In this paper, we explore \textit{summary-to-article generation}: the task of generating long articles given a short summary, which provides finer-grained content control for the generated text.To prevent sequence-to-sequence (seq2seq) models from degenerating into language models and better controlling the long text to be generated, we propose a hierarchical generation approach which first generates a sketch of intermediate length based on the summary and then completes the article by enriching the generated sketch.To mitigate the discrepancy between the ``oracle'' sketch used during training and the noisy sketch generated during inference, we propose an end-to-end joint training framework based on multi-agent reinforcement learning.For evaluation, we use text summarization corpora by reversing their inputs and outputs, and introduce a novel evaluation method that employs a summarization system to summarize the generated article and test its match with the original input summary.Experiments show that our proposed hierarchical generation approach can generate a coherent and relevant article based on the given summary, yielding significant improvements upon conventional seq2seq models.

Wir untersuchen die Aufgabe der Generierung von Zusammenfassungen zu Artikeln und schlagen ein hierarchisches Generierungsschema zusammen mit einem gemeinsamen Ende-zu-Ende Reinforcement Learning Framework zum Trainieren des hierarchischen Modells vor.

Um das Problem der Degeneration bei der Generierung von Zusammenfassungen zu Artikeln zu lösen, wird in diesem Artikel ein hierarchischer Generierungsansatz vorgeschlagen, der zunächst eine Zwischenversion des Artikels und dann den vollständigen Artikel generiert.


When training a deep neural network for supervised image classification, one can broadly distinguish between two types of latent features of images that will drive the classification of class Y. Following the notation of Gong et al. (2016), we can divide features broadly into the classes of(i) â€œcoreâ€ or â€œconditionally invariantâ€ features X^ci whose distribution P(X^ci | Y) does not change substantially across domains and(ii) â€œstyleâ€ or â€œorthogonalâ€ features X^orth whose distribution P(X^orth | Y) can change substantially across domains.These latter orthogonal features would generally include features such as position, rotation, image quality or brightness but also more complex ones like hair color or posture for images of persons.We try to guard against future adversarial domain shifts by ideally just using the â€œconditionally invariantâ€ features for classification.In contrast to previous work, we assume that the domain itself is not observed and hence a latent variable.We can hence not directly see the distributional change of features across different domains. We do assume, however, that we can sometimes observe a so-called identifier or ID variable.We might know, for example, that two images show the same person, with ID referring to the identity of the person.In data augmentation, we generate several images from the same original image, with ID referring to the relevant original image.The method requires only a small fraction of images to have an ID variable.We provide a causal framework for the problem by adding the ID variable to the model of Gong et al. (2016).However, we are interested in settings where we cannot observe the domain directly and we treat domain as a latent variable.If two or more samples share the same class and identifier, (Y, ID)=(y,i), then we treat those samples as counterfactuals under different style interventions on the orthogonal or style features.Using this grouping-by-ID approach, we regularize the network to provide near constant output across samples that share the same ID by penalizing with an appropriate graph Laplacian.This is shown to substantially improve performance in settings where domains change in terms of image quality, brightness, color changes, and more complex changes such as changes in movement and posture.We show links to questions of interpretability, fairness and transfer learning.

Wir schlagen eine kontrafaktische Regularisierung vor, um uns vor nachteiligen Domänenverschiebungen zu schützen, die durch Verschiebungen in der Verteilung der latenten "Stilmerkmale" von Bildern entstehen.

Die Arbeit erörtert Möglichkeiten zum Schutz vor nachteiligen Domänenverschiebungen mit kontrafaktischer Regularisierung durch das Erlernen eines Klassifikators, der gegenüber oberflächlichen Veränderungen (oder "Stil"-Merkmalen) in der Vorstellungswelt invariant ist.

Diese Arbeit zielt auf eine robuste Bildklassifizierung gegen nachteilige Veränderungen im Bereich ab, und das Ziel wird erreicht, indem die Verwendung von sich ändernden Stilmerkmalen vermieden wird.


Gradient-based meta-learning algorithms require several steps of gradient descent to adapt to newly incoming tasks.This process becomes more costly as the number  of  samples  increases.  Moreover,  the  gradient  updates  suffer  from  several sources of noise leading to a degraded performance.   In this work,  we propose a meta-learning algorithm equipped with the GradiEnt Component COrrections, aGECCO cell for short, which generates a multiplicative corrective low-rank matrix which (after vectorization) corrects the estimated gradients. GECCO contains a simple decoder-like network with learnable parameters, an attention module and a so-called context input parameter. The context parameter of GECCO is updated to  generate  a  low-rank  corrective  term  for  the  network  gradients.   As  a  result, meta-learning requires only a few of gradient updates to absorb new task (often, a single update is sufficient in the few-shot scenario). While previous approaches address this problem by altering the learning rates, factorising network parameters or directly learning feature corrections from features and/or gradients, GECCO is an off-the-shelf generator-like unit that performs element-wise gradient corrections without the need to â€˜observeâ€™ the features and/or the gradients directly.  We show that our GECCO(i) accelerates learning,(ii) performs robust corrections of the gradients corrupted by a noise, and(iii) leads to notable improvements over existing gradient-based meta-learning algorithms.

Wir schlagen einen Meta-Lerner vor, der sich schnell an mehrere Aufgaben anpassen kann, sogar in einem Schritt in einer Few-Shot Einstellung.

In diesem Beitrag wird eine Methode zum Meta-Lernen eines Gradientenkorrekturmoduls vorgeschlagen, bei der die Vorkonditionierung durch ein neuronales Netz parametrisiert wird und ein zweistufiger Gradientenaktualisierungsprozess während der Anpassung eingebaut wird.


Discriminative  question  answering  models  can  overfit  to  superficial  biases  in datasets,  because their loss function saturates when any clue makes the answer likely.  We introduce generative models of the joint distribution of questions and answers, which are trained to explain the whole question, not just to answer it.Our  question  answering  (QA)  model  is  implemented  by  learning  a  prior  over answers,  and  a  conditional  language  model  to  generate  the  question  given  the answerâ€”allowing scalable and interpretable many-hop reasoning as the question is generated word-by-word.  Our model achieves competitive performance with specialised discriminative models on the SQUAD and CLEVR benchmarks, indicating that it is a more general architecture for language understanding and reasoning than previous work.The model greatly improves generalisation both from biased training data and to adversarial testing data, achieving a new state-of-the-art on ADVERSARIAL SQUAD.We will release our code.

Modelle zur Beantwortung von Fragen, die die gemeinsame Verteilung von Fragen und Antworten modellieren, können mehr lernen als unterschiedliche Modelle.

In diesem Beitrag wird ein generativer Ansatz für die textuelle und visuelle Qualitätssicherung vorgeschlagen, bei dem eine gemeinsame Verteilung über den Frage- und Antwortraum unter Berücksichtigung des Kontexts erlernt wird, wodurch komplexere Beziehungen erfasst werden.

In diesem Beitrag wird ein generatives Modell für die Beantwortung von Fragen vorgestellt und vorgeschlagen, p(q,a|c) zu modellieren, faktorisiert als p(a|c) * p(q|a,c).

Die Autoren schlagen ein generatives QA-Modell vor, das die Verteilung von Fragen und Antworten in einem Dokument/Kontext gemeinsam optimiert.


In this paper, we turn our attention to the interworking between the activation functions and the batch normalization, which is a virtually mandatory technique to train deep networks currently.We propose the activation function Displaced Rectifier Linear Unit (DReLU) by conjecturing that extending the identity function of ReLU to the third quadrant enhances compatibility with batch normalization.Moreover, we used statistical tests to compare the impact of using distinct activation functions (ReLU, LReLU, PReLU, ELU, and DReLU) on the learning speed and test accuracy performance of standardized VGG and Residual Networks state-of-the-art models.These convolutional neural networks were trained on CIFAR-100 and CIFAR-10, the most commonly used deep learning computer vision datasets.The results showed DReLU speeded up learning in all models and datasets.Besides, statistical significant performance assessments (p<0.05) showed DReLU enhanced the test accuracy presented by ReLU in all scenarios.Furthermore, DReLU showed better test accuracy than any other tested activation function in all experiments with one exception, in which case it presented the second best performance.Therefore, this work demonstrates that it is possible to increase performance replacing ReLU by an enhanced activation function.

Es wird eine neue Aktivierungsfunktion namens Displaced Rectifier Linear Unit vorgeschlagen. Es wurde gezeigt, dass sie die Trainings- und Inferenzleistung von Batch normalisierten neuronalen Netzen verbessern kann.

Die Arbeit vergleicht und rät ab von der Verwendung von Batch-Normalisierung nach der Verwendung von Rectifier Linear Units.

In dieser Arbeit wird eine Aktivierungsfunktion, genannt "displaced ReLU", vorgeschlagen, um die Leistung von CNNs zu verbessern, die eine Batch-Normalisierung verwenden.


Encoding the input scale information explicitly into the representation learned by a convolutional neural network (CNN) is beneficial for many vision tasks especially when dealing with multiscale input signals.We study, in this paper, a scale-equivariant CNN architecture with joint convolutions across the space and the scaling group, which is shown to be both sufficient and necessary to achieve scale-equivariant representations.To reduce the model complexity and computational burden, we decompose the convolutional filters under two pre-fixed separable bases and truncate the expansion to low-frequency components.A further benefit of the truncated filter expansion is the improved deformation robustness of the equivariant representation.Numerical experiments demonstrate that the proposed scale-equivariant neural network with decomposed convolutional filters (ScDCFNet) achieves significantly improved performance in multiscale image classification and better interpretability than regular CNNs at a reduced model size.

Wir konstruieren skalenäquivariante Convolutional Neural Networks in der allgemeinsten Form, die sowohl rechnerisch effizient als auch nachweislich deformationsresistent sind.

Die Autoren schlagen eine CNN-Architektur vor, die theoretisch äquivariant zu isotropen Skalierungen und Translationen ist, indem eine zusätzliche Skalendimension zu den Aktivierungstensoren hinzugefügt wird.


In this paper, we diagnose deep neural networks for 3D point cloud processing to explore the utility of different network architectures.We propose a number of hypotheses on the effects of specific network architectures on the representation capacity of DNNs.In order to prove the hypotheses, we design five metrics to diagnose various types of DNNs from the following perspectives, information discarding, information concentration, rotation robustness, adversarial robustness, and neighborhood inconsistency.We conduct comparative studies based on such metrics to verify the hypotheses, which may shed new lights on the architectural design of neural networks.Experiments demonstrated the effectiveness of our method.The code will be released when this paper is accepted.

Wir diagnostizieren tiefe neuronale Netze für die 3D-Punktwolkenverarbeitung, um den Nutzen verschiedener Netzarchitekturen zu untersuchen.

Die Arbeit untersucht verschiedene neuronale Netzwerkarchitekturen für die Verarbeitung von 3D-Punktwolken und schlägt Metriken für die Robustheit gegenüber nachteiligen Einflüssen, die Rotationsrobustheit und die Nachbarschaftskonsistenz vor.


In this work we construct flexible joint distributions from low-dimensional conditional semi-implicit distributions.Explicitly defining the structure of the approximation allows to make the variational lower bound tighter, resulting in more accurate inference.

Die Nutzung der Struktur von Verteilungen verbessert die semi-implizite variationale Inferenz.


Imitation learning from human-expert demonstrations has been shown to be greatly helpful for challenging reinforcement learning problems with sparse environment rewards.However, it is very difficult to achieve similar success without relying on expert demonstrations.Recent works on self-imitation learning showed that imitating the agent's own past good experience could indirectly drive exploration in some environments, but these methods often lead to sub-optimal and myopic behavior.To address this issue, we argue that exploration in diverse directions by imitating diverse trajectories, instead of focusing on limited good trajectories, is more desirable for the hard-exploration tasks.We propose a new method of learning a trajectory-conditioned policy to imitate diverse trajectories from the agent's own past experiences and show that such self-imitation helps avoid myopic behavior and increases the chance of finding a globally optimal solution for hard-exploration tasks, especially when there are misleading rewards.Our method significantly outperforms existing self-imitation learning and count-based exploration methods on various hard-exploration tasks with local optima.In particular, we report a state-of-the-art score of more than 20,000 points on Montezumas Revenge without using expert demonstrations or resetting to arbitrary states.

Self-Imitation Learning von verschiedenen Trajektorien mit trajektorienbedingten Regeln.

Diese Arbeit befasst sich mit schwierigen Explorationsaufgaben, indem die Selbstimitation auf eine vielfältige Auswahl von Trajektorien aus der Vergangenheit angewendet wird, um eine effizientere Exploration in spärlichen Belohnungsproblemen zu ermöglichen und SOTA-Ergebnisse zu erzielen.


We present a method that trains large capacity neural networks with significantly improved accuracy and lower dynamic computational cost.This is achieved by gating the deep-learning architecture on a fine-grained-level.Individual convolutional maps are turned on/off conditionally on features in the network.To achieve this, we introduce a new residual block architecture that gates convolutional channels in a fine-grained manner.We also introduce a generally applicable tool batch-shaping that matches the marginal aggregate posteriors of features in a neural network to a pre-specified prior distribution.We use this novel technique to force gates to be more conditional on the data.We present results on CIFAR-10 and ImageNet datasets for image classification, and Cityscapes for semantic segmentation.Our results show that our method can slim down large architectures conditionally, such that the average computational cost on the data is on par with a smaller architecture, but with higher accuracy.In particular, on ImageNet, our ResNet50 and ResNet34 gated networks obtain 74.60% and 72.55% top-1 accuracy compared to the 69.76% accuracy of the baseline ResNet18 model, for similar complexity.We also show that the resulting networks automatically learn to use more features for difficult examples and fewer features for simple examples.

Eine Methode, die neuronale Netze mit großer Kapazität mit deutlich verbesserter Genauigkeit und geringeren dynamischen Rechenkosten trainiert.

Ein Verfahren zum Trainieren eines Netzes mit großer Kapazität, von dem nur Teile zum Zeitpunkt der Inferenz abhängig von der Eingabe verwendet werden, unter Verwendung einer feinkörnigen bedingten Auswahl und einer neuen Regularisierungsmethode, dem "Batch Shaping".


With a view to bridging the gap between deep learning and symbolic AI, we present a novel end-to-end neural network architecture that learns to form propositional representations with an explicitly relational structure from raw pixel data.In order to evaluate and analyse the architecture, we introduce a family of simple visual relational reasoning tasks of varying complexity.We show that the proposed architecture, when pre-trained on a curriculum of such tasks, learns to generate reusable representations that better facilitate subsequent learning on previously unseen tasks when compared to a number of baseline architectures.The workings of a successfully trained model are visualised to shed some light on how the architecture functions.

Wir stellen eine differenzierbare Ende-zu-Ende Architektur vor, die lernt, Pixel auf Prädikate abzubilden, und evaluieren sie anhand einer Reihe einfacher relationaler Argumentationsaufgaben.

Eine Netzwerkarchitektur, die auf dem Multi-Head Self-Attention Modul basiert, um eine neue Form von relationalen Darstellungen zu erlernen, die die Dateneffizienz und die Generalisierungsfähigkeit beim Lernen von Curriculums verbessert.


In natural language inference, the semantics of some words do not affect the inference.Such information is considered superficial and brings overfitting.How can we represent and discard such superficial information?In this paper, we use first order logic (FOL) - a classic technique from meaning representation language â€“ to explain what information is superficial for a given sentence pair.Such explanation also suggests two inductive biases according to its properties.We proposed a neural network-based approach that utilizes the two inductive biases.We obtain substantial improvements over extensive experiments.

Wir verwenden neuronale Netze, um oberflächliche Informationen für die Inferenz natürlicher Sprache zu projizieren, indem wir die oberflächlichen Informationen aus der Perspektive der Logik erster Ordnung definieren und identifizieren.

In diesem Beitrag wird versucht, oberflächliche Informationen in der natürlichsprachlichen Inferenz zu reduzieren, um eine Überanpassung zu verhindern, und es wird ein neuronales Graphennetz zur Modellierung der Beziehung zwischen Prämisse und Hypothese eingeführt.

Ein Ansatz zur Behandlung der Inferenz natürlicher Sprache unter Verwendung der Logik erster Ordnung und zur Ergänzung von NLI-Modellen mit logischen Informationen, um die Inferenz zu verbessern.


We propose an approach to training machine learning models that are fair in the sense that their performance is invariant under certain perturbations to the features.For example, the performance of a resume screening system should be invariant under changes to the name of the applicant.We formalize this intuitive notion of fairness by connecting it to the original  notion of individual fairness put forth by Dwork et al and show that the proposed approach achieves this notion of fairness.We also demonstrate the effectiveness of the approach on two machine learning tasks that are susceptible to gender and racial biases.

Algorithmus für das Training eines individuell fairen Klassifizierers unter Verwendung von adversarialer Robustheit.

In diesem Beitrag wird eine neue Definition von algorithmischer Fairness und ein Algorithmus vorgeschlagen, mit dem ein ML-Modell gefunden werden kann, das die Fairness-Bedingungen erfüllt.


In this paper, we propose a Seed-Augment-Train/Transfer (SAT) framework that contains a synthetic seed image dataset generation procedure for languages with different numeral systems using freely available open font file datasets.This seed dataset of images is then augmented to create a purely synthetic training dataset, which is in turn used to train a deep neural network and test on held-out real world handwritten digits dataset spanning five Indic scripts, Kannada, Tamil, Gujarati, Malayalam, and Devanagari.We showcase the efficacy of this approach both qualitatively, by training a Boundary-seeking GAN (BGAN) that generates realistic digit images in the five languages, and also qualitatively by testing a CNN trained on the synthetic data on the real-world datasets.This establishes not only an interesting nexus between the font-datasets-world and transfer learning but also provides a recipe for universal-digit classification in any script.

Sind Seeding und Augmentation alles, was Sie für die Klassifizierung von Ziffern in jeder Sprache brauchen?

In diesem Beitrag werden neue Datensätze für fünf Sprachen vorgestellt und ein neuer Rahmen (SAT) für die Erstellung von Schriftbilddatensätzen für die universelle Ziffernklassifikation vorgeschlagen.


An important research direction in machine learning has centered around developing meta-learning algorithms to tackle few-shot learning.An especially successful algorithm has been Model Agnostic Meta-Learning (MAML), a method that consists of two optimization loops, with the outer loop finding a meta-initialization, from which the inner loop can efficiently learn new tasks.Despite MAML's popularity, a fundamental open question remains -- is the effectiveness of MAML due to the meta-initialization being primed for rapid learning (large, efficient changes in the representations) or due to feature reuse,  with the meta initialization already containing high quality features?We investigate this question, via ablation studies and analysis of the latent representations, finding that feature reuse is the dominant factor.This leads to the ANIL (Almost No Inner Loop) algorithm, a simplification of MAML where we remove the inner loop for all but the (task-specific) head of the underlying neural network.ANIL matches MAML's performance on benchmark few-shot image classification and RL and offers computational improvements over MAML.We further study the precise contributions of the head and body of the network, showing that performance on the test tasks is entirely determined by the quality of the learned features, and we can remove even the head of the network (the NIL algorithm).We conclude with a discussion of the rapid learning vs feature reuse question for meta-learning algorithms more broadly.

Der Erfolg von MAML beruht auf der Wiederverwendung von Merkmalen aus der Meta-Initialisierung, die auch zu einer natürlichen Vereinfachung des Algorithmus führt, indem die innere Schleife für den Netzkörper sowie andere Erkenntnisse über den Kopf und den Körper entfernt werden.

In der Arbeit wird festgestellt, dass die Wiederverwendung von Merkmalen der wichtigste Faktor für den Erfolg von MAML ist, und es werden neue Algorithmen vorgeschlagen, die wesentlich weniger Rechenzeit benötigen als MAML.


Model training remains a dominant financial cost and time investment in machine learning applications.Developing and debugging models often involve iterative training, further exacerbating this issue.With growing interest in increasingly complex models, there is a need for techniques that help to reduce overall training effort.While incremental training can save substantial time and cost by training an existing model on a small subset of data, little work has explored policies for determining when incremental training provides adequate model performance versus full retraining.We provide a method-agnostic algorithm for deciding when to incrementally train versus fully train.We call this setting of non-deterministic full- or incremental training ``Mixed Setting Training".Upon evaluation in slot-filling tasks, we find that this algorithm provides a bounded error, avoids catastrophic forgetting, and results in a significant speedup over a policy of always fully training.

Wir stellen einen methodenunabhängigen Algorithmus zur Verfügung, um zu entscheiden, wann ein inkrementelles Training und wann ein vollständiges Training durchgeführt werden soll. Dieser Algorithmus bietet eine signifikante Beschleunigung gegenüber dem vollständigen Training und verhindert katastrophales Vergessen.

Diese Arbeit schlägt einen Ansatz vor, um zu entscheiden, wann ein Modell im Rahmen einer iterativen Modellentwicklung bei Slot-Filling-Aufgaben inkrementell oder vollständig neu trainiert werden soll.


Neural networks have succeeded in many reasoning tasks.Empirically, these tasks require specialized network structures, e.g., Graph Neural Networks (GNNs) perform well on many such tasks, while less structured networks fail.Theoretically, there is limited understanding of why and when a network structure generalizes better than other equally expressive ones.We develop a framework to characterize which reasoning tasks a network can learn well, by studying how well its structure aligns with the algorithmic structure of the relevant reasoning procedure.We formally define algorithmic alignment and derive a sample complexity bound that decreases with better alignment.This framework explains the empirical success of popular reasoning models and suggests their limitations.We unify seemingly different reasoning tasks, such as intuitive physics, visual question answering, and shortest paths, via the lens of a powerful algorithmic paradigm, dynamic programming (DP).We show that GNNs can learn DP and thus solve these tasks.On several reasoning tasks, our theory aligns with empirical results.

Wir entwickeln einen theoretischen Rahmen, um zu charakterisieren, welche Denkaufgaben ein neuronales Netz gut lernen kann.

In dem Beitrag wird ein Maß für die Klassen der algorithmischen Ausrichtung vorgeschlagen, mit dem gemessen wird, wie "nah" neuronale Netze an bekannten Algorithmen sind, und das die Verbindung zwischen mehreren Klassen bekannter Algorithmen und Architekturen neuronaler Netze nachweist.


Cell-cell interactions have an integral role in tumorigenesis as they are critical in governing immune responses.As such, investigating specific cell-cell interactions has the potential to not only expand upon the understanding of tumorigenesis, but also guide clinical management of patient responses to cancer immunotherapies.A recent imaging technique for exploring cell-cell interactions, multiplexed ion beam imaging by time-of-flight (MIBI-TOF), allows for cells to be quantified in 36 different protein markers at sub-cellular resolutions in situ as high resolution multiplexed images.To explore the MIBI images, we propose a GAN for multiplexed data with protein specific attention.By conditioning image generation on cell types, sizes, and neighborhoods through semantic segmentation maps, we are able to observe how these factors affect cell-cell interactions simultaneously in different protein channels.Furthermore, we design a set of metrics and offer the first insights towards cell spatial orientations, cell protein expressions, and cell neighborhoods.Our model, cell-cell interaction GAN (CCIGAN), outperforms or matches existing image synthesis methods on all conventional measures and significantly outperforms on biologically motivated metrics.To our knowledge, we are the first to systematically model multiple cellular protein behaviors and interactions under simulated conditions through image synthesis.

Wir erforschen Zell-Zell Interaktionen in verschiedenen Kontexten der Tumorumgebung, die in hochgradig multiplexen Bildern beobachtet werden, durch Bildsynthese unter Verwendung einer neuartigen Aufmerksamkeits GAN Architektur.

Eine neue Methode zur Modellierung von Daten, die durch Multiplex-Ionenstrahl Imaging per Time of Flight (MIBI-TOF) generiert werden, durch Lernen der Many-to-Many Zuordnung zwischen Zelltypen und Expressionsniveaus von Proteinmarkern.


Machine learning models for question-answering (QA), where given a question and a passage, the learner must select some span in the passage as an answer, are known to be brittle.By inserting a single nuisance sentence into the passage, an adversary can fool the model into selecting the wrong span.A promising new approach for QA decomposes the task into two stages:(i) select relevant sentences from the passage; and(ii) select a span among those sentences.Intuitively, if the sentence selector excludes the offending sentence, then the downstream span selector will be robust.While recent work has hinted at the potential robustness of two-stage QA, these methods have never, to our knowledge, been explicitly combined with adversarial training.This paper offers a thorough empirical investigation of adversarial robustness, demonstrating that although the two-stage approach lags behind single-stage span selection, adversarial training improves its performance significantly, leading to an improvement of over 22 points in F1 score over the adversarially-trained single-stage model.

Ein zweistufiger Ansatz, der aus einer Satzauswahl und einer anschließenden Abstandauswahl besteht, kann im Vergleich zu einem einstufigen Modell, das auf der Grundlage des gesamten Kontexts trainiert wird, robuster gegenüber adversarischen Angriffen gemacht werden.

In diesem Beitrag wird ein bestehendes Modell untersucht und es wird festgestellt, dass eine zweistufige trainierte QS-Methode im Vergleich zu anderen Methoden nicht widerstandsfähiger gegen Angriffe von außen ist.


The aim of this study is to introduce a formal framework for analysis and synthesis of driver assistance systems.It applies formal methods to the verification of a stochastic human driver model built using the cognitive architecture ACT-R, and then bootstraps safety in semi-autonomous vehicles through the design of provably correct Advanced Driver Assistance Systems.The main contributions include the integration of probabilistic ACT-R models in the formal analysis of semi-autonomous systems and an abstraction technique that enables a finite representation of a large dimensional, continuous system in the form of a Markov model.The effectiveness of the method is illustrated in several case studies under various conditions.

Verifizierung eines menschlichen Fahrermodells auf der Grundlage einer kognitiven Architektur und Synthese eines korrekt konstruierten ADAS auf dieser Grundlage.


In contrast to the older writing system of the 19th century, modern Hawaiian orthography employs characters for long vowels and glottal stops.These extra characters account for about one-third of the phonemes in Hawaiian, so including them makes a big difference to reading comprehension and pronunciation.However, transliterating between older and newer texts is a laborious task when performed manually.We introduce two related methods to help solve this transliteration problem automatically, given that there were not enough data to train an end-to-end deep learning model.One approach is implemented, end-to-end, using finite state transducers (FSTs).The other is a hybrid deep learning approach which approximately composes an FST with a recurrent neural network (RNN).We find that the hybrid approach outperforms the end-to-end FST by partitioning the original problem into one part that can be modelled by hand, using an FST, and into another part, which is easily solved by an RNN trained on the available data.

Ein neuartiger, hybrider Deep-Learning-Ansatz bietet die beste Lösung für ein Problem mit begrenzten Daten (das für den Erhalt der hawaiischen Sprache wichtig ist).


In many real-world settings, a learning model must perform few-shot classification: learn to classify examples from unseen classes using only a few labeled examples per class.Additionally, to be safely deployed, it should have the ability to detect out-of-distribution inputs: examples that do not belong to any of the classes.While both few-shot classification and out-of-distribution detection are popular topics,their combination has not been studied.In this work, we propose tasks for out-of-distribution detection in the few-shot setting and establish benchmark datasets, based on four popular few-shot classification datasets.  Then, we propose two new methods for this task and investigate their performance.In sum, we establish baseline out-of-distribution detection results using standard metrics on new benchmark datasets and show improved results with our proposed methods.

Wir untersuchen quantitativ die Out-of-Distribution Erkennung in einer Situation mit wenigen Aufnahmen, ermitteln die grundlegenden Ergebnisse mit ProtoNet, MAML und ABML und verbessern sie.

In dem Artikel werden zwei neue Konfidenzwerte vorgeschlagen, die für die Erkennung von Out-of-Distribution bei der Few-Shot Klassifizierung besser geeignet sind, und es wird gezeigt, dass ein auf Distanzmetriken basierender Ansatz die Leistung verbessert.


While modern  generative  models are able to synthesize high-fidelity, visually appealing images, successfully generating examples that are useful for recognition tasks remains an elusive goal.To this end, our key insight is that the examples should be synthesized to recover classifier decision boundaries that would be learned from a large amount of real examples.More concretely, we treat a classifier trained on synthetic examples as ''student'' and a classifier trained on real examples as ''teacher''.By introducing knowledge distillation into a meta-learning framework, we encourage the generative model to produce examples in a way that enables the student classifier to mimic the behavior of the teacher.To mitigate the potential gap between student and teacher classifiers, we further propose to distill the knowledge in a progressive manner, either by gradually strengthening the teacher or weakening the student.We demonstrate the use of our model-agnostic distillation approach to deal with data scarcity, significantly improving few-shot learning performance on miniImageNet and ImageNet1K benchmarks.

In diesem Beitrag wird eine progressive Wissensdestillation für das Lernen generativer Modelle vorgestellt, die auf Erkennungsaufgaben ausgerichtet sind.

In diesem Beitrag wird gezeigt, wie man durch einfaches bis schweres Curriculum-Lernen ein generatives Modell trainieren kann, um die Few-Shot Klassifizierung zu verbessern.


Deep neural networks provide state-of-the-art performance for many applications of interest.Unfortunately they are known to be vulnerable to adversarial examples, formed by applying small but malicious perturbations to the original inputs.Moreover, the perturbations can transfer across models: adversarial examples generated for a specific model will often mislead other unseen models.Consequently  the adversary can leverage it to attack against the deployed black-box systems. In this work, we demonstrate that the adversarial perturbation can be decomposed into two components: model-specific and data-dependent one, and it is the latter that mainly contributes to the transferability.Motivated by this understanding, we propose to craft adversarial examples by utilizing the noise reduced gradient (NRG) which approximates the data-dependent component.Experiments on various classification models trained on ImageNet demonstrates that the new approach enhances the transferability dramatically.We also find that low-capacity models have more powerful attack capability than high-capacity counterparts, under the condition that they have comparable test performance.  These insights give rise to a principled manner to construct adversarial examples with high success rates and could potentially provide us guidance for designing effective defense approaches against black-box attacks.

Wir schlagen eine neue Methode zur Verbesserung der Übertragbarkeit von Negativbeispielen vor, indem wir den Störungsreduzierten Gradienten verwenden.

In dieser Arbeit wird postuliert, dass eine Störung aus einer modellspezifischen und einer datenspezifischen Komponente besteht und dass die Verstärkung der letzteren am besten für adversarial Angriffe geeignet ist.

In dieser Arbeit geht es darum, die Übertragbarkeit von Beispielen aus der Praxis von einem Modell auf ein anderes Modell zu verbessern.


We present the iterative two-pass decomposition flow to accelerate existing convolutional neural networks (CNNs).  The proposed rank selection algorithm can effectively determine the proper ranks of the target convolutional layers for the low rank approximation.Our two-pass CP-decomposition helps prevent from the instability problem.The iterative flow makes the decomposition of the deeper networks systematic.The experiment results shows that VGG16 can be accelerated with a 6.2x measured speedup while the accuracy drop remains only 1.2%.

Wir stellen den iterativen Zwei-Pass-CP Zerlegungsfluss vor, um bestehende Convolutional Neural Networks (CNNs) effektiv zu beschleunigen.

In der Arbeit wird ein neuartiger Arbeitsablauf für die Beschleunigung und Komprimierung von CNNs vorgeschlagen, und es wird eine Methode zur Bestimmung des Zielrangs der einzelnen Schichten angesichts der angestrebten Gesamtbeschleunigung vorgeschlagen.

Diese Arbeit befasst sich mit dem Problem des Lernens einer Tensor-Filter-Operation niedrigen Ranges für Filterschichten in tiefen neuronalen Netzen (DNNs).


We introduce LiPopt, a polynomial optimization framework for computing increasingly tighter upper bound on the Lipschitz constant of neural networks.The underlying optimization problems boil down to either linear (LP) or semidefinite (SDP) programming.We show how to use the sparse connectivity of a network, to significantly reduce the complexity of computation.This is specially useful for convolutional as well as pruned neural networks.We conduct experiments on networks with random weights as well as networks trained on MNIST, showing that in the particular case of the $\ell_\infty$-Lipschitz constant, our approach yields superior estimates as compared to other baselines available in the literature.

LP-basierte obere Schranken für die Lipschitz-Konstante von neuronalen Netzen.

Die Autoren untersuchen das Problem der Schätzung der Lipschitz-Konstante eines tiefen neuronalen Netzes mit ELO Aktivierungsfunktion und formulieren es als polynomielles Optimierungsproblem.


Although few-shot learning research has advanced rapidly with the help of meta-learning, its practical usefulness is still limited because most of the researches assumed that all meta-training and meta-testing examples came from a single domain.We propose a simple but effective way for few-shot classification in which a task distribution spans multiple domains including previously unseen ones during meta-training.The key idea is to build a pool of embedding models which have their own metric spaces and to learn to select the best one for a particular task through multi-domain meta-learning.This simplifies task-specific adaptation over a complex task distribution as a simple selection problem rather than modifying the model with a number of parameters at meta-testing time.Inspired by common multi-task learning techniques, we let all models in the pool share a base network and add a separate modulator to each model to refine the base network in its own way.This architecture allows the pool to maintain representational diversity and each model to have domain-invariant representation as well. Experiments show that our selection scheme outperforms other few-shot classification algorithms when target tasks could come from many different domains.They also reveal that aggregating outputs from all constituent models is effective for tasks from unseen domains showing the effectiveness of our framework.

Wir befassen uns mit der Few-Shot Klassifizierung in mehreren Bereichen, indem wir mehrere Modelle erstellen, um diese komplexe Aufgabenverteilung auf kollektive Weise darzustellen, und die aufgabenspezifische Anpassung als Auswahlproblem aus diesen vortrainierten Modellen vereinfachen.

Diese Arbeit befasst sich mit der Few-shot Klassifikation mit vielen verschiedenen Domänen, indem ein Pool von Einbettungsmodellen aufgebaut wird, um domäneninvariante und domänenspezifische Merkmale zu erfassen, ohne die Anzahl der Parameter signifikant zu erhöhen.


Still in 2019, many scanned documents come into businesses in non-digital format.Text to be extracted from real world documents is often nestled inside rich formatting, such as tabular structures or forms with fill-in-the-blank boxes or underlines whose ink often touches or even strikes through the ink of the text itself.Such ink artifacts can severely interfere with the performance of recognition algorithms or other downstream processing tasks.In this work, we propose DeepErase, a neural preprocessor to erase ink artifacts from text images.We devise a method to programmatically augment text images with real artifacts, and use them to train a segmentation network in an weakly supervised manner.In additional to high segmentation accuracy, we show that our cleansed images achieve a significant boost in downstream recognition accuracy by popular OCR software such as Tesseract 4.0.We test DeepErase on out-of-distribution datasets (NIST SDB) of scanned IRS tax return forms and achieve double-digit improvements in recognition accuracy over baseline for both printed and handwritten text.

Neural-basierte Entfernung von Tintenartefakten in Dokumenten (Unterstreichungen, Flecken usw.) ohne manuell beschriftete Trainingsdaten.


Black-box adversarial attacks require a large number of attempts before finding successful adversarial examples that are visually indistinguishable from the original input.Current approaches relying on substitute model training, gradient estimation or genetic algorithms often require an excessive number of queries.Therefore, they are not suitable for real-world systems where the maximum query number is limited due to cost.We propose a query-efficient black-box attack which uses Bayesian optimisation in combination with Bayesian model selection to optimise over the adversarial perturbation and the optimal degree of search space dimension reduction.We demonstrate empirically that our method can achieve comparable success rates with 2-5 times fewer queries compared to previous state-of-the-art black-box attacks.

Wir schlagen einen abfrageeffizienten Black-Box-Angriff vor, der Bayes'sche Optimierung in Kombination mit Bayes'scher Modellauswahl verwendet, um die Störung des Gegners und den optimalen Grad der Dimensionsreduktion des Suchraums zu optimieren.

Die Autoren schlagen vor, die Bayes'sche Optimierung mit einem GP Surrogate für die Erzeugung von adversarial Bildern zu verwenden, indem sie die additive Struktur ausnutzen und die Bayes'sche Modellauswahl verwenden, um eine optimale Dimensionalitätsreduktion zu bestimmen.


Learning multimodal representations is a fundamentally complex research problem due to the presence of multiple heterogeneous sources of information.Although the presence of multiple modalities provides additional valuable information, there are two key challenges to address when learning from multimodal data:1) models must learn the complex intra-modal and cross-modal interactions for prediction and2) models must be robust to unexpected missing or noisy modalities during testing.In this paper, we propose to optimize for a joint generative-discriminative objective across multimodal data and labels.We introduce a model that factorizes representations into two sets of independent factors: multimodal discriminative and modality-specific generative factors.Multimodal discriminative factors are shared across all modalities and contain joint multimodal features required for discriminative tasks such as sentiment prediction.Modality-specific generative factors are unique for each modality and contain the information required for generating data.Experimental results show that our model is able to learn meaningful multimodal representations that achieve state-of-the-art or competitive performance on six multimodal datasets.Our model demonstrates flexible generative capabilities by conditioning on independent factors and can reconstruct missing modalities without significantly impacting performance.Lastly, we interpret our factorized representations to understand the interactions that influence multimodal learning.

Wir schlagen ein Modell zum Erlernen faktorisierter multimodaler Repräsentationen vor, die diskriminativ, generativ und interpretierbar sind.

Diese Arbeit stellt ein "Multimodales Faktorisierungsmodell" vor, das Repräsentationen in gemeinsame multimodale diskriminierende Faktoren und modalitätsspezifische generative Faktoren aufteilt.


The successful application of flexible, general learning algorithms to real-world robotics applications is often limited by their poor data-efficiency.To address the challenge, domains with more than one dominant task of interest encourage the sharing of information across tasks to limit required experiment time.To this end, we investigate compositional inductive biases in the form of hierarchical policies as a mechanism for knowledge transfer across tasks in reinforcement learning (RL).We demonstrate that this type of hierarchy enables positive transfer while mitigating negative interference.Furthermore, we demonstrate the benefits of additional incentives to efficiently decompose task solutions.Our experiments show that these incentives are naturally given in multitask learning and can be easily introduced for single objectives.We design an RL algorithm that enables stable and fast learning of structured policies and the effective reuse of both behavior components and transition data across tasks in an off-policy setting.Finally, we evaluate our algorithm in simulated environments as well as physical robot experiments and  demonstrate substantial improvements in data data-efficiency over competitive baselines.

Wir entwickeln einen hierarchischen, akteurskritischen Algorithmus für den kompositorischen Transfer durch die gemeinsame Nutzung von Richtlinienkomponenten und demonstrieren die Komponentenspezialisierung und die damit verbundenen direkten Vorteile in Multitasking-Domänen sowie seine Anpassung für Einzelaufgaben.

Eine Kombination verschiedener Lerntechniken für den Erwerb von Strukturen und das Lernen mit asymmetrischen Daten, die zum Trainieren einer HRL-Politik verwendet werden.

Die Autoren stellen eine hierarchische Policy-Struktur vor, die sowohl für Single-Task- als auch für Multitask Reinforcement Learning verwendet werden kann, und bewerten die Nützlichkeit der Struktur bei komplexen Roboteraufgaben.


In this paper, we study the representational power of deep neural networks (DNN) that belong to the family of piecewise-linear (PWL) functions, based on PWL activation units such as rectifier or maxout.We investigate the complexity of such networks by studying the number of linear regions of the PWL function.Typically, a PWL function from a DNN can be seen as a large family of linear functions acting on millions of such regions.We directly build upon the work of MontÂ´ufar et al. (2014), MontÂ´ufar (2017), and Raghu et al. (2017) by refining the upper and lower bounds on the number of linear regions for rectified and maxout networks.In addition to achieving tighter bounds, we also develop a novel method to perform exact numeration or counting of the number of linear regions with a mixed-integer linear formulation that maps the input space to output.We use this new capability to visualize how the number of linear regions change while training DNNs.

Wir zählen empirisch die Anzahl der linearen Bereiche von Gleichrichternetzen und verfeinern die oberen und unteren Grenzen.

In diesem Beitrag werden verbesserte Grenzwerte für die Zählung der Anzahl linearer Regionen in ReLU-Netzen vorgestellt.


Convolutional neural networks memorize part of their training data, which is why strategies such as data augmentation and drop-out are employed to mitigate over- fitting.This paper considers the related question of â€œmembership inferenceâ€, where the goal is to determine if an image was used during training.We con- sider membership tests over either ensembles of samples or individual samples.First, we show how to detect if a dataset was used to train a model, and in particular whether some validation images were used at train time.Then, we introduce a new approach to infer membership when a few of the top layers are not available or have been fine-tuned, and show that lower layers still carry information about the training samples.To support our findings, we conduct large-scale experiments on Imagenet and subsets of YFCC-100M with modern architectures such as VGG and Resnet.

Wir analysieren die Erinnerungseigenschaften durch ein Convnet der Trainingsmenge und schlagen verschiedene Anwendungsfälle vor, in denen wir einige Informationen über die Trainingsmenge extrahieren können.

Beleuchtet die Verallgemeinerungs-/Erinnerungseigenschaften von großen und tiefen ConvNets und versucht, Verfahren zu entwickeln, mit denen festgestellt werden kann, ob eine Eingabe für ein trainiertes ConvNet tatsächlich zum Trainieren des Netzes verwendet wurde.


While Generative Adversarial Networks (GANs) have empirically produced impressive results on learning complex real-world distributions, recent works have shown that they suffer from lack of diversity or mode collapse.The theoretical work of Arora et al. (2017a) suggests a dilemma about GANsâ€™ statistical properties: powerful discriminators cause overfitting, whereas weak discriminators cannot detect mode collapse.By contrast, we show in this paper that GANs can in principle learn distributions in Wasserstein distance (or KL-divergence in many cases) with polynomial sample complexity, if the discriminator class has strong distinguishing power against the particular generator class (instead of against all possible generators).For various generator classes such as mixture of Gaussians, exponential families, and invertible and injective neural networks generators, we design corresponding discriminators (which are often neural nets of specific architectures) such that the Integral Probability Metric (IPM) induced by the discriminators can provably approximate the Wasserstein distance and/or KL-divergence.This implies that if the training is successful, then the learned distribution is close to the true distribution in Wasserstein distance or KL divergence, and thus cannot drop modes.Our preliminary experiments show that on synthetic datasets the test IPM is well correlated with KL divergence or the Wasserstein distance, indicating that the lack of diversity in GANs may be caused by the sub-optimality in optimization instead of statistical inefficiency.

GANs können Verteilungen im Prinzip stichprobeneffizient lernen, wenn die Diskriminatorklasse kompakt ist und eine starke Unterscheidungskraft gegenüber der jeweiligen Generatorklasse hat.

Vorschlagen des Begriffs der eingeschränkten Approximierbarkeit und liefern einer Komplexitätsgrenze für Stichproben, die polynomiell in der Dimension ist und bei der Untersuchung mangelnder Vielfalt in GANs nützlich ist.

Analysiert, dass die Integrale Wahrscheinlichkeitsmetrik unter einigen milden Annahmen eine gute Annäherung an den Wassersteinabstand sein kann.


Understanding the optimization trajectory is critical to understand training of deep neural networks.We show how the hyperparameters of stochastic gradient descent influence the covariance of the gradients (K) and the Hessian of the training loss (H) along this trajectory.Based on a theoretical model, we predict that using a high learning rate or a small batch size in the early phase of training leads SGD to regions of the parameter space with (1) reduced spectral norm of K, and (2) improved conditioning of K and H. We show that the point on the trajectory after which these effects hold, which we refer to as the break-even point, is reached early during training.We demonstrate these effects empirically for a range of deep neural networks applied to multiple different tasks.Finally, we apply our analysis to networks with batch normalization (BN) layers and find that it is necessary to use a high learning rate to achieve loss smoothing effects attributed previously to BN alone.

In der frühen Phase des Trainings von tiefen neuronalen Netzen gibt es einen "Break-even-Point", der die Eigenschaften der gesamten Optimierungskurve bestimmt.

In dieser Arbeit wird die Optimierung von tiefen neuronalen Netzen analysiert, indem untersucht wird, wie die Hyperparameter Batch-Größe und Schrittgröße die Lerntrajektorien verändern.


Graph Convolution Network (GCN) has been recognized as one of the most effective graph models for semi-supervised learning, but it extracts merely the first-order or few-order neighborhood information through information propagation, which suffers performance drop-off for deeper structure.Existing approaches that deal with the higher-order neighbors tend to take advantage of adjacency matrix power.In this paper, we assume a seemly trivial condition that the higher-order neighborhood information may be similar to that of the first-order neighbors.Accordingly, we present an unsupervised approach to describe such similarities and learn the weight matrices of higher-order neighbors automatically through Lasso that minimizes the feature loss between the first-order and higher-order neighbors, based on which we formulate the new convolutional filter for GCN to learn the better node representations.Our model, called higher-order weighted GCN (HWGCN), has achieved the state-of-the-art results on a number of node classification tasks over Cora, Citeseer and Pubmed datasets.

Wir schlagen HWGCN vor, um die relevanten Nachbarschaftsinformationen auf verschiedenen Ebenen zu mischen, um die Knotenrepräsentationen besser zu lernen.

Die Autoren schlagen eine Variante von GCN, HWGCN, vor, die eine Convolution jenseits von 1-Schritt-Nachbarn berücksichtigt und mit modernen Methoden vergleichbar ist.


The performance of deep neural networks is often attributed to their automated, task-related feature construction.It remains an open question, though, why this leads to solutions with good generalization, even in cases where the number of parameters is larger than the number of samples.Back in the 90s, Hochreiter and Schmidhuber observed that flatness of the loss surface around a local minimum correlates with low generalization error.For several flatness measures, this correlation has been empirically validated.However, it has recently been shown that existing measures of flatness cannot theoretically be related to generalization: if a network uses ReLU activations, the network function can be reparameterized without changing its output in such a way that flatness is changed almost arbitrarily.This paper proposes a natural modification of existing flatness measures that results in invariance to reparameterization.The proposed measures imply a robustness of the network to changes in the input and the hidden layers.Connecting this feature robustness to generalization leads to a generalized definition of the representativeness of data.With this, the generalization error of a model trained on representative data can be bounded by its feature robustness which depends on our novel flatness measure.

Wir führen ein neuartiges Maß für die Flachheit bei lokalen Minima der Verlustfläche tiefer neuronaler Netze ein, das gegenüber schichtweisen Neuparametrisierungen invariant ist, und stellen eine Verbindung zwischen Flachheit und Merkmalsrobustheit und Generalisierung her.

Die Autoren schlagen einen Begriff der Merkmalsrobustheit vor, der gegenüber einer Neuskalierung der Gewichtung invariant ist, und erörtern die Beziehung des Begriffs zur Generalisierung.

In diesem Beitrag wird ein Begriff der Merkmalsrobustheit definiert und mit der Epsilon-Repräsentativität einer Funktion kombiniert, um einen Zusammenhang zwischen der Flachheit von Minima und der Generalisierung in tiefen neuronalen Netzen zu beschreiben.


Bayesian methods have been successfully applied to sparsify weights of neural networks and to remove structure units from the networks, e.g.neurons.We apply and further develop this approach for gated recurrent architectures.Specifically, in addition to sparsification of individual weights and neurons, we propose to sparsify preactivations of gates and information flow in LSTM.It makes some gates and information flow components constant, speeds up forward pass and improves compression.Moreover, the resulting structure of gate sparsity is interpretable and depends on the task.

Wir schlagen vor, die Voraktivierungen von Gattern und den Informationsfluss im LSTM zu sparsam zu gestalten, um sie konstant zu machen und das Spärlichkeitsniveau der Neuronen zu erhöhen.

In diesem Papier wird eine Sparsification Methode für rekurrente neuronale Netze vorgeschlagen, bei der Neuronen mit Null-Präaktivierungen eliminiert werden, um kompakte Netze zu erhalten.


Improving the accuracy of numerical methods remains a central challenge in many disciplines and is especially important for nonlinear simulation problems.A representative example of such problems is fluid flow, which has been thoroughly studied to arrive at efficient simulations of complex flow phenomena.This paper presents a data-driven approach that learns to improve the accuracy of numerical solvers.The proposed method utilizes an advanced numerical scheme with a fine simulation resolution to acquire reference data.We, then, employ a neural network that infers a correction to move a coarse thus quickly obtainable result closer to the reference data.We provide insights into the targeted learning problem with different learning approaches: fully supervised learning methods with a naive and an optimized data acquisition as well as an unsupervised learning method with a differentiable Navier-Stokes solver.While our approach is very general and applicable to arbitrary partial differential equation models, we specifically highlight gains in accuracy for fluid flow simulations.

Wir stellen einen Ansatz für neuronale Netze zur Unterstützung von Lösern partieller Differentialgleichungen vor.

Die Autoren zielen darauf ab, die Genauigkeit numerischer Solver zu verbessern, indem sie ein neuronales Netz auf simulierten Referenzdaten trainieren, das den numerischen Solver korrigiert.


A patientâ€™s health information is generally fragmented across silos.Though it is technically feasible to unite data for analysis in a manner that underpins a rapid learning healthcare system, privacy concerns and regulatory barriers limit data centralization.Machine learning can be conducted in a federated manner on patient datasets with the same set of variables, but separated across sites of care.But federated learning cannot handle the situation where different data types for a givenpatient are separated vertically across different organizations.We call methods that enable machine learning model training on data separated by two or more degrees â€œconfederated machine learning.â€We built and evaluated a confederated machinelearningmodel to stratify the risk of accidental falls among the elderly.

Eine konföderierte Lernmethode, die Modelle aus horizontal und vertikal getrennten medizinischen Daten trainiert.

Eine "verbündete" maschinelle Lernmethode, die über die Grenzen medizinischer Daten hinweg lernt, die sowohl horizontal als auch vertikal getrennt sind.


Existing neural networks are vulnerable to "adversarial examples"---created by adding maliciously designed small perturbations in inputs to induce a misclassification by the networks.The most investigated defense strategy is adversarial training which augments training data with adversarial examples.However, applying single-step adversaries in adversarial training does not support the robustness of the networks, instead, they will even make the networks to be overfitted.In contrast to the single-step, multi-step training results in the state-of-the-art performance on MNIST and CIFAR10, yet it needs a massive amount of time.Therefore, we propose a method, Stochastic Quantized Activation (SQA) that solves overfitting problems in single-step adversarial training and fastly achieves the robustness comparable to the multi-step.SQA attenuates the adversarial effects by providing random selectivity to activation functions and allows the network to learn robustness with only single-step training.Throughout the experiment, our method demonstrates the state-of-the-art robustness against one of the strongest white-box attacks as PGD training, but with much less computational cost.Finally, we visualize the learning process of the network with SQA to handle strong adversaries, which is different from existing methods.

In dieser Arbeit wird eine stochastische quantisierte Aktivierung vorgeschlagen, die das Problem der Überanpassung beim FGSM-Training löst und schnell eine Robustheit erreicht, die mit dem mehrstufigen Training vergleichbar ist.

In dem Papier wird ein Modell zur Verbesserung des gegnerischen Trainings vorgeschlagen, indem zufällige Störungen in die Aktivierungen einer der verborgenen Schichten eingeführt werden


Neural activity is highly variable in response to repeated stimuli.We used an open dataset, the Allen Brain Observatory, to quantify the distribution of responses to repeated natural movie presentations.A large fraction of responses are best fit by log-normal distributions or Gaussian mixtures with two components.These distributions are similar to those from units in deep neural networks with dropout.Using a separate set of electrophysiological recordings, we constructed a population coupling model as a control for state-dependent activity fluctuations and found that the model residuals also show non-Gaussian distributions.We then analyzed responses across trials from multiple sections of different movie clips and observed that the noise in cortex aligns better with in-clip versus out-of-clip stimulus variations.We argue that noise is useful for generalization when it moves along representations of different exemplars in-class, similar to the structure of cortical noise.

Wir untersuchen die Struktur von Störfaktoren im Gehirn und stellen fest, dass es zur Generalisierung beitragen kann, indem es die Repräsentationen entlang der Reizvariationen innerhalb einer Klasse verschiebt.


Unsupervised domain adaptation has received significant attention in recent years.Most of existing works tackle the closed-set scenario, assuming that the source and target domains share the exactly same categories.In practice, nevertheless, a target domain often contains samples of classes unseen in source domain (i.e., unknown class).The extension of domain adaptation from closed-set to such open-set situation is not trivial since the target samples in unknown class are not expected to align with the source.In this paper, we address this problem by augmenting the state-of-the-art domain adaptation technique, Self-Ensembling, with category-agnostic clusters in target domain.Specifically, we present Self-Ensembling with Category-agnostic Clusters (SE-CC) --- a novel architecture that steers domain adaptation with the additional guidance of category-agnostic clusters that are specific to target domain.These clustering information provides domain-specific visual cues, facilitating the generalization of Self-Ensembling for both closed-set and open-set scenarios.Technically, clustering is firstly performed over all the unlabeled target samples to obtain the category-agnostic clusters, which reveal the underlying data space structure peculiar to target domain.A clustering branch is capitalized on to ensure that the learnt representation preserves such underlying structure by matching the estimated assignment distribution over clusters to the inherent cluster distribution for each target sample.Furthermore, SE-CC enhances the learnt representation with mutual information maximization.Extensive experiments are conducted on Office and VisDA datasets for both open-set and closed-set domain adaptation, and superior results are reported when comparing to the state-of-the-art approaches.

Wir stellen ein neues Design vor, d.h. Self-Ensembling mit kategorie-agnostischen Clustern, sowohl für Closed-Set- als auch für Open-Set-Domain-Adaption.

Ein neuer Ansatz zur Anpassung von offenen Domänen, bei dem die Kategorien der Quelldomäne in den Kategorien der Zieldomäne enthalten sind, um Ausreißerkategorien herauszufiltern und eine Anpassung innerhalb der gemeinsamen Klassen zu ermöglichen.


We present Spectral Inference Networks, a framework for learning eigenfunctions of linear operators by stochastic optimization.Spectral Inference Networks generalize Slow Feature Analysis to generic symmetric operators, and are closely related to Variational Monte Carlo methods from computational physics.As such, they can be a powerful tool for unsupervised representation learning from video or graph-structured data.We cast training Spectral Inference Networks as a bilevel optimization problem, which allows for online learning of multiple eigenfunctions.We show results of training Spectral Inference Networks on problems in quantum mechanics and feature learning for videos on synthetic datasets.Our results demonstrate that Spectral Inference Networks accurately recover eigenfunctions of linear operators and can discover interpretable representations from video in a fully unsupervised manner.

Wir zeigen, wie man spektrale Zerlegungen von linearen Operatoren mit Deep Learning erlernen kann, und nutzen dies für unüberwachtes Lernen ohne generatives Modell.

Die Autoren schlagen vor, ein Deep Learning Framework zu verwenden, um die Berechnung der größten Eigenvektoren zu lösen.

In dieser Arbeit wird ein Rahmen zum Erlernen von Eigenfunktionen über einen stochastischen Prozess vorgestellt und vorgeschlagen, die Herausforderung der Berechnung von Eigenfunktionen in einem groß angelegten Kontext durch Annäherung mit Hilfe eines zweiphasigen stochastischen Optimierungsprozesses zu bewältigen.


The Tensor-Train factorization (TTF) is an efficient way to compress large weight matrices of fully-connected layers and recurrent layers in recurrent neural networks (RNNs).However, high Tensor-Train ranks for all the core tensors of parameters need to be element-wise fixed, which results in an unnecessary redundancy of model parameters.This work applies Riemannian stochastic gradient descent (RSGD) to train core tensors of parameters in the Riemannian Manifold before finding vectors of lower Tensor-Train ranks for parameters.The paper first presents the RSGD algorithm with a convergence analysis and then tests it on more advanced Tensor-Train RNNs such as bi-directional GRU/LSTM and Encoder-Decoder RNNs with a Tensor-Train attention model.The experiments on digit recognition and machine translation tasks suggest the effectiveness of the RSGD algorithm for Tensor-Train RNNs.

Anwendung des Riemannschen SGD (RSGD) Algorithmus für das Training von Tensor-Train RNNs zur weiteren Reduzierung der Modellparameter.

Die Arbeit schlägt vor, den Riemannschen stochastischen Gradientenalgorithmus für das Lernen von Tensoren mit niedrigem Rang in tiefen Netzwerken zu verwenden.

Vorschlagen eines Algorithmus zur Optimierung von neuronalen Netzen, der durch Tensor-Train-Zerlegung parametrisiert ist, basierend auf der Riemannschen Optimierung und Ranganpassung, und entwerfen einer bidirektionale TT-LSTM-Architektur.


In this paper, we consider the problem of learning control policies that optimize areward function while satisfying constraints due to considerations of safety, fairness, or other costs.We propose a new algorithm - Projection Based ConstrainedPolicy Optimization (PCPO), an iterative method for optimizing policies in a two-step process - the first step performs an unconstrained update while the secondstep reconciles the constraint violation by projection the policy back onto the constraint set.We theoretically analyze PCPO and provide a lower bound on rewardimprovement, as well as an upper bound on constraint violation for each policy update.We further characterize the convergence of PCPO with projection basedon two different metrics - L2 norm and Kullback-Leibler divergence.Our empirical results over several control tasks demonstrate that our algorithm achievessuperior performance, averaging more than 3.5 times less constraint violation andaround 15% higher reward compared to state-of-the-art methods.

Wir schlagen einen neuen Algorithmus vor, der einschränkungsbefriedigende Strategien lernt, und bieten eine theoretische Analyse und empirische Demonstration im Kontext des Reinforcement Learning mit Einschränkungen.

In diesem Beitrag wird ein Algorithmus zur Optimierung von Richtlinien mit Beschränkungen vorgestellt, der einen zweistufigen Optimierungsprozess verwendet, bei dem Richtlinien, die die Beschränkung nicht erfüllen, in die Beschränkungsmenge zurückprojiziert werden können.


Deep networks face challenges of ensuring their robustness against inputs that cannot be effectively represented by information learned from training data.We attribute this vulnerability to the limitations inherent to activation-based representation.To complement the learned information from activation-based representation, we propose utilizing a gradient-based representation that explicitly focuses on missing information.In addition, we propose a directional constraint on the gradients as an objective during training to improve the characterization of missing information.To validate the effectiveness of the proposed approach, we compare the anomaly detection performance of gradient-based and activation-based representations.We show that the gradient-based representation outperforms the activation-based representation by 0.093 in CIFAR-10 and 0.361 in CURE-TSR datasets in terms of AUROC averaged over all classes.Also, we propose an anomaly detection algorithm that uses the gradient-based representation, denoted as GradCon, and validate its performance on three benchmarking datasets.The proposed method outperforms the majority of the state-of-the-art algorithms in CIFAR-10, MNIST, and fMNIST datasets with an average AUROC of 0.664, 0.973, and 0.934, respectively.

Wir schlagen eine gradientenbasierte Darstellung zur Charakterisierung von Informationen vor, die tiefe Netzwerke nicht gelernt haben.

Die Autoren stellen Darstellungen vor, die auf Gradienten in Bezug auf die Gewichte basieren, um Informationen zu ergänzen, die im Trainingsdatensatz für tiefe Netzwerke fehlen.


Medical images may contain various types of artifacts with different patterns and mixtures, which depend on many factors such as scan setting, machine condition, patientsâ€™ characteristics, surrounding environment, etc.However, existing deep learning based artifact reduction methods are restricted by their training set with specific predetermined artifact type and pattern.As such, they have limited clinical adoption.In this paper, we introduce a â€œZero-Shotâ€ medical image Artifact Reduction (ZSAR) framework, which leverages the power of deep learning but without using general pre-trained networks or any clean image reference.Specifically, we utilize the low internal visual entropy of an image and train a light-weight image-specific artifact reduction network to reduce artifacts in an image at test-time.We use Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) as vehicles to show that ZSAR can reduce artifacts better than state-of-the-art both qualitatively and quantitatively, while using shorter execution time.To the best of our knowledge, this is the first deep learning framework that reduces artifacts in medical images without using a priori training set.

Wir führen ein Zero-Shot Framework zur Reduzierung von Artefakten in medizinischen Bildern ein, das die Leistungsfähigkeit von Deep Learning nutzt, ohne jedoch allgemeine vortrainierte Netzwerke oder eine saubere Bildreferenz zu verwenden.


Attribution methods provide insights into the decision-making of machine learning models like artificial neural networks.For a given input sample, they assign a relevance score to each individual input variable, such as the pixels of an image.In this work we adapt the information bottleneck concept for attribution.By adding noise to intermediate feature maps we restrict the flow of information and can quantify (in bits) how much information image regions provide.We compare our method against ten baselines using three different metrics on VGG-16 and ResNet-50, and find that our methods outperform all baselines in five out of six settings.The methodâ€™s information-theoretic foundation provides an absolute frame of reference for attribution values (bits) and a guarantee that regions scored close to zero are not necessary for the network's decision.

Wir wenden das Konzept des Informationsengpasses auf die Zuordnung an.

In diesem Beitrag wird eine neuartige, auf Störungen basierende Methode zur Berechnung von Attributions- bzw. Ähnlichkeitskarten für Bildklassifizierer auf der Grundlage tiefer neuronaler Netze vorgeschlagen, bei der künstliches Störungen in eine frühe Schicht des Netzes injiziert wird.


Recurrent Neural Networks (RNNs) are used in state-of-the-art models in domains such as speech recognition, machine translation, and language modelling.Sparsity is a technique to reduce compute and memory requirements of deep learning models.Sparse RNNs are easier to deploy on devices and high-end server processors.Even though sparse operations need less compute and memory relative to their dense counterparts, the speed-up observed by using sparse operations is less than expected on different hardware platforms.In order to address this issue, we investigate two different approaches to induce block sparsity in RNNs: pruning blocks of weights in a layer and using group lasso regularization with pruning to create blocks of weights with zeros.Using these techniques, we can create block-sparse RNNs with sparsity ranging from 80% to 90% with a small loss in accuracy.This technique allows us to reduce the model size by roughly 10x.Additionally, we can prune a larger dense network to recover this loss in accuracy while maintaining high block sparsity and reducing the overall parameter count.Our technique works with a variety of block sizes up to 32x32.Block-sparse RNNs eliminate overheads related to data storage and irregular memory accesses while increasing hardware efficiency compared to unstructured sparsity.

Wir zeigen, dass die RNNs gepruned werden können, um eine Blocksparsität zu erzeugen, die eine Beschleunigung für spärliche Operationen auf vorhandener Hardware ermöglicht.

Die Autoren schlagen einen Block Sparsity Pruning-Ansatz zur Komprimierung von RNNs vor, bei dem Gruppen-LASSO zur Förderung der Sparsity und zum Pruning verwendet wird, allerdings mit einem sehr speziellen Zeitplan für das Pruning und das Pruning-Gewicht.


Value iteration networks are an approximation of the value iteration (VI) algorithm implemented with convolutional neural networks to make VI fully differentiable.In this work, we study these networks in the context of robot motion planning, with a focus on applications to planetary rovers.The key challenging task in learning-based motion planning is to learn a transformation from terrain observations to a suitable navigation reward function.In order to deal with complex terrain observations and policy learning, we propose a value iteration recurrence, referred to as the soft value iteration network (SVIN).SVIN is designed to produce more effective training gradients through the value iteration network.It relies on a soft policy model, where the policy is represented with a probability distribution over all possible actions, rather than a deterministic policy that returns only the best action.We demonstrate the effectiveness of the proposed method in robot motion planning scenarios.In particular, we study the application of SVIN to very challenging problems in planetary rover navigation and present early training results on data gathered by the  Curiosity rover that is currently operating on Mars.

Wir schlagen eine Verbesserung der Wert-Iterationsnetzwerke vor, mit Anwendungen für die Pfadplanung von Planeten-Rovern.

In dieser Arbeit wird eine Belohnungsfunktion auf der Grundlage von Expertentrajektorien mit Hilfe eines Value Iteration Module erlernt, um den Planungsschritt differenzierbar zu machen.


Transformer networks have lead to important progress in language modeling and machine translation.These models include two consecutive modules, a feed-forward layer and a self-attention layer.The latter allows the network to capture long term dependencies and are often regarded as the key ingredient in the success of Transformers.Building upon this intuition, we propose a new model that solely consists of attention layers.More precisely, we augment the self-attention layers with persistent memory vectors that play a similar role as the feed-forward layer.Thanks to these vectors, we can remove the feed-forward layer without degrading the performance of a transformer.Our evaluation shows the benefits brought by our model on standard character and word level language modeling benchmarks.

Ein neuartiger Attention Layer, der die Selbstaufmerksamkeit und Feed-Forward-Teilschichten von Transformer-Netzen kombiniert.

In diesem Beitrag wird eine Modifikation des Transformer-Modells vorgeschlagen, bei der die Aufmerksamkeit über "persistente" Speichervektoren in die Selbstaufmerksamkeitsschicht integriert wird, was zu einer Leistung führt, die mit bestehenden Modellen vergleichbar ist, während weniger Parameter benötigt werden.


This work views neural networks as data generating systems and applies anomalous pattern detection techniques on that data in order to detect when a network is processing a group of anomalous inputs.  Detecting anomalies is a critical component for multiple machine learning problems including detecting the presence of adversarial noise added to inputs.More broadly, this work is a step towards giving neural networks the ability to detect groups of out-of-distribution samples.  This work introduces ``Subset Scanning methods from the anomalous pattern detection domain to the task of detecting anomalous inputs to neural networks.  Subset Scanning allows us to answer the question: "``Which subset of inputs have larger-than-expected activations at which subset of nodes?"  Framing the adversarial detection problem this way allows us to identify systematic patterns in the activation space that span multiple adversarially noised images.  Such images are ``"weird together".  Leveraging this common anomalous pattern, we show increased detection power as the proportion of noised images increases in a test set.   Detection power and accuracy results are provided for targeted adversarial noise added to CIFAR-10 images on a 20-layer ResNet using the Basic Iterative Method attack.

Wir finden effizient eine Untergruppe von Bildern, die höhere Aktivierungen als erwartet für eine Untergruppe von Knoten aufweisen.  Diese Bilder erscheinen anomaler und sind leichter zu erkennen, wenn sie als Gruppe betrachtet werden.

In dem Beitrag wird ein Verfahren zur Erkennung anomaler Eingaben vorgeschlagen, das auf einem "Subset-Scanning"-Ansatz zur Erkennung anomaler Aktivierungen im Deep Learning Netzwerk basiert.


Stability is a fundamental property of dynamical systems, yet to this date it has had little bearing on the practice of recurrent neural networks.In this work, we conduct a thorough investigation of stable recurrent models.Theoretically, we prove stable recurrent neural networks are well approximated by feed-forward networks for the purpose of both inference and training by gradient descent.Empirically, we demonstrate stable recurrent models often perform as well as their unstable counterparts on benchmark sequence tasks.Taken together, these findings shed light on the effective power of recurrent networks and suggest much of sequence learning happens, or can be made to happen, in the stable regime.Moreover, our results help to explain why in many cases practitioners succeed in replacing recurrent models by feed-forward models.

Stabile rekurrente Modelle können durch Feed Forward Netzwerke approximiert werden und schneiden empirisch genauso gut ab wie instabile Modelle bei Benchmark-Aufgaben.

Studien zur Stabilität von RNNs und Untersuchung der spektralen Normalisierung für sequenzielle Vorhersagen.


Weight-sharing plays a significant role in the success of many deep neural networks, by increasing memory efficiency and incorporating useful inductive priors about the problem into the network.But understanding how weight-sharing can be used effectively in general is a topic that has not been studied extensively.Chen et al. (2015) proposed HashedNets, which augments a multi-layer perceptron with a hash table, as a method for neural network compression.We generalize this method into a framework (ArbNets) that allows for efficient arbitrary weight-sharing, and use it to study the role of weight-sharing in neural networks.We show that common neural networks can be expressed as ArbNets with different hash functions.We also present two novel hash functions, the Dirichlet hash and the Neighborhood hash, and use them to demonstrate experimentally that balanced and deterministic weight-sharing helps with the performance of a neural network.

Untersuchung der Rolle der Gewichtsteilung in neuronalen Netzen unter Verwendung von Hash-Funktionen und feststellen, dass eine ausgewogene und deterministische Hash-Funktion die Netzleistung verbessert.

Vorschlag für ArbNets zur systematischeren Untersuchung der Gewichtsteilung durch Definition der Gewichtsteilungsfunktion als Hash-Funktion.


We introduce Neural Markov Logic Networks (NMLNs), a statistical relational learning system that borrows ideas from Markov logic.Like Markov Logic Networks (MLNs), NMLNs are an exponential-family model for modelling distributions over possible worlds, but unlike MLNs, they do not rely on explicitly specified first-order logic rules.Instead, NMLNs learn an implicit representation of such rules as a neural network that acts as a potential function on fragments of the relational structure.Interestingly, any MLN can be represented as an NMLN.Similarly to recently proposed Neural theorem provers (NTPs) (Rocktaschel at al. 2017), NMLNs can exploit embeddings of constants but, unlike NTPs, NMLNs work well also in their absence.This is extremely important for predicting in settings other than the transductive one.We showcase the potential of NMLNs on knowledge-base completion tasks and on generation of molecular (graph) data.

Wir stellen ein statistisches relationales Lernsystem vor, das Ideen aus der Markov-Logik entlehnt, aber eine implizite Repräsentation von Regeln als neuronales Netz erlernt.

Das Papier bietet eine Erweiterung der Markov-Logik-Netzwerke, indem es ihre Abhängigkeit von vordefinierten Logikregeln erster Ordnung aufhebt, um mehr Domänen in Wissensdatenbank-Vervollständigungsaufgaben zu modellieren.


Using variational Bayes neural networks, we develop an algorithm capable of accumulating knowledge into a prior from multiple different tasks.This results in a rich prior capable of few-shot learning on new tasks.The posterior can go beyond the mean field approximation and yields good uncertainty on the performed experiments.Analysis on toy tasks show that it can learn from significantly different tasks while finding similarities among them.Experiments on Mini-Imagenet reach state of the art with 74.5% accuracy on 5 shot learning.Finally, we provide two new benchmarks, each showing a failure mode of existing meta learning algorithms such as MAML and prototypical Networks.

Eine skalierbare Methode zum Erlernen eines aussagekräftigen Priors für neuronale Netze über mehrere Aufgaben hinweg.

Die Arbeit stellt eine Methode für die Ausbildung eines probabilistischen Modells für Multitasks Transfer Learning durch die Einführung einer latenten Variable pro Aufgabe, um die Gemeinsamkeit in den Aufgaben Instanzen zu erfassen.

In der Arbeit wird ein Variationsansatz für das Meta-Lernen vorgeschlagen, der latente Variablen verwendet, die aufgabenspezifischen Datensätzen entsprechen.

Ziel ist es, einen Prior über neuronale Netze für mehrere Aufgaben zu lernen.


Sequential data often originates from diverse environments.Across them exist both shared regularities and environment specifics.To learn robust cross-environment descriptions of sequences we introduce disentangled state space models (DSSM).In the latent space of DSSM environment-invariant state dynamics is explicitly disentangled from environment-specific information governing that dynamics.We empirically show that such separation enables robust prediction, sequence manipulation and environment characterization.We also propose an unsupervised VAE-based training procedure to learn DSSM as Bayesian filters.In our experiments, we demonstrate state-of-the-art performance in controlled generation and prediction of bouncing ball video sequences across varying gravitational influences.

Die Arbeit stellt ein generatives Zustandsraummodell vor, das eine globale latente Variable E verwendet, um umweltspezifische Informationen zu erfassen.


In this work, we approach one-shot and few-shot learning problems as methods for finding good prototypes for each class, where these prototypes are generalizable to new data samples and classes.We propose a metric learner that learns a Bregman divergence by learning its underlying convex function.Bregman divergences are a good candidate for this framework given they are the only class of divergences with the property that the best representative of a set of points is given by its mean.We propose a flexible extension to prototypical networks to enable joint learning of the embedding and the divergence, while preserving computational efficiency.Our preliminary results are comparable with the prior work on the Omniglot and Mini-ImageNet datasets, two standard benchmarks for one-shot and few-shot learning.We argue that our model can be used for other tasks that involve metric learning or tasks that require approximate convexity such as structured prediction and data completion.

Bregman Divergenz Lernen für Few-Shot Learning.


Motivated by the flexibility of biological neural networks whose connectivity structure changes significantly during their lifetime,we introduce the Unrestricted Recursive Network (URN) and demonstrate that it can exhibit similar flexibility during training via gradient descent.We show empirically that many of the different neural network structures commonly used in practice today (including fully connected, locally connected and residual networks of differ-ent depths and widths) can emerge dynamically from the same URN.These different structures can be derived using gradient descent on a single general loss function where the structure of the data and the relative strengths of various regulator terms determine the structure of the emergent network.We show that this loss function and the regulators arise naturally when considering the symmetries of the network as well as the geometric properties of the input data.

Wir stellen einen Netzwerkrahmen vor, der seine Struktur während des Trainings verändern kann, und zeigen, dass er zu verschiedenen ML-Netzwerk-Archetypen wie MLPs und LCNs konvergieren kann.


We present CROSSGRAD , a method to use multi-domain training data to learn a classifier that generalizes to new domains.CROSSGRAD does not need an adaptation phase via labeled or unlabeled data, or domain features in the new domain.Most existing domain adaptation methods attempt to erase domain signals using techniques like domain adversarial training.In contrast, CROSSGRAD is free to use domain signals for predicting labels, if it can prevent overfitting on training domains.We conceptualize the task in a Bayesian setting, in which a sampling step is implemented as data augmentation, based on domain-guided perturbations of input instances.CROSSGRAD jointly trains a label and a domain classifier on examples perturbed by loss gradients of each otherâ€™s objectives.This enables us to directly perturb inputs, without separating and re-mixing domain signals while making various distributional assumptions.Empirical evaluation on three different applications where this setting is natural establishes that (1) domain-guided perturbation provides consistently better generalization to unseen domains, compared to generic instance perturbation methods, and (2) data augmentation is a more stable and accurate method than domain adversarial training.

Die bereichsbezogene Datenerweiterung bietet eine robuste und stabile Methode der Bereichsgeneralisierung

In diesem Artikel wird ein Ansatz zur Generalisierung von Domänen durch domänenabhängige Datenerweiterung vorgeschlagen

Die Autoren stellen die CrossGrad-Methode vor, die sowohl eine Label-Klassifizierungsaufgabe als auch eine Domänen-Klassifizierungsaufgabe trainiert.


We present sketch-rnn, a recurrent neural network able to construct stroke-based drawings of common objects.The model is trained on a dataset of human-drawn images representing many different classes.We outline a framework for conditional and unconditional sketch generation, and describe new robust training methods for generating coherent sketch drawings in a vector format.

Wir untersuchen Alternativen zu traditionellen Pixelbildmodellierungsansätzen und schlagen ein generatives Modell für Vektorbilder vor.

In diesem Beitrag wird eine neuronale Netzwerkarchitektur zur Erzeugung von Skizzenzeichnungen vorgestellt, die sich an den variationalen Autoencoder anlehnt.


Wilson et al. (2017) showed that, when the stepsize schedule is properly designed, stochastic gradient generalizes better than ADAM (Kingma & Ba, 2014).In light of recent work on hypergradient methods (Baydin et al., 2018), we revisit these claims to see if such methods close the gap between the most popular optimizers.As a byproduct, we analyze the true benefit of these hypergradient methods compared to more classical schedules, such as the fixed decay of Wilson et al. (2017).In particular, we observe they are of marginal help since their performance varies significantly when tuning their hyperparameters.Finally, as robustness is a critical quality of an optimizer, we provide a sensitivity analysis of these gradient based optimizers to assess how challenging their tuning is.

Wir stellen eine Studie vor, die versucht zu sehen, wie die jüngste Online-Lernratenanpassung die Schlussfolgerung von Wilson et al. 2018 über adaptive Gradientenmethoden erweitert, zusammen mit einem Vergleich und einer Sensitivitätsanalyse.

Berichtet über die Ergebnisse von Tests verschiedener Methoden zur Anpassung der Schrittweite, einschließlich einfachem SGD, SGD mit Neserov-Momentum und ADAM, und vergleicht diese Methoden mit und ohne Hypergradient.


Despite an ever growing literature on reinforcement learning algorithms and applications, much less is known about their statistical inference.In this paper, we investigate the large-sample behaviors of the Q-value estimates with closed-form characterizations of the asymptotic variances.This allows us to efficiently construct confidence regions for Q-value and optimal value functions, and to develop policies to minimize their estimation errors.This also leads to a policy exploration strategy that relies on estimating the relative discrepancies among the Q estimates.Numerical experiments show superior performances of our exploration strategy than other benchmark approaches.

Wir untersuchen das Verhalten der Q-Wert-Schätzungen bei großen Stichproben und schlagen eine effiziente Erkundungsstrategie vor, die sich auf die Schätzung der relativen Diskrepanzen zwischen den Q-Schätzungen stützt.

In diesem Beitrag wird ein reiner Explorationsalgorithmus für das Reinforcement Learning vorgestellt, der auf einer asymptotischen Analyse der Q-Werte und ihrer Konvergenz zur zentralen Grenzverteilung basiert und damit bessere Ergebnisse als die Benchmark-Explorationsalgorithmen erzielt.


We perform completely unsupervised one-sided image to image translation between a source domain $X$ and a target domain $Y$ such that we preserve relevant underlying shared semantics (e.g., class, size, shape, etc). In particular, we are interested in a more difficult case than those typically addressed in the literature, where the source and target are ``far" enough that reconstruction-style or pixel-wise approaches fail.We argue that transferring (i.e., \emph{translating}) said relevant information should involve both discarding source domain-specific information while incorporate target domain-specific information, the latter of which we model with a noisy prior distribution. In order to avoid the degenerate case where the generated samples are only explained by the prior distribution, we propose to minimize an estimate of the mutual information between the generated sample and the sample from the prior distribution.We discover that the architectural choices are an important factor to consider in order to preserve the shared semantic between $X$ and $Y$. We show state of the art results on the MNIST to SVHN task for unsupervised image to image translation.

Wir trainieren ein Bild-zu-Bild-Übersetzungsnetzwerk, das als Eingabe das Quellbild und eine Probe aus einer vorherigen Verteilung nimmt, um eine Probe aus der Zielverteilung zu erzeugen

Diese Arbeit formalisiert das Problem der unbeaufsichtigten Übersetzung und schlägt ein erweitertes GAN Framework vor, der die gegenseitige Information nutzt, um den degenerierten Fall zu vermeiden.

In dieser Arbeit wird das Problem der unbeaufsichtigten Eins-zu-Viel-Bild-Übersetzung formuliert und das Problem durch Minimierung der gegenseitigen Information gelöst.


Identifying salient points in images is a crucial component for visual odometry, Structure-from-Motion or SLAM algorithms.Recently, several learned keypoint methods have demonstrated compelling performance on challenging benchmarks.  However, generating consistent and accurate training data for interest-point detection in natural images still remains challenging, especially for human annotators.We introduce IO-Net (i.e. InlierOutlierNet), a novel proxy task for the self-supervision of keypoint detection, description and matching.By making the sampling of inlier-outlier sets from point-pair correspondences fully differentiable within the keypoint learning framework, we show that are able to simultaneously self-supervise keypoint description and improve keypoint matching.Second, we introduce KeyPointNet, a keypoint-network architecture that is especially amenable to robust keypoint detection and description.We design the network to allow local keypoint aggregation to avoid artifacts due to spatial discretizations commonly used for this task, and we improve fine-grained keypoint descriptor performance by taking advantage of efficient sub-pixel convolutions to upsample the descriptor feature-maps to a higher operating resolution.Through extensive experiments and ablative analysis, we show that the proposed self-supervised keypoint learning method greatly improves the quality of feature matching and homography estimation on challenging benchmarks over the state-of-the-art.

Lernen, um unterscheidbare Schlüsselpunkte aus einer Proxy-Aufgabe zu extrahieren, Zurückweisung von Ausreißern.

Dieser Beitrag widmet sich dem selbstüberwachten Lernen von lokalen Merkmalen unter Verwendung von Neural Guided RANSAC als zusätzlicher Hilfsverlustlieferant zur Verbesserung der Deskriptorinterpolation.


We study the role of intrinsic motivation as an exploration bias for reinforcement learning in sparse-reward synergistic tasks, which are tasks where multiple agents must work together to achieve a goal they could not individually.Our key idea is that a good guiding principle for intrinsic motivation in synergistic tasks is to take actions which affect the world in ways that would not be achieved if the agents were acting on their own.Thus, we propose to incentivize agents to take (joint) actions whose effects cannot be predicted via a composition of the predicted effect for each individual agent.We study two instantiations of this idea, one based on the true states encountered, and another based on a dynamics model trained concurrently with the policy.While the former is simpler, the latter has the benefit of being analytically differentiable with respect to the action taken.We validate our approach in robotic bimanual manipulation tasks with sparse rewards; we find that our approach yields more efficient learning than both1) training with only the sparse reward and2) using the typical surprise-based formulation of intrinsic motivation, which does not bias toward synergistic behavior.Videos are available on the project webpage: https://sites.google.com/view/iclr2020-synergistic.

Wir schlagen eine Formulierung der intrinsischen Motivation vor, die sich als Explorationsverzerrung in synergetischen Multi-Agenten-Aufgaben mit geringer Belohnung eignet, indem sie die Agenten dazu ermutigt, die Welt auf eine Weise zu beeinflussen, die nicht möglich wäre, wenn sie einzeln handeln würden.

Der Beitrag konzentriert sich auf die Nutzung intrinsischer Motivation zur Verbesserung des Explorationsprozesses von Agenten mit Reinforcement Learning bei Aufgaben, die von mehreren Agenten zu bewältigen sind.


A general graph-structured neural network architecture operates on graphs through two core components: (1) complex enough message functions; (2) a fixed information aggregation process.In this paper, we present the Policy Message Passing algorithm, which takes a probabilistic perspective and reformulates the whole information aggregation as stochastic sequential processes.The algorithm works on a much larger search space, utilizes reasoning history to perform inference, and is robust to noisy edges.We apply our algorithm to multiple complex graph reasoning and prediction tasks and show that our algorithm consistently outperforms state-of-the-art graph-structured models by a significant margin.

Ein probabilistischer Inferenzalgorithmus auf der Grundlage eines neuronalen Netzes für graphisch-strukturierte Modelle.

Diese Arbeit stellt Policy Message Passing vor, ein neuronales Graphen-Netzwerk mit einem Inferenz-Mechanismus, der den Kanten auf rekurrente Weise Nachrichten zuweist, was auf eine konkurrenzfähige Leistung bei visuellen Argumentationsaufgaben hindeutet.