Transferring knowledge across tasks to improve data-efficiency is one ofthe open key challenges in the area of global optimization algorithms.Readilyavailable algorithms are typically designed to be universal optimizers and, thus,often suboptimal for specific tasks.We propose a novel transfer learning method toobtain customized optimizers within the well-established framework of Bayesianoptimization, allowing our algorithm to utilize the proven generalizationcapabilities of Gaussian processes.Using reinforcement learning to meta-train anacquisition function (AF) on a set of related tasks, the proposed method learns toextract implicit structural information and to exploit it for improved data-efficiency.We present experiments on a sim-to-real transfer task as well as on several simulatedfunctions and two hyperparameter search problems.The results show that ouralgorithm (1) automatically identifies structural properties of objective functionsfrom available source tasks or simulations, (2) performs favourably in settings withboth scarse and abundant source data, and (3) falls back to the performance levelof general AFs if no structure is present.

Wir führen effizientes und flexibles Transferlernen im Framework der Bayes'schen Optimierung durch meta-gelernte neuronale Erfassungsfunktionen durch.

Die Autoren stellen MetaBO vor, das Reinforcement Learning zum Meta-Lernen der Erfassungsfunktion für Bayes'sche Optimierung einsetzt und eine zunehmende Effizienz der Stichprobe bei neuen Aufgaben zeigt.

Die Autoren schlagen eine auf Meta-Learning basierende Alternative zu Standard-Erfassungsfunktionen (AFs) vor, bei der ein vortrainiertes neuronales Netz Erfassungswerte in Abhängigkeit von handverlesenen Merkmalen ausgibt.


We study the evolution of internal representations during deep neural network (DNN) training, aiming to demystify the compression aspect of the information bottleneck theory.The theory suggests that DNN training comprises a rapid fitting phase followed by a slower compression phase, in which the mutual information I(X;T) between the input X and internal representations T decreases.Several papers observe compression of estimated mutual information on different DNN models, but the true I(X;T) over these networks is provably either constant (discrete X) or infinite (continuous X).This work explains the discrepancy between theory and experiments, and clarifies what was actually measured by these past works.To this end, we introduce an auxiliary (noisy) DNN framework for which I(X;T) is a meaningful quantity that depends on the network's parameters.This noisy framework is shown to be a good proxy for the original (deterministic) DNN both in terms of performance and the learned representations.We then develop a rigorous estimator for I(X;T) in noisy DNNs and observe compression in various models.By relating I(X;T) in the noisy DNN to an information-theoretic communication problem, we show that compression is driven by the progressive clustering of hidden representations of inputs from the same class.Several methods to directly monitor clustering of hidden representations, both in noisy and deterministic DNNs, are used to show that meaningful clusters form in the T space.Finally, we return to the estimator of I(X;T) employed in past works, and demonstrate that while it fails to capture the true (vacuous) mutual information, it does serve as a measure for clustering.This clarifies the past observations of compression and isolates the geometric clustering of hidden representations as the true phenomenon of interest.

Deterministische tiefe neuronale Netze verwerfen keine Informationen, aber sie clustern ihre Eingaben.

Dieses Papier bietet einen prinzipiellen Weg, um die Kompressionsphrase in tiefen neuronalen Netzen zu untersuchen, indem es einen theoretisch fundierten Entropieschätzer zur Schätzung der gegenseitigen Information bereitstellt.


A central challenge in multi-agent reinforcement learning is the induction of coordination between agents of a team.In this work, we investigate how to promote inter-agent coordination using policy regularization and discuss two possible avenues respectively based on inter-agent modelling and synchronized sub-policy selection.We test each approach in four challenging continuous control tasks with sparse rewards and compare them against three baselines including MADDPG, a state-of-the-art multi-agent reinforcement learning algorithm.To ensure a fair comparison, we rely on a thorough hyper-parameter selection and training methodology that allows a fixed hyper-parameter search budget for each algorithm and environment.We consequently assess both the hyper-parameter sensitivity, sample-efficiency and asymptotic performance of each learning method.Our experiments show that the proposed methods lead to significant improvements on cooperative problems.We further analyse the effects of the proposed regularizations on the behaviors learned by the agents.

Wir schlagen Regularisierungsziele für Multi-Agenten-RL-Algorithmen vor, die die Koordination bei kooperativen Aufgaben fördern.

In dieser Arebit werden zwei Methoden vorgeschlagen, um Agenten dazu zu bringen, koordinierte Verhaltensweisen zu erlernen, und beide werden in Multi-Agenten-Domänen von angemessener Komplexität rigoros evaluiert.

In diesem Artikel werden zwei Methoden vorgeschlagen, die auf MADDPG aufbauen, um die Zusammenarbeit zwischen dezentralen MARL-Agenten zu fördern.


Multimodal sentiment analysis is a core research area that studies speaker sentiment expressed from the language, visual, and acoustic modalities.The central challenge in multimodal learning involves inferring joint representations that can process and relate information from these modalities.However, existing work learns joint representations using multiple modalities as input and may be sensitive to noisy or missing modalities at test time.With the recent success of sequence to sequence models in machine translation, there is an opportunity to explore new ways of learning joint representations that may not require all input modalities at test time.In this paper, we propose a method to learn robust joint representations by translating between modalities.Our method is based on the key insight that translation from a source to a target modality provides a method of learning joint representations using only the source modality as input.We augment modality translations with a cycle consistency loss to ensure that our joint representations retain maximal information from all modalities.Once our translation model is trained with paired multimodal data, we only need data from the source modality at test-time for prediction.This ensures that our model remains robust from perturbations or missing target modalities.We train our model with a coupled translation-prediction objective and it achieves new state-of-the-art results on multimodal sentiment analysis datasets: CMU-MOSI, ICT-MMMO, and YouTube.Additional experiments show that our model learns increasingly discriminative joint representations with more input modalities while maintaining robustness to perturbations of all other modalities.

Wir stellen ein Modell vor, das robuste gemeinsame Repräsentationen erlernt, indem es hierarchische zyklische Übersetzungen zwischen mehreren Modalitäten durchführt.

In diesem Beitrag wird das Multimodal Cyclic Translation Network (MCTN) vorgestellt und für die multimodale Sentiment Analyse evaluiert.


The geometric properties of loss surfaces, such as the local flatness of a solution, are associated with generalization in deep learning.The Hessian is often used to understand these geometric properties.We investigate the differences between the eigenvalues of the neural network Hessian evaluated over the empirical dataset, the Empirical Hessian, and the eigenvalues of the Hessian under the data generating distribution, which we term the True Hessian.Under mild assumptions, we use random matrix theory to show that the True Hessian has eigenvalues of smaller absolute value than the Empirical Hessian.We support these results for different SGD schedules on both a 110-Layer ResNet and VGG-16.To perform these experiments we propose a framework for spectral visualization, based on GPU accelerated stochastic Lanczos quadrature.This approach is an order of magnitude faster than state-of-the-art methods for spectral visualization, and can be generically used to investigate the spectral properties of matrices in deep learning.

Das Verständnis der Hessian Eigenwerte des neuronalen Netzes unter der datenerzeugenden Verteilung.

Diese Arbeit analysiert das Spektrum der Hessian Matrix großer neuronaler Netze, mit einer Analyse der Max/Min-Eigenwerte und einer Visualisierung der Spektren unter Verwendung eines Lanczos-Quadratur-Ansatzes.

In dieser Arbeit wird die Theorie der Zufallsmatrix verwendet, um die Spektrumverteilung der empirischen Hessian und der wahren Hessian für Deep Learning zu untersuchen, und es wird eine effiziente Methode zur Visualisierung des Spektrums vorgeschlagen.


Summarization of long sequences into a concise statement is a core problem in natural language processing, requiring non-trivial understanding of the input.Based on the promising results of graph neural networks on highly structured data, we develop a framework to extend existing sequence encoders with a graph component that can reason about long-distance relationships in weakly structured data such as text.In an extensive evaluation, we show that the resulting hybrid sequence-graph models outperform both pure sequence models as well as pure graph models on a range of summarization tasks.

Ein einfacher Trick zur Verbesserung von Sequenzmodellen: Kombinieren Sie sie mit einem Graphenmodell

In diesem Beitrag wird ein strukturelles Zusammenfassungsmodell mit einem graphenbasierten Encoder vorgestellt, der auf RNN basiert.

Diese Arbeit kombiniert Graph Neuronale Netze mit einem sequentiellen Ansatz zur abstrakten Zusammenfassung, der im Vergleich zu externen Baselines über alle Datensätze hinweg effektiv ist.


In probabilistic classification, a discriminative model based on Gaussian mixture exhibits flexible fitting capability.Nevertheless, it is difficult to determine the number of components.We propose a sparse classifier based on a discriminative Gaussian mixture model (GMM), which is named sparse discriminative Gaussian mixture (SDGM).In the SDGM, a GMM-based discriminative model is trained by sparse Bayesian learning.This learning algorithm improves the generalization capability by obtaining a sparse solution and automatically determines the number of components by removing redundant components.The SDGM can be embedded into neural networks (NNs) such as convolutional NNs and can be trained in an end-to-end manner.Experimental results indicated that the proposed method prevented overfitting by obtaining sparsity.Furthermore, we demonstrated that the proposed method outperformed a fully connected layer with the softmax function in certain cases when it was used as the last layer of a deep NN.

Ein spärlicher Klassifikator auf der Grundlage eines diskriminativen Gaußschen Mischmodells, das auch in ein neuronales Netz eingebettet werden kann.

Diese Arbeit stellt ein Gaußsches Mischungsmodell vor, das mit Hilfe von Gradientenabstiegsargumenten trainiert wird, die es ermöglichen, Spärlichkeit zu induzieren und die trainierbaren Modellschichtparameter zu reduzieren.

In dieser Arbeit wird ein Klassifikator, genannt SDGM, vorgeschlagen, der auf einer diskriminativen Gaußschen Mischung und ihrer spärlichen Parameterschätzung basiert.


We recently observed that convolutional filters initializedfarthest apart from each other using offthe-shelf pre-computed Grassmannian subspacepacking codebooks performed surprisingly wellacross many datasets.Through this short paper,weâ€™d like to disseminate some initial results in thisregard in the hope that we stimulate the curiosityof the deep-learning community towards consideringclassical Grassmannian subspace packingresults as a source of new ideas for more efficientinitialization strategies.

Initialisierung von Gewichten mit handelsüblichen Grassmann'schen Codebüchern, schnelleres Training und bessere Genauigkeit


Domain adaptation is critical for success in new, unseen environments.Adversarial adaptation models applied in feature spaces discover domain invariant representations, but are difficult to visualize and sometimes fail to capture pixel-level and low-level domain shifts.Recent work has shown that generative adversarial networks combined with cycle-consistency constraints are surprisingly effective at  mapping images between domains, even without the use of aligned image pairs.We propose a novel discriminatively-trained Cycle-Consistent Adversarial Domain Adaptation model.CyCADA adapts representations at both the pixel-level and feature-level, enforces cycle-consistency while leveraging a task loss, and does not require aligned pairs.  Our model can be applied in a variety of visual recognition and prediction settings.We show new state-of-the-art results across multiple adaptation tasks, including digit classification and semantic segmentation of road scenes demonstrating transfer from synthetic to real world domains.

Ein unüberwachter Ansatz zur Bereichsanpassung, der sich sowohl auf der Pixel- als auch auf der Merkmalsebene anpasst

Dieser Artikel schlägt einen Ansatz zur Domänenanpassung vor, indem es den CycleGAN um aufgabenspezifische Verlustfunktionen erweitert und den Verlust sowohl über Pixel als auch über Merkmale auferlegt.

In dieser Arbeit wird die Verwendung von CycleGANs für die Bereichsanpassung vorgeschlagen

Diese Arbeit macht eine neuartige Erweiterung der bisherigen Arbeit auf CycleGAN durch die Kopplung mit adversarial Anpassungsansätzen, einschließlich einer neuen Funktion und semantischem Verlust in das übergeordnete Ziel der CycleGAN, mit klaren Vorteilen.


Stemming is the process of removing affixes( i.e. prefixes, infixes and suffixes) that improve the accuracy and performance of information retrieval systems.This paper presents the reduction of Amharic words to corresponding stem where with the intention that it preserves semantic information.The proposed approach efficiently removes affixes from an Amharic word.The process of removing such affixes (prefixes, infixes and suffixes) from a word to its base form is called stemming.While many stemmers exist for dominant languages such as English, under resourced languages such as Amharic which lacks such powerful tool support.In this paper, we design a light Amharic stemmer relying on the rules that receives an Amharic word and then it finds a match to the beginning of a word to the possible prefixes and to its ending with the possible suffixes and finally it checks whether it has infix.The final result is the stem if there is any prefix, infix or/and suffix, otherwise it remains in one of the earlier states.The technique does not rely on any additional resource (e.g. dictionary) to verify the generated stem.The performance of the generated stemmer is evaluated using manually annotated Amharic words.The result is compared with current state-of-the-art stemmer for Amharic showing an increase of 7% in stemmer correctness.

Amharic Light Stemmer wurde entwickelt, um die Leistung der Amharic Sentiment Classification zu verbessern.

In diesem Beitrag wird das Stemming für morphologisch reichhaltige Sprachen mit einem leichten Stemmer untersucht, der Affixe nur so weit entfernt, dass die ursprüngliche semantische Information im Wort erhalten bleibt.

In diesem Beitrag wird eine Technik zur Amharic Light Stemming vorgeschlagen, die eine Kaskade von Transformationen verwendet, die die Form standardisieren und Suffixe, Präfixe und Infixe entfernen.


Place and grid-cells are known to aid navigation in animals and humans.Together with concept cells, they allow humans to form an internal representation of the external world, namely the concept space.We investigate the presence of such a space in deep neural networks by plotting the activation profile of its hidden layer neurons.Although place cell and concept-cell like properties are found, grid-cell like firing patterns are absent thereby indicating a lack of path integration or feature transformation functionality in trained networks.Overall, we present a plausible inadequacy in current deep learning practices that restrict deep networks from performing analogical reasoning and memory retrieval tasks.

Wir untersuchten, ob einfache tiefe Netze über gitterzellenartige künstliche Neuronen verfügen, während der Gedächtnisabruf im erlernten Konzeptraum erfolgt.


We develop a comprehensive description of the active inference framework, as proposed by Friston (2010), under a machine-learning compliant perspective.Stemming from a biological inspiration and the auto-encoding principles, a sketch of a cognitive architecture is proposed that should provide ways to implement estimation-oriented control policies.  Computer simulations illustrate the effectiveness of the approach through a foveated inspection of the input data.The pros and cons of the control policy are analyzed in detail, showing interesting promises in terms of processing compression.Though optimizing future posterior entropy over the actions set is shown enough to attain locally optimal action selection, offline calculation using class-specific saliency maps is shown better for it saves processing costs through saccades pathways pre-processing, with a negligible effect on the recognition/compression rates.

Vor- und Nachteile der Sakkaden-basierten Computer Vision unter der Perspektive des voraussagenden Codens.

Stellt einen rechnerischen Rahmen für das Problem des aktiven Sehens vor und erklärt, wie die Kontrollpolitik erlernt werden kann, um die Entropie der nachträglichen Überzeugung zu reduzieren.


Graphs possess exotic features like variable size and absence of natural ordering of the nodes that make them difficult to analyze and compare.To circumvent this problem and learn on graphs, graph feature representation is required.Main difficulties with feature extraction lie in the trade-off between expressiveness, consistency and efficiency, i.e. the capacity to extract features that represent the structural information of the graph while being deformation-consistent and isomorphism-invariant.While state-of-the-art methods enhance expressiveness with powerful graph neural-networks, we propose to leverage natural spectral properties of graphs to study a simple graph feature: the graph Laplacian spectrum (GLS).We analyze the representational power of this object that satisfies both isomorphism-invariance, expressiveness and deformation-consistency.In particular, we propose a theoretical analysis based on graph perturbation to understand what kind of comparison between graphs we do when comparing GLS.To do so, we derive bounds for the distance between GLS that are related to the divergence to isomorphism, a standard computationally expensive graph divergence.Finally, we experiment GLS as graph representation through consistency tests and classification tasks, and show that it is a strong graph feature representation baseline.

Wir untersuchen theoretisch die Konsistenz des Laplacian-Spektrums und verwenden es als Ganzgrapheneinbettung

In diesem Beitrag wird das Laplacian-Spektrum eines Graphen als Mittel zur Erstellung einer Darstellung verwendet, die zum Vergleich und zur Klassifizierung von Graphen eingesetzt werden kann.

In dieser Arbeit wird vorgeschlagen, das Graph-Laplacian-Spektrum zum Erlernen der Graphdarstellung zu verwenden.


Adversarial training, a method for learning robust deep networks, is typically assumed to be more expensive than traditional training due to the necessity of constructing adversarial examples via a first-order method like projected gradient decent (PGD).  In this paper, we make the surprising discovery that it is possible to train empirically robust models using a much weaker and cheaper adversary, an approach that was previously believed to be ineffective, rendering the method no more costly than standard training in practice.  Specifically, we show that adversarial training with the fast gradient sign method (FGSM), when combined with random initialization, is as effective as PGD-based training but has significantly lower cost.  Furthermore we show that FGSM adversarial training can be further accelerated by using standard techniques for efficient training of deep networks, allowing us to learn a robust CIFAR10 classifier with 45% robust accuracy at epsilon=8/255 in 6 minutes, and a robust ImageNet classifier with 43% robust accuracy at epsilon=2/255 in 12 hours, in comparison to past work based on ``free'' adversarial training which took 10 and 50 hours to reach the same respective thresholds.

Das FGSM-basierte adversarial Training mit Randomisierung funktioniert genauso gut wie das PGD-basierte adversarial Training: Wir können damit einen robusten Klassifikator auf einem einzigen Rechner in 6 Minuten auf CIFAR10 und in 12 Stunden auf ImageNet trainieren.

In diesem Artikel wird die Random+FGSM Methode überarbeitet, um robuste Modelle gegen starke PID-Umgehungsangriffe schneller als frühere Methoden zu trainieren.

Die Hauptbehauptung dieser Arbeit ist, dass eine einfache Strategie der Randomisierung plus Fast-Gradient-Sign-Methode (FGSM) adversariales Training zu robusten neuronalen Netzen führt.


In seeking for sparse and efficient neural network models, many previous works investigated on enforcing L1 or L0 regularizers to encourage weight sparsity during training.The L0 regularizer measures the parameter sparsity directly and is invariant to the scaling of parameter values.But it cannot provide useful gradients and therefore requires complex optimization techniques.The L1 regularizer is almost everywhere differentiable and can be easily optimized with gradient descent.Yet it is not scale-invariant and causes the same shrinking rate to all parameters, which is inefficient in increasing sparsity.Inspired by the Hoyer measure (the ratio between L1 and L2 norms) used in traditional compressed sensing problems, we present DeepHoyer, a set of sparsity-inducing regularizers that are both differentiable almost everywhere and scale-invariant.Our experiments show that enforcing DeepHoyer regularizers can produce even sparser neural network models than previous works, under the same accuracy level.We also show that DeepHoyer can be applied to both element-wise and structural pruning.

Wir schlagen fast überall differenzierbare und skaleninvariante Regularisierer für das DNN Pruning vor, die zu Supremum Sparsity durch Standard SGD Training führen können.

Diese Arbeit schlägt einen skaleninvarianten Regularisierer (DeepHoyer) vor, der durch das Hoyer-Maß inspiriert ist, um Spärlichkeit in neuronalen Netzen zu erzwingen.


Self-supervision, in which a target task is improved without external supervision, has primarily been explored in settings that assume the availability of additional data.However, in many cases, particularly in healthcare, one may not have access to additional data (labeled or otherwise).In such settings, we hypothesize that self-supervision based solely on the structure of the data at-hand can help.We explore a novel self-supervision framework for time-series data, in which multiple auxiliary tasks (e.g., forecasting) are included to improve overall performance on a sequence-level target task without additional training data.We call this approach limited self-supervision, as we limit ourselves to only the data at-hand.We demonstrate the utility of limited self-supervision on three sequence-level classification tasks, two pertaining to real clinical data and one using synthetic data.Within this framework, we introduce novel forms of self-supervision and demonstrate their utility in improving performance on the target task.Our results indicate that limited self-supervision leads to a consistent improvement over a supervised baseline, across a range of domains.In particular, for the task of identifying atrial fibrillation from small amounts of electrocardiogram data, we observe a nearly 13% improvement in the area under the receiver operating characteristics curve (AUC-ROC) relative to the baseline (AUC-ROC=0.55 vs. AUC-ROC=0.62).Limited self-supervision applied to sequential data can aid in learning intermediate representations, making it particularly applicable in settings where data collection is difficult.

Wir zeigen, dass zusätzliche unbeschriftete Daten für selbstüberwachte Hilfsaufgaben nicht erforderlich sind, um für die Klassifizierung von Zeitreihen nützlich zu sein, und stellen neue und effektive Hilfsaufgaben vor.

In diesem Beitrag wird eine selbstüberwachte Methode für das Lernen aus Zeitreihendaten im Gesundheitswesen vorgeschlagen, bei der Hilfsaufgaben auf der Grundlage der internen Struktur der Daten entworfen werden, um mehr beschriftete Hilfsaufgaben für das Training zu erstellen.

In diesem Artikel wird ein Ansatz für selbstüberwachtes Lernen auf Zeitreihen vorgeschlagen.


Are neural networks biased toward simple functions?Does depth always help learn more complex features?Is training the last layer of a network as good as training all layers?These questions seem unrelated at face value, but in this work we give all of them a common treatment from the spectral perspective.We will study the spectra of the *Conjugate Kernel, CK,* (also called the *Neural Network-Gaussian Process Kernel*), and the *Neural Tangent Kernel, NTK*.Roughly, the CK and the NTK tell us respectively ``"what a network looks like at initialization" and "``what a network looks like during and after training."Their spectra then encode valuable information about the initial distribution and the training and generalization properties of neural networks.By analyzing the eigenvalues, we lend novel insights into the questions put forth at the beginning, and we verify these insights by extensive experiments of neural networks.We believe the computational tools we develop here for analyzing the spectra of CK and NTK serve as a solid foundation for future studies of deep neural networks.We have open-sourced the code for it and for generating the plots in this paper at github.com/jxVmnLgedVwv6mNcGCBy/NNspectra.

Eigenwerte von Conjugate (auch bekannt als NNGP) und Neural Tangent Kernel können in geschlossener Form über den Booleschen Würfel berechnet werden und zeigen die Auswirkungen von Hyperparametern auf die induktive Verzerrung, das Training und die Generalisierung neuronaler Netze.

In diesem Beitrag wird eine Spektralanalyse des konjugierten Kernels neuronaler Netze und des neuronalen Tangentenkernels auf booleschen Würfeln durchgeführt, um zu klären, warum tiefe Netze auf einfache Funktionen ausgerichtet sind.


To communicate, to ground hypotheses, to analyse data, neuroscientists often refer to divisions of the brain.Here we consider atlases used to parcellate the brain when studying brain function.We discuss the meaning and the validity of these parcellations, from a conceptual point of view as well as by running various analytical tasks on popular functional brain parcellations.

Alle funktionellen Gehirnparzellierungen sind falsch, aber einige sind nützlich.


High-dimensional sparse reward tasks present major challenges for reinforcement learning agents.  In this work we use imitation learning to address two of these challenges:  how to learn a useful representation of the world e.g.  from pixels, and how to explore efficiently given the rarity of a reward signal?We show that adversarial imitation can work well even in this high dimensional observation space.Surprisingly the adversary itself, acting as the learned reward function, can be tiny, comprising as few as 128 parameters, and can be easily trained using the most basic GAN formulation.Our approach removes limitations present in most contemporary imitation approaches: requiring no demonstrator actions (only video), no special initial conditions or warm starts, and no explicit tracking of any single demo.The proposed agent can solve a challenging robot manipulation task of block stacking from only video demonstrations and sparse reward, in which the non-imitating agents fail to learn completely.  Furthermore, our agent learns much faster than competing approaches that depend on hand-crafted, staged dense reward functions, and also better compared to standard GAIL baselines.Finally, we develop a new adversarial goal recognizer that in some cases allows the agent to learn stacking without any task reward, purely from imitation.

Nachahmung aus Pixeln, mit spärlicher oder keiner Belohnung, unter Verwendung von Off-Policy-RL und einer winzigen, adversarisch erlernten Belohnungsfunktion.

Das Papier schlägt die Verwendung eines "minimalen Gegners" beim generativen adversarial Imitationslernen in hochdimensionalen visuellen Räumen vor.

Dieses Papier zielt darauf ab, das Problem der Schätzung von spärlichen Belohnungen in einer hochdimensionalen Eingabeumgebung zu lösen.


In this paper we show strategies to easily identify fake samples generated with the Generative Adversarial Network framework.One strategy is based on the statistical analysis and comparison of raw pixel values and features extracted from them.The other strategy learns formal specifications from the real data and shows that fake samples violate the specifications of the real data.We show that fake samples produced with GANs have a universal signature that can be used to identify fake samples.We provide results on MNIST, CIFAR10, music and speech data.

Wir zeigen Strategien zur einfachen Identifizierung gefälschter Proben, die mit dem Generative Adversarial Network Framework erzeugt wurden.

Es wird gezeigt, dass gefälschte Beispiele, die mit gängigen GAN-Implementierungen (Generative Adversarial Network) erstellt wurden, mit verschiedenen statistischen Verfahren leicht identifiziert werden können.

Diese Arbeit schlägt Statistiken vor, um gefälschte Daten, die mit GANs generiert wurden, auf der Grundlage einfacher marginaler Statistiken oder formaler Spezifikationen, die automatisch aus realen Daten generiert wurden, zu identifizieren.


Efforts to reduce the numerical precision of computations in deep learning training have yielded systems that aggressively quantize weights and activations, yet employ wide high-precision accumulators for partial sums in inner-product operations to preserve the quality of convergence.The absence of any framework to analyze the precision requirements of partial sum accumulations results in conservative design choices.This imposes an upper-bound on the reduction of complexity of multiply-accumulate units.We present a statistical approach to analyze the impact of reduced accumulation precision on deep learning training.Observing that a bad choice for accumulation precision results in loss of information that manifests itself as a reduction in variance in an ensemble of partial sums, we derive a set of equations that relate this variance to the length of accumulation and the minimum number of bits needed for accumulation.We apply our analysis to three benchmark networks: CIFAR-10 ResNet 32, ImageNet ResNet 18 and ImageNet AlexNet.In each case, with accumulation precision set in accordance with our proposed equations, the networks successfully converge to the single precision floating-point baseline.We also show that reducing accumulation precision further degrades the quality of the trained network, proving that our equations produce tight bounds.Overall this analysis enables precise tailoring of computation hardware to the application, yielding area- and power-optimal systems.

Wir stellen einen analytischen Rahmen vor, um die Anforderungen an die Akkumulations-Bitbreite in allen drei Deep Learning Trainings GEMMs zu bestimmen, und verifizieren die Gültigkeit und Dichtigkeit unserer Methode durch Benchmark Experimente.

Die Autoren schlagen eine analytische Methode zur Vorhersage der Anzahl der Mantissenbits vor, die für partielle Summierungen für Convolutional und Fully-Connected Layers benötigt werden.

Die Autoren führen eine gründliche Analyse der numerischen Präzision durch, die für die Akkumulationsoperationen beim Training neuronaler Netze erforderlich ist, und zeigen die theoretischen Auswirkungen einer Verringerung der Anzahl der Bits im Fließkomma-Akkumulator.


Unsupervised domain adaptation is a promising avenue to enhance the performance of deep neural networks on a target domain, using labels only from a source domain.However, the two predominant methods, domain discrepancy reduction learning and semi-supervised learning, are not readily applicable when source and target domains do not share a common label space.This paper addresses the above scenario by learning a representation space that retains discriminative power on both the (labeled) source and (unlabeled) target domains while keeping representations for the two domains well-separated.Inspired by a theoretical analysis, we first reformulate the disjoint classification task, where the source and target domains correspond to non-overlapping class labels, to a verification one.To handle both within and cross domain verifications, we propose a Feature Transfer Network (FTN) to separate the target feature space from the original source space while aligned with a transformed source space.Moreover, we present a non-parametric multi-class entropy minimization loss to further boost the discriminative power of FTNs on the target domain.In experiments, we first illustrate how FTN works in a controlled setting of adapting from MNIST-M to MNIST with disjoint digit classes between the two domains and then demonstrate the effectiveness of FTNs through state-of-the-art performances on a cross-ethnicity face recognition problem.

Eine neue Theorie der unüberwachten Domänenanpassung für metrisches Distanzlernen und ihre Anwendung auf die Gesichtserkennung bei verschiedenen ethnischen Variationen.

Schlägt ein neuartiges Merkmalstransfernetzwerk vor, das den Verlust der gegnerischen Domäne und den Verlust der Domänentrennung optimiert.


In this paper, we consider the problem of training neural networks (NN).To promote a NN with specific structures, we explicitly take into consideration the nonsmooth regularization (such as L1-norm) and constraints (such as interval constraint).This is formulated as a constrained nonsmooth nonconvex optimization problem, and we propose a convergent proximal-type stochastic gradient descent (Prox-SGD) algorithm.We show that under properly selected learning rates, momentum eventually resembles the unknown real gradient and thus is crucial in analyzing the convergence.We establish that with probability 1, every limit point of the sequence generated by the proposed Prox-SGD is a stationary point.Then the Prox-SGD is tailored to train a sparse neural network and a binary neural network, and the theoretical analysis is also supported by extensive numerical tests.

Wir schlagen einen konvergenten stochastischen Gradientenabstiegsalgorithmus vom proximalen Typ für eingeschränkte, nicht-glatte, nicht-konvexe Optimierungsprobleme vor.

Dieses Papier schlägt Prox-SGD vor, einen theoretischen Rahmen für stochastische Optimierungsalgorithmen, die asymptotisch zur Stationarität konvergieren für glatte nicht-konvexe Verluste + konvexe Constraints/Regulierer.

Diese Arbeit schlägt einen neuen gradientenbasierten stochastischen Optimierungsalgorithmus mit Gradientenmittelung vor, indem die Theorie für proximale Algorithmen an die nicht-konvexe Umgebung angepasst wird.


The loss of a few neurons in a brain rarely results in any visible loss of function.However, the insight into what â€œfewâ€ means in this context is unclear.How many random neuron failures will it take to lead to a visible loss of function?In this paper, we address the fundamental question of the impact of the crash of a random subset of neurons on the overall computation of a neural network and the error in the output it produces.We study fault tolerance of neural networks subject to small random neuron/weight crash failures in a probabilistic setting.We give provable guarantees on the robustness of the network to these crashes.Our main contribution is a bound on the error in the output of a network under small random Bernoulli crashes proved by using a Taylor expansion in the continuous limit, where close-by neurons at a layer are similar.The failure mode we adopt in our model is characteristic of neuromorphic hardware, a promising technology to speed up artificial neural networks, as well as of biological networks.We show that our theoretical bounds can be used to compare the fault tolerance of different architectures and to design a regularizer improving the fault tolerance of a given architecture.We design an algorithm achieving fault tolerance using a reasonable number of neurons.In addition to the theoretical proof, we also provide experimental validation of our results and suggest a connection to the generalization capacity problem.

Wir geben eine Schranke für NNs für den Ausgabefehler bei zufälligen Gewichtsausfällen an, indem wir eine Taylor-Erweiterung für den kontinuierlichen Grenzwert verwenden, bei dem benachbarte Neuronen ähnlich sind

In diesem Beitrag wird das Problem des Herausfallens von Neuronen aus einem neuronalen Netz betrachtet. Es wird gezeigt, dass es ausreicht, mit Dropout zu trainieren, wenn das Ziel darin besteht, gegenüber zufällig herausfallenden Neuronen während der Auswertung robust zu werden.

Dieser Beitrag untersucht die Auswirkung von Löschungen zufälliger Neuronen auf die Vorhersagegenauigkeit einer trainierten Architektur, mit der Anwendung auf die Fehleranalyse und den spezifischen Kontext neuromorpher Hardware.


Truly intelligent agents need to capture the interplay of all their senses to build a rich physical understanding of their world.In robotics, we have seen tremendous progress in using visual and tactile perception; however we have often ignored a key sense: sound.This is primarily due to lack of data that captures the interplay of action and sound.In this work, we perform the first large-scale study of the interactions between sound and robotic action.To do this, we create the largest available sound-action-vision dataset with 15,000 interactions on 60 objects using our robotic platform Tilt-Bot.By tilting objects and allowing them to crash into the walls of a robotic tray, we collect rich four-channel audio information.Using this data, we explore the synergies between sound and action, and present three key insights.First, sound is indicative of fine-grained object class information, e.g., sound can differentiate a metal screwdriver from a metal wrench.Second, sound also contains information about the causal effects of an action, i.e. given the sound produced, we can predict what action was applied on the object.Finally, object representations derived from audio embeddings are indicative of implicit physical properties.We demonstrate that on previously unseen objects, audio embeddings generated through interactions can predict forward models 24% better than passive visual embeddings.

Wir erforschen und untersuchen die Synergien zwischen Klang und Aktion.

In diesem Beitrag werden die Verbindungen zwischen Aktion und Ton durch die Erstellung eines Ton-Aktions-Sicht-Datensatzes mit einem Neigungsroboter untersucht.

In diesem Beitrag wird die Rolle von Audio bei der Wahrnehmung von Objekten und Handlungen untersucht, und es wird gezeigt, wie auditive Informationen beim Erlernen von Modellen der Vorwärts- und Rückwärtsdynamik helfen können.


Hierarchical label structures widely exist in many machine learning tasks, ranging from those with explicit label hierarchies such as image classification to the ones that have latent label hierarchies such as semantic segmentation.Unfortunately, state-of-the-art methods often utilize cross-entropy loss which in-explicitly assumes the independence among class labels.Motivated by the fact that class members from the same hierarchy need to be similar to each others, we design a new training diagram called Hierarchical Complement Objective Training (HCOT).In HCOT, in addition to maximizing the probability of the ground truth class, we also neutralize the probabilities of rest of the classes in a hierarchical fashion, making the model take advantage of the label hierarchy explicitly.We conduct our method on both image classification and semantic segmentation.Results show that HCOT outperforms state-of-the-art models in CIFAR100, Imagenet, and PASCAL-context.Our experiments also demonstrate that HCOT can be applied on tasks with latent label hierarchies, which is a common characteristic in many machine learning tasks.

Wir schlagen Hierarchical Complement Objective Training vor, ein neuartiges Trainingsparadigma zur effektiven Nutzung der Kategorienhierarchie im Beschriftungsraum sowohl bei der Bildklassifikation als auch bei der semantischen Segmentierung.

Eine Methode, die die Entropie der posterioren Verteilung über die Klassen reguliert, was für Bildklassifizierungs- und Segmentierungsaufgaben nützlich sein kann


There is a growing interest in automated neural architecture search (NAS).To improve the efficiency of NAS, previous approaches adopt  weight sharing method to force all models share the same set of weights.  However, it has been observed that a model performing better with shared weights does not necessarily perform  better when trained alone.In this paper, we analyse existing weight sharing one-shot NAS approaches from a Bayesian point of view and identify the posterior fading problem, which compromises the effectiveness of shared weights.To alleviate this problem, we present a practical approach to guide the parameter posterior towards its true distribution.Moreover, a hard latency constraint is introduced during the search so that the desired latency can be achieved.The resulted method, namely Posterior Convergent NAS (PC-NAS), achieves state-of-the-art performance under standard GPU latency constraint on ImageNet.In our small search space, our model PC-NAS-S attains76.8% top-1 accuracy, 2.1% higher than MobileNetV2 (1.4x) with the same latency.When adopted to our large search space, PC-NAS-L achieves 78.1% top-1 accuracy within 11ms.The discovered architecture also transfers well to other computer vision applications such as object detection and person re-identification.

Diese Arbeit identifiziert das Problem der bestehenden Gewichtsteilung bei der Suche nach neuronalen Architekturen und schlägt eine praktische Methode vor, die gute Ergebnisse erzielt.

Der Autor identifiziert ein Problem mit NAS, das als Posterior Fading bezeichnet wird, und führt Posterior Convergent NAS ein, um diesen Effekt abzuschwächen.


Noisy labels are very common in real-world training data, which lead to poor generalization on test data because of overfitting to the noisy labels.In this paper, we claim that such overfitting can be avoided by "early stopping" training a deep neural network before the noisy labels are severely memorized.Then, we resume training the early stopped network using a "maximal safe set," which maintains a collection of almost certainly true-labeled samples at each epoch since the early stop point.Putting them all together, our novel two-phase training method, called Prestopping, realizes noise-free training under any type of label noise for practical use.Extensive experiments using four image benchmark data sets verify that our method significantly outperforms four state-of-the-art methods in test error by 0.4â€“8.2 percent points under existence of real-world noise.

Wir schlagen einen neuartigen zweistufigen Trainingsansatz vor, der auf einem "frühen Stoppen" für robustes Training auf störhaften Etiketten basiert.

In dem Papier wird untersucht, wie frühes Stoppen bei der Optimierung hilft, sichere Beispiele zu finden.

In diesem Papier wird eine zweistufige Trainingsmethode für das Lernen mit störhaften Labels vorgeschlagen.


Learning when to communicate and doing that effectively is essential in multi-agent tasks.Recent works show that continuous communication allows efficient training with back-propagation in multi-agent scenarios, but have been restricted to fully-cooperative tasks.In this paper, we present Individualized Controlled Continuous Communication Model (IC3Net) which has better training efficiency than simple continuous communication model, and can be applied to semi-cooperative and competitive settings along with the cooperative settings.IC3Net controls continuous communication with a gating mechanism and uses individualized rewards foreach agent to gain better performance and scalability while fixing credit assignment issues.Using variety of tasks including StarCraft BroodWars explore and combat scenarios, we show that our network yields improved performance and convergence rates than the baselines as the scale increases.Our results convey that IC3Net agents learn when to communicate based on the scenario and profitability.

Wir stellen IC3Net vor, ein einzelnes Netzwerk, das zum Trainieren von Agenten in kooperativen, kompetitiven und gemischten Szenarien verwendet werden kann. Wir zeigen auch, dass Agenten mit unserem Modell lernen können, wann sie kommunizieren sollen.

Der Autor schlägt eine neue Architektur für Multi-Agenten Reinforcement Learning vor, die mehrere LSTM Controller mit verbundenen Gewichten verwendet, die sich gegenseitig einen kontinuierlichen Vektor übermitteln.

Die Autoren schlagen ein interessantes Gating-Schema vor, das es den Agenten erlaubt, in einer Multi-Agenten RL Umgebung zu kommunizieren.


Neural sequence-to-sequence models are a recently proposed family of approaches used in abstractive summarization of text documents, useful for producing condensed versions of source text narratives without being restricted to using only words from the original text.Despite the advances in abstractive summarization, custom generation of summaries (e.g. towards a user's preference) remains unexplored.In this paper, we present CATS, an abstractive neural summarization model, that summarizes content in a sequence-to-sequence fashion but also introduces a new mechanism to control the underlying latent topic distribution of the produced summaries.Our experimental results on the well-known CNN/DailyMail dataset show that our model achieves state-of-the-art performance.

Wir stellen das erste neuronale abstrakte Zusammenfassungsmodell vor, das in der Lage ist, die generierten Zusammenfassungen individuell anzupassen.


We propose a software framework based on ideas of the Learning-Compression algorithm , that allows one to compress any neural network by different compression mechanisms (pruning, quantization, low-rank, etc.).By design, the learning of the neural net (handled by SGD) is decoupled from the compression of its parameters (handled by a signal compression function), so that the framework can be easily extended to handle different combinations of neural net and compression type.In addition, it has other advantages, such as easy integration with deep learning frameworks, efficient training time, competitive practical performance in the loss-compression tradeoff, and reasonable convergence guarantees.Our toolkit is written in Python and Pytorch and we plan to make it available by the workshop time, and eventually open it for contributions from the community.

Wir schlagen ein Software-Framework vor, das auf den Ideen des Lern-Kompressions-Algorithmus basiert, der es erlaubt, jedes neuronale Netzwerk durch verschiedene Kompressionsmechanismen (Pruning, Quantisierung, Low-Rank, etc.) zu komprimieren.

In diesem Beitrag wird der Entwurf einer Softwarebibliothek vorgestellt, die es dem Benutzer erleichtert, seine Netzwerke zu komprimieren, indem sie die Details der Kompressionsmethoden verbirgt.


This work seeks the possibility of generating the human face from voice solely based on the audio-visual data without any human-labeled annotations.To this end, we propose a multi-modal learning framework that links the inference stage and generation stage.First, the inference networks are trained to match the speaker identity between the two different modalities.Then the pre-trained inference networks cooperate with the generation network by giving conditional information about the voice.

In diesem Beitrag wird eine Methode zur durchgängigen multimodalen Generierung von menschlichen Gesichtern aus Sprache vorgeschlagen, die auf einem selbstüberwachten Lernsystem basiert.

In diesem Artikel wird ein multimodales Learning Framework vorgestellt, das die Inferenzphase und die Generierungsphase miteinander verbindet, auf der Suche nach der Möglichkeit, das menschliche Gesicht ausschließlich aus der Stimme zu generieren.

Diese Arbeit zielt darauf ab, einen bedingten Rahmen für die Erzeugung von Gesichtsbildern aus Audiosignalen zu schaffen.


We present a simple neural model that given a formula and a property tries to answer the question whether the formula has the given property, for example whether a propositional formula is always true.The structure of the formula is captured by a feedforward neural network recursively built for the given formula in a top-down manner.The results of this network are then processed by two recurrent neural networks.One of the interesting aspects of our model is how propositional atoms are treated.For example, the model is insensitive to their names, it only matters whether they are the same or distinct.

Es wird ein Top-Down-Ansatz zur rekursiven Darstellung von Satzformeln durch neuronale Netze vorgestellt.

Diese Arbeit bietet ein neues neuronales Netzmodell für logische Formeln, das Informationen über eine gegebene Formel sammelt, indem es ihren Parse-Baum von oben nach unten durchläuft.

Der Beitrag verfolgt den Weg eines baumstrukturierten Netzwerks, das isomorph zum Parse-Baum einer propositionalen Kalkülformel ist, aber Informationen von oben nach unten und nicht von unten nach oben weitergibt.


Despite significant advances in the field of deep Reinforcement Learning (RL), today's algorithms still fail to learn human-level policies consistently over a set of diverse tasks such as Atari 2600 games.We identify three key challenges that any algorithm needs to master in order to perform well on all games:  processing diverse reward distributions, reasoning over long time horizons, and exploring efficiently.  In this paper, we propose an algorithm that addresses each of these challenges and is able to learn human-level policies on nearly all Atari games.A new transformed Bellman operator allows our algorithm to process rewards of varying densities and scales; an auxiliary temporal consistency loss allows us to train stably using a discount factor of 0.999 (instead of 0.99) extending the effective planning horizon by an order of magnitude; and we ease the exploration problem by using human demonstrations that guide the agent towards rewarding states.When tested on a set of 42 Atari games, our algorithm exceeds the performance  of an average human on 40 games using a common set of hyper parameters.

Ape-X DQfD = Distributed (many actors + one learner + prioritized replay) DQN mit Demonstrationen zur Optimierung der unclipped 0,999-discounted return auf Atari.

Diese Arbeit schlägt drei Erweiterungen (Bellman-Update, temporaler Konsistenzverlust und Expertendemonstration) für DQN vor, um die Lernleistung bei Atari-Spielen zu verbessern und übertrifft damit die State-of-the-Art-Ergebnisse für Atari-Spiele.

In dieser Arbeit wird ein transformierter Bellman-Operator vorgeschlagen, der darauf abzielt, die Sensitivität gegenüber nicht abgegrenzter Belohnung, die Robustheit gegenüber dem Wert des Diskontierungsfaktors und das Explorationsproblem zu lösen.


The knowledge that humans hold about a problem often extends far beyond a set of training data and output labels.While the success of deep learning mostly relies on supervised training, important properties cannot be inferred efficiently from end-to-end annotations alone, for example causal relations or domain-specific invariances.We present a general technique to supplement supervised training with prior knowledge expressed as relations between training instances.We illustrate the method on the task of visual question answering to exploit various auxiliary annotations, including relations of equivalence and of logical entailment between questions.Existing methods to use these annotations, including auxiliary losses and data augmentation, cannot guarantee the strict inclusion of these relations into the model since they require a careful balancing against the end-to-end objective.Our method uses these relations to shape the embedding space of the model, and treats them as strict constraints on its learned representations.%The resulting model encodes relations that better generalize across instances.In the context of VQA, this approach brings significant improvements in accuracy and robustness, in particular over the common practice of incorporating the constraints as a soft regularizer.We also show that incorporating this type of prior knowledge with our method brings consistent improvements, independently from the amount of supervised data used.It demonstrates the value of an additional training signal that is otherwise difficult to extract from end-to-end annotations alone.

Trainingsmethode zur Erzwingung strenger Beschränkungen für gelernte Einbettungen während des überwachten Trainings. Angewandt auf visuelles Fragen beantworten.

Die Autoren schlagen ein Framework vor, um zusätzliches semantisches Vorwissen in das traditionelle Training von Deep-Learning-Modellen einzubeziehen, um den Einbettungsraum anstelle des Parameterraums zu regularisieren.

Der Beitrag plädiert für die Kodierung von externem Wissen in der linguistischen Einbettungsschicht eines multimodalen neuronalen Netzes in Form einer Reihe von harten Einschränkungen.


Artificial neural networks revolutionized many areas of computer science in recent years since they provide solutions to a number of previously unsolved problems.On the other hand, for many problems, classic algorithms exist, which typically exceed the accuracy and stability of neural networks.To combine these two concepts, we present a new kind of neural networksâ€”algorithmic neural networks (AlgoNets).These networks integrate smooth versions of classic algorithms into the topology of neural networks.Our novel reconstructive adversarial network (RAN) enables solving inverse problems without or with only weak supervision.

Lösung inverser Probleme durch Verwendung glatter Approximationen der Forward Algorithmen zum Trainieren der inversen Modelle.


Pointwise localization allows more precise localization and accurate interpretability, compared to bounding box, in applications where objects are highly unstructured such as in medical domain.In this work, we focus on  weakly supervised localization (WSL) where a model is trained to classify an image and localize regions of interest at pixel-level using only global image annotation.Typical convolutional attentions maps are prune to high false positive regions.To alleviate this issue, we propose a new deep learning method for WSL, composed of a localizer and a classifier, where the localizer is constrained to determine relevant and irrelevant regions using conditional entropy (CE) with the aim to reduce false positive regions.Experimental results on a public medical dataset and two natural datasets, using Dice index, show that, compared to state of the art WSL methods, our proposal can provide significant improvements in terms of image-level classification and pixel-level localization (low false positive) with robustness to overfitting.A public reproducible PyTorch implementation is provided.

Eine Deep Learning Methode für schwach überwachte punktuelle Lokalisierung, die nur auf der Bildebene lernt. Es stützt sich auf die bedingte Entropie zur Lokalisierung relevanter und irrelevanter Regionen mit dem Ziel, falsch positive Regionen zu minimieren.

In dieser Arbeit wird das Problem der WSL mit Hilfe eines neuartigen Designs von Regularisierungsbegriffen und eines rekursiven Löschalgorithmus untersucht.

In diesem Beitrag wird ein neuer, schwach überwachter Ansatz zum Erlernen der Objektsegmentierung mit Klassenbezeichnungen auf Bildebene vorgestellt.


Model-based reinforcement learning has been empirically demonstrated as a successful strategy to improve sample efficiency.Particularly, Dyna architecture, as an elegant model-based architecture integrating learning and planning, provides huge flexibility of using a model.One of the most important components in Dyna is called search-control, which refers to the process of generating state or state-action pairs from which we query the model to acquire simulated experiences.Search-control is critical to improve learning efficiency.In this work, we propose a simple and novel search-control strategy by searching high frequency region on value function.Our main intuition is built on Shannon sampling theorem from signal processing, which indicates that a high frequency signal requires more samples to reconstruct.We empirically show that a high frequency function is more difficult to approximate.This suggests a search-control strategy: we should use states in high frequency region of the value function to query the model to acquire more samples.We develop a simple strategy to locally measure the frequency of a function by gradient norm, and provide theoretical justification for this approach.We then apply our strategy to search-control in Dyna, and conduct experiments to show its property and effectiveness on benchmark domains.

Erfassen von Zuständen aus dem Hochfrequenzbereich für die Suchkontrolle in Dyna.

Die Autoren schlagen vor, die Probenentnahme im Hochfrequenzbereich durchzuführen, um die Effizienz der Probenentnahme zu erhöhen.

In dieser Arbeit wird ein neuer Weg vorgeschlagen, um Zustände auszuwählen, von denen aus Übergänge im Dyna-Algorithmus durchgeführt werden.


We propose a new architecture for distributed image compression from a group of distributed data sources.The work is motivated by practical needs of data-driven codec design, low power consumption, robustness, and data privacy.The proposed architecture, which we refer to as Distributed Recurrent Autoencoder for Scalable Image Compression (DRASIC), is able to train distributed encoders and one joint decoder on correlated data sources.Its compression capability is much better than the method of training codecs separately.Meanwhile, for 10 distributed sources, our distributed system remarkably performs within 2 dB peak signal-to-noise ratio (PSNR) of that of a single codec trained with all data sources.We experiment distributed sources with different correlations and show how our methodology well matches the Slepian-Wolf Theorem in Distributed Source Coding (DSC).Our method is also shown to be robust to the lack of presence of encoded data from a number of distributed sources.Moreover, it is scalable in the sense that codes can be decoded simultaneously at more than one compression quality level.To the best of our knowledge, this is the first data-driven DSC framework for general distributed code design with deep learning.

Wir stellen ein datengesteuertes Distributed Source Coding Framework vor, das auf Distributed Recurrent Autoencoder for Scalable Image Compression (DRASIC) basiert.

In dem Artikel wird ein verteilter rekurrenter Autoencoder für die Bildkomprimierung vorgeschlagen, der ein ConvLSTM verwendet, um binäre Codes zu lernen, die nach und nach aus den Residuals der zuvor kodierten Informationen aufgebaut werden

Die Autoren schlagen eine Methode zum Trainieren von Bildkompressionsmodellen auf mehreren Quellen vor, mit einem separaten Encoder für jede Quelle und einem gemeinsamen Decoder.


Long short-term memory networks (LSTMs) were introduced to combat vanishing gradients in simple recurrent neural networks (S-RNNs) by augmenting them with additive recurrent connections controlled by gates.We present an alternate view to explain the success of LSTMs: the gates themselves are powerful recurrent models that provide more representational power than previously appreciated.We do this by showing that the LSTM's gates can be decoupled from the embedded S-RNN, producing a restricted class of RNNs where the main recurrence computes an element-wise weighted sum of context-independent functions of the inputs.Experiments on a range of challenging NLP problems demonstrate that the simplified gate-based models work substantially better than S-RNNs, and often just as well as the original LSTMs, strongly suggesting that the gates are doing much more in practice than just alleviating vanishing gradients.

Gates übernehmen in LSTMs die ganze schwere Arbeit, indem sie elementweise gewichtete Summen berechnen, und das Entfernen des internen einfachen RNN verschlechtert die Leistung des Modells nicht.

In diesem Beitrag wird eine vereinfachte LSTM Variante vorgeschlagen, bei der die Nichtlinearität von Inhaltselement und Ausgangs Gate entfernt wird

Diese Arbeit präsentiert eine Analyse von LSTMS, die zeigt, dass sie eine Form haben, in der der Inhalt der Speicherzelle bei jedem Schritt eine gewichtete Kombination der Inhaltsaktualisierten Werte ist, die bei jedem Zeitschritt berechnet werden, und bietet eine Vereinfachung von LSTMs, die den Wert berechnen, mit dem die Speicherzelle bei jedem Zeitschritt in Bezug auf eine deterministische Funktion der Eingabe anstatt einer Funktion der Eingabe und des aktuellen Kontexts.

Diese Arbeit schlägt eine neue Einsicht in LSTM vor, bei der der Kern eine elementweise gewichtete Summe ist, und argumentiert, dass LSTM redundant ist, indem nur Eingabe- und Vergessensgatter zur Berechnung der Gewichte beibehalten werden.


Machine learning algorithms designed to characterize, monitor, and intervene on human health (ML4H) are expected to perform safely and reliably when operating at scale, potentially outside strict human supervision.This requirement warrants a stricter attention to issues of reproducibility than other fields of machine learning.In this work, we conduct a systematic evaluation of over 100 recently published ML4H research papers along several dimensions related to reproducibility we identified.We find that the field of ML4H compares poorly to more established machine learning fields, particularly concerning data accessibility and code accessibility.  Finally, drawing from success in other fields of science, we propose recommendations to data providers, academic publishers, and the ML4H research community in order to promote reproducible research moving forward.

Bei der Analyse von mehr als 300 Beiträgen auf aktuellen Konferenzen zum maschinellen Lernen haben wir festgestellt, dass Anwendungen des maschinellen Lernens für die Gesundheit (ML4H) in Bezug auf die Reproduzierbarkeit hinter anderen Bereichen des maschinellen Lernens zurückbleiben.

In diesem Beitrag wird der Stand der Reproduzierbarkeit für ML-Anwendungen im Gesundheitswesen quantitativ und qualitativ untersucht und es werden Empfehlungen für eine bessere Reproduzierbarkeit der Forschung gegeben.


We propose a solution for evaluation of mathematical expression.However, instead of designing a single end-to-end model we propose a Lego bricks style architecture.In this architecture instead of training a complex end-to-end neural network, many small networks can be trained independently each accomplishing one specific operation and acting a single lego brick.More difficult or complex task can then be solved using a combination of these smaller network.In this work we first identify 8 fundamental operations that are commonly used to solve arithmetic operations (such as 1 digit multiplication, addition, subtraction, sign calculator etc).These fundamental operations are then learned using simple feed forward neural networks.We then shows that different operations can be designed simply by reusing these smaller networks.As an example we reuse these smaller networks to develop larger and a more complex network to solve n-digit multiplication, n-digit division, and cross product.This bottom-up strategy not only introduces reusability, we also show that it allows to generalize for computations involving n-digits and we show results for up to 7 digit numbers.Unlike existing methods, our solution also generalizes for both positive as well as negative numbers.

Wir trainieren viele kleine Netze, jedes für eine bestimmte Operation, die dann kombiniert werden, um komplexe Operationen durchzuführen.

In diesem Artikel wird vorgeschlagen, neuronale Netze zur Auswertung mathematischer Ausdrücke zu verwenden, indem 8 kleine Bausteine für 8 grundlegende Operationen, z. B. Addition, Subtraktion usw., entworfen werden und dann mehrstellige Multiplikationen und Divisionen mit diesen kleinen Bausteinen entworfen werden.

In dem Papier wird eine Methode zur Entwicklung eines NN-basierten Bewertungssystems für mathematische Ausdrücke vorgeschlagen.


In standard generative adversarial network (SGAN), the discriminator estimates the probability that the input data is real.The generator is trained to increase the probability that fake data is real.We argue that it should also simultaneously decrease the probability that real data is real because1) this would account for a priori knowledge that half of the data in the mini-batch is fake,2) this would be observed with divergence minimization, and3) in optimal settings, SGAN would be equivalent to integral probability metric (IPM) GANs. We show that this property can be induced by using a relativistic discriminator which estimate the probability that the given real data is more realistic than a randomly sampled fake data.We also present a variant in which the discriminator estimate the probability that the given real data is more realistic than fake data, on average.We generalize both approaches to non-standard GAN loss functions and we refer to them respectively as Relativistic GANs (RGANs) and Relativistic average GANs (RaGANs).We show that IPM-based GANs are a subset of RGANs which use the identity function. Empirically, we observe that1) RGANs and RaGANs are significantly more stable and generate higher quality data samples than their non-relativistic counterparts,2) Standard RaGAN with gradient penalty generate data of better quality than WGAN-GP while only requiring a single discriminator update per generator update (reducing the time taken for reaching the state-of-the-art by 400%), and3) RaGANs are able to generate plausible high resolutions images (256x256) from a very small sample (N=2011), while GAN and LSGAN cannot; these images are of significantly better quality than the ones generated by WGAN-GP and SGAN with spectral normalization.The code is freely available on https://github.com/AlexiaJM/RelativisticGAN.

Verbesserung der Qualität und Stabilität von GANs unter Verwendung eines relativistischen Diskriminators; IPM-GANs (wie WGAN-GP) sind ein Spezialfall.

Diese Arbeit schlägt einen relativistischen Diskriminator vor, der in einigen Situationen hilft, obwohl er etwas empfindlich auf Hyperparameter, Architekturen und Datensätze reagiert.

In dieser Arbeit betrachten die Autoren eine Variante des GAN, indem sie gleichzeitig die Wahrscheinlichkeit verringern, dass die Daten für den Generator real sind.


Some of the most successful applications of deep reinforcement learning to challenging domains in discrete and continuous control have used policy gradient methods in the on-policy setting.However, policy gradients can suffer from large variance that may limit performance, and in practice require carefully tuned entropy regularization to prevent policy collapse.As an alternative to policy gradient algorithms, we introduce V-MPO, an on-policy adaptation of Maximum a Posteriori Policy Optimization (MPO) that performs policy iteration based on a learned state-value function.We show that V-MPO surpasses previously reported scores for both the Atari-57 and DMLab-30 benchmark suites in the multi-task setting, and does so reliably without importance weighting, entropy regularization, or population-based tuning of hyperparameters.On individual DMLab and Atari levels, the proposed algorithm can achieve scores that are substantially higher than has previously been reported.V-MPO is also applicable to problems with high-dimensional, continuous action spaces, which we demonstrate in the context of learning to control simulated humanoids with 22 degrees of freedom from full state observations and 56 degrees of freedom from pixel observations, as well as example OpenAI Gym tasks where V-MPO achieves substantially higher asymptotic scores than previously reported.

Eine auf Zustandswertfunktionen basierende Version von MPO, die gute Ergebnisse bei einer Vielzahl von Aufgaben in der diskreten und kontinuierlichen Steuerung erzielt.

In diesem Beitrag wird ein Algorithmus für On-Policy Reinforcement Learning vorgestellt, der sowohl kontinuierliche/diskrete Steuerung als auch Single-/Multi-Task-Lernen ermöglicht und sowohl niedrigdimensionale Zustände als auch Pixel verwendet.

Das Papier schlägt eine Online-Variante von MPO, V-MPO, vor, die die V-Funktion erlernt und die nicht-parametrische Verteilung in Richtung der Vorteile aktualisiert.


Turing complete computation and reasoning are often regarded as necessary pre- cursors to general intelligence.There has been a significant body of work studying neural networks that mimic general computation, but these networks fail to generalize to data distributions that are outside of their training set.We study this problem through the lens of fundamental computer science problems: sorting and graph processing.We modify the masking mechanism of a transformer in order to allow them to implement rudimentary functions with strong generalization.We call this model the Neural Execution Engine, and show that it learns, through supervision, to numerically compute the basic subroutines comprising these algorithms with near perfect accuracy.Moreover, it retains this level of accuracy while generalizing to unseen data and long sequences outside of the training distribution.

Wir schlagen neuronale Ausführungs Engines (NEEs) vor, die eine gelernte Maske und überwachte Ausführungsspuren nutzen, um die Funktionalität von Unterprogrammen zu imitieren und eine starke Generalisierung zu zeigen.

In diesem Beitrag wird das Problem des Aufbaus einer Programmausführungs Engine mit neuronalen Netzen untersucht und ein Transformer-basiertes Modell zum Erlernen grundlegender Unterprogramme vorgeschlagen, das in mehreren Standardalgorithmen angewendet wird.

Diese Arbeit befasst sich mit dem Problem des Entwurfs neuronaler Netzwerkarchitekturen, die allgemeine Programme lernen und implementieren können.


Meta-learning is a promising strategy for learning to efficiently learn within new tasks, using data gathered from a distribution of tasks.However, the meta-learning literature thus far has focused on the task segmented setting, where at train-time, offline data is assumed to be split according to the underlying task, and at test-time, the algorithms are optimized to learn in a single task.In this work, we enable the application of generic meta-learning algorithms to settings where this task segmentation is unavailable, such as continual online learning with a time-varying task.We present meta-learning via online changepoint analysis (MOCA), an approach which augments a meta-learning algorithm with a differentiable Bayesian changepoint detection scheme.The framework allows both training and testing directly on time series data without segmenting it into discrete tasks.We demonstrate the utility of this approach on a nonlinear meta-regression benchmark as well as two meta-image-classification benchmarks.

Die Bayes'sche Changepoint-Erkennung ermöglicht Meta-Learning direkt aus Zeitreihendaten.

Diese Arbeit betrachtet Meta-Lernen in einer Aufgabe unsegmentierten Umgebung und wendet Bayesian Online Änderungspunkt Erkennung mit Meta-Lernen an.

In diesem Beitrag wird das Meta-Lernen auf nicht segmentierte Aufgaben ausgedehnt, wobei das MOCA Framework ein Bayessches Changepoint Schätzschema zur Erkennung von Aufgabenänderungen verwendet.


People with high-frequency hearing loss rely on hearing aids that employ frequency lowering algorithms.These algorithms shift some of the sounds from the high frequency band to the lower frequency band where the sounds become more perceptible for the people with the condition.Fricative phonemes have an important part of their content concentrated in high frequency bands.It is important that the frequency lowering algorithm is activated exactly for the duration of a fricative phoneme, and kept off at all other times.Therefore, timely (with zero delay) and accurate fricative phoneme detection is a key problem for high quality hearing aids.In this paper we present a deep learning based fricative phoneme detection algorithm that has zero detection delay and achieves state-of-the-art fricative phoneme detection accuracy on the TIMIT Speech Corpus.All reported results are reproducible and come with easy to use code that could serve as a baseline for future research.

Ein auf tiefem Lernen basierender Ansatz zur Erkennung von fricative Phoneme ohne Verzögerung.

In diesem Beitrag werden Methoden des überwachten tiefen Lernens angewandt, um die exakte Dauer eines fricative Phoneme zu erkennen und so den Algorithmus zur praktischen Frequenzabsenkung zu verbessern.


Sequence-to-sequence models with soft attention have been successfully applied to a wide variety of problems, but their decoding process incurs a quadratic time and space cost and is inapplicable to real-time sequence transduction.To address these issues, we propose Monotonic Chunkwise Attention (MoChA), which adaptively splits the input sequence into small chunks over which soft attention is computed.We show that models utilizing MoChA can be trained efficiently with standard backpropagation while allowing online and linear-time decoding at test time.When applied to online speech recognition, we obtain state-of-the-art results and match the performance of a model using an offline soft attention mechanism.In document summarization experiments where we do not expect monotonic alignments, we show significantly improved performance compared to a baseline monotonic attention-based model.

Ein Online- und Linearzeit-Aufmerksamkeitsmechanismus, der schwache Aufmerksamkeit über adaptiv platzierte Teile der Eingabesequenz ausübt.

In diesem Beitrag wird eine kleine Modifikation der monotonen Aufmerksamkeit in [1] vorgeschlagen, indem dem durch die monotone Aufmerksamkeit vorhergesagten Segment eine schwache Aufmerksamkeit hinzugefügt wird.

Die Arbeit schlägt eine Erweiterung eines früheren monotonen Aufmerksamkeitsmodells (Raffel et al. 2017) vor, um ein Fenster fester Größe bis zur Ausrichtungsposition zu beachten.


We present a framework for automatically ordering image patches that enables in-depth analysis of dataset relationship to learnability of a classification task using convolutional neural network.An image patch is a group of pixels residing in a continuous area contained in the sample.Our preliminary experimental results show that an informed smart shuffling of patches at a sample level can expedite training by exposing important features at early stages of training.In addition, we conduct systematic experiments and provide evidence that CNNâ€™s generalization capabilities do not correlate with human recognizable features present in training samples.We utilized the framework not only to show that spatial locality of features within samples do not correlate with generalization, but also to expedite convergence while achieving similar generalization performance.Using multiple network architectures and datasets, we show that ordering image regions using mutual information measure between adjacent patches, enables CNNs to converge in a third of the total steps required to train the same network without patch ordering.

Entwicklung neuer Techniken, die sich auf die Neuordnung von Bereichen stützen, um eine detaillierte Analyse der Beziehung zwischen Datensatz und Trainings- und Generalisierungsleistung zu ermöglichen.


Producing agents that can generalize to a wide range of environments is a significant challenge in reinforcement learning.One method for overcoming this issue is domain randomization, whereby at the start of each training episode some parameters of the environment are randomized so that the agent is exposed to many possible variations.However, domain randomization is highly inefficient and may lead to policies with high variance across domains.In this work, we formalize the domain randomization problem, and show that minimizing the policy's Lipschitz constant with respect to the randomization parameters leads to low variance in the learned policies.We propose a method where the agent only needs to be trained on one variation of the environment, and its learned state representations are regularized during training to minimize this constant.We conduct experiments that demonstrate that our technique leads to more efficient and robust learning than standard domain randomization, while achieving equal generalization scores.

Wir produzieren Reinforcement Learning Agenten, die sich mit Hilfe einer neuartigen Regularisierungstechnik gut auf eine Vielzahl von Umgebungen verallgemeinern lassen.

Diese Arbeit stellt die Herausforderung der hohen Varianzregelungen bei der Domänenrandomisierung für Reinforcement Learning vor und konzentriert sich hauptsächlich auf das Problem der visuellen Randomisierung, bei der sich die verschiedenen randomisierten Domänen nur im Zustandsraum unterscheiden und die zugrunde liegenden Belohnungen und Dynamiken gleich sind.

Um die Generalisierungsfähigkeit von Deep-RL-Agenten bei Aufgaben mit unterschiedlichen visuellen Mustern zu verbessern, wurde in diesem Beitrag eine einfache Regularisierungstechnik für die Domänenrandomisierung vorgeschlagen.


Claims from the fields of network neuroscience and connectomics suggest that topological models of the brain involving complex networks are of particular use and interest.The field of deep neural networks has mostly left inspiration from these claims out.In this paper, we propose three architectures and use each of them to explore the intersection of network neuroscience and deep learning in an attempt to bridge the gap between the two fields.Using the teachings from network neuroscience and connectomics, we show improvements over the ResNet architecture, we show a possible connection between early training and the spectral properties of the network, and we show the trainability of a DNN based on the neuronal network of C.Elegans.

Wir erforschen den Schnittpunkt von Netzwerk Neurowissenschaften und Deep Learning.


Creating a knowledge base that is accurate, up-to-date and complete remains a significant challenge despite substantial efforts in automated knowledge base construction.  In this paper, we present Alexandria -- a system for unsupervised, high-precision knowledge base construction.Alexandria uses a probabilistic program to define a process of converting knowledge base facts into unstructured text.  Using probabilistic inference, we can invert this program and so retrieve facts, schemas and entities from web text.The use of a probabilistic program allows uncertainty in the text to be propagated through to the retrieved facts, which increases accuracy and helps merge facts from multiple sources.Because Alexandria does not require labelled training data, knowledge bases can be constructed with the minimum of manual input.We demonstrate this by constructing a high precision (typically 97\%+) knowledge base for people from a single seed fact.

In diesem Beitrag wird ein System für die unüberwachte, hochpräzise Konstruktion von Knowledge Bases vorgestellt, das ein probabilistisches Programm verwendet, um einen Prozess der Umwandlung von Fakten aus der Knowledge Base in unstrukturierten Text zu definieren.

Überblick über die bestehende Knowledge Base, die mit einem probabilistischen Modell erstellt wird, wobei der Ansatz zur Erstellung der Knowledge Base im Vergleich zu anderen Knowledge Base Ansätzen wie YAGO2, NELL, Knowledge Vault und DeepDive bewertet wird.

Diese Arbeit verwendet ein probabilistisches Programm, das den Prozess beschreibt, durch den Fakten, die Entitäten beschreiben, in Texten und einer großen Anzahl von Webseiten realisiert werden können, um zu lernen, wie man Fakten über Personen anhand eines einzigen Seed-Fakts extrahiert.


Recent advances have made it possible to create deep complex-valued neural networks.Despite this progress, many challenging learning tasks have yet to leverage the power of complex representations.Building on recent advances, we propose a new deep complex-valued method for signal retrieval and extraction in the frequency domain.As a case study, we perform audio source separation in the Fourier domain.Our new method takes advantage of the convolution theorem which states that the Fourier transform of two convolved signals is the elementwise product of their Fourier transforms.Our novel method is based on a complex-valued version of Feature-Wise Linear Modulation (FiLM) and serves as the keystone of our proposed signal extraction method.We also introduce a new and explicit amplitude and phase-aware loss, which is scale and time invariant, taking into account the complex-valued components of the spectrogram.Using the Wall Street Journal Dataset, we compared our phase-aware loss to several others that operate both in the time and frequency domains and demonstrate the effectiveness of our proposed signal extraction method and proposed loss.

Neue Methode zur Signalextraktion im Fourier-Bereich


We propose an implementation of GNN that predicts and imitates the motion be- haviors from observed swarm trajectory data.The networkâ€™s ability to capture interaction dynamics in swarms is demonstrated through transfer learning.We finally discuss the inherent availability and challenges in the scalability of GNN, and proposed a method to improve it with layer-wise tuning and mixing of data enabled by padding.

Verbesserung der Skalierbarkeit graphischer neuronaler Netze beim Imitation Learning und der Vorhersage von Schwarmbewegungen

In der Arbeit wird ein neues Zeitreihenmodell für das Lernen einer Folge von Graphen vorgeschlagen.

Diese Arbeit befasst sich mit Problemen der Sequenzvorhersage in einem Multiagentensystem.


Embedding layers are commonly used to map discrete symbols into continuous embedding vectors that reflect their semantic meanings.Despite their effectiveness, the number of parameters in an embedding layer increases linearly with the number of symbols and poses a critical challenge on memory and storage constraints.In this work, we propose a generic and end-to-end learnable compression framework termed differentiable product quantization (DPQ).We present two instantiations of DPQ that leverage different approximation techniques to enable differentiability in end-to-end learning.Our method can readily serve as a drop-in alternative for any existing embedding layer.Empirically, DPQ offers significant compression ratios (14-238x) at negligible or no performance cost on 10 datasets across three different language tasks.

Wir schlagen ein differenzierbares Produktquantisierungsverfahren vor, das die Größe der Einbettungsschicht in einem Ende-zu-Ende Training ohne Leistungseinbußen reduzieren kann.

Diese Arbeit befasst sich mit Methoden zur Komprimierung von Einbettungsschichten für Inferenzen mit geringem Speicherbedarf, wobei komprimierte Einbettungen zusammen mit den aufgabenspezifischen Modellen in einer differenzierbaren Ende-zu-Ende Methode gelernt werden.


For multi-valued functions---such as when the conditional distribution on targets given the inputs is multi-modal---standard regression approaches are not always desirable because they provide the conditional mean.Modal regression approaches aim to instead find the conditional mode, but are restricted to nonparametric approaches.Such approaches can be difficult to scale, and make it difficult to benefit from parametric function approximation, like neural networks, which can learn complex relationships between inputs and targets.In this work, we propose a parametric modal regression algorithm, by using the implicit function theorem to develop an objective for learning a joint parameterized function over inputs and targets.We empirically demonstrate on several synthetic problems that our method(i) can learn multi-valued functions and produce the conditional modes,(ii) scales well to high-dimensional inputs and(iii) is even more effective for certain unimodal problems, particularly for high frequency data where the joint function over inputs and targets can better capture the complex relationship between them.We conclude by showing that our method provides small improvements on two regression datasets that have asymmetric distributions over the targets.

Wir stellen einen einfachen und neuartigen modalen Regressionsalgorithmus vor, der sich leicht auf große Probleme übertragen lässt.

Diese Arbeit schlägt einen impliziten Funktionsansatz zum Erlernen der Modi der multimodalen Regression vor.

In der vorliegenden Arbeit wird ein parametrischer Ansatz zur Schätzung des bedingten Modus unter Verwendung des Impliziten Funktionssatzes für multimodale Verteilungen vorgeschlagen.


Deep reinforcement learning algorithms require large amounts of experience to learn an individual task.While in principle meta-reinforcement learning (meta-RL) algorithms enable agents to learn new skills from small amounts of experience, several major challenges preclude their practicality.Current methods rely heavily on on-policy experience, limiting their sample efficiency.They also lack mechanisms to reason about task uncertainty when adapting to new tasks, limiting their effectiveness in sparse reward problems.In this paper, we address these challenges by developing an off-policy meta-RL algorithm that disentangles task inference and control.In our approach, we perform online probabilistic filtering of latent task variables to infer how to solve a new task from small amounts of experience.This probabilistic interpretation enables posterior sampling for structured and efficient exploration.We demonstrate how to integrate these task variables with off-policy RL algorithms to achieve both meta-training and adaptation efficiency.Our method outperforms prior algorithms in sample efficiency by 20-100X as well as in asymptotic performance on several meta-RL benchmarks.

Effiziente Meta-RL durch die Kombination von Variationsschlussfolgerung von probabilistischen Aufgabenvariablen mit Off-Policy RL.

In diesem Beitrag wird vorgeschlagen, während der Meta-Trainingszeit Off-Policy RL einzusetzen, um die Stichprobeneffizienz von Meta-RL Methoden erheblich zu verbessern.


Knowledge bases, massive collections of facts (RDF triples) on diverse topics, support vital modern applications.However, existing knowledge bases contain very little data compared to the wealth of information on the Web.This is because the industry standard in knowledge base creation and augmentation suffers from a serious bottleneck: they rely on domain experts to identify appropriate web sources to extract data from.Efforts to fully automate knowledge extraction have failed to improve this standard: these automated systems are able to retrieve much more data and from a broader range of sources, but they suffer from very low precision and recall.As a result, these large-scale extractions remain unexploited.In this paper, we present MIDAS, a system that harnesses the results of automated knowledge extraction pipelines to repair the bottleneck in industrial knowledge creation and augmentation processes.MIDAS automates the suggestion of good-quality web sources and describes what to extract with respect to augmenting an existing knowledge base.We make three major contributions.First, we introduce a novel concept, web source slices, to describe the contents of a web source.Second, we define a profit function to quantify the value of a web source slice with respect to augmenting an existing knowledge base.Third, we develop effective and highly-scalable algorithms to derive high-profit web source slices.We demonstrate that MIDAS produces high-profit results and outperforms the baselines significantly on both real-word and synthetic datasets.

Dieser Artikel konzentriert sich auf die Identifizierung von qualitativ hochwertigen Webquellen für industrielle Knowledge Base Augmentation Pipelines.


We explore the match prediction problem where one seeks to estimate the likelihood of a group of M items preferred over another, based on partial group comparison data.Challenges arise in practice.As existing state-of-the-art algorithms are tailored to certain statistical models, we have different best algorithms across distinct scenarios.Worse yet, we have no prior knowledge on the underlying model for a given scenario.These call for a unified approach that can be universally applied to a wide range of scenarios and achieve consistently high performances.To this end, we incorporate deep learning architectures so as to reflect the key structural features that most state-of-the-art algorithms, some of which are optimal in certain settings, share in common.This enables us to infer hidden models underlying a given dataset, which govern in-group interactions and statistical patterns of comparisons, and hence to devise the best algorithm tailored to the dataset at hand.Through extensive experiments on synthetic and real-world datasets, we evaluate our framework in comparison to state-of-the-art algorithms.It turns out that our framework consistently leads to the best performance across all datasets in terms of cross entropy loss and prediction accuracy, while the state-of-the-art algorithms suffer from inconsistent performances across different datasets.Furthermore, we show that it can be easily extended to attain satisfactory performances in rank aggregation tasks, suggesting that it can be adaptable for other tasks as well.

Wir untersuchen die Vorzüge des Einsatzes neuronaler Netze bei dem Problem der Vorhersage von Übereinstimmungen, bei dem es darum geht, die Wahrscheinlichkeit abzuschätzen, dass eine Gruppe von M Gegenständen gegenüber einer anderen bevorzugt wird, und zwar auf der Grundlage partieller Gruppenvergleichsdaten.

In diesem Beitrag wird eine Lösung für das Problem der Rangfolge von Mengen durch ein tiefes neuronales Netz vorgeschlagen und eine Architektur für diese Aufgabe entworfen, die sich an früheren, manuell entwickelten Algorithmen orientiert.

In diesem Beitrag wird eine Technik zur Lösung des Problems der Treffervorhersage mithilfe einer Deep-Learning-Architektur vorgestellt.


Recurrent Neural Networks (RNNs) are designed to handle sequential data but suffer from vanishing or exploding gradients.  Recent work on Unitary Recurrent Neural Networks (uRNNs) have been used to address this issue and in some cases, exceed the capabilities of Long Short-Term Memory networks (LSTMs).  We propose a simpler and novel update scheme to maintain orthogonal recurrent weight matrices without using complex valued matrices.This is done by parametrizing with a skew-symmetric matrix using the Cayley transform.Such a parametrization is unable to represent matrices with negative one eigenvalues, but this limitation is overcome by scaling the recurrent weight matrix by a diagonal matrix consisting of ones and negative ones.  The proposed training scheme involves a straightforward gradient calculation and update step.In several experiments, the proposed scaled Cayley orthogonal recurrent neural network (scoRNN) achieves superior results with fewer trainable parameters than other unitary RNNs.

Ein neuartiger Ansatz zur Erhaltung orthogonaler rekurrenter Gewichtsmatrizen in einem RNN.

Stellt ein Schema zum Lernen der rekurrenten Parametermatrix in einem neuronalen Netz vor, das die Cayley-Transformation und eine skalierende Gewichtsmatrix verwendet.

Diese Arbeit schlägt eine RNN-Reparametrisierung der rekurrenten Gewichte mit einer schiefsymmetrischen Matrix unter Verwendung der Cayley-Transformation vor, um die rekurrente Gewichtsmatrix orthogonal zu halten.

Die neuartige Parametrisierung von RNNs ermöglicht die relativ einfache Darstellung orthogonaler Gewichtsmatrizen.


A large number of natural language processing tasks exist to analyze syntax, semantics, and information content of human language.These seemingly very different tasks are usually solved by specially designed architectures.In this paper, we provide the simple insight that a great variety of tasks can be represented in a single unified format consisting of labeling spans and relations between spans, thus a single task-independent model can be used across different tasks.We perform extensive experiments to test this insight on 10 disparate tasks as broad as dependency parsing (syntax), semantic role labeling (semantics), relation extraction (information content), aspect based sentiment analysis (sentiment), and many others, achieving comparable performance as state-of-the-art specialized models.We further demonstrate benefits in multi-task learning.We convert these datasets into a unified format to build a benchmark, which provides a holistic testbed for evaluating future models for generalized natural language analysis.

Wir verwenden ein einziges Modell, um eine Vielzahl von Aufgaben zur Analyse natürlicher Sprache zu lösen, indem wir sie in einem einheitlichen Span-Relation-Format formulieren.

Diese Arbeit verallgemeinert eine breite Palette von Aufgaben zur Verarbeitung natürlicher Sprache in einem einzigen, auf Spans basierenden Rahmen und schlägt eine allgemeine Architektur zur Lösung all dieser Probleme vor.

In dieser Arbeit wird eine einheitliche Formulierung für verschiedene NLP-Aufgaben auf Phrasen- und Token-Ebene vorgestellt.


Large matrix inversions have often been cited as a major impediment to scaling Gaussian process (GP) models.With the use of GPs as building blocks for ever more sophisticated Bayesian deep learning models, removing these impediments is a necessary step for achieving large scale results.We present a variational approximation for a wide range of GP models that does not require a matrix inverse to be performed at each optimisation step.Our bound instead directly parameterises a free matrix, which is an additional variational parameter.At the local maxima of the bound, this matrix is equal to the matrix inverse.We prove that our bound gives the same guarantees as earlier variational approximations.We demonstrate some beneficial properties of the bound experimentally, although significant wall clock time speed improvements will require future improvements in optimisation and implementation.

Wir stellen eine untere Variationsschranke für GP-Modelle vor, die ohne die Berechnung teurer Matrixoperationen wie Inversen optimiert werden kann und dabei die gleichen Garantien wie bestehende Variationsannäherungen bietet.


It has been shown that using geometric spaces with non-zero curvature instead of plain Euclidean spaces with zero curvature improves performance on a range of Machine Learning tasks for learning representations.Recent work has leveraged these geometries to learn latent variable models like Variational Autoencoders (VAEs) in spherical and hyperbolic spaces with constant curvature.While these approaches work well on particular kinds of data that they were designed for e.g.~tree-like data for a hyperbolic VAE, there exists no generic approach unifying all three models.We develop a Mixed-curvature Variational Autoencoder, an efficient way to train a VAE whose latent space is a product of constant curvature Riemannian manifolds, where the per-component curvature can be learned.This generalizes the Euclidean VAE to curved latent spaces, as the model essentially reduces to the Euclidean VAE if curvatures of all latent space components go to 0.

Variationale Autoencoder mit latenten Räumen, die als Produkte von Riemannschen Mannigfaltigkeiten mit konstanter Krümmung modelliert sind, verbessern die Bildrekonstruktion gegenüber Varianten mit nur einer Mannigfaltigkeit.

In diesem Artikel wird eine allgemeine Formulierung des Begriffs einer VAE mit einem latenten Raum, der aus einer gekrümmten Mannigfaltigkeit besteht, vorgestellt.

In diese geht Arbeit es um die Entwicklung von VAEs in nicht-euklidischen Räumen.


Machine learning algorithms for generating molecular structures offer a promising new approach to drug discovery.We cast molecular optimization as a translation problem, where the goal is to map an input compound to a target compound with improved biochemical properties.Remarkably, we observe that when generated molecules are iteratively fed back into the translator, molecular compound attributes improve with each step.We show that this finding is invariant to the choice of translation model, making this a "black box" algorithm.We call this method Black Box Recursive Translation (BBRT), a new inference method for molecular property optimization.This simple, powerful technique operates strictly on the inputs and outputs of any translation model.We obtain new state-of-the-art results for molecular property optimization tasks using our simple drop-in replacement with well-known sequence and graph-based models.Our method provides a significant boost in performance relative to its non-recursive peers with just a simple "``for" loop.Further, BBRT is highly interpretable, allowing users to map the evolution of newly discovered compounds from known starting points.

Wir stellen einen Black-Box-Algorithmus für die wiederholte Optimierung von Verbindungen unter Verwendung eines Übersetzungs Frameworks vor.

Die Autoren stellen die Moleküloptimierung als ein equence-to-sequence Problem dar und erweitern bestehende Methoden zur Verbesserung von Molekülen. Sie zeigen, dass dies für die Optimierung von logP, nicht aber von QED von Vorteil ist.

Die Arbeit baut auf bestehenden Übersetzungsmodellen auf, die für die molekulare Optimierung entwickelt wurden, und verwendet iterative sequence-to-sequence oder Graph-zu-Graph Übersetzungsmodelle.