pwc-archive/papers-with-abstracts huggingface dataset:
Stable Recurrent Models
Authors: John Miller, Moritz Hardt
Date: 2018-05-25 00:00:00
Proceeding: ICLR 2019 5

Stability is a fundamental property of dynamical systems, yet to this date it
has had little bearing on the practice of recurrent neural networks. In this
work, we conduct a thorough investigation of stable recurrent models.
Theoretically, we prove stable recurrent neural networks are well approximated
by feed-forward networks for the purpose of both inference and training by
gradient descent. Empirically, we demonstrate stable recurrent models often
perform as well as their unstable counterparts on benchmark sequence tasks.
Taken together, these findings shed light on the effective power of recurrent
networks and suggest much of sequence learning happens, or can be made to
happen, in the stable regime. Moreover, our results help to explain why in many
cases practitioners succeed in replacing recurrent models by feed-forward
models.
AdapterNet - learning input transformation for domain adaptation
Authors: Alon Hazan, Yoel Shoshan, Daniel Khapun, Roy Aladjem, Vadim Ratner
Date: 2018-05-29 00:00:00
Tasks: Domain Adaptation

Deep neural networks have demonstrated impressive performance in various
machine learning tasks. However, they are notoriously sensitive to changes in
data distribution. Often, even a slight change in the distribution can lead to
drastic performance reduction. Artificially augmenting the data may help to
some extent, but in most cases, fails to achieve model invariance to the data
distribution. Some examples where this sub-class of domain adaptation can be
valuable are various imaging modalities such as thermal imaging, X-ray,
ultrasound, and MRI, where changes in acquisition parameters or acquisition
device manufacturer will result in a different representation of the same
input. Our work shows that standard fine-tuning fails to adapt the model in
certain important cases. We propose a novel method of adapting to a new data
source, and demonstrate near perfect adaptation on a customized ImageNet
benchmark. Moreover, our method does not require any samples from the original
data set, it is completely explainable and can be tailored to the task.
Polyglot Semantic Role Labeling
Authors: Phoebe Mulcaire, Swabha Swayamdipta, Noah Smith
Date: 2018-05-29 00:00:00
Proceeding: ACL 2018 7
Tasks: Dependency Parsing, Semantic Dependency Parsing, Semantic Role Labeling

Previous approaches to multilingual semantic dependency parsing treat
languages independently, without exploiting the similarities between semantic
structures across languages. We experiment with a new approach where we combine
resources from a pair of languages in the CoNLL 2009 shared task to build a
polyglot semantic role labeler. Notwithstanding the absence of parallel data,
and the dissimilarity in annotations between languages, our approach results in
an improvement in SRL performance on multiple languages over a monolingual
baseline. Analysis of the polyglot model shows it to be advantageous in
lower-resource settings.
Deep Neural Networks for Swept Volume Prediction Between Configurations
Authors: Hao-Tien Lewis Chiang, Aleksandra Faust, Lydia Tapia
Date: 2018-05-29 00:00:00
Tasks: Motion Planning

Swept Volume (SV), the volume displaced by an object when it is moving along
a trajectory, is considered a useful metric for motion planning. First, SV has
been used to identify collisions along a trajectory, because it directly
measures the amount of space required for an object to move. Second, in
sampling-based motion planning, SV is an ideal distance metric, because it
correlates to the likelihood of success of the expensive local planning step
between two sampled configurations. However, in both of these applications,
traditional SV algorithms are too computationally expensive for efficient
motion planning. In this work, we train Deep Neural Networks (DNNs) to learn
the size of SV for specific robot geometries. Results for two robots, a 6
degree of freedom (DOF) rigid body and a 7 DOF fixed-based manipulator,
indicate that the network estimations are very close to the true size of SV and
is more than 1500 times faster than a state of the art SV estimation algorithm.
Adversarial Noise Attacks of Deep Learning Architectures -- Stability Analysis via Sparse Modeled Signals
Authors: Yaniv Romano, Aviad Aberdam, Jeremias Sulam, Michael Elad
Date: 2018-05-29 00:00:00
Tasks: General Classification

Despite their impressive performance, deep convolutional neural networks (CNNs) have been shown to be sensitive to small adversarial perturbations. These nuisances, which one can barely notice, are powerful enough to fool sophisticated and well performing classifiers, leading to ridiculous misclassification results. In this paper we analyze the stability of state-of-the-art deep-learning classification machines to adversarial perturbations, where we assume that the signals belong to the (possibly multi-layer) sparse representation model. We start with convolutional sparsity and then proceed to its multi-layered version, which is tightly connected to CNNs. Our analysis links between the stability of the classification to noise and the underlying structure of the signal, quantified by the sparsity of its representation under a fixed dictionary. In addition, we offer similar stability theorems for two practical pursuit algorithms, which are posed as two different deep-learning architectures - the layered Thresholding and the layered Basis Pursuit. Our analysis establishes the better robustness of the later to adversarial attacks. We corroborate these theoretical results by numerical experiments on three datasets: MNIST, CIFAR-10 and CIFAR-100.
Observe and Look Further: Achieving Consistent Performance on Atari
Authors: Tobias Pohlen, Bilal Piot, Todd Hester, Mohammad Gheshlaghi Azar, Dan Horgan, David Budden, Gabriel Barth-Maron, Hado van Hasselt, John Quan, Mel Večerík, Matteo Hessel, Rémi Munos, Olivier Pietquin
Date: 2018-05-29 00:00:00
Tasks: Atari Games, Deep Reinforcement Learning, Montezuma's Revenge, Reinforcement Learning, Reinforcement Learning (RL)

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
$\gamma = 0.999$ (instead of $\gamma = 0.99$) 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. Furthermore, it is
the first deep RL algorithm to solve the first level of Montezuma's Revenge.
Playing hard exploration games by watching YouTube
Authors: Yusuf Aytar, Tobias Pfaff, David Budden, Tom Le Paine, Ziyu Wang, Nando de Freitas
Date: 2018-05-29 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Deep Reinforcement Learning, Montezuma's Revenge, Reinforcement Learning

Deep reinforcement learning methods traditionally struggle with tasks where
environment rewards are particularly sparse. One successful method of guiding
exploration in these domains is to imitate trajectories provided by a human
demonstrator. However, these demonstrations are typically collected under
artificial conditions, i.e. with access to the agent's exact environment setup
and the demonstrator's action and reward trajectories. Here we propose a
two-stage method that overcomes these limitations by relying on noisy,
unaligned footage without access to such data. First, we learn to map unaligned
videos from multiple sources to a common representation using self-supervised
objectives constructed over both time and modality (i.e. vision and sound).
Second, we embed a single YouTube video in this representation to construct a
reward function that encourages an agent to imitate human gameplay. This method
of one-shot imitation allows our agent to convincingly exceed human-level
performance on the infamously hard exploration games Montezuma's Revenge,
Pitfall! and Private Eye for the first time, even if the agent is not presented
with any environment rewards.
Mirror, Mirror, on the Wall, Who's Got the Clearest Image of Them All? - A Tailored Approach to Single Image Reflection Removal
Authors: Daniel Heydecker, Georg Maierhofer, Angelica I. Aviles-Rivero, Qingnan Fan, Dong-Dong Chen, Carola-Bibiane Schönlieb, Sabine Süsstrunk
Date: 2018-05-29 00:00:00
Tasks: All, Reflection Removal

Removing reflection artefacts from a single image is a problem of both
theoretical and practical interest, which still presents challenges because of
the massively ill-posed nature of the problem. In this work, we propose a
technique based on a novel optimisation problem. Firstly, we introduce a simple
user interaction scheme, which helps minimise information loss in
reflection-free regions. Secondly, we introduce an $H^2$ fidelity term, which
preserves fine detail while enforcing global colour similarity. We show that
this combination allows us to mitigate some major drawbacks of the existing
methods for reflection removal. We demonstrate, through numerical and visual
experiments, that our method is able to outperform the state-of-the-art methods
and compete with recent deep-learning approaches.
Focal onset seizure prediction using convolutional networks
Authors: Haidar Khan, Lara Marcuse, Madeline Fields, Kalina Swann, Bülent Yener
Date: 2018-05-29 00:00:00
Tasks: EEG, Electroencephalogram (EEG), Prediction, Seizure prediction

Objective: This work investigates the hypothesis that focal seizures can be
predicted using scalp electroencephalogram (EEG) data. Our first aim is to
learn features that distinguish between the interictal and preictal regions.
The second aim is to define a prediction horizon in which the prediction is as
accurate and as early as possible, clearly two competing objectives. Methods:
Convolutional filters on the wavelet transformation of the EEG signal are used
to define and learn quantitative signatures for each period: interictal,
preictal, and ictal. The optimal seizure prediction horizon is also learned
from the data as opposed to making an a priori assumption. Results:
Computational solutions to the optimization problem indicate a ten-minute
seizure prediction horizon. This result is verified by measuring
Kullback-Leibler divergence on the distributions of the automatically extracted
features. Conclusion: The results on the EEG database of 204 recordings
demonstrate that (i) the preictal phase transition occurs approximately ten
minutes before seizure onset, and (ii) the prediction results on the test set
are promising, with a sensitivity of 87.8% and a low false prediction rate of
0.142 FP/h. Our results significantly outperform a random predictor and other
seizure prediction algorithms. Significance: We demonstrate that a robust set
of features can be learned from scalp EEG that characterize the preictal state
of focal seizures.
Adversarial Regularizers in Inverse Problems
Authors: Sebastian Lunz, Ozan Öktem, Carola-Bibiane Schönlieb
Date: 2018-05-29 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Denoising

Inverse Problems in medical imaging and computer vision are traditionally
solved using purely model-based methods. Among those variational regularization
models are one of the most popular approaches. We propose a new framework for
applying data-driven approaches to inverse problems, using a neural network as
a regularization functional. The network learns to discriminate between the
distribution of ground truth images and the distribution of unregularized
reconstructions. Once trained, the network is applied to the inverse problem by
solving the corresponding variational problem. Unlike other data-based
approaches for inverse problems, the algorithm can be applied even if only
unsupervised training data is available. Experiments demonstrate the potential
of the framework for denoising on the BSDS dataset and for computed tomography
reconstruction on the LIDC dataset.
Multi-View Semantic Labeling of 3D Point Clouds for Automated Plant Phenotyping
Authors: Bernhard Japes, Jennifer Mack, Florian Rist, Katja Herzog, Reinhard Töpfer, Volker Steinhage
Date: 2018-05-10 00:00:00
Tasks: Feature Engineering, Management, Plant Phenotyping

Semantic labeling of 3D point clouds is important for the derivation of 3D
models from real world scenarios in several economic fields such as building
industry, facility management, town planning or heritage conservation. In
contrast to these most common applications, we describe in this study the
semantic labeling of 3D point clouds derived from plant organs by
high-precision scanning. Our approach is optimized for the task of plant
phenotyping with its very specific challenges and is employing a deep learning
framework. Thereby, we report important experiences concerning detailed
parameter initialization and optimization techniques. By evaluating our
approach with challenging datasets we achieve state-of-the-art results without
difficult and time consuming feature engineering as being necessary in
traditional approaches to semantic labeling.
Human-in-the-Loop Interpretability Prior
Authors: Isaac Lage, Andrew Slavin Ross, Been Kim, Samuel J. Gershman, Finale Doshi-Velez
Date: 2018-05-29 00:00:00
Proceeding: NeurIPS 2018 12

We often desire our models to be interpretable as well as accurate. Prior
work on optimizing models for interpretability has relied on easy-to-quantify
proxies for interpretability, such as sparsity or the number of operations
required. In this work, we optimize for interpretability by directly including
humans in the optimization loop. We develop an algorithm that minimizes the
number of user studies to find models that are both predictive and
interpretable and demonstrate our approach on several data sets. Our human
subjects results show trends towards different proxy notions of
interpretability on different datasets, which suggests that different proxies
are preferred on different tasks.
On gradient regularizers for MMD GANs
Authors: Michael Arbel, Danica J. Sutherland, Mikołaj Bińkowski, Arthur Gretton
Date: 2018-05-29 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Image Generation

We propose a principled method for gradient-based regularization of the critic of GAN-like models trained by adversarially optimizing the kernel of a Maximum Mean Discrepancy (MMD). We show that controlling the gradient of the critic is vital to having a sensible loss function, and devise a method to enforce exact, analytical gradient constraints at no additional cost compared to existing approximate techniques based on additive regularizers. The new loss function is provably continuous, and experiments show that it stabilizes and accelerates training, giving image generation models that outperform state-of-the art methods on $160 \times 160$ CelebA and $64 \times 64$ unconditional ImageNet.
Entrainment profiles: Comparison by gender, role, and feature set
Authors: Uwe D. Reichel, Štefan Beňuš, Katalin Mády
Date: 2018-05-29 00:00:00

We examine prosodic entrainment in cooperative game dialogs for new feature
sets describing register, pitch accent shape, and rhythmic aspects of
utterances. For these as well as for established features we present
entrainment profiles to detect within- and across-dialog entrainment by the
speakers' gender and role in the game. It turned out, that feature sets undergo
entrainment in different quantitative and qualitative ways, which can partly be
attributed to their different functions. Furthermore, interactions between
speaker gender and role (describer vs. follower) suggest gender-dependent
strategies in cooperative solution-oriented interactions: female describers
entrain most, male describers least. Our data suggests a slight advantage of
the latter strategy on task success.
Structured Disentangled Representations
Authors: Babak Esmaeili, Hao Wu, Sarthak Jain, Alican Bozkurt, N. Siddharth, Brooks Paige, Dana H. Brooks, Jennifer Dy, Jan-Willem van de Meent
Date: 2018-04-06 00:00:00
Tasks: Disentanglement

Deep latent-variable models learn representations of high-dimensional data in
an unsupervised manner. A number of recent efforts have focused on learning
representations that disentangle statistically independent axes of variation by
introducing modifications to the standard objective function. These approaches
generally assume a simple diagonal Gaussian prior and as a result are not able
to reliably disentangle discrete factors of variation. We propose a two-level
hierarchical objective to control relative degree of statistical independence
between blocks of variables and individual variables within blocks. We derive
this objective as a generalization of the evidence lower bound, which allows us
to explicitly represent the trade-offs between mutual information between data
and representation, KL divergence between representation and prior, and
coverage of the support of the empirical data distribution. Experiments on a
variety of datasets demonstrate that our objective can not only disentangle
discrete variables, but that doing so also improves disentanglement of other
variables and, importantly, generalization even to unseen combinations of
factors.
Optimisation and Illumination of a Real-world Workforce Scheduling and Routing Application via Map-Elites
Authors: Neil Urquhart, Emma Hart
Date: 2018-05-29 00:00:00
Tasks: Scheduling

Workforce Scheduling and Routing Problems (WSRP) are very common in many
practical domains, and usually, have a number of objectives. Illumination
algorithms such as Map-Elites (ME) have recently gained traction in application
to {\em design} problems, in providing multiple diverse solutions as well as
illuminating the solution space in terms of user-defined characteristics, but
typically require significant computational effort to produce the solution
archive. We investigate whether ME can provide an effective approach to solving
WSRP, a {\em repetitive} problem in which solutions have to be produced quickly
and often. The goals of the paper are two-fold. The first is to evaluate
whether ME can provide solutions of competitive quality to an Evolutionary
Algorithm (EA) in terms of a single objective function, and the second to
examine its ability to provide a repertoire of solutions that maximise user
choice. We find that very small computational budgets favour the EA in terms of
quality, but ME outperforms the EA at larger budgets, provides a more diverse
array of solutions, and lends insight to the end-user.
Like a Baby: Visually Situated Neural Language Acquisition
Authors: Alexander G. Ororbia, Ankur Mali, Matthew A. Kelly, David Reitter
Date: 2018-05-29 00:00:00
Proceeding: ACL 2019 7
Tasks: Language Acquisition, Language Modeling, Language Modelling

We examine the benefits of visual context in training neural language models to perform next-word prediction. A multi-modal neural architecture is introduced that outperform its equivalent trained on language alone with a 2\% decrease in perplexity, even when no visual context is available at test. Fine-tuning the embeddings of a pre-trained state-of-the-art bidirectional language model (BERT) in the language modeling framework yields a 3.5\% improvement. The advantage for training with visual context when testing without is robust across different languages (English, German and Spanish) and different models (GRU, LSTM, $\Delta$-RNN, as well as those that use BERT embeddings). Thus, language models perform better when they learn like a baby, i.e, in a multi-modal environment. This finding is compatible with the theory of situated cognition: language is inseparable from its physical context.
Lightly-supervised Representation Learning with Global Interpretability
Authors: Marco A. Valenzuela-Escárcega, Ajay Nagesh, Mihai Surdeanu
Date: 2018-05-29 00:00:00
Proceeding: WS 2019 6
Tasks: Representation Learning

We propose a lightly-supervised approach for information extraction, in
particular named entity classification, which combines the benefits of
traditional bootstrapping, i.e., use of limited annotations and
interpretability of extraction patterns, with the robust learning approaches
proposed in representation learning. Our algorithm iteratively learns custom
embeddings for both the multi-word entities to be extracted and the patterns
that match them from a few example entities per category. We demonstrate that
this representation-based approach outperforms three other state-of-the-art
bootstrapping approaches on two datasets: CoNLL-2003 and OntoNotes.
Additionally, using these embeddings, our approach outputs a
globally-interpretable model consisting of a decision list, by ranking patterns
based on their proximity to the average entity embedding in a given class. We
show that this interpretable model performs close to our complete bootstrapping
model, proving that representation learning can be used to produce
interpretable models with small loss in performance.
Faithful Inversion of Generative Models for Effective Amortized Inference
Authors: Stefan Webb, Adam Golinski, Robert Zinkov, N. Siddharth, Tom Rainforth, Yee Whye Teh, Frank Wood
Date: 2017-12-01 00:00:00
Proceeding: NeurIPS 2018 12

Inference amortization methods share information across multiple
posterior-inference problems, allowing each to be carried out more efficiently.
Generally, they require the inversion of the dependency structure in the
generative model, as the modeller must learn a mapping from observations to
distributions approximating the posterior. Previous approaches have involved
inverting the dependency structure in a heuristic way that fails to capture
these dependencies correctly, thereby limiting the achievable accuracy of the
resulting approximations. We introduce an algorithm for faithfully, and
minimally, inverting the graphical model structure of any generative model.
Such inverses have two crucial properties: (a) they do not encode any
independence assertions that are absent from the model and; (b) they are local
maxima for the number of true independencies encoded. We prove the correctness
of our approach and empirically show that the resulting minimally faithful
inverses lead to better inference amortization than existing heuristic
approaches.