pwc-archive/papers-with-abstracts huggingface dataset:
The RLLChatbot: a solution to the ConvAI challenge
Authors: Nicolas Gontier, Koustuv Sinha, Peter Henderson, Iulian Serban, Michael Noseworthy, Prasanna Parthasarathi, Joelle Pineau
Date: 2018-11-07 00:00:00
Tasks: Reinforcement Learning, Text Generation

Current conversational systems can follow simple commands and answer basic
questions, but they have difficulty maintaining coherent and open-ended
conversations about specific topics. Competitions like the Conversational
Intelligence (ConvAI) challenge are being organized to push the research
development towards that goal. This article presents in detail the RLLChatbot
that participated in the 2017 ConvAI challenge. The goal of this research is to
better understand how current deep learning and reinforcement learning tools
can be used to build a robust yet flexible open domain conversational agent. We
provide a thorough description of how a dialog system can be built and trained
from mostly public-domain datasets using an ensemble model. The first
contribution of this work is a detailed description and analysis of different
text generation models in addition to novel message ranking and selection
methods. Moreover, a new open-source conversational dataset is presented.
Training on this data significantly improves the Recall@k score of the ranking
and selection mechanisms compared to our baseline model responsible for
selecting the message returned at each interaction.
Greedy Frank-Wolfe Algorithm for Exemplar Selection
Authors: Gary Cheng, Armin Askari, Kannan Ramchandran, Laurent El Ghaoui
Date: 2018-11-06 00:00:00
Tasks: Dictionary Learning

In this paper, we consider the problem of selecting representatives from a data set for arbitrary supervised/unsupervised learning tasks. We identify a subset $S$ of a data set $A$ such that 1) the size of $S$ is much smaller than $A$ and 2) $S$ efficiently describes the entire data set, in a way formalized via convex optimization. In order to generate $|S| = k$ exemplars, our kernelizable algorithm, Frank-Wolfe Sparse Representation (FWSR), only needs to execute $\approx k$ iterations with a per-iteration cost that is quadratic in the size of $A$. This is in contrast to other state of the art methods which need to execute until convergence with each iteration costing an extra factor of $d$ (dimension of the data). Moreover, we also provide a proof of linear convergence for our method. We support our results with empirical experiments; we test our algorithm against current methods in three different experimental setups on four different data sets. FWSR outperforms other exemplar finding methods both in speed and accuracy in almost all scenarios.
Proceedings of the 2018 Workshop on Compositional Approaches in Physics, NLP, and Social Sciences
Authors: Martha Lewis, Bob Coecke, Jules Hedges, Dimitri Kartsaklis, Dan Marsden
Date: 2018-11-06 00:00:00

The ability to compose parts to form a more complex whole, and to analyze a
whole as a combination of elements, is desirable across disciplines. This
workshop bring together researchers applying compositional approaches to
physics, NLP, cognitive science, and game theory. Within NLP, a long-standing
aim is to represent how words can combine to form phrases and sentences. Within
the framework of distributional semantics, words are represented as vectors in
vector spaces. The categorical model of Coecke et al. [2010], inspired by
quantum protocols, has provided a convincing account of compositionality in
vector space models of NLP. There is furthermore a history of vector space
models in cognitive science. Theories of categorization such as those developed
by Nosofsky [1986] and Smith et al. [1988] utilise notions of distance between
feature vectors. More recently G\"ardenfors [2004, 2014] has developed a model
of concepts in which conceptual spaces provide geometric structures, and
information is represented by points, vectors and regions in vector spaces. The
same compositional approach has been applied to this formalism, giving
conceptual spaces theory a richer model of compositionality than previously
[Bolt et al., 2018]. Compositional approaches have also been applied in the
study of strategic games and Nash equilibria. In contrast to classical game
theory, where games are studied monolithically as one global object,
compositional game theory works bottom-up by building large and complex games
from smaller components. Such an approach is inherently difficult since the
interaction between games has to be considered. Research into categorical
compositional methods for this field have recently begun [Ghani et al., 2018].
Moreover, the interaction between the three disciplines of cognitive science,
linguistics and game theory is a fertile ground for research. Game theory in
cognitive science is a well-established area [Camerer, 2011]. Similarly game
theoretic approaches have been applied in linguistics [J\"ager, 2008]. Lastly,
the study of linguistics and cognitive science is intimately intertwined
[Smolensky and Legendre, 2006, Jackendoff, 2007]. Physics supplies
compositional approaches via vector spaces and categorical quantum theory,
allowing the interplay between the three disciplines to be examined.
ACE: An Actor Ensemble Algorithm for Continuous Control with Tree Search
Authors: Shangtong Zhang, Hao Chen, Hengshuai Yao
Date: 2018-11-06 00:00:00
Tasks: continuous-control, Continuous Control, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Value prediction

In this paper, we propose an actor ensemble algorithm, named ACE, for
continuous control with a deterministic policy in reinforcement learning. In
ACE, we use actor ensemble (i.e., multiple actors) to search the global maxima
of the critic. Besides the ensemble perspective, we also formulate ACE in the
option framework by extending the option-critic architecture with deterministic
intra-option policies, revealing a relationship between ensemble and options.
Furthermore, we perform a look-ahead tree search with those actors and a
learned value prediction model, resulting in a refined value estimation. We
demonstrate a significant performance boost of ACE over DDPG and its variants
in challenging physical robot simulators.
Reconstructing Speech Stimuli From Human Auditory Cortex Activity Using a WaveNet Approach
Authors: Ran Wang, Yao Wang, Adeen Flinker
Date: 2018-11-06 00:00:00
Tasks: speech-recognition, Speech Recognition

The superior temporal gyrus (STG) region of cortex critically contributes to
speech recognition. In this work, we show that a proposed WaveNet, with limited
available data, is able to reconstruct speech stimuli from STG intracranial
recordings. We further investigate the impulse response of the fitted model for
each recording electrode and observe phoneme level temporospectral tuning
properties for the recorded area of cortex. This discovery is consistent with
previous studies implicating the posterior STG (pSTG) in a phonetic
representation of speech and provides detailed acoustic features that certain
electrode sites possibly extract during speech recognition.
Deep Reinforcement Learning via L-BFGS Optimization
Authors: Jacob Rafati, Roummel F. Marcia
Date: 2018-11-06 00:00:00
Tasks: Atari Games, Deep Reinforcement Learning, Q-Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Second-order methods

Reinforcement Learning (RL) algorithms allow artificial agents to improve
their action selections so as to increase rewarding experiences in their
environments. Deep Reinforcement Learning algorithms require solving a
nonconvex and nonlinear unconstrained optimization problem. Methods for solving
the optimization problems in deep RL are restricted to the class of first-order
algorithms, such as stochastic gradient descent (SGD). The major drawback of
the SGD methods is that they have the undesirable effect of not escaping saddle
points and their performance can be seriously obstructed by ill-conditioning.
Furthermore, SGD methods require exhaustive trial and error to fine-tune many
learning parameters. Using second derivative information can result in improved
convergence properties, but computing the Hessian matrix for large-scale
problems is not practical. Quasi-Newton methods require only first-order
gradient information, like SGD, but they can construct a low rank approximation
of the Hessian matrix and result in superlinear convergence. The limited-memory
Broyden-Fletcher-Goldfarb-Shanno (L-BFGS) approach is one of the most popular
quasi-Newton methods that construct positive definite Hessian approximations.
In this paper, we introduce an efficient optimization method, based on the
limited memory BFGS quasi-Newton method using line search strategy -- as an
alternative to SGD methods. Our method bridges the disparity between first
order methods and second order methods by continuing to use gradient
information to calculate a low-rank Hessian approximations. We provide formal
convergence analysis as well as empirical results on a subset of the classic
ATARI 2600 games. Our results show a robust convergence with preferred
generalization characteristics, as well as fast training time and no need for
the experience replaying mechanism.
Training Domain Specific Models for Energy-Efficient Object Detection
Authors: Kentaro Yoshioka, Edward Lee, Mark Horowitz
Date: 2018-11-06 00:00:00
Tasks: Computational Efficiency, Object, object-detection, Object Detection

We propose an end-to-end framework for training domain specific models (DSMs)
to obtain both high accuracy and computational efficiency for object detection
tasks. DSMs are trained with distillation \cite{hinton2015distilling} and focus
on achieving high accuracy at a limited domain (e.g. fixed view of an
intersection). We argue that DSMs can capture essential features well even with
a small model size, enabling higher accuracy and efficiency than traditional
techniques. In addition, we improve the training efficiency by reducing the
dataset size by culling easy to classify images from the training set. For the
limited domain, we observed that compact DSMs significantly surpass the
accuracy of COCO trained models of the same size. By training on a compact
dataset, we show that with an accuracy drop of only 3.6\%, the training time
can be reduced by 93\%. The codes are uploaded in
https://github.com/kentaroy47/training-domain-specific-models.
Automatic Assessment of Full Left Ventricular Coverage in Cardiac Cine Magnetic Resonance Imaging with Fisher-Discriminative 3D CNN
Authors: Le Zhang, Ali Gooya, Marco Pereanez, Bo Dong, Stefan K. Piechnik, Stefan Neubauer, Steffen E. Petersen, Alejandro F. Frangi
Date: 2018-11-06 00:00:00
Tasks: Diagnostic

Cardiac magnetic resonance (CMR) images play a growing role in the diagnostic
imaging of cardiovascular diseases. Full coverage of the left ventricle (LV),
from base to apex, is a basic criterion for CMR image quality and necessary for
accurate measurement of cardiac volume and functional assessment. Incomplete
coverage of the LV is identified through visual inspection, which is
time-consuming and usually done retrospectively in the assessment of large
imaging cohorts. This paper proposes a novel automatic method for determining
LV coverage from CMR images by using Fisher-discriminative three-dimensional
(FD3D) convolutional neural networks (CNNs). In contrast to our previous method
employing 2D CNNs, this approach utilizes spatial contextual information in CMR
volumes, extracts more representative high-level features and enhances the
discriminative capacity of the baseline 2D CNN learning framework, thus
achieving superior detection accuracy. A two-stage framework is proposed to
identify missing basal and apical slices in measurements of CMR volume. First,
the FD3D CNN extracts high-level features from the CMR stacks. These image
representations are then used to detect the missing basal and apical slices.
Compared to the traditional 3D CNN strategy, the proposed FD3D CNN minimizes
within-class scatter and maximizes between-class scatter. We performed
extensive experiments to validate the proposed method on more than 5,000
independent volumetric CMR scans from the UK Biobank study, achieving low error
rates for missing basal/apical slice detection (4.9\%/4.6\%). The proposed
method can also be adopted for assessing LV coverage for other types of CMR
image data.
CAB: Continuous Adaptive Blending Estimator for Policy Evaluation and Learning
Authors: Yi Su, Lequn Wang, Michele Santacatterina, Thorsten Joachims
Date: 2018-11-06 00:00:00
Tasks: counterfactual, Recommendation Systems

The ability to perform offline A/B-testing and off-policy learning using logged contextual bandit feedback is highly desirable in a broad range of applications, including recommender systems, search engines, ad placement, and personalized health care. Both offline A/B-testing and off-policy learning require a counterfactual estimator that evaluates how some new policy would have performed, if it had been used instead of the logging policy. In this paper, we identify a family of counterfactual estimators which subsumes most such estimators proposed to date. Our analysis of this family identifies a new estimator - called Continuous Adaptive Blending (CAB) - which enjoys many advantageous theoretical and practical properties. In particular, it can be substantially less biased than clipped Inverse Propensity Score (IPS) weighting and the Direct Method, and it can have less variance than Doubly Robust and IPS estimators. In addition, it is sub-differentiable such that it can be used for learning, unlike the SWITCH estimator. Experimental results show that CAB provides excellent evaluation accuracy and outperforms other counterfactual estimators in terms of learning performance.
Bidirectional Quaternion Long-Short Term Memory Recurrent Neural Networks for Speech Recognition
Authors: Titouan Parcollet, Mohamed Morchid, Georges Linarès, Renato de Mori
Date: 2018-11-06 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), speech-recognition, Speech Recognition

Recurrent neural networks (RNN) are at the core of modern automatic speech
recognition (ASR) systems. In particular, long-short term memory (LSTM)
recurrent neural networks have achieved state-of-the-art results in many speech
recognition tasks, due to their efficient representation of long and short term
dependencies in sequences of inter-dependent features. Nonetheless, internal
dependencies within the element composing multidimensional features are weakly
considered by traditional real-valued representations. We propose a novel
quaternion long-short term memory (QLSTM) recurrent neural network that takes
into account both the external relations between the features composing a
sequence, and these internal latent structural dependencies with the quaternion
algebra. QLSTMs are compared to LSTMs during a memory copy-task and a realistic
application of speech recognition on the Wall Street Journal (WSJ) dataset.
QLSTM reaches better performances during the two experiments with up to $2.8$
times less learning parameters, leading to a more expressive representation of
the information.
Sparse and Smooth Signal Estimation: Convexification of L0 Formulations
Authors: Alper Atamturk, Andres Gomez, Shaoning Han
Date: 2018-11-06 00:00:00

Signal estimation problems with smoothness and sparsity priors can be naturally modeled as quadratic optimization with $\ell_0$-"norm" constraints. Since such problems are non-convex and hard-to-solve, the standard approach is, instead, to tackle their convex surrogates based on $\ell_1$-norm relaxations. In this paper, we propose a new iterative (convex) conic quadratic relaxations that exploit not only the $\ell_0$-"norm" terms, but also the fitness and smoothness functions. The iterative convexification approach substantially closes the gap between the $\ell_0$-"norm" and its $\ell_1$ surrogate. These stronger relaxations lead to significantly better estimators than $\ell_1$-norm approaches and also allow one to utilize affine sparsity priors. In addition, the parameters of the model and the resulting estimators are easily interpretable. Experiments with a tailored Lagrangian decomposition method indicate that the proposed iterative convex relaxations \rev{yield solutions within 1\% of the exact $\ell_0$ approach, and can tackle instances with up to 100,000 variables under one minute.
Building Corpora for Single-Channel Speech Separation Across Multiple Domains
Authors: Matthew Maciejewski, Gregory Sell, Leibny Paola Garcia-Perera, Shinji Watanabe, Sanjeev Khudanpur
Date: 2018-11-06 00:00:00
Tasks: Speech Separation

To date, the bulk of research on single-channel speech separation has been
conducted using clean, near-field, read speech, which is not representative of
many modern applications. In this work, we develop a procedure for constructing
high-quality synthetic overlap datasets, necessary for most deep learning-based
separation frameworks. We produced datasets that are more representative of
realistic applications using the CHiME-5 and Mixer 6 corpora and evaluate
standard methods on this data to demonstrate the shortcomings of current
source-separation performance. We also demonstrate the value of a wide variety
of data in training robust models that generalize well to multiple conditions.
Deep Probabilistic Ensembles: Approximate Variational Inference through KL Regularization
Authors: Kashyap Chitta, Jose M. Alvarez, Adam Lesnikowski
Date: 2018-11-06 00:00:00
Tasks: Active Learning, General Classification, Variational Inference

In this paper, we introduce Deep Probabilistic Ensembles (DPEs), a scalable
technique that uses a regularized ensemble to approximate a deep Bayesian
Neural Network (BNN). We do so by incorporating a KL divergence penalty term
into the training objective of an ensemble, derived from the evidence lower
bound used in variational inference. We evaluate the uncertainty estimates
obtained from our models for active learning on visual classification. Our
approach steadily improves upon active learning baselines as the annotation
budget is increased.
Molecular Transformer - A Model for Uncertainty-Calibrated Chemical Reaction Prediction
Authors: Philippe Schwaller, Teodoro Laino, Théophile Gaudin, Peter Bolgar, Costas Bekas, Alpha A. Lee
Date: 2018-11-06 00:00:00
Tasks: Chemical Reaction Prediction, Machine Translation, Translation

Organic synthesis is one of the key stumbling blocks in medicinal chemistry. A necessary yet unsolved step in planning synthesis is solving the forward problem: given reactants and reagents, predict the products. Similar to other work, we treat reaction prediction as a machine translation problem between SMILES strings of reactants-reagents and the products. We show that a multi-head attention Molecular Transformer model outperforms all algorithms in the literature, achieving a top-1 accuracy above 90% on a common benchmark dataset. Our algorithm requires no handcrafted rules, and accurately predicts subtle chemical transformations. Crucially, our model can accurately estimate its own uncertainty, with an uncertainty score that is 89% accurate in terms of classifying whether a prediction is correct. Furthermore, we show that the model is able to handle inputs without reactant-reagent split and including stereochemistry, which makes our method universally applicable.
MixTrain: Scalable Training of Verifiably Robust Neural Networks
Authors: Shiqi Wang, Yizheng Chen, Ahmed Abdou, Suman Jana
Date: 2018-11-06 00:00:00

Making neural networks robust against adversarial inputs has resulted in an
arms race between new defenses and attacks. The most promising defenses,
adversarially robust training and verifiably robust training, have limitations
that restrict their practical applications. The adversarially robust training
only makes the networks robust against a subclass of attackers and we reveal
such weaknesses by developing a new attack based on interval gradients. By
contrast, verifiably robust training provides protection against any L-p
norm-bounded attacker but incurs orders of magnitude more computational and
memory overhead than adversarially robust training.
  We propose two novel techniques, stochastic robust approximation and dynamic
mixed training, to drastically improve the efficiency of verifiably robust
training without sacrificing verified robustness. We leverage two critical
insights: (1) instead of over the entire training set, sound
over-approximations over randomly subsampled training data points are
sufficient for efficiently guiding the robust training process; and (2) We
observe that the test accuracy and verifiable robustness often conflict after
certain training epochs. Therefore, we use a dynamic loss function to
adaptively balance them for each epoch.
  We designed and implemented our techniques as part of MixTrain and evaluated
it on six networks trained on three popular datasets including MNIST, CIFAR,
and ImageNet-200. Our evaluations show that MixTrain can achieve up to $95.2\%$
verified robust accuracy against $L_\infty$ norm-bounded attackers while taking
$15$ and $3$ times less training time than state-of-the-art verifiably robust
training and adversarially robust training schemes, respectively. Furthermore,
MixTrain easily scales to larger networks like the one trained on ImageNet-200,
significantly outperforming the existing verifiably robust training methods.
State Aggregation Learning from Markov Transition Data
Authors: Yaqi Duan, Zheng Tracy Ke, Mengdi Wang
Date: 2018-11-06 00:00:00
Proceeding: NeurIPS 2019 12

State aggregation is a popular model reduction method rooted in optimal control. It reduces the complexity of engineering systems by mapping the system's states into a small number of meta-states. The choice of aggregation map often depends on the data analysts' knowledge and is largely ad hoc. In this paper, we propose a tractable algorithm that estimates the probabilistic aggregation map from the system's trajectory. We adopt a soft-aggregation model, where each meta-state has a signature raw state, called an anchor state. This model includes several common state aggregation models as special cases. Our proposed method is a simple two-step algorithm: The first step is spectral decomposition of empirical transition matrix, and the second step conducts a linear transformation of singular vectors to find their approximate convex hull. It outputs the aggregation distributions and disaggregation distributions for each meta-state in explicit forms, which are not obtainable by classical spectral methods. On the theoretical side, we prove sharp error bounds for estimating the aggregation and disaggregation distributions and for identifying anchor states. The analysis relies on a new entry-wise deviation bound for singular vectors of the empirical transition matrix of a Markov process, which is of independent interest and cannot be deduced from existing literature. The application of our method to Manhattan traffic data successfully generates a data-driven state aggregation map with nice interpretations.