pwc-archive/papers-with-abstracts huggingface dataset:
Identifying nonlinear dynamical systems via generative recurrent neural networks with applications to fMRI
Authors: Georgia Koppe, Hazem Toutounji, Peter Kirsch, Stefanie Lis, Daniel Durstewitz
Date: 2019-02-19 00:00:00
Tasks: Time Series, Time Series Analysis

A major tenet in theoretical neuroscience is that cognitive and behavioral processes are ultimately implemented in terms of the neural system dynamics. Accordingly, a major aim for the analysis of neurophysiological measurements should lie in the identification of the computational dynamics underlying task processing. Here we advance a state space model (SSM) based on generative piecewise-linear recurrent neural networks (PLRNN) to assess dynamics from neuroimaging data. In contrast to many other nonlinear time series models which have been proposed for reconstructing latent dynamics, our model is easily interpretable in neural terms, amenable to systematic dynamical systems analysis of the resulting set of equations, and can straightforwardly be transformed into an equivalent continuous-time dynamical system. The major contributions of this paper are the introduction of a new observation model suitable for functional magnetic resonance imaging (fMRI) coupled to the latent PLRNN, an efficient stepwise training procedure that forces the latent model to capture the 'true' underlying dynamics rather than just fitting (or predicting) the observations, and of an empirical measure based on the Kullback-Leibler divergence to evaluate from empirical time series how well this goal of approximating the underlying dynamics has been achieved. We validate and illustrate the power of our approach on simulated 'ground-truth' dynamical (benchmark) systems as well as on actual experimental fMRI time series, and demonstrate that the latent dynamics harbors task-related nonlinear structure that a linear dynamical model fails to capture. Given that fMRI is one of the most common techniques for measuring brain activity non-invasively in human subjects, this approach may provide a novel step toward analyzing aberrant (nonlinear) dynamics for clinical assessment or neuroscientific research.
Measuring Compositionality in Representation Learning
Authors: Jacob Andreas
Date: 2019-02-19 00:00:00
Proceeding: ICLR 2019 5
Tasks: BIG-bench Machine Learning, Representation Learning

Many machine learning algorithms represent input data with vector embeddings
or discrete codes. When inputs exhibit compositional structure (e.g. objects
built from parts or procedures from subroutines), it is natural to ask whether
this compositional structure is reflected in the the inputs' learned
representations. While the assessment of compositionality in languages has
received significant attention in linguistics and adjacent fields, the machine
learning literature lacks general-purpose tools for producing graded
measurements of compositional structure in more general (e.g. vector-valued)
representation spaces. We describe a procedure for evaluating compositionality
by measuring how well the true representation-producing model can be
approximated by a model that explicitly composes a collection of inferred
representational primitives. We use the procedure to provide formal and
empirical characterizations of compositional structure in a variety of
settings, exploring the relationship between compositionality and learning
dynamics, human judgments, representational similarity, and generalization.
A spelling correction model for end-to-end speech recognition
Authors: Jinxi Guo, Tara N. Sainath, Ron J. Weiss
Date: 2019-02-19 00:00:00
Tasks: Language Modeling, Language Modelling, speech-recognition, Speech Recognition, Spelling Correction

Attention-based sequence-to-sequence models for speech recognition jointly
train an acoustic model, language model (LM), and alignment mechanism using a
single neural network and require only parallel audio-text pairs. Thus, the
language model component of the end-to-end model is only trained on transcribed
audio-text pairs, which leads to performance degradation especially on rare
words. While there have been a variety of work that look at incorporating an
external LM trained on text-only data into the end-to-end framework, none of
them have taken into account the characteristic error distribution made by the
model. In this paper, we propose a novel approach to utilizing text-only data,
by training a spelling correction (SC) model to explicitly correct those
errors. On the LibriSpeech dataset, we demonstrate that the proposed model
results in an 18.6% relative improvement in WER over the baseline model when
directly correcting top ASR hypothesis, and a 29.0% relative improvement when
further rescoring an expanded n-best list using an external LM.
Simplifying Graph Convolutional Networks
Authors: Felix Wu, Tianyi Zhang, Amauri Holanda de Souza Jr., Christopher Fifty, Tao Yu, Kilian Q. Weinberger
Date: 2019-02-19 00:00:00
Tasks: Graph Regression, Image Classification, Node Classification, Node Classification on Non-Homophilic (Heterophilic) Graphs, Relation Extraction, Sentiment Analysis, Skeleton Based Action Recognition, Text Classification

Graph Convolutional Networks (GCNs) and their variants have experienced significant attention and have become the de facto methods for learning graph representations. GCNs derive inspiration primarily from recent deep learning approaches, and as a result, may inherit unnecessary complexity and redundant computation. In this paper, we reduce this excess complexity through successively removing nonlinearities and collapsing weight matrices between consecutive layers. We theoretically analyze the resulting linear model and show that it corresponds to a fixed low-pass filter followed by a linear classifier. Notably, our experimental evaluation demonstrates that these simplifications do not negatively impact accuracy in many downstream applications. Moreover, the resulting model scales to larger datasets, is naturally interpretable, and yields up to two orders of magnitude speedup over FastGCN.
Emergent Coordination Through Competition
Authors: Si-Qi Liu, Guy Lever, Josh Merel, Saran Tunyasuvunakool, Nicolas Heess, Thore Graepel
Date: 2019-02-19 00:00:00
Proceeding: ICLR 2019 5
Tasks: continuous-control, Continuous Control, Reinforcement Learning, Reinforcement Learning (RL)

We study the emergence of cooperative behaviors in reinforcement learning
agents by introducing a challenging competitive multi-agent soccer environment
with continuous simulated physics. We demonstrate that decentralized,
population-based training with co-play can lead to a progression in agents'
behaviors: from random, to simple ball chasing, and finally showing evidence of
cooperation. Our study highlights several of the challenges encountered in
large scale multi-agent training in continuous control. In particular, we
demonstrate that the automatic optimization of simple shaping rewards, not
themselves conducive to co-operative behavior, can lead to long-horizon team
behavior. We further apply an evaluation scheme, grounded by game theoretic
principals, that can assess agent performance in the absence of pre-defined
evaluation tasks or human baselines.
Recovery of a mixture of Gaussians by sum-of-norms clustering
Authors: Tao Jiang, Stephen Vavasis, Chen Wen Zhai
Date: 2019-02-19 00:00:00
Tasks: Clustering

Sum-of-norms clustering is a method for assigning $n$ points in
$\mathbb{R}^d$ to $K$ clusters, $1\le K\le n$, using convex optimization.
Recently, Panahi et al.\ proved that sum-of-norms clustering is guaranteed to
recover a mixture of Gaussians under the restriction that the number of samples
is not too large. The purpose of this note is to lift this restriction, i.e.,
show that sum-of-norms clustering with equal weights can recover a mixture of
Gaussians even as the number of samples tends to infinity. Our proof relies on
an interesting characterization of clusters computed by sum-of-norms clustering
that was developed inside a proof of the agglomeration conjecture by Chiquet et
al. Because we believe this theorem has independent interest, we restate and
reprove the Chiquet et al.\ result herein.
Bayesian Exploration with Heterogeneous Agents
Authors: Nicole Immorlica, Jieming Mao, Aleksandrs Slivkins, Zhiwei Steven Wu
Date: 2019-02-19 00:00:00
Tasks: Recommendation Systems

It is common in recommendation systems that users both consume and produce
information as they make strategic choices under uncertainty. While a social
planner would balance "exploration" and "exploitation" using a multi-armed
bandit algorithm, users' incentives may tilt this balance in favor of
exploitation. We consider Bayesian Exploration: a simple model in which the
recommendation system (the "principal") controls the information flow to the
users (the "agents") and strives to incentivize exploration via information
asymmetry. A single round of this model is a version of a well-known "Bayesian
Persuasion game" from [Kamenica and Gentzkow]. We allow heterogeneous users,
relaxing a major assumption from prior work that users have the same
preferences from one time step to another. The goal is now to learn the best
personalized recommendations. One particular challenge is that it may be
impossible to incentivize some of the user types to take some of the actions,
no matter what the principal does or how much time she has. We consider several
versions of the model, depending on whether and when the user types are
reported to the principal, and design a near-optimal "recommendation policy"
for each version. We also investigate how the model choice and the diversity of
user types impact the set of actions that can possibly be "explored" by each
type.
An entropic feature selection method in perspective of Turing formula
Authors: Jingyi Shi, Jialin Zhang, Yaorong Ge
Date: 2019-02-19 00:00:00
Tasks: feature selection

Health data are generally complex in type and small in sample size. Such
domain-specific challenges make it difficult to capture information reliably
and contribute further to the issue of generalization. To assist the analytics
of healthcare datasets, we develop a feature selection method based on the
concept of Coverage Adjusted Standardized Mutual Information (CASMI). The main
advantages of the proposed method are: 1) it selects features more efficiently
with the help of an improved entropy estimator, particularly when the sample
size is small, and 2) it automatically learns the number of features to be
selected based on the information from sample data. Additionally, the proposed
method handles feature redundancy from the perspective of joint-distribution.
The proposed method focuses on non-ordinal data, while it works with numerical
data with an appropriate binning method. A simulation study comparing the
proposed method to six widely cited feature selection methods shows that the
proposed method performs better when measured by the Information Recovery
Ratio, particularly when the sample size is small.
Global Convergence of Adaptive Gradient Methods for An Over-parameterized Neural Network
Authors: Xiaoxia Wu, Simon S. Du, Rachel Ward
Date: 2019-02-19 00:00:00

Adaptive gradient methods like AdaGrad are widely used in optimizing neural networks. Yet, existing convergence guarantees for adaptive gradient methods require either convexity or smoothness, and, in the smooth setting, only guarantee convergence to a stationary point. We propose an adaptive gradient method and show that for two-layer over-parameterized neural networks -- if the width is sufficiently large (polynomially) -- then the proposed method converges \emph{to the global minimum} in polynomial time, and convergence is robust, \emph{ without the need to fine-tune hyper-parameters such as the step-size schedule and with the level of over-parametrization independent of the training error}. Our analysis indicates in particular that over-parametrization is crucial for the harnessing the full potential of adaptive gradient methods in the setting of neural networks.
A novel repetition normalized adversarial reward for headline generation
Authors: Peng Xu, Pascale Fung
Date: 2019-02-19 00:00:00
Tasks: Headline Generation, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Text Generation

While reinforcement learning can effectively improve language generation
models, it often suffers from generating incoherent and repetitive phrases
\cite{paulus2017deep}. In this paper, we propose a novel repetition normalized
adversarial reward to mitigate these problems. Our repetition penalized reward
can greatly reduce the repetition rate and adversarial training mitigates
generating incoherent phrases. Our model significantly outperforms the baseline
model on ROUGE-1\,(+3.24), ROUGE-L\,(+2.25), and a decreased repetition-rate
(-4.98\%).
Adaptive Cross-Modal Few-Shot Learning
Authors: Chen Xing, Negar Rostamzadeh, Boris N. Oreshkin, Pedro O. Pinheiro
Date: 2019-02-19 00:00:00
Proceeding: NeurIPS 2019 12
Tasks: Few-Shot Image Classification, Few-Shot Learning, General Classification, image-classification, Image Classification, Meta-Learning

Metric-based meta-learning techniques have successfully been applied to few-shot classification problems. In this paper, we propose to leverage cross-modal information to enhance metric-based few-shot learning methods. Visual and semantic feature spaces have different structures by definition. For certain concepts, visual features might be richer and more discriminative than text ones. While for others, the inverse might be true. Moreover, when the support from visual information is limited in image classification, semantic representations (learned from unsupervised text corpora) can provide strong prior knowledge and context to help learning. Based on these two intuitions, we propose a mechanism that can adaptively combine information from both modalities according to new image categories to be learned. Through a series of experiments, we show that by this adaptive combination of the two modalities, our model outperforms current uni-modality few-shot learning methods and modality-alignment methods by a large margin on all benchmarks and few-shot scenarios tested. Experiments also show that our model can effectively adjust its focus on the two modalities. The improvement in performance is particularly large when the number of shots is very small.
Cost-Sensitive Diagnosis and Learning Leveraging Public Health Data
Authors: Mohammad Kachuee, Kimmo Karkkainen, Orpaz Goldstein, Davina Zamanzadeh, Majid Sarrafzadeh
Date: 2019-02-19 00:00:00
Tasks: Reinforcement Learning

Traditionally, machine learning algorithms rely on the assumption that all features of a given dataset are available for free. However, there are many concerns such as monetary data collection costs, patient discomfort in medical procedures, and privacy impacts of data collection that require careful consideration in any real-world health analytics system. An efficient solution would only acquire a subset of features based on the value it provides while considering acquisition costs. Moreover, datasets that provide feature costs are very limited, especially in healthcare. In this paper, we provide a health dataset as well as a method for assigning feature costs based on the total level of inconvenience asking for each feature entails. Furthermore, based on the suggested dataset, we provide a comparison of recent and state-of-the-art approaches to cost-sensitive feature acquisition and learning. Specifically, we analyze the performance of major sensitivity-based and reinforcement learning based methods in the literature on three different problems in the health domain, including diabetes, heart disease, and hypertension classification.
Directional Regularized Tensor Modeling for Video Rain Streaks Removal
Authors: Zhaoyang Sun, Shengwu Xiong, Ryan Wen Liu
Date: 2019-02-19 00:00:00
Tasks: Object Recognition, Rain Removal, Video deraining

Outdoor videos sometimes contain unexpected rain streaks due to the rainy
weather, which bring negative effects on subsequent computer vision
applications, e.g., video surveillance, object recognition and tracking, etc.
In this paper, we propose a directional regularized tensor-based video
deraining model by taking into consideration the arbitrary direction of rain
streaks. In particular, the sparsity of rain streaks in spatial and derivative
domains, the spatiotemporal sparsity and low-rank property of video background
are incorporated into the proposed method. Different from many previous methods
under the assumption of vertically falling rain streaks, we consider a more
realistic assumption that all the rain streaks in a video fall in an
approximately similar arbitrary direction. The resulting complicated
optimization problem will be effectively solved through an alternating
direction method. Comprehensive experiments on both synthetic and realistic
datasets have demonstrated the superiority of the proposed deraining method.
Variational Regularized Transmission Refinement for Image Dehazing
Authors: Qiaoling Shu, Chuansheng Wu, Zhe Xiao, Ryan Wen Liu
Date: 2019-02-19 00:00:00
Tasks: Image Dehazing

High-quality dehazing performance is highly dependent upon the accurate
estimation of transmission map. In this work, the coarse estimation version is
first obtained by weightedly fusing two different transmission maps, which are
generated from foreground and sky regions, respectively. A hybrid variational
model with promoted regularization terms is then proposed to assisting in
refining transmission map. The resulting complicated optimization problem is
effectively solved via an alternating direction algorithm. The final haze-free
image can be effectively obtained according to the refined transmission map and
atmospheric scattering model. Our dehazing framework has the capacity of
preserving important image details while suppressing undesirable artifacts,
even for hazy images with large sky regions. Experiments on both synthetic and
realistic images have illustrated that the proposed method is competitive with
or even outperforms the state-of-the-art dehazing techniques under different
imaging conditions.
A Walk-based Model on Entity Graphs for Relation Extraction
Authors: Fenia Christopoulou, Makoto Miwa, Sophia Ananiadou
Date: 2019-02-19 00:00:00
Proceeding: ACL 2018 7
Tasks: Position, Relation, Relation Extraction, Sentence

We present a novel graph-based neural network model for relation extraction. Our model treats multiple pairs in a sentence simultaneously and considers interactions among them. All the entities in a sentence are placed as nodes in a fully-connected graph structure. The edges are represented with position-aware contexts around the entity pairs. In order to consider different relation paths between two entities, we construct up to l-length walks between each pair. The resulting walks are merged and iteratively used to update the edge representations into longer walks representations. We show that the model achieves performance comparable to the state-of-the-art systems on the ACE 2005 dataset without using any external tools.
Investigating Generalisation in Continuous Deep Reinforcement Learning
Authors: Chenyang Zhao, Olivier Sigaud, Freek Stulp, Timothy M. Hospedales
Date: 2019-02-19 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Deep Reinforcement Learning has shown great success in a variety of control
tasks. However, it is unclear how close we are to the vision of putting Deep RL
into practice to solve real world problems. In particular, common practice in
the field is to train policies on largely deterministic simulators and to
evaluate algorithms through training performance alone, without a train/test
distinction to ensure models generalise and are not overfitted. Moreover, it is
not standard practice to check for generalisation under domain shift, although
robustness to such system change between training and testing would be
necessary for real-world Deep RL control, for example, in robotics. In this
paper we study these issues by first characterising the sources of uncertainty
that provide generalisation challenges in Deep RL. We then provide a new
benchmark and thorough empirical evaluation of generalisation challenges for
state of the art Deep RL methods. In particular, we show that, if
generalisation is the goal, then common practice of evaluating algorithms based
on their training performance leads to the wrong conclusions about algorithm
choice. Finally, we evaluate several techniques for improving generalisation
and draw conclusions about the most robust techniques to date.
An improved uncertainty propagation method for robust i-vector based speaker recognition
Authors: Dayana Ribas, Emmanuel Vincent
Date: 2019-02-15 00:00:00
Tasks: Speaker Recognition, Speaker Verification, Speech Enhancement

The performance of automatic speaker recognition systems degrades when facing
distorted speech data containing additive noise and/or reverberation.
Statistical uncertainty propagation has been introduced as a promising paradigm
to address this challenge. So far, different uncertainty propagation methods
have been proposed to compensate noise and reverberation in i-vectors in the
context of speaker recognition. They have achieved promising results on small
datasets such as YOHO and Wall Street Journal, but little or no improvement on
the larger, highly variable NIST Speaker Recognition Evaluation (SRE) corpus.
In this paper, we propose a complete uncertainty propagation method, whereby we
model the effect of uncertainty both in the computation of unbiased Baum-Welch
statistics and in the derivation of the posterior expectation of the i-vector.
We conduct experiments on the NIST-SRE corpus mixed with real domestic noise
and reverberation from the CHiME-2 corpus and preprocessed by multichannel
speech enhancement. The proposed method improves the equal error rate (EER) by
4% relative compared to a conventional i-vector based speaker verification
baseline. This is to be compared with previous methods which degrade
performance.
Evaluating model calibration in classification
Authors: Juozas Vaicenavicius, David Widmann, Carl Andersson, Fredrik Lindsten, Jacob Roll, Thomas B. Schön
Date: 2019-02-19 00:00:00
Tasks: Classification, Decision Making, General Classification, model

Probabilistic classifiers output a probability distribution on target classes
rather than just a class prediction. Besides providing a clear separation of
prediction and decision making, the main advantage of probabilistic models is
their ability to represent uncertainty about predictions. In safety-critical
applications, it is pivotal for a model to possess an adequate sense of
uncertainty, which for probabilistic classifiers translates into outputting
probability distributions that are consistent with the empirical frequencies
observed from realized outcomes. A classifier with such a property is called
calibrated. In this work, we develop a general theoretical calibration
evaluation framework grounded in probability theory, and point out subtleties
present in model calibration evaluation that lead to refined interpretations of
existing evaluation techniques. Lastly, we propose new ways to quantify and
visualize miscalibration in probabilistic classification, including novel
multidimensional reliability diagrams.