pwc-archive/papers-with-abstracts huggingface dataset:
Best-Choice Edge Grafting for Efficient Structure Learning of Markov Random Fields
Authors: Walid Chaabene, Bert Huang
Date: 2017-05-25 00:00:00

Incremental methods for structure learning of pairwise Markov random fields
(MRFs), such as grafting, improve scalability by avoiding inference over the
entire feature space in each optimization step. Instead, inference is performed
over an incrementally grown active set of features. In this paper, we address
key computational bottlenecks that current incremental techniques still suffer
by introducing best-choice edge grafting, an incremental, structured method
that activates edges as groups of features in a streaming setting. The method
uses a reservoir of edges that satisfy an activation condition, approximating
the search for the optimal edge to activate. It also reorganizes the search
space using search-history and structure heuristics. Experiments show a
significant speedup for structure learning and a controllable trade-off between
the speed and quality of learning.
Machine Teaching for Inverse Reinforcement Learning: Algorithms and Applications
Authors: Daniel S. Brown, Scott Niekum
Date: 2018-05-20 00:00:00
Tasks: Decision Making, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Sequential Decision Making

Inverse reinforcement learning (IRL) infers a reward function from demonstrations, allowing for policy improvement and generalization. However, despite much recent interest in IRL, little work has been done to understand the minimum set of demonstrations needed to teach a specific sequential decision-making task. We formalize the problem of finding maximally informative demonstrations for IRL as a machine teaching problem where the goal is to find the minimum number of demonstrations needed to specify the reward equivalence class of the demonstrator. We extend previous work on algorithmic teaching for sequential decision-making tasks by showing a reduction to the set cover problem which enables an efficient approximation algorithm for determining the set of maximally-informative demonstrations. We apply our proposed machine teaching algorithm to two novel applications: providing a lower bound on the number of queries needed to learn a policy using active IRL and developing a novel IRL algorithm that can learn more efficiently from informative demonstrations than a standard IRL approach.
Fighting Offensive Language on Social Media with Unsupervised Text Style Transfer
Authors: Cicero Nogueira dos Santos, Igor Melnyk, Inkit Padhi
Date: 2018-05-20 00:00:00
Proceeding: ACL 2018 7
Tasks: Style Transfer, Text Style Transfer, Unsupervised Text Style Transfer

We introduce a new approach to tackle the problem of offensive language in
online social media. Our approach uses unsupervised text style transfer to
translate offensive sentences into non-offensive ones. We propose a new method
for training encoder-decoders using non-parallel data that combines a
collaborative classifier, attention and the cycle consistency loss.
Experimental results on data from Twitter and Reddit show that our method
outperforms a state-of-the-art text style transfer system in two out of three
quantitative metrics and produces reliable non-offensive transferred sentences.
Consistent Estimation of Propensity Score Functions with Oversampled Exposed Subjects
Authors: Sherri Rose
Date: 2018-05-20 00:00:00
Tasks: Causal Inference

Observational cohort studies with oversampled exposed subjects are typically
implemented to understand the causal effect of a rare exposure. Because the
distribution of exposed subjects in the sample differs from the source
population, estimation of a propensity score function (i.e., probability of
exposure given baseline covariates) targets a nonparametrically nonidentifiable
parameter. Consistent estimation of propensity score functions is an important
component of various causal inference estimators, including double robust
machine learning and inverse probability weighted estimators. This paper
develops the use of the probability of exposure from the source population in a
flexible computational implementation that can be used with any algorithm that
allows observation weighting to produce consistent estimators of propensity
score functions. Simulation studies and a hypothetical health policy
intervention data analysis demonstrate low empirical bias and variance for
these propensity score function estimators with observation weights in finite
samples.
Multi-Step Prediction of Dynamic Systems with Recurrent Neural Networks
Authors: Nima Mohajerin, Steven L. Waslander
Date: 2018-05-20 00:00:00
Tasks: Prediction

Recurrent Neural Networks (RNNs) can encode rich dynamics which makes them
suitable for modeling dynamic systems. To train an RNN for multi-step
prediction of dynamic systems, it is crucial to efficiently address the state
initialization problem, which seeks proper values for the RNN initial states at
the beginning of each prediction interval. In this work, the state
initialization problem is addressed using Neural Networks (NNs) to effectively
train a variety of RNNs for modeling two aerial vehicles, a helicopter and a
quadrotor, from experimental data. It is shown that the RNN initialized by the
NN-based initialization method outperforms the state of the art. Further, a
comprehensive study of RNNs trained for multi-step prediction of the two aerial
vehicles is presented. The multi-step prediction of the quadrotor is enhanced
using a hybrid model which combines a simplified physics-based motion model of
the vehicle with RNNs. While the maximum translational and rotational
velocities in the quadrotor dataset are about 4 m/s and 3.8 rad/s,
respectively, the hybrid model produces predictions, over 1.9 second, which
remain within 9 cm/s and 0.12 rad/s of the measured translational and
rotational velocities, with 99\% confidence on the test dataset
Learning Graph-Level Representations with Recurrent Neural Networks
Authors: Yu Jin, Joseph F. JaJa
Date: 2018-05-20 00:00:00
Tasks: General Classification, Graph Classification

Recently a variety of methods have been developed to encode graphs into
low-dimensional vectors that can be easily exploited by machine learning
algorithms. The majority of these methods start by embedding the graph nodes
into a low-dimensional vector space, followed by using some scheme to aggregate
the node embeddings. In this work, we develop a new approach to learn
graph-level representations, which includes a combination of unsupervised and
supervised learning components. We start by learning a set of node
representations in an unsupervised fashion. Graph nodes are mapped into node
sequences sampled from random walk approaches approximated by the
Gumbel-Softmax distribution. Recurrent neural network (RNN) units are modified
to accommodate both the node representations as well as their neighborhood
information. Experiments on standard graph classification benchmarks
demonstrate that our proposed approach achieves superior or comparable
performance relative to the state-of-the-art algorithms in terms of convergence
speed and classification accuracy. We further illustrate the effectiveness of
the different components used by our approach.
Learning Deep Disentangled Embeddings with the F-Statistic Loss
Authors: Karl Ridgeway, Michael C. Mozer
Date: 2018-02-14 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Few-Shot Learning

Deep-embedding methods aim to discover representations of a domain that make
explicit the domain's class structure and thereby support few-shot learning.
Disentangling methods aim to make explicit compositional or factorial
structure. We combine these two active but independent lines of research and
propose a new paradigm suitable for both goals. We propose and evaluate a novel
loss function based on the $F$ statistic, which describes the separation of two
or more distributions. By ensuring that distinct classes are well separated on
a subset of embedding dimensions, we obtain embeddings that are useful for
few-shot learning. By not requiring separation on all dimensions, we encourage
the discovery of disentangled representations. Our embedding method matches or
beats state-of-the-art, as evaluated by performance on recall@$k$ and few-shot
learning tasks. Our method also obtains performance superior to a variety of
alternatives on disentangling, as evaluated by two key properties of a
disentangled representation: modularity and explicitness. The goal of our work
is to obtain more interpretable, manipulable, and generalizable deep
representations of concepts and categories.
Direct Runge-Kutta Discretization Achieves Acceleration
Authors: Jingzhao Zhang, Aryan Mokhtari, Suvrit Sra, Ali Jadbabaie
Date: 2018-05-01 00:00:00
Proceeding: NeurIPS 2018 12

We study gradient-based optimization methods obtained by directly
discretizing a second-order ordinary differential equation (ODE) related to the
continuous limit of Nesterov's accelerated gradient method. When the function
is smooth enough, we show that acceleration can be achieved by a stable
discretization of this ODE using standard Runge-Kutta integrators.
Specifically, we prove that under Lipschitz-gradient, convexity and
order-$(s+2)$ differentiability assumptions, the sequence of iterates generated
by discretizing the proposed second-order ODE converges to the optimal solution
at a rate of $\mathcal{O}({N^{-2\frac{s}{s+1}}})$, where $s$ is the order of
the Runge-Kutta numerical integrator. Furthermore, we introduce a new local
flatness condition on the objective, under which rates even faster than
$\mathcal{O}(N^{-2})$ can be achieved with low-order integrators and only
gradient information. Notably, this flatness condition is satisfied by several
standard loss functions used in machine learning. We provide numerical
experiments that verify the theoretical rates predicted by our results.
BourGAN: Generative Networks with Metric Embeddings
Authors: Chang Xiao, Peilin Zhong, Changxi Zheng
Date: 2018-05-19 00:00:00
Proceeding: NeurIPS 2018 12

This paper addresses the mode collapse for generative adversarial networks
(GANs). We view modes as a geometric structure of data distribution in a metric
space. Under this geometric lens, we embed subsamples of the dataset from an
arbitrary metric space into the l2 space, while preserving their pairwise
distance distribution. Not only does this metric embedding determine the
dimensionality of the latent space automatically, it also enables us to
construct a mixture of Gaussians to draw latent space random vectors. We use
the Gaussian mixture model in tandem with a simple augmentation of the
objective function to train GANs. Every major step of our method is supported
by theoretical analysis, and our experiments on real and synthetic data confirm
that the generator is able to produce samples spreading over most of the modes
while avoiding unwanted samples, outperforming several recent GAN variants on a
number of metrics and offering new features.
Learning to Search via Retrospective Imitation
Authors: Jialin Song, Ravi Lanka, Albert Zhao, Aadyot Bhatnagar, Yisong Yue, Masahiro Ono
Date: 2018-04-03 00:00:00
Tasks: Imitation Learning

We study the problem of learning a good search policy for combinatorial search spaces. We propose retrospective imitation learning, which, after initial training by an expert, improves itself by learning from \textit{retrospective inspections} of its own roll-outs. That is, when the policy eventually reaches a feasible solution in a combinatorial search tree after making mistakes and backtracks, it retrospectively constructs an improved search trace to the solution by removing backtracks, which is then used to further train the policy. A key feature of our approach is that it can iteratively scale up, or transfer, to larger problem sizes than those solved by the initial expert demonstrations, thus dramatically expanding its applicability beyond that of conventional imitation learning. We showcase the effectiveness of our approach on a range of tasks, including synthetic maze solving and combinatorial problems expressed as integer programs.
Distributed Stochastic Optimization via Adaptive SGD
Authors: Ashok Cutkosky, Robert Busa-Fekete
Date: 2018-02-16 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Stochastic Optimization

Stochastic convex optimization algorithms are the most popular way to train
machine learning models on large-scale data. Scaling up the training process of
these models is crucial, but the most popular algorithm, Stochastic Gradient
Descent (SGD), is a serial method that is surprisingly hard to parallelize. In
this paper, we propose an efficient distributed stochastic optimization method
by combining adaptivity with variance reduction techniques. Our analysis yields
a linear speedup in the number of machines, constant memory footprint, and only
a logarithmic number of communication rounds. Critically, our approach is a
black-box reduction that parallelizes any serial online learning algorithm,
streamlining prior analysis and allowing us to leverage the significant
progress that has been made in designing adaptive algorithms. In particular, we
achieve optimal convergence rates without any prior knowledge of smoothness
parameters, yielding a more robust algorithm that reduces the need for
hyperparameter tuning. We implement our algorithm in the Spark distributed
framework and exhibit dramatic performance gains on large-scale logistic
regression problems.
Sampling-Free Variational Inference of Bayesian Neural Networks by Variance Backpropagation
Authors: Manuel Haussmann, Fred A. Hamprecht, Melih Kandemir
Date: 2018-05-19 00:00:00
Tasks: Variational Inference

We propose a new Bayesian Neural Net formulation that affords variational inference for which the evidence lower bound is analytically tractable subject to a tight approximation. We achieve this tractability by (i) decomposing ReLU nonlinearities into the product of an identity and a Heaviside step function, (ii) introducing a separate path that decomposes the neural net expectation from its variance. We demonstrate formally that introducing separate latent binary variables to the activations allows representing the neural network likelihood as a chain of linear operations. Performing variational inference on this construction enables a sampling-free computation of the evidence lower bound which is a more effective approximation than the widely applied Monte Carlo sampling and CLT related techniques. We evaluate the model on a range of regression and classification tasks against BNN inference alternatives, showing competitive or improved performance over the current state-of-the-art.
Robust and Fast Decoding of High-Capacity Color QR Codes for Mobile Applications
Authors: Zhibo Yang, Huanle Xu, Jianyuan Deng, Chen Change Loy, Wing Cheong Lau
Date: 2017-04-21 00:00:00
Tasks: distortion correction

The use of color in QR codes brings extra data capacity, but also inflicts
tremendous challenges on the decoding process due to chromatic distortion,
cross-channel color interference and illumination variation. Particularly, we
further discover a new type of chromatic distortion in high-density color QR
codes, cross-module color interference, caused by the high density which also
makes the geometric distortion correction more challenging. To address these
problems, we propose two approaches, namely, LSVM-CMI and QDA-CMI, which
jointly model these different types of chromatic distortion. Extended from SVM
and QDA, respectively, both LSVM-CMI and QDA-CMI optimize over a particular
objective function to learn a color classifier. Furthermore, a robust geometric
transformation method and several pipeline refinements are proposed to boost
the decoding performance for mobile applications. We put forth and implement a
framework for high-capacity color QR codes equipped with our methods, called
HiQ. To evaluate the performance of HiQ, we collect a challenging large-scale
color QR code dataset, CUHK-CQRC, which consists of 5390 high-density color QR
code samples. The comparison with the baseline method [2] on CUHK-CQRC shows
that HiQ at least outperforms [2] by 188% in decoding success rate and 60% in
bit error rate. Our implementation of HiQ in iOS and Android also demonstrates
the effectiveness of our framework in real-world applications.
Learning a face space for experiments on human identity
Authors: Jordan W. Suchow, Joshua C. Peterson, Thomas L. Griffiths
Date: 2018-05-19 00:00:00
Tasks: Decoder

Generative models of human identity and appearance have broad applicability
to behavioral science and technology, but the exquisite sensitivity of human
face perception means that their utility hinges on the alignment of the model's
representation to human psychological representations and the photorealism of
the generated images. Meeting these requirements is an exacting task, and
existing models of human identity and appearance are often unworkably abstract,
artificial, uncanny, or biased. Here, we use a variational autoencoder with an
autoregressive decoder to learn a face space from a uniquely diverse dataset of
portraits that control much of the variation irrelevant to human identity and
appearance. Our method generates photorealistic portraits of fictive identities
with a smooth, navigable latent space. We validate our model's alignment with
human sensitivities by introducing a psychophysical Turing test for images,
which humans mostly fail. Lastly, we demonstrate an initial application of our
model to the problem of fast search in mental space to obtain detailed "police
sketches" in a small number of trials.
Closed-Loop Policies for Operational Tests of Safety-Critical Systems
Authors: Jeremy Morton, Tim A. Wheeler, Mykel J. Kochenderfer
Date: 2017-07-25 00:00:00
Tasks: Scheduling

Manufacturers of safety-critical systems must make the case that their
product is sufficiently safe for public deployment. Much of this case often
relies upon critical event outcomes from real-world testing, requiring
manufacturers to be strategic about how they allocate testing resources in
order to maximize their chances of demonstrating system safety. This work
frames the partially observable and belief-dependent problem of test scheduling
as a Markov decision process, which can be solved efficiently to yield
closed-loop manufacturer testing policies. By solving for policies over a wide
range of problem formulations, we are able to provide high-level guidance for
manufacturers and regulators on issues relating to the testing of
safety-critical systems. This guidance spans an array of topics, including
circumstances under which manufacturers should continue testing despite
observed incidents, when manufacturers should test aggressively, and when
regulators should increase or reduce the real-world testing requirements for an
autonomous vehicle.
Improved Support Recovery Guarantees for the Group Lasso With Applications to Structural Health Monitoring
Authors: Mojtaba Kadkhodaie Elyaderani, Swayambhoo Jain, Jeffrey Druce, Stefano Gonella, Jarvis Haupt
Date: 2017-08-29 00:00:00
Tasks: Structural Health Monitoring

This paper considers the problem of estimating an unknown high dimensional
signal from noisy linear measurements, {when} the signal is assumed to possess
a \emph{group-sparse} structure in a {known,} fixed dictionary. We consider
signals generated according to a natural probabilistic model, and establish new
conditions under which the set of indices of the non-zero groups of the signal
(called the group-level support) may be accurately estimated via the group
Lasso. Our results strengthen existing coherence-based analyses that exhibit
the well-known "square root" bottleneck, allowing for the number of recoverable
nonzero groups to be nearly as large as the total number of groups. We also
establish a sufficient recovery condition relating the number of nonzero groups
and the signal to noise ratio (quantified in terms of the ratio of the squared
Euclidean norms of nonzero groups and the variance of the random additive
{measurement} noise), and validate this trend empirically. Finally, we examine
the implications of our results in the context of a structural health
monitoring application, where the group Lasso approach facilitates demixing of
a propagating acoustic wavefield, acquired on the material surface by a
scanning laser Doppler vibrometer, into antithetical components, one of which
indicates the locations of internal material defects.
On Attention Models for Human Activity Recognition
Authors: Vishvak S Murahari, Thomas Ploetz
Date: 2018-05-19 00:00:00
Tasks: Activity Recognition, Human Activity Recognition, Time Series, Time Series Analysis

Most approaches that model time-series data in human activity recognition
based on body-worn sensing (HAR) use a fixed size temporal context to represent
different activities. This might, however, not be apt for sets of activities
with individ- ually varying durations. We introduce attention models into HAR
research as a data driven approach for exploring relevant temporal context.
Attention models learn a set of weights over input data, which we leverage to
weight the temporal context being considered to model each sensor reading. We
construct attention models for HAR by adding attention layers to a state-
of-the-art deep learning HAR model (DeepConvLSTM) and evaluate our approach on
benchmark datasets achieving sig- nificant increase in performance. Finally, we
visualize the learned weights to better understand what constitutes relevant
temporal context.
Learning Hierarchical Visual Representations in Deep Neural Networks Using Hierarchical Linguistic Labels
Authors: Joshua C. Peterson, Paul Soulos, Aida Nematzadeh, Thomas L. Griffiths
Date: 2018-05-19 00:00:00

Modern convolutional neural networks (CNNs) are able to achieve human-level
object classification accuracy on specific tasks, and currently outperform
competing models in explaining complex human visual representations. However,
the categorization problem is posed differently for these networks than for
humans: the accuracy of these networks is evaluated by their ability to
identify single labels assigned to each image. These labels often cut
arbitrarily across natural psychological taxonomies (e.g., dogs are separated
into breeds, but never jointly categorized as "dogs"), and bias the resulting
representations. By contrast, it is common for children to hear both "dog" and
"Dalmatian" to describe the same stimulus, helping to group perceptually
disparate objects (e.g., breeds) into a common mental class. In this work, we
train CNN classifiers with multiple labels for each image that correspond to
different levels of abstraction, and use this framework to reproduce classic
patterns that appear in human generalization behavior.
Long-term face tracking in the wild using deep learning
Authors: Kunlei Zhang, Elaheh Rashedi, Elaheh Barati, Xue-wen Chen
Date: 2018-05-19 00:00:00
Tasks: Deep Learning, Face Detection, Face Verification, Metric Learning

This paper investigates long-term face tracking of a specific person given
his/her face image in a single frame as a query in a video stream. Through
taking advantage of pre-trained deep learning models on big data, a novel
system is developed for accurate video face tracking in the unconstrained
environments depicting various people and objects moving in and out of the
frame. In the proposed system, we present a detection-verification-tracking
method (dubbed as 'DVT') which accomplishes the long-term face tracking task
through the collaboration of face detection, face verification, and
(short-term) face tracking. An offline trained detector based on cascaded
convolutional neural networks localizes all faces appeared in the frames, and
an offline trained face verifier based on deep convolutional neural networks
and similarity metric learning decides if any face or which face corresponds to
the queried person. An online trained tracker follows the face from frame to
frame. When validated on a sitcom episode and a TV show, the DVT method
outperforms tracking-learning-detection (TLD) and face-TLD in terms of recall
and precision. The proposed system is also tested on many other types of videos
and shows very promising results.