pwc-archive/papers-with-abstracts huggingface dataset:
The Atari Grand Challenge Dataset
Authors: Vitaly Kurin, Sebastian Nowozin, Katja Hofmann, Lucas Beyer, Bastian Leibe
Date: 2017-05-31 00:00:00
Tasks: Imitation Learning, Reinforcement Learning, Reinforcement Learning (RL)

Recent progress in Reinforcement Learning (RL), fueled by its combination,
with Deep Learning has enabled impressive results in learning to interact with
complex virtual environments, yet real-world applications of RL are still
scarce. A key limitation is data efficiency, with current state-of-the-art
approaches requiring millions of training samples. A promising way to tackle
this problem is to augment RL with learning from human demonstrations. However,
human demonstration data is not yet readily available. This hinders progress in
this direction. The present work addresses this problem as follows. We (i)
collect and describe a large dataset of human Atari 2600 replays -- the largest
and most diverse such data set publicly released to date, (ii) illustrate an
example use of this dataset by analyzing the relation between demonstration
quality and imitation learning performance, and (iii) outline possible research
directions that are opened up by our work.
Non-Markovian Control with Gated End-to-End Memory Policy Networks
Authors: Julien Perez, Tomi Silander
Date: 2017-05-31 00:00:00
Tasks: OpenAI Gym, Reinforcement Learning

Partially observable environments present an important open challenge in the
domain of sequential control learning with delayed rewards. Despite numerous
attempts during the two last decades, the majority of reinforcement learning
algorithms and associated approximate models, applied to this context, still
assume Markovian state transitions. In this paper, we explore the use of a
recently proposed attention-based model, the Gated End-to-End Memory Network,
for sequential control. We call the resulting model the Gated End-to-End Memory
Policy Network. More precisely, we use a model-free value-based algorithm to
learn policies for partially observed domains using this memory-enhanced neural
network. This model is end-to-end learnable and it features unbounded memory.
Indeed, because of its attention mechanism and associated non-parametric
memory, the proposed model allows us to define an attention mechanism over the
observation stream unlike recurrent models. We show encouraging results that
illustrate the capability of our attention-based model in the context of the
continuous-state non-stationary control problem of stock trading. We also
present an OpenAI Gym environment for simulated stock exchange and explain its
relevance as a benchmark for the field of non-Markovian decision process
learning.
Class Specific Feature Selection for Interval Valued Data Through Interval K-Means Clustering
Authors: D. S. Guru, N. Vinay Kumar
Date: 2017-05-31 00:00:00
Tasks: Clustering, feature selection

In this paper, a novel feature selection approach for supervised interval
valued features is proposed. The proposed approach takes care of selecting the
class specific features through interval K-Means clustering. The kernel of
K-Means clustering algorithm is modified to adapt interval valued data. During
training, a set of samples corresponding to a class is fed into the interval
K-Means clustering algorithm, which clusters features into K distinct clusters.
Hence, there are K number of features corresponding to each class.
Subsequently, corresponding to each class, the cluster representatives are
chosen. This procedure is repeated for all the samples of remaining classes.
During testing the feature indices correspond to each class are used for
validating the given dataset through classification using suitable symbolic
classifiers. For experimentation, four standard supervised interval datasets
are used. The results show the superiority of the proposed model when compared
with the other existing state-of-the-art feature selection methods.
Analysis of the Effect of Dependency Information on Predicate-Argument Structure Analysis and Zero Anaphora Resolution
Authors: Koichiro Yoshino, Shinsuke Mori, Satoshi Nakamura
Date: 2017-05-31 00:00:00
Tasks: Dependency Parsing, POS

This paper investigates and analyzes the effect of dependency information on
predicate-argument structure analysis (PASA) and zero anaphora resolution (ZAR)
for Japanese, and shows that a straightforward approach of PASA and ZAR works
effectively even if dependency information was not available. We constructed an
analyzer that directly predicts relationships of predicates and arguments with
their semantic roles from a POS-tagged corpus. The features of the system are
designed to compensate for the absence of syntactic information by using
features used in dependency parsing as a reference. We also constructed
analyzers that use the oracle dependency and the real dependency parsing
results, and compared with the system that does not use any syntactic
information to verify that the improvement provided by dependencies is not
crucial.
Online to Offline Conversions, Universality and Adaptive Minibatch Sizes
Authors: Kfir. Y. Levy
Date: 2017-05-30 00:00:00
Proceeding: NeurIPS 2017

We present an approach towards convex optimization that relies on a novel
scheme which converts online adaptive algorithms into offline methods. In the
offline optimization setting, our derived methods are shown to obtain
favourable adaptive guarantees which depend on the harmonic sum of the queried
gradients. We further show that our methods implicitly adapt to the objective's
structure: in the smooth case fast convergence rates are ensured without any
prior knowledge of the smoothness parameter, while still maintaining guarantees
in the non-smooth setting. Our approach has a natural extension to the
stochastic setting, resulting in a lazy version of SGD (stochastic GD), where
minibathces are chosen \emph{adaptively} depending on the magnitude of the
gradients. Thus providing a principled approach towards choosing minibatch
sizes.
Gaze Distribution Analysis and Saliency Prediction Across Age Groups
Authors: Onkar Krishna, Kiyoharu Aizawa, Andrea Helo, Rama Pia
Date: 2017-05-20 00:00:00
Tasks: Prediction, Saliency Prediction

Knowledge of the human visual system helps to develop better computational
models of visual attention. State-of-the-art models have been developed to
mimic the visual attention system of young adults that, however, largely ignore
the variations that occur with age. In this paper, we investigated how visual
scene processing changes with age and we propose an age-adapted framework that
helps to develop a computational model that can predict saliency across
different age groups. Our analysis uncovers how the explorativeness of an
observer varies with age, how well saliency maps of an age group agree with
fixation points of observers from the same or different age groups, and how age
influences the center bias. We analyzed the eye movement behavior of 82
observers belonging to four age groups while they explored visual scenes.
Explorativeness was quantified in terms of the entropy of a saliency map, and
area under the curve (AUC) metrics was used to quantify the agreement analysis
and the center bias. These results were used to develop age adapted saliency
models. Our results suggest that the proposed age-adapted saliency model
outperforms existing saliency models in predicting the regions of interest
across age groups.
Bridge Simulation and Metric Estimation on Landmark Manifolds
Authors: Stefan Sommer, Alexis Arnaudon, Line Kuhnel, Sarang Joshi
Date: 2017-05-31 00:00:00

We present an inference algorithm and connected Monte Carlo based estimation
procedures for metric estimation from landmark configurations distributed
according to the transition distribution of a Riemannian Brownian motion
arising from the Large Deformation Diffeomorphic Metric Mapping (LDDMM) metric.
The distribution possesses properties similar to the regular Euclidean normal
distribution but its transition density is governed by a high-dimensional PDE
with no closed-form solution in the nonlinear case. We show how the density can
be numerically approximated by Monte Carlo sampling of conditioned Brownian
bridges, and we use this to estimate parameters of the LDDMM kernel and thus
the metric structure by maximum likelihood.
Spectral Norm Regularization for Improving the Generalizability of Deep Learning
Authors: Yuichi Yoshida, Takeru Miyato
Date: 2017-05-31 00:00:00
Tasks: Deep Learning, Sensitivity

We investigate the generalizability of deep learning based on the sensitivity
to input perturbation. We hypothesize that the high sensitivity to the
perturbation of data degrades the performance on it. To reduce the sensitivity
to perturbation, we propose a simple and effective regularization method,
referred to as spectral norm regularization, which penalizes the high spectral
norm of weight matrices in neural networks. We provide supportive evidence for
the abovementioned hypothesis by experimentally confirming that the models
trained using spectral norm regularization exhibit better generalizability than
other baseline methods.
Neural Attribute Machines for Program Generation
Authors: Matthew Amodio, Swarat Chaudhuri, Thomas W. Reps
Date: 2017-05-25 00:00:00
Tasks: Attribute

Recurrent neural networks have achieved remarkable success at generating sequences with complex structures, thanks to advances that include richer embeddings of input and cures for vanishing gradients. Trained only on sequences from a known grammar, though, they can still struggle to learn rules and constraints of the grammar. Neural Attribute Machines (NAMs) are equipped with a logical machine that represents the underlying grammar, which is used to teach the constraints to the neural machine by (i) augmenting the input sequence, and (ii) optimizing a custom loss function. Unlike traditional RNNs, NAMs are exposed to the grammar, as well as samples from the language of the grammar. During generation, NAMs make significantly fewer violations of the constraints of the underlying grammar than RNNs trained only on samples from the language of the grammar.
RGB-D Salient Object Detection Based on Discriminative Cross-modal Transfer Learning
Authors: Hao Chen, Y. F. Li, Dan Su
Date: 2017-03-01 00:00:00
Tasks: object-detection, Object Detection, RGB-D Salient Object Detection, RGB Salient Object Detection, Saliency Detection, Salient Object Detection, Transfer Learning

In this work, we propose to utilize Convolutional Neural Networks to boost
the performance of depth-induced salient object detection by capturing the
high-level representative features for depth modality. We formulate the
depth-induced saliency detection as a CNN-based cross-modal transfer problem to
bridge the gap between the "data-hungry" nature of CNNs and the unavailability
of sufficient labeled training data in depth modality. In the proposed
approach, we leverage the auxiliary data from the source modality effectively
by training the RGB saliency detection network to obtain the task-specific
pre-understanding layers for the target modality. Meanwhile, we exploit the
depth-specific information by pre-training a modality classification network
that encourages modal-specific representations during the optimizing course.
Thus, it could make the feature representations of the RGB and depth modalities
as discriminative as possible. These two modules are pre-trained independently
and then stitched to initialize and optimize the eventual depth-induced
saliency detection model. Experiments demonstrate the effectiveness of the
proposed novel pre-training strategy as well as the significant and consistent
improvements of the proposed approach over other state-of-the-art methods.
Micro Fourier Transform Profilometry ($μ$FTP): 3D shape measurement at 10,000 frames per second
Authors: Chao Zuo, Tianyang Tao, Shijie Feng, Lei Huang, Anand Asundi, Qian Chen
Date: 2017-05-31 00:00:00

Recent advances in imaging sensors and digital light projection technology
have facilitated a rapid progress in 3D optical sensing, enabling 3D surfaces
of complex-shaped objects to be captured with improved resolution and accuracy.
However, due to the large number of projection patterns required for phase
recovery and disambiguation, the maximum fame rates of current 3D shape
measurement techniques are still limited to the range of hundreds of frames per
second (fps). Here, we demonstrate a new 3D dynamic imaging technique, Micro
Fourier Transform Profilometry ($\mu$FTP), which can capture 3D surfaces of
transient events at up to 10,000 fps based on our newly developed high-speed
fringe projection system. Compared with existing techniques, $\mu$FTP has the
prominent advantage of recovering an accurate, unambiguous, and dense 3D point
cloud with only two projected patterns. Furthermore, the phase information is
encoded within a single high-frequency fringe image, thereby allowing
motion-artifact-free reconstruction of transient events with temporal
resolution of 50 microseconds. To show $\mu$FTP's broad utility, we use it to
reconstruct 3D videos of 4 transient scenes: vibrating cantilevers, rotating
fan blades, bullet fired from a toy gun, and balloon's explosion triggered by a
flying dart, which were previously difficult or even unable to be captured with
conventional approaches.
Adversarial Generation of Natural Language
Authors: Sai Rajeswar, Sandeep Subramanian, Francis Dutil, Christopher Pal, Aaron Courville
Date: 2017-05-31 00:00:00
Proceeding: WS 2017 8
Tasks: Image Generation, Language Modeling, Language Modelling, Sentence

Generative Adversarial Networks (GANs) have gathered a lot of attention from
the computer vision community, yielding impressive results for image
generation. Advances in the adversarial generation of natural language from
noise however are not commensurate with the progress made in generating images,
and still lag far behind likelihood based methods. In this paper, we take a
step towards generating natural language with a GAN objective alone. We
introduce a simple baseline that addresses the discrete output space problem
without relying on gradient estimators and show that it is able to achieve
state-of-the-art results on a Chinese poem generation dataset. We present
quantitative results on generating sentences from context-free and
probabilistic context-free grammars, and qualitative language modeling results.
A conditional version is also described that can generate sequences conditioned
on sentence characteristics.
Sequential Dynamic Decision Making with Deep Neural Nets on a Test-Time Budget
Authors: Henghui Zhu, Feng Nan, Ioannis Paschalidis, Venkatesh Saligrama
Date: 2017-05-31 00:00:00
Tasks: Decision Making, Feature Engineering, Reinforcement Learning, Reinforcement Learning (RL)

Deep neural network (DNN) based approaches hold significant potential for
reinforcement learning (RL) and have already shown remarkable gains over
state-of-art methods in a number of applications. The effectiveness of DNN
methods can be attributed to leveraging the abundance of supervised data to
learn value functions, Q-functions, and policy function approximations without
the need for feature engineering. Nevertheless, the deployment of DNN-based
predictors with very deep architectures can pose an issue due to computational
and other resource constraints at test-time in a number of applications. We
propose a novel approach for reducing the average latency by learning a
computationally efficient gating function that is capable of recognizing states
in a sequential decision process for which policy prescriptions of a shallow
network suffices and deeper layers of the DNN have little marginal utility. The
overall system is adaptive in that it dynamically switches control actions
based on state-estimates in order to reduce average latency without sacrificing
terminal performance. We experiment with a number of alternative loss-functions
to train gating functions and shallow policies and show that in a number of
applications a speed-up of up to almost 5X can be obtained with little loss in
performance.
The ALAMO approach to machine learning
Authors: Zachary T. Wilson, Nikolaos V. Sahinidis
Date: 2017-05-31 00:00:00
Tasks: BIG-bench Machine Learning

ALAMO is a computational methodology for leaning algebraic functions from
data. Given a data set, the approach begins by building a low-complexity,
linear model composed of explicit non-linear transformations of the independent
variables. Linear combinations of these non-linear transformations allow a
linear model to better approximate complex behavior observed in real processes.
The model is refined, as additional data are obtained in an adaptive fashion
through error maximization sampling using derivative-free optimization. Models
built using ALAMO can enforce constraints on the response variables to
incorporate first-principles knowledge. The ability of ALAMO to generate simple
and accurate models for a number of reaction problems is demonstrated. The
error maximization sampling is compared with Latin hypercube designs to
demonstrate its sampling efficiency. ALAMO's constrained regression methodology
is used to further refine concentration models, resulting in models that
perform better on validation data and satisfy upper and lower bounds placed on
model outputs.
Unsupervised Learning of Disentangled Representations from Video
Authors: Emily Denton, Vighnesh Birodkar
Date: 2017-05-31 00:00:00
Proceeding: NeurIPS 2017 12

We present a new model DrNET that learns disentangled image representations
from video. Our approach leverages the temporal coherence of video and a novel
adversarial loss to learn a representation that factorizes each frame into a
stationary part and a temporally varying component. The disentangled
representation can be used for a range of tasks. For example, applying a
standard LSTM to the time-vary components enables prediction of future frames.
We evaluate our approach on a range of synthetic and real videos, demonstrating
the ability to coherently generate hundreds of steps into the future.
Emergent Predication Structure in Hidden State Vectors of Neural Readers
Authors: Hai Wang, Takeshi Onishi, Kevin Gimpel, David Mcallester
Date: 2016-11-23 00:00:00
Proceeding: WS 2017 8
Tasks: Reading Comprehension

A significant number of neural architectures for reading comprehension have
recently been developed and evaluated on large cloze-style datasets. We present
experiments supporting the emergence of "predication structure" in the hidden
state vectors of these readers. More specifically, we provide evidence that the
hidden state vectors represent atomic formulas $\Phi[c]$ where $\Phi$ is a
semantic property (predicate) and $c$ is a constant symbol entity identifier.
Does the Geometry of Word Embeddings Help Document Classification? A Case Study on Persistent Homology Based Representations
Authors: Paul Michel, Abhilasha Ravichander, Shruti Rijhwani
Date: 2017-05-31 00:00:00
Tasks: Classification, Clustering, Document Classification, Document Embedding, General Classification, Sentiment Analysis, Sentiment Classification, Word Embeddings

We investigate the pertinence of methods from algebraic topology for text
data analysis. These methods enable the development of
mathematically-principled isometric-invariant mappings from a set of vectors to
a document embedding, which is stable with respect to the geometry of the
document in the selected metric space. In this work, we evaluate the utility of
these topology-based document representations in traditional NLP tasks,
specifically document clustering and sentiment classification. We find that the
embeddings do not benefit text analysis. In fact, performance is worse than
simple techniques like $\textit{tf-idf}$, indicating that the geometry of the
document does not provide enough variability for classification on the basis of
topic or sentiment in the chosen datasets.
Towards Learned Clauses Database Reduction Strategies Based on Dominance Relationship
Authors: Jerry Lonlac, Engelbert Mephu Nguifo
Date: 2017-05-31 00:00:00
Tasks: Diversity

Clause Learning is one of the most important components of a conflict driven
clause learning (CDCL) SAT solver that is effective on industrial instances.
Since the number of learned clauses is proved to be exponential in the worse
case, it is necessary to identify the most relevant clauses to maintain and
delete the irrelevant ones. As reported in the literature, several learned
clauses deletion strategies have been proposed. However the diversity in both
the number of clauses to be removed at each step of reduction and the results
obtained with each strategy creates confusion to determine which criterion is
better. Thus, the problem to select which learned clauses are to be removed
during the search step remains very challenging. In this paper, we propose a
novel approach to identify the most relevant learned clauses without favoring
or excluding any of the proposed measures, but by adopting the notion of
dominance relationship among those measures. Our approach bypasses the problem
of the diversity of results and reaches a compromise between the assessments of
these measures. Furthermore, the proposed approach also avoids another
non-trivial problem which is the amount of clauses to be deleted at each
reduction of the learned clause database.
High Dimensional Structured Superposition Models
Authors: Qilong Gu, Arindam Banerjee
Date: 2017-05-30 00:00:00
Proceeding: NeurIPS 2016 12
Tasks: Vocal Bursts Intensity Prediction

High dimensional superposition models characterize observations using
parameters which can be written as a sum of multiple component parameters, each
with its own structure, e.g., sum of low rank and sparse matrices, sum of
sparse and rotated sparse vectors, etc. In this paper, we consider general
superposition models which allow sum of any number of component parameters, and
each component structure can be characterized by any norm. We present a simple
estimator for such models, give a geometric condition under which the
components can be accurately estimated, characterize sample complexity of the
estimator, and give high probability non-asymptotic bounds on the componentwise
estimation error. We use tools from empirical processes and generic chaining
for the statistical analysis, and our results, which substantially generalize
prior work on superposition models, are in terms of Gaussian widths of suitable
sets.
Optimization of Tree Ensembles
Authors: Velibor V. Mišić
Date: 2017-05-30 00:00:00
Tasks: Drug Design

Tree ensemble models such as random forests and boosted trees are among the most widely used and practically successful predictive models in applied machine learning and business analytics. Although such models have been used to make predictions based on exogenous, uncontrollable independent variables, they are increasingly being used to make predictions where the independent variables are controllable and are also decision variables. In this paper, we study the problem of tree ensemble optimization: given a tree ensemble that predicts some dependent variable using controllable independent variables, how should we set these variables so as to maximize the predicted value? We formulate the problem as a mixed-integer optimization problem. We theoretically examine the strength of our formulation, provide a hierarchy of approximate formulations with bounds on approximation quality and exploit the structure of the problem to develop two large-scale solution methods, one based on Benders decomposition and one based on iteratively generating tree split constraints. We test our methodology on real data sets, including two case studies in drug design and customized pricing, and show that our methodology can efficiently solve large-scale instances to near or full optimality, and outperforms solutions obtained by heuristic approaches. In our drug design case, we show how our approach can identify compounds that efficiently trade-off predicted performance and novelty with respect to existing, known compounds. In our customized pricing case, we show how our approach can efficiently determine optimal store-level prices under a random forest model that delivers excellent predictive accuracy.