pwc-archive/papers-with-abstracts huggingface dataset:
Revisiting the Arcade Learning Environment: Evaluation Protocols and Open Problems for General Agents
Authors: Marlos C. Machado, Marc G. Bellemare, Erik Talvitie, Joel Veness, Matthew Hausknecht, Michael Bowling
Date: 2017-09-18 00:00:00
Tasks: Atari Games

The Arcade Learning Environment (ALE) is an evaluation platform that poses
the challenge of building AI agents with general competency across dozens of
Atari 2600 games. It supports a variety of different problem settings and it
has been receiving increasing attention from the scientific community, leading
to some high-profile success stories such as the much publicized Deep
Q-Networks (DQN). In this article we take a big picture look at how the ALE is
being used by the research community. We show how diverse the evaluation
methodologies in the ALE have become with time, and highlight some key concerns
when evaluating agents in the ALE. We use this discussion to present some
methodological best practices and provide new benchmark results using these
best practices. To further the progress in the field, we introduce a new
version of the ALE that supports multiple game modes and provides a form of
stochasticity we call sticky actions. We conclude this big picture look by
revisiting challenges posed when the ALE was introduced, summarizing the
state-of-the-art in various problems and highlighting problems that remain
open.
Text Generation Based on Generative Adversarial Nets with Latent Variable
Authors: Heng Wang, Zengchang Qin, Tao Wan
Date: 2017-12-01 00:00:00
Tasks: Language Modeling, Language Modelling, Text Generation

In this paper, we propose a model using generative adversarial net (GAN) to
generate realistic text. Instead of using standard GAN, we combine variational
autoencoder (VAE) with generative adversarial net. The use of high-level latent
random variables is helpful to learn the data distribution and solve the
problem that generative adversarial net always emits the similar data. We
propose the VGAN model where the generative model is composed of recurrent
neural network and VAE. The discriminative model is a convolutional neural
network. We train the model via policy gradient. We apply the proposed model to
the task of text generation and compare it to other recent neural network based
models, such as recurrent neural network language model and SeqGAN. We evaluate
the performance of the model by calculating negative log-likelihood and the
BLEU score. We conduct experiments on three benchmark datasets, and results
show that our model outperforms other previous models.
Structured Deep Neural Network Pruning via Matrix Pivoting
Authors: Ranko Sredojevic, Shaoyi Cheng, Lazar Supic, Rawan Naous, Vladimir Stojanovic
Date: 2017-12-01 00:00:00
Tasks: Diversity, Network Pruning, Sensor Fusion

Deep Neural Networks (DNNs) are the key to the state-of-the-art machine
vision, sensor fusion and audio/video signal processing. Unfortunately, their
computation complexity and tight resource constraints on the Edge make them
hard to leverage on mobile, embedded and IoT devices. Due to great diversity of
Edge devices, DNN designers have to take into account the hardware platform and
application requirements during network training. In this work we introduce
pruning via matrix pivoting as a way to improve network pruning by compromising
between the design flexibility of architecture-oblivious and performance
efficiency of architecture-aware pruning, the two dominant techniques for
obtaining resource-efficient DNNs. We also describe local and global network
optimization techniques for efficient implementation of the resulting pruned
networks. In combination, the proposed pruning and implementation result in
close to linear speed up with the reduction of network coefficients during
pruning.
Audio Cover Song Identification using Convolutional Neural Network
Authors: Sungkyun Chang, Juheon Lee, Sang Keun Choe, Kyogu Lee
Date: 2017-12-01 00:00:00
Tasks: Cover song identification, Relation

In this paper, we propose a new approach to cover song identification using a CNN (convolutional neural network). Most previous studies extract the feature vectors that characterize the cover song relation from a pair of songs and used it to compute the (dis)similarity between the two songs. Based on the observation that there is a meaningful pattern between cover songs and that this can be learned, we have reformulated the cover song identification problem in a machine learning framework. To do this, we first build the CNN using as an input a cross-similarity matrix generated from a pair of songs. We then construct the data set composed of cover song pairs and non-cover song pairs, which are used as positive and negative training samples, respectively. The trained CNN outputs the probability of being in the cover song relation given a cross-similarity matrix generated from any two pieces of music and identifies the cover song by ranking on the probability. Experimental results show that the proposed algorithm achieves performance better than or comparable to the state-of-the-art.
Generative Adversarial Networks for Electronic Health Records: A Framework for Exploring and Evaluating Methods for Predicting Drug-Induced Laboratory Test Trajectories
Authors: Alexandre Yahi, Rami Vanguri, Noémie Elhadad, Nicholas P. Tatonetti
Date: 2017-12-01 00:00:00
Tasks: Predicting Drug-Induced Laboratory Test Effects, Representation Learning, Time Series, Time Series Analysis

Generative Adversarial Networks (GANs) represent a promising class of
generative networks that combine neural networks with game theory. From
generating realistic images and videos to assisting musical creation, GANs are
transforming many fields of arts and sciences. However, their application to
healthcare has not been fully realized, more specifically in generating
electronic health records (EHR) data. In this paper, we propose a framework for
exploring the value of GANs in the context of continuous laboratory time series
data. We devise an unsupervised evaluation method that measures the predictive
power of synthetic laboratory test time series. Further, we show that when it
comes to predicting the impact of drug exposure on laboratory test data,
incorporating representation learning of the training cohorts prior to training
GAN models is beneficial.
Distance-based Camera Network Topology Inference for Person Re-identification
Authors: Yeong-Jun Cho, Kuk-Jin Yoon
Date: 2017-12-01 00:00:00
Tasks: Person Re-Identification

In this paper, we propose a novel distance-based camera network topology
inference method for efficient person re-identification. To this end, we first
calibrate each camera and estimate relative scales between cameras. Using the
calibration results of multiple cameras, we calculate the speed of each person
and infer the distance between cameras to generate distance-based camera
network topology. The proposed distance-based topology can be applied
adaptively to each person according to its speed and handle diverse transition
time of people between non-overlapping cameras. To validate the proposed
method, we tested the proposed method using an open person re-identification
dataset and compared to state-of-the-art methods. The experimental results show
that the proposed method is effective for person re-identification in the
large-scale camera network with various people transition time.
Susceptibility Propagation by Using Diagonal Consistency
Authors: Muneki Yasuda, Kazuyuki Tanaka
Date: 2017-12-01 00:00:00

A susceptibility propagation that is constructed by combining a belief
propagation and a linear response method is used for approximate computation
for Markov random fields. Herein, we formulate a new, improved susceptibility
propagation by using the concept of a diagonal matching method that is based on
mean-field approaches to inverse Ising problems. The proposed susceptibility
propagation is robust for various network structures, and it is reduced to the
ordinary susceptibility propagation and to the adaptive
Thouless-Anderson-Palmer equation in special cases.
An Improved Naive Bayes Classifier-based Noise Detection Technique for Classifying User Phone Call Behavior
Authors: Iqbal H. Sarker, Muhammad Ashad Kabir, Alan Colman, Jun Han
Date: 2017-10-12 00:00:00
Tasks: Classification, General Classification

The presence of noisy instances in mobile phone data is a fundamental issue
for classifying user phone call behavior (i.e., accept, reject, missed and
outgoing), with many potential negative consequences. The classification
accuracy may decrease and the complexity of the classifiers may increase due to
the number of redundant training samples. To detect such noisy instances from a
training dataset, researchers use naive Bayes classifier (NBC) as it identifies
misclassified instances by taking into account independence assumption and
conditional probabilities of the attributes. However, some of these
misclassified instances might indicate usages behavioral patterns of individual
mobile phone users. Existing naive Bayes classifier based noise detection
techniques have not considered this issue and, thus, are lacking in
classification accuracy. In this paper, we propose an improved noise detection
technique based on naive Bayes classifier for effectively classifying users'
phone call behaviors. In order to improve the classification accuracy, we
effectively identify noisy instances from the training dataset by analyzing the
behavioral patterns of individuals. We dynamically determine a noise threshold
according to individual's unique behavioral patterns by using both the naive
Bayes classifier and Laplace estimator. We use this noise threshold to identify
noisy instances. To measure the effectiveness of our technique in classifying
user phone call behavior, we employ the most popular classification algorithm
(e.g., decision tree). Experimental results on the real phone call log dataset
show that our proposed technique more accurately identifies the noisy instances
from the training datasets that leads to better classification accuracy.
Constraint Answer Set Solver EZCSP and Why Integration Schemas Matter
Authors: Marcello Balduccini, Yuliya Lierler
Date: 2017-02-14 00:00:00

Researchers in answer set programming and constraint programming have spent
significant efforts in the development of hybrid languages and solving
algorithms combining the strengths of these traditionally separate fields.
These efforts resulted in a new research area: constraint answer set
programming. Constraint answer set programming languages and systems proved to
be successful at providing declarative, yet efficient solutions to problems
involving hybrid reasoning tasks. One of the main contributions of this paper
is the first comprehensive account of the constraint answer set language and
solver EZCSP, a mainstream representative of this research area that has been
used in various successful applications. We also develop an extension of the
transition systems proposed by Nieuwenhuis et al. in 2006 to capture Boolean
satisfiability solvers. We use this extension to describe the EZCSP algorithm
and prove formal claims about it. The design and algorithmic details behind
EZCSP clearly demonstrate that the development of the hybrid systems of this
kind is challenging. Many questions arise when one faces various design choices
in an attempt to maximize system's benefits. One of the key decisions that a
developer of a hybrid solver makes is settling on a particular integration
schema within its implementation. Thus, another important contribution of this
paper is a thorough case study based on EZCSP, focused on the various
integration schemas that it provides.
  Under consideration in Theory and Practice of Logic Programming (TPLP).
Video retrieval based on deep convolutional neural network
Authors: Yj Dong, JG Li
Date: 2017-12-01 00:00:00
Tasks: Retrieval, Triplet, Video Retrieval

Recently, with the enormous growth of online videos, fast video retrieval
research has received increasing attention. As an extension of image hashing
techniques, traditional video hashing methods mainly depend on hand-crafted
features and transform the real-valued features into binary hash codes. As
videos provide far more diverse and complex visual information than images,
extracting features from videos is much more challenging than that from images.
Therefore, high-level semantic features to represent videos are needed rather
than low-level hand-crafted methods. In this paper, a deep convolutional neural
network is proposed to extract high-level semantic features and a binary hash
function is then integrated into this framework to achieve an end-to-end
optimization. Particularly, our approach also combines triplet loss function
which preserves the relative similarity and difference of videos and
classification loss function as the optimization objective. Experiments have
been performed on two public datasets and the results demonstrate the
superiority of our proposed method compared with other state-of-the-art video
retrieval methods.
Experimental learning of quantum states
Authors: Andrea Rocchetto, Scott Aaronson, Simone Severini, Gonzalo Carvacho, Davide Poderini, Iris Agresti, Marco Bentivegna, Fabio Sciarrino
Date: 2017-11-30 00:00:00
Tasks: Learning Theory

The number of parameters describing a quantum state is well known to grow
exponentially with the number of particles. This scaling clearly limits our
ability to do tomography to systems with no more than a few qubits and has been
used to argue against the universal validity of quantum mechanics itself.
However, from a computational learning theory perspective, it can be shown
that, in a probabilistic setting, quantum states can be approximately learned
using only a linear number of measurements. Here we experimentally demonstrate
this linear scaling in optical systems with up to 6 qubits. Our results
highlight the power of computational learning theory to investigate quantum
information, provide the first experimental demonstration that quantum states
can be "probably approximately learned" with access to a number of copies of
the state that scales linearly with the number of qubits, and pave the way to
probing quantum states at new, larger scales.
Label Efficient Learning of Transferable Representations across Domains and Tasks
Authors: Zelun Luo, Yuliang Zou, Judy Hoffman, Li Fei-Fei
Date: 2017-11-30 00:00:00
Proceeding: NeurIPS 2017
Tasks: Action Recognition, Metric Learning, Object Recognition, Temporal Action Localization, Transfer Learning

We propose a framework that learns a representation transferable across
different domains and tasks in a label efficient manner. Our approach battles
domain shift with a domain adversarial loss, and generalizes the embedding to
novel task using a metric learning-based approach. Our model is simultaneously
optimized on labeled source data and unlabeled or sparsely labeled data in the
target domain. Our method shows compelling results on novel classes within a
new domain even when only a few labeled examples per class are available,
outperforming the prevalent fine-tuning approach. In addition, we demonstrate
the effectiveness of our framework on the transfer learning task from image
object recognition to video action recognition.
Towards Personalized Modeling of the Female Hormonal Cycle: Experiments with Mechanistic Models and Gaussian Processes
Authors: Iñigo Urteaga, David J. Albers, Marija Vlajic Wheeler, Anna Druet, Hans Raffauf, Noémie Elhadad
Date: 2017-11-30 00:00:00
Tasks: Gaussian Processes

In this paper, we introduce a novel task for machine learning in healthcare,
namely personalized modeling of the female hormonal cycle. The motivation for
this work is to model the hormonal cycle and predict its phases in time, both
for healthy individuals and for those with disorders of the reproductive
system. Because there are individual differences in the menstrual cycle, we are
particularly interested in personalized models that can account for individual
idiosyncracies, towards identifying phenotypes of menstrual cycles. As a first
step, we consider the hormonal cycle as a set of observations through time. We
use a previously validated mechanistic model to generate realistic hormonal
patterns, and experiment with Gaussian process regression to estimate their
values over time. Specifically, we are interested in the feasibility of
predicting menstrual cycle phases under varying learning conditions: number of
cycles used for training, hormonal measurement noise and sampling rates, and
informed vs. agnostic sampling of hormonal measurements. Our results indicate
that Gaussian processes can help model the female menstrual cycle. We discuss
the implications of our experiments in the context of modeling the female
menstrual cycle.
Prediction Under Uncertainty with Error-Encoding Networks
Authors: Mikael Henaff, Junbo Zhao, Yann Lecun
Date: 2017-11-14 00:00:00
Tasks: Prediction, Video Prediction

In this work we introduce a new framework for performing temporal predictions
in the presence of uncertainty. It is based on a simple idea of disentangling
components of the future state which are predictable from those which are
inherently unpredictable, and encoding the unpredictable components into a
low-dimensional latent variable which is fed into a forward model. Our method
uses a supervised training objective which is fast and easy to train. We
evaluate it in the context of video prediction on multiple datasets and show
that it is able to consistently generate diverse predictions without the need
for alternating minimization over a latent space or adversarial training.
Computing LPMLN Using ASP and MLN Solvers
Authors: Joohyung Lee, Samidh Talsania, Yi Wang
Date: 2017-07-19 00:00:00

LPMLN is a recent addition to probabilistic logic programming languages. Its
main idea is to overcome the rigid nature of the stable model semantics by
assigning a weight to each rule in a way similar to Markov Logic is defined. We
present two implementations of LPMLN, $\text{LPMLN2ASP}$ and
$\text{LPMLN2MLN}$. System $\text{LPMLN2ASP}$ translates LPMLN programs into
the input language of answer set solver $\text{CLINGO}$, and using weak
constraints and stable model enumeration, it can compute most probable stable
models as well as exact conditional and marginal probabilities. System
$\text{LPMLN2MLN}$ translates LPMLN programs into the input language of Markov
Logic solvers, such as $\text{ALCHEMY}$, $\text{TUFFY}$, and $\text{ROCKIT}$,
and allows for performing approximate probabilistic inference on LPMLN
programs. We also demonstrate the usefulness of the LPMLN systems for computing
other languages, such as ProbLog and Pearl's Causal Models, that are shown to
be translatable into LPMLN. (Under consideration for acceptance in TPLP)
Blind Gain and Phase Calibration via Sparse Spectral Methods
Authors: Yanjun Li, Kiryung Lee, Yoram Bresler
Date: 2017-11-30 00:00:00
Tasks: Inverse Rendering

Blind gain and phase calibration (BGPC) is a bilinear inverse problem
involving the determination of unknown gains and phases of the sensing system,
and the unknown signal, jointly. BGPC arises in numerous applications, e.g.,
blind albedo estimation in inverse rendering, synthetic aperture radar
autofocus, and sensor array auto-calibration. In some cases, sparse structure
in the unknown signal alleviates the ill-posedness of BGPC. Recently there has
been renewed interest in solutions to BGPC with careful analysis of error
bounds. In this paper, we formulate BGPC as an eigenvalue/eigenvector problem,
and propose to solve it via power iteration, or in the sparsity or joint
sparsity case, via truncated power iteration. Under certain assumptions, the
unknown gains, phases, and the unknown signal can be recovered simultaneously.
Numerical experiments show that power iteration algorithms work not only in the
regime predicted by our main results, but also in regimes where theoretical
analysis is limited. We also show that our power iteration algorithms for BGPC
compare favorably with competing algorithms in adversarial conditions, e.g.,
with noisy measurement or with a bad initial estimate.
Semantic Photometric Bundle Adjustment on Natural Sequences
Authors: Rui Zhu, Chaoyang Wang, Chen-Hsuan Lin, Ziyan Wang, Simon Lucey
Date: 2017-11-30 00:00:00
Tasks: Object, Object Reconstruction

The problem of obtaining dense reconstruction of an object in a natural
sequence of images has been long studied in computer vision. Classically this
problem has been solved through the application of bundle adjustment (BA). More
recently, excellent results have been attained through the application of
photometric bundle adjustment (PBA) methods -- which directly minimize the
photometric error across frames. A fundamental drawback to BA & PBA, however,
is: (i) their reliance on having to view all points on the object, and (ii) for
the object surface to be well textured. To circumvent these limitations we
propose semantic PBA which incorporates a 3D object prior, obtained through
deep learning, within the photometric bundle adjustment problem. We demonstrate
state of the art performance in comparison to leading methods for object
reconstruction across numerous natural sequences.
Graph Distillation for Action Detection with Privileged Modalities
Authors: Zelun Luo, Jun-Ting Hsieh, Lu Jiang, Juan Carlos Niebles, Li Fei-Fei
Date: 2017-11-30 00:00:00
Proceeding: ECCV 2018 9
Tasks: Action Classification, Action Detection, Transfer Learning

We propose a technique that tackles action detection in multimodal videos
under a realistic and challenging condition in which only limited training data
and partially observed modalities are available. Common methods in transfer
learning do not take advantage of the extra modalities potentially available in
the source domain. On the other hand, previous work on multimodal learning only
focuses on a single domain or task and does not handle the modality discrepancy
between training and testing. In this work, we propose a method termed graph
distillation that incorporates rich privileged information from a large-scale
multimodal dataset in the source domain, and improves the learning in the
target domain where training data and modalities are scarce. We evaluate our
approach on action classification and detection tasks in multimodal videos, and
show that our model outperforms the state-of-the-art by a large margin on the
NTU RGB+D and PKU-MMD benchmarks. The code is released at
http://alan.vision/eccv18_graph/.
Face Recognition Using Scattering Convolutional Network
Authors: Shervin Minaee, Amirali Abdolrashidi, Yao Wang
Date: 2016-07-30 00:00:00
Tasks: Face Recognition

Face recognition has been an active research area in the past few decades. In
general, face recognition can be very challenging due to variations in
viewpoint, illumination, facial expression, etc. Therefore it is essential to
extract features which are invariant to some or all of these variations. Here a
new image representation, called scattering transform/network, has been used to
extract features from faces. The scattering transform is a kind of
convolutional network which provides a powerful multi-layer representation for
signals. After extraction of scattering features, PCA is applied to reduce the
dimensionality of the data and then a multi-class support vector machine is
used to perform recognition. The proposed algorithm has been tested on three
face datasets and achieved a very high recognition rate.