pwc-archive/papers-with-abstracts huggingface dataset:
Low-rank and Sparse Soft Targets to Learn Better DNN Acoustic Models
Authors: Pranay Dighe, Afsaneh Asaei, Herve Bourlard
Date: 2016-10-18 00:00:00
Tasks: speech-recognition, Speech Recognition

Conventional deep neural networks (DNN) for speech acoustic modeling rely on
Gaussian mixture models (GMM) and hidden Markov model (HMM) to obtain binary
class labels as the targets for DNN training. Subword classes in speech
recognition systems correspond to context-dependent tied states or senones. The
present work addresses some limitations of GMM-HMM senone alignments for DNN
training. We hypothesize that the senone probabilities obtained from a DNN
trained with binary labels can provide more accurate targets to learn better
acoustic models. However, DNN outputs bear inaccuracies which are exhibited as
high dimensional unstructured noise, whereas the informative components are
structured and low-dimensional. We exploit principle component analysis (PCA)
and sparse coding to characterize the senone subspaces. Enhanced probabilities
obtained from low-rank and sparse reconstructions are used as soft-targets for
DNN acoustic modeling, that also enables training with untranscribed data.
Experiments conducted on AMI corpus shows 4.6% relative reduction in word error
rate.
Further properties of the forward-backward envelope with applications to difference-of-convex programming
Authors: Tianxiang Liu, Ting Kei Pong
Date: 2016-05-01 00:00:00

In this paper, we further study the forward-backward envelope first
introduced in [28] and [30] for problems whose objective is the sum of a proper
closed convex function and a twice continuously differentiable possibly
nonconvex function with Lipschitz continuous gradient. We derive sufficient
conditions on the original problem for the corresponding forward-backward
envelope to be a level-bounded and Kurdyka-{\L}ojasiewicz function with an
exponent of $\frac12$; these results are important for the efficient
minimization of the forward-backward envelope by classical optimization
algorithms. In addition, we demonstrate how to minimize some
difference-of-convex regularized least squares problems by minimizing a
suitably constructed forward-backward envelope. Our preliminary numerical
results on randomly generated instances of large-scale $\ell_{1-2}$ regularized
least squares problems [37] illustrate that an implementation of this approach
with a limited-memory BFGS scheme usually outperforms standard first-order
methods such as the nonmonotone proximal gradient method in [35].
From Traditional to Modern : Domain Adaptation for Action Classification in Short Social Video Clips
Authors: Aditya Singh, Saurabh Saini, Rajvi Shah, P. J. Narayanan
Date: 2016-10-18 00:00:00
Tasks: Action Classification, Data Augmentation, Domain Adaptation, General Classification

Short internet video clips like vines present a significantly wild
distribution compared to traditional video datasets. In this paper, we focus on
the problem of unsupervised action classification in wild vines using
traditional labeled datasets. To this end, we use a data augmentation based
simple domain adaptation strategy. We utilise semantic word2vec space as a
common subspace to embed video features from both, labeled source domain and
unlablled target domain. Our method incrementally augments the labeled source
with target samples and iteratively modifies the embedding function to bring
the source and target distributions together. Additionally, we utilise a
multi-modal representation that incorporates noisy semantic information
available in form of hash-tags. We show the effectiveness of this simple
adaptation technique on a test set of vines and achieve notable improvements in
performance.
Fast Sampling for Bayesian Max-Margin Models
Authors: Wenbo Hu, Jun Zhu, Bo Zhang
Date: 2015-04-27 00:00:00
Tasks: Link Prediction, Text Categorization

Bayesian max-margin models have shown superiority in various practical
applications, such as text categorization, collaborative prediction, social
network link prediction and crowdsourcing, and they conjoin the flexibility of
Bayesian modeling and predictive strengths of max-margin learning. However,
Monte Carlo sampling for these models still remains challenging, especially for
applications that involve large-scale datasets. In this paper, we present the
stochastic subgradient Hamiltonian Monte Carlo (HMC) methods, which are easy to
implement and computationally efficient. We show the approximate detailed
balance property of subgradient HMC which reveals a natural and validated
generalization of the ordinary HMC. Furthermore, we investigate the variants
that use stochastic subsampling and thermostats for better scalability and
mixing. Using stochastic subgradient Markov Chain Monte Carlo (MCMC), we
efficiently solve the posterior inference task of various Bayesian max-margin
models and extensive experimental results demonstrate the effectiveness of our
approach.
Deep Identity-aware Transfer of Facial Attributes
Authors: Mu Li, WangMeng Zuo, David Zhang
Date: 2016-10-18 00:00:00
Tasks: Attribute, Denoising, Face Hallucination, Hallucination, Image-to-Image Translation

This paper presents a Deep convolutional network model for Identity-Aware
Transfer (DIAT) of facial attributes. Given the source input image and the
reference attribute, DIAT aims to generate a facial image that owns the
reference attribute as well as keeps the same or similar identity to the input
image. In general, our model consists of a mask network and an attribute
transform network which work in synergy to generate a photo-realistic facial
image with the reference attribute. Considering that the reference attribute
may be only related to some parts of the image, the mask network is introduced
to avoid the incorrect editing on attribute irrelevant region. Then the
estimated mask is adopted to combine the input and transformed image for
producing the transfer result. For joint training of transform network and mask
network, we incorporate the adversarial attribute loss, identity-aware adaptive
perceptual loss, and VGG-FACE based identity loss. Furthermore, a denoising
network is presented to serve for perceptual regularization to suppress the
artifacts in transfer result, while an attribute ratio regularization is
introduced to constrain the size of attribute relevant region. Our DIAT can
provide a unified solution for several representative facial attribute transfer
tasks, e.g., expression transfer, accessory removal, age progression, and
gender transfer, and can be extended for other face enhancement tasks such as
face hallucination. The experimental results validate the effectiveness of the
proposed method. Even for the identity-related attribute (e.g., gender), our
DIAT can obtain visually impressive results by changing the attribute while
retaining most identity-aware features.
Master's Thesis : Deep Learning for Visual Recognition
Authors: Rémi Cadène, Nicolas Thome, Matthieu Cord
Date: 2016-10-18 00:00:00
Tasks: Deep Learning, Weakly-supervised Learning

The goal of our research is to develop methods advancing automatic visual
recognition. In order to predict the unique or multiple labels associated to an
image, we study different kind of Deep Neural Networks architectures and
methods for supervised features learning. We first draw up a state-of-the-art
review of the Convolutional Neural Networks aiming to understand the history
behind this family of statistical models, the limit of modern architectures and
the novel techniques currently used to train deep CNNs. The originality of our
work lies in our approach focusing on tasks with a low amount of data. We
introduce different models and techniques to achieve the best accuracy on
several kind of datasets, such as a medium dataset of food recipes (100k
images) for building a web API, or a small dataset of satellite images (6,000)
for the DSG online challenge that we've won. We also draw up the
state-of-the-art in Weakly Supervised Learning, introducing different kind of
CNNs able to localize regions of interest. Our last contribution is a
framework, build on top of Torch7, for training and testing deep models on any
visual recognition tasks and on datasets of any scale.
Identifiability and Transportability in Dynamic Causal Networks
Authors: Gilles Blondel, Marta Arias, Ricard Gavaldà
Date: 2016-10-18 00:00:00

In this paper we propose a causal analog to the purely observational Dynamic
Bayesian Networks, which we call Dynamic Causal Networks. We provide a sound
and complete algorithm for identification of Dynamic Causal Net- works, namely,
for computing the effect of an intervention or experiment, based on passive
observations only, whenever possible. We note the existence of two types of
confounder variables that affect in substantially different ways the iden-
tification procedures, a distinction with no analog in either Dynamic Bayesian
Networks or standard causal graphs. We further propose a procedure for the
transportability of causal effects in Dynamic Causal Network settings, where
the re- sult of causal experiments in a source domain may be used for the
identification of causal effects in a target domain.
Online Contrastive Divergence with Generative Replay: Experience Replay without Storing Data
Authors: Decebal Constantin Mocanu, Maria Torres Vega, Eric Eaton, Peter Stone, Antonio Liotta
Date: 2016-10-18 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Conceived in the early 1990s, Experience Replay (ER) has been shown to be a
successful mechanism to allow online learning algorithms to reuse past
experiences. Traditionally, ER can be applied to all machine learning paradigms
(i.e., unsupervised, supervised, and reinforcement learning). Recently, ER has
contributed to improving the performance of deep reinforcement learning. Yet,
its application to many practical settings is still limited by the memory
requirements of ER, necessary to explicitly store previous observations. To
remedy this issue, we explore a novel approach, Online Contrastive Divergence
with Generative Replay (OCD_GR), which uses the generative capability of
Restricted Boltzmann Machines (RBMs) instead of recorded past experiences. The
RBM is trained online, and does not require the system to store any of the
observed data points. We compare OCD_GR to ER on 9 real-world datasets,
considering a worst-case scenario (data points arriving in sorted order) as
well as a more realistic one (sequential random-order data points). Our results
show that in 64.28% of the cases OCD_GR outperforms ER and in the remaining
35.72% it has an almost equal performance, while having a considerably reduced
space complexity (i.e., memory usage) at a comparable time complexity.
Weighted Positive Binary Decision Diagrams for Exact Probabilistic Inference
Authors: Giso H. Dal, Peter J. F. Lucas
Date: 2016-10-18 00:00:00

Recent work on weighted model counting has been very successfully applied to
the problem of probabilistic inference in Bayesian networks. The probability
distribution is encoded into a Boolean normal form and compiled to a target
language, in order to represent local structure expressed among conditional
probabilities more efficiently. We show that further improvements are possible,
by exploiting the knowledge that is lost during the encoding phase and
incorporating it into a compiler inspired by Satisfiability Modulo Theories.
Constraints among variables are used as a background theory, which allows us to
optimize the Shannon decomposition. We propose a new language, called Weighted
Positive Binary Decision Diagrams, that reduces the cost of probabilistic
inference by using this decomposition variant to induce an arithmetic circuit
of reduced size.
SYSTRAN's Pure Neural Machine Translation Systems
Authors: Josep Crego, Jungi Kim, Guillaume Klein, Anabel Rebollo, Kathy Yang, Jean Senellart, Egor Akhanov, Patrice Brunelle, Aurelien Coquard, Yongchao Deng, Satoshi Enoue, Chiyo Geiss, Joshua Johanson, Ardas Khalsa, Raoum Khiari, Byeongil Ko, Catherine Kobus, Jean Lorieux, Leidiana Martins, Dang-Chuan Nguyen, Alexandra Priori, Thomas Riccardi, Natalia Segal, Christophe Servan, Cyril Tiquet, Bo wang, Jin Yang, Dakun Zhang, Jing Zhou, Peter Zoldan
Date: 2016-10-18 00:00:00
Tasks: Machine Translation, NMT, Translation

Since the first online demonstration of Neural Machine Translation (NMT) by
LISA, NMT development has recently moved from laboratory to production systems
as demonstrated by several entities announcing roll-out of NMT engines to
replace their existing technologies. NMT systems have a large number of
training configurations and the training process of such systems is usually
very long, often a few weeks, so role of experimentation is critical and
important to share. In this work, we present our approach to production-ready
systems simultaneously with release of online demonstrators covering a large
variety of languages (12 languages, for 32 language pairs). We explore
different practical choices: an efficient and evolutive open-source framework;
data preparation; network architecture; additional implemented features; tuning
for production; etc. We discuss about evaluation methodology, present our first
findings and we finally outline further work.
  Our ultimate goal is to share our expertise to build competitive production
systems for "generic" translation. We aim at contributing to set up a
collaborative framework to speed-up adoption of the technology, foster further
research efforts and enable the delivery and adoption to/by industry of
use-case specific engines integrated in real production workflows. Mastering of
the technology would allow us to build translation engines suited for
particular needs, outperforming current simplest/uniform systems.
Addressing Community Question Answering in English and Arabic
Authors: Giovanni Da San Martino, Alberto Barrón-Cedeño, Salvatore Romeo, Alessandro Moschitti, Shafiq Joty, Fahad A. Al Obaidli, Kateryna Tymoshenko, Antonio Uva
Date: 2016-10-18 00:00:00
Tasks: Community Question Answering, Learning-To-Rank, Machine Translation, Question Answering, Reranking, Re-Ranking

This paper studies the impact of different types of features applied to
learning to re-rank questions in community Question Answering. We tested our
models on two datasets released in SemEval-2016 Task 3 on "Community Question
Answering". Task 3 targeted real-life Web fora both in English and Arabic. Our
models include bag-of-words features (BoW), syntactic tree kernels (TKs), rank
features, embeddings, and machine translation evaluation features. To the best
of our knowledge, structural kernels have barely been applied to the question
reranking task, where they have to model paraphrase relations. In the case of
the English question re-ranking task, we compare our learning to rank (L2R)
algorithms against a strong baseline given by the Google-generated ranking
(GR). The results show that i) the shallow structures used in our TKs are
robust enough to noisy data and ii) improving GR is possible, but effective BoW
features and TKs along with an accurate model of GR features in the used L2R
algorithm are required. In the case of the Arabic question re-ranking task, for
the first time we applied tree kernels on syntactic trees of Arabic sentences.
Our approaches to both tasks obtained the second best results on SemEval-2016
subtasks B on English and D on Arabic.
Diagnosis of aerospace structure defects by a HPC implemented soft computing algorithm
Authors: Gianni D'Angelo, Salvatore Rampone
Date: 2016-10-18 00:00:00

This study concerns with the diagnosis of aerospace structure defects by
applying a HPC parallel implementation of a novel learning algorithm, named
U-BRAIN. The Soft Computing approach allows advanced multi-parameter data
processing in composite materials testing. The HPC parallel implementation
overcomes the limits due to the great amount of data and the complexity of data
processing. Our experimental results illustrate the effectiveness of the
U-BRAIN parallel implementation as defect classifier in aerospace structures.
The resulting system is implemented on a Linux-based cluster with multi-core
architecture.
Shape-based defect classification for Non Destructive Testing
Authors: Gianni D'Angelo, Salvatore Rampone
Date: 2016-10-18 00:00:00
Tasks: Classification, General Classification, Specificity

The aim of this work is to classify the aerospace structure defects detected
by eddy current non-destructive testing. The proposed method is based on the
assumption that the defect is bound to the reaction of the probe coil impedance
during the test. Impedance plane analysis is used to extract a feature vector
from the shape of the coil impedance in the complex plane, through the use of
some geometric parameters. Shape recognition is tested with three different
machine-learning based classifiers: decision trees, neural networks and Naive
Bayes. The performance of the proposed detection system are measured in terms
of accuracy, sensitivity, specificity, precision and Matthews correlation
coefficient. Several experiments are performed on dataset of eddy current
signal samples for aircraft structures. The obtained results demonstrate the
usefulness of our approach and the competiveness against existing descriptors.
Analysis and Implementation of an Asynchronous Optimization Algorithm for the Parameter Server
Authors: Arda Aytekin, Hamid Reza Feyzmahdavian, Mikael Johansson
Date: 2016-10-18 00:00:00

This paper presents an asynchronous incremental aggregated gradient algorithm
and its implementation in a parameter server framework for solving regularized
optimization problems. The algorithm can handle both general convex (possibly
non-smooth) regularizers and general convex constraints. When the empirical
data loss is strongly convex, we establish linear convergence rate, give
explicit expressions for step-size choices that guarantee convergence to the
optimum, and bound the associated convergence factors. The expressions have an
explicit dependence on the degree of asynchrony and recover classical results
under synchronous operation. Simulations and implementations on commercial
compute clouds validate our findings.
Generating captions without looking beyond objects
Authors: Hendrik Heuer, Christof Monz, Arnold W. M. Smeulders
Date: 2016-10-12 00:00:00
Tasks: Caption Generation, Image Captioning, Language Modeling, Language Modelling, Translation

This paper explores new evaluation perspectives for image captioning and
introduces a noun translation task that achieves comparative image caption
generation performance by translating from a set of nouns to captions. This
implies that in image captioning, all word categories other than nouns can be
evoked by a powerful language model without sacrificing performance on n-gram
precision. The paper also investigates lower and upper bounds of how much
individual word categories in the captions contribute to the final BLEU score.
A large possible improvement exists for nouns, verbs, and prepositions.
Efficient Metric Learning for the Analysis of Motion Data
Authors: Babak Hosseini, Barbara Hammer
Date: 2016-10-17 00:00:00
Tasks: Dynamic Time Warping, General Classification, Metric Learning

We investigate metric learning in the context of dynamic time warping (DTW),
the by far most popular dissimilarity measure used for the comparison and
analysis of motion capture data. While metric learning enables a
problem-adapted representation of data, the majority of methods has been
proposed for vectorial data only. In this contribution, we extend the popular
principle offered by the large margin nearest neighbors learner (LMNN) to DTW
by treating the resulting component-wise dissimilarity values as features. We
demonstrate that this principle greatly enhances the classification accuracy in
several benchmarks. Further, we show that recent auxiliary concepts such as
metric regularization can be transferred from the vectorial case to
component-wise DTW in a similar way. We illustrate that metric regularization
constitutes a crucial prerequisite for the interpretation of the resulting
relevance profiles.
Spatio-Temporal Attention Models for Grounded Video Captioning
Authors: Mihai Zanfir, Elisabeta Marinoiu, Cristian Sminchisescu
Date: 2016-10-17 00:00:00
Tasks: image-classification, Image Classification, Temporal Localization, Video Captioning

Automatic video captioning is challenging due to the complex interactions in
dynamic real scenes. A comprehensive system would ultimately localize and track
the objects, actions and interactions present in a video and generate a
description that relies on temporal localization in order to ground the visual
concepts. However, most existing automatic video captioning systems map from
raw video data to high level textual description, bypassing localization and
recognition, thus discarding potentially valuable information for content
localization and generalization. In this work we present an automatic video
captioning model that combines spatio-temporal attention and image
classification by means of deep neural network structures based on long
short-term memory. The resulting system is demonstrated to produce
state-of-the-art results in the standard YouTube captioning benchmark while
also offering the advantage of localizing the visual concepts (subjects, verbs,
objects), with no grounding supervision, over space and time.
Real-time analysis of cataract surgery videos using statistical models
Authors: Katia Charrière, Gwenolé Quellec, Mathieu Lamard, David Martiano, Guy Cazuguel, Gouenou Coatrieux, Béatrice Cochener
Date: 2016-10-18 00:00:00
Tasks: Retrieval, Video Retrieval

The automatic analysis of the surgical process, from videos recorded during
surgeries, could be very useful to surgeons, both for training and for
acquiring new techniques. The training process could be optimized by
automatically providing some targeted recommendations or warnings, similar to
the expert surgeon's guidance. In this paper, we propose to reuse videos
recorded and stored during cataract surgeries to perform the analysis. The
proposed system allows to automatically recognize, in real time, what the
surgeon is doing: what surgical phase or, more precisely, what surgical step he
or she is performing. This recognition relies on the inference of a multilevel
statistical model which uses 1) the conditional relations between levels of
description (steps and phases) and 2) the temporal relations among steps and
among phases. The model accepts two types of inputs: 1) the presence of
surgical tools, manually provided by the surgeons, or 2) motion in videos,
automatically analyzed through the Content Based Video retrieval (CBVR)
paradigm. Different data-driven statistical models are evaluated in this paper.
For this project, a dataset of 30 cataract surgery videos was collected at
Brest University hospital. The system was evaluated in terms of area under the
ROC curve. Promising results were obtained using either the presence of
surgical tools ($A_z$ = 0.983) or motion analysis ($A_z$ = 0.759). The
generality of the method allows to adapt it to any kinds of surgeries. The
proposed solution could be used in a computer assisted surgery tool to support
surgeons during the surgery.