pwc-archive/papers-with-abstracts huggingface dataset:
Describing Common Human Visual Actions in Images
Authors: Matteo Ruggero Ronchi, Pietro Perona
Date: 2015-06-07 00:00:00

Which common human actions and interactions are recognizable in monocular
still images? Which involve objects and/or other people? How many is a person
performing at a time? We address these questions by exploring the actions and
interactions that are detectable in the images of the MS COCO dataset. We make
two main contributions. First, a list of 140 common `visual actions', obtained
by analyzing the largest on-line verb lexicon currently available for English
(VerbNet) and human sentences used to describe images in MS COCO. Second, a
complete set of annotations for those `visual actions', composed of
subject-object and associated verb, which we call COCO-a (a for `actions').
COCO-a is larger than existing action datasets in terms of number of actions
and instances of these actions, and is unique because it is data-driven, rather
than experimenter-biased. Other unique features are that it is exhaustive, and
that all subjects and objects are localized. A statistical analysis of the
accuracy of our annotations and of each action, interaction and subject-object
combination is provided.
Fast Mixing for Discrete Point Processes
Authors: Patrick Rebeschini, Amin Karbasi
Date: 2015-06-06 00:00:00
Tasks: Point Processes

We investigate the systematic mechanism for designing fast mixing Markov
chain Monte Carlo algorithms to sample from discrete point processes under the
Dobrushin uniqueness condition for Gibbs measures. Discrete point processes are
defined as probability distributions $\mu(S)\propto \exp(\beta f(S))$ over all
subsets $S\in 2^V$ of a finite set $V$ through a bounded set function
$f:2^V\rightarrow \mathbb{R}$ and a parameter $\beta>0$. A subclass of discrete
point processes characterized by submodular functions (which include
log-submodular distributions, submodular point processes, and determinantal
point processes) has recently gained a lot of interest in machine learning and
shown to be effective for modeling diversity and coverage. We show that if the
set function (not necessarily submodular) displays a natural notion of decay of
correlation, then, for $\beta$ small enough, it is possible to design fast
mixing Markov chain Monte Carlo methods that yield error bounds on marginal
approximations that do not depend on the size of the set $V$. The sufficient
conditions that we derive involve a control on the (discrete) Hessian of set
functions, a quantity that has not been previously considered in the
literature. We specialize our results for submodular functions, and we discuss
canonical examples where the Hessian can be easily controlled.
Risk-Sensitive and Robust Decision-Making: a CVaR Optimization Approach
Authors: Yin-Lam Chow, Aviv Tamar, Shie Mannor, Marco Pavone
Date: 2015-06-06 00:00:00
Proceeding: NeurIPS 2015 12
Tasks: Decision Making

In this paper we address the problem of decision making within a Markov
decision process (MDP) framework where risk and modeling errors are taken into
account. Our approach is to minimize a risk-sensitive conditional-value-at-risk
(CVaR) objective, as opposed to a standard risk-neutral expectation. We refer
to such problem as CVaR MDP. Our first contribution is to show that a CVaR
objective, besides capturing risk sensitivity, has an alternative
interpretation as expected cost under worst-case modeling errors, for a given
error budget. This result, which is of independent interest, motivates CVaR
MDPs as a unifying framework for risk-sensitive and robust decision making. Our
second contribution is to present an approximate value-iteration algorithm for
CVaR MDPs and analyze its convergence rate. To our knowledge, this is the first
solution algorithm for CVaR MDPs that enjoys error guarantees. Finally, we
present results from numerical experiments that corroborate our theoretical
findings and show the practicality of our approach.
First-Take-All: Temporal Order-Preserving Hashing for 3D Action Videos
Authors: Jun Ye, Hao Hu, Kai Li, Guo-Jun Qi, Kien A. Hua
Date: 2015-06-06 00:00:00
Tasks: 3D Action Recognition, Action Recognition, All, Temporal Action Localization

With the prevalence of the commodity depth cameras, the new paradigm of user
interfaces based on 3D motion capturing and recognition have dramatically
changed the way of interactions between human and computers. Human action
recognition, as one of the key components in these devices, plays an important
role to guarantee the quality of user experience. Although the model-driven
methods have achieved huge success, they cannot provide a scalable solution for
efficiently storing, retrieving and recognizing actions in the large-scale
applications. These models are also vulnerable to the temporal translation and
warping, as well as the variations in motion scales and execution rates. To
address these challenges, we propose to treat the 3D human action recognition
as a video-level hashing problem and propose a novel First-Take-All (FTA)
Hashing algorithm capable of hashing the entire video into hash codes of fixed
length. We demonstrate that this FTA algorithm produces a compact
representation of the video invariant to the above mentioned variations,
through which action recognition can be solved by an efficient nearest neighbor
search by the Hamming distance between the FTA hash codes. Experiments on the
public 3D human action datasets shows that the FTA algorithm can reach a
recognition accuracy higher than 80%, with about 15 bits per frame considering
there are 65 frames per video over the datasets.
Hybridized Feature Extraction and Acoustic Modelling Approach for Dysarthric Speech Recognition
Authors: Megha Rughani, D. Shivakrishna
Date: 2015-06-06 00:00:00
Tasks: Acoustic Modelling, Automatic Speech Recognition, Automatic Speech Recognition (ASR), speech-recognition, Speech Recognition

Dysarthria is malfunctioning of motor speech caused by faintness in the human
nervous system. It is characterized by the slurred speech along with physical
impairment which restricts their communication and creates the lack of
confidence and affects the lifestyle. This paper attempt to increase the
efficiency of Automatic Speech Recognition (ASR) system for unimpaired speech
signal. It describes state of art of research into improving ASR for speakers
with dysarthria by means of incorporated knowledge of their speech production.
Hybridized approach for feature extraction and acoustic modelling technique
along with evolutionary algorithm is proposed for increasing the efficiency of
the overall system. Here number of feature vectors are varied and tested the
system performance. It is observed that system performance is boosted by
genetic algorithm. System with 16 acoustic features optimized with genetic
algorithm has obtained highest recognition rate of 98.28% with training time of
5:30:17.
ModDrop: adaptive multi-modal gesture recognition
Authors: Natalia Neverova, Christian Wolf, Graham W. Taylor, Florian Nebout
Date: 2014-12-31 00:00:00
Tasks: Gesture Recognition

We present a method for gesture detection and localisation based on
multi-scale and multi-modal deep learning. Each visual modality captures
spatial information at a particular spatial scale (such as motion of the upper
body or a hand), and the whole system operates at three temporal scales. Key to
our technique is a training strategy which exploits: i) careful initialization
of individual modalities; and ii) gradual fusion involving random dropping of
separate channels (dubbed ModDrop) for learning cross-modality correlations
while preserving uniqueness of each modality-specific representation. We
present experiments on the ChaLearn 2014 Looking at People Challenge gesture
recognition track, in which we placed first out of 17 teams. Fusing multiple
modalities at several spatial and temporal scales leads to a significant
increase in recognition rates, allowing the model to compensate for errors of
the individual classifiers as well as noise in the separate channels.
Futhermore, the proposed ModDrop training technique ensures robustness of the
classifier to missing signals in one or several channels to produce meaningful
predictions from any number of available modalities. In addition, we
demonstrate the applicability of the proposed fusion scheme to modalities of
arbitrary nature by experiments on the same dataset augmented with audio.
A Near-Optimal Dynamic Learning Algorithm for Online Matching Problems with Concave Returns
Authors: Xiao Alison Chen, Zizhuo Wang
Date: 2013-07-23 00:00:00

We consider an online matching problem with concave returns. This problem is
a significant generalization of the Adwords allocation problem and has vast
applications in online advertising. In this problem, a sequence of items arrive
sequentially and each has to be allocated to one of the bidders, who bid a
certain value for each item. At each time, the decision maker has to allocate
the current item to one of the bidders without knowing the future bids and the
objective is to maximize the sum of some concave functions of each bidder's
aggregate value. In this work, we propose an algorithm that achieves
near-optimal performance for this problem when the bids arrive in a random
order and the input data satisfies certain conditions. The key idea of our
algorithm is to learn the input data pattern dynamically: we solve a sequence
of carefully chosen partial allocation problems and use their optimal solutions
to assist with the future decision. Our analysis belongs to the primal-dual
paradigm, however, the absence of linearity of the objective function and the
dynamic feature of the algorithm makes our analysis quite unique.
On the consistency theory of high dimensional variable screening
Authors: Xiangyu Wang, Chenlei Leng, David B. Dunson
Date: 2015-02-24 00:00:00
Proceeding: NeurIPS 2015 12
Tasks: Computational Efficiency, Dimensionality Reduction, feature selection, Vocal Bursts Intensity Prediction

Variable screening is a fast dimension reduction technique for assisting high
dimensional feature selection. As a preselection method, it selects a moderate
size subset of candidate variables for further refining via feature selection
to produce the final model. The performance of variable screening depends on
both computational efficiency and the ability to dramatically reduce the number
of variables without discarding the important ones. When the data dimension $p$
is substantially larger than the sample size $n$, variable screening becomes
crucial as 1) Faster feature selection algorithms are needed; 2) Conditions
guaranteeing selection consistency might fail to hold. This article studies a
class of linear screening methods and establishes consistency theory for this
special class. In particular, we prove the restricted diagonally dominant (RDD)
condition is a necessary and sufficient condition for strong screening
consistency. As concrete examples, we show two screening methods $SIS$ and
$HOLP$ are both strong screening consistent (subject to additional constraints)
with large probability if $n > O((\rho s + \sigma/\tau)^2\log p)$ under random
designs. In addition, we relate the RDD condition to the irrepresentable
condition, and highlight limitations of $SIS$.
Selective Greedy Equivalence Search: Finding Optimal Bayesian Networks Using a Polynomial Number of Score Evaluations
Authors: David Maxwell Chickering, Christopher Meek
Date: 2015-06-06 00:00:00

We introduce Selective Greedy Equivalence Search (SGES), a restricted version
of Greedy Equivalence Search (GES). SGES retains the asymptotic correctness of
GES but, unlike GES, has polynomial performance guarantees. In particular, we
show that when data are sampled independently from a distribution that is
perfect with respect to a DAG ${\cal G}$ defined over the observable variables
then, in the limit of large data, SGES will identify ${\cal G}$'s equivalence
class after a number of score evaluations that is (1) polynomial in the number
of nodes and (2) exponential in various complexity measures including
maximum-number-of-parents, maximum-clique-size, and a new measure called {\em
v-width} that is at least as small as---and potentially much smaller than---the
other two. More generally, we show that for any hereditary and
equivalence-invariant property $\Pi$ known to hold in ${\cal G}$, we retain the
large-sample optimality guarantees of GES even if we ignore any GES deletion
operator during the backward phase that results in a state for which $\Pi$ does
not hold in the common-descendants subgraph.
Low-Cost Learning via Active Data Procurement
Authors: Jacob Abernethy, Yi-Ling Chen, Chien-Ju Ho, Bo Waggoner
Date: 2015-02-20 00:00:00

We design mechanisms for online procurement of data held by strategic agents
for machine learning tasks. The challenge is to use past data to actively price
future data and give learning guarantees even when an agent's cost for
revealing her data may depend arbitrarily on the data itself. We achieve this
goal by showing how to convert a large class of no-regret algorithms into
online posted-price and learning mechanisms. Our results in a sense parallel
classic sample complexity guarantees, but with the key resource being money
rather than quantity of data: With a budget constraint $B$, we give robust risk
(predictive error) bounds on the order of $1/\sqrt{B}$. Because we use an
active approach, we can often guarantee to do significantly better by
leveraging correlations between costs and data.
  Our algorithms and analysis go through a model of no-regret learning with $T$
arriving pairs (cost, data) and a budget constraint of $B$. Our regret bounds
for this model are on the order of $T/\sqrt{B}$ and we give lower bounds on the
same order.
Hybrid Orthogonal Projection and Estimation (HOPE): A New Framework to Probe and Learn Neural Networks
Authors: Shiliang Zhang, Hui Jiang
Date: 2015-02-03 00:00:00
Tasks: Image Classification, speech-recognition, Speech Recognition

In this paper, we propose a novel model for high-dimensional data, called the
Hybrid Orthogonal Projection and Estimation (HOPE) model, which combines a
linear orthogonal projection and a finite mixture model under a unified
generative modeling framework. The HOPE model itself can be learned
unsupervised from unlabelled data based on the maximum likelihood estimation as
well as discriminatively from labelled data. More interestingly, we have shown
the proposed HOPE models are closely related to neural networks (NNs) in a
sense that each hidden layer can be reformulated as a HOPE model. As a result,
the HOPE framework can be used as a novel tool to probe why and how NNs work,
more importantly, to learn NNs in either supervised or unsupervised ways. In
this work, we have investigated the HOPE framework to learn NNs for several
standard tasks, including image recognition on MNIST and speech recognition on
TIMIT. Experimental results have shown that the HOPE framework yields
significant performance gains over the current state-of-the-art methods in
various types of NN learning problems, including unsupervised feature learning,
supervised or semi-supervised learning.
A Hierarchical Neural Autoencoder for Paragraphs and Documents
Authors: Jiwei Li, Minh-Thang Luong, Dan Jurafsky
Date: 2015-06-02 00:00:00
Proceeding: IJCNLP 2015 7
Tasks: Sentence, Text Generation

Natural language generation of coherent long texts like paragraphs or longer
documents is a challenging problem for recurrent networks models. In this
paper, we explore an important step toward this generation task: training an
LSTM (Long-short term memory) auto-encoder to preserve and reconstruct
multi-sentence paragraphs. We introduce an LSTM model that hierarchically
builds an embedding for a paragraph from embeddings for sentences and words,
then decodes this embedding to reconstruct the original paragraph. We evaluate
the reconstructed paragraph using standard metrics like ROUGE and Entity Grid,
showing that neural models are able to encode texts in a way that preserve
syntactic, semantic, and discourse coherence. While only a first step toward
generating coherent text units from neural models, our work has the potential
to significantly impact natural language generation and
summarization\footnote{Code for the three models described in this paper can be
found at www.stanford.edu/~jiweil/ .
Global Gene Expression Analysis Using Machine Learning Methods
Authors: Min Xu
Date: 2015-06-05 00:00:00
Tasks: BIG-bench Machine Learning, Classification, Clustering, feature selection, General Classification

Microarray is a technology to quantitatively monitor the expression of large
number of genes in parallel. It has become one of the main tools for global
gene expression analysis in molecular biology research in recent years. The
large amount of expression data generated by this technology makes the study of
certain complex biological problems possible and machine learning methods are
playing a crucial role in the analysis process. At present, many machine
learning methods have been or have the potential to be applied to major areas
of gene expression analysis. These areas include clustering, classification,
dynamic modeling and reverse engineering.
  In this thesis, we focus our work on using machine learning methods to solve
the classification problems arising from microarray data. We first identify the
major types of the classification problems; then apply several machine learning
methods to solve the problems and perform systematic tests on real and
artificial datasets. We propose improvement to existing methods. Specifically,
we develop a multivariate and a hybrid feature selection method to obtain high
classification performance for high dimension classification problems. Using
the hybrid feature selection method, we are able to identify small sets of
features that give predictive accuracy that is as good as that from other
methods which require many more features.
Gene selection for cancer classification using a hybrid of univariate and multivariate feature selection methods
Authors: Min Xu, Rudy Setiono
Date: 2015-06-05 00:00:00
Tasks: Cancer Classification, feature selection, General Classification

Various approaches to gene selection for cancer classification based on
microarray data can be found in the literature and they may be grouped into two
categories: univariate methods and multivariate methods. Univariate methods
look at each gene in the data in isolation from others. They measure the
contribution of a particular gene to the classification without considering the
presence of the other genes. In contrast, multivariate methods measure the
relative contribution of a gene to the classification by taking the other genes
in the data into consideration. Multivariate methods select fewer genes in
general. However, the selection process of multivariate methods may be
sensitive to the presence of irrelevant genes, noises in the expression and
outliers in the training data. At the same time, the computational cost of
multivariate methods is high. To overcome the disadvantages of the two types of
approaches, we propose a hybrid method to obtain gene sets that are small and
highly discriminative.
  We devise our hybrid method from the univariate Maximum Likelihood method
(LIK) and the multivariate Recursive Feature Elimination method (RFE). We
analyze the properties of these methods and systematically test the
effectiveness of our proposed method on two cancer microarray datasets. Our
experiments on a leukemia dataset and a small, round blue cell tumors dataset
demonstrate the effectiveness of our hybrid method. It is able to discover sets
consisting of fewer genes than those reported in the literature and at the same
time achieve the same or better prediction accuracy.
Machine Assisted Authentication of Paper Currency: an Experiment on Indian Banknotes
Authors: Ankush Roy, Biswajit Halder, Utpal Garain, David S. Doermann
Date: 2014-01-02 00:00:00

Automatic authentication of paper money has been targeted. Indian bank notes
are taken as reference to show how a system can be developed for discriminating
fake notes from genuine ones. Image processing and pattern recognition
techniques are used to design the overall approach. The ability of the embedded
security aspects is thoroughly analysed for detecting fake currencies. Real
forensic samples are involved in the experiment that shows a high precision
machine can be developed for authentication of paper money. The system
performance is reported in terms of both accuracy and processing speed.
Comparison with human subjects namely forensic experts and bank staffs clearly
shows its applicability for mass checking of currency notes in the real world.
The analysis of security features to protect counterfeiting highlights some
facts that should be taken care of in future designing of currency notes.
Automatic tracking of protein vesicles
Authors: Min Xu
Date: 2015-06-05 00:00:00
Tasks: Multiple Object Tracking, Object, Object Tracking, Video Object Tracking

With the advance of fluorescence imaging technologies, recently cell
biologists are able to record the movement of protein vesicles within a living
cell. Automatic tracking of the movements of these vesicles become key for
qualitative analysis of dynamics of theses vesicles. In this thesis, we
formulate such tracking problem as video object tracking problem, and design a
dynamic programming method for tracking single object. Our experiments on
simulation data show that the method can identify a track with high accuracy
which is robust to the choose of tracking parameters and presence of high level
noise. We then extend this method to the tracking multiple objects using the
track elimination strategy. In multiple object tracking, the above approach
often fails to correctly identify a track when two tracks cross. We solve this
problem by incorporating the Kalman filter into the dynamic programming
framework. Our experiments on simulated data show that the tracking accuracy is
significantly improved.
Local Nonstationarity for Efficient Bayesian Optimization
Authors: Ruben Martinez-Cantin
Date: 2015-06-05 00:00:00
Tasks: Bayesian Optimization, BIG-bench Machine Learning, Experimental Design, global-optimization, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Bayesian optimization has shown to be a fundamental global optimization
algorithm in many applications: ranging from automatic machine learning,
robotics, reinforcement learning, experimental design, simulations, etc. The
most popular and effective Bayesian optimization relies on a surrogate model in
the form of a Gaussian process due to its flexibility to represent a prior over
function. However, many algorithms and setups relies on the stationarity
assumption of the Gaussian process. In this paper, we present a novel
nonstationary strategy for Bayesian optimization that is able to outperform the
state of the art in Bayesian optimization both in stationary and nonstationary
problems.
Large-scale Simple Question Answering with Memory Networks
Authors: Antoine Bordes, Nicolas Usunier, Sumit Chopra, Jason Weston
Date: 2015-06-05 00:00:00
Tasks: Question Answering, Transfer Learning

Training large-scale question answering systems is complicated because
training sources usually cover a small portion of the range of possible
questions. This paper studies the impact of multitask and transfer learning for
simple question answering; a setting for which the reasoning required to answer
is quite easy, as long as one can retrieve the correct evidence given a
question, which can be difficult in large-scale conditions. To this end, we
introduce a new dataset of 100k questions that we use in conjunction with
existing benchmarks. We conduct our study within the framework of Memory
Networks (Weston et al., 2015) because this perspective allows us to eventually
scale up to more complex reasoning, and show that Memory Networks can be
successfully trained to achieve excellent performance.