pwc-archive/papers-with-abstracts huggingface dataset:
Phased Exploration with Greedy Exploitation in Stochastic Combinatorial Partial Monitoring Games
Authors: Sougata Chaudhuri, Ambuj Tewari
Date: 2016-08-23 00:00:00
Proceeding: NeurIPS 2016 12

Partial monitoring games are repeated games where the learner receives
feedback that might be different from adversary's move or even the reward
gained by the learner. Recently, a general model of combinatorial partial
monitoring (CPM) games was proposed \cite{lincombinatorial2014}, where the
learner's action space can be exponentially large and adversary samples its
moves from a bounded, continuous space, according to a fixed distribution. The
paper gave a confidence bound based algorithm (GCB) that achieves
$O(T^{2/3}\log T)$ distribution independent and $O(\log T)$ distribution
dependent regret bounds. The implementation of their algorithm depends on two
separate offline oracles and the distribution dependent regret additionally
requires existence of a unique optimal action for the learner. Adopting their
CPM model, our first contribution is a Phased Exploration with Greedy
Exploitation (PEGE) algorithmic framework for the problem. Different algorithms
within the framework achieve $O(T^{2/3}\sqrt{\log T})$ distribution independent
and $O(\log^2 T)$ distribution dependent regret respectively. Crucially, our
framework needs only the simpler "argmax" oracle from GCB and the distribution
dependent regret does not require existence of a unique optimal action. Our
second contribution is another algorithm, PEGE2, which combines gap estimation
with a PEGE algorithm, to achieve an $O(\log T)$ regret bound, matching the GCB
guarantee but removing the dependence on size of the learner's action space.
However, like GCB, PEGE2 requires access to both offline oracles and the
existence of a unique optimal action. Finally, we discuss how our algorithm can
be efficiently applied to a CPM problem of practical interest: namely, online
ranking with feedback at the top.
Perceptron like Algorithms for Online Learning to Rank
Authors: Sougata Chaudhuri, Ambuj Tewari
Date: 2015-08-04 00:00:00
Tasks: General Classification, Information Retrieval, Learning-To-Rank, Retrieval

Perceptron is a classic online algorithm for learning a classification
function. In this paper, we provide a novel extension of the perceptron
algorithm to the learning to rank problem in information retrieval. We consider
popular listwise performance measures such as Normalized Discounted Cumulative
Gain (NDCG) and Average Precision (AP). A modern perspective on perceptron for
classification is that it is simply an instance of online gradient descent
(OGD), during mistake rounds, using the hinge loss function. Motivated by this
interpretation, we propose a novel family of listwise, large margin ranking
surrogates. Members of this family can be thought of as analogs of the hinge
loss. Exploiting a certain self-bounding property of the proposed family, we
provide a guarantee on the cumulative NDCG (or AP) induced loss incurred by our
perceptron-like algorithm. We show that, if there exists a perfect oracle
ranker which can correctly rank each instance in an online sequence of ranking
data, with some margin, the cumulative loss of perceptron algorithm on that
sequence is bounded by a constant, irrespective of the length of the sequence.
This result is reminiscent of Novikoff's convergence theorem for the
classification perceptron. Moreover, we prove a lower bound on the cumulative
loss achievable by any deterministic algorithm, under the assumption of
existence of perfect oracle ranker. The lower bound shows that our perceptron
bound is not tight, and we propose another, \emph{purely online}, algorithm
which achieves the lower bound. We provide empirical results on simulated and
large commercial datasets to corroborate our theoretical results.
Artificial Neural Networks for Detection of Malaria in RBCs
Authors: Purnima Pandit, A. Anand
Date: 2016-08-23 00:00:00

Malaria is one of the most common diseases caused by mosquitoes and is a
great public health problem worldwide. Currently, for malaria diagnosis the
standard technique is microscopic examination of a stained blood film. We
propose use of Artificial Neural Networks (ANN) for the diagnosis of the
disease in the red blood cell. For this purpose features / parameters are
computed from the data obtained by the digital holographic images of the blood
cells and is given as input to ANN which classifies the cell as the infected
one or otherwise.
Which techniques does your application use?: An information extraction framework for scientific articles
Authors: Soham Dan, Sanyam Agarwal, Mayank Singh, Pawan Goyal, Animesh Mukherjee
Date: 2016-08-23 00:00:00
Tasks: Articles, Language Modeling, Language Modelling

Every field of research consists of multiple application areas with various
techniques routinely used to solve problems in these wide range of application
areas. With the exponential growth in research volumes, it has become difficult
to keep track of the ever-growing number of application areas as well as the
corresponding problem solving techniques. In this paper, we consider the
computational linguistics domain and present a novel information extraction
system that automatically constructs a pool of all application areas in this
domain and appropriately links them with corresponding problem solving
techniques. Further, we categorize individual research articles based on their
application area and the techniques proposed/used in the article. k-gram based
discounting method along with handwritten rules and bootstrapped pattern
learning is employed to extract application areas. Subsequently, a language
modeling approach is proposed to characterize each article based on its
application area. Similarly, regular expressions and high-scoring noun phrases
are used for the extraction of the problem solving techniques. We propose a
greedy approach to characterize each article based on the techniques. Towards
the end, we present a table representing the most frequent techniques adopted
for a particular application area. Finally, we propose three use cases
presenting an extensive temporal analysis of the usage of techniques and
application areas.
Softplus Regressions and Convex Polytopes
Authors: Mingyuan Zhou
Date: 2016-08-23 00:00:00
Tasks: Bayesian Inference, Data Augmentation, regression

To construct flexible nonlinear predictive distributions, the paper
introduces a family of softplus function based regression models that convolve,
stack, or combine both operations by convolving countably infinite stacked
gamma distributions, whose scales depend on the covariates. Generalizing
logistic regression that uses a single hyperplane to partition the covariate
space into two halves, softplus regressions employ multiple hyperplanes to
construct a confined space, related to a single convex polytope defined by the
intersection of multiple half-spaces or a union of multiple convex polytopes,
to separate one class from the other. The gamma process is introduced to
support the convolution of countably infinite (stacked) covariate-dependent
gamma distributions. For Bayesian inference, Gibbs sampling derived via novel
data augmentation and marginalization techniques is used to deconvolve and/or
demix the highly complex nonlinear predictive distribution. Example results
demonstrate that softplus regressions provide flexible nonlinear decision
boundaries, achieving classification accuracies comparable to that of kernel
support vector machine while requiring significant less computation for
out-of-sample prediction.
A Survey of Visual Analysis of Human Motion and Its Applications
Authors: Qifei Wang
Date: 2016-08-02 00:00:00
Tasks: Action Recognition, Temporal Action Localization

This paper summarizes the recent progress in human motion analysis and its
applications. In the beginning, we reviewed the motion capture systems and the
representation model of human's motion data. Next, we sketched the advanced
human motion data processing technologies, including motion data filtering,
temporal alignment, and segmentation. The following parts overview the
state-of-the-art approaches of action recognition and dynamics measuring since
these two are the most active research areas in human motion analysis. The last
part discusses some emerging applications of the human motion analysis in
healthcare, human robot interaction, security surveillance, virtual reality and
animation. The promising research topics of human motion analysis in the future
is also summarized in the last part.
Towards Machine Comprehension of Spoken Content: Initial TOEFL Listening Comprehension Test by Machine
Authors: Bo-Hsiang Tseng, Sheng-syun Shen, Hung-Yi Lee, Lin-shan Lee
Date: 2016-08-23 00:00:00
Tasks: Reading Comprehension, Sentence

Multimedia or spoken content presents more attractive information than plain
text content, but it's more difficult to display on a screen and be selected by
a user. As a result, accessing large collections of the former is much more
difficult and time-consuming than the latter for humans. It's highly attractive
to develop a machine which can automatically understand spoken content and
summarize the key information for humans to browse over. In this endeavor, we
propose a new task of machine comprehension of spoken content. We define the
initial goal as the listening comprehension test of TOEFL, a challenging
academic English examination for English learners whose native language is not
English. We further propose an Attention-based Multi-hop Recurrent Neural
Network (AMRNN) architecture for this task, achieving encouraging results in
the initial tests. Initial results also have shown that word-level attention is
probably more robust than sentence-level attention for this task with ASR
errors.
Exploiting Semantic Information and Deep Matching for Optical Flow
Authors: Min Bai, Wenjie Luo, Kaustav Kundu, Raquel Urtasun
Date: 2016-04-06 00:00:00
Tasks: Autonomous Driving, Optical Flow Estimation

We tackle the problem of estimating optical flow from a monocular camera in
the context of autonomous driving. We build on the observation that the scene
is typically composed of a static background, as well as a relatively small
number of traffic participants which move rigidly in 3D. We propose to estimate
the traffic participants using instance-level segmentation. For each traffic
participant, we use the epipolar constraints that govern each independent
motion for faster and more accurate estimation. Our second contribution is a
new convolutional net that learns to perform flow matching, and is able to
estimate the uncertainty of its matches. This is a core element of our flow
estimation pipeline. We demonstrate the effectiveness of our approach in the
challenging KITTI 2015 flow benchmark, and show that our approach outperforms
published approaches by a large margin.
A Recurrent Encoder-Decoder Network for Sequential Face Alignment
Authors: Xi Peng, Rogerio S. Feris, Xiaoyu Wang, Dimitris N. Metaxas
Date: 2016-08-19 00:00:00
Tasks: Decoder, Face Alignment

We propose a novel recurrent encoder-decoder network model for real-time
video-based face alignment. Our proposed model predicts 2D facial point maps
regularized by a regression loss, while uniquely exploiting recurrent learning
at both spatial and temporal dimensions. At the spatial level, we add a
feedback loop connection between the combined output response map and the
input, in order to enable iterative coarse-to-fine face alignment using a
single network model. At the temporal level, we first decouple the features in
the bottleneck of the network into temporal-variant factors, such as pose and
expression, and temporal-invariant factors, such as identity information.
Temporal recurrent learning is then applied to the decoupled temporal-variant
features, yielding better generalization and significantly more accurate
results at test time. We perform a comprehensive experimental analysis, showing
the importance of each component of our proposed model, as well as superior
results over the state-of-the-art in standard datasets.
Unifying Decision Trees Split Criteria Using Tsallis Entropy
Authors: Yisen Wang, Chaobing Song, Shu-Tao Xia
Date: 2015-11-25 00:00:00

The construction of efficient and effective decision trees remains a key
topic in machine learning because of their simplicity and flexibility. A lot of
heuristic algorithms have been proposed to construct near-optimal decision
trees. ID3, C4.5 and CART are classical decision tree algorithms and the split
criteria they used are Shannon entropy, Gain Ratio and Gini index respectively.
All the split criteria seem to be independent, actually, they can be unified in
a Tsallis entropy framework. Tsallis entropy is a generalization of Shannon
entropy and provides a new approach to enhance decision trees' performance with
an adjustable parameter $q$. In this paper, a Tsallis Entropy Criterion (TEC)
algorithm is proposed to unify Shannon entropy, Gain Ratio and Gini index,
which generalizes the split criteria of decision trees. More importantly, we
reveal the relations between Tsallis entropy with different $q$ and other split
criteria. Experimental results on UCI data sets indicate that the TEC algorithm
achieves statistically significant improvement over the classical algorithms.
Five dimensions of reasoning in the wild
Authors: Don Perlis
Date: 2016-08-23 00:00:00

Reasoning does not work well when done in isolation from its significance,
both to the needs and interests of an agent and with respect to the wider
world. Moreover, those issues may best be handled with a new sort of data
structure that goes beyond the knowledge base and incorporates aspects of
perceptual knowledge and even more, in which a kind of anticipatory action may
be key.
Seq-NMS for Video Object Detection
Authors: Wei Han, Pooya Khorrami, Tom Le Paine, Prajit Ramachandran, Mohammad Babaeizadeh, Honghui Shi, Jianan Li, Shuicheng Yan, Thomas S. Huang
Date: 2016-02-26 00:00:00
Tasks: General Classification, Object, object-detection, Object Detection, Object Proposal Generation, Object Recognition, Video Object Detection

Video object detection is challenging because objects that are easily
detected in one frame may be difficult to detect in another frame within the
same clip. Recently, there have been major advances for doing object detection
in a single image. These methods typically contain three phases: (i) object
proposal generation (ii) object classification and (iii) post-processing. We
propose a modification of the post-processing phase that uses high-scoring
object detections from nearby frames to boost scores of weaker detections
within the same clip. We show that our method obtains superior results to
state-of-the-art single image object detection techniques. Our method placed
3rd in the video object detection (VID) task of the ImageNet Large Scale Visual
Recognition Challenge 2015 (ILSVRC2015).
A Systematic Approach for Cross-source Point Cloud Registration by Preserving Macro and Micro Structures
Authors: Xiaoshui Huang, Jian Zhang, Lixin Fan, Qiang Wu, Chun Yuan
Date: 2016-08-18 00:00:00
Tasks: graph construction, Graph Matching, Point Cloud Registration

We propose a systematic approach for registering cross-source point clouds.
The compelling need for cross-source point cloud registration is motivated by
the rapid development of a variety of 3D sensing techniques, but many existing
registration methods face critical challenges as a result of the large
variations in cross-source point clouds. This paper therefore illustrates a
novel registration method which successfully aligns two cross-source point
clouds in the presence of significant missing data, large variations in point
density, scale difference and so on. The robustness of the method is attributed
to the extraction of macro and micro structures. Our work has three main
contributions: (1) a systematic pipeline to deal with cross-source point cloud
registration; (2) a graph construction method to maintain macro and micro
structures; (3) a new graph matching method is proposed which considers the
global geometric constraint to robustly register these variable graphs.
Compared to most of the related methods, the experiments show that the proposed
method successfully registers in cross-source datasets, while other methods
have difficulty achieving satisfactory results. The proposed method also shows
great ability in same-source datasets.
LFADS - Latent Factor Analysis via Dynamical Systems
Authors: David Sussillo, Rafal Jozefowicz, L. F. Abbott, Chethan Pandarinath
Date: 2016-08-22 00:00:00

Neuroscience is experiencing a data revolution in which many hundreds or
thousands of neurons are recorded simultaneously. Currently, there is little
consensus on how such data should be analyzed. Here we introduce LFADS (Latent
Factor Analysis via Dynamical Systems), a method to infer latent dynamics from
simultaneously recorded, single-trial, high-dimensional neural spiking data.
LFADS is a sequential model based on a variational auto-encoder. By making a
dynamical systems hypothesis regarding the generation of the observed data,
LFADS reduces observed spiking to a set of low-dimensional temporal factors,
per-trial initial conditions, and inferred inputs. We compare LFADS to existing
methods on synthetic data and show that it significantly out-performs them in
inferring neural firing rates and latent dynamics.
Removing Manually-Generated Boilerplate from Electronic Texts: Experiments with Project Gutenberg e-Books
Authors: Owen Kaser, Daniel Lemire
Date: 2007-07-13 00:00:00

Collaborative work on unstructured or semi-structured documents, such as in
literature corpora or source code, often involves agreed upon templates
containing metadata. These templates are not consistent across users and over
time. Rule-based parsing of these templates is expensive to maintain and tends
to fail as new documents are added. Statistical techniques based on frequent
occurrences have the potential to identify automatically a large fraction of
the templates, thus reducing the burden on the programmers. We investigate the
case of the Project Gutenberg corpus, where most documents are in ASCII format
with preambles and epilogues that are often copied and pasted or manually
typed. We show that a statistical approach can solve most cases though some
documents require knowledge of English. We also survey various technical
solutions that make our approach applicable to large data sets.
ROCS-Derived Features for Virtual Screening
Authors: Steven Kearnes, Vijay Pande
Date: 2016-06-06 00:00:00
Tasks: BIG-bench Machine Learning

Rapid overlay of chemical structures (ROCS) is a standard tool for the
calculation of 3D shape and chemical ("color") similarity. ROCS uses unweighted
sums to combine many aspects of similarity, yielding parameter-free models for
virtual screening. In this report, we decompose the ROCS color force field into
"color components" and "color atom overlaps", novel color similarity features
that can be weighted in a system-specific manner by machine learning
algorithms. In cross-validation experiments, these additional features
significantly improve virtual screening performance (ROC AUC scores) relative
to standard ROCS.
Multi-Dueling Bandits and Their Application to Online Ranker Evaluation
Authors: Brian Brost, Yevgeny Seldin, Ingemar J. Cox, Christina Lioma
Date: 2016-08-22 00:00:00
Tasks: Online Ranker Evaluation

New ranking algorithms are continually being developed and refined,
necessitating the development of efficient methods for evaluating these
rankers. Online ranker evaluation focuses on the challenge of efficiently
determining, from implicit user feedback, which ranker out of a finite set of
rankers is the best. Online ranker evaluation can be modeled by dueling ban-
dits, a mathematical model for online learning under limited feedback from
pairwise comparisons. Comparisons of pairs of rankers is performed by
interleaving their result sets and examining which documents users click on.
The dueling bandits model addresses the key issue of which pair of rankers to
compare at each iteration, thereby providing a solution to the
exploration-exploitation trade-off. Recently, methods for simultaneously
comparing more than two rankers have been developed. However, the question of
which rankers to compare at each iteration was left open. We address this
question by proposing a generalization of the dueling bandits model that uses
simultaneous comparisons of an unrestricted number of rankers. We evaluate our
algorithm on synthetic data and several standard large-scale online ranker
evaluation datasets. Our experimental results show that the algorithm yields
orders of magnitude improvement in performance compared to stateof- the-art
dueling bandit algorithms.
Single-shot Adaptive Measurement for Quantum-enhanced Metrology
Authors: Pantita Palittapongarnpim, Peter Wittek, Barry C. Sanders
Date: 2016-08-22 00:00:00
Tasks: Decision Making, Reinforcement Learning

Quantum-enhanced metrology aims to estimate an unknown parameter such that
the precision scales better than the shot-noise bound. Single-shot adaptive
quantum-enhanced metrology (AQEM) is a promising approach that uses feedback to
tweak the quantum process according to previous measurement outcomes.
Techniques and formalism for the adaptive case are quite different from the
usual non-adaptive quantum metrology approach due to the causal relationship
between measurements and outcomes. We construct a formal framework for AQEM by
modeling the procedure as a decision-making process, and we derive the
imprecision and the Cram\'{e}r-Rao lower bound with explicit dependence on the
feedback policy. We also explain the reinforcement learning approach for
generating quantum control policies, which is adopted due to the optimal policy
being non-trivial to devise. Applying a learning algorithm based on
differential evolution enables us to attain imprecision for adaptive
interferometric phase estimation, which turns out to be SQL when non-entangled
particles are used in the scheme.
Fast Randomized Singular Value Thresholding for Low-rank Optimization
Authors: Tae-Hyun Oh, Yasuyuki Matsushita, Yu-Wing Tai, In So Kweon
Date: 2015-09-01 00:00:00
Tasks: Clustering

Rank minimization can be converted into tractable surrogate problems, such as
Nuclear Norm Minimization (NNM) and Weighted NNM (WNNM). The problems related
to NNM, or WNNM, can be solved iteratively by applying a closed-form proximal
operator, called Singular Value Thresholding (SVT), or Weighted SVT, but they
suffer from high computational cost of Singular Value Decomposition (SVD) at
each iteration. We propose a fast and accurate approximation method for SVT,
that we call fast randomized SVT (FRSVT), with which we avoid direct
computation of SVD. The key idea is to extract an approximate basis for the
range of the matrix from its compressed matrix. Given the basis, we compute
partial singular values of the original matrix from the small factored matrix.
In addition, by developping a range propagation method, our method further
speeds up the extraction of approximate basis at each iteration. Our
theoretical analysis shows the relationship between the approximation bound of
SVD and its effect to NNM via SVT. Along with the analysis, our empirical
results quantitatively and qualitatively show that our approximation rarely
harms the convergence of the host algorithms. We assess the efficiency and
accuracy of the proposed method on various computer vision problems, e.g.,
subspace clustering, weather artifact removal, and simultaneous multi-image
alignment and rectification.