pwc-archive/papers-with-abstracts huggingface dataset:
Enhancing Person Re-identification in a Self-trained Subspace
Authors: Xun Yang, Meng Wang, Richang Hong, Qi Tian, Yong Rui
Date: 2017-04-20 00:00:00
Tasks: Person Re-Identification

Despite the promising progress made in recent years, person re-identification
(re-ID) remains a challenging task due to the complex variations in human
appearances from different camera views. For this challenging problem, a large
variety of algorithms have been developed in the fully-supervised setting,
requiring access to a large amount of labeled training data. However, the main
bottleneck for fully-supervised re-ID is the limited availability of labeled
training samples. To address this problem, in this paper, we propose a
self-trained subspace learning paradigm for person re-ID which effectively
utilizes both labeled and unlabeled data to learn a discriminative subspace
where person images across disjoint camera views can be easily matched. The
proposed approach first constructs pseudo pairwise relationships among
unlabeled persons using the k-nearest neighbors algorithm. Then, with the
pseudo pairwise relationships, the unlabeled samples can be easily combined
with the labeled samples to learn a discriminative projection by solving an
eigenvalue problem. In addition, we refine the pseudo pairwise relationships
iteratively, which further improves the learning performance. A multi-kernel
embedding strategy is also incorporated into the proposed approach to cope with
the non-linearity in person's appearance and explore the complementation of
multiple kernels. In this way, the performance of person re-ID can be greatly
enhanced when training data are insufficient. Experimental results on six
widely-used datasets demonstrate the effectiveness of our approach and its
performance can be comparable to the reported results of most state-of-the-art
fully-supervised methods while using much fewer labeled data.
Multi-dueling Bandits with Dependent Arms
Authors: Yanan Sui, Vincent Zhuang, Joel W. Burdick, Yisong Yue
Date: 2017-04-29 00:00:00
Tasks: Thompson Sampling

The dueling bandits problem is an online learning framework for learning from
pairwise preference feedback, and is particularly well-suited for modeling
settings that elicit subjective or implicit human feedback. In this paper, we
study the problem of multi-dueling bandits with dependent arms, which extends
the original dueling bandits setting by simultaneously dueling multiple arms as
well as modeling dependencies between arms. These extensions capture key
characteristics found in many real-world applications, and allow for the
opportunity to develop significantly more efficient algorithms than were
possible in the original setting. We propose the \selfsparring algorithm, which
reduces the multi-dueling bandits problem to a conventional bandit setting that
can be solved using a stochastic bandit algorithm such as Thompson Sampling,
and can naturally model dependencies using a Gaussian process prior. We present
a no-regret analysis for multi-dueling setting, and demonstrate the
effectiveness of our algorithm empirically on a wide range of simulation
settings.
Lifelong Learning CRF for Supervised Aspect Extraction
Authors: Lei Shu, Hu Xu, Bing Liu
Date: 2017-04-29 00:00:00
Proceeding: ACL 2017 7
Tasks: Aspect Extraction, Lifelong learning

This paper makes a focused contribution to supervised aspect extraction. It
shows that if the system has performed aspect extraction from many past domains
and retained their results as knowledge, Conditional Random Fields (CRF) can
leverage this knowledge in a lifelong learning manner to extract in a new
domain markedly better than the traditional CRF without using this prior
knowledge. The key innovation is that even after CRF training, the model can
still improve its extraction with experiences in its applications.
Explaining the Success of AdaBoost and Random Forests as Interpolating Classifiers
Authors: Abraham J. Wyner, Matthew Olson, Justin Bleich, David Mease
Date: 2015-04-28 00:00:00

There is a large literature explaining why AdaBoost is a successful
classifier. The literature on AdaBoost focuses on classifier margins and
boosting's interpretation as the optimization of an exponential likelihood
function. These existing explanations, however, have been pointed out to be
incomplete. A random forest is another popular ensemble method for which there
is substantially less explanation in the literature. We introduce a novel
perspective on AdaBoost and random forests that proposes that the two
algorithms work for similar reasons. While both classifiers achieve similar
predictive accuracy, random forests cannot be conceived as a direct
optimization procedure. Rather, random forests is a self-averaging,
interpolating algorithm which creates what we denote as a "spikey-smooth"
classifier, and we view AdaBoost in the same light. We conjecture that both
AdaBoost and random forests succeed because of this mechanism. We provide a
number of examples and some theoretical justification to support this
explanation. In the process, we question the conventional wisdom that suggests
that boosting algorithms for classification require regularization or early
stopping and should be limited to low complexity classes of learners, such as
decision stumps. We conclude that boosting should be used like random forests:
with large decision trees and without direct regularization or early stopping.
Funnel Libraries for Real-Time Robust Feedback Motion Planning
Authors: Anirudha Majumdar, Russ Tedrake
Date: 2016-01-15 00:00:00
Tasks: Motion Planning

We consider the problem of generating motion plans for a robot that are
guaranteed to succeed despite uncertainty in the environment, parametric model
uncertainty, and disturbances. Furthermore, we consider scenarios where these
plans must be generated in real-time, because constraints such as obstacles in
the environment may not be known until they are perceived (with a noisy sensor)
at runtime. Our approach is to pre-compute a library of "funnels" along
different maneuvers of the system that the state is guaranteed to remain within
(despite bounded disturbances) when the feedback controller corresponding to
the maneuver is executed. We leverage powerful computational machinery from
convex optimization (sums-of-squares programming in particular) to compute
these funnels. The resulting funnel library is then used to sequentially
compose motion plans at runtime while ensuring the safety of the robot. A major
advantage of the work presented here is that by explicitly taking into account
the effect of uncertainty, the robot can evaluate motion plans based on how
vulnerable they are to disturbances.
  We demonstrate and validate our method using extensive hardware experiments
on a small fixed-wing airplane avoiding obstacles at high speed (~12 mph),
along with thorough simulation experiments of ground vehicle and quadrotor
models navigating through cluttered environments. To our knowledge, these
demonstrations constitute one of the first examples of provably safe and robust
control for robotic systems with complex nonlinear dynamics that need to plan
in real-time in environments with complex geometric constraints.
Learning to Skim Text
Authors: Adams Wei Yu, Hongrae Lee, Quoc V. Le
Date: 2017-04-23 00:00:00
Proceeding: ACL 2017 7
Tasks: Document Classification, General Classification, Machine Translation, Question Answering, Sentiment Analysis, Translation

Recurrent Neural Networks are showing much promise in many sub-areas of
natural language processing, ranging from document classification to machine
translation to automatic question answering. Despite their promise, many
recurrent models have to read the whole text word by word, making it slow to
handle long documents. For example, it is difficult to use a recurrent network
to read a book and answer questions about it. In this paper, we present an
approach of reading text while skipping irrelevant information if needed. The
underlying model is a recurrent network that learns how far to jump after
reading a few words of the input text. We employ a standard policy gradient
method to train the model to make discrete jumping decisions. In our benchmarks
on four different tasks, including number prediction, sentiment analysis, news
article classification and automatic Q\&A, our proposed model, a modified LSTM
with jumping, is up to 6 times faster than the standard sequential LSTM, while
maintaining the same or even better accuracy.
Learning with Changing Features
Authors: Amit Dhurandhar, Steve Hanneke, Liu Yang
Date: 2017-04-29 00:00:00
Tasks: Change Point Detection

In this paper we study the setting where features are added or change
interpretation over time, which has applications in multiple domains such as
retail, manufacturing, finance. In particular, we propose an approach to
provably determine the time instant from which the new/changed features start
becoming relevant with respect to an output variable in an agnostic
(supervised) learning setting. We also suggest an efficient version of our
approach which has the same asymptotic performance. Moreover, our theory also
applies when we have more than one such change point. Independent post analysis
of a change point identified by our method for a large retailer revealed that
it corresponded in time with certain unflattering news stories about a brand
that resulted in the change in customer behavior. We also applied our method to
data from an advanced manufacturing plant identifying the time instant from
which downstream features became relevant. To the best of our knowledge this is
the first work that formally studies change point detection in a distribution
independent agnostic setting, where the change point is based on the changing
relationship between input and output.
Extending and Improving Wordnet via Unsupervised Word Embeddings
Authors: Mikhail Khodak, Andrej Risteski, Christiane Fellbaum, Sanjeev Arora
Date: 2017-04-29 00:00:00
Tasks: Clustering, Word Embeddings

This work presents an unsupervised approach for improving WordNet that builds
upon recent advances in document and sense representation via distributional
semantics. We apply our methods to construct Wordnets in French and Russian,
languages which both lack good manual constructions.1 These are evaluated on
two new 600-word test sets for word-to-synset matching and found to improve
greatly upon synset recall, outperforming the best automated Wordnets in
F-score. Our methods require very few linguistic resources, thus being
applicable for Wordnet construction in low-resources languages, and may further
be applied to sense clustering and other Wordnet improvements.
Opening the Black Box of Deep Neural Networks via Information
Authors: Ravid Shwartz-Ziv, Naftali Tishby
Date: 2017-03-02 00:00:00
Tasks: Information Plane

Despite their great success, there is still no comprehensive theoretical
understanding of learning with Deep Neural Networks (DNNs) or their inner
organization. Previous work proposed to analyze DNNs in the \textit{Information
Plane}; i.e., the plane of the Mutual Information values that each layer
preserves on the input and output variables. They suggested that the goal of
the network is to optimize the Information Bottleneck (IB) tradeoff between
compression and prediction, successively, for each layer.
  In this work we follow up on this idea and demonstrate the effectiveness of
the Information-Plane visualization of DNNs. Our main results are: (i) most of
the training epochs in standard DL are spent on {\emph compression} of the
input to efficient representation and not on fitting the training labels. (ii)
The representation compression phase begins when the training errors becomes
small and the Stochastic Gradient Decent (SGD) epochs change from a fast drift
to smaller training error into a stochastic relaxation, or random diffusion,
constrained by the training error value. (iii) The converged layers lie on or
very close to the Information Bottleneck (IB) theoretical bound, and the maps
from the input to any hidden layer and from this hidden layer to the output
satisfy the IB self-consistent equations. This generalization through noise
mechanism is unique to Deep Neural Networks and absent in one layer networks.
(iv) The training time is dramatically reduced when adding more hidden layers.
Thus the main advantage of the hidden layers is computational. This can be
explained by the reduced relaxation time, as this it scales super-linearly
(exponentially for simple diffusion) with the information compression from the
previous layer.
Predicting and Understanding Law-Making with Word Vectors and an Ensemble Model
Authors: John J. Nay
Date: 2016-07-07 00:00:00
Tasks: Language Modeling, Language Modelling, Sentence

Out of nearly 70,000 bills introduced in the U.S. Congress from 2001 to 2015,
only 2,513 were enacted. We developed a machine learning approach to
forecasting the probability that any bill will become law. Starting in 2001
with the 107th Congress, we trained models on data from previous Congresses,
predicted all bills in the current Congress, and repeated until the 113th
Congress served as the test. For prediction we scored each sentence of a bill
with a language model that embeds legislative vocabulary into a
high-dimensional, semantic-laden vector space. This language representation
enables our investigation into which words increase the probability of
enactment for any topic. To test the relative importance of text and context,
we compared the text model to a context-only model that uses variables such as
whether the bill's sponsor is in the majority party. To test the effect of
changes to bills after their introduction on our ability to predict their final
outcome, we compared using the bill text and meta-data available at the time of
introduction with using the most recent data. At the time of introduction
context-only predictions outperform text-only, and with the newest data
text-only outperforms context-only. Combining text and context always performs
best. We conducted a global sensitivity analysis on the combined model to
determine important variables predicting enactment.
The Problem of Coincidence in A Theory of Temporal Multiple Recurrence
Authors: B. O. Akinkunmi
Date: 2017-04-29 00:00:00

Logical theories have been developed which have allowed temporal reasoning
about eventualities (a la Galton) such as states, processes, actions, events,
processes and complex eventualities such as sequences and recurrences of other
eventualities. This paper presents the problem of coincidence within the
framework of a first order logical theory formalising temporal multiple
recurrence of two sequences of fixed duration eventualities and presents a
solution to it The coincidence problem is described as: if two complex
eventualities (or eventuality sequences) consisting respectively of component
eventualities x0, x1,....,xr and y0, y1, ..,ys both recur over an interval k
and all eventualities are of fixed durations, is there a sub-interval of k over
which the incidence xt and yu for t between 0..r and s between 0..s coincide.
The solution presented here formalises the intuition that a solution can be
found by temporal projection over a cycle of the multiple recurrence of both
sequences.
Two Algorithms for Deciding Coincidence In Double Temporal Recurrence of Eventuality Sequences
Authors: Babatunde Opeoluwa Akinkunmi, Adesoji A. Adegbola
Date: 2017-04-29 00:00:00

Let two sequences of eventualities x (signifying the sequence, x0,x1, x2,...,xn-1) and y (signifying the sequence, y0, y1, y2,..,yn-1) both recur over the same time interval and it is required to determine whether or not a subinterval exists within the said interval which is a common subinterval of the intervals of occurrence of xp and yq. This paper presents two algorithms for solving the problem. the first explores an arbitrary cycle of the double recurrence for the existence of such an interval. its worst case running time is quadratic. The other algorithm is based on the novel notion of gcd-partitions and has a linear worst case running time. If the eventuality sequence pair (W,z) is a gcd-partition for the double recurrence (x, y),then, from a certain property of gcd-partitions, within any cycle of the double recurrence, there exists r and s such that intervals of occurrence of xp and yq are non-disjoint with the interval of co-occurrence of wr and zs. As such, a coincidence between xp and yq occurs within a cycle of the double recurrence if and only if such r and s exist so that the interval of co-occurrence of wr and zs shares a common interval with the common interval of occurrences of xp and yq. The algorithm systematically reduces the number of wr and zs pairs to be explored in the process of finding the existence of the coincidence.
Improved underwater image enhancement algorithms based on partial differential equations (PDEs)
Authors: U. A. Nnolim
Date: 2017-04-29 00:00:00
Tasks: Image Enhancement

The experimental results of improved underwater image enhancement algorithms
based on partial differential equations (PDEs) are presented in this report.
This second work extends the study of previous work and incorporating several
improvements into the revised algorithm. Experiments show the evidence of the
improvements when compared to previously proposed approaches and other
conventional algorithms found in the literature.
Estimating 3D Trajectories from 2D Projections via Disjunctive Factored Four-Way Conditional Restricted Boltzmann Machines
Authors: Decebal Constantin Mocanu, Haitham Bou Ammar, Luis Puig, Eric Eaton, Antonio Liotta
Date: 2016-04-20 00:00:00
Tasks: Future prediction, Time Series, Time Series Analysis

Estimation, recognition, and near-future prediction of 3D trajectories based
on their two dimensional projections available from one camera source is an
exceptionally difficult problem due to uncertainty in the trajectories and
environment, high dimensionality of the specific trajectory states, lack of
enough labeled data and so on. In this article, we propose a solution to solve
this problem based on a novel deep learning model dubbed Disjunctive Factored
Four-Way Conditional Restricted Boltzmann Machine (DFFW-CRBM). Our method
improves state-of-the-art deep learning techniques for high dimensional
time-series modeling by introducing a novel tensor factorization capable of
driving forth order Boltzmann machines to considerably lower energy levels, at
no computational costs. DFFW-CRBMs are capable of accurately estimating,
recognizing, and performing near-future prediction of three-dimensional
trajectories from their 2D projections while requiring limited amount of
labeled data. We evaluate our method on both simulated and real-world data,
showing its effectiveness in predicting and classifying complex ball
trajectories and human activities.
Cnvlutin2: Ineffectual-Activation-and-Weight-Free Deep Neural Network Computing
Authors: Patrick Judd, Alberto Delmas, Sayeh Sharify, Andreas Moshovos
Date: 2017-04-29 00:00:00

We discuss several modifications and extensions over the previous proposed
Cnvlutin (CNV) accelerator for convolutional and fully-connected layers of Deep
Learning Network. We first describe different encodings of the activations that
are deemed ineffectual. The encodings have different memory overhead and energy
characteristics. We propose using a level of indirection when accessing
activations from memory to reduce their memory footprint by storing only the
effectual activations. We also present a modified organization that detects the
activations that are deemed as ineffectual while fetching them from memory.
This is different than the original design that instead detected them at the
output of the preceding layer. Finally, we present an extended CNV that can
also skip ineffectual weights.
Semi-supervised sequence tagging with bidirectional language models
Authors: Matthew E. Peters, Waleed Ammar, Chandra Bhagavatula, Russell Power
Date: 2017-04-29 00:00:00
Proceeding: ACL 2017 7
Tasks: Chunking, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER

Pre-trained word embeddings learned from unlabeled text have become a
standard component of neural network architectures for NLP tasks. However, in
most cases, the recurrent network that operates on word-level representations
to produce context sensitive representations is trained on relatively little
labeled data. In this paper, we demonstrate a general semi-supervised approach
for adding pre- trained context embeddings from bidirectional language models
to NLP systems and apply it to sequence labeling tasks. We evaluate our model
on two standard datasets for named entity recognition (NER) and chunking, and
in both cases achieve state of the art results, surpassing previous systems
that use other forms of transfer or joint learning with additional labeled data
and task specific gazetteers.
Learning to Ask: Neural Question Generation for Reading Comprehension
Authors: Xinya Du, Junru Shao, Claire Cardie
Date: 2017-04-29 00:00:00
Proceeding: ACL 2017 7
Tasks: Question Generation, Question-Generation, Reading Comprehension, Sentence

We study automatic question generation for sentences from text passages in
reading comprehension. We introduce an attention-based sequence learning model
for the task and investigate the effect of encoding sentence- vs.
paragraph-level information. In contrast to all previous work, our model does
not rely on hand-crafted rules or a sophisticated NLP pipeline; it is instead
trainable end-to-end via sequence-to-sequence learning. Automatic evaluation
results show that our system significantly outperforms the state-of-the-art
rule-based system. In human evaluations, questions generated by our system are
also rated as being more natural (i.e., grammaticality, fluency) and as more
difficult to answer (in terms of syntactic and lexical divergence from the
original text and reasoning needed to answer).
The Impact of Coevolution and Abstention on the Emergence of Cooperation
Authors: Marcos Cardinot, Colm O'Riordan, Josephine Griffith
Date: 2017-04-28 00:00:00

This paper explores the Coevolutionary Optional Prisoner's Dilemma (COPD)
game, which is a simple model to coevolve game strategy and link weights of
agents playing the Optional Prisoner's Dilemma game. We consider a population
of agents placed in a lattice grid with boundary conditions. A number of Monte
Carlo simulations are performed to investigate the impacts of the COPD game on
the emergence of cooperation. Results show that the coevolutionary rules enable
cooperators to survive and even dominate, with the presence of abstainers in
the population playing a key role in the protection of cooperators against
exploitation from defectors. We observe that in adverse conditions such as when
the initial population of abstainers is too scarce/abundant, or when the
temptation to defect is very high, cooperation has no chance of emerging.
However, when the simple coevolutionary rules are applied, cooperators
flourish.
Generative Modeling with Conditional Autoencoders: Building an Integrated Cell
Authors: Gregory R. Johnson, Rory M. Donovan-Maiye, Mary M. Maleckar
Date: 2017-04-28 00:00:00

We present a conditional generative model to learn variation in cell and
nuclear morphology and the location of subcellular structures from microscopy
images. Our model generalizes to a wide range of subcellular localization and
allows for a probabilistic interpretation of cell and nuclear morphology and
structure localization from fluorescence images. We demonstrate the
effectiveness of our approach by producing photo-realistic cell images using
our generative model. The conditional nature of the model provides the ability
to predict the localization of unobserved structures given cell and nuclear
morphology.