pwc-archive/papers-with-abstracts huggingface dataset:
Centralized and Decentralized Global Outer-synchronization of Asymmetric Recurrent Time-varying Neural Network by Data-sampling
Authors: Wenlian Lu, Ren Zheng, Tianping Chen
Date: 2016-04-02 00:00:00

In this paper, we discuss the outer-synchronization of the asymmetrically
connected recurrent time-varying neural networks. By both centralized and
decentralized discretization data sampling principles, we derive several
sufficient conditions based on diverse vector norms that guarantee that any two
trajectories from different initial values of the identical neural network
system converge together. The lower bounds of the common time intervals between
data samples in centralized and decentralized principles are proved to be
positive, which guarantees exclusion of Zeno behavior. A numerical example is
provided to illustrate the efficiency of the theoretical results.
Embedding Lexical Features via Low-Rank Tensors
Authors: Mo Yu, Mark Dredze, Raman Arora, Matthew Gormley
Date: 2016-04-02 00:00:00
Proceeding: NAACL 2016 6
Tasks: Relation Extraction

Modern NLP models rely heavily on engineered features, which often combine
word and contextual information into complex lexical features. Such combination
results in large numbers of features, which can lead to over-fitting. We
present a new model that represents complex lexical features---comprised of
parts for words, contextual information and labels---in a tensor that captures
conjunction information among these parts. We apply low-rank tensor
approximations to the corresponding parameter tensors to reduce the parameter
space and improve prediction speed. Furthermore, we investigate two methods for
handling features that include $n$-grams of mixed lengths. Our model achieves
state-of-the-art results on tasks in relation extraction, PP-attachment, and
preposition disambiguation.
Stability of Analytic Neural Networks with Event-triggered Synaptic Feedbacks
Authors: Ren Zheng, Xinlei Yi, Wenlian Lu, Tianping Chen
Date: 2016-04-02 00:00:00

In this paper, we investigate stability of a class of analytic neural
networks with the synaptic feedback via event-triggered rules. This model is
general and include Hopfield neural network as a special case. These
event-trigger rules can efficiently reduces loads of computation and
information transmission at synapses of the neurons. The synaptic feedback of
each neuron keeps a constant value based on the outputs of the other neurons at
its latest triggering time but changes at its next triggering time, which is
determined by certain criterion. It is proved that every trajectory of the
analytic neural network converges to certain equilibrium under this
event-triggered rule for all initial values except a set of zero measure. The
main technique of the proof is the Lojasiewicz inequality to prove the
finiteness of trajectory length. The realization of this event-triggered rule
is verified by the exclusion of Zeno behaviors. Numerical examples are provided
to illustrate the efficiency of the theoretical results.
3D-R2N2: A Unified Approach for Single and Multi-view 3D Object Reconstruction
Authors: Christopher B. Choy, Danfei Xu, JunYoung Gwak, Kevin Chen, Silvio Savarese
Date: 2016-04-02 00:00:00
Tasks: 3D Object Reconstruction, 3D Reconstruction, Object, Object Reconstruction

Inspired by the recent success of methods that employ shape priors to achieve
robust 3D reconstructions, we propose a novel recurrent neural network
architecture that we call the 3D Recurrent Reconstruction Neural Network
(3D-R2N2). The network learns a mapping from images of objects to their
underlying 3D shapes from a large collection of synthetic data. Our network
takes in one or more images of an object instance from arbitrary viewpoints and
outputs a reconstruction of the object in the form of a 3D occupancy grid.
Unlike most of the previous works, our network does not require any image
annotations or object class labels for training or testing. Our extensive
experimental analysis shows that our reconstruction framework i) outperforms
the state-of-the-art methods for single view reconstruction, and ii) enables
the 3D reconstruction of objects in situations when traditional SFM/SLAM
methods fail (because of lack of texture and/or wide baseline).
Leaving Some Stones Unturned: Dynamic Feature Prioritization for Activity Detection in Streaming Video
Authors: Yu-Chuan Su, Kristen Grauman
Date: 2016-04-01 00:00:00
Tasks: Action Detection, Activity Detection, Activity Recognition, feature selection

Current approaches for activity recognition often ignore constraints on
computational resources: 1) they rely on extensive feature computation to
obtain rich descriptors on all frames, and 2) they assume batch-mode access to
the entire test video at once. We propose a new active approach to activity
recognition that prioritizes "what to compute when" in order to make timely
predictions. The main idea is to learn a policy that dynamically schedules the
sequence of features to compute on selected frames of a given test video. In
contrast to traditional static feature selection, our approach continually
re-prioritizes computation based on the accumulated history of observations and
accounts for the transience of those observations in ongoing video. We develop
variants to handle both the batch and streaming settings. On two challenging
datasets, our method provides significantly better accuracy than alternative
techniques for a wide range of computational budgets.
Revisiting Summarization Evaluation for Scientific Articles
Authors: Arman Cohan, Nazli Goharian
Date: 2016-04-01 00:00:00
Proceeding: LREC 2016 5
Tasks: Articles, Text Summarization

Evaluation of text summarization approaches have been mostly based on metrics
that measure similarities of system generated summaries with a set of human
written gold-standard summaries. The most widely used metric in summarization
evaluation has been the ROUGE family. ROUGE solely relies on lexical overlaps
between the terms and phrases in the sentences; therefore, in cases of
terminology variations and paraphrasing, ROUGE is not as effective. Scientific
article summarization is one such case that is different from general domain
summarization (e.g. newswire data). We provide an extensive analysis of ROUGE's
effectiveness as an evaluation metric for scientific summarization; we show
that, contrary to the common belief, ROUGE is not much reliable in evaluating
scientific summaries. We furthermore show how different variants of ROUGE
result in very different correlations with the manual Pyramid scores. Finally,
we propose an alternative metric for summarization evaluation which is based on
the content relevance between a system generated summary and the corresponding
human written summaries. We call our metric SERA (Summarization Evaluation by
Relevance Analysis). Unlike ROUGE, SERA consistently achieves high correlations
with manual scores which shows its effectiveness in evaluation of scientific
article summarization.
Large-Scale Electron Microscopy Image Segmentation in Spark
Authors: Stephen M. Plaza, Stuart E. Berg
Date: 2016-04-01 00:00:00
Tasks: Electron Microscopy Image Segmentation, Image Segmentation, Segmentation, Semantic Segmentation

The emerging field of connectomics aims to unlock the mysteries of the brain
by understanding the connectivity between neurons. To map this connectivity, we
acquire thousands of electron microscopy (EM) images with nanometer-scale
resolution. After aligning these images, the resulting dataset has the
potential to reveal the shapes of neurons and the synaptic connections between
them. However, imaging the brain of even a tiny organism like the fruit fly
yields terabytes of data. It can take years of manual effort to examine such
image volumes and trace their neuronal connections. One solution is to apply
image segmentation algorithms to help automate the tracing tasks. In this
paper, we propose a novel strategy to apply such segmentation on very large
datasets that exceed the capacity of a single machine. Our solution is robust
to potential segmentation errors which could otherwise severely compromise the
quality of the overall segmentation, for example those due to poor classifier
generalizability or anomalies in the image dataset. We implement our algorithms
in a Spark application which minimizes disk I/O, and apply them to a few large
EM datasets, revealing both their effectiveness and scalability. We hope this
work will encourage external contributions to EM segmentation by providing 1) a
flexible plugin architecture that deploys easily on different cluster
environments and 2) an in-memory representation of segmentation that could be
conducive to new advances.
Reinforcement learning based local search for grouping problems: A case study on graph coloring
Authors: Yangming Zhou, Jin-Kao Hao, Béatrice Duval
Date: 2016-04-01 00:00:00
Tasks: Combinatorial Optimization, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Grouping problems aim to partition a set of items into multiple mutually
disjoint subsets according to some specific criterion and constraints. Grouping
problems cover a large class of important combinatorial optimization problems
that are generally computationally difficult. In this paper, we propose a
general solution approach for grouping problems, i.e., reinforcement learning
based local search (RLS), which combines reinforcement learning techniques with
descent-based local search. The viability of the proposed approach is verified
on a well-known representative grouping problem (graph coloring) where a very
simple descent-based coloring algorithm is applied. Experimental studies on
popular DIMACS and COLOR02 benchmark graphs indicate that RLS achieves
competitive performances compared to a number of well-known coloring
algorithms.
Person Re-identification in Appearance Impaired Scenarios
Authors: Mengran Gou, Xikang Zhang, Angels Rates-Borras, Sadjad Asghari-Esfeden, Mario Sznaier, Octavia Camps
Date: 2016-04-01 00:00:00
Tasks: Person Re-Identification

Person re-identification is critical in surveillance applications. Current
approaches rely on appearance based features extracted from a single or
multiple shots of the target and candidate matches. These approaches are at a
disadvantage when trying to distinguish between candidates dressed in similar
colors or when targets change their clothing. In this paper we propose a
dynamics-based feature to overcome this limitation. The main idea is to capture
soft biometrics from gait and motion patterns by gathering dense short
trajectories (tracklets) which are Fisher vector encoded. To illustrate the
merits of the proposed features we introduce three new "appearance-impaired"
datasets. Our experiments on the original and the appearance impaired datasets
demonstrate the benefits of incorporating dynamics-based information with
appearance-based information to re-identification algorithms.
How to Transfer? Zero-Shot Object Recognition via Hierarchical Transfer of Semantic Attributes
Authors: Ziad Al-Halah, Rainer Stiefelhagen
Date: 2016-04-01 00:00:00
Tasks: Attribute, Object Recognition, Transfer Learning

Attribute based knowledge transfer has proven very successful in visual
object analysis and learning previously unseen classes. However, the common
approach learns and transfers attributes without taking into consideration the
embedded structure between the categories in the source set. Such information
provides important cues on the intra-attribute variations. We propose to
capture these variations in a hierarchical model that expands the knowledge
source with additional abstraction levels of attributes. We also provide a
novel transfer approach that can choose the appropriate attributes to be shared
with an unseen class. We evaluate our approach on three public datasets:
aPascal, Animals with Attributes and CUB-200-2011 Birds. The experiments
demonstrate the effectiveness of our model with significant improvement over
state-of-the-art.
A Semisupervised Approach for Language Identification based on Ladder Networks
Authors: Ehud Ben-Reuven, Jacob Goldberger
Date: 2016-04-01 00:00:00
Tasks: Denoising, Language Identification

In this study we address the problem of training a neuralnetwork for language
identification using both labeled and unlabeled speech samples in the form of
i-vectors. We propose a neural network architecture that can also handle
out-of-set languages. We utilize a modified version of the recently proposed
Ladder Network semisupervised training procedure that optimizes the
reconstruction costs of a stack of denoising autoencoders. We show that this
approach can be successfully applied to the case where the training dataset is
composed of both labeled and unlabeled acoustic data. The results show enhanced
language identification on the NIST 2015 language identification dataset.
Automated Alertness and Emotion Detection for Empathic Feedback During E-Learning
Authors: S. L. Happy, A. Dasgupta, P. Patnaik, A. Routray
Date: 2016-04-01 00:00:00

In the context of education technology, empathic interaction with the user
and feedback by the learning system using multiple inputs such as video, voice
and text inputs is an important area of research. In this paper, a
nonintrusive, standalone model for intelligent assessment of alertness and
emotional state as well as generation of appropriate feedback has been
proposed. Using the non-intrusive visual cues, the system classifies emotion
and alertness state of the user, and provides appropriate feedback according to
the detected cognitive state using facial expressions, ocular parameters,
postures, and gestures. Assessment of alertness level using ocular parameters
such as PERCLOS and saccadic parameters, emotional state from facial expression
analysis, and detection of both relevant cognitive and emotional states from
upper body gestures and postures has been proposed. Integration of such a
system in e-learning environment is expected to enhance students performance
through interaction, feedback, and positive mood induction.
A strengthening of rational closure in DLs: reasoning about multiple aspects
Authors: Valentina Gliozzi
Date: 2016-04-01 00:00:00

We propose a logical analysis of the concept of typicality, central in human
cognition (Rosch,1978). We start from a previously proposed extension of the
basic Description Logic ALC (a computationally tractable fragment of First
Order Logic, used to represent concept inclusions and ontologies) with a
typicality operator T that allows to consistently represent the attribution to
classes of individuals of properties with exceptions (as in the classic example
(i) typical birds fly, (ii) penguins are birds but (iii) typical penguins don't
fly). We then strengthen this extension in order to separately reason about the
typicality with respect to different aspects (e.g., flying, having nice
feather: in the previous example, penguins may not inherit the property of
flying, for which they are exceptional, but can nonetheless inherit other
properties, such as having nice feather).
A SAT model to mine flexible sequences in transactional datasets
Authors: Rémi Coletta, Benjamin Negrevergne
Date: 2016-04-01 00:00:00

Traditional pattern mining algorithms generally suffer from a lack of
flexibility. In this paper, we propose a SAT formulation of the problem to
successfully mine frequent flexible sequences occurring in transactional
datasets. Our SAT-based approach can easily be extended with extra constraints
to address a broad range of pattern mining applications. To demonstrate this
claim, we formulate and add several constraints, such as gap and span
constraints, to our model in order to extract more specific patterns. We also
use interactive solving to perform important derived tasks, such as closed
pattern mining or maximal pattern mining. Finally, we prove the practical
feasibility of our SAT model by running experiments on two real datasets.
COEVOLVE: A Joint Point Process Model for Information Diffusion and Network Co-evolution
Authors: Mehrdad Farajtabar, Yichen Wang, Manuel Gomez Rodriguez, Shuang Li, Hongyuan Zha, Le Song
Date: 2015-07-08 00:00:00
Proceeding: NeurIPS 2015 12

Information diffusion in online social networks is affected by the underlying
network topology, but it also has the power to change it. Online users are
constantly creating new links when exposed to new information sources, and in
turn these links are alternating the way information spreads. However, these
two highly intertwined stochastic processes, information diffusion and network
evolution, have been predominantly studied separately, ignoring their
co-evolutionary dynamics.
  We propose a temporal point process model, COEVOLVE, for such joint dynamics,
allowing the intensity of one process to be modulated by that of the other.
This model allows us to efficiently simulate interleaved diffusion and network
events, and generate traces obeying common diffusion and network patterns
observed in real-world networks. Furthermore, we also develop a convex
optimization framework to learn the parameters of the model from historical
diffusion and network evolution traces. We experimented with both synthetic
data and data gathered from Twitter, and show that our model provides a good
fit to the data as well as more accurate predictions than alternatives.
Matrix Completion under Interval Uncertainty
Authors: Jakub Marecek, Peter Richtarik, Martin Takac
Date: 2014-08-11 00:00:00
Tasks: Collaborative Filtering, Matrix Completion

Matrix completion under interval uncertainty can be cast as matrix completion
with element-wise box constraints. We present an efficient
alternating-direction parallel coordinate-descent method for the problem. We
show that the method outperforms any other known method on a benchmark in image
in-painting in terms of signal-to-noise ratio, and that it provides
high-quality solutions for an instance of collaborative filtering with
100,198,805 recommendations within 5 minutes.
Relations between assumption-based approaches in nonmonotonic logic and formal argumentation
Authors: Jesse Heyninck, Christian Straßer
Date: 2016-04-01 00:00:00

In this paper we make a contribution to the unification of formal models of
defeasible reasoning. We present several translations between formal
argumentation frameworks and nonmonotonic logics for reasoning with plausible
assumptions. More specifically, we translate adaptive logics into
assumption-based argumentation and ASPIC+, ASPIC+ into assumption-based
argumentation and a fragment of assumption-based argumentation into adaptive
logics. Adaptive logics are closely related to Makinson's default assumptions
and to a significant class of systems within the tradition of preferential
semantics in the vein of KLM and Shoham. Thus, our results also provide close
links between formal argumentation and the latter approaches.
Learning a Pose Lexicon for Semantic Action Recognition
Authors: Lijuan Zhou, Wanqing Li, Philip Ogunbona
Date: 2016-04-01 00:00:00
Tasks: Action Recognition, Temporal Action Localization, Translation, Zero-Shot Action Recognition

This paper presents a novel method for learning a pose lexicon comprising
semantic poses defined by textual instructions and their associated visual
poses defined by visual features. The proposed method simultaneously takes two
input streams, semantic poses and visual pose candidates, and statistically
learns a mapping between them to construct the lexicon. With the learned
lexicon, action recognition can be cast as the problem of finding the maximum
translation probability of a sequence of semantic poses given a stream of
visual pose candidates. Experiments evaluating pre-trained and zero-shot action
recognition conducted on MSRC-12 gesture and WorkoutSu-10 exercise datasets
were used to verify the efficacy of the proposed method.
It's Moving! A Probabilistic Model for Causal Motion Segmentation in Moving Camera Videos
Authors: Pia Bideau, Erik Learned-Miller
Date: 2016-04-01 00:00:00
Tasks: Motion Segmentation, Optical Flow Estimation, Segmentation

The human ability to detect and segment moving objects works in the presence
of multiple objects, complex background geometry, motion of the observer, and
even camouflage. In addition to all of this, the ability to detect motion is
nearly instantaneous. While there has been much recent progress in motion
segmentation, it still appears we are far from human capabilities. In this
work, we derive from first principles a new likelihood function for assessing
the probability of an optical flow vector given the 3D motion direction of an
object. This likelihood uses a novel combination of the angle and magnitude of
the optical flow to maximize the information about the true motions of objects.
Using this new likelihood and several innovations in initialization, we develop
a motion segmentation algorithm that beats current state-of-the-art methods by
a large margin. We compare to five state-of-the-art methods on two established
benchmarks, and a third new data set of camouflaged animals, which we introduce
to push motion segmentation to the next level.
Good Practice in CNN Feature Transfer
Authors: Liang Zheng, Yali Zhao, Shengjin Wang, Jingdong Wang, Qi Tian
Date: 2016-04-01 00:00:00
Tasks: General Classification, Image Retrieval

The objective of this paper is the effective transfer of the Convolutional
Neural Network (CNN) feature in image search and classification.
Systematically, we study three facts in CNN transfer. 1) We demonstrate the
advantage of using images with a properly large size as input to CNN instead of
the conventionally resized one. 2) We benchmark the performance of different
CNN layers improved by average/max pooling on the feature maps. Our observation
suggests that the Conv5 feature yields very competitive accuracy under such
pooling step. 3) We find that the simple combination of pooled features
extracted across various CNN layers is effective in collecting evidences from
both low and high level descriptors. Following these good practices, we are
capable of improving the state of the art on a number of benchmarks to a large
margin.
The Goldilocks Principle: Reading Children's Books with Explicit Memory Representations
Authors: Felix Hill, Antoine Bordes, Sumit Chopra, Jason Weston
Date: 2015-11-07 00:00:00
Tasks: Articles, Language Modelling

We introduce a new test of how well language models capture meaning in
children's books. Unlike standard language modelling benchmarks, it
distinguishes the task of predicting syntactic function words from that of
predicting lower-frequency words, which carry greater semantic content. We
compare a range of state-of-the-art models, each with a different way of
encoding what has been previously read. We show that models which store
explicit representations of long-term contexts outperform state-of-the-art
neural language models at predicting semantic content words, although this
advantage is not observed for syntactic function words. Interestingly, we find
that the amount of text encoded in a single memory representation is highly
influential to the performance: there is a sweet-spot, not too big and not too
small, between single words and full sentences that allows the most meaningful
information in a text to be effectively retained and recalled. Further, the
attention over such window-based memories can be trained effectively through
self-supervision. We then assess the generality of this principle by applying
it to the CNN QA benchmark, which involves identifying named entities in
paraphrased summaries of news articles, and achieve state-of-the-art
performance.