pwc-archive/papers-with-abstracts huggingface dataset:
Posterior Sampling for Reinforcement Learning Without Episodes
Authors: Ian Osband, Benjamin Van Roy
Date: 2016-08-09 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

This is a brief technical note to clarify some of the issues with applying
the application of the algorithm posterior sampling for reinforcement learning
(PSRL) in environments without fixed episodes. In particular, this paper aims
to:
  - Review some of results which have been proven for finite horizon MDPs
(Osband et al 2013, 2014a, 2014b, 2016) and also for MDPs with finite ergodic
structure (Gopalan et al 2014).
  - Review similar results for optimistic algorithms in infinite horizon
problems (Jaksch et al 2010, Bartlett and Tewari 2009, Abbasi-Yadkori and
Szepesvari 2011), with particular attention to the dynamic episode growth.
  - Highlight the delicate technical issue which has led to a fault in the
proof of the lazy-PSRL algorithm (Abbasi-Yadkori and Szepesvari 2015). We
present an explicit counterexample to this style of argument. Therefore, we
suggest that the Theorem 2 in (Abbasi-Yadkori and Szepesvari 2015) be instead
considered a conjecture, as it has no rigorous proof.
  - Present pragmatic approaches to apply PSRL in infinite horizon problems. We
conjecture that, under some additional assumptions, it will be possible to
obtain bounds $O( \sqrt{T} )$ even without episodic reset.
  We hope that this note serves to clarify existing results in the field of
reinforcement learning and provides interesting motivation for future work.
OnionNet: Sharing Features in Cascaded Deep Classifiers
Authors: Martin Simonovsky, Nikos Komodakis
Date: 2016-08-09 00:00:00
Tasks: Image Retrieval, object-detection, Object Detection, Patch Matching, Retrieval

The focus of our work is speeding up evaluation of deep neural networks in
retrieval scenarios, where conventional architectures may spend too much time
on negative examples. We propose to replace a monolithic network with our novel
cascade of feature-sharing deep classifiers, called OnionNet, where subsequent
stages may add both new layers as well as new feature channels to the previous
ones. Importantly, intermediate feature maps are shared among classifiers,
preventing them from the necessity of being recomputed. To accomplish this, the
model is trained end-to-end in a principled way under a joint loss. We validate
our approach in theory and on a synthetic benchmark. As a result demonstrated
in three applications (patch matching, object detection, and image retrieval),
our cascade can operate significantly faster than both monolithic networks and
traditional cascades without sharing at the cost of marginal decrease in
precision.
External Lexical Information for Multilingual Part-of-Speech Tagging
Authors: Benoît Sagot
Date: 2016-06-12 00:00:00
Tasks: Part-Of-Speech Tagging

Morphosyntactic lexicons and word vector representations have both proven
useful for improving the accuracy of statistical part-of-speech taggers. Here
we compare the performances of four systems on datasets covering 16 languages,
two of these systems being feature-based (MEMMs and CRFs) and two of them being
neural-based (bi-LSTMs). We show that, on average, all four approaches perform
similarly and reach state-of-the-art results. Yet better performances are
obtained with our feature-based models on lexically richer datasets (e.g. for
morphologically rich languages), whereas neural-based results are higher on
datasets with less lexical variability (e.g. for English). These conclusions
hold in particular for the MEMM models relying on our system MElt, which
benefited from newly designed features. This shows that, under certain
conditions, feature-based approaches enriched with morphosyntactic lexicons are
competitive with respect to neural methods.
PAC-Bayesian Theorems for Domain Adaptation with Specialization to Linear Classifiers
Authors: Pascal Germain, Amaury Habrard, François Laviolette, Emilie Morvant
Date: 2015-03-24 00:00:00
Tasks: Domain Adaptation

In this paper, we provide two main contributions in PAC-Bayesian theory for
domain adaptation where the objective is to learn, from a source distribution,
a well-performing majority vote on a different target distribution. On the one
hand, we propose an improvement of the previous approach proposed by Germain et
al. (2013), that relies on a novel distribution pseudodistance based on a
disagreement averaging, allowing us to derive a new tighter PAC-Bayesian domain
adaptation bound for the stochastic Gibbs classifier. We specialize it to
linear classifiers, and design a learning algorithm which shows interesting
results on a synthetic problem and on a popular sentiment annotation task. On
the other hand, we generalize these results to multisource domain adaptation
allowing us to take into account different source domains. This study opens the
door to tackle domain adaptation tasks by making use of all the PAC-Bayesian
tools.
Mean Box Pooling: A Rich Image Representation and Output Embedding for the Visual Madlibs Task
Authors: Ashkan Mokarian, Mateusz Malinowski, Mario Fritz
Date: 2016-08-09 00:00:00

We present Mean Box Pooling, a novel visual representation that pools over
CNN representations of a large number, highly overlapping object proposals. We
show that such representation together with nCCA, a successful multimodal
embedding technique, achieves state-of-the-art performance on the Visual
Madlibs task. Moreover, inspired by the nCCA's objective function, we extend
classical CNN+LSTM approach to train the network by directly maximizing the
similarity between the internal representation of the deep learning
architecture and candidate answers. Again, such approach achieves a significant
improvement over the prior work that also uses CNN+LSTM approach on Visual
Madlibs.
A deep language model for software code
Authors: Hoa Khanh Dam, Truyen Tran, Trang Pham
Date: 2016-08-09 00:00:00
Tasks: Deep Learning, Language Modeling, Language Modelling, model

Existing language models such as n-grams for software code often fail to
capture a long context where dependent code elements scatter far apart. In this
paper, we propose a novel approach to build a language model for software code
to address this particular issue. Our language model, partly inspired by human
memory, is built upon the powerful deep learning-based Long Short Term Memory
architecture that is capable of learning long-term dependencies which occur
frequently in software code. Results from our intrinsic evaluation on a corpus
of Java projects have demonstrated the effectiveness of our language model.
This work contributes to realizing our vision for DeepSoft, an end-to-end,
generic deep learning-based framework for modeling software and its development
process.
Robust Visual Knowledge Transfer via EDA
Authors: Lei Zhang, David Zhang
Date: 2015-05-17 00:00:00
Tasks: Domain Adaptation, Object Recognition, Transfer Learning

We address the problem of visual knowledge adaptation by leveraging labeled
patterns from source domain and a very limited number of labeled instances in
target domain to learn a robust classifier for visual categorization. This
paper proposes a new extreme learning machine based cross-domain network
learning framework, that is called Extreme Learning Machine (ELM) based Domain
Adaptation (EDA). It allows us to learn a category transformation and an ELM
classifier with random projection by minimizing the l_(2,1)-norm of the network
output weights and the learning error simultaneously. The unlabeled target
data, as useful knowledge, is also integrated as a fidelity term to guarantee
the stability during cross domain learning. It minimizes the matching error
between the learned classifier and a base classifier, such that many existing
classifiers can be readily incorporated as base classifiers. The network output
weights cannot only be analytically determined, but also transferrable.
Additionally, a manifold regularization with Laplacian graph is incorporated,
such that it is beneficial to semi-supervised learning. Extensively, we also
propose a model of multiple views, referred as MvEDA. Experiments on benchmark
visual datasets for video event recognition and object recognition, demonstrate
that our EDA methods outperform existing cross-domain learning methods.
Steerable Principal Components for Space-Frequency Localized Images
Authors: Boris Landa, Yoel Shkolnisky
Date: 2016-08-09 00:00:00
Tasks: Numerical Integration

This paper describes a fast and accurate method for obtaining steerable
principal components from a large dataset of images, assuming the images are
well localized in space and frequency. The obtained steerable principal
components are optimal for expanding the images in the dataset and all of their
rotations. The method relies upon first expanding the images using a series of
two-dimensional Prolate Spheroidal Wave Functions (PSWFs), where the expansion
coefficients are evaluated using a specially designed numerical integration
scheme. Then, the expansion coefficients are used to construct a
rotationally-invariant covariance matrix which admits a block-diagonal
structure, and the eigen-decomposition of its blocks provides us with the
desired steerable principal components. The proposed method is shown to be
faster then existing methods, while providing appropriate error bounds which
guarantee its accuracy.
Deep Attributes Driven Multi-Camera Person Re-identification
Authors: Chi Su, Shiliang Zhang, Junliang Xing, Wen Gao, Qi Tian
Date: 2016-05-11 00:00:00
Tasks: Attribute, Metric Learning, Person Re-Identification, Triplet

The visual appearance of a person is easily affected by many factors like
pose variations, viewpoint changes and camera parameter differences. This makes
person Re-Identification (ReID) among multiple cameras a very challenging task.
This work is motivated to learn mid-level human attributes which are robust to
such visual appearance variations. And we propose a semi-supervised attribute
learning framework which progressively boosts the accuracy of attributes only
using a limited number of labeled data. Specifically, this framework involves a
three-stage training. A deep Convolutional Neural Network (dCNN) is first
trained on an independent dataset labeled with attributes. Then it is
fine-tuned on another dataset only labeled with person IDs using our defined
triplet loss. Finally, the updated dCNN predicts attribute labels for the
target dataset, which is combined with the independent dataset for the final
round of fine-tuning. The predicted attributes, namely \emph{deep attributes}
exhibit superior generalization ability across different datasets. By directly
using the deep attributes with simple Cosine distance, we have obtained
surprisingly good accuracy on four person ReID datasets. Experiments also show
that a simple metric learning modular further boosts our method, making it
significantly outperform many recent works.
Deeply Semantic Inductive Spatio-Temporal Learning
Authors: Jakob Suchan, Mehul Bhatt, Carl Schultz
Date: 2016-08-09 00:00:00
Tasks: Inductive logic programming

We present an inductive spatio-temporal learning framework rooted in
inductive logic programming. With an emphasis on visuo-spatial language, logic,
and cognition, the framework supports learning with relational spatio-temporal
features identifiable in a range of domains involving the processing and
interpretation of dynamic visuo-spatial imagery. We present a prototypical
system, and an example application in the domain of computing for visual arts
and computational cognitive science.
Tracking Objects with Higher Order Interactions using Delayed Column Generation
Authors: Shaofei Wang, Steffen Wolf, Charless Fowlkes, Julian Yarkony
Date: 2015-12-08 00:00:00

We study the problem of multi-target tracking and data association in video.
We formulate this in terms of selecting a subset of high-quality tracks subject
to the constraint that no pair of selected tracks is associated with a common
detection (of an object). This objective is equivalent to the classic NP-hard
problem of finding a maximum-weight set packing (MWSP) where tracks correspond
to sets and is made further difficult since the number of candidate tracks
grows exponentially in the number of detections. We present a relaxation of
this combinatorial problem that uses a column generation formulation where the
pricing problem is solved via dynamic programming to efficiently explore the
space of tracks. We employ row generation to tighten the bound in such a way as
to preserve efficient inference in the pricing problem. We show the practical
utility of this algorithm for tracking problems in natural and biological video
datasets.
A Factorization Approach to Inertial Affine Structure from Motion
Authors: Roberto Tron
Date: 2016-08-09 00:00:00

We consider the problem of reconstructing a 3-D scene from a moving camera
with high frame rate using the affine projection model. This problem is
traditionally known as Affine Structure from Motion (Affine SfM), and can be
solved using an elegant low-rank factorization formulation. In this paper, we
assume that an accelerometer and gyro are rigidly mounted with the camera, so
that synchronized linear acceleration and angular velocity measurements are
available together with the image measurements. We extend the standard Affine
SfM algorithm to integrate these measurements through the use of image
derivatives.
End-to-End Localization and Ranking for Relative Attributes
Authors: Krishna Kumar Singh, Yong Jae Lee
Date: 2016-08-09 00:00:00
Tasks: Attribute

We propose an end-to-end deep convolutional network to simultaneously
localize and rank relative visual attributes, given only weakly-supervised
pairwise image comparisons. Unlike previous methods, our network jointly learns
the attribute's features, localization, and ranker. The localization module of
our network discovers the most informative image region for the attribute,
which is then used by the ranking module to learn a ranking model of the
attribute. Our end-to-end framework also significantly speeds up processing and
is much faster than previous methods. We show state-of-the-art ranking results
on various relative attribute datasets, and our qualitative localization
results clearly demonstrate our network's ability to learn meaningful image
patches.
Towards Biologically Plausible Deep Learning
Authors: Yoshua Bengio, Dong-Hyun Lee, Jorg Bornschein, Thomas Mesnard, Zhouhan Lin
Date: 2015-02-14 00:00:00
Tasks: Deep Learning, Denoising, Reinforcement Learning, Representation Learning

Neuroscientists have long criticised deep learning algorithms as incompatible
with current knowledge of neurobiology. We explore more biologically plausible
versions of deep representation learning, focusing here mostly on unsupervised
learning but developing a learning mechanism that could account for supervised,
unsupervised and reinforcement learning. The starting point is that the basic
learning rule believed to govern synaptic weight updates
(Spike-Timing-Dependent Plasticity) arises out of a simple update rule that
makes a lot of sense from a machine learning point of view and can be
interpreted as gradient descent on some objective function so long as the
neuronal dynamics push firing rates towards better values of the objective
function (be it supervised, unsupervised, or reward-driven). The second main
idea is that this corresponds to a form of the variational EM algorithm, i.e.,
with approximate rather than exact posteriors, implemented by neural dynamics.
Another contribution of this paper is that the gradients required for updating
the hidden states in the above variational interpretation can be estimated
using an approximation that only requires propagating activations forward and
backward, with pairs of layers learning to form a denoising auto-encoder.
Finally, we extend the theory about the probabilistic interpretation of
auto-encoders to justify improved sampling schemes based on the generative
interpretation of denoising auto-encoders, and we validate all these ideas on
generative learning tasks.
Mouse Movement and Probabilistic Graphical Models Based E-Learning Activity Recognition Improvement Possibilistic Model
Authors: Anis Elbahi, Mohamed Nazih Omri, Mohamed Ali Mahjoub, Kamel Garrouch
Date: 2016-08-08 00:00:00
Tasks: Activity Recognition

Automatically recognizing the e-learning activities is an important task for
improving the online learning process. Probabilistic graphical models such as
hidden Markov models and conditional random fields have been successfully used
in order to identify a Web users activity. For such models, the sequences of
observation are crucial for training and inference processes. Despite the
efficiency of these probabilistic graphical models in segmenting and labeling
stochastic sequences, their performance is adversely affected by the imperfect
quality of data used for the construction of sequences of observation. In this
paper, a formalism of the possibilistic theory will be used in order to propose
a new approach for observation sequences preparation. The eminent contribution
of our approach is to evaluate the effect of possibilistic reasoning during the
generation of observation sequences on the effectiveness of hidden Markov
models and conditional random fields models. Using a dataset containing 51 real
manipulations related to three types of learners tasks, the preliminary
experiments demonstrate that the sequences of observation obtained based on
possibilistic reasoning significantly improve the performance of hidden Marvov
models and conditional random fields models in the automatic recognition of the
e-learning activities.
Biconvex Relaxation for Semidefinite Programming in Computer Vision
Authors: Sohil Shah, Abhay Kumar, Carlos Castillo, David Jacobs, Christoph Studer, Tom Goldstein
Date: 2016-05-31 00:00:00
Tasks: Metric Learning

Semidefinite programming is an indispensable tool in computer vision, but
general-purpose solvers for semidefinite programs are often too slow and memory
intensive for large-scale problems. We propose a general framework to
approximately solve large-scale semidefinite problems (SDPs) at low complexity.
Our approach, referred to as biconvex relaxation (BCR), transforms a general
SDP into a specific biconvex optimization problem, which can then be solved in
the original, low-dimensional variable space at low complexity. The resulting
biconvex problem is solved using an efficient alternating minimization (AM)
procedure. Since AM has the potential to get stuck in local minima, we propose
a general initialization scheme that enables BCR to start close to a global
optimum - this is key for our algorithm to quickly converge to optimal or
near-optimal solutions. We showcase the efficacy of our approach on three
applications in computer vision, namely segmentation, co-segmentation, and
manifold metric learning. BCR achieves solution quality comparable to
state-of-the-art SDP methods with speedups between 4X and 35X. At the same
time, BCR handles a more general set of SDPs than previous approaches, which
are more specialized.
A Thorough Examination of the CNN/Daily Mail Reading Comprehension Task
Authors: Danqi Chen, Jason Bolton, Christopher D. Manning
Date: 2016-06-09 00:00:00
Proceeding: ACL 2016 8
Tasks: Articles, Reading Comprehension

Enabling a computer to understand a document so that it can answer
comprehension questions is a central, yet unsolved goal of NLP. A key factor
impeding its solution by machine learned systems is the limited availability of
human-annotated data. Hermann et al. (2015) seek to solve this problem by
creating over a million training examples by pairing CNN and Daily Mail news
articles with their summarized bullet points, and show that a neural network
can then be trained to give good performance on this task. In this paper, we
conduct a thorough examination of this new reading comprehension task. Our
primary aim is to understand what depth of language understanding is required
to do well on this task. We approach this from one side by doing a careful
hand-analysis of a small subset of the problems and from the other by showing
that simple, carefully designed systems can obtain accuracies of 73.6% and
76.6% on these two datasets, exceeding current state-of-the-art results by
7-10% and approaching what we believe is the ceiling for performance on this
task.
Sparse recovery via Orthogonal Least-Squares under presence of Noise
Authors: Abolfazl Hashemi, Haris Vikalo
Date: 2016-08-08 00:00:00

We consider the Orthogonal Least-Squares (OLS) algorithm for the recovery of
a $m$-dimensional $k$-sparse signal from a low number of noisy linear
measurements. The Exact Recovery Condition (ERC) in bounded noisy scenario is
established for OLS under certain condition on nonzero elements of the signal.
The new result also improves the existing guarantees for Orthogonal Matching
Pursuit (OMP) algorithm. In addition, This framework is employed to provide
probabilistic guarantees for the case that the coefficient matrix is drawn at
random according to Gaussian or Bernoulli distribution where we exploit some
concentration properties. It is shown that under certain conditions, OLS
recovers the true support in $k$ iterations with high probability. This in turn
demonstrates that ${\cal O}\left(k\log m\right)$ measurements is sufficient for
exact recovery of sparse signals via OLS.
Parallelizing Word2Vec in Shared and Distributed Memory
Authors: Shihao Ji, Nadathur Satish, Sheng Li, Pradeep Dubey
Date: 2016-04-15 00:00:00
Tasks: CPU, Machine Translation, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), Question Answering, Sentiment Analysis, Translation

Word2Vec is a widely used algorithm for extracting low-dimensional vector
representations of words. It generated considerable excitement in the machine
learning and natural language processing (NLP) communities recently due to its
exceptional performance in many NLP applications such as named entity
recognition, sentiment analysis, machine translation and question answering.
State-of-the-art algorithms including those by Mikolov et al. have been
parallelized for multi-core CPU architectures but are based on vector-vector
operations that are memory-bandwidth intensive and do not efficiently use
computational resources. In this paper, we improve reuse of various data
structures in the algorithm through the use of minibatching, hence allowing us
to express the problem using matrix multiply operations. We also explore
different techniques to distribute word2vec computation across nodes in a
compute cluster, and demonstrate good strong scalability up to 32 nodes. In
combination, these techniques allow us to scale up the computation near
linearly across cores and nodes, and process hundreds of millions of words per
second, which is the fastest word2vec implementation to the best of our
knowledge.
Topic Modelling and Event Identification from Twitter Textual Data
Authors: Marina Sokolova, Kanyi Huang, Stan Matwin, Joshua Ramisch, Vera Sazonova, Renee Black, Chris Orwa, Sidney Ochieng, Nanjira Sambuli
Date: 2016-08-08 00:00:00

The tremendous growth of social media content on the Internet has inspired
the development of the text analytics to understand and solve real-life
problems. Leveraging statistical topic modelling helps researchers and
practitioners in better comprehension of textual content as well as provides
useful information for further analysis. Statistical topic modelling becomes
especially important when we work with large volumes of dynamic text, e.g.,
Facebook or Twitter datasets. In this study, we summarize the message content
of four data sets of Twitter messages relating to challenging social events in
Kenya. We use Latent Dirichlet Allocation (LDA) topic modelling to analyze the
content. Our study uses two evaluation measures, Normalized Mutual Information
(NMI) and topic coherence analysis, to select the best LDA models. The obtained
LDA results show that the tool can be effectively used to extract discussion
topics and summarize them for further manual analysis