pwc-archive/papers-with-abstracts huggingface dataset:
Learning Discriminative Bayesian Networks from High-dimensional Continuous Neuroimaging Data
Authors: Luping Zhou, Lei Wang, Lingqiao Liu, Philip Ogunbona, Dinggang Shen
Date: 2015-06-23 00:00:00
Tasks: Vocal Bursts Intensity Prediction

Due to its causal semantics, Bayesian networks (BN) have been widely employed
to discover the underlying data relationship in exploratory studies, such as
brain research. Despite its success in modeling the probability distribution of
variables, BN is naturally a generative model, which is not necessarily
discriminative. This may cause the ignorance of subtle but critical network
changes that are of investigation values across populations. In this paper, we
propose to improve the discriminative power of BN models for continuous
variables from two different perspectives. This brings two general
discriminative learning frameworks for Gaussian Bayesian networks (GBN). In the
first framework, we employ Fisher kernel to bridge the generative models of GBN
and the discriminative classifiers of SVMs, and convert the GBN parameter
learning to Fisher kernel learning via minimizing a generalization error bound
of SVMs. In the second framework, we employ the max-margin criterion and build
it directly upon GBN models to explicitly optimize the classification
performance of the GBNs. The advantages and disadvantages of the two frameworks
are discussed and experimentally compared. Both of them demonstrate strong
power in learning discriminative parameters of GBNs for neuroimaging based
brain network analysis, as well as maintaining reasonable representation
capacity. The contributions of this paper also include a new Directed Acyclic
Graph (DAG) constraint with theoretical guarantee to ensure the graph validity
of GBN.
A Feature-Based Analysis on the Impact of Set of Constraints for e-Constrained Differential Evolution
Authors: Shayan Poursoltan, Frank Neumann
Date: 2015-06-23 00:00:00
Tasks: Evolutionary Algorithms

Different types of evolutionary algorithms have been developed for
constrained continuous optimization. We carry out a feature-based analysis of
evolved constrained continuous optimization instances to understand the
characteristics of constraints that make problems hard for evolutionary
algorithm. In our study, we examine how various sets of constraints can
influence the behaviour of e-Constrained Differential Evolution. Investigating
the evolved instances, we obtain knowledge of what type of constraints and
their features make a problem difficult for the examined algorithm.
Modality-dependent Cross-media Retrieval
Authors: Yunchao Wei, Yao Zhao, Zhenfeng Zhu, Shikui Wei, Yanhui Xiao, Jiashi Feng, Shuicheng Yan
Date: 2015-06-22 00:00:00
Tasks: Retrieval

In this paper, we investigate the cross-media retrieval between images and
text, i.e., using image to search text (I2T) and using text to search images
(T2I). Existing cross-media retrieval methods usually learn one couple of
projections, by which the original features of images and text can be projected
into a common latent space to measure the content similarity. However, using
the same projections for the two different retrieval tasks (I2T and T2I) may
lead to a tradeoff between their respective performances, rather than their
best performances. Different from previous works, we propose a
modality-dependent cross-media retrieval (MDCR) model, where two couples of
projections are learned for different cross-media retrieval tasks instead of
one couple of projections. Specifically, by jointly optimizing the correlation
between images and text and the linear regression from one modal space (image
or text) to the semantic space, two couples of mappings are learned to project
images and text from their original feature spaces into two common latent
subspaces (one for I2T and the other for T2I). Extensive experiments show the
superiority of the proposed MDCR compared with other methods. In particular,
based the 4,096 dimensional convolutional neural network (CNN) visual feature
and 100 dimensional LDA textual feature, the mAP of the proposed method
achieves 41.5\%, which is a new state-of-the-art performance on the Wikipedia
dataset.
Detection and Analysis of Emotion From Speech Signals
Authors: Assel Davletcharova, Sherin Sugathan, Bibia Abraham, Alex Pappachen James
Date: 2015-06-23 00:00:00
Tasks: Attribute, Classification, Emotion Classification, General Classification

Recognizing emotion from speech has become one the active research themes in
speech processing and in applications based on human-computer interaction. This
paper conducts an experimental study on recognizing emotions from human speech.
The emotions considered for the experiments include neutral, anger, joy and
sadness. The distinuishability of emotional features in speech were studied
first followed by emotion classification performed on a custom dataset. The
classification was performed for different classifiers. One of the main feature
attribute considered in the prepared dataset was the peak-to-peak distance
obtained from the graphical representation of the speech signals. After
performing the classification tests on a dataset formed from 30 different
subjects, it was found that for getting better accuracy, one should consider
the data collected from one person rather than considering the data from a
group of people.
DeepStereo: Learning to Predict New Views from the World's Imagery
Authors: John Flynn, Ivan Neulander, James Philbin, Noah Snavely
Date: 2015-06-22 00:00:00
Proceeding: CVPR 2016 6

Deep networks have recently enjoyed enormous success when applied to
recognition and classification problems in computer vision, but their use in
graphics problems has been limited. In this work, we present a novel deep
architecture that performs new view synthesis directly from pixels, trained
from a large number of posed image sets. In contrast to traditional approaches
which consist of multiple complex stages of processing, each of which require
careful tuning and can fail in unexpected ways, our system is trained
end-to-end. The pixels from neighboring views of a scene are presented to the
network which then directly produces the pixels of the unseen view. The
benefits of our approach include generality (we only require posed image sets
and can easily apply our method to different domains), and high quality results
on traditionally difficult scenes. We believe this is due to the end-to-end
nature of our system which is able to plausibly generate pixels according to
color, depth, and texture priors learnt automatically from the training data.
To verify our method we show that it can convincingly reproduce known test
views from nearby imagery. Additionally we show images rendered from novel
viewpoints. To our knowledge, our work is the first to apply deep learning to
the problem of new view synthesis from sets of real-world, natural imagery.
Parallel training of DNNs with Natural Gradient and Parameter Averaging
Authors: Daniel Povey, Xiaohui Zhang, Sanjeev Khudanpur
Date: 2014-10-27 00:00:00
Tasks: GPU, speech-recognition, Speech Recognition

We describe the neural-network training framework used in the Kaldi speech
recognition toolkit, which is geared towards training DNNs with large amounts
of training data using multiple GPU-equipped or multi-core machines. In order
to be as hardware-agnostic as possible, we needed a way to use multiple
machines without generating excessive network traffic. Our method is to average
the neural network parameters periodically (typically every minute or two), and
redistribute the averaged parameters to the machines for further training. Each
machine sees different data. By itself, this method does not work very well.
However, we have another method, an approximate and efficient implementation of
Natural Gradient for Stochastic Gradient Descent (NG-SGD), which seems to allow
our periodic-averaging method to work well, as well as substantially improving
the convergence of SGD on a single machine.
Skip-Thought Vectors
Authors: Ryan Kiros, Yukun Zhu, Ruslan Salakhutdinov, Richard S. Zemel, Antonio Torralba, Raquel Urtasun, Sanja Fidler
Date: 2015-06-22 00:00:00
Proceeding: NeurIPS 2015 12
Tasks: Decoder, Sentence

We describe an approach for unsupervised learning of a generic, distributed
sentence encoder. Using the continuity of text from books, we train an
encoder-decoder model that tries to reconstruct the surrounding sentences of an
encoded passage. Sentences that share semantic and syntactic properties are
thus mapped to similar vector representations. We next introduce a simple
vocabulary expansion method to encode words that were not seen as part of
training, allowing us to expand our vocabulary to a million words. After
training our model, we extract and evaluate our vectors with linear models on 8
tasks: semantic relatedness, paraphrase detection, image-sentence ranking,
question-type classification and 4 benchmark sentiment and subjectivity
datasets. The end result is an off-the-shelf encoder that can produce highly
generic sentence representations that are robust and perform well in practice.
We will make our encoder publicly available.
Aligning Books and Movies: Towards Story-like Visual Explanations by Watching Movies and Reading Books
Authors: Yukun Zhu, Ryan Kiros, Richard Zemel, Ruslan Salakhutdinov, Raquel Urtasun, Antonio Torralba, Sanja Fidler
Date: 2015-06-22 00:00:00
Proceeding: ICCV 2015 12
Tasks: Descriptive, Diversity, Sentence, Sentence Embedding, Sentence-Embedding

Books are a rich source of both fine-grained information, how a character, an
object or a scene looks like, as well as high-level semantics, what someone is
thinking, feeling and how these states evolve through a story. This paper aims
to align books to their movie releases in order to provide rich descriptive
explanations for visual content that go semantically far beyond the captions
available in current datasets. To align movies and books we exploit a neural
sentence embedding that is trained in an unsupervised way from a large corpus
of books, as well as a video-text neural embedding for computing similarities
between movie clips and sentences in the book. We propose a context-aware CNN
to combine information from multiple sources. We demonstrate good quantitative
performance for movie/book alignment and show several qualitative examples that
showcase the diversity of tasks our model can be used for.
A Neural Network Approach to Context-Sensitive Generation of Conversational Responses
Authors: Alessandro Sordoni, Michel Galley, Michael Auli, Chris Brockett, Yangfeng Ji, Margaret Mitchell, Jian-Yun Nie, Jianfeng Gao, Bill Dolan
Date: 2015-06-22 00:00:00
Proceeding: HLT 2015 5
Tasks: Information Retrieval, Machine Translation, Response Generation, Retrieval, Translation

We present a novel response generation system that can be trained end to end
on large quantities of unstructured Twitter conversations. A neural network
architecture is used to address sparsity issues that arise when integrating
contextual information into classic statistical models, allowing the system to
take into account previous dialog utterances. Our dynamic-context generative
models show consistent gains over both context-sensitive and
non-context-sensitive Machine Translation and Information Retrieval baselines.
A Rank-Corrected Procedure for Matrix Completion with Fixed Basis Coefficients
Authors: Weimin Miao, Shaohua Pan, Defeng Sun
Date: 2012-10-13 00:00:00
Tasks: Low-Rank Matrix Completion, Matrix Completion, Quantum State Tomography

For the problems of low-rank matrix completion, the efficiency of the
widely-used nuclear norm technique may be challenged under many circumstances,
especially when certain basis coefficients are fixed, for example, the low-rank
correlation matrix completion in various fields such as the financial market
and the low-rank density matrix completion from the quantum state tomography.
To seek a solution of high recovery quality beyond the reach of the nuclear
norm, in this paper, we propose a rank-corrected procedure using a nuclear
semi-norm to generate a new estimator. For this new estimator, we establish a
non-asymptotic recovery error bound. More importantly, we quantify the
reduction of the recovery error bound for this rank-corrected procedure.
Compared with the one obtained for the nuclear norm penalized least squares
estimator, this reduction can be substantial (around 50%). We also provide
necessary and sufficient conditions for rank consistency in the sense of Bach
(2008). Very interestingly, these conditions are highly related to the concept
of constraint nondegeneracy in matrix optimization. As a byproduct, our results
provide a theoretical foundation for the majorized penalty method of Gao and
Sun (2010) and Gao (2010) for structured low-rank matrix optimization problems.
Extensive numerical experiments demonstrate that our proposed rank-corrected
procedure can simultaneously achieve a high recovery accuracy and capture the
low-rank structure.
Adaptive Digital Scan Variable Pixels
Authors: Sherin Sugathan, Reshma Scaria, Alex Pappachen James
Date: 2015-06-22 00:00:00

The square and rectangular shape of the pixels in the digital images for
sensing and display purposes introduces several inaccuracies in the
representation of digital images. The major disadvantage of square pixel shapes
is the inability to accurately capture and display the details in the objects
having variable orientations to edges, shapes and regions. This effect can be
observed by the inaccurate representation of diagonal edges in low resolution
square pixel images. This paper explores a less investigated idea of using
variable shaped pixels for improving visual quality of image scans without
increasing the square pixel resolution. The proposed adaptive filtering
technique reports an improvement in image PSNR.
A cognitive neural architecture able to learn and communicate through natural language
Authors: Bruno Golosio, Angelo Cangelosi, Olesya Gamotina, Giovanni Luca Masala
Date: 2015-06-10 00:00:00
Tasks: Incremental Learning

Communicative interactions involve a kind of procedural knowledge that is
used by the human brain for processing verbal and nonverbal inputs and for
language production. Although considerable work has been done on modeling human
language abilities, it has been difficult to bring them together to a
comprehensive tabula rasa system compatible with current knowledge of how
verbal information is processed in the brain. This work presents a cognitive
system, entirely based on a large-scale neural architecture, which was
developed to shed light on the procedural knowledge involved in language
elaboration. The main component of this system is the central executive, which
is a supervising system that coordinates the other components of the working
memory. In our model, the central executive is a neural network that takes as
input the neural activation states of the short-term memory and yields as
output mental actions, which control the flow of information among the working
memory components through neural gating mechanisms. The proposed system is
capable of learning to communicate through natural language starting from
tabula rasa, without any a priori knowledge of the structure of phrases,
meaning of words, role of the different classes of words, only by interacting
with a human through a text-based interface, using an open-ended incremental
learning process. It is able to learn nouns, verbs, adjectives, pronouns and
other word classes, and to use them in expressive language. The model was
validated on a corpus of 1587 input sentences, based on literature on early
language assessment, at the level of about 4-years old child, and produced 521
output sentences, expressing a broad range of language processing
functionalities.
Multi-path Convolutional Neural Networks for Complex Image Classification
Authors: Mingming Wang
Date: 2015-06-15 00:00:00
Tasks: Classification, General Classification, image-classification, Image Classification, Object Recognition

Convolutional Neural Networks demonstrate high performance on ImageNet
Large-Scale Visual Recognition Challenges contest. Nevertheless, the published
results only show the overall performance for all image classes. There is no
further analysis why certain images get worse results and how they could be
improved. In this paper, we provide deep performance analysis based on
different types of images and point out the weaknesses of convolutional neural
networks through experiment. We design a novel multiple paths convolutional
neural network, which feeds different versions of images into separated paths
to learn more comprehensive features. This model has better presentation for
image than the traditional single path model. We acquire better classification
results on complex validation set on both top 1 and top 5 scores than the best
ILSVRC 2013 classification model.
Target Tracking In Real Time Surveillance Cameras and Videos
Authors: Nayyab Naseem, Mehreen Sirshar
Date: 2015-06-22 00:00:00
Tasks: Video Background Subtraction

Security concerns has been kept on increasing, so it is important for
everyone to keep their property safe from thefts and destruction. So the need
for surveillance techniques are also increasing. The system has been developed
to detect the motion in a video. A system has been developed for real time
applications by using the techniques of background subtraction and frame
differencing. In this system, motion is detected from the webcam or from the
real time video. Background subtraction and frames differencing method has been
used to detect the moving target. In background subtraction method, current
frame is subtracted from the referenced frame and then the threshold is
applied. If the difference is greater than the threshold then it is considered
as the pixel from the moving object, otherwise it is considered as background
pixel. Similarly, two frames difference method takes difference between two
continuous frames. Then that resultant difference frame is thresholded and the
amount of difference pixels is calculated.
Representation Learning for cold-start recommendation
Authors: Gabriella Contardo, Ludovic Denoyer, Thierry Artieres
Date: 2014-12-22 00:00:00
Tasks: Collaborative Filtering, Representation Learning

A standard approach to Collaborative Filtering (CF), i.e. prediction of user
ratings on items, relies on Matrix Factorization techniques. Representations
for both users and items are computed from the observed ratings and used for
prediction. Unfortunatly, these transductive approaches cannot handle the case
of new users arriving in the system, with no known rating, a problem known as
user cold-start. A common approach in this context is to ask these incoming
users for a few initialization ratings. This paper presents a model to tackle
this twofold problem of (i) finding good questions to ask, (ii) building
efficient representations from this small amount of information. The model can
also be used in a more standard (warm) context. Our approach is evaluated on
the classical CF problem and on the cold-start problem on four different
datasets showing its ability to improve baseline performance in both cases.
Task-Oriented Learning of Word Embeddings for Semantic Relation Classification
Authors: Kazuma Hashimoto, Pontus Stenetorp, Makoto Miwa, Yoshimasa Tsuruoka
Date: 2015-02-28 00:00:00
Proceeding: CONLL 2015 7
Tasks: Classification, General Classification, Relation, Relation Classification, Word Embeddings

We present a novel learning method for word embeddings designed for relation
classification. Our word embeddings are trained by predicting words between
noun pairs using lexical relation-specific features on a large unlabeled
corpus. This allows us to explicitly incorporate relation-specific information
into the word embeddings. The learned word embeddings are then used to
construct feature vectors for a relation classification model. On a
well-established semantic relation classification task, our method
significantly outperforms a baseline based on a previously introduced word
embedding method, and compares favorably to previous state-of-the-art models
that use syntactic information or manually constructed external resources.
Understanding Neural Networks Through Deep Visualization
Authors: Jason Yosinski, Jeff Clune, Anh Nguyen, Thomas Fuchs, Hod Lipson
Date: 2015-06-22 00:00:00
Tasks: Interpretable Machine Learning

Recent years have produced great advances in training large, deep neural
networks (DNNs), including notable successes in training convolutional neural
networks (convnets) to recognize natural images. However, our understanding of
how these models work, especially what computations they perform at
intermediate layers, has lagged behind. Progress in the field will be further
accelerated by the development of better tools for visualizing and interpreting
neural nets. We introduce two such tools here. The first is a tool that
visualizes the activations produced on each layer of a trained convnet as it
processes an image or video (e.g. a live webcam stream). We have found that
looking at live activations that change in response to user input helps build
valuable intuitions about how convnets work. The second tool enables
visualizing features at each layer of a DNN via regularized optimization in
image space. Because previous versions of this idea produced less recognizable
images, here we introduce several new regularization methods that combine to
produce qualitatively clearer, more interpretable visualizations. Both tools
are open source and work on a pre-trained convnet with minimal setup.
PAC-Bayes Iterated Logarithm Bounds for Martingale Mixtures
Authors: Akshay Balsubramani
Date: 2015-06-22 00:00:00

We give tight concentration bounds for mixtures of martingales that are
simultaneously uniform over (a) mixture distributions, in a PAC-Bayes sense;
and (b) all finite times. These bounds are proved in terms of the martingale
variance, extending classical Bernstein inequalities, and sharpening and
simplifying prior work.
Regularization Path of Cross-Validation Error Lower Bounds
Authors: Atsushi Shibagaki, Yoshiki Suzuki, Masayuki Karasuyama, Ichiro Takeuchi
Date: 2015-02-09 00:00:00
Proceeding: NeurIPS 2015 12

Careful tuning of a regularization parameter is indispensable in many machine
learning tasks because it has a significant impact on generalization
performances. Nevertheless, current practice of regularization parameter tuning
is more of an art than a science, e.g., it is hard to tell how many grid-points
would be needed in cross-validation (CV) for obtaining a solution with
sufficiently small CV error. In this paper we propose a novel framework for
computing a lower bound of the CV errors as a function of the regularization
parameter, which we call regularization path of CV error lower bounds. The
proposed framework can be used for providing a theoretical approximation
guarantee on a set of solutions in the sense that how far the CV error of the
current best solution could be away from best possible CV error in the entire
range of the regularization parameters. We demonstrate through numerical
experiments that a theoretically guaranteed a choice of regularization
parameter in the above sense is possible with reasonable computational costs.
Answer Sequence Learning with Neural Networks for Answer Selection in Community Question Answering
Authors: Xiaoqiang Zhou, Baotian Hu, Qingcai Chen, Buzhou Tang, Xiaolong Wang
Date: 2015-06-22 00:00:00
Proceeding: IJCNLP 2015 7
Tasks: Answer Selection, Community Question Answering, Question Answering

In this paper, the answer selection problem in community question answering
(CQA) is regarded as an answer sequence labeling task, and a novel approach is
proposed based on the recurrent architecture for this problem. Our approach
applies convolution neural networks (CNNs) to learning the joint representation
of question-answer pair firstly, and then uses the joint representation as
input of the long short-term memory (LSTM) to learn the answer sequence of a
question for labeling the matching quality of each answer. Experiments
conducted on the SemEval 2015 CQA dataset shows the effectiveness of our
approach.