pwc-archive/papers-with-abstracts huggingface dataset:
Formulating Semantics of Probabilistic Argumentation by Characterizing Subgraphs: Theory and Empirical Results
Authors: Beishui Liao, Kang Xu, Huaxin Huang
Date: 2016-08-01 00:00:00

In existing literature, while approximate approaches based on Monte-Carlo
simulation technique have been proposed to compute the semantics of
probabilistic argumentation, how to improve the efficiency of computation
without using simulation technique is still an open problem. In this paper, we
address this problem from the following two perspectives. First, conceptually,
we define specific properties to characterize the subgraphs of a PrAG with
respect to a given extension, such that the probability of a set of arguments E
being an extension can be defined in terms of these properties, without (or
with less) construction of subgraphs. Second, computationally, we take
preferred semantics as an example, and develop algorithms to evaluate the
efficiency of our approach. The results show that our approach not only
dramatically decreases the time for computing p(E^\sigma), but also has an
attractive property, which is contrary to that of existing approaches: the
denser the edges of a PrAG are or the bigger the size of a given extension E
is, the more efficient our approach computes p(E^\sigma). Meanwhile, it is
shown that under complete and preferred semantics, the problems of determining
p(E^\sigma) are fixed-parameter tractable.
Unifying Multi-Domain Multi-Task Learning: Tensor and Neural Network Perspectives
Authors: Yongxin Yang, Timothy M. Hospedales
Date: 2016-11-28 00:00:00
Tasks: Domain Adaptation, Multi-Task Learning, Zero-Shot Learning

Multi-domain learning aims to benefit from simultaneously learning across
several different but related domains. In this chapter, we propose a single
framework that unifies multi-domain learning (MDL) and the related but better
studied area of multi-task learning (MTL). By exploiting the concept of a
\emph{semantic descriptor} we show how our framework encompasses various
classic and recent MDL/MTL algorithms as special cases with different semantic
descriptor encodings. As a second contribution, we present a higher order
generalisation of this framework, capable of simultaneous
multi-task-multi-domain learning. This generalisation has two mathematically
equivalent views in multi-linear algebra and gated neural networks
respectively. Moreover, by exploiting the semantic descriptor, it provides
neural networks the capability of zero-shot learning (ZSL), where a classifier
is generated for an unseen class without any training data; as well as
zero-shot domain adaptation (ZSDA), where a model is generated for an unseen
domain without any training data. In practice, this framework provides a
powerful yet easy to implement method that can be flexibly applied to MTL, MDL,
ZSL and ZSDA.
Private Topic Modeling
Authors: Mijung Park, James Foulds, Kamalika Chaudhuri, Max Welling
Date: 2016-09-14 00:00:00
Tasks: Variational Inference

We develop a privatised stochastic variational inference method for Latent
Dirichlet Allocation (LDA). The iterative nature of stochastic variational
inference presents challenges: multiple iterations are required to obtain
accurate posterior distributions, yet each iteration increases the amount of
noise that must be added to achieve a reasonable degree of privacy. We propose
a practical algorithm that overcomes this challenge by combining: (1) an
improved composition method for differential privacy, called the moments
accountant, which provides a tight bound on the privacy cost of multiple
variational inference iterations and thus significantly decreases the amount of
additive noise; and (2) privacy amplification resulting from subsampling of
large-scale data. Focusing on conjugate exponential family models, in our
private variational inference, all the posterior distributions will be
privatised by simply perturbing expected sufficient statistics. Using Wikipedia
data, we illustrate the effectiveness of our algorithm for large-scale data.
Dictionary Learning with Equiprobable Matching Pursuit
Authors: Fredrik Sandin, Sergio Martin-del-Campo
Date: 2016-11-28 00:00:00
Tasks: Denoising, Dictionary Learning

Sparse signal representations based on linear combinations of learned atoms
have been used to obtain state-of-the-art results in several practical signal
processing applications. Approximation methods are needed to process
high-dimensional signals in this way because the problem to calculate optimal
atoms for sparse coding is NP-hard. Here we study greedy algorithms for
unsupervised learning of dictionaries of shift-invariant atoms and propose a
new method where each atom is selected with the same probability on average,
which corresponds to the homeostatic regulation of a recurrent convolutional
neural network. Equiprobable selection can be used with several greedy
algorithms for dictionary learning to ensure that all atoms adapt during
training and that no particular atom is more likely to take part in the linear
combination on average. We demonstrate via simulation experiments that
dictionary learning with equiprobable selection results in higher entropy of
the sparse representation and lower reconstruction and denoising errors, both
in the case of ordinary matching pursuit and orthogonal matching pursuit with
shift-invariant dictionaries. Furthermore, we show that the computational costs
of the matching pursuits are lower with equiprobable selection, leading to
faster and more accurate dictionary learning algorithms.
Tagger: Deep Unsupervised Perceptual Grouping
Authors: Klaus Greff, Antti Rasmus, Mathias Berglund, Tele Hotloo Hao, Jürgen Schmidhuber, Harri Valpola
Date: 2016-06-21 00:00:00
Proceeding: NeurIPS 2016 12
Tasks: General Classification, Segmentation

We present a framework for efficient perceptual inference that explicitly
reasons about the segmentation of its inputs and features. Rather than being
trained for any specific segmentation, our framework learns the grouping
process in an unsupervised manner or alongside any supervised task. By
enriching the representations of a neural network, we enable it to group the
representations of different objects in an iterative manner. By allowing the
system to amortize the iterative inference of the groupings, we achieve very
fast convergence. In contrast to many other recently proposed methods for
addressing multi-object scenes, our system does not assume the inputs to be
images and can therefore directly handle other modalities. For multi-digit
classification of very cluttered images that require texture segmentation, our
method offers improved classification performance over convolutional networks
despite being fully connected. Furthermore, we observe that our system greatly
improves on the semi-supervised result of a baseline Ladder network on our
dataset, indicating that segmentation can also improve sample efficiency.
Bethe Projections for Non-Local Inference
Authors: Luke Vilnis, David Belanger, Daniel Sheldon, Andrew McCallum
Date: 2015-03-04 00:00:00
Tasks: Handwriting Recognition, Structured Prediction, Variational Inference

Many inference problems in structured prediction are naturally solved by
augmenting a tractable dependency structure with complex, non-local auxiliary
objectives. This includes the mean field family of variational inference
algorithms, soft- or hard-constrained inference using Lagrangian relaxation or
linear programming, collective graphical models, and forms of semi-supervised
learning such as posterior regularization. We present a method to
discriminatively learn broad families of inference objectives, capturing
powerful non-local statistics of the latent variables, while maintaining
tractable and provably fast inference using non-Euclidean projected gradient
descent with a distance-generating function given by the Bethe entropy. We
demonstrate the performance and flexibility of our method by (1) extracting
structured citations from research papers by learning soft global constraints,
(2) achieving state-of-the-art results on a widely-used handwriting recognition
task using a novel learned non-convex inference procedure, and (3) providing a
fast and highly scalable algorithm for the challenging problem of inference in
a collective graphical model applied to bird migration.
Deep Convolutional Neural Networks and Data Augmentation for Environmental Sound Classification
Authors: Justin Salamon, Juan Pablo Bello
Date: 2016-08-15 00:00:00
Proceeding: IEEE Signal Processing Letters 2017 1
Tasks: Data Augmentation, Dictionary Learning, Environmental Sound Classification, General Classification, Sound Classification

The ability of deep convolutional neural networks (CNN) to learn
discriminative spectro-temporal patterns makes them well suited to
environmental sound classification. However, the relative scarcity of labeled
data has impeded the exploitation of this family of high-capacity models. This
study has two primary contributions: first, we propose a deep convolutional
neural network architecture for environmental sound classification. Second, we
propose the use of audio data augmentation for overcoming the problem of data
scarcity and explore the influence of different augmentations on the
performance of the proposed CNN architecture. Combined with data augmentation,
the proposed model produces state-of-the-art results for environmental sound
classification. We show that the improved performance stems from the
combination of a deep, high-capacity model and an augmented training set: this
combination outperforms both the proposed CNN without augmentation and a
"shallow" dictionary learning model with augmentation. Finally, we examine the
influence of each augmentation on the model's classification accuracy for each
class, and observe that the accuracy for each class is influenced differently
by each augmentation, suggesting that the performance of the model could be
improved further by applying class-conditional data augmentation.
Multi-resolution Data Fusion for Super-Resolution Electron Microscopy
Authors: Suhas Sreehari, S. V. Venkatakrishnan, Katherine L. Bouman, Jeffrey P. Simmons, Lawrence F. Drummy, Charles A. Bouman
Date: 2016-11-28 00:00:00
Tasks: Super-Resolution

Perhaps surprisingly, the total electron microscopy (EM) data collected to
date is less than a cubic millimeter. Consequently, there is an enormous demand
in the materials and biological sciences to image at greater speed and lower
dosage, while maintaining resolution. Traditional EM imaging based on
homogeneous raster-order scanning severely limits the volume of high-resolution
data that can be collected, and presents a fundamental limitation to
understanding physical processes such as material deformation, crack
propagation, and pyrolysis.
  We introduce a novel multi-resolution data fusion (MDF) method for
super-resolution computational EM. Our method combines innovative data
acquisition with novel algorithmic techniques to dramatically improve the
resolution/volume/speed trade-off. The key to our approach is to collect the
entire sample at low resolution, while simultaneously collecting a small
fraction of data at high resolution. The high-resolution measurements are then
used to create a material-specific patch-library that is used within the
"plug-and-play" framework to dramatically improve super-resolution of the
low-resolution data. We present results using FEI electron microscope data that
demonstrate super-resolution factors of 4x, 8x, and 16x, while substantially
maintaining high image quality and reducing dosage.
Improving Multi-Document Summarization via Text Classification
Authors: Ziqiang Cao, Wenjie Li, Sujian Li, Furu Wei
Date: 2016-11-28 00:00:00
Tasks: Classification, Document Summarization, General Classification, Multi-Document Summarization, text-classification, Text Classification

Developed so far, multi-document summarization has reached its bottleneck due
to the lack of sufficient training data and diverse categories of documents.
Text classification just makes up for these deficiencies. In this paper, we
propose a novel summarization system called TCSum, which leverages plentiful
text classification data to improve the performance of multi-document
summarization. TCSum projects documents onto distributed representations which
act as a bridge between text classification and summarization. It also utilizes
the classification results to produce summaries of different styles. Extensive
experiments on DUC generic multi-document summarization datasets show that,
TCSum can achieve the state-of-the-art performance without using any
hand-crafted features and has the capability to catch the variations of summary
styles with respect to different text categories.
Joint Copying and Restricted Generation for Paraphrase
Authors: Ziqiang Cao, Chuwei Luo, Wenjie Li, Sujian Li
Date: 2016-11-28 00:00:00
Tasks: Abstractive Text Summarization, Decoder, Informativeness, Text Generation, Text Simplification

Many natural language generation tasks, such as abstractive summarization and
text simplification, are paraphrase-orientated. In these tasks, copying and
rewriting are two main writing modes. Most previous sequence-to-sequence
(Seq2Seq) models use a single decoder and neglect this fact. In this paper, we
develop a novel Seq2Seq model to fuse a copying decoder and a restricted
generative decoder. The copying decoder finds the position to be copied based
on a typical attention model. The generative decoder produces words limited in
the source-specific vocabulary. To combine the two decoders and determine the
final output, we develop a predictor to predict the mode of copying or
rewriting. This predictor can be guided by the actual writing mode in the
training data. We conduct extensive experiments on two different paraphrase
datasets. The result shows that our model outperforms the state-of-the-art
approaches in terms of both informativeness and language quality.
Efficient Convolutional Auto-Encoding via Random Convexification and Frequency-Domain Minimization
Authors: Meshia Cédric Oveneke, Mitchel Aliosha-Perez, Yong Zhao, Dongmei Jiang, Hichem Sahli
Date: 2016-11-28 00:00:00

The omnipresence of deep learning architectures such as deep convolutional
neural networks (CNN)s is fueled by the synergistic combination of
ever-increasing labeled datasets and specialized hardware. Despite the
indisputable success, the reliance on huge amounts of labeled data and
specialized hardware can be a limiting factor when approaching new
applications. To help alleviating these limitations, we propose an efficient
learning strategy for layer-wise unsupervised training of deep CNNs on
conventional hardware in acceptable time. Our proposed strategy consists of
randomly convexifying the reconstruction contractive auto-encoding (RCAE)
learning objective and solving the resulting large-scale convex minimization
problem in the frequency domain via coordinate descent (CD). The main
advantages of our proposed learning strategy are: (1) single tunable
optimization parameter; (2) fast and guaranteed convergence; (3) possibilities
for full parallelization. Numerical experiments show that our proposed learning
strategy scales (in the worst case) linearly with image size, number of filters
and filter size.
A Unified Maximum Likelihood Approach for Optimal Distribution Property Estimation
Authors: Jayadev Acharya, Hirakendu Das, Alon Orlitsky, Ananda Theertha Suresh
Date: 2016-11-09 00:00:00

The advent of data science has spurred interest in estimating properties of
distributions over large alphabets. Fundamental symmetric properties such as
support size, support coverage, entropy, and proximity to uniformity, received
most attention, with each property estimated using a different technique and
often intricate analysis tools.
  We prove that for all these properties, a single, simple, plug-in
estimator---profile maximum likelihood (PML)---performs as well as the best
specialized techniques. This raises the possibility that PML may optimally
estimate many other symmetric properties.
Robust Variational Inference
Authors: Michael Figurnov, Kirill Struminsky, Dmitry Vetrov
Date: 2016-11-28 00:00:00
Tasks: Variational Inference

Variational inference is a powerful tool for approximate inference. However,
it mainly focuses on the evidence lower bound as variational objective and the
development of other measures for variational inference is a promising area of
research. This paper proposes a robust modification of evidence and a lower
bound for the evidence, which is applicable when the majority of the training
set samples are random noise objects. We provide experiments for variational
autoencoders to show advantage of the objective over the evidence lower bound
on synthetic datasets obtained by adding uninformative noise objects to MNIST
and OMNIGLOT. Additionally, for the original MNIST and OMNIGLOT datasets we
observe a small improvement over the non-robust evidence lower bound.
AutoMOS: Learning a non-intrusive assessor of naturalness-of-speech
Authors: Brian Patton, Yannis Agiomyrgiannakis, Michael Terry, Kevin Wilson, Rif A. Saurous, D. Sculley
Date: 2016-11-28 00:00:00
Tasks: text-to-speech, Text to Speech

Developers of text-to-speech synthesizers (TTS) often make use of human
raters to assess the quality of synthesized speech. We demonstrate that we can
model human raters' mean opinion scores (MOS) of synthesized speech using a
deep recurrent neural network whose inputs consist solely of a raw waveform.
Our best models provide utterance-level estimates of MOS only moderately
inferior to sampled human ratings, as shown by Pearson and Spearman
correlations. When multiple utterances are scored and averaged, a scenario
common in synthesizer quality assessment, AutoMOS achieves correlations
approaching those of human raters. The AutoMOS model has a number of
applications, such as the ability to explore the parameter space of a speech
synthesizer without requiring a human-in-the-loop.
Who's that Actor? Automatic Labelling of Actors in TV series starting from IMDB Images
Authors: Rahaf Aljundi, Punarjay Chakravarty, Tinne Tuytelaars
Date: 2016-11-28 00:00:00
Tasks: Clustering, Domain Adaptation, Graph Matching

In this work, we aim at automatically labeling actors in a TV series. Rather
than relying on transcripts and subtitles, as has been demonstrated in the
past, we show how to achieve this goal starting from a set of example images of
each of the main actors involved, collected from the Internet Movie Database
(IMDB). The problem then becomes one of domain adaptation: actors' IMDB photos
are typically taken at awards ceremonies and are quite different from their
appearances in TV series. In each series as well, there is considerable change
in actor appearance due to makeup, lighting, ageing, etc. To bridge this gap,
we propose a graph-matching based self-labelling algorithm, which we coin HSL
(Hungarian Self Labeling). Further, we propose a new edge cost to be used in
this context, as well as an extension that is more robust to outliers, where
prototypical faces for each of the actors are selected based on a hierarchical
clustering procedure. We conduct experiments with 15 episodes from 3 different
TV series and demonstrate automatic annotation with an accuracy of 90% and up.
Proceedings of NIPS 2016 Workshop on Interpretable Machine Learning for Complex Systems
Authors: Andrew Gordon Wilson, Been Kim, William Herlands
Date: 2016-11-28 00:00:00
Tasks: BIG-bench Machine Learning, Interpretable Machine Learning

This is the Proceedings of NIPS 2016 Workshop on Interpretable Machine
Learning for Complex Systems, held in Barcelona, Spain on December 9, 2016
Pixel-Level Domain Transfer
Authors: Donggeun Yoo, Namil Kim, Sunggyun Park, Anthony S. Paek, In So Kweon
Date: 2016-03-24 00:00:00
Tasks: Conditional Image Generation, Image Generation

We present an image-conditional image generation model. The model transfers
an input domain to a target domain in semantic level, and generates the target
image in pixel level. To generate realistic target images, we employ the
real/fake-discriminator as in Generative Adversarial Nets, but also introduce a
novel domain-discriminator to make the generated image relevant to the input
image. We verify our model through a challenging task of generating a piece of
clothing from an input image of a dressed person. We present a high quality
clothing dataset containing the two domains, and succeed in demonstrating
decent results.
Learning to Compose Words into Sentences with Reinforcement Learning
Authors: Dani Yogatama, Phil Blunsom, Chris Dyer, Edward Grefenstette, Wang Ling
Date: 2016-11-28 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

We use reinforcement learning to learn tree-structured neural networks for
computing representations of natural language sentences. In contrast with prior
work on tree-structured models in which the trees are either provided as input
or predicted using supervision from explicit treebank annotations, the tree
structures in this work are optimized to improve performance on a downstream
task. Experiments demonstrate the benefit of learning task-specific composition
orders, outperforming both sequential encoders and recursive encoders based on
treebank annotations. We analyze the induced trees and show that while they
discover some linguistically intuitive structures (e.g., noun phrases, simple
verb phrases), they are different than conventional English syntactic
structures.
On the Role and the Importance of Features for Background Modeling and Foreground Detection
Authors: Thierry Bouwmans, Caroline Silva, Cristina Marghes, Mohammed Sami Zitouni, Harish Bhaskar, Carl Frelicot
Date: 2016-11-28 00:00:00

Background modeling has emerged as a popular foreground detection technique
for various applications in video surveillance. Background modeling methods
have become increasing efficient in robustly modeling the background and hence
detecting moving objects in any visual scene. Although several background
subtraction and foreground detection have been proposed recently, no
traditional algorithm today still seem to be able to simultaneously address all
the key challenges of illumination variation, dynamic camera motion, cluttered
background and occlusion. This limitation can be attributed to the lack of
systematic investigation concerning the role and importance of features within
background modeling and foreground detection. With the availability of a rather
large set of invariant features, the challenge is in determining the best
combination of features that would improve accuracy and robustness in
detection. The purpose of this study is to initiate a rigorous and
comprehensive survey of features used within background modeling and foreground
detection. Further, this paper presents a systematic experimental and
statistical analysis of techniques that provide valuable insight on the trends
in background modeling and use it to draw meaningful recommendations for
practitioners. In this paper, a preliminary review of the key characteristics
of features based on the types and sizes is provided in addition to
investigating their intrinsic spectral, spatial and temporal properties.
Furthermore, improvements using statistical and fuzzy tools are examined and
techniques based on multiple features are benchmarked against reliability and
selection criterion. Finally, a description of the different resources
available such as datasets and codes is provided.