pwc-archive/papers-with-abstracts huggingface dataset:
Tunnel Effects in Cognition: A new Mechanism for Scientific Discovery and Education
Authors: Antoine Cornuéjols, Andrée Tiberghien, Gérard Collet
Date: 2017-07-16 00:00:00
Tasks: scientific discovery

It is quite exceptional, if it ever happens, that a new conceptual domain be
built from scratch. Usually, it is developed and mastered in interaction, both
positive and negative, with other more operational existing domains. Few
reasoning mechanisms have been proposed to account for the interplay of
different conceptual domains and the transfer of information from one to
another. Analogical reasoning is one, blending is another. This paper presents
a new mechanism, called 'tunnel effect', that may explain, in part, how
scientists and students reason while constructing a new conceptual domain. One
experimental study with high school students and analyses from the history of
science, particularly about the birth of classical thermodynamics, provide
evidence and illustrate this mechanism. The knowledge organization, processes
and conditions for its appearance are detailed and put into the perspective of
a computational model. Specifically, we put forward the hypothesis that two
levels of knowledge, notional and conceptual, cooperate in the scientific
discovery process when a new conceptual domain is being built. The type of
conceptual learning that can be associated with tunnel effect is discussed and
a thorough comparison is made with analogical reasoning in order to underline
the main features of the new proposed mechanism.
Simplified End-to-End MMI Training and Voting for ASR
Authors: Lior Fritz, David Burshtein
Date: 2017-03-30 00:00:00
Tasks: Decoder, General Classification, Language Modeling, Language Modelling, speech-recognition, Speech Recognition

A simplified speech recognition system that uses the maximum mutual
information (MMI) criterion is considered. End-to-end training using gradient
descent is suggested, similarly to the training of connectionist temporal
classification (CTC). We use an MMI criterion with a simple language model in
the training stage, and a standard HMM decoder. Our method compares favorably
to CTC in terms of performance, robustness, decoding time, disk footprint and
quality of alignments. The good alignments enable the use of a straightforward
ensemble method, obtained by simply averaging the predictions of several neural
network models, that were trained separately end-to-end. The ensemble method
yields a considerable reduction in the word error rate.
Generative Adversarial Network based on Resnet for Conditional Image Restoration
Authors: Meng Wang, Huafeng Li, Fang Li
Date: 2017-07-16 00:00:00
Tasks: Attribute, Generative Adversarial Network, Image Restoration

The GANs promote an adversarive game to approximate complex and jointed
example probability. The networks driven by noise generate fake examples to
approximate realistic data distributions. Later the conditional GAN merges
prior-conditions as input in order to transfer attribute vectors to the
corresponding data. However, the CGAN is not designed to deal with the high
dimension conditions since indirect guide of the learning is inefficiency. In
this paper, we proposed a network ResGAN to generate fine images in terms of
extremely degenerated images. The coarse images aligned to attributes are
embedded as the generator inputs and classifier labels. In generative network,
a straight path similar to the Resnet is cohered to directly transfer the
coarse images to the higher layers. And adversarial training is circularly
implemented to prevent degeneration of the generated images. Experimental
results of applying the ResGAN to datasets MNIST, CIFAR10/100 and CELEBA show
its higher accuracy to the state-of-art GANs.
Listening while Speaking: Speech Chain by Deep Learning
Authors: Andros Tjandra, Sakriani Sakti, Satoshi Nakamura
Date: 2017-07-16 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Deep Learning, speech-recognition, Speech Recognition, Speech Synthesis, text-to-speech, Text to Speech, Text-To-Speech Synthesis

Despite the close relationship between speech perception and production,
research in automatic speech recognition (ASR) and text-to-speech synthesis
(TTS) has progressed more or less independently without exerting much mutual
influence on each other. In human communication, on the other hand, a
closed-loop speech chain mechanism with auditory feedback from the speaker's
mouth to her ear is crucial. In this paper, we take a step further and develop
a closed-loop speech chain model based on deep learning. The
sequence-to-sequence model in close-loop architecture allows us to train our
model on the concatenation of both labeled and unlabeled data. While ASR
transcribes the unlabeled speech features, TTS attempts to reconstruct the
original speech waveform based on the text from ASR. In the opposite direction,
ASR also attempts to reconstruct the original text transcription given the
synthesized speech. To the best of our knowledge, this is the first deep
learning model that integrates human speech perception and production
behaviors. Our experimental results show that the proposed approach
significantly improved the performance more than separate systems that were
only trained with labeled data.
Optical Music Recognition with Convolutional Sequence-to-Sequence Models
Authors: Eelco van der Wel, Karen Ullrich
Date: 2017-07-16 00:00:00
Tasks: Deep Learning, Information Retrieval, Music Information Retrieval, Retrieval

Optical Music Recognition (OMR) is an important technology within Music
Information Retrieval. Deep learning models show promising results on OMR
tasks, but symbol-level annotated data sets of sufficient size to train such
models are not available and difficult to develop. We present a deep learning
architecture called a Convolutional Sequence-to-Sequence model to both move
towards an end-to-end trainable OMR pipeline, and apply a learning process that
trains on full sentences of sheet music instead of individually labeled
symbols. The model is trained and evaluated on a human generated data set, with
various image augmentations based on real-world scenarios. This data set is the
first publicly available set in OMR research with sufficient size to train and
evaluate deep learning models. With the introduced augmentations a pitch
recognition accuracy of 81% and a duration accuracy of 94% is achieved,
resulting in a note level accuracy of 80%. Finally, the model is compared to
commercially available methods, showing a large improvements over these
applications.
Automated Detection of Non-Relevant Posts on the Russian Imageboard "2ch": Importance of the Choice of Word Representations
Authors: Amir Bakarov, Olga Gureenkova
Date: 2017-07-16 00:00:00
Tasks: Word Embeddings

This study considers the problem of automated detection of non-relevant posts
on Web forums and discusses the approach of resolving this problem by
approximation it with the task of detection of semantic relatedness between the
given post and the opening post of the forum discussion thread. The
approximated task could be resolved through learning the supervised classifier
with a composed word embeddings of two posts. Considering that the success in
this task could be quite sensitive to the choice of word representations, we
propose a comparison of the performance of different word embedding models. We
train 7 models (Word2Vec, Glove, Word2Vec-f, Wang2Vec, AdaGram, FastText,
Swivel), evaluate embeddings produced by them on dataset of human judgements
and compare their performance on the task of non-relevant posts detection. To
make the comparison, we propose a dataset of semantic relatedness with posts
from one of the most popular Russian Web forums, imageboard "2ch", which has
challenging lexical and grammatical features.
A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problem
Authors: Zhengyao Jiang, Dixing Xu, Jinjun Liang
Date: 2017-06-30 00:00:00
Tasks: Deep Reinforcement Learning, Management, Portfolio Optimization, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Financial portfolio management is the process of constant redistribution of a
fund into different financial products. This paper presents a
financial-model-free Reinforcement Learning framework to provide a deep machine
learning solution to the portfolio management problem. The framework consists
of the Ensemble of Identical Independent Evaluators (EIIE) topology, a
Portfolio-Vector Memory (PVM), an Online Stochastic Batch Learning (OSBL)
scheme, and a fully exploiting and explicit reward function. This framework is
realized in three instants in this work with a Convolutional Neural Network
(CNN), a basic Recurrent Neural Network (RNN), and a Long Short-Term Memory
(LSTM). They are, along with a number of recently reviewed or published
portfolio-selection strategies, examined in three back-test experiments with a
trading period of 30 minutes in a cryptocurrency market. Cryptocurrencies are
electronic and decentralized alternatives to government-issued money, with
Bitcoin as the best-known example of a cryptocurrency. All three instances of
the framework monopolize the top three positions in all experiments,
outdistancing other compared trading algorithms. Although with a high
commission rate of 0.25% in the backtests, the framework is able to achieve at
least 4-fold returns in 50 days.
Minimax deviation strategies for machine learning and recognition with short learning samples
Authors: Michail Schlesinger, Evgeniy Vodolazskiy
Date: 2017-07-16 00:00:00
Tasks: BIG-bench Machine Learning

The article is devoted to the problem of small learning samples in machine
learning. The flaws of maximum likelihood learning and minimax learning are
looked into and the concept of minimax deviation learning is introduced that is
free of those flaws.
FML-based Dynamic Assessment Agent for Human-Machine Cooperative System on Game of Go
Authors: Chang-Shing Lee, Mei-Hui Wang, Sheng-Chi Yang, Pi-Hsia Hung, Su-Wei Lin, Nan Shuo, Naoyuki Kubota, Chun-Hsun Chou, Ping-Chiang Chou, Chia-Hsiu Kao
Date: 2017-07-16 00:00:00
Tasks: Decision Making, Game of Go

In this paper, we demonstrate the application of Fuzzy Markup Language (FML)
to construct an FML-based Dynamic Assessment Agent (FDAA), and we present an
FML-based Human-Machine Cooperative System (FHMCS) for the game of Go. The
proposed FDAA comprises an intelligent decision-making and learning mechanism,
an intelligent game bot, a proximal development agent, and an intelligent
agent. The intelligent game bot is based on the open-source code of Facebook
Darkforest, and it features a representational state transfer application
programming interface mechanism. The proximal development agent contains a
dynamic assessment mechanism, a GoSocket mechanism, and an FML engine with a
fuzzy knowledge base and rule base. The intelligent agent contains a GoSocket
engine and a summarization agent that is based on the estimated win rate,
real-time simulation number, and matching degree of predicted moves.
Additionally, the FML for player performance evaluation and linguistic
descriptions for game results commentary are presented. We experimentally
verify and validate the performance of the FDAA and variants of the FHMCS by
testing five games in 2016 and 60 games of Google Master Go, a new version of
the AlphaGo program, in January 2017. The experimental results demonstrate that
the proposed FDAA can work effectively for Go applications.
Spectrum Estimation from Samples
Authors: Weihao Kong, Gregory Valiant
Date: 2016-01-30 00:00:00

We consider the problem of approximating the set of eigenvalues of the
covariance matrix of a multivariate distribution (equivalently, the problem of
approximating the "population spectrum"), given access to samples drawn from
the distribution. The eigenvalues of the covariance of a distribution contain
basic information about the distribution, including the presence or lack of
structure in the distribution, the effective dimensionality of the
distribution, and the applicability of higher-level machine learning and
multivariate statistical tools. We consider this fundamental recovery problem
in the regime where the number of samples is comparable, or even sublinear in
the dimensionality of the distribution in question. First, we propose a
theoretically optimal and computationally efficient algorithm for recovering
the moments of the eigenvalues of the population covariance matrix. We then
leverage this accurate moment recovery, via a Wasserstein distance argument, to
show that the vector of eigenvalues can be accurately recovered. We provide
finite--sample bounds on the expected error of the recovered eigenvalues, which
imply that our estimator is asymptotically consistent as the dimensionality of
the distribution and sample size tend towards infinity, even in the sublinear
sample regime where the ratio of the sample size to the dimensionality tends to
zero. In addition to our theoretical results, we show that our approach
performs well in practice for a broad range of distributions and sample sizes.
RED: Reinforced Encoder-Decoder Networks for Action Anticipation
Authors: Jiyang Gao, Zhenheng Yang, Ram Nevatia
Date: 2017-07-16 00:00:00
Tasks: Action Anticipation, Decoder

Action anticipation aims to detect an action before it happens. Many real
world applications in robotics and surveillance are related to this predictive
capability. Current methods address this problem by first anticipating visual
representations of future frames and then categorizing the anticipated
representations to actions. However, anticipation is based on a single past
frame's representation, which ignores the history trend. Besides, it can only
anticipate a fixed future time. We propose a Reinforced Encoder-Decoder (RED)
network for action anticipation. RED takes multiple history representations as
input and learns to anticipate a sequence of future representations. One
salient aspect of RED is that a reinforcement module is adopted to provide
sequence-level supervision; the reward function is designed to encourage the
system to make correct predictions as early as possible. We test RED on
TVSeries, THUMOS-14 and TV-Human-Interaction datasets for action anticipation
and achieve state-of-the-art performance on all datasets.
Open-Set Language Identification
Authors: Shervin Malmasi
Date: 2017-07-16 00:00:00
Tasks: General Classification, Language Identification, One-Class Classification

We present the first open-set language identification experiments using
one-class classification. We first highlight the shortcomings of traditional
feature extraction methods and propose a hashing-based feature vectorization
approach as a solution. Using a dataset of 10 languages from different writing
systems, we train a One- Class Support Vector Machine using only a monolingual
corpus for each language. Each model is evaluated against a test set of data
from all 10 languages and we achieve an average F-score of 0.99, highlighting
the effectiveness of this approach for open-set language identification.
Fast Low-rank Shared Dictionary Learning for Image Classification
Authors: Tiep Vu, Vishal Monga
Date: 2016-10-27 00:00:00
Tasks: Classification, Dictionary Learning, General Classification, image-classification, Image Classification

Despite the fact that different objects possess distinct class-specific
features, they also usually share common patterns. This observation has been
exploited partially in a recently proposed dictionary learning framework by
separating the particularity and the commonality (COPAR). Inspired by this, we
propose a novel method to explicitly and simultaneously learn a set of common
patterns as well as class-specific features for classification with more
intuitive constraints. Our dictionary learning framework is hence characterized
by both a shared dictionary and particular (class-specific) dictionaries. For
the shared dictionary, we enforce a low-rank constraint, i.e. claim that its
spanning subspace should have low dimension and the coefficients corresponding
to this dictionary should be similar. For the particular dictionaries, we
impose on them the well-known constraints stated in the Fisher discrimination
dictionary learning (FDDL). Further, we develop new fast and accurate
algorithms to solve the subproblems in the learning step, accelerating its
convergence. The said algorithms could also be applied to FDDL and its
extensions. The efficiencies of these algorithms are theoretically and
experimentally verified by comparing their complexities and running time with
those of other well-known dictionary learning methods. Experimental results on
widely used image datasets establish the advantages of our method over
state-of-the-art dictionary learning methods.
Sentence Simplification with Deep Reinforcement Learning
Authors: Xingxing Zhang, Mirella Lapata
Date: 2017-03-31 00:00:00
Proceeding: EMNLP 2017 9
Tasks: Decoder, Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Sentence, Sentence Compression, Text Simplification, Translation

Sentence simplification aims to make sentences easier to read and understand.
Most recent approaches draw on insights from machine translation to learn
simplification rewrites from monolingual corpora of complex and simple
sentences. We address the simplification problem with an encoder-decoder model
coupled with a deep reinforcement learning framework. Our model, which we call
{\sc Dress} (as shorthand for {\bf D}eep {\bf RE}inforcement {\bf S}entence
{\bf S}implification), explores the space of possible simplifications while
learning to optimize a reward function that encourages outputs which are
simple, fluent, and preserve the meaning of the input. Experiments on three
datasets demonstrate that our model outperforms competitive simplification
systems.
SegAN: Adversarial Network with Multi-scale $L_1$ Loss for Medical Image Segmentation
Authors: Yuan Xue, Tao Xu, Han Zhang, Rodney Long, Xiaolei Huang
Date: 2017-06-06 00:00:00
Tasks: Brain Tumor Segmentation, Image Segmentation, Medical Image Segmentation, Segmentation, Semantic Segmentation, Tumor Segmentation

Inspired by classic generative adversarial networks (GAN), we propose a novel
end-to-end adversarial neural network, called SegAN, for the task of medical
image segmentation. Since image segmentation requires dense, pixel-level
labeling, the single scalar real/fake output of a classic GAN's discriminator
may be ineffective in producing stable and sufficient gradient feedback to the
networks. Instead, we use a fully convolutional neural network as the segmentor
to generate segmentation label maps, and propose a novel adversarial critic
network with a multi-scale $L_1$ loss function to force the critic and
segmentor to learn both global and local features that capture long- and
short-range spatial relationships between pixels. In our SegAN framework, the
segmentor and critic networks are trained in an alternating fashion in a
min-max game: The critic takes as input a pair of images, (original_image $*$
predicted_label_map, original_image $*$ ground_truth_label_map), and then is
trained by maximizing a multi-scale loss function; The segmentor is trained
with only gradients passed along by the critic, with the aim to minimize the
multi-scale loss function. We show that such a SegAN framework is more
effective and stable for the segmentation task, and it leads to better
performance than the state-of-the-art U-net segmentation method. We tested our
SegAN method using datasets from the MICCAI BRATS brain tumor segmentation
challenge. Extensive experimental results demonstrate the effectiveness of the
proposed SegAN with multi-scale loss: on BRATS 2013 SegAN gives performance
comparable to the state-of-the-art for whole tumor and tumor core segmentation
while achieves better precision and sensitivity for Gd-enhance tumor core
segmentation; on BRATS 2015 SegAN achieves better performance than the
state-of-the-art in both dice score and precision.
Matroids Hitting Sets and Unsupervised Dependency Grammar Induction
Authors: Nicholas Harvey, Vahab Mirrokni, David Karger, Virginia Savova, Leonid Peshkin
Date: 2017-05-24 00:00:00
Tasks: Dependency Grammar Induction

This paper formulates a novel problem on graphs: find the minimal subset of
edges in a fully connected graph, such that the resulting graph contains all
spanning trees for a set of specifed sub-graphs. This formulation is motivated
by an un-supervised grammar induction problem from computational linguistics.
We present a reduction to some known problems and algorithms from graph theory,
provide computational complexity results, and describe an approximation
algorithm.
Modified Alpha-Rooting Color Image Enhancement Method On The Two-Side 2-D Quaternion Discrete Fourier Transform And The 2-D Discrete Fourier Transform
Authors: Artyom M. Grigoryan, Aparna John, Sos S. Agaian
Date: 2017-07-15 00:00:00
Tasks: Image Enhancement

Color in an image is resolved into 3 or 4 color components and 2-Dimages of
these components are stored in separate channels. Most of the color image
enhancement algorithms are applied channel-by-channel on each image. But such a
system of color image processing is not processing the original color. When a
color image is represented as a quaternion image, processing is done in
original colors. This paper proposes an implementation of the quaternion
approach of enhancement algorithm for enhancing color images and is referred as
the modified alpha-rooting by the two-dimensional quaternion discrete Fourier
transform (2-D QDFT). Enhancement results of this proposed method are compared
with the channel-by-channel image enhancement by the 2-D DFT. Enhancements in
color images are quantitatively measured by the color enhancement measure
estimation (CEME), which allows for selecting optimum parameters for processing
by the genetic algorithm. Enhancement of color images by the quaternion based
method allows for obtaining images which are closer to the genuine
representation of the real original color.
Vertical stratification of forest canopy for segmentation of under-story trees within small-footprint airborne LiDAR point clouds
Authors: Hamid Hamraz, Marco A. Contreras, Jun Zhang
Date: 2016-12-31 00:00:00
Tasks: Segmentation

Airborne LiDAR point cloud representing a forest contains 3D data, from which
vertical stand structure even of understory layers can be derived. This paper
presents a tree segmentation approach for multi-story stands that stratifies
the point cloud to canopy layers and segments individual tree crowns within
each layer using a digital surface model based tree segmentation method. The
novelty of the approach is the stratification procedure that separates the
point cloud to an overstory and multiple understory tree canopy layers by
analyzing vertical distributions of LiDAR points within overlapping locales.
The procedure does not make a priori assumptions about the shape and size of
the tree crowns and can, independent of the tree segmentation method, be
utilized to vertically stratify tree crowns of forest canopies. We applied the
proposed approach to the University of Kentucky Robinson Forest - a natural
deciduous forest with complex and highly variable terrain and vegetation
structure. The segmentation results showed that using the stratification
procedure strongly improved detecting understory trees (from 46% to 68%) at the
cost of introducing a fair number of over-segmented understory trees (increased
from 1% to 16%), while barely affecting the overall segmentation quality of
overstory trees. Results of vertical stratification of the canopy showed that
the point density of understory canopy layers were suboptimal for performing a
reasonable tree segmentation, suggesting that acquiring denser LiDAR point
clouds would allow more improvements in segmenting understory trees. As shown
by inspecting correlations of the results with forest structure, the
segmentation approach is applicable to a variety of forest types.