pwc-archive/papers-with-abstracts huggingface dataset:
#Exploration: A Study of Count-Based Exploration for Deep Reinforcement Learning
Authors: Haoran Tang, Rein Houthooft, Davis Foote, Adam Stooke, Xi Chen, Yan Duan, John Schulman, Filip De Turck, Pieter Abbeel
Date: 2016-11-15 00:00:00
Proceeding: NeurIPS 2017 12
Tasks: Atari Games, continuous-control, Continuous Control, Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Count-based exploration algorithms are known to perform near-optimally when
used in conjunction with tabular reinforcement learning (RL) methods for
solving small discrete Markov decision processes (MDPs). It is generally
thought that count-based methods cannot be applied in high-dimensional state
spaces, since most states will only occur once. Recent deep RL exploration
strategies are able to deal with high-dimensional continuous state spaces
through complex heuristics, often relying on optimism in the face of
uncertainty or intrinsic motivation. In this work, we describe a surprising
finding: a simple generalization of the classic count-based approach can reach
near state-of-the-art performance on various high-dimensional and/or continuous
deep RL benchmarks. States are mapped to hash codes, which allows to count
their occurrences with a hash table. These counts are then used to compute a
reward bonus according to the classic count-based exploration theory. We find
that simple hash functions can achieve surprisingly good results on many
challenging tasks. Furthermore, we show that a domain-dependent learned hash
code may further improve these results. Detailed analysis reveals important
aspects of a good hash function: 1) having appropriate granularity and 2)
encoding information relevant to solving the MDP. This exploration strategy
achieves near state-of-the-art performance on both continuous control tasks and
Atari 2600 games, hence providing a simple yet powerful baseline for solving
MDPs that require considerable exploration.
Capturing Reliable Fine-Grained Sentiment Associations by Crowdsourcing and Best-Worst Scaling
Authors: Svetlana Kiritchenko, Saif M. Mohammad
Date: 2017-12-05 00:00:00
Tasks: Sentiment Analysis, Stance Detection

Access to word-sentiment associations is useful for many applications,
including sentiment analysis, stance detection, and linguistic analysis.
However, manually assigning fine-grained sentiment association scores to words
has many challenges with respect to keeping annotations consistent. We apply
the annotation technique of Best-Worst Scaling to obtain real-valued sentiment
association scores for words and phrases in three different domains: general
English, English Twitter, and Arabic Twitter. We show that on all three domains
the ranking of words by sentiment remains remarkably consistent even when the
annotation process is repeated with a different set of annotators. We also, for
the first time, determine the minimum difference in sentiment association that
is perceptible to native speakers of a language.
OLÉ: Orthogonal Low-rank Embedding, A Plug and Play Geometric Loss for Deep Learning
Authors: José Lezama, Qiang Qiu, Pablo Musé, Guillermo Sapiro
Date: 2017-12-05 00:00:00
Tasks: General Classification, image-classification, Image Classification, Metric Learning, Object Recognition, Small Data Image Classification, Triplet

Deep neural networks trained using a softmax layer at the top and the
cross-entropy loss are ubiquitous tools for image classification. Yet, this
does not naturally enforce intra-class similarity nor inter-class margin of the
learned deep representations. To simultaneously achieve these two goals,
different solutions have been proposed in the literature, such as the pairwise
or triplet losses. However, such solutions carry the extra task of selecting
pairs or triplets, and the extra computational burden of computing and learning
for many combinations of them. In this paper, we propose a plug-and-play loss
term for deep networks that explicitly reduces intra-class variance and
enforces inter-class margin simultaneously, in a simple and elegant geometric
manner. For each class, the deep features are collapsed into a learned linear
subspace, or union of them, and inter-class subspaces are pushed to be as
orthogonal as possible. Our proposed Orthogonal Low-rank Embedding (OL\'E) does
not require carefully crafting pairs or triplets of samples for training, and
works standalone as a classification loss, being the first reported deep metric
learning framework of its kind. Because of the improved margin between features
of different classes, the resulting deep networks generalize better, are more
discriminative, and more robust. We demonstrate improved classification
performance in general object recognition, plugging the proposed loss term into
existing off-the-shelf architectures. In particular, we show the advantage of
the proposed loss in the small data/model scenario, and we significantly
advance the state-of-the-art on the Stanford STL-10 benchmark.
Automated Pruning for Deep Neural Network Compression
Authors: Franco Manessi, Alessandro Rozza, Simone Bianco, Paolo Napoletano, Raimondo Schettini
Date: 2017-12-05 00:00:00
Tasks: Neural Network Compression, Transfer Learning

In this work we present a method to improve the pruning step of the current
state-of-the-art methodology to compress neural networks. The novelty of the
proposed pruning technique is in its differentiability, which allows pruning to
be performed during the backpropagation phase of the network training. This
enables an end-to-end learning and strongly reduces the training time. The
technique is based on a family of differentiable pruning functions and a new
regularizer specifically designed to enforce pruning. The experimental results
show that the joint optimization of both the thresholds and the network weights
permits to reach a higher compression rate, reducing the number of weights of
the pruned network by a further 14% to 33% compared to the current
state-of-the-art. Furthermore, we believe that this is the first study where
the generalization capabilities in transfer learning tasks of the features
extracted by a pruned network are analyzed. To achieve this goal, we show that
the representations learned using the proposed pruning methodology maintain the
same effectiveness and generality of those learned by the corresponding
non-compressed network on a set of different recognition tasks.
Phylogenetics of Indo-European Language families via an Algebro-Geometric Analysis of their Syntactic Structures
Authors: Kevin Shu, Andrew Ortegaray, Robert Berwick, Matilde Marcolli
Date: 2017-12-05 00:00:00

Using Phylogenetic Algebraic Geometry, we analyze computationally the phylogenetic tree of subfamilies of the Indo-European language family, using data of syntactic structures. The two main sources of syntactic data are the SSWL database and Longobardi's recent data of syntactic parameters. We compute phylogenetic invariants and likelihood functions for two sets of Germanic languages, a set of Romance languages, a set of Slavic languages and a set of early Indo-European languages, and we compare the results with what is known through historical linguistics.
Restricted Isometry Property of Gaussian Random Projection for Finite Set of Subspaces
Authors: Gen Li, Yuantao Gu
Date: 2017-04-07 00:00:00
Tasks: Clustering, compressed sensing, Dimensionality Reduction, LEMMA

Dimension reduction plays an essential role when decreasing the complexity of
solving large-scale problems. The well-known Johnson-Lindenstrauss (JL) Lemma
and Restricted Isometry Property (RIP) admit the use of random projection to
reduce the dimension while keeping the Euclidean distance, which leads to the
boom of Compressed Sensing and the field of sparsity related signal processing.
Recently, successful applications of sparse models in computer vision and
machine learning have increasingly hinted that the underlying structure of high
dimensional data looks more like a union of subspaces (UoS). In this paper,
motivated by JL Lemma and an emerging field of Compressed Subspace Clustering
(CSC), we study for the first time the RIP of Gaussian random matrices for the
compression of two subspaces based on the generalized projection $F$-norm
distance. We theoretically prove that with high probability the affinity or
distance between two projected subspaces are concentrated around their
estimates. When the ambient dimension after projection is sufficiently large,
the affinity and distance between two subspaces almost remain unchanged after
random projection. Numerical experiments verify the theoretical work.
Linear Convergence of An Iterative Phase Retrieval Algorithm with Data Reuse
Authors: Gen Li, Yuchen Jiao, Yuantao Gu
Date: 2017-12-05 00:00:00
Tasks: Retrieval

Phase retrieval has been an attractive but difficult problem rising from
physical science, and there has been a gap between state-of-the-art theoretical
convergence analyses and the corresponding efficient retrieval methods.
Firstly, these analyses all assume that the sensing vectors and the iterative
updates are independent, which only fits the ideal model with infinite
measurements but not the reality, where data are limited and have to be reused.
Secondly, the empirical results of some efficient methods, such as the
randomized Kaczmarz method, show linear convergence, which is beyond existing
theoretical explanations considering its randomness and reuse of data. In this
work, we study for the first time, without the independence assumption, the
convergence behavior of the randomized Kaczmarz method for phase retrieval.
Specifically, beginning from taking expectation of the squared estimation error
with respect to the index of measurement by fixing the sensing vector and the
error in the previous step, we discard the independence assumption, rigorously
derive the upper and lower bounds of the reduction of the mean squared error,
and prove the linear convergence. This work fills the gap between a fast
converging algorithm and its theoretical understanding. The proposed
methodology may contribute to the study of other iterative algorithms for phase
retrieval and other problems in the broad area of signal processing and machine
learning.
Object Referring in Visual Scene with Spoken Language
Authors: Arun Balajee Vasudevan, Dengxin Dai, Luc van Gool
Date: 2017-11-10 00:00:00
Tasks: Object

Object referring has important applications, especially for human-machine
interaction. While having received great attention, the task is mainly attacked
with written language (text) as input rather than spoken language (speech),
which is more natural. This paper investigates Object Referring with Spoken
Language (ORSpoken) by presenting two datasets and one novel approach. Objects
are annotated with their locations in images, text descriptions and speech
descriptions. This makes the datasets ideal for multi-modality learning. The
approach is developed by carefully taking down ORSpoken problem into three
sub-problems and introducing task-specific vision-language interactions at the
corresponding levels. Experiments show that our method outperforms competing
methods consistently and significantly. The approach is also evaluated in the
presence of audio noise, showing the efficacy of the proposed vision-language
interaction methods in counteracting background noise.
Inducing Distant Supervision in Suggestion Mining through Part-of-Speech Embeddings
Authors: Sapna Negi, Paul Buitelaar
Date: 2017-09-21 00:00:00
Tasks: Classification, General Classification, POS, Sentence, Sentence Classification, Suggestion mining, Word Embeddings

Mining suggestion expressing sentences from a given text is a less
investigated sentence classification task, and therefore lacks hand labeled
benchmark datasets. In this work, we propose and evaluate two approaches for
distant supervision in suggestion mining. The distant supervision is obtained
through a large silver standard dataset, constructed using the text from
wikiHow and Wikipedia. Both the approaches use a LSTM based neural network
architecture to learn a classification model for suggestion mining, but vary in
their method to use the silver standard dataset. The first approach directly
trains the classifier using this dataset, while the second approach only learns
word embeddings from this dataset. In the second approach, we also learn POS
embeddings, which interestingly gives the best classification accuracy.
Recognizing Gender from Human Facial Regions using Genetic Algorithm
Authors: Avirup Bhattacharyya, Rajkumar Saini, Partha Pratim Roy, Debi Prosad Dogra, Samarjit Kar
Date: 2017-12-05 00:00:00
Tasks: General Classification

Recently, recognition of gender from facial images has gained a lot of
importance. There exist a handful of research work that focus on feature
extraction to obtain gender specific information from facial images. However,
analyzing different facial regions and their fusion help in deciding the gender
of a person from facial images. In this paper, we propose a new approach to
identify gender from frontal facial images that is robust to background,
illumination, intensity, and facial expression. In our framework, first the
frontal face image is divided into a number of distinct regions based on facial
landmark points that are obtained by the Chehra model proposed by Asthana et
al. The model provides 49 facial landmark points covering different regions of
the face, e.g. forehead, left eye, right eye, lips. Next, a face image is
segmented into facial regions using landmark points and features are extracted
from each region. The Compass LBP feature, a variant of LBP feature, has been
used in our framework to obtain discriminative gender-specific information.
Following this, a Support Vector Machine based classifier has been used to
compute the probability scores from each facial region. Finally, the
classification scores obtained from individual regions are combined with a
genetic algorithm based learning to improve the overall classification
accuracy. The experiments have been performed on popular face image datasets
such as Adience, cFERET (color FERET), LFW and two sketch datasets, namely CUFS
and CUFSF. Through experiments, we have observed that, the proposed method
outperforms existing approaches.
On Deterministic Sampling Patterns for Robust Low-Rank Matrix Completion
Authors: Morteza Ashraphijuo, Vaneet Aggarwal, Xiaodong Wang
Date: 2017-12-05 00:00:00
Tasks: Low-Rank Matrix Completion, Matrix Completion, valid

In this letter, we study the deterministic sampling patterns for the
completion of low rank matrix, when corrupted with a sparse noise, also known
as robust matrix completion. We extend the recent results on the deterministic
sampling patterns in the absence of noise based on the geometric analysis on
the Grassmannian manifold. A special case where each column has a certain
number of noisy entries is considered, where our probabilistic analysis
performs very efficiently. Furthermore, assuming that the rank of the original
matrix is not given, we provide an analysis to determine if the rank of a valid
completion is indeed the actual rank of the data corrupted with sparse noise by
verifying some conditions.
Autonomous development and learning in artificial intelligence and robotics: Scaling up deep learning to human--like learning
Authors: Pierre-Yves Oudeyer
Date: 2017-12-05 00:00:00

Autonomous lifelong development and learning is a fundamental capability of
humans, differentiating them from current deep learning systems. However, other
branches of artificial intelligence have designed crucial ingredients towards
autonomous learning: curiosity and intrinsic motivation, social learning and
natural interaction with peers, and embodiment. These mechanisms guide
exploration and autonomous choice of goals, and integrating them with deep
learning opens stimulating perspectives. Deep learning (DL) approaches made
great advances in artificial intelligence, but are still far away from human
learning. As argued convincingly by Lake et al., differences include human
capabilities to learn causal models of the world from very little data,
leveraging compositional representations and priors like intuitive physics and
psychology. However, there are other fundamental differences between current DL
systems and human learning, as well as technical ingredients to fill this gap,
that are either superficially, or not adequately, discussed by Lake et al.
These fundamental mechanisms relate to autonomous development and learning.
They are bound to play a central role in artificial intelligence in the future.
Current DL systems require engineers to manually specify a task-specific
objective function for every new task, and learn through off-line processing of
large training databases. On the contrary, humans learn autonomously open-ended
repertoires of skills, deciding for themselves which goals to pursue or value,
and which skills to explore, driven by intrinsic motivation/curiosity and
social learning through natural interaction with peers. Such learning processes
are incremental, online, and progressive. Human child development involves a
progressive increase of complexity in a curriculum of learning where skills are
explored, acquired, and built on each other, through particular ordering and
timing. Finally, human learning happens in the physical world, and through
bodily and physical experimentation, under severe constraints on energy, time,
and computational resources. In the two last decades, the field of
Developmental and Cognitive Robotics (Cangelosi and Schlesinger, 2015, Asada et
al., 2009), in strong interaction with developmental psychology and
neuroscience, has achieved significant advances in computational
MRF-Based Multispectral Image Fusion Using an Adaptive Approach Based on Edge-Guided Interpolation
Authors: Mohammad Reza Khosravi, Mohammad Sharif-Yazd, Mohammad Kazem Moghimi, Ahmad Keshavarz, Habib Rostami, Suleiman Mansouri
Date: 2015-12-28 00:00:00

In interpretation of remote sensing images, it is possible that some images
which are supplied by different sensors become incomprehensible. For better
visual perception of these images, it is essential to operate series of
pre-processing and elementary corrections and then operate a series of main
processing steps for more precise analysis on the images. There are several
approaches for processing which are depended on the type of remote sensing
images. The discussed approach in this article, i.e. image fusion, is the use
of natural colors of an optical image for adding color to a grayscale satellite
image which gives us the ability for better observation of the HR image of OLI
sensor of Landsat-8. This process with emphasis on details of fusion technique
has previously been performed; however, we are going to apply the concept of
the interpolation process. In fact, we see many important software tools such
as ENVI and ERDAS as the most famous remote sensing image processing tools have
only classical interpolation techniques (such as bi-linear (BL) and
bi-cubic/cubic convolution (CC)). Therefore, ENVI- and ERDAS-based researches
in image fusion area and even other fusion researches often dont use new and
better interpolators and are mainly concentrated on the fusion algorithms
details for achieving a better quality, so we only focus on the interpolation
impact on fusion quality in Landsat-8 multispectral images. The important
feature of this approach is to use a statistical, adaptive, and edge-guided
interpolation method for improving the color quality in the images in practice.
Numerical simulations show selecting the suitable interpolation techniques in
MRF-based images creates better quality than the classical interpolators.
Deep Learning for automatic sale receipt understanding
Authors: Rizlène Raoui-Outach, Cécile Million-Rousseau, Alexandre Benoit, Patrick Lambert
Date: 2017-12-05 00:00:00
Tasks: Deep Learning

As a general rule, data analytics are now mandatory for companies. Scanned
document analysis brings additional challenges introduced by paper damages and
scanning quality.In an industrial context, this work focuses on the automatic
understanding of sale receipts which enable access to essential and accurate
consumption statistics. Given an image acquired with a smart-phone, the
proposed work mainly focuses on the first steps of the full tool chain which
aims at providing essential information such as the store brand, purchased
products and related prices with the highest possible confidence. To get this
high confidence level, even if scanning is not perfectly controlled, we propose
a double check processing tool-chain using Deep Convolutional Neural Networks
(DCNNs) on one hand and more classical image and text processings on another
hand.The originality of this work relates in this double check processing and
in the joint use of DCNNs for different applications and text analysis.
Learning to Warm-Start Bayesian Hyperparameter Optimization
Authors: Jungtaek Kim, Saehoon Kim, Seungjin Choi
Date: 2017-10-17 00:00:00
Tasks: Bayesian Optimization, global-optimization, Hyperparameter Optimization, Metric Learning

Hyperparameter optimization aims to find the optimal hyperparameter
configuration of a machine learning model, which provides the best performance
on a validation dataset. Manual search usually leads to get stuck in a local
hyperparameter configuration, and heavily depends on human intuition and
experience. A simple alternative of manual search is random/grid search on a
space of hyperparameters, which still undergoes extensive evaluations of
validation errors in order to find its best configuration. Bayesian
optimization that is a global optimization method for black-box functions is
now popular for hyperparameter optimization, since it greatly reduces the
number of validation error evaluations required, compared to random/grid
search. Bayesian optimization generally finds the best hyperparameter
configuration from random initialization without any prior knowledge. This
motivates us to let Bayesian optimization start from the configurations that
were successful on similar datasets, which are able to remarkably minimize the
number of evaluations. In this paper, we propose deep metric learning to learn
meta-features over datasets such that the similarity over them is effectively
measured by Euclidean distance between their associated meta-features. To this
end, we introduce a Siamese network composed of deep feature and meta-feature
extractors, where deep feature extractor provides a semantic representation of
each instance in a dataset and meta-feature extractor aggregates a set of deep
features to encode a single representation over a dataset. Then, our learned
meta-features are used to select a few datasets similar to the new dataset, so
that hyperparameters in similar datasets are adopted as initializations to
warm-start Bayesian hyperparameter optimization.
Deep learning for semantic segmentation of remote sensing images with rich spectral content
Authors: A Hamida, A. Benoît, P. Lambert, L Klein, C Amar, N. Audebert, S. Lefèvre
Date: 2017-12-05 00:00:00
Tasks: Deep Learning, Semantic Segmentation

With the rapid development of Remote Sensing acquisition techniques, there is
a need to scale and improve processing tools to cope with the observed increase
of both data volume and richness. Among popular techniques in remote sensing,
Deep Learning gains increasing interest but depends on the quality of the
training data. Therefore, this paper presents recent Deep Learning approaches
for fine or coarse land cover semantic segmentation estimation. Various 2D
architectures are tested and a new 3D model is introduced in order to jointly
process the spatial and spectral dimensions of the data. Such a set of networks
enables the comparison of the different spectral fusion schemes. Besides, we
also assess the use of a " noisy ground truth " (i.e. outdated and low spatial
resolution labels) for training and testing the networks.