pwc-archive/papers-with-abstracts huggingface dataset:
What Catches the Eye? Visualizing and Understanding Deep Saliency Models
Authors: Sen He, Ali Borji, Yang Mi, Nicolas Pugeault
Date: 2018-03-15 00:00:00
Tasks: Prediction

Deep convolutional neural networks have demonstrated high performances for
fixation prediction in recent years. How they achieve this, however, is less
explored and they remain to be black box models. Here, we attempt to shed light
on the internal structure of deep saliency models and study what features they
extract for fixation prediction. Specifically, we use a simple yet powerful
architecture, consisting of only one CNN and a single resolution input,
combined with a new loss function for pixel-wise fixation prediction during
free viewing of natural scenes. We show that our simple method is on par or
better than state-of-the-art complicated saliency models. Furthermore, we
propose a method, related to saliency model evaluation metrics, to visualize
deep models for fixation prediction. Our method reveals the inner
representations of deep models for fixation prediction and provides evidence
that saliency, as experienced by humans, is likely to involve high-level
semantic knowledge in addition to low-level perceptual cues. Our results can be
useful to measure the gap between current saliency models and the human
inter-observer model and to build new models to close this gap.
Speaker Clustering With Neural Networks And Audio Processing
Authors: Maxime Jumelle, Taqiyeddine Sakmeche
Date: 2018-03-22 00:00:00
Tasks: Clustering

Speaker clustering is the task of differentiating speakers in a recording. In
a way, the aim is to answer "who spoke when" in audio recordings. A common
method used in industry is feature extraction directly from the recording
thanks to MFCC features, and by using well-known techniques such as Gaussian
Mixture Models (GMM) and Hidden Markov Models (HMM). In this paper, we studied
neural networks (especially CNN) followed by clustering and audio processing in
the quest to reach similar accuracy to state-of-the-art methods.
Risk-sensitive Inverse Reinforcement Learning via Semi- and Non-Parametric Methods
Authors: Sumeet Singh, Jonathan Lacotte, Anirudha Majumdar, Marco Pavone
Date: 2017-11-28 00:00:00
Tasks: Decision Making, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

The literature on Inverse Reinforcement Learning (IRL) typically assumes that
humans take actions in order to minimize the expected value of a cost function,
i.e., that humans are risk neutral. Yet, in practice, humans are often far from
being risk neutral. To fill this gap, the objective of this paper is to devise
a framework for risk-sensitive IRL in order to explicitly account for a human's
risk sensitivity. To this end, we propose a flexible class of models based on
coherent risk measures, which allow us to capture an entire spectrum of risk
preferences from risk-neutral to worst-case. We propose efficient
non-parametric algorithms based on linear programming and semi-parametric
algorithms based on maximum likelihood for inferring a human's underlying risk
measure and cost function for a rich class of static and dynamic
decision-making settings. The resulting approach is demonstrated on a simulated
driving game with ten human participants. Our method is able to infer and mimic
a wide range of qualitatively different driving styles from highly risk-averse
to risk-neutral in a data-efficient manner. Moreover, comparisons of the
Risk-Sensitive (RS) IRL approach with a risk-neutral model show that the RS-IRL
framework more accurately captures observed participant behavior both
qualitatively and quantitatively, especially in scenarios where catastrophic
outcomes such as collisions can occur.
Unsupervised Adversarial Learning of 3D Human Pose from 2D Joint Locations
Authors: Yasunori Kudo, Keisuke Ogaki, Yusuke Matsui, Yuri Odagiri
Date: 2018-03-22 00:00:00
Tasks: 3D Human Pose Estimation, 3D Pose Estimation, Pose Estimation

The task of three-dimensional (3D) human pose estimation from a single image
can be divided into two parts: (1) Two-dimensional (2D) human joint detection
from the image and (2) estimating a 3D pose from the 2D joints. Herein, we
focus on the second part, i.e., a 3D pose estimation from 2D joint locations.
The problem with existing methods is that they require either (1) a 3D pose
dataset or (2) 2D joint locations in consecutive frames taken from a video
sequence. We aim to solve these problems. For the first time, we propose a
method that learns a 3D human pose without any 3D datasets. Our method can
predict a 3D pose from 2D joint locations in a single image. Our system is
based on the generative adversarial networks, and the networks are trained in
an unsupervised manner. Our primary idea is that, if the network can predict a
3D human pose correctly, the 3D pose that is projected onto a 2D plane should
not collapse even if it is rotated perpendicularly. We evaluated the
performance of our method using Human3.6M and the MPII dataset and showed that
our network can predict a 3D pose well even if the 3D dataset is not available
during training.
An Analysis of Neural Language Modeling at Multiple Scales
Authors: Stephen Merity, Nitish Shirish Keskar, Richard Socher
Date: 2018-03-22 00:00:00
Tasks: GPU, Language Modeling, Language Modelling

Many of the leading approaches in language modeling introduce novel, complex
and specialized architectures. We take existing state-of-the-art word level
language models based on LSTMs and QRNNs and extend them to both larger
vocabularies as well as character-level granularity. When properly tuned, LSTMs
and QRNNs achieve state-of-the-art results on character-level (Penn Treebank,
enwik8) and word-level (WikiText-103) datasets, respectively. Results are
obtained in only 12 hours (WikiText-103) to 2 days (enwik8) using a single
modern GPU.
AON: Towards Arbitrarily-Oriented Text Recognition
Authors: Zhanzhan Cheng, Yangliu Xu, Fan Bai, Yi Niu, ShiLiang Pu, Shuigeng Zhou
Date: 2017-11-12 00:00:00
Proceeding: CVPR 2018 6
Tasks: Decoder, Optical Character Recognition, Optical Character Recognition (OCR), Scene Text Recognition

Recognizing text from natural images is a hot research topic in computer
vision due to its various applications. Despite the enduring research of
several decades on optical character recognition (OCR), recognizing texts from
natural images is still a challenging task. This is because scene texts are
often in irregular (e.g. curved, arbitrarily-oriented or seriously distorted)
arrangements, which have not yet been well addressed in the literature.
Existing methods on text recognition mainly work with regular (horizontal and
frontal) texts and cannot be trivially generalized to handle irregular texts.
In this paper, we develop the arbitrary orientation network (AON) to directly
capture the deep features of irregular texts, which are combined into an
attention-based decoder to generate character sequence. The whole network can
be trained end-to-end by using only images and word-level annotations.
Extensive experiments on various benchmarks, including the CUTE80,
SVT-Perspective, IIIT5k, SVT and ICDAR datasets, show that the proposed
AON-based method achieves the-state-of-the-art performance in irregular
datasets, and is comparable to major existing methods in regular datasets.
ISLAND: In-Silico Prediction of Proteins Binding Affinity Using Sequence Descriptors
Authors: Wajid Arshad Abbasi, Fahad Ul Hassan, Adiba Yaseen, Fayyaz ul Amir Afsar Minhas
Date: 2017-11-22 00:00:00

Determination of binding affinity of proteins in the formation of protein
complexes requires sophisticated, expensive and time-consuming experimentation
which can be replaced with computational methods. Most computational prediction
techniques require protein structures which limit their applicability to
protein complexes with known structures. In this work, we explore sequence
based protein binding affinity prediction using machine learning. Our paper
highlights the fact that the generalization performance of even the state of
the art sequence-only predictor of binding affinity is far from satisfactory
and that the development of effective and practical methods in this domain is
still an open problem. We also propose a novel sequence-only predictor of
binding affinity called ISLAND which gives better accuracy than existing
methods over the same validation set as well as on external independent test
dataset. A cloud-based webserver implementation of ISLAND and its Python code
are available at the URL:
http://faculty.pieas.edu.pk/fayyaz/software.html#island.
Entrenamiento de una red neuronal para el reconocimiento de imagenes de lengua de senas capturadas con sensores de profundidad
Authors: Rivas P. Pedro E., Velarde-Anaya Omar, Gonzalez-Lopez Samuel, Rivas P. Pablo, Alvarez-Torres Norma Angelica
Date: 2018-03-22 00:00:00
Tasks: Classification, General Classification, image-classification, Image Classification

Due to the growth of the population with hearing problems, devices have been
developed that facilitate the inclusion of deaf people in society, using
technology as a communication tool, such as vision systems. Then, a solution to
this problem is presented using neural networks and autoencoders for the
classification of American Sign Language images. As a result, 99.5% accuracy
and an error of 0.01684 were obtained for image classification
From Shannon's Channel to Semantic Channel via New Bayes' Formulas for Machine Learning
Authors: Chenguang Lu
Date: 2018-03-22 00:00:00
Tasks: Bayesian Inference, BIG-bench Machine Learning, Two-sample testing

A group of transition probability functions form a Shannon's channel whereas
a group of truth functions form a semantic channel. By the third kind of Bayes'
theorem, we can directly convert a Shannon's channel into an optimized semantic
channel. When a sample is not big enough, we can use a truth function with
parameters to produce the likelihood function, then train the truth function by
the conditional sampling distribution. The third kind of Bayes' theorem is
proved. A semantic information theory is simply introduced. The semantic
information measure reflects Popper's hypothesis-testing thought. The Semantic
Information Method (SIM) adheres to maximum semantic information criterion
which is compatible with maximum likelihood criterion and Regularized Least
Squares criterion. It supports Wittgenstein's view: the meaning of a word lies
in its use. Letting the two channels mutually match, we obtain the Channels'
Matching (CM) algorithm for machine learning. The CM algorithm is used to
explain the evolution of the semantic meaning of natural language, such as "Old
age". The semantic channel for medical tests and the confirmation measures of
test-positive and test-negative are discussed. The applications of the CM
algorithm to semi-supervised learning and non-supervised learning are simply
introduced. As a predictive model, the semantic channel fits variable sources
and hence can overcome class-imbalance problem. The SIM strictly distinguishes
statistical probability and logical probability and uses both at the same time.
This method is compatible with the thoughts of Bayes, Fisher, Shannon, Zadeh,
Tarski, Davidson, Wittgenstein, and Popper.It is a competitive alternative to
Bayesian inference.
PersonLab: Person Pose Estimation and Instance Segmentation with a Bottom-Up, Part-Based, Geometric Embedding Model
Authors: George Papandreou, Tyler Zhu, Liang-Chieh Chen, Spyros Gidaris, Jonathan Tompson, Kevin Murphy
Date: 2018-03-22 00:00:00
Proceeding: ECCV 2018 9
Tasks: Instance Segmentation, Keypoint Detection, Multi-Person Pose Estimation, Pose Estimation, Semantic Segmentation

We present a box-free bottom-up approach for the tasks of pose estimation and
instance segmentation of people in multi-person images using an efficient
single-shot model. The proposed PersonLab model tackles both semantic-level
reasoning and object-part associations using part-based modeling. Our model
employs a convolutional network which learns to detect individual keypoints and
predict their relative displacements, allowing us to group keypoints into
person pose instances. Further, we propose a part-induced geometric embedding
descriptor which allows us to associate semantic person pixels with their
corresponding person instance, delivering instance-level person segmentations.
Our system is based on a fully-convolutional architecture and allows for
efficient inference, with runtime essentially independent of the number of
people present in the scene. Trained on COCO data alone, our system achieves
COCO test-dev keypoint average precision of 0.665 using single-scale inference
and 0.687 using multi-scale inference, significantly outperforming all previous
bottom-up pose estimation systems. We are also the first bottom-up method to
report competitive results for the person class in the COCO instance
segmentation task, achieving a person category average precision of 0.417.
Deep Net Triage: Analyzing the Importance of Network Layers via Structural Compression
Authors: Theodore S. Nowak, Jason J. Corso
Date: 2018-01-15 00:00:00
Tasks: Knowledge Distillation

Despite their prevalence, deep networks are poorly understood. This is due,
at least in part, to their highly parameterized nature. As such, while certain
structures have been found to work better than others, the significance of a
model's unique structure, or the importance of a given layer, and how these
translate to overall accuracy, remains unclear. In this paper, we analyze these
properties of deep neural networks via a process we term deep net triage. Like
medical triage---the assessment of the importance of various wounds---we assess
the importance of layers in a neural network, or as we call it, their
criticality. We do this by applying structural compression, whereby we reduce a
block of layers to a single layer. After compressing a set of layers, we apply
a combination of initialization and training schemes, and look at network
accuracy, convergence, and the layer's learned filters to assess the
criticality of the layer. We apply this analysis across four data sets of
varying complexity. We find that the accuracy of the model does not depend on
which layer was compressed; that accuracy can be recovered or exceeded after
compression by fine-tuning across the entire model; and, lastly, that Knowledge
Distillation can be used to hasten convergence of a compressed network, but
constrains the accuracy attainable to that of the base model.
Local Binary Pattern Networks
Authors: Jeng-Hau Lin, Yunfan Yang, Rajesh Gupta, Zhuowen Tu
Date: 2018-03-19 00:00:00
Tasks: Binarization

Memory and computation efficient deep learning architec- tures are crucial to
continued proliferation of machine learning capabili- ties to new platforms and
systems. Binarization of operations in convo- lutional neural networks has
shown promising results in reducing model size and computing efficiency. In
this paper, we tackle the problem us- ing a strategy different from the
existing literature by proposing local binary pattern networks or LBPNet, that
is able to learn and perform binary operations in an end-to-end fashion.
LBPNet1 uses local binary comparisons and random projection in place of
conventional convolu- tion (or approximation of convolution) operations. These
operations can be implemented efficiently on different platforms including
direct hard- ware implementation. We applied LBPNet and its variants on
standard benchmarks. The results are promising across benchmarks while provid-
ing an important means to improve memory and speed efficiency that is
particularly suited for small footprint devices and hardware accelerators.
Adaptive Estimation in Structured Factor Models with Applications to Overlapping Clustering
Authors: Xin Bing, Florentina Bunea, Yang Ning, Marten Wegkamp
Date: 2017-04-23 00:00:00
Tasks: Clustering

This work introduces a novel estimation method, called LOVE, of the entries and structure of a loading matrix A in a sparse latent factor model X = AZ + E, for an observable random vector X in Rp, with correlated unobservable factors Z \in RK, with K unknown, and independent noise E. Each row of A is scaled and sparse. In order to identify the loading matrix A, we require the existence of pure variables, which are components of X that are associated, via A, with one and only one latent factor. Despite the fact that the number of factors K, the number of the pure variables, and their location are all unknown, we only require a mild condition on the covariance matrix of Z, and a minimum of only two pure variables per latent factor to show that A is uniquely defined, up to signed permutations. Our proofs for model identifiability are constructive, and lead to our novel estimation method of the number of factors and of the set of pure variables, from a sample of size n of observations on X. This is the first step of our LOVE algorithm, which is optimization-free, and has low computational complexity of order p2. The second step of LOVE is an easily implementable linear program that estimates A. We prove that the resulting estimator is minimax rate optimal up to logarithmic factors in p. The model structure is motivated by the problem of overlapping variable clustering, ubiquitous in data science. We define the population level clusters as groups of those components of X that are associated, via the sparse matrix A, with the same unobservable latent factor, and multi-factor association is allowed. Clusters are respectively anchored by the pure variables, and form overlapping sub-groups of the p-dimensional random vector X. The Latent model approach to OVErlapping clustering is reflected in the name of our algorithm, LOVE.
Information Theoretic Interpretation of Deep learning
Authors: Tianchen Zhao
Date: 2018-03-21 00:00:00
Tasks: Deep Learning

We interpret part of the experimental results of Shwartz-Ziv and Tishby
[2017]. Inspired by these results, we established a conjecture of the dynamics
of the machinary of deep neural network. This conjecture can be used to explain
the counterpart result by Saxe et al. [2018].
Single-Shot Bidirectional Pyramid Networks for High-Quality Object Detection
Authors: Xiongwei Wu, Daoxin Zhang, Jianke Zhu, Steven C. H. Hoi
Date: 2018-03-22 00:00:00
Tasks: Object, object-detection, Object Detection, Vocal Bursts Intensity Prediction

Recent years have witnessed many exciting achievements for object detection
using deep learning techniques. Despite achieving significant progresses, most
existing detectors are designed to detect objects with relatively low-quality
prediction of locations, i.e., often trained with the threshold of Intersection
over Union (IoU) set to 0.5 by default, which can yield low-quality or even
noisy detections. It remains an open challenge for how to devise and train a
high-quality detector that can achieve more precise localization (i.e.,
IoU$>$0.5) without sacrificing the detection performance. In this paper, we
propose a novel single-shot detection framework of Bidirectional Pyramid
Networks (BPN) towards high-quality object detection, which consists of two
novel components: (i) a Bidirectional Feature Pyramid structure for more
effective and robust feature representations; and (ii) a Cascade Anchor
Refinement to gradually refine the quality of predesigned anchors for more
effective training. Our experiments showed that the proposed BPN achieves the
best performances among all the single-stage object detectors on both PASCAL
VOC and MS COCO datasets, especially for high-quality detections.
Positive-unlabeled convolutional neural networks for particle picking in cryo-electron micrographs
Authors: Tristan Bepler, Andrew Morin, Julia Brasch, Lawrence Shapiro, Alex J. Noble, Bonnie Berger
Date: 2018-03-22 00:00:00

Cryo-electron microscopy (cryoEM) is an increasingly popular method for
protein structure determination. However, identifying a sufficient number of
particles for analysis (often >100,000) can take months of manual effort.
Current computational approaches are limited by high false positive rates and
require significant ad-hoc post-processing, especially for unusually shaped
particles. To address this shortcoming, we develop Topaz, an efficient and
accurate particle picking pipeline using neural networks trained with few
labeled particles by newly leveraging the remaining unlabeled particles through
the framework of positive-unlabeled (PU) learning. Remarkably, despite using
minimal labeled particles, Topaz allows us to improve reconstruction resolution
by up to 0.15 {\AA} over published particles on three public cryoEM datasets
without any post-processing. Furthermore, we show that our novel
generalized-expectation criteria approach to PU learning outperforms existing
general PU learning approaches when applied to particle detection, especially
for challenging datasets of non-globular proteins. We expect Topaz to be an
essential component of cryoEM analysis.
High-order Tensor Completion for Data Recovery via Sparse Tensor-train Optimization
Authors: Longhao Yuan, Qibin Zhao, Jianting Cao
Date: 2017-11-07 00:00:00
Tasks: Vocal Bursts Intensity Prediction

In this paper, we aim at the problem of tensor data completion. Tensor-train
decomposition is adopted because of its powerful representation ability and
linear scalability to tensor order. We propose an algorithm named Sparse
Tensor-train Optimization (STTO) which considers incomplete data as sparse
tensor and uses first-order optimization method to find the factors of
tensor-train decomposition. Our algorithm is shown to perform well in
simulation experiments at both low-order cases and high-order cases. We also
employ a tensorization method to transform data to a higher-order form to
enhance the performance of our algorithm. The results of image recovery
experiments in various cases manifest that our method outperforms other
completion algorithms. Especially when the missing rate is very high, e.g.,
90\% to 99\%, our method is significantly better than the state-of-the-art
methods.
Residual Networks: Lyapunov Stability and Convex Decomposition
Authors: Kamil Nar, Shankar Sastry
Date: 2018-03-22 00:00:00

While training error of most deep neural networks degrades as the depth of
the network increases, residual networks appear to be an exception. We show
that the main reason for this is the Lyapunov stability of the gradient descent
algorithm: for an arbitrarily chosen step size, the equilibria of the gradient
descent are most likely to remain stable for the parametrization of residual
networks. We then present an architecture with a pair of residual networks to
approximate a large class of functions by decomposing them into a convex and a
concave part. Some parameters of this model are shown to change little during
training, and this imperfect optimization prevents overfitting the data and
leads to solutions with small Lipschitz constants, while providing clues about
the generalization of other deep networks.