pwc-archive/papers-with-abstracts huggingface dataset:
A Deterministic and Generalized Framework for Unsupervised Learning with Restricted Boltzmann Machines
Authors: Eric W. Tramel, Marylou Gabrié, Andre Manoel, Francesco Caltagirone, Florent Krzakala
Date: 2017-02-10 00:00:00
Tasks: Denoising

Restricted Boltzmann machines (RBMs) are energy-based neural-networks which
are commonly used as the building blocks for deep architectures neural
architectures. In this work, we derive a deterministic framework for the
training, evaluation, and use of RBMs based upon the Thouless-Anderson-Palmer
(TAP) mean-field approximation of widely-connected systems with weak
interactions coming from spin-glass theory. While the TAP approach has been
extensively studied for fully-visible binary spin systems, our construction is
generalized to latent-variable models, as well as to arbitrarily distributed
real-valued spin systems with bounded support. In our numerical experiments, we
demonstrate the effective deterministic training of our proposed models and are
able to show interesting features of unsupervised learning which could not be
directly observed with sampling. Additionally, we demonstrate how to utilize
our TAP-based framework for leveraging trained RBMs as joint priors in
denoising problems.
Deep Convolutional Neural Networks for Interpretable Analysis of EEG Sleep Stage Scoring
Authors: Albert Vilamala, Kristoffer H. Madsen, Lars K. Hansen
Date: 2017-10-02 00:00:00
Tasks: EEG, Electroencephalogram (EEG), Sleep Stage Detection, Transfer Learning

Sleep studies are important for diagnosing sleep disorders such as insomnia,
narcolepsy or sleep apnea. They rely on manual scoring of sleep stages from raw
polisomnography signals, which is a tedious visual task requiring the workload
of highly trained professionals. Consequently, research efforts to purse for an
automatic stage scoring based on machine learning techniques have been carried
out over the last years. In this work, we resort to multitaper spectral
analysis to create visually interpretable images of sleep patterns from EEG
signals as inputs to a deep convolutional network trained to solve visual
recognition tasks. As a working example of transfer learning, a system able to
accurately classify sleep stages in new unseen patients is presented.
Evaluations in a widely-used publicly available dataset favourably compare to
state-of-the-art results, while providing a framework for visual interpretation
of outcomes.
Adaptive Smoothing in fMRI Data Processing Neural Networks
Authors: Albert Vilamala, Kristoffer Hougaard Madsen, Lars Kai Hansen
Date: 2017-10-02 00:00:00

Functional Magnetic Resonance Imaging (fMRI) relies on multi-step data
processing pipelines to accurately determine brain activity; among them, the
crucial step of spatial smoothing. These pipelines are commonly suboptimal,
given the local optimisation strategy they use, treating each step in
isolation. With the advent of new tools for deep learning, recent work has
proposed to turn these pipelines into end-to-end learning networks. This change
of paradigm offers new avenues to improvement as it allows for a global
optimisation. The current work aims at benefitting from this paradigm shift by
defining a smoothing step as a layer in these networks able to adaptively
modulate the degree of smoothing required by each brain volume to better
accomplish a given data analysis task. The viability is evaluated on real fMRI
data where subjects did alternate between left and right finger tapping tasks.
Learning Predictive Leading Indicators for Forecasting Time Series Systems with Unknown Clusters of Forecast Tasks
Authors: Magda Gregorova, Alexandros Kalousis, Stephane Marchand-Maillet
Date: 2017-10-02 00:00:00
Tasks: Time Series, Time Series Analysis

We present a new method for forecasting systems of multiple interrelated time
series. The method learns the forecast models together with discovering leading
indicators from within the system that serve as good predictors improving the
forecast accuracy and a cluster structure of the predictive tasks around these.
The method is based on the classical linear vector autoregressive model (VAR)
and links the discovery of the leading indicators to inferring sparse graphs of
Granger causality. We formulate a new constrained optimisation problem to
promote the desired sparse structures across the models and the sharing of
information amongst the learning tasks in a multi-task manner. We propose an
algorithm for solving the problem and document on a battery of synthetic and
real-data experiments the advantages of our new method over baseline VAR models
as well as the state-of-the-art sparse VAR learning methods.
Indirect Match Highlights Detection with Deep Convolutional Neural Networks
Authors: Marco Godi, Paolo Rota, Francesco Setti
Date: 2017-10-02 00:00:00

Highlights in a sport video are usually referred as actions that stimulate
excitement or attract attention of the audience. A big effort is spent in
designing techniques which find automatically highlights, in order to
automatize the otherwise manual editing process. Most of the state-of-the-art
approaches try to solve the problem by training a classifier using the
information extracted on the tv-like framing of players playing on the game
pitch, learning to detect game actions which are labeled by human observers
according to their perception of highlight. Obviously, this is a long and
expensive work. In this paper, we reverse the paradigm: instead of looking at
the gameplay, inferring what could be exciting for the audience, we directly
analyze the audience behavior, which we assume is triggered by events happening
during the game. We apply deep 3D Convolutional Neural Network (3D-CNN) to
extract visual features from cropped video recordings of the supporters that
are attending the event. Outputs of the crops belonging to the same frame are
then accumulated to produce a value indicating the Highlight Likelihood (HL)
which is then used to discriminate between positive (i.e. when a highlight
occurs) and negative samples (i.e. standard play or time-outs). Experimental
results on a public dataset of ice-hockey matches demonstrate the effectiveness
of our method and promote further research in this new exciting direction.
Selecting the top-quality item through crowd scoring
Authors: Alessandro Nordio, Alberto Tarable, Emilio Leonardi, Marco Ajmone Marsan
Date: 2015-12-23 00:00:00
Tasks: Recommendation Systems

We investigate crowdsourcing algorithms for finding the top-quality item
within a large collection of objects with unknown intrinsic quality values.
This is an important problem with many relevant applications, for example in
networked recommendation systems. The core of the algorithms is that objects
are distributed to crowd workers, who return a noisy and biased evaluation. All
received evaluations are then combined, to identify the top-quality object. We
first present a simple probabilistic model for the system under investigation.
Then, we devise and study a class of efficient adaptive algorithms to assign in
an effective way objects to workers. We compare the performance of several
algorithms, which correspond to different choices of the design
parameters/metrics. In the simulations we show that some of the algorithms
achieve near optimal performance for a suitable setting of the system
parameters.
Attentive Convolution: Equipping CNNs with RNN-style Attention Mechanisms
Authors: Wenpeng Yin, Hinrich Schütze
Date: 2017-10-02 00:00:00
Proceeding: TACL 2018 1
Tasks: Claim Verification, Natural Language Inference, Representation Learning, Sentence, Sentiment Analysis

In NLP, convolutional neural networks (CNNs) have benefited less than
recurrent neural networks (RNNs) from attention mechanisms. We hypothesize that
this is because the attention in CNNs has been mainly implemented as attentive
pooling (i.e., it is applied to pooling) rather than as attentive convolution
(i.e., it is integrated into convolution). Convolution is the differentiator of
CNNs in that it can powerfully model the higher-level representation of a word
by taking into account its local fixed-size context in the input text t^x. In
this work, we propose an attentive convolution network, ATTCONV. It extends the
context scope of the convolution operation, deriving higher-level features for
a word not only from local context, but also information extracted from
nonlocal context by the attention mechanism commonly used in RNNs. This
nonlocal context can come (i) from parts of the input text t^x that are distant
or (ii) from extra (i.e., external) contexts t^y. Experiments on sentence
modeling with zero-context (sentiment analysis), single-context (textual
entailment) and multiple-context (claim verification) demonstrate the
effectiveness of ATTCONV in sentence representation learning with the
incorporation of context. In particular, attentive convolution outperforms
attentive pooling and is a strong competitor to popular attentive RNNs.
Semantic Video Segmentation by Gated Recurrent Flow Propagation
Authors: David Nilsson, Cristian Sminchisescu
Date: 2016-12-28 00:00:00
Proceeding: CVPR 2018 6
Tasks: Optical Flow Estimation, Segmentation, Semantic Segmentation, Video Segmentation, Video Semantic Segmentation

Semantic video segmentation is challenging due to the sheer amount of data
that needs to be processed and labeled in order to construct accurate models.
In this paper we present a deep, end-to-end trainable methodology to video
segmentation that is capable of leveraging information present in unlabeled
data in order to improve semantic estimates. Our model combines a convolutional
architecture and a spatio-temporal transformer recurrent layer that are able to
temporally propagate labeling information by means of optical flow, adaptively
gated based on its locally estimated uncertainty. The flow, the recognition and
the gated temporal propagation modules can be trained jointly, end-to-end. The
temporal, gated recurrent flow propagation component of our model can be
plugged into any static semantic segmentation architecture and turn it into a
weakly supervised video processing one. Our extensive experiments in the
challenging CityScapes and Camvid datasets, and based on multiple deep
architectures, indicate that the resulting model can leverage unlabeled
temporal frames, next to a labeled one, in order to improve both the video
segmentation accuracy and the consistency of its temporal labeling, at no
additional annotation cost and with little extra computation.
Temporal shape super-resolution by intra-frame motion encoding using high-fps structured light
Authors: Yuki Shiba, Satoshi Ono, Ryo Furukawa, Shinsaku Hiura, Hiroshi Kawasaki
Date: 2017-10-02 00:00:00
Proceeding: ICCV 2017 10
Tasks: Object, Super-Resolution

One of the solutions of depth imaging of moving scene is to project a static
pattern on the object and use just a single image for reconstruction. However,
if the motion of the object is too fast with respect to the exposure time of
the image sensor, patterns on the captured image are blurred and reconstruction
fails. In this paper, we impose multiple projection patterns into each single
captured image to realize temporal super resolution of the depth image
sequences. With our method, multiple patterns are projected onto the object
with higher fps than possible with a camera. In this case, the observed pattern
varies depending on the depth and motion of the object, so we can extract
temporal information of the scene from each single image. The decoding process
is realized using a learning-based approach where no geometric calibration is
needed. Experiments confirm the effectiveness of our method where sequential
shapes are reconstructed from a single image. Both quantitative evaluations and
comparisons with recent techniques were also conducted.
Depth estimation using structured light flow -- analysis of projected pattern flow on an object's surface --
Authors: Ryo Furukawa, Ryusuke Sagawa, Hiroshi Kawasaki
Date: 2017-10-02 00:00:00
Proceeding: ICCV 2017
Tasks: Depth Estimation

Shape reconstruction techniques using structured light have been widely
researched and developed due to their robustness, high precision, and density.
Because the techniques are based on decoding a pattern to find correspondences,
it implicitly requires that the projected patterns be clearly captured by an
image sensor, i.e., to avoid defocus and motion blur of the projected pattern.
Although intensive researches have been conducted for solving defocus blur, few
researches for motion blur and only solution is to capture with extremely fast
shutter speed. In this paper, unlike the previous approaches, we actively
utilize motion blur, which we refer to as a light flow, to estimate depth.
Analysis reveals that minimum two light flows, which are retrieved from two
projected patterns on the object, are required for depth estimation. To
retrieve two light flows at the same time, two sets of parallel line patterns
are illuminated from two video projectors and the size of motion blur of each
line is precisely measured. By analyzing the light flows, i.e. lengths of the
blurs, scene depth information is estimated. In the experiments, 3D shapes of
fast moving objects, which are inevitably captured with motion blur, are
successfully reconstructed by our technique.
Online control of the false discovery rate with decaying memory
Authors: Aaditya Ramdas, Fanny Yang, Martin J. Wainwright, Michael. I. Jordan
Date: 2017-10-02 00:00:00
Proceeding: NeurIPS 2017 12
Tasks: Unity

In the online multiple testing problem, p-values corresponding to different
null hypotheses are observed one by one, and the decision of whether or not to
reject the current hypothesis must be made immediately, after which the next
p-value is observed. Alpha-investing algorithms to control the false discovery
rate (FDR), formulated by Foster and Stine, have been generalized and applied
to many settings, including quality-preserving databases in science and
multiple A/B or multi-armed bandit tests for internet commerce. This paper
improves the class of generalized alpha-investing algorithms (GAI) in four
ways: (a) we show how to uniformly improve the power of the entire class of
monotone GAI procedures by awarding more alpha-wealth for each rejection,
giving a win-win resolution to a recent dilemma raised by Javanmard and
Montanari, (b) we demonstrate how to incorporate prior weights to indicate
domain knowledge of which hypotheses are likely to be non-null, (c) we allow
for differing penalties for false discoveries to indicate that some hypotheses
may be more important than others, (d) we define a new quantity called the
decaying memory false discovery rate (mem-FDR) that may be more meaningful for
truly temporal applications, and which alleviates problems that we describe and
refer to as "piggybacking" and "alpha-death". Our GAI++ algorithms incorporate
all four generalizations simultaneously, and reduce to more powerful variants
of earlier algorithms when the weights and decay are all set to unity. Finally,
we also describe a simple method to derive new online FDR rules based on an
estimated false discovery proportion.
The Dutch's Real World Financial Institute: Introducing Quantum-Like Bayesian Networks as an Alternative Model to deal with Uncertainty
Authors: Catarina Moreira, Emmanuel Haven, Sandro Sozzo, Andreas Wichert
Date: 2017-10-02 00:00:00

In this work, we analyse and model a real life financial loan application
belonging to a sample bank in the Netherlands. The log is robust in terms of
data, containing a total of 262 200 event logs, belonging to 13 087 different
credit applications. The dataset is heterogeneous and consists of a mixture of
computer generated automatic processes and manual human tasks. The goal is to
work out a decision model, which represents the underlying tasks that make up
the loan application service, and to assess potential areas of improvement of
the institution's internal processes. To this end we study the impact of
incomplete event logs for the extraction and analysis of business processes. It
is quite common that event logs are incomplete with several amounts of missing
information (for instance, workers forget to register their tasks). Absence of
data is translated into a drastic decrease of precision and compromises the
decision models, leading to biased and unrepresentative results. We investigate
how classical probabilistic models are affected by incomplete event logs and we
explore quantum-like probabilistic inferences as an alternative mathematical
model to classical probability. This work represents a first step towards
systematic investigation of the impact of quantum interference in a real life
large scale decision scenario. The results obtained in this study indicate
that, under high levels of uncertainty, the quantum-like models generate
quantum interference terms, which allow an additional non-linear
parameterisation of the data. Experimental results attest the efficiency of the
quantum-like Bayesian networks, since the application of interference terms is
able to reduce the error percentage of inferences performed over quantum-like
models when compared to inferences produced by classical models.
SE3-Pose-Nets: Structured Deep Dynamics Models for Visuomotor Planning and Control
Authors: Arunkumar Byravan, Felix Leeb, Franziska Meier, Dieter Fox
Date: 2017-10-02 00:00:00
Tasks: Decoder

In this work, we present an approach to deep visuomotor control using
structured deep dynamics models. Our deep dynamics model, a variant of
SE3-Nets, learns a low-dimensional pose embedding for visuomotor control via an
encoder-decoder structure. Unlike prior work, our dynamics model is structured:
given an input scene, our network explicitly learns to segment salient parts
and predict their pose-embedding along with their motion modeled as a change in
the pose space due to the applied actions. We train our model using a pair of
point clouds separated by an action and show that given supervision only in the
form of point-wise data associations between the frames our network is able to
learn a meaningful segmentation of the scene along with consistent poses. We
further show that our model can be used for closed-loop control directly in the
learned low-dimensional pose space, where the actions are computed by
minimizing error in the pose space using gradient-based methods, similar to
traditional model-based control. We present results on controlling a Baxter
robot from raw depth data in simulation and in the real world and compare
against two baseline deep networks. Our method runs in real-time, achieves good
prediction of scene dynamics and outperforms the baseline methods on multiple
control runs. Video results can be found at:
https://rse-lab.cs.washington.edu/se3-structured-deep-ctrl/
DeepSafe: A Data-driven Approach for Checking Adversarial Robustness in Neural Networks
Authors: Divya Gopinath, Guy Katz, Corina S. Pasareanu, Clark Barrett
Date: 2017-10-02 00:00:00
Tasks: Adversarial Robustness, Clustering, Collision Avoidance, Machine Translation, speech-recognition, Speech Recognition

Deep neural networks have become widely used, obtaining remarkable results in domains such as computer vision, speech recognition, natural language processing, audio recognition, social network filtering, machine translation, and bio-informatics, where they have produced results comparable to human experts. However, these networks can be easily fooled by adversarial perturbations: minimal changes to correctly-classified inputs, that cause the network to mis-classify them. This phenomenon represents a concern for both safety and security, but it is currently unclear how to measure a network's robustness against such perturbations. Existing techniques are limited to checking robustness around a few individual input points, providing only very limited guarantees. We propose a novel approach for automatically identifying safe regions of the input space, within which the network is robust against adversarial perturbations. The approach is data-guided, relying on clustering to identify well-defined geometric regions as candidate safe regions. We then utilize verification techniques to confirm that these regions are safe or to provide counter-examples showing that they are not safe. We also introduce the notion of targeted robustness which, for a given target label and region, ensures that a NN does not map any input in the region to the target label. We evaluated our technique on the MNIST dataset and on a neural network implementation of a controller for the next-generation Airborne Collision Avoidance System for unmanned aircraft (ACAS Xu). For these networks, our approach identified multiple regions which were completely safe as well as some which were only safe for specific labels. It also discovered several adversarial perturbations of interest.
Weighted-SVD: Matrix Factorization with Weights on the Latent Factors
Authors: Hung-Hsuan Chen
Date: 2017-10-02 00:00:00
Tasks: Recommendation Systems

The Matrix Factorization models, sometimes called the latent factor models,
are a family of methods in the recommender system research area to (1) generate
the latent factors for the users and the items and (2) predict users' ratings
on items based on their latent factors. However, current Matrix Factorization
models presume that all the latent factors are equally weighted, which may not
always be a reasonable assumption in practice. In this paper, we propose a new
model, called Weighted-SVD, to integrate the linear regression model with the
SVD model such that each latent factor accompanies with a corresponding weight
parameter. This mechanism allows the latent factors have different weights to
influence the final ratings. The complexity of the Weighted-SVD model is
slightly larger than the SVD model but much smaller than the SVD++ model. We
compared the Weighted-SVD model with several latent factor models on five
public datasets based on the Root-Mean-Squared-Errors (RMSEs). The results show
that the Weighted-SVD model outperforms the baseline methods in all the
experimental datasets under almost all settings.
Unified Framework for Automated Person Re-identification and Camera Network Topology Inference in Camera Networks
Authors: Yeong-Jun Cho, Jae-Han Park, Su-A Kim, Kyuewang Lee, Kuk-Jin Yoon
Date: 2017-04-24 00:00:00
Tasks: Person Re-Identification

Person re-identification in large-scale multi-camera networks is a
challenging task because of the spatio-temporal uncertainty and high complexity
due to large numbers of cameras and people. To handle these difficulties,
additional information such as camera network topology should be provided,
which is also difficult to automatically estimate. In this paper, we propose a
unified framework which jointly solves both person re-id and camera network
topology inference problems. The proposed framework takes general multi-camera
network environments into account. To effectively show the superiority of the
proposed framework, we also provide a new person re-id dataset with full
annotations, named SLP, captured in the synchronized multi-camera network.
Experimental results show that the proposed methods are promising for both
person re-id and camera topology inference tasks.