pwc-archive/papers-with-abstracts huggingface dataset:
Learning to Reconstruct Texture-less Deformable Surfaces from a Single View
Authors: Jan Bednařík, Pascal Fua, Mathieu Salzmann
Date: 2018-03-23 00:00:00
Tasks: 3D Reconstruction

Recent years have seen the development of mature solutions for reconstructing
deformable surfaces from a single image, provided that they are relatively
well-textured. By contrast, recovering the 3D shape of texture-less surfaces
remains an open problem, and essentially relates to Shape-from-Shading. In this
paper, we introduce a data-driven approach to this problem. We introduce a
general framework that can predict diverse 3D representations, such as meshes,
normals, and depth maps. Our experiments show that meshes are ill-suited to
handle texture-less 3D reconstruction in our context. Furthermore, we
demonstrate that our approach generalizes well to unseen objects, and that it
yields higher-quality reconstructions than a state-of-the-art SfS technique,
particularly in terms of normal estimates. Our reconstructions accurately model
the fine details of the surfaces, such as the creases of a T-Shirt worn by a
person.
Structured Uncertainty Prediction Networks
Authors: Garoe Dorta, Sara Vicente, Lourdes Agapito, Neill D. F. Campbell, Ivor Simpson
Date: 2018-02-20 00:00:00
Proceeding: CVPR 2018 6
Tasks: Denoising, Image Denoising, Prediction

This paper is the first work to propose a network to predict a structured
uncertainty distribution for a synthesized image. Previous approaches have been
mostly limited to predicting diagonal covariance matrices. Our novel model
learns to predict a full Gaussian covariance matrix for each reconstruction,
which permits efficient sampling and likelihood evaluation.
  We demonstrate that our model can accurately reconstruct ground truth
correlated residual distributions for synthetic datasets and generate plausible
high frequency samples for real face images. We also illustrate the use of
these predicted covariances for structure preserving image denoising.
Context Encoding for Semantic Segmentation
Authors: Hang Zhang, Kristin Dana, Jianping Shi, Zhongyue Zhang, Xiaogang Wang, Ambrish Tyagi, Amit Agrawal
Date: 2018-03-23 00:00:00
Proceeding: CVPR 2018 6
Tasks: image-classification, Image Classification, Segmentation, Semantic Segmentation, Thermal Image Segmentation

Recent work has made significant progress in improving spatial resolution for
pixelwise labeling with Fully Convolutional Network (FCN) framework by
employing Dilated/Atrous convolution, utilizing multi-scale features and
refining boundaries. In this paper, we explore the impact of global contextual
information in semantic segmentation by introducing the Context Encoding
Module, which captures the semantic context of scenes and selectively
highlights class-dependent featuremaps. The proposed Context Encoding Module
significantly improves semantic segmentation results with only marginal extra
computation cost over FCN. Our approach has achieved new state-of-the-art
results 51.7% mIoU on PASCAL-Context, 85.9% mIoU on PASCAL VOC 2012. Our single
model achieves a final score of 0.5567 on ADE20K test set, which surpass the
winning entry of COCO-Place Challenge in 2017. In addition, we also explore how
the Context Encoding Module can improve the feature representation of
relatively shallow networks for the image classification on CIFAR-10 dataset.
Our 14 layer network has achieved an error rate of 3.45%, which is comparable
with state-of-the-art approaches with over 10 times more layers. The source
code for the complete system are publicly available.
Explicit Reasoning over End-to-End Neural Architectures for Visual Question Answering
Authors: Somak Aditya, Yezhou Yang, Chitta Baral
Date: 2018-03-23 00:00:00
Tasks: Question Answering, Visual Question Answering, Visual Question Answering (VQA)

Many vision and language tasks require commonsense reasoning beyond
data-driven image and natural language processing. Here we adopt Visual
Question Answering (VQA) as an example task, where a system is expected to
answer a question in natural language about an image. Current state-of-the-art
systems attempted to solve the task using deep neural architectures and
achieved promising performance. However, the resulting systems are generally
opaque and they struggle in understanding questions for which extra knowledge
is required. In this paper, we present an explicit reasoning layer on top of a
set of penultimate neural network based systems. The reasoning layer enables
reasoning and answering questions where additional knowledge is required, and
at the same time provides an interpretable interface to the end users.
Specifically, the reasoning layer adopts a Probabilistic Soft Logic (PSL) based
engine to reason over a basket of inputs: visual relations, the semantic parse
of the question, and background ontological knowledge from word2vec and
ConceptNet. Experimental analysis of the answers and the key evidential
predicates generated on the VQA dataset validate our approach.
Dist-GAN: An Improved GAN using Distance Constraints
Authors: Ngoc-Trung Tran, Tuan-Anh Bui, Ngai-Man Cheung
Date: 2018-03-23 00:00:00
Proceeding: ECCV 2018 9
Tasks: Image Generation

We introduce effective training algorithms for Generative Adversarial
Networks (GAN) to alleviate mode collapse and gradient vanishing. In our
system, we constrain the generator by an Autoencoder (AE). We propose a
formulation to consider the reconstructed samples from AE as "real" samples for
the discriminator. This couples the convergence of the AE with that of the
discriminator, effectively slowing down the convergence of discriminator and
reducing gradient vanishing. Importantly, we propose two novel distance
constraints to improve the generator. First, we propose a latent-data distance
constraint to enforce compatibility between the latent sample distances and the
corresponding data sample distances. We use this constraint to explicitly
prevent the generator from mode collapse. Second, we propose a
discriminator-score distance constraint to align the distribution of the
generated samples with that of the real samples through the discriminator
score. We use this constraint to guide the generator to synthesize samples that
resemble the real ones. Our proposed GAN using these distance constraints,
namely Dist-GAN, can achieve better results than state-of-the-art methods
across benchmark datasets: synthetic, MNIST, MNIST-1K, CelebA, CIFAR-10 and
STL-10 datasets. Our code is published here (https://github.com/tntrung/gan)
for research.
Defeasible Reasoning in SROEL: from Rational Entailment to Rational Closure
Authors: Laura Giordano, Daniele Theseider Dupré
Date: 2018-03-23 00:00:00
Tasks: Negation

In this work we study a rational extension $SROEL^R T$ of the low complexity
description logic SROEL, which underlies the OWL EL ontology language. The
extension involves a typicality operator T, whose semantics is based on Lehmann
and Magidor's ranked models and allows for the definition of defeasible
inclusions. We consider both rational entailment and minimal entailment. We
show that deciding instance checking under minimal entailment is in general
$\Pi^P_2$-hard, while, under rational entailment, instance checking can be
computed in polynomial time. We develop a Datalog calculus for instance
checking under rational entailment and exploit it, with stratified negation,
for computing the rational closure of simple KBs in polynomial time.
Inequity aversion improves cooperation in intertemporal social dilemmas
Authors: Edward Hughes, Joel Z. Leibo, Matthew G. Phillips, Karl Tuyls, Edgar A. Duéñez-Guzmán, Antonio García Castañeda, Iain Dunning, Tina Zhu, Kevin R. McKee, Raphael Koster, Heather Roff, Thore Graepel
Date: 2018-03-23 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Multi-agent Reinforcement Learning, Reinforcement Learning

Groups of humans are often able to find ways to cooperate with one another in
complex, temporally extended social dilemmas. Models based on behavioral
economics are only able to explain this phenomenon for unrealistic stateless
matrix games. Recently, multi-agent reinforcement learning has been applied to
generalize social dilemma problems to temporally and spatially extended Markov
games. However, this has not yet generated an agent that learns to cooperate in
social dilemmas as humans do. A key insight is that many, but not all, human
individuals have inequity averse social preferences. This promotes a particular
resolution of the matrix game social dilemma wherein inequity-averse
individuals are personally pro-social and punish defectors. Here we extend this
idea to Markov games and show that it promotes cooperation in several types of
sequential social dilemma, via a profitable interaction with policy
learnability. In particular, we find that inequity aversion improves temporal
credit assignment for the important class of intertemporal social dilemmas.
These results help explain how large-scale cooperation may emerge and persist.
Reviving and Improving Recurrent Back-Propagation
Authors: Renjie Liao, Yuwen Xiong, Ethan Fetaya, Lisa Zhang, KiJung Yoon, Xaq Pitkow, Raquel Urtasun, Richard Zemel
Date: 2018-03-16 00:00:00
Proceeding: ICML 2018 7
Tasks: Document Classification, Hyperparameter Optimization

In this paper, we revisit the recurrent back-propagation (RBP) algorithm, discuss the conditions under which it applies as well as how to satisfy them in deep neural networks. We show that RBP can be unstable and propose two variants based on conjugate gradient on the normal equations (CG-RBP) and Neumann series (Neumann-RBP). We further investigate the relationship between Neumann-RBP and back propagation through time (BPTT) and its truncated version (TBPTT). Our Neumann-RBP has the same time complexity as TBPTT but only requires constant memory, whereas TBPTT's memory cost scales linearly with the number of truncation steps. We examine all RBP variants along with BPTT and TBPTT in three different application domains: associative memory with continuous Hopfield networks, document classification in citation networks using graph neural networks and hyperparameter optimization for fully connected networks. All experiments demonstrate that RBPs, especially the Neumann-RBP variant, are efficient and effective for optimizing convergent recurrent neural networks. Code is released at: \url{https://github.com/lrjconan/RBP}.
Trace your sources in large-scale data: one ring to find them all
Authors: Alexander Böttcher, Wieland Brendel, Bernhard Englitz, Matthias Bethge
Date: 2018-03-23 00:00:00
Tasks: All, blind source separation

An important preprocessing step in most data analysis pipelines aims to
extract a small set of sources that explain most of the data. Currently used
algorithms for blind source separation (BSS), however, often fail to extract
the desired sources and need extensive cross-validation. In contrast, their
rarely used probabilistic counterparts can get away with little
cross-validation and are more accurate and reliable but no simple and scalable
implementations are available. Here we present a novel probabilistic BSS
framework (DECOMPOSE) that can be flexibly adjusted to the data, is extensible
and easy to use, adapts to individual sources and handles large-scale data
through algorithmic efficiency. DECOMPOSE encompasses and generalises many
traditional BSS algorithms such as PCA, ICA and NMF and we demonstrate
substantial improvements in accuracy and robustness on artificial and real
data.
A mosaic of Chu spaces and Channel Theory with applications to Object Identification and Mereological Complexity
Authors: Chris Fields, James F. Glazebrook
Date: 2018-03-23 00:00:00
Tasks: Object

Chu Spaces and Channel Theory are well established areas of investigation in
the general context of category theory. We review a range of examples and
applications of these methods in logic and computer science, including Formal
Concept Analysis, distributed systems and ontology development. We then employ
these methods to describe human object perception, beginning with the
construction of uncategorized object files and proceeding through
categorization, individual object identification and the tracking of object
identity through time. We investigate the relationship between abstraction and
mereological categorization, particularly as these affect object identity
tracking. This we accomplish in terms of information flow that is semantically
structured in terms of local logics, while at the same time this framework also
provides an inferential mechanism towards identification and perception. We
show how a mereotopology naturally emerges from the representation of
classifications by simplicial complexes, and briefly explore the emergence of
geometric relations and interactions between objects.
SqueezeNext: Hardware-Aware Neural Network Design
Authors: Amir Gholami, Kiseok Kwon, Bichen Wu, Zizheng Tai, Xiangyu Yue, Peter Jin, Sicheng Zhao, Kurt Keutzer
Date: 2018-03-23 00:00:00

One of the main barriers for deploying neural networks on embedded systems
has been large memory and power consumption of existing neural networks. In
this work, we introduce SqueezeNext, a new family of neural network
architectures whose design was guided by considering previous architectures
such as SqueezeNet, as well as by simulation results on a neural network
accelerator. This new network is able to match AlexNet's accuracy on the
ImageNet benchmark with $112\times$ fewer parameters, and one of its deeper
variants is able to achieve VGG-19 accuracy with only 4.4 Million parameters,
($31\times$ smaller than VGG-19). SqueezeNext also achieves better top-5
classification accuracy with $1.3\times$ fewer parameters as compared to
MobileNet, but avoids using depthwise-separable convolutions that are
inefficient on some mobile processor platforms. This wide range of accuracy
gives the user the ability to make speed-accuracy tradeoffs, depending on the
available resources on the target hardware. Using hardware simulation results
for power and inference speed on an embedded system has guided us to design
variations of the baseline model that are $2.59\times$/$8.26\times$ faster and
$2.25\times$/$7.5\times$ more energy efficient as compared to
SqueezeNet/AlexNet without any accuracy degradation.
On the difficulty of a distributional semantics of spoken language
Authors: Grzegorz Chrupała, Lieke Gelderloos, Ákos Kádár, Afra Alishahi
Date: 2018-03-23 00:00:00
Proceeding: WS 2019 1

In the domain of unsupervised learning most work on speech has focused on
discovering low-level constructs such as phoneme inventories or word-like
units. In contrast, for written language, where there is a large body of work
on unsupervised induction of semantic representations of words, whole sentences
and longer texts. In this study we examine the challenges of adapting these
approaches from written to spoken language. We conjecture that unsupervised
learning of the semantics of spoken language becomes feasible if we abstract
from the surface variability. We simulate this setting with a dataset of
utterances spoken by a realistic but uniform synthetic voice. We evaluate two
simple unsupervised models which, to varying degrees of success, learn semantic
representations of speech fragments. Finally we present inconclusive results on
human speech, and discuss the challenges inherent in learning distributional
semantic representations on unrestricted natural spoken language.
2CoBel : An Efficient Belief Function Extension for Two-dimensional Continuous Spaces
Authors: Nicola Pellicanò, Sylvie Le Hégarat-Mascle, Emanuel Aldea
Date: 2018-03-23 00:00:00
Tasks: Decision Making, Vocal Bursts Valence Prediction

This paper introduces an innovative approach for handling 2D compound
hypotheses within the Belief Function Theory framework. We propose a
polygon-based generic rep- resentation which relies on polygon clipping
operators. This approach allows us to account in the computational cost for the
precision of the representation independently of the cardinality of the
discernment frame. For the BBA combination and decision making, we propose
efficient algorithms which rely on hashes for fast lookup, and on a topological
ordering of the focal elements within a directed acyclic graph encoding their
interconnections. Additionally, an implementation of the functionalities
proposed in this paper is provided as an open source library. Experimental
results on a pedestrian localization problem are reported. The experiments show
that the solution is accurate and that it fully benefits from the scalability
of the 2D search space granularity provided by our representation.
Audio-Visual Event Localization in Unconstrained Videos
Authors: Yapeng Tian, Jing Shi, Bochen Li, Zhiyao Duan, Chenliang Xu
Date: 2018-03-23 00:00:00
Proceeding: ECCV 2018 9
Tasks: audio-visual event localization, Temporal Localization

In this paper, we introduce a novel problem of audio-visual event
localization in unconstrained videos. We define an audio-visual event as an
event that is both visible and audible in a video segment. We collect an
Audio-Visual Event(AVE) dataset to systemically investigate three temporal
localization tasks: supervised and weakly-supervised audio-visual event
localization, and cross-modality localization. We develop an audio-guided
visual attention mechanism to explore audio-visual correlations, propose a dual
multimodal residual network (DMRN) to fuse information over the two modalities,
and introduce an audio-visual distance learning network to handle the
cross-modality localization. Our experiments support the following findings:
joint modeling of auditory and visual modalities outperforms independent
modeling, the learned attention can capture semantics of sounding objects,
temporal alignment is important for audio-visual fusion, the proposed DMRN is
effective in fusing audio-visual features, and strong correlations between the
two modalities enable cross-modality localization.
The Convergence of Stochastic Gradient Descent in Asynchronous Shared Memory
Authors: Dan Alistarh, Christopher De Sa, Nikola Konstantinov
Date: 2018-03-23 00:00:00
Tasks: BIG-bench Machine Learning

Stochastic Gradient Descent (SGD) is a fundamental algorithm in machine
learning, representing the optimization backbone for training several classic
models, from regression to neural networks. Given the recent practical focus on
distributed machine learning, significant work has been dedicated to the
convergence properties of this algorithm under the inconsistent and noisy
updates arising from execution in a distributed environment. However,
surprisingly, the convergence properties of this classic algorithm in the
standard shared-memory model are still not well-understood.
  In this work, we address this gap, and provide new convergence bounds for
lock-free concurrent stochastic gradient descent, executing in the classic
asynchronous shared memory model, against a strong adaptive adversary. Our
results give improved upper and lower bounds on the "price of asynchrony" when
executing the fundamental SGD algorithm in a concurrent setting. They show that
this classic optimization tool can converge faster and with a wider range of
parameters than previously known under asynchronous iterations. At the same
time, we exhibit a fundamental trade-off between the maximum delay in the
system and the rate at which SGD can converge, which governs the set of
parameters under which this algorithm can still work efficiently.
Effective deep learning training for single-image super-resolution in endomicroscopy exploiting video-registration-based reconstruction
Authors: Daniele Ravì, Agnieszka Barbara Szczotka, Dzhoshkun Ismail Shakir, Stephen P. Pereira, Tom Vercauteren
Date: 2018-03-23 00:00:00
Tasks: Image Quality Assessment, Image Super-Resolution, Super-Resolution, Synthetic Data Generation

Purpose: Probe-based Confocal Laser Endomicroscopy (pCLE) is a recent imaging
modality that allows performing in vivo optical biopsies. The design of pCLE
hardware, and its reliance on an optical fibre bundle, fundamentally limits the
image quality with a few tens of thousands fibres, each acting as the
equivalent of a single-pixel detector, assembled into a single fibre bundle.
Video-registration techniques can be used to estimate high-resolution (HR)
images by exploiting the temporal information contained in a sequence of
low-resolution (LR) images. However, the alignment of LR frames, required for
the fusion, is computationally demanding and prone to artefacts. Methods: In
this work, we propose a novel synthetic data generation approach to train
exemplar-based Deep Neural Networks (DNNs). HR pCLE images with enhanced
quality are recovered by the models trained on pairs of estimated HR images
(generated by the video-registration algorithm) and realistic synthetic LR
images. Performance of three different state-of-the-art DNNs techniques were
analysed on a Smart Atlas database of 8806 images from 238 pCLE video
sequences. The results were validated through an extensive Image Quality
Assessment (IQA) that takes into account different quality scores, including a
Mean Opinion Score (MOS). Results: Results indicate that the proposed solution
produces an effective improvement in the quality of the obtained reconstructed
image. Conclusion: The proposed training strategy and associated DNNs allows us
to perform convincing super-resolution of pCLE images.
What Do We Understand About Convolutional Networks?
Authors: Isma Hadji, Richard P. Wildes
Date: 2018-03-23 00:00:00

This document will review the most prominent proposals using multilayer
convolutional architectures. Importantly, the various components of a typical
convolutional network will be discussed through a review of different
approaches that base their design decisions on biological findings and/or sound
theoretical bases. In addition, the different attempts at understanding
ConvNets via visualizations and empirical studies will be reviewed. The
ultimate goal is to shed light on the role of each layer of processing involved
in a ConvNet architecture, distill what we currently understand about ConvNets
and highlight critical open problems.
Gaussian and exponential lateral connectivity on distributed spiking neural network simulation
Authors: Elena Pastorelli, Pier Stanislao Paolucci, Francesco Simula, Andrea Biagioni, Fabrizio Capuani, Paolo Cretaro, Giulia De Bonis, Francesca Lo Cicero, Alessandro Lonardo, Michele Martinelli, Luca Pontisso, Piero Vicini, Roberto Ammendola
Date: 2018-03-23 00:00:00
Tasks: Neural Network simulation

We measured the impact of long-range exponentially decaying intra-areal
lateral connectivity on the scaling and memory occupation of a distributed
spiking neural network simulator compared to that of short-range Gaussian
decays. While previous studies adopted short-range connectivity, recent
experimental neurosciences studies are pointing out the role of longer-range
intra-areal connectivity with implications on neural simulation platforms.
Two-dimensional grids of cortical columns composed by up to 11 M point-like
spiking neurons with spike frequency adaption were connected by up to 30 G
synapses using short- and long-range connectivity models. The MPI processes
composing the distributed simulator were run on up to 1024 hardware cores,
hosted on a 64 nodes server platform. The hardware platform was a cluster of
IBM NX360 M5 16-core compute nodes, each one containing two Intel Xeon Haswell
8-core E5-2630 v3 processors, with a clock of 2.40 G Hz, interconnected through
an InfiniBand network, equipped with 4x QDR switches.