pwc-archive/papers-with-abstracts huggingface dataset:
A Convolutional Network for Sleep Stages Classification
Authors: Isaac Fernández-Varela, Elena Hernández-Pereira, Diego Alvarez-Estevez, Vicente Moret-Bonillo
Date: 2019-02-15 00:00:00
Tasks: Classification, General Classification

Sleep stages classification is a crucial task in the context of sleep
studies. It involves the simultaneous analysis of multiple signals recorded
during sleep. However, it is complex and tedious, and even the trained expert
can spend several hours scoring a single night recording. Multiple automatic
methods have tried to solve these problems in the past, most of them by
classifying a feature vector that is engineered for a specific dataset. In this
work, we avoid this bias using a deep learning model that learns relevant
features without human intervention. Particularly, we propose an ensemble of 5
convolutional networks that achieves a kappa index of 0.83 when classifying a
dataset of 500 sleep recordings.
Empty Cities: Image Inpainting for a Dynamic-Object-Invariant Space
Authors: Berta Bescos, José Neira, Roland Siegwart, Cesar Cadena
Date: 2018-09-20 00:00:00
Tasks: Image Inpainting, Semantic Segmentation, Visual Place Recognition

In this paper we present an end-to-end deep learning framework to turn images
that show dynamic content, such as vehicles or pedestrians, into realistic
static frames. This objective encounters two main challenges: detecting all the
dynamic objects, and inpainting the static occluded background with plausible
imagery. The second problem is approached with a conditional generative
adversarial model that, taking as input the original dynamic image and its
dynamic/static binary mask, is capable of generating the final static image.
The former challenge is addressed by the use of a convolutional network that
learns a multi-class semantic segmentation of the image.
  These generated images can be used for applications such as augmented reality
or vision-based robot localization purposes. To validate our approach, we show
both qualitative and quantitative comparisons against other state-of-the-art
inpainting methods by removing the dynamic objects and hallucinating the static
structure behind them. Furthermore, to demonstrate the potential of our
results, we carry out pilot experiments that show the benefits of our proposal
for visual place recognition.
SVM-based Deep Stacking Networks
Authors: Jingyuan Wang, Kai Feng, Junjie Wu
Date: 2019-02-15 00:00:00
Tasks: Math

The deep network model, with the majority built on neural networks, has been
proved to be a powerful framework to represent complex data for high
performance machine learning. In recent years, more and more studies turn to
nonneural network approaches to build diverse deep structures, and the Deep
Stacking Network (DSN) model is one of such approaches that uses stacked
easy-to-learn blocks to build a parameter-training-parallelizable deep network.
In this paper, we propose a novel SVM-based Deep Stacking Network (SVM-DSN),
which uses the DSN architecture to organize linear SVM classifiers for deep
learning. A BP-like layer tuning scheme is also proposed to ensure holistic and
local optimizations of stacked SVMs simultaneously. Some good math properties
of SVM, such as the convex optimization, is introduced into the DSN framework
by our model. From a global view, SVM-DSN can iteratively extract data
representations layer by layer as a deep neural network but with
parallelizability, and from a local view, each stacked SVM can converge to its
optimal solution and obtain the support vectors, which compared with neural
networks could lead to interesting improvements in anti-saturation and
interpretability. Experimental results on both image and text data sets
demonstrate the excellent performances of SVM-DSN compared with some
competitive benchmark models.
Shepherding Hordes of Markov Chains
Authors: Milan Ceska, Nils Jansen, Sebastian Junges, Joost-Pieter Katoen
Date: 2019-02-15 00:00:00

This paper considers large families of Markov chains (MCs) that are defined
over a set of parameters with finite discrete domains. Such families occur in
software product lines, planning under partial observability, and sketching of
probabilistic programs. Simple questions, like `does at least one family member
satisfy a property?', are NP-hard. We tackle two problems: distinguish family
members that satisfy a given quantitative property from those that do not, and
determine a family member that satisfies the property optimally, i.e., with the
highest probability or reward. We show that combining two well-known
techniques, MDP model checking and abstraction refinement, mitigates the
computational complexity. Experiments on a broad set of benchmarks show that in
many situations, our approach is able to handle families of millions of MCs,
providing superior scalability compared to existing solutions.
Generating Natural Language Explanations for Visual Question Answering using Scene Graphs and Visual Attention
Authors: Shalini Ghosh, Giedrius Burachas, Arijit Ray, Avi Ziskind
Date: 2019-02-15 00:00:00
Tasks: Explanation Generation, Language Modeling, Language Modelling, Question Answering, Visual Question Answering, Visual Question Answering (VQA)

In this paper, we present a novel approach for the task of eXplainable
Question Answering (XQA), i.e., generating natural language (NL) explanations
for the Visual Question Answering (VQA) problem. We generate NL explanations
comprising of the evidence to support the answer to a question asked to an
image using two sources of information: (a) annotations of entities in an image
(e.g., object labels, region descriptions, relation phrases) generated from the
scene graph of the image, and (b) the attention map generated by a VQA model
when answering the question. We show how combining the visual attention map
with the NL representation of relevant scene graph entities, carefully selected
using a language model, can give reasonable textual explanations without the
need of any additional collected data (explanation captions, etc). We run our
algorithms on the Visual Genome (VG) dataset and conduct internal user-studies
to demonstrate the efficacy of our approach over a strong baseline. We have
also released a live web demo showcasing our VQA and textual explanation
generation using scene graphs and visual attention.
Efficient Deep Learning of GMMs
Authors: Shirin Jalali, Carl Nuzman, Iraj Saniee
Date: 2019-02-15 00:00:00
Tasks: Deep Learning, General Classification

We show that a collection of Gaussian mixture models (GMMs) in $R^{n}$ can be
optimally classified using $O(n)$ neurons in a neural network with two hidden
layers (deep neural network), whereas in contrast, a neural network with a
single hidden layer (shallow neural network) would require at least
$O(\exp(n))$ neurons or possibly exponentially large coefficients. Given the
universality of the Gaussian distribution in the feature spaces of data, e.g.,
in speech, image and text, our result sheds light on the observed efficiency of
deep neural networks in practical classification problems.
Deep Spiking Neural Network with Spike Count based Learning Rule
Authors: Jibin Wu, Yansong Chua, Malu Zhang, Qu Yang, Guoqi Li, Haizhou Li
Date: 2019-02-15 00:00:00

Deep spiking neural networks (SNNs) support asynchronous event-driven
computation, massive parallelism and demonstrate great potential to improve the
energy efficiency of its synchronous analog counterpart. However, insufficient
attention has been paid to neural encoding when designing SNN learning rules.
Remarkably, the temporal credit assignment has been performed on rate-coded
spiking inputs, leading to poor learning efficiency. In this paper, we
introduce a novel spike-based learning rule for rate-coded deep SNNs, whereby
the spike count of each neuron is used as a surrogate for gradient
backpropagation. We evaluate the proposed learning rule by training deep
spiking multi-layer perceptron (MLP) and spiking convolutional neural network
(CNN) on the UCI machine learning and MNIST handwritten digit datasets. We show
that the proposed learning rule achieves state-of-the-art accuracies on all
benchmark datasets. The proposed learning rule allows introducing latency,
spike rate and hardware constraints into the SNN learning, which is superior to
the indirect approach in which conventional artificial neural networks are
first trained and then converted to SNNs. Hence, it allows direct deployment to
the neuromorphic hardware and supports efficient inference. Notably, a test
accuracy of 98.40% was achieved on the MNIST dataset in our experiments with
only 10 simulation time steps, when the same latency constraint is imposed
during training.
Network Offloading Policies for Cloud Robotics: a Learning-based Approach
Authors: Sandeep Chinchali, Apoorva Sharma, James Harrison, Amine Elhafsi, Daniel Kang, Evgenya Pergament, Eyal Cidon, Sachin Katti, Marco Pavone
Date: 2019-02-15 00:00:00
Tasks: Decision Making, Deep Reinforcement Learning, object-detection, Object Detection, Reinforcement Learning, Sequential Decision Making

Today's robotic systems are increasingly turning to computationally expensive
models such as deep neural networks (DNNs) for tasks like localization,
perception, planning, and object detection. However, resource-constrained
robots, like low-power drones, often have insufficient on-board compute
resources or power reserves to scalably run the most accurate, state-of-the art
neural network compute models. Cloud robotics allows mobile robots the benefit
of offloading compute to centralized servers if they are uncertain locally or
want to run more accurate, compute-intensive models. However, cloud robotics
comes with a key, often understated cost: communicating with the cloud over
congested wireless networks may result in latency or loss of data. In fact,
sending high data-rate video or LIDAR from multiple robots over congested
networks can lead to prohibitive delay for real-time applications, which we
measure experimentally. In this paper, we formulate a novel Robot Offloading
Problem --- how and when should robots offload sensing tasks, especially if
they are uncertain, to improve accuracy while minimizing the cost of cloud
communication? We formulate offloading as a sequential decision making problem
for robots, and propose a solution using deep reinforcement learning. In both
simulations and hardware experiments using state-of-the art vision DNNs, our
offloading strategy improves vision task performance by between 1.3-2.6x of
benchmark offloading strategies, allowing robots the potential to significantly
transcend their on-board sensing accuracy but with limited cost of cloud
communication.
Learning to Adaptively Scale Recurrent Neural Networks
Authors: Hao Hu, Liqiang Wang, Guo-Jun Qi
Date: 2019-02-15 00:00:00
Tasks: Time Series, Time Series Analysis

Recent advancements in recurrent neural network (RNN) research have
demonstrated the superiority of utilizing multiscale structures in learning
temporal representations of time series. Currently, most of multiscale RNNs use
fixed scales, which do not comply with the nature of dynamical temporal
patterns among sequences. In this paper, we propose Adaptively Scaled Recurrent
Neural Networks (ASRNN), a simple but efficient way to handle this problem.
Instead of using predefined scales, ASRNNs are able to learn and adjust scales
based on different temporal contexts, making them more flexible in modeling
multiscale patterns. Compared with other multiscale RNNs, ASRNNs are bestowed
upon dynamical scaling capabilities with much simpler structures, and are easy
to be integrated with various RNN cells. The experiments on multiple sequence
modeling tasks indicate ASRNNs can efficiently adapt scales based on different
sequence contexts and yield better performances than baselines without
dynamical scaling abilities.
Lightweight Feature Fusion Network for Single Image Super-Resolution
Authors: Wenming Yang, Wei Wang, Xuechen Zhang, Shuifa Sun, Qingmin Liao
Date: 2019-02-15 00:00:00
Tasks: Image Super-Resolution, Super-Resolution

Single image super-resolution(SISR) has witnessed great progress as
convolutional neural network(CNN) gets deeper and wider. However, enormous
parameters hinder its application to real world problems. In this letter, We
propose a lightweight feature fusion network (LFFN) that can fully explore
multi-scale contextual information and greatly reduce network parameters while
maximizing SISR results. LFFN is built on spindle blocks and a softmax feature
fusion module (SFFM). Specifically, a spindle block is composed of a dimension
extension unit, a feature exploration unit and a feature refinement unit. The
dimension extension layer expands low dimension to high dimension and
implicitly learns the feature maps which is suitable for the next unit. The
feature exploration unit performs linear and nonlinear feature exploration
aimed at different feature maps. The feature refinement layer is used to fuse
and refine features. SFFM fuses the features from different modules in a
self-adaptive learning manner with softmax function, making full use of
hierarchical information with a small amount of parameter cost. Both
qualitative and quantitative experiments on benchmark datasets show that LFFN
achieves favorable performance against state-of-the-art methods with similar
parameters.
AutoQ: Automated Kernel-Wise Neural Network Quantization
Authors: Qian Lou, Feng Guo, Lantao Liu, Minje Kim, Lei Jiang
Date: 2019-02-15 00:00:00
Proceeding: ICLR 2020 1
Tasks: AutoML, Deep Reinforcement Learning, Quantization, Reinforcement Learning

Network quantization is one of the most hardware friendly techniques to enable the deployment of convolutional neural networks (CNNs) on low-power mobile devices. Recent network quantization techniques quantize each weight kernel in a convolutional layer independently for higher inference accuracy, since the weight kernels in a layer exhibit different variances and hence have different amounts of redundancy. The quantization bitwidth or bit number (QBN) directly decides the inference accuracy, latency, energy and hardware overhead. To effectively reduce the redundancy and accelerate CNN inferences, various weight kernels should be quantized with different QBNs. However, prior works use only one QBN to quantize each convolutional layer or the entire CNN, because the design space of searching a QBN for each weight kernel is too large. The hand-crafted heuristic of the kernel-wise QBN search is so sophisticated that domain experts can obtain only sub-optimal results. It is difficult for even deep reinforcement learning (DRL) Deep Deterministic Policy Gradient (DDPG)-based agents to find a kernel-wise QBN configuration that can achieve reasonable inference accuracy. In this paper, we propose a hierarchical-DRL-based kernel-wise network quantization technique, AutoQ, to automatically search a QBN for each weight kernel, and choose another QBN for each activation layer. Compared to the models quantized by the state-of-the-art DRL-based schemes, on average, the same models quantized by AutoQ reduce the inference latency by 54.06\%, and decrease the inference energy consumption by 50.69\%, while achieving the same inference accuracy.
Lipschitz Generative Adversarial Nets
Authors: Zhiming Zhou, Jiadong Liang, Yuxuan Song, Lantao Yu, Hongwei Wang, Wei-Nan Zhang, Yong Yu, Zhihua Zhang
Date: 2019-02-15 00:00:00
Tasks: Informativeness

In this paper, we study the convergence of generative adversarial networks (GANs) from the perspective of the informativeness of the gradient of the optimal discriminative function. We show that GANs without restriction on the discriminative function space commonly suffer from the problem that the gradient produced by the discriminator is uninformative to guide the generator. By contrast, Wasserstein GAN (WGAN), where the discriminative function is restricted to 1-Lipschitz, does not suffer from such a gradient uninformativeness problem. We further show in the paper that the model with a compact dual form of Wasserstein distance, where the Lipschitz condition is relaxed, may also theoretically suffer from this issue. This implies the importance of Lipschitz condition and motivates us to study the general formulation of GANs with Lipschitz constraint, which leads to a new family of GANs that we call Lipschitz GANs (LGANs). We show that LGANs guarantee the existence and uniqueness of the optimal discriminative function as well as the existence of a unique Nash equilibrium. We prove that LGANs are generally capable of eliminating the gradient uninformativeness problem. According to our empirical analysis, LGANs are more stable and generate consistently higher quality samples compared with WGAN.
Crime Analysis using Open Source Information
Authors: Sarwat Nizamani, Nasrullah Memon, Azhar Ali Shah, Sehrish Nizamani, Saad Nizamani, Imdad Ali Ismaili
Date: 2019-02-15 00:00:00
Tasks: Clustering

In this paper, we present a method of crime analysis from open source
information. We employed un-supervised methods of data mining to explore the
facts regarding the crimes of an area of interest. The analysis is based on
well known clustering and association techniques. The results show that the
proposed method of crime analysis is efficient and gives a broad picture of the
crimes of an area to analyst without much effort. The analysis is evaluated
using manual approach, which reveals that the results produced by the proposed
approach are comparable to the manual analysis, while a great amount of time is
saved.
ProxSARAH: An Efficient Algorithmic Framework for Stochastic Composite Nonconvex Optimization
Authors: Nhan H. Pham, Lam M. Nguyen, Dzung T. Phan, Quoc Tran-Dinh
Date: 2019-02-15 00:00:00

We propose a new stochastic first-order algorithmic framework to solve
stochastic composite nonconvex optimization problems that covers both
finite-sum and expectation settings. Our algorithms rely on the SARAH estimator
introduced in (Nguyen et al, 2017) and consist of two steps: a proximal
gradient and an averaging step making them different from existing nonconvex
proximal-type algorithms. The algorithms only require an average smoothness
assumption of the nonconvex objective term and additional bounded variance
assumption if applied to expectation problems. They work with both constant and
adaptive step-sizes, while allowing single sample and mini-batches. In all
these cases, we prove that our algorithms can achieve the best-known complexity
bounds. One key step of our methods is new constant and adaptive step-sizes
that help to achieve desired complexity bounds while improving practical
performance. Our constant step-size is much larger than existing methods
including proximal SVRG schemes in the single sample case. We also specify the
algorithm to the non-composite case that covers existing state-of-the-arts in
terms of complexity bounds. Our update also allows one to trade-off between
step-sizes and mini-batch sizes to improve performance. We test the proposed
algorithms on two composite nonconvex problems and neural networks using
several well-known datasets.
Probabilistic Relational Agent-based Models
Authors: Paul Cohen
Date: 2019-02-15 00:00:00

PRAM puts agent-based models on a sound probabilistic footing as a basis for
integrating agent-based and probabilistic models. It extends the themes of
probabilistic relational models and lifted inference to incorporate dynamical
models and simulation. It can also be much more efficient than agent-based
simulation.
Breaking the Spatio-Angular Trade-off for Light Field Super-Resolution via LSTM Modelling on Epipolar Plane Images
Authors: Hao Zhu, Mantang Guo, Hongdong Li, Qing Wang, Antonio Robles-Kelly
Date: 2019-02-15 00:00:00
Tasks: Super-Resolution

Light-field cameras (LFC) have received increasing attention due to their
wide-spread applications. However, current LFCs suffer from the well-known
spatio-angular trade-off, which is considered as an inherent and fundamental
limit for LFC designs. In this paper, by doing a detailed geometrical optical
analysis of the sampling process in an LFC, we show that the effective sampling
resolution is generally higher than the number of micro-lenses. This
contribution makes it theoretically possible to break the resolution trade-off.
Our second contribution is an epipolar plane image (EPI) based super-resolution
method, which can super-resolve the spatial and angular dimensions
simultaneously. We prove that the light field is a 2D series, thus, a
specifically designed CNN-LSTM network is proposed to capture the continuity
property of the EPI. Rather than leveraging semantic information, our network
focuses on extracting geometric continuity in the EPI. This gives our method an
improved generalization ability and makes it applicable to a wide range of
previously unseen scenes. Experiments on both synthetic and real light fields
demonstrate the improvements over state-of-the-art, especially in large
disparity areas.
Cycle-Consistency for Robust Visual Question Answering
Authors: Meet Shah, Xinlei Chen, Marcus Rohrbach, Devi Parikh
Date: 2019-02-15 00:00:00
Proceeding: CVPR 2019 6
Tasks: Question Answering, Question Generation, Question-Generation, Visual Question Answering, Visual Question Answering (VQA)

Despite significant progress in Visual Question Answering over the years,
robustness of today's VQA models leave much to be desired. We introduce a new
evaluation protocol and associated dataset (VQA-Rephrasings) and show that
state-of-the-art VQA models are notoriously brittle to linguistic variations in
questions. VQA-Rephrasings contains 3 human-provided rephrasings for 40k
questions spanning 40k images from the VQA v2.0 validation dataset. As a step
towards improving robustness of VQA models, we propose a model-agnostic
framework that exploits cycle consistency. Specifically, we train a model to
not only answer a question, but also generate a question conditioned on the
answer, such that the answer predicted for the generated question is the same
as the ground truth answer to the original question. Without the use of
additional annotations, we show that our approach is significantly more robust
to linguistic variations than state-of-the-art VQA models, when evaluated on
the VQA-Rephrasings dataset. In addition, our approach outperforms
state-of-the-art approaches on the standard VQA and Visual Question Generation
tasks on the challenging VQA v2.0 dataset.
Massively Parallel Benders Decomposition for Correlation Clustering
Authors: Margret Keuper, Jovita Lukasik, Maneesh Singh, Julian Yarkony
Date: 2019-02-15 00:00:00
Tasks: Clustering, graph partitioning, Image Segmentation, Semantic Segmentation

We tackle the problem of graph partitioning for image segmentation using correlation clustering (CC), which we treat as an integer linear program (ILP). We reformulate optimization in the ILP so as to admit efficient optimization via Benders decomposition, a classic technique from operations research. Our Benders decomposition formulation has many subproblems, each associated with a node in the CC instance's graph, which are solved in parallel. Each Benders subproblem enforces the cycle inequalities corresponding to the negative weight edges attached to its corresponding node in the CC instance. We generate Magnanti-Wong Benders rows in addition to standard Benders rows, to accelerate optimization. Our Benders decomposition approach provides a promising new avenue to accelerate optimization for CC, and allows for massive parallelization.