pwc-archive/papers-with-abstracts huggingface dataset:
ReLeQ: A Reinforcement Learning Approach for Deep Quantization of Neural Networks
Authors: Ahmed T. Elthakeb, Prannoy Pilligundla, FatemehSadat Mireshghallah, Amir Yazdanbakhsh, Hadi Esmaeilzadeh
Date: 2018-11-05 00:00:00
Tasks: Deep Reinforcement Learning, Quantization, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Deep Neural Networks (DNNs) typically require massive amount of computation resource in inference tasks for computer vision applications. Quantization can significantly reduce DNN computation and storage by decreasing the bitwidth of network encodings. Recent research affirms that carefully selecting the quantization levels for each layer can preserve the accuracy while pushing the bitwidth below eight bits. However, without arduous manual effort, this deep quantization can lead to significant accuracy loss, leaving it in a position of questionable utility. As such, deep quantization opens a large hyper-parameter space (bitwidth of the layers), the exploration of which is a major challenge. We propose a systematic approach to tackle this problem, by automating the process of discovering the quantization levels through an end-to-end deep reinforcement learning framework (ReLeQ). We adapt policy optimization methods to the problem of quantization, and focus on finding the best design decisions in choosing the state and action spaces, network architecture and training framework, as well as the tuning of various hyperparamters. We show how ReLeQ can balance speed and quality, and provide an asymmetric general solution for quantization of a large variety of deep networks (AlexNet, CIFAR-10, LeNet, MobileNet-V1, ResNet-20, SVHN, and VGG-11) that virtually preserves the accuracy (=< 0.3% loss) while minimizing the computation and storage cost. With these DNNs, ReLeQ enables conventional hardware to achieve 2.2x speedup over 8-bit execution. Similarly, a custom DNN accelerator achieves 2.0x speedup and energy reduction compared to 8-bit runs. These encouraging results mark ReLeQ as the initial step towards automating the deep quantization of neural networks.
Role of Awareness and Universal Context in a Spiking Conscious Neural Network (SCNN): A New Perspective and Future Directions
Authors: Ahsan Adeel
Date: 2018-11-05 00:00:00

Awareness plays a major role in human cognition and adaptive behaviour,
though mechanisms involved remain unknown. Awareness is not an objectively
established fact, therefore, despite extensive research, scientists have not
been able to fully interpret its contribution in multisensory integration and
precise neural firing, hence, questions remain: (1) How the biological neuron
integrates the incoming multisensory signals with respect to different
situations? (2) How are the roles of incoming multisensory signals defined
(selective amplification/attenuation) that help neuron(s) to originate a
precise neural firing complying with the anticipated behavioural-constraint of
the environment? (3) How are the external environment and anticipated behaviour
integrated? Recently, scientists have exploited deep learning to integrate
multimodal cues and capture context-dependent meanings. Yet, these methods
suffer from imprecise behavioural representation. In this research, we
introduce a new theory on the role of awareness and universal context that can
help answering the aforementioned crucial neuroscience questions. Specifically,
we propose a class of spiking conscious neuron in which the output depends on
three functionally distinctive integrated input variables: receptive field
(RF), local contextual field (LCF), and universal contextual field (UCF). The
RF defines the incoming ambiguous sensory signal, LCF defines the modulatory
signal coming from other parts of the brain, and UCF defines the awareness. It
is believed that the conscious neuron inherently contains enough knowledge
about the situation in which the problem is to be solved based on past learning
and reasoning and it defines the precise role of incoming multisensory signals
to originate a precise neural firing (exhibiting switch-like behaviour). It is
shown that the conscious neuron helps modelling a more precise human behaviour.
Combining Subgoal Graphs with Reinforcement Learning to Build a Rational Pathfinder
Authors: Junjie Zeng, Long Qin, Yue Hu, Cong Hu, Quanjun Yin
Date: 2018-11-05 00:00:00
Tasks: Motion Planning, Optimal Motion Planning, Pathfinder, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

In this paper, we present a hierarchical path planning framework called SG-RL
(subgoal graphs-reinforcement learning), to plan rational paths for agents
maneuvering in continuous and uncertain environments. By "rational", we mean
(1) efficient path planning to eliminate first-move lags; (2) collision-free
and smooth for agents with kinematic constraints satisfied. SG-RL works in a
two-level manner. At the first level, SG-RL uses a geometric path-planning
method, i.e., Simple Subgoal Graphs (SSG), to efficiently find optimal abstract
paths, also called subgoal sequences. At the second level, SG-RL uses an RL
method, i.e., Least-Squares Policy Iteration (LSPI), to learn near-optimal
motion-planning policies which can generate kinematically feasible and
collision-free trajectories between adjacent subgoals. The first advantage of
the proposed method is that SSG can solve the limitations of sparse reward and
local minima trap for RL agents; thus, LSPI can be used to generate paths in
complex environments. The second advantage is that, when the environment
changes slightly (i.e., unexpected obstacles appearing), SG-RL does not need to
reconstruct subgoal graphs and replan subgoal sequences using SSG, since LSPI
can deal with uncertainties by exploiting its generalization ability to handle
changes in environments. Simulation experiments in representative scenarios
demonstrate that, compared with existing methods, SG-RL can work well on
large-scale maps with relatively low action-switching frequencies and shorter
path lengths, and SG-RL can deal with small changes in environments. We further
demonstrate that the design of reward functions and the types of training
environments are important factors for learning feasible policies.
Learning to Explicitate Connectives with Seq2Seq Network for Implicit Discourse Relation Classification
Authors: Wei Shi, Vera Demberg
Date: 2018-11-05 00:00:00
Proceeding: WS 2019 5
Tasks: Classification, Discourse Parsing, General Classification, Implicit Discourse Relation Classification, Relation, Relation Classification

Implicit discourse relation classification is one of the most difficult steps
in discourse parsing. The difficulty stems from the fact that the coherence
relation must be inferred based on the content of the discourse relational
arguments. Therefore, an effective encoding of the relational arguments is of
crucial importance. We here propose a new model for implicit discourse relation
classification, which consists of a classifier, and a sequence-to-sequence
model which is trained to generate a representation of the discourse relational
arguments by trying to predict the relational arguments including a suitable
implicit connective. Training is possible because such implicit connectives
have been annotated as part of the PDTB corpus. Along with a memory network,
our model could generate more refined representations for the task. And on the
now standard 11-way classification, our method outperforms previous state of
the art systems on the PDTB benchmark on multiple settings including cross
validation.
The External Interface for Extending WASP
Authors: Carmine Dodaro, Francesco Ricca
Date: 2018-11-05 00:00:00

Answer set programming (ASP) is a successful declarative formalism for
knowledge representation and reasoning. The evaluation of ASP programs is
nowadays based on the Conflict-Driven Clause Learning (CDCL) backtracking
search algorithm. Recent work suggested that the performance of CDCL-based
implementations can be considerably improved on specific benchmarks by
extending their solving capabilities with custom heuristics and propagators.
However, embedding such algorithms into existing systems requires expert
knowledge of the internals of ASP implementations. The development of effective
solver extensions can be made easier by providing suitable programming
interfaces. In this paper, we present the interface for extending the
CDCL-based ASP solver WASP. The interface is both general, i.e. it can be used
for providing either new branching heuristics and propagators, and external,
i.e. the implementation of new algorithms requires no internal modifications of
WASP. Moreover, we review the applications of the interface witnessing it can
be successfully used to extend WASP for solving effectively hard instances of
both real-world and synthetic problems. Under consideration in Theory and
Practice of Logic Programming (TPLP).
GEMRank: Global Entity Embedding For Collaborative Filtering
Authors: Arash Khoeini, Bita Shams, Saman Haratizadeh
Date: 2018-11-05 00:00:00
Tasks: Collaborative Filtering, Recommendation Systems

Recently, word embedding algorithms have been applied to map the entities of
recommender systems, such as users and items, to new feature spaces using
textual element-context relations among them. Unlike many other domains, this
approach has not achieved a desired performance in collaborative filtering
problems, probably due to unavailability of appropriate textual data. In this
paper we propose a new recommendation framework, called GEMRank that can be
applied when the user-item matrix is the sole available souce of information.
It uses the concept of profile co-occurrence for defining relations among
entities and applies a factorization method for embedding the users and items.
GEMRank then feeds the extracted representations to a neural network model to
predict user-item like/dislike relations which the final recommendations are
made based on. We evaluated GEMRank in an extensive set of experiments against
state of the art recommendation methods. The results show that GEMRank
significantly outperforms the baseline algorithms in a variety of data sets
with different degrees of density.
Matrix Completion With Variational Graph Autoencoders: Application in Hyperlocal Air Quality Inference
Authors: Tien Huu Do, Duc Minh Nguyen, Evaggelia Tsiligianni, Angel Lopez Aguirre, Valerio Panzica La Manna, Frank Pasveer, Wilfried Philips, Nikos Deligiannis
Date: 2018-11-05 00:00:00
Tasks: Air Quality Inference, Matrix Completion

Inferring air quality from a limited number of observations is an essential
task for monitoring and controlling air pollution. Existing inference methods
typically use low spatial resolution data collected by fixed monitoring
stations and infer the concentration of air pollutants using additional types
of data, e.g., meteorological and traffic information. In this work, we focus
on street-level air quality inference by utilizing data collected by mobile
stations. We formulate air quality inference in this setting as a graph-based
matrix completion problem and propose a novel variational model based on graph
convolutional autoencoders. Our model captures effectively the spatio-temporal
correlation of the measurements and does not depend on the availability of
additional information apart from the street-network topology. Experiments on a
real air quality dataset, collected with mobile stations, shows that the
proposed model outperforms state-of-the-art approaches.
Exact multiplicative updates for convolutional $β$-NMF in 2D
Authors: Pedro J. Villasana T., Stanislaw Gorlow
Date: 2018-11-05 00:00:00

In this paper, we extend the $\beta$-CNMF to two dimensions and derive exact
multiplicative updates for its factors. The new updates generalize and correct
the nonnegative matrix factor deconvolution previously proposed by Schmidt and
M{\o}rup. We show by simulation that the updates lead to a monotonically
decreasing $\beta$-divergence in terms of the mean and the standard deviation
and that the corresponding convergence curves are consistent across the most
common values for $\beta$.
Leveraging Random Label Memorization for Unsupervised Pre-Training
Authors: Vinaychandran Pondenkandath, Michele Alberti, Sammer Puran, Rolf Ingold, Marcus Liwicki
Date: 2018-11-05 00:00:00
Tasks: Action Recognition, Memorization, Temporal Action Localization, Unsupervised Pre-training

We present a novel approach to leverage large unlabeled datasets by
pre-training state-of-the-art deep neural networks on randomly-labeled
datasets. Specifically, we train the neural networks to memorize arbitrary
labels for all the samples in a dataset and use these pre-trained networks as a
starting point for regular supervised learning. Our assumption is that the
"memorization infrastructure" learned by the network during the random-label
training proves to be beneficial for the conventional supervised learning as
well. We test the effectiveness of our pre-training on several video action
recognition datasets (HMDB51, UCF101, Kinetics) by comparing the results of the
same network with and without the random label pre-training. Our approach
yields an improvement - ranging from 1.5% on UCF-101 to 5% on Kinetics - in
classification accuracy, which calls for further research in this direction.
Real-time Driver Drowsiness Detection for Android Application Using Deep Neural Networks Techniques
Authors: Rateb Jabbar, Khalifa Al-Khalifa, Mohamed Kharbeche, Wael Alhajyaseen, Mohsen Jafari, Shan Jiang
Date: 2018-11-05 00:00:00

Road crashes and related forms of accidents are a common cause of injury and
death among the human population. According to 2015 data from the World Health
Organization, road traffic injuries resulted in approximately 1.25 million
deaths worldwide, i.e. approximately every 25 seconds an individual will
experience a fatal crash. While the cost of traffic accidents in Europe is
estimated at around 160 billion Euros, driver drowsiness accounts for
approximately 100,000 accidents per year in the United States alone as reported
by The American National Highway Traffic Safety Administration (NHTSA). In this
paper, a novel approach towards real-time drowsiness detection is proposed.
This approach is based on a deep learning method that can be implemented on
Android applications with high accuracy. The main contribution of this work is
the compression of heavy baseline model to a lightweight model. Moreover,
minimal network structure is designed based on facial landmark key point
detection to recognize whether the driver is drowsy. The proposed model is able
to achieve an accuracy of more than 80%. Keywords: Driver Monitoring System;
Drowsiness Detection; Deep Learning; Real-time Deep Neural Network; Android.
ConvS2S-VC: Fully convolutional sequence-to-sequence voice conversion
Authors: Hirokazu Kameoka, Kou Tanaka, Damian Kwasny, Takuhiro Kaneko, Nobukatsu Hojo
Date: 2018-11-05 00:00:00
Tasks: Speech Enhancement, Voice Conversion

This paper proposes a voice conversion (VC) method using sequence-to-sequence (seq2seq or S2S) learning, which flexibly converts not only the voice characteristics but also the pitch contour and duration of input speech. The proposed method, called ConvS2S-VC, has three key features. First, it uses a model with a fully convolutional architecture. This is particularly advantageous in that it is suitable for parallel computations using GPUs. It is also beneficial since it enables effective normalization techniques such as batch normalization to be used for all the hidden layers in the networks. Second, it achieves many-to-many conversion by simultaneously learning mappings among multiple speakers using only a single model instead of separately learning mappings between each speaker pair using a different model. This enables the model to fully utilize available training data collected from multiple speakers by capturing common latent features that can be shared across different speakers. Owing to this structure, our model works reasonably well even without source speaker information, thus making it able to handle any-to-many conversion tasks. Third, we introduce a mechanism, called the conditional batch normalization that switches batch normalization layers in accordance with the target speaker. This particular mechanism has been found to be extremely effective for our many-to-many conversion model. We conducted speaker identity conversion experiments and found that ConvS2S-VC obtained higher sound quality and speaker similarity than baseline methods. We also found from audio examples that it could perform well in various tasks including emotional expression conversion, electrolaryngeal speech enhancement, and English accent conversion.
Continual Occlusions and Optical Flow Estimation
Authors: Michal Neoral, Jan Šochman, Jiří Matas
Date: 2018-11-05 00:00:00
Tasks: Occlusion Estimation, Optical Flow Estimation

Two optical flow estimation problems are addressed: i) occlusion estimation
and handling, and ii) estimation from image sequences longer than two frames.
The proposed ContinualFlow method estimates occlusions before flow, avoiding
the use of flow corrupted by occlusions for their estimation. We show that
providing occlusion masks as an additional input to flow estimation improves
the standard performance metric by more than 25\% on both KITTI and Sintel. As
a second contribution, a novel method for incorporating information from past
frames into flow estimation is introduced. The previous frame flow serves as an
input to occlusion estimation and as a prior in occluded regions, i.e. those
without visual correspondences. By continually using the previous frame flow,
ContinualFlow performance improves further by 18\% on KITTI and 7\% on Sintel,
achieving top performance on KITTI and Sintel.
Semi-Semantic Line-Cluster Assisted Monocular SLAM for Indoor Environments
Authors: Ting Sun, Dezhen Song, Dit-yan Yeung, Ming Liu
Date: 2018-11-05 00:00:00
Tasks: Simultaneous Localization and Mapping

This paper presents a novel method to reduce the scale drift for indoor
monocular simultaneous localization and mapping (SLAM). We leverage the prior
knowledge that in the indoor environment, the line segments form tight
clusters, e.g. many door frames in a straight corridor are of the same shape,
size and orientation, so the same edges of these door frames form a tight line
segment cluster. We implement our method in the popular ORB-SLAM2, which also
serves as our baseline. In the front end we detect the line segments in each
frame and incrementally cluster them in the 3D space. In the back end, we
optimize the map imposing the constraint that the line segments of the same
cluster should be the same. Experimental results show that our proposed method
successfully reduces the scale drift for indoor monocular SLAM.
Task Embedded Coordinate Update: A Realizable Framework for Multivariate Non-convex Optimization
Authors: Yiyang Wang, Risheng Liu, Long Ma, Xiaoliang Song
Date: 2018-11-05 00:00:00

We in this paper propose a realizable framework TECU, which embeds
task-specific strategies into update schemes of coordinate descent, for
optimizing multivariate non-convex problems with coupled objective functions.
On one hand, TECU is capable of improving algorithm efficiencies through
embedding productive numerical algorithms, for optimizing univariate
sub-problems with nice properties. From the other side, it also augments
probabilities to receive desired results, by embedding advanced techniques in
optimizations of realistic tasks. Integrating both numerical algorithms and
advanced techniques together, TECU is proposed in a unified framework for
solving a class of non-convex problems. Although the task embedded strategies
bring inaccuracies in sub-problem optimizations, we provide a realizable
criterion to control the errors, meanwhile, to ensure robust performances with
rigid theoretical analyses. By respectively embedding ADMM and a residual-type
CNN in our algorithm framework, the experimental results verify both efficiency
and effectiveness of embedding task-oriented strategies in coordinate descent
for solving practical problems.
Low-Rank Phase Retrieval via Variational Bayesian Learning
Authors: Kaihui Liu, Jiayi Wang, Zhengli Xing, Linxiao Yang, Jun Fang
Date: 2018-11-05 00:00:00
Tasks: Retrieval

In this paper, we consider the problem of low-rank phase retrieval whose
objective is to estimate a complex low-rank matrix from magnitude-only
measurements. We propose a hierarchical prior model for low-rank phase
retrieval, in which a Gaussian-Wishart hierarchical prior is placed on the
underlying low-rank matrix to promote the low-rankness of the matrix. Based on
the proposed hierarchical model, a variational expectation-maximization (EM)
algorithm is developed. The proposed method is less sensitive to the choice of
the initialization point and works well with random initialization. Simulation
results are provided to illustrate the effectiveness of the proposed algorithm.
SPNet: Deep 3D Object Classification and Retrieval using Stereographic Projection
Authors: Mohsen Yavartanoo, Eu Young Kim, Kyoung Mu Lee
Date: 2018-11-05 00:00:00
Tasks: 3D Object Classification, Classification, General Classification, GPU, Object, Retrieval

We propose an efficient Stereographic Projection Neural Network (SPNet) for
learning representations of 3D objects. We first transform a 3D input volume
into a 2D planar image using stereographic projection. We then present a
shallow 2D convolutional neural network (CNN) to estimate the object category
followed by view ensemble, which combines the responses from multiple views of
the object to further enhance the predictions. Specifically, the proposed
approach consists of four stages: (1) Stereographic projection of a 3D object,
(2) view-specific feature learning, (3) view selection and (4) view ensemble.
The proposed approach performs comparably to the state-of-the-art methods while
having substantially lower GPU memory as well as network parameters. Despite
its lightness, the experiments on 3D object classification and shape retrievals
demonstrate the high performance of the proposed method.
You Only Search Once: Single Shot Neural Architecture Search via Direct Sparse Optimization
Authors: Xinbang Zhang, Zehao Huang, Naiyan Wang
Date: 2018-11-05 00:00:00
Tasks: Neural Architecture Search, Reinforcement Learning

Recently Neural Architecture Search (NAS) has aroused great interest in both
academia and industry, however it remains challenging because of its huge and
non-continuous search space. Instead of applying evolutionary algorithm or
reinforcement learning as previous works, this paper proposes a Direct Sparse
Optimization NAS (DSO-NAS) method. In DSO-NAS, we provide a novel model pruning
view to NAS problem. In specific, we start from a completely connected block,
and then introduce scaling factors to scale the information flow between
operations. Next, we impose sparse regularizations to prune useless connections
in the architecture. Lastly, we derive an efficient and theoretically sound
optimization method to solve it. Our method enjoys both advantages of
differentiability and efficiency, therefore can be directly applied to large
datasets like ImageNet. Particularly, On CIFAR-10 dataset, DSO-NAS achieves an
average test error 2.84\%, while on the ImageNet dataset DSO-NAS achieves
25.4\% test error under 600M FLOPs with 8 GPUs in 18 hours.