pwc-archive/papers-with-abstracts huggingface dataset:
Derivative-Free Methods for Policy Optimization: Guarantees for Linear Quadratic Systems
Authors: Dhruv Malik, Ashwin Pananjady, Kush Bhatia, Koulik Khamaru, Peter L. Bartlett, Martin J. Wainwright
Date: 2018-12-20 00:00:00

We study derivative-free methods for policy optimization over the class of linear policies. We focus on characterizing the convergence rate of these methods when applied to linear-quadratic systems, and study various settings of driving noise and reward feedback. We show that these methods provably converge to within any pre-specified tolerance of the optimal policy with a number of zero-order evaluations that is an explicit polynomial of the error tolerance, dimension, and curvature properties of the problem. Our analysis reveals some interesting differences between the settings of additive driving noise and random initialization, as well as the settings of one-point and two-point reward feedback. Our theory is corroborated by extensive simulations of derivative-free methods on these systems. Along the way, we derive convergence rates for stochastic zero-order optimization algorithms when applied to a certain class of non-convex problems.
Recommendation System based on Semantic Scholar Mining and Topic modeling: A behavioral analysis of researchers from six conferences
Authors: Hamed Jelodar, Yongli Wang, Mahdi Rabbani, Ru-xin Zhao, SeyedValyAllah Ayobi, Peng Hu, Isma Masood
Date: 2018-12-20 00:00:00
Tasks: Recommendation Systems

Recommendation systems have an important place to help online users in the
internet society. Recommendation Systems in computer science are of very
practical use these days in various aspects of the Internet portals, such as
social networks, and library websites. There are several approaches to
implement recommendation systems, Latent Dirichlet Allocation (LDA) is one the
popular techniques in Topic Modeling. Recently, researchers have proposed many
approaches based on Recommendation Systems and LDA. According to importance of
the subject, in this paper we discover the trends of the topics and find
relationship between LDA topics and Scholar-Context-documents. In fact, We
apply probabilistic topic modeling based on Gibbs sampling algorithms for a
semantic mining from six conference publications in computer science from DBLP
dataset. According to our experimental results, our semantic framework can be
effective to help organizations to better organize these conferences and cover
future research topics.
SQuantizer: Simultaneous Learning for Both Sparse and Low-precision Neural Networks
Authors: Mi Sun Park, Xiaofan Xu, Cormac Brick
Date: 2018-12-20 00:00:00
Tasks: Machine Translation, Neural Architecture Search, object-detection, Object Detection, Quantization, speech-recognition, Speech Recognition

Deep neural networks have achieved state-of-the-art accuracies in a wide
range of computer vision, speech recognition, and machine translation tasks.
However the limits of memory bandwidth and computational power constrain the
range of devices capable of deploying these modern networks. To address this
problem, we propose SQuantizer, a new training method that jointly optimizes
for both sparse and low-precision neural networks while maintaining high
accuracy and providing a high compression rate. This approach brings
sparsification and low-bit quantization into a single training pass, employing
these techniques in an order demonstrated to be optimal. Our method achieves
state-of-the-art accuracies using 4-bit and 2-bit precision for ResNet18,
MobileNet-v2 and ResNet50, even with high degree of sparsity. The compression
rates of 18x for ResNet18 and 17x for ResNet50, and 9x for MobileNet-v2 are
obtained when SQuantizing both weights and activations within 1% and 2% loss in
accuracy for ResNets and MobileNet-v2 respectively. An extension of these
techniques to object detection also demonstrates high accuracy on YOLO-v3.
Additionally, our method allows for fast single pass training, which is
important for rapid prototyping and neural architecture search techniques.
Finally extensive results from this simultaneous training approach allows us to
draw some useful insights into the relative merits of sparsity and
quantization.
Efficient logic architecture in training gradient boosting decision tree for high-performance and edge computing
Authors: Takuya Tanaka, Ryosuke Kasahara, Daishiro Kobayashi
Date: 2018-12-20 00:00:00
Tasks: Binary Classification, CPU, Edge-computing, General Classification, GPU

This study proposes a logic architecture for the high-speed and power
efficiently training of a gradient boosting decision tree model of binary
classification. We implemented the proposed logic architecture on an FPGA and
compared training time and power efficiency with three general GBDT software
libraries using CPU and GPU. The training speed of the logic architecture on
the FPGA was 26-259 times faster than the software libraries. The power
efficiency of the logic architecture was 90-1,104 times higher than the
software libraries. The results show that the logic architecture suits for
high-performance and edge computing.
Finite-time optimality of Bayesian predictors
Authors: Daniil Ryabko
Date: 2018-12-20 00:00:00

The problem of sequential probability forecasting is considered in the most general setting: a model set C is given, and it is required to predict as well as possible if any of the measures (environments) in C is chosen to generate the data. No assumptions whatsoever are made on the model class C, in particular, no independence or mixing assumptions; C may not be measurable; there may be no predictor whose loss is sublinear, etc. It is shown that the cumulative loss of any possible predictor can be matched by that of a Bayesian predictor whose prior is discrete and is concentrated on C, up to an additive term of order $\log n$, where $n$ is the time step. The bound holds for every $n$ and every measure in C. This is the first non-asymptotic result of this kind. In addition, a non-matching lower bound is established: it goes to infinity with $n$ but may do so arbitrarily slow.
FlowQA: Grasping Flow in History for Conversational Machine Comprehension
Authors: Hsin-Yuan Huang, Eunsol Choi, Wen-tau Yih
Date: 2018-10-06 00:00:00
Proceeding: ICLR 2019 5
Tasks: Question Answering, Reading Comprehension, Spoken Language Understanding

Conversational machine comprehension requires the understanding of the
conversation history, such as previous question/answer pairs, the document
context, and the current question. To enable traditional, single-turn models to
encode the history comprehensively, we introduce Flow, a mechanism that can
incorporate intermediate representations generated during the process of
answering previous questions, through an alternating parallel processing
structure. Compared to approaches that concatenate previous questions/answers
as input, Flow integrates the latent semantics of the conversation history more
deeply. Our model, FlowQA, shows superior performance on two recently proposed
conversational challenges (+7.2% F1 on CoQA and +4.0% on QuAC). The
effectiveness of Flow also shows in other tasks. By reducing sequential
instruction understanding to conversational machine comprehension, FlowQA
outperforms the best models on all three domains in SCONE, with +1.8% to +4.4%
improvement in accuracy.
TD-Regularized Actor-Critic Methods
Authors: Simone Parisi, Voot Tangkaratt, Jan Peters, Mohammad Emtiyaz Khan
Date: 2018-12-19 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Actor-critic methods can achieve incredible performance on difficult
reinforcement learning problems, but they are also prone to instability. This
is partly due to the interaction between the actor and critic during learning,
e.g., an inaccurate step taken by one of them might adversely affect the other
and destabilize the learning. To avoid such issues, we propose to regularize
the learning objective of the actor by penalizing the temporal difference (TD)
error of the critic. This improves stability by avoiding large steps in the
actor update whenever the critic is highly inaccurate. The resulting method,
which we call the TD-regularized actor-critic method, is a simple plug-and-play
approach to improve stability and overall performance of the actor-critic
methods. Evaluations on standard benchmarks confirm this.
Multisource and Multitemporal Data Fusion in Remote Sensing
Authors: Pedram Ghamisi, Behnood Rasti, Naoto Yokoya, Qunming Wang, Bernhard Hofle, Lorenzo Bruzzone, Francesca Bovolo, Mingmin Chi, Katharina Anders, Richard Gloaguen, Peter M. Atkinson, Jon Atli Benediktsson
Date: 2018-12-19 00:00:00

The sharp and recent increase in the availability of data captured by
different sensors combined with their considerably heterogeneous natures poses
a serious challenge for the effective and efficient processing of remotely
sensed data. Such an increase in remote sensing and ancillary datasets,
however, opens up the possibility of utilizing multimodal datasets in a joint
manner to further improve the performance of the processing approaches with
respect to the application at hand. Multisource data fusion has, therefore,
received enormous attention from researchers worldwide for a wide variety of
applications. Moreover, thanks to the revisit capability of several spaceborne
sensors, the integration of the temporal information with the spatial and/or
spectral/backscattering information of the remotely sensed data is possible and
helps to move from a representation of 2D/3D data to 4D data structures, where
the time variable adds new information as well as challenges for the
information extraction algorithms. There are a huge number of research works
dedicated to multisource and multitemporal data fusion, but the methods for the
fusion of different modalities have expanded in different paths according to
each research community. This paper brings together the advances of multisource
and multitemporal data fusion approaches with respect to different research
communities and provides a thorough and discipline-specific starting point for
researchers at different levels (i.e., students, researchers, and senior
researchers) willing to conduct novel investigations on this challenging topic
by supplying sufficient detail and references.
Fast Approximate Geodesics for Deep Generative Models
Authors: Nutan Chen, Francesco Ferroni, Alexej Klushyn, Alexandros Paraschos, Justin Bayer, Patrick van der Smagt
Date: 2018-12-19 00:00:00

The length of the geodesic between two data points along a Riemannian manifold, induced by a deep generative model, yields a principled measure of similarity. Current approaches are limited to low-dimensional latent spaces, due to the computational complexity of solving a non-convex optimisation problem. We propose finding shortest paths in a finite graph of samples from the aggregate approximate posterior, that can be solved exactly, at greatly reduced runtime, and without a notable loss in quality. Our approach, therefore, is hence applicable to high-dimensional problems, e.g., in the visual domain. We validate our approach empirically on a series of experiments using variational autoencoders applied to image data, including the Chair, FashionMNIST, and human movement data sets.
Statistical learning of geometric characteristics of wireless networks
Authors: Antoine Brochard, Bartłomiej Błaszczyszyn, Stéphane Mallat, Sixin Zhang
Date: 2018-12-19 00:00:00
Tasks: Point Processes, regression

Motivated by the prediction of cell loads in cellular networks, we formulate
the following new, fundamental problem of statistical learning of geometric
marks of point processes: An unknown marking function, depending on the
geometry of point patterns, produces characteristics (marks) of the points. One
aims at learning this function from the examples of marked point patterns in
order to predict the marks of new point patterns. To approximate (interpolate)
the marking function, in our baseline approach, we build a statistical
regression model of the marks with respect some local point distance
representation. In a more advanced approach, we use a global data
representation via the scattering moments of random measures, which build
informative and stable to deformations data representation, already proven
useful in image analysis and related application domains. In this case, the
regression of the scattering moments of the marked point patterns with respect
to the non-marked ones is combined with the numerical solution of the inverse
problem, where the marks are recovered from the estimated scattering moments.
Considering some simple, generic marks, often appearing in the modeling of
wireless networks, such as the shot-noise values, nearest neighbour distance,
and some characteristics of the Voronoi cells, we show that the scattering
moments can capture similar geometry information as the baseline approach, and
can reach even better performance, especially for non-local marking functions.
Our results motivate further development of statistical learning tools for
stochastic geometry and analysis of wireless networks, in particular to predict
cell loads in cellular networks from the locations of base stations and traffic
demand.
A comparative study of texture attributes for characterizing subsurface structures in seismic volumes
Authors: Zhiling Long, Yazeed Alaudah, Muhammad Ali Qureshi, Yuting Hu, Zhen Wang, Motaz Alfarraj, Ghassan AlRegib, Asjad Amin, Mohamed Deriche, Suhail Al-Dharrab, Haibin Di
Date: 2018-12-19 00:00:00
Tasks: Attribute, Seismic Interpretation

In this paper, we explore how to computationally characterize subsurface
geological structures presented in seismic volumes using texture attributes.
For this purpose, we conduct a comparative study of typical texture attributes
presented in the image processing literature. We focus on spatial attributes in
this study and examine them in a new application for seismic interpretation,
i.e., seismic volume labeling. For this application, a data volume is
automatically segmented into various structures, each assigned with its
corresponding label. If the labels are assigned with reasonable accuracy, such
volume labeling will help initiate an interpretation process in a more
effective manner. Our investigation proves the feasibility of accomplishing
this task using texture attributes. Through the study, we also identify
advantages and disadvantages associated with each attribute.
Automatic Classifiers as Scientific Instruments: One Step Further Away from Ground-Truth
Authors: Jacob Whitehill, Anand Ramakrishnan
Date: 2018-12-19 00:00:00

Automatic machine learning-based detectors of various psychological and social phenomena (e.g., emotion, stress, engagement) have great potential to advance basic science. However, when a detector $d$ is trained to approximate an existing measurement tool (e.g., a questionnaire, observation protocol), then care must be taken when interpreting measurements collected using $d$ since they are one step further removed from the underlying construct. We examine how the accuracy of $d$, as quantified by the correlation $q$ of $d$'s outputs with the ground-truth construct $U$, impacts the estimated correlation between $U$ (e.g., stress) and some other phenomenon $V$ (e.g., academic performance). In particular: (1) We show that if the true correlation between $U$ and $V$ is $r$, then the expected sample correlation, over all vectors $\mathcal{T}^n$ whose correlation with $U$ is $q$, is $qr$. (2) We derive a formula for the probability that the sample correlation (over $n$ subjects) using $d$ is positive given that the true correlation is negative (and vice-versa); this probability can be substantial (around $20-30\%$) for values of $n$ and $q$ that have been used in recent affective computing studies. %We also show that this probability decreases monotonically in $n$ and in $q$. (3) With the goal to reduce the variance of correlations estimated by an automatic detector, we show that training multiple neural networks $d^{(1)},\ldots,d^{(m)}$ using different training architectures and hyperparameters for the same detection task provides only limited ``coverage'' of $\mathcal{T}^n$.
Towards an Evolvable Cancer Treatment Simulator
Authors: Richard J. Preen, Larry Bull, Andrew Adamatzky
Date: 2018-12-19 00:00:00
Tasks: Evolutionary Algorithms, Two-sample testing

The use of high-fidelity computational simulations promises to enable high-throughput hypothesis testing and optimisation of cancer therapies. However, increasing realism comes at the cost of increasing computational requirements. This article explores the use of surrogate-assisted evolutionary algorithms to optimise the targeted delivery of a therapeutic compound to cancerous tumour cells with the multicellular simulator, PhysiCell. The use of both Gaussian process models and multi-layer perceptron neural network surrogate models are investigated. We find that evolutionary algorithms are able to effectively explore the parameter space of biophysical properties within the agent-based simulations, minimising the resulting number of cancerous cells after a period of simulated treatment. Both model-assisted algorithms are found to outperform a standard evolutionary algorithm, demonstrating their ability to perform a more effective search within the very small evaluation budget. This represents the first use of efficient evolutionary algorithms within a high-throughput multicellular computing approach to find therapeutic design optima that maximise tumour regression.
D3D: Distilled 3D Networks for Video Action Recognition
Authors: Jonathan C. Stroud, David A. Ross, Chen Sun, Jia Deng, Rahul Sukthankar
Date: 2018-12-19 00:00:00
Tasks: Action Classification, Action Recognition, Optical Flow Estimation, Temporal Action Localization

State-of-the-art methods for video action recognition commonly use an
ensemble of two networks: the spatial stream, which takes RGB frames as input,
and the temporal stream, which takes optical flow as input. In recent work,
both of these streams consist of 3D Convolutional Neural Networks, which apply
spatiotemporal filters to the video clip before performing classification.
Conceptually, the temporal filters should allow the spatial stream to learn
motion representations, making the temporal stream redundant. However, we still
see significant benefits in action recognition performance by including an
entirely separate temporal stream, indicating that the spatial stream is
"missing" some of the signal captured by the temporal stream. In this work, we
first investigate whether motion representations are indeed missing in the
spatial stream of 3D CNNs. Second, we demonstrate that these motion
representations can be improved by distillation, by tuning the spatial stream
to predict the outputs of the temporal stream, effectively combining both
models into a single stream. Finally, we show that our Distilled 3D Network
(D3D) achieves performance on par with two-stream approaches, using only a
single model and with no need to compute optical flow.
Detecting GAN-generated Imagery using Color Cues
Authors: Scott McCloskey, Michael Albright
Date: 2018-12-19 00:00:00
Tasks: Image Forensics

Image forensics is an increasingly relevant problem, as it can potentially
address online disinformation campaigns and mitigate problematic aspects of
social media. Of particular interest, given its recent successes, is the
detection of imagery produced by Generative Adversarial Networks (GANs), e.g.
`deepfakes'. Leveraging large training sets and extensive computing resources,
recent work has shown that GANs can be trained to generate synthetic imagery
which is (in some ways) indistinguishable from real imagery. We analyze the
structure of the generating network of a popular GAN implementation, and show
that the network's treatment of color is markedly different from a real camera
in two ways. We further show that these two cues can be used to distinguish
GAN-generated imagery from camera imagery, demonstrating effective
discrimination between GAN imagery and real camera images used to train the
GAN.
Unconstrained Iris Segmentation using Convolutional Neural Networks
Authors: Sohaib Ahmad, Benjamin Fuller
Date: 2018-12-19 00:00:00
Tasks: Iris Recognition, Iris Segmentation, Segmentation

The extraction of consistent and identifiable features from an image of the
human iris is known as iris recognition. Identifying which pixels belong to the
iris, known as segmentation, is the first stage of iris recognition. Errors in
segmentation propagate to later stages. Current segmentation approaches are
tuned to specific environments. We propose using a convolution neural network
for iris segmentation. Our algorithm is accurate when trained in a single
environment and tested in multiple environments. Our network builds on the Mask
R-CNN framework (He et al., ICCV 2017). Our approach segments faster than
previous approaches including the Mask R-CNN network. Our network is accurate
when trained on a single environment and tested with a different sensors
(either visible light or near-infrared). Its accuracy degrades when trained
with a visible light sensor and tested with a near-infrared sensor (and vice
versa). A small amount of retraining of the visible light model (using a few
samples from a near-infrared dataset) yields a tuned network accurate in both
settings. For training and testing, this work uses the Casia v4 Interval, Notre
Dame 0405, Ubiris v2, and IITD datasets.
A Novel Large-scale Ordinal Regression Model
Authors: Yong Shi, Huadong Wang, Xin Shen, Lingfeng Niu
Date: 2018-12-19 00:00:00
Tasks: model, regression

Ordinal regression (OR) is a special multiclass classification problem where
an order relation exists among the labels. Recent years, people share their
opinions and sentimental judgments conveniently with social networks and
E-Commerce so that plentiful large-scale OR problems arise. However, few
studies have focused on this kind of problems. Nonparallel Support Vector
Ordinal Regression (NPSVOR) is a SVM-based OR model, which learns a hyperplane
for each rank by solving a series of independent sub-optimization problems and
then ensembles those learned hyperplanes to predict. The previous studies are
focused on its nonlinear case and got a competitive testing performance, but
its training is time consuming, particularly for large-scale data. In this
paper, we consider NPSVOR's linear case and design an efficient training method
based on the dual coordinate descent method (DCD). To utilize the order
information among labels in prediction, a new prediction function is also
proposed. Extensive contrast experiments on the text OR datasets indicate that
the carefully implemented DCD is very suitable for training large data.
DeepWiTraffic: Low Cost WiFi-Based Traffic Monitoring System Using Deep Learning
Authors: Myounggyu Won, Sayan Sahu, Kyung-Joon Park
Date: 2018-12-19 00:00:00
Tasks: General Classification

A traffic monitoring system (TMS) is an integral part of Intelligent Transportation Systems (ITS). It is an essential tool for traffic analysis and planning. One of the biggest challenges is, however, the high cost especially in covering the huge rural road network. In this paper, we propose to address the problem by developing a novel TMS called DeepWiTraffic. DeepWiTraffic is a low-cost, portable, and non-intrusive solution that is built only with two WiFi transceivers. It exploits the unique WiFi Channel State Information (CSI) of passing vehicles to perform detection and classification of vehicles. Spatial and temporal correlations of CSI amplitude and phase data are identified and analyzed using a machine learning technique to classify vehicles into five different types: motorcycles, passenger vehicles, SUVs, pickup trucks, and large trucks. A large amount of CSI data and ground-truth video data are collected over a month period from a real-world two-lane rural roadway to validate the effectiveness of DeepWiTraffic. The results validate that DeepWiTraffic is an effective TMS with the average detection accuracy of 99.4% and the average classification accuracy of 91.1% in comparison with state-of-the-art non-intrusive TMSs.