pwc-archive/papers-with-abstracts huggingface dataset:
Unsupervised Basis Function Adaptation for Reinforcement Learning
Authors: Edward Barker, Charl Ras
Date: 2017-03-23 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

When using reinforcement learning (RL) algorithms it is common, given a large
state space, to introduce some form of approximation architecture for the value
function (VF). The exact form of this architecture can have a significant
effect on an agent's performance, however, and determining a suitable
approximation architecture can often be a highly complex task. Consequently
there is currently interest among researchers in the potential for allowing RL
algorithms to adaptively generate (i.e. to learn) approximation architectures.
One relatively unexplored method of adapting approximation architectures
involves using feedback regarding the frequency with which an agent has visited
certain states to guide which areas of the state space to approximate with
greater detail. In this article we will: (a) informally discuss the potential
advantages offered by such methods; (b) introduce a new algorithm based on such
methods which adapts a state aggregation approximation architecture on-line and
is designed for use in conjunction with SARSA; (c) provide theoretical results,
in a policy evaluation setting, regarding this particular algorithm's
complexity, convergence properties and potential to reduce VF error; and
finally (d) test experimentally the extent to which this algorithm can improve
performance given a number of different test problems. Taken together our
results suggest that our algorithm (and potentially such methods more
generally) can provide a versatile and computationally lightweight means of
significantly boosting RL performance given suitable conditions which are
commonly encountered in practice.
Diversification-Based Learning in Computing and Optimization
Authors: Fred Glover, Jin-Kao Hao
Date: 2017-03-23 00:00:00
Tasks: BIG-bench Machine Learning, Metaheuristic Optimization

Diversification-Based Learning (DBL) derives from a collection of principles
and methods introduced in the field of metaheuristics that have broad
applications in computing and optimization. We show that the DBL framework goes
significantly beyond that of the more recent Opposition-based learning (OBL)
framework introduced in Tizhoosh (2005), which has become the focus of numerous
research initiatives in machine learning and metaheuristic optimization. We
unify and extend earlier proposals in metaheuristic search (Glover, 1997,
Glover and Laguna, 1997) to give a collection of approaches that are more
flexible and comprehensive than OBL for creating intensification and
diversification strategies in metaheuristic search. We also describe potential
applications of DBL to various subfields of machine learning and optimization.
Diversification Methods for Zero-One Optimization
Authors: Fred Glover
Date: 2017-01-30 00:00:00
Tasks: Binarization, Clustering, Scheduling

We introduce new diversification methods for zero-one optimization that
significantly extend strategies previously introduced in the setting of
metaheuristic search. Our methods incorporate easily implemented strategies for
partitioning assignments of values to variables, accompanied by processes
called augmentation and shifting which create greater flexibility and
generality. We then show how the resulting collection of diversified solutions
can be further diversified by means of permutation mappings, which equally can
be used to generate diversified collections of permutations for applications
such as scheduling and routing. These methods can be applied to non-binary
vectors by the use of binarization procedures and by Diversification-Based
Learning (DBL) procedures which also provide connections to applications in
clustering and machine learning. Detailed pseudocode and numerical
illustrations are provided to show the operation of our methods and the
collections of solutions they create.
Changing Fashion Cultures
Authors: Kaori Abe, Teppei Suzuki, Shunya Ueta, Akio Nakamura, Yutaka Satoh, Hirokatsu Kataoka
Date: 2017-03-23 00:00:00
Tasks: Cultural Vocal Bursts Intensity Prediction, Time Series, Time Series Analysis

The paper presents a novel concept that analyzes and visualizes worldwide
fashion trends. Our goal is to reveal cutting-edge fashion trends without
displaying an ordinary fashion style. To achieve the fashion-based analysis, we
created a new fashion culture database (FCDB), which consists of 76 million
geo-tagged images in 16 cosmopolitan cities. By grasping a fashion trend of
mixed fashion styles,the paper also proposes an unsupervised fashion trend
descriptor (FTD) using a fashion descriptor, a codeword vetor, and temporal
analysis. To unveil fashion trends in the FCDB, the temporal analysis in FTD
effectively emphasizes consecutive features between two different times. In
experiments, we clearly show the analysis of fashion trends and fashion-based
city similarity. As the result of large-scale data collection and an
unsupervised analyzer, the proposed approach achieves world-level fashion
visualization in a time series. The code, model, and FCDB will be publicly
available after the construction of the project page.
Perspective: Energy Landscapes for Machine Learning
Authors: Andrew J. Ballard, Ritankar Das, Stefano Martiniani, Dhagash Mehta, Levent Sagun, Jacob D. Stevenson, David J. Wales
Date: 2017-03-23 00:00:00
Tasks: BIG-bench Machine Learning

Machine learning techniques are being increasingly used as flexible
non-linear fitting and prediction tools in the physical sciences. Fitting
functions that exhibit multiple solutions as local minima can be analysed in
terms of the corresponding machine learning landscape. Methods to explore and
visualise molecular potential energy landscapes can be applied to these machine
learning landscapes to gain new insight into the solution space involved in
training and the nature of the corresponding predictions. In particular, we can
define quantities analogous to molecular structure, thermodynamics, and
kinetics, and relate these emergent properties to the structure of the
underlying landscape. This Perspective aims to describe these analogies with
examples from recent applications, and suggest avenues for new
interdisciplinary research.
Bidirectional-Convolutional LSTM Based Spectral-Spatial Feature Learning for Hyperspectral Image Classification
Authors: Qingshan Liu, Feng Zhou, Renlong Hang, Xiao-Tong Yuan
Date: 2017-03-23 00:00:00
Tasks: General Classification, Hyperspectral Image Classification, image-classification, Image Classification

This paper proposes a novel deep learning framework named
bidirectional-convolutional long short term memory (Bi-CLSTM) network to
automatically learn the spectral-spatial feature from hyperspectral images
(HSIs). In the network, the issue of spectral feature extraction is considered
as a sequence learning problem, and a recurrent connection operator across the
spectral domain is used to address it. Meanwhile, inspired from the widely used
convolutional neural network (CNN), a convolution operator across the spatial
domain is incorporated into the network to extract the spatial feature.
Besides, to sufficiently capture the spectral information, a bidirectional
recurrent connection is proposed. In the classification phase, the learned
features are concatenated into a vector and fed to a softmax classifier via a
fully-connected operator. To validate the effectiveness of the proposed
Bi-CLSTM framework, we compare it with several state-of-the-art methods,
including the CNN framework, on three widely used HSIs. The obtained results
show that Bi-CLSTM can improve the classification performance as compared to
other methods.
Data Driven Exploratory Attacks on Black Box Classifiers in Adversarial Domains
Authors: Tegjyot Singh Sethi, Mehmed Kantardzic
Date: 2017-03-23 00:00:00
Tasks: BIG-bench Machine Learning

While modern day web applications aim to create impact at the civilization
level, they have become vulnerable to adversarial activity, where the next
cyber-attack can take any shape and can originate from anywhere. The increasing
scale and sophistication of attacks, has prompted the need for a data driven
solution, with machine learning forming the core of many cybersecurity systems.
Machine learning was not designed with security in mind, and the essential
assumption of stationarity, requiring that the training and testing data follow
similar distributions, is violated in an adversarial domain. In this paper, an
adversary's view point of a classification based system, is presented. Based on
a formal adversarial model, the Seed-Explore-Exploit framework is presented,
for simulating the generation of data driven and reverse engineering attacks on
classifiers. Experimental evaluation, on 10 real world datasets and using the
Google Cloud Prediction Platform, demonstrates the innate vulnerability of
classifiers and the ease with which evasion can be carried out, without any
explicit information about the classifier type, the training data or the
application domain. The proposed framework, algorithms and empirical
evaluation, serve as a white hat analysis of the vulnerabilities, and aim to
foster the development of secure machine learning frameworks.
Detailed Surface Geometry and Albedo Recovery from RGB-D Video Under Natural Illumination
Authors: Xinxin Zuo, Sen Wang, Jiangbin Zheng, Ruigang Yang
Date: 2017-02-06 00:00:00
Proceeding: ICCV 2017 10
Tasks: Object, Optical Flow Estimation

In this paper we present a novel approach for depth map enhancement from an RGB-D video sequence. The basic idea is to exploit the shading information in the color image. Instead of making assumption about surface albedo or controlled object motion and lighting, we use the lighting variations introduced by casual object movement. We are effectively calculating photometric stereo from a moving object under natural illuminations. The key technical challenge is to establish correspondences over the entire image set. We therefore develop a lighting insensitive robust pixel matching technique that out-performs optical flow method in presence of lighting variations. In addition we present an expectation-maximization framework to recover the surface normal and albedo simultaneously, without any regularization term. We have validated our method on both synthetic and real datasets to show its superior performance on both surface details recovery and intrinsic decomposition.
Localizing and Orienting Street Views Using Overhead Imagery
Authors: Nam Vo, James Hays
Date: 2016-07-30 00:00:00
Tasks: General Classification, Retrieval, Triplet

In this paper we aim to determine the location and orientation of a
ground-level query image by matching to a reference database of overhead (e.g.
satellite) images. For this task we collect a new dataset with one million
pairs of street view and overhead images sampled from eleven U.S. cities. We
explore several deep CNN architectures for cross-domain matching --
Classification, Hybrid, Siamese, and Triplet networks. Classification and
Hybrid architectures are accurate but slow since they allow only partial
feature precomputation. We propose a new loss function which significantly
improves the accuracy of Siamese and Triplet embedding networks while
maintaining their applicability to large-scale retrieval tasks like image
geolocalization. This image matching task is challenging not just because of
the dramatic viewpoint difference between ground-level and overhead imagery but
because the orientation (i.e. azimuth) of the street views is unknown making
correspondence even more difficult. We examine several mechanisms to match in
spite of this -- training for rotation invariance, sampling possible rotations
at query time, and explicitly predicting relative rotation of ground and
overhead images with our deep networks. It turns out that explicit orientation
supervision also improves location prediction accuracy. Our best performing
architectures are roughly 2.5 times as accurate as the commonly used Siamese
network baseline.
Robust Estimation of Self-Exciting Generalized Linear Models with Application to Neuronal Modeling
Authors: Abbas Kazemipour, Min Wu, Behtash Babadi
Date: 2015-07-14 00:00:00
Tasks: compressed sensing

We consider the problem of estimating self-exciting generalized linear models
from limited binary observations, where the history of the process serves as
the covariate. We analyze the performance of two classes of estimators, namely
the $\ell_1$-regularized maximum likelihood and greedy estimators, for a
canonical self-exciting process and characterize the sampling tradeoffs
required for stable recovery in the non-asymptotic regime. Our results extend
those of compressed sensing for linear and generalized linear models with
i.i.d. covariates to those with highly inter-dependent covariates. We further
provide simulation studies as well as application to real spiking data from the
mouse's lateral geniculate nucleus and the ferret's retinal ganglion cells
which agree with our theoretical predictions.
Computational Complexity of Testing Proportional Justified Representation
Authors: Haris Aziz, Shenwei Huang
Date: 2016-12-20 00:00:00

We consider a committee voting setting in which each voter approves of a
subset of candidates and based on the approvals, a target number of candidates
are selected. Aziz et al. (2015) proposed two representation axioms called
justified representation and extended justified representation. Whereas the
former can be tested as well as achieved in polynomial time, the latter
property is coNP-complete to test and no polynomial-time algorithm is known to
achieve it. Interestingly, S{\'a}nchez-Fern{\'a}ndez et~al. (2016) proposed an
intermediate property called proportional justified representation that admits
a polynomial-time algorithm to achieve. The complexity of testing proportional
justified representation has remained an open problem. In this paper, we settle
the complexity by proving that testing proportional justified representation is
coNP-complete. We complement the complexity result by showing that the problem
admits efficient algorithms if any of the following parameters are bounded: (1)
number of voters (2) number of candidates (3) maximum number of candidates
approved by a voter (4) maximum number of voters approving a given candidate.
Two-Stream RNN/CNN for Action Recognition in 3D Videos
Authors: Rui Zhao, Haider Ali, Patrick van der Smagt
Date: 2017-03-22 00:00:00
Tasks: Action Recognition, Temporal Action Localization, Vocal Bursts Valence Prediction

The recognition of actions from video sequences has many applications in
health monitoring, assisted living, surveillance, and smart homes. Despite
advances in sensing, in particular related to 3D video, the methodologies to
process the data are still subject to research. We demonstrate superior results
by a system which combines recurrent neural networks with convolutional neural
networks in a voting approach. The gated-recurrent-unit-based neural networks
are particularly well-suited to distinguish actions based on long-term
information from optical tracking data; the 3D-CNNs focus more on detailed,
recent information from video data. The resulting features are merged in an SVM
which then classifies the movement. In this architecture, our method improves
recognition rates of state-of-the-art methods by 14% on standard data sets.
Robust method for finding sparse solutions to linear inverse problems using an L2 regularization
Authors: Gonzalo H Otazu
Date: 2017-01-03 00:00:00
Tasks: L2 Regularization

We analyzed the performance of a biologically inspired algorithm called the
Corrected Projections Algorithm (CPA) when a sparseness constraint is required
to unambiguously reconstruct an observed signal using atoms from an
overcomplete dictionary. By changing the geometry of the estimation problem,
CPA gives an analytical expression for a binary variable that indicates the
presence or absence of a dictionary atom using an L2 regularizer. The
regularized solution can be implemented using an efficient real-time
Kalman-filter type of algorithm. The smoother L2 regularization of CPA makes it
very robust to noise, and CPA outperforms other methods in identifying known
atoms in the presence of strong novel atoms in the signal.
Random Features for Compositional Kernels
Authors: Amit Daniely, Roy Frostig, Vineet Gupta, Yoram Singer
Date: 2017-03-22 00:00:00
Tasks: Diversity

We describe and analyze a simple random feature scheme (RFS) from prescribed
compositional kernels. The compositional kernels we use are inspired by the
structure of convolutional neural networks and kernels. The resulting scheme
yields sparse and efficiently computable features. Each random feature can be
represented as an algebraic expression over a small number of (random) paths in
a composition tree. Thus, compositional random features can be stored
compactly. The discrete nature of the generation process enables de-duplication
of repeated features, further compacting the representation and increasing the
diversity of the embeddings. Our approach complements and can be combined with
previous random feature schemes.
Nonnegative Matrix Factorization Requires Irrationality
Authors: Dmitry Chistikov, Stefan Kiefer, Ines Marušić, Mahsa Shirmohammadi, James Worrell
Date: 2016-05-22 00:00:00
Tasks: Open-Ended Question Answering

Nonnegative matrix factorization (NMF) is the problem of decomposing a given
nonnegative $n \times m$ matrix $M$ into a product of a nonnegative $n \times
d$ matrix $W$ and a nonnegative $d \times m$ matrix $H$. A longstanding open
question, posed by Cohen and Rothblum in 1993, is whether a rational matrix $M$
always has an NMF of minimal inner dimension $d$ whose factors $W$ and $H$ are
also rational. We answer this question negatively, by exhibiting a matrix for
which $W$ and $H$ require irrational entries.
Detecting Unseen Falls from Wearable Devices using Channel-wise Ensemble of Autoencoders
Authors: Shehroz S. Khan, Babak Taati
Date: 2016-10-12 00:00:00
Tasks: Activity Recognition, One-Class Classification

A fall is an abnormal activity that occurs rarely, so it is hard to collect
real data for falls. It is, therefore, difficult to use supervised learning
methods to automatically detect falls. Another challenge in using machine
learning methods to automatically detect falls is the choice of engineered
features. In this paper, we propose to use an ensemble of autoencoders to
extract features from different channels of wearable sensor data trained only
on normal activities. We show that the traditional approach of choosing a
threshold as the maximum of the reconstruction error on the training normal
data is not the right way to identify unseen falls. We propose two methods for
automatic tightening of reconstruction error from only the normal activities
for better identification of unseen falls. We present our results on two
activity recognition datasets and show the efficacy of our proposed method
against traditional autoencoder models and two standard one-class
classification methods.
Classification-based RNN machine translation using GRUs
Authors: Ri Wang, Maysum Panju, Mahmood Gohari
Date: 2017-03-22 00:00:00
Tasks: Classification, General Classification, Machine Translation, Translation

We report the results of our classification-based machine translation model,
built upon the framework of a recurrent neural network using gated recurrent
units. Unlike other RNN models that attempt to maximize the overall conditional
log probability of sentences against sentences, our model focuses a
classification approach of estimating the conditional probability of the next
word given the input sequence. This simpler approach using GRUs was hoped to be
comparable with more complicated RNN models, but achievements in this
implementation were modest and there remains a lot of room for improving this
classification approach.
Designing Neural Network Architectures using Reinforcement Learning
Authors: Bowen Baker, Otkrist Gupta, Nikhil Naik, Ramesh Raskar
Date: 2016-11-07 00:00:00
Tasks: General Classification, image-classification, Image Classification, Q-Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

At present, designing convolutional neural network (CNN) architectures
requires both human expertise and labor. New architectures are handcrafted by
careful experimentation or modified from a handful of existing networks. We
introduce MetaQNN, a meta-modeling algorithm based on reinforcement learning to
automatically generate high-performing CNN architectures for a given learning
task. The learning agent is trained to sequentially choose CNN layers using
$Q$-learning with an $\epsilon$-greedy exploration strategy and experience
replay. The agent explores a large but finite space of possible architectures
and iteratively discovers designs with improved performance on the learning
task. On image classification benchmarks, the agent-designed networks
(consisting of only standard convolution, pooling, and fully-connected layers)
beat existing networks designed with the same layer types and are competitive
against the state-of-the-art methods that use more complex layer types. We also
outperform existing meta-modeling approaches for network design on image
classification tasks.
Decision structure of risky choice
Authors: Lamb Wubin, Naixin Ren
Date: 2017-01-30 00:00:00
Tasks: Decision Making

As we know, there is a controversy about the decision making under risk
between economists and psychologists. We discuss to build a unified theory of
risky choice, which would explain both of compensatory and non-compensatory
theories. For risky choice, according to cognition ability, we argue that
people could not build a continuous and accurate subjective probability world,
but several order concepts, such as small, middle and large probability. People
make decisions based on information, experience, imagination and other things.
All of these things are so huge that people have to prepare some strategies.
That is, people have different strategies when facing to different situations.
The distributions of these things have different decision structures. More
precisely, decision making is a process of simplifying the decision structure.
However, the process of decision structure simplifying is not stuck in a rut,
but through different path when facing problems repeatedly. It is why
preference reversal always happens when making decisions. The most efficient
way to simplify the decision structure is calculating expected value or making
decisions based on one or two dimensions. We also argue that the deliberation
time at least has four parts, which are consist of substitution time, first
order time, second order time and calculation time. Decision structure also can
simply explain the phenomenon of paradoxes and anomalies. JEL Codes: C10, D03,
D81
Randomized Kernel Methods for Least-Squares Support Vector Machines
Authors: M. Andrecut
Date: 2017-03-22 00:00:00
Tasks: Classification, General Classification, Multi-class Classification, regression

The least-squares support vector machine is a frequently used kernel method
for non-linear regression and classification tasks. Here we discuss several
approximation algorithms for the least-squares support vector machine
classifier. The proposed methods are based on randomized block kernel matrices,
and we show that they provide good accuracy and reliable scaling for
multi-class classification problems with relatively large data sets. Also, we
present several numerical experiments that illustrate the practical
applicability of the proposed methods.