pwc-archive/papers-with-abstracts huggingface dataset:
Generating OWA weights using truncated distributions
Authors: Maxime Lenormand
Date: 2017-09-13 00:00:00
Tasks: Decision Making

Ordered weighted averaging (OWA) operators have been widely used in decision
making these past few years. An important issue facing the OWA operators' users
is the determination of the OWA weights. This paper introduces an OWA
determination method based on truncated distributions that enables intuitive
generation of OWA weights according to a certain level of risk and trade-off.
These two dimensions are represented by the two first moments of the truncated
distribution. We illustrate our approach with the well-know normal distribution
and the definition of a continuous parabolic decision-strategy space. We
finally study the impact of the number of criteria on the results.
Model Trees for Identifying Exceptional Players in the NHL Draft
Authors: Oliver Schulte, Yejia Liu, Chao Li
Date: 2018-02-23 00:00:00

Drafting strong players is crucial for the team success. We describe a new
data-driven interpretable approach for assessing draft prospects in the
National Hockey League. Successful previous approaches have built a predictive
model based on player features, or derived performance predictions from the
observed performance of comparable players in a cohort. This paper develops
model tree learning, which incorporates strengths of both model-based and
cohort-based approaches. A model tree partitions the feature space according to
the values of discrete features, or learned thresholds for continuous features.
Each leaf node in the tree defines a group of players, easily described to
hockey experts, with its own group regression model. Compared to a single
model, the model tree forms an ensemble that increases predictive power.
Compared to cohort-based approaches, the groups of comparables are discovered
from the data, without requiring a similarity metric. The performance
predictions of the model tree are competitive with the state-of-the-art
methods, which validates our model empirically. We show in case studies that
the model tree player ranking can be used to highlight strong and weak points
of players.
Diffusion Maps meet Nyström
Authors: N. Benjamin Erichson, Lionel Mathelin, Steven L. Brunton, J. Nathan Kutz
Date: 2018-02-23 00:00:00
Tasks: Dimensionality Reduction, Time Series, Time Series Analysis

Diffusion maps are an emerging data-driven technique for non-linear
dimensionality reduction, which are especially useful for the analysis of
coherent structures and nonlinear embeddings of dynamical systems. However, the
computational complexity of the diffusion maps algorithm scales with the number
of observations. Thus, long time-series data presents a significant challenge
for fast and efficient embedding. We propose integrating the Nystr\"om method
with diffusion maps in order to ease the computational demand. We achieve a
speedup of roughly two to four times when approximating the dominant diffusion
map components.
Generating Natural Adversarial Examples
Authors: Zhengli Zhao, Dheeru Dua, Sameer Singh
Date: 2017-10-31 00:00:00
Proceeding: ICLR 2018 1
Tasks: Adversarial Attack, image-classification, Image Classification, Machine Translation, Natural Language Inference, Translation

Due to their complex nature, it is hard to characterize the ways in which
machine learning models can misbehave or be exploited when deployed. Recent
work on adversarial examples, i.e. inputs with minor perturbations that result
in substantially different model predictions, is helpful in evaluating the
robustness of these models by exposing the adversarial scenarios where they
fail. However, these malicious perturbations are often unnatural, not
semantically meaningful, and not applicable to complicated domains such as
language. In this paper, we propose a framework to generate natural and legible
adversarial examples that lie on the data manifold, by searching in semantic
space of dense and continuous data representation, utilizing the recent
advances in generative adversarial networks. We present generated adversaries
to demonstrate the potential of the proposed approach for black-box classifiers
for a wide range of applications such as image classification, textual
entailment, and machine translation. We include experiments to show that the
generated adversaries are natural, legible to humans, and useful in evaluating
and analyzing black-box classifiers.
Costate-focused models for reinforcement learning
Authors: Bita Behrouzi, Xuefei Liu, Douglas Tweed
Date: 2017-11-15 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Many recent algorithms for reinforcement learning are model-free and founded
on the Bellman equation. Here we present a method founded on the costate
equation and models of the state dynamics. We use the costate -- the gradient
of cost with respect to state -- to improve the policy and also to "focus" the
model, training it to detect and mimic those features of the environment that
are most relevant to its task. We show that this method can handle difficult
time-optimal control problems, driving deterministic or stochastic mechanical
systems quickly to a target. On these tasks it works well compared to deep
deterministic policy gradient, a recent Bellman method. And because it creates
a model, the costate method can also learn from mental practice.
Behavioral-clinical phenotyping with type 2 diabetes self-monitoring data
Authors: Matthew E. Levine, David J. Albers, Marissa Burgermaster, Patricia G. Davidson, Arlene M. Smaldone, Lena Mamykina
Date: 2018-02-23 00:00:00
Tasks: Clustering, Nutrition, Vocal Bursts Type Prediction

Objective: To evaluate unsupervised clustering methods for identifying
individual-level behavioral-clinical phenotypes that relate personal biomarkers
and behavioral traits in type 2 diabetes (T2DM) self-monitoring data. Materials
and Methods: We used hierarchical clustering (HC) to identify groups of meals
with similar nutrition and glycemic impact for 6 individuals with T2DM who
collected self-monitoring data. We evaluated clusters on: 1) correspondence to
gold standards generated by certified diabetes educators (CDEs) for 3
participants; 2) face validity, rated by CDEs, and 3) impact on CDEs' ability
to identify patterns for another 3 participants. Results: Gold standard (GS)
included 9 patterns across 3 participants. Of these, all 9 were re-discovered
using HC: 4 GS patterns were consistent with patterns identified by HC (over
50% of meals in a cluster followed the pattern); another 5 were included as
sub-groups in broader clusers. 50% (9/18) of clusters were rated over 3 on
5-point Likert scale for validity, significance, and being actionable. After
reviewing clusters, CDEs identified patterns that were more consistent with
data (70% reduction in contradictions between patterns and participants'
records). Discussion: Hierarchical clustering of blood glucose and
macronutrient consumption appears suitable for discovering behavioral-clinical
phenotypes in T2DM. Most clusters corresponded to gold standard and were rated
positively by CDEs for face validity. Cluster visualizations helped CDEs
identify more robust patterns in nutrition and glycemic impact, creating new
possibilities for visual analytic solutions. Conclusion: Machine learning
methods can use diabetes self-monitoring data to create personalized
behavioral-clinical phenotypes, which may prove useful for delivering
personalized medicine.
Best-Effort Inductive Logic Programming via Fine-grained Cost-based Hypothesis Generation
Authors: Peter Schüller, Mishal Benz
Date: 2017-07-10 00:00:00
Tasks: Inductive logic programming

We describe the Inspire system which participated in the first competition on
Inductive Logic Programming (ILP). Inspire is based on Answer Set Programming
(ASP). The distinguishing feature of Inspire is an ASP encoding for hypothesis
space generation: given a set of facts representing the mode bias, and a set of
cost configuration parameters, each answer set of this encoding represents a
single rule that is considered for finding a hypothesis that entails the given
examples. Compared with state-of-the-art methods that use the length of the
rule body as a metric for rule complexity, our approach permits a much more
fine-grained specification of the shape of hypothesis candidate rules. The
Inspire system iteratively increases the rule cost limit and thereby increases
the search space until it finds a suitable hypothesis. The system searches for
a hypothesis that entails a single example at a time, utilizing an ASP encoding
derived from the encoding used in XHAIL. We perform experiments with the
development and test set of the ILP competition. For comparison we also adapted
the ILASP system to process competition instances. Experimental results show
that the cost parameters for the hypothesis search space are an important
factor for finding hypotheses to competition instances within tight resource
bounds.
Edge-Based Recognition of Novel Objects for Robotic Grasping
Authors: Amirhossein Jabalameli, Nabil Ettehadi, Aman Behal
Date: 2018-02-23 00:00:00
Tasks: Robotic Grasping

In this paper, we investigate the problem of grasping novel objects in
unstructured environments. To address this problem, consideration of the object
geometry, reachability and force closure analysis are required. We propose a
framework for grasping unknown objects by localizing contact regions on the
contours formed by a set of depth edges in a single view 2D depth image.
According to the edge geometric features obtained from analyzing the data of
the depth map, the contact regions are determined. Finally,We validate the
performance of the approach by applying it to the scenes with both single and
multiple objects, using Baxter manipulator.
Backpropagation through the Void: Optimizing control variates for black-box gradient estimation
Authors: Will Grathwohl, Dami Choi, Yuhuai Wu, Geoffrey Roeder, David Duvenaud
Date: 2017-10-31 00:00:00
Proceeding: ICLR 2018 1
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Gradient-based optimization is the foundation of deep learning and
reinforcement learning. Even when the mechanism being optimized is unknown or
not differentiable, optimization using high-variance or biased gradient
estimates is still often the best strategy. We introduce a general framework
for learning low-variance, unbiased gradient estimators for black-box functions
of random variables. Our method uses gradients of a neural network trained
jointly with model parameters or policies, and is applicable in both discrete
and continuous settings. We demonstrate this framework for training discrete
latent-variable models. We also give an unbiased, action-conditional extension
of the advantage actor-critic reinforcement learning algorithm.
A PAC-Bayesian Approach to Spectrally-Normalized Margin Bounds for Neural Networks
Authors: Behnam Neyshabur, Srinadh Bhojanapalli, Nathan Srebro
Date: 2017-07-29 00:00:00
Proceeding: ICLR 2018 1

We present a generalization bound for feedforward neural networks in terms of
the product of the spectral norm of the layers and the Frobenius norm of the
weights. The generalization bound is derived using a PAC-Bayes analysis.
Can you find a face in a HEVC bitstream?
Authors: Saeed Ranjbar Alvar, Hyomin Choi, Ivan V. Bajic
Date: 2017-10-30 00:00:00

Finding faces in images is one of the most important tasks in computer
vision, with applications in biometrics, surveillance, human-computer
interaction, and other areas. In our earlier work, we demonstrated that it is
possible to tell whether or not an image contains a face by only examining the
HEVC syntax, without fully reconstructing the image. In the present work we
move further in this direction by showing how to localize faces in HEVC-coded
images, without full reconstruction. We also demonstrate the benefits that such
approach can have in privacy-friendly face localization.
Eigenoption Discovery through the Deep Successor Representation
Authors: Marlos C. Machado, Clemens Rosenbaum, Xiaoxiao Guo, Miao Liu, Gerald Tesauro, Murray Campbell
Date: 2017-10-30 00:00:00
Proceeding: ICLR 2018 1
Tasks: Atari Games, Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Representation Learning

Options in reinforcement learning allow agents to hierarchically decompose a
task into subtasks, having the potential to speed up learning and planning.
However, autonomously learning effective sets of options is still a major
challenge in the field. In this paper we focus on the recently introduced idea
of using representation learning methods to guide the option discovery process.
Specifically, we look at eigenoptions, options obtained from representations
that encode diffusive information flow in the environment. We extend the
existing algorithms for eigenoption discovery to settings with stochastic
transitions and in which handcrafted features are not available. We propose an
algorithm that discovers eigenoptions while learning non-linear state
representations from raw pixels. It exploits recent successes in the deep
reinforcement learning literature and the equivalence between proto-value
functions and the successor representation. We use traditional tabular domains
to provide intuition about our approach and Atari 2600 games to demonstrate its
potential.
Target-adaptive CNN-based pansharpening
Authors: Giuseppe Scarpa, Sergio Vitale, Davide Cozzolino
Date: 2017-09-18 00:00:00
Tasks: Pansharpening

We recently proposed a convolutional neural network (CNN) for remote sensing
image pansharpening obtaining a significant performance gain over the state of
the art. In this paper, we explore a number of architectural and training
variations to this baseline, achieving further performance gains with a
lightweight network which trains very fast. Leveraging on this latter property,
we propose a target-adaptive usage modality which ensures a very good
performance also in the presence of a mismatch w.r.t. the training set, and
even across different sensors. The proposed method, published online as an
off-the-shelf software tool, allows users to perform fast and high-quality
CNN-based pansharpening of their own target images on general-purpose hardware.
Contextual Bandits with Stochastic Experts
Authors: Rajat Sen, Karthikeyan Shanmugam, Nihal Sharma, Sanjay Shakkottai
Date: 2018-02-23 00:00:00
Tasks: Multi-Armed Bandits

We consider the problem of contextual bandits with stochastic experts, which is a variation of the traditional stochastic contextual bandit with experts problem. In our problem setting, we assume access to a class of stochastic experts, where each expert is a conditional distribution over the arms given a context. We propose upper-confidence bound (UCB) algorithms for this problem, which employ two different importance sampling based estimators for the mean reward for each expert. Both these estimators leverage information leakage among the experts, thus using samples collected under all the experts to estimate the mean reward of any given expert. This leads to instance dependent regret bounds of $\mathcal{O}\left(\lambda(\pmb{\mu})\mathcal{M}\log T/\Delta \right)$, where $\lambda(\pmb{\mu})$ is a term that depends on the mean rewards of the experts, $\Delta$ is the smallest gap between the mean reward of the optimal expert and the rest, and $\mathcal{M}$ quantifies the information leakage among the experts. We show that under some assumptions $\lambda(\pmb{\mu})$ is typically $\mathcal{O}(\log N)$, where $N$ is the number of experts. We implement our algorithm with stochastic experts generated from cost-sensitive classification oracles and show superior empirical performance on real-world datasets, when compared to other state of the art contextual bandit algorithms.
Estimating Graphlet Statistics via Lifting
Authors: Kirill Paramonov, Dmitry Shemetov, James Sharpnack
Date: 2018-02-23 00:00:00

Exploratory analysis over network data is often limited by the ability to efficiently calculate graph statistics, which can provide a model-free understanding of the macroscopic properties of a network. We introduce a framework for estimating the graphlet count---the number of occurrences of a small subgraph motif (e.g. a wedge or a triangle) in the network. For massive graphs, where accessing the whole graph is not possible, the only viable algorithms are those that make a limited number of vertex neighborhood queries. We introduce a Monte Carlo sampling technique for graphlet counts, called {\em Lifting}, which can simultaneously sample all graphlets of size up to $k$ vertices for arbitrary $k$. This is the first graphlet sampling method that can provably sample every graphlet with positive probability and can sample graphlets of arbitrary size $k$. We outline variants of lifted graphlet counts, including the ordered, unordered, and shotgun estimators, random walk starts, and parallel vertex starts. We prove that our graphlet count updates are unbiased for the true graphlet count and have a controlled variance for all graphlets. We compare the experimental performance of lifted graphlet counts to the state-of-the art graphlet sampling procedures: Waddling and the pairwise subgraph random walk.
Automatic Speech Recognition and Topic Identification for Almost-Zero-Resource Languages
Authors: Matthew Wiesner, Chunxi Liu, Lucas Ondel, Craig Harman, Vimal Manohar, Jan Trmal, Zhongqiang Huang, Najim Dehak, Sanjeev Khudanpur
Date: 2018-02-23 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Humanitarian, speech-recognition, Speech Recognition

Automatic speech recognition (ASR) systems often need to be developed for
extremely low-resource languages to serve end-uses such as audio content
categorization and search. While universal phone recognition is natural to
consider when no transcribed speech is available to train an ASR system in a
language, adapting universal phone models using very small amounts (minutes
rather than hours) of transcribed speech also needs to be studied, particularly
with state-of-the-art DNN-based acoustic models. The DARPA LORELEI program
provides a framework for such very-low-resource ASR studies, and provides an
extrinsic metric for evaluating ASR performance in a humanitarian assistance,
disaster relief setting. This paper presents our Kaldi-based systems for the
program, which employ a universal phone modeling approach to ASR, and describes
recipes for very rapid adaptation of this universal ASR system. The results we
obtain significantly outperform results obtained by many competing approaches
on the NIST LoReHLT 2017 Evaluation datasets.
FearNet: Brain-Inspired Model for Incremental Learning
Authors: Ronald Kemker, Christopher Kanan
Date: 2017-11-28 00:00:00
Proceeding: ICLR 2018 1
Tasks: Audio Classification, Incremental Learning, model

Incremental class learning involves sequentially learning classes in bursts
of examples from the same class. This violates the assumptions that underlie
methods for training standard deep neural networks, and will cause them to
suffer from catastrophic forgetting. Arguably, the best method for incremental
class learning is iCaRL, but it requires storing training examples for each
class, making it challenging to scale. Here, we propose FearNet for incremental
class learning. FearNet is a generative model that does not store previous
examples, making it memory efficient. FearNet uses a brain-inspired dual-memory
system in which new memories are consolidated from a network for recent
memories inspired by the mammalian hippocampal complex to a network for
long-term storage inspired by medial prefrontal cortex. Memory consolidation is
inspired by mechanisms that occur during sleep. FearNet also uses a module
inspired by the basolateral amygdala for determining which memory system to use
for recall. FearNet achieves state-of-the-art performance at incremental class
learning on image (CIFAR-100, CUB-200) and audio classification (AudioSet)
benchmarks.
Longitudinal Face Aging in the Wild - Recent Deep Learning Approaches
Authors: Chi Nhan Duong, Khoa Luu, Kha Gia Quach, Tien D. Bui
Date: 2018-02-23 00:00:00
Tasks: Deep Learning

Face Aging has raised considerable attentions and interest from the computer
vision community in recent years. Numerous approaches ranging from purely image
processing techniques to deep learning structures have been proposed in
literature. In this paper, we aim to give a review of recent developments of
modern deep learning based approaches, i.e. Deep Generative Models, for Face
Aging task. Their structures, formulation, learning algorithms as well as
synthesized results are also provided with systematic discussions. Moreover,
the aging databases used in most methods to learn the aging process are also
reviewed.
On the Discrimination-Generalization Tradeoff in GANs
Authors: Pengchuan Zhang, Qiang Liu, Dengyong Zhou, Tao Xu, Xiaodong He
Date: 2017-11-07 00:00:00
Proceeding: ICLR 2018 1
Tasks: Generalization Bounds

Generative adversarial training can be generally understood as minimizing
certain moment matching loss defined by a set of discriminator functions,
typically neural networks. The discriminator set should be large enough to be
able to uniquely identify the true distribution (discriminative), and also be
small enough to go beyond memorizing samples (generalizable). In this paper, we
show that a discriminator set is guaranteed to be discriminative whenever its
linear span is dense in the set of bounded continuous functions. This is a very
mild condition satisfied even by neural networks with a single neuron. Further,
we develop generalization bounds between the learned distribution and true
distribution under different evaluation metrics. When evaluated with neural
distance, our bounds show that generalization is guaranteed as long as the
discriminator set is small enough, regardless of the size of the generator or
hypothesis set. When evaluated with KL divergence, our bound provides an
explanation on the counter-intuitive behaviors of testing likelihood in GAN
training. Our analysis sheds lights on understanding the practical performance
of GANs.
Convolutional Neural Networks Via Node-Varying Graph Filters
Authors: Fernando Gama, Geert Leus, Antonio G. Marques, Alejandro Ribeiro
Date: 2017-10-27 00:00:00

Convolutional neural networks (CNNs) are being applied to an increasing
number of problems and fields due to their superior performance in
classification and regression tasks. Since two of the key operations that CNNs
implement are convolution and pooling, this type of networks is implicitly
designed to act on data described by regular structures such as images.
Motivated by the recent interest in processing signals defined in irregular
domains, we advocate a CNN architecture that operates on signals supported on
graphs. The proposed design replaces the classical convolution not with a
node-invariant graph filter (GF), which is the natural generalization of
convolution to graph domains, but with a node-varying GF. This filter extracts
different local features without increasing the output dimension of each layer
and, as a result, bypasses the need for a pooling stage while involving only
local operations. A second contribution is to replace the node-varying GF with
a hybrid node-varying GF, which is a new type of GF introduced in this paper.
While the alternative architecture can still be run locally without requiring a
pooling stage, the number of trainable parameters is smaller and can be
rendered independent of the data dimension. Tests are run on a synthetic source
localization problem and on the 20NEWS dataset.