pwc-archive/papers-with-abstracts huggingface dataset:
Born Again Neural Networks
Authors: Tommaso Furlanello, Zachary C. Lipton, Michael Tschannen, Laurent Itti, Anima Anandkumar
Date: 2018-05-12 00:00:00
Proceeding: ICML 2018 7
Tasks: Image Classification, Knowledge Distillation, Language Modeling

Knowledge distillation (KD) consists of transferring knowledge from one
machine learning model (the teacher}) to another (the student). Commonly, the
teacher is a high-capacity model with formidable performance, while the student
is more compact. By transferring knowledge, one hopes to benefit from the
student's compactness. %we desire a compact model with performance close to the
teacher's. We study KD from a new perspective: rather than compressing models,
we train students parameterized identically to their teachers. Surprisingly,
these {Born-Again Networks (BANs), outperform their teachers significantly,
both on computer vision and language modeling tasks. Our experiments with BANs
based on DenseNets demonstrate state-of-the-art performance on the CIFAR-10
(3.5%) and CIFAR-100 (15.5%) datasets, by validation error. Additional
experiments explore two distillation objectives: (i) Confidence-Weighted by
Teacher Max (CWTM) and (ii) Dark Knowledge with Permuted Predictions (DKPP).
Both methods elucidate the essential components of KD, demonstrating a role of
the teacher outputs on both predicted and non-predicted classes. We present
experiments with students of various capacities, focusing on the under-explored
case where students overpower teachers. Our experiments show significant
advantages from transferring knowledge between DenseNets and ResNets in either
direction.
Improving Predictive Uncertainty Estimation using Dropout -- Hamiltonian Monte Carlo
Authors: Diego Vergara, Sergio Hernández, Matias Valdenegro-Toro, Felipe Jorquera
Date: 2018-05-12 00:00:00
Tasks: Autonomous Driving, Bayesian Inference, General Classification, image-classification, Image Classification

Estimating predictive uncertainty is crucial for many computer vision tasks, from image classification to autonomous driving systems. Hamiltonian Monte Carlo (HMC) is an sampling method for performing Bayesian inference. On the other hand, Dropout regularization has been proposed as an approximate model averaging technique that tends to improve generalization in large scale models such as deep neural networks. Although, HMC provides convergence guarantees for most standard Bayesian models, it does not handle discrete parameters arising from Dropout regularization. In this paper, we present a robust methodology for improving predictive uncertainty in classification problems, based on Dropout and Hamiltonian Monte Carlo. Even though Dropout induces a non-smooth energy function with no such convergence guarantees, the resulting discretization of the Hamiltonian proves empirical success. The proposed method allows to effectively estimate the predictive accuracy and to provide better generalization for difficult test examples.
A Simple and Effective Model-Based Variable Importance Measure
Authors: Brandon M. Greenwell, Bradley C. Boehmke, Andrew J. McCarthy
Date: 2018-05-12 00:00:00

In the era of "big data", it is becoming more of a challenge to not only
build state-of-the-art predictive models, but also gain an understanding of
what's really going on in the data. For example, it is often of interest to
know which, if any, of the predictors in a fitted model are relatively
influential on the predicted outcome. Some modern algorithms---like random
forests and gradient boosted decision trees---have a natural way of quantifying
the importance or relative influence of each feature. Other algorithms---like
naive Bayes classifiers and support vector machines---are not capable of doing
so and model-free approaches are generally used to measure each predictor's
importance. In this paper, we propose a standardized, model-based approach to
measuring predictor importance across the growing spectrum of supervised
learning algorithms. Our proposed method is illustrated through both simulated
and real data examples. The R code to reproduce all of the figures in this
paper is available in the supplementary materials.
Incremental Learning Framework Using Cloud Computing
Authors: Kumarjit Pathak, Prabhukiran G, Jitin Kapila, Nikit Gawande
Date: 2018-05-12 00:00:00
Tasks: Cloud Computing, Incremental Learning

High volume of data, perceived as either challenge or opportunity. Deep
learning architecture demands high volume of data to effectively back propagate
and train the weights without bias. At the same time, large volume of data
demands higher capacity of the machine where it could be executed seamlessly.
Budding data scientist along with many research professionals face frequent
disconnection issue with cloud computing framework (working without dedicated
connection) due to free subscription to the platform. Similar issues also
visible while working on local computer where computer may run out of resource
or power sometimes and researcher has to start training the models all over
again. In this paper, we intend to provide a way to resolve this issue and
progressively training the neural network even after having frequent
disconnection or resource outage without loosing much of the progress
Generating Rescheduling Knowledge using Reinforcement Learning in a Cognitive Architecture
Authors: Jorge A. Palombarini, Juan Cruz Barsce, Ernesto C. Martínez
Date: 2018-05-12 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Scheduling

In order to reach higher degrees of flexibility, adaptability and autonomy in
manufacturing systems, it is essential to develop new rescheduling
methodologies which resort to cognitive capabilities, similar to those found in
human beings. Artificial cognition is important for designing planning and
control systems that generate and represent knowledge about heuristics for
repair-based scheduling. Rescheduling knowledge in the form of decision rules
is used to deal with unforeseen events and disturbances reactively in real
time, and take advantage of the ability to act interactively with the user to
counteract the effects of disruptions. In this work, to achieve the
aforementioned goals, a novel approach to generate rescheduling knowledge in
the form of dynamic first-order logical rules is proposed. The proposed
approach is based on the integration of reinforcement learning with artificial
cognitive capabilities involving perception and reasoning/learning skills
embedded in the Soar cognitive architecture. An industrial example is discussed
showing that the approach enables the scheduling system to assess its
operational range in an autonomic way, and to acquire experience through
intensive simulation while performing repair tasks.
A Cognitive Approach to Real-time Rescheduling using SOAR-RL
Authors: Juan Cruz Barsce, Jorge A. Palombarini, Ernesto C. Martínez
Date: 2018-05-12 00:00:00

Ensuring flexible and efficient manufacturing of customized products in an
increasing dynamic and turbulent environment without sacrificing cost
effectiveness, product quality and on-time delivery has become a key issue for
most industrial enterprises. A promising approach to cope with this challenge
is the integration of cognitive capabilities in systems and processes with the
aim of expanding the knowledge base used to perform managerial and operational
tasks. In this work, a novel approach to real-time rescheduling is proposed in
order to achieve sustainable improvements in flexibility and adaptability of
production systems through the integration of artificial cognitive
capabilities, involving perception, reasoning/learning and planning skills.
Moreover, an industrial example is discussed where the SOAR cognitive
architecture capabilities are integrated in a software prototype, showing that
the approach enables the rescheduling system to respond to events in an
autonomic way, and to acquire experience through intensive simulation while
performing repair tasks.
Extended pipeline for content-based feature engineering in music genre recognition
Authors: Tina Raissi, Alessandro Tibo, Paolo Bientinesi
Date: 2018-05-12 00:00:00
Tasks: Feature Engineering, General Classification, Music Genre Recognition

We present a feature engineering pipeline for the construction of musical
signal characteristics, to be used for the design of a supervised model for
musical genre identification. The key idea is to extend the traditional
two-step process of extraction and classification with additive stand-alone
phases which are no longer organized in a waterfall scheme. The whole system is
realized by traversing backtrack arrows and cycles between various stages. In
order to give a compact and effective representation of the features, the
standard early temporal integration is combined with other selection and
extraction phases: on the one hand, the selection of the most meaningful
characteristics based on information gain, and on the other hand, the inclusion
of the nonlinear correlation between this subset of features, determined by an
autoencoder. The results of the experiments conducted on GTZAN dataset reveal a
noticeable contribution of this methodology towards the model's performance in
classification task.
Towards Autonomous Reinforcement Learning: Automatic Setting of Hyper-parameters using Bayesian Optimization
Authors: Juan Cruz Barsce, Jorge A. Palombarini, Ernesto C. Martínez
Date: 2018-05-12 00:00:00
Tasks: Bayesian Optimization, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Self-Driving Cars

With the increase of machine learning usage by industries and scientific
communities in a variety of tasks such as text mining, image recognition and
self-driving cars, automatic setting of hyper-parameter in learning algorithms
is a key factor for achieving satisfactory performance regardless of user
expertise in the inner workings of the techniques and methodologies. In
particular, for a reinforcement learning algorithm, the efficiency of an agent
learning a control policy in an uncertain environment is heavily dependent on
the hyper-parameters used to balance exploration with exploitation. In this
work, an autonomous learning framework that integrates Bayesian optimization
with Gaussian process regression to optimize the hyper-parameters of a
reinforcement learning algorithm, is proposed. Also, a bandits-based approach
to achieve a balance between computational costs and decreasing uncertainty
about the Q-values, is presented. A gridworld example is used to highlight how
hyper-parameter configurations of a learning algorithm (SARSA) are iteratively
improved based on two performance functions.
Agreement Rate Initialized Maximum Likelihood Estimator for Ensemble Classifier Aggregation and Its Application in Brain-Computer Interface
Authors: Dongrui Wu, Vernon J. Lawhern, Stephen Gordon, Brent J. Lance, Chin-Teng Lin
Date: 2018-05-12 00:00:00
Tasks: Brain Computer Interface, Ensemble Learning, General Classification

Ensemble learning is a powerful approach to construct a strong learner from
multiple base learners. The most popular way to aggregate an ensemble of
classifiers is majority voting, which assigns a sample to the class that most
base classifiers vote for. However, improved performance can be obtained by
assigning weights to the base classifiers according to their accuracy. This
paper proposes an agreement rate initialized maximum likelihood estimator
(ARIMLE) to optimally fuse the base classifiers. ARIMLE first uses a simplified
agreement rate method to estimate the classification accuracy of each base
classifier from the unlabeled samples, then employs the accuracies to
initialize a maximum likelihood estimator (MLE), and finally uses the
expectation-maximization algorithm to refine the MLE. Extensive experiments on
visually evoked potential classification in a brain-computer interface
application show that ARIMLE outperforms majority voting, and also achieves
better or comparable performance with several other state-of-the-art classifier
combination approaches.
Offline EEG-Based Driver Drowsiness Estimation Using Enhanced Batch-Mode Active Learning (EBMAL) for Regression
Authors: Dongrui Wu, Vernon J. Lawhern, Stephen Gordon, Brent J. Lance, Chin-Teng Lin
Date: 2018-05-12 00:00:00
Tasks: Active Learning, Brain Computer Interface, Diversity, EEG, Electroencephalogram (EEG), regression

There are many important regression problems in real-world brain-computer
interface (BCI) applications, e.g., driver drowsiness estimation from EEG
signals. This paper considers offline analysis: given a pool of unlabeled EEG
epochs recorded during driving, how do we optimally select a small number of
them to label so that an accurate regression model can be built from them to
label the rest? Active learning is a promising solution to this problem, but
interestingly, to our best knowledge, it has not been used for regression
problems in BCI so far. This paper proposes a novel enhanced batch-mode active
learning (EBMAL) approach for regression, which improves upon a baseline active
learning algorithm by increasing the reliability, representativeness and
diversity of the selected samples to achieve better regression performance. We
validate its effectiveness using driver drowsiness estimation from EEG signals.
However, EBMAL is a general approach that can also be applied to many other
offline regression problems beyond BCI.
Active Semi-supervised Transfer Learning (ASTL) for Offline BCI Calibration
Authors: Dongrui Wu
Date: 2018-05-12 00:00:00
Tasks: Active Learning, Brain Computer Interface, EEG, Electroencephalogram (EEG), Subject Transfer, Transfer Learning

Single-trial classification of event-related potentials in
electroencephalogram (EEG) signals is a very important paradigm of
brain-computer interface (BCI). Because of individual differences, usually some
subject-specific calibration data are required to tailor the classifier for
each subject. Transfer learning has been extensively used to reduce such
calibration data requirement, by making use of auxiliary data from
similar/relevant subjects/tasks. However, all previous research assumes that
all auxiliary data have been labeled. This paper considers a more general
scenario, in which part of the auxiliary data could be unlabeled. We propose
active semi-supervised transfer learning (ASTL) for offline BCI calibration,
which integrates active learning, semi-supervised learning, and transfer
learning. Using a visual evoked potential oddball task and three different EEG
headsets, we demonstrate that ASTL can achieve consistently good performance
across subjects and headsets, and it outperforms some state-of-the-art
approaches in the literature.
Analogical Reasoning on Chinese Morphological and Semantic Relations
Authors: Shen Li, Zhe Zhao, Renfen Hu, Wensi Li, Tao Liu, Xiaoyong Du
Date: 2018-05-12 00:00:00
Proceeding: ACL 2018 7
Tasks: Word Embeddings

Analogical reasoning is effective in capturing linguistic regularities. This
paper proposes an analogical reasoning task on Chinese. After delving into
Chinese lexical knowledge, we sketch 68 implicit morphological relations and 28
explicit semantic relations. A big and balanced dataset CA8 is then built for
this task, including 17813 questions. Furthermore, we systematically explore
the influences of vector representations, context features, and corpora on
analogical reasoning. With the experiments, CA8 is proved to be a reliable
benchmark for evaluating Chinese word embeddings.
Pool-Based Sequential Active Learning for Regression
Authors: Dongrui Wu
Date: 2018-05-12 00:00:00
Tasks: Active Learning, Diversity, Informativeness, regression

Active learning is a machine learning approach for reducing the data labeling
effort. Given a pool of unlabeled samples, it tries to select the most useful
ones to label so that a model built from them can achieve the best possible
performance. This paper focuses on pool-based sequential active learning for
regression (ALR). We first propose three essential criteria that an ALR
approach should consider in selecting the most useful unlabeled samples:
informativeness, representativeness, and diversity, and compare four existing
ALR approaches against them. We then propose a new ALR approach using passive
sampling, which considers both the representativeness and the diversity in both
the initialization and subsequent iterations. Remarkably, this approach can
also be integrated with other existing ALR approaches in the literature to
further improve the performance. Extensive experiments on 11 UCI, CMU StatLib,
and UFL Media Core datasets from various domains verified the effectiveness of
our proposed ALR approaches.
Learning Symmetric and Low-energy Locomotion
Authors: Wenhao Yu, Greg Turk, C. Karen Liu
Date: 2018-01-24 00:00:00
Tasks: Deep Reinforcement Learning, Motion Generation, Reinforcement Learning

Learning locomotion skills is a challenging problem. To generate realistic
and smooth locomotion, existing methods use motion capture, finite state
machines or morphology-specific knowledge to guide the motion generation
algorithms. Deep reinforcement learning (DRL) is a promising approach for the
automatic creation of locomotion control. Indeed, a standard benchmark for DRL
is to automatically create a running controller for a biped character from a
simple reward function. Although several different DRL algorithms can
successfully create a running controller, the resulting motions usually look
nothing like a real runner. This paper takes a minimalist learning approach to
the locomotion problem, without the use of motion examples, finite state
machines, or morphology-specific knowledge. We introduce two modifications to
the DRL approach that, when used together, produce locomotion behaviors that
are symmetric, low-energy, and much closer to that of a real person. First, we
introduce a new term to the loss function (not the reward function) that
encourages symmetric actions. Second, we introduce a new curriculum learning
method that provides modulated physical assistance to help the character with
left/right balance and forward movement. The algorithm automatically computes
appropriate assistance to the character and gradually relaxes this assistance,
so that eventually the character learns to move entirely without help. Because
our method does not make use of motion capture data, it can be applied to a
variety of character morphologies. We demonstrate locomotion controllers for
the lower half of a biped, a full humanoid, a quadruped, and a hexapod. Our
results show that learned policies are able to produce symmetric, low-energy
gaits. In addition, speed-appropriate gait patterns emerge without any guidance
from motion examples or contact planning.
Do Outliers Ruin Collaboration?
Authors: Mingda Qiao
Date: 2018-05-12 00:00:00
Proceeding: ICML 2018 7

We consider the problem of learning a binary classifier from $n$ different
data sources, among which at most an $\eta$ fraction are adversarial. The
overhead is defined as the ratio between the sample complexity of learning in
this setting and that of learning the same hypothesis class on a single data
distribution. We present an algorithm that achieves an $O(\eta n + \ln n)$
overhead, which is proved to be worst-case optimal. We also discuss the
potential challenges to the design of a computationally efficient learning
algorithm with a small overhead.
Face Recognition Techniques: A Survey
Authors: Raunak Dave, Ankit Vyas, Nikita P Desai
Date: 2018-03-20 00:00:00
Tasks: Face Recognition, Survey

Nowadays research has expanded to extracting auxiliary information from various biometric techniques like fingerprints, face, iris, palm and voice . This information contains some major features like gender, age, beard, mustache, scars, height, hair, skin color, glasses, weight, facial marks and tattoos. All this information contributes strongly to identification of human. The major challenges that come across face recognition are to find age & gender of the person. This paper contributes a survey of various face recognition techniques for finding the age and gender. The existing techniques are discussed based on their performances. This paper also provides future directions for further research.
Unsupervised Representation Learning with Laplacian Pyramid Auto-encoders
Authors: Qilu Zhao, Zongmin Li
Date: 2018-01-16 00:00:00
Tasks: Representation Learning

Scale-space representation has been popular in computer vision community due
to its theoretical foundation. The motivation for generating a scale-space
representation of a given data set originates from the basic observation that
real-world objects are composed of different structures at different scales.
Hence, it's reasonable to consider learning features with image pyramids
generated by smoothing and down-sampling operations. In this paper we propose
Laplacian pyramid auto-encoders, a straightforward modification of the deep
convolutional auto-encoder architecture, for unsupervised representation
learning. The method uses multiple encoding-decoding sub-networks within a
Laplacian pyramid framework to reconstruct the original image and the low pass
filtered images. The last layer of each encoding sub-network also connects to
an encoding layer of the sub-network in the next level, which aims to reverse
the process of Laplacian pyramid generation. Experimental results showed that
Laplacian pyramid benefited the classification and reconstruction performance
of deep auto-encoder approaches, and batch normalization is critical to get
deep auto-encoders approaches to begin learning.
Utilitarian Welfare and Representation Guarantees of Approval-Based Multiwinner Rules
Authors: Martin Lackner, Piotr Skowron
Date: 2018-01-04 00:00:00

To choose a suitable multiwinner voting rule is a hard and ambiguous task. Depending on the context, it varies widely what constitutes the choice of an ``optimal'' subset of alternatives. In this paper, we provide a quantitative analysis of multiwinner voting rules using methods from the theory of approximation algorithms---we estimate how well multiwinner rules approximate two extreme objectives: a representation criterion defined via the Approval Chamberlin--Courant rule and a utilitarian criterion defined via Multiwinner Approval Voting. With both theoretical and experimental methods, we classify multiwinner rules in terms of their quantitative alignment with these two opposing objectives. Our results provide fundamental information about the nature of multiwinner rules and, in particular, about the necessary tradeoffs when choosing such a rule.