pwc-archive/papers-with-abstracts huggingface dataset:
Single Image Reflection Removal Using Deep Encoder-Decoder Network
Authors: Zhixiang Chi, Xiaolin Wu, Xiao Shu, Jinjin Gu
Date: 2018-01-31 00:00:00
Tasks: Decoder, Reflection Removal

Image of a scene captured through a piece of transparent and reflective
material, such as glass, is often spoiled by a superimposed layer of reflection
image. While separating the reflection from a familiar object in an image is
mentally not difficult for humans, it is a challenging, ill-posed problem in
computer vision. In this paper, we propose a novel deep convolutional
encoder-decoder method to remove the objectionable reflection by learning a map
between image pairs with and without reflection. For training the neural
network, we model the physical formation of reflections in images and
synthesize a large number of photo-realistic reflection-tainted images from
reflection-free images collected online. Extensive experimental results show
that, although the neural network learns only from synthetic data, the proposed
method is effective on real-world images, and it significantly outperforms the
other tested state-of-the-art techniques.
Improved Image Segmentation via Cost Minimization of Multiple Hypotheses
Authors: Marc Bosch, Christopher M. Gifford, Austin G. Dress, Clare W. Lau, Jeffrey G. Skibo, Gordon A. Christie
Date: 2018-01-31 00:00:00
Tasks: Diversity, Image Segmentation, Segmentation, Semantic Segmentation

Image segmentation is an important component of many image understanding
systems. It aims to group pixels in a spatially and perceptually coherent
manner. Typically, these algorithms have a collection of parameters that
control the degree of over-segmentation produced. It still remains a challenge
to properly select such parameters for human-like perceptual grouping. In this
work, we exploit the diversity of segments produced by different choices of
parameters. We scan the segmentation parameter space and generate a collection
of image segmentation hypotheses (from highly over-segmented to
under-segmented). These are fed into a cost minimization framework that
produces the final segmentation by selecting segments that: (1) better describe
the natural contours of the image, and (2) are more stable and persistent among
all the segmentation hypotheses. We compare our algorithm's performance with
state-of-the-art algorithms, showing that we can achieve improved results. We
also show that our framework is robust to the choice of segmentation kernel
that produces the initial set of hypotheses.
A Flexible Procedure for Mixture Proportion Estimation in Positive-Unlabeled Learning
Authors: Zhenfeng Lin, James P. Long
Date: 2018-01-30 00:00:00

Positive--unlabeled (PU) learning considers two samples, a positive set P with observations from only one class and an unlabeled set U with observations from two classes. The goal is to classify observations in U. Class mixture proportion estimation (MPE) in U is a key step in PU learning. Blanchard et al. [2010] showed that MPE in PU learning is a generalization of the problem of estimating the proportion of true null hypotheses in multiple testing problems. Motivated by this idea, we propose reducing the problem to one dimension via construction of a probabilistic classifier trained on the P and U data sets followed by application of a one--dimensional mixture proportion method from the multiple testing literature to the observation class probabilities. The flexibility of this framework lies in the freedom to choose the classifier and the one--dimensional MPE method. We prove consistency of two mixture proportion estimators using bounds from empirical process theory, develop tuning parameter free implementations, and demonstrate that they have competitive performance on simulated waveform data and a protein signaling problem.
Modelling contextuality by probabilistic programs with hypergraph semantics
Authors: Peter D. Bruza
Date: 2018-01-31 00:00:00
Tasks: Probabilistic Programming

Models of a phenomenon are often developed by examining it under different
experimental conditions, or measurement contexts. The resultant probabilistic
models assume that the underlying random variables, which define a measurable
set of outcomes, can be defined independent of the measurement context. The
phenomenon is deemed contextual when this assumption fails. Contextuality is an
important issue in quantum physics. However, there has been growing speculation
that it manifests outside the quantum realm with human cognition being a
particularly prominent area of investigation. This article contributes the
foundations of a probabilistic programming language that allows convenient
exploration of contextuality in wide range of applications relevant to
cognitive science and artificial intelligence. Specific syntax is proposed to
allow the specification of "measurement contexts". Each such context delivers a
partial model of the phenomenon based on the associated experimental condition
described by the measurement context. The probabilistic program is translated
into a hypergraph in a modular way. Recent theoretical results from the field
of quantum physics show that contextuality can be equated with the possibility
of constructing a probabilistic model on the resulting hypergraph. The use of
hypergraphs opens the door for a theoretically succinct and efficient
computational semantics sensitive to modelling both contextual and
non-contextual phenomena. Finally, this article raises awareness of
contextuality beyond quantum physics and to contribute formal methods to detect
its presence by means of hypergraph semantics.
Comparing approaches for mitigating intergroup variability in personality recognition
Authors: Guozhen An, Rivka Levitan
Date: 2018-01-31 00:00:00
Tasks: General Classification

Personality have been found to predict many life outcomes, and there have
been huge interests on automatic personality recognition from a speaker's
utterance. Previously, we achieved accuracies between 37%-44% for three-way
classification of high, medium or low for each of the Big Five personality
traits (Openness to Experience, Conscientiousness, Extraversion, Agreeableness,
Neuroticism). We show here that we can improve performance on this task by
accounting for heterogeneity of gender and L1 in our data, which has English
speech from female and male native speakers of Chinese and Standard American
English (SAE). We experiment with personalizing models by L1 and gender and
normalizing features by speaker, L1 group, and/or gender.
NYTRO: When Subsampling Meets Early Stopping
Authors: Tomas Angles, Raffaello Camoriano, Alessandro Rudi, Lorenzo Rosasco
Date: 2015-10-19 00:00:00
Tasks: Model Selection, regression

Early stopping is a well known approach to reduce the time complexity for
performing training and model selection of large scale learning machines. On
the other hand, memory/space (rather than time) complexity is the main
constraint in many applications, and randomized subsampling techniques have
been proposed to tackle this issue. In this paper we ask whether early stopping
and subsampling ideas can be combined in a fruitful way. We consider the
question in a least squares regression setting and propose a form of randomized
iterative regularization based on early stopping and subsampling. In this
context, we analyze the statistical and computational properties of the
proposed method. Theoretical results are complemented and validated by a
thorough experimental analysis.
Optimizing Non-decomposable Measures with Deep Networks
Authors: Amartya Sanyal, Pawan Kumar, Purushottam Kar, Sanjay Chawla, Fabrizio Sebastiani
Date: 2018-01-31 00:00:00

We present a class of algorithms capable of directly training deep neural
networks with respect to large families of task-specific performance measures
such as the F-measure and the Kullback-Leibler divergence that are structured
and non-decomposable. This presents a departure from standard deep learning
techniques that typically use squared or cross-entropy loss functions (that are
decomposable) to train neural networks. We demonstrate that directly training
with task-specific loss functions yields much faster and more stable
convergence across problems and datasets. Our proposed algorithms and
implementations have several novel features including (i) convergence to first
order stationary points despite optimizing complex objective functions; (ii)
use of fewer training samples to achieve a desired level of convergence, (iii)
a substantial reduction in training time, and (iv) a seamless integration of
our implementation into existing symbolic gradient frameworks. We implement our
techniques on a variety of deep architectures including multi-layer perceptrons
and recurrent neural networks and show that on a variety of benchmark and real
data sets, our algorithms outperform traditional approaches to training deep
networks, as well as some recent approaches to task-specific training of neural
networks.
Generalized End-to-End Loss for Speaker Verification
Authors: Li Wan, Quan Wang, Alan Papir, Ignacio Lopez Moreno
Date: 2017-10-28 00:00:00
Tasks: Domain Adaptation, Speaker Verification

In this paper, we propose a new loss function called generalized end-to-end (GE2E) loss, which makes the training of speaker verification models more efficient than our previous tuple-based end-to-end (TE2E) loss function. Unlike TE2E, the GE2E loss function updates the network in a way that emphasizes examples that are difficult to verify at each step of the training process. Additionally, the GE2E loss does not require an initial stage of example selection. With these properties, our model with the new loss function decreases speaker verification EER by more than 10%, while reducing the training time by 60% at the same time. We also introduce the MultiReader technique, which allows us to do domain adaptation - training a more accurate model that supports multiple keywords (i.e. "OK Google" and "Hey Google") as well as multiple dialects.
Dynamics of Driver's Gaze: Explorations in Behavior Modeling & Maneuver Prediction
Authors: Sujitha Martin, Sourabh Vora, Kevan Yuen, Mohan M. Trivedi
Date: 2018-01-31 00:00:00

The study and modeling of driver's gaze dynamics is important because, if and
how the driver is monitoring the driving environment is vital for driver
assistance in manual mode, for take-over requests in highly automated mode and
for semantic perception of the surround in fully autonomous mode. We developed
a machine vision based framework to classify driver's gaze into context rich
zones of interest and model driver's gaze behavior by representing gaze
dynamics over a time period using gaze accumulation, glance duration and glance
frequencies. As a use case, we explore the driver's gaze dynamic patterns
during maneuvers executed in freeway driving, namely, left lane change
maneuver, right lane change maneuver and lane keeping. It is shown that
condensing gaze dynamics into durations and frequencies leads to recurring
patterns based on driver activities. Furthermore, modeling these patterns show
predictive powers in maneuver detection up to a few hundred milliseconds a
priori.
Attention-Based Models for Text-Dependent Speaker Verification
Authors: F A Rezaur Rahman Chowdhury, Quan Wang, Ignacio Lopez Moreno, Li Wan
Date: 2017-10-28 00:00:00
Tasks: Image Captioning, Machine Translation, Speaker Recognition, Speaker Verification, speech-recognition, Speech Recognition, Text-Dependent Speaker Verification, Translation

Attention-based models have recently shown great performance on a range of
tasks, such as speech recognition, machine translation, and image captioning
due to their ability to summarize relevant information that expands through the
entire length of an input sequence. In this paper, we analyze the usage of
attention mechanisms to the problem of sequence summarization in our end-to-end
text-dependent speaker recognition system. We explore different topologies and
their variants of the attention layer, and compare different pooling methods on
the attention weights. Ultimately, we show that attention-based models can
improves the Equal Error Rate (EER) of our speaker verification system by
relatively 14% compared to our non-attention LSTM baseline model.
Recursive Feature Generation for Knowledge-based Learning
Authors: Lior Friedman, Shaul Markovitch
Date: 2018-01-31 00:00:00
Tasks: General Classification, Inductive Learning, text-classification, Text Classification

When humans perform inductive learning, they often enhance the process with
background knowledge. With the increasing availability of well-formed
collaborative knowledge bases, the performance of learning algorithms could be
significantly enhanced if a way were found to exploit these knowledge bases. In
this work, we present a novel algorithm for injecting external knowledge into
induction algorithms using feature generation. Given a feature, the algorithm
defines a new learning task over its set of values, and uses the knowledge base
to solve the constructed learning task. The resulting classifier is then used
as a new feature for the original problem. We have applied our algorithm to the
domain of text classification using large semantic knowledge bases. We have
shown that the generated features significantly improve the performance of
existing learning algorithms.
Paradoxes in Fair Computer-Aided Decision Making
Authors: Andrew Morgan, Rafael Pass
Date: 2017-11-29 00:00:00
Tasks: Decision Making

Computer-aided decision making--where a human decision-maker is aided by a
computational classifier in making a decision--is becoming increasingly
prevalent. For instance, judges in at least nine states make use of algorithmic
tools meant to determine "recidivism risk scores" for criminal defendants in
sentencing, parole, or bail decisions. A subject of much recent debate is
whether such algorithmic tools are "fair" in the sense that they do not
discriminate against certain groups (e.g., races) of people.
  Our main result shows that for "non-trivial" computer-aided decision making,
either the classifier must be discriminatory, or a rational decision-maker
using the output of the classifier is forced to be discriminatory. We further
provide a complete characterization of situations where fair computer-aided
decision making is possible.
Composite Gaussian Processes: Scalable Computation and Performance Analysis
Authors: Xiuming Liu, Dave Zachariah, Edith C. H. Ngai
Date: 2018-01-31 00:00:00
Tasks: Gaussian Processes

Gaussian process (GP) models provide a powerful tool for prediction but are
computationally prohibitive using large data sets. In such scenarios, one has
to resort to approximate methods. We derive an approximation based on a
composite likelihood approach using a general belief updating framework, which
leads to a recursive computation of the predictor as well as of learning the
hyper-parameters. We then provide an analysis of the derived composite GP model
in predictive and information-theoretic terms. Finally, we evaluate the
approximation with both synthetic data and a real-world application.
Incremental kernel PCA and the Nyström method
Authors: Fredrik Hallgren, Paul Northrop
Date: 2018-01-31 00:00:00

Incremental versions of batch algorithms are often desired, for increased
time efficiency in the streaming data setting, or increased memory efficiency
in general. In this paper we present a novel algorithm for incremental kernel
PCA, based on rank one updates to the eigendecomposition of the kernel matrix,
which is more computationally efficient than comparable existing algorithms. We
extend our algorithm to incremental calculation of the Nystr\"om approximation
to the kernel matrix, the first such algorithm proposed. Incremental
calculation of the Nystr\"om approximation leads to further gains in memory
efficiency, and allows for empirical evaluation of when a subset of sufficient
size has been obtained.
In Defense of Classical Image Processing: Fast Depth Completion on the CPU
Authors: Jason Ku, Ali Harakeh, Steven L. Waslander
Date: 2018-01-31 00:00:00
Tasks: CPU, Depth Completion

With the rise of data driven deep neural networks as a realization of
universal function approximators, most research on computer vision problems has
moved away from hand crafted classical image processing algorithms. This paper
shows that with a well designed algorithm, we are capable of outperforming
neural network based methods on the task of depth completion. The proposed
algorithm is simple and fast, runs on the CPU, and relies only on basic image
processing operations to perform depth completion of sparse LIDAR depth data.
We evaluate our algorithm on the challenging KITTI depth completion benchmark,
and at the time of submission, our method ranks first on the KITTI test server
among all published methods. Furthermore, our algorithm is data independent,
requiring no training data to perform the task at hand. The code written in
Python will be made publicly available at https://github.com/kujason/ip_basic.
The Heart of an Image: Quantum Superposition and Entanglement in Visual Perception
Authors: Jonito Aerts Arguelles
Date: 2018-01-31 00:00:00

We analyse the way in which the principle that 'the whole is greater than the
sum of its parts' manifests itself with phenomena of visual perception. For
this investigation we use insights and techniques coming from quantum
cognition, and more specifically we are inspired by the correspondence of this
principle with the phenomenon of the conjunction effect in human cognition. We
identify entities of meaning within artefacts of visual perception and rely on
how such entities are modelled for corpuses of texts such as the webpages of
the World-Wide Web for our study of how they appear in phenomena of visual
perception. We identify concretely the conjunction effect in visual artefacts
and analyse its structure in the example of a photograph. We also analyse
quantum entanglement between different aspects of meaning in artefacts of
visual perception. We confirm its presence by showing that well elected
experiments on images retrieved accordingly by Google Images give rise to
probabilities and expectation values violating the Clauser Horne Shimony Holt
version of Bell's inequalities. We point out how this approach can lead to a
mathematical description of the meaning content of a visual artefact such as a
photograph.
Technical Report: Adjudication of Coreference Annotations via Answer Set Optimization
Authors: Peter Schüller
Date: 2018-01-31 00:00:00

We describe the first automatic approach for merging coreference annotations
obtained from multiple annotators into a single gold standard. This merging is
subject to certain linguistic hard constraints and optimization criteria that
prefer solutions with minimal divergence from annotators. The representation
involves an equivalence relation over a large number of elements. We use Answer
Set Programming to describe two representations of the problem and four
objective functions suitable for different datasets. We provide two
structurally different real-world benchmark datasets based on the METU-Sabanci
Turkish Treebank and we report our experiences in using the Gringo, Clasp, and
Wasp tools for computing optimal adjudication results on these datasets.
Fusarium Damaged Kernels Detection Using Transfer Learning on Deep Neural Network Architecture
Authors: Márcio Nicolau, Márcia Barrocas Moreira Pimentel, Casiane Salete Tibola, José Mauricio Cunha Fernandes, Willingthon Pavan
Date: 2018-01-31 00:00:00
Tasks: GPU, Transfer Learning

The present work shows the application of transfer learning for a pre-trained
deep neural network (DNN), using a small image dataset ($\approx$ 12,000) on a
single workstation with enabled NVIDIA GPU card that takes up to 1 hour to
complete the training task and archive an overall average accuracy of $94.7\%$.
The DNN presents a $20\%$ score of misclassification for an external test
dataset. The accuracy of the proposed methodology is equivalent to ones using
HSI methodology $(81\%-91\%)$ used for the same task, but with the advantage of
being independent on special equipment to classify wheat kernel for FHB
symptoms.
Cluster-based Approach to Improve Affect Recognition from Passively Sensed Data
Authors: Mawulolo K. Ameko, Lihua Cai, Mehdi Boukhechba, Alexander Daros, Philip I. Chow, Bethany A. Teachman, Matthew S. Gerber, Laura E. Barnes
Date: 2018-01-31 00:00:00

Negative affect is a proxy for mental health in adults. By being able to
predict participants' negative affect states unobtrusively, researchers and
clinicians will be better positioned to deliver targeted, just-in-time mental
health interventions via mobile applications. This work attempts to personalize
the passive recognition of negative affect states via group-based modeling of
user behavior patterns captured from mobility, communication, and activity
patterns. Results show that group models outperform generalized models in a
dataset based on two weeks of users' daily lives.
Model compression for faster structural separation of macromolecules captured by Cellular Electron Cryo-Tomography
Authors: Jialiang Guo, Bo Zhou, Xiangrui Zeng, Zachary Freyberg, Min Xu
Date: 2018-01-31 00:00:00
Tasks: Classification, General Classification, Knowledge Distillation, Model Compression

Electron Cryo-Tomography (ECT) enables 3D visualization of macromolecule
structure inside single cells. Macromolecule classification approaches based on
convolutional neural networks (CNN) were developed to separate millions of
macromolecules captured from ECT systematically. However, given the fast
accumulation of ECT data, it will soon become necessary to use CNN models to
efficiently and accurately separate substantially more macromolecules at the
prediction stage, which requires additional computational costs. To speed up
the prediction, we compress classification models into compact neural networks
with little in accuracy for deployment. Specifically, we propose to perform
model compression through knowledge distillation. Firstly, a complex teacher
network is trained to generate soft labels with better classification
feasibility followed by training of customized student networks with simple
architectures using the soft label to compress model complexity. Our tests
demonstrate that our compressed models significantly reduce the number of
parameters and time cost while maintaining similar classification accuracy.
Modeling Variations of First-Order Horn Abduction in Answer Set Programming
Authors: Peter Schüller
Date: 2015-12-30 00:00:00
Tasks: Natural Language Understanding

We study abduction in First Order Horn logic theories where all atoms can be
abduced and we are looking for preferred solutions with respect to three
objective functions: cardinality minimality, coherence, and weighted abduction.
We represent this reasoning problem in Answer Set Programming (ASP), in order
to obtain a flexible framework for experimenting with global constraints and
objective functions, and to test the boundaries of what is possible with ASP.
Realizing this problem in ASP is challenging as it requires value invention and
equivalence between certain constants, because the Unique Names Assumption does
not hold in general. To permit reasoning in cyclic theories, we formally
describe fine-grained variations of limiting Skolemization. We identify term
equivalence as a main instantiation bottleneck, and improve the efficiency of
our approach with on-demand constraints that were used to eliminate the same
bottleneck in state-of-the-art solvers. We evaluate our approach experimentally
on the ACCEL benchmark for plan recognition in Natural Language Understanding.
Our encodings are publicly available, modular, and our approach is more
efficient than state-of-the-art solvers on the ACCEL benchmark.