pwc-archive/papers-with-abstracts huggingface dataset:
Computer Aided Detection of Anemia-like Pallor
Authors: Sohini Roychowdhury, Donny Sun, Matthew Bihis, Johnny Ren, Paul Hage, Humairat H. Rahman
Date: 2017-03-17 00:00:00
Tasks: Benchmarking, Classification, General Classification, Multi-class Classification

Paleness or pallor is a manifestation of blood loss or low hemoglobin
concentrations in the human blood that can be caused by pathologies such as
anemia. This work presents the first automated screening system that utilizes
pallor site images, segments, and extracts color and intensity-based features
for multi-class classification of patients with high pallor due to anemia-like
pathologies, normal patients and patients with other abnormalities. This work
analyzes the pallor sites of conjunctiva and tongue for anemia screening
purposes. First, for the eye pallor site images, the sclera and conjunctiva
regions are automatically segmented for regions of interest. Similarly, for the
tongue pallor site images, the inner and outer tongue regions are segmented.
Then, color-plane based feature extraction is performed followed by machine
learning algorithms for feature reduction and image level classification for
anemia. In this work, a suite of classification algorithms image-level
classifications for normal (class 0), pallor (class 1) and other abnormalities
(class 2). The proposed method achieves 86% accuracy, 85% precision and 67%
recall in eye pallor site images and 98.2% accuracy and precision with 100%
recall in tongue pallor site images for classification of images with pallor.
The proposed pallor screening system can be further fine-tuned to detect the
severity of anemia-like pathologies using controlled set of local images that
can then be used for future benchmarking purposes.
Dynamic Neural Turing Machine with Soft and Hard Addressing Schemes
Authors: Caglar Gulcehre, Sarath Chandar, Kyunghyun Cho, Yoshua Bengio
Date: 2016-06-30 00:00:00
Tasks: Natural Language Inference, Question Answering

We extend neural Turing machine (NTM) model into a dynamic neural Turing
machine (D-NTM) by introducing a trainable memory addressing scheme. This
addressing scheme maintains for each memory cell two separate vectors, content
and address vectors. This allows the D-NTM to learn a wide variety of
location-based addressing strategies including both linear and nonlinear ones.
We implement the D-NTM with both continuous, differentiable and discrete,
non-differentiable read/write mechanisms. We investigate the mechanisms and
effects of learning to read and write into a memory through experiments on
Facebook bAbI tasks using both a feedforward and GRUcontroller. The D-NTM is
evaluated on a set of Facebook bAbI tasks and shown to outperform NTM and LSTM
baselines. We have done extensive analysis of our model and different
variations of NTM on bAbI task. We also provide further experimental results on
sequential pMNIST, Stanford Natural Language Inference, associative recall and
copy tasks.
Predictive Coding for Dynamic Vision : Development of Functional Hierarchy in a Multiple Spatio-Temporal Scales RNN Model
Authors: Minkyu Choi, Jun Tani
Date: 2016-06-06 00:00:00

The current paper presents a novel recurrent neural network model, the
predictive multiple spatio-temporal scales RNN (P-MSTRNN), which can generate
as well as recognize dynamic visual patterns in the predictive coding
framework. The model is characterized by multiple spatio-temporal scales
imposed on neural unit dynamics through which an adequate spatio-temporal
hierarchy develops via learning from exemplars. The model was evaluated by
conducting an experiment of learning a set of whole body human movement
patterns which was generated by following a hierarchically defined movement
syntax. The analysis of the trained model clarifies what types of
spatio-temporal hierarchy develop in dynamic neural activity as well as how
robust generation and recognition of movement patterns can be achieved by using
the error minimization principle.
Improving Document Clustering by Eliminating Unnatural Language
Authors: Myungha Jang, Jinho D. Choi, James Allan
Date: 2017-03-16 00:00:00
Tasks: Clustering

Technical documents contain a fair amount of unnatural language, such as
tables, formulas, pseudo-codes, etc. Unnatural language can be an important
factor of confusing existing NLP tools. This paper presents an effective method
of distinguishing unnatural language from natural language, and evaluates the
impact of unnatural language detection on NLP tasks such as document
clustering. We view this problem as an information extraction task and build a
multiclass classification model identifying unnatural language components into
four categories. First, we create a new annotated corpus by collecting slides
and papers in various formats, PPT, PDF, and HTML, where unnatural language
components are annotated into four categories. We then explore features
available from plain text to build a statistical model that can handle any
format as long as it is converted into plain text. Our experiments show that
removing unnatural language components gives an absolute improvement in
document clustering up to 15%. Our corpus and tool are publicly available.
Fractal Dimension Invariant Filtering and Its CNN-based Implementation
Authors: Hongteng Xu, Junchi Yan, Nils Persson, Weiyao Lin, Hongyuan Zha
Date: 2016-03-19 00:00:00
Proceeding: CVPR 2017 7
Tasks: Texture Classification

Fractal analysis has been widely used in computer vision, especially in
texture image processing and texture analysis. The key concept of fractal-based
image model is the fractal dimension, which is invariant to bi-Lipschitz
transformation of image, and thus capable of representing intrinsic structural
information of image robustly. However, the invariance of fractal dimension
generally does not hold after filtering, which limits the application of
fractal-based image model. In this paper, we propose a novel fractal dimension
invariant filtering (FDIF) method, extending the invariance of fractal
dimension to filtering operations. Utilizing the notion of local
self-similarity, we first develop a local fractal model for images. By adding a
nonlinear post-processing step behind anisotropic filter banks, we demonstrate
that the proposed filtering method is capable of preserving the local
invariance of the fractal dimension of image. Meanwhile, we show that the FDIF
method can be re-instantiated approximately via a CNN-based architecture, where
the convolution layer extracts anisotropic structure of image and the nonlinear
layer enhances the structure via preserving local fractal dimension of image.
The proposed filtering method provides us with a novel geometric interpretation
of CNN-based image model. Focusing on a challenging image processing task ---
detecting complicated curves from the texture-like images, the proposed method
obtains superior results to the state-of-art approaches.
Supervised Learning Based Algorithm Selection for Deep Neural Networks
Authors: Shaohuai Shi, Pengfei Xu, Xiaowen Chu
Date: 2017-02-10 00:00:00

Many recent deep learning platforms rely on third-party libraries (such as
cuBLAS) to utilize the computing power of modern hardware accelerators (such as
GPUs). However, we observe that they may achieve suboptimal performance because
the library functions are not used appropriately. In this paper, we target at
optimizing the operations of multiplying a matrix with the transpose of another
matrix (referred to as NT operation hereafter), which contribute about half of
the training time of fully connected deep neural networks. Rather than directly
calling the library function, we propose a supervised learning based algorithm
selection approach named MTNN, which uses a gradient boosted decision tree to
select one from two alternative NT implementations intelligently: (1) calling
the cuBLAS library function; (2) calling our proposed algorithm TNN that uses
an efficient out-of-place matrix transpose. We evaluate the performance of MTNN
on two modern GPUs: NVIDIA GTX 1080 and NVIDIA Titan X Pascal. MTNN can achieve
96\% of prediction accuracy with very low computational overhead, which results
in an average of 54\% performance improvement on a range of NT operations. To
further evaluate the impact of MTNN on the training process of deep neural
networks, we have integrated MTNN into a popular deep learning platform Caffe.
Our experimental results show that the revised Caffe can outperform the
original one by an average of 28\%. Both MTNN and the revised Caffe are
open-source.
Automatic Skin Lesion Analysis using Large-scale Dermoscopy Images and Deep Residual Networks
Authors: Lei Bi, Jinman Kim, Euijoon Ahn, Dagan Feng
Date: 2017-03-12 00:00:00
Tasks: Decision Making, image-classification, Image Classification

Malignant melanoma has one of the most rapidly increasing incidences in the
world and has a considerable mortality rate. Early diagnosis is particularly
important since melanoma can be cured with prompt excision. Dermoscopy images
play an important role in the non-invasive early detection of melanoma [1].
However, melanoma detection using human vision alone can be subjective,
inaccurate and poorly reproducible even among experienced dermatologists. This
is attributed to the challenges in interpreting images with diverse
characteristics including lesions of varying sizes and shapes, lesions that may
have fuzzy boundaries, different skin colors and the presence of hair [2].
Therefore, the automatic analysis of dermoscopy images is a valuable aid for
clinical decision making and for image-based diagnosis to identify diseases
such as melanoma [1-4]. Deep residual networks (ResNets) has achieved
state-of-the-art results in image classification and detection related problems
[5-8]. In this ISIC 2017 skin lesion analysis challenge [9], we propose to
exploit the deep ResNets for robust visual features learning and
representations.
Syntax-Preserving Belief Change Operators for Logic Programs
Authors: Sebastian Binnewies, Zhiqiang Zhuang, Kewen Wang, Bela Stantic
Date: 2017-03-15 00:00:00

Recent methods have adapted the well-established AGM and belief base
frameworks for belief change to cover belief revision in logic programs. In
this study here, we present two new sets of belief change operators for logic
programs. They focus on preserving the explicit relationships expressed in the
rules of a program, a feature that is missing in purely semantic approaches
that consider programs only in their entirety. In particular, operators of the
latter class fail to satisfy preservation and support, two important properties
for belief change in logic programs required to ensure intuitive results.
  We address this shortcoming of existing approaches by introducing partial
meet and ensconcement constructions for logic program belief change, which
allow us to define syntax-preserving operators that satisfy preservation and
support. Our work is novel in that our constructions not only preserve more
information from a logic program during a change operation than existing ones,
but they also facilitate natural definitions of contraction operators, the
first in the field to the best of our knowledge.
  In order to evaluate the rationality of our operators, we translate the
revision and contraction postulates from the AGM and belief base frameworks to
the logic programming setting. We show that our operators fully comply with the
belief base framework and formally state the interdefinability between our
operators. We further propose an algorithm that is based on modularising a
logic program to reduce partial meet and ensconcement revisions or contractions
to performing the operation only on the relevant modules of that program.
Finally, we compare our approach to two state-of-the-art logic program revision
methods and demonstrate that our operators address the shortcomings of one and
generalise the other method.
Towards Closing the Energy Gap Between HOG and CNN Features for Embedded Vision
Authors: Amr Suleiman, Yu-Hsin Chen, Joel Emer, Vivienne Sze
Date: 2017-03-17 00:00:00
Tasks: Self-Driving Cars

Computer vision enables a wide range of applications in robotics/drones,
self-driving cars, smart Internet of Things, and portable/wearable electronics.
For many of these applications, local embedded processing is preferred due to
privacy and/or latency concerns. Accordingly, energy-efficient embedded vision
hardware delivering real-time and robust performance is crucial. While deep
learning is gaining popularity in several computer vision algorithms, a
significant energy consumption difference exists compared to traditional
hand-crafted approaches. In this paper, we provide an in-depth analysis of the
computation, energy and accuracy trade-offs between learned features such as
deep Convolutional Neural Networks (CNN) and hand-crafted features such as
Histogram of Oriented Gradients (HOG). This analysis is supported by
measurements from two chips that implement these algorithms. Our goal is to
understand the source of the energy discrepancy between the two approaches and
to provide insight about the potential areas where CNNs can be improved and
eventually approach the energy-efficiency of HOG while maintaining its
outstanding performance accuracy.
Online Multilinear Dictionary Learning
Authors: Thiernithi Variddhisai, Danilo Mandic
Date: 2017-03-07 00:00:00
Tasks: Dictionary Learning

A method for online tensor dictionary learning is proposed. With the assumption of separable dictionaries, tensor contraction is used to diminish a $N$-way model of $\mathcal{O}\left(L^N\right)$ into a simple matrix equation of $\mathcal{O}\left(NL^2\right)$ with a real-time capability. To avoid numerical instability due to inversion of sparse matrix, a class of stochastic gradient with memory is formulated via a least-square solution to guarantee convergence and robustness. Both gradient descent with exact line search and Newton's method are discussed and realized. Extensions onto how to deal with bad initialization and outliers are also explained in detail. Experiments on two synthetic signals confirms an impressive performance of our proposed method.
Fast Fourier Color Constancy
Authors: Jonathan T. Barron, Yun-Ta Tsai
Date: 2016-11-23 00:00:00
Proceeding: CVPR 2017 7
Tasks: Color Constancy

We present Fast Fourier Color Constancy (FFCC), a color constancy algorithm which solves illuminant estimation by reducing it to a spatial localization task on a torus. By operating in the frequency domain, FFCC produces lower error rates than the previous state-of-the-art by 13-20% while being 250-3000 times faster. This unconventional approach introduces challenges regarding aliasing, directional statistics, and preconditioning, which we address. By producing a complete posterior distribution over illuminants instead of a single illuminant estimate, FFCC enables better training techniques, an effective temporal smoothing technique, and richer methods for error analysis. Our implementation of FFCC runs at ~700 frames per second on a mobile device, allowing it to be used as an accurate, real-time, temporally-coherent automatic white balance algorithm.
Adaptivity to Noise Parameters in Nonparametric Active Learning
Authors: Andrea Locatelli, Alexandra Carpentier, Samory Kpotufe
Date: 2017-03-16 00:00:00
Tasks: Active Learning, General Classification

This work addresses various open questions in the theory of active learning
for nonparametric classification. Our contributions are both statistical and
algorithmic: -We establish new minimax-rates for active learning under common
\textit{noise conditions}. These rates display interesting transitions -- due
to the interaction between noise \textit{smoothness and margin} -- not present
in the passive setting. Some such transitions were previously conjectured, but
remained unconfirmed. -We present a generic algorithmic strategy for adaptivity
to unknown noise smoothness and margin; our strategy achieves optimal rates in
many general situations; furthermore, unlike in previous work, we avoid the
need for \textit{adaptive confidence sets}, resulting in strictly milder
distributional requirements.
Git Blame Who?: Stylistic Authorship Attribution of Small, Incomplete Source Code Fragments
Authors: Edwin Dauber, Aylin Caliskan, Richard Harang, Gregory Shearer, Michael Weisman, Frederica Nelson, Rachel Greenstadt
Date: 2017-01-20 00:00:00
Tasks: Authorship Attribution

Program authorship attribution has implications for the privacy of programmers who wish to contribute code anonymously. While previous work has shown that complete files that are individually authored can be attributed, we show here for the first time that accounts belonging to open source contributors containing short, incomplete, and typically uncompilable fragments can also be effectively attributed. We propose a technique for authorship attribution of contributor accounts containing small source code samples, such as those that can be obtained from version control systems or other direct comparison of sequential versions. We show that while application of previous methods to individual small source code samples yields an accuracy of about 73% for 106 programmers as a baseline, by ensembling and averaging the classification probabilities of a sufficiently large set of samples belonging to the same author we achieve 99% accuracy for assigning the set of samples to the correct author. Through these results, we demonstrate that attribution is an important threat to privacy for programmers even in real-world collaborative environments such as GitHub. Additionally, we propose the use of calibration curves to identify samples by unknown and previously unencountered authors in the open world setting. We show that we can also use these calibration curves in the case that we do not have linking information and thus are forced to classify individual samples directly. This is because the calibration curves allow us to identify which samples are more likely to have been correctly attributed. Using such a curve can help an analyst choose a cut-off point which will prevent most misclassifications, at the cost of causing the rejection of some of the more dubious correct attributions.
Diet Networks: Thin Parameters for Fat Genomics
Authors: Adriana Romero, Pierre Luc Carrier, Akram Erraqabi, Tristan Sylvain, Alex Auvolat, Etienne Dejoie, Marc-André Legault, Marie-Pierre Dubé, Julie G. Hussin, Yoshua Bengio
Date: 2016-11-28 00:00:00
Tasks: Parameter Prediction

Learning tasks such as those involving genomic data often poses a serious
challenge: the number of input features can be orders of magnitude larger than
the number of training examples, making it difficult to avoid overfitting, even
when using the known regularization techniques. We focus here on tasks in which
the input is a description of the genetic variation specific to a patient, the
single nucleotide polymorphisms (SNPs), yielding millions of ternary inputs.
Improving the ability of deep learning to handle such datasets could have an
important impact in precision medicine, where high-dimensional data regarding a
particular patient is used to make predictions of interest. Even though the
amount of data for such tasks is increasing, this mismatch between the number
of examples and the number of inputs remains a concern. Naive implementations
of classifier neural networks involve a huge number of free parameters in their
first layer: each input feature is associated with as many parameters as there
are hidden units. We propose a novel neural network parametrization which
considerably reduces the number of free parameters. It is based on the idea
that we can first learn or provide a distributed representation for each input
feature (e.g. for each position in the genome where variations are observed),
and then learn (with another neural network called the parameter prediction
network) how to map a feature's distributed representation to the vector of
parameters specific to that feature in the classifier neural network (the
weights which link the value of the feature to each of the hidden units). We
show experimentally on a population stratification task of interest to medical
studies that the proposed approach can significantly reduce both the number of
parameters and the error rate of the classifier.
Particle Value Functions
Authors: Chris J. Maddison, Dieterich Lawson, George Tucker, Nicolas Heess, Arnaud Doucet, andriy mnih, Yee Whye Teh
Date: 2017-03-16 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

The policy gradients of the expected return objective can react slowly to
rare rewards. Yet, in some cases agents may wish to emphasize the low or high
returns regardless of their probability. Borrowing from the economics and
control literature, we review the risk-sensitive value function that arises
from an exponential utility and illustrate its effects on an example. This
risk-sensitive value function is not always applicable to reinforcement
learning problems, so we introduce the particle value function defined by a
particle filter over the distributions of an agent's experience, which bounds
the risk-sensitive one. We illustrate the benefit of the policy gradients of
this objective in Cliffworld.
Reservoir Computing and Extreme Learning Machines using Pairs of Cellular Automata Rules
Authors: Nathan McDonald
Date: 2017-03-16 00:00:00

A framework for implementing reservoir computing (RC) and extreme learning
machines (ELMs), two types of artificial neural networks, based on 1D
elementary Cellular Automata (CA) is presented, in which two separate CA rules
explicitly implement the minimum computational requirements of the reservoir
layer: hyperdimensional projection and short-term memory. CAs are cell-based
state machines, which evolve in time in accordance with local rules based on a
cells current state and those of its neighbors. Notably, simple single cell
shift rules as the memory rule in a fixed edge CA afforded reasonable success
in conjunction with a variety of projection rules, potentially significantly
reducing the optimal solution search space. Optimal iteration counts for the CA
rule pairs can be estimated for some tasks based upon the category of the
projection rule. Initial results support future hardware realization, where CAs
potentially afford orders of magnitude reduction in size, weight, and power
(SWaP) requirements compared with floating point RC implementations.
Low-rank and Sparse NMF for Joint Endmembers' Number Estimation and Blind Unmixing of Hyperspectral Images
Authors: Paris V. Giampouras, Athanasios A. Rontogiannis, Konstantinos D. Koutroumbas
Date: 2017-03-16 00:00:00
Tasks: Hyperspectral Unmixing

Estimation of the number of endmembers existing in a scene constitutes a
critical task in the hyperspectral unmixing process. The accuracy of this
estimate plays a crucial role in subsequent unsupervised unmixing steps i.e.,
the derivation of the spectral signatures of the endmembers (endmembers'
extraction) and the estimation of the abundance fractions of the pixels. A
common practice amply followed in literature is to treat endmembers' number
estimation and unmixing, independently as two separate tasks, providing the
outcome of the former as input to the latter. In this paper, we go beyond this
computationally demanding strategy. More precisely, we set forth a multiple
constrained optimization framework, which encapsulates endmembers' number
estimation and unsupervised unmixing in a single task. This is attained by
suitably formulating the problem via a low-rank and sparse nonnegative matrix
factorization rationale, where low-rankness is promoted with the use of a
sophisticated $\ell_2/\ell_1$ norm penalty term. An alternating proximal
algorithm is then proposed for minimizing the emerging cost function. The
results obtained by simulated and real data experiments verify the
effectiveness of the proposed approach.