pwc-archive/papers-with-abstracts huggingface dataset:
An EMG Gesture Recognition System with Flexible High-Density Sensors and Brain-Inspired High-Dimensional Classifier
Authors: Ali Moin, Andy Zhou, Abbas Rahimi, Simone Benatti, Alisha Menon, Senam Tamakloe, Jonathan Ting, Natasha Yamamoto, Yasser Khan, Fred Burghardt, Luca Benini, Ana C. Arias, Jan M. Rabaey
Date: 2018-02-28 00:00:00
Tasks: EMG Gesture Recognition, General Classification, Gesture Recognition, One-Shot Learning, Robust classification, Vocal Bursts Intensity Prediction

EMG-based gesture recognition shows promise for human-machine interaction.
Systems are often afflicted by signal and electrode variability which degrades
performance over time. We present an end-to-end system combating this
variability using a large-area, high-density sensor array and a robust
classification algorithm. EMG electrodes are fabricated on a flexible substrate
and interfaced to a custom wireless device for 64-channel signal acquisition
and streaming. We use brain-inspired high-dimensional (HD) computing for
processing EMG features in one-shot learning. The HD algorithm is tolerant to
noise and electrode misplacement and can quickly learn from few gestures
without gradient descent or back-propagation. We achieve an average
classification accuracy of 96.64% for five gestures, with only 7% degradation
when training and testing across different days. Our system maintains this
accuracy when trained with only three trials of gestures; it also demonstrates
comparable accuracy with the state-of-the-art when trained with one trial.
CBMV: A Coalesced Bidirectional Matching Volume for Disparity Estimation
Authors: Konstantinos Batsos, Changjiang Cai, Philippos Mordohai
Date: 2018-04-05 00:00:00
Proceeding: CVPR 2018 6
Tasks: Disparity Estimation, Stereo Matching, Stereo Matching Hand

Recently, there has been a paradigm shift in stereo matching with
learning-based methods achieving the best results on all popular benchmarks.
The success of these methods is due to the availability of training data with
ground truth; training learning-based systems on these datasets has allowed
them to surpass the accuracy of conventional approaches based on heuristics and
assumptions. Many of these assumptions, however, had been validated extensively
and hold for the majority of possible inputs. In this paper, we generate a
matching volume leveraging both data with ground truth and conventional wisdom.
We accomplish this by coalescing diverse evidence from a bidirectional matching
process via random forest classifiers. We show that the resulting matching
volume estimation method achieves similar accuracy to purely data-driven
alternatives on benchmarks and that it generalizes to unseen data much better.
In fact, the results we submitted to the KITTI and ETH3D benchmarks were
generated using a classifier trained on the Middlebury 2014 dataset.
Automated Classification of Text Sentiment
Authors: Emmanuel Dufourq, Bruce A. Bassett
Date: 2018-04-05 00:00:00
Tasks: Classification, General Classification, Sentiment Analysis, Sociology

The ability to identify sentiment in text, referred to as sentiment analysis,
is one which is natural to adult humans. This task is, however, not one which a
computer can perform by default. Identifying sentiments in an automated,
algorithmic manner will be a useful capability for business and research in
their search to understand what consumers think about their products or
services and to understand human sociology. Here we propose two new Genetic
Algorithms (GAs) for the task of automated text sentiment analysis. The GAs
learn whether words occurring in a text corpus are either sentiment or
amplifier words, and their corresponding magnitude. Sentiment words, such as
'horrible', add linearly to the final sentiment. Amplifier words in contrast,
which are typically adjectives/adverbs like 'very', multiply the sentiment of
the following word. This increases, decreases or negates the sentiment of the
following word. The sentiment of the full text is then the sum of these terms.
This approach grows both a sentiment and amplifier dictionary which can be
reused for other purposes and fed into other machine learning algorithms. We
report the results of multiple experiments conducted on large Amazon data sets.
The results reveal that our proposed approach was able to outperform several
public and/or commercial sentiment analysis algorithms.
Squeeze-and-Excitation Networks
Authors: Jie Hu, Li Shen, Samuel Albanie, Gang Sun, Enhua Wu
Date: 2017-09-05 00:00:00
Proceeding: CVPR 2018 6
Tasks: Image Classification, Object Detection

The central building block of convolutional neural networks (CNNs) is the convolution operator, which enables networks to construct informative features by fusing both spatial and channel-wise information within local receptive fields at each layer. A broad range of prior research has investigated the spatial component of this relationship, seeking to strengthen the representational power of a CNN by enhancing the quality of spatial encodings throughout its feature hierarchy. In this work, we focus instead on the channel relationship and propose a novel architectural unit, which we term the "Squeeze-and-Excitation" (SE) block, that adaptively recalibrates channel-wise feature responses by explicitly modelling interdependencies between channels. We show that these blocks can be stacked together to form SENet architectures that generalise extremely effectively across different datasets. We further demonstrate that SE blocks bring significant improvements in performance for existing state-of-the-art CNNs at slight additional computational cost. Squeeze-and-Excitation Networks formed the foundation of our ILSVRC 2017 classification submission which won first place and reduced the top-5 error to 2.251%, surpassing the winning entry of 2016 by a relative improvement of ~25%. Models and code are available at https://github.com/hujie-frank/SENet.
Iris Recognition After Death
Authors: Mateusz Trokielewicz, Adam Czajka, Piotr Maciejewicz
Date: 2018-04-05 00:00:00
Tasks: Iris Recognition

This paper presents a comprehensive study of post-mortem human iris
recognition carried out for 1,200 near-infrared and 1,787 visible-light samples
collected from 37 deceased individuals kept in the mortuary conditions. We used
four independent iris recognition methods (three commercial and one academic)
to analyze genuine and impostor comparison scores and check the dynamics of
iris quality decay over a period of up to 814 hours after death. This study
shows that post-mortem iris recognition may be close-to-perfect approximately 5
to 7 hours after death and occasionally is still viable even 21 days after
death. These conclusions contradict the statements present in past literature
that the iris is unusable as a biometrics shortly after death, and show that
the dynamics of post-mortem changes to the iris that are important for
biometric identification are more moderate than previously hypothesized. The
paper contains a thorough medical commentary that helps to understand which
post-mortem metamorphoses of the eye may impact the performance of automatic
iris recognition. We also show that post-mortem iris recognition works equally
well for images taken in near-infrared and when the red channel of
visible-light sample is used. However, cross-wavelength matching presents
significantly worse performance. This paper conforms to reproducible research
and the database used in this study is made publicly available to facilitate
research of post-mortem iris recognition. To our knowledge, this paper offers
the most comprehensive evaluation of post-mortem iris recognition and the
largest database of post-mortem iris images.
Efficient and accurate inversion of multiple scattering with deep learning
Authors: Yu Sun, Zhihao Xia, Ulugbek S. Kamilov
Date: 2018-03-18 00:00:00
Tasks: Deep Learning, Image Reconstruction

Image reconstruction under multiple light scattering is crucial in a number
of applications such as diffraction tomography. The reconstruction problem is
often formulated as a nonconvex optimization, where a nonlinear measurement
model is used to account for multiple scattering and regularization is used to
enforce prior constraints on the object. In this paper, we propose a powerful
alternative to this optimization-based view of image reconstruction by
designing and training a deep convolutional neural network that can invert
multiple scattered measurements to produce a high-quality image of the
refractive index. Our results on both simulated and experimental datasets show
that the proposed approach is substantially faster and achieves higher imaging
quality compared to the state-of-the-art methods based on optimization.
Highly corrupted image inpainting through hypoelliptic diffusion
Authors: Ugo Boscain, Roman Chertovskih, Jean-Paul Gauthier, Dario Prandi, Alexey Remizov
Date: 2015-02-25 00:00:00
Tasks: Image Inpainting

We present a new image inpainting algorithm, the Averaging and Hypoelliptic
Evolution (AHE) algorithm, inspired by the one presented in [SIAM J. Imaging
Sci., vol. 7, no. 2, pp. 669--695, 2014] and based upon a semi-discrete
variation of the Citti-Petitot-Sarti model of the primary visual cortex V1. The
AHE algorithm is based on a suitable combination of sub-Riemannian hypoelliptic
diffusion and ad-hoc local averaging techniques. In particular, we focus on
reconstructing highly corrupted images (i.e. where more than the 80% of the
image is missing), for which we obtain reconstructions comparable with the
state-of-the-art.
Crowd-Labeling Fashion Reviews with Quality Control
Authors: Iurii Chernushenko, Felix A. Gers, Alexander Löser, Alessandro Checco
Date: 2018-04-05 00:00:00
Tasks: Aspect-Based Sentiment Analysis, Aspect-Based Sentiment Analysis (ABSA), Sentiment Analysis

We present a new methodology for high-quality labeling in the fashion domain
with crowd workers instead of experts. We focus on the Aspect-Based Sentiment
Analysis task. Our methods filter out inaccurate input from crowd workers but
we preserve different worker labeling to capture the inherent high variability
of the opinions. We demonstrate the quality of labeled data based on Facebook's
FastText framework as a baseline.
A reinforcement learning algorithm for building collaboration in multi-agent systems
Authors: Mehmet Emin Aydin, Ryan Fellows
Date: 2017-11-28 00:00:00
Tasks: Q-Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

This paper presents a proof-of concept study for demonstrating the viability
of building collaboration among multiple agents through standard Q learning
algorithm embedded in particle swarm optimisation. Collaboration is formulated
to be achieved among the agents via some sort competition, where the agents are
expected to balance their action in such a way that none of them drifts away of
the team and none intervene any fellow neighbours territory. Particles are
devised with Q learning algorithm for self training to learn how to act as
members of a swarm and how to produce collaborative/collective behaviours. The
produced results are supportive to the algorithmic structures suggesting that a
substantive collaboration can be build via proposed learning algorithm.
Quantum Entanglement in Deep Learning Architectures
Authors: Yoav Levine, Or Sharir, Nadav Cohen, Amnon Shashua
Date: 2018-03-26 00:00:00
Tasks: BIG-bench Machine Learning, Deep Learning, Image Classification

Modern deep learning has enabled unprecedented achievements in various
domains. Nonetheless, employment of machine learning for wave function
representations is focused on more traditional architectures such as restricted
Boltzmann machines (RBMs) and fully-connected neural networks. In this letter,
we establish that contemporary deep learning architectures, in the form of deep
convolutional and recurrent networks, can efficiently represent highly
entangled quantum systems. By constructing Tensor Network equivalents of these
architectures, we identify an inherent reuse of information in the network
operation as a key trait which distinguishes them from standard Tensor Network
based representations, and which enhances their entanglement capacity. Our
results show that such architectures can support volume-law entanglement
scaling, polynomially more efficiently than presently employed RBMs. Thus,
beyond a quantification of the entanglement capacity of leading deep learning
architectures, our analysis formally motivates a shift of trending
neural-network based wave function representations closer to the
state-of-the-art in machine learning.
Multi-level Activation for Segmentation of Hierarchically-nested Classes
Authors: Marie Piraud, Anjany Sekuboyina, Bjoern H. Menze
Date: 2018-04-05 00:00:00
Tasks: Image Segmentation, Multi-class Classification, Segmentation, Semantic Segmentation

For many biological image segmentation tasks, including topological
knowledge, such as the nesting of classes, can greatly improve results.
However, most `out-of-the-box' CNN models are still blind to such prior
information. In this paper, we propose a novel approach to encode this
information, through a multi-level activation layer and three compatible
losses. We benchmark all of them on nuclei segmentation in bright-field
microscopy cell images from the 2018 Data Science Bowl challenge, offering an
exemplary segmentation task with cells and nested subcellular structures. Our
scheme greatly speeds up learning, and outperforms standard multi-class
classification with soft-max activation and a previously proposed method
stemming from it, improving the Dice score significantly (p-values<0.007). Our
approach is conceptually simple, easy to implement and can be integrated in any
CNN architecture. It can be generalized to a higher number of classes, with or
without further relations of containment.
Towards radiologist-level cancer risk assessment in CT lung screening using deep learning
Authors: Stojan Trajanovski, Dimitrios Mavroeidis, Christine Leon Swisher, Binyam Gebrekidan Gebre, Bastiaan S. Veeling, Rafael Wiemker, Tobias Klinder, Amir Tahmasebi, Shawn M. Regis, Christoph Wald, Brady J. McKee, Sebastian Flacke, Heber MacMahon, Homer Pien
Date: 2018-04-05 00:00:00
Tasks: Computed Tomography (CT)

Importance: Lung cancer is the leading cause of cancer mortality in the US,
responsible for more deaths than breast, prostate, colon and pancreas cancer
combined and it has been recently demonstrated that low-dose computed
tomography (CT) screening of the chest can significantly reduce this death
rate.
  Objective: To compare the performance of a deep learning model to
state-of-the-art automated algorithms and radiologists as well as assessing the
robustness of the algorithm in heterogeneous datasets.
  Design, Setting, and Participants: Three low-dose CT lung cancer screening
datasets from heterogeneous sources were used, including National Lung
Screening Trial (NLST, n=3410), Lahey Hospital and Medical Center (LHMC,
n=3174) data, Kaggle competition data (from both stages, n=1595+505) and the
University of Chicago data (UCM, a subset of NLST, annotated by radiologists,
n=197). Relevant works on automated methods for Lung Cancer malignancy
estimation have used significantly less data in size and diversity. At the
first stage, our framework employs a nodule detector; while in the second
stage, we use both the image area around the nodules and nodule features as
inputs to a neural network that estimates the malignancy risk for the entire CT
scan. We trained our two-stage algorithm on a part of the NLST dataset, and
validated it on the other datasets.
  Results, Conclusions, and Relevance: The proposed deep learning model: (a)
generalizes well across all three data sets, achieving AUC between 86% to 94%;
(b) has better performance than the widely accepted PanCan Risk Model,
achieving 11% better AUC score; (c) has improved performance compared to the
state-of-the-art represented by the winners of the Kaggle Data Science Bowl
2017 competition on lung cancer screening; (d) has comparable performance to
radiologists in estimating cancer risk at a patient level.
Large Scale Local Online Similarity/Distance Learning Framework based on Passive/Aggressive
Authors: Baida Hamdan, Davood Zabihzadeh, Monsefi Reza
Date: 2018-04-05 00:00:00
Tasks: Metric Learning

Similarity/Distance measures play a key role in many machine learning,
pattern recognition, and data mining algorithms, which leads to the emergence
of metric learning field. Many metric learning algorithms learn a global
distance function from data that satisfy the constraints of the problem.
However, in many real-world datasets that the discrimination power of features
varies in the different regions of input space, a global metric is often unable
to capture the complexity of the task. To address this challenge, local metric
learning methods are proposed that learn multiple metrics across the different
regions of input space. Some advantages of these methods are high flexibility
and the ability to learn a nonlinear mapping but typically achieves at the
expense of higher time requirement and overfitting problem. To overcome these
challenges, this research presents an online multiple metric learning
framework. Each metric in the proposed framework is composed of a global and a
local component learned simultaneously. Adding a global component to a local
metric efficiently reduce the problem of overfitting. The proposed framework is
also scalable with both sample size and the dimension of input data. To the
best of our knowledge, this is the first local online similarity/distance
learning framework based on PA (Passive/Aggressive). In addition, for
scalability with the dimension of input data, DRP (Dual Random Projection) is
extended for local online learning in the present work. It enables our methods
to be run efficiently on high-dimensional datasets, while maintains their
predictive performance. The proposed framework provides a straightforward local
extension to any global online similarity/distance learning algorithm based on
PA.
Abstractive Tabular Dataset Summarization via Knowledge Base Semantic Embeddings
Authors: Paul Azunre, Craig Corcoran, David Sullivan, Garrett Honke, Rebecca Ruppel, Sandeep Verma, Jonathon Morgan
Date: 2018-04-04 00:00:00
Tasks: Abstractive Text Summarization, Descriptive

This paper describes an abstractive summarization method for tabular data
which employs a knowledge base semantic embedding to generate the summary.
Assuming the dataset contains descriptive text in headers, columns and/or some
augmenting metadata, the system employs the embedding to recommend a
subject/type for each text segment. Recommendations are aggregated into a small
collection of super types considered to be descriptive of the dataset by
exploiting the hierarchy of types in a pre-specified ontology. Using February
2015 Wikipedia as the knowledge base, and a corresponding DBpedia ontology as
types, we present experimental results on open data taken from several
sources--OpenML, CKAN and data.world--to illustrate the effectiveness of the
approach.
Improved Training for Self-Training by Confidence Assessments
Authors: Gal Hyams, Daniel Greenfeld, Dor Bank
Date: 2017-09-30 00:00:00
Tasks: General Classification, Segmentation, Semantic Segmentation, Semi-Supervised Semantic Segmentation

It is well known that for some tasks, labeled data sets may be hard to
gather. Therefore, we wished to tackle here the problem of having insufficient
training data. We examined learning methods from unlabeled data after an
initial training on a limited labeled data set. The suggested approach can be
used as an online learning method on the unlabeled test set. In the general
classification task, whenever we predict a label with high enough confidence,
we treat it as a true label and train the data accordingly. For the semantic
segmentation task, a classic example for an expensive data labeling process, we
do so pixel-wise. Our suggested approaches were applied on the MNIST data-set
as a proof of concept for a vision classification task and on the ADE20K
data-set in order to tackle the semi-supervised semantic segmentation problem.
Deep Image Prior
Authors: Dmitry Ulyanov, Andrea Vedaldi, Victor Lempitsky
Date: 2017-11-29 00:00:00
Proceeding: CVPR 2018 6
Tasks: Denoising, Feature Upsampling, Image Denoising, Image Generation, Image Inpainting, Image Restoration, Inductive Bias, Jpeg Compression Artifact Reduction, Super-Resolution

Deep convolutional networks have become a popular tool for image generation and restoration. Generally, their excellent performance is imputed to their ability to learn realistic image priors from a large number of example images. In this paper, we show that, on the contrary, the structure of a generator network is sufficient to capture a great deal of low-level image statistics prior to any learning. In order to do so, we show that a randomly-initialized neural network can be used as a handcrafted prior with excellent results in standard inverse problems such as denoising, super-resolution, and inpainting. Furthermore, the same prior can be used to invert deep neural representations to diagnose them, and to restore images based on flash-no flash input pairs. Apart from its diverse applications, our approach highlights the inductive bias captured by standard generator network architectures. It also bridges the gap between two very popular families of image restoration methods: learning-based methods using deep convolutional networks and learning-free methods based on handcrafted image priors such as self-similarity. Code and supplementary material are available at https://dmitryulyanov.github.io/deep_image_prior .
A Human Mixed Strategy Approach to Deep Reinforcement Learning
Authors: Ngoc Duy Nguyen, Saeid Nahavandi, Thanh Nguyen
Date: 2018-04-05 00:00:00
Tasks: Atari Games, Deep Reinforcement Learning, Efficient Exploration, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

In 2015, Google's DeepMind announced an advancement in creating an autonomous
agent based on deep reinforcement learning (DRL) that could beat a professional
player in a series of 49 Atari games. However, the current manifestation of DRL
is still immature, and has significant drawbacks. One of DRL's imperfections is
its lack of "exploration" during the training process, especially when working
with high-dimensional problems. In this paper, we propose a mixed strategy
approach that mimics behaviors of human when interacting with environment, and
create a "thinking" agent that allows for more efficient exploration in the DRL
training process. The simulation results based on the Breakout game show that
our scheme achieves a higher probability of obtaining a maximum score than does
the baseline DRL algorithm, i.e., the asynchronous advantage actor-critic
method. The proposed scheme therefore can be applied effectively to solving a
complicated task in a real-world application.