pwc-archive/papers-with-abstracts huggingface dataset:
Crick-net: A Convolutional Neural Network based Classification Approach for Detecting Waist High No Balls in Cricket
Authors: Md. Harun-Ur-Rashid, Shekina Khatun, Mehe Zabin Trisha, Nafis Neehal, Md. Zahid Hasan
Date: 2018-05-15 00:00:00
Tasks: Decision Making, General Classification

Cricket is undoubtedly one of the most popular games in this modern era. As human beings are prone to error, there remains a constant need for automated analysis and decision making of different events in this game. Simultaneously, with advent and advances in Artificial Intelligence and Computer Vision, application of these two in different domains has become an emerging trend. Applying several computer vision techniques in analyzing different Cricket events and automatically coming into decisions has become popular in recent days. In this paper, we have deployed a CNN based classification method with Inception V3 in order to automatically detect and differentiate waist high no balls with fair balls. Our approach achieves an overall average accuracy of 88% with a fairly low cross-entropy value.
Harvesting Paragraph-Level Question-Answer Pairs from Wikipedia
Authors: Xinya Du, Claire Cardie
Date: 2018-05-15 00:00:00
Proceeding: ACL 2018 7
Tasks: Articles, Question Generation, Question-Generation, Sentence

We study the task of generating from Wikipedia articles question-answer pairs
that cover content beyond a single sentence. We propose a neural network
approach that incorporates coreference knowledge via a novel gating mechanism.
Compared to models that only take into account sentence-level information
(Heilman and Smith, 2010; Du et al., 2017; Zhou et al., 2017), we find that the
linguistic knowledge introduced by the coreference representation aids question
generation significantly, producing models that outperform the current
state-of-the-art. We apply our system (composed of an answer span extraction
system and the passage-level QG system) to the 10,000 top-ranking Wikipedia
articles and create a corpus of over one million question-answer pairs. We also
provide a qualitative analysis for this large-scale generated corpus from
Wikipedia.
Feedback-Based Tree Search for Reinforcement Learning
Authors: Daniel R. Jiang, Emmanuel Ekwedike, Han Liu
Date: 2018-05-15 00:00:00
Proceeding: ICML 2018 7
Tasks: AI Agent, Model-based Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Inspired by recent successes of Monte-Carlo tree search (MCTS) in a number of
artificial intelligence (AI) application domains, we propose a model-based
reinforcement learning (RL) technique that iteratively applies MCTS on batches
of small, finite-horizon versions of the original infinite-horizon Markov
decision process. The terminal condition of the finite-horizon problems, or the
leaf-node evaluator of the decision tree generated by MCTS, is specified using
a combination of an estimated value function and an estimated policy function.
The recommendations generated by the MCTS procedure are then provided as
feedback in order to refine, through classification and regression, the
leaf-node evaluator for the next iteration. We provide the first sample
complexity bounds for a tree search-based RL algorithm. In addition, we show
that a deep neural network implementation of the technique can create a
competitive AI agent for the popular multi-player online battle arena (MOBA)
game King of Glory.
CLINIQA: A Machine Intelligence Based Clinical Question Answering System
Authors: M A H Zahid, Ankush Mittal, R. C. Joshi, G. Atluri
Date: 2018-05-15 00:00:00
Tasks: Answer Selection, Information Retrieval, Question Answering, Retrieval

The recent developments in the field of biomedicine have made large volumes
of biomedical literature available to the medical practitioners. Due to the
large size and lack of efficient searching strategies, medical practitioners
struggle to obtain necessary information available in the biomedical
literature. Moreover, the most sophisticated search engines of age are not
intelligent enough to interpret the clinicians' questions. These facts reflect
the urgent need of an information retrieval system that accepts the queries
from medical practitioners' in natural language and returns the answers quickly
and efficiently. In this paper, we present an implementation of a machine
intelligence based CLINIcal Question Answering system (CLINIQA) to answer
medical practitioner's questions. The system was rigorously evaluated on
different text mining algorithms and the best components for the system were
selected. The system makes use of Unified Medical Language System for semantic
analysis of both questions and medical documents. In addition, the system
employs supervised machine learning algorithms for classification of the
documents, identifying the focus of the question and answer selection.
Effective domain-specific heuristics are designed for answer ranking. The
performance evaluation on hundred clinical questions shows the effectiveness of
our approach.
CaMKII activation supports reward-based neural network optimization through Hamiltonian sampling
Authors: Zhaofei Yu, David Kappel, Robert Legenstein, Sen Song, Feng Chen, Wolfgang Maass
Date: 2016-06-01 00:00:00

Synaptic plasticity is implemented and controlled through over thousand
different types of molecules in the postsynaptic density and presynaptic
boutons that assume a staggering array of different states through
phosporylation and other mechanisms. One of the most prominent molecule in the
postsynaptic density is CaMKII, that is described in molecular biology as a
"memory molecule" that can integrate through auto-phosporylation Ca-influx
signals on a relatively large time scale of dozens of seconds. The functional
impact of this memory mechanism is largely unknown. We show that the
experimental data on the specific role of CaMKII activation in dopamine-gated
spine consolidation suggest a general functional role in speeding up
reward-guided search for network configurations that maximize reward
expectation. Our theoretical analysis shows that stochastic search could in
principle even attain optimal network configurations by emulating one of the
most well-known nonlinear optimization methods, simulated annealing. But this
optimization is usually impeded by slowness of stochastic search at a given
temperature. We propose that CaMKII contributes a momentum term that
substantially speeds up this search. In particular, it allows the network to
overcome saddle points of the fitness function. The resulting improved
stochastic policy search can be understood on a more abstract level as
Hamiltonian sampling, which is known to be one of the most efficient stochastic
search methods.
Integrating Local Context and Global Cohesiveness for Open Information Extraction
Authors: Qi Zhu, Xiang Ren, Jingbo Shang, Yu Zhang, Ahmed El-Kishky, Jiawei Han
Date: 2018-04-26 00:00:00
Tasks: Open Information Extraction, Relation, Sentence

Extracting entities and their relations from text is an important task for
understanding massive text corpora. Open information extraction (IE) systems
mine relation tuples (i.e., entity arguments and a predicate string to describe
their relation) from sentences. These relation tuples are not confined to a
predefined schema for the relations of interests. However, current Open IE
systems focus on modeling local context information in a sentence to extract
relation tuples, while ignoring the fact that global statistics in a large
corpus can be collectively leveraged to identify high-quality sentence-level
extractions. In this paper, we propose a novel Open IE system, called ReMine,
which integrates local context signals and global structural signals in a
unified, distant-supervision framework. Leveraging facts from external
knowledge bases as supervision, the new system can be applied to many different
domains to facilitate sentence-level tuple extractions using corpus-level
statistics. Our system operates by solving a joint optimization problem to
unify (1) segmenting entity/relation phrases in individual sentences based on
local context; and (2) measuring the quality of tuples extracted from
individual sentences with a translating-based objective. Learning the two
subtasks jointly helps correct errors produced in each subtask so that they can
mutually enhance each other. Experiments on two real-world corpora from
different domains demonstrate the effectiveness, generality, and robustness of
ReMine when compared to state-of-the-art open IE systems.
Classification of Household Materials via Spectroscopy
Authors: Zackory Erickson, Nathan Luskey, Sonia Chernova, Charles C. Kemp
Date: 2018-05-10 00:00:00
Tasks: Classification, General Classification, Material Classification, Material Recognition, Object

Recognizing an object's material can inform a robot on the object's fragility
or appropriate use. To estimate an object's material during manipulation, many
prior works have explored the use of haptic sensing. In this paper, we explore
a technique for robots to estimate the materials of objects using spectroscopy.
We demonstrate that spectrometers provide several benefits for material
recognition, including fast response times and accurate measurements with low
noise. Furthermore, spectrometers do not require direct contact with an object.
To explore this, we collected a dataset of spectral measurements from two
commercially available spectrometers during which a robotic platform interacted
with 50 flat material objects, and we show that a neural network model can
accurately analyze these measurements. Due to the similarity between
consecutive spectral measurements, our model achieved a material classification
accuracy of 94.6% when given only one spectral sample per object. Similar to
prior works with haptic sensors, we found that generalizing material
recognition to new objects posed a greater challenge, for which we achieved an
accuracy of 79.1% via leave-one-object-out cross-validation. Finally, we
demonstrate how a PR2 robot can leverage spectrometers to estimate the
materials of everyday objects found in the home. From this work, we find that
spectroscopy poses a promising approach for material classification during
robotic manipulation.
A Dynamic Model for Traffic Flow Prediction Using Improved DRN
Authors: Zeren Tan, Ruimin Li
Date: 2018-05-02 00:00:00
Tasks: Management, Prediction

Real-time traffic flow prediction can not only provide travelers with
reliable traffic information so that it can save people's time, but also assist
the traffic management agency to manage traffic system. It can greatly improve
the efficiency of the transportation system. Traditional traffic flow
prediction approaches usually need a large amount of data but still give poor
performances. With the development of deep learning, researchers begin to pay
attention to artificial neural networks (ANNs) such as RNN and LSTM. However,
these ANNs are very time-consuming. In our research, we improve the Deep
Residual Network and build a dynamic model which previous researchers hardly
use. We firstly integrate the input and output of the $i^{th}$ layer to the
input of the $i+1^{th}$ layer and prove that each layer will fit a simpler
function so that the error rate will be much smaller. Then, we use the concept
of online learning in our model to update pre-trained model during prediction.
Our result shows that our model has higher accuracy than some state-of-the-art
models. In addition, our dynamic model can perform better in practical
applications.
Causal Reasoning for Algorithmic Fairness
Authors: Joshua R. Loftus, Chris Russell, Matt J. Kusner, Ricardo Silva
Date: 2018-05-15 00:00:00
Tasks: Decision Making, Fairness

In this work, we argue for the importance of causal reasoning in creating
fair algorithms for decision making. We give a review of existing approaches to
fairness, describe work in causality necessary for the understanding of causal
approaches, argue why causality is necessary for any approach that wishes to be
fair, and give a detailed analysis of the many recent approaches to
causality-based fairness.
Gradient-Leaks: Understanding and Controlling Deanonymization in Federated Learning
Authors: Tribhuvanesh Orekondy, Seong Joon Oh, Yang Zhang, Bernt Schiele, Mario Fritz
Date: 2018-05-15 00:00:00
Tasks: Data Augmentation, Federated Learning, Speech Recognition

Federated Learning (FL) systems are gaining popularity as a solution to training Machine Learning (ML) models from large-scale user data collected on personal devices (e.g., smartphones) without their raw data leaving the device. At the core of FL is a network of anonymous user devices sharing training information (model parameter updates) computed locally on personal data. However, the type and degree to which user-specific information is encoded in the model updates is poorly understood. In this paper, we identify model updates encode subtle variations in which users capture and generate data. The variations provide a strong statistical signal, allowing an adversary to effectively deanonymize participating devices using a limited set of auxiliary data. We analyze resulting deanonymization attacks on diverse tasks on real-world (anonymized) user-generated data across a range of closed- and open-world scenarios. We study various strategies to mitigate the risks of deanonymization. As random perturbation methods do not offer convincing operating points, we propose data-augmentation strategies which introduces adversarial biases in device data and thereby, offer substantial protection against deanonymization threats with little effect on utility.
Multiplicative Updates for Convolutional NMF Under $β$-Divergence
Authors: Pedro J. Villasana T., Stanislaw Gorlow, Arvind T. Hariraman
Date: 2018-03-14 00:00:00

In this letter, we generalize the convolutional NMF by taking the
$\beta$-divergence as the contrast function and present the correct
multiplicative updates for its factors in closed form. The new updates unify
the $\beta$-NMF and the convolutional NMF. We state why almost all of the
existing updates are inexact and approximative w.r.t. the convolutional data
model. We show that our updates are stable and that their convergence
performance is consistent across the most common values of $\beta$.
Graph Signal Sampling via Reinforcement Learning
Authors: Oleksii Abramenko, Alexander Jung
Date: 2018-05-15 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

We formulate the problem of sampling and recovering clustered graph signal as
a multi-armed bandit (MAB) problem. This formulation lends naturally to
learning sampling strategies using the well-known gradient MAB algorithm. In
particular, the sampling strategy is represented as a probability distribution
over the individual arms of the MAB and optimized using gradient ascent. Some
illustrative numerical experiments indicate that the sampling strategies based
on the gradient MAB algorithm outperform existing sampling methods.
A Purely End-to-end System for Multi-speaker Speech Recognition
Authors: Hiroshi Seki, Takaaki Hori, Shinji Watanabe, Jonathan Le Roux, John R. Hershey
Date: 2018-05-15 00:00:00
Proceeding: ACL 2018 7
Tasks: speech-recognition, Speech Recognition

Recently, there has been growing interest in multi-speaker speech
recognition, where the utterances of multiple speakers are recognized from
their mixture. Promising techniques have been proposed for this task, but
earlier works have required additional training data such as isolated source
signals or senone alignments for effective learning. In this paper, we propose
a new sequence-to-sequence framework to directly decode multiple label
sequences from a single speech sequence by unifying source separation and
speech recognition functions in an end-to-end manner. We further propose a new
objective function to improve the contrast between the hidden vectors to avoid
generating similar hypotheses. Experimental results show that the model is
directly able to learn a mapping from a speech mixture to multiple label
sequences, achieving 83.1 % relative improvement compared to a model trained
without the proposed objective. Interestingly, the results are comparable to
those produced by previous end-to-end works featuring explicit separation and
recognition modules.
Critical Learning Periods in Deep Neural Networks
Authors: Alessandro Achille, Matteo Rovere, Stefano Soatto
Date: 2017-11-24 00:00:00
Tasks: Disentanglement, Representation Learning

Similar to humans and animals, deep artificial neural networks exhibit
critical periods during which a temporary stimulus deficit can impair the
development of a skill. The extent of the impairment depends on the onset and
length of the deficit window, as in animal models, and on the size of the
neural network. Deficits that do not affect low-level statistics, such as
vertical flipping of the images, have no lasting effect on performance and can
be overcome with further training. To better understand this phenomenon, we use
the Fisher Information of the weights to measure the effective connectivity
between layers of a network during training. Counterintuitively, information
rises rapidly in the early phases of training, and then decreases, preventing
redistribution of information resources in a phenomenon we refer to as a loss
of "Information Plasticity". Our analysis suggests that the first few epochs
are critical for the creation of strong connections that are optimal relative
to the input data distribution. Once such strong connections are created, they
do not appear to change during additional training. These findings suggest that
the initial learning transient, under-scrutinized compared to asymptotic
behavior, plays a key role in determining the outcome of the training process.
Our findings, combined with recent theoretical results in the literature, also
suggest that forgetting (decrease of information in the weights) is critical to
achieving invariance and disentanglement in representation learning. Finally,
critical periods are not restricted to biological systems, but can emerge
naturally in learning systems, whether biological or artificial, due to
fundamental constrains arising from learning dynamics and information
processing.
Robust and Efficient Graph Correspondence Transfer for Person Re-identification
Authors: Qin Zhou, Heng Fan, Hua Yang, Hang Su, Shibao Zheng, Shuang Wu, Haibin Ling
Date: 2018-05-15 00:00:00
Tasks: Graph Matching, Person Re-Identification

Spatial misalignment caused by variations in poses and viewpoints is one of
the most critical issues that hinders the performance improvement in existing
person re-identification (Re-ID) algorithms. To address this problem, in this
paper, we present a robust and efficient graph correspondence transfer (REGCT)
approach for explicit spatial alignment in Re-ID. Specifically, we propose to
establish the patch-wise correspondences of positive training pairs via graph
matching. By exploiting both spatial and visual contexts of human appearance in
graph matching, meaningful semantic correspondences can be obtained. To
circumvent the cumbersome \emph{on-line} graph matching in testing phase, we
propose to transfer the \emph{off-line} learned patch-wise correspondences from
the positive training pairs to test pairs. In detail, for each test pair, the
training pairs with similar pose-pair configurations are selected as
references. The matching patterns (i.e., the correspondences) of the selected
references are then utilized to calculate the patch-wise feature distances of
this test pair. To enhance the robustness of correspondence transfer, we design
a novel pose context descriptor to accurately model human body configurations,
and present an approach to measure the similarity between a pair of pose
context descriptors. Meanwhile, to improve testing efficiency, we propose a
correspondence template ensemble method using the voting mechanism, which
significantly reduces the amount of patch-wise matchings involved in distance
calculation. With aforementioned strategies, the REGCT model can effectively
and efficiently handle the spatial misalignment problem in Re-ID. Extensive
experiments on five challenging benchmarks, including VIPeR, Road, PRID450S,
3DPES and CUHK01, evidence the superior performance of REGCT over other
state-of-the-art approaches.
Leveraging human knowledge in tabular reinforcement learning: A study of human subjects
Authors: Ariel Rosenfeld, Moshe Cohen, Matthew E. Taylor, Sarit Kraus
Date: 2018-05-15 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Reinforcement Learning (RL) can be extremely effective in solving complex,
real-world problems. However, injecting human knowledge into an RL agent may
require extensive effort and expertise on the human designer's part. To date,
human factors are generally not considered in the development and evaluation of
possible RL approaches. In this article, we set out to investigate how
different methods for injecting human knowledge are applied, in practice, by
human designers of varying levels of knowledge and skill. We perform the first
empirical evaluation of several methods, including a newly proposed method
named SASS which is based on the notion of similarities in the agent's
state-action space. Through this human study, consisting of 51 human
participants, we shed new light on the human factors that play a key role in
RL. We find that the classical reward shaping technique seems to be the most
natural method for most designers, both expert and non-expert, to speed up RL.
However, we further find that our proposed method SASS can be effectively and
efficiently combined with reward shaping, and provides a beneficial alternative
to using only a single speedup method with minimal human designer effort
overhead.
Multi-label Classification of Surgical Tools with Convolutional Neural Networks
Authors: Jonas Prellberg, Oliver Kramer
Date: 2018-05-15 00:00:00
Tasks: General Classification, Multi-Label Classification, MUlTI-LABEL-ClASSIFICATION

Automatic tool detection from surgical imagery has a multitude of useful
applications, such as real-time computer assistance for the surgeon. Using the
successful residual network architecture, a system that can distinguish 21
different tools in cataract surgery videos is created. The videos are provided
as part of the 2017 CATARACTS challenge and pose difficulties found in many
real-world datasets, for example a strong class imbalance. The construction of
the detection system is guided by a wide array of experiments that explore
different design decisions.
Continuous Learning in a Hierarchical Multiscale Neural Network
Authors: Thomas Wolf, Julien Chaumond, Clement Delangue
Date: 2018-05-15 00:00:00
Proceeding: ACL 2018 7
Tasks: Language Modeling, Language Modelling, Meta-Learning

We reformulate the problem of encoding a multi-scale representation of a
sequence in a language model by casting it in a continuous learning framework.
We propose a hierarchical multi-scale language model in which short time-scale
dependencies are encoded in the hidden state of a lower-level recurrent neural
network while longer time-scale dependencies are encoded in the dynamic of the
lower-level network by having a meta-learner update the weights of the
lower-level neural network in an online meta-learning fashion. We use elastic
weights consolidation as a higher-level to prevent catastrophic forgetting in
our continuous learning framework.