pwc-archive/papers-with-abstracts huggingface dataset:
WorldTree: A Corpus of Explanation Graphs for Elementary Science Questions supporting Multi-Hop Inference
Authors: Peter A. Jansen, Elizabeth Wainwright, Steven Marmorstein, Clayton T. Morrison
Date: 2018-02-08 00:00:00
Proceeding: LREC 2018 5
Tasks: Question Answering, World Knowledge

Developing methods of automated inference that are able to provide users with
compelling human-readable justifications for why the answer to a question is
correct is critical for domains such as science and medicine, where user trust
and detecting costly errors are limiting factors to adoption. One of the
central barriers to training question answering models on explainable inference
tasks is the lack of gold explanations to serve as training data. In this paper
we present a corpus of explanations for standardized science exams, a recent
challenge task for question answering. We manually construct a corpus of
detailed explanations for nearly all publicly available standardized elementary
science question (approximately 1,680 3rd through 5th grade questions) and
represent these as "explanation graphs" -- sets of lexically overlapping
sentences that describe how to arrive at the correct answer to a question
through a combination of domain and world knowledge. We also provide an
explanation-centered tablestore, a collection of semi-structured tables that
contain the knowledge to construct these elementary science explanations.
Together, these two knowledge resources map out a substantial portion of the
knowledge required for answering and explaining elementary science exams, and
provide both structured and free-text training data for the explainable
inference task.
Thompson Sampling for Dynamic Pricing
Authors: Ravi Ganti, Matyas Sustik, Quoc Tran, Brian Seaman
Date: 2018-02-08 00:00:00
Tasks: Active Learning, Thompson Sampling

In this paper we apply active learning algorithms for dynamic pricing in a
prominent e-commerce website. Dynamic pricing involves changing the price of
items on a regular basis, and uses the feedback from the pricing decisions to
update prices of the items. Most popular approaches to dynamic pricing use a
passive learning approach, where the algorithm uses historical data to learn
various parameters of the pricing problem, and uses the updated parameters to
generate a new set of prices. We show that one can use active learning
algorithms such as Thompson sampling to more efficiently learn the underlying
parameters in a pricing problem. We apply our algorithms to a real e-commerce
system and show that the algorithms indeed improve revenue compared to pricing
algorithms that use passive learning.
A Savage-Like Axiomatization for Nonstandard Expected Utility
Authors: Grant Molnar
Date: 2017-01-12 00:00:00
Tasks: Decision Making

Since Leonard Savage's epoch-making "Foundations of Statistics", Subjective
Expected Utility Theory has been the presumptive model for decision-making.
Savage provided an act-based axiomatization of standard expected utility
theory. In this article, we provide a Savage-like axiomatization of nonstandard
expected utility theory. It corresponds to a weakening of Savage's 6th axiom.
Streaming Binary Sketching based on Subspace Tracking and Diagonal Uniformization
Authors: Anne Morvan, Antoine Souloumiac, Cédric Gouy-Pailler, Jamal Atif
Date: 2017-05-22 00:00:00

In this paper, we address the problem of learning compact
similarity-preserving embeddings for massive high-dimensional streams of data
in order to perform efficient similarity search. We present a new online method
for computing binary compressed representations -sketches- of high-dimensional
real feature vectors. Given an expected code length $c$ and high-dimensional
input data points, our algorithm provides a $c$-bits binary code for preserving
the distance between the points from the original high-dimensional space. Our
algorithm does not require neither the storage of the whole dataset nor a
chunk, thus it is fully adaptable to the streaming setting. It also provides
low time complexity and convergence guarantees. We demonstrate the quality of
our binary sketches through experiments on real data for the nearest neighbors
search task in the online setting.
PoTrojan: powerful neural-level trojan designs in deep learning models
Authors: Minhui Zou, Yang Shi, Chengliang Wang, Fangyu Li, WenZhan Song, Yu Wang
Date: 2018-02-08 00:00:00
Tasks: Deep Learning

With the popularity of deep learning (DL), artificial intelligence (AI) has been applied in many areas of human life. Neural network or artificial neural network (NN), the main technique behind DL, has been extensively studied to facilitate computer vision and natural language recognition. However, the more we rely on information technology, the more vulnerable we are. That is, malicious NNs could bring huge threat in the so-called coming AI era. In this paper, for the first time in the literature, we propose a novel approach to design and insert powerful neural-level trojans or PoTrojan in pre-trained NN models. Most of the time, PoTrojans remain inactive, not affecting the normal functions of their host NN models. PoTrojans could only be triggered in very rare conditions. Once activated, however, the PoTrojans could cause the host NN models to malfunction, either falsely predicting or classifying, which is a significant threat to human society of the AI era. We would explain the principles of PoTrojans and the easiness of designing and inserting them in pre-trained deep learning models. PoTrojans doesn't modify the existing architecture or parameters of the pre-trained models, without re-training. Hence, the proposed method is very efficient.
Optimization Methods for Large-Scale Machine Learning
Authors: Léon Bottou, Frank E. Curtis, Jorge Nocedal
Date: 2016-06-15 00:00:00
Tasks: BIG-bench Machine Learning, Text Classification

This paper provides a review and commentary on the past, present, and future
of numerical optimization algorithms in the context of machine learning
applications. Through case studies on text classification and the training of
deep neural networks, we discuss how optimization problems arise in machine
learning and what makes them challenging. A major theme of our study is that
large-scale machine learning represents a distinctive setting in which the
stochastic gradient (SG) method has traditionally played a central role while
conventional gradient-based nonlinear optimization techniques typically falter.
Based on this viewpoint, we present a comprehensive theory of a
straightforward, yet versatile SG algorithm, discuss its practical behavior,
and highlight opportunities for designing algorithms with improved performance.
This leads to a discussion about the next generation of optimization methods
for large-scale machine learning, including an investigation of two main
streams of research on techniques that diminish noise in the stochastic
directions and methods that make use of second-order derivative approximations.
Detection of Adversarial Training Examples in Poisoning Attacks through Anomaly Detection
Authors: Andrea Paudice, Luis Muñoz-González, Andras Gyorgy, Emil C. Lupu
Date: 2018-02-08 00:00:00
Tasks: Anomaly Detection, BIG-bench Machine Learning, Data Poisoning, Intrusion Detection, Network Intrusion Detection, Outlier Detection

Machine learning has become an important component for many systems and
applications including computer vision, spam filtering, malware and network
intrusion detection, among others. Despite the capabilities of machine learning
algorithms to extract valuable information from data and produce accurate
predictions, it has been shown that these algorithms are vulnerable to attacks.
Data poisoning is one of the most relevant security threats against machine
learning systems, where attackers can subvert the learning process by injecting
malicious samples in the training data. Recent work in adversarial machine
learning has shown that the so-called optimal attack strategies can
successfully poison linear classifiers, degrading the performance of the system
dramatically after compromising a small fraction of the training dataset. In
this paper we propose a defence mechanism to mitigate the effect of these
optimal poisoning attacks based on outlier detection. We show empirically that
the adversarial examples generated by these attack strategies are quite
different from genuine points, as no detectability constrains are considered to
craft the attack. Hence, they can be detected with an appropriate pre-filtering
of the training dataset.
Algorithmic Bidding for Virtual Trading in Electricity Markets
Authors: Sevi Baltaoglu, Lang Tong, Qing Zhao
Date: 2018-02-08 00:00:00

We consider the problem of optimal bidding for virtual trading in
two-settlement electricity markets. A virtual trader aims to arbitrage on the
differences between day-ahead and real-time market prices; both prices,
however, are random and unknown to market participants. An online learning
algorithm is proposed to maximize the cumulative payoff over a finite number of
trading sessions by allocating the trader's budget among his bids for K options
in each session. It is shown that the proposed algorithm converges, with an
almost optimal convergence rate, to the global optimal corresponding to the
case when the underlying price distribution is known. The proposed algorithm is
also generalized for trading strategies with a risk measure. By using both
cumulative payoff and Sharpe ratio as performance metrics, evaluations were
performed based on historical data spanning ten year period of NYISO and PJM
markets. It was shown that the proposed strategy outperforms standard
benchmarks and the S&P 500 index over the same period.
A New Multi Criteria Decision Making Method: Approach of Logarithmic Concept (APLOCO)
Authors: Tevfik Bulut
Date: 2018-02-08 00:00:00
Tasks: Decision Making

The primary aim of the study is to introduce APLOCO method which is developed
for the solution of multicriteria decision making problems both theoretically
and practically. In this context, application subject of APLACO constitutes
evaluation of investment potential of different cities in metropolitan status
in Turkey. The secondary purpose of the study is to identify the independent
variables affecting the factories in the operating phase and to estimate the
effect levels of independent variables on the dependent variable in the
organized industrial zones (OIZs), whose mission is to reduce regional
development disparities and to mobilize local production dynamics. For this
purpose, the effect levels of independent variables on dependent variables have
been determined using the multilayer perceptron (MLP) method, which has a wide
use in artificial neural networks (ANNs). The effect levels derived from MLP
have been then used as the weight levels of the decision criteria in APLOCO.
The independent variables included in MLP are also used as the decision
criteria in APLOCO. According to the results obtained from APLOCO, Istanbul
city is the best alternative in term of the investment potential and other
alternatives are Manisa, Denizli, Izmir, Kocaeli, Bursa, Ankara, Adana, and
Antalya, respectively. Although APLOCO is used to solve the ranking problem in
order to show application process in the paper, it can be employed easily in
the solution of classification and selection problems. On the other hand, the
study also shows a rare example of the nested usage of APLOCO which is one of
the methods of operation research as well as MLP used in determination of
weights.
Precision medicine as a control problem: Using simulation and deep reinforcement learning to discover adaptive, personalized multi-cytokine therapy for sepsis
Authors: Brenden K. Petersen, Jiachen Yang, Will S. Grathwohl, Chase Cockrell, Claudio Santiago, Gary An, Daniel M. Faissol
Date: 2018-02-08 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Sepsis is a life-threatening condition affecting one million people per year
in the US in which dysregulation of the body's own immune system causes damage
to its tissues, resulting in a 28 - 50% mortality rate. Clinical trials for
sepsis treatment over the last 20 years have failed to produce a single
currently FDA approved drug treatment. In this study, we attempt to discover an
effective cytokine mediation treatment strategy for sepsis using a previously
developed agent-based model that simulates the innate immune response to
infection: the Innate Immune Response agent-based model (IIRABM). Previous
attempts at reducing mortality with multi-cytokine mediation using the IIRABM
have failed to reduce mortality across all patient parameterizations and
motivated us to investigate whether adaptive, personalized multi-cytokine
mediation can control the trajectory of sepsis and lower patient mortality. We
used the IIRABM to compute a treatment policy in which systemic patient
measurements are used in a feedback loop to inform future treatment. Using deep
reinforcement learning, we identified a policy that achieves 0% mortality on
the patient parameterization on which it was trained. More importantly, this
policy also achieves 0.8% mortality over 500 randomly selected patient
parameterizations with baseline mortalities ranging from 1 - 99% (with an
average of 49%) spanning the entire clinically plausible parameter space of the
IIRABM. These results suggest that adaptive, personalized multi-cytokine
mediation therapy could be a promising approach for treating sepsis. We hope
that this work motivates researchers to consider such an approach as part of
future clinical trials. To the best of our knowledge, this work is the first to
consider adaptive, personalized multi-cytokine mediation therapy for sepsis,
and is the first to exploit deep reinforcement learning on a biological
simulation.
Uncertainty quantification in graph-based classification of high dimensional data
Authors: Andrea L. Bertozzi, Xiyang Luo, Andrew M. Stuart, Konstantinos C. Zygalakis
Date: 2017-03-26 00:00:00
Tasks: Binary Classification, Classification, General Classification, Uncertainty Quantification, Vocal Bursts Intensity Prediction

Classification of high dimensional data finds wide-ranging applications. In
many of these applications equipping the resulting classification with a
measure of uncertainty may be as important as the classification itself. In
this paper we introduce, develop algorithms for, and investigate the properties
of, a variety of Bayesian models for the task of binary classification; via the
posterior distribution on the classification labels, these methods
automatically give measures of uncertainty. The methods are all based around
the graph formulation of semi-supervised learning.
  We provide a unified framework which brings together a variety of methods
which have been introduced in different communities within the mathematical
sciences. We study probit classification in the graph-based setting, generalize
the level-set method for Bayesian inverse problems to the classification
setting, and generalize the Ginzburg-Landau optimization-based classifier to a
Bayesian setting; we also show that the probit and level set approaches are
natural relaxations of the harmonic function approach introduced in [Zhu et al
2003].
  We introduce efficient numerical methods, suited to large data-sets, for both
MCMC-based sampling as well as gradient-based MAP estimation. Through numerical
experiments we study classification accuracy and uncertainty quantification for
our models; these experiments showcase a suite of datasets commonly used to
evaluate graph-based semi-supervised learning algorithms.
Learning and Querying Fast Generative Models for Reinforcement Learning
Authors: Lars Buesing, Theophane Weber, Sebastien Racaniere, S. M. Ali Eslami, Danilo Rezende, David P. Reichert, Fabio Viola, Frederic Besse, Karol Gregor, Demis Hassabis, Daan Wierstra
Date: 2018-02-08 00:00:00
Tasks: Atari Games, Decision Making, Model-based Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), State Space Models

A key challenge in model-based reinforcement learning (RL) is to synthesize
computationally efficient and accurate environment models. We show that
carefully designed generative models that learn and operate on compact state
representations, so-called state-space models, substantially reduce the
computational costs for predicting outcomes of sequences of actions. Extensive
experiments establish that state-space models accurately capture the dynamics
of Atari games from the Arcade Learning Environment from raw pixels. The
computational speed-up of state-space models while maintaining high accuracy
makes their application in RL feasible: We demonstrate that agents which query
these models for decision making outperform strong model-free baselines on the
game MSPACMAN, demonstrating the potential of using learned environment models
for planning.
Go With the Flow, on Jupiter and Snow. Coherence From Model-Free Video Data without Trajectories
Authors: Abd AlRahman AlMomani, Erik M. Bollt
Date: 2016-08-25 00:00:00

Viewing a data set such as the clouds of Jupiter, coherence is readily
apparent to human observers, especially the Great Red Spot, but also other
great storms and persistent structures. There are now many different
definitions and perspectives mathematically describing coherent structures, but
we will take an image processing perspective here. We describe an image
processing perspective inference of coherent sets from a fluidic system
directly from image data, without attempting to first model underlying flow
fields, related to a concept in image processing called motion tracking. In
contrast to standard spectral methods for image processing which are generally
related to a symmetric affinity matrix, leading to standard spectral graph
theory, we need a not symmetric affinity which arises naturally from the
underlying arrow of time. We develop an anisotropic, directed diffusion
operator corresponding to flow on a directed graph, from a directed affinity
matrix developed with coherence in mind, and corresponding spectral graph
theory from the graph Laplacian. Our methodology is not offered as more
accurate than other traditional methods of finding coherent sets, but rather
our approach works with alternative kinds of data sets, in the absence of
vector field. Our examples will include partitioning the weather and cloud
structures of Jupiter, and a local to Potsdam, N.Y. lake-effect snow event on
Earth, as well as the benchmark test double-gyre system.
Adversarial Advantage Actor-Critic Model for Task-Completion Dialogue Policy Learning
Authors: Baolin Peng, Xiujun Li, Jianfeng Gao, Jingjing Liu, Yun-Nung Chen, Kam-Fai Wong
Date: 2017-10-31 00:00:00
Tasks: Task-Completion Dialogue Policy Learning

This paper presents a new method --- adversarial advantage actor-critic
(Adversarial A2C), which significantly improves the efficiency of dialogue
policy learning in task-completion dialogue systems. Inspired by generative
adversarial networks (GAN), we train a discriminator to differentiate
responses/actions generated by dialogue agents from responses/actions by
experts. Then, we incorporate the discriminator as another critic into the
advantage actor-critic (A2C) framework, to encourage the dialogue agent to
explore state-action within the regions where the agent takes actions similar
to those of the experts. Experimental results in a movie-ticket booking domain
show that the proposed Adversarial A2C can accelerate policy exploration
efficiently.
Visualizations for an Explainable Planning Agent
Authors: Tathagata Chakraborti, Kshitij P. Fadnis, Kartik Talamadupula, Mishal Dholakia, Biplav Srivastava, Jeffrey O. Kephart, Rachel K. E. Bellamy
Date: 2017-09-13 00:00:00
Tasks: Decision Making

In this paper, we report on the visualization capabilities of an Explainable
AI Planning (XAIP) agent that can support human in the loop decision making.
Imposing transparency and explainability requirements on such agents is
especially important in order to establish trust and common ground with the
end-to-end automated planning system. Visualizing the agent's internal
decision-making processes is a crucial step towards achieving this. This may
include externalizing the "brain" of the agent -- starting from its sensory
inputs, to progressively higher order decisions made by it in order to drive
its planning components. We also show how the planner can bootstrap on the
latest techniques in explainable planning to cast plan visualization as a plan
explanation problem, and thus provide concise model-based visualization of its
plans. We demonstrate these functionalities in the context of the automated
planning components of a smart assistant in an instrumented meeting space.
Texture Segmentation Based Video Compression Using Convolutional Neural Networks
Authors: Chichen Fu, Di Chen, Edward J. Delp, Zoe Liu, Fengqing Zhu
Date: 2018-02-08 00:00:00
Tasks: Texture Classification, Video Compression

There has been a growing interest in using different approaches to improve
the coding efficiency of modern video codec in recent years as demand for
web-based video consumption increases. In this paper, we propose a model-based
approach that uses texture analysis/synthesis to reconstruct blocks in texture
regions of a video to achieve potential coding gains using the AV1 codec
developed by the Alliance for Open Media (AOM). The proposed method uses
convolutional neural networks to extract texture regions in a frame, which are
then reconstructed using a global motion model. Our preliminary results show an
increase in coding efficiency while maintaining satisfactory visual quality.
A Generalization Method of Partitioned Activation Function for Complex Number
Authors: HyeonSeok Lee, Hyo Seon Park
Date: 2018-02-08 00:00:00

A method to convert real number partitioned activation function into complex
number one is provided. The method has 4em variations; 1 has potential to get
holomorphic activation, 2 has potential to conserve complex angle, and the last
1 guarantees interaction between real and imaginary parts. The method has been
applied to LReLU and SELU as examples. The complex number activation function
is an building block of complex number ANN, which has potential to properly
deal with complex number problems. But the complex activation is not well
established yet. Therefore, we propose a way to extend the partitioned real
activation to complex number.
Practical Issues of Action-conditioned Next Image Prediction
Authors: Donglai Zhu, Hao Chen, Hengshuai Yao, Masoud Nosrati, Peyman Yadmellat, Yunfei Zhang
Date: 2018-02-08 00:00:00
Tasks: Decoder, Prediction, SSIM

The problem of action-conditioned image prediction is to predict the expected
next frame given the current camera frame the robot observes and an action
selected by the robot. We provide the first comparison of two recent popular
models, especially for image prediction on cars. Our major finding is that
action tiling encoding is the most important factor leading to the remarkable
performance of the CDNA model. We present a light-weight model by action tiling
encoding which has a single-decoder feedforward architecture same as
[action_video_prediction_honglak]. On a real driving dataset, the CDNA model
achieves ${0.3986} \times 10^{-3}$ MSE and ${0.9846}$ Structure SIMilarity
(SSIM) with a network size of about {\bfseries ${12.6}$ million} parameters.
With a small network of fewer than {\bfseries ${1}$ million} parameters, our
new model achieves a comparable performance to CDNA at ${0.3613} \times
10^{-3}$ MSE and ${0.9633}$ SSIM. Our model requires less memory, is more
computationally efficient and is advantageous to be used inside self-driving
vehicles.