pwc-archive/papers-with-abstracts huggingface dataset:
Mining Novel Multivariate Relationships in Time Series Data Using Correlation Networks
Authors: Saurabh Agrawal, Michael Steinbach, Daniel Boley, Snigdhansu Chatterjee, Gowtham Atluri, Anh The Dang, Stefan Liess, Vipin Kumar
Date: 2018-10-06 00:00:00
Tasks: Time Series, Time Series Analysis

In many domains, there is significant interest in capturing novel
relationships between time series that represent activities recorded at
different nodes of a highly complex system. In this paper, we introduce
multipoles, a novel class of linear relationships between more than two time
series. A multipole is a set of time series that have strong linear dependence
among themselves, with the requirement that each time series makes a
significant contribution to the linear dependence. We demonstrate that most
interesting multipoles can be identified as cliques of negative correlations in
a correlation network. Such cliques are typically rare in a real-world
correlation network, which allows us to find almost all multipoles efficiently
using a clique-enumeration approach. Using our proposed framework, we
demonstrate the utility of multipoles in discovering new physical phenomena in
two scientific domains: climate science and neuroscience. In particular, we
discovered several multipole relationships that are reproducible in multiple
other independent datasets and lead to novel domain insights.
Co-Stack Residual Affinity Networks with Multi-level Attention Refinement for Matching Text Sequences
Authors: Yi Tay, Luu Anh Tuan, Siu Cheung Hui
Date: 2018-10-06 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Paraphrase Identification, Question Answering

Learning a matching function between two text sequences is a long standing
problem in NLP research. This task enables many potential applications such as
question answering and paraphrase identification. This paper proposes Co-Stack
Residual Affinity Networks (CSRAN), a new and universal neural architecture for
this problem. CSRAN is a deep architecture, involving stacked (multi-layered)
recurrent encoders. Stacked/Deep architectures are traditionally difficult to
train, due to the inherent weaknesses such as difficulty with feature
propagation and vanishing gradients. CSRAN incorporates two novel components to
take advantage of the stacked architecture. Firstly, it introduces a new
bidirectional alignment mechanism that learns affinity weights by fusing
sequence pairs across stacked hierarchies. Secondly, it leverages a multi-level
attention refinement component between stacked recurrent layers. The key
intuition is that, by leveraging information across all network hierarchies, we
can not only improve gradient flow but also improve overall performance. We
conduct extensive experiments on six well-studied text sequence matching
datasets, achieving state-of-the-art performance on all.
FD-GAN: Pose-guided Feature Distilling GAN for Robust Person Re-identification
Authors: Yixiao Ge, Zhuowan Li, Haiyu Zhao, Guojun Yin, Shuai Yi, Xiaogang Wang, Hongsheng Li
Date: 2018-10-06 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Generative Adversarial Network, Person Re-Identification

Person re-identification (reID) is an important task that requires to
retrieve a person's images from an image dataset, given one image of the person
of interest. For learning robust person features, the pose variation of person
images is one of the key challenges. Existing works targeting the problem
either perform human alignment, or learn human-region-based representations.
Extra pose information and computational cost is generally required for
inference. To solve this issue, a Feature Distilling Generative Adversarial
Network (FD-GAN) is proposed for learning identity-related and pose-unrelated
representations. It is a novel framework based on a Siamese structure with
multiple novel discriminators on human poses and identities. In addition to the
discriminators, a novel same-pose loss is also integrated, which requires
appearance of a same person's generated images to be similar. After learning
pose-unrelated person features with pose guidance, no auxiliary pose
information and additional computational cost is required during testing. Our
proposed FD-GAN achieves state-of-the-art performance on three person reID
datasets, which demonstrates that the effectiveness and robust feature
distilling capability of the proposed FD-GAN.
Scaling All-Goals Updates in Reinforcement Learning Using Convolutional Neural Networks
Authors: Fabio Pardo, Vitaly Levdik, Petar Kormushev
Date: 2018-10-06 00:00:00
Proceeding: ICLR 2019 5
Tasks: All, Montezuma's Revenge, Navigate, Q-Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), SNES Games, Sokoban

Being able to reach any desired location in the environment can be a valuable asset for an agent. Learning a policy to navigate between all pairs of states individually is often not feasible. An all-goals updating algorithm uses each transition to learn Q-values towards all goals simultaneously and off-policy. However the expensive numerous updates in parallel limited the approach to small tabular cases so far. To tackle this problem we propose to use convolutional network architectures to generate Q-values and updates for a large number of goals at once. We demonstrate the accuracy and generalization qualities of the proposed method on randomly generated mazes and Sokoban puzzles. In the case of on-screen goal coordinates the resulting mapping from frames to distance-maps directly informs the agent about which places are reachable and in how many steps. As an example of application we show that replacing the random actions in epsilon-greedy exploration by several actions towards feasible goals generates better exploratory trajectories on Montezuma's Revenge and Super Mario All-Stars games.
Adaptive Geo-Topological Independence Criterion
Authors: Baihan Lin, Nikolaus Kriegeskorte
Date: 2018-10-06 00:00:00

Testing two potentially multivariate variables for statistical dependence on the basis finite samples is a fundamental statistical challenge. Here we explore a family of tests that adapt to the complexity of the relationship between the variables, promising robust power across scenarios. Building on the distance correlation, we introduce a family of adaptive independence criteria based on nonlinear monotonic transformations of distances. We show that these criteria, like the distance correlation and RKHS-based criteria, provide dependence indicators. We propose a class of adaptive (multi-threshold) test statistics, which form the basis for permutation tests. These tests empirically outperform some of the established tests in average and worst-case statistical sensitivity across a range of univariate and multivariate relationships, offer useful insights to the data and may deserve further exploration.
Actor-Attention-Critic for Multi-Agent Reinforcement Learning
Authors: Shariq Iqbal, Fei Sha
Date: 2018-10-05 00:00:00
Proceeding: ICLR 2019 5
Tasks: Multi-agent Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Reinforcement learning in multi-agent scenarios is important for real-world applications but presents challenges beyond those seen in single-agent settings. We present an actor-critic algorithm that trains decentralized policies in multi-agent settings, using centrally computed critics that share an attention mechanism which selects relevant information for each agent at every timestep. This attention mechanism enables more effective and scalable learning in complex multi-agent environments, when compared to recent approaches. Our approach is applicable not only to cooperative settings with shared rewards, but also individualized reward settings, including adversarial settings, as well as settings that do not provide global states, and it makes no assumptions about the action spaces of the agents. As such, it is flexible enough to be applied to most multi-agent learning problems.
On the Art and Science of Machine Learning Explanations
Authors: Patrick Hall
Date: 2018-10-05 00:00:00
Tasks: BIG-bench Machine Learning

This text discusses several popular explanatory methods that go beyond the error measurements and plots traditionally used to assess machine learning models. Some of the explanatory methods are accepted tools of the trade while others are rigorously derived and backed by long-standing theory. The methods, decision tree surrogate models, individual conditional expectation (ICE) plots, local interpretable model-agnostic explanations (LIME), partial dependence plots, and Shapley explanations, vary in terms of scope, fidelity, and suitable application domain. Along with descriptions of these methods, this text presents real-world usage recommendations supported by a use case and public, in-depth software examples for reproducibility.
Network Distance Based on Laplacian Flows on Graphs
Authors: Dianbin Bao, Kisung You, Lizhen Lin
Date: 2018-10-05 00:00:00
Tasks: Clustering, Dimensionality Reduction

Distance plays a fundamental role in measuring similarity between objects.
Various visualization techniques and learning tasks in statistics and machine
learning such as shape matching, classification, dimension reduction and
clustering often rely on some distance or similarity measure. It is of
tremendous importance to have a distance that can incorporate the underlying
structure of the object. In this paper, we focus on proposing such a distance
between network objects. Our key insight is to define a distance based on the
long term diffusion behavior of the whole network. We first introduce a dynamic
system on graphs called Laplacian flow. Based on this Laplacian flow, a new
version of diffusion distance between networks is proposed. We will demonstrate
the utility of the distance and its advantage over various existing distances
through explicit examples. The distance is also applied to subsequent learning
tasks such as clustering network objects.
CDF Transform-and-Shift: An effective way to deal with datasets of inhomogeneous cluster densities
Authors: Ye Zhu, Kai Ming Ting, Mark Carman, Maia Angelova
Date: 2018-10-05 00:00:00
Tasks: Anomaly Detection, Clustering, Clustering Algorithms Evaluation

The problem of inhomogeneous cluster densities has been a long-standing issue for distance-based and density-based algorithms in clustering and anomaly detection. These algorithms implicitly assume that all clusters have approximately the same density. As a result, they often exhibit a bias towards dense clusters in the presence of sparse clusters. Many remedies have been suggested; yet, we show that they are partial solutions which do not address the issue satisfactorily. To match the implicit assumption, we propose to transform a given dataset such that the transformed clusters have approximately the same density while all regions of locally low density become globally low density -- homogenising cluster density while preserving the cluster structure of the dataset. We show that this can be achieved by using a new multi-dimensional Cumulative Distribution Function in a transform-and-shift method. The method can be applied to every dataset, before the dataset is used in many existing algorithms to match their implicit assumption without algorithmic modification. We show that the proposed method performs better than existing remedies.
Interval Estimation of Individual-Level Causal Effects Under Unobserved Confounding
Authors: Nathan Kallus, Xiaojie Mao, Angela Zhou
Date: 2018-10-05 00:00:00

We study the problem of learning conditional average treatment effects (CATE)
from observational data with unobserved confounders. The CATE function maps
baseline covariates to individual causal effect predictions and is key for
personalized assessments. Recent work has focused on how to learn CATE under
unconfoundedness, i.e., when there are no unobserved confounders. Since CATE
may not be identified when unconfoundedness is violated, we develop a
functional interval estimator that predicts bounds on the individual causal
effects under realistic violations of unconfoundedness. Our estimator takes the
form of a weighted kernel estimator with weights that vary adversarially. We
prove that our estimator is sharp in that it converges exactly to the tightest
bounds possible on CATE when there may be unobserved confounders. Further, we
study personalized decision rules derived from our estimator and prove that
they achieve optimal minimax regret asymptotically. We assess our approach in a
simulation study as well as demonstrate its application in the case of hormone
replacement therapy by comparing conclusions from a real observational study
and clinical trial.
Entity Tracking Improves Cloze-style Reading Comprehension
Authors: Luong Hoang, Sam Wiseman, Alexander M. Rush
Date: 2018-10-05 00:00:00
Proceeding: EMNLP 2018 10
Tasks: LAMBADA, Reading Comprehension

Reading comprehension tasks test the ability of models to process long-term
context and remember salient information. Recent work has shown that relatively
simple neural methods such as the Attention Sum-Reader can perform well on
these tasks; however, these systems still significantly trail human
performance. Analysis suggests that many of the remaining hard instances are
related to the inability to track entity-references throughout documents. This
work focuses on these hard entity tracking cases with two extensions: (1)
additional entity features, and (2) training with a multi-task tracking
objective. We show that these simple modifications improve performance both
independently and in combination, and we outperform the previous state of the
art on the LAMBADA dataset, particularly on difficult entity examples.
Scalable Micro-planned Generation of Discourse from Structured Data
Authors: Anirban Laha, Parag Jain, Abhijit Mishra, Karthik Sankaranarayanan
Date: 2018-10-05 00:00:00
Proceeding: CL 2019 12
Tasks: Knowledge Graphs, Sentence, Text Generation

We present a framework for generating natural language description from structured data such as tables; the problem comes under the category of data-to-text natural language generation (NLG). Modern data-to-text NLG systems typically employ end-to-end statistical and neural architectures that learn from a limited amount of task-specific labeled data, and therefore, exhibit limited scalability, domain-adaptability, and interpretability. Unlike these systems, ours is a modular, pipeline-based approach, and does not require task-specific parallel data. It rather relies on monolingual corpora and basic off-the-shelf NLP tools. This makes our system more scalable and easily adaptable to newer domains. Our system employs a 3-staged pipeline that: (i) converts entries in the structured data to canonical form, (ii) generates simple sentences for each atomic entry in the canonicalized representation, and (iii) combines the sentences to produce a coherent, fluent and adequate paragraph description through sentence compounding and co-reference replacement modules. Experiments on a benchmark mixed-domain dataset curated for paragraph description from tables reveals the superiority of our system over existing data-to-text approaches. We also demonstrate the robustness of our system in accepting other popular datasets covering diverse data types such as Knowledge Graphs and Key-Value maps.
Physics Guided Recurrent Neural Networks For Modeling Dynamical Systems: Application to Monitoring Water Temperature And Quality In Lakes
Authors: Xiaowei Jia, Anuj Karpatne, Jared Willard, Michael Steinbach, Jordan Read, Paul C Hanson, Hilary A Dugan, Vipin Kumar
Date: 2018-10-05 00:00:00
Tasks: scientific discovery

In this paper, we introduce a novel framework for combining scientific
knowledge within physics-based models and recurrent neural networks to advance
scientific discovery in many dynamical systems. We will first describe the use
of outputs from physics-based models in learning a hybrid-physics-data model.
Then, we further incorporate physical knowledge in real-world dynamical systems
as additional constraints for training recurrent neural networks. We will apply
this approach on modeling lake temperature and quality where we take into
account the physical constraints along both the depth dimension and time
dimension. By using scientific knowledge to guide the construction and learning
the data-driven model, we demonstrate that this method can achieve better
prediction accuracy as well as scientific consistency of results.
Adaptive Clinical Trials: Exploiting Sequential Patient Recruitment and Allocation
Authors: Onur Atan, William R. Zame, Mihaela van der Schaar
Date: 2018-10-05 00:00:00

Randomized Controlled Trials (RCTs) are the gold standard for comparing the
effectiveness of a new treatment to the current one (the control). Most RCTs
allocate the patients to the treatment group and the control group by uniform
randomization. We show that this procedure can be highly sub-optimal (in terms
of learning) if -- as is often the case -- patients can be recruited in cohorts
(rather than all at once), the effects on each cohort can be observed before
recruiting the next cohort, and the effects are heterogeneous across
identifiable subgroups of patients. We formulate the patient allocation problem
as a finite stage Markov Decision Process in which the objective is to minimize
a given weighted combination of type-I and type-II errors. Because finding the
exact solution to this Markov Decision Process is computationally intractable,
we propose an algorithm -- \textit{Knowledge Gradient for Randomized Controlled
Trials} (RCT-KG) -- that yields an approximate solution. We illustrate our
algorithm on a synthetic dataset with Bernoulli outcomes and compare it with
uniform randomization. For a given size of trial our method achieves
significant reduction in error, and to achieve a prescribed level of confidence
(in identifying whether the treatment is superior to the control), our method
requires many fewer patients. Our approach uses what has been learned from the
effects on previous cohorts to recruit patients to subgroups and allocate
patients (to treatment/control) within subgroups in a way that promotes more
efficient learning.
A New Method for the Semantic Integration of Multiple OWL Ontologies using Alignments
Authors: Inès Osman
Date: 2018-10-05 00:00:00

This work is done as part of a master's thesis project. The goal is to
integrate two or more ontologies (of the same or close domains) in a new
consistent and coherent OWL ontology to insure semantic interoperability
between them. To do this, we have chosen to create a bridge ontology that
includes all source ontologies and their bridging axioms in a customized way.
In addition, we introduced a new criterion for obtaining an ontology of better
quality (having the minimum of semantic/logical conflicts). We have also
proposed new terminology and definitions that clarify the unclear and misplaced
"integration" and "merging" notions that are randomly used in state-of-the-art
works. Finally, we tested and evaluated our OIA2R tool using ontologies and
reference alignments of the OAEI campaign. It turned out that it is generic,
efficient and powerful enough.
Artificial Intelligence Assisted Power Grid Hardening in Response to Extreme Weather Events
Authors: Rozhin Eskandarpour, Amin Khodaei, A. Paaso, N. M. Abdullah
Date: 2018-10-05 00:00:00

In this paper, an artificial intelligence based grid hardening model is
proposed with the objective of improving power grid resilience in response to
extreme weather events. At first, a machine learning model is proposed to
predict the component states (either operational or outage) in response to the
extreme event. Then, these predictions are fed into a hardening model, which
determines strategic locations for placement of distributed generation (DG)
units. In contrast to existing literature in hardening and resilience
enhancement, this paper co-optimizes grid economic and resilience objectives by
considering the intricate dependencies of the two. The numerical simulations on
the standard IEEE 118-bus test system illustrate the merits and applicability
of the proposed hardening model. The results indicate that the proposed
hardening model through decentralized and distributed local energy resources
can produce a more robust solution that can protect the system significantly
against multiple component outages due to an extreme event.
Generic Model-Agnostic Convolutional Neural Network for Single Image Dehazing
Authors: Zheng Liu, Botao Xiao, Muhammad Alrabeiah, Keyan Wang, Jun Chen
Date: 2018-10-05 00:00:00
Tasks: Image Dehazing, Single Image Dehazing

Haze and smog are among the most common environmental factors impacting image quality and, therefore, image analysis. This paper proposes an end-to-end generative method for image dehazing. It is based on designing a fully convolutional neural network to recognize haze structures in input images and restore clear, haze-free images. The proposed method is agnostic in the sense that it does not explore the atmosphere scattering model. Somewhat surprisingly, it achieves superior performance relative to all existing state-of-the-art methods for image dehazing even on SOTS outdoor images, which are synthesized using the atmosphere scattering model. Project detail and code can be found here: https://github.com/Seanforfun/GMAN_Net_Haze_Removal
Learning to Encode Text as Human-Readable Summaries using Generative Adversarial Networks
Authors: Yau-Shian Wang, Hung-Yi Lee
Date: 2018-10-05 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Abstractive Text Summarization

Auto-encoders compress input data into a latent-space representation and
reconstruct the original data from the representation. This latent
representation is not easily interpreted by humans. In this paper, we propose
training an auto-encoder that encodes input text into human-readable sentences,
and unpaired abstractive summarization is thereby achieved. The auto-encoder is
composed of a generator and a reconstructor. The generator encodes the input
text into a shorter word sequence, and the reconstructor recovers the generator
input from the generator output. To make the generator output human-readable, a
discriminator restricts the output of the generator to resemble human-written
sentences. By taking the generator output as the summary of the input text,
abstractive summarization is achieved without document-summary pairs as
training data. Promising results are shown on both English and Chinese corpora.
Deep Generative Video Compression
Authors: Jun Han, Salvator Lombardo, Christopher Schroers, Stephan Mandt
Date: 2018-10-05 00:00:00
Proceeding: NeurIPS 2019 12
Tasks: Diversity, Image Compression, Temporal Sequences, Video Compression

The usage of deep generative models for image compression has led to impressive performance gains over classical codecs while neural video compression is still in its infancy. Here, we propose an end-to-end, deep generative modeling approach to compress temporal sequences with a focus on video. Our approach builds upon variational autoencoder (VAE) models for sequential data and combines them with recent work on neural image compression. The approach jointly learns to transform the original sequence into a lower-dimensional representation as well as to discretize and entropy code this representation according to predictions of the sequential VAE. Rate-distortion evaluations on small videos from public data sets with varying complexity and diversity show that our model yields competitive results when trained on generic video content. Extreme compression performance is achieved when training the model on specialized content.