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Sep 19, 2018
Computation and Language

In this paper we introduce a crowd-sourced conversations dataset that is grounded in a predefined set of documents which is available for download 4. We perform multiple automatic and human judgment based analysis to understand the value the informa…

A Dataset for Document Grounded Conversations

This paper introduces a document grounded dataset for text conversations. We define "Document Grounded Conversations" as conversations that are about…
Nov 30, 2016
By dolev
Distributed
Parallel
and Cluster Computing

We formalized and explored the notion of stateless computation. Our investigation of stateless computation focused on two important aspects: (1) guaranteeing and verifying self-stabilization of stateless computation systems and (2) analyzing the com…

Stateless Computation

We present and explore a model of stateless and self-stabilizing distributed computation, inspired by real-world applications such as routing on toda…
May 04, 2014
Computation and Language

We have shown that a statistically significant improvement in the performance of NER system for a given language can be obtained when the training data is supplemented with word clusters from a secondary language(s) which is written using the same a…

"Translation can't change a name": Using Multilingual Data for Named …

Named Entities (NEs) are often written with no orthographic changes across different languages that share a common alphabet. We show that this can be…
May 09, 2012
By mossel
Computer Science and Game Theory
Probability

We thank anonymous referees for helpful comments.

A Smooth Transition from Powerlessness to Absolute Power

We study the phase transition of the coalitional manipulation problem for generalized scoring rules. Previously it has been shown that, under some co…
Oct 18, 2010
By ilias
Machine Learning

We have established two hardness results for proper agnostic learning of low-degree PTFs. Our results show that even if there exist low-degree PTFs that are almost perfect hypotheses, it is computationally hard to find low-degree PTF hypotheses that…

Hardness Results for Agnostically Learning Low-Degree Polynomial Th…

Hardness results for maximum agreement problems have close connections to hardness results for proper learning in computational learning theory. In t…
Feb 11, 2018
Machine Learning
Computers and Society

Understanding Convolutional Networks with APPLE : Automatic Patch P…

With the success of deep learning, recent efforts have been focused on analyzing how learned networks make their classifications. We are interested i…
Apr 23, 2018
By srijan
Social and Information Networks
Computation and Language
Computers and Society
Digital Libraries

False Information on Web and Social Media: A Survey

False information can be created and spread easily through the web and social media platforms, resulting in widespread real-world impact. Characteriz…
Mar 05, 2017
Machine Learning
Computation and Language

Using Graphs of Classifiers to Impose Declarative Constraints on Se…

We propose a general approach to modeling semi-supervised learning (SSL) algorithms. Specifically, we present a declarative language for modeling bot…
Sep 30, 2014
Computation and Language
Machine Learning
Neural and Evolutionary Computation

A Deep Learning Approach to Data-driven Parameterizations for Stati…

Nearly all Statistical Parametric Speech Synthesizers today use Mel Cepstral coefficients as the vocal tract parameterization of the speech signal. M…
Jun 24, 2017
Machine Learning

Preserving Intermediate Objectives: One Simple Trick to Improve Learn…

Hierarchical models are utilized in a wide variety of problems which are characterized by task hierarchies, where predictions on smaller subtasks are…
Nov 21, 2017
By gzheng
Machine Learning

Likelihood Almost Free Inference Networks

Variational inference for latent variable models is prevalent in various machine learning problems, typically solved by maximizing the Evidence Lower…
Apr 03, 2014
Machine Learning

Subspace Learning from Extremely Compressed Measurements

We consider learning the principal subspace of a large set of vectors from an extremely small number of compressive measurements of each vector. Our …
May 12, 2016
Computer Vision

Going Deeper into First-Person Activity Recognition

We bring together ideas from recent work on feature design for egocentric action recognition under one framework by exploring the use of deep convolu…
Jul 18, 2011
Computer Science and Game Theory
Artificial Intelligence
Multiagent Systems

Computing Strong Game-Theoretic Strategies in Jotto

We develop a new approach that computes approximate equilibrium strategies in Jotto, a popular word game. Jotto is an extremely large two-player game…
May 29, 2019
By haohanw
Computer Vision

Learning Robust Global Representations by Penalizing Local Predictive…

Despite their renowned predictive power on i.i.d. data, convolutional neural networks are known to rely more on high-frequency patterns that humans d…
Jan 26, 2017
Robotics

Game-Theoretic Modeling of Human Adaptation in Human-Robot Collaborat…

In human-robot teams, humans often start with an inaccurate model of the robot capabilities. As they interact with the robot, they infer the robot's …
Oct 28, 2017
By ruil
Computation and Language

Phase Conductor on Multi-layered Attentions for Machine Comprehension

Attention models have been intensively studied to improve NLP tasks such as machine comprehension via both question-aware passage attention model and…
Apr 12, 2014
By nlao
Artificial Intelligence

Efficient Inference and Learning in a Large Knowledge Base: Reasoning…

One important challenge for probabilistic logics is reasoning with very large knowledge bases (KBs) of imperfect information, such as those produced …
May 28, 2019
By ntopin
Machine Learning
Artificial Intelligence

Generation of Policy-Level Explanations for Reinforcement Learning

Though reinforcement learning has greatly benefited from the incorporation of neural networks, the inability to verify the correctness of such system…
Jun 13, 2016
By pedrom
Computation and Language
Information Retrieval
Social and Information Networks

Graph-Community Detection for Cross-Document Topic Segment Relationsh…

In this paper we propose a graph-community detection approach to identify cross-document relationships at the topic segment level. Given a set of rel…
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