Kevin Lin

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berkeley college ∙ University of Washington ∙ Allen Institute for Artificial Intelligence

Cross-Domain Complementary Learning with Synthetic Data for Multi-Person Part Segmentation

The success of supervised deep learning depends on the training labels. ...
07/11/2019 ∙ by Kevin Lin, et al. ∙ 1 ∙ share

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Optimization for Compressed Sensing: the Simplex Method and Kronecker Sparsification

In this paper we present two new approaches to efficiently solve large-s...
12/16/2013 ∙ by Robert Vanderbei, et al. ∙ 0 ∙ share

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Supervised Learning of Semantics-Preserving Hash via Deep Convolutional Neural Networks

This paper presents a simple yet effective supervised deep hash approach...
07/01/2015 ∙ by Huei-Fang Yang, et al. ∙ 0 ∙ share

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Adversarial Ranking for Language Generation

Generative adversarial networks (GANs) have great successes on synthesiz...
05/31/2017 ∙ by Kevin Lin, et al. ∙ 0 ∙ share

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Adversarial Learning for Fine-grained Image Search

Fine-grained image search is still a challenging problem due to the diff...
07/06/2018 ∙ by Kevin Lin, et al. ∙ 0 ∙ share

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DeepBase: Deep Inspection of Neural Networks

Although deep learning models perform remarkably across a range of tasks...
08/13/2018 ∙ by Thibault Sellam, et al. ∙ 0 ∙ share

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Post-Selection Inference for Changepoint Detection Algorithms with Application to Copy Number Variation Data

Changepoint detection methods are used in many areas of science and engi...
12/10/2018 ∙ by Sangwon Hyun, et al. ∙ 0 ∙ share

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Covariance-based sample selection for heterogenous data: Applications to gene expression and autism risk gene detection

Risk for autism can be influenced by genetic mutations in hundreds of ge...
12/19/2018 ∙ by Kevin Lin, et al. ∙ 0 ∙ share

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Subgoals, Problem Solving Phases, and Sources of Knowledge: A Complex Mangle

Educational researchers have increasingly drawn attention to how student...
01/05/2019 ∙ by Kevin Lin, et al. ∙ 0 ∙ share

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Grammar-based Neural Text-to-SQL Generation

The sequence-to-sequence paradigm employed by neural text-to-SQL models ...
05/30/2019 ∙ by Kevin Lin, et al. ∙ 0 ∙ share

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Reasoning Over Paragraph Effects in Situations

A key component of successfully reading a passage of text is the ability...
08/16/2019 ∙ by Kevin Lin, et al. ∙ 0 ∙ share

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Statistically and Computationally Efficient Change Point Localization in Regression Settings

Detecting when the underlying distribution changes from the observed tim...
06/26/2019 ∙ by Daren Wang, et al. ∙ 0 ∙ share

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QuaRTz: An Open-Domain Dataset of Qualitative Relationship Questions

We introduce the first open-domain dataset, called QuaRTz, for reasoning...
09/08/2019 ∙ by Oyvind Tafjord, et al. ∙ 0 ∙ share

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Neural Module Networks for Reasoning over Text

Answering compositional questions that require multiple steps of reasoni...
12/10/2019 ∙ by Nitish Gupta, et al. ∙ 0 ∙ share

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Kevin Lin

Featured Co-authors

Han Liu

Xiaodong He

Fan Yang

Dan Roth

Jonathan Berant

Sameer Singh

Carl Vondrick

Rebecca Willett

Matt Gardner

Peter Clark

Ryan J. Tibshirani

Cross-Domain Complementary Learning with Synthetic Data for Multi-Person Part Segmentation

Optimization for Compressed Sensing: the Simplex Method and Kronecker Sparsification

Supervised Learning of Semantics-Preserving Hash via Deep Convolutional Neural Networks

Adversarial Ranking for Language Generation

Adversarial Learning for Fine-grained Image Search

DeepBase: Deep Inspection of Neural Networks

Post-Selection Inference for Changepoint Detection Algorithms with Application to Copy Number Variation Data

Covariance-based sample selection for heterogenous data: Applications to gene expression and autism risk gene detection

Subgoals, Problem Solving Phases, and Sources of Knowledge: A Complex Mangle

Grammar-based Neural Text-to-SQL Generation

Reasoning Over Paragraph Effects in Situations

Statistically and Computationally Efficient Change Point Localization in Regression Settings

QuaRTz: An Open-Domain Dataset of Qualitative Relationship Questions

Neural Module Networks for Reasoning over Text