Sameer Singh

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University of Massachusetts Amherst ∙ University of California, Irvine ∙ University of Illinois at Urbana-Champaign ∙ University of Washington

Assistant Professor at UC Irvine since 2016, Postdoctoral Research Associate at University of Washington from 2013-2016, Research Assistant at University of Massachusetts Amherst from 2008-2013, Research Intern at Microsoft 2012, Research Intern at Google 2010

Featured Co-authors

Anima Anandkumar
95 publications
Noah A. Smith
72 publications
Honglak Lee
72 publications
Andrew McCallum
70 publications
Dan Roth
68 publications
Luke Zettlemoyer
66 publications
Sebastian Riedel
63 publications
Hannaneh Hajishirzi
50 publications
Jonathan Berant
46 publications
Kevin Gimpel
43 publications
Yang Chen
43 publications

Image Augmentations for GAN Training

Data augmentations have been widely studied to improve the accuracy and ...
06/04/2020 ∙ by Zhengli Zhao, et al. ∙ 18 ∙ share

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Beyond Accuracy: Behavioral Testing of NLP models with CheckList

Although measuring held-out accuracy has been the primary approach to ev...
05/08/2020 ∙ by Marco Tulio Ribeiro, et al. ∙ 0 ∙ share

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Obtaining Faithful Interpretations from Compositional Neural Networks

Neural module networks (NMNs) are a popular approach for modeling compos...
05/02/2020 ∙ by Sanjay Subramanian, et al. ∙ 22 ∙ share

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Evaluating NLP Models via Contrast Sets

Standard test sets for supervised learning evaluate in-distribution gene...
04/06/2020 ∙ by Matt Gardner, et al. ∙ 0 ∙ share

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Improved Consistency Regularization for GANs

Recent work has increased the performance of Generative Adversarial Netw...
02/11/2020 ∙ by Zhengli Zhao, et al. ∙ 38 ∙ share

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ORB: An Open Reading Benchmark for Comprehensive Evaluation of Machine Reading Comprehension

Reading comprehension is one of the crucial tasks for furthering researc...
12/29/2019 ∙ by Dheeru Dua, et al. ∙ 0 ∙ share

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Explain Your Move: Understanding Agent Actions Using Focused Feature Saliency

As deep reinforcement learning (RL) is applied to more tasks, there is a...
12/23/2019 ∙ by Piyush Gupta, et al. ∙ 10 ∙ 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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How can we fool LIME and SHAP? Adversarial Attacks on Post hoc Explanation Methods

As machine learning black boxes are increasingly being deployed in domai...
11/06/2019 ∙ by Dylan Slack, et al. ∙ 20 ∙ share

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Memory Augmented Recursive Neural Networks

Recursive neural networks have shown an impressive performance for model...
11/05/2019 ∙ by Forough Arabshahi, et al. ∙ 18 ∙ share

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Improving Differentially Private Models with Active Learning

Broad adoption of machine learning techniques has increased privacy conc...
10/02/2019 ∙ by Zhengli Zhao, et al. ∙ 26 ∙ share

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AllenNLP Interpret: A Framework for Explaining Predictions of NLP Models

Neural NLP models are increasingly accurate but are imperfect and opaque...
09/19/2019 ∙ by Eric Wallace, et al. ∙ 0 ∙ share

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Do NLP Models Know Numbers? Probing Numeracy in Embeddings

The ability to understand and work with numbers (numeracy) is critical f...
09/17/2019 ∙ by Eric Wallace, et al. ∙ 0 ∙ share

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Knowledge Enhanced Contextual Word Representations

Contextual word representations, typically trained on unstructured, unla...
09/09/2019 ∙ by Matthew E. Peters, et al. ∙ 0 ∙ share

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Universal Adversarial Triggers for Attacking and Analyzing NLP

Adversarial examples highlight model vulnerabilities and are useful for ...
08/20/2019 ∙ by Eric Wallace, et al. ∙ 0 ∙ share

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Universal Adversarial Triggers for NLP

Adversarial examples highlight model vulnerabilities and are useful for ...
08/20/2019 ∙ by Eric Wallace, et al. ∙ 0 ∙ share

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Compositional Questions Do Not Necessitate Multi-hop Reasoning

Multi-hop reading comprehension (RC) questions are challenging because t...
06/07/2019 ∙ by Sewon Min, et al. ∙ 6 ∙ share

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Investigating Robustness and Interpretability of Link Prediction via Adversarial Modifications

Representing entities and relations in an embedding space is a well-stud...
05/02/2019 ∙ by Pouya Pezeshkpour, et al. ∙ 6 ∙ share

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PoMo: Generating Entity-Specific Post-Modifiers in Context

We introduce entity post-modifier generation as an instance of a collabo...
04/05/2019 ∙ by Jun Seok Kang, et al. ∙ 0 ∙ share

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DROP: A Reading Comprehension Benchmark Requiring Discrete Reasoning Over Paragraphs

Reading comprehension has recently seen rapid progress, with systems mat...
03/01/2019 ∙ by Dheeru Dua, et al. ∙ 0 ∙ share

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Embedding Multimodal Relational Data for Knowledge Base Completion

Representing entities and relations in an embedding space is a well-stud...
09/05/2018 ∙ by Pouya Pezeshkpour, et al. ∙ 4 ∙ share

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Interpretation of Natural Language Rules in Conversational Machine Reading

Most work in machine reading focuses on question answering problems wher...
08/28/2018 ∙ by Marzieh Saeidi, et al. ∙ 0 ∙ share

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Compact Factorization of Matrices Using Generalized Round-Rank

Matrix factorization is a well-studied task in machine learning for comp...
05/01/2018 ∙ by Pouya Pezeshkpour, et al. ∙ 0 ∙ share

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Combining Symbolic and Function Evaluation Expressions In Neural Programs

Neural programming involves training neural networks to learn programs f...
01/12/2018 ∙ by Forough Arabshahi, et al. ∙ 0 ∙ share

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Multimodal Attribute Extraction

The broad goal of information extraction is to derive structured informa...
11/29/2017 ∙ by Robert L. Logan IV, et al. ∙ 0 ∙ share

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Generating Natural Adversarial Examples

Due to their complex nature, it is hard to characterize the ways in whic...
10/31/2017 ∙ by Zhengli Zhao, et al. ∙ 0 ∙ share

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Relational Learning and Feature Extraction by Querying over Heterogeneous Information Networks

Many real world systems need to operate on heterogeneous information net...
07/25/2017 ∙ by Parisa Kordjamshidi, et al. ∙ 0 ∙ share

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Programs as Black-Box Explanations

Recent work in model-agnostic explanations of black-box machine learning...
11/22/2016 ∙ by Sameer Singh, et al. ∙ 0 ∙ share

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Nothing Else Matters: Model-Agnostic Explanations By Identifying Prediction Invariance

At the core of interpretable machine learning is the question of whether...
11/17/2016 ∙ by Marco Tulio Ribeiro, et al. ∙ 0 ∙ share

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Model-Agnostic Interpretability of Machine Learning

Understanding why machine learning models behave the way they do empower...
06/16/2016 ∙ by Marco Tulio Ribeiro, et al. ∙ 0 ∙ share

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"Why Should I Trust You?": Explaining the Predictions of Any Classifier

Despite widespread adoption, machine learning models remain mostly black...
02/16/2016 ∙ by Marco Tulio Ribeiro, et al. ∙ 0 ∙ share

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Anytime Belief Propagation Using Sparse Domains

Belief Propagation has been widely used for marginal inference, however ...
11/14/2013 ∙ by Sameer Singh, et al. ∙ 0 ∙ share

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Compiling Relational Database Schemata into Probabilistic Graphical Models

Instead of requiring a domain expert to specify the probabilistic depend...
12/05/2012 ∙ by Sameer Singh, et al. ∙ 0 ∙ share

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Distantly Labeling Data for Large Scale Cross-Document Coreference

Cross-document coreference, the problem of resolving entity mentions acr...
05/24/2010 ∙ by Sameer Singh, et al. ∙ 0 ∙ share

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