Search Results for author: Adam Fisch

Found 15 papers, 12 papers with code

CapWAP: Image Captioning with a Purpose

no code implementations EMNLP 2020 Adam Fisch, Kenton Lee, Ming-Wei Chang, Jonathan Clark, Regina Barzilay

In this task, we use question-answer (QA) pairs{---}a natural expression of information need{---}from users, instead of reference captions, for both training and post-inference evaluation.

Image Captioning Question Answering +1

Conformal Prediction Sets with Limited False Positives

1 code implementation15 Feb 2022 Adam Fisch, Tal Schuster, Tommi Jaakkola, Regina Barzilay

We propose to trade coverage for a notion of precision by enforcing that the presence of incorrect candidates in the predicted conformal sets (i. e., the total number of false positives) is bounded according to a user-specified tolerance.

Trading Coverage for Precision: Conformal Prediction with Limited False Discoveries

no code implementations29 Sep 2021 Adam Fisch, Tal Schuster, Tommi S. Jaakkola, Regina Barzilay

In this paper, we develop a new approach to conformal prediction in which we aim to output a precise set of promising prediction candidates that is guaranteed to contain a limited number of incorrect answers.

Drug Discovery

Consistent Accelerated Inference via Confident Adaptive Transformers

no code implementations EMNLP 2021 Tal Schuster, Adam Fisch, Tommi Jaakkola, Regina Barzilay

In this work, we present CATs -- Confident Adaptive Transformers -- in which we simultaneously increase computational efficiency, while guaranteeing a specifiable degree of consistency with the original model with high confidence.

Few-shot Conformal Prediction with Auxiliary Tasks

1 code implementation17 Feb 2021 Adam Fisch, Tal Schuster, Tommi Jaakkola, Regina Barzilay

We develop a novel approach to conformal prediction when the target task has limited data available for training.

Drug Discovery Meta-Learning

Making Pre-trained Language Models Better Few-shot Learners

5 code implementations ACL 2021 Tianyu Gao, Adam Fisch, Danqi Chen

We present LM-BFF--better few-shot fine-tuning of language models--a suite of simple and complementary techniques for fine-tuning language models on a small number of annotated examples.

Few-Shot Learning

CapWAP: Captioning with a Purpose

1 code implementation9 Nov 2020 Adam Fisch, Kenton Lee, Ming-Wei Chang, Jonathan H. Clark, Regina Barzilay

In this task, we use question-answer (QA) pairs---a natural expression of information need---from users, instead of reference captions, for both training and post-inference evaluation.

Image Captioning Question Answering +1

Efficient Conformal Prediction via Cascaded Inference with Expanded Admission

1 code implementation ICLR 2021 Adam Fisch, Tal Schuster, Tommi Jaakkola, Regina Barzilay

This set is guaranteed to contain a correct answer with high probability, and is well-suited for many open-ended classification tasks.

Drug Discovery

MRQA 2019 Shared Task: Evaluating Generalization in Reading Comprehension

1 code implementation WS 2019 Adam Fisch, Alon Talmor, Robin Jia, Minjoon Seo, Eunsol Choi, Danqi Chen

We present the results of the Machine Reading for Question Answering (MRQA) 2019 shared task on evaluating the generalization capabilities of reading comprehension systems.

Multi-Task Learning Question Answering +1

StarSpace: Embed All The Things!

2 code implementations12 Sep 2017 Ledell Wu, Adam Fisch, Sumit Chopra, Keith Adams, Antoine Bordes, Jason Weston

A framework for training and evaluating AI models on a variety of openly available dialogue datasets.

Collaborative Filtering Text Classification +1

ParlAI: A Dialog Research Software Platform

18 code implementations EMNLP 2017 Alexander H. Miller, Will Feng, Adam Fisch, Jiasen Lu, Dhruv Batra, Antoine Bordes, Devi Parikh, Jason Weston

We introduce ParlAI (pronounced "par-lay"), an open-source software platform for dialog research implemented in Python, available at http://parl. ai.

reinforcement-learning Visual Question Answering +1

Reading Wikipedia to Answer Open-Domain Questions

9 code implementations ACL 2017 Danqi Chen, Adam Fisch, Jason Weston, Antoine Bordes

This paper proposes to tackle open- domain question answering using Wikipedia as the unique knowledge source: the answer to any factoid question is a text span in a Wikipedia article.

Open-Domain Question Answering Reading Comprehension

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