Search Results for author: Jonathan Malmaud

Found 8 papers, 5 papers with code

Measuring The Impact Of Programming Language Distribution

1 code implementation3 Feb 2023 Gabriel Orlanski, Kefan Xiao, Xavier Garcia, Jeffrey Hui, Joshua Howland, Jonathan Malmaud, Jacob Austin, Rishabh Singh, Michele Catasta

Training a model on a balanced corpus results in, on average, 12. 34% higher $pass@k$ across all tasks and languages compared to the baseline.

Code Translation Translation

Pareto-Optimal Quantized ResNet Is Mostly 4-bit

4 code implementations7 May 2021 Amirali Abdolrashidi, Lisa Wang, Shivani Agrawal, Jonathan Malmaud, Oleg Rybakov, Chas Leichner, Lukasz Lew

In this work, we use ResNet as a case study to systematically investigate the effects of quantization on inference compute cost-quality tradeoff curves.

Quantization

Bridging Information-Seeking Human Gaze and Machine Reading Comprehension

no code implementations CONLL 2020 Jonathan Malmaud, Roger Levy, Yevgeni Berzak

In this work, we analyze how human gaze during reading comprehension is conditioned on the given reading comprehension question, and whether this signal can be beneficial for machine reading comprehension.

Machine Reading Comprehension Multiple-choice +1

STARC: Structured Annotations for Reading Comprehension

1 code implementation ACL 2020 Yevgeni Berzak, Jonathan Malmaud, Roger Levy

We present STARC (Structured Annotations for Reading Comprehension), a new annotation framework for assessing reading comprehension with multiple choice questions.

Multiple-choice Reading Comprehension

What's Cookin'? Interpreting Cooking Videos using Text, Speech and Vision

1 code implementation5 Mar 2015 Jonathan Malmaud, Jonathan Huang, Vivek Rathod, Nick Johnston, Andrew Rabinovich, Kevin Murphy

We present a novel method for aligning a sequence of instructions to a video of someone carrying out a task.

Keyword Spotting

ClusterCluster: Parallel Markov Chain Monte Carlo for Dirichlet Process Mixtures

no code implementations8 Apr 2013 Dan Lovell, Jonathan Malmaud, Ryan P. Adams, Vikash K. Mansinghka

Applied to mixture modeling, our approach enables the Dirichlet process to simultaneously learn clusters that describe the data and superclusters that define the granularity of parallelization.

Density Estimation Time Series +1

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