Search Results for author:
Found 5 papers, 2 papers with code
NNSmith: Generating Diverse and Valid Test Cases for Deep Learning Compilers
In this work, we propose a new fuzz testing approach for finding bugs in deep-learning compilers.
Measuring the Effect of Training Data on Deep Learning Predictions via Randomized Experiments
Our algorithm estimates the AME, a quantity that measures the expected (average) marginal effect of adding a data point to a subset of the training data, sampled from a given distribution.
Hop: Heterogeneity-Aware Decentralized Training
Based on a unique characteristic of decentralized training that we have identified, the iteration gap, we propose a queue-based synchronization mechanism that can efficiently implement backup workers and bounded staleness in the decentralized setting.
New Characterizations and Efficient Local Search for General Integer Linear Programming
We propose a new local search framework that switches between three modes, namely Search, Improve, and Restore modes.
RAAMove: A Corpus for Analyzing Moves in Research Article Abstracts
In this paper, we introduce RAAMove, a comprehensive multi-domain corpus dedicated to the annotation of move structures in RA abstracts.
