88 citations · 92 across the 2 of their papers we have counts for
4 papers
New Characterizations and Efficient Local Search for General Integer Linear Programming
Peng Lin, Shaowei Cai, Mengchuan Zou +1
Integer linear programming (ILP) models a wide range of practical combinatorial optimization problems and significantly impacts industry and management sectors. This work proposes…
NNSmith: Generating Diverse and Valid Test Cases for Deep Learning Compilers
Jiawei Liu, Jinkun Lin, Fabian Ruffy +4
Deep-learning (DL) compilers such as TVM and TensorRT are increasingly being used to optimize deep neural network (DNN) models to meet performance, resource utilization and other r…
Measuring the Effect of Training Data on Deep Learning Predictions via Randomized Experiments
Jinkun Lin, Anqi Zhang, Mathias Lecuyer +3
We develop a new, principled algorithm for estimating the contribution of training data points to the behavior of a deep learning model, such as a specific prediction it makes. Our…
Hop: Heterogeneity-Aware Decentralized Training
Qinyi Luo, Jinkun Lin, Youwei Zhuo +1
Recent work has shown that decentralized algorithms can deliver superior performance over centralized ones in the context of machine learning. The two approaches, with the main dif…