181 citations · 377 across the 15 of their papers we have counts for
16 papers
PyTorch FSDP: Experiences on Scaling Fully Sharded Data Parallel
Yanli Zhao, Andrew Gu, Rohan Varma +15
It is widely acknowledged that large models have the potential to deliver superior performance across a broad range of domains. Despite the remarkable progress made in the field of…
BenchDirect: A Directed Language Model for Compiler Benchmarks
Foivos Tsimpourlas, Pavlos Petoumenos, Min Xu +4
The exponential increase of hardware-software complexity has made it impossible for compiler engineers to find the right optimization heuristics manually. Predictive models have be…
BenchPress: A Deep Active Benchmark Generator
Foivos Tsimpourlas, Pavlos Petoumenos, Min Xu +4
We develop BenchPress, the first ML benchmark generator for compilers that is steerable within feature space representations of source code. BenchPress synthesizes compiling functi…
Deep Active Learning with Noise Stability
Xingjian Li, Pengkun Yang, Yangcheng Gu +4
Uncertainty estimation for unlabeled data is crucial to active learning. With a deep neural network employed as the backbone model, the data selection process is highly challenging…
Boosting Active Learning via Improving Test Performance
Tianyang Wang, Xingjian Li, Pengkun Yang +5
Central to active learning (AL) is what data should be selected for annotation. Existing works attempt to select highly uncertain or informative data for annotation. Nevertheless,…
Objects in Semantic Topology
Shuo Yang, Peize Sun, Yi Jiang +6
A more realistic object detection paradigm, Open-World Object Detection, has arisen increasing research interests in the community recently. A qualified open-world object detector…