1 citations · 1 across the 2 of their papers we have counts for
3 papers
cs.PL2026
DLCB: Ahead-of-Time Compilation for Dynamic Deep Learning
Alexander Collins, Bin Fan, Evghenii Gaburov +4
Deep learning workloads are increasingly deployed in settings where tensor shapes are not fully known at compile time. Batch sizes vary across requests, sequence lengths differ bet…
cs.PL2022★ 1 cited
Axon: A Language for Dynamic Shapes in Deep Learning Graphs
Alexander Collins, Vinod Grover
Axon is a language that enables shape and rank inference for tensors in a Deep Learning graphs. It aims to make shapes implicit and inferred, in a similar manner to how types are i…
cs.PL2020
Probabilistic Programming with CuPPL
Alexander Collins, Vinod Grover
Probabilistic Programming Languages (PPLs) are a powerful tool in machine learning, allowing highly expressive generative models to be expressed succinctly. They couple complex inf…