Search Results for author:
Found 5 papers, 2 papers with code
Bidirectional Reactive Programming for Machine Learning
Reactive languages are dedicated to the programming of systems which interact continuously and concurrently with their environment.
The Next 700 ML-Enabled Compiler Optimizations
There is a growing interest in enhancing compiler optimizations with ML models, yet interactions between compilers and ML frameworks remain challenging.
RL4ReAl: Reinforcement Learning for Register Allocation
We aim to automate decades of research and experience in register allocation, leveraging machine learning.
Hierarchical Reinforcement Learning
Multi-agent Reinforcement Learning
+2
MLIR: A Compiler Infrastructure for the End of Moore's Law
This work presents MLIR, a novel approach to building reusable and extensible compiler infrastructure.
Tensor Comprehensions: Framework-Agnostic High-Performance Machine Learning Abstractions
Deep learning models with convolutional and recurrent networks are now ubiquitous and analyze massive amounts of audio, image, video, text and graph data, with applications in automatic translation, speech-to-text, scene understanding, ranking user preferences, ad placement, etc.
