78 citations · 187 across the 21 of their papers we have counts for
5 papers · 1 filter
Analyzing a Caching Model
Leon Sixt, Evan Zheran Liu, Marie Pellat +4
Machine Learning has been successfully applied in systems applications such as memory prefetching and caching, where learned models have been shown to outperform heuristics. Howeve…
Data-Driven Offline Optimization For Architecting Hardware Accelerators
Aviral Kumar, Amir Yazdanbakhsh, Milad Hashemi +2
Industry has gradually moved towards application-specific hardware accelerators in order to attain higher efficiency. While such a paradigm shift is already starting to show promis…
Apollo: Transferable Architecture Exploration
Amir Yazdanbakhsh, Christof Angermueller, Berkin Akin +7
The looming end of Moore's Law and ascending use of deep learning drives the design of custom accelerators that are optimized for specific neural architectures. Architecture explor…
Two Sides of the Same Coin: Heterophily and Oversmoothing in Graph Convolutional Neural Networks
Yujun Yan, Milad Hashemi, Kevin Swersky +2
In node classification tasks, graph convolutional neural networks (GCNs) have demonstrated competitive performance over traditional methods on diverse graph data. However, it is kn…
Oops I Took A Gradient: Scalable Sampling for Discrete Distributions
Will Grathwohl, Kevin Swersky, Milad Hashemi +2
We propose a general and scalable approximate sampling strategy for probabilistic models with discrete variables. Our approach uses gradients of the likelihood function with respec…