19 citations · 56 across the 12 of their papers we have counts for
3 papers · 1 filter
Designing Counterfactual Generators using Deep Model Inversion
Jayaraman J. Thiagarajan, Vivek Narayanaswamy, Deepta Rajan +3
Explanation techniques that synthesize small, interpretable changes to a given image while producing desired changes in the model prediction have become popular for introspecting b…
First-Generation Inference Accelerator Deployment at Facebook
Michael Anderson, Benny Chen, Stephen Chen +112
In this paper, we provide a deep dive into the deployment of inference accelerators at Facebook. Many of our ML workloads have unique characteristics, such as sparse memory accesse…
Training Stacked Denoising Autoencoders for Representation Learning
Jason Liang, Keith Kelly
We implement stacked denoising autoencoders, a class of neural networks that are capable of learning powerful representations of high dimensional data. We describe stochastic gradi…