165 citations · 336 across the 5 of their papers we have counts for
5 papers
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…
Variational Deep Embedding: An Unsupervised and Generative Approach to Clustering
Zhuxi Jiang, Yin Zheng, Huachun Tan +2
Clustering is among the most fundamental tasks in computer vision and machine learning. In this paper, we propose Variational Deep Embedding (VaDE), a novel unsupervised generative…
Neural Autoregressive Collaborative Filtering for Implicit Feedback
Yin Zheng, Cailiang Liu, Bangsheng Tang +1
This paper proposes implicit CF-NADE, a neural autoregressive model for collaborative filtering tasks using implicit feedback ( e.g. click, watch, browse behaviors). We first conve…
A Neural Autoregressive Approach to Collaborative Filtering
Yin Zheng, Bangsheng Tang, Wenkui Ding +1
This paper proposes CF-NADE, a neural autoregressive architecture for collaborative filtering (CF) tasks, which is inspired by the Restricted Boltzmann Machine (RBM) based CF model…
Width-parameterized SAT: Time-Space Tradeoffs
Shiteng Chen, Tiancheng Lou, Periklis Papakonstantinou +1
Width parameterizations of SAT, such as tree-width and path-width, enable the study of computationally more tractable and practical SAT instances. We give two simple algorithms. On…