71 citations · 107 across the 4 of their papers we have counts for
4 papers
LightLDA: Big Topic Models on Modest Compute Clusters
Jinhui Yuan, Fei Gao, Qirong Ho +6
When building large-scale machine learning (ML) programs, such as big topic models or deep neural nets, one usually assumes such tasks can only be attempted with industrial-sized c…
Model-Parallel Inference for Big Topic Models
Xun Zheng, Jin Kyu Kim, Qirong Ho +1
In real world industrial applications of topic modeling, the ability to capture gigantic conceptual space by learning an ultra-high dimensional topical representation, i.e., the so…
High-Performance Distributed ML at Scale through Parameter Server Consistency Models
Wei Dai, Abhimanu Kumar, Jinliang Wei +3
As Machine Learning (ML) applications increase in data size and model complexity, practitioners turn to distributed clusters to satisfy the increased computational and memory deman…
Primitives for Dynamic Big Model Parallelism
Seunghak Lee, Jin Kyu Kim, Xun Zheng +3
When training large machine learning models with many variables or parameters, a single machine is often inadequate since the model may be too large to fit in memory, while trainin…