176 citations · 277 across the 5 of their papers we have counts for
6 papers
A Berkeley View of Systems Challenges for AI
Ion Stoica, Dawn Song, Raluca Ada Popa +11
With the increasing commoditization of computer vision, speech recognition and machine translation systems and the widespread deployment of learning-based back-end technologies suc…
Shift: A Zero FLOP, Zero Parameter Alternative to Spatial Convolutions
Bichen Wu, Alvin Wan, Xiangyu Yue +6
Neural networks rely on convolutions to aggregate spatial information. However, spatial convolutions are expensive in terms of model size and computation, both of which grow quadra…
Composing Meta-Policies for Autonomous Driving Using Hierarchical Deep Reinforcement Learning
Richard Liaw, Sanjay Krishnan, Animesh Garg +3
Rather than learning new control policies for each new task, it is possible, when tasks share some structure, to compose a "meta-policy" from previously learned policies. This pape…
Hemingway: Modeling Distributed Optimization Algorithms
Xinghao Pan, Shivaram Venkataraman, Zizheng Tai +1
Distributed optimization algorithms are widely used in many industrial machine learning applications. However choosing the appropriate algorithm and cluster size is often difficult…
Scalable Linear Causal Inference for Irregularly Sampled Time Series with Long Range Dependencies
Francois W. Belletti, Evan R. Sparks, Michael J. Franklin +2
Linear causal analysis is central to a wide range of important application spanning finance, the physical sciences, and engineering. Much of the existing literature in linear causa…
GraphLab: A Distributed Framework for Machine Learning in the Cloud
Yucheng Low, Joseph Gonzalez, Aapo Kyrola +2
Machine Learning (ML) techniques are indispensable in a wide range of fields. Unfortunately, the exponential increase of dataset sizes are rapidly extending the runtime of sequenti…