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researcher

Joseph E. Gonzalez

UC Berkeley

80 papers hereh-index 6840.8k citations221 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • middle author54
  • last author24

Across the 78 of 80 papers where every author was matched, so the position is known.

fields
  • cs.LG30
  • cs.CV21
  • cs.DC9
  • cs.RO9
  • cs.CL3
  • cs.AI2
affiliations
  • UC Berkeley
Homepage
same name
  • Joseph E. Gonzalez — 28 papers
  • Joseph E. Gonzalez — 3 papers

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20112022
most citedA Berkeley View of Systems Challenges for AI

176 citations · 699 across the 41 of their papers we have counts for

collaborators
Showing 2017Show all

4 papers · 1 filter

cs.AI2017★ 176 cited

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…

cs.CV2017★ 41 cited

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…

cs.AI2017★ 17 cited

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…

cs.DC2017★ 22 cited

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…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.