◍wovepaper
SearchResearchersInstitutions
Sign in
researcher

John Canny

4 papers here

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

author position
  • middle author1
  • last author3

Across the 4 of 4 papers where every author was matched, so the position is known.

fields
  • cs.LG2
  • cs.CV1
  • cs.HC1
ORCID 0000-0002-7161-7927

identity via Semantic Scholar / OpenAlex

most citedRisk Averse Robust Adversarial Reinforcement Learning

11 citations · 33 across the 4 of their papers we have counts for

collaborators

4 papers

cs.HC2019★ 8 cited

Sketchforme: Composing Sketched Scenes from Text Descriptions for Interactive Applications

Forrest Huang, John F. Canny

Sketching and natural languages are effective communication media for interactive applications. We introduce Sketchforme, the first neural-network-based system that can generate sk…

cs.LG2019★ 11 cited

Risk Averse Robust Adversarial Reinforcement Learning

Xinlei Pan, Daniel Seita, Yang Gao +1

Deep reinforcement learning has recently made significant progress in solving computer games and robotic control tasks. A known problem, though, is that policies overfit to the tra…

cs.CV2019★ 5 cited

Periphery-Fovea Multi-Resolution Driving Model guided by Human Attention

Ye Xia, Jinkyu Kim, John Canny +2

Inspired by human vision, we propose a new periphery-fovea multi-resolution driving model that predicts vehicle speed from dash camera videos. The peripheral vision module of the m…

cs.LG2014★ 9 cited

SAME but Different: Fast and High-Quality Gibbs Parameter Estimation

Huasha Zhao, Biye Jiang, John Canny

Gibbs sampling is a workhorse for Bayesian inference but has several limitations when used for parameter estimation, and is often much slower than non-sampling inference methods. S…

◍wovepaper

Papers, researchers and institutions, woven together.

Explore
  • Search
  • Researchers
  • Institutions
Account
  • Library
  • Chat
Data
  • arXiv.org
  • Semantic Scholar
  • OpenAlex
  • Latest RSS
AboutContactPrivacyDevelopersllms.txtopenapi.json
Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.