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Object Detection with Discriminatively Trained Part Based Models
- Pedro F. Felzenszwalb, Ross B. Girshick, David A. McAllester, D. Ramanan
- Computer ScienceIEEE Transactions on Pattern Analysis and Machine…
- 1 September 2010
We describe an object detection system based on mixtures of multiscale deformable part models. Our system is able to represent highly variable object classes and achieves state-of-the-art results in…
Policy Gradient Methods for Reinforcement Learning with Function Approximation
- R. Sutton, David A. McAllester, Satinder Singh, Y. Mansour
- Computer ScienceNIPS
- 29 November 1999
TLDR
A discriminatively trained, multiscale, deformable part model
- Pedro F. Felzenszwalb, David A. McAllester, D. Ramanan
- Computer ScienceIEEE Conference on Computer Vision and Pattern…
- 23 June 2008
TLDR
Cascade object detection with deformable part models
- Pedro F. Felzenszwalb, Ross B. Girshick, David A. McAllester
- Computer ScienceIEEE Computer Society Conference on Computer…
- 13 June 2010
TLDR
Resolution Theorem Proving
- L. Bachmair, H. Ganzinger, David A. McAllester, C. Lynch
- Computer ScienceHandbook of Automated Reasoning
- 2001
Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence
- David A. McAllester
- Computer ScienceUAI
- 18 June 2009
Some PAC-Bayesian Theorems
- David A. McAllester
- Computer ScienceCOLT' 98
- 24 July 1998
TLDR
Systematic Nonlinear Planning
- David A. McAllester, David Rosenblitt
- MathematicsAAAI
- 14 July 1991
TLDR
Evidence for Invariants in Local Search
- David A. McAllester, B. Selman, Henry A. Kautz
- Computer ScienceAAAI/IAAI
- 27 July 1997
TLDR
A PAC-Bayesian Approach to Spectrally-Normalized Margin Bounds for Neural Networks
- Behnam Neyshabur, Srinadh Bhojanapalli, David A. McAllester, Nathan Srebro
- Computer Science, MathematicsICLR
- 29 July 2017
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