1997 - Fellow of the Association for the Advancement of Artificial Intelligence (AAAI) For contributions to the fields of knowledge representation, reasoning, search and planning.
1957 - Fellow of John Simon Guggenheim Memorial Foundation
David McAllester mostly deals with Artificial intelligence, Algorithm, Theoretical computer science, Mathematical optimization and Machine learning. His studies examine the connections between Artificial intelligence and genetics, as well as such issues in Computer vision, with regards to False positive rate. The study incorporates disciplines such as Pascal and Bootstrapping in addition to Machine learning.
In his study, which falls under the umbrella issue of Object detection, Histogram and Pruning is strongly linked to Pattern recognition. His research integrates issues of Probabilistic latent semantic analysis and Grammar in his study of Discriminative model. His work deals with themes such as Cognitive neuroscience of visual object recognition, Linear discriminant analysis and Latent variable, which intersect with Probabilistic latent semantic analysis.
His main research concerns Artificial intelligence, Algorithm, Theoretical computer science, Inference and Computer vision. His biological study spans a wide range of topics, including Machine learning, Pattern recognition and Natural language processing. His Algorithm research incorporates themes from Discrete mathematics, Viterbi algorithm, Markov chain and Logic programming.
His Theoretical computer science study incorporates themes from Programming language, Set and Backtracking. The various areas that David McAllester examines in his Object detection study include Pascal and Image segmentation. His study focuses on the intersection of Discriminative model and fields such as Probabilistic latent semantic analysis with connections in the field of Latent variable.
David McAllester mainly investigates Artificial intelligence, Natural language processing, Reading comprehension, Isomorphism and Machine learning. His study in Artificial intelligence is interdisciplinary in nature, drawing from both Predicate, Computer vision and Pattern recognition. David McAllester studies Pattern recognition, focusing on Discriminative model in particular.
David McAllester interconnects Iterative method, Latent variable, Support vector machine and Object detection in the investigation of issues within Discriminative model. His Isomorphism study deals with Type theory intersecting with Rule of inference, Natural number and Theoretical computer science. His Machine learning research incorporates themes from Domain and Contextual image classification.
His primary areas of investigation include Artificial intelligence, Natural language processing, Mathematical optimization, Mutual information and Work. His work deals with themes such as Contrast, Variety and Computer vision, which intersect with Artificial intelligence. His work on Optical flow, Epipolar geometry and Monocular as part of general Computer vision study is frequently linked to Flow estimation, therefore connecting diverse disciplines of science.
His Natural language processing research integrates issues from Simple and Fraction.
This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.
Object Detection with Discriminatively Trained Part-Based Models
P F Felzenszwalb;R B Girshick;D McAllester;D Ramanan.
IEEE Transactions on Pattern Analysis and Machine Intelligence (2010)
Policy Gradient Methods for Reinforcement Learning with Function Approximation
Richard S Sutton;David A. McAllester;Satinder P. Singh;Yishay Mansour.
neural information processing systems (1999)
A discriminatively trained, multiscale, deformable part model
P. Felzenszwalb;D. McAllester;D. Ramanan.
computer vision and pattern recognition (2008)
Cascade object detection with deformable part models
Pedro F. Felzenszwalb;Ross B. Girshick;David McAllester.
computer vision and pattern recognition (2010)
Systematic nonlinear planning
David McAllester;David Rosenblitt.
national conference on artificial intelligence (1991)
Exploring Generalization in Deep Learning
Behnam Neyshabur;Srinadh Bhojanapalli;David McAllester;Nathan Srebro.
neural information processing systems (2017)
Evidence for invariants in local search
David McAllester;Bart Selman;Henry Kautz.
national conference on artificial intelligence (1997)
F. Benhamou;D. McAllester;P. van Hentenryck.
international conference on logic programming (1994)
Encoding plans in propositional logic
Henry A. Kautz;David A. McAllester;Bart Selman.
principles of knowledge representation and reasoning (1996)
Some PAC-Bayesian theorems
David A. McAllester.
conference on learning theory (1998)
If you think any of the details on this page are incorrect, let us know.
We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below: