D. George

发表

Letter to the Editor: Research Priorities for Robust and Beneficial Artificial Intelligence: An Open Letter

Thomas G. Dietterich, Eric Horvitz, Francesca Rossi, 2015, AI Mag..

Hierarchical Temporal Memory Concepts , Theory , and Terminology

D. George, J. Hawkins, 2006 .

Schema Networks: Zero-shot Transfer with a Generative Causal Model of Intuitive Physics pdf

Dileep George, Tom Silver, Miguel Lázaro-Gredilla, 2017, ICML.

A generative vision model that trains with high data efficiency and breaks text-based CAPTCHAs

Bhaskara Marthi, Wolfgang Lehrach, Dileep George, 2017, Science.

Invariant Pattern Recognition using Bayesian Inference on Hierarchical Sequences

D. George, Dileep George, 2004 .

2 Short-term Research Priorities 2 . 1 Optimizing AI ’ s Economic Impact

D. Hassabis, D. Parkes, L. Kaelbling, 2022 .

Cortical Microcircuits from a Generative Vision Model

J. S. Guntupalli, David A. Mély, Nick J. Hay, 2018, bioRxiv.

Explaining Visual Cortex Phenomena using Recursive Cortical Network

J. S. Guntupalli, D. George, M. Lázaro-Gredilla, 2018, bioRxiv.

Learning attention-controllable border-ownership for objectness inference and binding

D. George, M. Lázaro-Gredilla, A. Dedieu, 2021, bioRxiv.

Different clones for different contexts: Hippocampal cognitive maps as higher-order graphs of a cloned HMM

J. S. Guntupalli, D. George, M. Lázaro-Gredilla, 2019, bioRxiv.

PGMax: Factor Graphs for Discrete Probabilistic Graphical Models and Loopy Belief Propagation in JAX

D. George, Nishanth Kumar, Guangyao Zhou, 2022, ArXiv.

Interactive learning of cognitive programs

D. George, M. Lázaro-Gredilla, Sunayana Rane, 2020 .

Space is a latent sequence: Structured sequence learning as a unified theory of representation in the hippocampus

J. S. Guntupalli, Rajkumar Vasudeva Raju, D. George, 2022, 2212.01508.

PushWorld: A benchmark for manipulation planning with tools and movable obstacles

D. George, M. Lázaro-Gredilla, Ken Kansky, 2023, ArXiv.

Behavior Is Everything: Towards Representing Concepts with Sensorimotor Contingencies

Tom Silver, Dileep George, D. Scott Phoenix, 2018, AAAI.

Teaching Compositionality to CNNs

Yi Liu, Hua-Yan Wang, Dileep George, 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

Explaining Visual Cortex Phenomena using Recursive Cortical Network

Miguel Lazaro-Gredilla, Wolfgang Lehrach, Dileep George, 2018, bioRxiv.

A detailed mathematical theory of thalamic and cortical microcircuits based on inference in a generative vision model

Wolfgang Lehrach, Dileep George, Miguel Lázaro-Gredilla, 2020, bioRxiv.

From CAPTCHA to Commonsense: How Brain Can Teach Us About Artificial Intelligence

Dileep George, Miguel Lázaro-Gredilla, J. S. Guntupalli, 2020, Frontiers in Computational Neuroscience.

Sequence memory for prediction, inference and behaviour

Dileep George, Jeff Hawkins, Jamie Niemasik, 2009, Philosophical Transactions of the Royal Society B: Biological Sciences.

Learning cognitive maps as structured graphs for vicarious evaluation

Dileep George, Miguel Lázaro-Gredilla, J. S. Guntupalli, 2019, bioRxiv.

Towards a Mathematical Theory of Cortical Micro-circuits

Dileep George, Jeff Hawkins, D. George, 2009, PLoS Comput. Biol..

Fast exploration and learning of latent graphs with aliased observations

D. George, M. Lázaro-Gredilla, Ishani Deshpande, 2023, ArXiv.

Beyond imitation: Zero-shot task transfer on robots by learning concepts as cognitive programs

Dileep George, J. S. Guntupalli, Dianhuan Lin, 2018, Science Robotics.

How the brain might work: a hierarchical and temporal model for learning and recognition

Dileep George, D. George, 2008 .

A backward pass through a CNN using a generative model of its activations pdf

Yi Liu, Hua-Yan Wang, Dileep George, 2016, ArXiv.

Graph schemas as abstractions for transfer learning, inference, and planning

J. S. Guntupalli, Rajkumar Vasudeva Raju, Daniel P. Sawyer, 2023, ArXiv.

Neuroscience needs Network Science

Thomas E. Nichols, István A. Kovács, Dániel L. Barabási, 2023, ArXiv.

3D Neural Embedding Likelihood for Robust Probabilistic Inverse Graphics

Vikash K. Mansinghka, J. Tenenbaum, D. George, 2023, 2302.03744.

Clone-structured graph representations enable flexible learning and vicarious evaluation of cognitive maps

J. S. Guntupalli, D. George, M. Lázaro-Gredilla, 2021, Nature Communications.

Computing with inter-spike interval codes in networks of integrate and fire neurons

Friedrich T. Sommer, Dileep George, F. Sommer, 2005, Neurocomputing.

Inducing Biological Models from Temporal Gene Expression Data

Kazumi Saito, Stephen D. Bay, Jeff Shrager, 2003, Discovery Science.

Discovering Ecosystem Models from Time-Series Data

Kazumi Saito, Pat Langley, Stephen D. Bay, 2003, Discovery Science.

PushWorld: A benchmark for manipulation planning with tools and movable obstacles

D. George, M. Lázaro-Gredilla, Ken Kansky, 2023, ArXiv.

Learning cognitive maps for vicarious evaluation

Miguel Lazaro-Gredilla, Dileep George, J. S. Guntupalli, 2019 .

Query Training: Learning a Worse Model to Infer Better Marginals in Undirected Graphical Models with Hidden Variables

Wolfgang Lehrach, Dileep George, Antoine Dedieu, 2020, AAAI.

A hierarchical Bayesian model of invariant pattern recognition in the visual cortex

D. George, J. Hawkins, D. George, 2005, Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005..

Schema Networks: Zero-shot Transfer with a Generative Causal Model of Intuitive Physics

David A. Mély, Szymon Sidor, D. George, 2017, ICML.

Schema-learning and rebinding as mechanisms of in-context learning and emergence

Rajkumar Vasudeva Raju, M. Shanahan, D. George, 2023, NeurIPS.

Learning higher-order sequential structure with cloned HMMs pdf

Wolfgang Lehrach, Miguel Lázaro-Gredilla, Dileep George, 2019, ArXiv.