Discovering Ecosystem Models from Time-Series Data. - Dileep George; Kazumi Saito; Pat Langley; Stephen D. Bay; Kevin R. Arrigo (Discovery Science, 2003)

Inducing Biological Models from Temporal Gene Expression Data. - Kazumi Saito; Dileep George; Stephen D. Bay; Jeff Shrager (Discovery Science, 2003)

Robust Induction of Process Models from Time-Series Data. - Pat Langley; Dileep George; Stephen D. Bay; Kazumi Saito (ICML, 2003)

A hierarchical Bayesian model of invariant pattern recognition in the visual cortex. - Dileep George; Jeff Hawkins (IJCNN, 2005)

Computing with inter-spike interval codes in networks of integrate and fire neurons. - Dileep George; Friedrich T. Sommer (Neurocomputing, 2005)

Hierarchical Temporal Memory: Theory and Applications. - Dileep George (EMBC (Supplement), 2006)

How to make computers that work like the brain. - Dileep George (DAC, 2009)

Towards a Mathematical Theory of Cortical Micro-circuits. - Dileep George; Jeff Hawkins (PLoS Comput. Biol., 2009)

Letter to the Editor: Research Priorities for Robust and Beneficial Artificial Intelligence: An Open Letter. - Stuart Russell 001; Tom Dietterich; Eric Horvitz; Bart Selman; Francesca Rossi 001; Demis Hassabis; Shane Legg; Mustafa Suleyman; Dileep George; D. Scott Phoenix (AI Mag., 2015)

Generative Shape Models: Joint Text Recognition and Segmentation with Very Little Training Data. - Xinghua Lou; Ken Kansky; Wolfgang Lehrach; C. C. Laan; Bhaskara Marthi; D. Scott Phoenix; Dileep George (NIPS, 2016)

Hierarchical compositional feature learning. - Miguel Lázaro-Gredilla; Yi Liu 055; D. Scott Phoenix; Dileep George (CoRR, 2016)

A backward pass through a CNN using a generative model of its activations. - Hua-Yan Wang; Anna Chen; Yi Liu 055; Dileep George; D. Scott Phoenix (CoRR, 2016)

Teaching Compositionality to CNNs. - Austin Stone; Hua-Yan Wang; Michael Stark 003; Yi Liu 055; D. Scott Phoenix; Dileep George (CVPR, 2017)

Schema Networks: Zero-shot Transfer with a Generative Causal Model of Intuitive Physics. - Ken Kansky; Tom Silver; David A. Mély; Mohamed Eldawy; Miguel Lázaro-Gredilla; Xinghua Lou; Nimrod Dorfman; Szymon Sidor; D. Scott Phoenix; Dileep George (ICML, 2017)

Behavior Is Everything: Towards Representing Concepts with Sensorimotor Contingencies. - Nicholas Hay; Michael Stark 003; Alexander Schlegel; Carter Wendelken; Dennis Park; Eric Purdy; Tom Silver; D. Scott Phoenix; Dileep George (AAAI, 2018)

Cortical Microcircuits from a Generative Vision Model. - Dileep George; Alexander Lavin; J. Swaroop Guntupalli; David A. Mély; Nick Hay; Miguel Lázaro-Gredilla (CoRR, 2018)

Beyond imitation: Zero-shot task transfer on robots by learning concepts as cognitive programs. - Miguel Lázaro-Gredilla; Dianhuan Lin; J. Swaroop Guntupalli; Dileep George (CoRR, 2018)

Learning higher-order sequential structure with cloned HMMs. - Antoine Dedieu; Nishad Gothoskar; Scott Swingle; Wolfgang Lehrach; Miguel Lázaro-Gredilla; Dileep George (CoRR, 2019)

What can the brain teach us about building artificial intelligence? - Dileep George (CoRR, 2019)

Learning undirected models via query training. - Miguel Lázaro-Gredilla; Wolfgang Lehrach; Dileep George (CoRR, 2019)

Beyond imitation: Zero-shot task transfer on robots by learning concepts as cognitive programs. - Dianhuan Lin; J. Swaroop Guntupalli; Dileep George (Sci. Robotics, 2019)

A Model of Fast Concept Inference with Object-Factorized Cognitive Programs. - Daniel P. Sawyer; Miguel Lázaro-Gredilla; Dileep George (CogSci, 2020)

Learning a generative model for robot control using visual feedback. - Nishad Gothoskar; Miguel Lázaro-Gredilla; Abhishek Agarwal; Yasemin Bekiroglu; Dileep George (CoRR, 2020)

From proprioception to long-horizon planning in novel environments: A hierarchical RL model. - Nishad Gothoskar; Miguel Lázaro-Gredilla; Dileep George (CoRR, 2020)

Query Training: Learning and inference for directed and undirected graphical models. - Miguel Lázaro-Gredilla; Wolfgang Lehrach; Nishad Gothoskar; Guangyao Zhou; Antoine Dedieu; Dileep George (CoRR, 2020)

From CAPTCHA to Commonsense: How Brain Can Teach Us About Artificial Intelligence. - Dileep George; Miguel Lázaro-Gredilla; J. Swaroop Guntupalli (Frontiers Comput. Neurosci., 2020)

Sample-Efficient L0-L2 Constrained Structure Learning of Sparse Ising Models. - Antoine Dedieu; Miguel Lázaro-Gredilla; Dileep George (AAAI, 2021)

Query Training: Learning a Worse Model to Infer Better Marginals in Undirected Graphical Models with Hidden Variables. - Miguel Lázaro-Gredilla; Wolfgang Lehrach; Nishad Gothoskar; Guangyao Zhou; Antoine Dedieu; Dileep George (AAAI, 2021)

Perturb-and-max-product: Sampling and learning in discrete energy-based models. - Miguel Lázaro-Gredilla; Antoine Dedieu; Dileep George (NeurIPS, 2021)

Graphical Models with Attention for Context-Specific Independence and an Application to Perceptual Grouping. - Guangyao Zhou; Wolfgang Lehrach; Antoine Dedieu; Miguel Lázaro-Gredilla; Dileep George (CoRR, 2021)

DURableVS: Data-efficient Unsupervised Recalibrating Visual Servoing via online learning in a structured generative model. - Nishad Gothoskar; Miguel Lázaro-Gredilla; Yasemin Bekiroglu; Abhishek Agarwal; Joshua B. Tenenbaum; Vikash K. Mansinghka; Dileep George (ICRA, 2022)

PGMax: Factor Graphs for Discrete Probabilistic Graphical Models and Loopy Belief Propagation in JAX. - Guangyao Zhou; Nishanth Kumar; Miguel Lázaro-Gredilla; Shrinu Kushagra; Dileep George (CoRR, 2022)

3D Neural Embedding Likelihood: Probabilistic Inverse Graphics for Robust 6D Pose Estimation. - Guangyao Zhou; Nishad Gothoskar; Lirui Wang; Joshua B. Tenenbaum; Dan Gutfreund; Miguel Lázaro-Gredilla; Dileep George; Vikash K. Mansinghka (ICCV, 2023)

Learning Noisy OR Bayesian Networks with Max-Product Belief Propagation. - Antoine Dedieu; Guangyao Zhou; Dileep George; Miguel Lázaro-Gredilla (ICML, 2023)

Schema-learning and rebinding as mechanisms of in-context learning and emergence. - Sivaramakrishnan Swaminathan; Antoine Dedieu; Rajkumar Vasudeva Raju; Murray Shanahan; Miguel Lázaro-Gredilla; Dileep George (NeurIPS, 2023)

PushWorld: A benchmark for manipulation planning with tools and movable obstacles. - Ken Kansky; Skanda Vaidyanath; Scott Swingle; Xinghua Lou; Miguel Lázaro-Gredilla; Dileep George (CoRR, 2023)

3D Neural Embedding Likelihood for Robust Sim-to-Real Transfer in Inverse Graphics. - Guangyao Zhou; Nishad Gothoskar; Lirui Wang; Joshua B. Tenenbaum; Dan Gutfreund; Miguel Lázaro-Gredilla; Dileep George; Vikash K. Mansinghka (CoRR, 2023)

Graph schemas as abstractions for transfer learning, inference, and planning. - J. Swaroop Guntupalli; Rajkumar Vasudeva Raju; Shrinu Kushagra; Carter Wendelken; Danny Sawyer; Ishan Deshpande; Guangyao Zhou; Miguel Lázaro-Gredilla; Dileep George (CoRR, 2023)

Fast exploration and learning of latent graphs with aliased observations. - Miguel Lázaro-Gredilla; Ishan Deshpande; Sivaramakrishnan Swaminathan; Meet Dave; Dileep George (CoRR, 2023)

Learning Cognitive Maps from Transformer Representations for Efficient Planning in Partially Observed Environments. - Antoine Dedieu; Wolfgang Lehrach; Guangyao Zhou; Dileep George; Miguel Lázaro-Gredilla (ICML, 2024)

What type of inference is planning? - Miguel Lázaro-Gredilla; Li Yang Ku; Kevin P. Murphy; Dileep George (NeurIPS, 2024)