Dileep George

Li Yang Ku

Kevin P. Murphy

CoRR, 2024

Learning Cognitive Maps from Transformer Representations for Efficient Planning in Partially Observed Environments.

[BibT_eX]

[DOI]

Proceedings of the Forty-first International Conference on Machine Learning, 2024

2023

Fast exploration and learning of latent graphs with aliased observations.

[BibT_eX]

[DOI]

Sivaramakrishnan Swaminathan

Ishan Deshpande

Meet Dave

CoRR, 2023

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

[BibT_eX]

[DOI]

Rajkumar Vasudeva Raju

CoRR, 2023

3D Neural Embedding Likelihood for Robust Sim-to-Real Transfer in Inverse Graphics.

[BibT_eX]

[DOI]

Vikash K. Mansinghka

CoRR, 2023

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

[BibT_eX]

[DOI]

Sivaramakrishnan Swaminathan

CoRR, 2023

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

[BibT_eX]

[DOI]

Rajkumar Vasudeva Raju

Murray Shanahan

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Learning Noisy OR Bayesian Networks with Max-Product Belief Propagation.

[BibT_eX]

[DOI]

Guangyao Zhou

Proceedings of the International Conference on Machine Learning, 2023

3D Neural Embedding Likelihood: Probabilistic Inverse Graphics for Robust 6D Pose Estimation.

[BibT_eX]

[DOI]

Vikash K. Mansinghka

Proceedings of the IEEE/CVF International Conference on Computer Vision, 2023

2022

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

[BibT_eX]

[DOI]

Guangyao Zhou

Nishanth Kumar

Shrinu Kushagra

CoRR, 2022

DURableVS: Data-efficient Unsupervised Recalibrating Visual Servoing via online learning in a structured generative model.

[BibT_eX]

[DOI]

Nishad Gothoskar

Proceedings of the 2022 International Conference on Robotics and Automation, 2022

2021

Graphical Models with Attention for Context-Specific Independence and an Application to Perceptual Grouping.

[BibT_eX]

[DOI]

Guangyao Zhou

Wolfgang Lehrach

CoRR, 2021

Perturb-and-max-product: Sampling and learning in discrete energy-based models.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

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

[BibT_eX]

[DOI]

Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence, 2021

Sample-Efficient L0-L2 Constrained Structure Learning of Sparse Ising Models.

[BibT_eX]

[DOI]

Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence, 2021

2020

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

[BibT_eX]

[DOI]

Frontiers Comput. Neurosci., 2020

Query Training: Learning and inference for directed and undirected graphical models.

[BibT_eX]

[DOI]

CoRR, 2020

From proprioception to long-horizon planning in novel environments: A hierarchical RL model.

[BibT_eX]

[DOI]

Nishad Gothoskar

CoRR, 2020

Learning a generative model for robot control using visual feedback.

[BibT_eX]

[DOI]

Nishad Gothoskar

Abhishek Agarwal

Yasemin Bekiroglu

CoRR, 2020

A Model of Fast Concept Inference with Object-Factorized Cognitive Programs.

[BibT_eX]

[DOI]

Daniel P. Sawyer

Proceedings of the 42th Annual Meeting of the Cognitive Science Society, 2020

2019

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

[BibT_eX]

[DOI]

Dianhuan Lin

Sci. Robotics, 2019

Learning undirected models via query training.

[BibT_eX]

[DOI]

Wolfgang Lehrach

CoRR, 2019

What can the brain teach us about building artificial intelligence?

[BibT_eX]

[DOI]

CoRR, 2019

Learning higher-order sequential structure with cloned HMMs.

[BibT_eX]

[DOI]

CoRR, 2019

2018

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

[BibT_eX]

[DOI]

Dianhuan Lin

CoRR, 2018

Cortical Microcircuits from a Generative Vision Model.

[BibT_eX]

[DOI]

Alexander Lavin

David A. Mély

Nick Hay

CoRR, 2018

Behavior Is Everything: Towards Representing Concepts with Sensorimotor Contingencies.

[BibT_eX]

[DOI]

Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, 2018

2017

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

[BibT_eX]

[DOI]

Proceedings of the 34th International Conference on Machine Learning, 2017

Teaching Compositionality to CNNs.

[BibT_eX]

[DOI]

Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition, 2017

2016

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

[BibT_eX]

[DOI]

CoRR, 2016

Hierarchical compositional feature learning.

[BibT_eX]

[DOI]

Yi Liu

D. Scott Phoenix

CoRR, 2016

Generative Shape Models: Joint Text Recognition and Segmentation with Very Little Training Data.

[BibT_eX]

[DOI]

Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

2015

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

[BibT_eX]

[DOI]

AI Mag., 2015

2009

Towards a Mathematical Theory of Cortical Micro-circuits.

[BibT_eX]

[DOI]

Jeff Hawkins

PLoS Comput. Biol., 2009

How to make computers that work like the brain.

[BibT_eX]

[DOI]

Proceedings of the 46th Design Automation Conference, 2009

2006

Hierarchical Temporal Memory: Theory and Applications.

[BibT_eX]

[DOI]

Proceedings of the 28th International Conference of the IEEE Engineering in Medicine and Biology Society, 2006

2005

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

[BibT_eX]

[DOI]