Dileep George
12 papers · 2016–2024 · 6 venues · across top CS/AI venues
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Research topics
Artificial Intelligence
(11)
Machine Learning
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Bayesian & Probabilistic · Artificial Intelligence
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Core Methods
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Deep Learning
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Core AI
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Optimization & Theory
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Architectures
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Learning Types · Deep Learning
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Computer Vision
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Natural Language Processing
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Bayesian & Probabilistic · Machine Learning
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Learning Types · Machine Learning
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Computer Science
(1)
Mathematics & Optimization
(1)
Keywords
generative model
(3)
probabilistic graphical model
(3)
approximate inference
(2)
variational inference
(2)
markov random field
(2)
belief propagation
(2)
sample complexity
(1)
probabilistic modeling
(1)
image classification
(1)
markov decision process
(1)
uncertainty quantification
(1)
in-context learning
(1)
structure learning
(1)
depth estimation
(1)
feature disentanglement
(1)
object recognition
(1)
instance segmentation
(1)
ising model
(1)
transformer
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bayesian inference
(1)
Papers
Learning Cognitive Maps from Transformer Representations for Efficient Planning in Partially Observed Environments
ICML 2024
PGMax: Factor Graphs for Discrete Probabilistic Graphical Models and Loopy Belief Propagation in JAX
JMLR 2024
What type of inference is planning?
NeurIPS 2024
3D Neural Embedding Likelihood: Probabilistic Inverse Graphics for Robust 6D Pose Estimation
ICCV 2023
Learning Noisy OR Bayesian Networks with Max-Product Belief Propagation
ICML 2023
Schema-learning and rebinding as mechanisms of in-context learning and emergence
NeurIPS 2023
Perturb-and-max-product: Sampling and learning in discrete energy-based models
NeurIPS 2021
Query Training: Learning a Worse Model to Infer Better Marginals in Undirected Graphical Models with Hidden Variables
AAAI 2021
Sample-Efficient L0-L2 Constrained Structure Learning of Sparse Ising Models
AAAI 2021
Schema Networks: Zero-shot Transfer with a Generative Causal Model of Intuitive Physics
ICML 2017
Teaching Compositionality to CNNs
CVPR 2017
Generative Shape Models: Joint Text Recognition and Segmentation with Very Little Training Data
NeurIPS 2016