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05/01/2023
Learning Controllable Adaptive Simulation for Multi-resolution Physics
Simulating the time evolution of physical systems is pivotal in many sci...
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07/04/2022
ViRel: Unsupervised Visual Relations Discovery with Graph-level Analogy
Visual relations form the basis of understanding our compositional world...
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06/30/2022
ZeroC: A Neuro-Symbolic Model for Zero-shot Concept Recognition and Acquisition at Inference Time
Humans have the remarkable ability to recognize and acquire novel visual...
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06/15/2022
Learning to Accelerate Partial Differential Equations via Latent Global Evolution
Simulating the time evolution of Partial Differential Equations (PDEs) o...
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06/15/2022
Learning Large-scale Subsurface Simulations with a Hybrid Graph Network Simulator
Subsurface simulations use computational models to predict the flow of f...
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10/24/2020
Graph Information Bottleneck
Representation learning of graph-structured data is challenging because ...
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06/18/2020
AI Feynman 2.0: Pareto-optimal symbolic regression exploiting graph modularity
We present an improved method for symbolic regression that seeks to fit ...
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01/11/2020
Intelligence, physics and information – the tradeoff between accuracy and simplicity in machine learning
How can we enable machines to make sense of the world, and become better...
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01/07/2020
Discovering Nonlinear Relations with Minimum Predictive Information Regularization
Identifying the underlying directional relations from observational time...
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01/07/2020
Phase Transitions for the Information Bottleneck in Representation Learning
In the Information Bottleneck (IB), when tuning the relative strength be...
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08/23/2019
Pareto-optimal data compression for binary classification tasks
The goal of lossy data compression is to reduce the storage cost of a da...
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07/17/2019
Learnability for the Information Bottleneck
The Information Bottleneck (IB) method (tishby2000information) provides ...
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10/24/2018
Toward an AI Physicist for Unsupervised Learning
We investigate opportunities and challenges for improving unsupervised m...
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07/26/2018
Meta-learning autoencoders for few-shot prediction
Compared to humans, machine learning models generally require significan...
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05/04/2017
