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07/24/2023
Predicting Ordinary Differential Equations with Transformers
We develop a transformer-based sequence-to-sequence model that recovers ...
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11/05/2022
Discovering ordinary differential equations that govern time-series
Natural laws are often described through differential equations yet find...
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01/25/2022
Explanatory Learning: Beyond Empiricism in Neural Networks
We introduce Explanatory Learning (EL), a framework to let machines use ...
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06/11/2021
Neural Symbolic Regression that Scales
Symbolic equations are at the core of scientific discovery. The task of ...
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09/01/2020
Learning explanations that are hard to vary
In this paper, we investigate the principle that `good explanations are ...
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04/23/2020
Divide-and-Conquer Monte Carlo Tree Search For Goal-Directed Planning
Standard planners for sequential decision making (including Monte Carlo ...
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12/03/2018
Generalization in anti-causal learning
The ability to learn and act in novel situations is still a prerogative ...
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08/14/2018
Adaptive Skip Intervals: Temporal Abstraction for Recurrent Dynamical Models
We introduce a method which enables a recurrent dynamics model to be tem...
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12/04/2017
Learning Independent Causal Mechanisms
Independent causal mechanisms are a central concept in the study of caus...
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06/08/2017
Avoiding Discrimination through Causal Reasoning
Recent work on fairness in machine learning has focused on various stati...
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03/07/2017
Convolutional Recurrent Neural Networks for Bird Audio Detection
Bird sounds possess distinctive spectral structure which may exhibit sma...
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04/04/2016
