BayLearn 2021: Poster B-02: What Can I Do Here? Learning New Skills by Imagining Visual Affordances

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BayLearn 2021: Poster B-20: Decomposing Variance from Mini-Batch Order and Parameter Initialization

(4:37)

BayLearn 2021: Poster B-9: ML-CI: Machine Learning Confidence Intervals for Covid-19 forecasts

(5:29)

BayLearn 2021: Poster A-10: Deep Reinforcement Learning at the Edge of the Statistical Precipice

(6:11)

BayLearn 2021: Poster A-04: Offline Reinforcement Learning for Mobile Notifications

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BayLearn 2021: Poster A-11: Accuracy on the Line

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BayLearn 2021: Poster B-15: Evolving Reinforcement Learning Algorithms

(4:37)

Offline Reinforcement Learning: BayLearn 2021 Keynote Talk

(45:14)

BayLearn 2021:Poster B-11:Distributed Full-batch Training of GNNs Using Sequential Rematerialization

(4:4)

BayLearn 2021: Poster A-1: LocoProp: Enhancing BackProp via Local Loss Optimization

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BayLearn 2021: Poster B-12: Understanding and Improving Fairness-Accuracy Trade-offs in Multi-Task..

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BayLearn 2021: Poster B-01: Correspondence between neuroevolution and gradient descent

(4:57)

BayLearn 2021: Poster A-21: The Evolution of Out-of-Distribution RobustnessThroughout Fine-Tuning

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BayLearn 2021: Poster A-19: Interval Deep Learning

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BayLearn 2021: Poster B-03: Convolutional Networks Can Develop Blind Spots

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BayLearn 2021:Poster A-3:A Near-Optimal Method for Minimizing the Maximum of N Convex Loss Functions

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BayLearn 2021: Poster B-16: Sparcle: Spatial Reassignment of spots to cells via maximum likelihood

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