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Found 4 papers, 4 papers with code
Learning from Integral Losses in Physics Informed Neural Networks
Our numerical results confirm the existence of the aforementioned bias in practice, and also show that our proposed delayed target approach can lead to accurate solutions with comparable quality to ones estimated with a large number of samples.
Robust Model-Based Optimization for Challenging Fitness Landscapes
Protein design, a grand challenge of the day, involves optimization on a fitness landscape, and leading methods adopt a model-based approach where a model is trained on a training set (protein sequences and fitness) and proposes candidates to explore next.
Truly Deterministic Policy Optimization
In this paper, we present a policy gradient method that avoids exploratory noise injection and performs policy search over the deterministic landscape.
On the Importance of Firth Bias Reduction in Few-Shot Classification
In this work, we demonstrate the effectiveness of Firth bias reduction in few-shot classification.
