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Found 7 papers, 2 papers with code
Bayesian Neural Networks with Domain Knowledge Priors
Bayesian neural networks (BNNs) have recently gained popularity due to their ability to quantify model uncertainty.
Spectrally Transformed Kernel Regression
Unlabeled data is a key component of modern machine learning.
Learning with Explanation Constraints
In this paper, we formalize this notion as learning from explanation constraints and provide a learning theoretic framework to analyze how such explanations can improve the learning of our models.
Nash Equilibria and Pitfalls of Adversarial Training in Adversarial Robustness Games
Adversarial training is a standard technique for training adversarially robust models.
Label Propagation with Weak Supervision
Semi-supervised learning and weakly supervised learning are important paradigms that aim to reduce the growing demand for labeled data in current machine learning applications.
Sharp asymptotics on the compression of two-layer neural networks
In this paper, we study the compression of a target two-layer neural network with N nodes into a compressed network with M<N nodes.
Improving Transformation Invariance in Contrastive Representation Learning
We propose methods to strengthen the invariance properties of representations obtained by contrastive learning.
