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Found 6 papers, 2 papers with code
Improved Group Robustness via Classifier Retraining on Independent Splits
Given a limited amount of group labels at training time, Just Train Twice (Liu et al., 2021), or JTT in short, is a two-stage method that infers a pseudo group label for every unlabeled example first, then applies group DRO based on the inferred group labels.
Linear Constraints Learning for Spiking Neurons
In the ubiquitous Iris and MNIST datasets, our algorithm achieves competitive predictive performance with state-of-the-art approaches.
Multi-view Audio and Music Classification
The learned embedding in the subnetworks are then concatenated to form the multi-view embedding for classification similar to a simple concatenation network.
Fair and Optimal Cohort Selection for Linear Utilities
The rise of algorithmic decision-making has created an explosion of research around the fairness of those algorithms.
Constraints on Hebbian and STDP learned weights of a spiking neuron
In the case of pure Hebbian learning, we find that the normalised weights equal the promotion probabilities of weights up to correction terms that depend on the learning rate and are usually small.
Self-Attention Generative Adversarial Network for Speech Enhancement
Existing generative adversarial networks (GANs) for speech enhancement solely rely on the convolution operation, which may obscure temporal dependencies across the sequence input.
