Search Results for author:
Found 4 papers, 4 papers with code
Few-Max: Few-Shot Domain Adaptation for Unsupervised Contrastive Representation Learning
Pretraining on a large source data set and fine-tuning on the target samples is prone to overfitting in the few-shot regime, where only a small number of target samples are available.
Hyperbolic Graph Embedding with Enhanced Semi-Implicit Variational Inference
We show that the existing semi-implicit variational inference objective provably reduces information in the observed graph.
Long Short-Term Memory Spiking Networks and Their Applications
The developed architecture and method for backpropagation within LSTM-based SNNs enable them to learn long-term dependencies with comparable results to conventional LSTMs.
Learning Representations by Maximizing Mutual Information in Variational Autoencoders
Variational autoencoders (VAEs) have ushered in a new era of unsupervised learning methods for complex distributions.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
