Semi-supervised learning aims to train a model using limited labels.
Sta...
This paper studies graph-structured prediction for supervised learning o...
Patch-based models, e.g., Vision Transformers (ViTs) and Mixers, have sh...
Trying to capture the sample-label relationship, conditional generative
...
In the deep learning era, long video generation of high-quality still re...
Contrastive self-supervised learning has shown impressive results in lea...
Abstract reasoning, i.e., inferring complicated patterns from given
obse...
Pre-trained language models have achieved state-of-the-art accuracies on...
Recent discoveries on neural network pruning reveal that, with a careful...
Novelty detection, i.e., identifying whether a given sample is drawn fro...
Generative adversarial networks (GANs) have shown outstanding performanc...
Generative adversarial networks (GANs) have shown outstanding performanc...
Magnitude-based pruning is one of the simplest methods for pruning neura...
Conditional generative adversarial networks (cGANs) have gained a
consid...
Unsupervised image-to-image translation has gained considerable attentio...
We study contextual multi-armed bandit problems under linear realizabili...