Deep Learning
Reconciling Modern Deep Learning with Traditional Optimization Analyses: The Intrinsic Learning Rate
Recent works (e.g., (Li and Arora, 2020)) suggest that the use of popular normalization schemes (including Batch Normalization) in today's deep learning can
CogView2: Faster and Better Text-to-Image Generation via Hierarchical Transformers Ming Ding
A lion man is typing in the o ffi ce. A beautiful girl is hugging a husky. A lion teacher wearing a suit is in front of a blackboard. A robot is riding under the blue and cloudy sky. Several youths are talking in a bar. A young woman is taking photos.
Transformer in Transformer Kai Han 1,2 An Xiao 2 Enhua Wu
Features of both words and sentences will be aggregated to enhance the representation ability.
Motif-based Graph Self-Supervised Learning for Molecular Property Prediction
To bridge this gap, we propose Motif-based Graph Self-supervised Learning (MGSSL) by introducing a novel self-supervised motif generation framework for GNNs.
6b055b95d689b1f704d8f92191cdb788-Paper-Conference.pdf
The receptive field (RF), which determines the region of time series to be "seen"
