NeurIPS

Long Range Graph Benchmark
Graph Neural Networks (GNNs) that are based on the message passing (MP) paradigm generally exchange information between 1-hop neighbors …
Recipe for a General, Powerful, Scalable Graph Transformer
We propose a recipe on how to build a general, powerful, scalable (GPS) graph Transformer with linear complexity and state-of-the-art …
How Should Pre-Trained Language Models Be Fine-Tuned Towards Adversarial Robustness?
The fine-tuning of pre-trained language models has a great success in many NLP fields. Yet, it is strikingly vulnerable to adversarial …
Contrastive Learning for Neural Topic Model
Recent empirical studies show that adversarial topic models (ATM) can successfully capture semantic patterns of the document by …
Compositional De-Attention Networks
Attentional models are distinctly characterized by their ability to learn relative importance, i.e., assigning a different weight to …
Densely Connected Attention Propagation for Reading Comprehension
We propose DecaProp (Densely Connected Attention Propagation), a new densely connected neural architecture for reading comprehension …
Recurrently Controlled Recurrent Networks
Recurrent neural networks (RNNs) such as long short-term memory and gated recurrent units are pivotal building blocks across a broad …