3 accepted papers
This paper is an extended abstract of our ICLR 2024 Outstanding Paper Award work. Modeling long-range dependencies across sequences is a longstanding goal in machine learning. While state space models reportedly outperform Transformers on benchmarks like Long Range Arena, we show that random initial
Predictions over graphs play a crucial role in various domains, including social networks and medicine. Graph Neural Networks (GNNs) have emerged as the dominant approach for learning on graph data. Although a graph-structure is provided as input to the GNN, in some cases the best solution can be ob…
Modeling long-range dependencies across sequences is a longstanding goal in machine learning and has led to architectures, such as state space models, that dramatically outperform Transformers on long sequences. However, these impressive empirical gains have been by and large demonstrated on benchma…