Genre
A new perspective on building efficient and expressive 3D equivariant graph neural networks
Geometric deep learning enables the encoding of physical symmetries in modeling 3D objects.
QGym: Scalable Simulation and Benchmarking of Queuing Network Controllers
Queuing network control determines the allocation of scarce resources to manage congestion, a fundamental problem in manufacturing, communications, and healthcare.
Optimized Feature Generation for Tabular Data via LLMs with Decision Tree Reasoning
We use decision trees to convey this reasoning information, as they can be easily represented in natural language, effectively providing knowledge from prior experiments ( i.e., the impact of the generated features on performance) to
Unraveling the Gradient Descent Dynamics of Transformers
By analyzing the loss landscape of a single Transformer layer using Softmax and Gaussian attention kernels, our work provides concrete answers to these questions.
Multi-Agent Meta-Reinforcement Learning: Sharper Convergence Rates with Task Similarity
We further provide numerical simulations to corroborate our theoretical findings.
Differentiable sorting for censored time-to-event data
This bias becomes particularly noticeable when the study's endpoint can be inferred from the