Deep Learning
Decomposable Transformer Point Processes
Despite the flexibility of these methods, their inference is based on the computationally intensive thinning algorithm.
Meta-Learning with Neural Bandit Scheduler Y unzhe Qi
However, this assumption can possibly fail in real-world scenarios.
Improving the Worst-Case Bidirectional Communication Complexity for Nonconvex Distributed Optimization under Function Similarity
Effective communication between the server and workers plays a key role in distributed optimization. In this paper, we focus on optimizing communication, uncovering inefficiencies in prevalent downlink compression approaches.
Variational Annealing on Graphs for Combinatorial Optimization
We demonstrate that this assumption imposes performance limitations in particular on difficult problem instances.
Provable and Efficient Dataset Distillation for Kernel Ridge Regression
Our code is available at GitHub.
SM3-Text-to-Query: Synthetic M ulti-M odel Medical Text-to-Query Benchmark
Text-to-Query systems have surprisingly not been investigated so far.
Prediction and Control in Continual Reinforcement Learning
Deep reinforcement learning (RL) has achieved remarkable successes in complex tasks, e.g Go [