2 citations · 5 across the 8 of their papers we have counts for
8 papers
Advancing Real-time Pandemic Forecasting Using Large Language Models: A COVID-19 Case Study
Hongru Du, Jianan Zhao, Yang Zhao +5
Forecasting the short-term spread of an ongoing disease outbreak is a formidable challenge due to the complexity of contributing factors, some of which can be characterized through…
A Classical Architecture For Digital Quantum Computers
Fang Zhang, Xing Zhu, Rui Chao +15
Scaling bottlenecks the making of digital quantum computers, posing challenges from both the quantum and the classical components. We present a classical architecture to cope with…
Two-Memory Reinforcement Learning
Zhao Yang, Thomas. M. Moerland, Mike Preuss +1
While deep reinforcement learning has shown important empirical success, it tends to learn relatively slow due to slow propagation of rewards information and slow update of paramet…
Harmonizing Base and Novel Classes: A Class-Contrastive Approach for Generalized Few-Shot Segmentation
Weide Liu, Zhonghua Wu, Yang Zhao +4
Current methods for few-shot segmentation (FSSeg) have mainly focused on improving the performance of novel classes while neglecting the performance of base classes. To overcome th…
deHuBERT: Disentangling Noise in a Self-supervised Model for Robust Speech Recognition
Dianwen Ng, Ruixi Zhang, Jia Qi Yip +7
Existing self-supervised pre-trained speech models have offered an effective way to leverage massive unannotated corpora to build good automatic speech recognition (ASR). However,…
First Go, then Post-Explore: the Benefits of Post-Exploration in Intrinsic Motivation
Zhao Yang, Thomas M. Moerland, Mike Preuss +1
Go-Explore achieved breakthrough performance on challenging reinforcement learning (RL) tasks with sparse rewards. The key insight of Go-Explore was that successful exploration req…