13 citations · 41 across the 10 of their papers we have counts for
10 papers
LongLLMLingua: Accelerating and Enhancing LLMs in Long Context Scenarios via Prompt Compression
Huiqiang Jiang, Qianhui Wu, Xufang Luo +4
In long context scenarios, large language models (LLMs) face three main challenges: higher computational cost, performance reduction, and position bias. Research indicates that LLM…
Seeing through the Brain: Image Reconstruction of Visual Perception from Human Brain Signals
Yu-Ting Lan, Kan Ren, Yansen Wang +4
Seeing is believing, however, the underlying mechanism of how human visual perceptions are intertwined with our cognitions is still a mystery. Thanks to the recent advances in both…
Protecting the Future: Neonatal Seizure Detection with Spatial-Temporal Modeling
Ziyue Li, Yuchen Fang, You Li +7
A timely detection of seizures for newborn infants with electroencephalogram (EEG) has been a common yet life-saving practice in the Neonatal Intensive Care Unit (NICU). However, i…
End-to-End Word-Level Pronunciation Assessment with MASK Pre-training
Yukang Liang, Kaitao Song, Shaoguang Mao +6
Pronunciation assessment is a major challenge in the computer-aided pronunciation training system, especially at the word (phoneme)-level. To obtain word (phoneme)-level scores, cu…
MLCopilot: Unleashing the Power of Large Language Models in Solving Machine Learning Tasks
Lei Zhang, Yuge Zhang, Kan Ren +2
The field of machine learning (ML) has gained widespread adoption, leading to significant demand for adapting ML to specific scenarios, which is yet expensive and non-trivial. The…
Online Streaming Video Super-Resolution with Convolutional Look-Up Table
Guanghao Yin, Zefan Qu, Xinyang Jiang +8
Online video streaming has fundamental limitations on the transmission bandwidth and computational capacity and super-resolution is a promising potential solution. However, applyin…
Unsupervised Video Anomaly Detection for Stereotypical Behaviours in Autism
Jiaqi Gao, Xinyang Jiang, Yuqing Yang +2
Monitoring and analyzing stereotypical behaviours is important for early intervention and care taking in Autism Spectrum Disorder (ASD). This paper focuses on automatically detecti…
Towards Inference Efficient Deep Ensemble Learning
Ziyue Li, Kan Ren, Yifan Yang +3
Ensemble methods can deliver surprising performance gains but also bring significantly higher computational costs, e.g., can be up to 2048X in large-scale ensemble tasks. However,…
Reinforcement Learning with Automated Auxiliary Loss Search
Tairan He, Yuge Zhang, Kan Ren +5
A good state representation is crucial to solving complicated reinforcement learning (RL) challenges. Many recent works focus on designing auxiliary losses for learning informative…
Improving Hypernasality Estimation with Automatic Speech Recognition in Cleft Palate Speech
Kaitao Song, Teng Wan, Bixia Wang +9
Hypernasality is an abnormal resonance in human speech production, especially in patients with craniofacial anomalies such as cleft palate. In clinical application, hypernasality e…