Search Results for author:
Found 4 papers, 2 papers with code
Uncertainty Quantification for In-Context Learning of Large Language Models
Existing works have been devoted to quantifying the uncertainty in LLM's response, but they often overlook the complex nature of LLMs and the uniqueness of in-context learning.
Multi-Loss Convolutional Network with Time-Frequency Attention for Speech Enhancement
By combining the DPRNN module with Convolution Recurrent Network (CRN), the DPCRN obtained a promising performance in speech separation with a limited model size.
Towards High-Quality and Efficient Video Super-Resolution via Spatial-Temporal Data Overfitting
To reconcile such, we propose a novel method for high-quality and efficient video resolution upscaling tasks, which leverages the spatial-temporal information to accurately divide video into chunks, thus keeping the number of chunks as well as the model size to minimum.
Detecting Problem Statements in Peer Assessments
We attempt to automate the process of deciding whether a review comment detects a problem.
