Search Results for author:
Found 12 papers, 5 papers with code
PaDeLLM-NER: Parallel Decoding in Large Language Models for Named Entity Recognition
In this study, we aim to reduce generation latency for Named Entity Recognition (NER) with Large Language Models (LLMs).
Bridging Cross-task Protocol Inconsistency for Distillation in Dense Object Detection
Thus, the optimum of the distillation loss does not necessarily lead to the optimal student classification scores for dense object detectors.
MoCLIM: Towards Accurate Cancer Subtyping via Multi-Omics Contrastive Learning with Omics-Inference Modeling
Precision medicine fundamentally aims to establish causality between dysregulated biochemical mechanisms and cancer subtypes.
Understanding and Unifying Fourteen Attribution Methods with Taylor Interactions
Various attribution methods have been developed to explain deep neural networks (DNNs) by inferring the attribution/importance/contribution score of each input variable to the final output.
Cancer Subtyping by Improved Transcriptomic Features Using Vector Quantized Variational Autoencoder
As such, existing methods often impose unrealistic assumptions to extract useful features from the data while avoiding overfitting to spurious correlations.
Automated Cancer Subtyping via Vector Quantization Mutual Information Maximization
Cancer subtyping is crucial for understanding the nature of tumors and providing suitable therapy.
Multi-Tier Platform for Cognizing Massive Electroencephalogram
A spiking neural network (SNN) based tier is designed to distill the principle information in terms of spike-streams from the rare features, which maintains the temporal implication in the nature of EEGs.
Automated Sleep Staging via Parallel Frequency-Cut Attention
This paper proposes a novel framework for automatically capturing the time-frequency nature of electroencephalogram (EEG) signals of human sleep based on the authoritative sleep medicine guidance.
Adaptive Spike-Like Representation of EEG Signals for Sleep Stages Scoring
Recently there has seen promising results on automatic stage scoring by extracting spatio-temporal features from electroencephalogram (EEG).
Cancer Subtyping via Embedded Unsupervised Learning on Transcriptomics Data
In this paper, we propose to investigate automatic subtyping from an unsupervised learning perspective by directly constructing the underlying data distribution itself, hence sufficient data can be generated to alleviate the issue of overfitting.
Improving Ranking Correlation of Supernet with Candidates Enhancement and Progressive Training
One-shot neural architecture search (NAS) applies weight-sharing supernet to reduce the unaffordable computation overhead of automated architecture designing.
Cascade Bagging for Accuracy Prediction with Few Training Samples
To alleviate this problem, we propose a novel framework to train an accuracy predictor under few training samples.
