active learning
5 notes, newest first.
Neuron-Aware Active Few-Shot Learning for LLMs
The paper introduces a new method called NeuFS for Active Few-Shot Learning (AFSL) in Language Models (LLMs). Instead of relying on output-level signals to identify valuable unlabeled samples, it focuses on the…
Neuron-Aware Active Few-Shot Learning for LLMs
The research presents NeuFS, a new approach for Active Few-Shot Learning (AFSL) that focuses on using a model's internal dynamics to identify valuable unlabeled samples for annotation. This method is designed to reduce…
Detecting the Undetectable: Enhancing Unsupervised time series Anomaly Detection via Active Learning
The authors propose a novel framework using active learning to enhance unsupervised time series anomaly detection in complex data. The framework aims to improve the ability of models to detect subtle and noisy…
Hierarchical Reinforcement Learning for Neural Network Compression (HiReLC): Pruning and Quantization
The paper presents HiReLC, a framework that uses reinforcement learning to compress deep neural networks by pruning and quantizing them. The framework divides the compression search into two levels: low-level agents…
Hierarchical Reinforcement Learning for Neural Network Compression (HiReLC): Pruning and Quantization
The paper introduces HiReLC, a hierarchical reinforcement learning framework that automates the joint quantization and structured pruning of deep neural networks. It operates at two levels: low-level agents (LLAs)…
