Search Results for author:
Found 7 papers, 5 papers with code
GeoGalactica: A Scientific Large Language Model in Geoscience
To our best knowledge, it is the largest language model for the geoscience domain.
Mind's Mirror: Distilling Self-Evaluation Capability and Comprehensive Thinking from Large Language Models
While techniques such as chain-of-thought (CoT) distillation have displayed promise in distilling LLMs into small language models (SLMs), there is a risk that distilled SLMs may still inherit flawed reasoning and hallucinations from LLMs.
Pushing Large Language Models to the 6G Edge: Vision, Challenges, and Opportunities
In both aspects, considering the inherent resource limitations at the edge, we discuss various cutting-edge techniques, including split learning/inference, parameter-efficient fine-tuning, quantization, and parameter-sharing inference, to facilitate the efficient deployment of LLMs.
K2: A Foundation Language Model for Geoscience Knowledge Understanding and Utilization
Large language models (LLMs) have achieved great success in general domains of natural language processing.
ExcelFormer: Can a DNN be a Sure Bet for Tabular Prediction?
In this paper, we delve into this question: Can we develop a deep learning model that serves as a "sure bet" solution for a wide range of tabular prediction tasks, while also being user-friendly for casual users?
Rethinking Cost-sensitive Classification in Deep Learning via Adversarial Data Augmentation
This necessitates rethinking cost-sensitive classification in DNNs.
Reprint: a randomized extrapolation based on principal components for data augmentation
This paper presents REPRINT, a simple and effective hidden-space data augmentation method for imbalanced data classification.
