Search Results for author:
Found 7 papers, 3 papers with code
Data Augmentation using LLMs: Data Perspectives, Learning Paradigms and Challenges
In the rapidly evolving field of machine learning (ML), data augmentation (DA) has emerged as a pivotal technique for enhancing model performance by diversifying training examples without the need for additional data collection.
Panda LLM: Training Data and Evaluation for Open-Sourced Chinese Instruction-Following Large Language Models
This project focuses on enhancing open-source large language models through instruction-tuning and providing comprehensive evaluations of their performance.
Fast Graph Generation via Spectral Diffusion
In this paper, we argue that running full-rank diffusion SDEs on the whole graph adjacency matrix space hinders diffusion models from learning graph topology generation, and hence significantly deteriorates the quality of generated graph data.
Domain Confused Contrastive Learning for Unsupervised Domain Adaptation
In this work, we study Unsupervised Domain Adaptation (UDA) in a challenging self-supervised approach.
Domain-Augmented Domain Adaptation
Unsupervised domain adaptation (UDA) enables knowledge transfer from the labelled source domain to the unlabeled target domain by reducing the cross-domain discrepancy.
Re-ranking With Constraints on Diversified Exposures for Homepage Recommender System
The homepage recommendation on most E-commerce applications places items in a hierarchical manner, where different channels display items in different styles.
Mitigating Performance Saturation in Neural Marked Point Processes: Architectures and Loss Functions
Neural marked point processes possess good interpretability of probabilistic models as well as the representational power of neural networks.
