Search Results for author:
Found 8 papers, 2 papers with code
Deep Transferring Quantization
Specifically, we propose a method named deep transferring quantization (DTQ) to effectively exploit the knowledge in a pre-trained full-precision model.
Large-scale Generative Simulation Artificial Intelligence: the Next Hotspot in Generative AI
The concept of GenAI has been developed for decades.
AUC Optimization from Multiple Unlabeled Datasets
In this paper, we study the problem of building an AUC (area under ROC curve) optimization model from multiple unlabeled datasets, which maximizes the pairwise ranking ability of the classifier.
Weakly Supervised AUC Optimization: A Unified Partial AUC Approach
Since acquiring perfect supervision is usually difficult, real-world machine learning tasks often confront inaccurate, incomplete, or inexact supervision, collectively referred to as weak supervision.
Think Outside the Code: Brainstorming Boosts Large Language Models in Code Generation
We demonstrate that Brainstorm significantly enhances the ability of LLMs to solve competition-level programming problems, resulting in a more than 50% increase in the pass@$k$ metrics for ChatGPT on the CodeContests benchmark, achieving state-of-the-art performance.
Trivial bundle embeddings for learning graph representations
Embedding scale-free networks to hyperbolic spaces offer an exciting alternative but incurs distortions when embedding assortative networks with latent geometries not hyperbolic.
AlphaGomoku: An AlphaGo-based Gomoku Artificial Intelligence using Curriculum Learning
In this project, we combine AlphaGo algorithm with Curriculum Learning to crack the game of Gomoku.
Social Media Would Not Lie: Prediction of the 2016 Taiwan Election via Online Heterogeneous Data
First, public opinions in online media dominate traditional polls in Taiwan election prediction in terms of both predictive power and timeliness.
