Search Results for author:
Found 6 papers, 3 papers with code
Conifer: Improving Complex Constrained Instruction-Following Ability of Large Language Models
To address this challenge, we introduce Conifer, a novel instruction tuning dataset, designed to enhance LLMs to follow multi-level instructions with complex constraints.
Open-World Semi-Supervised Learning for Node Classification
Open-world semi-supervised learning (Open-world SSL) for node classification, that classifies unlabeled nodes into seen classes or multiple novel classes, is a practical but under-explored problem in the graph community.
Continual Transfer Learning for Cross-Domain Click-Through Rate Prediction at Taobao
Therefore, it is crucial to perform cross-domain CTR prediction to transfer knowledge from large domains to small domains to alleviate the data sparsity issue.
Learn to Play Tetris with Deep Reinforcement Learning
We also applied several state of the art reinforcement learning algorithms such as Dreamer, DrQ, and Plan2Explore in the real-world Tetris game environment.
Generating Diverse and Descriptive Image Captions Using Visual Paraphrases
Recently there has been significant progress in image captioning with the help of deep learning.
Learning towards Minimum Hyperspherical Energy
In light of this intuition, we reduce the redundancy regularization problem to generic energy minimization, and propose a minimum hyperspherical energy (MHE) objective as generic regularization for neural networks.
