2 citations · 2 across the 5 of their papers we have counts for
5 papers
From Compression to Accountability: Harmless Copyright Protection for Dataset Distillation
Yan Liang, Ziyuan Yang, Mengyu Sun +2
Large-scale datasets have been a key driving force behind the rapid progress of deep learning, but their storage, computational, and energy costs have become increasingly prohibiti…
EMA: Efficient Model Adaptation for Learning-based Systems
Daiyang Yu, Xinyu Chen, Yihan Zhang +3
Machine learning (ML) is increasingly applied to optimize system performance in tasks such as resource management and network simulation. Unlike traditional ML tasks (e.g., image c…
A Multi-agent AI System for Deep Learning Model Migration from TensorFlow to JAX
Stoyan Nikolov, Bernhard Konrad, Moritz Gronbach +5
The rapid development of AI-based products and their underlying models has led to constant innovation in deep learning frameworks. Google has been pioneering machine learning usage…
Gated Integration of Low-Rank Adaptation for Continual Learning of Large Language Models
Yan-Shuo Liang, Jia-Rui Chen, Wu-Jun Li
Continual learning (CL), which requires the model to learn multiple tasks sequentially, is crucial for large language models (LLMs). Recently, low-rank adaptation~(LoRA), one of th…
InfLoRA: Interference-Free Low-Rank Adaptation for Continual Learning
Yan-Shuo Liang, Wu-Jun Li
Continual learning requires the model to learn multiple tasks sequentially. In continual learning, the model should possess the ability to maintain its performance on old tasks (st…