12 citations · 23 across the 8 of their papers we have counts for
5 papers · 1 filter
Variational Task Vector Composition
Boyuan Zhang, Yingjun Du, Xiantong Zhen +1
Task vectors capture how a model changes during fine-tuning by recording the difference between pre-trained and task-specific weights. The composition of task vectors, a key operat…
Reducing Fine-Tuning Memory Overhead by Approximate and Memory-Sharing Backpropagation
Yuchen Yang, Yingdong Shi, Cheems Wang +3
Fine-tuning pretrained large models to downstream tasks is an important problem, which however suffers from huge memory overhead due to large-scale parameters. This work strives to…
Episodic Multi-Task Learning with Heterogeneous Neural Processes
Jiayi Shen, Xiantong Zhen, Qi +2
This paper focuses on the data-insufficiency problem in multi-task learning within an episodic training setup. Specifically, we explore the potential of heterogeneous information a…
Variational Multi-Task Learning with Gumbel-Softmax Priors
Jiayi Shen, Xiantong Zhen, Marcel Worring +1
Multi-task learning aims to explore task relatedness to improve individual tasks, which is of particular significance in the challenging scenario that only limited data is availabl…
Learning to Learn Kernels with Variational Random Features
Xiantong Zhen, Haoliang Sun, Yingjun Du +4
In this work, we introduce kernels with random Fourier features in the meta-learning framework to leverage their strong few-shot learning ability. We propose meta variational rando…