5 citations · 21 across the 16 of their papers we have counts for
10 papers · 1 filter
Reimagining Parameter Space Exploration with Diffusion Models
Lijun Zhang, Xiao Liu, Hui Guan
Adapting neural networks to new tasks typically requires task-specific fine-tuning, which is time-consuming and reliant on labeled data. We explore a generative alternative that pr…
ASTRA: Communication-Efficient Acceleration for Multi-Device Transformer Inference
Xiao Liu, Lijun Zhang, Deepak Ganesan +1
Multi-device inference can reduce Transformer latency by parallelizing computation. However, existing methods require high inter-device bandwidth, making them impractical for bandw…
Thinking Forward: Memory-Efficient Federated Finetuning of Language Models
Kunjal Panchal, Nisarg Parikh, Sunav Choudhary +3
Finetuning large language models (LLMs) in federated learning (FL) settings has become increasingly important as it allows resource-constrained devices to finetune a model using pr…
Attacking All Tasks at Once Using Adversarial Examples in Multi-Task Learning
Lijun Zhang, Xiao Liu, Kaleel Mahmood +2
Visual content understanding frequently relies on multi-task models to extract robust representations of a single visual input for multiple downstream tasks. However, in comparison…
Structured Pruning for Multi-Task Deep Neural Networks
Siddhant Garg, Lijun Zhang, Hui Guan
Although multi-task deep neural network (DNN) models have computation and storage benefits over individual single-task DNN models, they can be further optimized via model compressi…
Flow: Per-Instance Personalized Federated Learning Through Dynamic Routing
Kunjal Panchal, Sunav Choudhary, Nisarg Parikh +2
Personalization in Federated Learning (FL) aims to modify a collaboratively trained global model according to each client. Current approaches to personalization in FL are at a coar…
Uplifting Message Passing Neural Network with Graph Original Information
Xiao Liu, Lijun Zhang, Hui Guan
Message passing neural networks (MPNNs) learn the representation of graph-structured data based on graph original information, including node features and graph structures, and hav…
A Tree-Structured Multi-Task Model Recommender
Lijun Zhang, Xiao Liu, Hui Guan
Tree-structured multi-task architectures have been employed to jointly tackle multiple vision tasks in the context of multi-task learning (MTL). The major challenge is to determine…
AutoMTL: A Programming Framework for Automating Efficient Multi-Task Learning
Lijun Zhang, Xiao Liu, Hui Guan
Multi-task learning (MTL) jointly learns a set of tasks by sharing parameters among tasks. It is a promising approach for reducing storage costs while improving task accuracy for m…
Rethinking Hard-Parameter Sharing in Multi-Domain Learning
Lijun Zhang, Qizheng Yang, Xiao Liu +1
Hard parameter sharing in multi-domain learning (MDL) allows domains to share some of the model parameters to reduce storage cost while improving prediction accuracy. One common sh…