3 citations · 3 across the 9 of their papers we have counts for
9 papers
Transferable FB-GNN-MBE Framework for Potential Energy Surfaces: Data-Adaptive Transfer Learning in Deep Learned Many-Body Expansion Theory
Siqi Chen, Zhiqiang Wang, Yili Shen +8
Mechanistic understanding and rational design of complex chemical systems depend on fast and accurate predictions of electronic structures beyond individual building blocks. Howeve…
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…
Memory Savings at What Cost? A Study of Alternatives to Backpropagation
Kunjal Panchal, Sunav Choudhary, Yuriy Brun +1
Forward-mode automatic differentiation (FmAD) and zero-order (ZO) optimization are increasingly proposed as memory-efficient, backpropagation-free alternatives for large language m…
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…
Integrating Graph Neural Networks and Many-Body Expansion Theory for Potential Energy Surfaces
Siqi Chen, Zhiqiang Wang, Xianqi Deng +8
Rational design of next-generation functional materials relied on quantitative predictions of their electronic structures beyond single building blocks. First-principles quantum me…
Aligned Vector Quantization for Edge-Cloud Collabrative Vision-Language Models
Xiao Liu, Lijun Zhang, Deepak Ganesan +1
Vision Language Models (VLMs) are central to Visual Question Answering (VQA) systems and are typically deployed in the cloud due to their high computational demands. However, this…
Assessing Simplification Levels in Neural Networks: The Impact of Hyperparameter Configurations on Complexity and Sensitivity
Huixin Guan
This paper presents an experimental study focused on understanding the simplification properties of neural networks under different hyperparameter configurations, specifically inve…
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…
Attack-Resilient Image Watermarking Using Stable Diffusion
Lijun Zhang, Xiao Liu, Antoni Viros Martin +3
Watermarking images is critical for tracking image provenance and proving ownership. With the advent of generative models, such as stable diffusion, that can create fake but realis…