3 citations · 6 across the 7 of their papers we have counts for
7 papers
Multigrid Training for Molecular Generation using Graph Neural Networks
Zixuan Ling, Paula Mercurio, Di Liu
Deep learning has demonstrated significant success for modeling biochemical molecular systems, where inputs are commonly represented as graphs or 3D grids. A major challenge is tha…
Towards Biologically Plausible Computing: A Comprehensive Comparison
Changze Lv, Yufei Gu, Zhengkang Guo +16
Backpropagation is a cornerstone algorithm in training neural networks for supervised learning, which uses a gradient descent method to update network weights by minimizing the dis…
Promoting Data and Model Privacy in Federated Learning through Quantized LoRA
JianHao Zhu, Changze Lv, Xiaohua Wang +7
Conventional federated learning primarily aims to secure the privacy of data distributed across multiple edge devices, with the global model dispatched to edge devices for paramete…
Decoding Continuous Character-based Language from Non-invasive Brain Recordings
Cenyuan Zhang, Xiaoqing Zheng, Ruicheng Yin +8
Deciphering natural language from brain activity through non-invasive devices remains a formidable challenge. Previous non-invasive decoders either require multiple experiments wit…
Advancing Parameter Efficiency in Fine-tuning via Representation Editing
Muling Wu, Wenhao Liu, Xiaohua Wang +7
Parameter Efficient Fine-Tuning (PEFT) techniques have drawn significant attention due to their ability to yield competitive results while updating only a small portion of the adju…
Aligning Large Language Models with Human Preferences through Representation Engineering
Wenhao Liu, Xiaohua Wang, Muling Wu +7
Aligning large language models (LLMs) with human preferences is crucial for enhancing their utility in terms of helpfulness, truthfulness, safety, harmlessness, and interestingness…
SpikeBERT: A Language Spikformer Learned from BERT with Knowledge Distillation
Changze Lv, Tianlong Li, Jianhan Xu +5
Spiking neural networks (SNNs) offer a promising avenue to implement deep neural networks in a more energy-efficient way. However, the network architectures of existing SNNs for la…