8 citations · 29 across the 44 of their papers we have counts for
15 papers · 1 filter
Biologically Plausible Learning via Bidirectional Spike-Based Distillation
Changze Lv, Yifei Wang, Yanxun Zhang +7
Developing biologically plausible learning algorithms that can achieve performance comparable to error backpropagation remains a longstanding challenge. Existing approaches often c…
Enhancing Model Privacy in Federated Learning with Random Masking and Quantization
Zhibo Xu, Jianhao Zhu, Jingwen Xu +7
The primary goal of traditional federated learning is to protect data privacy by enabling distributed edge devices to collaboratively train a shared global model while keeping raw…
SpikeSTAG: Spatial-Temporal Forecasting via GNN-SNN Collaboration
Bang Hu, Changze Lv, Mingjie Li +5
Spiking neural networks (SNNs), inspired by the spiking behavior of biological neurons, offer a distinctive approach for capturing the complexities of temporal data. However, their…
TripTailor: A Real-World Benchmark for Personalized Travel Planning
Yuanzhe Shen, Kaimin Wang, Changze Lv +2
The continuous evolution and enhanced reasoning capabilities of large language models (LLMs) have elevated their role in complex tasks, notably in travel planning, where demand for…
Beyond Single Labels: Improving Conversational Recommendation through LLM-Powered Data Augmentation
Haozhe Xu, Xiaohua Wang, Changze Lv +1
Conversational recommender systems (CRSs) enhance recommendation quality by engaging users in multi-turn dialogues, capturing nuanced preferences through natural language interacti…
Edge Intelligence with Spiking Neural Networks
Shuiguang Deng, Di Yu, Changze Lv +10
The convergence of artificial intelligence and edge computing has spurred growing interest in enabling intelligent services directly on resource-constrained devices. While traditio…
AMix-1: A Pathway to Test-Time Scalable Protein Foundation Model
Changze Lv, Jiang Zhou, Siyu Long +22
We introduce AMix-1, a powerful protein foundation model built on Bayesian Flow Networks and empowered by a systematic training methodology, encompassing pretraining scaling laws,…
Progressive Mastery: Customized Curriculum Learning with Guided Prompting for Mathematical Reasoning
Muling Wu, Qi Qian, Wenhao Liu +12
Large Language Models (LLMs) have achieved remarkable performance across various reasoning tasks, yet post-training is constrained by inefficient sample utilization and inflexible…
Improving Continual Pre-training Through Seamless Data Packing
Ruicheng Yin, Xuan Gao, Changze Lv +3
Continual pre-training has demonstrated significant potential in enhancing model performance, particularly in domain-specific scenarios. The most common approach for packing data b…
ECC-SNN: Cost-Effective Edge-Cloud Collaboration for Spiking Neural Networks
Di Yu, Changze Lv, Xin Du +5
Most edge-cloud collaboration frameworks rely on the substantial computational and storage capabilities of cloud-based artificial neural networks (ANNs). However, this reliance res…
RECAST: Expanding the Boundaries of LLMs' Complex Instruction Following with Multi-Constraint Data
Zhengkang Guo, Wenhao Liu, Mingchen Xie +13
Large language models (LLMs) are increasingly expected to tackle complex tasks, driven by their expanding applications and users' growing proficiency in crafting sophisticated prom…
Layer-Specific Scaling of Positional Encodings for Superior Long-Context Modeling
Zhenghua Wang, Yiran Ding, Changze Lv +5
Although large language models (LLMs) have achieved significant progress in handling long-context inputs, they still suffer from the ``lost-in-the-middle'' problem, where crucial i…
Explainable Synthetic Image Detection through Diffusion Timestep Ensembling
Yixin Wu, Feiran Zhang, Tianyuan Shi +7
Recent advances in diffusion models have enabled the creation of deceptively real images, posing significant security risks when misused. In this study, we empirically show that di…
Toward Relative Positional Encoding in Spiking Transformers
Changze Lv, Yansen Wang, Dongqi Han +4
Spiking neural networks (SNNs) are bio-inspired networks that mimic how neurons in the brain communicate through discrete spikes, which have great potential in various tasks due to…
Dendritic Localized Learning: Toward Biologically Plausible Algorithm
Changze Lv, Jingwen Xu, Yiyang Lu +7
Backpropagation is the foundational algorithm for training neural networks and a key driver of deep learning's success. However, its biological plausibility has been challenged due…