4 citations · 6 across the 5 of their papers we have counts for
5 papers
Path to Intelligence: Measuring Similarity between Human Brain and Large Language Model Beyond Language Task
Doai Ngo, Mingxuan Sun, Zhengji Zhang +3
Large language models (LLMs) have demonstrated human-like abilities in language-based tasks. While language is a defining feature of human intelligence, it emerges from more fundam…
SpikeLLM: Scaling up Spiking Neural Network to Large Language Models via Saliency-based Spiking
Xingrun Xing, Boyan Gao, Zheng Zhang +5
Recent advancements in large language models (LLMs) with billions of parameters have improved performance in various applications, but their inference processes demand significant…
Benchmarking Neural Decoding Backbones towards Enhanced On-edge iBCI Applications
Zhou Zhou, Guohang He, Zheng Zhang +5
Traditional invasive Brain-Computer Interfaces (iBCIs) typically depend on neural decoding processes conducted on workstations within laboratory settings, which prevents their ever…
SpikeLM: Towards General Spike-Driven Language Modeling via Elastic Bi-Spiking Mechanisms
Xingrun Xing, Zheng Zhang, Ziyi Ni +6
Towards energy-efficient artificial intelligence similar to the human brain, the bio-inspired spiking neural networks (SNNs) have advantages of biological plausibility, event-drive…
BiPFT: Binary Pre-trained Foundation Transformer with Low-rank Estimation of Binarization Residual Polynomials
Xingrun Xing, Li Du, Xinyuan Wang +4
Pretrained foundation models offer substantial benefits for a wide range of downstream tasks, which can be one of the most potential techniques to access artificial general intelli…