5 citations · 14 across the 15 of their papers we have counts for
6 papers · 1 filter
PretrainZero: Reinforcement Active Pretraining
Xingrun Xing, Zhiyuan Fan, Jie Lou +3
Mimicking human behavior to actively learning from general experience and achieve artificial general intelligence has always been a human dream. Recent reinforcement learning (RL)…
Breaking the Modality Wall: Time-step Mixup for Efficient Spiking Knowledge Transfer from Static to Event Domain
Yuqi Xie, Shuhan Ye, Yi Yu +7
The integration of event cameras and spiking neural networks (SNNs) promises energy-efficient visual intelligence, yet scarce event data and the sparsity of DVS outputs hinder effe…
Speed Always Wins: A Survey on Efficient Architectures for Large Language Models
Weigao Sun, Jiaxi Hu, Yucheng Zhou +12
Large Language Models (LLMs) have delivered impressive results in language understanding, generation, reasoning, and pushes the ability boundary of multimodal models. Transformer m…
SpikeVideoFormer: An Efficient Spike-Driven Video Transformer with Hamming Attention and Complexity
Shihao Zou, Qingfeng Li, Wei Ji +4
Spiking Neural Networks (SNNs) have shown competitive performance to Artificial Neural Networks (ANNs) in various vision tasks, while offering superior energy efficiency. However,…
Autoregressive Image Generation with Randomized Parallel Decoding
Haopeng Li, Jinyue Yang, Guoqi Li +1
We introduce ARPG, a novel visual Autoregressive model that enables Randomized Parallel Generation, addressing the inherent limitations of conventional raster-order approaches, whi…
EfficientLLM: Scalable Pruning-Aware Pretraining for Architecture-Agnostic Edge Language Models
Xingrun Xing, Zheng Liu, Shitao Xiao +6
Modern large language models (LLMs) driven by scaling laws, achieve intelligence emergency in large model sizes. Recently, the increasing concerns about cloud costs, latency, and p…