14 citations · 27 across the 9 of their papers we have counts for
10 papers
SolveEdit: Benchmarking Visual Problem Solving in Generative Models
Wenjie Shu, Yexin Liu, Harold Haodong Chen +9
Machine intelligence is often evaluated through abstract reasoning problems, yet many real-world problems are visual, such as arranging objects, repairing layouts, or tracing route…
LoopViT: Scaling Visual ARC with Looped Transformers
Wen-Jie Shu, Xuerui Qiu, Rui-Jie Zhu +3
Recent advances in visual reasoning have leveraged vision transformers to tackle the ARC-AGI benchmark. However, we argue that the feed-forward architecture, where computational de…
Quantized Spike-driven Transformer
Xuerui Qiu, Malu Zhang, Jieyuan Zhang +7
Spiking neural networks are emerging as a promising energy-efficient alternative to traditional artificial neural networks due to their spike-driven paradigm. However, recent resea…
When Spiking neural networks meet temporal attention image decoding and adaptive spiking neuron
Xuerui Qiu, Zheng Luan, Zhaorui Wang +1
Spiking Neural Networks (SNNs) are capable of encoding and processing temporal information in a biologically plausible way. However, most existing SNN-based methods for image tasks…
Advancing Spiking Neural Networks towards Multiscale Spatiotemporal Interaction Learning
Yimeng Shan, Malu Zhang, Rui-jie Zhu +3
Recent advancements in neuroscience research have propelled the development of Spiking Neural Networks (SNNs), which not only have the potential to further advance neuroscience res…
High-Performance Temporal Reversible Spiking Neural Networks with Training Memory and Inference Cost
JiaKui Hu, Man Yao, Xuerui Qiu +6
Multi-timestep simulation of brain-inspired Spiking Neural Networks (SNNs) boost memory requirements during training and increase inference energy cost. Current training methods ca…
Event-Driven Learning for Spiking Neural Networks
Wenjie Wei, Malu Zhang, Jilin Zhang +7
Brain-inspired spiking neural networks (SNNs) have gained prominence in the field of neuromorphic computing owing to their low energy consumption during feedforward inference on ne…
SynA-ResNet: Spike-driven ResNet Achieved through OR Residual Connection
Yimeng Shan, Xuerui Qiu, Rui-jie Zhu +3
Spiking Neural Networks (SNNs) have garnered substantial attention in brain-like computing for their biological fidelity and the capacity to execute energy-efficient spike-driven o…
Tensor Decomposition Based Attention Module for Spiking Neural Networks
Haoyu Deng, Ruijie Zhu, Xuerui Qiu +3
The attention mechanism has been proven to be an effective way to improve spiking neural network (SNN). However, based on the fact that the current SNN input data flow is split int…
Gated Attention Coding for Training High-performance and Efficient Spiking Neural Networks
Xuerui Qiu, Rui-Jie Zhu, Yuhong Chou +3
Spiking neural networks (SNNs) are emerging as an energy-efficient alternative to traditional artificial neural networks (ANNs) due to their unique spike-based event-driven nature.…