Search Results for author:
Found 9 papers, 5 papers with code
Biologically-Plausible Topology Improved Spiking Actor Network for Efficient Deep Reinforcement Learning
Diverging from the conventional direct linear weighted sum, the BPT-SAN models the local nonlinearities of dendritic trees within the inter-layer connections.
Fourier or Wavelet bases as counterpart self-attention in spikformer for efficient visual classification
Energy-efficient spikformer has been proposed by integrating the biologically plausible spiking neural network (SNN) and artificial Transformer, whereby the Spiking Self-Attention (SSA) is used to achieve both higher accuracy and lower computational cost.
Machine Translation Testing via Syntactic Tree Pruning
The results show that STP can accurately find 5, 073 unique erroneous translations in Google Translate and 5, 100 unique erroneous translations in Bing Microsoft Translator (400% more than state-of-the-art techniques), with 64. 5% and 65. 4% precision, respectively.
Attention-free Spikformer: Mixing Spike Sequences with Simple Linear Transforms
The results indicate that compared to the SOTA Spikformer with SSA, Spikformer with LT achieves higher Top-1 accuracy on neuromorphic datasets (i. e., CIFAR10-DVS and DVS128 Gesture) and comparable Top-1 accuracy on static datasets (i. e., CIFAR-10 and CIFAR-100).
Complex Dynamic Neurons Improved Spiking Transformer Network for Efficient Automatic Speech Recognition
The spiking neural network (SNN) using leaky-integrated-and-fire (LIF) neurons has been commonly used in automatic speech recognition (ASR) tasks.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Tuning Synaptic Connections instead of Weights by Genetic Algorithm in Spiking Policy Network
Learning from the interaction is the primary way biological agents know about the environment and themselves.
Recent Advances and New Frontiers in Spiking Neural Networks
In recent years, spiking neural networks (SNNs) have received extensive attention in brain-inspired intelligence due to their rich spatially-temporal dynamics, various encoding methods, and event-driven characteristics that naturally fit the neuromorphic hardware.
Universal adversarial perturbation for remote sensing images
Firstly, the former is used to generate the UAP, which can learn the distribution of perturbations better, and then the latter is used to find the sensitive regions concerned by the RSI classification model.
PlantStereo: A Stereo Matching Benchmark for Plant Surface Dense Reconstruction
In this paper, we aim to address the issue between datasets and models and propose a large scale stereo dataset with high accuracy disparity ground truth named PlantStereo.
