Search Results for author:
Found 16 papers, 9 papers with code
Ternary Spike: Learning Ternary Spikes for Spiking Neural Networks
To handle the problem, we propose a ternary spike neuron to transmit information.
Membrane Potential Batch Normalization for Spiking Neural Networks
All these BNs are suggested to be used after the convolution layer as usually doing in CNNs.
RMP-Loss: Regularizing Membrane Potential Distribution for Spiking Neural Networks
Spiking Neural Networks (SNNs) as one of the biology-inspired models have received much attention recently.
InfLoR-SNN: Reducing Information Loss for Spiking Neural Networks
The Spiking Neural Network (SNN) has attracted more and more attention recently.
Direct Learning-Based Deep Spiking Neural Networks: A Review
The spiking neural network (SNN), as a promising brain-inspired computational model with binary spike information transmission mechanism, rich spatially-temporal dynamics, and event-driven characteristics, has received extensive attention.
Joint A-SNN: Joint Training of Artificial and Spiking Neural Networks via Self-Distillation and Weight Factorization
In this paper, we propose a joint training framework of ANN and SNN, in which the ANN can guide the SNN's optimization.
Deep Dive Into Gradients: Better Optimization for 3D Object Detection With Gradient-Corrected IoU Supervision
In this paper, we propose a Gradient-Corrected IoU (GCIoU) loss to achieve fast and accurate 3D bounding box regression.
PeakConv: Learning Peak Receptive Field for Radar Semantic Segmentation
However, neither the regular convolution operation nor the modified ones are specific to interpret radar signals.
Alleviating Catastrophic Forgetting of Incremental Object Detection via Within-Class and Between-Class Knowledge Distillation
However, previous works on feature distillation heavily rely on low-level feature information, while under-exploring the importance of high-level semantic information.
IM-Loss: Information Maximization Loss for Spiking Neural Networks
To deal with this problem, the Information maximization loss (IM-Loss) that aims at maximizing the information flow in the SNN is proposed in the paper.
Ranked #6 on
Event data classification
on CIFAR10-DVS
Real Spike: Learning Real-valued Spikes for Spiking Neural Networks
Motivated by this assumption, a training-inference decoupling method for SNNs named as Real Spike is proposed, which not only enjoys both unshared convolution kernels and binary spikes in inference-time but also maintains both shared convolution kernels and Real-valued Spikes during training.
RecDis-SNN: Rectifying Membrane Potential Distribution for Directly Training Spiking Neural Networks
Unfortunately, with the propagation of binary spikes, the distribution of membrane potential will shift, leading to degeneration, saturation, and gradient mismatch problems, which would be disadvantageous to the network optimization and convergence.
A Note on Learning Rare Events in Molecular Dynamics using LSTM and Transformer
Therefore, to learn rare events of slow molecular dynamics by LSTM and Transformer, it is critical to choose proper temporal resolution (i. e., saving intervals of MD simulation trajectories) and state partition in high resolution data, since deep neural network models might not automatically disentangle slow dynamics from fast dynamics when both are present in data influencing each other.
ECKPN: Explicit Class Knowledge Propagation Network for Transductive Few-shot Learning
Specifically, we first employ the comparison module to explore the pairwise sample relations to learn rich sample representations in the instance-level graph.
A Biologically Plausible Supervised Learning Method for Spiking Neural Networks Using the Symmetric STDP Rule
By combining the sym-STDP rule with bio-plausible synaptic scaling and intrinsic plasticity of the dynamic threshold, our SNN model implemented SL well and achieved good performance in the benchmark recognition task (MNIST dataset).
Data-Driven Tight Frame for Cryo-EM Image Denoising and Conformational Classification
We introduce the use of data-driven tight frame (DDTF) algorithm for cryo-EM image denoising.
Computation Image and Video Processing
