ResearchTrend.AI
  • Papers
  • Communities
  • Events
  • Blog
  • Pricing
Papers
Communities
Social Events
Terms and Conditions
Pricing
Parameter LabParameter LabTwitterGitHubLinkedInBlueskyYoutube

© 2025 ResearchTrend.AI, All rights reserved.

  1. Home
  2. Papers
  3. 2010.01729
  4. Cited By
Revisiting Batch Normalization for Training Low-latency Deep Spiking
  Neural Networks from Scratch

Revisiting Batch Normalization for Training Low-latency Deep Spiking Neural Networks from Scratch

5 October 2020
Youngeun Kim
Priyadarshini Panda
ArXivPDFHTML

Papers citing "Revisiting Batch Normalization for Training Low-latency Deep Spiking Neural Networks from Scratch"

16 / 16 papers shown
Title
DISTA: Denoising Spiking Transformer with intrinsic plasticity and
  spatiotemporal attention
DISTA: Denoising Spiking Transformer with intrinsic plasticity and spatiotemporal attention
Boxun Xu
Hejia Geng
Yuxuan Yin
Peng Li
8
2
0
15 Nov 2023
Spike-based Neuromorphic Computing for Next-Generation Computer Vision
Spike-based Neuromorphic Computing for Next-Generation Computer Vision
Md. Sakib Hasan
Catherine D. Schuman
Zhongyang Zhang
Tauhidur Rahman
Garrett S. Rose
21
0
0
15 Oct 2023
Sparse-firing regularization methods for spiking neural networks with
  time-to-first spike coding
Sparse-firing regularization methods for spiking neural networks with time-to-first spike coding
Yusuke Sakemi
Kakei Yamamoto
T. Hosomi
Kazuyuki Aihara
40
7
0
24 Jul 2023
Bridging the Gap between ANNs and SNNs by Calibrating Offset Spikes
Bridging the Gap between ANNs and SNNs by Calibrating Offset Spikes
Zecheng Hao
Jianhao Ding
Tong Bu
Tiejun Huang
Zhaofei Yu
27
39
0
21 Feb 2023
Exploring Temporal Information Dynamics in Spiking Neural Networks
Exploring Temporal Information Dynamics in Spiking Neural Networks
Youngeun Kim
Yuhang Li
Hyoungseob Park
Yeshwanth Venkatesha
Anna Hambitzer
Priyadarshini Panda
8
31
0
26 Nov 2022
Wearable-based Human Activity Recognition with Spatio-Temporal Spiking
  Neural Networks
Wearable-based Human Activity Recognition with Spatio-Temporal Spiking Neural Networks
Yuhang Li
Ruokai Yin
Hyoungseob Park
Youngeun Kim
Priyadarshini Panda
13
14
0
14 Nov 2022
SpikeSim: An end-to-end Compute-in-Memory Hardware Evaluation Tool for
  Benchmarking Spiking Neural Networks
SpikeSim: An end-to-end Compute-in-Memory Hardware Evaluation Tool for Benchmarking Spiking Neural Networks
Abhishek Moitra
Abhiroop Bhattacharjee
Runcong Kuang
Gokul Krishnan
Yu Cao
Priyadarshini Panda
6
20
0
24 Oct 2022
Exploring Lottery Ticket Hypothesis in Spiking Neural Networks
Exploring Lottery Ticket Hypothesis in Spiking Neural Networks
Youngeun Kim
Yuhang Li
Hyoungseob Park
Yeshwanth Venkatesha
Ruokai Yin
Priyadarshini Panda
8
46
0
04 Jul 2022
Toward Robust Spiking Neural Network Against Adversarial Perturbation
Toward Robust Spiking Neural Network Against Adversarial Perturbation
Ling Liang
Kaidi Xu
Xing Hu
Lei Deng
Yuan Xie
AAML
18
13
0
12 Apr 2022
SATA: Sparsity-Aware Training Accelerator for Spiking Neural Networks
SATA: Sparsity-Aware Training Accelerator for Spiking Neural Networks
Ruokai Yin
Abhishek Moitra
Abhiroop Bhattacharjee
Youngeun Kim
Priyadarshini Panda
8
50
0
11 Apr 2022
Beyond Classification: Directly Training Spiking Neural Networks for
  Semantic Segmentation
Beyond Classification: Directly Training Spiking Neural Networks for Semantic Segmentation
Youngeun Kim
Joshua Chough
Priyadarshini Panda
40
79
0
14 Oct 2021
One Timestep is All You Need: Training Spiking Neural Networks with
  Ultra Low Latency
One Timestep is All You Need: Training Spiking Neural Networks with Ultra Low Latency
Sayeed Shafayet Chowdhury
Nitin Rathi
Kaushik Roy
16
39
0
01 Oct 2021
Training Energy-Efficient Deep Spiking Neural Networks with Single-Spike
  Hybrid Input Encoding
Training Energy-Efficient Deep Spiking Neural Networks with Single-Spike Hybrid Input Encoding
Gourav Datta
Souvik Kundu
P. Beerel
34
27
0
26 Jul 2021
Enabling Deep Spiking Neural Networks with Hybrid Conversion and Spike
  Timing Dependent Backpropagation
Enabling Deep Spiking Neural Networks with Hybrid Conversion and Spike Timing Dependent Backpropagation
Nitin Rathi
G. Srinivasan
Priyadarshini Panda
Kaushik Roy
116
292
0
04 May 2020
Inherent Adversarial Robustness of Deep Spiking Neural Networks: Effects
  of Discrete Input Encoding and Non-Linear Activations
Inherent Adversarial Robustness of Deep Spiking Neural Networks: Effects of Discrete Input Encoding and Non-Linear Activations
Saima Sharmin
Nitin Rathi
Priyadarshini Panda
Kaushik Roy
AAML
106
85
0
23 Mar 2020
Long short-term memory and learning-to-learn in networks of spiking
  neurons
Long short-term memory and learning-to-learn in networks of spiking neurons
G. Bellec
Darjan Salaj
Anand Subramoney
R. Legenstein
Wolfgang Maass
111
477
0
26 Mar 2018
1