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Progressive Tandem Learning for Pattern Recognition With Deep Spiking Neural Networks

https://doi.org/10.1109/tpami.2021.3114196

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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

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Spiking Deep Convolutional Neural Networks for Energy-Efficient Object Recognition

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Selective cortical representation of attended speaker in multi-talker speech perception

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Mastering the game of Go without human knowledge

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Towards artificial general intelligence with hybrid Tianjic chip architecture

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Towards spike-based machine intelligence with neuromorphic computing

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Training deep neural networks for binary communication with the Whetstone method

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Modelling auditory attention

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Gradient-based learning applied to document recognition

inferredcites →https://doi.org/10.1109/cvpr.2009.5206848openalex-snapshot

ImageNet: A large-scale hierarchical image database

inferredcites →https://doi.org/10.1109/cvpr.2016.90openalex-snapshot

Deep Residual Learning for Image Recognition

inferredcites →https://doi.org/10.1109/cvpr.2018.00286openalex-snapshot

Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference

inferredcites →https://doi.org/10.1109/icassp.2001.941023openalex-snapshot

Perceptual evaluation of speech quality (PESQ)-a new method for speech quality assessment of telephone networks and codecs

inferredcites →https://doi.org/10.1109/icassp.2016.7471631openalex-snapshot

Deep clustering: Discriminative embeddings for segmentation and separation

inferredcites →https://doi.org/10.1109/icassp.2019.8683855openalex-snapshot

SDR – Half-baked or Well Done?

inferredcites →https://doi.org/10.1109/ijcnn.2015.7280696openalex-snapshot

Fast-classifying, high-accuracy spiking deep networks through weight and threshold balancing

inferredcites →https://doi.org/10.1109/mm.2018.112130359openalex-snapshot

Loihi: A Neuromorphic Manycore Processor with On-Chip Learning

inferredcites →https://doi.org/10.1109/msp.2019.2931595openalex-snapshot

Surrogate Gradient Learning in Spiking Neural Networks: Bringing the Power of Gradient-Based Optimization to Spiking Neural Networks

inferredcites →https://doi.org/10.1109/mspec.2017.7864754openalex-snapshot

Deep learning reinvents the hearing aid

inferredcites →https://doi.org/10.1109/ssci.2017.8285427openalex-snapshot

Spike trains encoding and threshold rescaling method for deep spiking neural networks

inferredcites →https://doi.org/10.1109/tasl.2007.911054openalex-snapshot

Evaluation of Objective Quality Measures for Speech Enhancement

inferredcites →https://doi.org/10.1109/taslp.2017.2756440openalex-snapshot

Toward Human Parity in Conversational Speech Recognition

inferredcites →https://doi.org/10.1109/taslp.2019.2915167openalex-snapshot

Conv-TasNet: Surpassing Ideal Time–Frequency Magnitude Masking for Speech Separation

inferredcites →https://doi.org/10.1109/taslp.2020.2987429openalex-snapshot

SpEx: Multi-Scale Time Domain Speaker Extraction Network

inferredcites →https://doi.org/10.1109/tcds.2020.2971655openalex-snapshot

Deep Spiking Neural Networks With Binary Weights for Object Recognition

inferredcites →https://doi.org/10.1109/tnnls.2021.3095724openalex-snapshot

A Tandem Learning Rule for Effective Training and Rapid Inference of Deep Spiking Neural Networks

inferredcites →https://doi.org/10.1109/tnnls.2021.3119238openalex-snapshot

Spiking Deep Residual Networks

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Mapping from Frame-Driven to Frame-Free Event-Driven Vision Systems by Low-Rate Rate Coding and Coincidence Processing--Application to Feedforward ConvNets

inferredcites →https://doi.org/10.1126/science.1089662openalex-snapshot

Communication in Neuronal Networks

inferredcites →https://doi.org/10.1126/science.1254642openalex-snapshot

A million spiking-neuron integrated circuit with a scalable communication network and interface

inferredcites →https://doi.org/10.1126/science.aaa8685openalex-snapshot

Advances in natural language processing

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The Vanishing Gradient Problem During Learning Recurrent Neural Nets and Problem Solutions

inferredcites →https://doi.org/10.1145/3065386openalex-snapshot

ImageNet classification with deep convolutional neural networks

inferredcites →https://doi.org/10.1162/neco_a_01086openalex-snapshot

SuperSpike: Supervised Learning in Multilayer Spiking Neural Networks

inferredcites →https://doi.org/10.13140/rg.2.2.10967.06566openalex-snapshot

Training Spiking Deep Networks for Neuromorphic Hardware

inferredcites →https://doi.org/10.1371/journal.pcbi.1004643openalex-snapshot

Cultured Cortical Neurons Can Perform Blind Source Separation According to the Free-Energy Principle

inferredcites →https://doi.org/10.1609/aaai.v33i01.33011311openalex-snapshot

Direct Training for Spiking Neural Networks: Faster, Larger, Better

inferredcites →https://doi.org/10.21437/interspeech.2018-1893openalex-snapshot

Diarization is Hard: Some Experiences and Lessons Learned for the JHU Team in the Inaugural DIHARD Challenge

inferredcites →https://doi.org/10.21437/interspeech.2019-1410openalex-snapshot

Target Speaker Extraction for Multi-Talker Speaker Verification

inferredcites →https://doi.org/10.24963/ijcai.2019/189openalex-snapshot

STCA: Spatio-Temporal Credit Assignment with Delayed Feedback in Deep Spiking Neural Networks

inferredcites →https://doi.org/10.3389/fnins.2017.00682openalex-snapshot

Conversion of Continuous-Valued Deep Networks to Efficient Event-Driven Networks for Image Classification

inferredcites →https://doi.org/10.3389/fnins.2018.00331openalex-snapshot

Spatio-Temporal Backpropagation for Training High-Performance Spiking Neural Networks

inferredcites →https://doi.org/10.3389/fnins.2018.00774openalex-snapshot

Deep Learning With Spiking Neurons: Opportunities and Challenges

inferredcites →https://doi.org/10.3389/fnins.2019.00095openalex-snapshot

Going Deeper in Spiking Neural Networks: VGG and Residual Architectures

inferredcites →https://doi.org/10.3389/fnins.2020.00119openalex-snapshot

Enabling Spike-Based Backpropagation for Training Deep Neural Network Architectures

inferredcites →https://doi.org/10.35111/ewkm-cg47openalex-snapshot

CSR-I (WSJ0) Complete

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Very Deep Convolutional Networks for Large-Scale Image Recognition

inferredcites →https://doi.org/10.48550/arxiv.1412.6980openalex-snapshot

Adam: A Method for Stochastic Optimization

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inferredcites →https://doi.org/10.48550/arxiv.1503.02531openalex-snapshot

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inferredcites →https://doi.org/10.48550/arxiv.1510.00149openalex-snapshot

Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding

inferredcites →https://doi.org/10.48550/arxiv.1602.02830openalex-snapshot

Binarized Neural Networks: Training Deep Neural Networks with Weights and Activations Constrained to +1 or -1

inferredcites →https://doi.org/10.48550/arxiv.1609.03499openalex-snapshot

WaveNet: A Generative Model for Raw Audio

inferredcites →https://doi.org/10.48550/arxiv.1711.00937openalex-snapshot

Neural Discrete Representation Learning

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Channelwise Regional Integrate and Multiple Firing Neuron: Improving the Spatiotemporal Learning of Spiking Neural Networks

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Spatiotemporal Decoupled Learning for Spiking Neural Networks

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Attention Spiking Neural Networks

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Fast-SNN: Fast Spiking Neural Network by Converting Quantized ANN

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A Hybrid Neural Coding Approach for Pattern Recognition With Spiking Neural Networks

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Spiking Variational Policy Gradient for Brain Inspired Reinforcement Learning

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Scaling Spike-Driven Transformer With Efficient Spike Firing Approximation Training

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Mechanism of Selectivity in the Coupled FitzHugh – Nagumo Neurons

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Electroencephalography Decoding with Conditional Identification Generator

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Application Exploration of Multi-Scale Edge Detection and SIFT Feature Fusion in Digital Twin Pattern Recognition for Power Grids

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Q-SNNs: Quantized Spiking Neural Networks

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Toward End-to-End Bearing Fault Diagnosis for Industrial Scenarios with Spiking Neural Networks

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Machine learning Algorithm Predicting Deep Vein Thrombosis Based on Blood Routine Analysis

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Spiking Neural Networks: A Comprehensive Survey of Training Methodologies, Hardware Implementations and Applications

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A Hybrid Platform for Multi-Neurons Model with Optimum Co-Design Method

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Predicting deep vein thrombosis using machine learning and blood routine analysis

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BlocTrain: Block-Wise Conditional Training and Inference for Efficient Spike-Based Deep Learning

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Neuronal-Plasticity and Reward-Propagation Improved Recurrent Spiking Neural Networks

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Quantization Framework for Fast Spiking Neural Networks

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Direct learning-based deep spiking neural networks: a review

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Sharing leaky-integrate-and-fire neurons for memory-efficient spiking neural networks

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Efficient human activity recognition with spatio-temporal spiking neural networks

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Training multi-layer spiking neural networks with plastic synaptic weights and delays

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STCA-SNN: self-attention-based temporal-channel joint attention for spiking neural networks

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Chip-In-Loop SNN Proxy Learning: a new method for efficient training of spiking neural networks

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Rethinking skip connections in Spiking Neural Networks with Time-To-First-Spike coding

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Optimizing event-driven spiking neural network with regularization and cutoff

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SpikeAtConv: an integrated spiking-convolutional attention architecture for energy-efficient neuromorphic vision processing

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Improved Reptile Search Optimization Algorithm: Application on Regression and Classification Problems

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Damage Resistance of an fMRI-Spiking Neural Network Based on Speech Recognition Against Stochastic Attack

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Color Image Encryption Based on a Novel Fourth-Direction Hyperchaotic System

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Importance and Application of Machine Learning and Pattern Recognition in Business Intelligence Models to Transform the Real World for Decision-Making

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Stationary Distribution of Stochastic Hopfield Neural Network with Cross-Diffusion Under Time-Varying Modified Markov Switching

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Neuromorphic Computing: Cutting-Edge Advances and Future Directions

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