Search Results for author: Yukun Yang

Found 9 papers, 3 papers with code

Synaptic Dynamics Realize First-order Adaptive Learning and Weight Symmetry

no code implementations1 Dec 2022 Yukun Yang, Peng Li

Gradient-based first-order adaptive optimization methods such as the Adam optimizer are prevalent in training artificial networks, achieving the state-of-the-art results.

A Computational Framework of Cortical Microcircuits Approximates Sign-concordant Random Backpropagation

no code implementations15 May 2022 Yukun Yang, Peng Li

We employ the Hebbian rule operating in local compartments to update synaptic weights and achieve supervised learning in a biologically plausible manner.

Agreement or Disagreement in Noise-tolerant Mutual Learning?

1 code implementation29 Mar 2022 Jiarun Liu, Daguang Jiang, Yukun Yang, Ruirui Li

The state-of-the-art learning with noisy label method Co-teaching and Co-teaching+ confronts the noisy label by mutual-information between dual-network.

BioLeaF: A Bio-plausible Learning Framework for Training of Spiking Neural Networks

no code implementations14 Nov 2021 Yukun Yang, Peng Li

Our experiments show that the proposed framework demonstrates learning accuracy comparable to BP-based rules and may provide new insights on how learning is orchestrated in biological systems.

Training Deep Spiking Neural Networks with Bio-plausible Learning Rules

no code implementations29 Sep 2021 Yukun Yang, Peng Li

There exists a marked cleavage between the biological plausible approaches and the practical backpropagation-based approaches on how to train a deep spiking neural network (DSNN) with better performance.

Backpropagated Neighborhood Aggregation for Accurate Training of Spiking Neural Networks

no code implementations22 Jun 2021 Yukun Yang, Wenrui Zhang, Peng Li

While backpropagation (BP) has been applied to spiking neural networks (SNNs) achieving encouraging results, a key challenge involved is to backpropagate a continuous-valued loss over layers of spiking neurons exhibiting discontinuous all-or-none firing activities.

Temporal Surrogate Back-propagation for Spiking Neural Networks

no code implementations18 Nov 2020 Yukun Yang

Spiking neural networks (SNN) are usually more energy-efficient as compared to Artificial neural networks (ANN), and the way they work has a great similarity with our brain.

Defending Neural Backdoors via Generative Distribution Modeling

1 code implementation NeurIPS 2019 Ximing Qiao, Yukun Yang, Hai Li

An original trigger used by an attacker to build the backdoored model represents only a point in the space.

Backdoor Attack Image Generation

SwiftNet: Using Graph Propagation as Meta-knowledge to Search Highly Representative Neural Architectures

1 code implementation19 Jun 2019 Hsin-Pai Cheng, Tunhou Zhang, Yukun Yang, Feng Yan, Shi-Yu Li, Harris Teague, Hai Li, Yiran Chen

Designing neural architectures for edge devices is subject to constraints of accuracy, inference latency, and computational cost.

Neural Architecture Search

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