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Found 11 papers, 3 papers with code
SwiftNet: Using Graph Propagation as Meta-knowledge to Search Highly Representative Neural Architectures
Designing neural architectures for edge devices is subject to constraints of accuracy, inference latency, and computational cost.
Defending Neural Backdoors via Generative Distribution Modeling
An original trigger used by an attacker to build the backdoored model represents only a point in the space.
Temporal Surrogate Back-propagation for Spiking Neural Networks
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.
Backpropagated Neighborhood Aggregation for Accurate Training of Spiking Neural Networks
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.
Training Deep Spiking Neural Networks with Bio-plausible Learning Rules
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.
BioLeaF: A Bio-plausible Learning Framework for Training of Spiking Neural Networks
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.
Agreement or Disagreement in Noise-tolerant Mutual Learning?
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.
A Computational Framework of Cortical Microcircuits Approximates Sign-concordant Random Backpropagation
We employ the Hebbian rule operating in local compartments to update synaptic weights and achieve supervised learning in a biologically plausible manner.
Synaptic Dynamics Realize First-order Adaptive Learning and Weight Symmetry
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 theory for the sparsity emerged in the Forward Forward algorithm
This report explores the theory that explains the high sparsity phenomenon \citep{tosato2023emergent} observed in the forward-forward algorithm \citep{hinton2022forward}.
Coordinated Sparse Recovery of Label Noise
Based on CSR, this study designs a joint sample selection strategy and constructs a comprehensive and powerful learning framework called CSR+.
