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Found 7 papers, 3 papers with code
Introduction to dynamical mean-field theory of randomly connected neural networks with bidirectionally correlated couplings
Dynamical mean-field theory is a powerful physics tool used to analyze the typical behavior of neural networks, where neurons can be recurrently connected, or multiple layers of neurons can be stacked.
Statistical mechanics of continual learning: variational principle and mean-field potential
A variational Bayesian learning setting is thus proposed, where the neural networks are trained in a field-space, rather than gradient-ill-defined discrete-weight space, and furthermore, weight uncertainty is naturally incorporated, and modulates synaptic resources among tasks.
Graph Flow: Cross-layer Graph Flow Distillation for Dual Efficient Medical Image Segmentation
To tackle these problems, we propose Graph Flow, a comprehensive knowledge distillation framework, for both network-efficiency and annotation-efficiency medical image segmentation.
CoCo DistillNet: a Cross-layer Correlation Distillation Network for Pathological Gastric Cancer Segmentation
To tackle this problem, we propose CoCo DistillNet, a novel Cross-layer Correlation (CoCo) knowledge distillation network for pathological gastric cancer segmentation.
PAENet: A Progressive Attention-Enhanced Network for 3D to 2D Retinal Vessel Segmentation
In the three-dimensional feature learning path, we design a novel Adaptive Pooling Module (APM) and propose a new Quadruple Attention Module (QAM).
Ensemble perspective for understanding temporal credit assignment
Recurrent neural networks are widely used for modeling spatio-temporal sequences in both nature language processing and neural population dynamics.
Data-driven effective model shows a liquid-like deep learning
Here, we propose a statistical mechanics framework by directly building a least structured model of the high-dimensional weight space, considering realistic structured data, stochastic gradient descent training, and the computational depth of neural networks.
