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Found 9 papers, 2 papers with code
On Optimal Early Stopping: Over-informative versus Under-informative Parametrization
We demonstrate experimentally that our theoretical results on optimal early stopping time corresponds to the training process of deep neural networks.
Deformation Robust Roto-Scale-Translation Equivariant CNNs
Incorporating group symmetry directly into the learning process has proved to be an effective guideline for model design.
On Optimal Early Stopping: Overparametrization versus Underparametrization
Early stopping is a simple and widely used method to prevent over-training neural networks.
Quantifying Uncertainty in Deep Spatiotemporal Forecasting
Deep learning is gaining increasing popularity for spatiotemporal forecasting.
DeepGLEAM: A hybrid mechanistic and deep learning model for COVID-19 forecasting
We introduce DeepGLEAM, a hybrid model for COVID-19 forecasting.
Non-convex Learning via Replica Exchange Stochastic Gradient MCMC
Replica exchange Monte Carlo (reMC), also known as parallel tempering, is an important technique for accelerating the convergence of the conventional Markov Chain Monte Carlo (MCMC) algorithms.
Ranked #77 on
Image Classification
on CIFAR-100
(using extra training data)
RotEqNet: Rotation-Equivariant Network for Fluid Systems with Symmetric High-Order Tensors
Rotation symmetry is a general property for most symmetric fluid systems.
Learning with Collaborative Neural Network Group by Reflection
CNNG is a progression of neural systems that work cooperatively to deal with various errands independently in a similar learning framework.
Cortex Neural Network: learning with Neural Network groups
The Cortex Neural Network is an upper architecture of neural networks which motivated from cerebral cortex in the brain to handle different tasks in the same learning system.
