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Found 13 papers, 7 papers with code
Orthogonal Recurrent Neural Networks with Scaled Cayley Transform
Recurrent Neural Networks (RNNs) are designed to handle sequential data but suffer from vanishing or exploding gradients.
Adaptive Weighted Discriminator for Training Generative Adversarial Networks
Generative adversarial network (GAN) has become one of the most important neural network models for classical unsupervised machine learning.
Ranked #4 on
Conditional Image Generation
on CIFAR-100
Conditional Image Generation
Generative Adversarial Network
+1
Complex Unitary Recurrent Neural Networks using Scaled Cayley Transform
Recently, there have been several different RNN architectures that try to mitigate this issue by maintaining an orthogonal or unitary recurrent weight matrix.
Eigenvalue Normalized Recurrent Neural Networks for Short Term Memory
Several variants of recurrent neural networks (RNNs) with orthogonal or unitary recurrent matrices have recently been developed to mitigate the vanishing/exploding gradient problem and to model long-term dependencies of sequences.
Symmetry Structured Convolutional Neural Networks
We consider Convolutional Neural Networks (CNNs) with 2D structured features that are symmetric in the spatial dimensions.
On regularization for a convolutional kernel in neural networks
Convolutional neural network is an important model in deep learning.
Two-stage short-term wind power forecasting algorithm using different feature-learning models
Considering the overfitting issue, we propose a new moving window-based algorithm using a validation set in the first stage to update the training data in both stages with two different moving window processes. Experiments were conducted at three wind farms, and the results demonstrate that the model with single input multiple output structure obtains better forecasting accuracy compared to existing models.
Stochastic Gradient Descent with Nonlinear Conjugate Gradient-Style Adaptive Momentum
In this paper, we propose a novel \emph{adaptive momentum} for improving DNNs training; this adaptive momentum, with no momentum related hyperparameter required, is motivated by the nonlinear conjugate gradient (NCG) method.
Batch Normalization Preconditioning for Neural Network Training
Batch normalization (BN) is a popular and ubiquitous method in deep learning that has been shown to decrease training time and improve generalization performance of neural networks.
AUTM Flow: Atomic Unrestricted Time Machine for Monotonic Normalizing Flows
AUTM offers a versatile and efficient way to the design of normalizing flows with explicit inverse and unrestricted function classes or parameters.
Orthogonal Gated Recurrent Unit with Neumann-Cayley Transformation
In recent years, using orthogonal matrices has been shown to be a promising approach in improving Recurrent Neural Networks (RNNs) with training, stability, and convergence, particularly, to control gradients.
Breaking Time Invariance: Assorted-Time Normalization for RNNs
Methods such as Layer Normalization (LN) and Batch Normalization (BN) have proven to be effective in improving the training of Recurrent Neural Networks (RNNs).
SCP-GAN: Self-Correcting Discriminator Optimization for Training Consistency Preserving Metric GAN on Speech Enhancement Tasks
In recent years, Generative Adversarial Networks (GANs) have produced significantly improved results in speech enhancement (SE) tasks.
Ranked #2 on
Speech Enhancement
on VoiceBank + DEMAND
