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Learning rate
Training Autoencoders by Alternating Minimization
Q-Learning in Continuous State and Action Spaces
Comparative Analysis of Recurrent Neural Network Architectures for Reservoir Inflow Forecasting
Training Deep Networks Without Learning Rates Through Coin Betting
The Perceptron
Neural Networks a Simple Problem (Linear Regression)
Learning-Rate Annealing Methods for Deep Neural Networks
Arxiv:1912.08795V2 [Cs.LG] 16 Jun 2020 the Teacher and Student Network Logits
Towards Explaining the Regularization Effect of Initial Large Learning Rate in Training Neural Networks
How Does Learning Rate Decay Help Modern Neural Networks?
Natural Language Grammatical Inference with Recurrent Neural Networks
Perceptron.Pdf
Using Neural Network and Logistic Regression Analysis to Predict Prospective Mathematics Teachers’ Academic Success Upon Entering Graduate Education
Reconciling Modern Deep Learning with Traditional Optimization Analyses: the Intrinsic Learning Rate
Don't Decay the Learning Rate, Increase the Batch Size
Speedy Q-Learning
Structural Learning of Neural Networks Phd Defense
Machine Learning Basics Lecture 3: Perceptron Princeton University COS 495 Instructor: Yingyu Liang Perceptron Overview
Top View
Developing Creative AI to Generate Sculptural Objects
Learning with Random Learning Rates
Sparse Autoencoder
Towards Flatter Loss Surface Via Nonmonotonic Learning Rate Scheduling
On the Difficulty of Training Recurrent Neural Networks
A Novel Learning Rate Schedule in Optimization for Neural Networks and It’S Convergence
Variable Learning Rate Based Modification in Backpropagation
Stacked Denoising Autoencoders: Learning Useful Representations in a Deep Network with a Local Denoising Criterion
Neural Networks with Adaptive Learning Rate and Momentum Terms
Effect of Learning Rate on Artificial Neural Network in Machine Learning
Hallucinating Point Cloud Into 3D Sculptural Object
The Need for Small Learning Rates on Large Problems
Lecture 6: Training Neural Networks, Part 2
On the Dynamics of Gradient Descent for Autoencoders
An Optimized Back Propagation Learning Algorithm with Adaptive
On the Implicit Bias of Stochastic Gradient Descent
Improving the Convergence of the Backpropagation Algorithm Using Learning Rate Adaptation Methods
Weighted Double Q-Learning
An Online Backpropagation Algorithm with Validation Error-Based Adaptive Learning Rate
The Large Learning Rate Phase of Deep Learning: the Catapult Mechanism
Technical Note Q,-Learning
Forget the Learning Rate, Decay Loss
Improved Backpropagation Learning in Neural Networks with Windowed Momentum
Memorization in Overparameterized Autoencoders
Reinforcement Learning with Factored States and Actions
Intro to Deep Learning
Using Deep Q-Learning to Control Optimization Hyperparameters
A Comprehensive Analysis of Deep Regression
Reinforcement Learning
A Learning-Rate Schedule for Stochastic Gradient Methods to Matrix Factorization
Audio Deepdream: Optimizing Raw Audio with Convolutional Networks
An Empirical Study of Learning Rates in Deep Neural Networks for Speech Recognition
4 Perceptron Learning Rule
The Asymptotic Convergence-Rate of Q-Learning
No More Pesky Learning Rates
Predicting the Learning Rate of Gradient Descent for Accelerating Matrix Factorization
Recent Advances in Recurrent Neural Networks
No More Pesky Learning Rates
A Tutorial on Deep Learning Part 2: Autoencoders, Convolutional Neural Networks and Recurrent Neural Networks
Recurrent Neural Networks (Bianchi)
Learning the Learning Rate for Gradient Descent by Gradient Descent
The Simple Perceptron
Fine Tuning Image Input Sets for Deep Neural Networks Using Hallucinations
Perceptron Algorithm1
Demystifying Learning Rate Policies for High Accuracy Training of Deep Neural Networks
Online Learning Rate Adaptation with Hypergradient Descent
Introduction to Machine Learning
Learning in Variational Autoencoders with Kullback-Leibler and Renyi Integral Bounds
Deep Learning
Using Recurrent Neural Networks to Dream Sequences of Audio
Multi-Cell LSTM Based Neural Language Model
Least Mean Squares Regression
Learning Rates for Q-Learning
Control Batch Size and Learning Rate to Generalize Well: Theoretical and Empirical Evidence
Adaptive Learning Rate and Momentum for Training Deep Neural Networks