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Softmax function
Lecture 4 Feedforward Neural Networks, Backpropagation
Revisiting the Softmax Bellman Operator: New Benefits and New Perspective
On the Learning Property of Logistic and Softmax Losses for Deep Neural Networks
CS281B/Stat241b. Statistical Learning Theory. Lecture 7. Peter Bartlett
Loss Function Search for Face Recognition
Deep Neural Networks for Choice Analysis: Architecture Design with Alternative-Specific Utility Functions Shenhao Wang Baichuan
Pseudo-Learning Effects in Reinforcement Learning Model-Based Analysis: a Problem Of
Lecture 18: Wrapping up Classification Mark Hasegawa-Johnson, 3/9/2019
Categorical Data
Mixed Pattern Recognition Methodology on Wafer Maps with Pre-Trained Convolutional Neural Networks
Arxiv:1910.04465V2 [Cs.CV] 16 Oct 2019
Reinforcement Learning with Dynamic Boltzmann Softmax Updates Arxiv
Rethinking Feature Distribution for Loss Functions in Image Classification
STA 4273H: Statistical Machine Learning
An Adaptive Projection Alternative to the Softmax Function
TF-NAS: Rethinking Three Search Freedoms of Latency-Constrained Differentiable Neural Architecture Search
Dropmax: Adaptive Variational Softmax
Deepmellow: Removing the Need for a Target Network in Deep Q-Learning
Top View
Machine Learning and Statistics
Balanced Meta-Softmax for Long-Tailed Visual Recognition
Generating Speech in Different Speaking Styles Using Wavenet
Perceptrons Transcribed by Sophie Hao January 16, 2020
UNIVERSITY of CALIFORNIA Los Angeles Neural Architecture Search for Biological Sequences a Thesis Submitted in Partial Satisfact
Activation Functions in Neural Networks
CS 224D: Assignment #1
A Partially Interpretable Adaptive Softmax Regression for Credit Scoring
IMU-Based Locomotor Intention Prediction for Real-Time Use In
Activation Functions: Comparison of Trends in Practice and Research for Deep Learning
Notes on Backpropagation
Evidential Disambiguation of Latent Multimodality in Conditional Variational Autoencoders
A Novel Framework Using Deep Auto-Encoders Based Linear Model for Data Classification
4F10: Deep Learning
Word2vec Embeddings: CBOW and Skipgram
Deep Reinforcement Learning of Video Games
Improving Classification Performance of Softmax Loss Function Based On
Section 2: Backpropagation and Optimization 2.1 Backpropagation
Lecture 8 Multiclass/Log-Linear Models, Evaluation, and Human Labels
A Pseudo-Softmax Function for Hardware-Based High Speed Image Classification
7 the Backpropagation Algorithm
On Parameter Adaptation in Softmax-Based Cross-Entropy Loss for Improved Convergence Speed and Accuracy in DNN-Based Speaker Recognition
With Deep Learning CS224N/Ling284
Logistic Regression
Logistic Regression with an Intro to Sigmoid, Softmax, and Cross-Entropy
Graph Wavenet for Deep Spatial-Temporal Graph Modeling
Determining the Optimal Temperature Parameter for Softmax Function in Reinforcement Learning
Hardware Implementation of a Softmax-Like Function for Deep Learning †
Linear Discriminant Analysis Discriminant Analysis Or Classification, Can Be Viewed As a Special Type of Regression, Sometimes Named ×