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Differentiable function
Training Autoencoders by Alternating Minimization
Deep Learning Based Computer Generated Face Identification Using
Matrix Calculus
Differentiability in Several Variables: Summary of Basic Concepts
Lipschitz Recurrent Neural Networks
Neural Networks
Jointly Clustering with K-Means and Learning Representations
Differentiability of the Convolution
The Simple Essence of Automatic Differentiation
Expressivity of Deep Neural Networks∗
Differentiable Programming for Piecewise Polynomial
GAN and What It Is?
Differentiable Functions of Several Variables
Ganalyze: Toward Visual Definitions of Cognitive Image Properties
Differentiable Functions for Combining First-Order Constraints with Deep Learning Via Weighted Proof Tracing
Automatic Differentiation and Neural Networks 1 Introduction
Training Discrete-Valued Neural Networks with Sign Activations Using Weight Distributions
Tensor Calculus Arxiv:1610.04347V1 [Math.HO] 14 Oct 2016
Top View
A Mathematical Model for Automatic Differentiation in Machine Learning Jerome Bolte, Edouard Pauwels
Differentiable Programming and Its Applications to Dynamical Systems
Arxiv:1912.07651V2 [Cs.LG] 27 Aug 2020 Able At
Differentiability
AUTOMATIC DIFFERENTIATION of FUNCTIONS of DERIVATIVES in A
Selected Solutions to Exercises from Pavel Grinfeldgs Introduction to Tensor Analysis and the Calculus of Moving Surfaces
Alternating Direction Method of Multipliers for Sparse
Concrete Autoencoders: Differentiable Feature Selection and Reconstruction
Learning to Adaptively Scale Recurrent Neural Networks
EA-CG: an Approximate Second-Order Method for Training Fully-Connected Neural Networks
Continuous, Nowhere Differentiable Functions
Backpropagation in the Simply Typed Lambda-Calculus with Linear Negation
Learning Multiple Layers of Features from Tiny Images
Optimizing Data Usage Via Differentiable Rewards
COMP 551 – Applied Machine Learning Lecture 16: Deep Learning
Fast-Slow Recurrent Neural Networks
Tensorial Version of the Calculus of Variations
Deeply Aggrevated: Differentiable Imitation Learning for Sequential Prediction
Deep Generative Models Variational Autoencoders
Differentiable Approximation by Means of the Radon
Improving Discrete Latent Representations with Differentiable Approximation Bridges
Differentiable Functions
Lecture Notes on Distributions
Notes on Tensor Analysis in Differentiable
Differentiable Generator Nets
Monadic Deep Learning
Differentiable Functions
7 the Backpropagation Algorithm
An Overview of Artificial Neural Networks for Mathematicians
Parametric Exponential Linear Unit for Deep
Feedforward and Recurrent Neural Networks
6 Convolution, Smoothing, and Weak Convergence
The If-Problem in Automatic Differentiation
Universal Approximation Bounds for Superpositions of a Sigmoidal Function
On Autoencoder Scoring
Tensor Calculus
Neural Networks and Differential Equations
Universal Approximation Theory of Neural Networks by Simon Odense
Extending Differentiable Programming to Include Non-Differentiable
Detection of Synthetic Portrait Videos Using Biological Signals
Differentiable Programming for Image Processing and Deep Learning in Halide
Neural Networks: Backpropagation
Paper, We Present Swift for Tensorflow, a Platform Deploying Machine-Learned Models on Edge Devices Can for Machine Learning
Automatic Differentiation in Pytorch
Generative Adversarial Networks in Lip-Synchronized Deepfakes for Personalized Video Messages
Augmented Fréchet Inception Distance
Optimizing Millions of Hyperparameters by Implicit Differentiation
Tempered Distributions
On Correctness of Automatic Differentiation for Non-Differentiable Functions
Neural Query Language: a Knowledge Base Query Language for Tensorflow
Introduction to Tensor Calculus for General Relativity C 2000 Edmund Bertschinger
Difftaichi: Differentiable Programming for Physical Simulation
Differentiable Mask for Pruning Convolutional and Recurrent
Communication Learning Via Backpropagation in Discrete Channels with Unknown Noise
Neural Networks and Backpropagation
MATH 4330/5330, Fourier Analysis Section 9 Properties of the Fourier Transform
8 Differentiability and Derivatives
Difftaichi: Differentiable Programming for Physical
Harmonic Analysis: from Fourier to Haar Mar´Ia Cristina Pereyra Lesley A. Ward
Automatic Differentiation of Iterative Processes
How to Get a Neural Network to Do What You Want?
Neural Networks Learning the Network: Part 2
Universal Approximation with Deep Narrow Networks
Metaquant: Learning to Quantize by Learning to Penetrate Non-Differentiable Quantization
Semantics for Automatic Differentiation Towards Probabilistic Programming Literature Review and Dissertation Proposal
1 Adversarial Examples and the Deeper Riddle of Induction
Autoencoders, Deconvolution and Gans Outline
True Gradient-Based Training of Deep Binary
Metaquant: Learning to Quantize by Learning to Penetrate Non-Differentiable Quantization
Lecture 5: Value Function Approximation
Non-Differentiability of the Convolution of Differentiable Real Functions
LECTURE #20: ADVERSARIAL TRAINING Administrivia
On Correctness of Automatic Differentiation for Non-Differentiable Functions
Neural Expectation Maximization
Optimization Techniques for ML (2)
Notes on Tensor Calculus
Universal Approximation with Deep Narrow Networks