Keyphrases
Deep Learning
92%
Implicit Regularization
67%
Gradient Descent
52%
Deep Network
44%
Deep Learning Architectures
44%
Quantum Entanglement
43%
Convolutional Networks
35%
Tensor Factorization
33%
Deep Linear Networks
33%
Tensor Decomposition
29%
Inductive Bias
29%
Deep Convolutional Network
29%
Arithmetic Circuits
26%
Expressiveness
25%
Convolutional
22%
Matrix Completion
22%
Matrix Factorization
19%
Hierarchical Tensor
19%
Tensor Analysis
17%
Network Design
16%
Expressive Power
16%
Separation Rank
16%
Discrete Optimization
14%
Locally Connected Neural Network
14%
Deep Internal Learning
14%
Deep Learning Theory
14%
Zero-shot Super-resolution
14%
Super-resolution Algorithm
14%
Convergence to Global Optimum
14%
Deep Neural Network
14%
Expressive Efficiency
14%
Feature Space
14%
Dilated Convolutional Neural Network
14%
Deep Convolutional Neural Network (deep CNN)
14%
Temporal Extrapolation
14%
Convergence Analysis
14%
Deep Matrix Factorization
14%
Gradient Flow
14%
Rectifier Network
14%
Tensorization
14%
Infinite Depth
14%
Depth Limit
14%
Graph Neural Network
14%
Condition-dependent
14%
Implicit Bias
14%
Model Interaction
14%
Data Distribution
14%
Convolutional Neural Network
13%
Gradient-based Optimization
13%
Recurrent Neural Network
13%
Mathematics
Neural Network
100%
Deep Learning
75%
Regularization
60%
Factorization
38%
Tensor Decomposition
29%
Matrix (Mathematics)
25%
Complete Matrix
22%
Gradient Flow
21%
Expressiveness
20%
Polynomial
18%
Tensor
18%
Expressive Power
16%
Convolutional Neural Network
15%
Natural Image
14%
Tensor Analysis
14%
Necessary and Sufficient Condition
14%
Convergence Analysis
14%
Feature Space
14%
Mixed Tensor
14%
Global Optimum
14%
Deep Neural Network
14%
Neural Net
14%
Training Data
12%
Data Distribution
11%
Hypothesis Space
10%
Negligible Set
10%
Nonlinear
10%
Convolution
10%
Statistics
10%
Open Question
7%
Convex Problem
7%
Replicate
7%
Explicit Expression
7%
Volume Form
7%
Asymptotics
7%
Linear Separation
7%
Residual Network
7%
Numerics
7%
Rank Tensor
7%
Learning Task
7%
Condition Ii
7%
Linear Regression
7%
Probability Theory
7%
Loss Function
7%
Correlation Model
7%
Riemannian Geometry
7%
Weight Matrix
7%
Driving Force
7%
Speed Convergence
7%
Real-World Data
5%
Computer Science
Deep Learning
97%
Regularization
75%
Neural Network
73%
Convolutional Network
40%
Gradient Descent
34%
Matrix Factorization
25%
Tensor Analysis
16%
Conventional Wisdom
16%
Expressive Power
16%
Preconditioner
14%
Deep Convolutional Neural Networks
14%
Speed-up
14%
super resolution
14%
Sufficient Condition
14%
Mathematical Convergence
14%
Output Dimension
14%
Learning State
14%
Feature Space
14%
Data Distribution
14%
Interaction Model
14%
Graph Neural Network
14%
Convolutional Neural Network
13%
Machine Learning
9%
Residual Neural Network
7%
State Space
7%
Speed Convergence
7%
Constant Probability
7%
Tensor Network
7%
Boltzmann Machine
7%
Input Dimension
7%
Representation Function
7%
Hypothesis Space
7%
Arithmetic Circuit
7%
Input Partition
7%
