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Methodology
← Optimization & Theory
Deep Learning
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Optimization & Theory
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Theory
1072 directly classified papers
Papers per year
2007: 1
2010: 4
2011: 1
2012: 3
2013: 4
2014: 5
2015: 2
2016: 11
2017: 31
2018: 47
2019: 67
2020: 97
2021: 128
2022: 225
2023: 155
2024: 209
2025: 81
2026: 1
Papers
Defending Against Adversarial Attacks via Neural Dynamic System
NIPS 2022
Neural Network Architecture Beyond Width and Depth
NIPS 2022
A PAC-Bayesian Generalization Bound for Equivariant Networks
NIPS 2022
Size and depth of monotone neural networks: interpolation and approximation
NIPS 2022
Sketching based Representations for Robust Image Classification with Provable Guarantees
NIPS 2022
Transition to Linearity of General Neural Networks with Directed Acyclic Graph Architecture
NIPS 2022
Chaotic Dynamics are Intrinsic to Neural Network Training with SGD
NIPS 2022
How Powerful are K-hop Message Passing Graph Neural Networks
NIPS 2022
The alignment property of SGD noise and how it helps select flat minima: A stability analysis
NIPS 2022
Why Robust Generalization in Deep Learning is Difficult: Perspective of Expressive Power
NIPS 2022
Your Transformer May Not be as Powerful as You Expect
NIPS 2022
ExSum: From Local Explanations to Model Understanding
NAACL 2022
On the Effectiveness of Iterative Learning Control
L4DC 2022
Implicit Regularization with Polynomial Growth in Deep Tensor Factorization
ICML 2022
Exploring the Gap between Collapsed & Whitened Features in Self-Supervised Learning
ICML 2022
Sparse Double Descent: Where Network Pruning Aggravates Overfitting
ICML 2022
Robust Fine-Tuning of Deep Neural Networks with Hessian-based Generalization Guarantees
ICML 2022
Robustness Implies Generalization via Data-Dependent Generalization Bounds
ICML 2022
Implicit Bias of Linear Equivariant Networks
ICML 2022
Neural Tangent Kernel Analysis of Deep Narrow Neural Networks
ICML 2022
Generalization Guarantee of Training Graph Convolutional Networks with Graph Topology Sampling
ICML 2022
Hessian-Free High-Resolution Nesterov Acceleration For Sampling
ICML 2022
Benefits of Overparameterized Convolutional Residual Networks: Function Approximation under Smoothness Constraint
ICML 2022
Minimizing Control for Credit Assignment with Strong Feedback
ICML 2022
A Dynamical System Perspective for Lipschitz Neural Networks
ICML 2022
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