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Methodology
← Optimization & Theory
Machine Learning
›
Optimization & Theory
›
Neural Network Optimization
3648 directly classified papers
Papers per year
2001: 1
2003: 1
2005: 2
2006: 3
2007: 6
2008: 1
2009: 7
2010: 5
2011: 7
2012: 9
2013: 17
2014: 18
2015: 40
2016: 76
2017: 113
2018: 214
2019: 324
2020: 414
2021: 489
2022: 445
2023: 524
2024: 469
2025: 386
2026: 77
Papers
Error Feedback Fixes SignSGD and other Gradient Compression Schemes
ICML 2019
Understanding and correcting pathologies in the training of learned optimizers
ICML 2019
Shallow-Deep Networks: Understanding and Mitigating Network Overthinking
ICML 2019
Semi-flat minima and saddle points by embedding neural networks to overparameterization
NIPS 2019
Band-limited Training and Inference for Convolutional Neural Networks
ICML 2019
Communication trade-offs for Local-SGD with large step size
NIPS 2019
Understanding the Role of Momentum in Stochastic Gradient Methods
NIPS 2019
Meta-Descent for Online, Continual Prediction
AAAI 2019
Long Short-Term Memory with Dynamic Skip Connections
AAAI 2019
Are deep ResNets provably better than linear predictors?
NIPS 2019
Acceleration via Symplectic Discretization of High-Resolution Differential Equations
NIPS 2019
On the Impact of the Activation function on Deep Neural Networks Training
ICML 2019
Escaping Saddle Points with Adaptive Gradient Methods
ICML 2019
Non-normal Recurrent Neural Network (nnRNN): learning long time dependencies while improving expressivity with transient dynamics
NIPS 2019
Understanding and Controlling Memory in Recurrent Neural Networks
ICML 2019
A Deep Bi-directional Attention Network for Human Motion Recovery
IJCAI 2019
Estimating Information Flow in Deep Neural Networks
ICML 2019
Push-pull Feedback Implements Hierarchical Information Retrieval Efficiently
NIPS 2019
An Investigation into Neural Net Optimization via Hessian Eigenvalue Density
ICML 2019
Turbo Autoencoder: Deep learning based channel codes for point-to-point communication channels
NIPS 2019
Deep Neural Networks with Multi-Branch Architectures Are Intrinsically Less Non-Convex
AISTATS 2019
Making Asynchronous Stochastic Gradient Descent Work for Transformers
EMNLP 2019
Toward Understanding the Importance of Noise in Training Neural Networks
ICML 2019
Demystifying Dropout
ICML 2019
Leap-LSTM: Enhancing Long Short-Term Memory for Text Categorization
IJCAI 2019
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