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← Optimization & Theory
Machine Learning
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Optimization & Theory
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Neural Network Optimization
3,648 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
Learning the dynamics of autonomous nonlinear delay systems
L4DC 2023
Automatic Integration for Fast and Interpretable Neural Point Processes
L4DC 2023
Regret Guarantees for Online Deep Control
L4DC 2023
ModelKeeper: Accelerating DNN Training via Automated Training Warmup
NSDI 2023
Sequence-Based Plan Feasibility Prediction for Efficient Task and Motion Planning
RSS 2023
Reachability-based Trajectory Design with Neural Implicit Safety Constraints
RSS 2023
I2C-Huelva at SemEval-2023 Task 10: Ensembling Transformers Models for the Detection of Online Sexism
SEMEVAL 2023
UZH_CLyp at SemEval-2023 Task 9: Head-First Fine-Tuning and ChatGPT Data Generation for Cross-Lingual Learning in Tweet Intimacy Prediction
SEMEVAL 2023
Lon-eå at SemEval-2023 Task 11: A Comparison of Activation Functions for Soft and Hard Label Prediction
SEMEVAL 2023
Neural tangent kernel at initialization: linear width suffices
UAI 2023
On the Convergence of Continual Learning with Adaptive Methods
UAI 2023
Fed-LAMB: Layer-wise and Dimension-wise Locally Adaptive Federated Learning
UAI 2023
How to use dropout correctly on residual networks with batch normalization
UAI 2023
Phase-shifted adversarial training
UAI 2023
Random Reshuffling with Variance Reduction: New Analysis and Better Rates
UAI 2023
Improving Multi-Fidelity Optimization With a Recurring Learning Rate for Hyperparameter Tuning
WACV 2023
Couplformer: Rethinking Vision Transformer With Coupling Attention
WACV 2023
Accumulated Trivial Attention Matters in Vision Transformers on Small Datasets
WACV 2023
LCS: Learning Compressible Subspaces for Efficient, Adaptive, Real-Time Network Compression at Inference Time
WACV 2023
Self-Attentive Pooling for Efficient Deep Learning
WACV 2023
SERF: Towards Better Training of Deep Neural Networks Using Log-Softplus ERror Activation Function
WACV 2023
RNAS-MER: A Refined Neural Architecture Search With Hybrid Spatiotemporal Operations for Micro-Expression Recognition
WACV 2023
Are Straight-Through Gradients and Soft-Thresholding All You Need for Sparse Training?
WACV 2023
FLOAT: Fast Learnable Once-for-All Adversarial Training for Tunable Trade-Off Between Accuracy and Robustness
WACV 2023
Spike-Based Anytime Perception
WACV 2023
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