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Methodology
← Optimization & Theory
Deep Learning
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Optimization & Theory
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Optimization
1638 directly classified papers
Papers per year
2006: 5
2007: 2
2008: 4
2009: 2
2010: 2
2011: 3
2012: 8
2013: 25
2014: 19
2015: 22
2016: 31
2017: 42
2018: 68
2019: 104
2020: 148
2021: 174
2022: 178
2023: 209
2024: 345
2025: 244
2026: 3
Papers
Improving Perceptual Quality by Phone-Fortified Perceptual Loss Using Wasserstein Distance for Speech Enhancement
INTERSPEECH 2021
Residual Echo and Noise Cancellation with Feature Attention Module and Multi-Domain Loss Function
INTERSPEECH 2021
Dissecting User-Perceived Latency of On-Device E2E Speech Recognition
INTERSPEECH 2021
Fiedler Regularization: Learning Neural Networks with Graph Sparsity
ICML 2020
Optimization Learning: Perspective, Method, and Applications
IJCAI 2020
BaKer-Nets: Bayesian Random Kernel Mapping Networks
IJCAI 2020
Spectral Pruning: Compressing Deep Neural Networks via Spectral Analysis and its Generalization Error
IJCAI 2020
Accelerating Stratified Sampling SGD by Reconstructing Strata
IJCAI 2020
SMART: Robust and Efficient Fine-Tuning for Pre-trained Natural Language Models through Principled Regularized Optimization
ACL 2020
Improving Low Compute Language Modeling with In-Domain Embedding Initialisation
EMNLP 2020
A Greedy Bit-flip Training Algorithm for Binarized Knowledge Graph Embeddings
EMNLP 2020
A Batch Normalized Inference Network Keeps the KL Vanishing Away
ACL 2020
Linear Convergence of Randomized Primal-Dual Coordinate Method for Large-scale Linear Constrained Convex Programming
ICML 2020
Efficient Minimum Word Error Rate Training of RNN-Transducer for End-to-End Speech Recognition
INTERSPEECH 2020
Deep Unfolding Network for Image Super-Resolution
CVPR 2020
Error Estimation for Sketched SVD via the Bootstrap
ICML 2020
Taylor Expansion Policy Optimization
ICML 2020
Stochastic Gauss-Newton Algorithms for Nonconvex Compositional Optimization
ICML 2020
Tuning-free Plug-and-Play Proximal Algorithm for Inverse Imaging Problems
ICML 2020
DeltaGrad: Rapid retraining of machine learning models
ICML 2020
Stronger and Faster Wasserstein Adversarial Attacks
ICML 2020
Training Deep Energy-Based Models with f-Divergence Minimization
ICML 2020
Complexity of Finding Stationary Points of Nonconvex Nonsmooth Functions
ICML 2020
Bundle Adjustment on a Graph Processor
CVPR 2020
MAGSAC++, a Fast, Reliable and Accurate Robust Estimator
CVPR 2020
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