Research map / Learning theory and algorithms / Matrix and tensor methods
The Flip Side of the Reweighted Coin: Duality of Adaptive Dropout and Regularization
Read the paperCheck your idea against 108,967 papers
AI-extracted summary. Written by AI from the paper's title only (no summary yet). Numbers and claims can be wrong or missing context, so check the paper before you cite or build on it.
Closest papers by meaning
- Adaptive Dropout with Rademacher Complexity Regularization (ICLR 2018 · similarity 0.90)
- On the Regularization Properties of Structured Dropout (CVPR 2020 · similarity 0.90)
- On Dropout and Nuclear Norm Regularization (ICML 2019 · similarity 0.89)
- Global Minimizers of $\ell^p$-Regularized Objectives Yield the Sparsest ReLU Neural Networks (NeurIPS 2025 · similarity 0.88)
- Deep Weight Factorization: Sparse Learning Through the Lens of Artificial Symmetries (ICLR 2025 · similarity 0.88)
- Mask in the Mirror: Implicit Sparsification (ICLR 2025 · similarity 0.87)
Writing a paper in this area? scime draft is a free LaTeX editor with AI included, real-time co-authors and a check of your references and venue rules before you submit. It also imports OpenAI Prism projects.