Research map / Architectures and efficiency / RNNs and normalization
Boosting the Performance of Generic Deep Neural Network Frameworks with Log-supermodular CRFs
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
- Regularized Frank-Wolfe for Dense CRFs: Generalizing Mean Field and Beyond (NeurIPS 2021 · similarity 0.88)
- Multi-Scale Continuous CRFs as Sequential Deep Networks for Monocular Depth Estimation (CVPR 2017 · similarity 0.86)
- Gaussian Conditional Random Field Network for Semantic Segmentation (CVPR 2016 · similarity 0.86)
- End-To-End Training of Hybrid CNN-CRF Models for Stereo (CVPR 2017 · similarity 0.85)
- Deep Submodular Functions: Definitions and Learning (NeurIPS 2016 · similarity 0.85)
- CRONOS: Enhancing Deep Learning with Scalable GPU Accelerated Convex Neural Networks (NeurIPS 2024 · similarity 0.82)
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.