Research map / Architectures and efficiency / RNNs and normalization
Tversky Neural Networks: Psychologically Plausible Deep Learning with Differentiable Tversky Similarity
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
- Decoupling Semantic Similarity from Spatial Alignment for Neural Networks. (NeurIPS 2024 · similarity 0.85)
- Representational Similarity via Interpretable Visual Concepts (ICLR 2025 · similarity 0.84)
- Tracing Representation Progression: Analyzing and Enhancing Layer-Wise Similarity (ICLR 2025 · similarity 0.84)
- Differentiable Optimization of Similarity Scores Between Models and Brains (ICLR 2025 · similarity 0.83)
- Objective drives the consistency of representational similarity across datasets (ICML 2025 · similarity 0.83)
- Going Beyond Neural Network Feature Similarity: The Network Feature Complexity and Its Interpretation Using Category Theory (ICLR 2024 · similarity 0.83)
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.
