Measure Neural Mappings
cs.LG, stat.ML
Submitted: 2026-10-12
Updated: 2026-10-12
Comments: 65 pages, 6 figures, 7 tables
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Sources
- Learning Probabilistic Filters with Strictly Proper Scoring Rules
- Neural Machine Translation by Jointly Learning to Align and Translate
- Emergence of meta-stable clustering in mean-field transformer models
- A Unified Perspective on the Dynamics of Deep Transformers
- Quantitative Clustering in Mean-Field Transformer Models
- Propagation of Chaos in Contextual Flow Maps
- Stability of Measure-to-Measure Transformers on Sub-Gaussian Data
- In-Context Operator Learning on the Space of Probability Measures
- A short proof on the rate of convergence of the empirical measure for the Wasserstein distance
- An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
- Learn to Evolve: Self-supervised Neural JKO Operator for Wasserstein Gradient Flow
- Universal Approximation of Measure-to-Measure Operators by Pushforwards
- Attention Kernels for Learning Maps Between Heavy-Tailed Measures
- Continuous transformations of probability measures and their transport representations
- Minimax Confidence Intervals for the Sliced Wasserstein Distance
- Distribution learning via neural differential equations: minimal energy regularization and approximation theory
- GPT-4 Technical Report
- The Mean-Field Dynamics of Transformers
- LLaMA: Open and Efficient Foundation Language Models
- Measure-to-measure Regression with Transformers
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