mlsanityData to 14 Oct 2026

Functional Distribution Networks (FDN)

arXiv 2510.17794 ↗First posted 20 Oct 2025cs.LG stat.MLcited by 0

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Abstract (arXiv)

Modern probabilistic regressors often remain overconfident under distribution shift. Functional Distribution Networks (FDN) place input-conditioned distributions over network weights, producing predictive mixtures whose dispersion adapts to the input; we train them with a Monte Carlo beta-ELBO objective. We pair FDN with an evaluation protocol that separates interpolation from extrapolation and emphasizes simple OOD sanity checks. On controlled 1D tasks and small/medium UCI-style regression benchmarks, FDN remains competitive in accuracy with strong Bayesian, ensemble, dropout, and hypernetwork baselines, while providing strongly input-dependent, shift-aware uncertainty and competitive calibration under matched parameter and update budgets.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 18 of its 29 references that are in the map. First posted on arXiv 20 Oct 2025.

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References · 18

  1. 2021LoRA: Low-Rank Adaptation of Large Language Modelscited by 14,380
  2. 2017On Calibration of Modern Neural Networkscited by 4,960
  3. 2017Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networkscited by 4,897
  4. 2015Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learningcited by 4,448
  5. 2019Parameter-Efficient Transfer Learning for NLPcited by 4,236
  6. 2016Simple and Scalable Predictive Uncertainty Estimation using Deep Ensemblescited by 3,926
  7. 2017What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?cited by 2,352
  8. 2019Can You Trust Your Model's Uncertainty? Evaluating Predictive Uncertainty Under Dataset Shiftcited by 1,287
  9. 2016HyperNetworkscited by 1,027
  10. 2016Deep Variational Information Bottleneckcited by 930
  11. 2019A Simple Baseline for Bayesian Uncertainty in Deep Learningcited by 560
  12. 2015Weight Uncertainty in Neural Networkscited by 554
6 more
  1. 2015Importance Weighted Autoencoderscited by 456
  2. 2016Dynamic Filter Networkscited by 319
  3. 2018Neural Processescited by 311
  4. 2019Attentive Neural Processescited by 283
  5. 2017SMASH: One-Shot Model Architecture Search through HyperNetworkscited by 202
  6. 2017Bayesian Hypernetworkscited by 66

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