mlsanityData to 7 Oct 2026

Evidential Turing Processes

arXiv 2106.01216 ↗First posted 2 Jun 2021cs.LGcited by 8

Not on the map. Why?

Abstract (arXiv)

A probabilistic classifier with reliable predictive uncertainties i) fits successfully to the target domain data, ii) provides calibrated class probabilities in difficult regions of the target domain (e.g. class overlap), and iii) accurately identifies queries coming out of the target domain and rejects them. We introduce an original combination of Evidential Deep Learning, Neural Processes, and Neural Turing Machines capable of providing all three essential properties mentioned above for total uncertainty quantification. We observe our method on five classification tasks to be the only one that can excel all three aspects of total calibration with a single standalone predictor. Our unified solution delivers an implementation-friendly and compute efficient recipe for safety clearance and provides intellectual economy to an investigation of algorithmic roots of epistemic awareness in deep neural nets.

Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). Builds on 31 of its 52 references that are in the map. First posted on arXiv 2 Jun 2021.

Builds on

31 of 52 references.

References · 31

  1. 2017Attention Is All You Needcited by 59,932
  2. 2014Adam: A Method for Stochastic Optimizationcited by 36,960
  3. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,160
  4. 2017On Calibration of Modern Neural Networkscited by 4,860
  5. 2015Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learningcited by 4,416
  6. 2017Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithmscited by 4,283
  7. 2013Auto-Encoding Variational Bayescited by 3,980
  8. 2016Simple and Scalable Predictive Uncertainty Estimation using Deep Ensemblescited by 3,888
  9. 2019Benchmarking Neural Network Robustness to Common Corruptions and Perturbationscited by 2,857
  10. 2016Variational Inference: A Review for Statisticianscited by 1,466
  11. 2019Can You Trust Your Model's Uncertainty? Evaluating Predictive Uncertainty Under Dataset Shiftcited by 1,274
  12. 2018Evidential Deep Learning to Quantify Classification Uncertaintycited by 728
19 more
  1. 2014Neural Turing Machinescited by 630
  2. 2018Predictive Uncertainty Estimation via Prior Networkscited by 555
  3. 2019A Simple Baseline for Bayesian Uncertainty in Deep Learningcited by 555
  4. 2015Variational Dropout and the Local Reparameterization Trickcited by 489
  5. 2018Conditional Neural Processescited by 421
  6. 2018Accurate Uncertainties for Deep Learning Using Calibrated Regressioncited by 417
  7. 2018Deep Learning for Classical Japanese Literaturecited by 415
  8. 2019Beyond temperature scaling: Obtaining well-calibrated multiclass probabilities with Dirichlet calibrationcited by 359
  9. 2019Deep Evidential Regressioncited by 354
  10. 2018Neural Processescited by 307
  11. 2019Attentive Neural Processescited by 280
  12. 2017Variational Dropout Sparsifies Deep Neural Networkscited by 265
  13. 2019Convolutional Conditional Neural Processescited by 139
  14. 2019Reverse KL-Divergence Training of Prior Networks: Improved Uncertainty and Adversarial Robustnesscited by 104
  15. 2020Meta-Learning Stationary Stochastic Process Prediction with Convolutional Neural Processescited by 74
  16. 2019Sequential Neural Processescited by 55
  17. 2019The Functional Neural Processcited by 43
  18. 2020Uncertainty-Aware Deep Classifiers using Generative Modelscited by 41
  19. 2020Robustifying Sequential Neural Processescited by 15

Built on by

Cited by year

Citing papers per year: 2022: 2; 2023: 2; 2024: 3; 2025: 1.

8 papers.

  1. 2024A Comprehensive Survey on Evidential Deep Learning and Its Applicationscited by 25
  2. 2023EdVAE: Mitigating Codebook Collapse with Evidential Discrete Variational Autoencoderscited by 16
  3. 2024Revisiting Essential and Nonessential Settings of Evidential Deep Learningcited by 15
  4. 2023Beyond Unimodal: Generalising Neural Processes for Multimodal Uncertainty Estimationcited by 11
  5. 2022Evidential Conditional Neural Processescited by 10
  6. 2024Transitional Uncertainty with Layered Intermediate Predictionscited by 4
  7. 2025Overcoming Non-stationary Dynamics with Evidential Proximal Policy Optimizationcited by 2
  8. 2022Continual Learning of Multi-modal Dynamics with External Memorycited by 1