# Inhibited Softmax for Uncertainty Estimation in Neural Networks

arXiv: https://arxiv.org/abs/1810.01861 · HTML: https://mlsanity.com/paper/1810.01861/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Oct 2018.

## Built on by (most cited first, 19 of 19)

- [A Survey of Uncertainty in Deep Neural Networks](https://mlsanity.com/paper/2107.03342/) (2021)
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- [A Structured Review of Literature on Uncertainty in Machine Learning & Deep Learning](https://mlsanity.com/paper/2406.00332/) (2024)
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- [Confidence estimation of classification based on the distribution of the neural network output layer](https://mlsanity.com/paper/2210.07745/) (2022)
- [Revisiting Softmax for Uncertainty Approximation in Text Classification](https://mlsanity.com/paper/2210.14037/) (2022)
- [TEDL: A Two-stage Evidential Deep Learning Method for Classification Uncertainty Quantification](https://mlsanity.com/paper/2209.05522/) (2022)
- [To Softmax, or not to Softmax: that is the question when applying Active Learning for Transformer Models](https://mlsanity.com/paper/2210.03005/) (2022)
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- [Selective classification using a robust meta-learning approach](https://mlsanity.com/paper/2212.05987/) (2022)
- [Delay-SDE-net: A deep learning approach for time series modelling with memory and uncertainty estimates](https://mlsanity.com/paper/2303.08587/) (2023)
- [Sparse-shot Learning with Exclusive Cross-Entropy for Extremely Many Localisations](https://mlsanity.com/paper/2104.10425/) (2021)
- [Efficient IoT Inference via Context-Awareness](https://mlsanity.com/paper/2310.19112/) (2023)
- [Robust quantum dots charge autotuning using neural network uncertainty](https://mlsanity.com/paper/2406.05175/) (2024)
- [Gaussian Mixture based Evidential Learning for Stereo Matching](https://mlsanity.com/paper/2408.02796/) (2024)
- [Long-Term Alzheimers Disease Prediction: A Novel Image Generation Method Using Temporal Parameter Estimation with Normal Inverse Gamma Distribution on Uneven Time Series](https://mlsanity.com/paper/2511.21057/) (2025)
- [Quantifying Uncertainty in Machine Learning-Based Pervasive Systems: Application to Human Activity Recognition](https://mlsanity.com/paper/2512.09775/) (2025)
- [Noise reduction in BERT NER models for clinical entity extraction](https://mlsanity.com/paper/2603.00022/) (2026)

## Abstract (arXiv)

We present a new method for uncertainty estimation and out-of-distribution detection in neural networks with softmax output. We extend softmax layer with an additional constant input. The corresponding additional output is able to represent the uncertainty of the network. The proposed method requires neither additional parameters nor multiple forward passes nor input preprocessing nor out-of-distribution datasets. We show that our method performs comparably to more computationally expensive methods and outperforms baselines on our experiments from image recognition and sentiment analysis domains.