Summary of Fixed-mean Gaussian Processes For Post-hoc Bayesian Deep Learning, by Luis A. Ortega et al.
Fixed-Mean Gaussian Processes for Post-hoc Bayesian Deep Learning
by Luis A. Ortega, Simón Rodríguez-Santana, Daniel Hernández-Lobato
First submitted to arxiv on: 5 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Machine Learning (stat.ML)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper introduces a novel approach for performing post-hoc uncertainty estimation of pre-trained deep neural network (DNN) predictions, enhancing the original network with output confidence measures like error bars without compromising its accuracy. The proposed method, called fixed mean GP (FMGP), leverages variational inference and is architecture-agnostic, relying solely on the pre-trained model’s outputs to adjust predictive variances. FMGP demonstrates improved uncertainty estimation and computational efficiency compared to state-of-the-art methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper helps us better understand how deep neural networks make predictions by giving us a way to know how sure they are about their answers. It uses a special type of math called Gaussian processes to figure out what the network is likely to predict, and then adjusts this based on how confident the network was in its answer. This can be useful for many applications like image recognition or natural language processing. |
Keywords
* Artificial intelligence * Inference * Natural language processing * Neural network
