Summary of Uncertainty-aware Perceiver, by Euiyul Song
Uncertainty-Aware Perceiver
by EuiYul Song
First submitted to arxiv on: 4 Feb 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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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 Perceiver is a machine learning model that processes multiple inputs without making assumptions about how they relate to each other. This architecture provides efficient memory and computation usage with quadratic scalability. While it outperforms ResNet-50 and ViT on some accuracy metrics, it doesn’t account for predictive uncertainty and calibration. The Perceiver generalizes well across three datasets, models, metrics, and hyperparameters. However, its relative performance improvement is marginal compared to other models. To address this limitation, five Uncertainty-Aware Perceivers are developed to estimate uncertainty and measure their performance on various metrics. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The Perceiver is a machine learning model that helps computers understand multiple pieces of information without making assumptions about how they relate to each other. This makes the model very efficient and fast. The Perceiver can be used for different tasks, like image recognition or natural language processing, and it performs well on some datasets. However, it doesn’t account for uncertainty, which is important when computers make predictions. To solve this problem, researchers created five new models that can estimate uncertainty and perform better than the original Perceiver. |
Keywords
* Artificial intelligence * Machine learning * Natural language processing * Resnet * Vit
