Summary of Orchid: Flexible and Data-dependent Convolution For Sequence Modeling, by Mahdi Karami and Ali Ghodsi
Orchid: Flexible and Data-Dependent Convolution for Sequence Modeling
by Mahdi Karami, Ali Ghodsi
First submitted to arxiv on: 28 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 This paper introduces Orchid, a novel architecture designed to address the quadratic complexity of traditional attention mechanisms in deep learning. The Orchid model combines a new data-dependent global convolution layer with dedicated conditioning neural networks that maintain shift equivariance. This allows the model to capture long-range dependencies and in-context learning while maintaining quasilinear scalability for long sequences. The paper evaluates the proposed model across multiple domains, including language modeling and image classification, showing improved performance and generality compared to traditional attention-based architectures like BERT and Vision Transformers. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Orchid is a new deep learning architecture that helps computers process and understand long sequences of data, like sentences or images. It does this by using a special kind of convolution layer that adapts to the input sequence. This allows Orchid to be more efficient and accurate than other models, even with smaller model sizes. The paper shows that Orchid works well across different tasks and domains. |
Keywords
* Artificial intelligence * Attention * Bert * Deep learning * Image classification
