Summary of Bidirectional Long-range Parser For Sequential Data Understanding, by George Leotescu et al.
Bidirectional Long-Range Parser for Sequential Data Understanding
by George Leotescu, Daniel Voinea, Alin-Ionut Popa
First submitted to arxiv on: 8 Apr 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Computation and Language (cs.CL); 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 transformer is a powerful framework that has achieved remarkable performance on various tasks. However, it is inefficient when processing long-sequence data, which limits its scalability. To address this issue, we propose BLRP (Bidirectional Long-Range Parser), an attention mechanism designed to improve performance and efficiency on long-sequence tasks. BLRP combines local sliding window approaches with global bidirectional latent space synthesis techniques. Our approach demonstrates competitive results against state-of-the-art methods on the Long-Range-Arena and CIFAR benchmarks, showcasing its benefits and versatility in vision and language domains. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The transformer is a powerful tool that helps machines understand data better. However, it gets slower when dealing with really long pieces of data. We created a new way to look at data called BLRP (Bidirectional Long-Range Parser) that makes it faster and more efficient for working with long data. It uses two different techniques to process information from the past and the future at the same time. Our approach works well on both visual and language-based tasks, beating other state-of-the-art methods on certain benchmarks. |
Keywords
* Artificial intelligence * Attention * Latent space * Transformer
