Researchers have introduced the SETAREH Transform, a novel method utilizing Haar wavelets to approximate inner products in high-dimensional vector operations common in deep learning. This technique aims to reduce the costs associated with storing, transferring, and processing large vectors, particularly in distributed and edge-computing scenarios. By applying a sequence of permutations and Haar transforms, SETAREH preserves the exact inner product before truncation, retaining only a subset of transformed coefficient-input pairs to minimize representation costs while allowing for controllable approximation error. Experiments on synthetic data and a real-world age estimation task demonstrated SETAREH's ability to significantly reduce the number of required pairs compared to the original vector dimension. AI
IMPACT This method could improve efficiency in deep learning systems by reducing computational costs for high-dimensional vector operations.
RANK_REASON The cluster contains a research paper detailing a new computational method for deep learning. [lever_c_demoted from research: ic=1 ai=1.0]
- APPA-REAL dataset
- arXiv
- CatalyzeX Code Finder for Papers
- convolutional neural network
- DagsHub
- Gotit.pub
- Hugging Face
- ScienceCast
- SETAREH Transform
- Seyed Kamaledin Setarehdan
AI-generated summary · Google Gemini · from 1 sources. How we write summaries →
