Summary of Symbolnet: Neural Symbolic Regression with Adaptive Dynamic Pruning For Compression, by Ho Fung Tsoi et al.
SymbolNet: Neural Symbolic Regression with Adaptive Dynamic Pruning for Compression
by Ho Fung Tsoi, Vladimir Loncar, Sridhara Dasu, Philip Harris
First submitted to arxiv on: 18 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: High Energy Physics – Experiment (hep-ex); Instrumentation and Detectors (physics.ins-det)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 proposed neural network approach to symbolic regression, called SymbolNet, is designed to enable low-latency inference for high-dimensional inputs on custom hardware like FPGAs. This model compression technique allows for dynamic pruning of model weights, input features, and mathematical operators in a single training process, optimizing both training loss and expression complexity simultaneously. The framework introduces a sparsity regularization term for each pruning type, which can adaptively adjust its strength to achieve convergence at a target sparsity ratio. SymbolNet is effective on datasets with more than 10 inputs, demonstrated through experiments on the LHC jet tagging task (16 inputs), MNIST (784 inputs), and SVHN (3072 inputs). |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary SymbolNet is a new way to make computers understand mathematical equations quickly and efficiently. It’s like a shortcut for math problems that uses special hardware to solve them fast. This helps in places where computers need to do lots of calculations, like the Large Hadron Collider at CERN. The problem with current methods is they get stuck when dealing with very big datasets. SymbolNet solves this by allowing it to adapt and learn from the data while keeping the math simple. |
Keywords
* Artificial intelligence * Inference * Model compression * Neural network * Pruning * Regression * Regularization
