Summary of Temporal and Spatial Reservoir Ensembling Techniques For Liquid State Machines, by Anmol Biswas et al.
Temporal and Spatial Reservoir Ensembling Techniques for Liquid State Machines
by Anmol Biswas, Sharvari Ashok Medhe, Raghav Singhal, Udayan Ganguly
First submitted to arxiv on: 18 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Neural and Evolutionary Computing (cs.NE)
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 paper proposes two approaches to improve the performance of Liquid State Machines (LSMs), a type of Reservoir Computing model inspired by brain organization. The methods, Multi-Length Scale Reservoir Ensemble (MuLRE) and Temporal Excitation Partitioned Reservoir Ensemble (TEPRE), are designed to overcome the limitations of scaling up LSMs by increasing their size. The authors benchmark these approaches on three standard neuromorphic benchmarks: Neuromorphic-MNIST (N-MNIST), Spiking Heidelberg Digits (SHD), and DVSGesture datasets. They achieve state-of-the-art results, including 98.1% test accuracy on N-MNIST with a 3600-neuron LSM model and 77.8% test accuracy on the SHD dataset. The paper also introduces receptive field-based input weights to the Reservoir for vision tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at ways to improve performance in Liquid State Machines (LSMs), which are used for pattern recognition and temporal analysis. The authors want to make LSMs better by using more of them together, like a team. They test this idea on some special datasets that mimic how the brain works, and it does really well! One example is getting 98.1% right on a handwriting recognition task with a big model. |
Keywords
* Artificial intelligence * Pattern recognition
