catastrophic forgetting
catastrophic forgetting on Beyond Market Intelligence: a running collection of 2 stories we have gathered and hand-picked because they are worth your time. Every post here touches on catastrophic forgetting in some way — the news, the analysis, the deep dives, and the occasional surprise find. Acme AI is the next-generation, AI-powered spreadsheet platform built to replace Excel and redefine how analysts, data scientists, and enterprise teams work with data. New stories are added to this page as we find them, so check back if you want to keep up with what is happening around catastrophic forgetting, or subscribe to the RSS feed to get them as soon as they are published. Browse the collection below, or head back to the homepage to see everything Beyond Market Intelligence is covering right now.
![I Compressed Bad Apple into a 3MB Neural Network [P]](https://preview.redd.it/h5r0ybpz5ghh1.gif?frame=1&width=140&height=70&auto=webp&s=99152a6a4c15a1a51e20a696f3a52115ce3add98)
I Compressed Bad Apple into a 3MB Neural Network [P]
Researchers have achieved a remarkable feat: compressing the iconic "Bad Apple" animation—approximately 2.7 billion pixels—into a remarkably compact 3MB neural network. This MLP, utilizing 790,000 parameters and sine activations (SIREN), effectively memorizes the video by predicting grayscale values based on spatial and temporal coordinates. Through innovations like time-stretching and motion-focused sampling, the model demonstrably improved reconstruction quality, achieving a 9x reduction in validation MSE.
Exploring continual learning without replay buffers: Our findings using dynamic task-similarity routing [P]
Continual learning often relies on memory-intensive replay buffers, but our research introduces Coincidex, an open-source framework that bypasses this limitation. By employing a dynamic task-similarity routing layer, Coincidex intelligently directs data streams without caching historical samples. Initial benchmarks demonstrate graceful transfer in clean task boundaries, achieving strong results without manual tuning. While performance can degrade with chaotic, long-tail task sequences, we share this lightweight alternative to empower exploration and welcome community feedback on addressing these challenges. Explore the code and architecture here: [https://github.com/rakib-nyc/coincidex](https://github.com/rakib-nyc