[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"branding":3,"analytics":7,"article-green-elm-skips-backprop-trains-mnist-in-15-seconds":10,"sections":34},{"siteName":4,"siteTagline":5,"publisherName":4,"contactEmail":6},"The Revision","Tech news, decoded.","editor@therevision.news",{"gaMeasurementId":8,"adsenseClientId":9},"G-ZW2MV82GYR","ca-pub-8533917693782264",{"article":11},{"id":12,"slug":13,"title":14,"dek":15,"body_md":16,"tags_json":17,"published_at":18,"created_at":19,"updated_at":20,"status":21,"review_note":22,"review_notes":23,"image_url":22,"persona_id":22,"persona_name":22,"section":24,"tags":25,"sources":29,"feedback":33,"feedback_at":22,"cost_usd":33,"total_tokens":33},6971,"green-elm-skips-backprop-trains-mnist-in-15-seconds","Green-ELM skips backprop, trains MNIST in 1.5 seconds","Researchers built a neural network that trains via matrix math instead of gradient descent, hitting 97% MNIST accuracy in 1.5 seconds on a CPU.","A new neural network skips gradient descent entirely, solving for its output layer with a single matrix calculation instead of training over many epochs.\n\nThe method, called Green-ELM, projects input data into a very high-dimensional random feature space, then solves for the output layer weights in one step using the Moore-Penrose pseudoinverse plus LU and Cholesky decomposition, skipping backpropagation entirely. On MNIST digit recognition, a 4,000-dimensional version hits 98.10% accuracy, and a Fashion-MNIST test reaches 86.63%. A leaner CPU setup at 2,000 dimensions trains in 1.5 seconds and still scores 97.15% accuracy, which the researchers say is 11.6 times faster than a comparable SGD-trained baseline. They also paired the method with a pretrained ResNet-18 to extract features first, showing the one-shot solver works beyond simple digit datasets.\n\nThe pitch is real-time edge AI: devices that need a working model immediately, not after a lengthy training run. If a closed-form solve can really substitute for backprop on constrained hardware, that changes the math for on-device personalization and fast retraining. The researchers also propose an \"Empirical Scaling Hypothesis\" linking accuracy to feature-space dimensionality and dataset complexity, suggesting they see this as more than a one-off trick.\n\nFast, one-shot output layers are not new. Extreme learning machines have solved problems this way since the mid-2000s. Green-ELM's contribution is scaling that idea to far higher dimensions and pairing it with modern pretrained backbones. But MNIST and Fashion-MNIST are the easiest benchmarks in machine learning, and the edge-AI pitch still needs proving on harder, real-world data.","[\"ai\",\"machine-learning\",\"edge-ai\",\"research\"]","2026-09-18T04:00:00.000Z","2026-09-19T01:10:03.401Z","2026-09-19T01:10:15.481Z","published",null,[],"ai",[24,26,27,28],"machine-learning","edge-ai","research",[30],{"name":31,"url":32},"arXiv cs.AI","https:\u002F\u002Farxiv.org\u002Fabs\u002F2604.15613",0,{"sections":35},[36,39,43,48,53,57,61,66,70,75,80,85,90,95],{"name":37,"slug":24,"count":38,"latest_published_at":18},"AI",4082,{"name":40,"slug":41,"count":42,"latest_published_at":18},"Security","security",661,{"name":44,"slug":45,"count":46,"latest_published_at":47},"Policy","policy",339,"2026-09-17T12:00:00.000Z",{"name":49,"slug":50,"count":51,"latest_published_at":52},"Deals","deals",179,"2026-06-29T20:02:07.000Z",{"name":54,"slug":55,"count":56,"latest_published_at":18},"Hardware","hardware",155,{"name":58,"slug":59,"count":60,"latest_published_at":18},"Science","science",125,{"name":62,"slug":63,"count":64,"latest_published_at":65},"Consumer Tech","consumer-tech",99,"2026-09-09T17:27:33.000Z",{"name":67,"slug":68,"count":69,"latest_published_at":18},"Dev Tools","dev-tools",78,{"name":71,"slug":72,"count":73,"latest_published_at":74},"Software","software",75,"2026-09-10T20:41:21.000Z",{"name":76,"slug":77,"count":78,"latest_published_at":79},"Startups","startups",55,"2026-09-09T23:14:29.000Z",{"name":81,"slug":82,"count":83,"latest_published_at":84},"Gaming","gaming",43,"2026-09-10T12:18:06.000Z",{"name":86,"slug":87,"count":88,"latest_published_at":89},"General","general",41,"2026-09-08T01:57:23.000Z",{"name":91,"slug":92,"count":93,"latest_published_at":94},"Reviews","reviews",20,"2026-06-24T12:00:01.000Z",{"name":96,"slug":97,"count":98,"latest_published_at":99},"How-To","how-to",6,"2026-06-16T09:00:00.000Z"]