AI
Smaller, smarter music AI Foundation models are powerful—but huge and pricey to run. This study shows how to keep music understanding strong while trimming model size and compute. * What’s new: A lean architecture that swaps heavy self‑attention for Branchformer + SummaryMixing, plus a simple random quantizer to learn
AI
Smarter, faster explanations for what you’re recommended Big AI models can explain why you’re shown a product or movie—but wiring them directly into recommendation engines often slows everything down and muddles goals. "Prism" takes a cleaner route: it splits the system into two jobs. * Oracle:
AI
TL;DR LoRA adapters are small plug-in modules that fine-tune big language models cheaply. But one adapter per task doesn’t fit messy, real-world inputs. What’s new LoRA on the Go (LoGo) lets a model pick and blend the best adapters for each individual input—no labels, no extra
AI
Ever heard of the “lottery ticket” idea in AI? It says big neural nets hide small subnetworks that can perform just as well. This paper proves a strong version of that for the heart of Transformers: multi-head attention (MHA). * The big claim: Inside a randomly initialized MHA, there exists a
AI
Ever heard of the “lottery ticket” idea in AI? It says big neural nets hide small subnetworks that can perform just as well. This paper proves a strong version of that for the heart of Transformers: multi-head attention (MHA). * The big claim: Inside a randomly initialized MHA, there exists a
AI
Ever heard of the “lottery ticket” idea in AI? It says big neural nets hide small subnetworks that can perform just as well. This paper proves a strong version of that for the heart of Transformers: multi-head attention (MHA). * The big claim: Inside a randomly initialized MHA, there exists a
AI
Ever heard of the “lottery ticket” idea in AI? It says big neural nets hide small subnetworks that can perform just as well. This paper proves a strong version of that for the heart of Transformers: multi-head attention (MHA). * The big claim: Inside a randomly initialized MHA, there exists a