STARFlow: Scaling Latent Normalizing Flows for High-resolution Image Synthesis

We present STARFlow, a scalable generative model based on normalizing flows that achieves strong performance in high-resolution image synthesis. The core of STARFlow is Transformer Autoregressive Flow (TARFlow), which combines the expressive power of normalizing flows with the structured modeling capabilities of Autoregressive Transformers. We first establish the theoretical universality of TARFlow for modeling continuous …

Cloud quantum computing: A trillion-dollar opportunity with dangerous hidden risks

GUEST: Quantum computing (QC) brings with it a mix of groundbreaking possibilities and significant risks. Major tech players like IBM, Google, Microsoft and Amazon have already rolled out commercial QC cloud services, while specialized firms like Quantinuum and PsiQuantum have quickly achieved unicorn status. Experts predict that the global QC mark…Read More

Distillation Scaling Laws

We propose a distillation scaling law that estimates distilled model performance based on a compute budget and its allocation between the student and teacher. Our findings mitigate the risks associated with large-scale distillation by enabling compute-optimal allocation for both the teacher and student to maximize student performance. We provide compute-optimal distillation recipes for two key …

AI at light speed: How glass fibers could replace silicon brains

Imagine supercomputers that think with light instead of electricity. That s the breakthrough two European research teams have made, demonstrating how intense laser pulses through ultra-thin glass fibers can perform AI-like computations thousands of times faster than traditional electronics. Their system doesn t just break speed records it achieves near state-of-the-art results in tasks like …