Ever felt like trying to find a needle in a haystack? That’s part of the process of building and optimizing machine learning models, particularly complex ones like ensembles and neural networks, where several hyperparameters need to be manually set by us before training them.
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 …
How Alberta Health Services is using advanced AI to bolster its defenses as attackers increasingly target healthcare facilities.Read More
A week after Disney and Universal filed a landmark lawsuit against Midjourney, the generative AI startup’s new V1 video tool will make clips of Shrek, Deadpool, and other famous creations.
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 …
Read more “AI at light speed: How glass fibers could replace silicon brains”
Recently, text-based image generation models can automatically create high-resolution, high-quality images solely from natural language descriptions. However, when a typical example like the Stable Diffusion model is given the text “creative,” its ability to generate truly creative images remains limited.
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This post is divided into three parts; they are: • Why Attention is Needed • The Attention Operation • Multi-Head Attention (MHA) • Grouped-Query Attention (GQA) and Multi-Query Attention (MQA) Traditional neural networks struggle with long-range dependencies in sequences.
MLOps, or machine learning operations, is all about managing the end-to-end process of building, training, deploying, and maintaining machine learning models.
We study Variational Rectified Flow Matching, a framework that enhances classic rectified flow matching by modeling multi-modal velocity vector-fields. At inference time, classic rectified flow matching ‘moves’ samples from a source distribution to the target distribution by solving an ordinary differential equation via integration along a velocity vector-field. At training time, the velocity vector-field is …