GANs, diffusion models, and the architectures behind modern generative AI. Research-grade explainers on how generative models learn, where they fail, and the design choices that separate state-of-the-art methods from the rest, each tied to the paper it came from.
AlphaFold changed biology by cracking a puzzle that had resisted everyone for fifty years. Hand it a protein’s sequence and it tells you the shape that sequence folds into. The answer it gives is a single, crisp structure, and that…
Mac-Diff: A Diffusion Model for Diverse Protein Conformations Read More »
Ask a generative model to dream up a new crystal and it will usually hand you something that looks a lot like what it already knows. That is not a flaw. It was trained to reproduce the patterns in its…
Reinforcement Learning Guides Diffusion to New Crystals Read More »
Start with the scale of the problem, because it is what makes this work matter. Bacteria evolve, and every time we deploy an antibiotic widely, we hand them a reason to develop resistance to it. Over decades this has hollowed…
ApexGO Optimizes Peptide Antibiotics With Generative AI Read More »
Diffusion models got very good very fast at turning text into images. What they did not automatically get good at was matching what a person actually wants to see. A prompt asking for a cat sleeping in a sunbeam might…
Why DPO Is Beating RLHF at Aligning AI Images Read More »
Every hyperspectral sensor is fighting the same physics. To split light into dozens of narrow spectral bands the sensor has to gather less light per band, and less light per pixel means the imaging system trades spatial resolution for spectral…
Vector Quantized Priors for Sharper Hyperspectral image Fusion Read More »
Diffusion models and flow matching models have become the backbone of modern image and video generation, powering everything from text to image systems to text to 3D creation tools. Both families work the same basic way. They start from pure…
Implicit Generator Matching Distills Diffusion to One Step Read More »
Super resolution, the task of turning a low resolution image into a high resolution one, is familiar territory for ordinary photos. Hyperspectral imaging changes the game in two ways that the paper from Zhixiong Yang and colleagues at the National…
BKSR: A Unified Loop for Blind Hyperspectral Image Super Resolution Read More »
Semantic segmentation, the task of deciding which class every pixel belongs to, has always carried an awkward cost. To train a supervised model you need someone to color in every pixel of every training image across a fixed list of…
How iSeg Refines Stable Diffusion Attention for Segmentation Read More »
Reconstructing a static object from a scan is already a well studied problem, going back to foundational techniques like Marching Cubes and Poisson Surface Reconstruction. Add motion to the picture and the difficulty compounds. Early systems such as DynamicFusion, VolumeDeform,…
Motion2VecSets: Teaching AI to Guess the Missing Motion in 3D Scans Read More »
Image fusion sounds like a simple ask. Take an object out of one photo and put it convincingly into another. Anyone who has actually tried it in Photoshop knows how quickly convincing becomes the hard part. A pasted object needs…
DreamFuse Teaches Diffusion Models Real Image Fusion Read More »