ComfyAI·Oct 22Is Tabular Data Generation Really “Nearly Perfect,” or Are the Metrics Just Too Easy?Tabular data generation is widely seen as having made huge strides in recent years. Diffusion models like TabDiff and TabSyn now score…
Tobeornottobeisneveraquestion·Sep 29When a Good Score Isn’t EnoughGSoC 2026 with ML4SCI: fast calorimeter simulation with conditional flow matching
Zeromathai·Sep 18Diffusion Models: Learning to Generate by Reversing NoiseInstead of generating complex data in one step, diffusion models learn a sequence of probabilistic transitions that gradually turn noise bac
Zeromathai·Sep 16Deep Generative Models: Four Ways to Make Complex Distributions LearnableAutoregressive models, VAEs, normalizing flows, and GANs address the same distribution-learning problem through fundamentally different stru
Zeromathai·Sep 16The Hidden Cost of Turning Neural Networks Into Probability DistributionsWhy the normalizing constant exposes the tension between expressive generative models and tractable probability computation.
Zeromathai·Sep 12Modeling the Data Distribution: What Generative Models Are Really LearningGenerative modeling starts with observed samples and asks how a learnable probability model can capture the hidden distribution that…
Zeromathai·Sep 6Inverse Problems: Reasoning Backward from Observations to Possible CausesWhen observations do not uniquely determine their source, machine learning must reason over possible explanations rather than simply…
Asim Shah·Sep 4Deep Generative Models, Part 1: How Does a Machine Learn to Imagine?Part 1 of a series on Deep Generative Models — Overview → Autoencoders → VAEs → Diffusion Models
Zeromathai·Sep 4Generative Modeling: Learning the Structure Behind the DataThe key difference is not classification versus generation, but which probability relationship the model learns directly.
Prabhu M S·Aug 25Single Self-Attention Head in a Generative TransformerGenerative transformers use self-attention heads to understand the relationships between tokens in a sequence. These contextual…A response icon2A response icon2
