Tagged “deep-learning”

4 posts.

All Model Architecture19 Training & Alignment22 Inference & Serving20 Agents & Orchestration11 Reasoning & Evaluation27 Safety, Security & Governance7 Platforms & Practice22

Model Architecture 24 min

From Normalising Flows to Flow Matching: How the Change-of-Variables Idea Survived Its Own Architecture

For five years, normalising flows bent every layer around one number: the log-determinant of a Jacobian. Flow matching kept the invertible transport map, stopped computing that number during training, and ended up inside Stable D…

variational-and-flow-models flow-matching diffusion-models generative-ai ∑ ◫

Platforms & Practice 25 min

Learning Without Labels: The Collapse Problem at the Heart of Self-Supervised Learning

Every joint-embedding method has the same trivial solution available to it: map every input to the same vector. The history of self-supervised learning is the history of preventing that, and the methods that look most different f…

self-supervised-learning contrastive-learning representation-learning embeddings ∑ ◫

Reasoning & Evaluation 27 min

The Last Undefeated Baseline: Why Gradient-Boosted Trees Still Beat Deep Learning on Tabular Data

Deep learning took images in three years and text in five. It has been attacking tabular data since 2016 and still has not won. The reason is not compute or architecture; it is three specific inductive biases that make a tree the…

tabular gradient-boosting deep-learning xgboost ∑ ◫

Model Architecture 24 min

The Score Is All You Need: How Energy-Based Models, Langevin Dynamics and Diffusion Became One Theory

For decades the normalising constant made energy-based models nearly impossible to train at scale. Between 2019 and 2022 the field stopped computing it and learned its gradient instead, and score matching, Langevin sampling and d…

energy-based-and-score-models diffusion-models diffusion generative-ai ∑ ◫