DreamBooth
DreamBooth: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Generation Nataniel Ruiz Yuanzhen Li Varun Jampani Yael Pritch Michael Rubinstein Kfir Aberman Google Research It’s like a photo booth, but once the subject is captured, it can be synthesized wherever your dreams take you… [Paper] (new!) [Dataset] [BibTeX] Abstract Large text-to-image models achieved a remarkable leap in the
Pattern Recognition and Machine Learning - Microsoft Research
This leading textbook provides a comprehensive introduction to the fields of pattern recognition and machine learning. It is aimed at advanced undergraduates or first-year PhD students, as well as researchers and practitioners. No previous knowledge of pattern recognition or machine learning concepts is assumed. This is the first machine learning textbook to include a comprehensive coverage of rec
Transformer models: an introduction and catalog — 2023 Edition
Transformer models: an introduction and catalog — 2023 Edition January 16, 2023 52 minute read This post is now an ArXiV paper that you can print and cite. Update 05/2023 Another pretty large update after 4 months. I was invited to submit the article to a journal, so I decided to enlist some help from some LinkedIn colleages and completely revamp it. First off, we added a whole lot of new models,
Understanding Convolutions on Graphs
This article is one of two Distill publications about graph neural networks. Take a look at A Gentle Introduction to Graph Neural Networks for a companion view on many things graph and neural network related. Many systems and interactions - social networks, molecules, organizations, citations, physical models, transactions - can be represented quite naturally as graphs. How can we reason about and
A Gentle Introduction to Graph Neural Networks
Neural networks have been adapted to leverage the structure and properties of graphs. We explore the components needed for building a graph neural network - and motivate the design choices behind them. Hover over a node in the diagram below to see how it accumulates information from nodes around it through the layers of the network. Authors Affiliations Benjamin Sanchez-Lengeling Google Research E
Workshop on Visualization for AI Explainability
7th Workshop on Visualization for AI Explainability October 13, 2024 at IEEE VIS in St. Pete Beach, Florida The role of visualization in artificial intelligence (AI) gained significant attention in recent years. With the growing complexity of AI models, the critical need for understanding their inner-workings has increased. Visualization is potentially a powerful technique to fill such a critical
The Matrix Calculus You Need For Deep Learning
Terence Parr and Jeremy Howard (Terence is a tech lead at Google and ex-Professor of computer/data science in University of San Francisco's MS in Data Science program. You might know Terence as the creator of the ANTLR parser generator. For more material, see Jeremy's fast.ai courses and University of San Francisco's Data Institute in-person version of the deep learning course.) Please send commen
Pyro
NumPyro Release We’re excited to announce the release of NumPyro, a NumPy-backed Pyro using JAX for automatic differentiation and JIT compilation, with over 100x speedup for HMC and NUTS! See the examples and documentation for more details. Pyro is a universal probabilistic programming language (PPL) written in Python and supported by PyTorch on the backend. Pyro enables flexible and expressive de
MLU-Explain
MLU-EXPLAIN Visual explanations of core machine learning concepts Machine Learning University (MLU) is an education initiative from Amazon designed to teach machine learning theory and practical application. As part of that goal, MLU-Explain exists to teach important machine learning concepts through visual essays in a fun, informative, and accessible manner. Neural Networks Learn about neural net
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How does XGBoost really work?
MSR Cambridge Lecture Series: An Introduction to Graph Neural Networks: Models and Applications - Microsoft Research
EfficientNet: Improving Accuracy and Efficiency through AutoML and Model Scaling
https://scikit-learn.org/stable/modules/model_evaluation.html
Algorithms for Decision Making
https://www.cs.cornell.edu/courses/cs4780/2018fa/lectures/
10: Empirical Risk Minimization
5 Regression Loss Functions All Machine Learners Should Know
Statisticians say the darndest things
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