Graph Machine Learning @ ICML 2023
Magnificent beaches and tropical Hawaiian landscapes 🌴did not turn brave scientists away from attending the International Conference on Machine Learning in Honolulu and presenting their recent work! Let’s see what’s new in our favorite Graph Machine Learning area. Graph Transformers: Sparser, Faster, and DirectedWe presented GraphGPS about a year ago and it is pleasing to see many ICML papers bui
Predictions and hopes for Geometric & Graph ML in 2022
Image: ShutterstockThis post was co-authored with Petar Veličković. See also my last year’s prediction, Michael Galkin’s excellent post on the current state of affairs in Graph ML, a deeper dive into subgraph GNNs, techniques inspired by PDEs and differential geometry and algebraic topology, and how the concepts of symmetry and invariance form the picture of modern deep learning. Summing up impres
Geometric foundations of Deep Learning
Geometric Deep Learning is an attempt for geometric unification of a broad class of ML problems from the perspectives of symmetry and invariance. These principles not only underlie the breakthrough performance of convolutional neural networks and the recent success of graph neural networks but also provide a principled…
10 tips for a better Google Colab experience
Photo by Jason Tharsiman on UnsplashColab is a great tool for coding. I use it very often, for a large set of tasks, from traditional Machine Learning to Deep Learning applications using PyTorch, TensorFlow or OpenCV. With Google Colab, creating and sharing notebooks is intuitive and simple 😃 Here are 10 tips and tricks I gathered over time that will help you to get the most out of Google Colab.
Announcing PyCaret: An open source, low-code machine learning library in Python
We are excited to announce PyCaret, an open source machine learning library in Python to train and deploy supervised and unsupervised machine learning models in a low-code environment. PyCaret allows you to go from preparing data to deploying models within seconds from your choice of notebook environment. In comparison with the other open source machine learning libraries, PyCaret is an alternate
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Quantize Llama models with GGML and llama.cpp
Boosting image generation by intersecting GANs with Diffusion models
PyTorch Model Performance Analysis and Optimization — Part 2
Graph Neural Networks as gradient flows
Accelerating and scaling Temporal Graph Networks on the Graphcore IPU
Towards Geometric Deep Learning IV: Chemical Precursors of GNNs
The problem with credit-based pricing for data platforms
Assessing Image Similarity Using Inception V3 and FID Score
Graph Neural Networks through the lens of Differential Geometry and Algebraic Topology
Graph Neural Networks through the lens of Differential Geometry and Algebraic Topology
Bayesian Approximated Neural Network Example via JAX— Flexible Parameter Distributions for ODEs
What is Benford’s Law and why is it important for data science?
Handbook of Anomaly Detection with Python Outlier Detection — (12) Autoencoders
Monocular Bird’s-Eye-View Semantic Segmentation for Autonomous Driving
ECCV 2020 digest