Lists (5)
Starred repositories
TabICLv2: An open tabular foundation model
Notebooks, code and documents for the ISPRS Congress tutorial on Earth embeddings
[SIGSPATIAL 2026] Massive-STEPS: Massive Semantic Trajectories for Understanding POI Check-ins -- Dataset and Benchmarks
Unified Training of Universal Time Series Forecasting Transformers
Reimplementation of ICLR20 paper "Geom-GCN: Geometric Graph Convolutional Networks" based on PyTorch and PyTorch Geometric (PyG).
The repository implements the paper "Learning Graph Quantized Tokenizers for Transformers".
Vector (and Scalar) Quantization, in Pytorch
Explainable Boosted Scoring with Python: turning XGBoost, LightGBM, and CatBoost into explainable scorecards
Must-read papers on graph foundation models (GFMs)
Algorithms inspired by graph Laplacians: linear equation solvers, sparsification, clustering, optimization, etc.
TOpological Point Features: Node-Level Topological Representation Learning on Point Clouds
Topological Graph Neural Networks (ICLR 2022)
Implementation of the PersLay layer for persistence diagrams
The essence of my research, distilled for reusability. Enjoy 🥃!
NVIDIA cuOpt examples for decision optimization
Official implementation of the paper "Geographic mapping with unsupervised multi-modal representation learning from VHR images and POIs"
🚴 Call stack profiler for Python. Shows you why your code is slow!
💻 A fully functional local AWS cloud stack. Develop and test your cloud & Serverless apps offline
Arima, Sarima, LSTM, Prophet, DeepAR, Kats, Granger-causality, Autots
Repository containing the official PyTorch implementation of the paper Deep Random Features for Scalable Interpolation of Spatiotemporal Data
Code for UrbanCLIP: Learning Text-Enhanced Urban Region Profiling with Contrastive Language-Image Pre-Training [WWW 2024]
Official repository of paper "VecCity: A Taxonomy-guided Library for Map Entity Representation Learning".
Exploratory spatiotemporal data analysis and Geospatial distribution dynamics analysis
Graph Diffusion Convolution, as proposed in "Diffusion Improves Graph Learning" (NeurIPS 2019)
Database with posteriors of interest for Bayesian inference