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A lightweight, low-dependency, unified API to use all common reranking and cross-encoder models.
Representation Learning on Topological Domains
🐙 Guides, papers, lecture, notebooks and resources for prompt engineering
DoWhy is a Python library for causal inference that supports explicit modeling and testing of causal assumptions. DoWhy is based on a unified language for causal inference, combining causal graphic…
An open-source data logging library for machine learning models and data pipelines. 📚 Provides visibility into data quality & model performance over time. 🛡️ Supports privacy-preserving data collec…
Code for paper: SDE-Net: Equipping Deep Neural network with Uncertainty Estimates
Differentiable SDE solvers with GPU support and efficient sensitivity analysis.
Kalman Filter book using Jupyter Notebook. Focuses on building intuition and experience, not formal proofs. Includes Kalman filters,extended Kalman filters, unscented Kalman filters, particle filte…
Fast and Easy Infinite Neural Networks in Python
Notes for courses taken at Harvard (2015--2019)
Implementation of a differentiable CGP (Cartesian Genetic Programming)
Mars is a tensor-based unified framework for large-scale data computation which scales numpy, pandas, scikit-learn and Python functions.
This repository provides code for SVD and Importance sampling-based algorithms for large scale topic modeling.
Scalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library, for Python, R, Java, Scala, C++ and more. Runs on single machine, Hadoop, Spark, Dask, Flink and DataFlow
Python package built to ease deep learning on graph, on top of existing DL frameworks.
Automatically exported from code.google.com/p/word2vec
Supplementary Materials for Tshitoyan et al. "Unsupervised word embeddings capture latent knowledge from materials science literature", Nature (2019).
PyTorch implementation of the NIPS-17 paper "Poincaré Embeddings for Learning Hierarchical Representations"