🍊 📊 💡 Orange: Interactive data analysis
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Updated
Jun 16, 2025 - Python
🍊 📊 💡 Orange: Interactive data analysis
🆔 A python library for accurate and scalable fuzzy matching, record deduplication and entity-resolution.
MTEB: Massive Text Embedding Benchmark
A curated list of community detection research papers with implementations.
Interact, analyze and structure massive text, image, embedding, audio and video datasets
This is the library for the Unbounded Interleaved-State Recurrent Neural Network (UIS-RNN) algorithm, corresponding to the paper Fully Supervised Speaker Diarization.
🔴 MiniSom is a minimalistic implementation of the Self Organizing Maps
A Python toolkit/library for reality-centric machine/deep learning and data mining on partially-observed time series, including SOTA neural network models for scientific analysis tasks of imputation/classification/clustering/forecasting/anomaly detection/cleaning on incomplete industrial (irregularly-sampled) multivariate TS with NaN missing values
SCAN: Learning to Classify Images without Labels, incl. SimCLR. [ECCV 2020]
The official implementation of RAPTOR: Recursive Abstractive Processing for Tree-Organized Retrieval
pyclustering is a Python, C++ data mining library.
Time series distances: Dynamic Time Warping (fast DTW implementation in C)
Pocket-Sized Multimodal AI for content understanding and generation across multilingual texts, images, and 🔜 video, up to 5x faster than OpenAI CLIP and LLaVA 🖼️ & 🖋️
TensorFlow Similarity is a python package focused on making similarity learning quick and easy.
A scikit-learn based module for multi-label et. al. classification
Awesome Deep Graph Clustering is a collection of SOTA, novel deep graph clustering methods (papers, codes, and datasets).
A PyTorch implementation of "Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks" (KDD 2019).
Large-scale Point Cloud Semantic Segmentation with Superpoint Graphs
A robust streaming log template miner based on the Drain algorithm
Training of Locally Optimized Product Quantization (LOPQ) models for approximate nearest neighbor search of high dimensional data in Python and Spark.
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