Independent consultant

By Lars Buitinck, G. Louppe, Mathieu Blondel, F. Pedregosa, Andreas Mueller, Olivier Grisel, Vlad Niculae, Peter Prettenhofer, A. Gramfort, J. Grobler and 5 more. 2013

Subfields: Natural Language Processing

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In Natural Language Processing

Citations in the corpus

UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction (2018, methodology): “e Isomap [46] and Laplacian Eigenmaps [6] implementations in scikit-learn [10] were used.”
Data Synthesis based on Generative Adversarial Networks (2018, methodology): “try-level server, it took at most 20 minutes to generate synthetic tables, as shown in Table 4. For the model compatibility test, we programmed using the popular scikit-learn machine learning library [9]. 5.1.5 Parameter Setups 11 F-1 is the harmonic mean of precision and recall, and it is one of th”
openTSNE: a modular Python library for t-SNE dimensionality reduction and embedding (2019, neutral): “Most end-users often only need a quick and easy way to create informative visualizations of their data sets. openTSNE provides a high-level API that closely follows the style of scikit-learn (Buitinck et al. 2013), a popular and widely used machine learning toolkit.”
Interpreting Blackbox Models via Model Extraction (2017, methodology): “We implement our algorithm on top of scikit-learn [14], and evaluate it in two ways.”
Optimizing taxonomic classification of marker-gene amplicon sequences with QIIME 2’s q2-feature-classifier plugin (2018, background): “learn [7, 8] for marker gene taxonomy classification, and”
MEG and EEG data analysis with MNE-Python (2013, methodology): “…used for 2D graphics; Mayavi (Ramachandran and Varoquaux, 2010) is employed for 3D rendering; Scikit-Learn [Pedregosa et al. (2011) and Buitinck et al. (2013)] is required for decoding tasks; and the Python Data Analysis Library (Pandas) is used for interfacing with spreadsheet table oriented…”
Amazon SageMaker Autopilot: a white box AutoML solution at scale (2020, methodology): “Each such python script implements a Scikit-Learn Pipeline composed of native Scikit-Learn transformers [3, 16] and custom built transformers available in the open-source python library, sagemaker-scikit-learn-extension [1] developed by us.”
Autoregressive Image Generation using Residual Quantization (2022, methodology): “To visualize the nearest neighbors in the training images of FFHQ to generated images, we use a KD-tree [5], which is constructed by the VGG-16 features [44] of training images.”
Anti-Money Laundering in Bitcoin: Experimenting with Graph Convolutional Networks for Financial Forensics (2019, methodology): “We rst tested standard classication models for the licit/illicit prediction using three standard approaches: Logistic Regression (with default parameters from the scikit-learn Python package [4]), Random Forest (also from scikit-learn, with 50 estimators and 50 max features), and Multilayer Perceptr”
Survey on categorical data for neural networks (2020, unknown): “The Python Scikit-learn library [63] has a Category Encoders module, with an Ordinal Encoder.”
Big Data: Tutorial and guidelines on information and process fusion for analytics algorithms with MapReduce (2018, methodology): “FlinkML pipelines are inspired by the design introduced by sklearn in [60].”
Robust Smartphone App Identification via Encrypted Network Traffic Analysis (2017, methodology): “We leveraged the scikit-learn [34] machine learning libraries to implement the classifiers in our framework.”
TensorLy: Tensor Learning in Python (2016, methodology): “We aim at making the API simple and efficient, following that of scikit-learn (Buitinck et al., 2013), where possible.”
Themis-ml: A Fairness-Aware Machine Learning Interface for End-To-End Discrimination Discovery and Mitigation (2017, methodology): “Table 1 outlines a simple classiﬁcation system in terms of the core interfaces in scikit-learn (sklearn), which is a machine learning library in the Python programming language [16], and table 2 delineates some of the use cases that research or product teams might have to justify the use of an FMLI.”