Python 3 Reference Manual

By G. Rossum, F. Drake. 2009

3,662 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/fc1d1ba95d4b1ce49d50ac82f553b6236305b0b6

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Citations in the corpus

Cellpose3: one-click image restoration for improved cellular segmentation (2024, unknown): “The Cellpose code library is implemented in Python 3 [36], using pytorch, numpy, scipy, numba, opencv, im-agecodecs, tifffile, fastremap, and tqdm [37–45].”
Isaac Gym: High Performance GPU-Based Physics Simulation For Robot Learning (2021, methodology): “Thanks are also due to open-source community projects like Matplotib[31], Python[32], NumPy[33], PyTorch[34], Tensorboard[35], Tensorboard Aggregator[36] and SciencePlots[37] which we used heavily in this work.”
Cellpose 2.0: how to train your own model (2022, methodology): “The Cellpose code library is implemented in Python 3 [32], using pytorch, numpy, scipy, numba, opencv [20, 33–36].”
Choose a Transformer: Fourier or Galerkin (2021, methodology): “We would also like to thank the Python community [87, 68] for sharing and developing the tools that enabled this work, including PyTorch [69], NumPy [39], SciPy [89], Plotly [45] Seaborn [93], Matplotlib [43], and the Python team for Visual Studio Code.”
Evaluation metrics and statistical tests for machine learning (2024, methodology): “The CNNs were coded in Python (version: 3.9.9) 38 with packages TensorFlow (version: 2.7.0) 58 and Keras (version: 2.7.0) 59 .”
Continuous diffusion for categorical data (2022, methodology): “Finally, our proposed time warping heuristic may also be useful beyond diﬀusion models of categorical data. tainers of the open source software used in this work, including Python (Van Rossum and Drake, 2009), NumPy (Harris et al., 2020), SciPy (Virtanen et al., 2020), JAX (Bradbury et al., 2018), T”
NeuroKit2: A Python toolbox for neurophysiological signal processing (2020, methodology): “The package is implemented in Python 3 (Van Rossum & Drake, 2009), which means that NeuroKit2’s users benefit from an large number of learning resources and a vibrant community.”
MISIM: An End-to-End Neural Code Similarity System (2020, background): “, Python [42], JavaScript [17]), C/C++ programs found “in the wild” may not be well-formed nor exhaustively include all of their dependencies.”
Geometric Transformers for Protein Interface Contact Prediction (2021, background): “Moreover, the Geometric Transformer includes subtle architectural enhancements to the original Transformer architecture (Vaswani et al. (2017)) such as moving the network’s first normalization layer to precede any affinity score computations for improved training stability (Hussain et al.”
HANA: A HAndwritten NAme Database for Offline Handwritten Text Recognition (2021, unknown): “The code to run the neural networks is created in Python (Van Rossum and Drake, 2009) using PyTorch (Paszke, Gross, Massa, Lerer, Bradbury, Chanan, Killeen, Lin, Gimelshein, Antiga, et al., 2019).”
Towards Total Recall in Industrial Anomaly Detection (2021, methodology): “We implemented our models in Python 3.7 [51] and PyTorch [37].”