sha256 stringlengths 64 64 | language stringclasses 28
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|---|---|---|---|---|
84f004902b8011558e9e26ffd3c44438947c1b41cd6045ec63c078660e0787b9 | Jupyter | 41,105 | 962 | # %% [markdown]
# ## Script to parse behavioral data from pickle files
#
# The purpose of this notebook is to parse the behavioral data in the appropriate pickle files into a format that is easily usable for analysis and plotting.
#
# The pickle files for each animal contain dictionaries where the keys are session na... |
5e5b232bd90be5978e3988348ddd3fc241b6642e6232c123e8a38c5e43274d0f | Jupyter | 41,434 | 997 | # %%
import pandas as pd
import numpy as np
import os
from pathlib import Path
import matplotlib.pyplot as plt
import matplotlib.ticker as mtick
from matplotlib import gridspec
from matplotlib.ticker import ScalarFormatter
from matplotlib.ticker import FormatStrFormatter
import seaborn as sns
import sys
sys.path.append... |
3ec77360de751c77ec32e3734bc1d3e1548861b53a66a5e28335767bcec272c8 | Jupyter | 41,615 | 1,273 | # %% [markdown]
# ##### This notebook creates Transfer Learning (TL) experiment, with base model trained on Tox21+RTECS, transferred to predict on ClinTox
#
# Using split data already saved.
#
# RTECS dataset has been commented out.
#
# Notebook shows results for seed = 124, but we also ran on seed 122, 123.
#
# ... |
8ba800103487702fdff5f54c3bf856a5cdb9373dd97ec68a9d6cd02b4894abf2 | Jupyter | 42,311 | 1,307 | # %%
%pylab inline
# %%
from __future__ import print_function
from pandas import *
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torchvision import datasets, transforms
import numpy
import matplotlib.pyplot as plt
import matplotlib
import pickle
from scipy import s... |
a68381a6cdbb150d982a5cfec1a3bdda13beb7562b98c316a72cc2fc6519705b | Jupyter | 43,881 | 103 | # %% [markdown]
# # Defining populations of neurons
# In this tutorial we're going to define a population of Izhikevich neurons and configure individual neurons within it to operate in various regimes:
# ![image.png](data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAA1MAAAJDCAIAAABhe9nkAAAgAElEQVR4nOzdT2zbVro//MPB3SVAxcJAg... |
25bf57b0d5c6b2306408d9a2793bcee4be55b17016998e056336caaa36de8294 | Jupyter | 45,449 | 978 | # %%
import sys
import os
import glob
import collections
import shutil
import subprocess
import itertools
import numpy as np
import pandas as pd
from scipy import stats
from openeye import oechem
from scipy import stats
from sklearn import metrics
from sklearn.metrics import roc_auc_score, roc_curve
from sklearn.uti... |
bc89ea88d3563635fd21f46b8a8f4eeb0bb43ec5f9ae50f850b359154d22a364 | Jupyter | 46,493 | 1,305 | # %% [markdown]
# # Installation
# %%
%%writefile setup.sh
# maskrcnn_benchmark and coco api dependencies
pip install ninja yacs cython matplotlib tqdm opencv-python
# follow PyTorch installation in https://pytorch.org/get-started/locally/
# we give the instructions for CUDA 9.0
pip install -c pytorch pytorch-nightl... |
570a87f85cf1a78d177e6226f2fb6f9884ea876571145b5cfc6e4156b316864e | Jupyter | 46,564 | 921 | # %% [markdown]
# # Clean Bibliography
#
# To goal of this notebook is to clean your `.bib` file to ensure that it only contains the full first names of references that you have cited in your paper. The full first names will then be used to query the probabilistic gender classifier, [Gender API](https://gender-api.com... |
281853c222e4debafebee6666e4caffd5911cfa5e998da5d6b76d259bfb917cf | Jupyter | 46,662 | 1,131 | # %%
import sys
import os
import glob
import collections
import shutil
import subprocess
import numpy as np
import pandas as pd
from openeye import oechem
from scipy import stats
from sklearn.metrics import roc_auc_score, roc_curve
from sklearn.utils import resample
from sklearn.linear_model import LinearRegression
... |
4675e9686631ccba80785c01adc4d743ea7e3e3ac186d08131f42d546a8e8fba | Jupyter | 47,037 | 1,307 | # %%
"""
jupyter-nbconvert report_plots.ipynb --to python; python -u report_plots.py
"""
# %%
from __future__ import division
import os
import glob
import pickle
from natsort import natsorted
import argparse
import os
import sys
import numpy as np
import tqdm
import pandas as pd
import cv2
import scipy as sp
import ... |
6427dae2a7cd58e1edee1ccf735cd3743abfef270cc1c03956d32c18f5902d6e | Jupyter | 48,981 | 1,198 | # %%
import numpy as np
import operator
from scipy.optimize import linear_sum_assignment, OptimizeResult
from scipy._lib._util import check_random_state
import itertools
def quadratic_assignment(A, B, method="faq", options=None):
r"""
Approximates solution to the quadratic assignment problem and
the grap... |
347c5785e1f43e3fe98eb2e9f16a6be4803146ec12a5bfdaf7f746ed7f6f9377 | Jupyter | 49,171 | 1,398 | # %% [markdown]
# # Import Packages
# %%
# Default packages
import os
from os.path import join
import json
from datetime import date, datetime, timedelta
from joblib import load, dump
# Set current directory to "src"
os.chdir(join(os.getcwd(), os.pardir, "src"))
print(f"Current working directory: {os.getcwd()}")
# I... |
972f6ecf3f651cbbdf714a2b0bb07fb402d9382a3e83d54c2393f294e1b082df | Jupyter | 50,966 | 294 | # %%
#Load the required libraries
import numpy as np
import pandas as pd
from tensorflow import keras
from keras.models import Model,load_model
from keras import backend as K
from keras import models, layers
from keras.models import Sequential, model_from_json
from keras.layers.core import Dense
from matplotlib import... |
8cb5eb0178630650950cc98bb9c44869c19d56555d2412e39cee9082ac813772 | Jupyter | 50,971 | 297 | # %%
#Load the required libraries
import numpy as np
import pandas as pd
from tensorflow import keras
from keras.models import Model,load_model
from keras import backend as K
from keras import models, layers
from keras.models import Sequential, model_from_json
from keras.layers.core import Dense
from matplotlib import... |
d77bd17eca188e7333c5a257eccebbca368c97a71e31fff353a2b49b94d9a6f6 | Jupyter | 52,711 | 1,398 | # %%
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
plt.rcParams['pdf.fonttype'] = 42
plt.rcParams['ps.fonttype'] = 42
plt.rcParams['font.family'] = 'Arial'
import seaborn as sns
sns.set_context("talk")
cmap = ['#9B1D20','#03254E','#B4B8AB']
sns.set_palette(cmap, n_colors=3, desat=None, color_co... |
c5315710dcf75b9f739edf77165421916b3735033beaadf2b4b95c1292f693d7 | Jupyter | 56,316 | 1,486 | # %% [markdown]
# # Sentinel 2A Plastic Waste Exploration
# A starting point for exploring multispectral data from Sentinel 2A for TPA sites in Indonesia.
#
# ## Explorations:
# ### [1. Patch Visualization](#Exploration-1)
# For each known plastic waste site, define a rect centered on the known coordinates. Additional... |
15d6a25e2e3488ba5ddbb1af86af0cfaa312b8c3970ca8f7c782ab4fe63feb43 | Jupyter | 56,514 | 1,592 | # %%
"""
jupyter-nbconvert report_common_subspace.ipynb --to python; python -u report_common_subspace.py
"""
# %%
from __future__ import division
import os
import glob
import pickle
from natsort import natsorted
import argparse
import os
import sys
import numpy as np
import tqdm
import pandas as pd
import scipy as s... |
5320ceb41b54b7a90de712139bf88dce2e2125374d1e5b578f8c0c543c554023 | Jupyter | 58,584 | 1,293 | # %%
%run ../setup_notebook.ipynb
# %%
import matplotlib.image as mpimg
from mpl_toolkits.axes_grid1 import make_axes_locatable
from mpl_toolkits.axes_grid1.colorbar import colorbar as mpl_colorbar
from sklearn.mixture import GaussianMixture
import copy
import matplotlib.image as mpimg
import plotting_functions as pf... |
4c87e201994aa02a926d025231e11aa5ff4a6dc740c6171f21ef34d13b95efe5 | Jupyter | 59,251 | 1,732 | # %%
"""
jupyter-nbconvert report_differences.ipynb --to python; python -u report_differences.py
"""
# %%
from __future__ import division
import os
import glob
import pickle
from natsort import natsorted
import argparse
import os
import sys
import numpy as np
import tqdm
import pandas as pd
import numpy as np
from pp... |
2a9e5509f01e7b1ecef5efefdbc2575764fe4c5a43c887322bed6e034b33bb96 | Jupyter | 60,725 | 1,424 | # %% [markdown]
# %%
%run ../setup_notebook.ipynb
# %%
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import pickle
import seaborn as sns
import matplotlib.patches
import itertools
import matplotlib.image as mpimg
from mpl_toolkits.axes_grid1 import make_axes_locatable
from mpl_toolkits.axes_... |
7981a947df704749a30302a337c8556864a1dcce33b167f9610905818317773c | Jupyter | 62,235 | 1,784 | # %% [markdown]
# # Installation
# %%
%%writefile setup.sh
# maskrcnn_benchmark and coco api dependencies
pip install ninja yacs cython matplotlib tqdm opencv-python
# follow PyTorch installation in https://pytorch.org/get-started/locally/
# we give the instructions for CUDA 9.0
pip install -c pytorch pytorch-nightl... |
418a76a64fa00ea9465c9156816cccc62f77dcc60a492e2c3813f286e3ce8e89 | Jupyter | 62,548 | 1,610 | # %% [markdown]
# # Benchmark Results
# This notebook visualizes the output from the different models on different classification problems
# %%
import collections
import glob
import itertools
import json
import os
import numpy as np
import pandas as pd
import plotnine
import svgutils.transform as sg
from svgutils.com... |
22a25549814401c4df75496dbb7c5ef6f84da6b26b6f3b9dcb3154f09ff2c9ae | Jupyter | 64,295 | 1,141 | # %% [markdown]
# # Deception Detection supported by Machine Learning
# %% [markdown]
# ## Literature Review - Statistical analysis
# This notebook is devoted to performing some statistical analysis on the selected corpus.
#
# By statistical analysis we mean generating some tables and charts from the collected metada... |
0fd0e214303d2292fbb659587302409f7c7a002b5d5b5250c2a5c6041196b7dc | Jupyter | 65,315 | 1,935 | # %% [markdown]
# ## Installing SuStaIn and setting it up to run in a notebook
#
# To get SuStaIn up and running first you need to install the package. I'm using Anaconda and had some conflicts with existing packagaes so I had to create a new environment. For me the whole set up process looked like this...
#
# Step 1... |
451c546d1e988971c13fe4c66452508926100354c51c687964624783ab6a978c | Jupyter | 68,004 | 2,051 | # %% [markdown]
# ## Installing SuStaIn and setting it up to run in a notebook
#
# To get SuStaIn up and running first you need to install the package. I'm using Anaconda and had some conflicts with existing packagaes so I had to create a new environment. For me the whole set up process looked like this...
#
# Step 1... |
fb73588188d330b280678f24cdd177724b40e8996c8ec9a91a257224a457a783 | Jupyter | 75,379 | 1,564 | # %% [markdown]
# # Cerebellar volumetry and cortical thickness covariance
# ## Data:
# - 1. Freesurfer cortical thickness measures in Des/DKT atlas;
# - 2. Cerebellar lobular volumnes from SUIT/MDTB/MAGeT softwares;
# ## Atlases
# -1. Des 2009a: L+R: 74 regions + WhiteSurfArea_area + MeanThickness_thick... |
6f52fc75ad30625a289b7625cbba4d57c4a078e326a2a04fd57446f3e20d65ca | Jupyter | 75,587 | 2,118 | # %% [markdown]
# # Installation
# %%
%%writefile setup.sh
# maskrcnn_benchmark and coco api dependencies
pip install ninja yacs cython matplotlib tqdm opencv-python
# follow PyTorch installation in https://pytorch.org/get-started/locally/
# we give the instructions for CUDA 9.0
pip install -c pytorch pytorch-nightl... |
78775f76363eb89bc67f12dd25dc6d338406a809284bd510838b41a08111ec6c | Jupyter | 88,994 | 2,068 | # %% [markdown]
# ## Script to parse behavioral data from pickle files
#
# The purpose of this notebook is to parse the behavioral data in the appropriate pickle files into a format that is easily usable for analysis and plotting.
#
# The pickle files for each animal contain dictionaries where the keys are session na... |
cfe1ea6b11b3093e8ee9a45d972f23238d0140c6ea82834b1346189e3a7c4fe9 | Jupyter | 94,446 | 2,238 | # %%
from __future__ import division
import os
import copy
import math
import random
import scipy.stats as stats
import scipy.integrate as integrate
import numpy as np
import seaborn as sns
import pymworks
import pickle
import ExGUtils.pyexg as pyexg
from collections import Counter
from sympy import solve, Symbol
impor... |
0afa9273b7e97dddd7d45703c4f721b68d692e10d20524b2351347ba0598a513 | Jupyter | 104,755 | 2,905 | # %% [markdown]
# ## Unsupervised cross-domain translation via deep learning and adversarial attention neural networks and application to music-inspired protein designs
# #### Markus J. Buehler, MIT, 2022-2023
# #### mbuehler@MIT.EDU
# ###### Published in: Patterns, 2023
# %%
import numpy as np
import matplotlib.pyplo... |
82d4bf4c5cd51c742d4464e201aebd18d77d6ff2babb5d11e167c34db79e5a36 | Jupyter | 130,263 | 295 | # %%
import os
import itertools
import random
import numpy as np
import pandas as pd
import sklearn
from sklearn import metrics
from sklearn.metrics import confusion_matrix
from sklearn.ensemble import ExtraTreesClassifier
import matplotlib.pyplot as plt
%matplotlib inline
random_state = 42
print(np.__version__)
pri... |
0d803b4acc6b38eed07e1c6b77b077b351baabb35fb5d881004569086d6c34e3 | Jupyter | 141,250 | 221 | # %% [markdown]
# # Classification of a single digit
# In this series of tutorial, we are going to build an SNN capable of classifying MNIST by copying the weights obtained by training the following simple ANN using TensorFlow:
#
# 
# sys.path.append('/content/drive/MyDrive/neuro_research/stp_networks/'... |
c0caba14aed81b8346e4fd2b06d68416652da9d4c27aecb9fd12031ec30c5ff5 | License | 124 | 2 | Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
https://creativecommons.org/licenses/by-nc-sa/4.0/
|
9935afeb344f9353cef576cfd7e5635c80f40f2d812f4310410242786b140a4d | License | 131 | 3 | This work is licensed under a Creative Commons Attribution 4.0 International License
https://creativecommons.org/licenses/by/4.0/
|
31fcad21ef9ff65843a7453a5bae1d039f7c21b5e90c2ff3e7683ab440477695 | License | 584 | 13 | Copyright 2016 - Chris Miller
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in wri... |
53c5eb201f208d0134839004ebf85943411083965d936ead0ed18929f0c8dcfd | License | 653 | 5 | This work is the property of Virufy.org and licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License with the below addendum. To view a copy of this license, visit http://creativecommons.org/licenses/by-nc-sa/4.0/ or send a letter to Creative Commons, PO Box 1866, Mountain View... |
d48933dffac70a4f8f4ef9c79115eeca6c4892921fe1017d5f5e02c8ffe0e4f0 | License | 700 | 15 | jie
Copyright (C) 2022 Ludwig Institute for Cancer Research Ltd., Bing Ren, Bojing Jia
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later ... |
90a4b9979cf5b57cb0154ca289aac7518bcd4b95b141b2b25c8b6bd75d1da0ec | License | 767 | 17 | Code and pre-trained models for the paper:
Accurate decoding of the speech envelope using the VLAAI deep neural network.
Copyright (C) 2022 Bernd Accou
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundatio... |
113b431df85a872ef43142e708d31368be92d899faadc4b2793387ac2d9df41e | License | 1,022 | 21 | License
Copyright (c) FuZengBio
Permission is hereby granted, free of charge, to any person obtaining a copy
of the associated documentation files (the "Files"), to deal
in the Files without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
... |
98ae9724e73fd04c074df7a34a7123beb37ef663473961306cd5ab97a97a3aa1 | License | 1,039 | 7 | Copyright 2022
Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sel... |
20843858b6884746a2339f2800501da1c907a747520da801327b8def8ed95bbd | License | 1,049 | 7 | Copyright 2022 Leo Burgy
Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, ... |
4ae5448f9b95d5ecb2f189e32f3e9c79b8fd8770465b1979277c45eb44758c92 | License | 1,053 | 7 | Copyright <2017> <Xuhan Liu>
Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicen... |
c29092f7f01a9138bc61b28b2a5f3336eb3f2d2adc7e6ead9345db1692d46b99 | License | 1,053 | 7 | Copyright 2022 David Crompton
Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublice... |
f50ca824cdc269e42a045a67f95f62da441d955c0e0d301c2b1c45b36176d23f | License | 1,054 | 20 | Copyright 2020 Ivan Lazarevich
Permission is hereby granted, free of charge, to any person obtaining
a copy of this software and associated documentation files (the
"Software"), to deal in the Software without restriction, including
without limitation the rights to use, copy, modify, merge, publish,
distribute, sublic... |
31d9e5755af15c6ffab110c6ada9ed4ea4cdfd97894c91558579a808c2292e5a | License | 1,057 | 19 | Copyright 2021 Emerson F. Harkin
Permission is hereby granted, free of charge, to any person obtaining a copy of
this software and associated documentation files (the "Software"), to deal in
the Software without restriction, including without limitation the rights to
use, copy, modify, merge, publish, distribute, subl... |
afb11426e09da40a1ae4f8fa17ddcc6b6a52d14df04c29bc5bcd06eb8730624d | License | 1,057 | 21 | MIT License
Copyright (c) 2017
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, subl... |
6184e79fbbf7da1fd8fc9be9c0c81dc46c3a101578cc3859d6ff82544a96fd1e | License | 1,060 | 21 | MIT License
Copyright (c) 2023 Bob
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, s... |
a1399ca0246c4201858d53752acbc5a8cc821942ea93e7960b2f0017a30622e6 | License | 1,061 | 21 | MIT License
Copyright (c) 2022 CORE
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, ... |
d21e7c748b38b1192024619f29fc299fae083d7509543190a0423d33a19ded19 | License | 1,061 | 21 | MIT License
Copyright (c) 2020 jmpu
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, ... |
41cad25d29ec48c676d2916959e5658976446618cf6d13a34e59ec083012a6b7 | License | 1,062 | 21 | MIT License
Copyright (c) 2022 TIERS
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute,... |
5dcc10c37431645eace5dc33b1a0662d27f41ad9b74e3a0ef1b358dbf1825e04 | License | 1,062 | 21 | MIT License
Copyright (c) 2022 LCY02
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute,... |
93b9855a414d925075e89e0d5191e6aaf58296c67df4ca52050be64307a83d03 | License | 1,062 | 21 | MIT License
Copyright (c) 2022 ISSAI
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute,... |
a91dd7c7b6b71e23ca88322355624925510462a866648efbe1816eaf31416454 | License | 1,062 | 21 | MIT License
Copyright (c) 2020 mfcsc
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute,... |
b0cc8cb52430908e5fcfe2bf2fab3924fc5c991c08cb4f7178ffddfdec42def5 | License | 1,062 | 21 | MIT License
Copyright (c) 2022 ds4dh
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute,... |
e797991fe3fa75b75b0f47d996e31b3619df68409c78abc46ca9d00aec7f58bc | License | 1,062 | 21 | MIT License
Copyright (c) 2021 Vahid
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute,... |
51c409094d69883b2c10fa9814df6fdbd399c8771650c790b4fc3488e928a2ed | License | 1,063 | 21 | MIT License
Copyright (c) 2022 taopeng
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribut... |
847b4ad2a6c8ac47f334e7c15c7ffafd4490aa75acc667a21bf3562aa81f8b57 | License | 1,063 | 21 | MIT License
Copyright (c) 2023 jmasis
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute... |
a4e240a3188e7166d52287e0d1b23b33eb41344fb6daf3edfb110c2de6533a21 | License | 1,063 | 21 | MIT License
Copyright (c) 2021 Xiaoyu
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
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cf4822bb2d609b6aac8325093dee639c7b41791307707e448af05fbe200cc2f4 | License | 1,063 | 21 | MIT License
Copyright (c) 2017 VH-Lab
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d5a27a15336687ee81183c7dd31d1c468e5f4af84231c8c97b327f45367baee4 | License | 1,063 | 21 | MIT License
Copyright (c) 2021 icemtel
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0227425f8337b6dd85ddeb2de77d1a028dc0df4ca213dd1bdc8abd4f455fba7c | License | 1,064 | 21 | MIT License
Copyright (c) Philip Ewels
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0ff244e736eda94aa52e40c921365eaeb4d9591d305ff9f1a4a04eea02bc63b4 | License | 1,064 | 21 | MIT License
Copyright (c) 2023 YX Shao
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3bd675174ad068bd73f54724150d91cc179436d7e544de7a6ac409e1c79a3297 | License | 1,064 | 21 | MIT License
Copyright (c) 2020 carushi
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599cf10f775bfec67b1e38986d4c88ac5d6ff6a4be334feade3056fbc1aa0622 | License | 1,064 | 21 | MIT License
Copyright (c) 2019 igordot
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bc012bf18a9826fa77fecd4dfc1649994dec04fe6850a8f2b08735e9d2b6c8fb | License | 1,064 | 21 | MIT License
Copyright (c) 2021 grey891
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d082d4aee4cfec18732397aa4d5c34f7522103ab548fdb04f158fc9df7fd32e4 | License | 1,064 | 21 | MIT License
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d5628ac980c74f336343dcd20c0604552da04ca4c3927fcf37c33a15a4cbc391 | License | 1,064 | 21 | MIT License
Copyright (c) 2022 XK-Chen
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fb405dff76d14f290196c0732c56bc7e43f39cdc3550ebc8aa7a90198e371680 | License | 1,064 | 21 | MIT License
Copyright (c) 2021 ykubo82
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fd18cbfa1a6f857ad56a45c9423ff0ebb625206055ad8f89097193037a02e34f | License | 1,064 | 21 | MIT License
Copyright (c) 2023 brainML
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10813b2d50341a663600659b9615e15a4a4fbb5effb43364b7064f5f206385c4 | License | 1,065 | 21 | MIT License
Copyright (c) 2021 tiago939
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504bd9f2ad5b4f65244899e4068bc069ef505d45430e9dd724c51e26ae47d50c | License | 1,065 | 21 | MIT License
Copyright (c) Harshil Patel
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55233a80fdd7c9983b48b623eaa4264680882c763baf528b465794b5130dee37 | License | 1,065 | 21 | MIT License
Copyright (c) 2023 GraphXAI
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631ab817a00cf6feffeae5d3a3ffb18949f38b4f0681a4a634ad8a877f53c97e | License | 1,065 | 21 | MIT License
Copyright (c) 2018 UCF CRCV
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9ab4a76a887756af7723acf13417c332327a363f5117179f30f2426a661d376f | License | 1,065 | 21 | MIT License
Copyright (c) 2021 santavis
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afc38c8ffab7641c43aa28623f697ee3f42b38df9df9bbf211ff822ce0d7a9ed | License | 1,065 | 21 | MIT License
Copyright (c) 2022 epetrucc
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ced0e3ead8dba690ac24f8bab0dea1fda1500bfc12fc113593e0fa4243619d78 | License | 1,065 | 21 | MIT License
Copyright (c) 2021 Pugavkom
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dcfba4e38504511c34a016a71ce23bf31e7a2ab6a3f287f4b5b938a6788e6dfb | License | 1,065 | 21 | MIT License
Copyright (c) 2020 JiaQi Xu
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f4377f092d24511f9f77d8965ad5efccb1021fa27d2932859c49d175142cae6f | License | 1,065 | 21 | MIT License
Copyright (c) 2022 Aiwiscal
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111d8229e8888a423f7dee10a858ba6a2f8e0766c8afd84a92350b7d6cb877aa | License | 1,066 | 21 | MIT License
Copyright (c) 2021 Beery Lab
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2f66c9caf14294af8a87317868df0ec516f5345a5b15c2e8f28234481df409be | License | 1,066 | 21 | MIT License
Copyright (c) 2023 Ling KANG
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3124c0b1489a8c45f794ddf42ec05d4ff2b166054011f73af5e3ca034caec5f6 | License | 1,066 | 21 | MIT License
Copyright (c) 2023 MaHuidong
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4d6fb3d0d1ad9440cab75ed5826978630b7311701a7d1cbad15b9644b07f63cf | License | 1,066 | 21 | MIT License
Copyright (c) Mahbub Ul Alam
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570ae6d4b7c66ec48718986db5244538592eedfd64ea64f553968c612c4c6dbd | License | 1,066 | 21 | MIT License
Copyright (c) 2021 Pugavkomm
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ae702ad99618596b98e369b566f2dee663cc5804342a215545cf0f902ef469dd | License | 1,066 | 21 | MIT License
Copyright (c) 2021 Enes Avcu
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c48965dd4456d4f93d6a9926f262cf08ff75ce3e6e0b26562111d27871bcf386 | License | 1,066 | 21 | MIT License
Copyright (c) 2020 Hao Zhang
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cf70c88bd26308c5fdd6da02d298fec2eee451c30bf32ceb2de7a91aca70a1e0 | License | 1,066 | 21 | MIT License
Copyright (c) 2022 whbdupree
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d84fd9268f7328f4ea66688c54a10d40f90ae96d3601a4cc0e15c7acb344dafd | License | 1,066 | 21 | MIT License
Copyright (c) 2021 Fan Group
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e57c57db75d403791ab31db8919c568697472b715ef999624bed3e787de65515 | License | 1,066 | 21 | MIT License
Copyright (c) 2022 Wei-Liang
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00dbb0950bd7bc8d414c6f4ba57655fad4a62d1f95e0441d1c8864aa9b1e3689 | License | 1,067 | 21 | MIT License
Copyright (c) 2022 rosbashlab
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11bc41e03680fd4acfe43faaa316c86d11d0a7891d65f13e838bb7a0b9847806 | License | 1,067 | 21 | MIT License
Copyright (c) 2022 gfsilveira
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15e4b8f86b9ffc7979e06447960f8e81a412b86ebb6a716ea1ff015889041933 | License | 1,067 | 21 | MIT License
Copyright (c) 2020 Ge-Peng Ji
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2c2a6d44db4856d93036bac672bcbd7e2d14b43af8bfddfee5781d8f40afc00c | License | 1,067 | 21 | MIT License
Copyright (c) 2023 antoniozip
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594b3108584c44d0a614e7f62a02f3ee04256a6627ffa606bef784c112a2719a | License | 1,067 | 21 | MIT License
Copyright (c) 2021 Sumner-lab
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