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from __future__ import print_function | |
import numpy as np | |
from keras import backend as K | |
from keras.preprocessing import sequence | |
from keras.models import Model, Sequential | |
from keras.layers import Dense, Dropout, Embedding, LSTM, Wrapper, Input | |
from keras.datasets import imdb | |
from keras.utils.generic_utils import has_arg | |
import copy |
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from sklearn.metrics.pairwise import pairwise_distances | |
import numpy as np | |
import sklearn | |
print(sklearn.__version__) | |
print(np.__version__) | |
def posdef_check_value(d): | |
d[np.isnan(d)]=0 |
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Bulusu, 2020, https://arxiv.org/pdf/2003.06979.pdf, "ANOMALOUS INSTANCE DETECTION IN DEEP LEARNING: A SURVEY", | |
Meinke, 2020, https://arxiv.org/abs/1909.12180, "TOWARDS NEURAL NETWORKS THAT PROVABLY KNOWWHEN THEY DON’T KNOW" | |
Serrà, 2020, https://arxiv.org/pdf/1909.11480.pdfn "Input complexity and out-of-distribution detection with likelihood-based generative models" | |
Hendrycks, 2020, https://arxiv.org/pdf/1912.02781.pdf, "AUGMIX: A SIMPLE DATA PROCESSING METHOD TO IMPROVE ROBUSTNESS AND UNCERTAINTY" | |
Liu, 2019, http://openaccess.thecvf.com/content_CVPR_2019/html/Liu_Large-Scale_Long-Tailed_Recognition_in_an_Open_World_CVPR_2019_paper.html, "Large-Scale Long-Tailed Recognition in an Open World" |