################################### Create Readme ####################################
def plot_titles(titles):
return '\n' + "## " + titles[0] + '\n'
list_types = [["Classics", "Classic"], ["Empirical Study", "Empirical"],
["Surveys", "Survey", "survey"], ["Influentials", "Influential"],
["New Settings or Metrics", "Setting", "Metric"],
["Regularization Methods", "Regularization"],
["Distillation Methods", "Distillation"],
["Rehearsal Methods", "Rehearsal"],
["Generative Replay Methods", "Generative Replay"],
[
"Dynamic Architectures or Routing Methods", "Architectures",
"Dynamic Architecture"
], ["Hybrid Methods", "Hybrid"],
["Continual Few-Shot Learning", "Continual-Meta Learning"],
["Meta-Continual Learning"],
["Lifelong Reinforcement Learning", "Reinforcement"],
["Continual Generative Modeling", "Generative Modeling"],
["Applications"], ["Thesis"], ["Libraries", "Library"],
["Workshops", 'Workshop']]
generate_md_file(DB=bib_db,
list_classif=list_types,
key="keywords",
plot_title_fct=plot_titles,
filename="README.md",
add_comments=True)
conferences_list = [["ICLR", "International Conference on Learning Representations"],
["CVPR", "Conference on Computer Vision and Pattern Recognition"],
["ICCV", "International Conference on Computer Vision"],
["ECCV", "European Conference on Computer Vision"],
["NeuriPS", "NIPS", "Neural Information Processing Systems"],
["ICML", "International Conference on Machine Learning"],
["IJCAI", "International Joint Conference on Artificial Intelligence"],
["IJCNN", "International Joint Conference on Neural Networks"],
["ICANN", "International Conference on Artificial Neural Networks"]]
output_file = os.path.join("Mardown_Files", "Conferences_Bibliography.md")
generate_md_file(DB=bib_db,
list_classif=conferences_list,
key="booktitle",
plot_title_fct=plot_conf_title,
filename=output_file,
url=repository_url,
bibfile=bibfile_name,
add_comments=False)
################################### SORT BY AUTHORS ####################################
def plot_years_title(year):
return '\n' + "## Papers in " + year[0] + '\n'
years = []
for i in range(1950, 2021):
years.append([str(i)])
years.reverse()
