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We implement a Jordan network that learns arithmetic operations (addition and subtraction) and mimic human reaction time. This implementation was used in the ICCM 2019 paper titled "Simulating Problem Difficulty in Arithmetic Cognition Through Dynamic Connectionist Models".

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sungjae-cho/arithmetic-jordan-net

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data
data
 
 
measure_logs/cogsci2019
measure_logs/cogsci2019
 
 
plot_figures
plot_figures
 
 
result_statistics
result_statistics
 
 
run_info
run_info
 
 
01-2_generate_and_save_all_datasets.ipynb
01-2_generate_and_save_all_datasets.ipynb
 
 
01-3_save_carry_dataset_statistics.ipynb
01-3_save_carry_dataset_statistics.ipynb
 
 
01-4_visualize_carray_datasets_statistics.ipynb
01-4_visualize_carray_datasets_statistics.ipynb
 
 
01-5_get_prob_one_per_digit.ipynb
01-5_get_prob_one_per_digit.ipynb
 
 
01-6_print_carry_dataset_info.ipynb
01-6_print_carry_dataset_info.ipynb
 
 
02-1_plot_run_info_stat_cogsci2019.ipynb
02-1_plot_run_info_stat_cogsci2019.ipynb
 
 
02-1_plot_run_info_stat_iccm2019.ipynb
02-1_plot_run_info_stat_iccm2019.ipynb
 
 
02-2_plot_measures.ipynb
02-2_plot_measures.ipynb
 
 
02-3_see_run_info.ipynb
02-3_see_run_info.ipynb
 
 
03-1_Jordan_RNN.ipynb
03-1_Jordan_RNN.ipynb
 
 
LICENSE
LICENSE
 
 
README.md
README.md
 
 
anova.R
anova.R
 
 
change_experiment_name.ipynb
change_experiment_name.ipynb
 
 
cogsci_experiment.sh
cogsci_experiment.sh
 
 
cogsci_experiment_fixed_random.sh
cogsci_experiment_fixed_random.sh
 
 
cogsci_experiment_random.sh
cogsci_experiment_random.sh
 
 
config.py
config.py
 
 
data_utils.py
data_utils.py
 
 
device_num_alloc.sh
device_num_alloc.sh
 
 
generate_datasets_old.py
generate_datasets_old.py
 
 
get_df_run_info.ipynb
get_df_run_info.ipynb
 
 
iccm2019.ipynb
iccm2019.ipynb
 
 
iccm2019.sh
iccm2019.sh
 
 
iccm2019_vic.sh
iccm2019_vic.sh
 
 
jordan_cp9_add_sub_maxstep.ipynb
jordan_cp9_add_sub_maxstep.ipynb
 
 
jordan_cp9_add_sub_maxstep.sh
jordan_cp9_add_sub_maxstep.sh
 
 
log_cogsci2019.txt
log_cogsci2019.txt
 
 
log_cogsci2019_fixed_random.txt
log_cogsci2019_fixed_random.txt
 
 
log_cogsci2019_random.txt
log_cogsci2019_random.txt
 
 
log_cogsci2019_test.txt
log_cogsci2019_test.txt
 
 
mlp_run.py
mlp_run.py
 
 
mutual_info_and_conquest_epoch.ipynb
mutual_info_and_conquest_epoch.ipynb
 
 
rm_records.py
rm_records.py
 
 
rnn_run.py
rnn_run.py
 
 
rnn_run.sh
rnn_run.sh
 
 
rnn_run_v1.py
rnn_run_v1.py
 
 
run_info_utils.py
run_info_utils.py
 
 
test.py
test.py
 
 
test.sh
test.sh
 
 
utils.py
utils.py
 
 
write_embeddings_summary.py
write_embeddings_summary.py
 
 

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arithmetic-neural-net

Project for implementing arithmetic neural networks

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We implement a Jordan network that learns arithmetic operations (addition and subtraction) and mimic human reaction time. This implementation was used in the ICCM 2019 paper titled "Simulating Problem Difficulty in Arithmetic Cognition Through Dynamic Connectionist Models".

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