Demis Hassabis, Dharshan Kumaran, Raia Hadsell, Joel Veness, Razvan Pascanu, Agnieszka Grabska-Barwinska, James Kirkpatrick, Neil Rabinowitz, Claudia Clopath, Guillaume Desjardins, Kieran Milan, John Quan, and 2 more
We lifted 22 functions out of this paper's own repositories and ran 14 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| mmasana/FACIL | — | 4 of 5 |
| clam004/intro_continual_learning | — | 3 of 4 |
| catid/never_forget | pwc_unofficial | 2 of 4 |
| srvCodes/continual-learning-benchmark | — | 1 of 1 |
| codelion/adaptive-classifier | — | 1 of 1 |
| kamsyn95/CL_DNN | — | 1 of 1 |
| wjmacro/continualmt | — | 1 of 1 |
| wannabeOG/MAS-PyTorch | — | 1 of 1 |
| Minhchuyentoancbn/Continual-Learning | — | 0 of 1 |
| stijani/elastic-weight-consolidation-tf2 | — | 0 of 1 |
| shivamsaboo17/overcoming-catastrophic-forgetting-in-neural-networks | — | 0 of 1 |
| xduan7/hat-cl | pwc_unofficial | 0 of 1 |
| Function | Status | Where it lives |
|---|---|---|
| EWC | Ran | srvCodes/continual-learning-benchmark/train/ewc.py code served (permissive licence) · get_code("3ca3b2ad5cf08e7a") |
| EWC | Ran | codelion/adaptive-classifier/src/adaptive_classifier/ewc.py code served (permissive licence) · get_code("a1465fe6cdcb57d7") |
| EWCMultitask | Ran | kamsyn95/CL_DNN/models/ewc_modules.py pointer only (licence: NONE) · get_code("6aad701239ed7577") |
| ExemplarsDataset | Ran | mmasana/FACIL/src/approach/ewc.py code served (permissive licence) · get_code("39b8751422fdac64") |
| ExperimentLogger | Ran | mmasana/FACIL/src/approach/ewc.py code served (permissive licence) · get_code("1d10e03aa48307d3") |
| GradMultiply | Ran | wjmacro/continualmt/fairseq/modules/grad_multiply.py code served (permissive licence) · get_code("b26c848baf0f673d") |
| Inc_Learning_Appr | Ran | mmasana/FACIL/src/approach/ewc.py code served (permissive licence) · get_code("a47c1a346ec772ca") |
| MemoryDataset | Ran | mmasana/FACIL/src/approach/ewc.py code served (permissive licence) · get_code("c1ecdf9c6eed882e") |
| cauchy_naive | Ran | catid/never_forget/models/s4.py code served (permissive licence) · get_code("b7d9d94554e93b1b") |
| compute_omega_grads_norm | Ran | wannabeOG/MAS-PyTorch/utils/mas_utils.py code served (permissive licence) · get_code("a1703c053471f8d6") |
| log_vandermonde_naive | Ran | catid/never_forget/models/s4.py code served (permissive licence) · get_code("e25911109ba29783") |
| one_epoch_baseline | Ran | clam004/intro_continual_learning/contlearn/gettrainer.py code served (permissive licence) · get_code("4473672cd588ecd4") |
| test | Ran | clam004/intro_continual_learning/contlearn/gettrainer.py code served (permissive licence) · get_code("426df1649f33777b") |
| var2device | Ran | clam004/intro_continual_learning/contlearn/gettrainer.py code served (permissive licence) · get_code("aff2576823d89d61") |
| Appr | Not yet run | mmasana/FACIL/src/approach/ewc.py code served (permissive licence) · get_code("c1596b67340a6f97") |
| Approach | Not yet run | Minhchuyentoancbn/Continual-Learning/UCL/approaches/ewc.py pointer only (licence: NONE) · get_code("118dad0481c8d9e3") |
| EWC | Not yet run | stijani/elastic-weight-consolidation-tf2/module.py pointer only (licence: NONE) · get_code("5ebc601b0d506a2c") |
| ElasticWeightConsolidation | Not yet run | shivamsaboo17/overcoming-catastrophic-forgetting-in-neural-networks/elastic_weight_consolidation.py pointer only (licence: NONE) · get_code("9d058983f0e0d999") |
| elastic_weight_consolidation_training | Not yet run | clam004/intro_continual_learning/contlearn/gettrainer.py code served (permissive licence) · get_code("cbe2eeebdcb6a48a") |
| log_vandermonde_transpose_naive | Not yet run | catid/never_forget/models/s4.py code served (permissive licence) · get_code("7cfca855faa8e8dc") |
| read_servers_from_hostfile | Not yet run | catid/never_forget/command_client_grid.py code served (permissive licence) · get_code("5e7be12170048fbb") |
| register_mapping | Not yet run | xduan7/hat-cl/hat/modules/utils.py code served (permissive licence) · get_code("ca1097ecd04eb4a3") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
The ability to learn tasks in a sequential fashion is crucial to the development of artificial intelligence. Neural networks are not, in general, capable of this and it has been widely thought that catastrophic forgetting is an inevitable feature of connectionist models. We show that it is possible to overcome this limitation and train networks that can maintain expertise on tasks which they have not experienced for a long time. Our approach remembers old tasks by selectively slowing down learning on the weights important for those tasks. We demonstrate our approach is scalable and effective by solving a set of classification tasks based on the MNIST hand written digit dataset and by learning several Atari 2600 games sequentially.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("1612.00796")
get_code_for_paper("1612.00796")
have("1612.00796")
Connect an agent — have() is free.