We lifted 6 functions out of this paper's own repositories and ran 5 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 |
|---|---|---|
| igolan/bgd | canonical | 5 of 6 |
| Function | Status | Where it lives |
|---|---|---|
| adam | Ran | igolan/bgd/optimizers_lib/optimizers_lib.py code served (permissive licence) · get_code("e4de4e90f9014c20") |
| calculate_gain | Ran | igolan/bgd/nn_utils/init_utils.py code served (permissive licence) · get_code("35b9edc57d88d973") |
| kaiming_normal_std_ | Ran | igolan/bgd/nn_utils/init_utils.py code served (permissive licence) · get_code("7017609a5ade3c7f") |
| labels_trick | Ran | igolan/bgd/nn_utils/labels_trick.py code served (permissive licence) · get_code("2a7a7a7286986fde") |
| sgd | Ran | igolan/bgd/optimizers_lib/optimizers_lib.py code served (permissive licence) · get_code("184f03d97f32de50") |
| bgd | Not yet run | igolan/bgd/optimizers_lib/optimizers_lib.py code served (permissive licence) · get_code("996f532d849dc656") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Catastrophic forgetting is the notorious vulnerability of neural networks to the change of the data distribution while learning. This phenomenon has long been considered a major obstacle for allowing the use of learning agents in realistic continual learning settings. A large body of continual learning research assumes that task boundaries are known during training. However, research for scenarios in which task boundaries are unknown during training has been lacking. In this paper we present, for the first time, a method for preventing catastrophic forgetting (BGD) for scenarios with task boundaries that are unknown during training --- task-agnostic continual learning. Code of our algorithm is available at https://github.com/igolan/bgd.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("1803.10123")
get_code_for_paper("1803.10123")
have("1803.10123")
Connect an agent — have() is free.