Vineeth Balasubramanian, K Joseph
We lifted 20 functions out of this paper's own repositories and ran 4 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 |
|---|---|---|
| JosephKJ/merlin | — | 4 of 20 |
| Function | Status | Where it lives |
|---|---|---|
| Metrics | Ran | JosephKJ/merlin/lib/consolidate.py pointer only (licence: NONE) · get_code("4f1c61003fbdaab9") |
| construct_state_dict_from_weights | Ran | JosephKJ/merlin/lib/consolidate.py pointer only (licence: NONE) · get_code("13ec5834ed19bd95") |
| gen_bar_updater | Ran | JosephKJ/merlin/lib/consolidate.py pointer only (licence: NONE) · get_code("69c291c8cf8a5818") |
| one_hot | Ran | JosephKJ/merlin/lib/consolidate.py pointer only (licence: NONE) · get_code("8431f92b5e9cab82") |
| CHUNKED_VAE | Not yet run | JosephKJ/merlin/lib/consolidate.py pointer only (licence: NONE) · get_code("d4dbd015944bacde") |
| MNIST | Not yet run | JosephKJ/merlin/lib/consolidate.py pointer only (licence: NONE) · get_code("b82ee827d83c2eff") |
| compute_accuracy | Not yet run | JosephKJ/merlin/lib/consolidate.py pointer only (licence: NONE) · get_code("6d1affa42db4b765") |
| compute_offset | Not yet run | JosephKJ/merlin/lib/consolidate.py pointer only (licence: NONE) · get_code("79feca99ba9abfe0") |
| decode_chunked_model | Not yet run | JosephKJ/merlin/lib/consolidate.py pointer only (licence: NONE) · get_code("b0e208a3661a619b") |
| ensemble_and_evaluate | Not yet run | JosephKJ/merlin/lib/consolidate.py pointer only (licence: NONE) · get_code("5813ce0c4f476a48") |
| ensemble_and_evaluate_cumulative_prior | Not yet run | JosephKJ/merlin/lib/consolidate.py pointer only (licence: NONE) · get_code("da193d4ba7529139") |
| ensembled_prediction_for_a_task | Not yet run | JosephKJ/merlin/lib/consolidate.py pointer only (licence: NONE) · get_code("f9eaee6cea9ea9a5") |
| evaluate_classification_model | Not yet run | JosephKJ/merlin/lib/consolidate.py pointer only (licence: NONE) · get_code("d2dd1ab69b49be0e") |
| get_classification_loss | Not yet run | JosephKJ/merlin/lib/consolidate.py pointer only (licence: NONE) · get_code("52adafc4af204ff6") |
| get_weights_from_chunked_vae | Not yet run | JosephKJ/merlin/lib/consolidate.py pointer only (licence: NONE) · get_code("ee8b54e47e727fb2") |
| get_weights_from_chunked_vae_cumulative_prior | Not yet run | JosephKJ/merlin/lib/consolidate.py pointer only (licence: NONE) · get_code("fd90c498ce78b6f1") |
| log | Not yet run | JosephKJ/merlin/lib/consolidate.py pointer only (licence: NONE) · get_code("92a8f98b9d4d4a77") |
| recall | Not yet run | JosephKJ/merlin/lib/consolidate.py pointer only (licence: NONE) · get_code("8116d3e7e4ec6a28") |
| train_block_chunk | Not yet run | JosephKJ/merlin/lib/consolidate.py pointer only (licence: NONE) · get_code("4fdf6fab77d964a3") |
| train_vae | Not yet run | JosephKJ/merlin/lib/consolidate.py pointer only (licence: NONE) · get_code("6d62cdee8d38351f") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
The ability to continuously learn and adapt itself to new tasks, without losing grasp of already acquired knowledge is a hallmark of biological learning systems, which current deep learning systems fall short of. In this work, we present a novel methodology for continual learning called MERLIN: Meta-Consolidation for Continual Learning. We assume that weights of a neural network ψ, for solving task t, come from a meta-distribution p(ψ|t). This meta-distribution is learned and consolidated incrementally. We operate in the challenging online continual learning setting, where a data point is seen by the model only once. Our experiments with continual learning benchmarks of MNIST, CIFAR-10, CIFAR-100 and Mini-ImageNet datasets show consistent improvement over five baselines, including a recent state-of-the-art, corroborating the promise of MERLIN.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2010.00352")
get_code_for_paper("2010.00352")
have("2010.00352")
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