We lifted 3 functions out of this paper's own repositories and ran 3 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 |
|---|---|---|
| Westlake-AI/OpenBioSeq | canonical | 3 of 3 |
| Function | Status | Where it lives |
|---|---|---|
| annealing_cos | Ran | Westlake-AI/OpenBioSeq/openbioseq/core/hooks/addtional_scheduler.py code served (permissive licence) · get_code("9d9b0c55c72acf25") |
| get_position_from_periods | Ran | Westlake-AI/OpenBioSeq/openbioseq/core/hooks/addtional_scheduler.py code served (permissive licence) · get_code("b46e88c7adaa8b7b") |
| scaled_all_reduce | Ran | Westlake-AI/OpenBioSeq/openbioseq/core/hooks/precise_bn_hook.py code served (permissive licence) · get_code("84b0ff113e7a1efe") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Deep learning associated with neurological signals is poised to drive major advancements in diverse fields such as medical diagnostics, neurorehabilitation, and brain-computer interfaces. The challenge in harnessing the full potential of these signals lies in the dependency on extensive, high-quality annotated data, which is often scarce and expensive to acquire, requiring specialized infrastructure and domain expertise. To address the appetite for data in deep learning, we present Neuro-BERT, a self-supervised pre-training framework of neurological signals based on masked autoencoding in the Fourier domain. The intuition behind our approach is simple: frequency and phase distribution of neurological signals can reveal intricate neurological activities. We propose a novel pre-training task dubbed Fourier Inversion Prediction (FIP), which randomly masks out a portion of the input signal and then predicts the missing information using the Fourier inversion theorem. Pre-trained models can be potentially used for various downstream tasks such as sleep stage classification and gesture recognition. Unlike contrastive-based methods, which strongly rely on carefully hand-crafted augmentations and siamese structure, our approach works reasonably well with a simple transformer encoder with no augmentation requirements. By evaluating our method on several benchmark datasets, we show that Neuro-BERT improves downstream neurological-related tasks by a large margin.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2204.12440")
get_code_for_paper("2204.12440")
have("2204.12440")
Connect an agent — have() is free.