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Found 5 papers, 5 papers with code
Self-Distillation Improves DNA Sequence Inference
Self-supervised pretraining (SSP) has been recognized as a method to enhance prediction accuracy in various downstream tasks.
ChordMixer: A Scalable Neural Attention Model for Sequences with Different Lengths
Sequential data naturally have different lengths in many domains, with some very long sequences.
Ranked #4 on
Long-range modeling
on LRA
Paramixer: Parameterizing Mixing Links in Sparse Factors Works Better than Dot-Product Self-Attention
The overall computing cost of the new building block is as low as $O(N \log N)$.
Ranked #18 on
Long-range modeling
on LRA
Classification of Long Sequential Data using Circular Dilated Convolutional Neural Networks
Recurrent Neural Networks, Transformers, and Convolutional Neural Networks are three major techniques for learning from sequential data.
Sparse Factorization of Large Square Matrices
The sparse factorization method is tested for a variety of synthetic and real-world square matrices.
Ranked #17 on
Long-range modeling
on LRA
