4 citations · 6 across the 5 of their papers we have counts for
5 papers
Self-Distillation Improves DNA Sequence Inference
Tong Yu, Lei Cheng, Ruslan Khalitov +2
Self-supervised pretraining (SSP) has been recognized as a method to enhance prediction accuracy in various downstream tasks. However, its efficacy for DNA sequences remains somewh…
ChordMixer: A Scalable Neural Attention Model for Sequences with Different Lengths
Ruslan Khalitov, Tong Yu, Lei Cheng +1
Sequential data naturally have different lengths in many domains, with some very long sequences. As an important modeling tool, neural attention should capture long-range interacti…
Paramixer: Parameterizing Mixing Links in Sparse Factors Works Better than Dot-Product Self-Attention
Tong Yu, Ruslan Khalitov, Lei Cheng +1
Self-Attention is a widely used building block in neural modeling to mix long-range data elements. Most self-attention neural networks employ pairwise dot-products to specify the a…
Classification of Long Sequential Data using Circular Dilated Convolutional Neural Networks
Lei Cheng, Ruslan Khalitov, Tong Yu +1
Classification of long sequential data is an important Machine Learning task and appears in many application scenarios. Recurrent Neural Networks, Transformers, and Convolutional N…
Sparse Factorization of Large Square Matrices
Ruslan Khalitov, Tong Yu, Lei Cheng +1
Square matrices appear in many machine learning problems and models. Optimization over a large square matrix is expensive in memory and in time. Therefore an economic approximation…