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Scaling VQDNA Without Losing Efficiency and Performance

Design and validate scaling strategies that increase the capacity or parameter count of VQDNA while maintaining its demonstrated merits (e.g., parameter efficiency and performance), overcoming current computational constraints that limited scaling in this work.

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Background

The authors report that VQDNA has not been scaled to its maximum potential due to computational constraints. Despite strong results, they emphasize the need to explore scaling approaches that preserve benefits such as efficiency and accuracy.

By explicitly noting that this exploration remains open, the paper invites methods for capacity scaling that do not degrade the advantages of the HRQ/VQ tokenization pipeline.

References

There are several limitations in this work: (1) The superiority of VQDNA stems from its genome vocabulary learning, which is an additional training stage with extra costs compared to other models. Thus, there is still room for reducing its computational overhead to boost its applicability. (2) Due to the computational constraints, the model scale of VQDNA has not reached its maximum. How to scale up VQDNA while maintaining the gained merits is worth exploring. (3) As the HRQ vocabulary has shown great biological significance in SARS-CoV-2 mutations, broader applications in genomics with VQDNA, such as generation tasks, deserve to be studied. Overall, all these avenues remain open for our future research.

— VQDNA: Unleashing the Power of Vector Quantization for Multi-Species Genomic Sequence Modeling (2405.10812 - Li et al., 13 May 2024) in Section 6 (Conclusion and Discussion), Limitations and Future Works