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Distillation

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Interpretable pairwise distillations for generative protein sequence models.

PLoS computational biology
Many different types of generative models for protein sequences have been proposed in literature. Their uses include the prediction of mutational effects, protein design and the prediction of structural properties. Neural network (NN) architectures h...

Uncertainty-Aware Knowledge Distillation for Collision Identification of Collaborative Robots.

Sensors (Basel, Switzerland)
Human-robot interaction has received a lot of attention as collaborative robots became widely utilized in many industrial fields. Among techniques for human-robot interaction, collision identification is an indispensable element in collaborative robo...

Relieving the Incompatibility of Network Representation and Classification for Long-Tailed Data Distribution.

Computational intelligence and neuroscience
In the real-world scenario, data often have a long-tailed distribution and training deep neural networks on such an imbalanced dataset has become a great challenge. The main problem caused by a long-tailed data distribution is that common classes wil...

A New Hybrid BFOA-PSO Optimization Technique for Decoupling and Robust Control of Two-Coupled Distillation Column Process.

Computational intelligence and neuroscience
The two-coupled distillation column process is a physically complicated system in many aspects. Specifically, the nested interrelationship between system inputs and outputs constitutes one of the significant challenges in system control design. Mostl...