Search Results for author:
Found 8 papers, 4 papers with code
Dispersed EM-VAEs for Interpretable Text Generation
Interpretability is important in text generation for guiding the generation with interpretable attributes.
Functional Geometry Guided Protein Sequence and Backbone Structure Co-Design
In this paper, we propose NAEPro, a model to jointly design Protein sequence and structure based on automatically detected functional sites.
Joint Design of Protein Sequence and Structure based on Motifs
Designing novel proteins with desired functions is crucial in biology and chemistry.
Follow Your Path: a Progressive Method for Knowledge Distillation
However, it has been observed that a converged heavy teacher model is strongly constrained for learning a compact student network and could make the optimization subject to poor local optima.
Learning from deep model via exploring local targets
However, it has been observed that a converged heavy teacher model is strongly constrained for learning a compact student network and could make the optimization subject to poor local optima.
Variational Template Machine for Data-to-Text Generation
We propose the variational template machine (VTM), a novel method to generate text descriptions from data tables.
Kernelized Bayesian Softmax for Text Generation
Neural models for text generation require a softmax layer with proper token embeddings during the decoding phase.
Dispersed Exponential Family Mixture VAEs for Interpretable Text Generation
To enhance the controllability and interpretability, one can replace the Gaussian prior with a mixture of Gaussian distributions (GM-VAE), whose mixture components could be related to hidden semantic aspects of data.
