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Found 6 papers, 1 papers with code
ReFormer: The Relational Transformer for Image Captioning
To improve the quality of image captioning, we propose a novel architecture ReFormer -- a RElational transFORMER to generate features with relation information embedded and to explicitly express the pair-wise relationships between objects in the image.
Cubic Spline Smoothing Compensation for Irregularly Sampled Sequences
The marriage of recurrent neural networks and neural ordinary differential networks (ODE-RNN) is effective in modeling irregularly-observed sequences.
Fashion Captioning: Towards Generating Accurate Descriptions with Semantic Rewards
The goal of this work is to develop a novel learning framework for accurate and expressive fashion captioning.
Learning Continuous-Time Dynamics by Stochastic Differential Networks
Learning continuous-time stochastic dynamics is a fundamental and essential problem in modeling sporadic time series, whose observations are irregular and sparse in both time and dimension.
Adaptive Activation Network and Functional Regularization for Efficient and Flexible Deep Multi-Task Learning
In this paper, we propose a novel deep learning model called Task Adaptive Activation Network (TAAN) that can automatically learn the optimal network architecture for MTL.
Latent Part-of-Speech Sequences for Neural Machine Translation
In this work, we introduce a new latent variable model, LaSyn, that captures the co-dependence between syntax and semantics, while allowing for effective and efficient inference over the latent space.
