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Found 8 papers, 6 papers with code
Adversarial Dropout for Recurrent Neural Networks
Successful application processing sequential data, such as text and speech, requires an improved generalization performance of recurrent neural networks (RNNs).
Hierarchical Context enabled Recurrent Neural Network for Recommendation
The analyses on the user history require the robust sequential model to anticipate the transitions and the decays of user interests.
Sequential Recommendation with Relation-Aware Kernelized Self-Attention
This work merges the self-attention of the Transformer and the sequential recommendation by adding a probabilistic model of the recommendation task specifics.
BROS: A Pre-trained Language Model for Understanding Texts in Document
Although the recent advance in OCR enables the accurate extraction of text segments, it is still challenging to extract key information from documents due to the diversity of layouts.
Show, Attend and Distill:Knowledge Distillation via Attention-based Feature Matching
Knowledge distillation extracts general knowledge from a pre-trained teacher network and provides guidance to a target student network.
Ranked #36 on
Knowledge Distillation
on ImageNet
Refine Myself by Teaching Myself: Feature Refinement via Self-Knowledge Distillation
Knowledge distillation is a method of transferring the knowledge from a pretrained complex teacher model to a student model, so a smaller network can replace a large teacher network at the deployment stage.
BROS: A Pre-trained Language Model Focusing on Text and Layout for Better Key Information Extraction from Documents
On the other hand, this paper tackles the problem by going back to the basic: effective combination of text and layout.
Ranked #5 on
Relation Extraction
on FUNSD
Unknown-Aware Domain Adversarial Learning for Open-Set Domain Adaptation
Therefore, we propose Unknown-Aware Domain Adversarial Learning (UADAL), which $\textit{aligns}$ the source and the target-$\textit{known}$ distribution while simultaneously $\textit{segregating}$ the target-$\textit{unknown}$ distribution in the feature alignment procedure.
