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Found 12 papers, 5 papers with code
Entity Linking over Nested Named Entities for Russian
In this paper, we describe entity linking annotation over nested named entities in the recently released Russian NEREL dataset for information extraction.
Cross-modal Contrastive Learning with Asymmetric Co-attention Network for Video Moment Retrieval
Video moment retrieval is a challenging task requiring fine-grained interactions between video and text modalities.
NEREL-BIO: A Dataset of Biomedical Abstracts Annotated with Nested Named Entities
NEREL-BIO provides annotation for nested named entities as an extension of the scheme employed for NEREL.
NEREL: A Russian Dataset with Nested Named Entities, Relations and Events
In this paper, we present NEREL, a Russian dataset for named entity recognition and relation extraction.
Visualising Argumentation Graphs with Graph Embeddings and t-SNE
This paper applies t-SNE, a visualisation technique familiar from Deep Neural Network research to argumentation graphs by applying it to the output of graph embeddings generated using several different methods.
FatNet: A Feature-attentive Network for 3D Point Cloud Processing
The application of deep learning to 3D point clouds is challenging due to its lack of order.
FocusNet++: Attentive Aggregated Transformations for Efficient and Accurate Medical Image Segmentation
We propose a new residual block for convolutional neural networks and demonstrate its state-of-the-art performance in medical image segmentation.
Penalizing small errors using an Adaptive Logarithmic Loss
Loss functions are error metrics that quantify the difference between a prediction and its corresponding ground truth.
SAWNet: A Spatially Aware Deep Neural Network for 3D Point Cloud Processing
But their application to processing data lying on non-Euclidean domains is still a very active area of research.
FocusNet: An attention-based Fully Convolutional Network for Medical Image Segmentation
We propose a novel technique to incorporate attention within convolutional neural networks using feature maps generated by a separate convolutional autoencoder.
Multimodal deep learning for short-term stock volatility prediction
Stock market volatility forecasting is a task relevant to assessing market risk.
Evaluation of Complex-Valued Neural Networks on Real-Valued Classification Tasks
Complex-valued neural networks are not a new concept, however, the use of real-valued models has often been favoured over complex-valued models due to difficulties in training and performance.
