Search Results for author:
Found 7 papers, 1 papers with code
Composited-Nested-Learning with Data Augmentation for Nested Named Entity Recognition
Compared to Flat Named Entity Recognition (FNER), annotated resources are scarce in the corpus for NNER.
PyPop7: A Pure-Python Library for Population-Based Black-Box Optimization
In this paper, we present an open-source pure-Python library called PyPop7 for black-box optimization (BBO).
MSA-GCN:Multiscale Adaptive Graph Convolution Network for Gait Emotion Recognition
Most of the existing methods recognize emotions by focusing on local actions over time.
FedNoiL: A Simple Two-Level Sampling Method for Federated Learning with Noisy Labels
Federated learning (FL) aims at training a global model on the server side while the training data are collected and located at the local devices.
SemiNLL: A Framework of Noisy-Label Learning by Semi-Supervised Learning
We also instantiate our framework with different combinations, which set the new state of the art on benchmark-simulated and real-world datasets with noisy labels.
Confusable Learning for Large-class Few-Shot Classification
Specifically, our method maintains a dynamically updating confusion matrix, which analyzes confusable classes in the dataset.
Self-attention-based BiGRU and capsule network for named entity recognition
Finally, we propose to use CapsNet for entity recognition.
