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Found 9 papers, 6 papers with code
Translation as Cross-Domain Knowledge: Attention Augmentation for Unsupervised Cross-Domain Segmenting and Labeling Tasks
The nature of no word delimiter or inflection that can indicate segment boundaries or word semantics increases the difficulty of Chinese text understanding, and also intensifies the demand for word-level semantic knowledge to accomplish the tagging goal in Chinese segmenting and labeling tasks.
GA-SAM: Gradient-Strength based Adaptive Sharpness-Aware Minimization for Improved Generalization
It demonstrates that flat minima tend to imply better generalization abilities.
Adversarial Parameter Defense by Multi-Step Risk Minimization
To enhance neural networks, we propose the adversarial parameter defense algorithm that minimizes the average risk of multiple adversarial parameter corruptions.
Learning Robust Representation for Clustering through Locality Preserving Variational Discriminative Network
Recent deep learning based methods focus on learning clustering oriented representations.
Exploring the Vulnerability of Deep Neural Networks: A Study of Parameter Corruption
We argue that the vulnerability of model parameters is of crucial value to the study of model robustness and generalization but little research has been devoted to understanding this matter.
An Adaptive and Momental Bound Method for Stochastic Learning
The dynamic learning rate bounds are based on the exponential moving averages of the adaptive learning rates themselves, which smooth out unexpected large learning rates and stabilize the training of deep neural networks.
PKUSEG: A Toolkit for Multi-Domain Chinese Word Segmentation
Through this method, we generate synthetic data using a large amount of unlabeled data in the target domain and then obtain a word segmentation model for the target domain.
Exploration on Grounded Word Embedding: Matching Words and Images with Image-Enhanced Skip-Gram Model
Word embedding is designed to represent the semantic meaning of a word with low dimensional vectors.
Acquisition of Localization Confidence for Accurate Object Detection
The network acquires this confidence of localization, which improves the NMS procedure by preserving accurately localized bounding boxes.
Ranked #172 on
Object Detection
on COCO test-dev
