Search Results for author:
Found 11 papers, 2 papers with code
Building a Bridge: A Method for Image-Text Sarcasm Detection Without Pretraining on Image-Text Data
We use the pretrained Multi-Head Attention of BERT to model the text and image.
Correcting the Misuse: A Method for the Chinese Idiom Cloze Test
The cloze test for Chinese idioms is a new challenge in machine reading comprehension: given a sentence with a blank, choosing a candidate Chinese idiom which matches the context.
GUTS at SemEval-2022 Task 4: Adversarial Training and Balancing Methods for Patronizing and Condescending Language Detection
For Subtask B, framed as a multi-label classification problem, we utilize various improved multi-label cross-entropy loss functions and analyze the performance of our method.
Rethinking Mutual Information for Language Conditioned Skill Discovery on Imitation Learning
Language-conditioned robot behavior plays a vital role in executing complex tasks by associating human commands or instructions with perception and actions.
Enhancing Cloud-Based Large Language Model Processing with Elasticsearch and Transformer Models
We gain a comprehensive understanding of semantic search principles and acquire practical skills for implementing semantic search in real-world model application scenarios.
Enhanced User Interaction in Operating Systems through Machine Learning Language Models
This iterative optimization process can continuously improve the quality and performance of the product to meet the changing needs of users.
Data Pipeline Training: Integrating AutoML to Optimize the Data Flow of Machine Learning Models
With the increasing diversification and complexity of Data sources, as well as the rapid growth of data volumes, building an efficient Data Pipeline has become crucial for improving work efficiency and solving complex problems.
DocRED-FE: A Document-Level Fine-Grained Entity And Relation Extraction Dataset
Joint entity and relation extraction (JERE) is one of the most important tasks in information extraction.
AutoFed: Heterogeneity-Aware Federated Multimodal Learning for Robust Autonomous Driving
Object detection with on-board sensors (e. g., lidar, radar, and camera) play a crucial role in autonomous driving (AD), and these sensors complement each other in modalities.
Dynamic Interactional And Cooperative Network For Shield Machine
Then, according to the relationship, models were established for the control terminal, including SM rate prediction and SM anomaly detection.
Bilaterally Slimmable Transformer for Elastic and Efficient Visual Question Answering
Recent advances in Transformer architectures [1] have brought remarkable improvements to visual question answering (VQA).
