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Found 7 papers, 3 papers with code
ITNLP2022 at SemEval-2022 Task 8: Pre-trained Model with Data Augmentation and Voting for Multilingual News Similarity
This article introduces a system to solve the SemEval 2022 Task 8: Multilingual News Article Similarity.
COLE: A Hierarchical Generation Framework for Multi-Layered and Editable Graphic Design
Our COLE system comprises multiple fine-tuned Large Language Models (LLMs), Large Multimodal Models (LMMs), and Diffusion Models (DMs), each specifically tailored for design-aware layer-wise captioning, layout planning, reasoning, and the task of generating images and text.
AutoKG: Efficient Automated Knowledge Graph Generation for Language Models
Traditional methods of linking large language models (LLMs) to knowledge bases via the semantic similarity search often fall short of capturing complex relational dynamics.
OpsEval: A Comprehensive IT Operations Benchmark Suite for Large Language Models
Information Technology (IT) Operations (Ops), particularly Artificial Intelligence for IT Operations (AIOps), is the guarantee for maintaining the orderly and stable operation of existing information systems.
Revisiting DETR Pre-training for Object Detection
Motivated by the remarkable achievements of DETR-based approaches on COCO object detection and segmentation benchmarks, recent endeavors have been directed towards elevating their performance through self-supervised pre-training of Transformers while preserving a frozen backbone.
Novel Batch Active Learning Approach and Its Application to Synthetic Aperture Radar Datasets
Active learning improves the performance of machine learning methods by judiciously selecting a limited number of unlabeled data points to query for labels, with the aim of maximally improving the underlying classifier's performance.
Graph-based Active Learning for Surface Water and Sediment Detection in Multispectral Images
We develop a graph active learning pipeline (GAP) to detect surface water and in-river sediment pixels in satellite images.
