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Found 7 papers, 4 papers with code
Evaluating GPT-3.5 and GPT-4 on Grammatical Error Correction for Brazilian Portuguese
We investigate the effectiveness of GPT-3. 5 and GPT-4, two large language models, as Grammatical Error Correction (GEC) tools for Brazilian Portuguese and compare their performance against Microsoft Word and Google Docs.
Ignore Previous Prompt: Attack Techniques For Language Models
Transformer-based large language models (LLMs) provide a powerful foundation for natural language tasks in large-scale customer-facing applications.
Solo or Ensemble? Choosing a CNN Architecture for Melanoma Classification
We evaluate that claim for melanoma classification, over 9 CNNs architectures, in 5 sets of splits created on the ISIC Challenge 2017 dataset, and 3 repeated measures, resulting in 135 models.
Skin Lesion Synthesis with Generative Adversarial Networks
Skin cancer is by far the most common type of cancer.
Weakly Supervised Active Learning with Cluster Annotation
In this work, we introduce a novel framework that employs cluster annotation to boost active learning by reducing the number of human interactions required to train deep neural networks.
Data Augmentation for Skin Lesion Analysis
In this work, we investigate the impact of 13 data augmentation scenarios for melanoma classification trained on three CNNs (Inception-v4, ResNet, and DenseNet).
Deep-Learning Ensembles for Skin-Lesion Segmentation, Analysis, Classification: RECOD Titans at ISIC Challenge 2018
This extended abstract describes the participation of RECOD Titans in parts 1 to 3 of the ISIC Challenge 2018 "Skin Lesion Analysis Towards Melanoma Detection" (MICCAI 2018).
