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Found 9 papers, 2 papers with code
A Survey on Human-AI Teaming with Large Pre-Trained Models
In the rapidly evolving landscape of artificial intelligence (AI), the collaboration between human intelligence and AI systems, known as Human-AI (HAI) Teaming, has emerged as a cornerstone for advancing problem-solving and decision-making processes.
How Close is ChatGPT to Human Experts? Comparison Corpus, Evaluation, and Detection
We call the collected dataset the Human ChatGPT Comparison Corpus (HC3).
IDEA: Interactive DoublE Attentions from Label Embedding for Text Classification
Current text classification methods typically encode the text merely into embedding before a naive or complicated classifier, which ignores the suggestive information contained in the label text.
SAS: A Simple, Accurate and Scalable Node Classification Algorithm
Recent works have sought to address this problem using a two-stage approach, which first aggregates data along graph edges, then trains a classifier without using additional graph information.
Decentralized Statistical Inference with Unrolled Graph Neural Networks
In this paper, we investigate the decentralized statistical inference problem, where a network of agents cooperatively recover a (structured) vector from private noisy samples without centralized coordination.
Automatic Data Augmentation via Deep Reinforcement Learning for Effective Kidney Tumor Segmentation
Conventional data augmentation realized by performing simple pre-processing operations (\eg, rotation, crop, \etc) has been validated for its advantage in enhancing the performance for medical image segmentation.
Machine learning for automatic construction of pseudo-realistic pediatric abdominal phantoms
To assess the effects of radiation therapy, treatment plans are typically simulated on phantoms, i. e., virtual surrogates of patient anatomy.
From Data Quality to Model Quality: an Exploratory Study on Deep Learning
Nowadays, people strive to improve the accuracy of deep learning models.
