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Found 11 papers, 5 papers with code
Evaluating Large Language Models in Ophthalmology
Methods: A 100-item ophthalmology single-choice test was administered to three different LLMs (GPT-3. 5, GPT-4, and PaLM2) and three different professional levels (medical undergraduates, medical masters, and attending physicians), respectively.
Evaluating multiple large language models in pediatric ophthalmology
IMPORTANCE The response effectiveness of different large language models (LLMs) and various individuals, including medical students, graduate students, and practicing physicians, in pediatric ophthalmology consultations, has not been clearly established yet.
A Novel Deep Reinforcement Learning Based Automated Stock Trading System Using Cascaded LSTM Networks
More and more stock trading strategies are constructed using deep reinforcement learning (DRL) algorithms, but DRL methods originally widely used in the gaming community are not directly adaptable to financial data with low signal-to-noise ratios and unevenness, and thus suffer from performance shortcomings.
Palm Vein Recognition via Multi-task Loss Function and Attention Layer
With the improvement of arithmetic power and algorithm accuracy of personal devices, biological features are increasingly widely used in personal identification, and palm vein recognition has rich extractable features and has been widely studied in recent years.
Paint4Poem: A Dataset for Artistic Visualization of Classical Chinese Poems
We create abenchmark for Paint4Poem: we train two representative text-to-image generation models: AttnGAN and MirrorGAN, and evaluate theirperformance regarding painting pictorial quality, painting stylistic relevance, and semantic relevance between poems and paintings. The results indicate that the models are able to generate paintings that have good pictorial quality and mimic Feng Zikai's style, but thereflection of poem semantics is limited.
Category Aware Explainable Conversational Recommendation
We first perform an explainable user model in the form of preferences over the items' categories, and then use the category preferences to recommend items.
An Empirical Study of Clarifying Question-Based Systems
Search and recommender systems that take the initiative to ask clarifying questions to better understand users' information needs are receiving increasing attention from the research community.
Towards Question-based Recommender Systems
Previous conversational recommender systems ask users to express their preferences over items or item facets.
Learning to Ask: Question-based Sequential Bayesian Product Search
The method learns the product relevance as well as the reward of the potential questions to be asked to the user by being trained on the search history and purchase behavior of a specific user together with that of other users.
A Class-Incremental Learning Method Based on One Class Support Vector Machine
Then the support vectors of the old classes and the support vectors of the new class are reused to train 1VS1 classifiers for the confuse part.
HSI-CNN: A Novel Convolution Neural Network for Hyperspectral Image
In this paper, we propose a novel convolutional neural network framework for the characteristics of hyperspectral image data, called HSI-CNN.
