AIMC Topic: Knowledge

Clear Filters Showing 81 to 90 of 263 articles

Large language models encode clinical knowledge.

Nature
Large language models (LLMs) have demonstrated impressive capabilities, but the bar for clinical applications is high. Attempts to assess the clinical knowledge of models typically rely on automated evaluations based on limited benchmarks. Here, to a...

Close to the metal: Towards a material political economy of the epistemology of computation.

Social studies of science
This paper investigates the role of the materiality of computation in two domains: blockchain technologies and artificial intelligence (AI). Although historically designed as parallel computing accelerators for image rendering and videogames, graphic...

An End-to-End Framework for Joint Denoising and Classification of Hyperspectral Images.

IEEE transactions on neural networks and learning systems
Image denoising and classification are typically conducted separately and sequentially according to their respective objectives. In such a setup, where the two tasks are decoupled, the denoising operation does not optimally serve the classification t...

TCGAN: Convolutional Generative Adversarial Network for time series classification and clustering.

Neural networks : the official journal of the International Neural Network Society
Recent works have demonstrated the superiority of supervised Convolutional Neural Networks (CNNs) in learning hierarchical representations from time series data for successful classification. These methods require sufficiently large labeled data for ...

Learning Attentional Communication with a Common Network for Multiagent Reinforcement Learning.

Computational intelligence and neuroscience
For multiagent communication and cooperation tasks in partially observable environments, most of the existing works only use the information contained in hidden layers of a network at the current moment, limiting the source of information. In this pa...

Differentially private knowledge transfer for federated learning.

Nature communications
Extracting useful knowledge from big data is important for machine learning. When data is privacy-sensitive and cannot be directly collected, federated learning is a promising option that extracts knowledge from decentralized data by learning and exc...

How AI can distort human beliefs.

Science (New York, N.Y.)
Models can convey biases and false information to users.

Safe human-robot collaboration in construction: A conceptual perspective.

Journal of safety research
INTRODUCTION: Small mobile robots have become increasingly popular in the construction domain over the last few years. They are stable on rough terrains, can walk over small obstacles, climb stairs, and carry various sensors or arms to perform divers...

MedKPL: A heterogeneous knowledge enhanced prompt learning framework for transferable diagnosis.

Journal of biomedical informatics
Artificial Intelligence (AI) based diagnosis systems have emerged as powerful tools to reform traditional medical care. Each clinician now wants to have his own intelligent diagnostic partner to expand the range of services he can provide. However, t...

Integrating domain knowledge for biomedical text analysis into deep learning: A survey.

Journal of biomedical informatics
The past decade has witnessed an explosion of textual information in the biomedical field. Biomedical texts provide a basis for healthcare delivery, knowledge discovery, and decision-making. Over the same period, deep learning has achieved remarkable...