Artificial intelligence is transforming every sector of the global economy. From finance and healthcare to logistics, education, and national defense, large language models (LLMs) and other foundation ...
Learn the concept of in-context learning and why it’s a breakthrough for large language models. Clear and beginner-friendly explanation. #InContextLearning #DeepLearning #LLMs Trump Says He Is ...
ABSTRACT: This work contributes to the development of intelligent data-driven approaches to improve intrusion management in smart IoT environments. The proposed model combines a hybrid ...
Article Views are the COUNTER-compliant sum of full text article downloads since November 2008 (both PDF and HTML) across all institutions and individuals. These metrics are regularly updated to ...
According to Stanford AI Lab, researchers have successfully optimized the classic K-SVD algorithm to achieve performance on par with sparse autoencoders for interpreting transformer-based language ...
ABSTRACT: Magnetic Resonance Imaging (MRI) is commonly applied to clinical diagnostics owing to its high soft-tissue contrast and lack of invasiveness. However, its sensitivity to noise, attributable ...
Within the past few years, models that can predict the structure or function of proteins have been widely used for a variety of biological applications, such as identifying drug targets and designing ...
Abstract: Physical-layer secret key generation (PSKG) is a well-known and effective method for boosting wireless security in the Internet of Things (IoT). This technique creates cryptographic keys ...
According to Chris Olah, the central issue in the ongoing Sparse Autoencoder (SAE) debate is mechanistic faithfulness, which refers to how accurately an interpretability method reflects the internal ...
Introduction: Predicting the relationship between diseases and microbes can significantly enhance disease diagnosis and treatment, while providing crucial scientific support for public health, ...
Image Denoising with Autoencoders in R (University Project) Built a convolutional autoencoder in R using Keras/TensorFlow to perform image denoising on MNIST and CIFAR-10 datasets with varying levels ...
Electric Vehicle (EV) cost prediction involves analyzing complex, high-dimensional data that often contains noise, multicollinearity, and irrelevant features. Traditional regression models struggle to ...
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