A marriage of formal methods and LLMs seeks to harness the strengths of both.

Something extraordinary has happened, even if we haven’t fully realized it yet: algorithms are now capable of solving ...

CNN in deep learning is a special type of neural network that can understand images and visual information. It works just like human vision: first it detects edges, lines and then recognizes faces and ...

Learn how to build a fully connected, feedforward deep neural network from scratch in Python! This tutorial covers the theory, forward propagation, backpropagation, and coding step by step for a hands ...

This academic seminar series explores a broad range of topics such as data science, urban planning and urban networks and ...

STM-Graph is a Python framework for analyzing spatial-temporal urban data and doing predictions using Graph Neural Networks. It provides a complete end-to-end pipeline from raw event data to trained ...

School of Electronics Engineering (SENSE), Vellore Institute of Technology, Chennai, India Introduction: In recent years, Deep Learning (DL) architectures such as Convolutional Neural Network (CNN) ...

We will create a Deep Neural Network python from scratch. We are not going to use Tensorflow or any built-in model to write the code, but it's entirely from scratch in python. We will code Deep Neural ...

Abstract: This work investigates the generalization behavior of deep neural networks (DNNs), focusing on the phenomenon of “fooling examples,” where DNNs confidently classify inputs that appear random ...

For the first time, researchers at the Netherlands Institute for Neuroscience and Amsterdam UMC have identified what happens in neural networks deep within the brain during obsessive thoughts and ...

Abstract: This paper presents attention-based deep neural networks for high-dimensional microwave modeling to predict behavior of spatio-temporal modulated (STM) non-reciprocal bandpass filters ...