Researchers developed a more efficient way to control the outputs of a large language model, guiding it to generate text that adheres to a certain structure, like a programming language, and remains ...

One notable missing feature in most ANN models is top-down feedback, i.e. projections from higher-order layers to lower-order layers in the network. Top-down feedback is ubiquitous in the brain, and ...

This study addresses the growing demand for news text classification driven by the rapid expansion of internet information by proposing a classification algorithm based on a Bidirectional Gated ...

This project involves implementing a text classification model using Convolutional Neural Networks (CNNs) and Recurrent Neural ... output the loss and accuracy every 1,000 iterations. After training, ...

data through a neural encoder and then using a deep learning recurrent neural network transducer model to convert brain signals to sounds. The researchers used a recording of the patient's pre-injury ...

The design process behind an LLM consists of 3 main steps: Pre-Training, Unsupervised Learning, and Fine Tuning. (Image: Showmetech) For a large language model to perform a ... other architectures, ...

At RSAC, a security researcher from Checkmarx explains how malefactors can push LLMs off track by deliberately introducing ...

They’re based on the open-source Llama and Qwen language model families, which are developed by Meta Platforms Inc. and Alibaba Group Holding Ltd., respectively. Deep Cogito’s models use a ...

As quantum-inspired methods enter the AI arena, companies like Dynex are developing alternatives to mainstream LLMs. What ...