Category: Deep Learning Architectures
Description: Various neural network architectures designed for vision tasks.
Examples: Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Transformer-based models.
Use Cases: Image classification, style transfer, and feature extraction.
Will Deep Learning Transform Biosciences? – Evrim Ağacı
Deep learning (DL) is making waves across multiple fields, and nowhere is this more evident than in the realm of biosciences. One of the standout triumphs is AlphaFold’s ability to predict protein structures with remarkable precision. As research continues to evolve, this paper by Nicolae Sapoval, Amirali Aghazadeh, and colleagues sheds light on the profound impact and ongoing challenges of DL in the biosciences . Protein structure prediction, for instance, has been a persistent challenge in the field. AlphaFold’s success underscores a significant step forward. DL facilitates the representation of…
Read More
Why the newest LLMs use a MoE (Mixture of Experts) architecture – DataScienceCentral.com – Data Science Central
Specialization made necessary A hospital is overcrowded with experts and doctors each with their own specializations, solving unique problems. Surgeons, cardiologists, pediatricians—experts of all kinds join hands to provide care, often collaborating to get the patients the care they need. We can do the same with AI. Mixture of Experts (MoE) architecture in artificial intelligence is defined as a mix or blend of different “expert” models working together to deal with or respond to complex data inputs. When it comes to AI, every expert in an MoE model specializes in…
Read More
Colon cancer diagnosis by means of explainable deep learning | Scientific Reports – Nature.com
Abstract Early detection of the adenocarcinoma cancer in colon tissue by means of explainable deep learning, by classifying histological images and providing visual explainability on model prediction. Considering that in recent years, deep learning techniques have emerged as powerful techniques in medical image analysis, offering unprecedented accuracy and efficiency, in this paper we propose a method to automatically detect the presence of cancerous cells in colon tissue images. Various deep learning architectures are considered, with the aim of considering the best one in terms of quantitative and qualitative results. As…
Read More