What are the current limitations of deep learning, and what future breakthroughs are needed to overcome them?

Jul 7, 2023 ... predicted tumour types with a support vector machine using information on somatically mutated genes resulting in 49% accuracy in 28 tumour ... Feb 8, 2022 ... Deep learning is a subfield of machine learning that uses artificial neural networks, algorithms that learn patterns from large amounts of data ... Sep 21, 2017 ... Tishby began contemplating the information bottleneck around the time that other researchers were first mulling over deep neural networks, ... In machine learning, a popular subset of AI, algorithms are trained on labeled or unlabeled data to make predictions or categorize information. Deep learning, a ... Oct 26, 2022 ... In the human brain, each neuron interconnects with another neuron to receive information, process it, and pass it to other neurons. In much the ... May 21, 2024 ... Recently, information systems researchers also called for empowering machine learning ... learn the pattern of targeting different prospects. Aug 13, 2018 ... ... between each product category, but they will all have individual differences too. ... Informative priors for Bayesian Neural Networks. Informative ... Jun 22, 2018 ... ... learn in comparison with fully connected neural networks. As ... For example, deep learning is frequently used for the classification of ... Third, large music collections contain different modalities of information, i.e., audio, images, and text, and all these data are suitable to be exploited for ... Neural ranking models for information retrieval (IR) use shal- low or deep neural networks to rank search results in response to a query. Traditional learning ...