Results 31 to 40 of about 9,750,100 (244)
Quantum machine learning: a classical perspective [PDF]
, 2018 Recently, increased computational power and data availability, as well as algorithmic advances, have led machine learning techniques to impressive results in regression, classification, data-generation and reinforcement learning tasks.
Ben-David S +15 more
core +2 more sources
Multimodal Machine Learning: A Survey and Taxonomy [PDF]
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017 Our experience of the world is multimodal - we see objects, hear sounds, feel texture, smell odors, and taste flavors. Modality refers to the way in which something happens or is experienced and a research problem is characterized as multimodal when it ...
T. Baltrušaitis +2 more
semanticscholar +1 more source
Distributional Prototypical Methods for Reliable Explanation Space Construction
IEEE Access, 2023 As deep learning has been successfully deployed in diverse applications, there is an ever increasing need to explain its decision. To explain decisions, case-based reasoning has proved to be effective in many areas.
Hyungjun Joo +3 more
doaj +1 more source
De Computis, 2023 In recent years, deep learning (DL) has been the most popular computational approach in the field of machine learning (ML), achieving exceptional results on a variety of complex cognitive tasks, matching or even surpassing human performance.
Mohammad Mustafa Taye
semanticscholar +1 more source
Small data machine learning in materials science
npj Computational Materials, 2023 This review discussed the dilemma of small data faced by materials machine learning. First, we analyzed the limitations brought by small data. Then, the workflow of materials machine learning has been introduced.
Pengcheng Xu +3 more
semanticscholar +1 more source
Industry-scale application and evaluation of deep learning for drug target prediction [PDF]
, 2019 Artificial intelligence (AI) is undergoing a revolution thanks to the breakthroughs of machine learning algorithms in computer vision, speech recognition, natural language processing and generative modelling.
Ashby, Thomas J. +18 more
core +2 more sources
Machine Learning for Communications [PDF]
Entropy, 2021 Due to the proliferation of applications and services that run over communication networks, ranging from video streaming and data analytics to robotics and augmented reality, tomorrow’s networks will be faced with increasing challenges resulting from the explosive growth of data traffic demand with significantly varying performance requirements [...]
openaire +4 more sources
Interpretable Machine Learning
Hands-On Machine Learning with R, 2019 Interpretable machine learning has become a popular research direction as deep neural networks (DNNs) have become more powerful and their applications more mainstream, yet DNNs remain difficult to understand. Testing with Concept Activation Vectors, TCAV,
Bradley C. Boehmke, Brandon M. Greenwell
semanticscholar +1 more source
Machine Learned Learning Machines
, 2017 There are two common approaches for optimizing the performance of a machine: genetic algorithms and machine learning. A genetic algorithm is applied over many generations whereas machine learning works by applying feedback until the system meets a performance threshold. Though these are methods that typically operate separately, we combine evolutionary
Sheneman, Leigh, Hintze, Arend
openaire +2 more sources
Machine learning in automated text categorization [PDF]
CSUR, 2001 The automated categorization (or classification) of texts into predefined categories has witnessed a booming interest in the last 10 years, due to the increased availability of documents in digital form and the ensuing need to organize them.
F. Sebastiani
semanticscholar +1 more source