14 papers · ranked by Valyu relevance
Adem Kikaj, Giuseppe Marra, Floris Geerts, Robin Manhaeve + 1 more
Neurosymbolic AI (NeSy) aims to integrate the statistical strengths of neural networks with the interpretability and structure of symbolic reasoning. However, current NeSy frameworks like DEEPPROBLOG enforce a fixed flow where symbolic reasoning always follows neural processing. This restricts their ability to model…
Robin Manhaeve, Sebastijan Dumančić, Angelika Kimmig, Thomas Demeester + 1 more
'Thomas Demeester' 'Luc De Raedt'] We introduce DeepProbLog, a neural probabilistic logic programming language that incorporates deep learning by means of neural predicates. We show how existing inference and learning techniques of the underlying probabilistic logic programming language ProbLog can be adapted for the…
Marc Roig Vilamala, Tianwei Xing, Harrison Taylor, Luis Antonio Ribot García + 5 more
'Luis Antonio Ribot García' 'Mani Srivastava' 'Lance Kaplan' 'Alun Preece' 'Angelika Kimmig' 'Federico Cerutti'] In this paper, we present an approach to Complex Event Processing (CEP) that is based on DeepProbLog. This approach has the following objectives: (i) allowing the use of subsymbolic data as an input, (ii)…
Robin Manhaeve, Sebastijan Dumančić, Angelika Kimmig, Thomas Demeester + 1 more
'Thomas Demeester' 'Luc De Raedt'] We introduce DeepProbLog, a probabilistic logic programming language that incorporates deep learning by means of neural predicates. We show how existing inference and learning techniques can be adapted for the new language. Our experiments demonstrate that DeepProbLog supports (i)…
Marco Cominelli, Francesco Gringoli, Lance Kaplan, Mani Srivastava + 4 more
Inter-Modal Knowledge Transfer Authors: ['Marco Cominelli' 'Francesco Gringoli' 'Lance Kaplan' 'Mani Srivastava' 'Trevor Bihl' 'Erik Blasch' 'Nandini Iyer' 'Federico Cerutti'] © 2024 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future…
Chensi Cao, Feng Liu, Hai Tan, Deshou Song + 5 more
'Weizhong Li' 'Yiming Zhou' 'Xiaochen Bo' 'Zhi Xie'] Advances in biological and medical technologies have been providing us explosive volumes of biological and physiological data, such as medical images, electroencephalography, genomic and protein sequences. Learning from these data facilitates the understanding of…
Christian Bresciani, Federico Cerutti, Marco Cominelli
Lo studio analizza metodi innovativi per migliorare l'efficienza e l'adattabilita dei ` sistemi di riconoscimento delle attivita umane (HAR), fondamentali per numerose ` applicazioni come il monitoraggio sanitario, l'automazione industriale e i sistemi di sicurezza. I sistemi HAR tradizionali, basati su sensori…
Ali Madani, Ben Krause, Eric R. Greene, Subu Subramanian + 8 more
Bypassing nature’s evolutionary trajectory, de novo protein generation—defined as creating artificial protein sequences from scratch—could enable breakthrough solutions for biomedical and environmental challenges. Viewing amino acid sequences as a language, we demonstrate that a deep learning-based language model can…
Travers Ching, Daniel S. Himmelstein, Brett K. Beaulieu-Jones, Alexandr A. Kalinin + 23 more
Deep learning, which describes a class of machine learning algorithms, has recently showed impressive results across a variety of domains. Biology and medicine are data rich, but the data are complex and often ill-understood. Problems of this nature may be particularly well-suited to deep learning techniques. We…
Ruheng Wang, Yi Jiang, Junru Jin, Chenglin Yin + 7 more
Here, we present DeepBIO, the first-of-its-kind automated and interpretable deep-learning platform for biological sequence functional analysis. DeepBIO is a one-stop-shop web service that enables researchers to develop a new deep-learning architecture to answer any biological question. Specifically, given any…
Sijie Yang, Fei Zhu, Xinghong Ling, Quan Liu + 1 more
With the progress of medical technology, biomedical field ushered in the era of big data, based on which and driven by artificial intelligence technology, computational medicine has emerged. People need to extract the effective information contained in these big biomedical data to promote the development of precision…
Yu Li, Chao Huang, Lizhong Ding, Zhongxiao Li + 2 more
Deep learning, which is especially formidable in handling big data, has achieved great success in various fields, including bioinformatics. With the advances of the big data era in biology, it is foreseeable that deep learning will become increasingly important in the field and will be incorporated in vast majorities…
Amaryllis Mavragani, Yuehua Zhao, Chang Su, Yanbo Zhang + 4 more
'Donghun Kim' 'Woojin Jung' 'Yongjun Zhu'] Background Advances in biomedical research using deep learning techniques have generated a large volume of related literature. However, there is a lack of scientometric studies that provide a bird’s-eye view of them. This absence has led to a partial and fragmented…
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The rapid progress in Artificial Intelligence (AI) has led to extraordinary achievements across various domains, significantly impacting every aspect of daily life. This advancement is also revolutionizing research in numerous scientific areas, particularly within bioinformatics, chemistry, pharmaceuticals, and…