Results 101 to 110 of about 1,101,099 (365)
RankDNN: Learning to Rank for Few-Shot Learning
Proceedings of the AAAI Conference on Artificial Intelligence, 2023 This paper introduces a new few-shot learning pipeline that casts relevance ranking for image retrieval as binary ranking relation classification. In comparison to image classification, ranking relation classification is sample efficient and domain agnostic.
Guo, Qianyu +6 more
openaire +2 more sources
Molecular Oncology, EarlyView.This study presents a novel AI‐based diagnostic approach—comprehensive serum glycopeptide spectra analysis (CSGSA)—that integrates tumor markers and enriched glycopeptides from serum. Using a neural network model, this method accurately distinguishes liver and pancreatic cancers from healthy individuals.
Motoyuki Kohjima +6 more
wiley +1 more source
Mask Optimization for Image Inpainting
IEEE Access, 2018 This paper proposes a novel approach to image inpainting that optimizes the shape of masked regions given by users. In image inpainting, which removes and restores unwanted regions in images, users draw masks to specify the regions. However, it is widely
Mariko Isogawa +4 more
doaj +1 more source
Learning to Rank Question Answer Pairs with Holographic Dual LSTM Architecture
, 2017 We describe a new deep learning architecture for learning to rank question answer pairs. Our approach extends the long short-term memory (LSTM) network with holographic composition to model the relationship between question and answer representations. As
Chang Ming-Wei +12 more
core +1 more source
Learning to Rank Based on Subsequences [PDF]
2015 IEEE International Conference on Computer Vision (ICCV), 2015 We present a supervised learning to rank algorithm that effectively orders images by exploiting the structure in image sequences. Most often in the supervised learning to rank literature, ranking is approached either by analysing pairs of images or by optimizing a list-wise surrogate loss function on full sequences.
Fernando, Basura +3 more
openaire +2 more sources
Molecular Oncology, EarlyView.Proteomic and phosphoproteomic analyses were performed on lung adenocarcinoma (LUAD) tumors with EGFR, KRAS, or EML4–ALK alterations and wild‐type cases. Distinct protein expression and phosphorylation patterns were identified, especially in EGFR‐mutated tumors. Key altered pathways included vesicle transport and RNA splicing.
Fanni Bugyi +12 more
wiley +1 more source
Ranking to Learn and Learning to Rank: On the Role of Ranking in Pattern Recognition Applications
, 2017 European PhD Thesis.
openaire +2 more sources
To Model or to Intervene: A Comparison of Counterfactual and Online Learning to Rank from User Interactions [PDF]
Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, 2019 Learning to Rank (LTR) from user interactions is challenging as user feedback often contains high levels of bias and noise. At the moment, two methodologies for dealing with bias prevail in the field of LTR: counterfactual methods that learn from ...
R. Jagerman +2 more
semanticscholar +1 more source
Decrypting cancer's spatial code: from single cells to tissue niches
Molecular Oncology, EarlyView.Spatial transcriptomics maps gene activity across tissues, offering powerful insights into how cancer cells are organised, switch states and interact with their surroundings. This review outlines emerging computational, artificial intelligence (AI) and geospatial approaches to define cell states, uncover tumour niches and integrate spatial data with ...
Cenk Celik +4 more
wiley +1 more source
Improving Consumer Health Search with Field-Level Learning-to-Rank Techniques
InformationIn the area of consumer health search (CHS), there is an increasing concern about returning topically relevant and understandable health information to the user.
Hua Yang, Teresa Gonçalves
doaj +1 more source