Results 91 to 100 of about 23,776,920 (366)
Learning to Embed Categorical Features without Embedding Tables for Recommendation [PDF]
arXiv, 2020 Embedding learning of categorical features (e.g. user/item IDs) is at the core of various recommendation models including matrix factorization and neural collaborative filtering. The standard approach creates an embedding table where each row represents a dedicated embedding vector for every unique feature value.
arxiv
Molecular Oncology, EarlyView.Cancer stem cells are associated with aggressive disease, but a deep characterization of such markers is lacking in endometrial cancer. This study uses imaging mass cytometry to explore putative cancer stem cell markers in endometrial tumors and corresponding organoid models.
Hilde E. Lien +7 more
wiley +1 more source
Embedding Projector: Interactive Visualization and Interpretation of Embeddings [PDF]
arXiv, 2016 Embeddings are ubiquitous in machine learning, appearing in recommender systems, NLP, and many other applications. Researchers and developers often need to explore the properties of a specific embedding, and one way to analyze embeddings is to visualize them. We present the Embedding Projector, a tool for interactive visualization and interpretation of
arxiv
Project-Based Learning to Enhance Teaching Embedded Systems.
, 2016 Exposing engineering students during their education to real world problems and giving them the chance to apply what they learn in the classroom is a vital element of engineering education.
Belal H. Sababha +3 more
semanticscholar +1 more source
Stochastic variation in the FOXM1 transcription program mediates replication stress tolerance
Molecular Oncology, EarlyView.Cellular heterogeneity is a major cause of drug resistance in cancer. Segeren et al. used single‐cell transcriptomics to investigate gene expression events that correlate with sensitivity to the DNA‐damaging drugs gemcitabine and prexasertib. They show that dampened expression of transcription factor FOXM1 and its target genes protected cells against ...
Hendrika A. Segeren +4 more
wiley +1 more source
Systematic Error Modeling and Bias Estimation
Sensors, 2016 This paper analyzes the statistic properties of the systematic error in terms of range and bearing during the transformation process. Furthermore, we rely on a weighted nonlinear least square method to calculate the biases based on the proposed models ...
Feihu Zhang, Alois Knoll
doaj +1 more source
Molecular Oncology, EarlyView.Low expression of five purine metabolism‐related genes (ADSL, APRT, ADCY3, NME3, NME6) was correlated with poor survival in colorectal cancer. Immunohistochemistry analysis showed that low NME3 (early stage) and low ADSL/NME6 (late stage) levels were associated with high risk.
Sungyeon Kim +8 more
wiley +1 more source
A Survey on Network Embedding [PDF]
arXiv, 2017 Network embedding assigns nodes in a network to low-dimensional representations and effectively preserves the network structure. Recently, a significant amount of progresses have been made toward this emerging network analysis paradigm. In this survey, we focus on categorizing and then reviewing the current development on network embedding methods, and
arxiv
Hybrid Improved Document-level Embedding (HIDE) [PDF]
arXiv, 2020 In recent times, word embeddings are taking a significant role in sentiment analysis. As the generation of word embeddings needs huge corpora, many applications use pretrained embeddings. In spite of the success, word embeddings suffers from certain drawbacks such as it does not capture sentiment information of a word, contextual information in terms ...
arxiv