Results 81 to 90 of about 762,608 (309)
Blindfold learning of an accurate neural metric [PDF]
Proceedings of the National Academy of Sciences, 2017 The brain has no direct access to physical stimuli, but only to the spiking activity evoked in sensory organs. It is unclear how the brain can structure its representation of the world based on differences between those noisy, correlated responses alone. Here we show how to build a distance map of responses from the structure of the population activity
Gardella, Christophe +2 more
openaire +6 more sources
Systematic profiling of cancer‐fibroblast interactions reveals drug combinations in ovarian cancer
Molecular Oncology, EarlyView.Fibroblasts, cells in the tumor environment, support ovarian cancer cell growth and alter morphology and drug response. We used fibroblast and cancer cell co‐culture models to test 528 drugs and discovered new drugs for combination treatment. We showed that adding Vorinostat or Birinapant to standard chemotherapy may improve drug response, suggesting ...
Greta Gudoityte +10 more
wiley +1 more source
Deep Metric Learning with Angular Loss
, 2017 The modern image search system requires semantic understanding of image, and a key yet under-addressed problem is to learn a good metric for measuring the similarity between images.
Lin, Yuanqing +4 more
core +1 more source
Optimal reach estimation and metric learning
The Annals of Statistics, 2023 We study the estimation of the reach, an ubiquitous regularity parameter in manifold estimation and geometric data analysis. Given an i.i.d. sample over an unknown $d$-dimensional $\mathcal{C}^k$-smooth submanifold of $\mathbb{R}^D$, we provide optimal nonasymptotic bounds for the estimation of its reach.
Aamari, Eddie +2 more
openaire +3 more sources
Molecular Oncology, EarlyView.In patients treated with atezolizumab as a part of the MyPathway (NCT02091141) trial, pre‐treatment ctDNA tumor fraction at high levels was associated with poor outcomes (radiographic response, progression‐free survival, and overall survival) but better sensitivity for blood tumor mutational burden (bTMB).
Charles Swanton +17 more
wiley +1 more source
, 2018 Most popular machine learning algorithms like k-nearest neighbour, k-means, SVM uses a metric to identify the distance(or similarity) between data instances. It is clear that performances of these algorithm heavily depends on the metric being used. In absence of prior knowledge about data we can only use general purpose metrics like Euclidean distance,
openaire +2 more sources
LM-Metric: Learned Pair Weighting and Contextual Memory for Deep Metric Learning
Pattern Recognition, 2023 <p>Learned Pair Weighting and Contextual Memory for Deep Metric Learning. </p>
Shiyang Yan +4 more
openaire +1 more source
Molecular Oncology, EarlyView.A DIA‐MS‐based proteomics analysis of serum samples from GB patients and healthy controls showed that high levels of IL1R2 and low levels of CRTAC1 and HRG in serum are associated with poor survival outcomes for GB patients. These circulating proteins could serve as biomarkers for the prediction of outcome in patients with GB.
Anne Clavreul +11 more
wiley +1 more source
Online Metric-Weighted Linear Representations for Robust Visual Tracking
, 2015 In this paper, we propose a visual tracker based on a metric-weighted linear representation of appearance. In order to capture the interdependence of different feature dimensions, we develop two online distance metric learning methods using proximity ...
Dick, Anthony +4 more
core +1 more source
, 2017 The state-of-the-art online learning approaches are only capable of learning the metric for predefined tasks. In this paper, we consider lifelong learning problem to mimic "human learning", i.e., endowing a new capability to the learned metric for a new task from new online samples and incorporating previous experiences and knowledge.
Sun, Gan +3 more
openaire +2 more sources