Feb 9, 2017 · The results allow to quantify awareness in terms of context and selective attention and to propose such solution for use in the general case.

The results allow to quantify awareness in terms of context and selective attention and to propose such solution for use in the general case. Published in: 2016 ...

Oct 9, 2016 · The results allow to quantify awareness in terms of context and selective attention and to propose such solution for use in the general case.

The results allow to quantify awareness in terms of context and selective attention and to propose such solution for use in the general case. References 14.

May 9, 2017 · We present context-aware stacked convolutional neural networks (CNN) for classification of breast WSIs into normal/benign, DCIS, and invasive ductal carcinoma ...

Mar 27, 2023 · We discuss how such models may inform the study of the neural bases of context-dependent learning and, conversely, how a better understanding of ...

We propose contextual convolution (CoConv) for visual recognition. CoConv is a direct replacement of the stan- dard convolution, which is the core component ...

We found that neural networks perceive time through state evolution along stereotypical trajectories and produce time intervals by scaling evolution speed.

Apr 6, 2022 · For context-dependent decision-making, one rich solution is to project task representations onto low-dimensional and orthogonal manifolds.

Context-awareness in similarity measures and pattern discoveries of trajectories: a context-based dynamic time warping method. GIScience & Remote Sensing ...