The following pages link to ImageNet (Q32917):
Displaying 50 items.
- Comment: A brief survey of the current state of play for Bayesian computation in data science at big-data scale (Q1705540) (← links)
- Efficient benchmarking of algorithm configurators via model-based surrogates (Q1707462) (← links)
- Bayesian deep convolutional encoder-decoder networks for surrogate modeling and uncertainty quantification (Q1721865) (← links)
- Implications for firms with limited information to take advantage of reference price effect in competitive settings (Q1722722) (← links)
- An efficient mapreduce-based parallel clustering algorithm for distributed traffic subarea division (Q1723512) (← links)
- Gradient surfing: a new deterministic approach for low-dimensional global optimization (Q1730775) (← links)
- Statistics for data with geometric structure. Abstracts from the workshop held January 21--27, 2018 (Q1731971) (← links)
- Feature extraction by using dual-generalized discriminative common vectors (Q1735945) (← links)
- Cox processes for counting by detection (Q1735951) (← links)
- On the convergence of formally diverging neural net-based classifiers (Q1747388) (← links)
- Graph-induced restricted Boltzmann machines for document modeling (Q1750503) (← links)
- Lie group impression for deep learning (Q1751408) (← links)
- Linear feature transform and enhancement of classification on deep neural network (Q1785494) (← links)
- Exploring complex and big data (Q1787028) (← links)
- Deep network based on stacked orthogonal convex incremental ELM autoencoders (Q1792712) (← links)
- Theory and practice of hierarchical data-driven descent for optimal deformation estimation (Q1799997) (← links)
- A joint Gaussian process model for active visual recognition with expertise estimation in crowdsourcing (Q1800023) (← links)
- Large scale image annotation: learning to rank with joint word-image embeddings (Q1959605) (← links)
- Multi-source remote sensing image classification based on two-channel densely connected convolutional networks (Q1979588) (← links)
- Deep neural networks with a set of node-wise varying activation functions (Q1980402) (← links)
- Learning in the machine: to share or not to share? (Q1980412) (← links)
- Prior-based hierarchical segmentation highlighting structures of interest (Q1980883) (← links)
- How to construct low-altitude aerial image datasets for deep learning (Q1981016) (← links)
- A comparative study for glioma classification using deep convolutional neural networks (Q1981082) (← links)
- CPINet: parameter identification of path-dependent constitutive model with automatic denoising based on CNN-LSTM (Q1982319) (← links)
- Training of deep neural networks for the generation of dynamic movement primitives (Q1982413) (← links)
- Linear embedding by joint robust discriminant analysis and inter-class sparsity (Q1982415) (← links)
- Vulnerability of classifiers to evolutionary generated adversarial examples (Q1982418) (← links)
- Automated classification of cells into multiple classes in epithelial tissue of oral squamous cell carcinoma using transfer learning and convolutional neural network (Q1982424) (← links)
- Estimation of in vivo constitutive parameters of the aortic wall using a machine learning approach (Q1987805) (← links)
- Predicting the effective mechanical property of heterogeneous materials by image based modeling and deep learning (Q1987847) (← links)
- Machine learning materials physics: integrable deep neural networks enable scale bridging by learning free energy functions (Q1988102) (← links)
- Circumventing the solution of inverse problems in mechanics through deep learning: application to elasticity imaging (Q1988119) (← links)
- Forward stability of ResNet and its variants (Q1988347) (← links)
- Deep neural networks motivated by partial differential equations (Q1988348) (← links)
- ADMM-softmax: an ADMM approach for multinomial logistic regression (Q1988494) (← links)
- Application of the residue number system to reduce hardware costs of the convolutional neural network implementation (Q1998097) (← links)
- Deep UQ: learning deep neural network surrogate models for high dimensional uncertainty quantification (Q2002273) (← links)
- Local receptive fields based extreme learning machine with hybrid filter kernels for image classification (Q2002382) (← links)
- A novel approach for detecting the horizon using a convolutional neural network and multi-scale edge detection (Q2002385) (← links)
- Local color oppugnant quantized extrema patterns for image retrieval (Q2002412) (← links)
- A joint loss function for deep face recognition (Q2002420) (← links)
- Direct cellularity estimation on breast cancer histopathology images using transfer learning (Q2003647) (← links)
- A technical review of convolutional neural network-based mammographic breast cancer diagnosis (Q2003663) (← links)
- Convolution accelerator designs using fast algorithms (Q2004889) (← links)
- Non-iterative and fast deep learning: multilayer extreme learning machines (Q2005317) (← links)
- Properly-weighted graph Laplacian for semi-supervised learning (Q2019913) (← links)
- SCN: dilated silhouette convolutional network for video action recognition (Q2020345) (← links)
- Preconditioning Markov chain Monte Carlo method for geomechanical subsidence using multiscale method and machine learning technique (Q2020498) (← links)
- A physics-informed operator regression framework for extracting data-driven continuum models (Q2020813) (← links)
