Search Results for author:
Found 5 papers, 3 papers with code
Detecting Synapse Location and Connectivity by Signed Proximity Estimation and Pruning with Deep Nets
The few methods that computes direction along with contact location have only been demonstrated to work on either dyadic (most common in vertebrate brain) or polyadic (found in fruit fly brain) synapses, but not on both types.
Meta-Learning of Neural Architectures for Few-Shot Learning
The recent progress in neural architecture search (NAS) has allowed scaling the automated design of neural architectures to real-world domains, such as object detection and semantic segmentation.
Bag of Tricks for Neural Architecture Search
While neural architecture search methods have been successful in previous years and led to new state-of-the-art performance on various problems, they have also been criticized for being unstable, being highly sensitive with respect to their hyperparameters, and often not performing better than random search.
Neural Architecture Search for Dense Prediction Tasks in Computer Vision
The success of deep learning in recent years has lead to a rising demand for neural network architecture engineering.
Multi-objective Differentiable Neural Architecture Search
Pareto front profiling in multi-objective optimization (MOO), i. e. finding a diverse set of Pareto optimal solutions, is challenging, especially with expensive objectives like neural network training.
