Search Results for author:
Found 7 papers, 2 papers with code
Active Canny: Edge Detection and Recovery with Open Active Contour Models
This way, the strong edges are used to recover weak or missing edges by considering the local edge structures, instead of blindly linking them if gradient magnitudes are above some threshold.
Symbol Grounding Association in Multimodal Sequences with Missing Elements
We evaluated the proposed extension in the following scenarios: missing elements in one modality (visual or audio) and missing elements in both modalities (visual and sound).
Possible Mechanisms for Neural Reconfigurability and their Implications
The paper introduces a biologically and evolutionarily plausible neural architecture that allows a single group of neurons, or an entire cortical pathway, to be dynamically reconfigured to perform multiple, potentially very different computations.
On the Convergence of SGD Training of Neural Networks
Neural networks are usually trained by some form of stochastic gradient descent (SGD)).
The Effects of Hyperparameters on SGD Training of Neural Networks
The performance of neural network classifiers is determined by a number of hyperparameters, including learning rate, batch size, and depth.
Benchmarking of LSTM Networks
LSTM (Long Short-Term Memory) recurrent neural networks have been highly successful in a number of application areas.
Scene Labeling With LSTM Recurrent Neural Networks
This paper addresses the problem of pixel-level segmentation and classification of scene images with an entirely learning-based approach using Long Short Term Memory (LSTM) recurrent neural networks, which are commonly used for sequence classification.
