Search Results for author:
Found 8 papers, 3 papers with code
Optimizing Neural Networks through Activation Function Discovery and Automatic Weight Initialization
While present methods focus on hyperparameters and neural network topologies, other aspects of neural network design can be optimized as well.
Efficient Activation Function Optimization through Surrogate Modeling
Second, a characterization of the benchmark space was developed, leading to a new surrogate-based method for optimization.
AutoInit: Analytic Signal-Preserving Weight Initialization for Neural Networks
By analytically tracking the mean and variance of signals as they propagate through the network, AutoInit appropriately scales the weights at each layer to avoid exploding or vanishing signals.
Discovering Parametric Activation Functions
Recent studies have shown that the choice of activation function can significantly affect the performance of deep learning networks.
Evolutionary Optimization of Deep Learning Activation Functions
The choice of activation function can have a large effect on the performance of a neural network.
Improving Low-Resource Cross-lingual Document Retrieval by Reranking with Deep Bilingual Representations
In this paper, we propose to boost low-resource cross-lingual document retrieval performance with deep bilingual query-document representations.
Cross-Lingual Information Retrieval
Cross-Lingual Word Embeddings
+3
Preliminary Studies on a Large Face Database
We perform preliminary studies on a large longitudinal face database MORPH-II, which is a benchmark dataset in the field of computer vision and pattern recognition.
Random Subspace Two-dimensional LDA for Face Recognition
In this paper, a novel technique named random subspace two-dimensional LDA (RS-2DLDA) is developed for face recognition.
