Deep Learning for Metagenomic Data: using 2D Embeddings and Convolutional Neural Networks

– Convolution layers & Pooling layers + global architecture – Training algorithm + Dropout Regularization.. • Useful

This thesis studies empirical properties of deep convolutional neural net- works, and in particular the Scattering Transform.. Indeed, the theoretical anal- ysis of the latter is

To evaluate how our transfer method performs on this very different target task, we use a network pre-trained on 1512 ImageNet object classes and apply our transfer methodology to

The C- DSSM contains a word hashing layer that transforms each word into a letter-tri-gram input representation, a convolutional layer to extract local contextual features,

In this paper, we systematically depicted different cross- modality learning approaches of four methods based on Con- volutional Neural Networks for pedestrian recognition: (1)

To evaluate how our transfer method performs on this very different target task, we use a network pre-trained on 1512 ImageNet object classes and apply our transfer methodology to

The target output in visual recognition ranges from image-level labels (object/image classification) [23], lo- cation and extent of objects in the form of bounding boxes

We construct multi-modal concept repre- sentations by concatenating a skip-gram linguistic representation vector with a vi- sual concept representation vector com- puted using