Search Results for author:
Found 6 papers, 3 papers with code
Training Deep Neural Networks Without Batch Normalization
The main purpose of this work is to determine if it is possible to train networks effectively when batch normalization is removed through adaption of the training process.
P $\approx$ NP, at least in Visual Question Answering
In recent years, progress in the Visual Question Answering (VQA) field has largely been driven by public challenges and large datasets.
What do Deep Networks Like to See?
To do this, an autoencoder (AE) was fine-tuned on gradients from a pre-trained classifier with fixed parameters.
Ranked #818 on
Image Classification
on ImageNet
Adversarial Defense based on Structure-to-Signal Autoencoders
We analyze their robustness against several white-box and gray-box scenarios on the large ImageNet dataset.
Multi-Task Learning for Segmentation of Building Footprints with Deep Neural Networks
In this paper, we address the problem of preserving semantic segmentation boundaries in high resolution satellite imagery by introducing a new cascaded multi-task loss.
An Evolutionary Algorithm to Learn SPARQL Queries for Source-Target-Pairs: Finding Patterns for Human Associations in DBpedia
We show the scalability of the algorithm by running it against a SPARQL endpoint loaded with > 7. 9 billion triples.
