Search Results for author:
Found 9 papers, 5 papers with code
TEVR: Improving Speech Recognition by Token Entropy Variance Reduction
This paper presents TEVR, a speech recognition model designed to minimize the variation in token entropy w. r. t.
Ranked #1 on
Speech Recognition
on Common Voice German
(using extra training data)
Bio-inspired Min-Nets Improve the Performance and Robustness of Deep Networks
Min-Nets are inspired by end-stopped cortical cells with units that output the minimum of two learned filters.
Explainable COVID-19 Detection Using Chest CT Scans and Deep Learning
This paper explores how well deep learning models trained on chest CT images can diagnose COVID-19 infected people in a fast and automated process.
Feature Products Yield Efficient Networks
Such FP-blocks are inspired by models of end-stopped neurons, which are common in cortical areas V1 and especially in V2.
Deep Convolutional Neural Networks as Generic Feature Extractors
Deep convolutional neural networks, trained on large datasets, achieve convincing results and are currently the state-of-the-art approach for this task.
Ranked #34 on
Image Classification
on MNIST
A Hybrid Convolutional Variational Autoencoder for Text Generation
In this paper we explore the effect of architectural choices on learning a Variational Autoencoder (VAE) for text generation.
Recursive Autoconvolution for Unsupervised Learning of Convolutional Neural Networks
In visual recognition tasks, such as image classification, unsupervised learning exploits cheap unlabeled data and can help to solve these tasks more efficiently.
Recurrent Dropout without Memory Loss
This paper presents a novel approach to recurrent neural network (RNN) regularization.
Committees of deep feedforward networks trained with few data
Deep convolutional neural networks are known to give good results on image classification tasks.
Ranked #98 on
Image Classification
on STL-10
