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Striving for Simplicity: The All Convolutional Net
TLDR
It is found that max-pooling can simply be replaced by a convolutional layer with increased stride without loss in accuracy on several image recognition benchmarks. Expand
Efficient and Robust Automated Machine Learning
TLDR
This work introduces a robust new AutoML system based on scikit-learn, which improves on existing AutoML methods by automatically taking into account past performance on similar datasets, and by constructing ensembles from the models evaluated during the optimization. Expand
Deep learning with convolutional neural networks for EEG decoding and visualization
TLDR
This study shows how to design and train convolutional neural networks to decode task‐related information from the raw EEG without handcrafted features and highlights the potential of deep ConvNets combined with advanced visualization techniques for EEG‐based brain mapping. Expand
Embed to Control: A Locally Linear Latent Dynamics Model for Control from Raw Images
TLDR
Embed to Control is introduced, a method for model learning and control of non-linear dynamical systems from raw pixel images that is derived directly from an optimal control formulation in latent space and exhibits strong performance on a variety of complex control problems. Expand
Unsupervised and Semi-supervised Learning with Categorical Generative Adversarial Networks
In this paper we present a method for learning a discriminative classifier from unlabeled or partially labeled data. Our approach is based on an objective function that trades-off mutual informationExpand
Multimodal deep learning for robust RGB-D object recognition
TLDR
This paper leverages recent progress on Convolutional Neural Networks (CNNs) and proposes a novel RGB-D architecture for object recognition that is composed of two separate CNN processing streams - one for each modality - which are consecutively combined with a late fusion network. Expand
Discriminative Unsupervised Feature Learning with Convolutional Neural Networks
TLDR
This paper presents an approach for training a convolutional neural network using only unlabeled data and trains the network to discriminate between a set of surrogate classes, finding that this simple feature learning algorithm is surprisingly successful when applied to visual object recognition. Expand
Discriminative Unsupervised Feature Learning with Exemplar Convolutional Neural Networks
TLDR
While features learned with this approach cannot compete with class specific features from supervised training on a classification task, it is shown that they are advantageous on geometric matching problems, where they also outperform the SIFT descriptor. Expand
Speeding Up Automatic Hyperparameter Optimization of Deep Neural Networks by Extrapolation of Learning Curves
TLDR
This paper mimics the early termination of bad runs using a probabilistic model that extrapolates the performance from the first part of a learning curve, enabling state-of-the-art hyperparameter optimization methods for DNNs to find DNN settings that yield better performance than those chosen by human experts. Expand
Learning to generate chairs with convolutional neural networks
TLDR
This work trains a generative convolutional neural network which is able to generate images of objects given object type, viewpoint, and color and shows that the network can be used to find correspondences between different chairs from the dataset, outperforming existing approaches on this task. Expand
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