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Understanding the difficulty of training deep feedforward neural networks
The objective here is to understand better why standard gradient descent from random initialization is doing so poorly with deep neural networks, to better understand these recent relative successes and help design better algorithms in the future.
beta-VAE: Learning Basic Visual Concepts with a Constrained Variational Framework
Learning an interpretable factorised representation of the independent data generative factors of the world without supervision is an important precursor for the development of artificial…
Deep Sparse Rectifier Neural Networks
This paper shows that rectifying neurons are an even better model of biological neurons and yield equal or better performance than hyperbolic tangent networks in spite of the hard non-linearity and non-dierentiabil ity.
Theano: A Python framework for fast computation of mathematical expressions
The performance of Theano is compared against Torch7 and TensorFlow on several machine learning models and recently-introduced functionalities and improvements are discussed.
Domain Adaptation for Large-Scale Sentiment Classification: A Deep Learning Approach
A deep learning approach is proposed which learns to extract a meaningful representation for each review in an unsupervised fashion and clearly outperform state-of-the-art methods on a benchmark composed of reviews of 4 types of Amazon products.
Contractive Auto-Encoders: Explicit Invariance During Feature Extraction
It is found empirically that this penalty helps to carve a representation that better captures the local directions of variation dictated by the data, corresponding to a lower-dimensional non-linear manifold, while being more invariant to the vast majority of directions orthogonal to the manifold.
A semantic matching energy function for learning with multi-relational data
- Xavier Glorot, Antoine Bordes, J. Weston, Yoshua Bengio
- Computer ScienceMachine Learning
- 15 January 2013
A new neural network architecture designed to embed multi-relational graphs into a flexible continuous vector space in which the original data is kept and enhanced, demonstrating that it can scale up to tens of thousands of nodes and thousands of types of relation.
Joint Learning of Words and Meaning Representations for Open-Text Semantic Parsing
This work proposes a method that learns to assign MRs to a wide range of text thanks to a training scheme that combines learning from knowledge bases with learning from raw text.
A Clinically Applicable Approach to Continuous Prediction of Future Acute Kidney Injury
A deep learning approach that predicts the risk of acute kidney injury and provides confidence assessments and a list of the clinical features that are most salient to each prediction, alongside predicted future trajectories for clinically relevant blood tests are developed.
Clinically applicable deep learning for diagnosis and referral in retinal disease
A novel deep learning architecture performs device-independent tissue segmentation of clinical 3D retinal images followed by separate diagnostic classification that meets or exceeds human expert clinical diagnoses of retinal disease.