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Found 9 papers, 4 papers with code
Discovering Hidden Factors of Variation in Deep Networks
Deep learning has enjoyed a great deal of success because of its ability to learn useful features for tasks such as classification.
Discovering Hidden Factors of Variation in DeepNetworks
Deep learning has enjoyed a great deal of success because of its ability to learnuseful features for tasks such as classification.
Theano: A Python framework for fast computation of mathematical expressions
Since its introduction, it has been one of the most used CPU and GPU mathematical compilers - especially in the machine learning community - and has shown steady performance improvements.
Learning overcomplete, low coherence dictionaries with linear inference
Further, by comparing ICA algorithms on synthetic data and natural images to the computationally more expensive sparse coding solution, we show that the coherence control biases the exploration of the data manifold, sometimes yielding suboptimal solutions.
Deep learning as a tool for neural data analysis: speech classification and cross-frequency coupling in human sensorimotor cortex
A fundamental challenge in neuroscience is to understand what structure in the world is represented in spatially distributed patterns of neural activity from multiple single-trial measurements.
Spiking Linear Dynamical Systems on Neuromorphic Hardware for Low-Power Brain-Machine Interfaces
Neuromorphic architectures achieve low-power operation by using many simple spiking neurons in lieu of traditional hardware.
Unsupervised Discovery of Temporal Structure in Noisy Data with Dynamical Components Analysis
Linear dimensionality reduction methods are commonly used to extract low-dimensional structure from high-dimensional data.
Hangul Fonts Dataset: a Hierarchical and Compositional Dataset for Investigating Learned Representations
Thus, HFD enables the identification of shortcomings in existing methods, a critical first step toward developing new machine learning algorithms to extract hierarchical and compositional structure in the context of naturalistic variability.
Deep learning approaches for neural decoding: from CNNs to LSTMs and spikes to fMRI
Deep learning has been shown to be a useful tool for improving the accuracy and flexibility of neural decoding across a wide range of tasks, and we point out areas for future scientific development.
