Our model consists of a perceptual front-end based on convolutional neural networks and a dynamics predictor based on interaction networks.

Neural Information Processing Systems http://nips.cc/

Neuroscientists classify neurons into different types tha t perform similar computations at different locations in the visual field. Traditio nal methods for neural system identification do not capitalize on this separation o f "what" and "where". Learning deep convolutional feature spaces that are shared among many neurons provides an exciting path forward, but the architectural de sign needs to account for data limitations: While new experimental techniques enabl e recordings from thousands of neurons, experimental time is limited so that one ca n sample only a small fraction of each neuron's response space. Here, we show that a major bottleneck for fitting convolutional neural networks (CNNs) to neural d ata is the estimation of the individual receptive field locations - a problem that h as been scratched only at the surface thus far. W e propose a CNN architecture with a s parse readout layer factorizing the spatial (where) and feature (what) dimensi ons. Our network scales well to thousands of neurons and short recordings and can be t rained end-to-end. W e evaluate this architecture on ground-truth data to explo re the challenges and limitations of CNN-based system identification. Moreover, we show that our network model outperforms current state-of-the art system ide ntification models of mouse primary visual cortex.

High network communication cost for synchronizing gradients and parameters is the well-known bottleneck of distributed training.

Inferring topological and geometrical information from data can offer an alternative perspective on machine learning problems. Methods from topological data analysis, e.g., persistent homology, enable us to obtain such information, typically in the form

Generative Adversarial Nets (GANs) have shown promise in image generation and semi-supervised learning (SSL). However, existing GANs in SSL have two problems: (1) the generator and the discriminator (i.e. the classifier) may not be optimal at the same time; and (2) the generator cannot control the semantics of the generated samples.

We present a new approach to learn compressible representations in deep architectures with an end-to-end training strategy.

Compressive image recovery is a challenging problem that requires fast and accurate algorithms.

Well-calibrated uncertainty is crucial for many tasks in deep learning.

Neural Information Processing Systems http://nips.cc/