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

Where the constraint ensures that the solution is unique.

RMSProp avoids both the vanishing and exploding gradient problems.

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.

We propose a generalized Gibbs sampler algorithm for obtaining samples approximately distributed from a high-dimensional Gaussian distribution.

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

In this paper, we propose ELF, an Extensive, Lightweight and Flexible platform for fundamental reinforcement learning research. Using ELF, we implement a highly customizable real-time strategy (RTS) engine with three game environments (Mini-RTS, Capture the Flag and Tower Defense). Mini-RTS, as a miniature version of StarCraft, captures key game dynamics and runs at 40K frame-per-second (FPS) per core on a laptop. When coupled with modern reinforcement learning methods, the system can train a full-game bot against built-in AIs end-to-end in one day with 6 CPUs and 1 GPU. In addition, our platform is flexible in terms of environment-agent communication topologies, choices of RL methods, changes in game parameters, and can host existing C/C++-based game environments like ALE [4]. Using ELF, we thoroughly explore training parameters and show that a network with Leaky ReLU [17] and Batch Normalization [11] coupled with long-horizon training and progressive curriculum beats the rule-based built-in AI more than 70% of the time in the full game of Mini-RTS. Strong performance is also achieved on the other two games. In game replays, we show our agents learn interesting strategies.

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

Overall then, restart schemes generically accelerate accelerated methods.