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.

Humanity faces numerous problems of common-pool resource appropriation.

Efficiently aggregating data from different sources is a challenging problem, particularly when samples from each source are distributed differently.

Defiant Dems receive 24/7 protection from Capitol Police after Trump accused them of'seditious behavior' and threatened them with execution What Meghan's announcements in her pseudo-Royal court get wrong and why they'speak volumes', revealed by experts Presidential hopeful is dragged into criminal probe... as shock texts emerge: 'It will open Pandora's Box' Multiple cast members speak to Daily Mail and hurl ugly allegations at each other... and reveal co-stars they can't stand Family panic as Britney Spears takes'disturbing' measures... after world was shocked by her unrecognizable new look Everybody Loves Raymond stars now unrecognizable as they reunite for sitcom's 30th anniversary Democratic candidate gives bizarre defense after comments that she'hates' Nashville resurface Private school where teacher'had sex with five students as soon as they turned 16' - and it was LEGAL Kansas City Chiefs coach slams Donald Trump in brutal putdown: 'He has no idea what's going on' Anna Kepner's ex-boyfriend claims stepbrother'climbed on top of her' months before cheerleader was found dead on cruise Bruce Willis' daughter Rumer makes heartbreaking confession about famous father's dementia battle Truth about Ariana Grande and Cynthia Erivo's'secret marriage'... and the depressing reason insiders say their friendship could soon be OVER America's most forgiving wife lists enormous $6m NYC apartment she shares with disgraced CEO caught with woman on Coldplay kisscam Kessler twins who worked with Frank Sinatra and wowed Elvis Presley'paid a lot of money' to die together at 89 A lion's roar is undeniably one of the most fearsome sounds across the entire animal kingdom. Now, it turns out these majestic creatures produce not just one, but two distinct types of roar. That's according to researchers from the University of Exeter, who have identified a brand new type of growl in African lions. The animals - often referred to as the'King of the Jungle' - are best known for their full-throated roar, an immensely powerful vocalization that can be heard up to five miles away. However, using AI, the researchers were able to identify a second type of roar, which they've called the'intermediary roar'.

Evaluating the quality of synthetic data remains a persistent challenge in the Android malware domain due to instability and the lack of standardization among existing metrics. Experiments involving ten generative models and five balanced datasets demonstrate that the Super-Metric is more stable and consistent than traditional metrics, exhibiting stronger correlations with the actual performance of classifiers. Synthetic data generation has become an increasingly relevant strategy in cybersecurity [1], [2], [3], particularly as a way to mitigate the scarcity of real, complete, and high-quality datasets that limit the performance and generalization of machine learning models. Despite these advances, assessing the quality of synthetic data remains a complex and largely non-standardized methodological challenge [4], with no clear consensus on which metrics should be used or how to combine them consistently. The literature reports a significant fragmentation in the application of fidelity metrics, with studies identifying more than 65 distinct indicators used independently to assess fidelity [5]. This diversity hinders model-to-model comparison, reduces experimental reproducibility, and complicates the integrated interpretation of data quality.