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Found 11 papers, 2 papers with code
Improving Person Re-identification with Iterative Impression Aggregation
Not only does such a simple method improve the performance of the baseline models, it also achieves comparable performance with latest advanced re-ranking methods.
SSoC: Learning Spontaneous and Self-Organizing Communication for Multi-Agent Collaboration
By treating the communication behaviour as an explicit action, SSoC learns to organize communication in an effective and efficient way.
Revisiting the Master-Slave Architecture in Multi-Agent Deep Reinforcement Learning
Many tasks in artificial intelligence require the collaboration of multiple agents.
From Bayesian Sparsity to Gated Recurrent Nets
The iterations of many first-order algorithms, when applied to minimizing common regularized regression functions, often resemble neural network layers with pre-specified weights.
Collaborative Deep Reinforcement Learning for Joint Object Search
We examine the problem of joint top-down active search of multiple objects under interaction, e. g., person riding a bicycle, cups held by the table, etc..
Maximal Sparsity with Deep Networks?
The iterations of many sparse estimation algorithms are comprised of a fixed linear filter cascaded with a thresholding nonlinearity, which collectively resemble a typical neural network layer.
Curvature Wavefront Sensing for the Large Synoptic Survey Telescope
The Large Synoptic Survey Telescope (LSST) will use an active optics system (AOS) to maintain alignment and surface figure on its three large mirrors.
Instrumentation and Methods for Astrophysics
Background Subtraction via Generalized Fused Lasso Foreground Modeling
Background Subtraction (BS) is one of the key steps in video analysis.
Stable Feature Selection from Brain sMRI
Neuroimage analysis usually involves learning thousands or even millions of variables using only a limited number of samples.
Exploring Algorithmic Limits of Matrix Rank Minimization under Affine Constraints
While elegant theoretical conditions elucidate when this replacement is likely to be successful, they are highly restrictive and convex algorithms fail when the ambient rank is too high or when the constraint set is poorly structured.
LSST: from Science Drivers to Reference Design and Anticipated Data Products
About 90\% of the observing time will be devoted to a deep-wide-fast survey mode which will uniformly observe a 18, 000 deg$^2$ region about 800 times (summed over all six bands) during the anticipated 10 years of operations, and yield a coadded map to $r\sim27. 5$.
