Artificial Intelligence
Pursuing computing advances to create intelligent machines that complement human reasoning to augment and enrich our experience and competencies.
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NIPS: Oral Session 2 – Xiangyu Wang
Median Selection Subset Aggregation for Parallel Inference For massive data sets, efficient computation commonly relies on distributed algorithms that store and process subsets of the data on different machines, minimizing communication costs. Our focus is…
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NIPS: Oral Session 8 – Brooks Paige
Asynchronous Anytime Sequential Monte Carlo We introduce a new sequential Monte Carlo algorithm we call the particle cascade. The particle cascade is an asynchronous, anytime alternative to traditional sequential Monte Carlo algorithms that is amenable…
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NIPS: Oral Session 1 – Yurii Nesterov
Subgradient Methods for Huge-Scale Optimization Problems We consider a new class of huge-scale problems, the problems with sparse subgradients. The most important functions of this type are piece-wise linear. For optimization problems with uniform sparsity…
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NIPS: Oral Session 3 – Matthew Lawlor
Feedforward Learning of Mixture Models We develop a biologically-plausible learning rule that provably converges to the class means of general mixture models. This rule generalizes the classical BCM neural rule within a tensor framework, substantially…
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NIPS: Oral Session 8 – Chris J. Maddison
A* Sampling The problem of drawing samples from a discrete distribution can be converted into a discrete optimization problem. In this work, we show how sampling from a continuous distribution can be converted into an…
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NIPS: Oral Session 8 – Michael Schober
Probabilistic ODE Solvers with Runge-Kutta Means Runge-Kutta methods are the classic family of solvers for ordinary differential equations (ODEs), and the basis for the state of the art. Like most numerical methods, they return point…
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NIPS: Oral Session 4 – Yichuan Tang
Learning Generative Models with Visual Attention Attention has long been proposed by psychologists to be important for efficiently dealing with the massive amounts of sensory stimulus in the neocortex. Inspired by the attention models in…
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NIPS: Spotlight Session 3 – Neuroscience and Neural Coding Spotlights
P. Putzky, F. Franzen, G. Bassetto, J. Macke A Bayesian model for identifying hierarchically organised states in neural population activity L. Buesing, T. Machado, J. Cunningham, L. Paninski Clustered factor analysis of multineuronal spike data…
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NIPS: Oral Session 3 – Arunava Majumdar
Role of Coupled Networks in 21st Century Energy Infrastructure Our modern economy overwhelmingly depends on the electricity infrastructure or the grid. The architecture of the grid owes its origins to Tesla, Edison and their industrial…
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NIPS: Spotlight Session 2 – Large Scale Learning Spotlights
G. Bresler, G. Chen, D. Shah A Latent Source Model for Online Collaborative Filtering S. Lim, Y. Chen, H. Xu Clustering from Labels and Time-Varying Graphs W. Liu, C. Mu, S. Kumar, S. Chang Discrete…
