Results 61 to 70 of about 13,046,168 (188)

Nonparametric estimation over shrinking neighborhoods: Superefficiency and adaptation [PDF]

open access: yes, 2005
A theory of superefficiency and adaptation is developed under flexible performance measures which give a multiresolution view of risk and bridge the gap between pointwise and global estimation.
Cai, T. Tony, Low, Mark G.
core   +3 more sources

Computers and chess masters: The role of AI in transforming elite human performance

open access: yesBritish Journal of Psychology, EarlyView.
Abstract Advances in Artificial Intelligence (AI) have made significant strides in recent years, often supplementing rather than replacing human performance. The extent of their assistance at the highest levels of human performance remains unclear. We analyse over 11.6 million decisions of elite chess players, a domain commonly used as a testbed for AI
Merim Bilalić, Mario Graf, Nemanja Vaci
wiley   +1 more source

Computing Skinning Weights via Convex Duality

open access: yesComputer Graphics Forum, EarlyView.
We present an alternate optimization method to compute bounded biharmonic skinning weights. Our method relies on a dual formulation, which can be optimized with a nonnegative linear least squares setup. Abstract We study the problem of optimising for skinning weights through the lens of convex duality.
J. Solomon, O. Stein
wiley   +1 more source

Machine learning approaches to characterize the obesogenic urban exposome

open access: yesEnvironment International, 2022
Background: Characteristics of the urban environment may contain upstream drivers of obesity. However, research is lacking that considers the combination of environmental factors simultaneously.
Haykanush Ohanyan   +7 more
doaj   +1 more source

Information Recovery from Pairwise Measurements [PDF]

open access: yes, 2016
A variety of information processing tasks in practice involve recovering $n$ objects from single-shot graph-based measurements, particularly those taken over the edges of some measurement graph $\mathcal{G}$.
Chen, Yuxin, Goldsmith, Andrea J.
core  

Fast learning rate of multiple kernel learning: Trade-off between sparsity and smoothness [PDF]

open access: yes, 2013
We investigate the learning rate of multiple kernel learning (MKL) with $\ell_1$ and elastic-net regularizations. The elastic-net regularization is a composition of an $\ell_1$-regularizer for inducing the sparsity and an $\ell_2$-regularizer for ...
Sugiyama, Masashi, Suzuki, Taiji
core   +2 more sources

A Non‐Parametric Framework for Correlation Functions on Product Metric Spaces

open access: yesInternational Statistical Review, EarlyView.
Summary We propose a non‐parametric framework for analysing data defined over products of metric spaces, a versatile class encountered in various fields. This framework accommodates non‐stationarity and seasonality and is applicable to both local and global domains, such as the Earth's surface, as well as domains evolving over linear time or time ...
Pier Giovanni Bissiri   +3 more
wiley   +1 more source

Estimating Slump Flow and Compressive Strength of Self-Compacting Concrete Using Emotional Neural Networks

open access: yesApplied Sciences, 2020
The characteristics of fresh and hardened self-compacting concrete (SCC) are an essential requirement for construction projects. Moreover, the sensitivity of admixture contents of SCC in these properties is highly impacted by that cost. The current study
Mosbeh R. Kaloop   +3 more
doaj   +1 more source

On the Jacquet Conjecture on the Local Converse Problem for p-adic GL_n [PDF]

open access: yes, 2016
Based on previous results of Jiang, Nien and the third author, we prove that any two minimax unitarizable supercuspidals of GL_N that have the same depth and central character admit a special pair of Whittaker functions. This result gives a new reduction
Adrian, Moshe   +3 more
core   +1 more source

Financial Time Series Uncertainty: A Review of Probabilistic AI Applications

open access: yesJournal of Economic Surveys, EarlyView.
ABSTRACT Probabilistic machine learning models offer a distinct advantage over traditional deterministic approaches by quantifying both epistemic uncertainty (stemming from limited data or model knowledge) and aleatoric uncertainty (due to inherent randomness in the data), along with full distributional forecasts.
Sivert Eggen   +4 more
wiley   +1 more source

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