Concept
Half-normal distribution
Related publications (34)
A Theory of Finite-Width Neural Networks: Generalization, Scaling Laws, and the Loss Landscape
Deep learning has achieved remarkable success in various challenging tasks such as generating images from natural language or engaging in lengthy conversations with humans.The success in practice stems from the ability to successfully train massive neural ...
EPFL2023Distributionally Robust Inverse Covariance Estimation: The Wasserstein Shrinkage Estimator
Daniel Kuhn, Viet Anh Nguyen, Peyman Mohajerin Esfahani
We introduce a distributionally robust maximum likelihood estimation model with a Wasserstein ambiguity set to infer the inverse covariance matrix of a p-dimensional Gaussian random vector from n independent samples. The proposed model minimizes the worst ...
2020Wasserstein Distributionally Robust Learning
Many decision problems in science, engineering, and economics are affected by uncertainty, which is typically modeled by a random variable governed by an unknown probability distribution. For many practical applications, the probability distribution is onl ...
EPFL2020Integrating the Finite Element Method with a Data-Driven Approach for Dam Displacement Prediction
Zhenzhu Meng, Yating Hu, Chenfei Shao
Both numerical simulations and data-driven methods have been applied in dam's displacement modeling. For monitored displacement data-driven methods, the physical mechanism and structural correlations were rarely discussed. In order to take the spatial and ...
2020Particle number control for direct simulation Monte-Carlo methodology using kernel estimates
The efficiency of stochastic particle schemes for large scale simulations relies on the ability to preserve a uniform distribution of particles in the whole physical domain. While simple particle split and merge algorithms have been considered previously, ...
2019Concrete Dam Displacement Prediction Based on an ISODATA-GMM Clustering and Random Coefficient Model
Zhenzhu Meng, Yating Hu, Chenfei Shao
Displacement data modelling is of great importance for the safety control of concrete dams. The commonly used artificial intelligence method modelled the displacement data at each monitoring point individually, i.e., the data correlations between the monit ...
2019Fine-Grain Checkpointing with In-Cache-Line Logging
James Richard Larus, David Teksen Aksun, Nachshon Cohen
Non-Volatile Memory offers the possibility of implementing high-performance, durable data structures. However, achieving performance comparable to well-designed data structures in non-persistent (transient) memory is difficult, primarily because of the cos ...
ACM2019Wasserstein Distributionally Robust Kalman Filtering
Daniel Kuhn, Viet Anh Nguyen, Soroosh Shafieezadeh Abadeh, Peyman Mohajerin Esfahani
We study a distributionally robust mean square error estimation problem over a nonconvex Wasserstein ambiguity set containing only normal distributions. We show that the optimal estimator and the least favorable distribution form a Nash equilibrium. Despit ...
2018Learning to Reconstruct Texture-less Deformable Surfaces from a Single View
Pascal Fua, Mathieu Salzmann, Jan Bednarík
Recent years have seen the development of mature solutions for reconstructing deformable surfaces from a single image, provided that they are relatively well-textured. By contrast, recovering the 3D shape of texture-less surfaces remains an open problem, a ...
IEEE2018Learning to Reconstruct Texture-less Deformable Surfaces from a Single View
Pascal Fua, Mathieu Salzmann, Jan Bednarík
Recent years have seen the development of mature solutions for reconstructing deformable surfaces from a single image, provided that they are relatively well-textured. By contrast, recovering the 3D shape of texture-less surfaces remains an open problem, a ...
2018