Results 141 to 150 of about 15,911,760 (295)

On‐Chip Photonic Neural Network Architectures

Advanced Optical Materials, EarlyView.
This review presents a comprehensive overview of on‐chip photonic neural network architectures, covering key photonic building blocks, representative network types, and emerging applications. Recent advances, implementation challenges, and future directions are examined, highlighting the potential of integrated photonics to enable ultrafast, energy ...
Seokjin Hong, Berkay Neseli, Seungyeon Cho, Seungsoo Lee, Yeonjun Kim, Seungyoon Choi, Hyo‐Hoon Park, Hamza Kurt   +7 more
wiley   +1 more source

Bayesian active learning with model selection for spectral experiments

Scientific Reports
Active learning is a common approach to improve the efficiency of spectral experiments. Model selection from the candidates and parameter estimation are often required in the analysis of spectral experiments.
Tomohiro Nabika, Kenji Nagata, Masaichiro Mizumaki, Shun Katakami, Masato Okada   +4 more
doaj   +1 more source

Using the Bayesian information criterion to develop two-stage model-robust and model-sensitive designs. [PDF]

In this paper, we investigate use of the Bayesian Information Criterion (BIC) in the development of Bayesian two-stage designs robust to model uncertainty.
Ruggoo, Arvind, Vandebroek, Martina
core  

Default Bayesian model determination methods for generalised linear mixed models

, 2010
In this paper, we consider a default strategy for fully Bayesian model determination for GLMMs. We address the two key issues of default prior specification and computation.
Overstall, Anthony M., Overstall, Antony, Forster, Jonathan, Overstall, Antony M., Forster, Jonathan J.   +4 more
core   +1 more source

3D Printing of Soft Robotic Systems: Advances in Fabrication Strategies and Future Trends

Advanced Robotics Research, EarlyView.
Collectively, this review systematically examines 3D‐printed soft robotics, encompassing material selections, function integration, and manufacturing methodologies. Meanwhile, fabrication strategies are analyzed in order of increasing complexity, highlighting persistent challenges with proposed solutions.
Changjiang Liu, Bin Cui, Xumin Huang, Xiaoyu (Rayne) Zheng, T. Thang Vo‐Doan, Ruirui Qiao   +5 more
wiley   +1 more source

Identifying Physical Interactions in Contact‐Based Robot Manipulation for Learning from Demonstration

Advanced Robotics Research, EarlyView.
Robots can learn manipulation tasks from human demonstrations. This work proposes a versatile method to identify the physical interactions that occur in a demonstration, such as sequences of different contacts and interactions with mechanical constraints.
Alex Harm Gert‐Jan Overbeek, Herman van der Kooij, Douwe Dresscher, Mark Vlutters   +3 more
wiley   +1 more source

Bayesian Model Averaging in R [PDF]

Bayesian model averaging has increasingly witnessed applications across an array of empirical contexts. However, the dearth of available statistical software which allows one to engage in a model averaging exercise is limited.
Christopher F. Parmeter, Shahram Amini
core  

Continual Learning for Multimodal Data Fusion of a Soft Gripper

Advanced Robotics Research, EarlyView.
Models trained on a single data modality often struggle to generalize when exposed to a different modality. This work introduces a continual learning algorithm capable of incrementally learning different data modalities by leveraging both class‐incremental and domain‐incremental learning scenarios in an artificial environment where labeled data is ...
Nilay Kushawaha, Egidio Falotico
wiley   +1 more source

Comparing Gaussian graphical models with the posterior predictive distribution and Bayesian model selection. [PDF]

Psychol Methods, 2020
Williams DR, Rast P, Pericchi LR, Mulder J.   +3 more
europepmc   +1 more source

Bayesian stochastic model specification search for seasonal and calendar effects [PDF]

We apply a recent methodology, Bayesian stochastic model specification search (SMSS), for the selection of the unobserved components (level, slope, seasonal cycles, trading days effects) that are stochastically evolving over time.
Tommaso, Proietti, Stefano, Grassi
core