Results 111 to 120 of about 9,437,008 (290)

Bayesian Batch Active Learning as Sparse Subset Approximation

CoRR, 2019
Leveraging the wealth of unlabeled data produced in recent years provides great potential for improving supervised models. When the cost of acquiring labels is high, probabilistic active learning methods can be used to greedily select the most informative data points to be labeled.
Pinsler, R., Gordon, J., Nalisnick, E., Hernández-Lobato, J.M.   +3 more
openaire   +4 more sources

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 network classifiers in Weka [PDF]

, 2004
Various Bayesian network classifier learning algorithms are implemented in Weka [10].This note provides some user documentation and implementation details.
Remco R. Bouckaert, Bouckaert, Remco R.
core  

Problem-based learning and pedagogies of play

, 2023
The focus of this book is original research regarding the implementation of problem-based learning and pedagogies of play as active approaches to foster self-directed learning. With the Fourth Industrial Revolution (4IR) in mind, educational institutions
Benelrhali, Azeddine, du Toit-Brits, Charlene, Aboderin, Olukayode S, Erasmus, Ewie, Olivier, Louise, El Kharki, Khadija, Jacobson, Trudi E, Havenga, Marietjie, Nordgaard, Chanene, Oueriagli, Amane, Reyneke, Maryna, Bunt, Byron J., de Beer, Josef, Botha, Carolina, Golightly, Aubrey, Marais, Elma, Olivier, Jako, Burgos, Daniel, Dhakulkar, Amit, Mackey, Thomas P, Ouahbi, Ibrahim, Jordaan, Tertia, Berrada, Khalid, Sebatana, Motlhale J, Blignaut, Henry   +24 more
core   +1 more source

Lessons in groups ("LIG"): an original active way to study lessons [PDF]

, 2009
We present here research for deriving a collaborative learning situation which is not base don a Project or problem. The method mainly consists in working in small teams (4 or 5 students) in order to debate on a lesson which has been previously studied ...
Baudy, Pierre, Rabut, Christophe
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

Accelerating ligand discovery by combining Bayesian optimization with MMGBSA-based binding affinity calculations [PDF]

Digital Discovery
Predicting protein–ligand binding affinity with high accuracy is critical in structure-based drug discovery. While docking methods offer computational efficiency, they often lack the precision required for reliable affinity ranking.
Lucas Andersen, Max Rausch-Dupont, Alejandro Martínez León, Andrea Volkamer, Jochen S. Hub, Dietrich Klakow   +5 more
doaj   +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

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

Learning a bayesian network from ordinal data [PDF]

Bayesian networks are graphical models that represent the joint distributionof a set of variables using directed acyclic graphs. When the dependence structure is unknown (or partially known) the network can be learnt from data.
Flaminia Musella
core