Results 91 to 100 of about 137,057 (303)

Neuromorphic Electronics for Intelligence Everywhere: Emerging Devices, Flexible Platforms, and Scalable System Architectures

Advanced Materials, EarlyView.
The perspective presents an integrated view of neuromorphic technologies, from device physics to real‐time applicability, while highlighting the necessity of full‐stack co‐optimization. By outlining practical hardware‐level strategies to exploit device behavior and mitigate non‐idealities, it shows pathways for building efficient, scalable, and ...
Kapil Bhardwaj, Roshni Satheesh Babu, Yuxin Xia, Eva Bestelink, Radu Sporea, Nikos Hastas, Ioannis Zeimpekis, Dimitra G. Georgiadou, Sayani Majumdar   +8 more
wiley   +1 more source

Unsupervised Segmentation of Object Manipulation Operations from Multimodal Input [PDF]

, 2011
Barchunova A, Moringen J, Großekathöfer U, et al. Unsupervised Segmentation of Object Manipulation Operations from Multimodal Input. In: Hammer B, Villmann T, eds. Machine Learning Reports. New Challenges in Neural Computation.
Großekathöfer, Ulf, Barchunova, Alexandra ; https://orcid.org/, Moringen, Jan, Janssen, Herbert, Ritter, Helge ; https://orcid.org/, Haschke, Robert ; https://orcid.org/, Villmann, Thomas, Wachsmuth, Sven ; https://orcid.org/, Hammer, Barbara   +8 more
core   +1 more source

Multi-Objective Unsupervised Feature Selection and Cluster Based on Symbiotic Organism Search

Algorithms
Unsupervised learning is a type of machine learning that learns from data without human supervision. Unsupervised feature selection (UFS) is crucial in data analytics, which plays a vital role in enhancing the quality of results and reducing ...
Abbas Fadhil Jasim AL-Gburi, Mohd Zakree Ahmad Nazri, Mohd Ridzwan Bin Yaakub, Zaid Abdi Alkareem Alyasseri   +3 more
doaj   +1 more source

Organic Materials of Tomorrow: Horizons of Artificial Intelligence

Advanced Materials, EarlyView.
This review examines machine learning techniques accelerating the discovery of organic semiconductors by linking molecular structure to properties. Key methods include graph neural networks, generative models, and active learning. Applications to organic photovoltaics demonstrate practical impact.
Harold Mena, J. Terence Blaskovits, Kun‐Han Lin, Denis Andrienko   +3 more
wiley   +1 more source

A Perceptual Memory System for Affordance Learning in Humanoid Robots [PDF]

, 2011
Kammer M, Tscherepanow M, Schack T, Nagai Y. A Perceptual Memory System for Affordance Learning in Humanoid Robots. In: Honkela T, Duch W, Girolami M, Kaski S, eds.
Nagai, Yukie, Honkela, Timo, Marko Tscherepanow, Tscherepanow, Marko, Duch, Wlodzislaw, Kaski, Samuel, Marc Kammer, Yukie Nagai, Thomas Schack, Kammer, Marc, Girolami, Mark, Schack, Thomas   +11 more
core   +2 more sources

Cell Consistency Evaluation Method Based on Multiple Unsupervised Learning Algorithms

Big Data Mining and Analytics
Unsupervised learning algorithms can effectively solve sample imbalance. To address battery consistency anomalies in new energy vehicles, we adopt a variety of unsupervised learning algorithms to evaluate and predict the battery consistency of three ...
Jiang Chang, Xianglong Gu, Jieyun Wu, Debu Zhang   +3 more
doaj   +1 more source

A General Approach for Achieving Supervised Subspace Learning in Sparse Representation

IEEE Access, 2019
Over the past few decades, a large family of subspace learning algorithms based on dictionary learning have been designed to provide different solutions to learn subspace feature.
Jianshun Sang, Dezhong Peng, Yongsheng Sang   +2 more
doaj   +1 more source

In Situ Integrated Titanium Oxide Synaptic Phototransistor Enabling Multimodal Plasticity and Noise‐Robust Selective Attention

Advanced Materials Technologies, EarlyView.
An in situ integrated TiO2/SiOx/Al2O3 synaptic phototransistor couples ultraviolet and electrical stimuli within a scalable, CMOS‐compatible oxide stack. Multimodal plasticity, spike‐timing‐dependent learning, and bee‐inspired associative conditioning are achieved through trap‐mediated temporal dynamics.
Youngbin Yoon, Jaehee Lee, Jeongwook Kwon, Yongki Kim, Myunghun Shin, Jung Wook Lim   +5 more
wiley   +1 more source

Empirical Risk Minimization for Probabilistic Grammars: Sample Complexity and Hardness of Learning [PDF]

, 2012
Probabilistic grammars are generative statistical models that are useful for compositional and sequential structures. They are used ubiquitously in computational linguistics.
Noah A. Smith, Smith, N. A., Shay B. Cohen, Cohen, S. B.   +3 more
core   +1 more source

The Future of Research in Cognitive Robotics: Foundation Models or Developmental Cognitive Models?

Advanced Robotics Research, EarlyView.
Research in cognitive robotics founded on principles of developmental psychology and enactive cognitive science would yield what we seek in autonomous robots: the ability to perceive its environment, learn from experience, anticipate the outcome of events, act to pursue goals, and adapt to changing circumstances without resorting to training with ...
David Vernon
wiley   +1 more source