Results 51 to 60 of about 6,336,923 (296)

Adversary Aware Continual Learning

IEEE Access
Continual learning approaches are useful to help a model learn new information or new tasks sequentially, while also retaining the previously acquired information.
Muhammad Umer, Robi Polikar
doaj   +1 more source

Learning Invariant Representation for Continual Learning

CoRR, 2021
Accepted at the AAAI Meta-Learning for Computer Vision Workshop (2021)
Sokar, Ghada A.Z.N., Mocanu, Decebal Constantin, Pechenizkiy, Mykola   +2 more
openaire   +4 more sources

Monolithic Oxidation Enables Ultrathin Vertically Graded Tantalum Oxide for Low‐Voltage, Low‐Variability Memristive Switching

Advanced Functional Materials, EarlyView.
Monolithic UV‐ozone oxidation of Ta forms an ultrathin Ta2O5/TaOx bilayer enabling resistive switching with a vertical defect gradient. A stoichiometric surface layer over an oxygen‐deficient sublayer promotes localized filament nucleation near the top interface, enabling low‐voltage operation, and reduced cycle‐to‐cycle variability.
Seunghoon Yang, Dong Hyun Lee, Conan Weiland, Won‐Il Lee, Kim Kisslinger, Mingzhao Liu, Aswin kumar Anbalagan, Xiao Tong, Soumyajit Mandal, Nikhil Tiwale, Houk Jang, Chang‐Yong Nam   +11 more
wiley   +1 more source

Continuous Subspace Optimization for Continual Learning

CoRR
Accepted to NeurIPS ...
Quan Cheng 0001, Yuanyu Wan, Lingyu Wu, Chenping Hou, Lijun Zhang 0005   +4 more
openaire   +2 more sources

A Novel Class-wise Forgetting Detector in Continual Learning

, 2021
Deep learning model suffers from catastrophic forgetting when learning continuously from stream data. Existing strategies for continual learning suppose the forgetting always happens when learning a new task and only deals with the previous task's global
Xuan Cuong Pham, Alan Wee-chung Liew, Wang, Can, Liew, Alan Wee-Chung, Pham, Cuong X, Can Wang   +5 more
core   +1 more source

Electrolyte Engineering Challenges and Opportunities for Next‐Generation Aqueous Ammonium‐Ion Batteries

Advanced Materials, EarlyView.
Aqueous ammonium‐ion batteries (AAIBs) face a hydrogen‐bond paradox: the HB network enables fast NH4+ transport but triggers water decomposition. This review dissects this dilemma, evaluates multiple electrolyte engineering strategies, and outlines four future directions for next‐generation AAIB design ABSTRACT Aqueous ammonium‐ion batteries (AAIBs ...
Zi‐Hang Huang, Xiaoyu Liu, Shuai Mao, Lingfeng Zhu, Zhijun Wu, Hongge Pan, Tianyi Ma   +6 more
wiley   +1 more source

A continual learning framework to scale deep learning approaches for packaging equipment monitoring

, 2022
reservedNel campo dell'Industria 4.0, in cui l'Intelligenza Artificiale ha un ruolo importante nel business, c'è una sfida non banale da affrontare in cui le prestazioni dell'algoritmo si degradano nel tempo; questo fenomeno è noto come Catastrophic ...
DERONJIC, DENIS
core  

Advanced Manufacturing of Composite‐Based Systems for Energy Applications

Advanced Materials Technologies, EarlyView.
Advanced manufacturing enables the integration of polymers, ceramics, metal oxides, and composites into architected microstructures with tailored transport pathways. By coupling material selection, manufacturing strategy, and structural design, multifunctional energy systems can simultaneously improve electrochemical performance, thermal management ...
Sri Vaishnavi Thummalapalli, Dhanush Patil, Libin Yang, M. Taylor Sobczak, Anshul Kulkarni, Xiao Sun, Arunachalam Ramanathan, Tiantian Li, Ramaraja P. Ramasamy, Jason J. Locklin, Yanyu Chen, Xianqiao Wang, Arunachala Mada Kannan, Kenan Song   +13 more
wiley   +1 more source

Rethinking Continual Learning Through Self-Adaptive Learning

Machine Learning and Knowledge Extraction
Continual learning has made significant progress toward enabling adaptive machine learning under evolving environments, yet real-world deployment increasingly exposes systems to persistent harsh conditions, including distributional shifts, feature ...
Ehsan Hallaji, Roozbeh Razavi-Far
doaj   +1 more source

Self-Net: Lifelong Learning via Continual Self-Modeling

Frontiers in Artificial Intelligence, 2020
Learning a set of tasks over time, also known as continual learning (CL), is one of the most challenging problems in artificial intelligence. While recent approaches achieve some degree of CL in deep neural networks, they either (1) store a new network ...
Jaya Krishna Mandivarapu, Blake Camp, Rolando Estrada   +2 more
doaj   +1 more source