Results 71 to 80 of about 339,627 (329)

Advances and Perspectives in Graphene‐Based Quantum Dots Enabled Neuromorphic Devices

Advanced Science, EarlyView.
Graphene‐based QDs are zero‐dimensional carbon nanomaterials with pronounced quantum confinement and tunable electronic structures. Herein, we summarize their synthesis strategies and functionalization methods, and highlight their functional roles and operating mechanisms in devices, as well as recent advances in neuromorphic electronics. We anticipate
Yulin Zhen, Wei Zeng, Zherui Zhao, JiYu Zhao, Ziyan Huang, Zhuo Chen, Kui Zhou, Guanglong Ding, Su‐Ting Han, Ye Zhou   +9 more
wiley   +1 more source

Cosine Prompt-Based Class Incremental Semantic Segmentation for Point Clouds

Algorithms
Although current 3D semantic segmentation methods have achieved significant success, they suffer from catastrophic forgetting when confronted with dynamic, open environments.
Lei Guo, Hongye Li, Min Pang, Kaowei Liu, Xie Han, Fengguang Xiong   +5 more
doaj   +1 more source

What to Make and How to Make It: Combining Machine Learning and Statistical Learning to Design New Materials

Advanced Intelligent Discovery, EarlyView.
Combining machine learning and probabilistic statistical learning is a powerful way to discover and design new materials. A variety of machine learning approaches can be used to identify promising candidates for target applications, and causal inference can help identify potential ways to make them a reality.
Jonathan Y. C. Ting, Amanda S. Barnard
wiley   +1 more source

PackNet: Adding Multiple Tasks to a Single Network by Iterative Pruning

, 2018
This paper presents a method for adding multiple tasks to a single deep neural network while avoiding catastrophic forgetting. Inspired by network pruning techniques, we exploit redundancies in large deep networks to free up parameters that can then be ...
Lazebnik, Svetlana, Mallya, Arun
core   +1 more source

Parameter Efficient Fine-tuning of Self-supervised ViTs without Catastrophic Forgetting [PDF]

2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
Artificial neural networks often suffer from catastrophic forgetting, where learning new concepts leads to a complete loss of previously acquired knowledge.
Reza Akbarian Bafghi, Nidhin Harilal, C. Monteleoni, Maziar Raissi   +3 more
semanticscholar   +1 more source

Comparing the Latent Features of Universal Machine‐Learning Interatomic Potentials

Advanced Intelligent Systems, EarlyView.
This study quantitatively assesses how universal machine‐learning interatomic potentials encode the chemical space into latent features, showing unique model‐specific representations with high cross‐model reconstruction errors. It explores how training datasets, protocols, and targets affect these encodings.
Sofiia Chorna, Davide Tisi, Cesare Malosso, Wei Bin How, Michele Ceriotti, Sanggyu Chong   +5 more
wiley   +1 more source

History Repeats: Overcoming Catastrophic Forgetting For Event-Centric Temporal Knowledge Graph Completion [PDF]

, 2023
Mehrnoosh Mirtaheri, Mohammad Rostami, Aram Galstyan   +2 more
openalex   +1 more source

Bayesian Parameter-Efficient Fine-Tuning for Overcoming Catastrophic Forgetting [PDF]

IEEE/ACM Transactions on Audio Speech and Language Processing
We are motivated primarily by the adaptation of text-to-speech synthesis models; however we argue that more generic parameter-efficient fine-tuning (PEFT) is an appropriate framework to do such adaptation. Nevertheless, catastrophic forgetting remains an
Haolin Chen, Philip N. Garner
semanticscholar   +1 more source

Mindfulness and resilience: The experiences of global majority students in a mindfulness intervention Programme at a UK university

British Educational Research Journal, EarlyView.
Abstract Wellbeing in higher education (HE) in the United Kingdom has been increasingly prioritised for many institutions, with a growing demand for student support requests. There are various determinants in life that can influence mental health. As such, protected characteristics, including race, can indicate that students who are Black or Asian ...
Amy Bywater, Helen Keane
wiley   +1 more source

An Appraisal of Incremental Learning Methods

Entropy, 2020
As a special case of machine learning, incremental learning can acquire useful knowledge from incoming data continuously while it does not need to access the original data.
Yong Luo, Liancheng Yin, Wenchao Bai, Keming Mao   +3 more
doaj   +1 more source