Explainable artificial intelligence for mental health through transparency and interpretability for understandability
The literature on artificial intelligence (AI) or machine learning (ML) in mental health and
psychiatry lacks consensus on what “explainability” means. In the more general XAI …
psychiatry lacks consensus on what “explainability” means. In the more general XAI …
Toward the explainability, transparency, and universality of machine learning for behavioral classification in neuroscience
The use of rigorous ethological observation via machine learning techniques to understand
brain function (computational neuroethology) is a rapidly growing approach that is poised to …
brain function (computational neuroethology) is a rapidly growing approach that is poised to …
A cross-language investigation into jailbreak attacks in large language models
Large Language Models (LLMs) have become increasingly popular for their advanced text
generation capabilities across various domains. However, like any software, they face …
generation capabilities across various domains. However, like any software, they face …
Highly-optimized radar-based gesture recognition system with depthwise expansion module
The increasing integration of technology in our daily lives demands the development of
more convenient human–computer interaction (HCI) methods. Most of the current hand …
more convenient human–computer interaction (HCI) methods. Most of the current hand …
Transfer learning, alternative approaches, and visualization of a convolutional neural network for retrieval of the internuclear distance in a molecule from photoelectron …
NI Shvetsov-Shilovski, M Lein - Physical Review A, 2023 - APS
We investigate the application of deep learning to the retrieval of the internuclear distance in
the two-dimensional H 2+ molecule from the momentum distribution of photoelectrons …
the two-dimensional H 2+ molecule from the momentum distribution of photoelectrons …
Deep learning for retrieval of the internuclear distance in a molecule from interference patterns in photoelectron momentum distributions
NI Shvetsov-Shilovski, M Lein - Physical Review A, 2022 - APS
We use a convolutional neural network to retrieve the internuclear distance in the two-
dimensional H 2+ molecule ionized by a strong few-cycle laser pulse based on the …
dimensional H 2+ molecule ionized by a strong few-cycle laser pulse based on the …
Knowledge-Aware Neuron Interpretation for Scene Classification
Although neural models have achieved remarkable performance, they still encounter doubts
due to the intransparency. To this end, model prediction explanation is attracting more and …
due to the intransparency. To this end, model prediction explanation is attracting more and …
Neural network-based urban change monitoring with deep-temporal multispectral and SAR remote sensing data
G Zitzlsberger, M Podhorányi, V Svatoň, M Lazecký… - Remote Sensing, 2021 - mdpi.com
Remote-sensing-driven urban change detection has been studied in many ways for
decades for a wide field of applications, such as understanding socio-economic impacts …
decades for a wide field of applications, such as understanding socio-economic impacts …
Sniper: cloud-edge collaborative inference scheduling with neural network similarity modeling
W Liu, J Geng, Z Zhu, J Cao, Z Lian - Proceedings of the 59th ACM/IEEE …, 2022 - dl.acm.org
The cloud-edge collaborative inference demands scheduling the artificial intelligence (AI)
tasks efficiently to the appropriate edge smart device. However, the continuously iterative …
tasks efficiently to the appropriate edge smart device. However, the continuously iterative …
: Dynastic Data-Free Knowledge Distillation
Data-free knowledge distillation further broadens the applications of the distillation model.
Nevertheless, the problem of providing diverse data with rich expression patterns needs to …
Nevertheless, the problem of providing diverse data with rich expression patterns needs to …