Results 141 to 150 of about 11,006,319 (257)

Towards Imperceptible Backdoor Attack in Self-supervised Learning [PDF]

Self-supervised learning models are vulnerable to backdoor attacks. Existing backdoor attacks that are effective in self-supervised learning often involve noticeable triggers, like colored patches, which are vulnerable to human inspection. In this paper,
Du, Mengnan, Wang, Zhenting, Zhan, Chenlu, Jin, Mingyu, Zhang, Hanrong, Ma, Shiqing, Wang, Hongwei, Han, Tingxu   +7 more
core  

Performance of double contrast learning, self-supervised contrast learning and no contrast learning.

Performance of double contrast learning, self-supervised contrast learning and no contrast learning.
Wushour Silamu (16186844), Bing Zhang (129), Tao Liu (10785), Zunwang Ke (16186841), Yanbing Li (281394)   +4 more
core   +1 more source

Atomic Defects in Layered Transition Metal Dichalcogenides for Sustainable Energy Storage and the Intelligent Trends in Data Analytics

Advanced Science, EarlyView.
This review comprehensively summarizes the atomic defects in TMDs for their applications in sustainable energy storage devices, along with the latest progress in ML methodologies for high‐throughput TEM data analysis, offering insights on how ML‐empowered microscopy facilitates bridging structure–property correlation and inspires knowledge for precise ...
Zheng Luo, Ying Yang, Zizhen Fu, Fengqi Liu, Susu Fang, Kele Xu, Shanshan Wang   +6 more
wiley   +1 more source

Complexity begets simplicity: self-supervised learning for palaeontological images with few or no labels. [PDF]

J R Soc Interface
Rodgers N.
europepmc   +1 more source

Overcoming Artificial Structures in Resolution‐Enhanced Hi‐C Data by Signal Decomposition and Multi‐Scale Attention

Advanced Science, EarlyView.
Deep‐learning‐based signal enhancement is an effective way to recover high‐resolution details from a low‐resolution chromatin contact map. However, due to computational challenges, existing methods commonly divide up the contact map into small patches and create artificial discontinuities at patch boundaries.
Qinyao Li, Kelly Yichen Li, Chiara Nicoletti, Stephen Kwok‐Wing Tsui, Pier Lorenzo Puri, Qin Cao, Kevin Y. Yip   +6 more
wiley   +1 more source

Sustainable Materials Design With Multi‐Modal Artificial Intelligence

Advanced Science, EarlyView.
Critical mineral scarcity, high embodied carbon, and persistent pollution from materials processing intensify the need for sustainable materials design. This review frames the problem as multi‐objective optimization under heterogeneous, high‐dimensional evidence and highlights multi‐modal AI as an enabling pathway.
Tianyi Xu, Tianshuo Wei, Yan Ge, Bo Peng, Yue Li, Maolin Wang, Peng Wen, Chao Yang, Ye Wei   +8 more
wiley   +1 more source

Correction to: Towards Multi-Brain Decoding in Autism: A Self-Supervised Learning Approach. [PDF]

Neuroinformatics
Ranjbaran G, Moreau Q, Dubois A, Dumas G.   +3 more
europepmc   +1 more source

Photonic‐Enabled Energy‐Efficient Transparent Neuromorphic Computing Devices: A Review

Advanced Science, EarlyView.
Transparent photonic neuromorphic computing devices merge optics and brain‐inspired computing to overcome von Neumann bottlenecks with ultrafast, low‐energy processing. By exploiting transparent oxides, 2D materials, phase‐change materials, and hybrid heterostructures, these platforms enable photonic synapses, memory, and logic for see‐through edge ...
Shuvaraj Ghosh, Ki‐bum Lee, Junghyeon Lee, Seunghee Cho, Malkeshkumar Patel, Hangfei Li, Yu Wen, Ye Zhou, Joondong Kim   +8 more
wiley   +1 more source

Joint Supervised and Self-supervised Learning for MRI Reconstruction [PDF]

Magnetic Resonance Imaging (MRI) represents an important diagnostic modality; however, its inherently slow acquisition process poses challenges in obtaining fully-sampled $k$-space data under motion.
Sonke, Jan-Jakob, Yiasemis, George, Moriakov, Nikita, Teuwen, Jonas, Sánchez, Clara I.   +4 more
core  

AET5: A transcriptome-guided molecular generation framework with contrastive self-supervised learning. [PDF]

PLoS Comput Biol
Yuan Z, Zhang X, Lin G, Zou Q, Swain S, Ding Y, Tiwari P, Yuan S, Guo X.   +8 more
europepmc   +1 more source