Practical implementation of an astoundingly simple method for self-supervised learning that achieves a new state of the art (surpassing SimCLR) without contrastive learning and having to designate ...
In medical image analysis, acquiring large-scale labeled datasets remains challenging, and images often exhibit high overall similarity that requires expert-level interpretation, differing ...
Department of Chemistry, New York University, New York, New York 10003, United States Department of Chemistry, New York University, New York, New York 10003, United States Simons Center for ...
In this study, the authors proposed a pain identification model using facial expressions. An image extraction technique was developed using the liquid neural network to extract diverse images from the ...
Propose: Contrast-enhanced ultrasound has shown great promises for diagnosis and monitoring in a wide range of clinical conditions. Meanwhile, to obtain accurate and effective location of lesion in ...
The organization of the remaining part is given as follows, Section 2 introduces the preliminary for spiking neural networks. The characteristics and difficulties of the SNN are also analyzed in ...
Metagenomic binning is the step in building metagenome-assembled genomes (MAGs) when sequences predicted to originate from the same genome are automatically grouped together. The most widely-used ...
Yoshua Bengio, Yann LeCun, and Geoffrey Hinton are recipients of the 2018 ACM A.M. Turing Award for breakthroughs that have made deep neural networks a critical component of computing. Research on ...
This paper presents TorchANI, a PyTorch-based program for training/inference of ANI (ANAKIN-ME) deep learning models to obtain potential energy surfaces and other physical properties of molecular ...
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