Spiking neural networks (SNNs) offer a promising avenue to implement dee...
Endoscopic surgery is currently an important treatment method in the fie...
Large pre-trained language models (PLMs) have proven to be a crucial
com...
Existing studies have demonstrated that adversarial examples can be dire...
Recent studies have shown that deep neural networks are vulnerable to
in...
A sequence-to-sequence learning with neural networks has empirically pro...
Recently, few certified defense methods have been developed to provably
...
A popular strategy to train recurrent neural networks (RNNs), known as
“...
It is still a challenging task to learn a neural text generation model u...
Various robustness evaluation methodologies from different perspectives ...
We present an unsupervised word segmentation model, in which the learnin...
Deep neural network models are vulnerable to adversarial attacks. In man...
We present RepRank, an unsupervised graph-based ranking model for extrac...
One of the major challenges in coreference resolution is how to make use...
Despite neural networks have achieved prominent performance on many natu...
Many large-scale knowledge graphs are now available and ready to provide...
Inspired by a concept of content-addressable retrieval from cognitive
sc...