Scientists have developed a geometric deep learning method that can create a coherent picture of neuronal population activity during cognitive and motor tasks across experimental subjects and ...

Leveraging these estimated distributions as priors, the generalized-Gaussian cyclic variational autoencoder is then developed to infer domain-shared representations. The process is steered by a ...

and eventually leads to severely entangled feature representation. Motivated by this critical issue, this paper innovatively propose the FDTs relying on two-fold ideas: 1) Building a Semantic Instance ...

By introducing vascular structure as a prior constraint and constructing auxiliary information, the network achieves disentangled representation learning, effectively minimizing the interference of ...

In January 2012 I became Director of the Centre for Vision, Speech and Signal Processing (CVSSP ... CVSSP research spans audio and video processing, computer vision, machine learning, spatial audio, ...

Astronomers say they've detected a mysterious type of signal known as a fast radio burst coming from an ancient, dead galaxy billions of light years away. Figuratively speaking, it makes for one ...

AAAI 2024 Papers: Explore a comprehensive collection of innovative research papers presented at one of the premier artificial intelligence conferences. Seamlessly integrate code implementations for ...

Nature Research Intelligence Topics enable transformational understanding and discovery in research by categorising any document into meaningful, accessible topics. Read this blog to understand ...

Researchers say that recent predictions involving locations where intelligent aliens might be likeliest to intercept signals from Earth remain accurate, providing potential forecasts for when Earth ...

There is no way we can get rid of the proportional representation (PR) system, he continued. Proportionality is one of our great achievements. Our democracy is an inclusive democracy. There can be no ...

The resolution of these issues is crucial for constructing models of depression detection. Methods: In this paper, we propose a multi-task representation learning based on vision and audio for ...