The authors introduce a new algorithm for determining the minimal anisotropic Gaussian noise required to satisfy mutual ...

Many connections to other well-known techniques from machine learning and statistics are discussed, including support-vector machines, neural networks, splines, regularization networks, relevance ...

Researchers have developed a machine-learning workflow to optimize the output force of photo-actuated organic crystals. Using ...

A research group has developed an innovative machine learning technology that enables predictions beyond the distribution of ...

The researchers say machine learning gives them more freedom to study the data. Older methods expect things to follow a ...

A team of Lehigh University researchers has successfully predicted abnormal grain growth in simulated polycrystalline ...

New research reveals that individual neurons follow multiple learning rules simultaneously, challenging the long-held belief that synaptic plasticity operates uniformly throughout the brain.

“To truly evaluate and solve A.I.’s black-box paradox, we need to understand it on a psychological and architectural ...

The digital era has witnessed unprecedented technological advancements, with artificial intelligence emerging as one of the ...

By categorizing and filtering user input, you can better focus on driving AI improvement. This iterative process—blending automation with human review—ensures AI learns from high-quality data, leading ...

The heyday of video stores may be long over, but more people are pushing back against algorithm-driven culture in different ...

Then, we employ supervised regularization to minimize intra-class distances and maximize inter-class distances in clean samples and unsupervised regularization to assess the similarity between ...