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 ...

Data science platform Kaggle is hosting a Wikipedia dataset that’s specifically optimized for machine learning applications.

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 ...

In the digital era, Chirag Maheshwari, a researcher at the forefront of machine learning advancements, explores the evolution ...

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

Researchers developed a machine-learning-guided technique to solve complex, long-horizon planning problems more efficiently than some traditional approaches, while arriving at an optimal solution that ...

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 ...

“To truly evaluate and solve A.I.’s black-box paradox, we need to understand it on a psychological and architectural level—how it perceives, decides and why,” Hidenori Tanaka, PAI’s group leader, told ...

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