#neural networks

'AI twin generators can produce striking likenesses but often miss the human spark; they work best as creative tools for reimagining your image.'

Discover how CPUs, GPUs, NPUs, and TPUs differ in powering AI and machine learning workloads, including their strengths and ideal applications.

Scientists are leveraging AI neural networks to predict human behavior and explore the workings of the human mind, but challenges remain in interpreting these complex models.

New research reveals that generalization in flow matching models emerges from the neural network's approximation of velocity fields during early trajectories, challenging the belief that stochasticity drives this process.

Discover how AI voices have evolved from robotic tones to natural, human-like speech, transforming fields like accessibility, entertainment, and customer support.

Researchers reveal a new method using latent vector fields to understand how neural autoencoders balance memorization and generalization, enhancing interpretability without modifying models.

A novel AI framework introduces differentiable MCMC layers that enable neural networks to efficiently learn with inexact combinatorial solvers, significantly improving performance in complex optimization problems like vehicle routing.

Researchers introduce Effective State-Size (ESS), a new metric that measures actual memory utilization in sequence models, enhancing understanding and optimization of model performance.

Fudan University researchers have developed Lorsa, a sparse attention mechanism that disentangles atomic attention units hidden in transformer superposition, enhancing interpretability of large language models.