Abstract: This article explores generative antenna design (GAD) and optimization using neural network (NN)-based deep learning (DL) methods. We first outline traditional antenna design and the ...

A deep reinforcement learning framework optimizes silicon-based photonic crystal fiber modulators, achieving ultra-low ...

Google says its AI tutor delivered a year of math progress in eight weeks. I asked the DeepMind researcher behind the trial ...

China unveiled a comprehensive software platform in Beijing on Monday aimed at overcoming a long-standing bottleneck in its ...

Google on Monday unveiled the most significant upgrade to its autonomous research agent capabilities since the product's debut, launching two new agents — Deep Research and Deep Research Max — that ...

“The platforms should be absolutely begging Congress to regulate them, because the alternative is they get sued into oblivion by a bunch of law firms.” Hosted by Kevin Roose and Casey Newton Produced ...

Jennifer Paloma Rafferty and I briefly overlapped at Quinnipiac University, when she was working as a learning designer and I was teaching some online courses. I always thought that Paloma was super ...

Deep learning is a subset of machine learning that uses multi-layer neural networks to find patterns in complex, unstructured data like images, text, and audio. What sets deep learning apart is its ...

Physics-aware machine learning integrates domain-specific physical knowledge into machine learning models, leading to the development of physics-informed neural networks (PINNs). PINNs embed physical ...

Abstract: The non-convexity of rate-splitting precoder design precludes the direct use of efficient convex optimization algorithms. Instead, successive convex approximation (SCA)-based methods have ...

An important but unresolved question in deep learning for EEG decoding is which features neural networks learn to solve the task. Prior interpretability studies have mainly explained individual ...