Beyond Linearity: How Neural Networks Learn to Extrapolate
New research reveals that overparameterized neural networks exhibit surprisingly different extrapolation behaviors depending on their proximity to the training data’s origin.
New research reveals that overparameterized neural networks exhibit surprisingly different extrapolation behaviors depending on their proximity to the training data’s origin.

New research demonstrates how information theory can dramatically improve the efficiency of identifying underlying systems from observational data.

A new theoretical study reveals how driving a magnetic flux through a quantum ring creates energy sidebands, linking the system’s behavior to the AC Stark effect.

New research reveals fundamental incompatibilities in how we decompose information, challenging core assumptions in fields like neuroscience and machine learning.
Combining the power of ground-based telescopes with space-based technology could unlock the potential to directly image habitable worlds around distant stars.

New research reveals how even perfectly ordered quantum systems can generate surprising levels of complexity, challenging traditional notions of simplicity in many-body localization.

As gravitational wave astronomy matures, scientists are now coordinating efforts to confirm and characterize signals from merging supermassive black holes detected through pulsar timing arrays.

A new search for radio emissions from two exoplanets known for their potential to interact with their host stars has yielded no detectable signals.

A new analysis of particle dynamics reveals that quantum tunneling isn’t the instantaneous process previously thought, as particles can momentarily stall within potential barriers.
Researchers have developed a method for efficiently extracting underlying features from complex quantum or classical superpositions using Fourier analysis.