For the finetuning stage, we partition the 325,000 even experimental samples into training and validation sets (‘Sycamore data’ in Methods). This procedure allows us to train a decoder to high ...
Quantum low-density parity-check codes are a promising candidate for fault-tolerant quantum computing with considerably reduced overhead compared to the surface code. However, the lack of a practical ...
Last time, we looked into using a logic analyzer to decode SPI signals of LCD displays, which can help us reuse LCD screens from proprietary systems, or port LCD driver code from one platform to ...
Restoring some language for aphasia sufferers, like Bruce Willis and a million other Americans, could involve AI. Brain activity like this, measured in an fMRI machine, can be used to train a brain ...
HLS methodology allows the hardware design to be completed at a higher level of abstraction such as C/C++ algorithmic description. This provides significant time and cost savings, and paves the way ...
The work relies in part on a transformer model, similar to the ones that power ChatGPT. Alex Huth (left), Shailee Jain (center) and Jerry Tang (right) prepare to collect brain activity data in the ...
University of California, Davis researchers have developed a brain-computer interface (BCI) that enables computer cursor control and clicking, using neural signals from the speech motor cortex. One ...
Some results have been hidden because they may be inaccessible to you
Show inaccessible results