Abstract: In modern times, Deep Neural Networks (DNNs) become essential technology in advance wireless communication systems for automatic signal recognition and intelligent spectrum management. The ...
Abstract: Image processing is one of the most promising applications for quantum machine learning. Quanvolutional neural networks with nontrainable parameters are the preferred solution to run on ...
A multivariate analysis of electroencephalography activity reveals super-additive enhancements to the neural encoding of audiovisual stimuli, providing new insights into how the brain integrates ...
Neural encoding is the study of how neurons represent information with electrical activity (action potentials) at the level of individual cells or in networks of neurons. Studies of neural encoding ...
To mark the launch of our BBC micro:bit - the next gen campaign, we've put together a coding quiz to test your knowledge. See how you score on the eight questions below that are based around the ...
Next-generation sequencing refers to non-Sanger-based high-throughput DNA sequencing technologies. Millions or billions of DNA strands can be sequenced in parallel, yielding substantially more ...
A comprehensive LoRA (Low-Rank Adaptation) fine-tuning system built with Unsloth for sentiment analysis tasks. Supports efficient fine-tuning of Llama-2/Llama-3 models with complete training, ...
GGUF quantization implements Post-Training Quantization (PTQ): given an already-trained Llama-like model in high precision, it reduces the bit width of each individual weight. The resulting checkpoint ...
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