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 ...

Abstract: A growing amount of available data and computational power makes training neural networks over a network of devices, and distribution optimization in general, more realizable. As a ...

Abstract: In Deep Image Prior (DIP), a Convolutional Neural Network (CNN) is fitted to map a latent space to a degraded (e.g. noisy) image but in the process learns to reconstruct the clean image.

Instead of using RoPE’s low-dimensional limited rotations or ALiBi’s 1D linear bias, FEG builds position encoding on a higher-dimensional geometric structure. The idea is simple at a high level: Treat ...

The current Conformer implementation in Torchaudio is missing the relative sinusoidal positional encoding scheme that is a key component of the original Conformer architecture as described in the ...

Rotary Positional Embeddings (RoPE) is an advanced approach in artificial intelligence that enhances positional encoding in transformer models, especially for sequential data like language.