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Found 7 papers, 5 papers with code
Theory, Analysis, and Best Practices for Sigmoid Self-Attention
Attention is a key part of the transformer architecture.
dMel: Speech Tokenization made Simple
Large language models have revolutionized natural language processing by leveraging self-supervised pretraining on vast textual data.
Denoising LM: Pushing the Limits of Error Correction Models for Speech Recognition
Language models (LMs) have long been used to improve results of automatic speech recognition (ASR) systems, but they are unaware of the errors that ASR systems make.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
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Modulating human brain responses via optimal natural image selection and synthetic image generation
Furthermore, aTLfaces and FBA1 had higher activation in response to maximal synthetic images compared to maximal natural images.
Decoding natural image stimuli from fMRI data with a surface-based convolutional network
In this work, we propose a novel approach for this task, which we call Cortex2Image, to decode visual stimuli with high semantic fidelity and rich fine-grained detail.
Personalized visual encoding model construction with small data
Our approach shows the potential to use previously collected, deeply sampled data to efficiently create accurate, personalized encoding models and, subsequently, personalized optimal synthetic images for new individuals scanned under different experimental conditions.
NeuroGen: activation optimized image synthesis for discovery neuroscience
NeuroGen combines an fMRI-trained neural encoding model of human vision with a deep generative network to synthesize images predicted to achieve a target pattern of macro-scale brain activation.
