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Found 8 papers, 2 papers with code
Perception of Misalignment States for Sky Survey Telescopes with the Digital Twin and the Deep Neural Networks
To address this, we need a method to obtain misalignment states, aiding in the reconstruction of accurate point spread functions for data processing methods or facilitating adjustments of optical components for improved image quality.
Relaxed Attention for Transformer Models
The powerful modeling capabilities of all-attention-based transformer architectures often cause overfitting and - for natural language processing tasks - lead to an implicitly learned internal language model in the autoregressive transformer decoder complicating the integration of external language models.
Ranked #3 on
Lipreading
on LRS3-TED
(using extra training data)
Federated Split BERT for Heterogeneous Text Classification
To address this issue, we propose a framework, FedSplitBERT, which handles heterogeneous data and decreases the communication cost by splitting the BERT encoder layers into local part and global part.
Bandwidth-Scalable Fully Mask-Based Deep FCRN Acoustic Echo Cancellation and Postfiltering
Although today's speech communication systems support various bandwidths from narrowband to super-wideband and beyond, state-of-the art DNN methods for acoustic echo cancellation (AEC) are lacking modularity and bandwidth scalability.
Relaxed Attention: A Simple Method to Boost Performance of End-to-End Automatic Speech Recognition
Recently, attention-based encoder-decoder (AED) models have shown high performance for end-to-end automatic speech recognition (ASR) across several tasks.
Ranked #7 on
Speech Recognition
on WSJ eval92
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Multi-Encoder Learning and Stream Fusion for Transformer-Based End-to-End Automatic Speech Recognition
Stream fusion, also known as system combination, is a common technique in automatic speech recognition for traditional hybrid hidden Markov model approaches, yet mostly unexplored for modern deep neural network end-to-end model architectures.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Point Spread Function Estimation for Wide Field Small Aperture Telescopes with Deep Neural Networks and Calibration Data
Then we use these data to train a DNN (Tel--Net).
Point Spread Function Modelling for Wide Field Small Aperture Telescopes with a Denoising Autoencoder
For wide field small aperture telescopes, the point spread function is hard to model, because it is affected by many different effects and has strong temporal and spatial variations.
