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Partial Federated Learning
Federated Learning (FL) is a popular algorithm to train machine learning models on user data constrained to edge devices (for example, mobile phones) due to privacy concerns.
Coordinated Replay Sample Selection for Continual Federated Learning
Continual Federated Learning (CFL) combines Federated Learning (FL), the decentralized learning of a central model on a number of client devices that may not communicate their data, and Continual Learning (CL), the learning of a model from a continual stream of data without keeping the entire history.
End-to-end spoken language understanding using joint CTC loss and self-supervised, pretrained acoustic encoders
It is challenging to extract semantic meanings directly from audio signals in spoken language understanding (SLU), due to the lack of textual information.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
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Federated Learning with Noisy User Feedback
Federated learning (FL) has recently emerged as a method for training ML models on edge devices using sensitive user data and is seen as a way to mitigate concerns over data privacy.
Training Mixed-Domain Translation Models via Federated Learning
Training mixed-domain translation models is a complex task that demands tailored architectures and costly data preparation techniques.
Learnings from Federated Learning in the Real world
In this study, we evaluate the impact of such idiosyncrasies on Natural Language Understanding (NLU) models trained using FL.
Encoding Syntactic Knowledge in Transformer Encoder for Intent Detection and Slot Filling
We propose a novel Transformer encoder-based architecture with syntactical knowledge encoded for intent detection and slot filling.
