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Found 9 papers, 0 papers with code
i-Code V2: An Autoregressive Generation Framework over Vision, Language, and Speech Data
The convergence of text, visual, and audio data is a key step towards human-like artificial intelligence, however the current Vision-Language-Speech landscape is dominated by encoder-only models which lack generative abilities.
i-Code: An Integrative and Composable Multimodal Learning Framework
Human intelligence is multimodal; we integrate visual, linguistic, and acoustic signals to maintain a holistic worldview.
Sequence-level self-learning with multiple hypotheses
For untranscribed speech data, the hypothesis from an ASR system must be used as a label.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
Building a great multi-lingual teacher with sparsely-gated mixture of experts for speech recognition
The sparsely-gated Mixture of Experts (MoE) can magnify a network capacity with a little computational complexity.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Dynamic Gradient Aggregation for Federated Domain Adaptation
The proposed scheme is based on a weighted gradient aggregation using two-step optimization to offer a flexible training pipeline.
Federated Transfer Learning with Dynamic Gradient Aggregation
The target scenario is Acoustic Model training based on this platform.
TED: A Pretrained Unsupervised Summarization Model with Theme Modeling and Denoising
Text summarization aims to extract essential information from a piece of text and transform the text into a concise version.
Leveraging Lead Bias for Zero-shot Abstractive News Summarization
A typical journalistic convention in news articles is to deliver the most salient information in the beginning, also known as the lead bias.
Make Lead Bias in Your Favor: A Simple and Effective Method for News Summarization
For example, the pretrained model without finetuning outperforms pointer-generator network on CNN/DailyMail dataset.
