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Found 5 papers, 2 papers with code
USA-Net: Unified Semantic and Affordance Representations for Robot Memory
In order for robots to follow open-ended instructions like "go open the brown cabinet over the sink", they require an understanding of both the scene geometry and the semantics of their environment.
HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Units
Self-supervised approaches for speech representation learning are challenged by three unique problems: (1) there are multiple sound units in each input utterance, (2) there is no lexicon of input sound units during the pre-training phase, and (3) sound units have variable lengths with no explicit segmentation.
Ranked #4 on
Speech Recognition
on LibriSpeech test-other
On the Influence of Masking Policies in Intermediate Pre-training
Current NLP models are predominantly trained through a two-stage "pre-train then fine-tune" pipeline.
Generative Spoken Language Modeling from Raw Audio
We introduce Generative Spoken Language Modeling, the task of learning the acoustic and linguistic characteristics of a language from raw audio (no text, no labels), and a set of metrics to automatically evaluate the learned representations at acoustic and linguistic levels for both encoding and generation.
Ranked #1 on
Resynthesis
on LibriSpeech
Studying Strategically: Learning to Mask for Closed-book QA
Thus, our policy packs task-relevant knowledge into the parameters of a language model.
