No train, all gain: Improving frozen representations with self-supervised gradients.
A central challenge in advancing deep learning-based classification and retrieval tasks is achieving robust representations without the need for extensive ...
Tag: representations
The Semantic Hub: a cognitive approach to linguistic model representations
Language models have demonstrated remarkable capabilities in processing various types of data, including multilingual text, code, mathematical expressions, images, and ...
Representations of the pre-trained basic model for discovering breathing patterns in speech
The process of human speech production involves coordinated respiratory action to obtain acoustic speech signals. Typically, speech is produced by ...
International Conference on Learning Representations (ICLR) 2024
Apple is sponsoring the International Conference on Learning Representations (ICLR), which will take place in person from May 7 to ...
Investigation of salient representations and label variation models in dimensional analysis of speech emotions.
Model representations such as Bidirectional Encoder Representations of Transformers (BERT) and Hidden Units BERT (HuBERT) have helped achieve state-of-the-art performance ...
Google and MIT researchers present Synclr: a novel AI approach to learning visual representations exclusively from synthetic images and synthetic captions without real data
Raw and often unlabeled data can be retrieved and organized using representation learning. The ability of the model to develop ...
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