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NeurIPS 2022 Workshop Proposal Self-Supervised Learning: Theory and Practice
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NeurIPS 2021 Workshop: Self-Supervised Learning - Theory and Practice NeurIPS 2022 Workshop: Self-Supervised Learning - Theory and Practice Home Call for Papers Schedule Speakers Organizers Program Committee Accepted Papers In-person workshop at NeurIPS 2022 Date: December 3, 2022 Contact Email : sslneurips22@googlegroups.com Room: 391 (talk + posters); 393 (posters) Time : 08:15am to 5pm Central Time Schedule : here Self-supervised learning (SSL) is an unsupervised approach for representation learning without relying on human-provided labels. It creates auxiliary tasks on unlabeled input data and learns representations by solving these tasks. SSL has demonstrated great success on images (e.g., MoCo, PIRL, SimCLR, DINO, MAE), speech (e.g., CPC, HuBERT, wav2vec) and text (e.g., word2vec, BERT, RoBERTa, GPT, OPT) and has shown promising results in other data modalities, including graphs, time-series, audio, etc. On a wide variety of tasks, without...
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