Learning useful visual features and predictive representations.
LeCun studies how machines can learn useful representations from the structure of the world. This selection emphasizes self-supervised visual learning and predictive architectures that learn in a representation space, including joint-embedding predictive architectures.
10 papers
Selected research in representation learning. Read the full paper, including the methods, experiments, and reported results.
Paper & contextSelected research in representation learning. Read the full paper, including the methods, experiments, and reported results.
Paper & contextSelected research in representation learning. Read the full paper, including the methods, experiments, and reported results.
Paper & contextSelected research in representation learning. Read the full paper, including the methods, experiments, and reported results.
Paper & contextSelected research in representation learning. Read the full paper, including the methods, experiments, and reported results.
Paper & contextSelected research in representation learning. Read the full paper, including the methods, experiments, and reported results.
Paper & contextSelected research in representation learning. Read the full paper, including the methods, experiments, and reported results.
Paper & contextSelected research in representation learning. Read the full paper, including the methods, experiments, and reported results.
Paper & contextSelected research in representation learning. Read the full paper, including the methods, experiments, and reported results.
Paper & contextSelected research in representation learning. Read the full paper, including the methods, experiments, and reported results.
Paper & contextAn independent editorial profile. Inclusion does not imply Council membership or endorsement. Research is collaborative; coauthorship does not imply sole credit.
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