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    Professor Retweets Critique of Diffusion Models

    Kosta Derpanis shares a post questioning efficiency of diffusion and flow matching methods.

    KD
    KK
    2 Sources, 26d ago, first seen 26d ago

    TLDR

    Kosta Derpanis, Associate Professor at York University with ties to Samsung AI and the Vector Institute, retweeted a post by @KL_Div. The post states that diffusion and flow matching represent a standard generative AI approach yet remain slow, sample inefficient, and complicated. It asks whether such methods are truly required. The retweet forms part of visible AI-related conversation on the platform, with no further details or responses confirmed in the supplied evidence.

    Combined views

    32

    2 Sources, first seen 26d ago

    Combined views

    32

    2 Sources, first seen 26d ago

    46 reposts
    46 reposts

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    2 Sources

    @CSProfKGDRT @KL_Div: Diffusion/flow matching is a go-to generative AI paradigm, but it's slow, sample inefficient and complicated. Do we really nee…
    @kastnerkyleRT @KL_Div: Diffusion/flow matching is a go-to generative AI paradigm, but it's slow, sample inefficient and complicated. Do we really nee…

    2 Sources

    @CSProfKGDRT @KL_Div: Diffusion/flow matching is a go-to generative AI paradigm, but it's slow, sample inefficient and complicated. Do we really nee…
    @kastnerkyleRT @KL_Div: Diffusion/flow matching is a go-to generative AI paradigm, but it's slow, sample inefficient and complicated. Do we really nee…