ClaimsYann LeCun

“training with the energy loss reduces to an easily solvable least-squares minimization problem, which is convex”

LeCun et al., tutorial authors 2006 A Tutorial on Energy-Based Learning (yann.lecun.com) archived copy

Factual AI capabilities

With a linear parameterization, training with the energy loss reduces to a convex least-squares problem.

Not researched Checkable, but outside the sample that was researched.

Factual: Statements about the present or the past, checked against the record: did they hold up?

Cite this claim

Yann LeCun, “With a linear parameterization, training with the energy loss reduces to a convex least-squares problem.” (said 2006). Verdict: not researched. Reliability API, claim c_ea8ddd6aa8b0, method 0.1.0+c876030c, dataset release v1.1.2. https://epistacklabs.com/claims/c_ea8ddd6aa8b0

This claim is in dataset release v1.1.2, with the same verdict.

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More factual claims by Yann LeCun

  1. Mostly true Factual 18 March 2024
    “For starters, transformers are equivariant to permutations.”

    Transformers are equivariant to permutations, as ConvNets are equivariant to translations.

  2. Mostly true Factual 14 March 2024
    “If it were the case, we would have AI systems that could teach themselves to drive a car in 20 hours of practice, like any 17 year-old.”

    There are currently no AI systems that can teach themselves to drive a car in about 20 hours of practice, as a 17-year-old can.

  3. Mostly true Factual 8 March 2024
    “The architecture is good, the architecture of the encoder is good, but the fact that you train the system to reconstruct images does not lead it to produce to long, good generic features of images.”

    Training an encoder to reconstruct images does not lead it to produce good generic image features, though the encoder architecture is good.

  4. Mostly true Factual 8 March 2024
    “LLMs can do none of those or they can only do them in a very primitive way and they don’t really understand the physical world.”

    Current LLMs can do none of understanding the physical world, persistent memory, reasoning and planning, or only in a very primitive way.

  5. Mostly true Factual 8 March 2024
    “No, there’s one thing that autoregressive LLMs or that LLMs in general, not just the autoregressive one, but including the bird style bidirectional ones, are exploiting and its self supervised…”

    LLMs, including BERT-style bidirectional ones, exploit self-supervised learning, and LeCun has been a very strong advocate of self-supervised learning for many years.

All of them

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