Fun times with energy-based models
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Fun times with energy-based models
1–10 of 16 posts
Re: Fun times with energy-based models
#2I think the rationale for using tricks like score matching and contrastive divergence deserves a mention: the partition function is computationally expensive.
Since we're on the subject, what are EBMs good for today?
Re: Fun times with energy-based models
#3I think the rationale for using tricks like score matching and contrastive divergence deserves a mention: the partition function is computationally expensive. Since we're on the subject, what are EBMs good for today?
This paper lists the benefits in the introduction: https://proceedings.neurips.cc/paper_files/paper/2019/file/3...
- Simplicity and Stability: An EBM is the only object that needs to be trained and designed. Separate networks are not tuned to ensure balance.
- Sharing of Statistical Strength: Since the EBM is the only trained object, it requires fewer model parameters than approaches that use multiple networks.
- Adaptive Computation Time: Implicit sample generation is an iterative stochastic optimization process, which allows for a trade-off between generation quality and computation time.
- VAEs and flow-based models are bound by the manifold structure of the prior distribution and consequently have issues modelling discontinuous data manifolds, often assigning probability mass to areas unwarranted by the data. EBMs avoid this issue by directly modelling particular regions as high or lower energy.
- Compositionality: If we think of energy functions as costs for a certain goals or constraints, summation of two or more energies corresponds to satisfying all their goals or constraints.
Re: Fun times with energy-based models
#4I think the rationale for using tricks like score matching and contrastive divergence deserves a mention: the partition function is computationally expensive. Since we're on the subject, what are EBMs good for today?
EBMs show up all over the place, apparently even your classifier is an EBM :) (https://arxiv.org/abs/1912.03263).
Re: Fun times with energy-based models
#5I think the rationale for using tricks like score matching and contrastive divergence deserves a mention: the partition function is computationally expensive. Since we're on the subject, what are EBMs good for today?
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Re: Fun times with energy-based models
#6I think the rationale for using tricks like score matching and contrastive divergence deserves a mention: the partition function is computationally expensive. Since we're on the subject, what are EBMs good for today?
They're good for reinforcement learning. E.g. Cicero uses piKL which samples according to
p ∝ anchor_policy * exp(utility / temperature)
The utility is exactly the same as "energy". The article ignores entropy, but you can add in entropy regularization e.g. in soft actor-critic.
Re: Fun times with energy-based models
#7I think the rationale for using tricks like score matching and contrastive divergence deserves a mention: the partition function is computationally expensive. Since we're on the subject, what are EBMs good for today?
This paper lists the benefits in the introduction: https://proceedings.neurips.cc/paper_files/paper/2019/file/3... - Simplicity and Stability: An EBM is the only object that needs to be trained and designed. Separate networks are not tuned to ensure balance. - Sharing of Statistical Strength: Since the EBM is the only trained object, it requires fewer model parameters than approaches that use multiple networks. - Ada…
As far as I can tell, flow-based models are bound by the exact same requirements as energy based models (flow = diffusion/normalizing flow/flow-matching models). But they're absolutely right about VAEs. Those are a memetic virus that need to die off in favor of more theoretically grounded encoders.
Re: Fun times with energy-based models
#8I think the rationale for using tricks like score matching and contrastive divergence deserves a mention: the partition function is computationally expensive. Since we're on the subject, what are EBMs good for today?
Also: you are free to model p(x) without worrying about normalization, something that would be required to maximize likelihood.
Re: Fun times with energy-based models
#9If the author is reading: In the proof, at the end of Step 6, it's confusing that the "uv" term of the integration by parts is suddenly given a range from -∞ to ∞, as if we had previously assumed x ∈ ℝ. But elsewhere in the article, including the examples, we have higher-dimensional x's. I suggest to either 1) include the full multidimensional version from the paper, or 2) explicitly mention that this is the simple 1d case, the same result holds in ℝ^n, and refer to the paper.
Re: Fun times with energy-based models
#10We are working on a startup that is revisiting the math that underlies EBMs. If you want to work with us or invest, check out these links