Boltzmann Encoded Adversarial Machines
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Re: Boltzmann Encoded Adversarial Machines
#2Very interesting.
At a high level (ignoring many details) the main idea is to replace generator networks in GANs with Restricted Boltzman Machines, or RBMs, which are easier to train (more stable). The authors call this kind of architecture "Boltzmann Encoded Adversarial Machines," or BEAM for short.
The experiments provide persuasive evidence that BEAMs outperform GANs. Figure 3, in particular, I find very persuasive -- it compares the ability of different architectures to learn to generate low-dimensional mixtures of Gaussians, with BEAMs very clearly outperforming GANs. The results in higher-dimensional applications such as image generation also suggest that BEAMs outperform GANs, but the improvement is somewhat more subjective due to the nature of high-dimensional data. Obviously, these results need to be replicated by others.
It looks promising to me. That said, it's been years since I've touched an RBM -- I only have a vague recollection of how they work and how they're trained, layer by layer, as proposed by Hinton in 2006 or so. Time to re-read old papers!
Re: Boltzmann Encoded Adversarial Machines
#3Just what the AI/ML crowd needs in the midst of burgeoning fear of AI: a new technology with "Adversarial Machines" right there in the name.
Re: Boltzmann Encoded Adversarial Machines
#4Can someone explain the basic implications of this against current GANs and also provide a practical ML application?
Re: Boltzmann Encoded Adversarial Machines
#5Very interesting. At a high level (ignoring many details) the main idea is to replace generator networks in GANs with Restricted Boltzman Machines, or RBMs, which are easier to train (more stable). The authors call this kind of architecture "Boltzmann Encoded Adversarial Machines," or BEAM for short. The experiments provide persuasive evidence that BEAMs outperform GANs. Figure 3, in particular, I find very persuasiv…
To clarify: in the case of a BEAM both the generator and all but the top layer of the discriminator is replaced with an RBM. The adversary in this case operates on features encoded by the RBM, not raw data samples. Secondly the RBM is trained with a combined loss involving log-likelihood and the adversarial term.
Re: Boltzmann Encoded Adversarial Machines
#6It's amazing to see deep learning blast through all the benchmarks, for example in computer vision, over the last couple of years. At the same time something starts to feel off about having all these single-use asymmetric feedforward networks solving their own little task. Being trained in one direction, then used in the other, then thrown away. Maybe being chained together for a more complex task, but that seems to be about it for the average (real-world application) use case of deep learning nets.
I'm sure there's plenty of interesting work being done in ML to improve on this situation and come up with new architectures. Yet I was moderately surprised when I rediscovered Boltzmann machines recently, and found not much work seemed to be going on there at all (very little at NIPS 2017 for example?).
This BEAM seems intriguing, here's hoping it opens the door to a better understanding and modeling of our world.
Re: Boltzmann Encoded Adversarial Machines
#7Very interesting. At a high level (ignoring many details) the main idea is to replace generator networks in GANs with Restricted Boltzman Machines, or RBMs, which are easier to train (more stable). The authors call this kind of architecture "Boltzmann Encoded Adversarial Machines," or BEAM for short. The experiments provide persuasive evidence that BEAMs outperform GANs. Figure 3, in particular, I find very persuasiv…
To clarify: in the case of a BEAM both the generator and all but the top layer of the discriminator is replaced with an RBM. The adversary in this case operates on features encoded by the RBM, not raw data samples. Secondly the RBM is trained with a combined loss involving log-likelihood and the adversarial term.
Yes. For simplicity's and brevity's sake, I ignored many important details in my summary.
Re: Boltzmann Encoded Adversarial Machines
#8It's amazing to see deep learning blast through all the benchmarks, for example in computer vision, over the last couple of years. At the same time something starts to feel off about having all these single-use asymmetric feedforward networks solving their own little task. Being trained in one direction, then used in the other, then thrown away. Maybe being chained together for a more complex task, but that seems to…
RBMs went out of fashion after 2010-2011, as other architectures worked better than them in almost all of the tasks in vision.
Re: Boltzmann Encoded Adversarial Machines
#9Can someone explain the basic implications of this against current GANs and also provide a practical ML application?
I can try. (I am a coauthor of this paper)
First off, Unlearn.ai is a startup working to build new tools that make precision medicine a reality. We needed to be able to build generative models which allow us to
1. model multimodal data easily (consider medical datasets with categorical data, binary, and continuous, with various bounds etc. all mixed together)
2. be able to answer counterfactual questions about data (for example if I down regulate a gene how does this effect the rest of the gene expression?)
3. be able to build models which handle time-series data (give me a likely progression of this person's cognitive scores given their current scores and other indicators)
RBMs are natural candidates for models which handle these kind of issues quite well. 1. Although people have done work trying to get GANs to work well with multimodal data, it's pretty kludgy. 2. GANs do not provide a means of inference (contrast VAEs which can satisfy this demand). 3. We have built a solid extension of RBMs to temporal models which work quite well.
However, as explained in this paper, stock RBMs have significant training issues. This paper attempts to improve the situation.
Re: Boltzmann Encoded Adversarial Machines
#10It's amazing to see deep learning blast through all the benchmarks, for example in computer vision, over the last couple of years. At the same time something starts to feel off about having all these single-use asymmetric feedforward networks solving their own little task. Being trained in one direction, then used in the other, then thrown away. Maybe being chained together for a more complex task, but that seems to…
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