Why Deep Learning Works II: the Renormalization Group

Deep Learning is amazing.  But why is Deep Learning so successful?  Is Deep Learning just old-school Neural Networks on modern hardware?  Is it just that we have so much data now the methods work better?  Is Deep Learning just a really good at finding features. Researchers are working hard to sort this out.

Recently it has been shown that [1]

Unsupervised Deep Learning implements the Kadanoff Real Space Variational Renormalization Group (1975)

This means the success of Deep Learning is intimately related to some very deep and subtle ideas from Theoretical Physics.  In this post we examine this.

Unsupervised Deep Learning: AutoEncoder Flow Map

An AutoEncoder is a Unsupervised Deep Learning algorithm that learns how to represent an complex image or other data structure X .   There are several kinds of AutoEncoders; we care about so-called Neural Encoders–those using Deep Learning techniques to reconstruct the data:


The simplest Neural Encoder is a Restricted Boltzman Machine (RBM).  An RBM is non-linear, recursive, lossy function f(X) that maps the data X from visible nodes {v}  into hidden nodes {h} :


The RBM is learned by selecting the optimal parameters {b_{v}},{c_{h}},{w_{v,h}} that minimize some measure of the reconstruction error (see Training RBMs, below)

\min |\Vert f(X)-X\Vert

RBMs and other Deep Learning algos are formulated using classical Statistical Mechanics.  And that is where it gets interesting!

Multi Scale Feature Learning

In machine learning (ML), we map (visible) data into (hidden) features


The hidden units discover features at a coarser grain level of scale

unsupervised filters

With RBMs, when features are complex, we may stack them into a Deep Belief Network (DBM), so that we can learn at different levels of scale


and leads to multi-scale features in each layer


Deep Belief Networks are a Theory of Unsupervised MultiScale Feature Learning

Fixed Points and Flow Maps

We call f(X)  a flow map

f(X)\rightarrow X

eIf we apply the flow map to the data repeatedly, (we hope) it converges to a fixed point

\lim\limits_{n}f^{n}(f^{n-1}(\cdots(f^{1}(f^{0}(X))))\rightarrow f_{\infty}(X)

Notice that we usually expect to apply the same map each time f^{n}(x)=f(x) , however, for a computational theory we may need more flexibility.

Example: Linear Flow Map

The simplest example of a flow map is the simple linear map

X\rightarrow CX

so that

f(X)\sim CX

where C is a non-negative, low rank matrix

\mid C\mid\,\ll\,\mid X\mid

We have seen this before: this leads to a Convex form of NonNegative Matrix Factorization NMF


Convex NMF applies when we can specify the feature space and where the data naturally clusters.  Here, there are a few instances that are archetypes that define the convex hull of the data.

\{\mathbf{x}_{c}\}\in X\,,c=1,\cdots,\mid C\mid

Amazingly, many clustering problems are provably convex–but that’s a story for another post.

Example: Manifold Learning

Near a fixed point, we commonly approximate the flow map by a linear operator

f_{\infty}(X) \sim\mathbf{L}(X)

This lets us capture the structure of the true data manifold, and is usually described by the low lying eigen-spectra of


In the same spirit,  Semi & Unsupervised Manifold Learning, we model the data using a Laplacian operator \mathbf{L}(\sigma) , usually parameterized by a single scale parameter \sigma .


These methods include Spectral Clustering, Manifold Regularization , Laplacian SVM, etc.  Note that manifold learning methods, like the Manifold Tanget Classifier,  employ Contractive Auto Encoders and use several scale parameters to capture the local structure of the data manifold.

The Renormalization Group

In chemistry and physics, we frequently encounter problems that require a multi-scale description.   We need this for critical points and phase transitions, for natural crashes like earthquakes and avalanches,  for polymers and other macromolecules, for strongly correlated electronic systems, for quantum field theory, and, now, for Deep Learning.

A unifying idea across these systems is the Renormalization Group (RG) Theory.

Renormalization Group Theory is both a conceptual framework on how to think about physics on multiple scales as well as a technical & computational problem solving tool.

kenwilsonKen Wilson won the 1982 Nobel Prize in Physics for the development and application of his Momentum Space RG theory to phase transitions.

We used RG theory to model the recent BitCoin crash as a phase transition.

Wilson invented modern multi-scale modeling; the so-called Wilson Basis was an early form of Wavelets.  Wilson was also a big advocate of using supercomputers for solving problems.  Being a Nobel Laureate, he had great success promoting scientific computing.  It was thanks to him I had access to a Cray Y-MP when I was in high school because he was a professor at my undergrad, The Ohio State University.

 Here is the idea.  Consider a feature map  which transforms the data X to a different, more coarse grain scale


The RG theory requires that the Free Energy F(x) is rescaled, to reflect that

the Free Energy is both Size-Extensive and Scale Invariant near a Critical Point

This is not obvious — but it is essential to both having a conceptual understanding of complex, multi scale phenomena, and it is necessary to obtain very highly accurate numerical calculations.  In fact, being size extensive and/or size consistent is absolutely necessary for highly accurate quantum chemistry calculations of strongly correlated systems.  So it is pretty amazing but perhaps not surprising that this is necessary for large scale deep learning calculations also!

The Fundamental Renormalization Group Equation (RGE)


If we (can) apply the same map, F(x) , repeatedly, we obtain a RG recursion relation, which is the starting point for most analytic work in theoretical physics.   It is usually difficult to obtain an exact solution to the RGE (although it is illuminating when possible [20]).

Many RG formulations both approximate the exact RGE and/or only include relevant variables. To describe a multiscale system, it is essential to distinguish between these relevant and irrelevant variables.

Example: Linear Rescaling

Let’s say the feature map is a simple linear rescaling

\phi(x)=\lambda x

We can obtain a very elegant, approximate RG solution where F(x) obeys a complex (or log-periodic) power law.

\mathcal{F}(x)\sim x^{-(\alpha+i\beta)}

This behavior is thought to characterize Per-Bak style Self-Organized Criticality (SOC), which appears in many natural systems–and perhaps even in the brain itself.   Which leads to the argument that perhaps Deep Learning and Real Learning work so well because they operate like a system just near a phase transition–also known as the Sand Pile Model--operating at a state between order and chaos.

the Kadanoff Variational Renormalization Group (1975)

kadanoffLeo Kadanoff, now at the University of Chicago, invented some of the early ideas in Renormalization Group.  He is most famous for the Real Space formulation of RG, sometimes called the Block Spin approach.  He also developed an alternative approach, called the Variational Renormalization Group (VRG, 1975), which is, remarkably, what Unsupervised RBMs are implementing!

Let’s consider a traditional Neural Network–a Hopfield Associative Memory (HAM).  This is also known as an Ising model or a Spin Glass in statistical physics.

An HAM consists of only visible units; it stores memories explicitly and directly in them:


We specify the Energy — called the Hamiltonian \mathcal{H} — for the nodes.  Note that all the nodes are visible.  We write


The Hopfield model has only single B_{i} and pair-wise J_{i,j} interactions.

A general Hamiltonian might have many-body, multi-scale interactions:


The Partition Function is given as


And the Free Energy is


The idea was to mimic how our neurons were thought to store memories–although perhaps our neurons do not even do this.

Either way, Hopfield Neural Networks have many problems; most notably they may learn spurious patterns that never appeared in the training set. So they are pretty bad memories.

Hinton created the modern RBM to overcome the problems of the Hopfield model.  He used hidden units to represent the features in the data–not to memorize the data examples directly.


An RBM is specified Energy function for both the visible and hidden units


This also defines joint probability of simultaenously observing a configuration of hidden and visible spins


which is learned variationally, by minimizing the reconstruction error…or the cross entropy (KL divergence), plus some regularization (Dropout), using Greedy layer-wise unsupervised training, with the Contrastive Divergence (CD or PCD) algo, …

The specific details of an RBM Energy are not addressed by these general concepts; these details do not affect these arguments–although clearly they matter in practice !

It turns out that

Introducing Hidden Units in a Neural Network is a Scale Renormalization.  

When changing scale, we obtain an Effective Hamiltonian \tilde{\mathcal{H}}  that acts on a the new feature space (i.e the hidden units)


or, in operator form


This Effective Hamiltonian is not specified explicitly, but we know it can take the general form (of a spin funnel, actually)


The RG transform preservers the Free Energy (when properly rescaled):





Critical Trajectories and Renormalized Manifolds

The RG theory provides a way to iteratively update, or renormalize, the system Hamiltonian.  Each time we add a layer of hidden units (h1, h2, …), we have




We imagine that the flow map is attracted to a Critical Trajectory which naturally leads the algorithm to the fixed point.  At each step, when we apply another RG transform, we obtain a new, Renormalized Manifold, each one closer to the optimal data manifold.


Conceptually, the RG flow map is most useful when applied to critical phenomena–physical systems and/or simple models that undergo a phase transition.  And, as importantly, the small changes in the data should ‘wash away’ as noise and not affect the macroscopic / critical phenomena. Many systems–but not all–display this.

Where Hopfield Nets fail to be useful here, RBMs and Deep Learning systems shine.

We now show that these RG transformations are achieved by stacking RBMs and solving the RBM inference problem!

Kadanoff’s Variational Renormalization Group

As in many physics problems, we break the modeling problem into two parts:  one we know how to solve, and one we need to guess.

  1. we know the Hamiltonian at the most fine grained level of scale  \mathcal{H}(v)
  2. we seek the correlation \mathbf{V}(v,h) that couples to the next level scale

The joint Hamiltonian, or Energy function, is then given by


The Correlation V(v,h) is defined so that the partition function \mathcal{Z}  is not changed


This gives us


(Sometimes the Correlation V is called a Transfer Operator T, where V(v,h)=-T(v,h) )

We may now define a renormalized effective Hamilonian \tilde{\mathcal{H}(h)} that acts only on the hidden nodes


so that we may write


Because the partition function does not change, the Exact RGE preserves the Free Energy (up to a scale change, we we subsume into \mathcal{Z})


We generally can not solve the exact RGE–but we can try to minimize this Free Energy difference.

What Kadanoff showed, way back in 1975, is that we can accurately approximate the Exact Renormalization Group Equation by finding a lower bound using this formalism

Deep learning appears to be a real-space variational RG technique, specifically applicable to very complex, inhomogenous systems where the detailed scale transformations have to be learned from the data

RBMs expressed using Variational RG

We will now show how to express RBMs using the VRG formalism and provide some intuition

In an RBM, we simply want to learn the Energy function directly; we don’t specify the Hamiltonian for the visible or hidden units explicitly, like we would in physics.  The RBM Energy is just


We identify the Hamiltonian for the hidden units as the Renormalized Effective Hamiltonian from the VRG theory


RBM Hamiltonians / Marginal Probabilities

To obtain RBM Hamiltonians for just the visible \mathcal{H}^{RBM}(v) or hidden \mathcal{H}^{RBM}(h) nodes, we need to integrate out the other nodes; that is, we need to find the marginal probabilities.

P(v)=\sum\limits_{h}P(v,h)=\dfrac{e^{-\mathcal{H}^{RBM}(v) }}{\mathcal{Z}}=\dfrac{1}{\mathcal{Z}} \sum\limits_{h}e^{-\mathbf{E(v,h)}}



P(h)=\sum\limits_{v}P(v,h)=\dfrac{e^{-\mathcal{H}^{RBM}(h) }}{\mathcal{Z}}=\sum\limits_{v}\dfrac{e^{-\mathbf{E(v,h)}}}{\mathcal{Z}}


Training RBMs

To train an RBM, we apply Contrastive Divergence (CD), or, perhaps today, Persistent Contrastive Divergence (PCD).  We can kindof think of this as slowly approximating


In practice, however, RBM training minimizes the associated Free Energy difference \Delta\mathbf{F} … or something akin to this…to avoid overfitting.

In the “Practical Guide to Training Restricted Boltzmann Machines”, Hinton explains how to train an RBM (circa 2011).  Section 6 addresses “Monitoring the overfitting”

“it is possible to directly monitor the overfitting by comparing the free energies of training data and held out validation data…If the model is not overfitting at all, the average free energy should be about the same on training and validation data”

Other Objective Functions

Modern variants of Real Space VRG are not  “‘forced’ to minimize the global free energy” and have attempted other approaches such as Tensor-SVD Renormalization.  Likeswise, some RBM / DBM approaches do likewise may minimize a different objective.

In some methods, we minimize the KL Divergence; this has a very natural analog in VRG language [1].

Why Deep Learning Works: Lessons from Theoretical Physics

The Renormalization Group Theory provides new insights as to why Deep Learning works so amazingly well.  It is not, however, a complete theory. Rather, it is framework for beginning to understand what is an incredibly powerful, modern, applied tool.  Enjoy!


[1] An exact mapping between the Variational Renormalization Group and Deep Learning, 2014

[2] Variational Approximations for Renormalization Group Transformations, 1976

[3]  A Common Logic to Seeing Cats and Cosmos


[5] A Fundamental Theory to Model the Mind

[6] A Practical Guide to Training Restricted Boltzmann Machines, 2010

[7] On the importance of initialization and momentum in deep learning, 2013

[8] Dropout: A Simple Way to Prevent Neural Networks from Overfitting, 2014

[9] Ken Wilson, A Scientific Appreciation


[11] Training Products of Experts by Minimizing Contrastive Divergence, 2002

[12] Training Restricted Boltzmann Machines using Approximations to the Likelihood Gradient, 2008



[15] Scaling, universality, and renormalization: Three pillars of modern critical phenomena

[16] The Potential Energy of an Autoencoder, 2014

[17]  Contractive Auto-Encoders: Explicit Invariance During Feature Extraction, 2011

[18] Stacked Denoising Autoencoders: Learning Useful Representations in a Deep Network with a Local Denoising Criterion, 2010

[19] Quora:  What is Renormalization group theory?

[20] Renormalization group and critical localization, 1977


  1. (Let me prephrase that I am not sure if LaTeX / MathJax works in the comment section; I am just going to type it out so that you might copy-paste the equations into your post, in case my stuff is actually correct….):

    You write for the “renormalized Hamiltonian” acting only on the hidden units:
    \[ \tilde{\mathcal{H}}(v) = \sum_v \mathrm{e}^{-V(v,h) – \mathcal{H}(v)} \]

    I believe, \(\tilde{\mathcal{H}}(v)\) is a typo and should be \(\tilde{\mathcal{H}}(h)\) (since we are integrating out the visible degrees of freedom).

    Next, I wonder about the definition as such: in order for the partition function to be unchanged, shouldn’t the definition of \(\tilde{\mathcal{H}}(h)\) involve a logarithm? I.e.:
    \[\tilde{\mathcal{H}}(h) \rightarrow \tilde{\mathcal{H}}(h) = \log \sum_v \mathrm{e}^{-V(v,h) – \mathcal{H}(v)}\]

    This way we exactly get \( \mathrm{e}^{\tilde{\mathcal{H}}(h)} = \sum_v \mathrm{e}^{-V(v,h) – \mathcal{H}(v) \), and we can plug this directly into the partition function.


  2. This is great! I was asking around a couple months ago for some kind of explanation of the connection.

    I’ll read up on this!


  3. It seems however that deep learning networks do not have the most essential property of such a physical system. The hidden states in a sandpile are not some “higher-level” sand grains. They are macroscopic observables. A deep learning network, however, does explicitly represent the hidden states by neurons. I blame it on its non-recursive structure.

    What I don’t understand is how the “full connectivity” between a layer and its hidden representation (the next layer) does represent the locality as in block renormalization. It is fully connected, so why would it only get rid of local correlations? The coupling in spin lattices is normally only between neighbours.

    The renormalization group has a fixed point when we do the mapping an infinite number of times. Does this mean the network converges only with an infinite number of layers? I think such a “rolled out” version is hardly exhibiting “self-organized criticality”, that is, if scientist would agree on a definition of SOC :-).

    IMHO a single recurrent layer can exhibit renomalization group properties in a more fundamental way.

    Or am I misunderstanding everything here? Forgive my ignorance. Some explanations would be really appreciated!


    1. Thanks for stopping by and reading my post
      I think, as with lots of complexity theory, we rely a lot upon analogy. Recall that here we are trying to learn the parameters of the network / Ising model, so we are doing a kind of inverse statistical mechanics, as opposed to proposing a symmetric, uniform random distribution of couplings. It is known that , during the learning phase of an RBM, it is necessary to either run a few steps of CD or run PCD. That is, 1 step of CD, then 3 steps of CD, etc. This suggests to me that the system is evolving along something akin to a flow map, and that we can only invert the equations exactly when we are near the critical point. As we add more layers, we are effectively adding 1 more iteration of the RG group, as opposed to solving it exactly for all iterations.

      So, as in the picture, there are 2 lines of flow: the progress learning, and the number of RG transforms applied.

      Why do we only keep local interactions? Well, to me this s analogous to the electron correlation problem in quantum chemistry. We usually start with some Hartree Fock (i.e. mean field, product of experts) solution, and then add in correlations vi some perturbation / renormalization steps. The RBM model is an analogous products of experts solution, which we can solve easily. Does this mean we have to remove all local correlations–well there are lots of details necessary to prevent the system from collapsing. Indeed, I suspect that the RBM state, along with regularization and dropout, prevent the model from collapsing into a spin glass ground state. Here, I am at odds with LeCun, who thinks that the reason the Deep Learning systems work is exactly because they are a spin glass. Of course, removing these connections may simply be that there are too many of them and the system overtrains, spin glass or not.

      IMHO, this needs more investigation. And I agree with the RNN comment

      Finally, I want to pose the following question. Suppose we run an unsupervised clustering algorithms on some data, twice but w/ different parameters. Which set produces better clusters? Most algos have no answer, and this is a central, very practical problem in basic ML. Here, I think we could say the better set is the one that more closely conserves the free energy between layers. This is a property of the RG approach, or, in quantum chemistry, is related to what we call size-consistencey. This allows us to search a massive space of parameters — which is impossible for general non-convex searches. and I suspect this is a key idea as to why these methods perform so well.


      1. Maybe I am a bit slow. But to apply block renormalization in the network (N-dimensional spin lattice):
        * what are the neighbours of a node?
        * what exactly is the decimation step?
        I don’t understand the exact equivalence.

        Regarding number of clusters, I’m normally following a Bayesian approach and would just consider a prior on the distribution of items over clusters, using something like a Dirichlet Process or Pitman-Yor, etc. If this information is not available, I can understand that you’ll have to use something that just relates distributions over states on two different levels of abstraction. Free energy of some kind of quantity is probably involved. 🙂

        With respect to dropout I recommend “Dropout as a Bayesian Approximation: Insights and Applications” by Gal and Ghahramani. The relation with Gaussian Processes promises a dropout method for RNNs as well!


  4. 1. the variational approach is different from the real space decimation approach; roughly, the decimation step is like adding a layer

    2. what happened to the local connections ? in a Hopfield network, all nodes are visible. In an RBM, there visible nodes are connected through the hidden layer, but not directly. This does not mean the visible nodes are not connected. To see this, one would need to construct an effective Hamiltonian for the visible nodes that includes the hidden interactions as renormalized visible-visible interactions.

    Thanks for the references on the bayesian methods–ill check them out.


    1. 1. In a sandpile this would mean that all grains are connected to all grains on the next level. Then there would be some process (an inference process in the network). And magically, there will be local structure arising. And hey, this is a variational approximation to real-space renormalization. That’s not how it works… In the words of the authors: “Surprisingly, this local block spin structure emerges from the training process, suggesting the DNN is self-organizing to implement block spin renormalization”. I bet if you use textures as input of the network there is no such local structure arising. And local structure could just as well have originated from the ad-hoc applied L1 regularization (see Appendix).

      2. The problem is that the local connection should be there from the start. In normal renormalization group approaches large-scale correlations arise from short-scale correlations. Here the authors start with a restricted Boltzmann machine: hidden nodes are connected to all visible nodes. Newman and Watts have a renormalization group on a small-world network, but there the decimation operator is quite different. That’s why I asked what the exact decimation operator is. It is not in the paper, because it “emerges”. Miracle!

      If I may, let me ask one more detailed question…

      Equation 10, where is it actually used? Does the form of the network matter? I think if Eq. 10 would describe a black hole the rest would still stand, saying something very trivial. Namely that you can minimize the KL divergence between two layers in a network and that this can be described by Hamiltonians.


  5. The form of the network — eg 10 — is not used in this derivation.
    I personally don’t know how important this is.

    I have a more of experience with large scale perturbative ‘renormalization’ techniques from quantum chemistry. To me, these systems more resemble machine learning systems since they are large but finite, highly non-uniform, and not amenable to exact solutions. Also, it is not clear that we can apply RG arguments directly since we have to consider that we are learning the weights (i.e the H-V couplings)

    I would say that

    1. does eq 10 really matter? This is a key question

    Well, generally speaking, it is necessary to choose the correct zeroth order description, but also one that is amenable to computational exploration

    the RBM is also convenient computationally and it is unclear to me if similar results could be obtain using a formalism that includes local correlations directly. Perhaps adding them early on would improve the learning rate, or perhaps they would screw it up and cause it to overtrain?

    2. removing local interactions may be corrected , in many situations, by simply to run higher order calculations.

    if H= Ho+V , we can pick Ho to be diagonal in a localized basis (so there are no local interactions), and the off-diagonal parts just go into the interaction V.
    that is, as we learn the weights, i kinda assumed the RBM would be a mean field approximation and effectively include the local interactions this way

    I used this method (mean-field like H0, without overlap between sites) when computing effective Hamiltonians for pi-electron systems

    in an RG formalism this might mean taking a higher order approximation to the RG operator. In a DBN, this might mean adding more layers, running more epochs, etc.

    3. (1,2) , plus the danger of overtraining, implies to me that we may not / probably do not want to solve an Exact RGE
    Exact RG calculations can lead to funny behavior and the details of the system do matter See, for example

    so using variational RG is fine

    So I’m not sure what the beef is here? Or if this was a beef?

    It is true that the comparison is only partial; we set up the VRG formalism and apply the idea of finding a lower bound. What we can not do here is analyze the fixed point of the RG flow map using any kind of analytic theory. Moreover, this is the other ‘reaction coordinate’, which is the learning process. so it’s not like we are applying the RG transform, or the decimations, iteratively over and over–we have maybe a few layers, which corresponds to a few ‘decimations’ (if you want to call them that), and the ‘flow map’ is really saying that we are trying to find the Hamiltonian that is somewhere near the fixed point of the learning process, and not the fixed point of the RG iterations. Instead, what I assume is that when we get close to the the learned solution, the few ‘decimations’ are basically at the fixed point, so we only need a few to get a good solution.

    4. Do we need to have local correlations explicitly in the visible layer to obtain long range correlations?
    This is a good point and certainly something that the authors did not discuss.
    It is not obvious that a simple argument by analogy would be sufficient to resolve this

    These are highly non-uniform systems and it is unclear to me what the effective interactions look like
    So i assume that as we learn the RBM H-V couplings, they would effectively include local interactions.

    I think to understand the RBM system in terms of classical spatial models it is necessary to actually compute the, effective RBM Hamiltonian and the associated renormalized manny-body interactions.

    I assume the interactions of the visible units would give rise to a multi-scale spin Hamiltonian, of the kind suggested that arises in protein folding

    To me, this is the interesting connection, but there remain a lot of un-answered questions.
    In particular, what happens at the bottom of the funnel? Is it like a spin glass? Is it like the native state of a protein?
    Is the learning process driven by replica symmetry breaking? Or does this lead to overtraining?

    5. As to the paper itself–I think the authors recognized this old connection, and the knocked out some simple simulations so they could write a paper (as opposed to a blog post ). The simulations were interesting but they did not provide explicit code. I would say–lets try to reproduce it and add some textures


    1. Hi Charles, I’ve now tried several times to read your paper about obtaining the pi-electron Hamiltonians from scratch, but no way I’m gonna be able to understand it. 🙂

      Your suggestion about reproducing it with textures is a nice one. I don’t think I can do it coming month, but maybe I’ll have some time next month.


      1. Ah so let me clarify…that paper deals with effective Hamiltonians for zero-T, electronic structure problems. I just share where my thinking is on the problem today–a lot of which might sound like a proposal for a Phd candidate program

        One can think of the Hopfield Net / HAM as the zero-T limit of the Boltzman Net.

        Generally speaking, the Bolztman net did not work well at scale, even with hidden units, when it was fully connected.

        So we have 2 problems to deal with. !…should we build an effective Hamilonian for the zero-T case, or work at finite T and try to understand the spin-glass transition

        The HAM runs Hebbian (zero-T) learning and it appears to have all sorts of problems.

        I am kinda curious what happens if we introduce hidden nodes into this, apply something like dropout, and still run Hebbian learning, to fix some of the old problems in Hopfield and/or Boltzmann nets.

        If we just look at a HAM, this is a backwards way of looking at it–one usually first introduces T, then the hidden units access additional microstates via the entropy. In quantum mechanics, of course, the hidden states are virtual states–accessible by diagonalizing the energy operator. We dont even have an operator–we just have the ground state semi-classicial E(v) or E(h,v)

        Still, there is precedence a for a zero-T, renormalized HAM; the Schulten Neural gas is a like a simple, renormalized HAM that uses Hebbian (zero-T) learning

        Of course, it is one thing to propose a phenomenological correction to a model; it is something else
        entirely to compute the renormalization corrections exactly. The paper I shared does this, and it requires some pretty heavy quantum field theory to get this right — at least in the pure quantum mechanical sense (where that old paper of mind comes in)

        at one point i started a blog on the old approach

        but it is still a bit jumbled.

        Here, we don’t really have a quantum ground state, and the intent is different, so I would need to think a bit more about how to do this numerically, and whether it is possible to derive a form that allows learning of the weights using a Hebbian rule.

        So instead (or say after doing this), we need to consider the finite T / stochastic Boltzmann Net. Can we construct from E_RBM(v,h)–>E’_RBM(v). My idea is that the final E’_RBM(v) and associated (unrestricted) Boltzmann Machine would be connected very differently than a vanilla, fully connected HAM-style Boltzmann machine.
        This is where the Variational RG step comes in, and I was hoping one could just compute E(v) from the equations in the paper and on fully trained RBM.

        Indeed, it is understood that a single layer RBM is like a Hopfield net

        The question, what happens with multiple stacked layers, convolutional layers, etc

        That is, if we consider an RBM = stochastic Hopfield Net, with connectivity through the hidden nodes
        then I expect we can construct a renormalized Hopfield net, with no hidden nodes, and an Energy function with long range, multi-scale interactions.
        The zero-T limit would look like an Ising model with complex, non-uniform, many-body (3,4-body) interactions

        Or course, this is final energy, and we also want to understand the dynamics during learning. It is unclear if such a machine could naively be trained using the original Boltzmann machine style Hebbian Learning, or maybe just backprop+SGD
        So we would want some form of E(v) that is ‘learnable’.

        In spin physics one might just talke the derived/calculated final E(v) and study the ‘non-eq behavior’ using something formal like Glauber dynamics or some other traditional approach. That is, we study small deviations from the eq state by taking spin flips. For learning,I think this is similar to sampling one node at a time in a Boltzman net. This is known to be awful, and in ML we usually don’t something like wake-sleep, backprop, etc.

        Also, learning is probably very far from Eq and this kind of approach may be very bad. In fact, if the Spin Glass idea is right, then this could never work.

        I would be happy to just find a way to compute the Energy landscape with bare and renormalized E(v)

        Then, we we find a spin funnel, there is the question of what happens at the bottom of the funnel
        Do we encounter a spin-glass transition, or is there enough entropy in the system to avoid that?

        Now as to doing something practical–what I think might actually be useful is to compute the free energy at each layer and study the convergence. I am thinking that this could be a good metric for training an unsupervised RBM / DL algo. I finally got torch up and running on a GPU and I started looking at some of the old papers like

        I would like to run the lua torch RBM toolbox on this example and reproduce it–any idea if there is a worked example?


  6. I want to ask what is free energy ?
    Please explain with few words why deep learning works in context of Variational Renormalization Group? As far as i understand it scale the landscape and the model converge to the fixed point? Do we have some proof that this is global optimum ?



    1. the free energy is defined in the blog as the negative log of the partition function.
      Im not sure what the question here is ?

      why is there a global optimum..please see my previous post which discusses this

      why is this related to variational renormalization group? the basic conjecture is that a good model for why DL works is a model of a Hopfield-style spin glass that is being sampled at a subcritical point–a point below the
      spin glass phase.

      Note this is a different conjecture than what LeCun suggests is that DL is operating inside the spin glass phase.

      see, and his slides on the Geometry of the Loss Function

      A with many things in condensed matter theory–this conjecture is based on analogies. Here, I note
      RBMs are analogous to VRG and I assume the fixed point of the RBM flow map is something akin to the fixed point of the RG flow map

      The analogy here is a bit crude because in RG theory, one usually assumes the Hamilonian is fixed, whereas in DL, the Hamiltonian is being learned as part of the flow map. so I am conjecturing that the fixed point of the fixed Hamiltonian flow map, the stable attractor, is also a stable attractor of the varying Hamiltonian.

      Why might we believe this? Well, it depends on if you believe in the RB / Kadanoff idea of Universality and it applies here in some , perhaps crude, sense. If Universality is a good approach , then the ‘details’ of the Hamiltonian (i.e the weights) would not really change the underlying description we are close enough to the critical point. So if we can get close, we would converge quickly.


  7. hi Charlers,

    I would like to ask am I following the idea for critical points – when we train ta RBM with Gibbs sampling the configuration of the RBM is moved down on the variation manifold (the 4 image sample) to the critical point – where the network reach equilibrium and converge. Is that correct?
    What kind of image model exists at the critical equilibrium point – the average structure of the image or something else ?

    Thank you!


    1. Yes, it moves along the manifold. This is the intent of the discussion


      We should first understand that this is a model by analogy, as in most of condensed matter physics. Indeed, most practical models are very complex. Given that, and assuming it is a good analogy, is not clear that an RBM actually samples at a critical point since it is a variational lower bound to the free energy at the critical point. Additionally, the presence of Dropout may move the system to something like a subcritical point that avoids overtraining. Also, many systems are run with a finite number of steps, meaning that we may not be near convergence or even on the true statistical manifold. As to the second question, I don’t understand the question. The model is the model


      1. Thank you very much!

        The second question is what kind of image emerge on the critical point – but since it is the max likelihood learning it should be the most distribution with high probability.

        “Also, many systems are run with a finite number of steps, meaning that we may not be near convergence or even on the true statistical manifold.” – is that the case with contrastive divergence learning – where we do gibbs sampling only few steps? In that case we work with approximate energy landscape – in my opinion the energy function is shallow than the landscape which is converged at critical point.

        two very more questions – what is dropout and

        “is not clear that an RBM actually samples at a critical point since it is a variational lower bound to the free energy at the critical point” – the variational lower bound during Gibbs sampling may not reach critical point – the critical point might be lower than variational lower bound?

        10x very much


        1. The RBM is the lower bound.
          The intent here is to suggest that the free energy at every layer should be conserved during training. That is, it remains size consistent during the training phase. As for the surface itself, I suspect it is fairly convex most of the way until we reach the bottom.

          The best thing would be just to compute these things and look,


  8. 10x, 10x 🙂 ?
    so if the free energy for each layer is conserved what about the lower bound of variation free energy –
    as Hinton shows:

    “So maximizing the bound w.r.t. the weights in the higher layers
    is exactly equivalent to maximizing the log probability of
    a dataset in which h0 occurs with probability Q(h0|v0). If
    the bound becomes tighter, it is possible for log p(v0) to fall
    even though the lower bound on it increases, but log p(v0)
    can never fall below its value at step 2 of the greedy algorithm
    because the bound is tight at this point and the bound
    always increases.”

    The lower bound of variation free energy is increased but the free energy (itself) is preserved ?
    So as far as i understand the at each layer the energy is preserved but he energy landscape is rescale – we see the varational landscape in high abstraction level – only the deeper points are observed and the details are omitted. We just see the higher picture of the manifold – am i get the main idea?

    Thank you very much!

    Liked by 1 person

      1. you stated …

        “So if the free energy for each layer is conserved what about the lower bound of variation free energy ”


        “The lower bound of variation free energy is increased but the free energy (itself) is preserved ”

        I don’t understand the question? What Hinton paper are you referencing?

        Let us just go back to the Hinton paper in Science, 2006

        “It can be shown that adding an extra layer always improves a lower bound on the log probability that the model assigns to the training data, provided the number of feature detectors per layer does not decrease and their weights are initialized correctly”

        Of course, this is just saying that the RBM algorithm is a variational lower bound. The point here being that a very similar algorithm was first developed by Kadanoff for RG theory–10 years earlier.

        Also note

        “This bound does not apply when the higher layers have fewer feature detectors”

        So to get the proper lower bound you have to be careful how you set up the problem. In any real implementation, there are so many heuristics and modifications that a specific RBM implementation may not provide an exact lower bound. If you want a true lower bound to any problem you have to be careful. Even numerical instabilities could affect this.

        As to the statement

        “the free energy (itself) is preserved ?”

        I don’t know what this means?

        What I said was one expects the Free Energy of each layer to be related to the other layers via a scale transformation, and that this should hold throughout the optimization procedure. This would be the intent when constructing a renormalized Effective Hamiltonian. Now does it hold true for any specific implement, or does the implementation break this “size consistency”. Does this hold in practice? Who knows that the code is doing–you would need to run a simulation and check.

        I have no idea what it means to ‘preserve’ the total Free Energy? This has meaning a physical system (i.e. a chemical reaction) because Energy can be lost to the environment, through dissipation, etc. What do you mean?


        1. hi Charles,

          My question was – when adding additional layer on deep belief net do we get high abstraction view of the same energy landscape ? Is the “scale transformation” is just getting more abstract (without details of the small local optima) view of the energy landscape. With that renationalized high level view and tweaking the step of gradient descend we can get the global minimum of the energy landscape. This is my understanding about how the the energy surface between the layer is transformed and why we get global minimum with gradient descend – so it is that correct ?

          Could you recommend me some book (or tutorial) about RBMs, stacking RBM and deep learning ?

          Thank very much!


          1. No, it is just not an another abstract representation of the Energy landscape.

            As we add additional layers, the Energy landscape is further refined, or ‘renormalized’. In a sense, each layer is like taking 1 more iteration of a Renormalization Group transformation. I suspect this is can help explain very deep nets work better than shallow nets.

            In an earlier post I explained some old ideas of why the SGD may work–the final Energy landscape resembles what is called in protein folding the ‘spin glass of minimal frustration’.

            I don’t know of any books on this–these are my ideas.


            1. Great! It will be very good if we get some tutorial on that topic.
              It will b very useful for me to provide me some intuition for the renormalized energy surface –

              “As we add additional layers, the Energy landscape is further refined, or ‘renormalized’. In a sense, each layer is like taking 1 more iteration of a Renormalization Group transformation.”

              what the Renormalization Group transformation do with the energy landscape – is there some image or video to get intuition about that process ?

              Thank you very much!


              1. Do we get better energy surface that is near to the real energy surface – the same process as improving the variation low bound ?


                  1. better than the previous (lower level) variation approximation – by adding new layer we increase the low variation bound and we get better approximation of true log probability. So my question is what is the effect on the energy landscape when “taking 1 more iteration of a Renormalization Group transformation” ? I read that some kind of rescaleing is happen but could you give more information ..,


                    1. I conjecture that effect of the renormalizing the energy landscape is to make it more convex.

                      This is based on observations about the energy landscape of random heteropolymers. See part 1 of the blog.


    1. RBMs are one of the few successful models of unsupervised deep learning. The training methods you mention are technical adjustments to supervised learning algorithms.


    2. There is recent work on applying the TAP equations to improving training for RBMs. If I can get the code to work, Ill post on github. Next step might be to resum the TAP correction (i.e. find the right renormalization)


    1. RG is an analytic tool that is very well understood. I would say that DL is not well understood at all, so RG is just a starting point to begin to think about how statistical physic relates to RBMs. One idea I had is to try applying RG as a solver for RBMs, in the form of a diagrammatic resummation to the TAP equations.


        1. Thanks for the link. I am currently working on the problem from the point of view of improving RBM solvers. To that end, I view RG theory as one of a number of methods for treating strongly correlated systems. Progress has been made in applying the TAP equations to RBMs at low order (2nd, 3rd order). My approach is to see if I can incorporate higher order corrections from the TAP equations. This summer, I have succeeded in porting the RBM TAP corrections to python and I am working on producing a library for scikit-learn that others can test. I am looking for help finishing and testing the code if you are interested


        2. see:

          If you read the original work by Hinton (very old work), you will see he mentions the TAP theory, but back then no one could not get it to work. This has changed.

          TAP theory lets us treat RBMs as a mean-field theory, and lets us include systematic corrections, at low order. this suggests a way to apply an RG-like theory to obtain higher order corrections

          The main difficulty in applying an RG theory of sorts here would be to develop a diagrammatic formulation of the TAP theory, and then try to sum the terms to high order. To my understanding, no one has yet fully developed the diagrams. But this could also be done computationally.

          Even here, it is unclear which terms to resum, and it may or may not work. To that extent I agree with some of the Reddit comments. Still, there are lots of things to try, and it takes time to ferret them out.

          Of course, a diagrammatic resummation is not exactly RG theory, but for me it is close enough.

          More basic is to extend the existing EMF-RBM (TAP) theory to non-binary data sets. And to test it at scale. It is possible that a simple third order treatment may be good enough, when combined with newer ideas from DL.


    1. There has been a long history of attempting to apply physics-like symmetry transforms to object recognition. This was first tried using rotation invariant kernels in an SVM, that had something like a Lie-group transform built into them. 10-15 years ago, this is what you would see. These methods have pretty much disappeared, and only pop up from time to time.


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