# Posit AI Weblog: Infinite shock

Amongst deep studying practitioners, *Kullback-Leibler divergence* (KL divergence) is probably greatest identified for its function in coaching variational autoencoders (VAEs). To study an informative latent house, we don’t simply optimize for good reconstruction. Reasonably, we additionally impose a previous on the latent distribution, and goal to maintain them shut – usually, by minimizing KL divergence.

On this function, KL divergence acts like a watchdog; it’s a constraining, regularizing issue, and if anthropomorphized, would appear stern and extreme. If we go away it at that, nevertheless, we’ve seen only one aspect of its character, and are lacking out on its complement, an image of playfulness, journey, and curiosity. On this publish, we’ll check out that different aspect.

Whereas being impressed by a sequence of tweets by Simon de Deo, enumerating functions of KL divergence in an unlimited variety of disciplines,

we don’t aspire to offer a complete write-up right here – as talked about within the preliminary tweet, the subject might simply fill a complete semester of examine.

The rather more modest objectives of this publish, then, are

- to rapidly recap the function of KL divergence in coaching VAEs, and point out similar-in-character functions;
- for example that extra playful, adventurous “different aspect” of its character; and
- in a not-so-entertaining, however – hopefully – helpful method, differentiate KL divergence from associated ideas similar to cross entropy, mutual info, or free vitality.

Earlier than although, we begin with a definition and a few terminology.

## KL divergence in a nutshell

KL divergence is the anticipated worth of the logarithmic distinction in chances in accordance with two distributions, (p) and (q). Right here it’s in its discrete-probabilities variant:

[begin{equation}

D_{KL}(p||q) = sumlimits_{x} p(x) log(frac{p(x)}{q(x)})

tag{1}

end{equation}]

Notably, it’s uneven; that’s, (D_{KL}(p||q)) is just not the identical as (D_{KL}(q||p)). (Which is why it’s a *divergence*, not a *distance*.) This side will play an essential function in part 2 devoted to the “different aspect.”

To emphasize this asymmetry, KL divergence is typically referred to as *relative info* (as in “info of (p) relative to (q)”), or *info acquire*. We agree with one in all our sources that due to its universality and significance, KL divergence would most likely have deserved a extra informative identify; similar to, exactly, *info acquire*. (Which is much less ambiguous pronunciation-wise, as properly.)

## KL divergence, “villain”

In lots of machine studying algorithms, KL divergence seems within the context of *variational inference*. Usually, for life like information, actual computation of the posterior distribution is infeasible. Thus, some type of approximation is required. In variational inference, the true posterior (p^*) is approximated by a less complicated distribution, (q), from some tractable household.

To make sure we now have a superb approximation, we reduce – in principle, at the very least – the KL divergence of (q) relative to (p^*), thus changing inference by optimization.

In observe, once more for causes of intractability, the KL divergence minimized is that of (q) relative to an unnormalized distribution (widetilde{p})

[begin{equation}

J(q) = D_{KL}(q||widetilde{p})

tag{2}

end{equation}]

the place (widetilde{p}) is the joint distribution of parameters and information:

[begin{equation}

widetilde{p}(mathbf{x}) = p(mathbf{x}, mathcal{D}) = p^*(mathbf{x}) p(mathcal{D})

tag{3}

end{equation}]

and (p^*) is the true posterior:

[begin{equation}

p^*(mathbf{x}) = p(mathbf{x}|mathcal{D})

tag{4}

end{equation}]

Equal to that formulation (eq. (2)) – for a derivation see (Murphy 2012) – is that this, which reveals the optimization goal to be an higher sure on the unfavourable log-likelihood (NLL):

[begin{equation}

J(q) = D_{KL}(q||p^*) – log p(D)

tag{5}

end{equation}]

One more formulation – once more, see (Murphy 2012) for particulars – is the one we truly use when coaching (e.g.) VAEs. This one corresponds to the anticipated NLL plus the KL divergence between the approximation (q) and the imposed *prior* (p):

[begin{equation}

J(q) = D_{KL}(q||p) – E_q[- log p(mathcal{D}|mathbf{x})]

tag{6}

finish{equation}]

Negated, this formulation can also be referred to as the *ELBO*, for *proof decrease sure*. Within the VAE publish cited above, the ELBO was written

[begin{equation}

ELBO = E[log p(x|z)] – KL(q(z)||p(z))

tag{7}

finish{equation}]

with (z) denoting the latent variables ((q(z)) being the approximation, (p(z)) the prior, usually a multivariate regular).

### Past VAEs

Generalizing this “conservative” motion sample of KL divergence past VAEs, we are able to say that it expresses the standard of approximations. An essential space the place approximation takes place is (lossy) *compression*. KL divergence gives a strategy to quantify how a lot info is misplaced after we compress information.

Summing up, in these and related functions, KL divergence is “unhealthy” – though we don’t need it to be zero (or else, why hassle utilizing the algorithm?), we definitely wish to preserve it low. So now, let’s see the opposite aspect.

## KL divergence, good man

In a second class of functions, KL divergence is just not one thing to be minimized. In these domains, KL divergence is indicative of shock, disagreement, exploratory habits, or studying: This actually is the angle of *info acquire*.

### Shock

One area the place *shock*, not info per se, governs habits is notion. For instance, eyetracking research (e.g., (Itti and Baldi 2005)) confirmed that shock, as measured by KL divergence, was a greater predictor of visible consideration than info, measured by entropy. Whereas these research appear to have popularized the expression “Bayesian shock,” this compound is – I believe – not essentially the most informative one, as neither half provides a lot info to the opposite. In Bayesian updating, the magnitude of the distinction between prior and posterior displays the diploma of *shock* caused by the info – shock is an integral a part of the idea.

Thus, with KL divergence linked to shock, and shock rooted within the basic means of Bayesian updating, a course of that could possibly be used to explain the course of life itself, KL divergence itself turns into basic. We might get tempted to see it all over the place. Accordingly, it has been utilized in many fields to quantify unidirectional divergence.

For instance, (Zanardo 2017) have utilized it in buying and selling, measuring how a lot an individual disagrees with the market perception. Greater disagreement then corresponds to larger anticipated features from betting towards the market.

Nearer to the world of deep studying, it’s utilized in intrinsically motivated reinforcement studying (e.g., (Sun, Gomez, and Schmidhuber 2011)), the place an optimum coverage ought to maximize the long-term info acquire. That is doable as a result of like entropy, KL divergence is additive.

Though its asymmetry is related whether or not you employ KL divergence for regularization (part 1) or shock (this part), it turns into particularly evident when used for studying and shock.

### Asymmetry in motion

Wanting once more on the KL method

[begin{equation}

D_{KL}(p||q) = sumlimits_{x} p(x) log(frac{p(x)}{q(x)})

tag{1}

end{equation}]

the roles of (p) and (q) are essentially completely different. For one, the expectation is computed over the primary distribution ((p) in (1)). This side is essential as a result of the “order” (the respective roles) of (p) and (q) might need to be chosen in accordance with tractability (which distribution can we common over).

Secondly, the fraction contained in the (log) signifies that if (q) is ever zero at some extent the place (p) isn’t, the KL divergence will “blow up.” What this implies for distribution estimation basically is properly detailed in Murphy (2012). Within the context of shock, it signifies that if I study one thing I used to assume had likelihood zero, I shall be “infinitely shocked.”

To keep away from infinite shock, we are able to ensure our prior likelihood is rarely zero. However even then, the fascinating factor is that how a lot info we acquire in anybody occasion is dependent upon *how a lot info I had earlier than*. Let’s see a easy instance.

Assume that in my present understanding of the world, black swans most likely don’t exist, however they may … possibly 1 % of them is black. Put in a different way, my prior perception of a swan, ought to I encounter one, being black is (q = 0.01).

Now actually I *do* encounter one, and it’s black.

The knowledge I’ve gained is:

[begin{equation}

l(p,q) = 0 * log(frac{0}{0.99}) + 1 * log(frac{1}{0.01}) = 6.6 bits

tag{8}

end{equation}]

Conversely, suppose I’d been rather more undecided earlier than; say I’d have thought the chances have been 50:50.

On seeing a black swan, I get lots much less info:

[begin{equation}

l(p,q) = 0 * log(frac{0}{0.5}) + 1 * log(frac{1}{0.5}) = 1 bit

tag{9}

end{equation}]

This view of KL divergence, by way of shock and studying, is inspiring – it may lead one to seeing it in motion all over the place. Nonetheless, we nonetheless have the third and remaining process to deal with: rapidly examine KL divergence to different ideas within the space.

### Entropy

All of it begins with entropy, or *uncertainty*, or *info*, as formulated by Claude Shannon.

Entropy is the typical log likelihood of a distribution:

[begin{equation}

H(X) = – sumlimits_{x=1}^n p(x_i) log(p(x_i))

tag{10}

end{equation}]

As properly described in (DeDeo 2016), this formulation was chosen to fulfill 4 standards, one in all which is what we generally image as its “essence,” and one in all which is very fascinating.

As to the previous, if there are (n) doable states, entropy is maximal when all states are equiprobable. E.g., for a coin flip uncertainty is highest when coin bias is 0.5.

The latter has to do with *coarse-graining*, a change in “decision” of the state house. Say we now have 16 doable states, however we don’t actually care at that stage of element. We do care about 3 particular person states, however all the remainder are mainly the identical to us. Then entropy decomposes additively; complete (fine-grained) entropy is the entropy of the coarse-grained house, plus the entropy of the “lumped-together” group, weighted by their chances.

Subjectively, entropy displays our uncertainty whether or not an occasion will occur. Apparently although, it exists within the bodily world as properly: For instance, when ice melts, it turns into extra unsure the place particular person particles are. As reported by (DeDeo 2016), the variety of bits launched when one gram of ice melts is about 100 billion terabytes!

As fascinating as it’s, info per se might, in lots of instances, not be one of the best technique of characterizing human habits. Going again to the eyetracking instance, it’s utterly intuitive that individuals have a look at stunning elements of photos, not at white noise areas, that are the utmost you possibly can get by way of entropy.

As a deep studying practitioner, you’ve most likely been ready for the purpose at which we’d point out *cross entropy* – essentially the most generally used loss operate in categorization.

### Cross entropy

The cross entropy between distributions (p) and (q) is the entropy of (p) plus the KL divergence of (p) relative to (q). In case you’ve ever applied your personal classification community, you most likely acknowledge the sum on the very proper:

[begin{equation}

H(p,q) = H(p) + D_{KL}(p||q) = – sum p log(q)

tag{11}

end{equation}]

In info theory-speak, (H(p,q)) is the anticipated message size per datum when (q) is assumed however (p) is true.

Nearer to the world of machine studying, for mounted (p), minimizing cross entropy is equal to minimizing KL divergence.

### Mutual info

One other extraordinarily essential amount, utilized in many contexts and functions, is *mutual info*. Once more citing DeDeo, “you’ll be able to consider it as essentially the most normal type of correlation coefficient that you may measure.”

With two variables (X) and (Y), we are able to ask: How a lot can we find out about (X) after we find out about a person (y), (Y=y)? Averaged over all (y), that is the *conditional entropy*:

[begin{equation}

H(X|Y) = – sumlimits_{i} P(y_i) log(H(X|y_i))

tag{12}

end{equation}]

Now mutual info is entropy minus conditional entropy:

[begin{equation}

I(X, Y) = H(X) – H(X|Y) = H(Y) – H(Y|X)

tag{13}

end{equation}]

This amount – as required for a measure representing one thing like correlation – is symmetric: If two variables (X) and (Y) are associated, the quantity of knowledge (X) offers you about (Y) is the same as that (Y) offers you about (X).

KL divergence is a part of a household of divergences, referred to as *f-divergences*, used to measure directed distinction between likelihood distributions. Let’s additionally rapidly look one other information-theoretic measure that not like these, is a *distance*.

### Jensen-Shannon distance

In math, a *distance*, or *metric*, in addition to being non-negative has to fulfill two different standards: It should be symmetric, and it should obey the triangle inequality.

Each standards are met by the *Jensen-Shannon distance*. With (m) a combination distribution:

[begin{equation}

m_i = frac{1}{2}(p_i + q_i)

tag{14}

end{equation}]

the Jensen-Shannon distance is a median of KL divergences, one in all (m) relative to (p), the opposite of (m) relative to (q):

[begin{equation}

JSD = frac{1}{2}(KL(m||p) + KL(m||q))

tag{15}

end{equation}]

This is able to be a perfect candidate to make use of have been we involved in (undirected) distance between, not directed shock brought on by, distributions.

Lastly, let’s wrap up with a final time period, limiting ourselves to a fast glimpse at one thing complete books could possibly be written about.

### (Variational) Free Vitality

Studying papers on variational inference, you’re fairly more likely to hear individuals speaking not “simply” about KL divergence and/or the *ELBO* (which as quickly as you understand what it stands for, is simply what it’s), but additionally, one thing mysteriously referred to as *free vitality* (or: *variational free vitality*, in that context).

For sensible functions, it suffices to know that *variational free vitality* is unfavourable the ELBO, that’s, corresponds to equation (2). However for these , there may be *free vitality* as a central idea in thermodynamics.

On this publish, we’re primarily involved in how ideas are associated to KL divergence, and for this, we comply with the characterization John Baez offers in his aforementioned talk.

*Free* vitality, that’s, vitality in helpful type, is the anticipated vitality minus temperature instances entropy:

[begin{equation}

F = [E] -T H

tag{16}

finish{equation}]

Then, the additional free vitality of a system (Q) – in comparison with a system in equilibrium (P) – is proportional to their KL divergence, that’s, the data of (Q) relative to (P):

[begin{equation}

F(Q) – F(P) = k T KL(q||p)

tag{17}

end{equation}]

Talking of free vitality, there’s additionally the – not uncontroversial – free energy principle posited in neuroscience.. However sooner or later, we now have to cease, and we do it right here.

## Conclusion

Wrapping up, this publish has tried to do three issues: Having in thoughts a reader with background primarily in deep studying, begin with the “ordinary” use in coaching variational autoencoders; then present the – most likely much less acquainted – “different aspect”; and at last, present a synopsis of associated phrases and their functions.

In case you’re involved in digging deeper into the numerous varied functions, in a variety of various fields, no higher place to begin than from the Twitter thread, talked about above, that gave rise to this publish. Thanks for studying!

DeDeo, Simon. 2016. “Data Concept for Clever Individuals.”

*Nature Evaluations. Neuroscience*11 (February): 127–38. https://doi.org/10.1038/nrn2787.

*Advances in Neural Data Processing Methods 18 [Neural Information Processing Systems, NIPS 2005, December 5-8, 2005, Vancouver, British Columbia, Canada]*, 547–54. http://papers.nips.cc/paper/2822-bayesian-surprise-attracts-human-attention.

Murphy, Kevin. 2012. *Machine Studying: A Probabilistic Perspective*. MIT Press.

*CoRR*abs/1103.5708. http://arxiv.org/abs/1103.5708.

Zanardo, Enrico. 2017. “HOW TO MEASURE DISAGREEMENT ?” In.