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ref: -2019 tags: HSIC information bottleneck deep learning backprop gaussian kernel date: 10-06-2021 17:23 gmt revision:5      [head]

The HSIC Bottleneck: Deep learning without Back-propagation

In this work, the authors use a kernelized estimate of statistical independence as part of a 'information bottleneck' to set per-layer objective functions for learning useful features in a deep network. They use the HSIC, or Hilbert-schmidt independence criterion, as the independence measure.

The information bottleneck was proposed by Bailek (spikes..) et al in 1999, and aims to increase the mutual information between the layer representation and the labels while minimizing the mutual information between the representation and the input:

$\frac{min}{P_{T_i | X}} I(X; T_i) - \beta I(T_i; Y)$

Where $T_i$ is the hidden representation at layer i (later output), $X$ is the layer input, and $Y$ are the labels. By replacing $I()$ with the HSIC, and some derivation (?), they show that

$HSIC(D) = (m-1)^{-2} tr(K_X H K_Y H)$

Where $D = {(x_1,y_1), ... (x_m, y_m)}$ are samples and labels, $K_{X_{ij}} = k(x_i, x_j)$ and $K_{Y_{ij}} = k(y_i, y_j)$ -- that is, it's the kernel function applied to all pairs of (vectoral) input variables. H is the centering matrix. The kernel is simply a Gaussian kernel, $k(x,y) = exp(-1/2 ||x-y||^2/\sigma^2)$ . So, if all the x and y are on average independent, then the inner-product will be mean zero, the kernel will be mean one, and after centering will lead to zero trace. If the inner product is large within the realm of the derivative of the kernel, then the HSIC will be large (and negative, i think). In practice they use three different widths for their kernel, and they also center the kernel matrices.

But still, the feedback is an aggregate measure (the trace) of the product of two kernelized (a nonlinearity) outer-product spaces of similarities between inputs. it's not unimaginable that feedback networks could be doing something like this...

For example, a neural network could calculate & communicate aspects of joint statistics to reward / penalize weights within a layer of a network, and this is parallelizable / per layer / adaptable to an unsupervised learning regime. Indeed, that was done almost exactly by this paper: Kernelized information bottleneck leads to biologically plausible 3-factor Hebbian learning in deep networks albeit in a much less intelligible way.

Robust Learning with the Hilbert-Schmidt Independence Criterion

Is another, later, paper using the HSIC. Their interpretation: "This loss-function encourages learning models where the distribution of the residuals between the label and the model prediction is statistically independent of the distribution of the instances themselves." Hence, given above nomenclature, $E_X( P_{T_i | X} I(X ; T_i) ) = 0$ (I'm not totally sure about the weighting, but might be required given the definition of the HSIC.)

As I understand it, the HSIC loss is a kernellized loss between the input, output, and labels that encourages a degree of invariance to input ('covariate shift'). This is useful, but I'm unconvinced that making the layer output independent of the input is absolutely essential (??)

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ref: -0 tags: kernel regression structure discovery fitting gaussian process date: 09-24-2018 22:09 gmt revision:1  [head]
• Use Gaussian process kernels (squared exponential, periodic, linear, and ratio-quadratic)
• to model a kernel function, $k(x,x')$ which specifies how similar or correlated outputs $y$ and $y'$ are expected to be at two points x\$ and $x'$ .
• By defining the measure of similarity between inputs, the kernel determines the pattern of inductive generalization.
• This is different than modeling the mapping $y = f(x)$ .
• It's something more like $y' = N(m(x') + k(x,x'))$ -- check the appendix.
• Gaussian process models use a kernel to define the covariance between any two function values: $Cov(y,y') = k(x,x')$ .
• This kernel family is closed under addition and multiplication, and provides an interpretable structure.
• Search for kernel structure greedily & compositionally,
• then optimize parameters with conjugate gradients with restarts.
• This seems straightforwardly intuitive...
• Kernels are scored with the BIC.
• C.f. {842} -- "Because we learn expressions describing the covariance structure rather than the functions themselves, we are able to capture structure which does not have a simple parametric form."
• All their figure examples are 1-D time-series, which is kinda boring, but makes sense for creating figures.
• Tested on multidimensional (d=4) synthetic data too.
• Not sure how they back out modeling the covariance into actual predictions -- just draw (integrate) from the distribution?

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ref: work-0 tags: gaussian random variables mutual information SNR date: 01-16-2012 03:54 gmt revision:26       [head]

I've recently tried to determine the bit-rate of conveyed by one gaussian random process about another in terms of the signal-to-noise ratio between the two. Assume $x$ is the known signal to be predicted, and $y$ is the prediction.

Let's define $SNR(y) = \frac{Var(x)}{Var(err)}$ where $err = x-y$ . Note this is a ratio of powers; for the conventional SNR, $SNR_{dB} = 10*log_{10 } \frac{Var(x)}{Var(err)}$ . $Var(err)$ is also known as the mean-squared-error (mse).

Now, $Var(err) = \sum{ (x - y - sstrch \bar{err})^2 estrch} = Var(x) + Var(y) - 2 Cov(x,y)$ ; assume x and y have unit variance (or scale them so that they do), then

$\frac{2 - SNR(y)^{-1}}{2 } = Cov(x,y)$

We need the covariance because the mutual information between two jointly Gaussian zero-mean variables can be defined in terms of their covariance matrix: (see http://www.springerlink.com/content/v026617150753x6q/ ). Here Q is the covariance matrix,

$Q = \left[ \array{Var(x) & Cov(x,y) \\ Cov(x,y) & Var(y)} \right]$

$MI = \frac{1 }{2 } log \frac{Var(x) Var(y)}{det(Q)}$

$Det(Q) = 1 - Cov(x,y)^2$

Then $MI = - \frac{1 }{2 } log_2 \left[ 1 - Cov(x,y)^2 \right]$

or $MI = - \frac{1 }{2 } log_2 \left[ SNR(y)^{-1} - \frac{1 }{4 } SNR(y)^{-2} \right]$

This agrees with intuition. If we have a SNR of 10db, or 10 (power ratio), then we would expect to be able to break a random variable into about 10 different categories or bins (recall stdev is the sqrt of the variance), with the probability of the variable being in the estimated bin to be 1/2. (This, at least in my mind, is where the 1/2 constant comes from - if there is gaussian noise, you won't be able to determine exactly which bin the random variable is in, hence log_2 is an overestimator.)

Here is a table with the respective values, including the amplitude (not power) ratio representations of SNR. "

 SNR Amp. ratio MI (bits) 10 3.1 1.6 20 10 3.3 30 31 5.0 40 100 6.6 90 31e3 15
Note that at 90dB, you get about 15 bits of resolution. This makes sense, as 16-bit DACs and ADCs have (typically) 96dB SNR. good.

Now, to get the bitrate, you take the SNR, calculate the mutual information, and multiply it by the bandwidth (not the sampling rate in a discrete time system) of the signals. In our particular application, I think the bandwidth is between 1 and 2 Hz, hence we're getting 1.6-3.2 bits/second/axis, hence 3.2-6.4 bits/second for our normal 2D tasks. If you read this blog regularly, you'll notice that others have achieved 4bits/sec with one neuron and 6.5 bits/sec with dozens {271}.

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ref: work-0 tags: covariance matrix adaptation learning evolution continuous function normal gaussian statistics date: 06-30-2009 15:07 gmt revision:0 [head]
• Details a method of sampling + covariance matrix approximation to find the extrema of a continuous (but intractable) fitness function
• HAs flavors of RLS / Kalman filtering. Indeed, i think that kalman filtering may be a more principled method for optimization?
• Can be used in high-dimensional optimization problems like finding optimal weights for a neural network.
• Optimum-seeking is provided by weighting the stochastic samples (generated ala a particle filter or unscented kalman filter) by their fitness.
• Introductory material is quite good, actually...