## Composition, music theory, mostly Western.

Sometime you don’t want to generate a chord, or measure a chord, or
learn a chord,
you just want to write a chord.

## Helpful software for the musically vexed

• Fabrizio Poce’s
J74 progressive and J74 bassline
are some chord progression
generators from his library of very clever chord generators linked in to
Ableton Live’s scripting engine,
so if you
are using Ableton they might be very handy.
They are cheap (EUR12 + EUR15).
I use them myself, but they DO make Ableton crash a wee bit, so not really
suited for live performance, which is a pity because that would be a
wonderful unique selling point.
The realtime-oriented J74 HarmoTools from the same guy
are less sophisticated but worth trying, especially since they are free, and
he has lot of other clever hacks there too.
Basically, just go to this guy’s
site and try his stuff out. You don’t have to stop there.
• Odesi
(USD49) has been doing lots of advertising and has a very nice pop-interface.
It’s like Synfire-lite with a library of pop tricks and rhythms.
The desktop version tries to install gigabytes of synths of meagre merit on your machine,
which is a giant waste of space an time if you are using a computer with synths on,
which you are because this is 2016.
• Helio is free and cross platform and totally worth a shot.
There is a chord model in there and version control (!) but you might not notice the chord thing if you aren’t careful
• Mixtikl / Noatikl are grandaddy apps for this, although the creators doubtless put much effort into the sleek user interfaces, their complete inability to explain their app or provide compelling demonstrations or use cases leave me cold.
I get the feeling they had high-art aspirations but have ended up basically doing ambient noodles in order to sell product; Maybe I’m not being fair. (USD25/USD40)
• Rapid Compose (USD99/USD249) might make decent software, but can’t really explain why their app is nice or provide a demo version.
• synfire explains how it uses music theory to do large-scale scoring etc. Get the string section to behave itself or you’ll replace them with MIDIbots. (EUR996, so I won’t be buying it, but great demo video.)
• harmony builder does classical music theory for you.
USD39-USD219 depending on heinously complex pricing schemes.
• You can’t resist rolling your own?
sharp11 is a node.js music theory library for javascript with demo application to create jazz improv.
• Supercollider of course does this and everything else, but designing user interfaces for it will take years off your life. OTOH, if you are happy with text, this might be a goer.

## Constraint Composition

All of that too mainstream? Try a weird alternative formalism!
declarative musical composition by defining constraints which the notes must satisfy.
Sounds fun in the abstract but the details don’t grab me somehow.

The reference here is strasheela built on an obscure, unpopular, and apparently discontinued Prolog-like language called “Oz” or “Mozart”, because using existing languages is not a grand a gesture as claiming none of them are quire Turing complete enough for your special thingy.

That is a bit of a ghost town;
If you wanted to actually do this, you’d probably use overtone + minikanren (prolog-for-lisp) to do this, as with
the composing schemer,
or to be even more mainstream, just use a normal constraint solver in a normal language.
I am fond of python and ncvx.

• Anders, T., & Miranda, E. R.(2008). Higher-Order Constraint Applicators for Music Constraint Programming. In Proceedings of the 2008 International Computer Music Conference. Belfast, UK.
• Anders, T., & Miranda, E. R.(2010). Constraint Application with Higher-Order Programming for Modeling Music Theories. Computer Music Journal, 34(2), 25–38. DOI. Online.
• Anders, T., & Miranda, E. R.(2011). Constraint programming systems for modeling music theories and composition. ACM Computing Surveys, 43(4), 1–38. DOI. Online.
• Anders, T., & Miranda, E. R.(2009). A computational model that generalises Schoenberg’s guidelines for favourable chord progressions. In Proceedings of the Sound and Music Computing Conference. Citeseer. Online.

See original: Composition, music theory, mostly Western.

## Gaussian distribution and Erf and Normality

Stunts with Gaussian distributions.

Let’s start here with the basic thing.
The (univariate) standard Gaussian pdf

\begin{equation*}
\psi:x\mapsto \frac{1}{sqrt{2\pi}}\text{exp}\left(-\frac{x^2}{2}\right)
\end{equation*}

We define
.. math:

\Psi:x\mapsto \int_{-\infty}^x\psi{t} dt


This erf function is popular, isn’t it?
Unavoidable if you do computer algebra.
But I can never remember what it is.
There’s this scaling factor tacked on.

Well…

\begin{equation*}
\operatorname{erf}(x)\; =\; \frac{1}{\sqrt{\pi}} \int_{-x}^x e^{-t^2} \, dt
\end{equation*}
\begin{equation*}
\sqrt{\frac{\pi }{2}} \left(\text{erf}\left(\frac{x}{\sqrt{2}}\right)+1\right)
\end{equation*}

## Differential representation

Non-linear univariate DE represention.

\begin{equation*}
\begin{align*}
\sigma ^2 f'(x)+f(x) (x-\mu )&=0\\
f(0) &=\frac{e^{-\mu ^2/(2\sigma ^2)}}{\sqrt{2 \sigma^2\pi } }\\
L(x) &=(\sigma^2 D+x-\mu)
\end{align*}
\end{equation*}

Linear PDE representation as a diffusion equation (see, e.g. BoGK10)

\begin{equation*}
\begin{align*}
\frac{\partial}{\partial t)f(x;t) &=\frac{1}{2}\frac{\partial^2}{\partial x^2}f(x;t)\\
f(x;0)&=\delta(x-\mu)
\end{align*}
\end{equation*}

Look, it’s the diffusion equation of Wiener process.

## Roughness

\begin{equation*}
\begin{align*}
\| \frac{d}{dx}\phi_\sigma \|_2 &= \frac{1}{4\sqrt{\pi}\simga^3}\\
\| \left(\frac{d}{dx}\right)^n \phi_\sigma \|_2 &= \frac{\prod_{i<n}2n-1}{2^{n+1}\sqrt{\pi}\simga^{2n+1}}
\end{align*}
\end{equation*}

## Refs

Bote16
Botev, Z. I.(2016) The Normal Law Under Linear Restrictions: Simulation and Estimation via Minimax Tilting. Journal of the Royal Statistical Society: Series B (Statistical Methodology), n/a-n/a. DOI.
BoGK10
Botev, Z. I., Grotowski, J. F., & Kroese, D. P.(2010) Kernel density estimation via diffusion. The Annals of Statistics, 38(5), 2916–2957. DOI.

See original: Gaussian distribution and Erf and Normality

## Sparse regression and things that look a bit like it.

Related to compressed sensing but here we consider sampling complexity and the effect of measurement noise.

To discuss:

LARS, LASSO, de-biassed LASSO, Elastic net, etc.

## Implementations

I’m not going to mention LASSO in (generalised) linear regression,
since everything does that these days (Oh alright,
Jerome Friedman’s glmnet for R is the fastest,
and has a MATLAB version.

But SPAMS (C++, MATLAB, R, python) by Mairal himself, looks interesting.
It’s an optimisation library for many various in sparse problems.

### HOWTO

Sparse Filtering in Theano

## Eating Japanese Knotweed (and other daft ideas)

 Image: Wikipedia

There have been a number of calls(1,2,3,4) in recent weeks and months to control the invasive plant Japanese Knotweed, at least partially, by eating it. In recent days, Kerry County Council in Ireland heard from one member who, albeit with tongue-in-cheek, urged citizens to make wine, jelly and other sweet treats from the plant.

This strikes me as a terrible idea.

The plant itself is certainly edible - the Japanese have been eating it for years. It's Japanese name, itadori, means 'well being' and it seems to have some medicinal properties. It also tastes a bit like rhubarb apparently. I wouldn't know, I haven't tried it.

I haven't tried it for the same reason I don't advise you try it. Encouraging people to harvest and transport a regulated, invasive species is the perfect recipe (if you'll pardon the pun) for its continued and accelerated spread.

Japanese Knotweed (Fallopia japonica) is, as you will have guessed, native to Japan and the neighbouring region. It was introduced to the UK in the mid-19th century and quickly spread to Ireland and other parts of the world. Introduced as an ornamental plant, it quickly became a real problem.

The plant is capable of growing at a tremendous rate - 1 metre in a month- and forms big stands 2-3 metres in height. The early shoots are spear like, similar to asparagus in appearance and the plants produce delicate white flowers in late Summer. The real problem is underground where the plant forms tough rhizomes, adapted root-like organs, which remain in the soil even during the Winter when the rest of the plant dies back.

Japanese Knotweed thrives on disturbance and it is mainly spread by fragments of rhizome, crown or stem being accidentally or deliberately moved. This leads to some real (and expensive) problems including a massive reduction in biodiversity under the alien canopy; structural damage to buildings and infrastructure; and the significant cost of its removal.

Data from 2010 suggest that the plant costs the UK £165 million a year to control. If the plant were to be eradicated in the UK by current methods it would cost £1.56 billion. For one site alone, the 2012 London Olympic site, it cost £88 million to deal with this one invasive plant. Nobody wants Japanese Knotweed on their land.

 Image: Wikipedia

Imagine you go to the supermarket and buy a bunch of rhubarb. The first thing you do is chop the top and bottom off the stalks and chuck them on your compost heap. Do this with Japanese Knotweed and you end up costing yourself (and potentially your neighbours) thousands in a cleanup bill.

Harvesting Japanese Knotweed from the wild, no matter how careful you are, is also fraught with problems. The plant can easily regrow from small fragments the size of your fingernail. If we're lucky, you'll drop these fragments at the original, infested site. If not, you'll drop them on your walk back to the car or in your front garden when you unload the car.

Simply put, encouraging people to mess around with an invasive species like Japanese Knotweed is, in my view, irresponsible. It may also be illegal.

In Ireland, it is an offence to "plant, disperse or cause to disperse or otherwise cause to grow" the plant. It is also an offence if "he/she has in his/her possession for sale or for breeding/reproduction/transport....anything from which the plant can be reproduced or propagated".

In the meantime, there are chemical and physical control options and scientists in the UK are developing a biological control approach using a sap-sucking insect called Aphalara itadori. This is an old enemy of the plant, found in Japan and currently being tested in the UK to see if it will do the same job in this part of the world (and not eat anything else, by accident). The trials haven't been a total success with numbers surviving over winter too low to have much of an effect, but the tests are ongoing. Hopefully, before too long we will have a sustainable control option for this invasive plant. In the meantime, stop eating it.

See original: Eating Japanese Knotweed (and other daft ideas)

## Smoothing, regularisation, penalization and friends

In nonparametric statistics we might estimate simultaneously what look like
many, many parameters, which we constrain in some clever fashion,
which usually boils down to something we can interpret as a “smoothing”
parameters, controlling how many parameters we still have to model
from a subset of the original.

The “regularisation” nomenclature claims descent from Tikhonov, (eg TiGl65 etc) who wanted to solve ill-conditioned integral and differential equations, so it’s slightly more general.
“Smoothing” seems to be common in the
spline and
kernel estimate communities of
Wahba (Wahb90) and Silverman (Silv84) et al,
who usually actually want to smooth curves.

Penalization” has a geneology unknown to me, but is probably the least abstruse for common usage.

These are, AFAICT, more or less the same thing.
“smoothing” is more common in my communities which is fine,
but we have to remember that “smoothing” an estimator might not always infer smooth dynamics in the estimand;
it could be something else being smoothed, such as variance in the estimate of parameters of a rough function.

In every case, you wish to solve an ill-conditioned inverse problem, so you tame it by adding a penalty to solutions you feel one should be reluctant to accept.

TODO: make comprehensible

TODO: examples

TODO: discuss connection with model selection

TODO: discuss connection with compressed sensing.

The real classic approach here is spline smoothing of functional data.
More recent approaches are things like sparse regression.

## Refs

Bach00
Bach, F. (n.d.) Model-Consistent Sparse Estimation through the Bootstrap.
ChHS15
Chernozhukov, V., Hansen, C., & Spindler, M. (2015) Valid Post-Selection and Post-Regularization Inference: An Elementary, General Approach. Annual Review of Economics, 7(1), 649–688. DOI.
EHJT04
Efron, B., Hastie, T., Johnstone, I., & Tibshirani, R. (2004) Least angle regression. The Annals of Statistics, 32(2), 407–499. DOI.
FlHS13
Flynn, C. J., Hurvich, C. M., & Simonoff, J. S.(2013) Efficiency for Regularization Parameter Selection in Penalized Likelihood Estimation of Misspecified Models. arXiv:1302.2068 [Stat].
FrHT10
Friedman, J., Hastie, T., & Tibshirani, R. (2010) Regularization Paths for Generalized Linear Models via Coordinate Descent. Journal of Statistical Software, 33(1), 1–22. DOI.
JaFH13
Janson, L., Fithian, W., & Hastie, T. (2013) Effective Degrees of Freedom: A Flawed Metaphor. arXiv:1312.7851 [Stat].
KaRo14
Kaufman, S., & Rosset, S. (2014) When does more regularization imply fewer degrees of freedom? Sufficient conditions and counterexamples. Biometrika, 101(4), 771–784. DOI.
KoMi06
Koenker, R., & Mizera, I. (2006) Density estimation by total variation regularization. Advances in Statistical Modeling and Inference, 613–634.
LiRW10
Liu, H., Roeder, K., & Wasserman, L. (2010) Stability Approach to Regularization Selection (StARS) for High Dimensional Graphical Models. In J. D. Lafferty, C. K. I. Williams, J. Shawe-Taylor, R. S. Zemel, & A. Culotta (Eds.), Advances in Neural Information Processing Systems 23 (pp. 1432–1440). Curran Associates, Inc.
MeBü10
Meinshausen, N., & Bühlmann, P. (2010) Stability selection. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 72(4), 417–473. DOI.
Meye08
Meyer, M. C.(2008) Inference using shape-restricted regression splines. The Annals of Applied Statistics, 2(3), 1013–1033. DOI.
Silv84
Silverman, B. W.(1984) Spline Smoothing: The Equivalent Variable Kernel Method. The Annals of Statistics, 12(3), 898–916. DOI.
SmSM98
Smola, A. J., Schölkopf, B., & Müller, K.-R. (1998) The connection between regularization operators and support vector kernels. Neural Networks, 11(4), 637–649. DOI.
TKPS14
Tansey, W., Koyejo, O., Poldrack, R. A., & Scott, J. G.(2014) False discovery rate smoothing. arXiv:1411.6144 [Stat].
TiGl65
Tikhonov, A. N., & Glasko, V. B.(1965) Use of the regularization method in non-linear problems. USSR Computational Mathematics and Mathematical Physics, 5(3), 93–107. DOI.
Geer14
van de Geer, S. (2014) Weakly decomposable regularization penalties and structured sparsity. Scandinavian Journal of Statistics, 41(1), 72–86. DOI.
Wahb90
Wahba, G. (1990) Spline Models for Observational Data. . SIAM
WeMZ16
Weng, H., Maleki, A., & Zheng, L. (2016) Overcoming The Limitations of Phase Transition by Higher Order Analysis of Regularization Techniques. arXiv:1603.07377 [Cs, Math, Stat].
Wood00
Wood, S. N.(2000) Modelling and smoothing parameter estimation with multiple quadratic penalties. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 62(2), 413–428. DOI.
Wood08
Wood, S. N.(2008) Fast stable direct fitting and smoothness selection for generalized additive models. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 70(3), 495–518. DOI.
ZoHa05
Zou, H., & Hastie, T. (2005) Regularization and variable selection via the elastic net. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 67(2), 301–320. DOI.
ZoHT07
Zou, H., Hastie, T., & Tibshirani, R. (2007) On the “degrees of freedom” of the lasso. The Annals of Statistics, 35(5), 2173–2192. DOI.

See original: Smoothing, regularisation, penalization and friends

## DJing

Yet our sounds are also a vocabulary for those who detest the walled-off concentrations of wealth, and steal property back: the collectives that build their own sound systems, stage free parties, and invite DJs to perform. The international DJ becomes emblematic of global capitalism’s complicated cultural dimension. On flights and at the free Continental breakfasts in hotels, often the same soul-destroying hotel chains in each city, we get stuck chatting with our fellow Americans and Western Europeans, the executives eager to find compatriots. We make small talk with these consultants and deal-makers in the descending elevators in the evening—then go out to the city’s dead-end and unowned spaces or its luxury venues to soundtrack the night of the region’s youth, hungry for something new. DJ music is now the common art form of squatters and the nouveau riche; it is the soundtrack both for capital and for its opposition.

http://www.ibrahimshaath.co.uk/keyfinder/
tangerine echonest

audio software

## DJing software

So many choices, now. I use Ableton, but Traktor and Serrato are more designed for this.

Open source/ lower cost alternatives?

• flow8deck is made by the people who made mixedinkey, software for the musically vexed. It handles keychanges good.
• Traktor
• Serrato
• Djay

See original: DJing

## Moving the poors to marginal electorate

OK, Let’s start treating politics like the favour machine it is and behave accordingly;
NSW under Mike baird is a system wherew you buy favours with leverage.
I’d like it to be otherwise, buyt let’s look

Optimal marginalness.
Oerganised opposition menas we are more likely to claim council seats as a side benefit.

See original: Moving the poors to marginal electorate

## Recurrent neural networks

Feedback neural networks structured to have memory and a notion of “current” and “past” states, which can encode time (or whatever).

As someone who does a lot of signal processing for music, the notion that these generalise linear systems theory is suggestive of lots of interesting DSP applications.

The connection between these (IIR) and “convolutional” (FIR) neural networks is suggestive for the same reason.

## Flavours

### Vanilla

The main problem here is that they are unstable in the training phase unless you are clever.
See BeSF94. One solution is LSTM; see next.

### Long Short Term Memory (LSTM)

As always, Christopher Olah wins the visual explanation prize:
Understanding LSTM Networks
LSTM Networks for Sentiment Analysis:

In a traditional recurrent neural network, during the gradient back-propagation phase, the gradient signal can end up being multiplied a large number of times (as many as the number of timesteps) by the weight matrix associated with the connections between the neurons of the recurrent hidden layer. This means that, the magnitude of weights in the transition matrix can have a strong impact on the learning process.[…]

These issues are the main motivation behind the LSTM model which introduces a new structure called a memory cell…]. A memory cell is composed of four main elements: an input gate, a neuron with a self-recurrent connection (a connection to itself), a forget gate and an output gate. […]The gates serve to modulate the interactions between the memory cell itself and its environment.

#### GridRNN

A mini-genre.
KaDG15 et al connect recurrent cells across multiple axes, leading to a higher-rank MIMO system;
This is natural in many kinds of spatial random fields, and I am amazed it was uncommon enough to need formalizing in a paper; but it was and it did and good on Kalchbrenner et al.

TBD

### Liquid/ Echo State Machines

This sounds deliciously lazy;
Very roughly speaking, your first layer is a reservoir of random saturating IIR filters.
You fit a classifier on the outputs of this.
Easy to implement, that.
I wonder when it actually works, constraints on topology etc.

I wonder if you can use some kind of sparsifying transform on the recurrence operator?

These claim to be based on spiky models, but AFAICT this is not at all necessary.

Various claims are made about how hard they avoid the training difficulty of similarly basic RNNs by being essentially untrained; you use them as a feature factory for another supervised output algorithm.

Suggestive parallel with random projections.

From a dynamical systems perspective, there are two main classes of RNNs.
Models from the first class are characterized by an energy-minimizing
stochastic dynamics and symmetric connections.
The best known instantiations are Hopfield networks, Boltzmann machines, and
the recently emerging Deep Belief Networks.
These networks are mostly trained in some unsupervised learning scheme.
Typical targeted network functionalities in this field are associative
memories, data compression, the unsupervised modeling of data distributions,
and static pattern classification, where the model is run for multiple time
steps per single input instance to reach some type of convergence or
equilibrium
(but see e.g., TaHR06 for extension to temporal data).
The mathematical background is rooted in statistical physics.
In contrast, the second big class of RNN models typically features a
deterministic update dynamics and directed connections.
Systems from this class implement nonlinear filters, which
transform an input time series into an output time series.
The mathematical background here is nonlinear dynamical systems.
The standard training mode is supervised.
This survey is concerned only with RNNs of this second type, and
when we speak of RNNs later on, we will exclusively refer to such systems.

### Other

It’s still the wild west. Invent a category, name it and stake a claim.

## Practicalities

Variable sequence length:
https://gist.github.com/evanthebouncy/8e16148687e807a46e3f

seq2seq models with GRUs : Fun with Recurrent Neural Nets: One More Dive into CNTK and TensorFlow

## Refs

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Auer, P., Burgsteiner, H., & Maass, W. (2008) A learning rule for very simple universal approximators consisting of a single layer of perceptrons. Neural Networks, 21(5), 786–795. DOI.
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Bengio, Y., Simard, P., & Frasconi, P. (1994) Learning long-term dependencies with gradient descent is difficult. IEEE Transactions on Neural Networks, 5(2), 157–166. DOI.
BoBV12
Boulanger-Lewandowski, N., Bengio, Y., & Vincent, P. (2012) Modeling Temporal Dependencies in High-Dimensional Sequences: Application to Polyphonic Music Generation and Transcription. In 29th International Conference on Machine Learning.
BoLe06
Bown, O., & Lexer, S. (2006) Continuous-Time Recurrent Neural Networks for Generative and Interactive Musical Performance. In F. Rothlauf, J. Branke, S. Cagnoni, E. Costa, C. Cotta, R. Drechsler, … H. Takagi (Eds.), Applications of Evolutionary Computing (pp. 652–663). Springer Berlin Heidelberg
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Buhusi, C. V., & Meck, W. H.(2005) What makes us tick? Functional and neural mechanisms of interval timing. Nature Reviews Neuroscience, 6(10), 755–765. DOI.
CMBB14
Cho, K., van Merriënboer, B., Bahdanau, D., & Bengio, Y. (2014) On the properties of neural machine translation: Encoder-decoder approaches. arXiv Preprint arXiv:1409.1259.
CGCB14
Chung, J., Gulcehre, C., Cho, K., & Bengio, Y. (2014) Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling. In NIPS.
CGCB15
Chung, J., Gulcehre, C., Cho, K., & Bengio, Y. (2015) Gated Feedback Recurrent Neural Networks. arXiv:1502.02367 [Cs, Stat].
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Doelling, K. B., & Poeppel, D. (2015) Cortical entrainment to music and its modulation by expertise. Proceedings of the National Academy of Sciences, 112(45), E6233–E6242. DOI.
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Duan, Q., Park, J. H., & Wu, Z.-G. (2014) Exponential state estimator design for discrete-time neural networks with discrete and distributed time-varying delays. Complexity, 20(1), 38–48. DOI.
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Gal, Y. (2015) A Theoretically Grounded Application of Dropout in Recurrent Neural Networks. arXiv:1512.05287 [Stat].
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Gers, F. A., Schmidhuber, J., & Cummins, F. (2000) Learning to Forget: Continual Prediction with LSTM. Neural Computation, 12(10), 2451–2471. DOI.
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See original: Recurrent neural networks

## Generalised linear models

Using the machinery of linear regression to predict in
somewhat more general regressions.

This means you are still doing Maximum Likelihood regression,
but outside the setting of homoskedastic gaussian noise and linear response.

Not quite as fancy as generalised additive models,
but if you have to implement such models yourself,
less work. If you are using R this is not you.

To learn:

1. When we can do this? e.g. Must the response be from an exponential family for really real? Wikipedia mentions the “overdispersed exponential family” which is no such thing.
2. Does anything funky happen with regularisation?
3. Whether to merge this in with quasilikelihood.
4. Fitting variance parameters.

Pieces of the method follow.

## Response distribution

TBD. What constraints do we have here

## Linear Predictor

An invertible (monotonic?) function
relating the mean of the linear predictor and
the mean of the response distribution.

## Refs

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See original: Generalised linear models

## Artificial neural networks

Modern computational neural network methods reascend the hype phase transition.
a.k.a deep learning or extreme learning or double plus fancy brainbots or please can our department have a bigger computation budget it’s not to play video games i swear?.

Style transfer will be familiar to anyone who has ever taken hallucinogens or watched movies made by those who have, but you can’t usually put hallucinogens or film nights on the departmental budget so we have to make do with gigantic computing clusters.

But what are “artificial neural networks”?

Either

• a collection of incremental improvements machine learning techniques loosely inspired by real brains, that suurprisingly elicit the kind of results from machine learning networks that everyone was hoping we’d get by at least 20 years ago, or,
• the state-of-the-art in artificial kitten recognition.

## Why bother?

A classic —-

### The ultimate regression algorithm

It turns out that this particular learning model (class of learning models),
while often not apparently well suited to a given problem,
does very well on general on lots of things,
and very often can keep on doing better and better the more resources you throw at it.
Why burn three grad students on a perfect regression algorithm when you can use
one algorithm to solve a whole bunch of regression problems just as well?

This is more interesting for the business-dev people.

### Cool maths

Regularisation, function approximations, interesting manifold inference.

Even the stuff I’d assumed was trivial like backpropagation has a few wrinkles in practice.
See
Michael Nielson’s chapter and
Chrisopher Olah’s visual summary

TBD. Maybe.

See next.

## Generative art applications

Most neural networks are invertible, giving you generative models.
(e.g.
run the model forwards, it recognises melodies;
run it “backwards”, it composes melodies.

It’s not quite running it backwards, in this vein, the “deep dreaming” project does this.
See, say, the above image from
google’s tripped-out image recognition systems) or
Gatys, Ecker and Bethge’s deep art
Neural networks do Monet quite well.
I’ve a weakness for ideas that give me plausible deniability for making
generative art while doing my maths homework.

## Hip keywords for NN models

Not necessarily mutually exclusive;
some design patterns you can use.

See Tomasz Malisiewicz’s summary of Deep Learning Trends @ ICLR 2016

Train two networks to beat each other.
I have some intuitiuons why this might work, but need to learn more.

### Convolutional

Signal processing baked in to neural networks. Not so complicated if you have ever done signal processing, apart from the abstruse use of “depth” to mean 2 different things in the literature.

Generally uses FIR filters plus some smudgy “pooling”
(which is nonlinear downsampling),
although IIR is also making an appearance by running RNN on multiple axes.

### Spike-based

Most simulated neural networks are based on a continuous activation potential and discrete time, unlike spiking biological ones, which are driven by discrete events in continuous time.
There are a great many other differences.
What difference does this in particular make?
I suspect it make a difference regarding time.

### Recurrent neural networks

Feedback neural networks with memory and therefore a notion of time and state.
As someone who does a lot of signal processing for music, the notion that these generalise linear systems theory is suggestive of lots of interesting DSP applications.

The connection with these and convolutional neural networks is suggestive for the same reason.

#### Vanilla

The main problem here is that they are unstable in the training phase unless you are clever.
See BeSF94. One solution is LSTM; see next.

TBD

#### Long Short Term Memory (LSTM)

In a traditional recurrent neural network, during the gradient back-propagation phase, the gradient signal can end up being multiplied a large number of times (as many as the number of timesteps) by the weight matrix associated with the connections between the neurons of the recurrent hidden layer. This means that, the magnitude of weights in the transition matrix can have a strong impact on the learning process.[…]

These issues are the main motivation behind the LSTM model which introduces a new structure called a memory cell…]. A memory cell is composed of four main elements: an input gate, a neuron with a self-recurrent connection (a connection to itself), a forget gate and an output gate. […]The gates serve to modulate the interactions between the memory cell itself and its environment.

### Cortical learning algorithms

Is this a real thing, or pure hype? How does it distinguish itself from other deep learning techniques aside from name-checking biomimetic engineering?
NuPIC has made a big splash with their open source brain-esque learning, and have open-sourced it;
on that basis alone looks like it could be fun to explore.

Dunno.

TBD

## Related questions

• Artificial neural network are usually layers of linear projections
sandwiched between saturating nonlinear maps.
Why not more general nonlinearities?.
• Can you know in advance how long it will take to fit a classifier
or regression model for data of a given sort?
The process looks so mechanical…

## Regularisation in neural networks

L_1, L_2, dropout…

## Compression of neural networks

It seems we should be able to do better than a gigantic network with millions of parameters;
Once we have trained the graph, how can we simplify it, compress it, or prune it?

Quantizing to single bits.

## Encoding for neural networks

Neural networks take an inconvenient encoding format,
so general data has to be massaged.
Convolutional models are an important implicit encoding;
what else can we squeeze [in there/out of there]?

## Software stuff

Too many. Neural networks are intuitive enough that everyone builds their own library.

I use Tensorflow, plus a side order of Keras.

• R/MATLAB/Python/everything: MXNET.

• Lua: Torch

• MATLAB/Python: Caffe claims to be a “de facto standard”

• Python: Theano

• Python/C++: tensorflow seems to be the same thing as Theano,
but it’s backed by google so probably has better long-term prospects.
The construction of graphs is more explicit than in Theano, which I find easier to understand, although this means that you use the near-python syntax of Theano.
Also claims to compile to smartphones etc, although that looks buggy atm.

• Javascript (!) inference and training: convnetjs
* plus bonus interview
* sister project for recurrent networks: recurrentjs

• synapticjs is a very full-feature javasceript training, inference and visualisation of neural network, with really good documentation. Great learning resource, with plausible examples.

• javascript inference only, neocortexjt in the browser. Civilised.

• brainjs is unmaintained now but looked like a nice simple javascript neural netowrk library.

• mindjs is a simple one where you can see the moving parts.

• iphone: DeepBeliefSDK

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See original: Artificial neural networks

## Long memory models

Processes where we know that ancient history is still relevent for the future predictions, even if we know the recent history.

In my own mental map this is near-synonymous with stateful models
where we ignore the state, which I suppose is a kind of coarse graining.
If we aren’t concerned with a process but i.i.d. occurrences we might look at our
hidden variables differently.

This particular approach up being a popular simplification, because hidden states can be computationally difficult
to infer as well as having possibly high
sample complexity.
Maybe for other reasons too?

But in this formulation, we have a Markov process but
because we do not observe the whole state it looks non-Markov.
This is reasonably consistent with reality, where we believe
the current state of reality determines the future,
but we don’t know the whole current state.
Related: hidden variable quantum mechanics.

Note “long memory” is considered as a model for with time series, but clearly spatial random fields, or random fields indexed by any number of dimensions, with or without causality constraints, can have this property.

Main questions for me:

1. Can we use the “memory length” of a system to infer the number of hidden states for some class of interesting systems, or vice versa?
1. which classes?
1. Can we infer the memory length alone as a parameter of interest in some classes? (need making precise). Information criteria don’t do this model order selection consistently.

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See original: Long memory models

## Sample complexity

The machine-learning-ish approach to analysing estimator convergence.

TBD

See original: Sample complexity

## Hidden variables and latent factors

This isn’t a thing per se.
But I want a landing page for a few differnt ways to think about hidden variables.

If the hidden varaibles are random, I might think of hierarchical models, latent factors, deep networks, informative sampling, censored data…

If the hidden variables are determninistic I might think of algorithmic complexity.

Random thing:
Matrix factorisation to approximate latent factors and their interaction in recommender systems.

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See original: Hidden variables and latent factors

## Une approche pluridisciplinaire et pluri-scalaire des dynamiques paysagères du bassin minier du Nord-Pas-de-Calais

Ce texte revient sur la démarche de recherche participative et pluridisciplinaire menée durant trois ans dans le cadre du programme ITTECOP sur le territoire de la communauté d’agglomération d’Henin Carvin (CAHC) dans le Nord-Pas-de-Calais. Une caractéristique de cette recherche est d’avoir rassemblé une équipe de paysagistes et une de sociologues. La démarche part d’une conception du paysage comme cadre intégrateur de réflexion et d’action et outil capable de faire émerger une action territoriale concertée et durable. Le propos dépasse néanmoins une définition commune et désormais assez consensuelle du paysage comme « construction sociale », en ajoutant une interrogation sur la diversité des acteurs aménageurs, au-delà de l’image erronée de la toute-puissance des acteurs publics et aménageurs légitimes du territoire (ou à l’inverse de leur perte de pouvoir et de la privatisation des espaces urbains au profit des seuls acteurs économiques devenus tout puissants). La démarche scienti...