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Hito Steyerl | Politics of Post-Representation «DIS Magazine
From the militarization of social media to the corporatization of the art world, Hito Steyerl’s writings represent some of the most influential bodies of work in contemporary cultural criticism today.
camera  data  digital  images  hito  steyerl  information  photography  post  representation  smartphone  war  stream 
12 days ago by therourke
obligatory RITA post |
There’s this perception that making a character LGBTQ+ is an active decision while making them straight isn’t and that you should make the active decision only if required to. But, actually, the choice make a character straight, or male, or white is as active a decision as the choice them LGBTQ+ or female or a POC. And it’s a mistake to assume one of those choices is “political” when the other isn’t.
representation 
19 days ago by evilsofa
[1803.01422] DAGs with NO TEARS: Smooth Optimization for Structure Learning
Estimating the structure of directed acyclic graphs (DAGs, also known as Bayesian networks) is a challenging problem since the search space of DAGs is combinatorial and scales superexponentially with the number of nodes. Existing approaches rely on various local heuristics for enforcing the acyclicity constraint and are not well-suited to general purpose optimization packages for their solution. In this paper, we introduce a fundamentally different strategy: We formulate the structure learning problem as a smooth, constrained optimization problem over real matrices that avoids this combinatorial constraint entirely. This is achieved by a novel characterization of acyclicity that is not only smooth but also exact. The resulting nonconvex, constrained program involves smooth functions whose gradients are easy to compute and only involve elementary matrix operations. By using existing black-box optimization routines, our method uses global search to find an optimal DAG and can be implemented in about 50 lines of Python and outperforms existing methods without imposing any structural constraints.
graph-theory  combinatorics  algorithms  rather-interesting  computational-complexity  to-understand  to-write-about  nudge-targets  consider:looking-to-see  representation 
20 days ago by Vaguery
Three representations of the Ising model | Scientific Reports
Statistical models that analyse (pairwise) relations between variables encompass assumptions about the underlying mechanism that generated the associations in the observed data. In the present paper we demonstrate that three Ising model representations exist that, although each proposes a distinct theoretical explanation for the observed associations, are mathematically equivalent. This equivalence allows the researcher to interpret the results of one model in three different ways. We illustrate the ramifications of this by discussing concepts that are conceived as problematic in their traditional explanation, yet when interpreted in the context of another explanation make immediate sense.
representation  modeling  philosophy-of-science  rather-interesting  to-write-about  demonstrations-of-the-mangle 
24 days ago by Vaguery
The meaning of model equivalence: Network models, latent variables, and the theoretical space in between | Psych Networks
Recently, an important set of equivalent representations of the Ising model was published by Joost Kruis and Gunter Maris in Scientific Reports. The paper constructs elegant representations of the Ising model probability distribution in terms of a network model (which consists of direct relations between observables), a latent variable model (which consists of relations between a latent variable and observables, in which the latent variable acts as a common cause), and a common effect model (which also consists of relations between a latent variable and observables, but here the latent variable acts as a common effect). The latter equivalence is a novel contribution to the literature and a quite surprising finding, because it means that a formative model can be statistically equivalent to a reflective model, which one may not immediately expect (do note that this equivalence need not maintain dimensionality, so a model with a single common effect may translate in a higher-dimensional latent variable model).

However, the equivalence between the ordinary (reflective) latent variable models and network models has been with us for a long time, and I therefore was rather surprised at some people’s reaction to the paper and the blog post that accompanies it. Namely, it appears that some think that (a) the fact that network structures can mimic reflective latent variables and vice versa is a recent discovery, that (b) somehow spells trouble for the network approach itself (because, well, what’s the difference?). The first of these claims is sufficiently wrong to go through the trouble of refuting it, if only to set straight the historical record; the second is sufficiently interesting to investigate it a little more deeply. Hence the following notes.
dynamical-systems  models  models-and-modes  representation  philosophy-of-science  (in-practice)  to-write-about  via:several 
24 days ago by Vaguery

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