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ON THE GEOMETRY OF NASH EQUILIBRIA AND CORRELATED EQUILIBRIA
Abstract: It is well known that the set of correlated equilibrium distributions of an n-player noncooperative game is a convex polytope that includes all the Nash equilibrium distributions. We demonstrate an elementary yet surprising result: the Nash equilibria all lie on the boundary of the polytope.
pdf  nibble  papers  ORFE  game-theory  optimization  geometry  dimensionality  linear-algebra  equilibrium  structure  differential  correlation  iidness  acm  linear-programming  spatial  characterization  levers 
5 weeks ago by nhaliday
OSF | Near and Far Transfer in Cognitive Training: A Second-Order Meta- Analysis
In Models 1 (k = 99) and 2 (k = 119), we investigated the impact of working-memory training on near-transfer (i.e., memory) and far-transfer (e.g., reasoning, speed, and language) measures, respectively, and whether it is mediated by the type of population. Model 3 (k = 233) extended Model 2 by adding six meta-analyses assessing the far-transfer effects of other cognitive-training programs (video-games, music, chess, and exergames). Model 1 showed that working-memory training does induce near transfer, and that the size of this effect is moderated by the type of population. By contrast, Models 2 and 3 highlighted that far-transfer effects are small or null.
study  preprint  psychology  cog-psych  intelligence  generalization  dimensionality  psych-architecture  intervention  enhancement  practice 
february 2019 by nhaliday
Recent Advances for a Better Understanding of Deep Learning − Part I
Some issues in trying to understand the theory of deep learning.
This article deals with non-convex optimizations (as involved in the loss function), why stochastic gradient descent even works at all (and under what conditions), and the curse of dimensionality.
deeplearning  theory  sgd  stochasticgradientdescent  dimensionality  nonconvex  lossfunctions 
november 2018 by drmeme
t-SNE – Laurens van der Maaten
distributed stochastic neighbor embedding - dimensionality reduction for vizualization
statistics  visualization  dimensionality  dataviz  t-sne 
august 2018 by tswaterman
Rules of thumb for Deep Learning – Hacker Noon
With my few years of experience in training and using various available open source model networks, I have learned the hard way of setting various hyper parameters and using them efficiently. I have…
deep  learning  rules  of  thumb  dimensionality 
august 2018 by tranqy
lmcinnes/umap: Uniform Manifold Approximation and Projection
dimensionality reduction - mapping data back to a lower dimensional manifold
similar to the "topological learning" stuff
visualization  deeplearning  topology  dataviz  dimensionality 
july 2018 by tswaterman

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