Archive for July, 2015

Samuel J. Gershman, Eric J. Horvitz, Joshua B. Tenenbaum:
Computational Rationality: A Converging Paradigm for Intelligence in Brains, Minds, and Machines
Science 349(6245): 273-278, 2015

The article provides a shared computation-based view on concepts in AI, cognitive science and neuroscience. Advances that address challenges of perception and action under uncertainty are discussed.
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Manuel Watter, Jost Tobias Springenberg, Joschka Boedecker, Martin Riedmiller:
Embed to Control: A Locally Linear Latent Dynamics Model for Control from Raw Images
arXiv, 2015

This paper deals with the problem of model-based reinforcement learning (RL) from images. The idea behind model-based RL is to learn a model of the transition dynamics of the system/robot and use this model as a surrogate simulator. This is helpful if we want to minimize experiments with a (physical/mechanical) system. The added difficulty addressed in this paper is that this predictive transition model should be learned from raw images where only pixel information is available.

Central idea (more…)

Kurt T. Miller, Thomas L. Griffiths and Michael I. Jordan:
Nonparametric Latent Feature Model for Link Prediction
NIPS 2010

The objective of this paper is to predict links in social networks. The working assumption is that links depend on relational features between entities. The objective of the paper is to simultaneously infer the number of these features and learn which entities have each feature.
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arXiv has become a main source of information for statistics and machine learning. Daily email digests tell me what papers have been uploaded since yesterday, including authors, abstracts and a link to the paper. For me this is invaluable at the receiving side.

However, on the producing/publishing side, not everybody thinks that uploading papers to arXiv is a good idea. And there are several good reasons for this:
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Andrew Gordon Wilson and Hannes Nickisch:
Kernel Interpolation for Scalable Structured Gaussian Processes
ICML 2015

This paper was clearly one of my highlights at ICML and falls into the category of large-scale kernel machines, one of the trends at ICML. Wilson and Nickisch combine the advantages of inducing point and structure-exploiting (e.g., Kronecker/Toeplitz) approaches.

The key idea behind structured kernel interpolation is (more…)

Ali Rahimi and Ben Recht:
Random Features for Large Scale Kernel Machines
NIPS 2007

In this paper, the authors propose to map data to a low-dimensional Euclidean space, such that the inner product in this space is a close approximation of the inner product computed by a stationary (shift-invariant) kernel (in a potentially infinite-dimensional RKHS). The approach is based on Bochner’s theorem.

The central equation is this one: (more…)

Daniel Hernandez-Lobato and Jose Miguel Hernandez-Lobato:
Scalable Gaussian Process Classification via Expectation Propagation
arXiv

The paper is about large-scale Gaussian process classification. Unlike many others, the authors use Expectation Propagation (and not Variational Inference) for approximate inference. An approximate marginal likelihood expression is derived that factorizes over the data instances, which allows for distributed inference and training. Training is additionally sped up by using mini-batches of data.

An interesting part of the paper is that (more…)