arthegall + machinelearning 286
Jascha Sohl-Dickstein, "Efficient Methods for Unsupervised Learning of Probabilistic Models" (Thesis, from UC Berkeley)
4 days ago by arthegall
"Abstract exceeds arXiv space limitations -- see PDF." -- nice.
thesis
machinelearning
graphical-models
variational-methods
pdf
arxiv
4 days ago by arthegall
Frasconi et al. "kLog: A Language for Logical and Relational Learning with Kernels" (arXiv)
9 days ago by arthegall
Also reading this morning -- unforch, the refs in their PDF are messed up, and their source-code is missing some import, so I can't recompile it (from .tex source) unaided. GRrrrr.
machinelearning
arxiv
research-article
learning
programminglanguage
to-read
9 days ago by arthegall
Probabilistic Topic Models | April 2012 | Communications of the ACM
13 days ago by arthegall
David Blei surveys topic models for the Communications of the ACM.
acm
review-article
david-blei
machinelearning
nlp
topic-models
13 days ago by arthegall
[1203.3464] Gibbs Sampling in Open-Universe Stochastic Languages
7 weeks ago by arthegall
Gibbs sampling for BLOG-like models. Stuart Russell is one of the authors.
stuart-russel
machinelearning
blog
probabilistic-methods
graphical-models
arxiv
research-article
gibbs-sampling
7 weeks ago by arthegall
[1203.0697] Learning High-Dimensional Mixtures of Graphical Models
11 weeks ago by arthegall
"We now propose a method for learning the mixture components given n i.i.d. samples y_n
drawn from a graphical mixture model P(y). Our method proceeds in two stages. First, we estimate the graph G_∪ := U_{r}^{h=1} G_h, which is the union of the Markov graphs of the mixture. This is accomplished via a series of rank tests. Note that in the special case when G_h ≡ G_∪, this also gives the graph estimates of the component models. We then use the graph estimate hat{G}_∪ to obtain the pairwise marginals of the respective mixture components via a spectral decomposition method. Finally, we use the Chow-Liu algorithm to obtain tree approximations {T_h}_h of the individual mixture components." -- To do: review how this works in the context of gene expression experiments for transcription factor regulatory relationships, which are (presumably) mixtures of a couple different underlying models or modes.
gene-expression
bioinformatics
research-article
arxiv
via:cshalizi
graphical-models
mixture-models
machinelearning
drawn from a graphical mixture model P(y). Our method proceeds in two stages. First, we estimate the graph G_∪ := U_{r}^{h=1} G_h, which is the union of the Markov graphs of the mixture. This is accomplished via a series of rank tests. Note that in the special case when G_h ≡ G_∪, this also gives the graph estimates of the component models. We then use the graph estimate hat{G}_∪ to obtain the pairwise marginals of the respective mixture components via a spectral decomposition method. Finally, we use the Chow-Liu algorithm to obtain tree approximations {T_h}_h of the individual mixture components." -- To do: review how this works in the context of gene expression experiments for transcription factor regulatory relationships, which are (presumably) mixtures of a couple different underlying models or modes.
11 weeks ago by arthegall
[1112.6045] Comparing intermittency and network measurements of words and their dependency on authorship
january 2012 by arthegall
Other generic text features that can be used to determine authorship. 5th-grade science-fair project on steroids.
clustering
machinelearning
writing
authorship
classification
arxiv
research-article
nlp
january 2012 by arthegall
natural language processing blog: A technique for me is a task for you
october 2011 by arthegall
Some laughable, but also some thought-provoking, stuff in here.
by:hal-daume
machinelearning
nlp
october 2011 by arthegall
RDKit
august 2011 by arthegall
Whoa -- did *not* realize that Greg Landrum was (also) "The RDKit guy."
cheminformatics
machinelearning
greg-landrum
software
library
python
opensource
august 2011 by arthegall
rdkit (Google Code)
january 2011 by arthegall
"A toolkit for cheminformatics and machine learning." -- By one of the NIBR guys.
nibr
work
cheminformatics
informatics
bioinformatics
python
google-code
machinelearning
c++
from delicious
january 2011 by arthegall
James Petterson
september 2010 by arthegall
Hash kernels guy.
hash-kernels
machinelearning
research
homepage
september 2010 by arthegall
"Experiment in GP based on ImageMagick" (Notional Slurry)
september 2010 by arthegall
"If you skip this step—even with a downloaded library—you’re a baaaaaad genetic programmer. Turn in your Jaws and go back to machine learning land."
humor
genetic-programming
machinelearning
joke
via:cshalizi
by:Vaguery
imagemagick
testing
september 2010 by arthegall
Harrington, Hero, "Spatio-Temporal Graphical Model Selection" (arXiv)
july 2010 by arthegall
"We consider the problem of estimating the topology of spatial interactions in a discrete state, discrete time spatio-temporal graphical model where the interactions affect the temporal evolution of each agent in a network."
via:cshalizi
lasso
graphical-models
research-article
machinelearning
spatial-data
temporal-data
arxiv
july 2010 by arthegall
Viallon et al. "An empirical comparative study of approximate methods for binary graphical models; application to the search of associations among causes of death in French death certificates" (arXiv)
july 2010 by arthegall
"Through an extensive simulation study, we show that a simple modification of a method relying on a Gaussian approximation achieves good performance and is very fast. "
graphical-models
arxiv
research-article
machinelearning
mortality
via:cshalizi
july 2010 by arthegall
SLI | Projects / Belief Propagation
july 2010 by arthegall
"It turns out that the SAW [self-avoiding walk] tree is also closely connected to the behavior of belief propagation on G. Weitz (2006) and Jung and Shaw (2007) showed that marginalization in a binary, pairwise Markov random field defined on G can be performed exactly on the SAW tree, which suggested various approximations to exact inference corresponding to early termination of the SAW tree." -- Time to crack out that old Alan Sokal book (no, not *that* one).
alan-sokal
self-avoiding-walk
belief-propagation
machinelearning
graphical-models
inference
trees
graphs
july 2010 by arthegall
Chaudhuri, Monteleoni, & Sarwate, "Differentially Private Empirical Risk Minimization" (arXiv)
june 2010 by arthegall
"We provide general techniques to produce privacy-preserving approximations of classifiers learned via (regularized) empirical risk minimization (ERM). These algorithms are private under the {\it $\epsilon$-differential privacy} definition due to Dwork et al. (2006)."
privacy
security
machinelearning
asarwate
claire-monteleoni
arvix
research-article
optimization
perturbation
to-read
june 2010 by arthegall
Vishwanathan, et al. "Graph Kernels". JMLR (April 2010)
june 2010 by arthegall
To think about, in the context of RDF and query.
rdf
semanticweb
graph
query
kernel-methods
machinelearning
research-article
jmlr
via:cshalizi
june 2010 by arthegall
Pitch Identification Tutorial
april 2010 by arthegall
MLB.com apparently uses a custom-built neural network for doing real-time pitch identifications. But these graphs seem to imply that there might be simpler or more interpretable (offline) approaches to the same task...
data-mining
data
pitching
baseball
sports
neural-networks
clustering
machinelearning
pitchfx
april 2010 by arthegall
Viola-Jones object detection framework - Wikipedia
march 2010 by arthegall
Boosted networks of weak axis-aligned classifiers for face recognition (among other things).
wikipedia
face-recognition
algorithm
boosting
machinelearning
computer-vision
march 2010 by arthegall
Baluja & Covell, "Finding Images and Line Drawings in Document-Scanning Systems"
january 2010 by arthegall
Discovered by looking over the Google-list of ML papers. ( http://research.google.com/pubs/MachineLearning.html )
images
papers
annotation
documents
google
machinelearning
research-article
january 2010 by arthegall
The `Bow' Toolkit
december 2009 by arthegall
"The old text classifier system that won't go away..." (Oh, Rain-*bows*...)
text-classification
nlp
software
library
andrew-mccallum
classification
machinelearning
december 2009 by arthegall
SpringerLink - Book
november 2009 by arthegall
Machine Learning and Knowledge Discovery in Databases
European Conference, ECML PKDD 2009, Bled, Slovenia, September 7-11, 2009, Proceedings, Part II
book
springer
research
machinelearning
knowledge-base
database
European Conference, ECML PKDD 2009, Bled, Slovenia, September 7-11, 2009, Proceedings, Part II
november 2009 by arthegall
SpringerLink - Book
november 2009 by arthegall
Machine Learning and Knowledge Discovery in Databases
European Conference, ECML PKDD 2009, Bled, Slovenia, September 7-11, 2009, Proceedings, Part I
springer
book
research
knowledge-base
machinelearning
European Conference, ECML PKDD 2009, Bled, Slovenia, September 7-11, 2009, Proceedings, Part I
november 2009 by arthegall
CRF Project Page
november 2009 by arthegall
A reimplementation of some of the CRF papers (including the original one).
software
machinelearning
conditional-random-fields
research
november 2009 by arthegall
"What’s Wrong with Probability Notation?" (LingPipe Blog)
october 2009 by arthegall
It's a reasonable explication -- but I feel like every first-year CS graduate student has a similar personal revelation about notation when he or she first comes into contact with the machine learning or probabilistic AI literature. "This notation is horrible, but lambda calculus [or, more generally, a functional language, or some other technique I learned in my proglang course] will come to the rescue and clear all this up!" And then they work it out in the same way, have exactly the same realization (this is hard, people have tried and failed at it before), and move on. Or maybe they're Avi Pfeffer and they actually do something about it. But either way, it's a worthwhile exercise!
machinelearning
notation
programminglanguages
statistics
probability
computerscience
october 2009 by arthegall
Elements of Statistical Learning: data mining, inference, and prediction. 2nd Edition.
october 2009 by arthegall
Available for free as a PDF, online. (!!)
book
pdf
free
statistics
machinelearning
datamining
october 2009 by arthegall
Pennock, Wellman, "Toward a Market Model for Bayesian Inference," (UAI 1996)
september 2009 by arthegall
"We present a methodology for representing probabilistic relationships in a general-equilibrium economic model. Specifically, we define a precise mapping from a Bayesian network with binary nodes to a market price system where consumers and producers trade in uncertain propositions. We demonstrate the correspondence between the equilibrium prices of goods in this economy and the probabilities represented by the Bayesian network." -- (Noted in a link at this blog post: http://mblog.lib.umich.edu/strategic/archives/2009/09/market_reductio.html)
david-pennock
bayesian-networks
markets
uai
research-article
machinelearning
september 2009 by arthegall
Rai, Daume, and Venkatasubramanian, "Streamed Learning: One-Pass SVMs" (arXiv)
august 2009 by arthegall
Shaun! I think you know what I want to use this kind of thing for :-)
via:chl
machinelearning
svm
arxiv
research-article
streaming
online-algorithms
august 2009 by arthegall
"Log Sum of Exponentials" (LingPipe Blog)
june 2009 by arthegall
I was literally *just* writing up the portion of my thesis where I'm using this math... and I realize, I'm not checking for over/underflow correctly. Gotta go back and revise that, now. Thanks, LingPipe!!!
thesis
timely
logarithms
numerical-techniques
stability
floating-point-calculations
research
machinelearning
june 2009 by arthegall
"Why I Don't Buy Clustering Axioms" (natural language processing blog)
june 2009 by arthegall
In which Hal Daume re-discovers Section 5 of Kleinberg's paper, no?
clustering
kleinberg
machinelearning
consistency
missing-the-point
june 2009 by arthegall
Veronising...: Cooking for nerds: ingredient polyhedron and convex hull
may 2009 by arthegall
Time for: machine learning on recipes and ingredient lists. (Probably need to go through and code the 'labels' first: this one's a bread, this one's a cookie, this cookie is softer than that one, etc.)
machinelearning
cooking
recipes
data
may 2009 by arthegall
"Magnatagatune - a new research data set for MIR" (Music Machinery)
april 2009 by arthegall
"It contains:
* Human annotations collected by Edith Law’s TagATune game.
* The corresponding sound clips from magnatune.com, encoded in 16 kHz, 32kbps, mono mp3. (generously contributed by John Buckman, the founder of every MIR researcher’s favorite label Magnatune)
* A detailed analysis from The Echo Nest of the track’s structure and musical content, including rhythm, pitch and timbre.
* All the source code for generating the dataset distribution."
dataset
music
magnatagatune
research
machinelearning
luis-von-ahn
* Human annotations collected by Edith Law’s TagATune game.
* The corresponding sound clips from magnatune.com, encoded in 16 kHz, 32kbps, mono mp3. (generously contributed by John Buckman, the founder of every MIR researcher’s favorite label Magnatune)
* A detailed analysis from The Echo Nest of the track’s structure and musical content, including rhythm, pitch and timbre.
* All the source code for generating the dataset distribution."
april 2009 by arthegall
"Convergence is Relative: SGD vs. Pegasos, LibLinear, SVM^light, and SVM^perf" (LingPipe Blog)
april 2009 by arthegall
I've been recommending this paper to some people in the lab for (literally) months, now.
optimization
stochastic-gradient-descent
nati-srebro
pegasos
research-article
machinelearning
april 2009 by arthegall
Shalizi & Shalizi, "Blind Construction of Optimal Nonlinear Recursive Predictors for Discrete Sequences"
april 2009 by arthegall
CSSR is for discrete-timed discrete-valued time-series, right? (Downloading the code... now...)
cssr
cosma-shalizi
time-series
markov-chains
machinelearning
arxiv
research-article
april 2009 by arthegall
Robot scientist becomes first machine to discover new scientific knowledge
april 2009 by arthegall
I literally cannot wait to see what Wired does with this story... (Oh, no, wait. That's wrong: I totally *can* wait. In fact, I completely dread it.)
technology
futurism
journamalism
science
robot-scientist
machinelearning
april 2009 by arthegall
Calderbank, Jafarpour, Schapire, "Compressed Learning: Universal Sparse Dimensionality Reduction and Learning in the Measurement Domain" (PDF)
april 2009 by arthegall
Shaun, when you have a moment in the next week, do you want to^H^H^H^H^H^H would you be willing to talk about this?
compressed-sensing
learning
machinelearning
research
pdf
research-article
via:shivak
april 2009 by arthegall
"Compressed sensing and single-pixel cameras" (Terence Tao)
march 2009 by arthegall
I hadn't tagged this already? Srsly?
terence-tao
compressed-sensing
mathematics
optimization
machinelearning
signal-processing
statistics
compression
march 2009 by arthegall
Shalizi, Camperi, and Klinkner, "Discovering Functional Communities in Dynamical Networks" (arXiv)
march 2009 by arthegall
"In this paper, we lay out the problem of discovering_functional communities_, and describe an approach to doing so. This method combines recent work on measuring information sharing across stochastic networks with an existing and successful community-discovery algorithm for weighted networks. We illustrate it with an application to a large biophysical model of the transition from beta to gamma rhythms in the hippocampus."
via:cshalizi
arxiv
research-article
social-networks
networks
communities
machinelearning
march 2009 by arthegall
Learning Experiment Databases
march 2009 by arthegall
"An experiment database is a database designed to store learning experiments in full detail, aimed at providing a convenient platform for the study of learning algorithms." -- So, learning about learning algorithms, is it?
research
database
machinelearning
science
meta-learning
hyper-learning
results
march 2009 by arthegall
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