from data to knowledge: machine-learning with real-time and streaming applications
february 2012 by chl
cshalizi: 'intellectuals gathering in berkeley to argue about "knowledge" and "revolution".'
conf
ml
from delicious
february 2012 by chl
molecules from scratch without the fiendish physics | kurzweilai
february 2012 by chl
replacing schrödinger's equation with machine learning algorithms.
follow-up-refs
physics
ml
from delicious
february 2012 by chl
jellyfish
january 2012 by chl
"[...] software for large-scale matrix completion."
matrix-completion
ml
via:zenogantner
from delicious
january 2012 by chl
senna
january 2012 by chl
~3500 lines of c that can do pos tagging, chunking, named-entity recognition, semantic role labeling &c;.
srl
ner
pos-tagging
ronan-collobert
nn
ml
nlp
senna
from delicious
january 2012 by chl
stan - a c++ library for probability and sampling - google project hosting
january 2012 by chl
"[...] a directed graphical model compiler (like bugs) that uses adaptive hamiltonian monte carlo sampling to estimate posterior distributions for bayesian models."
graphical-models
bayes
ml
stan
from delicious
january 2012 by chl
hashing algorithms for large-scale learning - microsoft research
january 2012 by chl
"we compare b-bit minwise hashing with the count-min (cm) and vowpal wabbit (vw) algorithms, which have essentially the same variances as random projections. our theoretical and empirical comparisons illustrate that b-bit minwise hashing is significantly more accurate (at the same storage cost) than vw (and random projections) for binary data."
ml
random-projections
b-bit-minwise-hashing
vowpal-wabbit
hashing
from delicious
january 2012 by chl
#nips2011 day 1 highlights - alexandre passos' research blog
december 2011 by chl
RT @atpassos_ml: New blog post: #NIPS2011 Day 1 Highlights,
nips-2011
ml
NIPS2011
from delicious
december 2011 by chl
news:sgd-2.0_released [léon bottou]
october 2011 by chl
"this release provides implementations of the stochastic gradient descent and averaged stochastic gradient descent algorithms for linear svms and crfs. the latter sometimes shows vastly superior performance."
ml
sgd
by:léon-bottou
from delicious
october 2011 by chl
gaelvaroquaux/scikit-learn-tutorial - github
july 2011 by chl
Material from Gael Varoquaux's scikits.learn tutorial on statistical learning in Python #SciPy2011
scikits.learn
numpy
scipy
ml
tutorial
by:gael-varoquaux
SciPy2011
from delicious
july 2011 by chl
[1106.2429] efficient online learning via randomized rounding
june 2011 by chl
"most online algorithms used in machine learning today are based on variants of mirror descent or follow-the-leader. in this paper, we present an online algorithm based on a completely different approach, which combines "random playout" and randomized rounding of loss subgradients."
ml
online-learning
paper
later
via:shivak
from delicious
june 2011 by chl
youtube - the future of robotics and artificial intelligence (andrew ng, stanford university, stan 2011)
june 2011 by chl
The Future of Robotics and Artificial Intelligence (Andrew Ng, Stanford University, STAN 2011): #machine learning
watchlist
talk
by:andrew-ng
ml
robotics
machine
watchedlist
from delicious
june 2011 by chl
first thoughts on orthogonal matching pursuit « my programming and machine learning blog
june 2011 by chl
First thoughts on Orthogonal Matching Pursuit #machinelearning #sparsecoding #gsoc2011 /by Vlad Niculae
orthogonal-matching-pursuit
ml
later
sparsecoding
gsoc2011
machinelearning
from delicious
june 2011 by chl
a statistical learning/pattern recognition glossary
may 2011 by chl
A Statistical Learning/Pattern Recognition Glossary #machinelearning
ml
pr
glossary
machinelearning
from delicious
may 2011 by chl
sta 414/2104: statistical methods for machine learning and data mining
may 2011 by chl
Great lectures notes by Radford Neal on #machinelearning with #rstats,
ml
dm
stat
course
by:radford-neal
r
rstats
machinelearning
from delicious
may 2011 by chl
(3) random forests: how do random forests work in layman's terms? - quora
april 2011 by chl
"and so your friends now form a random forest."
random-forests
ml
from delicious
april 2011 by chl
twitter conversation with mathieuen
april 2011 by chl
"hash trick + cross-product features = polynomial kernel-like features in feature space"
ml
from delicious
april 2011 by chl
youtube - bay area vision meeting: unsupervised feature learning and deep learning
april 2011 by chl
andrew ng on (recent successes in) unsupervised feature learning, sparse coding &c.: / merci à @ogrisel
cv
ml
watchlist
via:ogrisel
google-techtalk
by:andrew-ng
watchedlist
from delicious
april 2011 by chl
related tags
! ⊕ #elasticnet ⊕ #FOSDEM ⊕ #liblinear ⊕ #libsvm ⊕ #machinelearning ⊕ #NIPS2010 ⊕ #python ⊕ #sleepdeprivedinthenameofscience ⊕ ***** ⊕ 2009-09-25 ⊕ aaai ⊕ aaai-2008 ⊕ abc-boost ⊕ abc-mart ⊕ academia ⊕ acceptance-rates ⊕ aco ⊕ active-learning ⊕ adaboost ⊕ adaline ⊕ adaption ⊕ adtree ⊕ ai ⊕ aiclass ⊕ aima ⊕ ais ⊕ aistats-2009 ⊕ aixi ⊕ alchemy ⊕ aleph ⊕ alg ⊕ announcement ⊕ apprenticeship-learning ⊕ approximate-closest-pairs ⊕ arduino ⊕ article ⊕ astronomy ⊕ astrostatistics ⊕ audio ⊕ auto-diff ⊕ autoencoders ⊕ autonomous-driving ⊕ avrim-blum ⊕ await ⊕ awesome ⊕ awk ⊕ azure ⊕ b-bit-minwise-hashing ⊕ backprop ⊕ bagging ⊕ bandits ⊕ bart ⊕ bayes ⊕ bayes-nets ⊕ bayesnet ⊕ bci ⊕ beam-search ⊕ belief-propagation ⊕ bell-labs ⊕ benchmark ⊕ berkeley ⊕ best-of ⊕ best-papers ⊕ bi ⊕ bias-variance-trade-off ⊕ bigchaos ⊕ bigml ⊕ billaboop ⊕ bing ⊕ bio ⊕ bio-inf ⊕ biology ⊕ blockbuster ⊕ blog ⊕ bolasso ⊕ bolt ⊕ boltzmann-machines ⊕ book ⊕ books ⊕ boosted-decision-trees ⊕ boosting ⊕ boosting? ⊕ bootstrapping ⊕ bugs ⊕ by:aaron-hertzman ⊕ by:agray0? ⊕ by:alex-gray ⊕ by:alexei-efros ⊕ by:alon-halevy ⊕ by:andrew-mccallum ⊕ by:andrew-ng ⊕ by:andriy-mnih ⊕ by:antonio-torralba ⊕ by:arthegall ⊕ by:bernhard-schölkopf ⊕ by:bill-triggs ⊕ by:bradford-cross ⊕ by:charles-elkan ⊕ by:charles-kemp ⊕ by:charles-sutton ⊕ by:cshalizi ⊕ by:curtis-poe ⊕ by:david-barber ⊕ by:david-m-blei ⊕ by:david-mackay ⊕ by:david-mechner ⊕ by:dwf ⊕ by:fernando-pereira ⊕ by:gael-varoquaux ⊕ by:geoff-hinton ⊕ by:georges-harik ⊕ by:hal-daumé ⊕ by:i2pi ⊕ by:james-hays ⊕ by:jason-rennie ⊕ by:josh-wills ⊕ by:joshua-tenenbaum ⊕ by:justin-domke ⊕ by:kevin-knight ⊕ by:kevin-murphy ⊕ by:longhai-li ⊕ by:léon-bottou ⊕ by:mblondel ⊕ by:mdreid ⊕ by:michael-jordan ⊕ by:mreid ⊕ by:noam-shazeer ⊕ by:ogrisel ⊕ by:pascal-vincent ⊕ by:paul-mineiro ⊕ by:percy-liang ⊕ by:peter-norvig ⊕ by:peter-turney ⊕ by:pfh ⊕ by:pprett ⊕ by:pskomoroch ⊕ by:radford-neal ⊕ by:reza-shadmehr ⊕ by:risi-kondor ⊕ by:rob-fergus ⊕ by:ronan-collobert ⊕ by:sam-roweis ⊕ by:sami-bengio ⊕ by:samy-bengio ⊕ by:scott-aaronson ⊕ by:sebastian-thrun ⊕ by:simon-funk ⊕ by:ted-dunning ⊕ by:tin-kam-ho ⊕ by:tom-dietterich ⊕ by:tom-mitchell ⊕ by:ulrike-von-luxburg ⊕ by:weezur ⊕ by:yair-weiss ⊕ by:yann-lecun ⊕ by:yehuda-koren ⊕ by:yoav-freund ⊕ by:yuri-lifshits ⊕ by:zoubin-ghahramani ⊕ c# ⊕ c++ ⊕ c4.5 ⊕ cacm ⊕ caltech ⊕ cam ⊕ cambridge ⊕ cbll ⊕ cf ⊕ challenge ⊕ cheatsheets ⊕ chess ⊕ cifar ⊕ classics ⊕ classification ⊕ classifier ⊕ classifiers ⊕ clojure ⊕ clustering ⊕ cmu ⊕ co-training ⊕ code ⊕ cog-sci ⊕ cognition ⊕ colt ⊕ colt-2008 ⊕ comp-bio ⊕ comp-go ⊕ comp-ling ⊕ comp-sci ⊕ competition ⊕ competitions ⊕ compressed-sensing ⊕ compressive-sampling ⊕ computer-assisted-discovery ⊕ conf ⊕ connectomics ⊕ controller ⊕ convolutional-neural-networks ⊕ cos242 ⊕ cotraining ⊕ counterintuition ⊕ course ⊕ course-notes ⊕ courses ⊕ crf ⊕ cross-entropy ⊕ cross-validation ⊕ crossover-random-fields ⊕ crowdsourced-data ⊕ crowdsourcing ⊕ cs ⊕ cs229 ⊕ ctr-prediction ⊕ ctypes ⊕ cuda ⊕ curr ⊕ curriculum ⊕ cv ⊕ cv? ⊕ cvpr ⊕ cvpr-2008 ⊕ data ⊕ data-analysis ⊕ data-mining ⊕ data-parallel ⊕ data-proc ⊕ data-to-knowledge-2012 ⊕ datadevroom ⊕ db ⊕ dbn ⊕ decision-trees ⊕ deep-learning ⊕ deep-nets ⊕ delta-lda ⊕ denoising-autoencoders ⊕ density-estimation ⊕ developmental-robotics ⊕ devroom ⊕ dictionary-learning ⊕ diffusion-maps ⊕ dim-red ⊕ dipre ⊕ discovery-of-structural-form ⊕ discussion ⊕ dist ⊕ distance-metric-learning ⊕ divergences ⊕ dlib ⊕ dm ⊕ docs ⊕ dolores-labs ⊕ dot-products ⊕ draft ⊕ dryad ⊕ dryadlinq ⊕ dsp ⊕ dup? ⊕ dupe ⊕ dupe? ⊕ dynamic-programming ⊕ eblearn ⊕ ecml-2008 ⊕ eda ⊕ edu ⊕ elastic-net ⊕ elasticnet ⊕ em ⊕ energy-based-models ⊕ engedu ⊕ ensemble-methods ⊕ epca ⊕ esl-2 ⊕ etl ⊕ eu:pascal ⊕ eval ⊕ evaluation ⊕ event ⊕ evolution ⊕ evolvability ⊕ excellent ⊕ exercises ⊕ expert-systems ⊕ exposition ⊕ extremely-randomized-trees ⊕ face-recognition ⊕ factor-graphs ⊕ factorie ⊕ fantastic ⊕ fastlib ⊕ feature-engineering ⊕ feature-selection ⊕ fest ⊕ filetype:pdf ⊕ fin ⊕ finance ⊕ flann ⊕ flightcaster ⊕ fm ⊕ follow-up ⊕ follow-up-links ⊕ follow-up-refs ⊕ food-for-thought ⊕ forum ⊕ forward-stagewise ⊕ fosdem ⊕ fosdem-2011 ⊕ free ⊕ frequent-itemset ⊕ from:earl ⊕ from:gavin ⊕ funding ⊕ funk-svd ⊕ furf ⊕ gaussian-process ⊕ gblearn2 ⊕ gbm ⊕ gbrt ⊕ gdd07 ⊕ gen ⊕ genetics ⊕ genomics ⊕ geo ⊕ geoff-hinton ⊕ gestures ⊕ gha ⊕ gibbs-sampler ⊕ glmnet ⊕ glossary ⊕ gmail ⊕ gmdh ⊕ go ⊕ google ⊕ google-bigquery ⊕ google-io-2010 ⊕ google-prediction-api ⊕ google-seti ⊕ google-sets ⊕ google-techtalk ⊕ gp ⊕ gpgpu ⊕ gps ⊕ gradient-boosting ⊕ gradient-descent ⊕ gram-schmidt ⊕ graphical-models ⊕ group-theory ⊕ groups ⊕ gsoc ⊕ gsoc2011 ⊕ hadoop ⊕ harik-shazeer ⊕ harmonic-analysis ⊕ has:via ⊕ hashing ⊕ haskell ⊕ hbc ⊕ hci ⊕ hebbian-learning ⊕ heh ⊕ helicopters ⊕ hi-di ⊕ hierarchical-clustering ⊕ history ⊕ hit-prediction ⊕ hit-song-science ⊕ hmm ⊕ hypothesis-testing ⊕ ic ⊕ ica ⊕ iccv-2011 ⊕ icml ⊕ icml-2008 ⊕ icml2007 ⊕ idea ⊕ ideas ⊕ idsia ⊕ ift6266 ⊕ ilp ⊕ im2gps ⊕ images ⊕ in-progress ⊕ in:nature ⊕ incanter ⊕ inductive-bias ⊕ inductive-biases ⊕ inequalities ⊕ infer.net ⊕ info-theory ⊕ information-forests ⊕ inmf ⊕ intelligence-amplification ⊕ interesting ⊕ interpretability ⊕ interview ⊕ intro ⊕ ip ⊕ ipad-app ⊕ ipam ⊕ ir ⊕ isomap ⊕ java ⊕ javascript ⊕ jboost ⊕ jmlr ⊕ job ⊕ journal ⊕ k-means ⊕ kdd-2009 ⊕ kdd-2011 ⊕ kdd-cup-2009 ⊕ kdd-cup-2011 ⊕ kepler ⊕ kernel-distance ⊕ kernel-functions ⊕ kernel-methods ⊕ kernel-pca ⊕ kiefer-wolfowitz ⊕ knn ⊕ krems ⊕ l-bfgs ⊕ lang:fr ⊕ laplacian-eigenmaps ⊕ lar ⊕ large-scale ⊕ lars ⊕ lasso ⊕ later ⊕ lda ⊕ learning ⊕ learning-theory ⊕ lecture ⊕ lecture-notes ⊕ lectures ⊕ lfd ⊕ lib ⊕ libfm ⊕ liblinear ⊕ libs ⊕ libsvm ⊕ lin-reg ⊕ linear-classifiers ⊕ linearity ⊕ ling ⊕ lingpipe ⊕ links ⊕ linq ⊕ lisp ⊕ list ⊕ logistic-regression ⊕ loss-functions ⊕ low-dimensional-codes ⊕ lsa ⊕ lsh ⊕ lsl ⊕ lt ⊕ lua ⊕ lucene ⊕ lush ⊕ lvq ⊕ m2py ⊕ machine ⊕ machinelearning ⊕ mahout ⊕ mapreduce ⊕ markov-logic ⊕ mart ⊕ materials ⊕ math ⊕ matlab ⊕ matplotlib ⊕ matrix-completion ⊕ max-ent ⊕ maxent ⊕ mcmc ⊕ mdp ⊕ media:document ⊕ media:html ⊕ media:pdf ⊕ media:ps ⊕ message-passing ⊕ meta-features ⊕ metric-embeddings ⊕ mf ⊕ mit ⊕ ml ⊖ ml-as-sport ⊕ ml-forum ⊕ ml-radar ⊕ mlclass ⊕ mloss ⊕ mlpa ⊕ mlpack ⊕ mlpy ⊕ mlss ⊕ mlss-2009 ⊕ mlss-2011 ⊕ mmds ⊕ mmds-2008 ⊕ mmmf ⊕ mmp ⊕ model-selection ⊕ mrf ⊕ msft ⊕ msr ⊕ msrc ⊕ mturk ⊕ multiclass-classifier ⊕ music ⊕ my:mblondel ⊕ mycin ⊕ n-gram ⊕ na ⊕ naive-bayes ⊕ naive-credal ⊕ nando-de-freitas ⊕ natural-gradient ⊕ navia ⊕ naïve-bayes ⊕ nca ⊕ ncap ⊕ nec ⊕ ner ⊕ netkit ⊕ neuro-sci ⊕ news.ycer ⊕ nflxprize ⊕ nflxprize-finale ⊕ nieme ⊕ nips ⊕ nips-2006 ⊕ nips-2007 ⊕ nips-2008 ⊕ nips-2009 ⊕ nips-2010 ⊕ nips-2011 ⊕ NIPS2010 ⊕ NIPS2011 ⊕ nlp ⊕ nltk ⊕ nmf ⊕ nn ⊕ no-free-lunch ⊕ node-harvest ⊕ noncon ⊕ nonlinearity ⊕ norb ⊕ notes ⊕ now! ⊕ npbayes ⊕ npde ⊕ numpy ⊕ nvidia ⊕ nyu ⊕ ocas ⊕ octave ⊕ ohmm ⊕ oja's-rule ⊕ omp ⊕ one-pass ⊕ online ⊕ online-alg ⊕ online-learning ⊕ online-pca ⊕ open-source ⊕ opencv ⊕ opencv-ml ⊕ opt ⊕ opt-2008 ⊕ orange ⊕ orthogonal-matching-pursuit ⊕ os ⊕ out-of-print ⊕ overcomplete-repr ⊕ p:m3p2r ⊕ panalogies ⊕ paper ⊕ papers ⊕ parallel ⊕ particle-filtering ⊕ pca ⊕ pdp ⊕ peer-review ⊕ pegasos ⊕ people ⊕ perception ⊕ perceptron ⊕ perceptrons ⊕ personalization ⊕ peter-norvig ⊕ philosophy ⊕ physics ⊕ pig ⊕ planet ⊕ plop ⊕ plsa ⊕ plsi ⊕ pmf ⊕ pmi-ir ⊕ pos-tagging ⊕ postgresql ⊕ pr ⊕ prediction ⊕ preprocessing ⊕ priority-inbox ⊕ prob ⊕ probabilistic-topic-models ⊕ proceedings ⊕ project-natal ⊕ prolog ⊕ psychology ⊕ pubs ⊕ pubtrack ⊕ pvm ⊕ pybrain ⊕ pycon-2009 ⊕ pycon-2011 ⊕ pysgd ⊕ python ⊕ q&a ⊕ q'd ⊕ qda ⊕ quantum-alg ⊕ r ⊕ random-features ⊕ random-forests ⊕ random-indexing ⊕ random-kitchen-sinks ⊕ random-naïve-bayes ⊕ random-projections ⊕ ranking ⊕ rating:fantastic ⊕ rbm ⊕ rc ⊕ reading-group ⊕ reading-list ⊕ readings ⊕ rec-sys ⊕ reddit ⊕ ref ⊕ refresh:1 ⊕ refresh:3 ⊕ refs ⊕ regression ⊕ regression-trees ⊕ regularization ⊕ reinforcement-learning ⊕ relational-bayesian-networks ⊕ relevance ⊕ research ⊕ research-changing-books ⊕ research-group ⊕ research-paper ⊕ resolve:hal3 ⊕ resources ⊕ retrospective ⊕ review-paper ⊕ revisit-in:2007-09 ⊕ ri ⊕ ril ⊕ rkhs ⊕ rnn ⊕ roadmap ⊕ robbins-monro ⊕ robot-science ⊕ robotics ⊕ roc ⊕ rocr ⊕ ronan-collobert ⊕ rprop ⊕ rstats ⊕ scala ⊕ sci ⊕ science ⊕ scikit ⊕ scikit-learn ⊕ scikits.learn ⊕ scipy ⊕ SciPy2011 ⊕ scratch-input ⊕ seam-carving ⊕ sebastian-seung ⊕ see ⊕ see-ml-class ⊕ self-driving-cars ⊕ self-parking-cars ⊕ self-taught-hashing ⊕ semantic-engine ⊕ semantic-hashing ⊕ semantic-texton-forests ⊕ semi-supervised-learning ⊕ seminars ⊕ semisup ⊕ senna ⊕ series:synthesis-lectures ⊕ sgd ⊕ shark ⊕ shell ⊕ shogun ⊕ shopping ⊕ sim-search ⊕ similarity ⊕ similarity-graphs ⊕ similarity-learning ⊕ similarity-metrics ⊕ similarity-search ⊕ simon-funk ⊕ single-pass ⊕ sleepdeprivedinthenameofscience ⊕ slides ⊕ snowbird ⊕ soda ⊕ soda-2009 ⊕ software ⊕ som ⊕ sound ⊕ sparse-codes ⊕ sparse-coding ⊕ sparse-representations ⊕ sparsecoding ⊕ specialist-gradient-descent ⊕ spectral-clustering ⊕ spectral-kernel-methods ⊕ srl ⊕ ssll-2009 ⊕ stan ⊕ stanford ⊕ stat ⊕ stat-mt ⊕ stat-nlp ⊕ statistical-learning-theory ⊕ statistical-relational-learning ⊕ statlog ⊕ stats ⊕ status:watched ⊕ stochastic-approximation ⊕ stochastic-discrimination ⊕ story ⊕ strat ⊕ structurally-random-matrices ⊕ summaries ⊕ summer-school ⊕ survey ⊕ svd ⊕ svm ⊕ sw ⊕ syllabus ⊕ symbolic-regression ⊕ synergetic-intelligence ⊕ t-sne ⊕ talk ⊕ talks ⊕ tanagra ⊕ teaching ⊕ text-mining ⊕ text-proc ⊕ tf-idf ⊕ theano ⊕ thesis ⊕ thesis/phd ⊕ toefl ⊕ tom-mitchell ⊕ topic-modeling ⊕ topic-models ⊕ topics ⊕ torch3 ⊕ torch5 ⊕ trees ⊕ trueskill ⊕ tutorial ⊕ tutorials ⊕ twitter ⊕ uai ⊕ uav ⊕ unsupervised-learning ⊕ vectors ⊕ via:* ⊕ via:? ⊕ via:agray0 ⊕ via:alstrup ⊕ via:arsyed ⊕ via:arthegall ⊕ via:atrilla ⊕ via:benmoran ⊕ via:bob-carpenter ⊕ via:brendano ⊕ via:britta ⊕ via:centr0id ⊕ via:csantos ⊕ via:cshalizi ⊕ via:cwensel ⊕ via:dabd ⊕ via:ded_maxim ⊕ via:der_mo ⊕ via:dwf ⊕ via:fractal76 ⊕ via:gappy ⊕ via:gappy3000 ⊕ via:gelman-gang ⊕ via:glinden ⊕ via:herrmann ⊕ via:innersmile0790 ⊕ via:javadi82 ⊕ via:jdkimball ⊕ via:jhammerb ⊕ via:jhofman ⊕ via:jolby ⊕ via:lemonodor ⊕ via:maciej ⊕ via:mikiobraun ⊕ via:mirwox ⊕ via:mreid ⊕ via:neuraxon77 ⊕ via:news.yc ⊕ via:nikete ⊕ via:ogrisel ⊕ via:pskomoroch ⊕ via:rocksportrocker ⊕ via:schaul ⊕ via:shivak ⊕ via:sidmitra ⊕ via:simon.belak ⊕ via:sonots ⊕ via:ssn ⊕ via:tahonen ⊕ via:tang ⊕ via:timwee ⊕ via:tisihara ⊕ via:tlipcon ⊕ via:trunov ⊕ via:vaguery ⊕ via:wnpxrz ⊕ via:yann-lecun ⊕ via:zenogantner ⊕ video ⊕ video-lecture ⊕ video-lectures ⊕ videos ⊕ vision ⊕ viz ⊕ vowpal-wabbit ⊕ watchedlist ⊕ watchlist ⊕ watchlist-source ⊕ weka ⊕ with:edwin-chen ⊕ with:geoff-hinton ⊕ with:tom-mitchell ⊕ with:vladimir-vapnik ⊕ with:yann-lecun ⊕ woah ⊕ wordnet ⊕ worio ⊕ workbench ⊕ workflow ⊕ wrapper ⊕ xbox ⊕ xrf ⊕ y! ⊕ y!-labs ⊕ yann-lecun ⊕ zite ⊕ ☆ ⊕Copy this bookmark: