hunch_net hunch_net-2008 hunch_net-2008-291 knowledge-graph by maker-knowledge-mining
Source: html
Introduction: The spock challenge for named entity recognition was won by Berno Stein , Sven Eissen, Tino Rub, Hagen Tonnies, Christof Braeutigam, and Martin Potthast .
sentIndex sentText sentNum sentScore
1 The spock challenge for named entity recognition was won by Berno Stein , Sven Eissen, Tino Rub, Hagen Tonnies, Christof Braeutigam, and Martin Potthast . [sent-1, score-2.178]
wordName wordTfidf (topN-words)
[('entity', 0.474), ('spock', 0.474), ('named', 0.414), ('martin', 0.395), ('recognition', 0.301), ('won', 0.268), ('challenge', 0.247)]
simIndex simValue blogId blogTitle
same-blog 1 1.0 291 hunch net-2008-03-07-Spock Challenge Winners
Introduction: The spock challenge for named entity recognition was won by Berno Stein , Sven Eissen, Tino Rub, Hagen Tonnies, Christof Braeutigam, and Martin Potthast .
2 0.47053963 239 hunch net-2007-04-18-$50K Spock Challenge
Introduction: Apparently, the company Spock is setting up a $50k entity resolution challenge . $50k is much less than the Netflix challenge, but it’s effectively the same as Netflix until someone reaches 10% . It’s also nice that the Spock challenge has a short duration. The (visible) test set is of size 25k and the training set has size 75k.
3 0.087646164 477 hunch net-2013-01-01-Deep Learning 2012
Introduction: 2012 was a tumultuous year for me, but it was undeniably a great year for deep learning efforts. Signs of this include: Winning a Kaggle competition . Wide adoption of deep learning for speech recognition . Significant industry support . Gains in image recognition . This is a rare event in research: a significant capability breakout. Congratulations are definitely in order for those who managed to achieve it. At this point, deep learning algorithms seem like a choice undeniably worth investigating for real applications with significant data.
4 0.06119936 418 hunch net-2010-12-02-Traffic Prediction Problem
Introduction: Slashdot points out the Traffic Prediction Challenge which looks pretty fun. The temporal aspect seems to be very common in many real-world problems and somewhat understudied.
5 0.046146136 424 hunch net-2011-02-17-What does Watson mean?
Introduction: Watson convincingly beat the best champion Jeopardy! players. The apparent significance of this varies hugely, depending on your background knowledge about the related machine learning, NLP, and search technology. For a random person, this might seem evidence of serious machine intelligence, while for people working on the system itself, it probably seems like a reasonably good assemblage of existing technologies with several twists to make the entire system work. Above all, I think we should congratulate the people who managed to put together and execute this project—many years of effort by a diverse set of highly skilled people were needed to make this happen. In academia, it’s pretty difficult for one professor to assemble that quantity of talent, and in industry it’s rarely the case that such a capable group has both a worthwhile project and the support needed to pursue something like this for several years before success. Alina invited me to the Jeopardy watching party
6 0.04265834 47 hunch net-2005-03-28-Open Problems for Colt
7 0.04250408 494 hunch net-2014-03-11-The New York ML Symposium, take 2
8 0.041312821 129 hunch net-2005-11-07-Prediction Competitions
9 0.040127631 105 hunch net-2005-08-23-(Dis)similarities between academia and open source programmers
10 0.038912132 310 hunch net-2008-07-15-Interesting papers at COLT (and a bit of UAI & workshops)
11 0.037902612 197 hunch net-2006-07-17-A Winner
12 0.036644999 194 hunch net-2006-07-11-New Models
13 0.035330769 94 hunch net-2005-07-13-Text Entailment at AAAI
14 0.034576032 138 hunch net-2005-12-09-Some NIPS papers
15 0.032790959 144 hunch net-2005-12-28-Yet more nips thoughts
16 0.031887725 360 hunch net-2009-06-15-In Active Learning, the question changes
17 0.030083297 420 hunch net-2010-12-26-NIPS 2010
18 0.029082494 202 hunch net-2006-08-10-Precision is not accuracy
19 0.028804559 280 hunch net-2007-12-20-Cool and Interesting things at NIPS, take three
20 0.028282743 190 hunch net-2006-07-06-Branch Prediction Competition
topicId topicWeight
[(0, 0.017), (1, 0.006), (2, -0.02), (3, -0.001), (4, 0.001), (5, 0.004), (6, -0.036), (7, 0.003), (8, -0.013), (9, -0.042), (10, -0.019), (11, 0.108), (12, -0.047), (13, -0.021), (14, -0.005), (15, 0.027), (16, 0.005), (17, 0.006), (18, 0.011), (19, 0.008), (20, -0.12), (21, 0.014), (22, -0.06), (23, -0.08), (24, 0.064), (25, -0.07), (26, -0.004), (27, 0.009), (28, 0.017), (29, 0.013), (30, 0.116), (31, 0.15), (32, 0.052), (33, 0.083), (34, -0.098), (35, -0.075), (36, 0.007), (37, 0.089), (38, -0.027), (39, 0.037), (40, 0.238), (41, -0.079), (42, -0.031), (43, -0.184), (44, -0.136), (45, 0.058), (46, 0.164), (47, -0.102), (48, 0.021), (49, 0.023)]
simIndex simValue blogId blogTitle
same-blog 1 0.99915761 291 hunch net-2008-03-07-Spock Challenge Winners
Introduction: The spock challenge for named entity recognition was won by Berno Stein , Sven Eissen, Tino Rub, Hagen Tonnies, Christof Braeutigam, and Martin Potthast .
2 0.89900273 239 hunch net-2007-04-18-$50K Spock Challenge
Introduction: Apparently, the company Spock is setting up a $50k entity resolution challenge . $50k is much less than the Netflix challenge, but it’s effectively the same as Netflix until someone reaches 10% . It’s also nice that the Spock challenge has a short duration. The (visible) test set is of size 25k and the training set has size 75k.
3 0.52040219 94 hunch net-2005-07-13-Text Entailment at AAAI
Introduction: Rajat Raina presented a paper on the technique they used for the PASCAL Recognizing Textual Entailment challenge. “Text entailment” is the problem of deciding if one sentence implies another. For example the previous sentence entails: Text entailment is a decision problem. One sentence can imply another. The challenge was of the form: given an original sentence and another sentence predict whether there was an entailment. All current techniques for predicting correctness of an entailment are at the “flail” stage—accuracies of around 58% where humans could achieve near 100% accuracy, so there is much room to improve. Apparently, there may be another PASCAL challenge on this problem in the near future.
4 0.40455478 275 hunch net-2007-11-29-The Netflix Crack
Introduction: A couple security researchers claim to have cracked the netflix dataset . The claims of success appear somewhat overstated to me, but the method of attack is valid and could plausibly be substantially improved so as to reveal the movie preferences of a small fraction of Netflix users. The basic idea is to use a heuristic similarity function between ratings in a public database (from IMDB) and an anonymized database (Netflix) to link ratings in the private database to public identities (in IMDB). They claim to have linked two of a few dozen IMDB users to anonymized netflix users. The claims seem a bit inflated to me, because (a) knowing the IMDB identity isn’t equivalent to knowing the person and (b) the claims of statistical significance are with respect to a model of the world they created (rather than one they created). Overall, this is another example showing that complete privacy is hard . It may be worth remembering that there are some substantial benefits from the Netf
5 0.37404165 171 hunch net-2006-04-09-Progress in Machine Translation
Introduction: I just visited ISI where Daniel Marcu and others are working on machine translation. Apparently, machine translation is rapidly improving. A particularly dramatic year was 2002->2003 when systems switched from word-based translation to phrase-based translation. From a (now famous) slide by Charles Wayne at DARPA (which funds much of the work on machine translation) here is some anecdotal evidence: 2002 2003 insistent Wednesday may recurred her trips to Libya tomorrow for flying. Cairo 6-4 ( AFP ) – An official announced today in the Egyptian lines company for flying Tuesday is a company “insistent for flying” may resumed a consideration of a day Wednesday tomorrow her trips to Libya of Security Council decision trace international the imposed ban comment. And said the official “the institution sent a speech to Ministry of Foreign Affairs of lifting on Libya air, a situation her recieving replying are so a trip will pull to Libya a morning Wednesday.” E
6 0.29739654 47 hunch net-2005-03-28-Open Problems for Colt
7 0.28421101 418 hunch net-2010-12-02-Traffic Prediction Problem
8 0.27991584 63 hunch net-2005-04-27-DARPA project: LAGR
9 0.2662369 70 hunch net-2005-05-12-Math on the Web
10 0.2634019 122 hunch net-2005-10-13-Site tweak
11 0.25817224 362 hunch net-2009-06-26-Netflix nearly done
12 0.23659782 302 hunch net-2008-05-25-Inappropriate Mathematics for Machine Learning
13 0.230399 190 hunch net-2006-07-06-Branch Prediction Competition
14 0.22090946 310 hunch net-2008-07-15-Interesting papers at COLT (and a bit of UAI & workshops)
15 0.21548219 32 hunch net-2005-02-27-Antilearning: When proximity goes bad
16 0.20770405 84 hunch net-2005-06-22-Languages of Learning
17 0.20569669 414 hunch net-2010-10-17-Partha Niyogi has died
18 0.20170736 284 hunch net-2008-01-18-Datasets
19 0.20115705 424 hunch net-2011-02-17-What does Watson mean?
20 0.1953903 155 hunch net-2006-02-07-Pittsburgh Mind Reading Competition
topicId topicWeight
[(64, 0.723)]
simIndex simValue blogId blogTitle
same-blog 1 1.0 291 hunch net-2008-03-07-Spock Challenge Winners
Introduction: The spock challenge for named entity recognition was won by Berno Stein , Sven Eissen, Tino Rub, Hagen Tonnies, Christof Braeutigam, and Martin Potthast .
2 0.6532104 155 hunch net-2006-02-07-Pittsburgh Mind Reading Competition
Introduction: Francisco Pereira points out a fun Prediction Competition . Francisco says: DARPA is sponsoring a competition to analyze data from an unusual functional Magnetic Resonance Imaging experiment. Subjects watch videos inside the scanner while fMRI data are acquired. Unbeknownst to these subjects, the videos have been seen by a panel of other subjects that labeled each instant with labels in categories such as representation (are there tools, body parts, motion, sound), location, presence of actors, emotional content, etc. The challenge is to predict all of these different labels on an instant-by-instant basis from the fMRI data. A few reasons why this is particularly interesting: This is beyond the current state of the art, but not inconceivably hard. This is a new type of experiment design current analysis methods cannot deal with. This is an opportunity to work with a heavily examined and preprocessed neuroimaging dataset. DARPA is offering prizes!
3 0.62953472 442 hunch net-2011-08-20-The Large Scale Learning Survey Tutorial
Introduction: Ron Bekkerman initiated an effort to create an edited book on parallel machine learning that Misha and I have been helping with. The breadth of efforts to parallelize machine learning surprised me: I was only aware of a small fraction initially. This put us in a unique position, with knowledge of a wide array of different efforts, so it is natural to put together a survey tutorial on the subject of parallel learning for KDD , tomorrow. This tutorial is not limited to the book itself however, as several interesting new algorithms have come out since we started inviting chapters. This tutorial should interest anyone trying to use machine learning on significant quantities of data, anyone interested in developing algorithms for such, and of course who has bragging rights to the fastest learning algorithm on planet earth (Also note the Modeling with Hadoop tutorial just before ours which deals with one way of trying to speed up learning algorithms. We have almost no
4 0.45312884 210 hunch net-2006-09-28-Programming Languages for Machine Learning Implementations
Introduction: Machine learning algorithms have a much better chance of being widely adopted if they are implemented in some easy-to-use code. There are several important concerns associated with machine learning which stress programming languages on the ease-of-use vs. speed frontier. Speed The rate at which data sources are growing seems to be outstripping the rate at which computational power is growing, so it is important that we be able to eak out every bit of computational power. Garbage collected languages ( java , ocaml , perl and python ) often have several issues here. Garbage collection often implies that floating point numbers are “boxed”: every float is represented by a pointer to a float. Boxing can cause an order of magnitude slowdown because an extra nonlocalized memory reference is made, and accesses to main memory can are many CPU cycles long. Garbage collection often implies that considerably more memory is used than is necessary. This has a variable effect. I
5 0.42378923 420 hunch net-2010-12-26-NIPS 2010
Introduction: I enjoyed attending NIPS this year, with several things interesting me. For the conference itself: Peter Welinder , Steve Branson , Serge Belongie , and Pietro Perona , The Multidimensional Wisdom of Crowds . This paper is about using mechanical turk to get label information, with results superior to a majority vote approach. David McAllester , Tamir Hazan , and Joseph Keshet Direct Loss Minimization for Structured Prediction . This is about another technique for directly optimizing the loss in structured prediction, with an application to speech recognition. Mohammad Saberian and Nuno Vasconcelos Boosting Classifier Cascades . This is about an algorithm for simultaneously optimizing loss and computation in a classifier cascade construction. There were several other papers on cascades which are worth looking at if interested. Alan Fern and Prasad Tadepalli , A Computational Decision Theory for Interactive Assistants . This paper carves out some
6 0.40979466 18 hunch net-2005-02-12-ROC vs. Accuracy vs. AROC
7 0.40663645 277 hunch net-2007-12-12-Workshop Summary—Principles of Learning Problem Design
8 0.098036706 343 hunch net-2009-02-18-Decision by Vetocracy
9 0.08029817 426 hunch net-2011-03-19-The Ideal Large Scale Learning Class
10 0.079963535 191 hunch net-2006-07-08-MaxEnt contradicts Bayes Rule?
11 0.072477952 49 hunch net-2005-03-30-What can Type Theory teach us about Machine Learning?
12 0.06818331 26 hunch net-2005-02-21-Problem: Cross Validation
13 0.067398958 128 hunch net-2005-11-05-The design of a computing cluster
14 0.066472791 136 hunch net-2005-12-07-Is the Google way the way for machine learning?
15 0.058933146 424 hunch net-2011-02-17-What does Watson mean?
16 0.056949012 105 hunch net-2005-08-23-(Dis)similarities between academia and open source programmers
17 0.055587467 205 hunch net-2006-09-07-Objective and subjective interpretations of probability
18 0.054360077 153 hunch net-2006-02-02-Introspectionism as a Disease
19 0.051696323 351 hunch net-2009-05-02-Wielding a New Abstraction
20 0.051009808 301 hunch net-2008-05-23-Three levels of addressing the Netflix Prize