hunch_net hunch_net-2006 hunch_net-2006-222 knowledge-graph by maker-knowledge-mining
Source: html
Introduction: One of the subsidiary roles of conferences is recruitment. NIPS is optimally placed in time for this because it falls right before the major recruitment season. I personally found job hunting embarrassing, and was relatively inept at it. I expect this is true of many people, because it is not something done often. The basic rule is: make the plausible hirers aware of your interest. Any corporate sponsor is a “plausible”, regardless of whether or not there is a booth. CRA and the acm job center are other reasonable sources. There are substantial differences between the different possibilities. Putting some effort into understanding the distinctions is a good idea, although you should always remember where the other person is coming from.
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same-blog 1 0.99999994 222 hunch net-2006-12-05-Recruitment Conferences
Introduction: One of the subsidiary roles of conferences is recruitment. NIPS is optimally placed in time for this because it falls right before the major recruitment season. I personally found job hunting embarrassing, and was relatively inept at it. I expect this is true of many people, because it is not something done often. The basic rule is: make the plausible hirers aware of your interest. Any corporate sponsor is a “plausible”, regardless of whether or not there is a booth. CRA and the acm job center are other reasonable sources. There are substantial differences between the different possibilities. Putting some effort into understanding the distinctions is a good idea, although you should always remember where the other person is coming from.
2 0.10925162 460 hunch net-2012-03-24-David Waltz
Introduction: has died . He lived a full life. I know him personally as a founder of the Center for Computational Learning Systems and the New York Machine Learning Symposium , both of which have sheltered and promoted the advancement of machine learning. I expect much of the New York area machine learning community will miss him, as well as many others around the world.
3 0.093597293 142 hunch net-2005-12-22-Yes , I am applying
Introduction: Every year about now hundreds of applicants apply for a research/teaching job with the timing governed by the university recruitment schedule. This time, it’s my turn—the hat’s in the ring, I am a contender, etc… What I have heard is that this year is good in both directions—both an increased supply and an increased demand for machine learning expertise. I consider this post a bit of an abuse as it is neither about general research nor machine learning. Please forgive me this once. My hope is that I will learn about new places interested in funding basic research—it’s easy to imagine that I have overlooked possibilities. I am not dogmatic about where I end up in any particular way. Several earlier posts detail what I think of as a good research environment, so I will avoid a repeat. A few more details seem important: Application. There is often a tension between basic research and immediate application. This tension is not as strong as might be expected in my case. As
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Introduction: Reviewers and students are sometimes greatly concerned by the distinction between: An open set and a closed set . A Supremum and a Maximum . An event which happens with probability 1 and an event that always happens. I don’t appreciate this distinction in machine learning & learning theory. All machine learning takes place (by definition) on a machine where every parameter has finite precision. Consequently, every set is closed, a maximal element always exists, and probability 1 events always happen. The fundamental issue here is that substantial parts of mathematics don’t appear well-matched to computation in the physical world, because the mathematics has concerns which are unphysical. This mismatched mathematics makes irrelevant distinctions. We can ask “what mathematics is appropriate to computation?” Andrej has convinced me that a pretty good answer to this question is constructive mathematics . So, here’s a basic challenge: Can anyone name a situati
5 0.077993408 242 hunch net-2007-04-30-COLT 2007
Introduction: Registration for COLT 2007 is now open. The conference will take place on 13-15 June, 2007, in San Diego, California, as part of the 2007 Federated Computing Research Conference (FCRC), which includes STOC, Complexity, and EC. The website for COLT: http://www.learningtheory.org/colt2007/index.html The early registration deadline is May 11, and the cutoff date for discounted hotel rates is May 9. Before registering, take note that the fees are substantially lower for members of ACM and/or SIGACT than for nonmembers. If you’ve been contemplating joining either of these two societies (annual dues: $99 for ACM, $18 for SIGACT), now would be a good time!
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same-blog 1 0.96349806 222 hunch net-2006-12-05-Recruitment Conferences
Introduction: One of the subsidiary roles of conferences is recruitment. NIPS is optimally placed in time for this because it falls right before the major recruitment season. I personally found job hunting embarrassing, and was relatively inept at it. I expect this is true of many people, because it is not something done often. The basic rule is: make the plausible hirers aware of your interest. Any corporate sponsor is a “plausible”, regardless of whether or not there is a booth. CRA and the acm job center are other reasonable sources. There are substantial differences between the different possibilities. Putting some effort into understanding the distinctions is a good idea, although you should always remember where the other person is coming from.
2 0.56869304 366 hunch net-2009-08-03-Carbon in Computer Science Research
Introduction: Al Gore ‘s film and gradually more assertive and thorough science has managed to mostly shift the debate on climate change from “Is it happening?” to “What should be done?” In that context, it’s worthwhile to think a bit about what can be done within computer science research. There are two things we can think about: Doing Research At a cartoon level, computer science research consists of some combination of commuting to&from; work, writing programs, running them on computers, writing papers, and presenting them at conferences. A typical computer has a power usage on the order of 100 Watts, which works out to 2.4 kiloWatt-hours/day. Looking up David MacKay ‘s reference on power usage per person , it becomes clear that this is a relatively minor part of the lifestyle, although it could become substantial if many more computers are required. Much larger costs are associated with commuting (which is in common with many people) and attending conferences. Since local commuti
3 0.56083089 491 hunch net-2013-11-21-Ben Taskar is gone
Introduction: I was not as personally close to Ben as Sam , but the level of tragedy is similar and I can’t help but be greatly saddened by the loss. Various news stories have coverage, but the synopsis is that he had a heart attack on Sunday and is survived by his wife Anat and daughter Aviv. There is discussion of creating a memorial fund for them, which I hope comes to fruition, and plan to contribute to. I will remember Ben as someone who thought carefully and comprehensively about new ways to do things, then fought hard and successfully for what he believed in. It is an ideal we strive for, that Ben accomplished. Edit: donations go here , and more information is here .
4 0.56013125 333 hunch net-2008-12-27-Adversarial Academia
Introduction: One viewpoint on academia is that it is inherently adversarial: there are finite research dollars, positions, and students to work with, implying a zero-sum game between different participants. This is not a viewpoint that I want to promote, as I consider it flawed. However, I know several people believe strongly in this viewpoint, and I have found it to have substantial explanatory power. For example: It explains why your paper was rejected based on poor logic. The reviewer wasn’t concerned with research quality, but rather with rejecting a competitor. It explains why professors rarely work together. The goal of a non-tenured professor (at least) is to get tenure, and a case for tenure comes from a portfolio of work that is undisputably yours. It explains why new research programs are not quickly adopted. Adopting a competitor’s program is impossible, if your career is based on the competitor being wrong. Different academic groups subscribe to the adversarial viewp
5 0.53170633 193 hunch net-2006-07-09-The Stock Prediction Machine Learning Problem
Introduction: …is discussed in this nytimes article . I generally expect such approaches to become more common since computers are getting faster, machine learning is getting better, and data is becoming more plentiful. This is another example where machine learning technology may have a huge economic impact. Some side notes: We-in-research know almost nothing about how these things are done (because it is typically a corporate secret). … but the limited discussion in the article seem naive from a machine learning viewpoint. The learning process used apparently often fails to take into account transaction costs. What little of the approaches is discussed appears modeling based. It seems plausible that more direct prediction methods can yield an edge. One difficulty with stock picking as a research topic is that it is inherently a zero sum game (for every winner, there is a loser). Much of the rest of research is positive sum (basically, everyone wins).
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same-blog 1 0.93154484 222 hunch net-2006-12-05-Recruitment Conferences
Introduction: One of the subsidiary roles of conferences is recruitment. NIPS is optimally placed in time for this because it falls right before the major recruitment season. I personally found job hunting embarrassing, and was relatively inept at it. I expect this is true of many people, because it is not something done often. The basic rule is: make the plausible hirers aware of your interest. Any corporate sponsor is a “plausible”, regardless of whether or not there is a booth. CRA and the acm job center are other reasonable sources. There are substantial differences between the different possibilities. Putting some effort into understanding the distinctions is a good idea, although you should always remember where the other person is coming from.
2 0.71111727 146 hunch net-2006-01-06-MLTV
Introduction: As part of a PASCAL project, the Slovenians have been filming various machine learning events and placing them on the web here . This includes, for example, the Chicago 2005 Machine Learning Summer School as well as a number of other summer schools, workshops, and conferences. There are some significant caveats here—for example, I can’t access it from Linux. Based upon the webserver logs, I expect that is a problem for most people—Computer scientists are particularly nonstandard in their choice of computing platform. Nevertheless, the core idea here is excellent and details of compatibility can be fixed later. With modern technology toys, there is no fundamental reason why the process of announcing new work at a conference should happen only once and only for the people who could make it to that room in that conference. The problems solved include: The multitrack vs. single-track debate. (“Sometimes the single track doesn’t interest me” vs. “When it’s multitrack I mis
3 0.63078058 68 hunch net-2005-05-10-Learning Reductions are Reductionist
Introduction: This is about a fundamental motivation for the investigation of reductions in learning. It applies to many pieces of work other than my own. The reductionist approach to problem solving is characterized by taking a problem, decomposing it into as-small-as-possible subproblems, discovering how to solve the subproblems, and then discovering how to use the solutions to the subproblems to solve larger problems. The reductionist approach to solving problems has often payed off very well. Computer science related examples of the reductionist approach include: Reducing computation to the transistor. All of our CPUs are built from transistors. Reducing rendering of images to rendering a triangle (or other simple polygons). Computers can now render near-realistic scenes in real time. The big breakthrough came from learning how to render many triangles quickly. This approach to problem solving extends well beyond computer science. Many fields of science focus on theories mak
4 0.57957196 34 hunch net-2005-03-02-Prior, “Prior” and Bias
Introduction: Many different ways of reasoning about learning exist, and many of these suggest that some method of saying “I prefer this predictor to that predictor” is useful and necessary. Examples include Bayesian reasoning, prediction bounds, and online learning. One difficulty which arises is that the manner and meaning of saying “I prefer this predictor to that predictor” differs. Prior (Bayesian) A prior is a probability distribution over a set of distributions which expresses a belief in the probability that some distribution is the distribution generating the data. “Prior” (Prediction bounds & online learning) The “prior” is a measure over a set of classifiers which expresses the degree to which you hope the classifier will predict well. Bias (Regularization, Early termination of neural network training, etc…) The bias is some (often implicitly specified by an algorithm) way of preferring one predictor to another. This only scratches the surface—there are yet more subt
5 0.5711087 141 hunch net-2005-12-17-Workshops as Franchise Conferences
Introduction: Founding a successful new conference is extraordinarily difficult. As a conference founder, you must manage to attract a significant number of good papers—enough to entice the participants into participating next year and to (generally) to grow the conference. For someone choosing to participate in a new conference, there is a very significant decision to make: do you send a paper to some new conference with no guarantee that the conference will work out? Or do you send it to another (possibly less related) conference that you are sure will work? The conference founding problem is a joint agreement problem with a very significant barrier. Workshops are a way around this problem, and workshops attached to conferences are a particularly effective means for this. A workshop at a conference is sure to have people available to speak and attend and is sure to have a large audience available. Presenting work at a workshop is not generally exclusive: it can also be presented at a confe
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