DRIV Meeting Organization, Fall 2008

Meeting Notes

Items for Discussion

(Please add or edit as you see fit.)

Purpose of the Meetings

  • To exchange technical ideas about artificial intelligence, computer vision, machine learning, and computational economics
  • To gain experience in conveying technical ideas
  • To rehearse talks to be given in other venues
  • To socialize
  • To address issues of logistics and resources

Points Raised in Recent Email Survey

Each of the following points was raised by someone, but no point was raised by a majority:

  • Venue, frequency, and enticements
    • Biweekly meetings are sufficiently frequent
    • We should hold meetings elsewhere occasionally: Twinnie's, lawn, Tyler's (Old Tobacco district), the Federal (across from Brightleaf Square), 3rd floor of North (the old CSEM), ...
    • Staying in LSRC is better
    • Some meetings could occur at lunch or dinner time
    • Beer
    • Milk
    • Cookies
  • Types of meeting
    • Presenting someone else's papers is intimidating
    • A reading-group style meeting is preferable
    • We need outside speakers
    • Some meetings could be allocated to brainstorming about a particular problem
  • Paper-reading meetings
    • Paper type
      • Papers should be visual, have little math, and convey simple and interesting ideas
      • Papers that appeal to the interests of a general audience, not be overly specialized
      • Look at papers on the boundaries between our areas of interest
    • Preparation for discussion
      • Have two or three people give one-minute overviews of what they think the next paper should be, to get people interested
      • Ditto with a good demo video, graphic, or some other nice display piece
      • Have someone give a five-minute crash course on the topic of the following week's paper
      • Send email with some background and key ideas (as short as possible) before the presentation
    • Discussion format
      • Start paper discussion by having everyone ask a question or make a comment about the paper
      • Understand at least part of the paper really thoroughly
      • Encourage discussion of how a paper fits within the literature of the field

Possible Upcoming Practice Talks, RIP Presentations, Defenses,...

  • Practice talk by Mingyu Guo in December
  • RIP talk by Mac Mason (date unknown)
  • RIP talk by Gavin Taylor (date unknown)
  • RIP talk by Josh Letchford (date unknown)
  • Thesis defense by Jeff Phillips (November or December)

Dates for Informal Research Presentations

  • Mid October: Mingyu Guo
  • October or November: Rolando Estrada
  • October 24st to November 14th: Seda Vural
  • End of October/early November: Susanna Ricco
  • Not on November 14 or 21: Zheng Li
  • After mid November: Anthony Yan
  • After mid November: Monika Schaeffer
  • End of semester: Steve Gu
  • End og semester: Sam Slee

Papers Suggested for Reading

  • S. Gray and D. H. Reiley. Measuring the Benefits to Sniping on eBay. Evidence from a Field Experiment, 2007. PDF. Suggested by Mingyu Guo.
  • A list of theoretical papers on electronic commerce. Suggested by Mingyu Guo.
  • J. Ting et al. Predicting EMG Data from M1 Neurons with Variational Bayesian Least Squares, NIPS, 2005. Link. Suggested by Zheng Li. A Tutorial on variational Bayes methods may be needed as a prerequisite.
  • Compressed sensing. Suggested by Anthony Yan.
    • E. J. Candes and M. B. Wakin. An Introduction to Compressive Sampling. IEEE Signal Processing Magazine, 2008. Link.
    • E. Candes and J. Romberg. Sparsity and Incoherence In Compressive Sampling. Inverse Problems, 23 (3), pp. 969-985, 2007. Link.
    • See also Rice web site on compressed sensing.
  • L. Fei-Fei and P. Perona. A Bayesian Hierarchical Model for Learning Natural Scene Categories. CVPR, 2005. Link. Suggested by Steve Gu.
  • M. Rubinstein, A. Shamir and S. Avidan. Improved Seam Carving for Video Retargeting. SIGGRAPH, 2008. Link. Suggested by Steve Gu.
  • A. A. Gooch, S. C. Olsen, J. Tumblin and B. Gooch. Color2Gray: Salience-Preserving Color Removal. SIGGRAPH, 2005. Link. Suggested by Steve Gu.
  • D. Dolgov and S. Thrun. Detection of Principle Directions in Unknown Environments for Autonomous Navigation. Robotics: Science and Systems, 2008. Link. Suggested by Mac Mason.
  • C. Vallespi-Gonzalez and T. Stentz. Prior Data and Kernel Conditional Random Fields for Obstacle Detection. Robotics: Science and Systems IV, 2008. Link. Suggested by Mac Mason.
  • Rosenhahn et al.. Markerless motion capture of man-machine interaction, CVPR, 2008. Link. Suggested by Susanna Ricco.
  • Laptev et al.. Learning realistic human actions from movies. CVPR, 2008. Link. Suggested by Susanna Ricco.
  • T. Joachims. Transductive Inference for Text Classification using Support Vector Machines. ICML, pages 200-209, 1999. Link. Suggested by Seda Vural.
  • R. Fergus, P. Perona, and A. Zisserman. A Sparse Object Category Model for Efficient Learning and Exhaustive Recognition. CVPR, pages 380-387, 2005. Link. Suggested by Seda Vural.
  • P. Duygulu, K. Barnard, N. Freitas, D. Forsyth. Object Recognition as Machine Translation: Learning a Lexicon for a Fixed Image Vocabulary. ECCV 2002, pages 97-112, 2002. Link. Suggested by Seda Vural.
  • J. Reisinger, P. Stone, and R. Miikkulainen . Online Kernel Selection for Bayesian Reinforcement Learning. ICML, 2008. Link. Suggested by Gavin Taylor.
  • R. Fitch and Z. Butler. Million Module March: Scalable Locomotion for Large Self-Reconfiguring Robots. International Journal of Robotics Research. 27(3-4):331-343, 2008. Link. Suggested by Sam Slee.
  • Latent Dirichlet allocation. Suggested by Ying Zheng.
    • D. M. Blei, A. Y. Ng, M. I. Jordan. Latent Dirichlet Allocation. Journal of Machine Learning Research, 2003. Link.
    • X. Wang and E. Grimson. Spatial Latent Dirichlet Allocation. NIPS, 2007. Link.
    • D. Newman ``et al. Distributed Inference for Latent Dirichlet Allocation. NIPS'', 2007. Link.
    • H. Kashima, K. Tsuda, and A. Inokuchi. Marginalized Kernels between Labeled Graphs. ICML, 2003. Link.
  • M. A. Carreira-Perpinan, R. J. Lister, and G. J. Goodhill. A Computational Model for the Development of Multiple Maps in Primary Visual Cortex. Cerebral Cortex 15:1222-1233., 2005. Link. Suggested by Rolando Estrada.
  • S. M. Kakade, A. T. Kalai, and K. Ligett. Playing games with approximation algorithms. ``STOC'', 2007. Link. Suggested by Josh Letchford.
  • M. Pollefeys and L. Van Gool. From Images to 3D Models. CACM, July 45(7):50-55, 2002. Link. Suggested by Monika Schaeffer.
  • Y. Baryshnikov and R. Ghrist. Target enumeration via integration over planar sensor networks, To appear in Proc. Robotics Systems & Science, 2008. Preprint. Suggested by Jeff Phillips.
  • D. Katz, J. Kenney, and O. Brock. How Can Robots Succeed in Unstructured Environments? Robot Manipulation, 2008. Link. Suggested by Jeff Phillips.
  • Overview of WAFR papers. Suggested by Jeff Phillips.
  • B. Glocker et al. Inter and Intra-modal Deformable Registration: Continuous Deformations Meet Efficient Optimal Linear Programming. Information Processing in Medical Imaging, 2007. Link. Suggested by Carlo Tomasi.
  • R. M. Rustamov. Laplace-Beltrami Eigenfunctions for Deformation Invariant Shape Representation. Eurographics symposium on Geometry processing, 2007. Link. Suggested by Carlo Tomasi.

Action Items

  • Appoint two or three DRIV Meeting coordinators, ideally one from each group. Coordinators have complete freedom of choice for their own participation, but they must keep the schedule running
  • Are we meeting on September 19 (grad student retreat)?
  • Next meeting