Contact

Evan Archer

Adresse: Spemannstrasse 41
Raum Nummer: 2.A.03.a
E-Mail: evan.archer

 

Bild von Archer, Evan

Evan Archer

Position: Doktorand  Abteilung: 

I'm a graduate student researcher, working on problems at the intersection of machine learning, statistics, and computational neuroscience. I received a master's degree in applied mathematics and bachelors in electrical engineering, both from The University of Texas at Austin. You can find out more abut me at my website.

Education

  • M.S. Computational and Applied Mathematics, The University of Texas at Austin, 2010
  • B.S. Electrical Engineering, The University of Texas at Austin, 2007

 


Publications (@Google Scholarmy website)

  • Journal Publications
    1. Archer E*, Park IM*, Pillow JW (2014). Bayesian entropy estimation for countable discrete distributionsJournal of Machine Learning Research (JMLR) [preprint|code(accepted)
      (*=equal contribution)
    2. Archer E, Park IM, Pillow JW (2013). Bayesian and quasi-Bayesian estimators for mutual information from discrete data. Entropy, 15(5): 1738-1755. [bibtex|pdf|link]
  • Conference Publications

    1. Archer E, Park IM, Pillow JW (2013). Bayesian entropy estimation for binary spike train data using parametric prior knowledge. Advances in Neural Information Processing Systems (NIPS) 26.
      (selected for spotlight presentation, top 5% of submitted) [bibtex|poster|pdf|code]
    2. Park IM*, Archer E*, Priebe N, Pillow JW (2013). Spectral methods for neural characterization using generalized quadratic models. Advances in Neural Information Processing Systems (NIPS) 26.
      (acceptance rate: 25%; *=equal contribution) [bibtex|poster|pdf]
    3. Park IM, Archer E, Latimer K, Pillow JW (2013). Universal models for binary spike patterns using centered Dirichlet processes. Advances in Neural Information Processing Systems (NIPS) 26.
      (acceptance rate: 25%) [bibtex|poster|pdf]
    4. Archer E*, Park IM*, Pillow JW (2012). Bayesian estimation of discrete entropy with mixtures of stick-breaking priors. Advances in Neural Information Processing Systems (NIPS) 25.
      [bibtex|poster|pdf|code] (acceptance rate: 25%; *=equal contribution)
  • Conference Abstracts
    1. Archer E, Pillow JW, Macke JH (2014). Low-d dynamical models of neural populations with common inputAreadne 2014, Santorini, Greece.
    2. Archer E, Pillow JW, Macke JH (2014). Low-dimensional models of neural population recordings with complex stimulus selectivity. Cosyne Abstracts 2014, Salt Lake City, USA.
    3. Park IM, Archer E, Latimer K, Pillow JW (2014). Scalable nonparametric models for binary spike patterns. Cosyne Abstracts 2014, Salt Lake City, USA. [abstract]
    4. Archer E, Park IM, Priebe N, Pillow JW (2013). Generalized quadratic models and moment-based dimensionality reduction. Bernstein Conference 2013, Tübingen, Germany.
    5. Park IM, Archer E, Pillow JW (2013). Bayesian entropy estimators for spike trains. CNS 2013, Paris, France. [abstract]
    6. Archer E, Park IM, Pillow JW (2013). Semi-parametric Bayesian entropy estimation for binary spike trains. Cosyne Abstracts 2013, Salt Lake City, USA. [abstract|poster]
    7. Park IM, Archer E, Priebe N, Pillow JW (2013). Got a moment or two? Neural models and linear dimensionality reduction. Cosyne Abstracts 2013, Salt Lake City, USA. [abstract|poster]
    8. Archer E, Park IM, Pillow JW (2012). Bayesian entropy estimation for infinite neural alphabets. Cosyne Abstracts 2012, Salt Lake City, USA. [abstract|poster|Memming's summary]
    9. Archer E, Priebe N, Pillow JW (2011). Voltage-triggered methods for intracellular neural characterization in visual cortex. Cosyne Abstracts 2011, Salt Lake City, USA. [abstract|poster]

Präferenzen: 
Referenzen pro Seite: Jahr: Medium:

  
Zeige Zusammenfassung

Artikel (1):

Archer E, Park IM und Pillow JW (Oktober-2014) Bayesian Entropy Estimation for Countable Discrete Distributions Journal of Machine Learning Research 15 2833−2868.

Beiträge zu Tagungsbänden (1):

Archer EW, Koster U, Pillow JW und Macke JH (2015) Low-dimensional models of neural population activity in sensory cortical circuits In: Advances in Neural Information Processing Systems 27, , Twenty-Eighth Annual Conference on Neural Information Processing Systems (NIPS 2014), Curran, Red Hook, NY, USA, 343-351.
pdf

Poster (5):

Speiser A, Turaga S, Archer E und Macke J (Februar-25-2017): Amortized inference for fast spike prediction from calcium imaging data, Computational and Systems Neuroscience Meeting (COSYNE 2017), Salt Lake City, UT, USA.
Archer E, Pillow J und Macke J (Oktober-2014): Low Dimensional Dynamical Models of Neural Populations with Common Input, 15th Conference of Junior Neuroscientists of Tübingen (NeNa 2014): The Changing Face of Publishing and Scientific Evaluation, Schramberg, Germany.
Archer E, Pillow JW und Macke JH (September-3-2014): Low-dimensional dynamical neural population models with shared stimulus drive, Bernstein Conference 2014, Göttingen, Germany.
Archer E, Pillow JW und Macke J (März-2014): Low-dimensional models of neural population recordings with complex stimulus selectivity, Computational and Systems Neuroscience Meeting (COSYNE 2014), Salt Lake City, UT, USA.
Park IM, Archer E, Latimer K und Pillow JW (März-2014): Scalable nonparametric models for binary spike patterns, Computational and Systems Neuroscience Meeting (COSYNE 2014), Salt Lake City, UT, USA.

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Last updated: Montag, 22.05.2017