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I am a graduate student in the Redwood Center for Theoretical Neuroscience, at University of California, Berkeley.  I am a member of [https://redwood.berkeley.edu/bruno/ Bruno Olshausen's] lab.
My new website is at [http://sohldickstein.com/ http://sohldickstein.com/]. Go there instead.


I am interested in how we learn to perceive the world.  That is, how we start receiving sensory input (seeing, hearing, smelling, touching, etc) ... and after a while figure out the way the world works.  We learn that the light intensity on adjacent patches of retina is correlated, that the world is made up of edges and surfaces, how occlusion works, that objects exist, that if you drop them they fall and make a noise and hurt...  We seem to infer most of human scale physics from examples of sensory input.
--


How this unsupervised learning problem is solved - how we learn the structure inherent in the world just by experiencing examples of it - is not understood.  This is the problem I am interested in tackling.
The information below is out of date.  I am now a postdoc in Surya Ganguli's lab at Stanford, and working with the Khan Academy.  A more complete update to follow soon. -Jascha (Aug 5, 2012)


There are two large (known) parts to this problem.  The first is the design of models which are flexible enough to describe *anything* without being unwieldy (greater flexibility frequently comes at the cost of an explosion in the number of parameters and/or more computationally costly implementation).  The second is practically training these models once they've been designed - estimating their parameters for a particular dataset (Almost any model you can write down is impractical to exactly evaluate - a factor called the "partition function", coming from the constraint that probabilities of all states must sum to 1, makes it intractable to compute.  This inability to exactly evaluate the model makes training exceedingly difficult.).  The majority of my work has been on parameter estimation in traditionally intractable models.
--


== Current projects ==
I am a graduate student in the [http://redwood.berkeley.edu Redwood Center for Theoretical Neuroscience], at the University of California, Berkeley.  I am a member of [https://redwood.berkeley.edu/bruno/ Bruno Olshausen's] lab, and the [http://biophysics.berkeley.edu/ Biophysics Graduate Group].  My email address is [mailto:jascha@berkeley.edu jascha@berkeley.edu].


I am working with Peter Battaglino and Michael DeWeese on a technique for parameter estimation in probabilistic models with intractable partition functions, involving minimization of probability flows.  See the [http://arxiv.org/abs/0906.4779 arXiv pre/e-print].
Most of my projects involve developing techniques to work with highly flexible but intractable probabilistic models, using ideas from statistical mechanics and dynamical systems.


I am working with Jimmy Wang and Bruno Olshausen to build a Lie algebraic model of the transformations which occur in natural video.  See [https://redwood.berkeley.edu/jwang/research.html Jimmy's web page], or a [http://asdf poster pdf].
My underlying interest is in how we learn to perceive the world.  There is evidence that much of our representation of the world is learned during development rather than being genetically hardwired - everything from the way light intensity is correlated on adjacent patches of the retina all the way up to rules for social interaction.  How this unsupervised learning problem is solved - how we learn the structure inherent in our sensory input by experiencing examples of it - is not well understood.  This is the problem I am interested in tackling.


I am working on my own to build probabilistic models out of unstructured recurrent neural networks, and train them on natural stimuli.  Nothing to show here yet, but I am eager to discuss.
== Projects ==


== Relevant publications ==
* '''Minimum Probability Flow (MPF) -''' A collaboration with Peter Battaglino and Michael R. DeWeese.  MPF is a technique for parameter estimation in un-normalized probabilistic models.  It proves to be an order of magnitude faster than competing techniques for the Ising model, and an effective tool for learning parameters for any non-normalizable distribution. See the [http://redwood.berkeley.edu/jascha/pdfs/icml.pdf ICML paper], the [http://prl.aps.org/abstract/PRL/v107/i22/e220601 PRL paper], and the [https://github.com/Sohl-Dickstein/Minimum-Probability-Flow-Learning released code].  If you are interested in using MPF in a continuous state space, you should use the method described in the Persistent MPF [http://redwood.berkeley.edu/jascha/pdfs/PMPF.pdf note].


J Sohl-Dickstein, P Battaglino, M DeWeese. Minimum probability flow learning. (2009) http://arxiv.org/abs/0906.4779
* '''Hamiltonian Annealed Importance Sampling (HAIS) -''' A collaboration with [http://www.cs.berkeley.edu/~bjc/ Jack Culpepper]. Allows the estimation of importance weights - and thus partition functions and log likelihoods - for intractable probabilistic models. See the [http://redwood.berkeley.edu/jascha/pdfs/HAIS.pdf tech report], and the [https://github.com/Sohl-Dickstein/Hamiltonian-Annealed-Importance-Sampling released code].


C Abbey, J Sohl-Dickstein, B Olshausen. Higher-order scene statistics of breast images. Proceedings of SPIE (2009) http://link.aip.org/link/?PSISDG/7263/726317/1
* '''Extensions to Hamiltonian Monte Carlo -'''  See the [http://redwood.berkeley.edu/jascha/pdfs/HMC_noflip.pdf note] below on modifying the rejection rules to less frequently negate the momentum, increasing mixing speed.  Additionally, ongoing work maintains an online low rank approximation to the inverse Hessian by the introduction of auxiliary Gaussian distributed variables with the Hessian as their coupling matrix.


POSTER - J Sohl-Dickstein, B Olshausen. Learning in energy based models via score matching. Cosyne (2007) - this (dense!) poster introduces a spatial derivation of score matching, applies it to learning in a Field of Experts model, and then extends Field of Experts to work with heterogeneous experts (to form a "tapestry of experts").
* '''Lie group models for transformations in natural video -''' A collaboration with Jimmy Wang and Bruno Olshausen.  We train first order differential operators on inter-frame differences in natural video, in order to learn a set of natural transformations. We further explore the use of these transformations in video compression.  See [http://arxiv.org/abs/1001.1027 the tech report], and the [http://redwood.berkeley.edu/jascha/pdfs/PID1615931.pdf DCC paper].
 
* '''A comparison of the log likelihoods of popular image models. -''' A collaboration with [http://www.cs.berkeley.edu/~bjc/ Jack Culpepper] and [https://redwood.berkeley.edu/cadieu/homepage/Home.html Charles Cadieu].  We use Hamiltonian Annealed Importance Sampling (HAIS - above) to compare the log likelihoods of popular image models trained via several parameter estimation techniques.
 
* '''Bilinear generative models for natural images -''' A collaboration with [http://www.cs.berkeley.edu/~bjc/ Jack Culpepper] and Bruno Olshausen.  See the [http://redwood.berkeley.edu/jascha/pdfs/culpepper-iccv13.pdf ICCV paper].
 
[[Image:nicol_bat.jpg|50px|thumb|right]]
* '''A device for human echolocation -''' A collaboration with Nicol Harper and Chris Rodgers.  (see stylish picture to right)  
 
* '''Statistical analysis of medical images of cancer patients -''' A collaboration with Joel Zylberberg and Michael DeWeese.  (See also an earlier project training statistical models on MRI and CT breast images - [http://link.aip.org/link/?PSISDG/7263/726317/1 SPIE publication].)
 
* '''Hessian-aware online optimization -'''  By rewriting the inverse Hessian in terms of its Taylor expansion, and then accumulating terms in this expansion in an online fashion, neat things can be done...
 
== Code ==
 
* '''[https://github.com/Sohl-Dickstein/Minimum-Probability-Flow-Learning MPF] -''' This repository contains Matlab code implementing Minimum Probability Flow learning (MPF) for several cases, specifically:
** '''MPF_ising/ -''' parameter estimation in the Ising model
** '''MPF_RBM_compare_log_likelihood/ -''' parameter estimation in Restricted Boltzmann Machines.  This directory also includes code comparing the log likelihood of small RBMs trained via pseudolikelihood and Contrastive Divergence to ones trained via MPF.
* '''[https://github.com/Sohl-Dickstein/Hamiltonian-Annealed-Importance-Sampling HAIS] -''' This repository contains Matlab code to perform partition function estimation, log likelihood estimation, and importance weight estimation in models with intractable partition functions and continuous state spaces, using Hamiltonian Annealed Importance Sampling (HAIS).  It can also be used for standard Hamiltonian Monte Carlo sampling (single step, with partial momentum refreshment).


== Notes ==
== Notes ==


I am intending to place short informal documents here on a variety of topics which interest me and I have something to say about.  If any of the following sound interesting to you, pester me and they will appear more quickly.
* [http://redwood.berkeley.edu/jascha/pdfs/HMC_reducedflip.pdf Hamiltonian Monte Carlo with Fewer Momentum Reversals] - Reduces the number of momentum reversals required in Hamiltonian Monte Carlo. This is accomplished by maintaining the net exchange of probability between states with opposite momenta, but reducing the rate of exchange in both directions such that it is 0 in one direction.
 
* [http://redwood.berkeley.edu/jascha/pdfs/independence_of_energy_function_contributions.pdf On the independence of linear contributions to an energy function] - Even in the overcomplete case where there are more experts than data dimensions, product-of-experts style models tend to learn decorrelated features.  This note provides motivation for this by Taylor expanding the KL divergence, and observing that there are terms in the expansion which specifically penalize similarity between the experts.
 
* [http://redwood.berkeley.edu/jascha/pdfs/PMPF.pdf Persistent Minimum Probability Flow] - Develops MPF in the case that non-data states are captured by persistent samples from the current estimate of the model distribution.  Analogous to Persistent CD.  This technique should be used for MPF in continuous state spaces.
 
* [http://redwood.berkeley.edu/jascha/pdfs/MPF_sampling.pdf Sampling the Connectivity Pattern in Minimum Probability Flow Learning] - Describes how the connectivity pattern between states in MPF can be described using a proposal distribution, rather than a deterministic rule.
 
* [http://redwood.berkeley.edu/jascha/pdfs/generic_entropy_091121.pdf Entropy of Generic Distributions] - Calculates the entropy that can be expected for a distribution drawn at random from the simplex of all possible distributions ([http://dittler.us/ John Schulman] points out that ET Jaynes deals with similar questions in chapter 11 of "Probability Theory: The Logic Of Science")
 
The following are titles for informal notes I intend to write, but haven't gotten to/finished yet.  If any of the following sound interesting to you, pester me and they will appear more quickly.
 
* Natural gradients explained via an analogy to signal whitening
* The [http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?arnumber=1467533 field of experts model] learns Gabor-like receptive fields when trained via minimum probability flow or score matching
* For small time bins, [http://www.jneurosci.org/cgi/content/abstract/25/47/11003 generalized linear models] and causal Boltzmann machines become equivalent
* How to construct phase space volume preserving recurrent networks
* Maximum likelihood learning as constraint satisfaction
* A spatial derivation of score matching
 
== Publications ==
 
J Sohl-Dickstein, P Battaglino, M DeWeese. New method for parameter estimation in probabilistic models: Minimum probability flow. Physical Review Letters (2011). http://prl.aps.org/abstract/PRL/v107/i22/e220601
 
J Sohl-Dickstein, P Battaglino, M DeWeese. Minimum probability flow learning. "Distinguished Paper" ICML (2011) http://redwood.berkeley.edu/jascha/pdfs/icml.pdf with supplementary material http://redwood.berkeley.edu/jascha/pdfs/supplementary_material_icml.pdf (also see the Persistent MPF [http://redwood.berkeley.edu/jascha/pdfs/PMPF.pdf note] for more on learning in continuous state spaces)
 
J Sohl-Dickstein, BJ Culpepper. Hamiltonian annealed importance sampling for partition function estimation.  Under submission, draft available as technical report. https://github.com/Sohl-Dickstein/Hamiltonian-Annealed-Importance-Sampling/blob/master/HAIS.pdf
 
A Hayes, J Grotzinger, L Edgar, SW Squyres, W Watters, J Sohl-Dickstein. Reconstruction of Eolian Bed Forms and Paleocurrents from Cross-Bedded Strata at Victoria Crater, Meridiani Planum, Mars, Journal of Geophysical Research (2011) http://www.agu.org/pubs/crossref/2011/2010JE003688.shtml


* Natural gradients made quick and dirty
CM Wang, J Sohl-Dickstein, I Tosik. Lie Group Transformation Models for Predictive Video Coding. Proceedings of the Data Compression Conference (2011) http://redwood.berkeley.edu/jascha/pdfs/DCC_2011_LieGroup.pdf
* A log bound on the growth of intelligence with system size
* The [http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?arnumber=1467533 field of experts model] learns Gabors when trained via score matching
* For small time bins, [http://www.jneurosci.org/cgi/content/abstract/25/47/11003 generalized linear models] (with exponential pointwise nonlinearities) and Boltzmann machines become equivalent
* How to construct a volume preserving recurrent network


== Interests ==
J Sohl-Dickstein, CM Wang, BA Olshausen. An Unsupervised Algorithm For Learning Lie Group Transformations. Under submission, draft available as technical report. http://arxiv.org/abs/1001.1027


If you know anything about the following, I would love to pick your brain:
BJ Culpepper, J Sohl-Dickstein, B Olshausen.  Building a better probabilistic model of images by factorization. 
International Conference on Computer Vision (2011) http://redwood.berkeley.edu/jascha/pdfs/culpepper-iccv13.pdf


* non-equilibrium statistical mechanics (Jaynes, Crooks, Jarzynski...)
C Abbey, J Sohl-Dickstein, BA Olshausen. Higher-order scene statistics of breast images. Proceedings of SPIE (2009) http://link.aip.org/link/?PSISDG/7263/726317/1
* criticality
* compressive sensing (specifically - connections to information theory / manifolds)
* echo state networks / liquid state machines (specifically - what can we say about the classes of transformations the input undergoes)
* Ways to close the sensori-motor loop. (What's the objective function for the brain?)
* Brain-machine interfaces (especially - thoughts about software on the machine side which is able to adapt intelligently, and online, to brain output)


== Papers from my previous life as a Martian ==
K Kinch,
J Sohl-Dickstein,
J Bell III,
JR Johnson,
W Goetz,
GA Landis.
Dust deposition on the Mars Exploration Rover Panoramic Camera (Pancam) calibration targets. Journal of Geophysical Research-Planets (2007) http://www.agu.org/pubs/crossref/2007/2006JE002807.shtml


Kinch et al. Dust deposition on the Mars Exploration Rover Panoramic Camera (Pancam) calibration targets. Journal of Geophysical Research-Planets (2007)
POSTER - J Sohl-Dickstein, BA Olshausen. Learning in energy based models via score matching. Cosyne (2007) - this (dense!) poster introduces a spatial derivation of score matching, applies it to learning in a Field of Experts model, and then extends Field of Experts to work with heterogeneous experts (to form a "tapestry of experts"). I'm including it as it hasn't been written up elsewhere. [http://redwood.berkeley.edu/jascha/pdfs/jascha_cosyne_07.pdf download poster]
<!-- POSTER - J Wang, J Sohl-Dickstein, BA Olshausen. Unsupervised learning of Lie group operators from natural movies.  Bay Area Vision Research Day (2009). [http://redwood.berkeley.edu/jascha/pdfs/jimmy_jascha_bavrd_09.pdf download poster] -->
<!-- POSTER - J Sohl-Dickstein, J Wang, B Olshausen. A Global Energy Function for Deep Belief Networks. Cosyne (2008) - some extensions to DBNs - fixes a problem which probably didn't need to be fixed.  The most interesting part may be noting the full joint distribution for a Deep Belief Network in the left column. [[Media:jascha_cosyne_08_poster.pdf|download poster]] -->


Johnson et al. Radiative transfer modeling of dust-coated Pancam calibration target materials: Laboratory visible/near-infrared spectrogoniometry. J. Geophys. Res (2006)
JR Johnson,
J Sohl-Dickstein,
WM Grundy,
RE Arvidson,
J Bell III,
P Christensen,
T Graff,
EA Guinness,
K Kinch,
R Morris,
MK Shepard.
Radiative transfer modeling of dust-coated Pancam calibration target materials: Laboratory visible/near-infrared spectrogoniometry. Journal of Geophysical Research (2006) http://www.agu.org/pubs/crossref/2006/2005JE002658.shtml


Joseph et al. In-flight calibration and performance of the Mars Exploration Rover Panoramic Camera (Pancam) Instruments. J. Geophys. Res (2006)
J Bell III,
J Joseph,
J Sohl-Dickstein,
H Arneson,
M Johnson,
M Lemmon,
D Savransky
In-flight calibration and performance of the Mars Exploration Rover Panoramic Camera (Pancam) instruments.
Journal of Geophysical Research (2006)
http://www.agu.org/pubs/crossref/2006/2005JE002444.shtml


Parker et al. Stratigraphy and sedimentology of a dry to wet eolian depositional system, Burns formation, Meridiani Planum, Mars. Earth and Planetary Science Letters (2005)
Parker et al. Stratigraphy and sedimentology of a dry to wet eolian depositional system, Burns formation, Meridiani Planum, Mars. Earth and Planetary Science Letters (2005)


Soderblom et al. Pancam multispectral imaging results from the Opportunity rover at Meridiani Planum. Science (2004)
Soderblom et al. Pancam multispectral imaging results from the Opportunity rover at Meridiani Planum. Science (2004) http://www.sciencemag.org/content/306/5702/1703


Soderblom et al. Pancam multispectral imaging results from the Spirit rover at Gusev crater. Science (2004)
Soderblom et al. Pancam multispectral imaging results from the Spirit rover at Gusev crater. Science (2004) http://www.sciencemag.org/content/305/5685/800


Smith et al. Athena microscopic imager investigation. Journal of Geophysical Research-Planets (2003)
Smith et al. Athena microscopic imager investigation. Journal of Geophysical Research-Planets (2003)


Bell et al. Hubble Space Telescope Imaging and Spectroscopy of Mars During 2001. American Geophysical Union (2001)
Bell et al. Hubble Space Telescope Imaging and Spectroscopy of Mars During 2001. American Geophysical Union (2001)

Latest revision as of 01:06, 22 September 2014

My new website is at http://sohldickstein.com/. Go there instead.

--

The information below is out of date. I am now a postdoc in Surya Ganguli's lab at Stanford, and working with the Khan Academy. A more complete update to follow soon. -Jascha (Aug 5, 2012)

--

I am a graduate student in the Redwood Center for Theoretical Neuroscience, at the University of California, Berkeley. I am a member of Bruno Olshausen's lab, and the Biophysics Graduate Group. My email address is jascha@berkeley.edu.

Most of my projects involve developing techniques to work with highly flexible but intractable probabilistic models, using ideas from statistical mechanics and dynamical systems.

My underlying interest is in how we learn to perceive the world. There is evidence that much of our representation of the world is learned during development rather than being genetically hardwired - everything from the way light intensity is correlated on adjacent patches of the retina all the way up to rules for social interaction. How this unsupervised learning problem is solved - how we learn the structure inherent in our sensory input by experiencing examples of it - is not well understood. This is the problem I am interested in tackling.

Projects

  • Minimum Probability Flow (MPF) - A collaboration with Peter Battaglino and Michael R. DeWeese. MPF is a technique for parameter estimation in un-normalized probabilistic models. It proves to be an order of magnitude faster than competing techniques for the Ising model, and an effective tool for learning parameters for any non-normalizable distribution. See the ICML paper, the PRL paper, and the released code. If you are interested in using MPF in a continuous state space, you should use the method described in the Persistent MPF note.
  • Hamiltonian Annealed Importance Sampling (HAIS) - A collaboration with Jack Culpepper. Allows the estimation of importance weights - and thus partition functions and log likelihoods - for intractable probabilistic models. See the tech report, and the released code.
  • Extensions to Hamiltonian Monte Carlo - See the note below on modifying the rejection rules to less frequently negate the momentum, increasing mixing speed. Additionally, ongoing work maintains an online low rank approximation to the inverse Hessian by the introduction of auxiliary Gaussian distributed variables with the Hessian as their coupling matrix.
  • Lie group models for transformations in natural video - A collaboration with Jimmy Wang and Bruno Olshausen. We train first order differential operators on inter-frame differences in natural video, in order to learn a set of natural transformations. We further explore the use of these transformations in video compression. See the tech report, and the DCC paper.
  • A comparison of the log likelihoods of popular image models. - A collaboration with Jack Culpepper and Charles Cadieu. We use Hamiltonian Annealed Importance Sampling (HAIS - above) to compare the log likelihoods of popular image models trained via several parameter estimation techniques.
  • Bilinear generative models for natural images - A collaboration with Jack Culpepper and Bruno Olshausen. See the ICCV paper.
Nicol bat.jpg
  • A device for human echolocation - A collaboration with Nicol Harper and Chris Rodgers. (see stylish picture to right)
  • Statistical analysis of medical images of cancer patients - A collaboration with Joel Zylberberg and Michael DeWeese. (See also an earlier project training statistical models on MRI and CT breast images - SPIE publication.)
  • Hessian-aware online optimization - By rewriting the inverse Hessian in terms of its Taylor expansion, and then accumulating terms in this expansion in an online fashion, neat things can be done...

Code

  • MPF - This repository contains Matlab code implementing Minimum Probability Flow learning (MPF) for several cases, specifically:
    • MPF_ising/ - parameter estimation in the Ising model
    • MPF_RBM_compare_log_likelihood/ - parameter estimation in Restricted Boltzmann Machines. This directory also includes code comparing the log likelihood of small RBMs trained via pseudolikelihood and Contrastive Divergence to ones trained via MPF.
  • HAIS - This repository contains Matlab code to perform partition function estimation, log likelihood estimation, and importance weight estimation in models with intractable partition functions and continuous state spaces, using Hamiltonian Annealed Importance Sampling (HAIS). It can also be used for standard Hamiltonian Monte Carlo sampling (single step, with partial momentum refreshment).

Notes

  • Hamiltonian Monte Carlo with Fewer Momentum Reversals - Reduces the number of momentum reversals required in Hamiltonian Monte Carlo. This is accomplished by maintaining the net exchange of probability between states with opposite momenta, but reducing the rate of exchange in both directions such that it is 0 in one direction.
  • On the independence of linear contributions to an energy function - Even in the overcomplete case where there are more experts than data dimensions, product-of-experts style models tend to learn decorrelated features. This note provides motivation for this by Taylor expanding the KL divergence, and observing that there are terms in the expansion which specifically penalize similarity between the experts.
  • Persistent Minimum Probability Flow - Develops MPF in the case that non-data states are captured by persistent samples from the current estimate of the model distribution. Analogous to Persistent CD. This technique should be used for MPF in continuous state spaces.
  • Entropy of Generic Distributions - Calculates the entropy that can be expected for a distribution drawn at random from the simplex of all possible distributions (John Schulman points out that ET Jaynes deals with similar questions in chapter 11 of "Probability Theory: The Logic Of Science")

The following are titles for informal notes I intend to write, but haven't gotten to/finished yet. If any of the following sound interesting to you, pester me and they will appear more quickly.

  • Natural gradients explained via an analogy to signal whitening
  • The field of experts model learns Gabor-like receptive fields when trained via minimum probability flow or score matching
  • For small time bins, generalized linear models and causal Boltzmann machines become equivalent
  • How to construct phase space volume preserving recurrent networks
  • Maximum likelihood learning as constraint satisfaction
  • A spatial derivation of score matching

Publications

J Sohl-Dickstein, P Battaglino, M DeWeese. New method for parameter estimation in probabilistic models: Minimum probability flow. Physical Review Letters (2011). http://prl.aps.org/abstract/PRL/v107/i22/e220601

J Sohl-Dickstein, P Battaglino, M DeWeese. Minimum probability flow learning. "Distinguished Paper" ICML (2011) http://redwood.berkeley.edu/jascha/pdfs/icml.pdf with supplementary material http://redwood.berkeley.edu/jascha/pdfs/supplementary_material_icml.pdf (also see the Persistent MPF note for more on learning in continuous state spaces)

J Sohl-Dickstein, BJ Culpepper. Hamiltonian annealed importance sampling for partition function estimation. Under submission, draft available as technical report. https://github.com/Sohl-Dickstein/Hamiltonian-Annealed-Importance-Sampling/blob/master/HAIS.pdf

A Hayes, J Grotzinger, L Edgar, SW Squyres, W Watters, J Sohl-Dickstein. Reconstruction of Eolian Bed Forms and Paleocurrents from Cross-Bedded Strata at Victoria Crater, Meridiani Planum, Mars, Journal of Geophysical Research (2011) http://www.agu.org/pubs/crossref/2011/2010JE003688.shtml

CM Wang, J Sohl-Dickstein, I Tosik. Lie Group Transformation Models for Predictive Video Coding. Proceedings of the Data Compression Conference (2011) http://redwood.berkeley.edu/jascha/pdfs/DCC_2011_LieGroup.pdf

J Sohl-Dickstein, CM Wang, BA Olshausen. An Unsupervised Algorithm For Learning Lie Group Transformations. Under submission, draft available as technical report. http://arxiv.org/abs/1001.1027

BJ Culpepper, J Sohl-Dickstein, B Olshausen. Building a better probabilistic model of images by factorization. International Conference on Computer Vision (2011) http://redwood.berkeley.edu/jascha/pdfs/culpepper-iccv13.pdf

C Abbey, J Sohl-Dickstein, BA Olshausen. Higher-order scene statistics of breast images. Proceedings of SPIE (2009) http://link.aip.org/link/?PSISDG/7263/726317/1

K Kinch, J Sohl-Dickstein, J Bell III, JR Johnson, W Goetz, GA Landis. Dust deposition on the Mars Exploration Rover Panoramic Camera (Pancam) calibration targets. Journal of Geophysical Research-Planets (2007) http://www.agu.org/pubs/crossref/2007/2006JE002807.shtml

POSTER - J Sohl-Dickstein, BA Olshausen. Learning in energy based models via score matching. Cosyne (2007) - this (dense!) poster introduces a spatial derivation of score matching, applies it to learning in a Field of Experts model, and then extends Field of Experts to work with heterogeneous experts (to form a "tapestry of experts"). I'm including it as it hasn't been written up elsewhere. download poster

JR Johnson, J Sohl-Dickstein, WM Grundy, RE Arvidson, J Bell III, P Christensen, T Graff, EA Guinness, K Kinch, R Morris, MK Shepard. Radiative transfer modeling of dust-coated Pancam calibration target materials: Laboratory visible/near-infrared spectrogoniometry. Journal of Geophysical Research (2006) http://www.agu.org/pubs/crossref/2006/2005JE002658.shtml

J Bell III, J Joseph, J Sohl-Dickstein, H Arneson, M Johnson, M Lemmon, D Savransky In-flight calibration and performance of the Mars Exploration Rover Panoramic Camera (Pancam) instruments. Journal of Geophysical Research (2006) http://www.agu.org/pubs/crossref/2006/2005JE002444.shtml

Parker et al. Stratigraphy and sedimentology of a dry to wet eolian depositional system, Burns formation, Meridiani Planum, Mars. Earth and Planetary Science Letters (2005)

Soderblom et al. Pancam multispectral imaging results from the Opportunity rover at Meridiani Planum. Science (2004) http://www.sciencemag.org/content/306/5702/1703

Soderblom et al. Pancam multispectral imaging results from the Spirit rover at Gusev crater. Science (2004) http://www.sciencemag.org/content/305/5685/800

Smith et al. Athena microscopic imager investigation. Journal of Geophysical Research-Planets (2003)

Bell et al. Hubble Space Telescope Imaging and Spectroscopy of Mars During 2001. American Geophysical Union (2001)

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