Josh Freeman

  



Quite the same Wikipedia. To install click the Add extension button. The source code for the WIKI 2 extension is being checked. Rotoworld fantasy sports news and analysis for NFL, MLB, NBA, NHL, CFB, Golf, EPL and NASCAR. Josh freeman was a young promising qb in the nfl, but he wasnt able to live up to expectations. Order Dre Moss Book: https://www.amazon.com/dp/B071RMC8PGMAKE. Coming into the 2009 NFL Draft, the Buccaneers had their eyes on a quarterback. There was a 6 foot 6, 250 pound quarterback out of Kansas State University that they had their eye on. That man was Josh Freeman. After passing for over 8,000 career yards, Freeman was getting a lot of attention.

A few years ago, Josh Freeman was the talk of the league. He was making significant strides as a quarterback, and he was the center of dozens of different starting battles and team controversies during his career. Within the span of the last few years, however, he has gone from being one of the most talked-about quarterbacks in the game to being almost entirely forgotten. What happened to him? Did he decide to retire from football? (That would be sort of surprising, seeing as he wouldn’t even be thirty years old yet.) In this article, I’ll talk about what caused Josh Freeman to drop into obscurity, and what he has been up to in 2016. Let’s investigate more closely.

Josh Freeman’s Early Career

Josh Freeman was born in January 1988, in Kansas City, Missouri. He attended high school at Grandview High School, which borders Kansas City to the south. In high school, Josh was considered an impressive NCAA prospect. During his high school career, he was able to set 10 school records, including passing touchdowns, completions, and touchdowns in a game. In three years of playing as quarterback, he was able to pass for over 7,000 yards. By the time he was in his senior year, he was considered a four-star recruit, and was rated one of the top pro-style quarterbacks in the country. He was rigorously recruited coming out of high school, but it’s worth noting that a lot of colleges wanted to bring Freeman on as a tight end. (At 6’5″ and 225 lbs, he was much larger than most college quarterbacks.) Josh had always wanted to be a quarterback in the NFL, however, so he refused all of those offers.

Ultimately, the young athlete ended up playing college football with the Kansas State Wildcats. During his freshman year, he was able to set a school freshman record by having a 52% completion rate, and 1,780 total passing yards. Not only that, but he was actually the first true freshman to start a game for the Wildcats since 1976. It’s safe to say that his freshman year was a massively significant one. Although he was able to bring Kansas State to any major bowl game wins or championships or anything like that, he still became a significant part of Kansas State history. His accumulated 8,427 total offensive yards became a school record, and he is still one of the only quarterbacks in the school’s history to have scored at least 60 touchdowns.

With a successful junior year behind him, Freeman decided to forgo his final year of college eligibility and enter the NFL draft early. In the weeks approaching the draft, NFL analysts predicts that Josh would be snatched up really early on. Quality quarterbacks are generally sought after in the first round of the draft, with desperate teams looking for a new central cog in their lackluster offense. Josh Freeman ended up being one of many quarterbacks drafted in the first round of the draft. In the 2009 NFL Draft, quarterbacks Matt Stafford and Mark Sanchez were also drafted in the first round. Ultimately, Freeman was drafted by the Tampa Bay Buccaneers, with their first round, 17th overall pick. With that, the rookie moved on down to South Florida.

Josh

Josh Freeman in the NFL

When Josh Freeman came onto the Tampa Bay Bucs, the team was worse for wear. To put it bluntly, the team had become the laughing stock of the NFL. Entering the 2009 season, Freeman was determined to turn he team around. In November, he started his first professional game against the Green Bay Packers. The team would go on to win this game. He had a phenomenal game, with 4 passing touchdowns and only a single interception. This victory ended up snapping the Buccaneers’s 11 game losing streak, and it made Josh Freeman the youngest quarterback in the team’s history to start and win his first starting game. The Buccaneers still had an abysmal overall record, however. Josh had shown that he had what it took to be a starting quarterback in the NFL, but he wasn’t promoted to that position until the team already had a 0-7 record. The fanbase, however, was still confident that the Bucs had a solid future ahead of them with this hotshot at the helm.

This would prove to be a fairly accurate prediction. Freeman was named the starting quarterback of the 2010 season, and he started in all 16 games of the season. (Although this isn’t really abnormal in the NFL, it was the first time that Tampa Bay had had a consistent starting quarterback since 2003.) Josh had a phenomenal season. With the help of offensive studs like Mike Williams and LeGarrette Blount, the Buccaneers had become one of the most impressive and surprising teams in the league. They ended their season with a 10-6 record, which immediately following their historically awful record in the year prior, was a godsend. Even though the team had put together one of their best seasons in years, they narrowly missed the playoff. They were beaten in the wild card tiebreaker by the Green Bay Packers, who would eventually go on to win the Super Bowl entirely. This was a huge blow to the team, to come so close and have nothing to show for it.

Optimism was high yet again in the 2011 season, but it seemed as though this optimism was not well placed. Poor leadership from the coaching staff, paired with a disappointing sophomore slump from Freeman, resulted in a 4-12 season record for Tampa Bay. Disgusted in the team’s sudden collapse, the brass had decided to clean house. In 2012, former Rutgers coach Greg Schiano came aboard as the new head coach. Following a weak start during the 2012 NFL season and reported rifts forming between Freeman, Schiano, and the rest of the team, Josh was ultimately cut from the team. This wasn’t considered the end of his career, however. Only three days after being officially released, Freeman was able to sign with the Minnesota Vikings, where he competed for a starting position against Christian Ponder and Matt Cassel.

Josh freeman football

He was able to start the Vikings’ week 7 game that year, but his performance was less than stellar. In the following game, he was benched due to medical issues. Josh was forced to ride the bench for the remainder of the season. After that, Freeman’s career started to fall off of the map. He had brief stints on the roster of teams like the New York Giants, the Miami Dolphins, and even the FXFL’s Brooklyn Bolts. He didn’t actually end up seeing the field again until 2015, following a signing by the Indianapolis Colts. He was able to play for half of the team’s final game of the season, with modest statistics. What is Josh Freeman doing today, though? Let’s catch up with our favorite hapless rookie.

What’s Josh Freeman Doing Now in 2018 – Recent Updates

Josh Freeman remained on the Indianapolis Colts for the majority of the off-season, and he was suspected to be slated as the team’s backup quarterback behind Andrew Luck. However, he was officially released from the team in March of 2016. Seeing as he never even completed his college education to enter the NFL draft early, you can bet that Josh is still working very hard to continue playing in the NFL. Earlier this month, it was reported that Freeman’s agent reached out to the Dallas Cowboys in hopes of getting a signing. It’s currently unknown as to whether or not that deal ended up panning out in any way. For all of his natural talent, Josh hasn’t actually been playing well since 2010, and it will be a hard sell for him to end up on any professional team. However, he hasn’t shown any sign of giving up yet.


Professor
Department of Brain and Cognitive Sciences
Massachusetts Institute of Technology

Email: jbt AT mit DOT edu
Phone: 617-452-2010 (office), 617-253-8335 (fax)
Mail: Building 46-4015, 77 Massachusetts Avenue, Cambridge, MA 02139
Curriculum Vitae (as of October 2017)

If you are an undergraduate at MIT interested in research with our group, please send a short email about your interests and background (as specific as possible), with resume/CV and any previous research papers, to cocosci-urop@mit.edu. Thanks!

Research interests

My colleagues and I in the Computational Cognitive Science group want to understand that most elusive aspect of human intelligence: our ability to learn so much about the world, so rapidly and flexibly. I like to ask, 'How do we humans get so much from so little?' and by that I mean how do we acquire our commonsense understanding of the world given what is clearly by today's engineering standards so little data, so little time, and so little energy.

Consider how given just a few relevant experiences, even young children can infer the meaning of a new word, the hidden properties of an object or substance, or the existence of a new causal relation or social rule. These inferences go far beyond the data given: after seeing three or four examples of 'horses', a two-year-old will confidently judge whether any new entity is a horse or not, and she will be mostly correct, except for the occasional donkey or camel.

We want to understand these everyday inductive leaps in computational terms. What is the underlying logic that supports reliable generalization from so little data? What are its cognitive and neural mechanisms, and how can we build more powerful learning machines based on the same principles?

These questions demand a multidisciplinary approach. Our group's research combines computational models (drawing chiefly on Bayesian statistics, probabilistic generative models, and probabilistic programming) with behavioral experiments in adults and children. Our models make strong quantitative predictions about behavior, but more importantly, they attempt to explain why cognition works, by viewing it as an approximation to ideal statistical inference given the structure of natural tasks and environments.

While our core interests are in human learning and reasoning, we also work actively in machine learning and artificial intelligence. These two programs are inseparable: bringing machine-learning algorithms closer to the capacities of human learning should lead to more powerful AI systems as well as more powerful theoretical paradigms for understanding human cognition.

Current research in our group explores the computational basis of many aspects of human cognition: learning concepts, judging similarity, inferring causal connections, forming perceptual representations, learning word meanings and syntactic principles in natural language, noticing coincidences and predicting the future, inferring the mental states of other people, and constructing intuitive theories of core domains, such as intuitive physics, psychology, biology, or social structure.

Up to date publications (courtesy of Google scholar)

Representative reading and talks

  • Human-level concept learning through probabilistic program induction.Lake, B., Salakhutdinov, R., and Tenenbaum, J. B. (2015). Science 350(6266), 1332-1338. doi: 10.1126/science.aab3050 (pdf)(supplement)(visual turing tests)(omniglot data set)(Bayesian program learning code)

  • Computational rationality: A converging paradigm for intelligence in brains, minds and machines..Gershman, S. J., Horvitz, E. J., and Tenenbaum, J. B. (2015). Science 349(6245), 273-278. doi: 10.1126/science.aac6076

  • Simulation as an engine of physical scene understanding. Battaglia, P. W., Hamrick, J. B., and Tenenbaum, J. B. (2013). Proceedings of the National Academy of Sciences 110(45), 18327-18332. doi: 10.1073/pnas.1306572110

  • How to Grow a Mind: Statistics, Structure, and Abstraction. Tenenbaum, J. B., Kemp, C., Griffiths, T. L., and Goodman, N. D. (2011). Science 331 (6022), 1279-1285. Supporting Online Material.

  • The discovery of structural form. Kemp, C. and Tenenbaum, J. B. (2008). Proceedings of the National Academy of Sciences. 105(31), 10687-10692. Supporting information. Commentary by K. J. Holyoak. Code and data sets.

  • Bayesian models of cognition. Griffiths, T. L., Kemp, C., and Tenenbaum, J. B. (2008). In Ron Sun (ed.), Cambridge Handbook of Computational Cognitive Modeling. Cambridge University Press.

  • Pure reasoning in 12-month-old infants as probabilistic inference. Teglas, E., Vul, E., Girotto, V., Gonzalez, M., Tenenbaum, J. B., and Bonatti, L. L. (2011). Science 332, 1054-1059. Supporting Online Material.

    • Special issue of Trends in Cognitive Science, July 2006 (Vol. 10, Issue 7), on 'Probabilistic Models of Cognition'.

  • Optimal predictions in everyday cognition. Griffiths, T. L. and Tenenbaum, J. B. (2006). Psychological Science 17(9), 767-773. Article in The Economist.

  • A global geometric framework for nonlinear dimensionality reduction. J. B. Tenenbaum, V. De Silva and J. C. Langford (2000). Science 290 (5500), 2319-2323. Website.

Papers (listed chronologically - very out of date!)

2011

  • A tutorial introuction to Bayesian models of cognitive development. Perfors, A., Tenenbaum, J. B., Griffiths, T. L.,and Xu, F. (in press) Cognition.

  • The learnability of abstract syntactic principles. Perfors, A, Tenenbaum, J. B. and Regier, T. (in press). Cognition.

  • Learning to learn causal models. Kemp, C., Goodman, N. & Tenenbaum, J. (in press).Cognitive Science.

  • Learning a theory of causality. N. D. Goodman, T. D. Ullman, and J. B. Tenenbaum (in press). Psychological Review.

  • Three ideal observer models for rule learning in simple languages. Frank, M. C. & Tenenbaum, J. B. (in press). Cognition. Code package.

  • Probabilistic models of cognition: Exploring representations and inductive biases. Griffiths, T. L., Chater, N., Kemp, C., Perfors, A., & Tenenbaum, J. B. (in press). Trends in Cognitive Sciences.

  • How to Grow a Mind: Statistics, Structure, and Abstraction. Tenenbaum, J. B., Kemp, C., Griffiths, T. L., and Goodman, N. D. (2011). Science 331 (6022), 1279-1285. Supporting Online Material.

  • Pure reasoning in 12-month-old infants as probabilistic inference. Teglas, E., Vul, E., Girotto, V., Gonzalez, M., Tenenbaum, J. B., and Bonatti, L. L. (2011). Science 332, 1054-1059. Supporting Online Material.

  • Theory acquisition as stochastic search. T. D. Ullman, N. D. Goodman and J. B. Tenenbaum (2010). Proceedings of the Thirty-Second Annual Conference of the Cognitive Science Society.

2010

  • A probabilistic model of theory formation. Kemp, C., Tenenbaum, J. B., Niyogi, S. & Griffiths, T. L. (2010).Cognition. 114(2), 165-196.Code and data sets.

  • Variability, negative evidence, and the acquisition of verb argument constructions.. Perfors, A. F, Wonnacott, E., & Tenenbaum, J.B. (in press). Journal of Child Langauge.

  • Help or hinder: Bayesian models of social goal inference. Ullman, T.D., Baker, C.L., Macindoe, O., Evans, O., Goodman, N.D., & Tenenbaum, J.B. (2010). Advances in Neural Information Processing Systems (Vol. 22, pp. 1874-1882).

  • The structure and dynamics of scientific theories: a hierarchical Bayesian perspective. L. Henderson,N. D. Goodman, J. B. Tenenbaum and J. F. Woodward. Phil. Sci. 77 (2), 172-200 (2010).

2009

  • Action understanding as inverse planning. Baker, C. L., Saxe, R., & Tenenbaum, J. B. (2009). Cognition, 113, 329-349. Supplementary material.

  • Theory-based causal induction. Griffiths, T. L., & Tenenbaum, J. B. (2009). Psychological Review, 116, 661-716.

  • Structured statistical models of inductive reasoning. Kemp, C. and Tenenbaum, J. B. (2009). Psychological Review, 116(1), 20-58.

  • Using speakers' referential intentions to model early cross-situational word learning. Frank, M. C., Goodman, N. D., and Tenenbaum, J. B. (2009). Psychological Science 20, 578-585.

  • Exact and approximate sampling by systematic stochastic search. Mansinghka, V. K., Roy, D. M., Jonas, E., and Tenenbaum, J. B. (2009). AISTATS 2009.

  • Cause and Intent: Social Reasoning in Causal Learning. Goodman, N.D., Baker, C.L., & Tenenbaum, J.B. (2009). In Proceedings of the Thirty-First Annual Conference of the Cognitive Science Society (pp. 2759-2764).

  • The infinite latent events model.D. Wingate, N. D. Goodman, D. M. Roy, and J. B. Tenenbaum (2009).Uncertainty in Artificial Intelligence 2009.

  • Explaining human multiple object tracking as resource-constrained approximate inference in a dynamic probabilistic model. . E. Vul, M. C. Frank, J. B. Tenenbaum, and G. Alvarez (2009). Advances in Neural Information Processing Systems 21.

  • Fragment grammars: Exploring computation and reuse in languageT O'Donnell, N. D. Goodman, J. B. Tenenbaum (2009).Technical Report MIT-CSAIL-TR-2009-013, Massachusetts Institute ofTechnology.
  • Learning a theory of causality.N. D. Goodman, T. Ullman, and J. B. Tenenbaum (2009). Proceedings of the Thirty-First AnnualConference of the Cognitive Science Society.
  • How tall Is tall? Compositionality, statistics, and gradable adjectives.L. Schmidt, N. D. Goodman, D. Barner, and J. B. Tenenbaum (2009). Proceedings of the Thirty-First AnnualConference of the Cognitive Science Society.
  • One and done: Globally optimal behavior from locally suboptimal decisions.E. Vul, N. D. Goodman, T. L. Griffiths, J. B. Tenenbaum (2009). Proceedings of the Thirty-First AnnualConference of the Cognitive Science Society.
  • Informative communication in word production and word learning.M. C. Frank, N. D. Goodman, P. Lai, and J. B. Tenenbaum (2009). Proceedings of the Thirty-First AnnualConference of the Cognitive Science Society.
  • Continuity of discourse provides information for word learning.M. C. Frank, N. D. Goodman, J. B. Tenenbaum, and A. Fernald (2009). Proceedings of the Thirty-First AnnualConference of the Cognitive Science Society.

2008

  • Inductive reasoning about causally transmitted properties. Shafto, P., Kemp, C., Baraff, E. R., Coley, J., and Tenenbaum, J. B. (2008). Cognition, 109, 175-192.

  • A rational analysis of rule-based concept learning. N. D. Goodman, J. B. Tenenbaum, J. Feldman, and T. L. Griffiths (2008). Cognitive Science, 32:1, 108-154.

  • The discovery of structural form. Kemp, C. and Tenenbaum, J. B. (2008). Proceedings of the National Academy of Sciences. 105(31), 10687-10692. Supporting information. Commentary by K. J. Holyoak. Code and data sets.

  • Bayesian models of cognition. Griffiths, T. L., Kemp, C., and Tenenbaum, J. B. (2008). In Ron Sun (ed.), Cambridge Handbook of Computational Cognitive Modeling. Cambridge University Press.

  • Compositionality in rational analysis: Grammar-based induction for concept learning. In M. Oaksford and N. Chater (Eds.). Goodman, N. D., Tenenbaum, J. B., Griffiths, T. L., & Feldman, J. (2008). The probabilistic mind: Prospects for rational models of cognition. Oxford: Oxford University Press.

  • Church: a language for generative models. Goodman, N. D., Mansinghka, V. K., Roy, D., Bonawitz, K., and Tenenbaum, J. B. (2008). Uncertainty in Artificial Intelligence 2008.

  • A Bayesian Model of the Acquisition of Compositional Semantics.S. T. Piantadosi, N. D. Goodman, B. A. Ellis, and J. B. Tenenbaum (2008). Proceedings of the Thirtieth AnnualConference of the Cognitive Science Society.
  • Theory acquisition and the language of thought.C. Kemp, N. D. Goodman, and J. B. Tenenbaum (2008). Proceedings of the Thirtieth AnnualConference of the Cognitive Science Society.
  • Structured correlation from the causal background.R. Mayrhofer, N. D. Goodman, M. Waldmann, and J. B. Tenenbaum (2008). Proceedings of the Thirtieth AnnualConference of the Cognitive Science Society.

  • Modeling semantic cognition as logical dimensionality reduction.Y. Katz, N. D. Goodman, K. Kersting, C. Kemp, and J. B. Tenenbaum (2008). Proceedings of the Thirtieth AnnualConference of the Cognitive Science Society.

  • Theory-based social goal inference. Baker, C.L., Goodman, N.D., & Tenenbaum, J.B. (2008). In Proceedings of the Thirtieth Annual Conference of the Cognitive Science Society (pp. 1447-1452).

  • A Bayesian framework for cross-situational word-learning. M. C. Frank, N. D. Goodman, and J. B. Tenenbaum (2008). Advances in Neural Information Processing Systems 20.

  • Learning and using relational theories. C. Kemp, N. D. Goodman, and J. B. Tenenbaum (2008). Advances in Neural Information Processing Systems 20.

2007

  • Theory-based Bayesian models of inductive reasoning. Tenenbaum, J. B., Kemp, C., and Shafto, P. (2007). In Feeney, A. & Heit, E. (eds.), Inductive reasoning. Cambridge University Press.

  • Causal inference in multisensory perception. Kording, K. P., Beierholm, U., Ma, W. J., Quartz, S., Tenenbaum, J. B., Shams, L. (2007). PLoS ONE. September 2007, Issue 9, e943.

  • The role of causality in judgment under uncertainty. Krynski, T. R. and Tenenbaum, J. B. (2007). Journal of Experimental Psychology: General 136(3), 430-450.

  • The dynamics of memory are a consequence of optimal adaptation to a changing body. Kording, K. P., Tenenbaum, J. B., and Shadmehr, R. (2007). Nature Neuroscience 10(6), 779-786.

  • Goal inference as inverse planning. Baker, C. L., Tenenbaum, J. B., and Saxe, R. R. (2007). Proceedings of the Twenty-Ninth Annual Conference of the Cognitive Science Society (pp. 779-784).

  • Modeling human performance in statistical word segmentation. Frank, M., Goldwater, S., Griffiths, T. L., Mansinghka, V. K., and Tenenbaum, J. B. (2007). Proceedings of the Twenty-Ninth Annual Conference of the Cognitive Science Society.

  • A rational analysis of rule-based concept learning. Goodman, N. D., Griffiths, T. L., Feldman, J., and Tenenbaum, J. B. (2007). Proceedings of the Twenty-Ninth Annual Conference of the Cognitive Science Society.

  • Learning grounded causal models. Goodman, N. D., Mansignhka, V. K., and Tenenbaum, J. B. (2007). Proceedings of the Twenty-Ninth Annual Conference of the Cognitive Science Society. [2007 Cognitive Science Computational Modeling Prize, Perception and Action category.]

  • Learning causal schemata. Kemp, C., Goodman, N. D., and Tenenbaum, J. B. (2007). Proceedings of the Twenty-Ninth Annual Conference of the Cognitive Science Society. [2007 Cognitive Science Computational Modeling Prize, Higher-Level Cognition category.]

  • Discovering syntactic hierarchies. Savova, V., Roy, D., Schmidt, L., and Tenenbaum, J. B. (2007). Proceedings of the Twenty-Ninth Annual Conference of the Cognitive Science Society.

  • Parametric embedding for class visualization. Iwata, T., Saito, K., Ueda, N., Stromsten, S., Griffiths, T. L., and Tenenbaum, J. B. (2007). Neural Computation 19, 2536-2556.

  • Learning annotated hierarchies from relational data. Roy, D., Kemp, C., Mansinghka, V., and Tenenbaum, J. B. (2007). Advances in Neural Information Processing Systems 19.

  • Combining causal and similarity-based reasoning. Kemp, C., Shafto, P., Berke, A., and Tenenbaum, J. B. (2007). Advances in Neural Information Processing Systems 19. [Honorable mention, Outstanding Student Paper award.]

  • Multiple timescales and uncertainty in motor adaptation. Kording, K., Tenenbaum, J. B., and Shadmehr, R. (2007). Advances in Neural Information Processing Systems 19.

  • Causal inference in sensorimotor integration. Kording, K. and Tenenbaum, J. B. (2007). Advances in Neural Information Processing Systems 19.

  • AClass: An online algorithm for generative classification. Mansinghka, V. K., Roy, D. M., Rifkin, R., and Tenenbaum, J. B. (2007). Eleventh International Conference on Artificial Intelligence and Statistics (AISTATS07).

  • Word learning as Bayesian inference. Xu, F. and Tenenbaum, J. B. (2007). Psychological Review 114(2).

  • Topics in semantic representation. Griffiths, T. L., Steyvers, M., and Tenenbaum, J. B. (2007). Psychological Review 114(2).

  • Bayesian networks, Bayesian learning, and cognitive development. Gopnik, A. and Tenenbaum, J. B. (2007). Developmental Science 10(3), 281-287.

  • Sensitivity to sampling in Bayesian word learning. Xu, F. and Tenenbaum, J. B. (2007). Developmental Science 10(3), 288-297.

  • Learning overhypotheses with hierarchical Bayesian models. Kemp, C., Perfors, A., and Tenenbaum, J. B. (2007). Developmental Science 10(3), 307-321.

  • From mere coincidences to meaningful discoveries. Griffiths, T. L. and Tenenbaum, J. B. (2007). Cognition 103(2), 180-226.

  • Intuitive theories as grammars for causal inference. Tenenbaum, J.B., Griffiths, T. L., and Niyogi, S. (2007). In Gopnik, A., & Schulz, L. (eds.), Causal learning: Psychology, philosophy, and computation. Oxford University Press.

  • Two proposals for causal grammars. Griffiths, T. L. and Tenenbaum, J. B. (2007). In Gopnik, A., & Schulz, L. (eds.), Causal learning: Psychology, philosophy, and computation. Oxford University Press.

2006

  • Optimal predictions in everyday cognition. Griffiths, T. L. and Tenenbaum, J. B. (2006). Psychological Science 17(9), 767-773. Article in The Economist

  • Statistics and the Bayesian mind. Griffiths, T. L. and Tenenbaum, J. B. (2006). Significance 3(3), 130-133.

  • Theory-based Bayesian models of inductive learning and reasoning. Tenenbaum, J. B., Griffiths, T. L., and Kemp, C. (2006). Trends in Cognitive Sciences, 10(7), 309-318.

  • Probabilistic models of cognition: Conceptual foundations. Chater, N., Tenenbaum, J. B., and Yuille, A. (2006). Trends in Cognitive Sciences, 10(7), 287-291.

  • Unsupervised topic modelling for multi-party spoken discourse. Purver, M., Kording, K. P., Griffiths, T. L., & Tenenbaum, J. B. (2006). Proceedings of Coling/ACL 2006.

  • Structured priors for structure learning. Mansinghka, V. K., Kemp, C., Tenenbaum, J. B., and Griffiths, T. L. (2006). Proceedings of the Twenty-Second Conference on Uncertainty in Artificial Intelligence (UAI 2006).

  • Learning systems of concepts with an infinite relational model. Kemp, C., Tenenbaum, J. B., Griffiths, T. L., Yamada, T., and Ueda, N. (2006). Proceedings of the Twenty-First National Conference on Artificial Intelligence (AAAI-06). IRM Code

  • Learning cross-cutting systems of categories. Shafto, P. Kemp, C., Mansignhka, V., Gordon, M., and Tenenbaum, J. B. (2006). Proceedings of the Twenty-Eighth Annual Conference of the Cognitive Science Society.

  • Poverty of the Stimulus? A rational approach. Perfors, A., Regier, T. and Tenenbaum, J. B. (2006). Proceedings of the Twenty-Eighth Annual Conference of the Cognitive Science Society.

  • Nonsense and sensibility: Inferring unseen possibilities. Schmidt, L. A., Kemp, C., and Tenenbaum, J. B. (2006). Proceedings of the Twenty-Eighth Annual Conference of the Cognitive Science Society.

  • Learning overhypotheses. Kemp, C., Perfors, A. and Tenenbaum, J. B. (2006). Proceedings of the Twenty-Eighth Annual Conference of the Cognitive Science Society.

  • Intuitive theories of mind: A rational approach to false belief. Goodman, N. D., Baker, C. L., Bonawitz, E. B., Mansinghka, V. K, Gopnik, A., Wellman, H., Schulz, L. & Tenenbaum, J. B. (2006). Proceedings of the Twenty-Eighth Annual Conference of the Cognitive Science Society (pp. 1382-1387).

  • Bayesian models of human action understanding. C. L. Baker, J. B. Tenenbaum, R. R. Saxe (2006). Advances in Neural Information Processing Systems 18 (pp. 99-106).

2005

  • Parametric Embedding for Class Visualization. T. Iwata, K. Saito, N. Ueda, S. Stromsten, T. L. Griffiths, J. B. Tenenbaum (2005). Advances in Neural Information Processing Systems 17.

  • Context-sensitive induction. Shafto, P., Kemp, C., Baraff, L., Coley, J., and Tenenbaum, J. B. (2005). Proceedings of the Twenty-Seventh Annual Conference of the Cognitive Science Society.

  • Word learning as Bayesian inference: Evidence from preschoolers. Xu, F. and Tenenbaum, J. B. (2005). Proceedings of the Twenty-Seventh Annual Conference of the Cognitive Science Society.

  • Integrating topics and syntax. T. L. Griffiths, M. Steyvers, D. Blei, and J. B. Tenenbaum (2005). Advances in Neural Information Processing Systems 17.

  • The large-scale structure of semantic networks: statistical analyses and a model of semantic growth. M. Steyvers, J. B. Tenenbaum (2005), Cognitive Science, 29(1).

  • A generative theory of similarity. Kemp, C., Bernstein, A., and Tenenbaum, J. B. (2005). Proceedings of the Twenty-Seventh Annual Conference of the Cognitive Science Society.

  • Secret agents: inferences about hidden causes by 10- and 12-month-old infants. Saxe, R., Tenenbaum, J.B., and Carey, S. (2005). Psychological Science 16(12), 995-1001.

  • Structure and strength in causal induction. Griffiths, T.L., & Tenenbaum, J.B. (2005). Cognitive Psychology 51, 334-384.
    (This paper was formerly titled 'Elemental causal induction.') MATLAB code for computing causal support.

2004

Josh Freeman Stats

  • Learning domain structures. Kemp, C. S., Perfors, A., and Tenenbaum, J. B. (2004). Proceedings of the Twenty-Sixth Annual Conference of the Cognitive Science Society.

  • Discovering latent classes in relational data. C. Kemp, T. L. Griffiths, & J. B. Tenenbaum (2004). MIT AI Memo 2004-019.

  • Semi-supervised learning with trees. Kemp, C., Griffiths, T. L, Stromsten, S., and Tenenbaum, J. B. (2004). Advances in Neural Information Processing Systems 16.

  • Hierarchical topic models and the nested Chinese restaurant process. D. Blei, T. L. Griffiths, M. I. Jordan, and J. B. Tenenbaum (2004). Advances in Neural Information Processing Systems 16.[Best Student Paper, NIPS 2003, Synthetic Systems Category.]

  • From algorithmic to subjective randomness. Griffiths, T. L. and Tenenbaum, J. B. (2004). Advances in Neural Information Processing Systems 16.[Best Student Paper, NIPS 2003, Natural Systems Category.]

  • Children's causal inferences from indirect evidence: Backwards blocking and Bayesian reasoning in preschoolers. D. Sobel, J. B. Tenenbaum, A. Gopnik (2004), Cognitive Science, 28, 303-333.

  • Using physical theories to infer hidden causal structure. Griffiths, T.L., Baraff, E.R., & Tenenbaum, J.B. (2004). Proceedings of the Twenty-Sixth Annual Conference of the Cognitive Science Society.[Marr Prize for Best Student Paper, Honorable Mention, Cognitive Science 2004.]

2003

Josh Freeman Career Earnings

  • Learning style translation for the lines of a drawing. W.T. Freeman, J.B. Tenenbaum, E. Pasztor (2003). ACM Transactions on Graphics 22 (1), January 2003, 33-46. (uncorrected proofs - SMALL pdf)

  • Theory-based induction. Kemp, C. S. and Tenenbaum, J. B. (2003). Proceedings of the Twenty-Fifth Annual Conference of the Cognitive Science Society.

  • The role of causal models in reasoning under uncertainty. Krynski, T. R. and Tenenbaum, J. B. (2003). Proceedings of the Twenty-Fifth Annual Conference of the Cognitive Science Society.

  • V1 neurons signal acquisition of an internal representation of stimulus location. Sharma, J., Dragoi, V., Tenenbaum, J. B., Miller, E. K., and Sur, M. (2003). Science, 300, 1758-1763.

  • Probability, algorithmic complexity, and subjective randomness. Griffiths, T. L. and Tenenbaum, J. B. (2003). Proceedings of the Twenty-Fifth Annual Conference of the Cognitive Science Society.

  • Learning causal laws. Tenenbaum, J. B. and Niyogi, S. (2003). Proceedings of the Twenty-Fifth Annual Conference of the Cognitive Science Society.

  • Dynamical causal learning. Danks, D., Griffiths, T.L., & Tenenbaum, J.B. (2003). Advances in Neural Information Processing Systems 15. Becker, S., Thrun, S., and Obermayer. (eds). Cambridge, MIT Press, 2003, 67-74.

  • Inferring causal networks from observations and interventions. M. Steyvers, J. B. Tenenbaum, E. J. Wagenmakers, B. Blum (2003), Cognitive Science 27: 453-489.

  • Theory-based causal inference. J. B. Tenenbaum, T. L. Griffiths (2003), Advances in Neural Information Processing Systems 15. Becker, S., Thrun, S., and Obermayer. (eds). Cambridge, MIT Press, 2003, 35-42.

2002

  • The Isomap Algorithm and Topological Stability. M. Balasubramanian, E. L. Shwartz, J. B. Tenenbaum, V. de Silva, and J. C. Langford (2002). Science Jan 4 2002: 7.

  • Global versus local methods in nonlinear dimensionality reduction. V. de Silva, J. B. Tenenbaum (2002). Advances in Neural Information Processing Systems 15. S. Becker, S., Thrun, S., and Obermayer, K. (eds). Cambridge, MIT Press, 2002, 705-712.

  • Unsupervised learning of curved manifolds. V. de Silva, J.B. Tenenbaum (2002). In D.D. Denison, M. H. Hansen, C. C. Holmes, B. Mallick and B. Yu (eds.), Nonlinear Estimation and Classification , Springer-Verlag, New York, 453-466.

  • Bayesian models of inductive generalization. N. Sanjana, J. B. Tenenbaum (2002), Advances in Neural Information Processing Systems 15. Becker, S., Thrun, S., Obermayer, K. (eds). Cambridge, MIT Press, 2002, 51-58. [Best Student Paper, Honorable Mention, NIPS 2001.]

2001

  • Generalization, similarity, and Bayesian inference. J. B. Tenenbaum, T. L. Griffiths (2001), Behavioral and Brain Sciences, 24 pp. 629-641.

  • Some specifics about generalization. J. B. Tenenbaum, T. L. Griffiths (2001), Behavioral and Brain Sciences, 24, pages 772-778.

  • The rational basis of representativeness. J. B. Tenenbaum, T. L. Griffiths (2001), 23rd Annual Conference of the Cognitive Science Society. 1036-1041.

  • Randomness and coincidences: Reconciling intuition and probability theory. T. L. Griffiths, J. B. Tenenbaum (2001), 23rd Annual Conference of the Cognitive Science Society. 370-375. Article in Psychology Today (single-page view)

  • Structure learning in human causal induction. J. B. Tenenbaum, T. L. Griffiths (2001), Advances in Neural Information Processing Systems 13. Leen, T., Dietterich, T., and Tresp, V., Cambridge, MIT Press, 2001, 59-65. (postscript)

2000

  • A global geometric framework for nonlinear dimensionality reduction. J. B. Tenenbaum, V. De Silva and J. C. Langford (2000). Science 290 (5500), 2319-2323. Website

  • Separating style and content with bilinear models. J. B. Tenenbaum, W. T. Freeman (2000). Neural Computation 12 (6), 1247-1283. [Conference version received the Outstanding PaperAward at IEEE CVPR 1997.]

  • Rules and similarity in concept learning. J. B. Tenenbaum (2000), Advances in Neural Information Processing Systems 12. Solla, S., Leen, T. and Muller, K. (eds). Cambridge, MIT Press, 2000, 59-65. (postscript)

  • Word learning as Bayesian inference. J. B. Tenenbaum, F. Xu (2000), Proceedings of the 22nd Annual Conference of the Cognitive Science Society(postscript)

  • Teacakes, trains, toxins, and taxicabs: A Bayesian account of predicting the future. T. L. Griffiths, J. B. Tenenbaum (2000), Proceedings of the 22nd Annual Conference of the Cognitive Science Society. 202-207. (postscript)

1999 and before

  • Bayesian modeling of human concept learning. J. B. Tenenbaum (1999), Advances in Neural Information Processing Systems 11. Kearns, M., Solla, S., and Cohn, D. (eds). Cambridge, MIT Press, 1999, 59-65. (postscript)

  • A Bayesian Framework for Concept Learning. J. B. Tenenbaum, Ph.D. Thesis, MIT, 1999

  • Mapping a manifold of perceptual observations. J. B. Tenenbaum (1998). Advances in Neural Information Processing Systems 10. Jordan, M., Kearns, M., and Solla, S. (eds). Cambridge, MIT Press, 1998, 682-688. (postscript)

  • Learning the structure of similarity. J. B. Tenenbaum (1995), Advances in Neural Information Processing Systems 8. Toretzky, D., Mozer, M., and Hasselmo, M. (eds). Cambridge, MIT Press, 1995, 3-9. (postscript)


Papers (by topic - this list is somewhat out of date)

Nonlinear dimensionality reduction

  • Parametric Embedding for Class Visualization. T. Iwata, K. Saito, N. Ueda, S. Stromsten, T. L. Griffiths, J. B. Tenenbaum (2005). Advances in Neural Information Processing Systems 17.

  • The Isomap Algorithm and Topological Stability. M. Balasubramanian, E. L. Shwartz, J. B. Tenenbaum, V. de Silva, and J. C. Langford (2002). Science Jan 4 2002: 7.

  • Global versus local methods in nonlinear dimensionality reduction. V. de Silva, J. B. Tenenbaum (2002). Advances in Neural Information Processing Systems 15. S. Becker, S., Thrun, S., and Obermayer, K. (eds). Cambridge, MIT Press, 2002, 705-712.

  • Unsupervised learning of curved manifolds. V. de Silva, J.B. Tenenbaum (2002). In D.D. Denison, M. H. Hansen, C. C. Holmes, B. Mallick and B. Yu (eds.), Nonlinear Estimation and Classification , Springer-Verlag, New York, 453-466.

  • A global geometric framework for nonlinear dimensionality reduction. J. B. Tenenbaum, V. De Silva and J. C. Langford (2000). Science 290 (5500), 2319-2323. Website

  • Mapping a manifold of perceptual observations. J. B. Tenenbaum (1998). Advances in Neural Information Processing Systems 10. Jordan, M., Kearns, M., and Solla, S. (eds). Cambridge, MIT Press, 1998, 682-688. (postscript)

Separating style and content

  • Learning style translation for the lines of a drawing. W.T. Freeman, J.B. Tenenbaum, E. Pasztor (2003). ACM Transactions on Graphics 22 (1), January 2003, 33-46. (uncorrected proofs - SMALL pdf)

  • Separating style and content with bilinear models. J. B. Tenenbaum, W. T. Freeman (2000). Neural Computation 12 (6), 1247-1283. [Conference version received the Outstanding Paper Award at IEEE CVPR 1997.]

Concept learning and generalization

  • Context-sensitive induction. Shafto, P., Kemp, C., Baraff, L., Coley, J., and Tenenbaum, J. B. (2005). Proceedings of the Twenty-Seventh Annual Conference of the Cognitive Science Society.

  • Learning domain structures. Kemp, C. S., Perfors, A., and Tenenbaum, J. B. (2004). Proceedings of the Twenty-Sixth Annual Conference of the Cognitive Science Society.

  • Discovering latent classes in relational data. C. Kemp, T. L. Griffiths, & J. B. Tenenbaum (2004). MIT AI Memo 2004-019.

  • Semi-supervised learning with trees. Kemp, C., Griffiths, T. L, Stromsten, S., and Tenenbaum, J. B. (2004). Advances in Neural Information Processing Systems 16.

  • Theory-based induction. Kemp, C. S. and Tenenbaum, J. B. (2003). Proceedings of the Twenty-Fifth Annual Conference of the Cognitive Science Society.

  • Bayesian models of inductive generalization. N. Sanjana, J. B. Tenenbaum (2002), Advances in Neural Information Processing Systems 15. Becker, S., Thrun, S., Obermayer, K. (eds). Cambridge, MIT Press, 2002, 51-58. [Best Student Paper, Honorable Mention, NIPS 2001.]

  • Rules and similarity in concept learning. J. B. Tenenbaum (2000), Advances in Neural Information Processing Systems 12. Solla, S., Leen, T. and Muller, K. (eds). Cambridge, MIT Press, 2000, 59-65. (postscript)

  • Bayesian modeling of human concept learning. J. B. Tenenbaum (1999), Advances in Neural Information Processing Systems 11. Kearns, M., Solla, S., and Cohn, D. (eds). Cambridge, MIT Press, 1999, 59-65. (postscript)

  • A Bayesian Framework for Concept Learning. J. B. Tenenbaum, Ph.D. Thesis, MIT, 1999.

Learning and Representing Word Meanings

  • Word learning as Bayesian inference: Evidence from preschoolers. Xu, F. and Tenenbaum, J. B. (2005). Proceedings of the Twenty-Seventh Annual Conference of the Cognitive Science Society.

  • Integrating topics and syntax. T. L. Griffiths, M. Steyvers, D. Blei, and J. B. Tenenbaum (2005). Advances in Neural Information Processing Systems 17.

  • The large-scale structure of semantic networks: statistical analyses and a model of semantic growth. M. Steyvers, J. B. Tenenbaum (2005), Cognitive Science, 29(1).

  • Hierarchical topic models and the nested Chinese restaurant process. D. Blei, T. L. Griffiths, M. I. Jordan, and J. B. Tenenbaum (2004). Advances in Neural Information Processing Systems 16.[Best Student Paper, NIPS 2003, Synthetic Systems Category.]

  • Word learning as Bayesian inference. J. B. Tenenbaum, F. Xu (2000), Proceedings of the 22nd Annual Conference of the Cognitive Science Society(postscript)

Learning and similarity

  • A generative theory of similarity. Kemp, C., Bernstein, A., and Tenenbaum, J. B. (2005). Proceedings of the Twenty-Seventh Annual Conference of the Cognitive Science Society.

  • Generalization, similarity, and Bayesian inference. J. B. Tenenbaum, T. L. Griffiths (2001), Behavioral and Brain Sciences, 24 pp. 629-641.

  • Some specifics about generalization. J. B. Tenenbaum, T. L. Griffiths (2001), Behavioral and Brain Sciences, 24, pages 772-778.

  • Learning the structure of similarity. J. B. Tenenbaum (1995), Advances in Neural Information Processing Systems 8. Toretzky, D., Mozer, M., and Hasselmo, M. (eds). Cambridge, MIT Press, 1995, 3-9. (postscript)

Josh Freeman Wa

Probabilistic reasoning

  • From mere coincidences to meaningful discoveries. Griffiths, T. L. and Tenenbaum, J. B. (in press). Cognition.

  • Optimal predictions in everyday cognition. Griffiths, T. L. and Tenenbaum, J. B. (in press). Psychological Science.Article in The Economist

  • From algorithmic to subjective randomness. Griffiths, T. L. and Tenenbaum, J. B. (2004). Advances in Neural Information Processing Systems 16.[Best Student Paper, NIPS 2003, Natural Systems Category.]

  • The role of causal models in reasoning under uncertainty. Krynski, T. R. and Tenenbaum, J. B. (2003). Proceedings of the Twenty-Fifth Annual Conference of the Cognitive Science Society.

  • V1 neurons signal acquisition of an internal representation of stimulus location. Sharma, J., Dragoi, V., Tenenbaum, J. B., Miller, E. K., and Sur, M. (2003). Science, 300, 1758-1763.

  • Probability, algorithmic complexity, and subjective randomness. Griffiths, T. L. and Tenenbaum, J. B. (2003). Proceedings of the Twenty-Fifth Annual Conference of the Cognitive Science Society.

  • The rational basis of representativeness. J. B. Tenenbaum, T. L. Griffiths (2001), 23rd Annual Conference of the Cognitive Science Society. 1036-1041.

  • Randomness and coincidences: Reconciling intuition and probability theory. T. L. Griffiths, J. B. Tenenbaum (2001), 23rd Annual Conference of the Cognitive Science Society. 370-375. Article in Psychology Today

  • Teacakes, trains, toxins, and taxicabs: A Bayesian account of predicting the future. T. L. Griffiths, J. B. Tenenbaum (2000), Proceedings of the 22nd Annual Conference of the Cognitive Science Society. 202-207. (postscript)

Causal learning and inference

  • Intuitive theories as grammars for causal inference. Tenenbaum, J.B., Griffiths, T. L., and Niyogi, S. (2007). In Gopnik, A., & Schulz, L. (eds.), Causal learning: Psychology, philosophy, and computation. Oxford University Press.

  • Two proposals for causal grammars. Griffiths, T. L. and Tenenbaum, J. B. (2007). In Gopnik, A., & Schulz, L. (eds.), Causal learning: Psychology, philosophy, and computation. Oxford University Press.

  • Secret agents: inferences about hidden causes by 10- and 12-month-old infants. Saxe, R., Tenenbaum, J.B., and Carey, S. (2005). Psychological Science 16(12), 995-1001.

  • Structure and strength in causal induction. Griffiths, T.L., & Tenenbaum, J.B. (2005). Cognitive Psychology 51(4), 285-386.
    (MATLAB code for computing causal support)

  • Children's causal inferences from indirect evidence: Backwards blocking and Bayesian reasoning in preschoolers. D. Sobel, J. B. Tenenbaum, A. Gopnik (2004), Cognitive Science, 28, 303-333.

  • Using physical theories to infer hidden causal structure. Griffiths, T.L., Baraff, E.R., & Tenenbaum, J.B. (2004). Proceedings of the Twenty-Sixth Annual Conference of the Cognitive Science Society. [Marr Prize for Best Student Paper, Honorable Mention, Cognitive Science 2004.]

  • Learning causal laws. Tenenbaum, J. B. and Niyogi, S. (2003). Proceedings of the Twenty-Fifth Annual Conference of the Cognitive Science Society.

  • Dynamical causal learning. Danks, D., Griffiths, T.L., & Tenenbaum, J.B. (2003). Advances in Neural Information Processing Systems 15. Becker, S., Thrun, S., and Obermayer. (eds). Cambridge, MIT Press, 2003, 67-74.

  • Inferring causal networks from observations and interventions. M. Steyvers, J. B. Tenenbaum, E. J. Wagenmakers, B. Blum (2003), Cognitive Science 27: 453-489.

  • Theory-based causal inference. J. B. Tenenbaum, T. L. Griffiths (2003), Advances in Neural Information Processing Systems 15. Becker, S., Thrun, S., and Obermayer. (eds). Cambridge, MIT Press, 2003, 35-42.

  • Structure learning in human causal induction. J. B. Tenenbaum, T. L. Griffiths (2001), Advances in Neural Information Processing Systems 13. Leen, T., Dietterich, T., and Tresp, V., Cambridge, MIT Press, 2001, 59-65. (postscript)

Theory of mind

  • Help or hinder: Bayesian models of social goal inference. Ullman, T.D., Baker, C.L., Macindoe, O., Evans, O., Goodman, N.D., & Tenenbaum, J.B. (2010). Advances in Neural Information Processing Systems (Vol. 22, pp. 1874-1882).

  • Action understanding as inverse planning. Baker, C. L., Saxe, R., & Tenenbaum, J. B. (2009). Cognition, 113, 329-349. Supplementary material.

  • Cause and Intent: Social Reasoning in Causal Learning. Goodman, N.D., Baker, C.L., & Tenenbaum, J.B. (2009). In Proceedings of the Thirty-First Annual Conference of the Cognitive Science Society (pp. 2759-2764).

  • Theory-based Social Goal Inference. Baker, C.L., Goodman, N.D., & Tenenbaum, J.B. (2008). In Proceedings of the Thirtieth Annual Conference of the Cognitive Science Society (pp. 1447-1452).

  • Goal inference as inverse planning. Baker, C. L., Tenenbaum, J. B., and Saxe, R. R. (2007). Proceedings of the Twenty-Ninth Annual Conference of the Cognitive Science Society (pp. 779-784).

  • Intuitive theories of mind: A rational approach to false belief. Goodman, N. D., Baker, C. L., Bonawitz, E. B., Mansinghka, V. K, Gopnik, A., Wellman, H., Schulz, L. & Tenenbaum, J. B. (2006). Proceedings of the Twenty-Eighth Annual Conference of the Cognitive Science Society (pp. 1382-1387).

  • Bayesian models of human action understanding. Baker, C. L, Tenenbaum, J. B. & Saxe, R. R. (2006). Advances in Neural Information Processing Systems 18 (pp. 99-106).

Lab mascots
Abigail 'Avishka' Lea Tenenbaum