1. And you can too!

2. 1972 2011 1992 SBS

3.  Introduction  Evidence for Statistics  Bays Law  Informative Priors  Joint Models  Inference  Conclusion

4. Two examples that seem to indicate that the brain is indeed processing statistical information

5.  Saffran, Aslin, Newport. “Statistical Learning in 8-Month-Old Infants”  The infants listen to strings of nonsense words with no auditory clues to word boundaries.  E.g., “bidakupa …” where “bidaku is the first word.  They learn to distinguish words from other combinations that occur (with less frequency) over word boundaries.

6. Speaker Light Child

7.  Based on Rosenholtz et. al. (2011) A B

8.  Based on Rosenholtz et. al. (2011) A N O B E L

9.  A much better idea than spatial subsampling Original patch~1000 pixels

10. Original patch  A rich set of statistics can capture a lot of useful information Patch synthesized to match ~1000 statistical parameters (Portilla & Simoncelli, 2000)

11.  Balas, Nakano, & Rosenholtz, JoV, 2009

12. To my mind, at least, it packs a lot of information

13. P(M|E) = P(M) P(E|M) P(E) M = Learned Model of the world E = Learner’s environment (sensory input)

14. P(M|E) =P(M) P(E|M) P(E) It divides up responsibility correctly. It requires a generative model. (big, joint) It (obliquely) suggests that as far as learning goes we ignore the programs that use the model. But which M?

15.  Don’t pick M. Integrate over all of them.  Pick the M that maximizes P(M)P(E|M).  Pick the average P(M) (Gibbs sampling). P(E) = Σ P(M)P(E|M) M

16. Don’t sweat it.

17. Three examples where they are critical

18. trees skyscrapersky bell dome temple buildings sky

19. Cut random surfaces (samples from a GP) with thresholds (as in Level Set Methods) Assign each pixel to the first surface which exceeds threshold (as in Layered Models) Duan, Guindani, & Gelfand, Generalized Spatial DP, 2007

20. Comparison: Potts Markov Random Field

21.  Based on the work of Goldwater et. al.  Separate one “word” from the next in child-directed speech.  E.g., yuwanttusiD6bUk You want to see the book

22.  Generative Story For each utterance: For each word w (or STOP) pick with probability P(w) If w=STOP break If we pick M to maximize P(E|M) the model memorizes the data. I.e., It creates one “word” which is the concatenation of all the words in that sentence.

23. Precision: 61.6Recall: 47.6 Example: youwant to see thebook

24.  Primarily based on Clark (2003)  Given a sequence of words, deduce their parts of speech (e.g., DT, NN, etc.)  Generative story: For each word position (i) in the text 1) propose part-of-speech (t) p(t|t-1) 2) propose a word (w) using p(w|t)

25.  We could put a Dirichlet prior on P(w|t)  But what we really want is sparse P(t|w)  Almost all words (by type) have only one part-of-speech  We do best by only allowing this.  E.g., “can” is only a model verb (we hope!)  Putting a sparse prior on P(word-type|t) also helps.

26. Two examples that show the strengths of modeling many phenomena jointly.

27.  Clark pos tagger also includes something sort of like a morphology model.  It assumes POS tags are correlated with spelling.  True morphology would recognize that “ride” “riding” and “rides” share a root.  I do not know of any true joint tagging- morphology model.

28.  Based on Haghighi & Klein 2010 Weiner said the problems were all Facebook’s fault. They should never have given him an account. (person) Type1 (organization) Type2 Obama, Weiner, father IBM, Facebook, company

29. Otherwise know as hardware.

30.  More generally it is not any mechanism that requires tracking all expectations.  Consider the word boundary. Between every two phonemes there may or may not be a boundary. abcde a|bcde ab|cde abc|de abcd|e a|b|cde …

31.  Start out with random guesses. Do (roughly) forever: Pick a random point. Compute p(split) and p(join). Pick r, 0<r<1: if p(split) > r split, p(split)+p(join) else join.

34.  First, the nice properties only hold for “exchangeable” distributions. It seems likely that most of the ones we care about are not (e.g., Haghighi & Klein)  But critically it assumes we have all the training data at once and go over it many times.

35.  Or something like it.  At the level of detail here, just think “beam search.”

36. NP NNS Dogs VBS like NP NNS bones VP S Information Barrier

37.  Or something like it.  At the level of detail here, just think “beam search.” (ROOT (Root (S (NP (NNS Dogs) (ROOT (NP (NNS Dogs) (ROOT (S (NP (NNS Dogs)) (VP (VBS eat)

38.  The brain operates by manipulating probabilities.  World-model induction is governed by Bayes Law  This implies we have a large joint generative model  It seems overwhelmingly likely that we have a very informative prior.  Something like particle filtering is the inference/use mechanism.