Sign inSign up
Bayesian models of human inductive learning thumbnail
Pause
Mute
Subtitles
Playback speed
0.25
0.5
0.75
1
1.25
1.5
1.75
2
Full screen

Bayesian models of human inductive learning

Published on Jun 22, 200727410 Views

Joshua B. Tenenbaum

In everyday learning and reasoning, people routinely draw successful generalizations from very limited evidence. Even young children can infer the meanings of words, hidden properties of objects, or t

ICML 2007 - Corvallis

Related categories

Machine LearningBayesian LearningDevelopmental Psychology

Chapter list

Bayesian models of human inductive learning Josh Tenenbaum MIT Department of Brain and Cognitive Sciences Computer Science and AI Lab (CSAIL)00:00
Lab members02:48
The probabilistic revolution in AI 03:20
Everyday inductive leaps05:00
Learning concepts from examples06:36
Everyday inductive leaps07:45
The solution09:22
The solution0110:08
The approach: from statistics to intelligence14:10
Outline16:33
The “shape bias” in word learning (Landau, Smith, Jones 1988)17:12
Is the shape bias learned?18:54
Transfer to real-world vocabulary21:01
Learning about feature variability24:06
Learning about feature variability0124:52
A hierarchical Bayesian model25:25
A hierarchical Bayesian model0126:07
A hierarchical Bayesian model0227:13
A hierarchical Bayesian model0327:52
A hierarchical Bayesian model0428:50
Learning the shape bias29:35
Learning the shape bias0130:23
Extensions31:43
Learning to transfer selectively33:02
Learning to transfer selectively0134:12
Outline36:02
Property induction36:29
The computational problem37:59
Hierarchical Bayesian Framework40:45
P(D|S): How the structure constrains the data of experience42:22
P(D|S): How the structure constrains the data of experience0142:53
P(D|S): How the structure constrains the data of experience0244:03
Structure S44:14
slide3444:57
[c.f., Lawrence, 2004; Smola & Kondor 2003]45:10
Structure S45:40
Cows have property P. Elephants have property P. Horses have property P.46:09
Testing different priors47:29
Learning about spatial properties 49:18
Hierarchical Bayesian Framework50:18
Discovering structural forms50:44
Discovering structural forms0151:04
People can discover structural forms51:24
The ultimate goal52:52
A “universal grammar” for structural forms53:20
Hierarchical Bayesian Framework54:46
slide4755:32
Structural forms from relational data57:23
Lab studies of learning structural forms57:31
Development of structural forms as more data are observed57:36
Beyond “Nativism” versus “Empiricism”01:00:09
Summary01:01:44