'Model learning' presentation slideshows

Unsupervised Learning of Visual Object Categories

Unsupervised Learning of Visual Object Categories. Michael Pfeiffer pfeiffer@igi.tugraz.at. References. M. Weber 1 , M. Welling 1 , P. Perona 1 (2000) Unsupervised Learning of Models for Recognition Li Fei-Fei 1 , R. Fergus 2 , P. Perona 1 (2003)

609 views • 51 slides

social networks analysis seminar introductory lecture #2

social networks analysis seminar introductory lecture #2. Danny Hendler and Yehonatan Cohen Advanced Topics in on-line Social Networks Analysis. Seminar schedule. Introductory lecture #1. 5/3/14. 10/3/14. Papers list published, students send their 3 preferences. 12/3/14.

624 views • 49 slides

social networks analysis seminar introductory lecture #2

social networks analysis seminar introductory lecture #2. Danny Hendler and Yehonatan Cohen Advanced Topics in on-line Social Networks Analysis. Seminar schedule. Introductory lecture #1. 5/3/14. 10/3/14. Papers list published, students send their 3 preferences. 12/3/14.

498 views • 49 slides

Hebbian Model Learning

Cognitive Neuroscience and Embodied Intelligence. Hebbian Model Learning. Based on a courses taught by Prof. Randall O'Reilly , University of Colorado, Prof. W?odzis?aw Duch , Uniwersytet Miko?aja Kopernika and http://wikipedia.org/.

417 views • 27 slides

Reinforcement Learning

Reinforcement Learning. CPS 370 Ron Parr. Limitations of the MDP Framework. Assumes that transition probabilities are known How do we discover these? Envelope method assumed this problem away SDS had trouble collecting enough data How do we store them? Big problems have big models

323 views • 27 slides

OPTIMOL: automatic Object Picture collecTion via Incremental MOdel Learning

310 views • 18 slides

Emergent Representations and Reasoning in Adaptive Agents

Emergent Representations and Reasoning in Adaptive Agents. Joost Broekens, Doug DeGroot Leiden University, LIACS, Leiden. {broekens, degroot}@liacs.nl. Overview. Introduction Interactivism Hypothesis Computational Model based on Interactivist Concepts Experiments Results Conclusion

258 views • 18 slides

Two papers in icfda14

Two papers in icfda14. Guimei Zhang MESA (Mechatronics, Embedded Systems and Automation) LAB School of Engineering, University of California, Merced E : guimei.zh@163.com Phone:209-658-4838 Lab : CAS Eng 820 ( T : 228-4398). June 30, 2014. Monday 4:00-6:00 PM

276 views • 16 slides

Social Action Tracking via Noise Tolerant Time-varying Factor Graphs

Social Action Tracking via Noise Tolerant Time-varying Factor Graphs. 1 Chenhao Tan, 1 Jie Tang, 2 Jimeng Sun, 3 Quan Lin , 4 Fengjiao Wang 1 Department of Computer Science and Technology, Tsinghua University, China 2 IBM TJ Watson Research Center, USA

293 views • 15 slides

Implementing the Common Core

Implementing the Common Core. Sandra M. Alberti Student Achievement Partners salberti@studentsachieve.net @ achievethecore. Initiatives. RtI. CCLS. DDI. SLO. APPR. DASA. Initiatives. RtI. CCLS. DDI. SLO. APPR. DASA. 2 Key Characteristics of Leading Organizations.

199 views • 11 slides

Knowledge Tracing

Knowledge Tracing. Parameters can be learned with the EM algorithm!. Modeling or measuring learning requires modeling knowledge Knowledge Tracing used to model learning. Parameters (probability of learning) (guess/slip) (prior). P(Skill: 0 ? 1). P(Skill: 0 ? 1). S. S. S. Latent

394 views • 3 slides