Travelled to:1 × Canada
1 × China
1 × Germany
1 × Israel
3 × USA
Collaborated with:N.Jetchev T.Lang N.A.Vien ∅ N.Vlassis A.J.Storkey S.Vijayakumar N.Plath S.Nakajima C.Friedrich Viktor Zielke Armin Lechler A.Verl Muhammad Usman Khalid Janik M. Hager Werner Kraus M.F.Huber F.V.Agakov E.V.Bonilla J.Cavazos B.Franke G.Fursin M.F.P.O'Boyle J.Thomson C.K.I.Williams
Talks about:learn (5) model (4) use (4) trajectori (3) relat (3) infer (3) probabilist (2) stochast (2) approxim (2) segment (2)
Person: Marc Toussaint
DBLP: Toussaint:Marc
Contributed to:
2014201120102009
200620062005
20172019Wrote 13 papers:
- ICML-c2-2014-NgoT #modelling #relational
- Model-Based Relational RL When Object Existence is Partially Observable (NAV, MT), pp. 559–567.
- ICML-2011-JetchevT #feedback #retrieval #using
- Task Space Retrieval Using Inverse Feedback Control (NJ, MT), pp. 449–456.
- ICML-2010-LangT #probability #reasoning #relational
- Probabilistic Backward and Forward Reasoning in Stochastic Relational Worlds (TL, MT), pp. 583–590.
- ICML-2009-JetchevT #learning #predict
- Trajectory prediction: learning to map situations to robot trajectories (NJ, MT), pp. 449–456.
- ICML-2009-LangT #approximate #probability #relational
- Approximate inference for planning in stochastic relational worlds (TL, MT), pp. 585–592.
- ICML-2009-PlathTN #classification #image #multi #random #segmentation #using
- Multi-class image segmentation using conditional random fields and global classification (NP, MT, SN), pp. 817–824.
- ICML-2009-Toussaint #approximate #optimisation #using
- Robot trajectory optimization using approximate inference (MT), pp. 1049–1056.
- ICML-2009-VlassisT #learning
- Model-free reinforcement learning as mixture learning (NV, MT), pp. 1081–1088.
- CGO-2006-AgakovBCFFOTTW #machine learning #optimisation #using
- Using Machine Learning to Focus Iterative Optimization (FVA, EVB, JC, BF, GF, MFPO, JT, MT, CKIW), pp. 295–305.
- ICML-2006-ToussaintS #markov #probability #process
- Probabilistic inference for solving discrete and continuous state Markov Decision Processes (MT, AJS), pp. 945–952.
- ICML-2005-ToussaintV #learning #modelling
- Learning discontinuities with products-of-sigmoids for switching between local models (MT, SV), pp. 904–911.
- CASE-2017-FriedrichZTLV #approach #maintenance #modelling #recognition
- Environment modeling for maintenance automation-a next-best-view approach for combining space exploration and object recognition tasks (CF, VZ, MT, AL, AV), pp. 1445–1450.
- CASE-2019-KhalidHKHT #segmentation
- Deep Workpiece Region Segmentation for Bin Picking (MUK, JMH, WK, MFH, MT), pp. 1138–1144.