Travelled to:1 × Canada
1 × Italy
1 × United Kingdom
2 × China
4 × USA
Collaborated with:F.J.Gomez Julian Togelius Y.Sun J.Koutník T.Schaul D.C.Ciresan U.Meier L.M.Gambardella R.Salustowicz M.Wiering ∅ Niels van Hoorn K.Greff M.B.Ring D.Wierstra A.Graves S.Fernández Sergey Karakovskiy Giuseppe Cuccu Alexandros Agapitos S.M.Lucas Andreas Konstantinidis 0002
Talks about:neural (5) network (4) hierarch (2) committe (2) classif (2) tempor (2) search (2) evolut (2) evolv (2) game (2)
Person: Jürgen Schmidhuber
DBLP: Schmidhuber:J=uuml=rgen
Contributed to:
20142012
2011201120092006199819961995
20082009
2013Wrote 16 papers:
- ICML-c2-2014-KoutnikGGS
- A Clockwork RNN (JK, KG, FJG, JS), pp. 1863–1871.
- ICML-2012-SunGS #kernel #on the #online #taxonomy
- On the Size of the Online Kernel Sparsification Dictionary (YS, FJG, JS), p. 79.
- ICDAR-2011-CiresanMGS #classification #network
- Convolutional Neural Network Committees for Handwritten Character Classification (DCC, UM, LMG, JS), pp. 1135–1139.
- ICDAR-2011-MeierCGS #recognition
- Better Digit Recognition with a Committee of Simple Neural Nets (UM, DCC, LMG, JS), pp. 1250–1254.
- ICML-2011-SunGRS #difference #fault #incremental
- Incremental Basis Construction from Temporal Difference Error (YS, FJG, MBR, JS), pp. 481–488.
- ICML-2009-YiWSS #probability #using
- Stochastic search using the natural gradient (YS, DW, TS, JS), pp. 1161–1168.
- ICML-2006-GravesFGS #classification #network #sequence
- Connectionist temporal classification: labelling unsegmented sequence data with recurrent neural networks (AG, SF, FJG, JS), pp. 369–376.
- ICML-1998-SalustowiczS #evolution #source code
- Evolving Structured Programs with Hierarchical Instructions and Skip Nodes (RS, JS), pp. 488–496.
- ICML-1996-WieringS
- Solving POMDPs with Levin Search and EIRA (MW, JS), pp. 534–542.
- ICML-1995-Schmidhuber #complexity
- Discovering Solutions with Low Kolmogorov Complexity and High Generalization Capability (JS), pp. 488–496.
- CIG-2008-AgapitosTLSK #evolution #generative #multi
- Generating diverse opponents with multiobjective evolution (AA, JT, SML, JS, AK0), pp. 135–142.
- CIG-2008-SchaulS #architecture #game studies #network #scalability
- A scalable neural network architecture for board games (TS, JS), pp. 357–364.
- CIG-2008-TogeliusS #automation #design #empirical #game studies
- An experiment in automatic game design (JT, JS), pp. 111–118.
- CIG-2009-HoornTS #learning
- Hierarchical controller learning in a First-Person Shooter (NvH, JT, JS), pp. 294–301.
- CIG-2009-TogeliusKKS #evolution
- Super mario evolution (JT, SK, JK, JS), pp. 156–161.
- FDG-2013-KoutnikCSG #evolution #network #scalability
- Evolving large-scale neural networks for vision-based TORCS (JK, GC, JS, FJG), pp. 206–212.