Tom Heskes

	2012
78		Tom Claassen, Tom Heskes: A Logical Characterization of Constraint-Based Causal Discovery CoRR abs/1202.3711: (2012)
77		L. Niels Cornelisse, Evgeni Tsivtsivadze, Marieke Meijer, Tjeerd Dijkstra, Tom Heskes, Matthijs Verhage: Molecular Machines in the Synapse: Overlapping Protein Sets Control Distinct Steps in Neurosecretion. PLoS Computational Biology 8(4): (2012)
	2011
76		Daniel Kühlwein, Josef Urban, Evgeni Tsivtsivadze, Herman Geuvers, Tom Heskes: Learning2Reason. Calculemus/MKM 2011: 298-300
75		Tom Claassen, Tom Heskes: A structure independent algorithm for causal discovery. ESANN 2011
74		John A. Quinn, Joris M. Mooij, Tom Heskes, Michael Biehl: Learning of causal relations. ESANN 2011
73		Marcel van Gerven, Eric Maris, Tom Heskes: A Markov Random Field Approach to Neural Encoding and Decoding. ICANN (2) 2011: 1-8
72		Perry Groot, Adriana Birlutiu, Tom Heskes: Learning from Multiple Annotators with Gaussian Processes. ICANN (2) 2011: 159-164
71		Daniel Kühlwein, Josef Urban, Evgeni Tsivtsivadze, Herman Geuvers, Tom Heskes: Multi-output Ranking for Automated Reasoning. KDIR 2011: 42-51
70		Joris M. Mooij, Dominik Janzing, Tom Heskes, Bernhard Schölkopf: On Causal Discovery with Cyclic Additive Noise Models. NIPS 2011: 639-647
69		Evgeni Tsivtsivadze, Josef Urban, Herman Geuvers, Tom Heskes: Semantic Graph Kernels for Automated Reasoning. SDM 2011: 795-803
68		Tom Claassen, Tom Heskes: A Logical Characterization of Constraint-Based Causal Discovery. UAI 2011: 135-144
67		Jesse Alama, Daniel Kühlwein, Evgeni Tsivtsivadze, Josef Urban, Tom Heskes: Premise Selection for Mathematics by Corpus Analysis and Kernel Methods CoRR abs/1108.3446: (2011)
66		Perry Groot, Tom Heskes, Tjeerd Dijkstra, James M. Kates: Predicting Preference Judgments of Individual Normal and Hearing-Impaired Listeners With Gaussian Processes. IEEE Transactions on Audio, Speech & Language Processing 19(4): 811-821 (2011)
65		Marco Baglietto, Lubica Benusková, Ivo Bukovsky, Tianping Chen, Tom Heskes, Kazushi Ikeda, Fakhri Karray, Rhee Man Kil, Robert A. Legenstein, Jinhu Lu, Yunqian Ma, Malik Magdon-Ismail, Michael G. Paulin, Robi Polikar, Danil V. Prokhorov, Marco Wiering, Vicente Zarzoso: Editorial: One Year as EiC, and Editorial-Board Changes at TNN. IEEE Transactions on Neural Networks 22(1): 1-7 (2011)
64		Botond Cseke, Tom Heskes: Properties of Bethe Free Energies and Message Passing in Gaussian Models. J. Artif. Intell. Res. (JAIR) 41: 1-24 (2011)
63		Botond Cseke, Tom Heskes: Approximate Marginals in Latent Gaussian Models. Journal of Machine Learning Research 12: 417-454 (2011)
62		Marcel van Gerven, Peter Kok, Floris P. de Lange, Tom Heskes: Dynamic decoding of ongoing perception. NeuroImage 57(3): 950-957 (2011)
	2010
61		Tjeerd Dijkstra, Evgeni Tsivtsivadze, Elena Marchiori, Tom Heskes: Pattern Recognition in Bioinformatics - 5th IAPR International Conference, PRIB 2010, Nijmegen, The Netherlands, September 22-24, 2010. Proceedings Springer 2010
60		Perry Groot, Adriana Birlutiu, Tom Heskes: Bayesian Monte Carlo for the Global Optimization of Expensive Functions. ECAI 2010: 249-254
59		Tom Claassen, Tom Heskes: Causal discovery in multiple models from different experiments. NIPS 2010: 415-423
58		Tom Heskes: Expectation Propagation. Encyclopedia of Machine Learning 2010: 383-387
57		Botond Cseke, Tom Heskes: Improving posterior marginal approximations in latent Gaussian models. Journal of Machine Learning Research - Proceedings Track 9: 121-128 (2010)
56		Marcel van Gerven, Floris P. de Lange, Tom Heskes: Neural Decoding with Hierarchical Generative Models. Neural Computation 22(12): 3127-3142 (2010)
55		Adriana Birlutiu, Perry Groot, Tom Heskes: Multi-task preference learning with an application to hearing aid personalization. Neurocomputing 73(7-9): 1177-1185 (2010)
	2009
54		Ali Bahramisharif, Marcel van Gerven, Tom Heskes: Exploring the impact of alternative feature representations on BCI classification. ESANN 2009
53		Adriana Birlutiu, Perry Groot, Tom Heskes: Multi-task Preference learning with Gaussian Processes. ESANN 2009
52		Marcel van Gerven, Botond Cseke, Robert Oostenveld, Tom Heskes: Bayesian Source Localization with the Multivariate Laplace Prior. NIPS 2009: 1901-1909
51		Rasa Jurgelenaite, Tjeerd Dijkstra, Clemens H. M. Kocken, Tom Heskes: Gene regulation in the intraerythrocytic cycle of Plasmodium falciparum. Bioinformatics 25(12): 1484-1491 (2009)
50		Marcel van Gerven, Ali Bahramisharif, Tom Heskes, Ole Jensen: Selecting features for BCI control based on a covert spatial attention paradigm. Neural Networks 22(9): 1271-1277 (2009)
	2008
49		Botond Cseke, Tom Heskes: Bounds on the Bethe Free Energy for Gaussian Networks. UAI 2008: 97-104
	2007
48		José Miguel Hernández-Lobato, Tjeerd Dijkstra, Tom Heskes: Regulator Discovery from Gene Expression Time Series of Malaria Parasites: a Hierachical Approach. NIPS 2007
47		Adriana Birlutiu, Tom Heskes: Expectation Propagation for Rating Players in Sports Competitions. PKDD 2007: 374-381
46		Marcel van Gerven, Rasa Jurgelenaite, Babs G. Taal, Tom Heskes, Peter J. F. Lucas: Predicting carcinoid heart disease with the noisy-threshold classifier. Artificial Intelligence in Medicine 40(1): 45-55 (2007)
45		Bart Bakker, Tom Heskes: Learning and approximate inference in dynamic hierarchical models. Computational Statistics & Data Analysis 52(2): 821-839 (2007)
	2006
44		Rasa Jurgelenaite, Tom Heskes: EM Algorithm for Symmetric Causal Independence Models. ECML 2006: 234-245
43		Rasa Jurgelenaite, Tom Heskes: Symmetric Causal Independence Models for Classification. Probabilistic Graphical Models 2006: 163-170
42		Tom Heskes: Convexity Arguments for Efficient Minimization of the Bethe and Kikuchi Free Energies. J. Artif. Intell. Res. (JAIR) 26: 153-190 (2006)
41		Onno Zoeter, Tom Heskes: Deterministic approximate inference techniques for conditionally Gaussian state space models. Statistics and Computing 16(3): 279-292 (2006)
	2005
40		Tom Heskes, Bert de Vries: Incremental Utility Elicitation for Adaptive Personalization. BNAIC 2005: 127-134
39		Rasa Jurgelenaite, Peter J. F. Lucas, Tom Heskes: Use of the Noisy Threshold Function in Building Bayesian Networks. BNAIC 2005: 158-165
38		Onno Zoeter, Tom Heskes: Gaussian Quadrature Based Expectation Propagation. BNAIC 2005: 407
37		Onno Zoeter, Tom Heskes: Change Point Problems in Linear Dynamical Systems. Journal of Machine Learning Research 6: 1999-2026 (2005)
36		Alexander Ypma, Tom Heskes: Novel approximations for inference in nonlinear dynamical systems using expectation propagation. Neurocomputing 69(1-3): 85-99 (2005)
	2004
35		Alexander Ypma, Tom Heskes: Novel approximations for inference and learning in nonlinear dynamical systems. ESANN 2004: 361-366
34		Tom Heskes: On the Uniqueness of Loopy Belief Propagation Fixed Points. Neural Computation 16(11): 2379-2413 (2004)
	2003
33		Onno Zoeter, Tom Heskes: Multi-scale Switching Linear Dynamical Systems. ICANN 2003: 562-572
32		Tom Heskes, Onno Zoeter, Wim Wiegerinck: Approximate Expectation Maximization. NIPS 2003
31		Tom Heskes, Kees Albers, Bert Kappen: Approximate Inference and Constrained Optimization. UAI 2003: 313-320
30		Onno Zoeter, Tom Heskes: Hierarchical Visualization of Time-Series Data Using Switching Linear Dynamical Systems. IEEE Trans. Pattern Anal. Mach. Intell. 25(10): 1202-1214 (2003)
29		Bart Bakker, Tom Heskes: Task Clustering and Gating for Bayesian Multitask Learning. Journal of Machine Learning Research 4: 83-99 (2003)
28		Tom Heskes, Jan-Joost Spanjers, Bart Bakker, Wim Wiegerinck: Optimising newspaper sales using neural-Bayesian technology. Neural Computing and Applications 12(3-4): 212-219 (2003)
27		Bart Bakker, Tom Heskes: Clustering ensembles of neural network models. Neural Networks 16(2): 261-269 (2003)
	2002
26		Bart Bakker, Tom Heskes: Model Clustering for Neural Network Ensembles. ICANN 2002: 383-388
25		Tom Heskes: Stable Fixed Points of Loopy Belief Propagation Are Local Minima of the Bethe Free Energy. NIPS 2002: 343-350
24		Wim Wiegerinck, Tom Heskes: Fractional Belief Propagation. NIPS 2002: 438-445
23		Tom Heskes, Onno Zoeter: Expectation Propogation for Approximate Inference in Dynamic Bayesian Networks. UAI 2002: 216-223
22		Wim Wiegerinck, Tom Heskes: IPF for Discrete Chain Factor Graphs. UAI 2002: 560-567
21		Alexander Ypma, Tom Heskes: Automatic Categorization of Web Pages and User Clustering with Mixtures of Hidden Markov Models. WEBKDD 2002: 35-49
20		Tom Heskes, Bart Bakker, Bert Kappen: Approximate algorithms for neural-Bayesian approaches. Theor. Comput. Sci. 287(1): 219-238 (2002)
	2001
19		Tom Heskes: Self-organizing maps, vector quantization, and mixture modeling. IEEE Transactions on Neural Networks 12(6): 1299-1305 (2001)
	2000
18		Tom Heskes: Empirical Bayes for Learning to Learn. ICML 2000: 367-374
17		Jakob Vogdrup Hansen, Tom Heskes: General Bias/Variance Decomposition with Target Independent Variance of Error Functions Derived from the Exponential Family of Distributions. ICPR 2000: 2207-2210
16		Tom Heskes, Jan-Joost Spanjers, Wim Wiegerinck: EM Algorithms for Self-Organizing Maps. IJCNN (6) 2000: 9-14
15		Tom Heskes: On "Natural" Learning and Pruning in Multilayered Perceptrons. Neural Computation 12(4): 881-901 (2000)
14		Piërre van de Laar, Tom Heskes: Input selection based on an ensemble. Neurocomputing 34(1-4): 227-238 (2000)
	1999
13		Bart Bakker, Tom Heskes: Model clustering by deterministic annealing. ESANN 1999: 87-92
12		Piërre van de Laar, Tom Heskes, Stan C. A. M. Gielen: Partial Retraining: A New Approach to Input Relevance Determination. Int. J. Neural Syst. 9(1): 75-85 (1999)
11		Piërre van de Laar, Tom Heskes: Pruning Using Parameter and Neuronal Metrics. Neural Computation 11(4): 977-993 (1999)
	1998
10		Tom Heskes: Solving a Huge Number of Similar Tasks: A Combination of Multi-Task Learning and a Hierarchical Bayesian Approach. ICML 1998: 233-241
9		Tom Heskes: Bias/Variance Decompositions for Likelihood-Based Estimators. Neural Computation 10(6): 1425-1433 (1998)
	1997
8		Piërre van de Laar, Stan C. A. M. Gielen, Tom Heskes: Input Selection with Partial Retraining. ICANN 1997: 469-474
7		Tom Heskes: Selecting Weighting Factors in Logarithmic Opinion Pools. NIPS 1997
6		Piërre van de Laar, Tom Heskes, Stan C. A. M. Gielen: Task-Dependent Learning of Attention. Neural Networks 10(6): 981-992 (1997)
	1996
5		Tom Heskes: Practical Confidence and Prediction Intervals. NIPS 1996: 176-182
4		Tom Heskes: Balancing Between Bagging and Bumping. NIPS 1996: 466-472
3		Tom Heskes: Transition times in self-organizing maps. Biological Cybernetics 75(1): 49-57 (1996)
	1994
2		Tom Heskes: Stochastics of on-line back-propagation. ESANN 1994
	1992
1		Tom Heskes, Stan C. A. M. Gielen: Retrieval of pattern sequences at variable speeds in a neural network with delays. Neural Networks 5(1): 145-152 (1992)

Coauthor Index

1	Jesse Alama	[67]
2	Kees Albers	[31]
3	Marco Baglietto	[65]
4	Ali Bahramisharif	[50] [54]
5	Bart Bakker	[13] [20] [26] [27] [28] [29] [45]
6	Lubica Benusková (Lubica Benuskova)	[65]
7	Michael Biehl	[74]
8	Adriana Birlutiu	[47] [53] [55] [60] [72]
9	Ivo Bukovsky	[65]
10	Tianping Chen	[65]
11	Tom Claassen	[59] [68] [75] [78]
12	L. Niels Cornelisse	[77]
13	Botond Cseke	[49] [52] [57] [63] [64]
14	Tjeerd Dijkstra (T. M. H. Dijkstra)	[48] [51] [61] [66] [77]
15	Marcel van Gerven	[46] [50] [52] [54] [56] [62] [73]
16	Herman Geuvers	[69] [71] [76]
17	Stan C. A. M. Gielen	[1] [6] [8] [12]
18	Perry Groot	[53] [55] [60] [66] [72]
19	Jakob Vogdrup Hansen	[17]
20	José Miguel Hernández-Lobato	[48]
21	Kazushi Ikeda	[65]
22	Dominik Janzing	[70]
23	Ole Jensen	[50]
24	Rasa Jurgelenaite	[39] [43] [44] [46] [51]
25	Hilbert J. Kappen (Bert Kappen)	[20] [31]
26	Fakhri Karray (Fakhreddine Karray)	[65]
27	James M. Kates	[66]
28	Rhee Man Kil	[65]
29	Clemens H. M. Kocken	[51]
30	Peter Kok	[62]
31	Daniel Kühlwein	[67] [71] [76]
32	Piërre van de Laar	[6] [8] [11] [12] [14]
33	Floris P. de Lange	[56] [62]
34	Robert A. Legenstein	[65]
35	Jinhu Lu (Jinhu Lü)	[65]
36	Peter J. F. Lucas	[39] [46]
37	Yunqian Ma	[65]
38	Malik Magdon-Ismail	[65]
39	Elena Marchiori	[61]
40	Eric Maris	[73]
41	Marieke Meijer	[77]
42	Joris M. Mooij	[70] [74]
43	Robert Oostenveld	[52]
44	Michael G. Paulin	[65]
45	Robi Polikar	[65]
46	Danil V. Prokhorov	[65]
47	John A. Quinn (John Alexander Quinn)	[74]
48	Bernhard Schölkopf	[70]
49	Jan-Joost Spanjers	[16] [28]
50	Babs G. Taal	[46]
51	Evgeni Tsivtsivadze	[61] [67] [69] [71] [76] [77]
52	Josef Urban	[67] [69] [71] [76]
53	Matthijs Verhage	[77]
54	Bert de Vries	[40]
55	Wim Wiegerinck	[16] [22] [24] [28] [32]
56	Marco Wiering (Marco A. Wiering)	[65]
57	Alexander Ypma	[21] [35] [36]
58	Vicente Zarzoso	[65]
59	Onno Zoeter	[23] [30] [32] [33] [37] [38] [41]

Colors in the list of coauthors

Last update Sat Jun 2 20:57:36 2012 CET by the DBLP Team — Data released under the ODC-BY 1.0 license — See also our legal information page