Botond Cseke - RankMe

26 papers A* 3A 2Journal 14Unranked 7

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Latent Matters: Learning Deep State-Space Models. CoRR Alexej Klushyn, Richard Kurle, Maximilian Soelch, Botond Cseke, Patrick van der Smagt
2025	J	`jnl`	FlowQ: Energy-Guided Flow Policies for Offline Reinforcement Learning. CoRR Marvin Alles, Nutan Chen, Patrick van der Smagt, Botond Cseke
2024	—	`conf`	Fast Preserving Local Distances and Topology in Auto-encoders. ICONIP (2) Nutan Chen, Patrick van der Smagt, Botond Cseke
2024	J	`jnl`	Guided Decoding for Robot Motion Generation and Adaption. CoRR Nutan Chen, Elie Aljalbout, Botond Cseke, Patrick van der Smagt
2024	—	`conf`	Guided Decoding for Robot On-line Motion Generation and Adaption. Humanoids Nutan Chen, Botond Cseke, Elie Aljalbout, Alexandros Paraschos, Marvin Alles, Patrick van der Smagt
2022	J	`jnl`	Constrained Probabilistic Movement Primitives for Robot Trajectory Adaptation. IEEE Trans. Robotics Felix Frank, Alexandros Paraschos, Patrick van der Smagt, Botond Cseke
2022	—	`conf`	Local Distance Preserving Auto-encoders using Continuous kNN Graphs. TAG-ML Nutan Chen, Patrick van der Smagt, Botond Cseke
2022	J	`jnl`	Local distance preserving auto-encoders using Continuous k-Nearest Neighbours graphs. CoRR Nutan Chen, Patrick van der Smagt, Botond Cseke
2021	J	`jnl`	Constrained Probabilistic Movement Primitives for Robot Trajectory Adaptation. CoRR Felix Frank, Alexandros Paraschos, Patrick van der Smagt, Botond Cseke
2021	A*	`conf`	Latent Matters: Learning Deep State-Space Models. NeurIPS Alexej Klushyn, Richard Kurle, Maximilian Soelch, Botond Cseke, Patrick van der Smagt
2020	A*	`conf`	Continual Learning with Bayesian Neural Networks for Non-Stationary Data. ICLR Richard Kurle, Botond Cseke, Alexej Klushyn, Patrick van der Smagt, Stephan Günnemann
2019	—	`conf`	Increasing the Generalisaton Capacity of Conditional VAEs. ICANN (2) Alexej Klushyn, Nutan Chen, Botond Cseke, Justin Bayer, Patrick van der Smagt
2019	J	`jnl`	Increasing the Generalisaton Capacity of Conditional VAEs. CoRR Alexej Klushyn, Nutan Chen, Botond Cseke, Justin Bayer, Patrick van der Smagt
2019	A*	`conf`	Learning Hierarchical Priors in VAEs. NeurIPS Alexej Klushyn, Nutan Chen, Richard Kurle, Botond Cseke, Patrick van der Smagt
2019	J	`jnl`	Learning Hierarchical Priors in VAEs. CoRR Alexej Klushyn, Nutan Chen, Richard Kurle, Botond Cseke, Patrick van der Smagt
2016	—	`conf`	f-GAN: Training Generative Neural Samplers using Variational Divergence Minimization. NIPS Sebastian Nowozin, Botond Cseke, Ryota Tomioka
2016	J	`jnl`	f-GAN: Training Generative Neural Samplers using Variational Divergence Minimization. CoRR Sebastian Nowozin, Botond Cseke, Ryota Tomioka
2014	J	`jnl`	Properties of Bethe Free Energies and Message Passing in Gaussian Models. CoRR Botond Cseke, Tom Heskes
2013	—	`conf`	Approximate inference in latent Gaussian-Markov models from continuous time observations. NIPS Botond Cseke, Manfred Opper, Guido Sanguinetti
2012	J	`jnl`	Bounds on the Bethe Free Energy for Gaussian Networks CoRR Botond Cseke, Tom Heskes
2011	J	`jnl`	Approximate Marginals in Latent Gaussian Models. J. Mach. Learn. Res. Botond Cseke, Tom Heskes
2011	J	`jnl`	Properties of Bethe Free Energies and Message Passing in Gaussian Models. J. Artif. Intell. Res. Botond Cseke, Tom Heskes
2010	J	`jnl`	Efficient Bayesian multivariate fMRI analysis using a sparsifying spatio-temporal prior. NeuroImage Marcel A. J. van Gerven, Botond Cseke, Floris P. de Lange, Tom Heskes
2010	A	`conf`	Improving posterior marginal approximations in latent Gaussian models. AISTATS Botond Cseke, Tom Heskes
2009	—	`conf`	Bayesian Source Localization with the Multivariate Laplace Prior. NIPS Marcel van Gerven, Botond Cseke, Robert Oostenveld, Tom Heskes
2008	A	`conf`	Bounds on the Bethe Free Energy for Gaussian Networks. UAI Botond Cseke, Tom Heskes