Evolving Memory Cell Structures for Sequence Learning. - Justin Bayer; Daan Wierstra; Julian Togelius; Jürgen Schmidhuber (ICANN (2), 2009)

PyBrain. - Tom Schaul; Justin Bayer; Daan Wierstra; Yi Sun 003; Martin Felder; Frank Sehnke; Thomas Rückstieß; Jürgen Schmidhuber (J. Mach. Learn. Res., 2010)

Learning Sequence Neighbourhood Metrics. - Justin Bayer; Christian Osendorfer; Patrick van der Smagt (ICANN (1), 2012)

Identification of Human Limb Stiffness in 5 DoF and Estimation via EMG. - Dominic Lakatos; Daniel Rüschen; Justin Bayer; Jörn Vogel; Patrick van der Smagt (ISER, 2012)

Training Neural Networks with Implicit Variance. - Justin Bayer; Christian Osendorfer; Sebastian Urban; Patrick van der Smagt (ICONIP (2), 2013)

Convolutional Neural Networks Learn Compact Local Image Descriptors. - Christian Osendorfer; Justin Bayer; Sebastian Urban; Patrick van der Smagt (ICONIP (3), 2013)

Computing grip force and torque from finger nail images using Gaussian processes. - Sebastian Urban; Justin Bayer; Christian Osendorfer; Göran Westling; Benoni B. Edin; Patrick van der Smagt (IROS, 2013)

Continuous robot control using surface electromyography of atrophic muscles. - Jörn Vogel; Justin Bayer; Patrick van der Smagt (IROS, 2013)

Unsupervised Feature Learning for low-level Local Image Descriptors - Christian Osendorfer; Justin Bayer; Patrick van der Smagt (ICLR (Workshop Poster), 2013)

Convolutional Neural Networks learn compact local image descriptors - Christian Osendorfer; Justin Bayer; Patrick van der Smagt (CoRR, 2013)

Estimating finger grip force from an image of the hand using Convolutional Neural Networks and Gaussian processes. - Nutan Chen; Sebastian Urban; Christian Osendorfer; Justin Bayer; Patrick van der Smagt (ICRA, 2014)

Learning Stochastic Recurrent Networks. - Justin Bayer; Christian Osendorfer (CoRR, 2014)

On Fast Dropout and its Applicability to Recurrent Networks. - Justin Bayer; Christian Osendorfer; Nutan Chen; Sebastian Urban; Patrick van der Smagt (ICLR (Poster), 2014)

Regularizing Recurrent Networks - On Injected Noise and Norm-based Methods. - Saahil Ognawala; Justin Bayer (CoRR, 2014)

Efficient movement representation by embedding Dynamic Movement Primitives in deep autoencoders. - Nutan Chen; Justin Bayer; Sebastian Urban; Patrick van der Smagt (Humanoids, 2015)

Measuring fingertip forces from camera images for random finger poses. - Nutan Chen; Sebastian Urban; Justin Bayer; Patrick van der Smagt (IROS, 2015)

Fast Adaptive Weight Noise. - Justin Bayer; Maximilian Karl; Daniela Korhammer; Patrick van der Smagt (CoRR, 2015)

Efficient Empowerment. - Maximilian Karl; Justin Bayer; Patrick van der Smagt (CoRR, 2015)

Learning Sequence Representations. - Justin Bayer (2015)

Theano: A Python framework for fast computation of mathematical expressions. - Rami Al-Rfou; Guillaume Alain; Amjad Almahairi; Christof Angermüller; Dzmitry Bahdanau; Nicolas Ballas; Frédéric Bastien; Justin Bayer; Anatoly Belikov; Alexander Belopolsky; Yoshua Bengio; Arnaud Bergeron; et al. (CoRR, 2016)

Unsupervised preprocessing for Tactile Data. - Maximilian Karl; Justin Bayer; Patrick van der Smagt (CoRR, 2016)

ML-based tactile sensor calibration: A universal approach. - Maximilian Karl; Artur Lohrer; Dhananjay Shah; Frederik Diehl; Max Fiedler; Saahil Ognawala; Justin Bayer; Patrick van der Smagt (CoRR, 2016)

Variational Inference for On-line Anomaly Detection in High-Dimensional Time Series. - Maximilian Soelch 001; Justin Bayer; Marvin Ludersdorfer; Patrick van der Smagt (CoRR, 2016)

Deep Variational Bayes Filters: Unsupervised Learning of State Space Models from Raw Data. - Maximilian Karl; Maximilian Soelch 001; Justin Bayer; Patrick van der Smagt (ICLR (Poster), 2017)

Key Insights into Hand Biomechanics: Human Grip Stiffness Can Be Decoupled from Force by Cocontraction and Predicted from Electromyography. - Hannes Höppner; Maximilian Große-Dunker; Georg Stillfried; Justin Bayer; Patrick van der Smagt (Frontiers Neurorobotics, 2017)

Metrics for Deep Generative Models. - Nutan Chen; Alexej Klushyn; Richard Kurle; Xueyan Jiang; Justin Bayer; Patrick van der Smagt (AISTATS, 2018)

Approximate Bayesian inference in spatial environments. - Atanas Mirchev; Baris Kayalibay; Patrick van der Smagt; Justin Bayer (CoRR, 2018)

Bayesian Learning of Neural Network Architectures. - Georgi Dikov; Justin Bayer (AISTATS, 2019)

Fast Approximate Geodesics for Deep Generative Models. - Nutan Chen; Francesco Ferroni; Alexej Klushyn; Alexandros Paraschos; Justin Bayer; Patrick van der Smagt (ICANN (2), 2019)

Increasing the Generalisaton Capacity of Conditional VAEs. - Alexej Klushyn; Nutan Chen; Botond Cseke; Justin Bayer; Patrick van der Smagt (ICANN (2), 2019)

On Deep Set Learning and the Choice of Aggregations. - Maximilian Soelch 001; Adnan Akhundov; Patrick van der Smagt; Justin Bayer (ICANN (1), 2019)

Unsupervised Real-Time Control Through Variational Empowerment. - Maximilian Karl; Philip Becker-Ehmck; Maximilian Soelch 001; Djalel Benbouzid; Patrick van der Smagt; Justin Bayer (ISRR, 2019)

Approximate Bayesian Inference in Spatial Environments. - Atanas Mirchev; Baris Kayalibay; Maximilian Soelch 001; Patrick van der Smagt; Justin Bayer (Robotics: Science and Systems, 2019)

Bayesian Learning of Neural Network Architectures. - Georgi Dikov; Patrick van der Smagt; Justin Bayer (CoRR, 2019)

Variational Tracking and Prediction with Generative Disentangled State-Space Models. - Adnan Akhundov; Maximilian Soelch 001; Justin Bayer; Patrick van der Smagt (CoRR, 2019)

Learning Flat Latent Manifolds with VAEs. - Nutan Chen; Alexej Klushyn; Francesco Ferroni; Justin Bayer; Patrick van der Smagt (ICML, 2020)

Mind the Gap when Conditioning Amortised Inference in Sequential Latent-Variable Models. - Justin Bayer; Maximilian Soelch 001; Atanas Mirchev; Baris Kayalibay; Patrick van der Smagt (ICLR, 2021)

Variational State-Space Models for Localisation and Dense 3D Mapping in 6 DoF. - Atanas Mirchev; Baris Kayalibay; Patrick van der Smagt; Justin Bayer (ICLR, 2021)

PRISM: Probabilistic Real-Time Inference in Spatial World Models. - Atanas Mirchev; Baris Kayalibay; Ahmed Agha; Patrick van der Smagt; Daniel Cremers; Justin Bayer (CoRL, 2022)

Tracking and Planning with Spatial World Models. - Baris Kayalibay; Atanas Mirchev; Patrick van der Smagt; Justin Bayer (L4DC, 2022)

Filter-Aware Model-Predictive Control. - Baris Kayalibay; Atanas Mirchev; Ahmed Agha; Patrick van der Smagt; Justin Bayer (L4DC, 2023)

Exploring Under Constraints with Model-Based Actor-Critic and Safety Filters. - Ahmed Agha; Baris Kayalibay; Atanas Mirchev; Patrick van der Smagt; Justin Bayer (CoRL, 2024)