Publications

Google Scholar Citations

Peer-reviewed journal articles
  1. Sparse Associative Memory. Heiko Hoffmann. Neural Computation, Vol. 31(5), pp. 998-1014, 2019. Available online at The MIT Press [pdf, 584K] 
  2. Impact of network topology on self-organized criticality. Heiko Hoffmann. Physical Review E, Vol. 97, 022313, 2018 [pdf, 719K] 
  3. Optimization by self-organized criticality. Heiko Hoffmann and David W Payton. Scientific Reports, Vol. 8, Article no. 2358, 2018. Available online at nature.com [pdf, 2.6M]  
  4. Suppressing cascades in a self-organized-critical model with non-contiguous spread of failures. Heiko Hoffmann and David W Payton. Chaos, Solitons & Fractals, Vol. 67, pp. 87-93, 2014. Available online at ScienceDirect [pdf, 644K] 
  5. Adaptive robotic tool use under variable grasps. Heiko Hoffmann, Zhichao Chen, Darren Earl, Derek Mitchell, Behnam Salemi, and Jivko Sinapov. Robotics and Autonomous Systems, Vol. 62, Issue 6, pp. 833-846, 2014. Available online at ScienceDirect [pdf, 2.4M] 
  6. Dynamical Movement Primitives: Learning Attractor Models for Motor Behaviors. Auke Jan Ijspeert, Jun Nakanishi, Heiko Hoffmann, Peter Pastor, and Stefan Schaal. Neural Computation, Vol. 25, No. 2, pp. 328-373, 2013. [pdf, 3.2M] Cited by 1,600+ (most cited article in Neural Computation since 2010) 
  7. Target switching in curved human arm movements is predicted by changing a single control parameter. Heiko Hoffmann. Experimental Brain Research, Vol. 208, Issue 1, pp. 73-87, 2011. Available online at springerlink.com [pdf, 636K] 
  8. Grasping to Extrafoveal Targets: A Robotic Model. Wolfram Schenck, Heiko Hoffmann, and Ralf Möller. New Ideas in Psychology, Vol. 29, pp. 235-259, 2011. Available online at ScienceDirect [pdf, 984K] 
  9. Computational models for neuromuscular function. Francisco Valero-Cuevas, Heiko Hoffmann, Manish Kurse, Jason Kutch, and Evangelos Theodorou (all authors contributed equally). IEEE Reviews in Biomedical Engineering, Vol. 2, pp. 110-135, 2009. [pdf, 1.6M] 
  10. Local dimensionality reduction for non-parametric regression. Heiko Hoffmann, Stefan Schaal, and Sethu Vijayakumar. Neural Processing Letters, Vol. 29, pp. 109-131, 2009. The original article is available online at Springerlink.com [pdf, 556K] 
  11. Perception through Visuomotor Anticipation in a Mobile Robot. Heiko Hoffmann. Neural Networks, Vol. 20, pp. 22-33, 2007. Available online at ScienceDirect [pdf, 1564K] 
  12. Kernel PCA for Novelty Detection. Heiko Hoffmann. Pattern Recognition, Vol. 40, pp. 863-874, 2007. Available online at ScienceDirect [pdf, 748K] [code] Cited by 800+
  13. Learning visuomotor transformations for gaze-control and grasping. Heiko Hoffmann, Wolfram Schenck, Ralf Möller. Biological Cybernetics, Vol. 93, pp. 119-130, 2005. Available online at springerlink.com [pdf, 620K] 
  14. Studies of crack dynamics in clay soil. I. Experimental methods, results and morphological quantification. Hans-Jörg Vogel, Heiko Hoffmann, Kurt Roth. Geoderma, Vol. 125, pp. 203-211, 2005. [pdf, 576K] 
  15. Studies of crack dynamics in clay soil. II. A physically based model for crack formation. Hans-Jörg Vogel, Heiko Hoffmann, Andreas Leopold, Kurt Roth. Geoderma, Vol. 125, pp. 213-223, 2005. [pdf, 596K] 
  16. An extension of Neural Gas to local PCA. Ralf Möller, Heiko Hoffmann. Neurocomputing, Vol. 62, pp. 305-326, 2004. [pdf, 1194K] 
  17. Cross-correlation studies with seismic noise. Heiko Hoffmann, John Winterflood, Yong-Jing Cheng, David G. Blair. Classical and Quantum Gravity, Vol. 19, pp. 1709-1716, 2002. [pdf, 152K] 
Peer-reviewed full-length conference articles
  1. Few-Shot Image Classification Along Sparse Graphs. Joseph F. Comer, Philip L. Jacobson, and Heiko Hoffmann. CVPR Workshop on Learning with Limited Labelled Data for Image and Video Understanding, 2022. [pdf, 848K] 
  2. Pooling by Sliced-Wasserstein Embedding. Navid Naderializadeh, Joseph F. Comer, Reed W. Andrews, Heiko Hoffmann, and Soheil Kolouri. Conference on Neural Information Processing Systems (NeurIPS), 2021. [pdf, 992K] 
  3. Wasserstein Embedding for Graph Learning. Soheil Kolouri, Navid Naderializadeh, Gustavo K. Rohde, and Heiko Hoffmann. International Conference on Learning Representations (ICLR), 2021. [pdf, 1.8M] 
  4. Universal Litmus Patterns: Revealing Backdoor Attacks in CNNs. Soheil Kolouri, Aniruddha Saha, Hamed Pirsiavash, and Heiko Hoffmann. Conference on Computer Vision and Pattern Recognition (CVPR), 2020. (oral presentation)[pdf, 2.6M] 
  5. Explainability Methods for Graph Convolutional Neural Networks. Phillip Pope, Soheil Kolouri, Mohammad Rostami, Charles E Martin, and Heiko Hoffmann. Conference on Computer Vision and Pattern Recognition (CVPR), 2019. (oral presentation, acceptance rate for orals: 5%) [pdf, 2.2M] 
  6. Sliced Wasserstein Distance for Learning Gaussian Mixture Models. Soheil Kolouri, Gustavo K. Rohde, and Heiko Hoffmann. Conference on Computer Vision and Pattern Recognition (CVPR), 2018. [pdf, 4.1M] 
  7. Explaining Distributed Neural Activations via Unsupervised Learning. Soheil Kolouri, Charles E Martin, and Heiko Hoffmann. Conference on Computer Vision and Pattern Recognition, Explainable Computer Vision Workshop, 2017. (oral presentation) [pdf, 1.6M] 
  8. Fast Re-learning of a Controller from Sparse Data. Charles E Martin and Heiko Hoffmann. IEEE International Conference on Systems, Man, and Cybernetics, 2014. (oral presentation) [pdf, 308K] 
  9. Fast pattern matching with time-delay neural networks. Heiko Hoffmann, Michael Howard, and Michael Daily. International Joint Conference on Neural Networks, 2011. (oral presentation) [pdf, 1.3M] 
  10. Toward Ultra High Speed Locomotors: Design and Test of a Cheetah Robot Hindlimb. M. Anthony Lewis, Matthew R Bunting, Behnam Salemi, and Heiko Hoffmann. IEEE International Conference on Robotics and Automation, 2011. (oral presentation) [pdf, 1.5M] 
  11. Biologically-inspired dynamical systems for movement generation: Automatic real-time goal adaptation and obstacle avoidance. Heiko Hoffmann, Peter Pastor, Dae-Hyung Park, and Stefan Schaal. IEEE International Conference on Robotics and Automation, 2009. (oral presentation) [pdf, 692K] 
  12. Learning and Generalization of Motor Skills by Learning from Demonstration. Peter Pastor, Heiko Hoffmann, Tamim Asfour, and Stefan Schaal. IEEE International Conference on Robotics and Automation, 2009. (oral presentation) [pdf, 1548K] 
  13. Movement reproduction and obstacle avoidance with dynamic movement primitives and potential fields. Dae-Hyung Park, Heiko Hoffmann, Peter Pastor, and Stefan Schaal. IEEE International Conference on Humanoid Robots, 2008. (oral presentation) [pdf, 672K] 
  14. Sensor-assisted adaptive motor control under continuously varying context. Heiko Hoffmann, Georgios Petkos, Sebastian Bitzer, and Sethu Vijayakumar. Proc. International Conference on Informatics in Control, Automation, and Robotics, presented at ICINCO, 2007. (oral presentation) [pdf, 1096K] 
  15. Action Selection and Mental Transformation Based on a Chain of Forward Models. Heiko Hoffmann, Ralf Möller. Proceedings of the 8th Conference on Simulation of Adaptive Behavior (SAB ’04), pp. 213-222, S. Schaal, A. Ijspeert, A. Billard, S. Vijayakumar, J. Hallam, and J.-A. Meyer (Eds.), MIT Press, 2004. (oral presentation) [pdf, 612K] 
  16. Learning internal models for eye-hand coordination in reaching and graspingWolfram Schenck, Heiko Hoffmann, Ralf Möller. Proc. EuroCogSci, pp. 289-294, F. Schmalhofer, R. M. Young, G. Katz (Eds.), Lawrence Erlbaum Associates: Mahwah, New Jersey, 2003. (oral presentation) [pdf, 872K] 
  17. Unsupervised Learning of a Kinematic Arm Model. Heiko Hoffmann, Ralf Möller. Artificial Neural Networks and Neural Information Processing – ICANN/ICONIP, O. Kaynak, E. Alpaydin, E. Oja, L. Xu (Eds.), 2003. (oral presentation), LNCS 2714, pp. 463-470, Springer, Heidelberg [pdf, 158K] 
Peer-reviewed short conference articles
  1. Interactive Audio-Tactile Annotation of 3D Point Clouds for Robotic Manipulation. Jivko Sinapov, Darren Earl, Derek Mitchell, and Heiko Hoffmann. ICRA Mobile Manipulation Workshop on Interactive Perception, 2013 [pdf, 370K]
  2. Simple finger movements require complex coordination of excursions and forces across all muscles. Jason Kutch, Manish Kurse, Heiko Hoffmann, Evangelos Theodorou, Rod Hentz, Caroline Leclercq, Isabella Fassola, and Francisco Valero-Cuevas. Annual Meeting for the American Society of Biomechanics, State College, Pennsylvania, August 26-29, 2009
  3. Optimization strategies in human reinforcement learning. Heiko Hoffmann, Evangelos Theodorou, and Stefan Schaal. Advances in Computational Motor Control VII, Symposium at the Society for Neuroscience Meeting, Washington DC, 2008 [pdf, 104K]
  4. Dynamic movement primitives for movement generation motivated by convergent force fields in frog. Heiko Hoffmann, Peter Pastor, and Stefan Schaal. Adaptive Motion of Animals and Machines (AMAM), Cleveland, Ohio, 2008[pdf, 212K]
  5. Movement generation by learning from demonstration and generalization to new targets. Peter Pastor, Heiko Hoffmann, and Stefan Schaal. Adaptive Motion of Animals and Machines (AMAM), Cleveland, Ohio, 2008 [pdf, 204K]
  6. Combining dynamic movement primitives and potential fields for online obstacle avoidance. Dae-Hyung Park, Heiko Hoffmann, and Stefan Schaal. Adaptive Motion of Animals and Machines (AMAM), Cleveland, Ohio, 2008 [pdf, 240K]
  7. Human movement generation based on convergent flow fields: A computational model and a behavioral experiment. Heiko Hoffmann and Stefan Schaal. Advances in Computational Motor Control VI, Symposium at the Society for Neuroscience Meeting, San Diego, 2007[pdf, 104K]
  8. Modellierung der Rissdynamik in tonigen BödenAndreas Leopold, Heiko Hoffmann, Hans-Jörg Vogel, Kurt Roth. Mitt. Dtsch. Bodenkundl. Ges., Vol. 102, pp. 109-110, 2003
Abstracts
  1. Are reaching movements planned in kinematic or dynamic coordinates? Alice Ellmer, Heiko Hoffmann, and Stefan Schaal. Neural Control of Movement, Naples, Florida, 2010 [html] [poster]
  2. Error-feedback control through a forward model predicts preferential control of task-relevant parameters as observed in human finger muscle activation. Heiko Hoffmann, Evangelos Theodorou, and Francisco Valero-Cuevas. Society for Neuroscience, Chicago, Il, 2009
  3. Muscle synergies may be artifacts of biomechanics rather than neural constraints, and are not necessary to simplify control. Jason Kutch, Manish Kurse, Heiko Hoffmann, Art Kuo, and Francisco Valero-Cuevas. Society for Neuroscience, Chicago, Il, 2009
  4. Control of muscle strain energy as a robust means to produce slow and accurate finger movements: Proof of concept via hardware and cadaver implementation. Heiko Hoffmann, Jason Kutch, Manish Kurse, and Francisco Valero-Cuevas. Neural Control of Movement, Waikoloa, Hawaii, 2009 [html] [poster]
  5. Human optimization strategies under reward feedback. Heiko Hoffmann, Evangelos Theodorou, and Stefan Schaal. Neural Control of Movement, Waikoloa, Hawaii, 2009 [html] [poster]
  6. Do humans plan continuous trajectories in kinematic coordinates? Heiko Hoffmann and Stefan Schaal. Society for Neuroscience, Washington, D.C., 2008 [html] [poster]
  7. Behavioral experiments on reinforcement learning in human motor control. Heiko Hoffmann, Evangelos Theodorou, and Stefan Schaal. Neural Control of Movement, Naples, Florida, 2008 [html]
  8. The dual role of uncertainty in force field learning. Michael Mistry, Evangelos Theodorou, Heiko Hoffmann, and Stefan Schaal. Neural Control of Movement, Naples, Florida, 2008 [html]
  9. A computational model of human trajectory planning based on convergent flow fields. Heiko Hoffmann and Stefan Schaal. Society for Neuroscience, San Diego, California, 2007 [html]
  10. A computational model of arm trajectory modification using dynamic movement primitives. Peyman Mohajerian, Heiko Hoffmann, Michael Mistry, and Stefan Schaal. Society for Neuroscience, San Diego, California, 2007
  11. Uncertain 3D force fields in reaching movements: Do humans favor robust or average performance? Michael Mistry, Evangelos Theodorou, Heiko Hoffmann, and Stefan Schaal. Society for Neuroscience, San Diego, California, 2007 
Invited articles
  1. SAR automatic target recognition with less labels. Joseph F. Comer, Reed W. Andrews, Navid Naderializadeh, Soheil Kolouri, and Heiko Hoffmann. Proc. SPIE 11394, Automatic Target Recognition XXX, 113940Q, 24 April 2020
  2. Rapid 3D registration using local subtree caching in Iterative Closest Point (ICP) algorithm. Ryan Uhlenbrock, Kyungnam Kim, Heiko Hoffmann, and Jean J. Dolne. SPIE Optics and Photonics, Unconventional and Indirect Imaging, Image Reconstruction, and Wavefront Sensing, 2017
  3. Classification and segmentation of orbital space-based objects against terrestrial distractors for the purpose of finding holes in Shape from Motion 3D reconstruction. T. Nathan Mundhenk, Arturo Flores, and Heiko Hoffmann. SPIE Electronic Imaging, 2014 [pdf, 8.9M]
  4. Biologically-inspired image processing for machine grasping. Heiko Hoffmann. The Neuromorphic Engineer, Vol. 2, Issue 2, 2005, p. 7 [pdf, 2.1M] 
Letters
  1. Failure-based resource allocation is insufficient to prevent future catastrophic blackouts. Heiko Hoffmann and David W. Payton. Science, eLetter in response to Small vulnerable sets determine large network cascades in power grids, Vol. 358, Issue 6365, eaan3184, 2017. Feb 1, 2018 
Books
  1. Unsupervised Learning of Visuomotor Associations. Heiko Hoffmann. MPI Series in Biological Cybernetics, Vol. 11, 2005, Logos Verlag Berlin, ISBN 3-8325-0858-9, PhD thesis, Faculty of Technology, Bielefeld University [pdf, 7800KB] [html]