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ORCIDAsja Fischer
Person information
- affiliation: University of Bonn, Institute of Computer Science, Germany
- affiliation: University of Montreal, Institute for Learning Algorithms, QC, Canada
- affiliation: University of Copenhagen, Department of Computer Science, Denmark
- affiliation: Ruhr University of Bochum, Institute for Neural Computation, Germany
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2020 – today
- 2024
[j14]Joachim Sicking
, Maram Akila, Maximilian Pintz, Tim Wirtz, Stefan Wrobel, Asja Fischer:
Wasserstein dropout. Mach. Learn. 113(5): 3161-3204 (2024)- [c43]Dmytro Velychko, Simon Damm, Asja Fischer, Jörg Lücke:
Learning Sparse Codes with Entropy-Based ELBOs. AISTATS 2024: 2089-2097 - [c42]Jonas Ricker, Denis Lukovnikov, Asja Fischer:
AEROBLADE: Training-Free Detection of Latent Diffusion Images Using Autoencoder Reconstruction Error. CVPR 2024: 9130-9140 - [c41]Yulian Sun, Li Duan, Ricardo Mendes, Derui Zhu, Yue Xia, Yong Li, Asja Fischer:
Exploiting Internal Randomness for Privacy in Vertical Federated Learning. ESORICS (2) 2024: 390-409 - [c40]Mike Laszkiewicz, Imant Daunhawer, Julia E. Vogt, Asja Fischer, Johannes Lederer:
Benchmarking the Fairness of Image Upsampling Methods. FAccT 2024: 489-517 - [c39]Linara Adilova, Maksym Andriushchenko, Michael Kamp, Asja Fischer, Martin Jaggi:
Layer-wise linear mode connectivity. ICLR 2024 - [c38]Mike Laszkiewicz, Jonas Ricker, Johannes Lederer, Asja Fischer:
Single-Model Attribution of Generative Models Through Final-Layer Inversion. ICML 2024 - [c37]Jonas Ricker, Dennis Assenmacher, Thorsten Holz, Asja Fischer, Erwin Quiring:
AI-Generated Faces in the Real World: A Large-Scale Case Study of Twitter Profile Images. RAID 2024: 513-530 - [c36]Joel Frank, Franziska Herbert, Jonas Ricker, Lea Schönherr, Thorsten Eisenhofer, Asja Fischer, Markus Dürmuth, Thorsten Holz:
A Representative Study on Human Detection of Artificially Generated Media Across Countries. SP 2024: 55-73 - [c35]Kira Maag, Asja Fischer:
Uncertainty-Based Detection of Adversarial Attacks in Semantic Segmentation. VISIGRAPP (2): VISAPP 2024: 37-46 - [c34]Jonas Ricker, Simon Damm, Thorsten Holz, Asja Fischer:
Towards the Detection of Diffusion Model Deepfakes. VISIGRAPP (4): VISAPP 2024: 446-457 - [c33]Kira Maag, Asja Fischer:
Uncertainty-weighted Loss Functions for Improved Adversarial Attacks on Semantic Segmentation. WACV 2024: 3894-3902 - [i53]Mike Laszkiewicz, Imant Daunhawer, Julia E. Vogt, Asja Fischer, Johannes Lederer:
Benchmarking the Fairness of Image Upsampling Methods. CoRR abs/2401.13555 (2024) - [i52]Jonas Ricker, Denis Lukovnikov, Asja Fischer:
AEROBLADE: Training-Free Detection of Latent Diffusion Images Using Autoencoder Reconstruction Error. CoRR abs/2401.17879 (2024) - [i51]Denis Lukovnikov, Asja Fischer:
Layout-to-Image Generation with Localized Descriptions using ControlNet with Cross-Attention Control. CoRR abs/2402.13404 (2024) - [i50]Laura Manduchi, Kushagra Pandey, Robert Bamler, Ryan Cotterell, Sina Däubener, Sophie Fellenz, Asja Fischer, Thomas Gärtner, Matthias Kirchler, Marius Kloft, Yingzhen Li, Christoph Lippert, Gerard de Melo, Eric T. Nalisnick, Björn Ommer, Rajesh Ranganath, Maja Rudolph, Karen Ullrich, Guy Van den Broeck, Julia E. Vogt, Yixin Wang, Florian Wenzel, Frank Wood, Stephan Mandt, Vincent Fortuin:
On the Challenges and Opportunities in Generative AI. CoRR abs/2403.00025 (2024) - [i49]Jonas Ricker, Dennis Assenmacher, Thorsten Holz, Asja Fischer, Erwin Quiring:
AI-Generated Faces in the Real World: A Large-Scale Case Study of Twitter Profile Images. CoRR abs/2404.14244 (2024) - [i48]Simon Damm, Mike Laszkiewicz, Johannes Lederer, Asja Fischer:
AnomalyDINO: Boosting Patch-based Few-shot Anomaly Detection with DINOv2. CoRR abs/2405.14529 (2024) - [i47]Sidak Pal Singh, Linara Adilova, Michael Kamp, Asja Fischer, Bernhard Schölkopf, Thomas Hofmann:
Landscaping Linear Mode Connectivity. CoRR abs/2406.16300 (2024) - [i46]Kira Maag, Roman Resner, Asja Fischer:
Detecting Adversarial Attacks in Semantic Segmentation via Uncertainty Estimation: A Deep Analysis. CoRR abs/2408.10021 (2024) - [i45]Karla Pizzi, Matías P. Pizarro B., Asja Fischer:
Reassessing Noise Augmentation Methods in the Context of Adversarial Speech. CoRR abs/2409.01813 (2024) - [i44]Sina Mavali, Jonas Ricker, David Pape, Yash Sharma, Asja Fischer, Lea Schönherr:
Fake It Until You Break It: On the Adversarial Robustness of AI-generated Image Detectors. CoRR abs/2410.01574 (2024) - [i43]Yulian Sun, Li Duan, Ricardo Mendes, Derui Zhu, Yue Xia, Yong Li, Asja Fischer:
Exploiting Internal Randomness for Privacy in Vertical Federated Learning. IACR Cryptol. ePrint Arch. 2024: 671 (2024) - 2023
- [c32]Simon Damm, Dennis Forster, Dmytro Velychko, Zhenwen Dai, Asja Fischer, Jörg Lücke:
The ELBO of Variational Autoencoders Converges to a Sum of Entropies. AISTATS 2023: 3931-3960 - [c31]Linara Adilova, Bernhard C. Geiger, Asja Fischer:
Information Plane Analysis for Dropout Neural Networks. ICLR 2023 - [e6]Massih-Reza Amini, Stéphane Canu, Asja Fischer, Tias Guns, Petra Kralj Novak, Grigorios Tsoumakas:
Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2022, Grenoble, France, September 19-23, 2022, Proceedings, Part I. Lecture Notes in Computer Science 13713, Springer 2023, ISBN 978-3-031-26386-6 [contents] - [e5]Massih-Reza Amini, Stéphane Canu, Asja Fischer, Tias Guns, Petra Kralj Novak, Grigorios Tsoumakas:
Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2022, Grenoble, France, September 19-23, 2022, Proceedings, Part II. Lecture Notes in Computer Science 13714, Springer 2023, ISBN 978-3-031-26389-7 [contents] - [e4]Massih-Reza Amini, Stéphane Canu, Asja Fischer, Tias Guns, Petra Kralj Novak, Grigorios Tsoumakas:
Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2022, Grenoble, France, September 19-23, 2022, Proceedings, Part III. Lecture Notes in Computer Science 13715, Springer 2023, ISBN 978-3-031-26408-5 [contents] - [e3]Massih-Reza Amini, Stéphane Canu, Asja Fischer, Tias Guns, Petra Kralj Novak, Grigorios Tsoumakas:
Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2022, Grenoble, France, September 19-23, 2022, Proceedings, Part IV. Lecture Notes in Computer Science 13716, Springer 2023, ISBN 978-3-031-26411-5 [contents] - [e2]Massih-Reza Amini, Stéphane Canu, Asja Fischer, Tias Guns, Petra Kralj Novak, Grigorios Tsoumakas:
Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2022, Grenoble, France, September 19-23, 2022, Proceedings, Part V. Lecture Notes in Computer Science 13717, Springer 2023, ISBN 978-3-031-26418-4 [contents] - [e1]Massih-Reza Amini, Stéphane Canu, Asja Fischer, Tias Guns, Petra Kralj Novak, Grigorios Tsoumakas:
Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2022, Grenoble, France, September 19-23, 2022, Proceedings, Part VI. Lecture Notes in Computer Science 13718, Springer 2023, ISBN 978-3-031-26421-4 [contents] - [d1]Jonas Ricker, Simon Damm, Thorsten Holz, Asja Fischer:
Towards the Detection of Diffusion Model Deepfakes (Dataset). Zenodo, 2023
- [i42]Linara Adilova, Bernhard C. Geiger, Asja Fischer:
Information Plane Analysis for Dropout Neural Networks. CoRR abs/2303.00596 (2023) - [i41]Kira Maag, Asja Fischer:
Uncertainty-based Detection of Adversarial Attacks in Semantic Segmentation. CoRR abs/2305.12825 (2023) - [i40]Matías P. Pizarro B., Dorothea Kolossa, Asja Fischer:
Leveraging characteristics of the output probability distribution for identifying adversarial audio examples. CoRR abs/2305.17000 (2023) - [i39]Mike Laszkiewicz, Jonas Ricker, Johannes Lederer, Asja Fischer:
Single-Model Attribution via Final-Layer Inversion. CoRR abs/2306.06210 (2023) - [i38]Linara Adilova, Asja Fischer, Martin Jaggi:
Layerwise Linear Mode Connectivity. CoRR abs/2307.06966 (2023) - [i37]Mike Laszkiewicz, Denis Lukovnikov, Johannes Lederer, Asja Fischer:
Set-Membership Inference Attacks using Data Watermarking. CoRR abs/2307.15067 (2023) - [i36]Kira Maag, Asja Fischer:
Uncertainty-weighted Loss Functions for Improved Adversarial Attacks on Semantic Segmentation. CoRR abs/2310.17436 (2023) - [i35]Dmytro Velychko, Simon Damm, Asja Fischer, Jörg Lücke:
Learning Sparse Codes with Entropy-Based ELBOs. CoRR abs/2311.01888 (2023) - [i34]Joel Frank, Franziska Herbert, Jonas Ricker, Lea Schönherr, Thorsten Eisenhofer, Asja Fischer, Markus Dürmuth, Thorsten Holz:
A Representative Study on Human Detection of Artificially Generated Media Across Countries. CoRR abs/2312.05976 (2023) - 2022
- [j13]Mehdi Ali, Max Berrendorf, Charles Tapley Hoyt, Laurent Vermue, Mikhail Galkin, Sahand Sharifzadeh, Asja Fischer, Volker Tresp, Jens Lehmann:
Bringing Light Into the Dark: A Large-Scale Evaluation of Knowledge Graph Embedding Models Under a Unified Framework. IEEE Trans. Pattern Anal. Mach. Intell. 44(12): 8825-8845 (2022) - [c30]Mike Laszkiewicz, Johannes Lederer, Asja Fischer:
Marginal Tail-Adaptive Normalizing Flows. ICML 2022: 12020-12048 - [c29]Sina Däubener, Asja Fischer:
How Sampling Impacts the Robustness of Stochastic Neural Networks. NeurIPS 2022 - [i33]Sina Däubener, Asja Fischer:
How Sampling Impacts the Robustness of Stochastic Neural Networks. CoRR abs/2204.10839 (2022) - [i32]Mike Laszkiewicz, Johannes Lederer, Asja Fischer:
Marginal Tail-Adaptive Normalizing Flows. CoRR abs/2206.10311 (2022) - [i31]Jonas Ricker, Simon Damm, Thorsten Holz, Asja Fischer:
Towards the Detection of Diffusion Model Deepfakes. CoRR abs/2210.14571 (2022) - 2021
- [j12]Nilesh Chakraborty, Denis Lukovnikov, Gaurav Maheshwari, Priyansh Trivedi, Jens Lehmann, Asja Fischer:
Introduction to neural network-based question answering over knowledge graphs. WIREs Data Mining Knowl. Discov. 11(3) (2021) - [c28]Denis Lukovnikov, Asja Fischer:
Insertion-based Tree Decoding. ACL/IJCNLP (Findings) 2021: 3201-3213 - [c27]Kai Brügge, Asja Fischer, Christian Igel:
On the convergence of the Metropolis algorithm with fixed-order updates for multivariate binary probability distributions. AISTATS 2021: 469-477 - [c26]Mike Laszkiewicz, Asja Fischer, Johannes Lederer:
Thresholded Adaptive Validation: Tuning the Graphical Lasso for Graph Recovery. AISTATS 2021: 1864-1872 - [c25]Denis Lukovnikov, Sina Däubener, Asja Fischer:
Detecting Compositionally Out-of-Distribution Examples in Semantic Parsing. EMNLP (Findings) 2021: 591-598 - [c24]Arne P. Raulf, Sina Däubener, Ben Hack, Axel Mosig, Asja Fischer:
SmoothLRP: Smoothing LRP by Averaging over Stochastic Input Variations. ESANN 2021 - [c23]Denis Lukovnikov, Asja Fischer:
Improving Breadth-Wise Backpropagation in Graph Neural Networks Helps Learning Long-Range Dependencies. ICML 2021: 7180-7191 - [c22]Diego Esteves, José Marcelino, Piyush Chawla, Asja Fischer, Jens Lehmann:
HORUS-NER: A Multimodal Named Entity Recognition Framework for Noisy Data. IDA 2021: 89-100 - [c21]Florian Linsner, Linara Adilova, Sina Däubener, Michael Kamp, Asja Fischer:
Approaches to Uncertainty Quantification in Federated Deep Learning. PKDD/ECML Workshops (1) 2021: 128-145 - [i30]Joachim Sicking, Maram Akila, Maximilian Pintz, Tim Wirtz, Asja Fischer, Stefan Wrobel:
A Novel Regression Loss for Non-Parametric Uncertainty Optimization. CoRR abs/2101.02726 (2021) - [i29]Mike Laszkiewicz, Johannes Lederer, Asja Fischer:
Copula-Based Normalizing Flows. CoRR abs/2107.07352 (2021) - [i28]Matias Pizarro, Dorothea Kolossa, Asja Fischer:
Robustifying automatic speech recognition by extracting slowly varying features. CoRR abs/2112.07400 (2021) - 2020
- [j11]Oswin Krause, Asja Fischer, Christian Igel:
Algorithms for estimating the partition function of restricted Boltzmann machines. Artif. Intell. 278 (2020) - [j10]Sven Koitka, Moon S. Kim, Ming Qu, Asja Fischer, Christoph M. Friedrich, Felix Nensa:
Mimicking the radiologists' workflow: Estimating pediatric hand bone age with stacked deep neural networks. Medical Image Anal. 64: 101743 (2020) - [c20]Joel Frank, Thorsten Eisenhofer, Lea Schönherr, Asja Fischer, Dorothea Kolossa, Thorsten Holz:
Leveraging Frequency Analysis for Deep Fake Image Recognition. ICML 2020: 3247-3258 - [c19]Oswin Krause, Asja Fischer, Christian Igel:
Algorithms for Estimating the Partition Function of Restricted Boltzmann Machines (Extended Abstract). IJCAI 2020: 5045-5049 - [c18]Sina Däubener, Lea Schönherr, Asja Fischer, Dorothea Kolossa:
Detecting Adversarial Examples for Speech Recognition via Uncertainty Quantification. INTERSPEECH 2020: 4661-4665 - [i27]Denis Lukovnikov, Asja Fischer, Jens Lehmann:
Pretrained Transformers for Simple Question Answering over Knowledge Graphs. CoRR abs/2001.11985 (2020) - [i26]Rostislav Nedelchev, Debanjan Chaudhuri, Jens Lehmann, Asja Fischer:
End-to-End Entity Linking and Disambiguation leveraging Word and Knowledge Graph Embeddings. CoRR abs/2002.11143 (2020) - [i25]Joel Frank, Thorsten Eisenhofer, Lea Schönherr, Asja Fischer, Dorothea Kolossa, Thorsten Holz:
Leveraging Frequency Analysis for Deep Fake Image Recognition. CoRR abs/2003.08685 (2020) - [i24]Mike Laszkiewicz, Asja Fischer, Johannes Lederer:
Thresholded Adaptive Validation: Tuning the Graphical Lasso for Graph Recovery. CoRR abs/2005.00466 (2020) - [i23]Mohammad Asif Khan, Fabien Cardinaux, Stefan Uhlich, Marc Ferras, Asja Fischer:
Unsupervised Cross-Domain Speech-to-Speech Conversion with Time-Frequency Consistency. CoRR abs/2005.07810 (2020) - [i22]Sina Däubener, Lea Schönherr, Asja Fischer, Dorothea Kolossa:
Detecting Adversarial Examples for Speech Recognition via Uncertainty Quantification. CoRR abs/2005.14611 (2020) - [i21]Mehdi Ali, Max Berrendorf, Charles Tapley Hoyt, Laurent Vermue, Mikhail Galkin, Sahand Sharifzadeh, Asja Fischer, Volker Tresp, Jens Lehmann:
Bringing Light Into the Dark: A Large-scale Evaluation of Knowledge Graph Embedding Models Under a Unified Framework. CoRR abs/2006.13365 (2020) - [i20]Kai Brügge, Asja Fischer, Christian Igel:
On the convergence of the Metropolis algorithm with fixed-order updates for multivariate binary probability distributions. CoRR abs/2006.14999 (2020) - [i19]Joachim Sicking, Maram Akila, Tim Wirtz, Sebastian Houben, Asja Fischer:
Characteristics of Monte Carlo Dropout in Wide Neural Networks. CoRR abs/2007.05434 (2020) - [i18]Denis Lukovnikov, Jens Lehmann, Asja Fischer:
Improving the Long-Range Performance of Gated Graph Neural Networks. CoRR abs/2007.09668 (2020) - [i17]Sina Däubener, Asja Fischer:
Investigating maximum likelihood based training of infinite mixtures for uncertainty quantification. CoRR abs/2008.03209 (2020) - [i16]Joachim Sicking, Maram Akila, Maximilian Pintz, Tim Wirtz, Asja Fischer, Stefan Wrobel:
Second-Moment Loss: A Novel Regression Objective for Improved Uncertainties. CoRR abs/2012.12687 (2020)
2010 – 2019
- 2019
- [c17]Stanislaw Jastrzebski, Zachary Kenton, Nicolas Ballas, Asja Fischer, Yoshua Bengio, Amos J. Storkey:
On the Relation Between the Sharpest Directions of DNN Loss and the SGD Step Length. ICLR (Poster) 2019 - [c16]Agustinus Kristiadi, Mohammad Asif Khan, Denis Lukovnikov, Jens Lehmann, Asja Fischer:
Incorporating Literals into Knowledge Graph Embeddings. ISWC (1) 2019: 347-363 - [c15]Denis Lukovnikov, Asja Fischer, Jens Lehmann:
Pretrained Transformers for Simple Question Answering over Knowledge Graphs. ISWC (1) 2019: 470-486 - [c14]Gaurav Maheshwari, Priyansh Trivedi, Denis Lukovnikov, Nilesh Chakraborty, Asja Fischer, Jens Lehmann:
Learning to Rank Query Graphs for Complex Question Answering over Knowledge Graphs. ISWC (1) 2019: 487-504 - [i15]Agustinus Kristiadi, Asja Fischer:
Predictive Uncertainty Quantification with Compound Density Networks. CoRR abs/1902.01080 (2019) - [i14]Nilesh Chakraborty, Denis Lukovnikov, Gaurav Maheshwari, Priyansh Trivedi, Jens Lehmann, Asja Fischer:
Introduction to Neural Network based Approaches for Question Answering over Knowledge Graphs. CoRR abs/1907.09361 (2019) - 2018
- [j9]Oswin Krause, Asja Fischer, Christian Igel:
Population-Contrastive-Divergence: Does consistency help with RBM training? Pattern Recognit. Lett. 102: 1-7 (2018) - [c13]Debanjan Chaudhuri, Agustinus Kristiadi, Jens Lehmann, Asja Fischer:
Improving Response Selection in Multi-Turn Dialogue Systems by Incorporating Domain Knowledge. CoNLL 2018: 497-507 - [c12]Stanislaw Jastrzebski, Zachary Kenton, Devansh Arpit, Nicolas Ballas, Asja Fischer, Yoshua Bengio, Amos J. Storkey:
Width of Minima Reached by Stochastic Gradient Descent is Influenced by Learning Rate to Batch Size Ratio. ICANN (3) 2018: 392-402 - [c11]Stanislaw Jastrzebski, Zachary Kenton, Devansh Arpit, Nicolas Ballas, Asja Fischer, Yoshua Bengio, Amos J. Storkey:
Finding Flatter Minima with SGD. ICLR (Workshop) 2018 - [c10]Henning Petzka, Asja Fischer, Denis Lukovnikov:
On the regularization of Wasserstein GANs. ICLR (Poster) 2018 - [i13]Agustinus Kristiadi, Mohammad Asif Khan, Denis Lukovnikov, Jens Lehmann, Asja Fischer:
Incorporating Literals into Knowledge Graph Embeddings. CoRR abs/1802.00934 (2018) - [i12]Stanislaw Jastrzebski, Zachary Kenton, Nicolas Ballas, Asja Fischer, Yoshua Bengio, Amos J. Storkey:
DNN's Sharpest Directions Along the SGD Trajectory. CoRR abs/1807.05031 (2018) - [i11]Debanjan Chaudhuri, Agustinus Kristiadi, Jens Lehmann, Asja Fischer:
Improving Response Selection in Multi-turn Dialogue Systems. CoRR abs/1809.03194 (2018) - [i10]Gaurav Maheshwari, Priyansh Trivedi, Denis Lukovnikov, Nilesh Chakraborty, Asja Fischer, Jens Lehmann:
Learning to Rank Query Graphs for Complex Question Answering over Knowledge Graphs. CoRR abs/1811.01118 (2018) - [i9]Denis Lukovnikov, Nilesh Chakraborty, Jens Lehmann, Asja Fischer:
Translating Natural Language to SQL using Pointer-Generator Networks and How Decoding Order Matters. CoRR abs/1811.05303 (2018) - 2017
- [j8]Björn Weghenkel, Asja Fischer, Laurenz Wiskott:
Graph-based predictable feature analysis. Mach. Learn. 106(9-10): 1359-1380 (2017) - [j7]Yoshua Bengio, Thomas Mesnard, Asja Fischer, Saizheng Zhang, Yuhuai Wu:
STDP-Compatible Approximation of Backpropagation in an Energy-Based Model. Neural Comput. 29(3): 555-577 (2017) - [c9]David Krueger, Nicolas Ballas, Stanislaw Jastrzebski, Devansh Arpit, Maxinder S. Kanwal, Tegan Maharaj, Emmanuel Bengio, Asja Fischer, Aaron C. Courville:
Deep Nets Don't Learn via Memorization. ICLR (Workshop) 2017 - [c8]Devansh Arpit, Stanislaw Jastrzebski, Nicolas Ballas, David Krueger, Emmanuel Bengio, Maxinder S. Kanwal, Tegan Maharaj, Asja Fischer, Aaron C. Courville, Yoshua Bengio, Simon Lacoste-Julien:
A Closer Look at Memorization in Deep Networks. ICML 2017: 233-242 - [c7]Denis Lukovnikov, Asja Fischer, Jens Lehmann, Sören Auer:
Neural Network-based Question Answering over Knowledge Graphs on Word and Character Level. WWW 2017: 1211-1220 - [i8]Devansh Arpit, Stanislaw Jastrzebski, Nicolas Ballas, David Krueger, Emmanuel Bengio, Maxinder S. Kanwal, Tegan Maharaj, Asja Fischer, Aaron C. Courville, Yoshua Bengio, Simon Lacoste-Julien:
A Closer Look at Memorization in Deep Networks. CoRR abs/1706.05394 (2017) - [i7]Henning Petzka, Asja Fischer, Denis Lukovnikov:
On the regularization of Wasserstein GANs. CoRR abs/1709.08894 (2017) - [i6]Stanislaw Jastrzebski, Zachary Kenton, Devansh Arpit, Nicolas Ballas, Asja Fischer, Yoshua Bengio, Amos J. Storkey:
Three Factors Influencing Minima in SGD. CoRR abs/1711.04623 (2017) - 2016
- [j6]Jan Melchior, Asja Fischer, Laurenz Wiskott:
How to Center Deep Boltzmann Machines. J. Mach. Learn. Res. 17: 99:1-99:61 (2016) - [c6]Jörg Bornschein, Samira Shabanian, Asja Fischer, Yoshua Bengio:
Bidirectional Helmholtz Machines. ICML 2016: 2511-2519 - [i5]Björn Weghenkel, Asja Fischer, Laurenz Wiskott:
Graph-based Predictable Feature Analysis. CoRR abs/1602.00554 (2016) - 2015
- [j5]Asja Fischer:
Training Restricted Boltzmann Machines. Künstliche Intell. 29(4): 441-444 (2015) - [j4]Asja Fischer, Christian Igel:
A bound for the convergence rate of parallel tempering for sampling restricted Boltzmann machines. Theor. Comput. Sci. 598: 102-117 (2015) - [c5]Dong-Hyun Lee, Saizheng Zhang, Asja Fischer, Yoshua Bengio:
Difference Target Propagation. ECML/PKDD (1) 2015: 498-515 - [i4]Jörg Bornschein, Samira Shabanian, Asja Fischer, Yoshua Bengio:
Training opposing directed models using geometric mean matching. CoRR abs/1506.03877 (2015) - [i3]Yoshua Bengio, Thomas Mesnard, Asja Fischer, Saizheng Zhang, Yuhai Wu:
An objective function for STDP. CoRR abs/1509.05936 (2015) - [i2]Oswin Krause, Asja Fischer, Christian Igel:
Population-Contrastive-Divergence: Does Consistency help with RBM training? CoRR abs/1510.01624 (2015) - 2014
- [j3]Asja Fischer, Christian Igel:
Training restricted Boltzmann machines: An introduction. Pattern Recognit. 47(1): 25-39 (2014) - 2013
- [j2]Kai Brügge, Asja Fischer, Christian Igel:
The flip-the-state transition operator for restricted Boltzmann machines. Mach. Learn. 93(1): 53-69 (2013) - [c4]Oswin Krause, Asja Fischer, Tobias Glasmachers, Christian Igel:
Approximation properties of DBNs with binary hidden units and real-valued visible units. ICML (1) 2013: 419-426 - [i1]Jan Melchior, Asja Fischer, Nan Wang, Laurenz Wiskott:
How to Center Binary Restricted Boltzmann Machines. CoRR abs/1311.1354 (2013) - 2012
- [c3]Asja Fischer, Christian Igel:
An Introduction to Restricted Boltzmann Machines. CIARP 2012: 14-36 - 2011
- [j1]Asja Fischer, Christian Igel:
Bounding the Bias of Contrastive Divergence Learning. Neural Comput. 23(3): 664-673 (2011) - [c2]Asja Fischer, Christian Igel:
Training RBMs based on the signs of the CD approximation of the log-likelihood derivatives. ESANN 2011 - 2010
- [c1]Asja Fischer, Christian Igel:
Empirical Analysis of the Divergence of Gibbs Sampling Based Learning Algorithms for Restricted Boltzmann Machines. ICANN (3) 2010: 208-217
Coauthor Index
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