Empirical Analysis of the Divergence of Gibbs Sampling Based Learning Algorithms for Restricted Boltzmann Machines. - Asja Fischer; Christian Igel (ICANN (3), 2010)

Training RBMs based on the signs of the CD approximation of the log-likelihood derivatives. - Asja Fischer; Christian Igel (ESANN, 2011)

Bounding the Bias of Contrastive Divergence Learning. - Asja Fischer; Christian Igel (Neural Comput., 2011)

An Introduction to Restricted Boltzmann Machines. - Asja Fischer; Christian Igel (CIARP, 2012)

Approximation properties of DBNs with binary hidden units and real-valued visible units. - Oswin Krause; Asja Fischer; Tobias Glasmachers; Christian Igel (ICML (1), 2013)

How to Center Binary Restricted Boltzmann Machines. - Jan Melchior; Asja Fischer; Nan Wang; Laurenz Wiskott (CoRR, 2013)

The flip-the-state transition operator for restricted Boltzmann machines. - Kai Brügge 001; Asja Fischer; Christian Igel (Mach. Learn., 2013)

Training restricted Boltzmann machines: An introduction. - Asja Fischer; Christian Igel (Pattern Recognit., 2014)

Difference Target Propagation. - Dong-Hyun Lee; Saizheng Zhang; Asja Fischer; Yoshua Bengio (ECML/PKDD (1), 2015)

An objective function for STDP. - Yoshua Bengio; Thomas Mesnard; Asja Fischer; Saizheng Zhang; Yuhai Wu (CoRR, 2015)

Training opposing directed models using geometric mean matching. - Jörg Bornschein; Samira Shabanian; Asja Fischer; Yoshua Bengio (CoRR, 2015)

Training Restricted Boltzmann Machines. - Asja Fischer (Künstliche Intell., 2015)

A bound for the convergence rate of parallel tempering for sampling restricted Boltzmann machines. - Asja Fischer; Christian Igel (Theor. Comput. Sci., 2015)

Bidirectional Helmholtz Machines. - Jörg Bornschein; Samira Shabanian; Asja Fischer; Yoshua Bengio (ICML, 2016)

How to Center Deep Boltzmann Machines. - Jan Melchior; Asja Fischer; Laurenz Wiskott (J. Mach. Learn. Res., 2016)

Deep Nets Don't Learn via Memorization. - David Krueger 001; Nicolas Ballas; Stanislaw Jastrzebski; Devansh Arpit; Maxinder S. Kanwal; Tegan Maharaj; Emmanuel Bengio; Asja Fischer; Aaron C. Courville (ICLR (Workshop), 2017)

A Closer Look at Memorization in Deep Networks. - Devansh Arpit; Stanislaw Jastrzebski; Nicolas Ballas; David Krueger 001; Emmanuel Bengio; Maxinder S. Kanwal; Tegan Maharaj; Asja Fischer; Aaron C. Courville; Yoshua Bengio; Simon Lacoste-Julien (ICML, 2017)

Neural Network-based Question Answering over Knowledge Graphs on Word and Character Level. - Denis Lukovnikov; Asja Fischer; Jens Lehmann 001; Sören Auer (WWW, 2017)

Three Factors Influencing Minima in SGD. - Stanislaw Jastrzebski; Zachary Kenton; Devansh Arpit; Nicolas Ballas; Asja Fischer; Yoshua Bengio; Amos J. Storkey (CoRR, 2017)

Graph-based predictable feature analysis. - Björn Weghenkel; Asja Fischer; Laurenz Wiskott (Mach. Learn., 2017)

STDP-Compatible Approximation of Backpropagation in an Energy-Based Model. - Yoshua Bengio; Thomas Mesnard; Asja Fischer; Saizheng Zhang; Yuhuai Wu (Neural Comput., 2017)

Improving Response Selection in Multi-Turn Dialogue Systems by Incorporating Domain Knowledge. - Debanjan Chaudhuri; Agustinus Kristiadi; Jens Lehmann 001; Asja Fischer (CoNLL, 2018)

Width of Minima Reached by Stochastic Gradient Descent is Influenced by Learning Rate to Batch Size Ratio. - Stanislaw Jastrzebski; Zachary Kenton; Devansh Arpit; Nicolas Ballas; Asja Fischer; Yoshua Bengio; Amos J. Storkey (ICANN (3), 2018)

Finding Flatter Minima with SGD. - Stanislaw Jastrzebski; Zachary Kenton; Devansh Arpit; Nicolas Ballas; Asja Fischer; Yoshua Bengio; Amos J. Storkey (ICLR (Workshop), 2018)

On the regularization of Wasserstein GANs. - Henning Petzka; Asja Fischer; Denis Lukovnikov (ICLR (Poster), 2018)

DNN's Sharpest Directions Along the SGD Trajectory. - Stanislaw Jastrzebski; Zachary Kenton; Nicolas Ballas; Asja Fischer; Yoshua Bengio; Amos J. Storkey (CoRR, 2018)

Improving Response Selection in Multi-turn Dialogue Systems. - Debanjan Chaudhuri; Agustinus Kristiadi; Jens Lehmann 001; Asja Fischer (CoRR, 2018)

Translating Natural Language to SQL using Pointer-Generator Networks and How Decoding Order Matters. - Denis Lukovnikov; Nilesh Chakraborty; Jens Lehmann 001; Asja Fischer (CoRR, 2018)

Population-Contrastive-Divergence: Does consistency help with RBM training? - Oswin Krause; Asja Fischer; Christian Igel (Pattern Recognit. Lett., 2018)

On the Relation Between the Sharpest Directions of DNN Loss and the SGD Step Length. - Stanislaw Jastrzebski; Zachary Kenton; Nicolas Ballas; Asja Fischer; Yoshua Bengio; Amos J. Storkey (ICLR (Poster), 2019)

Incorporating Literals into Knowledge Graph Embeddings. - Agustinus Kristiadi; Mohammad Asif Khan 001; Denis Lukovnikov; Jens Lehmann 001; Asja Fischer (ISWC (1), 2019)

Pretrained Transformers for Simple Question Answering over Knowledge Graphs. - Denis Lukovnikov; Asja Fischer; Jens Lehmann 001 (ISWC (1), 2019)

Learning to Rank Query Graphs for Complex Question Answering over Knowledge Graphs. - Gaurav Maheshwari 001; Priyansh Trivedi; Denis Lukovnikov; Nilesh Chakraborty; Asja Fischer; Jens Lehmann 001 (ISWC (1), 2019)

Predictive Uncertainty Quantification with Compound Density Networks. - Agustinus Kristiadi; Asja Fischer (CoRR, 2019)

Introduction to Neural Network based Approaches for Question Answering over Knowledge Graphs. - Nilesh Chakraborty; Denis Lukovnikov; Gaurav Maheshwari 001; Priyansh Trivedi; Jens Lehmann 001; Asja Fischer (CoRR, 2019)

Leveraging Frequency Analysis for Deep Fake Image Recognition. - Joel Frank; Thorsten Eisenhofer; Lea Schönherr; Asja Fischer; Dorothea Kolossa; Thorsten Holz (ICML, 2020)

Algorithms for Estimating the Partition Function of Restricted Boltzmann Machines (Extended Abstract). - Oswin Krause; Asja Fischer; Christian Igel (IJCAI, 2020)

Detecting Adversarial Examples for Speech Recognition via Uncertainty Quantification. - Sina Däubener; Lea Schönherr; Asja Fischer; Dorothea Kolossa (INTERSPEECH, 2020)

Algorithms for estimating the partition function of restricted Boltzmann machines. - Oswin Krause; Asja Fischer; Christian Igel (Artif. Intell., 2020)

End-to-End Entity Linking and Disambiguation leveraging Word and Knowledge Graph Embeddings. - Rostislav Nedelchev; Debanjan Chaudhuri; Jens Lehmann 001; Asja Fischer (CoRR, 2020)

Unsupervised Cross-Domain Speech-to-Speech Conversion with Time-Frequency Consistency. - Mohammad Asif Khan 001; Fabien Cardinaux; Stefan Uhlich; Marc Ferras; Asja Fischer (CoRR, 2020)

Characteristics of Monte Carlo Dropout in Wide Neural Networks. - Joachim Sicking; Maram Akila; Tim Wirtz; Sebastian Houben; Asja Fischer (CoRR, 2020)

Improving the Long-Range Performance of Gated Graph Neural Networks. - Denis Lukovnikov; Jens Lehmann 001; Asja Fischer (CoRR, 2020)

Investigating maximum likelihood based training of infinite mixtures for uncertainty quantification. - Sina Däubener; Asja Fischer (CoRR, 2020)

Second-Moment Loss: A Novel Regression Objective for Improved Uncertainties. - Joachim Sicking; Maram Akila; Maximilian Pintz; Tim Wirtz; Asja Fischer; Stefan Wrobel (CoRR, 2020)

Mimicking the radiologists' workflow: Estimating pediatric hand bone age with stacked deep neural networks. - Sven Koitka; Moon S. Kim 002; Ming Qu; Asja Fischer; Christoph M. Friedrich; Felix Nensa (Medical Image Anal., 2020)

Insertion-based Tree Decoding. - Denis Lukovnikov; Asja Fischer (ACL/IJCNLP (Findings), 2021)

On the convergence of the Metropolis algorithm with fixed-order updates for multivariate binary probability distributions. - Kai Brügge 001; Asja Fischer; Christian Igel (AISTATS, 2021)

Thresholded Adaptive Validation: Tuning the Graphical Lasso for Graph Recovery. - Mike Laszkiewicz; Asja Fischer; Johannes Lederer (AISTATS, 2021)

Detecting Compositionally Out-of-Distribution Examples in Semantic Parsing. - Denis Lukovnikov; Sina Däubener; Asja Fischer (EMNLP (Findings), 2021)

SmoothLRP: Smoothing LRP by Averaging over Stochastic Input Variations. - Arne P. Raulf; Sina Däubener; Ben Hack; Axel Mosig; Asja Fischer (ESANN, 2021)

Improving Breadth-Wise Backpropagation in Graph Neural Networks Helps Learning Long-Range Dependencies. - Denis Lukovnikov; Asja Fischer (ICML, 2021)

HORUS-NER: A Multimodal Named Entity Recognition Framework for Noisy Data. - Diego Esteves; José Marcelino; Piyush Chawla; Asja Fischer; Jens Lehmann 001 (IDA, 2021)

Approaches to Uncertainty Quantification in Federated Deep Learning. - Florian Linsner; Linara Adilova; Sina Däubener; Michael Kamp; Asja Fischer (PKDD/ECML Workshops (1), 2021)

A Novel Regression Loss for Non-Parametric Uncertainty Optimization. - Joachim Sicking; Maram Akila; Maximilian Pintz; Tim Wirtz; Asja Fischer; Stefan Wrobel (CoRR, 2021)

Copula-Based Normalizing Flows. - Mike Laszkiewicz; Johannes Lederer; Asja Fischer (CoRR, 2021)

Robustifying automatic speech recognition by extracting slowly varying features. - Matias Pizarro; Dorothea Kolossa; Asja Fischer (CoRR, 2021)

Introduction to neural network-based question answering over knowledge graphs. - Nilesh Chakraborty; Denis Lukovnikov; Gaurav Maheshwari 001; Priyansh Trivedi; Jens Lehmann 001; Asja Fischer (WIREs Data Mining Knowl. Discov., 2021)

Marginal Tail-Adaptive Normalizing Flows. - Mike Laszkiewicz; Johannes Lederer; Asja Fischer (ICML, 2022)

How Sampling Impacts the Robustness of Stochastic Neural Networks. - Sina Däubener; Asja Fischer (NeurIPS, 2022)

Bringing Light Into the Dark: A Large-Scale Evaluation of Knowledge Graph Embedding Models Under a Unified Framework. - Mehdi Ali; Max Berrendorf; Charles Tapley Hoyt; Laurent Vermue; Mikhail Galkin 001; Sahand Sharifzadeh; Asja Fischer; Volker Tresp; Jens Lehmann 001 (IEEE Trans. Pattern Anal. Mach. Intell., 2022)

The ELBO of Variational Autoencoders Converges to a Sum of Entropies. - Simon Damm; Dennis Forster; Dmytro Velychko; Zhenwen Dai; Asja Fischer; Jörg Lücke (AISTATS, 2023)

Information Plane Analysis for Dropout Neural Networks. - Linara Adilova; Bernhard C. Geiger; Asja Fischer (ICLR, 2023)

Leveraging characteristics of the output probability distribution for identifying adversarial audio examples. - Matías P. Pizarro B.; Dorothea Kolossa; Asja Fischer (CoRR, 2023)

Single-Model Attribution via Final-Layer Inversion. - Mike Laszkiewicz; Jonas Ricker; Johannes Lederer; Asja Fischer (CoRR, 2023)

Layerwise Linear Mode Connectivity. - Linara Adilova; Asja Fischer; Martin Jaggi (CoRR, 2023)

Set-Membership Inference Attacks using Data Watermarking. - Mike Laszkiewicz; Denis Lukovnikov; Johannes Lederer; Asja Fischer (CoRR, 2023)

Towards the Detection of Diffusion Model Deepfakes (Dataset). - Jonas Ricker; Simon Damm; Thorsten Holz; Asja Fischer (Zenodo, 2023)

Learning Sparse Codes with Entropy-Based ELBOs. - Dmytro Velychko; Simon Damm; Asja Fischer; Jörg Lücke (AISTATS, 2024)

AEROBLADE: Training-Free Detection of Latent Diffusion Images Using Autoencoder Reconstruction Error. - Jonas Ricker; Denis Lukovnikov; Asja Fischer (CVPR, 2024)

Benchmarking the Fairness of Image Upsampling Methods. - Mike Laszkiewicz; Imant Daunhawer; Julia E. Vogt; Asja Fischer; Johannes Lederer (FAccT, 2024)

Layer-wise linear mode connectivity. - Linara Adilova; Maksym Andriushchenko; Michael Kamp; Asja Fischer; Martin Jaggi (ICLR, 2024)

Single-Model Attribution of Generative Models Through Final-Layer Inversion. - Mike Laszkiewicz; Jonas Ricker; Johannes Lederer; Asja Fischer (ICML, 2024)

AI-Generated Faces in the Real World: A Large-Scale Case Study of Twitter Profile Images. - Jonas Ricker; Dennis Assenmacher; Thorsten Holz; Asja Fischer; Erwin Quiring (RAID, 2024)

A Representative Study on Human Detection of Artificially Generated Media Across Countries. - Joel Frank; Franziska Herbert; Jonas Ricker; Lea Schönherr; Thorsten Eisenhofer; Asja Fischer; Markus Dürmuth; Thorsten Holz (SP, 2024)

DistriBlock: Identifying adversarial audio samples by leveraging characteristics of the output distribution. - Matías P. Pizarro B.; Dorothea Kolossa; Asja Fischer (UAI, 2024)

Uncertainty-Based Detection of Adversarial Attacks in Semantic Segmentation. - Kira Maag; Asja Fischer (VISIGRAPP (2): VISAPP, 2024)

Towards the Detection of Diffusion Model Deepfakes. - Jonas Ricker; Simon Damm; Thorsten Holz; Asja Fischer (VISIGRAPP (4): VISAPP, 2024)

Uncertainty-weighted Loss Functions for Improved Adversarial Attacks on Semantic Segmentation. - Kira Maag; Asja Fischer (WACV, 2024)

Layout-to-Image Generation with Localized Descriptions using ControlNet with Cross-Attention Control. - Denis Lukovnikov; Asja Fischer (CoRR, 2024)

On the Challenges and Opportunities in Generative AI. - Laura Manduchi; Kushagra Pandey; Robert Bamler; Ryan Cotterell; Sina Däubener; Sophie Fellenz; Asja Fischer; Thomas Gärtner 004; Matthias Kirchler; Marius Kloft; Yingzhen Li; Christoph Lippert; et al. (CoRR, 2024)

Landscaping Linear Mode Connectivity. - Sidak Pal Singh; Linara Adilova; Michael Kamp; Asja Fischer; Bernhard Schölkopf; Thomas Hofmann (CoRR, 2024)

Detecting Adversarial Attacks in Semantic Segmentation via Uncertainty Estimation: A Deep Analysis. - Kira Maag; Roman Resner; Asja Fischer (CoRR, 2024)

Reassessing Noise Augmentation Methods in the Context of Adversarial Speech. - Karla Pizzi; Matías P. Pizarro B.; Asja Fischer (CoRR, 2024)

Fake It Until You Break It: On the Adversarial Robustness of AI-generated Image Detectors. - Sina Mavali; Jonas Ricker; David Pape; Yash Sharma; Asja Fischer; Lea Schönherr (CoRR, 2024)

Integrating uncertainty quantification into randomized smoothing based robustness guarantees. - Sina Däubener; Kira Maag; David Krueger 001; Asja Fischer (CoRR, 2024)

Single-Model Attribution for Spoofed Speech via Vocoder Fingerprints in an Open-World Setting. - Matias Pizarro; Mike Laszkiewicz; Dorothea Kolossa; Asja Fischer (CoRR, 2024)

Exploiting Internal Randomness for Privacy in Vertical Federated Learning. - Yulian Sun; Li Duan; Ricardo Mendes; Derui Zhu; Yue Xia; Yong Li 021; Asja Fischer (IACR Cryptol. ePrint Arch., 2024)

Towards the Detection of Diffusion Model Deepfakes (Additional Datasets). - Jonas Ricker; Simon Damm; Thorsten Holz; Asja Fischer (Zenodo, 2024)

Wasserstein dropout. - Joachim Sicking; Maram Akila; Maximilian Pintz; Tim Wirtz; Stefan Wrobel; Asja Fischer (Mach. Learn., 2024)

Black-Box Forgery Attacks on Semantic Watermarks for Diffusion Models. - Andreas Müller 025; Denis Lukovnikov; Jonas Thietke; Asja Fischer; Erwin Quiring (CVPR, 2025)

ELBO, regularized maximum likelihood, and their common one-sample approximation for training stochastic neural networks. - Sina Däubener; Simon Damm; Asja Fischer (UAI, 2025)

AnomalyDINO: Boosting Patch-based Few-Shot Anomaly Detection with DINOv2. - Simon Damm; Mike Laszkiewicz; Johannes Lederer; Asja Fischer (WACV, 2025)

Generative Models with ELBOs Converging to Entropy Sums. - Jan Warnken; Dmytro Velychko; Simon Damm; Asja Fischer; Jörg Lücke (CoRR, 2025)

Can LLMs Explain Themselves Counterfactually? - Zahra Dehghanighobadi; Asja Fischer; Muhammad Bilal Zafar (CoRR, 2025)

Towards A Correct Usage of Cryptography in Semantic Watermarks for Diffusion Models. - Jonas Thietke; Andreas Müller 025; Denis Lukovnikov; Asja Fischer; Erwin Quiring (CoRR, 2025)

Security Benefits and Side Effects of Labeling AI-Generated Images. - Sandra Höltervennhoff; Jonas Ricker; Maike M. Raphael; Charlotte Schwedes; Rebecca Weil; Asja Fischer; Thorsten Holz; Lea Schönherr; Sascha Fahl (CoRR, 2025)

RAID: A Dataset for Testing the Adversarial Robustness of AI-Generated Image Detectors. - Hicham Eddoubi; Jonas Ricker; Federico Cocchi; Lorenzo Baraldi 001; Angelo Sotgiu; Maura Pintor; Marcella Cornia; Lorenzo Baraldi 002; Asja Fischer; Rita Cucchiara; Battista Biggio (CoRR, 2025)

Towards an Optimal Control Perspective of ResNet Training. - Jens Püttschneider; Simon Heilig; Asja Fischer; Timm Faulwasser (CoRR, 2025)