Asja Fischer

Orcid: 0000-0002-1916-7033

Affiliations:
  • University of Bonn, Institute of Computer Science, Germany
  • University of Montreal, Institute for Learning Algorithms, QC, Canada
  • University of Copenhagen, Department of Computer Science, Denmark
  • Ruhr University of Bochum, Institute for Neural Computation, Germany


According to our database1, Asja Fischer authored at least 88 papers between 2010 and 2024.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

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Bibliography

2024
Wasserstein dropout.
Mach. Learn., May, 2024

Exploiting Internal Randomness for Privacy in Vertical Federated Learning.
IACR Cryptol. ePrint Arch., 2024

Integrating uncertainty quantification into randomized smoothing based robustness guarantees.
CoRR, 2024

Fake It Until You Break It: On the Adversarial Robustness of AI-generated Image Detectors.
CoRR, 2024

Reassessing Noise Augmentation Methods in the Context of Adversarial Speech.
CoRR, 2024

Detecting Adversarial Attacks in Semantic Segmentation via Uncertainty Estimation: A Deep Analysis.
CoRR, 2024

Landscaping Linear Mode Connectivity.
CoRR, 2024

AnomalyDINO: Boosting Patch-based Few-shot Anomaly Detection with DINOv2.
CoRR, 2024

On the Challenges and Opportunities in Generative AI.
CoRR, 2024

Layout-to-Image Generation with Localized Descriptions using ControlNet with Cross-Attention Control.
CoRR, 2024

Uncertainty-weighted Loss Functions for Improved Adversarial Attacks on Semantic Segmentation.
Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, 2024

Towards the Detection of Diffusion Model Deepfakes.
Proceedings of the 19th International Joint Conference on Computer Vision, 2024

Uncertainty-Based Detection of Adversarial Attacks in Semantic Segmentation.
Proceedings of the 19th International Joint Conference on Computer Vision, 2024

A Representative Study on Human Detection of Artificially Generated Media Across Countries.
Proceedings of the IEEE Symposium on Security and Privacy, 2024

AI-Generated Faces in the Real World: A Large-Scale Case Study of Twitter Profile Images.
Proceedings of the 27th International Symposium on Research in Attacks, 2024

Single-Model Attribution of Generative Models Through Final-Layer Inversion.
Proceedings of the Forty-first International Conference on Machine Learning, 2024

Layer-wise linear mode connectivity.
Proceedings of the Twelfth International Conference on Learning Representations, 2024

Benchmarking the Fairness of Image Upsampling Methods.
Proceedings of the 2024 ACM Conference on Fairness, Accountability, and Transparency, 2024

AEROBLADE: Training-Free Detection of Latent Diffusion Images Using Autoencoder Reconstruction Error.
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2024

Learning Sparse Codes with Entropy-Based ELBOs.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2024

2023
Towards the Detection of Diffusion Model Deepfakes (Dataset).
Dataset, January, 2023

Set-Membership Inference Attacks using Data Watermarking.
CoRR, 2023

Layerwise Linear Mode Connectivity.
CoRR, 2023

Single-Model Attribution via Final-Layer Inversion.
CoRR, 2023

Leveraging characteristics of the output probability distribution for identifying adversarial audio examples.
CoRR, 2023

Information Plane Analysis for Dropout Neural Networks.
Proceedings of the Eleventh International Conference on Learning Representations, 2023

The ELBO of Variational Autoencoders Converges to a Sum of Entropies.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2023

2022
Bringing Light Into the Dark: A Large-Scale Evaluation of Knowledge Graph Embedding Models Under a Unified Framework.
IEEE Trans. Pattern Anal. Mach. Intell., 2022

How Sampling Impacts the Robustness of Stochastic Neural Networks.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Marginal Tail-Adaptive Normalizing Flows.
Proceedings of the International Conference on Machine Learning, 2022

2021
Introduction to neural network-based question answering over knowledge graphs.
WIREs Data Mining Knowl. Discov., 2021

Robustifying automatic speech recognition by extracting slowly varying features.
CoRR, 2021

Copula-Based Normalizing Flows.
CoRR, 2021

A Novel Regression Loss for Non-Parametric Uncertainty Optimization.
CoRR, 2021

Approaches to Uncertainty Quantification in Federated Deep Learning.
Proceedings of the Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2021

HORUS-NER: A Multimodal Named Entity Recognition Framework for Noisy Data.
Proceedings of the Advances in Intelligent Data Analysis XIX, 2021

Improving Breadth-Wise Backpropagation in Graph Neural Networks Helps Learning Long-Range Dependencies.
Proceedings of the 38th International Conference on Machine Learning, 2021

SmoothLRP: Smoothing LRP by Averaging over Stochastic Input Variations.
Proceedings of the 29th European Symposium on Artificial Neural Networks, 2021

Detecting Compositionally Out-of-Distribution Examples in Semantic Parsing.
Proceedings of the Findings of the Association for Computational Linguistics: EMNLP 2021, 2021

Thresholded Adaptive Validation: Tuning the Graphical Lasso for Graph Recovery.
Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

On the convergence of the Metropolis algorithm with fixed-order updates for multivariate binary probability distributions.
Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

Insertion-based Tree Decoding.
Proceedings of the Findings of the Association for Computational Linguistics: ACL/IJCNLP 2021, 2021

2020
Mimicking the radiologists' workflow: Estimating pediatric hand bone age with stacked deep neural networks.
Medical Image Anal., 2020

Second-Moment Loss: A Novel Regression Objective for Improved Uncertainties.
CoRR, 2020

Investigating maximum likelihood based training of infinite mixtures for uncertainty quantification.
CoRR, 2020

Improving the Long-Range Performance of Gated Graph Neural Networks.
CoRR, 2020

Characteristics of Monte Carlo Dropout in Wide Neural Networks.
CoRR, 2020

Unsupervised Cross-Domain Speech-to-Speech Conversion with Time-Frequency Consistency.
CoRR, 2020

End-to-End Entity Linking and Disambiguation leveraging Word and Knowledge Graph Embeddings.
CoRR, 2020

Algorithms for estimating the partition function of restricted Boltzmann machines.
Artif. Intell., 2020

Detecting Adversarial Examples for Speech Recognition via Uncertainty Quantification.
Proceedings of the 21st Annual Conference of the International Speech Communication Association, 2020

Algorithms for Estimating the Partition Function of Restricted Boltzmann Machines (Extended Abstract).
Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, 2020

Leveraging Frequency Analysis for Deep Fake Image Recognition.
Proceedings of the 37th International Conference on Machine Learning, 2020

2019
Introduction to Neural Network based Approaches for Question Answering over Knowledge Graphs.
CoRR, 2019

Predictive Uncertainty Quantification with Compound Density Networks.
CoRR, 2019

Learning to Rank Query Graphs for Complex Question Answering over Knowledge Graphs.
Proceedings of the Semantic Web - ISWC 2019, 2019

Pretrained Transformers for Simple Question Answering over Knowledge Graphs.
Proceedings of the Semantic Web - ISWC 2019, 2019

Incorporating Literals into Knowledge Graph Embeddings.
Proceedings of the Semantic Web - ISWC 2019, 2019

On the Relation Between the Sharpest Directions of DNN Loss and the SGD Step Length.
Proceedings of the 7th International Conference on Learning Representations, 2019

2018
Population-Contrastive-Divergence: Does consistency help with RBM training?
Pattern Recognit. Lett., 2018

Translating Natural Language to SQL using Pointer-Generator Networks and How Decoding Order Matters.
CoRR, 2018

Improving Response Selection in Multi-turn Dialogue Systems.
CoRR, 2018

DNN's Sharpest Directions Along the SGD Trajectory.
CoRR, 2018

On the regularization of Wasserstein GANs.
Proceedings of the 6th International Conference on Learning Representations, 2018

Finding Flatter Minima with SGD.
Proceedings of the 6th International Conference on Learning Representations, 2018

Width of Minima Reached by Stochastic Gradient Descent is Influenced by Learning Rate to Batch Size Ratio.
Proceedings of the Artificial Neural Networks and Machine Learning - ICANN 2018, 2018

Improving Response Selection in Multi-Turn Dialogue Systems by Incorporating Domain Knowledge.
Proceedings of the 22nd Conference on Computational Natural Language Learning, 2018

2017
STDP-Compatible Approximation of Backpropagation in an Energy-Based Model.
Neural Comput., 2017

Graph-based predictable feature analysis.
Mach. Learn., 2017

Three Factors Influencing Minima in SGD.
CoRR, 2017

Neural Network-based Question Answering over Knowledge Graphs on Word and Character Level.
Proceedings of the 26th International Conference on World Wide Web, 2017

A Closer Look at Memorization in Deep Networks.
Proceedings of the 34th International Conference on Machine Learning, 2017

Deep Nets Don't Learn via Memorization.
Proceedings of the 5th International Conference on Learning Representations, 2017

2016
How to Center Deep Boltzmann Machines.
J. Mach. Learn. Res., 2016

Bidirectional Helmholtz Machines.
Proceedings of the 33nd International Conference on Machine Learning, 2016

2015
A bound for the convergence rate of parallel tempering for sampling restricted Boltzmann machines.
Theor. Comput. Sci., 2015

Training Restricted Boltzmann Machines.
Künstliche Intell., 2015

Training opposing directed models using geometric mean matching.
CoRR, 2015

An objective function for STDP.
CoRR, 2015

Difference Target Propagation.
Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2015

2014
Training restricted Boltzmann machines: An introduction.
Pattern Recognit., 2014

2013
The flip-the-state transition operator for restricted Boltzmann machines.
Mach. Learn., 2013

How to Center Binary Restricted Boltzmann Machines.
CoRR, 2013

Approximation properties of DBNs with binary hidden units and real-valued visible units.
Proceedings of the 30th International Conference on Machine Learning, 2013

2012
An Introduction to Restricted Boltzmann Machines.
Proceedings of the Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications, 2012

2011
Bounding the Bias of Contrastive Divergence Learning.
Neural Comput., 2011

Training RBMs based on the signs of the CD approximation of the log-likelihood derivatives.
Proceedings of the 19th European Symposium on Artificial Neural Networks, 2011

2010
Empirical Analysis of the Divergence of Gibbs Sampling Based Learning Algorithms for Restricted Boltzmann Machines.
Proceedings of the Artificial Neural Networks - ICANN 2010, 2010


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