Search Results for author: Denis Krompass

Found 7 papers, 2 papers with code

FedDAT: An Approach for Foundation Model Finetuning in Multi-Modal Heterogeneous Federated Learning

no code implementations21 Aug 2023 Haokun Chen, Yao Zhang, Denis Krompass, Jindong Gu, Volker Tresp

FedDAT is the first approach that enables an efficient distributed finetuning of foundation models for a variety of heterogeneous Vision-Language tasks.

Federated Learning Knowledge Distillation +1

FedPop: Federated Population-based Hyperparameter Tuning

no code implementations16 Aug 2023 Haokun Chen, Denis Krompass, Jindong Gu, Volker Tresp

Similar to conventional ML pipelines, the client local optimization and server aggregation procedure in FL are sensitive to the hyperparameter (HP) selection.

Computational Efficiency Evolutionary Algorithms +1

FRAug: Tackling Federated Learning with Non-IID Features via Representation Augmentation

no code implementations ICCV 2023 Haokun Chen, Ahmed Frikha, Denis Krompass, Jindong Gu, Volker Tresp

Real-world applications usually involve a distribution shift across the datasets of the different clients, which hurts the generalization ability of the clients to unseen samples from their respective data distributions.

Federated Learning

Tensor-Train Recurrent Neural Networks for Video Classification

1 code implementation ICML 2017 Yinchong Yang, Denis Krompass, Volker Tresp

The Recurrent Neural Networks and their variants have shown promising performances in sequence modeling tasks such as Natural Language Processing.

Classification General Classification +1

Towards a New Science of a Clinical Data Intelligence

no code implementations17 Nov 2013 Volker Tresp, Sonja Zillner, Maria J. Costa, Yi Huang, Alexander Cavallaro, Peter A. Fasching, Andre Reis, Martin Sedlmayr, Thomas Ganslandt, Klemens Budde, Carl Hinrichs, Danilo Schmidt, Philipp Daumke, Daniel Sonntag, Thomas Wittenberg, Patricia G. Oppelt, Denis Krompass

We argue that a science of a Clinical Data Intelligence is sensible in the context of a Big Data analysis, i. e., with data from many patients and with complete patient information.

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