77 citations · 196 across the 17 of their papers we have counts for
19 papers
Evaluating Federated Pre-Training: On the Reliability of Downstream Fine-Tuning and Intrinsic Evaluation
Claudia Grosser, Maike Heuer, Denis Krompass +1
Federated pre-training offers a way to train foundation models on private or distributed data without centralizing the underlying datasets. However, evaluating federated pre-traini…
A Comparative Study on How Data Normalization Affects Zero-Shot Generalization in Time Series Foundation Models
Ihab Ahmed, Denis Krompaß, Cheng Feng +1
We investigate input normalization methods for Time-Series Foundation Models (TSFMs). While normalization is well-studied in dataset-specific time-series models, it remains overloo…
Does Machine Unlearning Truly Remove Knowledge?
Haokun Chen, Yueqi Zhang, Yuan Bi +9
In recent years, Large Language Models (LLMs) have achieved remarkable advancements, drawing significant attention from the research community. Their capabilities are largely attri…
FedBiP: Heterogeneous One-Shot Federated Learning with Personalized Latent Diffusion Models
Haokun Chen, Hang Li, Yao Zhang +7
One-Shot Federated Learning (OSFL), a special decentralized machine learning paradigm, has recently gained significant attention. OSFL requires only a single round of client data o…
Towards Artwork Explanation in Large-scale Vision Language Models
Kazuki Hayashi, Yusuke Sakai, Hidetaka Kamigaito +2
Large-scale Vision-Language Models (LVLMs) output text from images and instructions, demonstrating capabilities in text generation and comprehension. However, it has not been clari…
Only the Curve Shape Matters: Training Foundation Models for Zero-Shot Multivariate Time Series Forecasting through Next Curve Shape Prediction
Cheng Feng, Long Huang, Denis Krompass
We present General Time Transformer (GTT), an encoder-only style foundation model for zero-shot multivariate time series forecasting. GTT is pretrained on a large dataset of 200M h…
FedDAT: An Approach for Foundation Model Finetuning in Multi-Modal Heterogeneous Federated Learning
Haokun Chen, Yao Zhang, Denis Krompass +2
Recently, foundation models have exhibited remarkable advancements in multi-modal learning. These models, equipped with millions (or billions) of parameters, typically require a su…
FedPop: Federated Population-based Hyperparameter Tuning
Haokun Chen, Denis Krompass, Jindong Gu +1
Federated Learning (FL) is a distributed machine learning (ML) paradigm, in which multiple clients collaboratively train ML models without centralizing their local data. Similar to…
CL-CrossVQA: A Continual Learning Benchmark for Cross-Domain Visual Question Answering
Yao Zhang, Haokun Chen, Ahmed Frikha +5
Visual Question Answering (VQA) is a multi-discipline research task. To produce the right answer, it requires an understanding of the visual content of images, the natural language…
FRAug: Tackling Federated Learning with Non-IID Features via Representation Augmentation
Haokun Chen, Ahmed Frikha, Denis Krompass +2
Federated Learning (FL) is a decentralized learning paradigm, in which multiple clients collaboratively train deep learning models without centralizing their local data, and hence…
Towards Data-Free Domain Generalization
Ahmed Frikha, Haokun Chen, Denis Krompaß +2
In this work, we investigate the unexplored intersection of domain generalization (DG) and data-free learning. In particular, we address the question: How can knowledge contained i…
Discovery of New Multi-Level Features for Domain Generalization via Knowledge Corruption
Ahmed Frikha, Denis Krompaß, Volker Tresp
Machine learning models that can generalize to unseen domains are essential when applied in real-world scenarios involving strong domain shifts. We address the challenging domain g…
ARCADe: A Rapid Continual Anomaly Detector
Ahmed Frikha, Denis Krompaß, Volker Tresp
Although continual learning and anomaly detection have separately been well-studied in previous works, their intersection remains rather unexplored. The present work addresses a le…
Few-Shot One-Class Classification via Meta-Learning
Ahmed Frikha, Denis Krompaß, Hans-Georg Köpken +1
Although few-shot learning and one-class classification (OCC), i.e., learning a binary classifier with data from only one class, have been separately well studied, their intersecti…
Tensor-Train Recurrent Neural Networks for Video Classification
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. These models, however, turn out t…
Predicting the Co-Evolution of Event and Knowledge Graphs
Cristóbal Esteban, Volker Tresp, Yinchong Yang +2
Embedding learning, a.k.a. representation learning, has been shown to be able to model large-scale semantic knowledge graphs. A key concept is a mapping of the knowledge graph to a…
Learning with Memory Embeddings
Volker Tresp, Cristóbal Esteban, Yinchong Yang +2
Embedding learning, a.k.a. representation learning, has been shown to be able to model large-scale semantic knowledge graphs. A key concept is a mapping of the knowledge graph to a…
Type-Constrained Representation Learning in Knowledge Graphs
Denis Krompaß, Stephan Baier, Volker Tresp
Large knowledge graphs increasingly add value to various applications that require machines to recognize and understand queries and their semantics, as in search or question answer…
Towards a New Science of a Clinical Data Intelligence
Volker Tresp, Sonja Zillner, Maria J. Costa +14
In this paper we define Clinical Data Intelligence as the analysis of data generated in the clinical routine with the goal of improving patient care. We define a science of a Clini…