64 citations · 113 across the 12 of their papers we have counts for
12 papers
Techniques for Peak Memory Reduction for LoRA Fine-tuning of LLMs on Edge Devices
Hassan Dbouk, Matthias Reisser, Prathamesh Mandke +2
Fine-tuning of Large Language Models (LLMs) using Low-Rank Adaptation (LoRA) on an end-user's data offers personalized experiences while keeping data private, but faces severe memo…
On Adaptivity in Zeroth-Order Optimization
Hassan Dbouk, Nidham Gazagnadou, Matthias Reisser +1
We investigate the effectiveness of adaptive zeroth-order (ZO) optimization for memory-constrained fine-tuning of large language models (LLMs). Contrary to prior claims, we show th…
Hollowed Net for On-Device Personalization of Text-to-Image Diffusion Models
Wonguk Cho, Seokeon Choi, Debasmit Das +4
Recent advancements in text-to-image diffusion models have enabled the personalization of these models to generate custom images from textual prompts. This paper presents an effici…
Stable Diffusion-based Data Augmentation for Federated Learning with Non-IID Data
Mahdi Morafah, Matthias Reisser, Bill Lin +1
The proliferation of edge devices has brought Federated Learning (FL) to the forefront as a promising paradigm for decentralized and collaborative model training while preserving t…
A Mutual Information Perspective on Federated Contrastive Learning
Christos Louizos, Matthias Reisser, Denis Korzhenkov
We investigate contrastive learning in the federated setting through the lens of SimCLR and multi-view mutual information maximization. In doing so, we uncover a connection between…
Hyperparameter Optimization through Neural Network Partitioning
Bruno Mlodozeniec, Matthias Reisser, Christos Louizos
Well-tuned hyperparameters are crucial for obtaining good generalization behavior in neural networks. They can enforce appropriate inductive biases, regularize the model and improv…
Quantization Robust Federated Learning for Efficient Inference on Heterogeneous Devices
Kartik Gupta, Marios Fournarakis, Matthias Reisser +2
Federated Learning (FL) is a machine learning paradigm to distributively learn machine learning models from decentralized data that remains on-device. Despite the success of standa…
DP-REC: Private & Communication-Efficient Federated Learning
Aleksei Triastcyn, Matthias Reisser, Christos Louizos
Privacy and communication efficiency are important challenges in federated training of neural networks, and combining them is still an open problem. In this work, we develop a meth…
An Expectation-Maximization Perspective on Federated Learning
Christos Louizos, Matthias Reisser, Joseph Soriaga +1
Federated learning describes the distributed training of models across multiple clients while keeping the data private on-device. In this work, we view the server-orchestrated fede…
Federated Mixture of Experts
Matthias Reisser, Christos Louizos, Efstratios Gavves +1
Federated learning (FL) has emerged as the predominant approach for collaborative training of neural network models across multiple users, without the need to gather the data at a…
Relaxed Quantization for Discretized Neural Networks
Christos Louizos, Matthias Reisser, Tijmen Blankevoort +2
Neural network quantization has become an important research area due to its great impact on deployment of large models on resource constrained devices. In order to train networks…
Decreasing nuclear volume concentrates DNA and enforces transcription factor-chromatin associations during Zebrafish genome activation
Matthias Reisser, J. Christof M. Gebhardt
Zygotic genome activation (ZGA), the onset of transcription after initial quiescence, is a major developmental step in many species, which occurs after ten cell divisions in Zebraf…