2 citations · 5 across the 12 of their papers we have counts for
12 papers
Prompt Compression via Activation Aggregation
Thibaud Ardoin, Semira Einsele, Evis Bregu +1
Large language models process prompts by propagating activations through dozens of layers before generating a response. We ask whether the task-relevant information contained in an…
LLM Self-Recognition: Steering and Retrieving Activation Signatures
Thibaud Ardoin, Jonas Schäfer, Gerhard Wunder
Recent advances in interpretability suggest that large language models (LLMs) implicitly encode signals in their generated text that enable self-recognition of their outputs. We de…
Robust Text Watermarking for Large Language Models via Dual Semantic Embeddings
Jonas Schäfer, Cezary Pilaszewicz, Gerhard Wunder
This work presents Dual-Embedding Watermarking (DEW), a semantic watermarking scheme for large language models (LLMs) that leverages contextual and token-level embeddings to enhanc…
ALIGN-FL: Architecture-independent Learning through Invariant Generative component sharing in Federated Learning
Mayank Gulati, Benedikt Groß, Gerhard Wunder
We present ALIGN-FL, a novel approach to distributed learning that addresses the challenge of learning from highly disjoint data distributions through selective sharing of generati…
Rethinking Explanation Evaluation under the Retraining Scheme
Yi Cai, Thibaud Ardoin, Mayank Gulati +1
Feature attribution has gained prominence as a tool for explaining model decisions, yet evaluating explanation quality remains challenging due to the absence of ground-truth explan…
Machine and Deep Learning for Indoor UWB Jammer Localization
Hamed Fard, Mahsa Kholghi, Benedikt Groß +1
Ultra-wideband (UWB) localization delivers centimeter-scale accuracy but is vulnerable to jamming attacks, creating security risks for asset tracking and intrusion detection in sma…
An Investigation into the Performance of Non-Contrastive Self-Supervised Learning Methods for Network Intrusion Detection
Hamed Fard, Tobias Schalau, Gerhard Wunder
Network intrusion detection, a well-explored cybersecurity field, has predominantly relied on supervised learning algorithms in the past two decades. However, their limitations in…
Tuning Block Size for Workload Optimization in Consortium Blockchain Networks
Narges Dadkhah, Somayeh Mohammadi, Gerhard Wunder
Determining the optimal block size is crucial for achieving high throughput in blockchain systems. Many studies have focused on tuning various components, such as databases, networ…
Transparent Neighborhood Approximation for Text Classifier Explanation
Yi Cai, Arthur Zimek, Eirini Ntoutsi +1
Recent literature highlights the critical role of neighborhood construction in deriving model-agnostic explanations, with a growing trend toward deploying generative models to impr…
DBNode: A Decentralized Storage System for Big Data Storage in Consortium Blockchains
Narges Dadkhah, Xuyang Ma, Katinka Wolter +1
Storing big data directly on a blockchain poses a substantial burden due to the need to maintain a consistent ledger across all nodes. Numerous studies in decentralized storage sys…
Perfectly Secure Key Agreement Over a Full Duplex Wireless Channel
Gerhard Wunder, Axel Flinth, Daniel Becker +1
Secret key generation (SKG) between authenticated devices is a pivotal task for secure communications. Diffie-Hellman (DH) is de-facto standard but not post-quantum secure. In this…
Model CBOR Serialization for Federated Learning
Koen Zandberg, Mayank Gulati, Gerhard Wunder +1
The typical federated learning workflow requires communication between a central server and a large set of clients synchronizing model parameters between each other. The current fr…