collaborators

7 papers

cs.AI2025

Who Has The Final Say? Conformity Dynamics in ChatGPT's Selections

Clarissa Sabrina Arlinghaus, Tristan Kenneweg, Barbara Hammer +1

Large language models (LLMs) such as ChatGPT are increasingly integrated into high-stakes decision-making, yet little is known about their susceptibility to social influence. We co…

cs.CV2025

Uncertainty-Aware Remaining Lifespan Prediction from Images

Tristan Kenneweg, Philip Kenneweg, Barbara Hammer

Predicting mortality-related outcomes from images offers the prospect of accessible, noninvasive, and scalable health screening. We present a method that leverages pretrained visio…

cs.CV2025

JEPA for RL: Investigating Joint-Embedding Predictive Architectures for Reinforcement Learning

Tristan Kenneweg, Philip Kenneweg, Barbara Hammer

Joint-Embedding Predictive Architectures (JEPA) have recently become popular as promising architectures for self-supervised learning. Vision transformers have been trained using JE…

cs.LG2024

No learning rates needed: Introducing SALSA -- Stable Armijo Line Search Adaptation

Philip Kenneweg, Tristan Kenneweg, Fabian Fumagalli +1

In recent studies, line search methods have been demonstrated to significantly enhance the performance of conventional stochastic gradient descent techniques across various dataset…

cs.LG2024

Improving Line Search Methods for Large Scale Neural Network Training

Philip Kenneweg, Tristan Kenneweg, Barbara Hammer

In recent studies, line search methods have shown significant improvements in the performance of traditional stochastic gradient descent techniques, eliminating the need for a spec…

cs.LG2024

Faster Convergence for Transformer Fine-tuning with Line Search Methods

Philip Kenneweg, Leonardo Galli, Tristan Kenneweg +1

Recent works have shown that line search methods greatly increase performance of traditional stochastic gradient descent methods on a variety of datasets and architectures [1], [2]…