1 citations · 2 across the 8 of their papers we have counts for
8 papers
Provably Reduced Sample Cost in Prior-Guided Hyperparameter Optimization
Leona Hennig, Jasmin Brandt, Lukas Fehring +3
Large-scale hyperparameter optimization (HPO) in automated machine learning (AutoML) consumes substantial computational resources, raising growing concerns about scalability and en…
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…
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…
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…
Generating Synthetic Genotypes using Diffusion Models
Philip Kenneweg, Raghuram Dandinasivara, Xiao Luo +2
In this paper, we introduce the first diffusion model designed to generate complete synthetic human genotypes, which, by standard protocols, one can straightforwardly expand into f…
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…
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…
Retrieval Augmented Generation Systems: Automatic Dataset Creation, Evaluation and Boolean Agent Setup
Tristan Kenneweg, Philip Kenneweg, Barbara Hammer
Retrieval Augmented Generation (RAG) systems have seen huge popularity in augmenting Large-Language Model (LLM) outputs with domain specific and time sensitive data. Very recently…