3 citations · 11 across the 13 of their papers we have counts for
7 papers · 1 filter
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…
Intelligent Learning Rate Distribution to reduce Catastrophic Forgetting in Transformers
Philip Kenneweg, Alexander Schulz, Sarah Schröder +1
Pretraining language models on large text corpora is a common practice in natural language processing. Fine-tuning of these models is then performed to achieve the best results on…
Debiasing Sentence Embedders through Contrastive Word Pairs
Philip Kenneweg, Sarah Schröder, Alexander Schulz +1
Over the last years, various sentence embedders have been an integral part in the success of current machine learning approaches to Natural Language Processing (NLP). Unfortunately…
Neural Architecture Search for Sentence Classification with BERT
Philip Kenneweg, Sarah Schröder, Barbara Hammer
Pre training of language models on large text corpora is common practice in Natural Language Processing. Following, fine tuning of these models is performed to achieve the best res…
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…
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]…
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…