14 citations · 30 across the 7 of their papers we have counts for
8 papers
Kolibri: A Sovereign European Model on the Pareto Frontier
Aleph Alpha, :, Moritz Althaus +65
This report introduces Kolibri, an English-German mixture-of-experts transformer with 78.1B total parameters and 3.46B active parameters per token, released as open weights under t…
CoRet: Improved Retriever for Code Editing
Fabio Fehr, Prabhu Teja Sivaprasad, Luca Franceschi +1
In this paper, we introduce CoRet, a dense retrieval model designed for code-editing tasks that integrates code semantics, repository structure, and call graph dependencies. The mo…
SWE-PolyBench: A multi-language benchmark for repository level evaluation of coding agents
Muhammad Shihab Rashid, Christian Bock, Yuan Zhuang +10
Coding agents powered by large language models have shown impressive capabilities in software engineering tasks, but evaluating their performance across diverse programming languag…
Choice of PEFT Technique in Continual Learning: Prompt Tuning is Not All You Need
Martin Wistuba, Prabhu Teja Sivaprasad, Lukas Balles +1
Recent Continual Learning (CL) methods have combined pretrained Transformers with prompt tuning, a parameter-efficient fine-tuning (PEFT) technique. We argue that the choice of pro…
Continual Learning with Low Rank Adaptation
Martin Wistuba, Prabhu Teja Sivaprasad, Lukas Balles +1
Recent work using pretrained transformers has shown impressive performance when fine-tuned with data from the downstream problem of interest. However, they struggle to retain that…
PAUMER: Patch Pausing Transformer for Semantic Segmentation
Evann Courdier, Prabhu Teja Sivaprasad, François Fleuret
We study the problem of improving the efficiency of segmentation transformers by using disparate amounts of computation for different parts of the image. Our method, PAUMER, accomp…
Test time Adaptation through Perturbation Robustness
Prabhu Teja Sivaprasad, François Fleuret
Data samples generated by several real world processes are dynamic in nature \textit{i.e.}, their characteristics vary with time. Thus it is not possible to train and tackle all po…
Optimizer Benchmarking Needs to Account for Hyperparameter Tuning
Prabhu Teja Sivaprasad, Florian Mai, Thijs Vogels +2
The performance of optimizers, particularly in deep learning, depends considerably on their chosen hyperparameter configuration. The efficacy of optimizers is often studied under n…