activity
20202026
most citedLLMs in the Loop: Leveraging Large Language Model Annotations for Active Learning in Low-Resource Languages

3 citations · 7 across the 7 of their papers we have counts for

collaborators

7 papers

cs.CL2026

When Confidence Takes the Wrong Path: Diagnosing Retrieval-State Lock-In in RAG

Sahib Julka

The trustworthiness of a retrieval-augmented generation (RAG) system depends on more than the answer it returns, yet many black-box uncertainty methods still read agreement among s…

cs.LG2024

Towards an Improved Metric for Evaluating Disentangled Representations

Sahib Julka, Yashu Wang, Michael Granitzer

Disentangled representation learning plays a pivotal role in making representations controllable, interpretable and transferable. Despite its significance in the domain, the quest…

cs.LG2024★ 2 cited

DriftGAN: Using historical data for Unsupervised Recurring Drift Detection

Christofer Fellicious, Sahib Julka, Lorenz Wendlinger +1

In real-world applications, input data distributions are rarely static over a period of time, a phenomenon known as concept drift. Such concept drifts degrade the model's predictio…

cs.CL2024★ 3 cited

LLMs in the Loop: Leveraging Large Language Model Annotations for Active Learning in Low-Resource Languages

Nataliia Kholodna, Sahib Julka, Mohammad Khodadadi +2

Low-resource languages face significant barriers in AI development due to limited linguistic resources and expertise for data labeling, rendering them rare and costly. The scarcity…

cs.CV2023

Knowledge distillation with Segment Anything (SAM) model for Planetary Geological Mapping

Sahib Julka, Michael Granitzer

Planetary science research involves analysing vast amounts of remote sensing data, which are often costly and time-consuming to annotate and process. One of the essential tasks in…

cs.CV2021

Conditional Generative Adversarial Networks for Speed Control in Trajectory Simulation

Sahib Julka, Vishal Sowrirajan, Joerg Schloetterer +1

Motion behaviour is driven by several factors -- goals, presence and actions of neighbouring agents, social relations, physical and social norms, the environment with its variable…

astro-ph.IM2020★ 2 cited

Lessons Learned from the 1st ARIEL Machine Learning Challenge: Correcting Transiting Exoplanet Light Curves for Stellar Spots

Nikolaos Nikolaou, Ingo P. Waldmann, Angelos Tsiaras +20

The last decade has witnessed a rapid growth of the field of exoplanet discovery and characterisation. However, several big challenges remain, many of which could be addressed usin…