9 papers
Your CLIP has 164 dimensions of noise: Exploring the embeddings covariance eigenspectrum of contrastively pretrained vision-language transformers
Jakub Grzywaczewski, Dawid PÅudowski, PrzemysÅaw Biecek
Contrastively pre-trained Vision-Language Models (VLMs) serve as powerful feature extractors. Yet, their shared latent spaces are prone to structural anomalies and act as repositor…
SwordBench: Evaluating Orthogonality of Steering Image Representations
Vladimir Zaigrajew, Dawid Pludowski, Hubert Baniecki +1
Steering or intervening on model representations at inference time to correct predictions is essential for AI interpretability and safety, yet existing evaluation protocols are lim…
Local Intrinsic Dimension Unveils Hallucinations in Diffusion Models
Bartlomiej Sobieski, Matthew Tivnan, Dawid PÅudowski +4
Diffusion models are prone to generating structural hallucinations - samples that match the statistical properties of the training data yet defy underlying structural rules, result…
Trojan horse hunt in deep forecasting models: Insights from the European Space Agency competition
Krzysztof Kotowski, Ramez Shendy, Jakub Nalepa +10
Forecasting plays a crucial role in modern safety-critical applications, such as space operations. However, the increasing use of deep forecasting models introduces a new security…
Fake or Real: The Impostor Hunt in Texts for Space Operations
Agata Kaczmarek, Dawid PÅudowski, Piotr WilczyÅski +6
The "Fake or Real" competition hosted on Kaggle (https://www.kaggle.com/competitions/fake-or-real-the-impostor-hunt ) is the second part of a series of follow-up competitions and h…
Divide, Specialize, and Route: A New Approach to Efficient Ensemble Learning
Jakub Piwko, JÄdrzej RuciÅski, Dawid PÅudowski +5
Ensemble learning has proven effective in boosting predictive performance, but traditional methods such as bagging, boosting, and dynamic ensemble selection (DES) suffer from high…