Zobrazeno 1 - 10
of 2 933
pro vyhledávání: '"A. Jamnik"'
Integrating AI in healthcare can greatly improve patient care and system efficiency. However, the lack of explainability in AI systems (XAI) hinders their clinical adoption, especially in multimodal settings that use increasingly complex model archit
Externí odkaz:
http://arxiv.org/abs/2412.15828
Computational analysis of whole slide images (WSIs) has seen significant research progress in recent years, with applications ranging across important diagnostic and prognostic tasks such as survival or cancer subtype prediction. Many state-of-the-ar
Externí odkaz:
http://arxiv.org/abs/2411.18225
Ontologies are useful for automatic machine processing of domain knowledge as they represent it in a structured format. Yet, constructing ontologies requires substantial manual effort. To automate part of this process, large language models (LLMs) ha
Externí odkaz:
http://arxiv.org/abs/2410.23584
Autor:
Zarlenga, Mateo Espinosa, Sankaranarayanan, Swami, Andrews, Jerone T. A., Shams, Zohreh, Jamnik, Mateja, Xiang, Alice
Deep neural networks trained via empirical risk minimisation often exhibit significant performance disparities across groups, particularly when group and task labels are spuriously correlated (e.g., "grassy background" and "cows"). Existing bias miti
Externí odkaz:
http://arxiv.org/abs/2409.17691
Data collection is often difficult in critical fields such as medicine, physics, and chemistry. As a result, classification methods usually perform poorly with these small datasets, leading to weak predictive performance. Increasing the training set
Externí odkaz:
http://arxiv.org/abs/2409.16118
Autor:
Ziarko, Alicja, Jiang, Albert Q., Piotrowski, Bartosz, Li, Wenda, Jamnik, Mateja, Miłoś, Piotr
Text embeddings are essential for many tasks, such as document retrieval, clustering, and semantic similarity assessment. In this paper, we study how to contrastively train text embedding models in a compute-optimal fashion, given a suite of pre-trai
Externí odkaz:
http://arxiv.org/abs/2406.04165
Tabular data is prevalent in many critical domains, yet it is often challenging to acquire in large quantities. This scarcity usually results in poor performance of machine learning models on such data. Data augmentation, a common strategy for perfor
Externí odkaz:
http://arxiv.org/abs/2406.01805
Learning holistic computational representations in physical, chemical or biological systems requires the ability to process information from different distributions and modalities within the same model. Thus, the demand for multimodal machine learnin
Externí odkaz:
http://arxiv.org/abs/2405.19950
Concept-based explainability methods provide insight into deep learning systems by constructing explanations using human-understandable concepts. While the literature on human reasoning demonstrates that we exploit relationships between concepts when
Externí odkaz:
http://arxiv.org/abs/2405.18217
The success of Large Language Models (LLMs), e.g., ChatGPT, is witnessed by their planetary popularity, their capability of human-like communication, and also by their steadily improved reasoning performance. However, it remains unclear whether LLMs
Externí odkaz:
http://arxiv.org/abs/2403.15297