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pro vyhledávání: '"A, Marttinen"'
Nursing notes, an important component of Electronic Health Records (EHRs), keep track of the progression of a patient's health status during a care episode. Distilling the key information in nursing notes through text summarization techniques can imp
Externí odkaz:
http://arxiv.org/abs/2407.04125
This work aims to improve generalization and interpretability of dynamical systems by recovering the underlying lower-dimensional latent states and their time evolutions. Previous work on disentangled representation learning within the realm of dynam
Externí odkaz:
http://arxiv.org/abs/2406.03337
Kernel Language Entropy: Fine-grained Uncertainty Quantification for LLMs from Semantic Similarities
Uncertainty quantification in Large Language Models (LLMs) is crucial for applications where safety and reliability are important. In particular, uncertainty can be used to improve the trustworthiness of LLMs by detecting factually incorrect model re
Externí odkaz:
http://arxiv.org/abs/2405.20003
Autor:
Alakuijala, Minttu, McLean, Reginald, Woungang, Isaac, Farsad, Nariman, Kaski, Samuel, Marttinen, Pekka, Yuan, Kai
Natural language is often the easiest and most convenient modality for humans to specify tasks for robots. However, learning to ground language to behavior typically requires impractical amounts of diverse, language-annotated demonstrations collected
Externí odkaz:
http://arxiv.org/abs/2405.19988
In this work we consider Code World Models, world models generated by a Large Language Model (LLM) in the form of Python code for model-based Reinforcement Learning (RL). Calling code instead of LLMs for planning has the advantages of being precise,
Externí odkaz:
http://arxiv.org/abs/2405.15383
Publikováno v:
IEEE International Workshop on Machine Learning for Signal Processing (MLSP) 2024
This paper explores the efficacy of diffusion-based generative models as neural operators for partial differential equations (PDEs). Neural operators are neural networks that learn a mapping from the parameter space to the solution space of PDEs from
Externí odkaz:
http://arxiv.org/abs/2405.07097
Publikováno v:
ACL Student Research Workshop 2024
State of the art Symbolic Regression (SR) methods currently build specialized models, while the application of Large Language Models (LLMs) remains largely unexplored. In this work, we introduce the first comprehensive framework that utilizes LLMs fo
Externí odkaz:
http://arxiv.org/abs/2404.19094
Autor:
Kumar, Yogesh, Marttinen, Pekka
We introduce eCLIP, an enhanced version of the CLIP model that integrates expert annotations in the form of radiologist eye-gaze heatmaps. It tackles key challenges in contrastive multi-modal medical imaging analysis, notably data scarcity and the "m
Externí odkaz:
http://arxiv.org/abs/2403.10153
Autor:
Poyraz, Onur, Marttinen, Pekka
Analysis of multivariate healthcare time series data is inherently challenging: irregular sampling, noisy and missing values, and heterogeneous patient groups with different dynamics violating exchangeability. In addition, interpretability and quanti
Externí odkaz:
http://arxiv.org/abs/2311.07867
Autor:
Odnoblyudova, Arina, Hizli, Çağlar, John, ST, Cognolato, Andrea, Juuti, Anne, Särkkä, Simo, Pietiläinen, Kirsi, Marttinen, Pekka
In biomedical applications it is often necessary to estimate a physiological response to a treatment consisting of multiple components, and learn the separate effects of the components in addition to the joint effect. Here, we extend existing probabi
Externí odkaz:
http://arxiv.org/abs/2311.03129