no code implementations • 5 Jun 2024 • Çağlar Hızlı, Çağatay Yıldız, Matthias Bethge, ST John, Pekka Marttinen
This work aims to improve generalization and interpretability of dynamical systems by recovering the underlying lower-dimensional latent states and their time evolutions.
1 code implementation • 30 May 2024 • Minttu Alakuijala, Reginald McLean, Isaac Woungang, Nariman Farsad, Samuel Kaski, Pekka Marttinen, Kai Yuan
Natural language is often the easiest and most convenient modality for humans to specify tasks for robots.
no code implementations • 30 May 2024 • Alexander Nikitin, Jannik Kossen, Yarin Gal, Pekka Marttinen
To address this problem, we propose Kernel Language Entropy (KLE), a novel method for uncertainty estimation in white- and black-box LLMs.
no code implementations • 24 May 2024 • Nicola Dainese, Matteo Merler, Minttu Alakuijala, Pekka Marttinen
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).
no code implementations • 11 May 2024 • Katsiaryna Haitsiukevich, Onur Poyraz, Pekka Marttinen, Alexander Ilin
In this work, we show that diffusion-based generative models exhibit many properties favourable for neural operators, and they can effectively generate the solution of a PDE conditionally on the parameter or recover the unobserved parts of the system.
no code implementations • 15 Mar 2024 • Yogesh Kumar, Pekka Marttinen
We introduce eCLIP, an enhanced version of the CLIP model that integrates expert annotations in the form of radiologist eye-gaze heatmaps.
1 code implementation • 14 Nov 2023 • Onur Poyraz, Pekka Marttinen
Experiments on challenging real-world epidemiological and semi-synthetic data demonstrate the advantages of the M-CHMM: improved data fit, capacity to efficiently handle missing and noisy measurements, improved prediction accuracy, and ability to identify interpretable subsets in the data.
1 code implementation • 6 Nov 2023 • Arina Odnoblyudova, Çağlar Hızlı, ST John, Andrea Cognolato, Anne Juuti, Simo Särkkä, Kirsi Pietiläinen, Pekka Marttinen
By differentiating treatment components, incorporating their dosages, and sharing statistical information across patients via a hierarchical multi-output Gaussian process, our method improves prediction accuracy over existing approaches, and allows us to interpret the different effects of carbohydrates and fat on the overall glucose response.
no code implementations • 12 Sep 2023 • Shaoxiong Ji, Wei Sun, Pekka Marttinen
We consider two interesting research questions: 1) how is information distributed over long documents, and 2) how does content reduction, such as token selection and text summarization, affect the information density in long documents.
no code implementations • 18 Jun 2023 • Antti Pöllänen, Pekka Marttinen
Building on existing results for measurement error models, we prove that our model's causal effect estimates are identifiable, even without knowledge of the measurement error variance or other side information.
no code implementations • 2 May 2023 • Manish Bhatia, Balram Meena, Vipin Kumar Rathi, Prayag Tiwari, Amit Kumar Jaiswal, Shagaf M Ansari, Ajay Kumar, Pekka Marttinen
We chose SCD as a model disease condition due to the presence of diverse erythrocyte morphologies in the blood samples of SCD patients.
1 code implementation • 17 Mar 2023 • Joel Honkamaa, Pekka Marttinen
Deep learning has emerged as a strong alternative for classical iterative methods for deformable medical image registration, where the goal is to find a mapping between the coordinate systems of two images.
no code implementations • 25 Jan 2023 • Shaoxiong Ji, Ya Gao, Pekka Marttinen
Adverse drug events (ADEs) are an important aspect of drug safety.
no code implementations • 9 Sep 2022 • Çağlar Hızlı, ST John, Anne Juuti, Tuure Saarinen, Kirsi Pietiläinen, Pekka Marttinen
Our model enables the estimation of a treatment policy from observational sequences of treatments and outcomes, and it can predict the interventional and counterfactual progression of the outcome after an intervention on the treatment policy (in contrast with the causal effect of a single treatment).
1 code implementation • 26 Aug 2022 • Joel Honkamaa, Umair Khan, Sonja Koivukoski, Mira Valkonen, Leena Latonen, Pekka Ruusuvuori, Pekka Marttinen
Cross-modality image synthesis is an active research topic with multiple medical clinically relevant applications.
1 code implementation • 4 Jul 2022 • Vishnu Raj, Tianyu Cui, Markus Heinonen, Pekka Marttinen
We present a simple approach to incorporate prior knowledge in BNNs based on external summary information about the predicted classification probabilities for a given dataset.
no code implementations • 16 Mar 2022 • Xiang Li, Yazhou Zhang, Prayag Tiwari, Dawei Song, Bin Hu, Meihong Yang, Zhigang Zhao, Neeraj Kumar, Pekka Marttinen
Hence, in this paper, we review from the perspective of researchers who try to take the first step on this topic.
no code implementations • 31 Jan 2022 • Tianyu Cui, Yogesh Kumar, Pekka Marttinen, Samuel Kaski
Similarity metrics such as representational similarity analysis (RSA) and centered kernel alignment (CKA) have been used to compare layer-wise representations between neural networks.
no code implementations • 8 Jan 2022 • Shaoxiong Ji, Wei Sun, Xiaobo Li, Hang Dong, Ara Taalas, Yijia Zhang, Honghan Wu, Esa Pitkänen, Pekka Marttinen
Automated medical coding, an essential task for healthcare operation and delivery, makes unstructured data manageable by predicting medical codes from clinical documents.
no code implementations • 7 Sep 2021 • Shaoxiong Ji, Pekka Marttinen
Multitask deep learning has been applied to patient outcome prediction from text, taking clinical notes as input and training deep neural networks with a joint loss function of multiple tasks.
2 code implementations • 6 Sep 2021 • Wei Sun, Shaoxiong Ji, Erik Cambria, Pekka Marttinen
Nevertheless, automated medical coding is still challenging because of the imbalanced class problem, complex code association, and noise in lengthy documents.
no code implementations • 31 Aug 2021 • Yogesh Kumar, Alexander Ilin, Henri Salo, Sangita Kulathinal, Maarit K. Leinonen, Pekka Marttinen
Despite the proven effectiveness of Transformer neural networks across multiple domains, their performance with Electronic Health Records (EHR) can be nuanced.
no code implementations • 27 May 2021 • Lang He, MingYue Niu, Prayag Tiwari, Pekka Marttinen, Rui Su, Jiewei Jiang, Chenguang Guo, Hongyu Wang, Songtao Ding, Zhongmin Wang, Wei Dang, Xiaoying Pan
Consequently, to improve current medical care, many scholars have used deep learning to extract a representation of depression cues in audio and video for automatic depression detection.
1 code implementation • 2 Apr 2021 • Wei Sun, Shaoxiong Ji, Erik Cambria, Pekka Marttinen
Medical coding translates professionally written medical reports into standardized codes, which is an essential part of medical information systems and health insurance reimbursement.
no code implementations • 11 Mar 2021 • Shaoxiong Ji, Matti Hölttä, Pekka Marttinen
In the clinical application of medical code assignment, diagnosis and procedure codes are inferred from lengthy clinical notes such as hospital discharge summaries.
no code implementations • NeurIPS 2021 • Severi Rissanen, Pekka Marttinen
Using deep latent variable models in causal inference has attracted considerable interest recently, but an essential open question is their ability to yield consistent causal estimates.
no code implementations • Findings (ACL) 2021 • Shaoxiong Ji, Shirui Pan, Pekka Marttinen
However, these methods are still ineffective as they do not fully encode and capture the lengthy and rich semantic information of medical notes nor explicitly exploit the interactions between the notes and codes.
no code implementations • EMNLP (ClinicalNLP) 2020 • Shaoxiong Ji, Erik Cambria, Pekka Marttinen
Medical code assignment, which predicts medical codes from clinical texts, is a fundamental task of intelligent medical information systems.
no code implementations • 24 Feb 2020 • Tianyu Cui, Aki Havulinna, Pekka Marttinen, Samuel Kaski
Encoding domain knowledge into the prior over the high-dimensional weight space of a neural network is challenging but essential in applications with limited data and weak signals.
1 code implementation • 2 Feb 2020 • Shaoxiong Ji, Shirui Pan, Erik Cambria, Pekka Marttinen, Philip S. Yu
In this survey, we provide a comprehensive review of knowledge graph covering overall research topics about 1) knowledge graph representation learning, 2) knowledge acquisition and completion, 3) temporal knowledge graph, and 4) knowledge-aware applications, and summarize recent breakthroughs and perspective directions to facilitate future research.
no code implementations • pproximateinference AABI Symposium 2019 • Marko Järvenpää, Aki Vehtari, Pekka Marttinen
Surrogate models can be used to accelerate approximate Bayesian computation (ABC).
no code implementations • 14 Oct 2019 • Marko Järvenpää, Aki Vehtari, Pekka Marttinen
We propose a numerical method to fully quantify the uncertainty in, for example, ABC posterior moments.
1 code implementation • 10 Jun 2019 • Guangyi Zhang, Reza Ashrafi, Anne Juuti, Kirsi Pietiläinen, Pekka Marttinen
Estimating the effect of a treatment on a given outcome, conditioned on a vector of covariates, is central in many applications.
1 code implementation • 3 May 2019 • Marko Järvenpää, Michael Gutmann, Aki Vehtari, Pekka Marttinen
We consider Bayesian inference when only a limited number of noisy log-likelihood evaluations can be obtained.
1 code implementation • 24 Jan 2019 • Tianyu Cui, Pekka Marttinen, Samuel Kaski
Estimating global pairwise interaction effects, i. e., the difference between the joint effect and the sum of marginal effects of two input features, with uncertainty properly quantified, is centrally important in science applications.
no code implementations • 27 Nov 2018 • Marko Järvenpää, Mohamad R. Abdul Sater, Georgia K. Lagoudas, Paul C. Blainey, Loren G. Miller, James A. McKinnell, Susan S. Huang, Yonatan H. Grad, Pekka Marttinen
Bacterial populations that colonize a host play important roles in host health, including serving as a reservoir that transmits to other hosts and from which invasive strains emerge, thus emphasizing the importance of understanding rates of acquisition and clearance of colonizing populations.
2 code implementations • 2 Aug 2017 • Jarno Lintusaari, Henri Vuollekoski, Antti Kangasrääsiö, Kusti Skytén, Marko Järvenpää, Pekka Marttinen, Michael U. Gutmann, Aki Vehtari, Jukka Corander, Samuel Kaski
The stand-alone ELFI graph can be used with any of the available inference methods without modifications.
no code implementations • 9 May 2017 • Iiris Sundin, Tomi Peltola, Muntasir Mamun Majumder, Pedram Daee, Marta Soare, Homayun Afrabandpey, Caroline Heckman, Samuel Kaski, Pekka Marttinen
Predicting the efficacy of a drug for a given individual, using high-dimensional genomic measurements, is at the core of precision medicine.
no code implementations • 3 Apr 2017 • Marko Järvenpää, Michael U. Gutmann, Arijus Pleska, Aki Vehtari, Pekka Marttinen
We propose to compute the uncertainty in the ABC posterior density, which is due to a lack of simulations to estimate this quantity accurately, and define a loss function that measures this uncertainty.
no code implementations • 7 Dec 2016 • Luana Micallef, Iiris Sundin, Pekka Marttinen, Muhammad Ammad-Ud-Din, Tomi Peltola, Marta Soare, Giulio Jacucci, Samuel Kaski
The main component of our approach is a user model that models the domain expert's knowledge of the relevance of different features for a prediction task.
no code implementations • 20 Oct 2016 • Marko Järvenpää, Michael Gutmann, Aki Vehtari, Pekka Marttinen
Approximate Bayesian computation (ABC) can be used for model fitting when the likelihood function is intractable but simulating from the model is feasible.
no code implementations • 27 Oct 2014 • Jussi Gillberg, Pekka Marttinen, Matti Pirinen, Antti J. Kangas, Pasi Soininen, Mehreen Ali, Aki S. Havulinna, Marjo-Riitta Marjo-Riitta Järvelin, Mika Ala-Korpela, Samuel Kaski
In high-dimensional data, structured noise caused by observed and unobserved factors affecting multiple target variables simultaneously, imposes a serious challenge for modeling, by masking the often weak signal.
no code implementations • 16 Oct 2013 • Jussi Gillberg, Pekka Marttinen, Matti Pirinen, Antti J. Kangas, Pasi Soininen, Marjo-Riitta Järvelin, Mika Ala-Korpela, Samuel Kaski
To facilitate the prediction of the weak effects, we constrain our model structure by introducing a novel Bayesian approach of sharing information between the regression model and the noise model.