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Pekka Marttinen
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2020 – today
- 2024
- [j34]Shaoxiong Ji, Xiaobo Li, Wei Sun, Hang Dong, Ara Taalas, Yijia Zhang, Honghan Wu, Esa Pitkänen, Pekka Marttinen:
A Unified Review of Deep Learning for Automated Medical Coding. ACM Comput. Surv. 56(12): 306:1-306:41 (2024) - [j33]Yogesh Kumar
, Alexander Ilin
Self-Supervised Forecasting in Electronic Health Records With Attention-Free Models. IEEE Trans. Artif. Intell. 5(8): 3926-3938 (2024) - [c23]Matteo Merler, Katsiaryna Haitsiukevich, Nicola Dainese, Pekka Marttinen:
In-Context Symbolic Regression: Leveraging Large Language Models for Function Discovery. ACL (Student Research Workshop) 2024: 589-606 - [c22]Ya Gao
Knowledge-augmented Graph Neural Networks with Concept-aware Attention for Adverse Drug Event Detection. LREC/COLING 2024: 9787-9798 - [c21]Nicola Dainese, Alexander Ilin, Pekka Marttinen:
Can docstring reformulation with an LLM improve code generation? EACL (Student Research Workshop) 2024: 296-312 - [c20]Sam Spilsbury, Pekka Marttinen, Alexander Ilin:
Generating Demonstrations for In-Context Compositional Generalization in Grounded Language Learning. EMNLP 2024: 15960-15991 - [c19]Chen He
VMS: Interactive Visualization to Support the Sensemaking and Selection of Predictive Models. IUI 2024: 229-244 - [i41]Yogesh Kumar, Pekka Marttinen:
Improving Medical Multi-modal Contrastive Learning with Expert Annotations. CoRR abs/2403.10153 (2024) - [i40]Katsiaryna Haitsiukevich, Onur Poyraz, Pekka Marttinen, Alexander Ilin:
Diffusion models as probabilistic neural operators for recovering unobserved states of dynamical systems. CoRR abs/2405.07097 (2024) - [i39]Nicola Dainese, Matteo Merler, Minttu Alakuijala, Pekka Marttinen:
Generating Code World Models with Large Language Models Guided by Monte Carlo Tree Search. CoRR abs/2405.15383 (2024) - [i38]Minttu Alakuijala, Reginald McLean, Isaac Woungang, Nariman Farsad, Samuel Kaski, Pekka Marttinen, Kai Yuan:
Video-Language Critic: Transferable Reward Functions for Language-Conditioned Robotics. CoRR abs/2405.19988 (2024) - [i37]Alexander Nikitin, Jannik Kossen, Yarin Gal, Pekka Marttinen:
Kernel Language Entropy: Fine-grained Uncertainty Quantification for LLMs from Semantic Similarities. CoRR abs/2405.20003 (2024) - [i36]Çaglar Hizli, Çagatay Yildiz, Matthias Bethge, St John, Pekka Marttinen:
Identifying latent state transition in non-linear dynamical systems. CoRR abs/2406.03337 (2024) - [i35]Ya Gao, Hans Moen, Saila Koivusalo, Miika Koskinen, Pekka Marttinen:
Query-Guided Self-Supervised Summarization of Nursing Notes. CoRR abs/2407.04125 (2024) - 2023
- [j32]Sophie Wharrie
HAPNEST: efficient, large-scale generation and evaluation of synthetic datasets for genotypes and phenotypes. Bioinform. 39(9) (2023) - [j31]Xiang Li
EEG Based Emotion Recognition: A Tutorial and Review. ACM Comput. Surv. 55(4): 79:1-79:57 (2023) - [j30]Tommi Gröhn
Quantifying Movement Behavior of Chronic Low Back Pain Patients in Virtual Reality. ACM Trans. Comput. Heal. 4(2): 11:1-11:24 (2023) - [j29]Joel Honkamaa
Deformation equivariant cross-modality image synthesis with paired non-aligned training data. Medical Image Anal. 90: 102940 (2023) - [j28]Saeed Karami, Farid Saberi-Movahed, Prayag Tiwari
Unsupervised feature selection based on variance-covariance subspace distance. Neural Networks 166: 188-203 (2023) - [j27]Fanni Ojala
Bayesian modeling of the impact of antibiotic resistance on the efficiency of MRSA decolonization. PLoS Comput. Biol. 19(10) (2023) - [j26]Wei Sun
Multitask Balanced and Recalibrated Network for Medical Code Prediction. ACM Trans. Intell. Syst. Technol. 14(1): 17:1-17:20 (2023) - [c18]Vishnu Raj, Tianyu Cui, Markus Heinonen, Pekka Marttinen:
Incorporating functional summary information in Bayesian neural networks using a Dirichlet process likelihood approach. AISTATS 2023: 6741-6763 - [c17]Shaoxiong Ji
Patient Outcome and Zero-shot Diagnosis Prediction with Hypernetwork-guided Multitask Learning. EACL 2023: 589-598 - [c16]Nicola Dainese, Pekka Marttinen, Alexander Ilin:
Reader: Model-based language-instructed reinforcement learning. EMNLP 2023: 16583-16599 - [c15]Caglar Hizli, S. T. John, Anne Tuulikki Juuti, Tuure Tapani Saarinen, Kirsi Hannele Pietiläinen, Pekka Marttinen:
Causal Modeling of Policy Interventions From Treatment-Outcome Sequences. ICML 2023: 13050-13084 - [c14]Arina Odnoblyudova, Caglar Hizli, St John
Nonparametric modeling of the composite effect of multiple nutrients on blood glucose dynamics. ML4H@NeurIPS 2023: 428-444 - [c13]Onur Poyraz, Pekka Marttinen:
Mixture of Coupled HMMs for Robust Modeling of Multivariate Healthcare Time Series. ML4H@NeurIPS 2023: 461-479 - [c12]Çaglar Hizli, St John, Anne Juuti, Tuure Saarinen, Kirsi Pietiläinen, Pekka Marttinen:
Temporal Causal Mediation through a Point Process: Direct and Indirect Effects of Healthcare Interventions. NeurIPS 2023 - [c11]Wei Sun, Shaoxiong Ji
Weak Supervision and Clustering-Based Sample Selection for Clinical Named Entity Recognition. ECML/PKDD (6) 2023: 444-459 - [i34]Shaoxiong Ji, Ya Gao, Pekka Marttinen:
Knowledge-augmented Graph Neural Networks with Concept-aware Attention for Adverse Drug Event Detection. CoRR abs/2301.10451 (2023) - [i33]Joel Honkamaa, Pekka Marttinen:
ASymReg: Robust symmetric image registration using anti-symmetric formulation and deformation inversion layers. CoRR abs/2303.10211 (2023) - [i32]Manish Bhatia, Balram Meena, Vipin Kumar Rathi, Prayag Tiwari
A Novel Deep Learning based Model for Erythrocytes Classification and Quantification in Sickle Cell Disease. CoRR abs/2305.01663 (2023) - [i31]Çaglar Hizli, S. T. John, Anne Juuti, Tuure Saarinen, Kirsi Pietiläinen, Pekka Marttinen
Temporal Causal Mediation through a Point Process: Direct and Indirect Effects of Healthcare Interventions. CoRR abs/2306.09656 (2023) - [i30]Antti Pöllänen, Pekka Marttinen:
Identifiable causal inference with noisy treatment and no side information. CoRR abs/2306.10614 (2023) - [i29]Shaoxiong Ji, Wei Sun, Pekka Marttinen:
Content Reduction, Surprisal and Information Density Estimation for Long Documents. CoRR abs/2309.06009 (2023) - [i28]Mikko Kytö, Saila Koivusalo, Heli Tuomonen, Lisbeth Strömberg, Antti Ruonala, Pekka Marttinen, Seppo Heinonen, Giulio Jacucci:
Supporting Management of Gestational Diabetes with Comprehensive Self-Tracking: Mixed-Method Study of Wearable Sensors. CoRR abs/2309.07437 (2023) - [i27]Arina Odnoblyudova, Çaglar Hizli, St John, Andrea Cognolato, Anne Juuti, Simo Särkkä, Kirsi Pietiläinen, Pekka Marttinen:
Nonparametric modeling of the composite effect of multiple nutrients on blood glucose dynamics. CoRR abs/2311.03129 (2023) - [i26]Onur Poyraz, Pekka Marttinen:
Mixture of Coupled HMMs for Robust Modeling of Multivariate Healthcare Time Series. CoRR abs/2311.07867 (2023) - 2022
- [j25]Lang He
Deep learning for depression recognition with audiovisual cues: A review. Inf. Fusion 80: 56-86 (2022) - [j24]Lang He
COVIDNet: An Automatic Architecture for COVID-19 Detection With Deep Learning From Chest X-Ray Images. IEEE Internet Things J. 9(13): 11376-11384 (2022) - [j23]Shaoxiong Ji
A Survey on Knowledge Graphs: Representation, Acquisition, and Applications. IEEE Trans. Neural Networks Learn. Syst. 33(2): 494-514 (2022) - [c10]Tianyu Cui, Yogesh Kumar, Pekka Marttinen, Samuel Kaski:
Deconfounded Representation Similarity for Comparison of Neural Networks. NeurIPS 2022 - [c9]Ya Gao
Contextualized Graph Embeddings for Adverse Drug Event Detection. ECML/PKDD (2) 2022: 605-620 - [i25]Shaoxiong Ji, Wei Sun, Hang Dong, Honghan Wu, Pekka Marttinen:
A Unified Review of Deep Learning for Automated Medical Coding. CoRR abs/2201.02797 (2022) - [i24]Tianyu Cui, Yogesh Kumar, Pekka Marttinen, Samuel Kaski:
Deconfounded Representation Similarity for Comparison of Neural Networks. CoRR abs/2202.00095 (2022) - [i23]Xiang Li, Yazhou Zhang, Prayag Tiwari, Dawei Song, Bin Hu, Meihong Yang, Zhigang Zhao, Neeraj Kumar, Pekka Marttinen:
EEG based Emotion Recognition: A Tutorial and Review. CoRR abs/2203.11279 (2022) - [i22]Vishnu Raj, Tianyu Cui, Markus Heinonen, Pekka Marttinen:
Look beyond labels: Incorporating functional summary information in Bayesian neural networks. CoRR abs/2207.01234 (2022) - [i21]Joel Honkamaa
Deformation equivariant cross-modality image synthesis with paired non-aligned training data. CoRR abs/2208.12491 (2022) - [i20]Çaglar Hizli, S. T. John, Anne Juuti, Tuure Saarinen, Kirsi Pietiläinen, Pekka Marttinen:
Joint Non-parametric Point Process model for Treatments and Outcomes: Counterfactual Time-series Prediction Under Policy Interventions. CoRR abs/2209.04142 (2022) - 2021
- [j22]Shaoxiong Ji
Does the magic of BERT apply to medical code assignment? A quantitative study. Comput. Biol. Medicine 139: 104998 (2021) - [j21]Guangyi Zhang
Errors-in-Variables Modeling of Personalized Treatment-Response Trajectories. IEEE J. Biomed. Health Informatics 25(1): 201-208 (2021) - [c8]Shaoxiong Ji
Medical Code Assignment with Gated Convolution and Note-Code Interaction. ACL/IJCNLP (Findings) 2021: 1034-1043 - [c7]Severi Rissanen, Pekka Marttinen:
A Critical Look at the Consistency of Causal Estimation with Deep Latent Variable Models. NeurIPS 2021: 4207-4217 - [c6]Wei Sun
Multitask Recalibrated Aggregation Network for Medical Code Prediction. ECML/PKDD (4) 2021: 367-383 - [i19]Severi Rissanen, Pekka Marttinen:
A Critical Look At The Identifiability of Causal Effects with Deep Latent Variable Models. CoRR abs/2102.06648 (2021) - [i18]Shaoxiong Ji, Matti Hölttä, Pekka Marttinen:
Does the Magic of BERT Apply to Medical Code Assignment? A Quantitative Study. CoRR abs/2103.06511 (2021) - [i17]Wei Sun, Shaoxiong Ji, Erik Cambria, Pekka Marttinen:
Multitask Recalibrated Aggregation Network for Medical Code Prediction. CoRR abs/2104.00952 (2021) - [i16]Lang He, Mingyue Niu, Prayag Tiwari, Pekka Marttinen, Rui Su, Jiewei Jiang, Chenguang Guo, Hongyu Wang, Songtao Ding, Zhongmin Wang, Wei Dang, Xiaoying Pan:
Deep Learning for Depression Recognition with Audiovisual Cues: A Review. CoRR abs/2106.00610 (2021) - [i15]Yogesh Kumar, Alexander Ilin, Henri Salo, Sangita Kulathinal, Maarit K. Leinonen, Pekka Marttinen:
Medical SANSformers: Training self-supervised transformers without attention for Electronic Medical Records. CoRR abs/2108.13672 (2021) - [i14]Wei Sun, Shaoxiong Ji, Erik Cambria, Pekka Marttinen:
Multi-task Balanced and Recalibrated Network for Medical Code Prediction. CoRR abs/2109.02418 (2021) - [i13]Shaoxiong Ji, Pekka Marttinen:
Patient Outcome and Zero-shot Diagnosis Prediction with Hypernetwork-guided Multitask Learning. CoRR abs/2109.03062 (2021) - 2020
- [c5]Shaoxiong Ji
Dilated Convolutional Attention Network for Medical Code Assignment from Clinical Text. ClinicalNLP@EMNLP 2020: 73-78 - [c4]Tianyu Cui, Pekka Marttinen
Learning Global Pairwise Interactions with Bayesian Neural Networks. ECAI 2020: 1087-1094 - [c3]Marko Järvenpää, Aki Vehtari, Pekka Marttinen:
Batch simulations and uncertainty quantification in Gaussian process surrogate approximate Bayesian computation. UAI 2020: 779-788 - [i12]Shaoxiong Ji, Shirui Pan, Erik Cambria, Pekka Marttinen, Philip S. Yu:
A Survey on Knowledge Graphs: Representation, Acquisition and Applications. CoRR abs/2002.00388 (2020) - [i11]Tianyu Cui, Aki S. Havulinna, Pekka Marttinen, Samuel Kaski:
Informative Gaussian Scale Mixture Priors for Bayesian Neural Networks. CoRR abs/2002.10243 (2020) - [i10]Shaoxiong Ji, Erik Cambria, Pekka Marttinen:
Dilated Convolutional Attention Network for Medical Code Assignment from Clinical Text. CoRR abs/2009.14578 (2020) - [i9]Shaoxiong Ji, Shirui Pan, Pekka Marttinen:
Medical Code Assignment with Gated Convolution and Note-Code Interaction. CoRR abs/2010.06975 (2020)
2010 – 2019
- 2019
- [j20]Jussi Gillberg, Pekka Marttinen
Modelling G×E with historical weather information improves genomic prediction in new environments. Bioinform. 35(20): 4045-4052 (2019) - [j19]Marko Järvenpää, Mohamad R. Abdul Sater
A Bayesian model of acquisition and clearance of bacterial colonization incorporating within-host variation. PLoS Comput. Biol. 15(4) (2019) - [c2]Yogesh Kumar, Henri Salo, Tuomo Nieminen, Kristian Vepsalainen, Sangita Kulathinal, Pekka Marttinen:
Predicting utilization of healthcare services from individual disease trajectories using RNNs with multi-headed attention. ML4H@NeurIPS 2019: 93-111 - [i8]Tianyu Cui, Pekka Marttinen, Samuel Kaski:
Recovering Pairwise Interactions Using Neural Networks. CoRR abs/1901.08361 (2019) - [i7]Marko Järvenpää, Michael U. Gutmann, Aki Vehtari, Pekka Marttinen:
Parallel Gaussian process surrogate method to accelerate likelihood-free inference. CoRR abs/1905.01252 (2019) - [i6]Guangyi Zhang, Reza A. Ashrafi, Anne Juuti, Kirsi Pietiläinen, Pekka Marttinen:
Errors-in-variables Modeling of Personalized Treatment-Response Trajectories. CoRR abs/1906.03989 (2019) - [i5]Marko Järvenpää, Aki Vehtari, Pekka Marttinen:
Batch simulations and uncertainty quantification in Gaussian process surrogate-based approximate Bayesian computation. CoRR abs/1910.06121 (2019) - 2018
- [j18]Aleksi Sipola
Bacmeta: simulator for genomic evolution in bacterial metapopulations. Bioinform. 34(13): 2308-2310 (2018) - [j17]Iiris Sundin
Improving genomics-based predictions for precision medicine through active elicitation of expert knowledge. Bioinform. 34(13): i395-i403 (2018) - [j16]Jarno Lintusaari, Henri Vuollekoski, Antti Kangasrääsiö, Kusti Skytén, Marko Järvenpää, Pekka Marttinen, Michael U. Gutmann, Aki Vehtari, Jukka Corander, Samuel Kaski:
ELFI: Engine for Likelihood-Free Inference. J. Mach. Learn. Res. 19: 16:1-16:7 (2018) - [i4]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:
A Bayesian model of acquisition and clearance of bacterial colonization. CoRR abs/1811.10958 (2018) - 2017
- [j15]Matti Pirinen
biMM: efficient estimation of genetic variances and covariances for cohorts with high-dimensional phenotype measurements. Bioinform. 33(15): 2405-2407 (2017) - [j14]Pekka Marttinen
Speciation trajectories in recombining bacterial species. PLoS Comput. Biol. 13(7) (2017) - [c1]Luana Micallef, Iiris Sundin
Interactive Elicitation of Knowledge on Feature Relevance Improves Predictions in Small Data Sets. IUI 2017: 547-552 - [i3]Iiris Sundin, Tomi Peltola, Muntasir Mamun Majumder, Pedram Daee, Marta Soare, Homayun Afrabandpey, Caroline Heckman, Samuel Kaski, Pekka Marttinen:
Improving drug sensitivity predictions in precision medicine through active expert knowledge elicitation. CoRR abs/1705.03290 (2017) - 2016
- [j13]Anna Cichonska
metaCCA: summary statistics-based multivariate meta-analysis of genome-wide association studies using canonical correlation analysis. Bioinform. 32(13): 1981-1989 (2016) - [j12]Jussi Gillberg, Pekka Marttinen, Matti Pirinen, Antti J. Kangas, Pasi Soininen, Mehreen Ali, Aki S. Havulinna, Marjo-Riitta Järvelin, Mika Ala-Korpela, Samuel Kaski:
Multiple Output Regression with Latent Noise. J. Mach. Learn. Res. 17: 122:1-122:35 (2016) - [i2]Luana Micallef, Iiris Sundin, Pekka Marttinen, Muhammad Ammad-ud-din, Tomi Peltola, Marta Soare, Giulio Jacucci, Samuel Kaski:
Interactive Elicitation of Knowledge on Feature Relevance Improves Predictions in Small Data Sets. CoRR abs/1612.02487 (2016) - 2014
- [j11]Pekka Marttinen
Assessing multivariate gene-metabolome associations with rare variants using Bayesian reduced rank regression. Bioinform. 30(14): 2026-2034 (2014) - 2013
- [i1]Jussi Gillberg, Pekka Marttinen, Matti Pirinen
Bayesian Information Sharing Between Noise And Regression Models Improves Prediction of Weak Effects. CoRR abs/1310.4362 (2013) - 2010
- [j10]Pekka Marttinen
Efficient Bayesian approach for multilocus association mapping including gene-gene interactions. BMC Bioinform. 11: 443 (2010)
2000 – 2009
- 2009
- [j9]Pekka Marttinen, Samuel Myllykangas, Jukka Corander:
Bayesian clustering and feature selection for cancer tissue samples. BMC Bioinform. 10 (2009) - [j8]Petri Törönen
Robust extraction of functional signals from gene set analysis using a generalized threshold free scoring function. BMC Bioinform. 10: 307 (2009) - [j7]Pekka Marttinen, Jukka Corander:
Bayesian learning of graphical vector autoregressions with unequal lag-lengths. Mach. Learn. 75(2): 217-243 (2009) - [j6]Pekka Marttinen, Jing Tang
Bayesian Clustering of Fuzzy Feature Vectors Using a Quasi-Likelihood Approach. IEEE Trans. Pattern Anal. Mach. Intell. 31(1): 74-85 (2009) - 2008
- [j5]Jukka Corander, Pekka Marttinen, Jukka Sirén
Enhanced Bayesian modelling in BAPS software for learning genetic structures of populations. BMC Bioinform. 9 (2008) - [j4]Pekka Marttinen, Adam Baldwin, William P. Hanage, Chris Dowson, Eshwar Mahenthiralingam
Bayesian modeling of recombination events in bacterial populations. BMC Bioinform. 9 (2008) - 2006
- [j3]Pekka Marttinen, Jukka Corander, Petri Törönen
Bayesian search of functionally divergent protein subgroups and their function specific residues. Bioinform. 22(20): 2466-2474 (2006) - [j2]Jukka Corander, Pekka Marttinen:
Bayesian Model Learning Based on Predictive Entropy. J. Log. Lang. Inf. 15(1-2): 5-20 (2006) - 2004
- [j1]Jukka Corander, Patrik Waldmann
BAPS 2: enhanced possibilities for the analysis of genetic population structure. Bioinform. 20(15): 2363-2369 (2004)
Coauthor Index
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last updated on 2024-11-21 20:30 CET by the dblp team
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