1 code implementation • 31 May 2022 • Lorenzo Pacchiardi, Ritabrata Dutta
However, generative networks only allow sampling from the parametrized distribution; for this reason, Ramesh et al. [2022] follows the common solution of adversarial training, where the generative network plays a min-max game against a "critic" network.
no code implementations • NAACL 2022 • Rilwan A. Adewoyin, Ritabrata Dutta, Yulan He
In this paper, we study the task of improving the cohesion and coherence of long-form text generated by language models.
1 code implementation • 15 Dec 2021 • Lorenzo Pacchiardi, Rilwan Adewoyin, Peter Dueben, Ritabrata Dutta
Adversarial-free minimization is possible for some scoring rules; hence, our framework avoids the cumbersome hyperparameter tuning and uncertainty underestimation due to unstable adversarial training, thus unlocking reliable use of generative networks in probabilistic forecasting.
2 code implementations • 20 Dec 2020 • Lorenzo Pacchiardi, Ritabrata Dutta
Bayesian Likelihood-Free Inference (LFI) approaches allow to obtain posterior distributions for stochastic models with intractable likelihood, by relying on model simulations.
no code implementations • 17 Dec 2020 • Sherman Lo, Peter Watson, Peter Dueben, Ritabrata Dutta
Here, we develop a method to make probabilistic precipitation predictions based on features that climate models can resolve well and that is not highly sensitive to the approximations used in individual models.
Computation Applications
no code implementations • 13 Oct 2020 • Ritabrata Dutta, Karim Zouaoui-Boudjeltia, Christos Kotsalos, Alexandre Rousseau, Daniel Ribeiro de Sousa, Jean-Marc Desmet, Alain Van Meerhaeghe, Antonietta Mira, Bastien Chopard
Cardio/cerebrovascular diseases (CVD) have become one of the major health issue in our societies.
1 code implementation • 20 Aug 2020 • Rilwan Adewoyin, Peter Dueben, Peter Watson, Yulan He, Ritabrata Dutta
Experiments show that our model consistently attains lower RMSE and MAE scores than a DL model prevalent in short term precipitation prediction and improves upon the rainfall predictions of a state-of-the-art dynamical weather model.
2 code implementations • 29 Jun 2020 • Ritabrata Dutta, Susana Gomes, Dante Kalise, Lorenzo Pacchiardi
A mathematical model for the COVID-19 pandemic spread in England is presented.
Applications Physics and Society Populations and Evolution
1 code implementation • 21 Jun 2020 • Christos Kotsalos, Karim Zouaoui Boudjeltia, Ritabrata Dutta, Jonas Latt, Bastien Chopard
The transport of platelets in blood is commonly assumed to obey an advection-diffusion equation.
Computational Physics Biological Physics
1 code implementation • 28 Sep 2019 • Lorenzo Pacchiardi, Pierre Kunzli, Marcel Schoengens, Bastien Chopard, Ritabrata Dutta
Using ABC, which depends on many simulations from the considered model, we develop an inferential framework to learn parameters of a stochastic numerical simulator of volcanic eruption.
Computation Applications
1 code implementation • 13 Nov 2017 • Ritabrata Dutta, Marcel Schoengens, Lorenzo Pacchiardi, Avinash Ummadisingu, Nicole Widmer, Jukka-Pekka Onnela, Antonietta Mira
Further, ABCpy enables ABC experts to easily develop new inference schemes and evaluate them in a standardized environment and to extend the library with new algorithms.
Computation
no code implementations • 26 Sep 2017 • Ritabrata Dutta, Antonietta Mira, Jukka-Pekka Onnela
Although the underlying processes of transmission are different, the network approach can be used to study the spread of pathogens in a contact network or the spread of rumors in an online social network.
1 code implementation • 30 Nov 2016 • Owen Thomas, Ritabrata Dutta, Jukka Corander, Samuel Kaski, Michael U. Gutmann
The popular synthetic likelihood approach infers the parameters by modelling summary statistics of the data by a Gaussian probability distribution.
no code implementations • 30 Mar 2016 • Ritabrata Dutta, Paul Blomstedt, Samuel Kaski
Hierarchical models are versatile tools for joint modeling of data sets arising from different, but related, sources.
no code implementations • 19 May 2015 • Paul Blomstedt, Ritabrata Dutta, Sohan Seth, Alvis Brazma, Samuel Kaski
For retrieval of gene expression experiments, we use a probabilistic model called product partition model, which induces a clustering of genes that show similar expression patterns across a number of samples.
no code implementations • 19 Feb 2015 • Michael U. Gutmann, Jukka Corander, Ritabrata Dutta, Samuel Kaski
This approach faces at least two major difficulties: The first difficulty is the choice of the discrepancy measure which is used to judge whether the simulated data resemble the observed data.
no code implementations • 18 Jul 2014 • Michael U. Gutmann, Ritabrata Dutta, Samuel Kaski, Jukka Corander
Increasingly complex generative models are being used across disciplines as they allow for realistic characterization of data, but a common difficulty with them is the prohibitively large computational cost to evaluate the likelihood function and thus to perform likelihood-based statistical inference.
no code implementations • 8 Oct 2013 • Ritabrata Dutta, Sohan Seth, Samuel Kaski
We address the problem of retrieving relevant experiments given a query experiment, motivated by the public databases of datasets in molecular biology and other experimental sciences, and the need of scientists to relate to earlier work on the level of actual measurement data.