Francisco Pereira

发表

Detecting Significant Multidimensional Spatial Clusters

Andrew W. Moore, Tom M. Mitchell, Daniel B. Neill, 2004, NIPS.

Closed-form supervised dimensionality reduction with generalized linear models

Geoffrey J. Gordon, Francisco Pereira, Genady Grabarnik, 2008, ICML '08.

Generating Text from Functional Brain Images

Francisco Pereira, Matthew Botvinick, Greg Detre, 2011, Front. Hum. Neurosci..

A comparative evaluation of off-the-shelf distributed semantic representations for modelling behavioural data

Francisco Pereira, Samuel Gershman, Samuel Ritter, 2016, Cognitive neuropsychology.

First Workshop on Computational Neurolinguistics Learning Semantic Features for Fmri Data from Definitional Text Concept Classification with Bayesian Multi-task Learning Wordnet Based Features for Predicting Brain Activity Associated with Meanings of Nouns an Investigation on Polysemy and Lexical Or

Bradford Z. Mahon, Nathaniel J. Smith, Ben D. Amsel, 2010 .

Transformada de Fourier aplicada al alineamiento de imágenes multidimensionales en GPU

Álvaro Ordóñez Iglesias, Francisco Pereira, D. B. Heras, 2017 .

Beyond brain blobs: machine learning classifiers as instruments for analyzing functional magnetic resonance imaging data

Tom M. Mitchell, Francisco Pereira, Tom Michael Mitchell, 2007 .

Two-frame 3D particle tracking

Francisco Pereira, Morteza Gharib, Heinrich Stüer, 2006 .

PREDICTION OF ADHD DIAGNOSIS 1 Network , anatomical , and nonimaging measures for the prediction of ADHD diagnosis in individual subjects

J. Rapin, L. Grady, J. Bohland, 2012 .

Machine learning methods for transportation under uncertainty

J. Dauwels, Francisco Pereira, Yu Jiang, 2020 .

A neural-embedded discrete choice model: Learning taste representation with strengthened interpretability

M. Ben-Akiva, P. C. Zegras, Francisco Pereira, 2020, Transportation Research Part B: Methodological.

Causality in Travel Mode Choice Modeling: A Novel Methodology that Combines Causal Discovery and Structural Equation Modeling

S. Derrible, C. Choudhury, Francisco Pereira, 2022, 2208.05624.

Predicting human perceived similarity across a wide range of object categories via sparse positive embeddings

Chris I. Baker, Martin N. Hebart, Charles Y. Zheng, 2018 .

Bayesian Active Learning with Fully Bayesian Gaussian Processes

Frederik Boe Huttel, Francisco Pereira, C. L. Azevedo, 2022, ArXiv.

Testing for context-dependent changes in neural encoding in naturalistic experiments

Francisco Pereira, Ye Chen, Xiaoyu Ma, 2022, ArXiv.

Piotroski, Graham e Greenblatt: Uma Abordagem Empírica do Value Investing no Mercado Acionário Brasileiro

Francisco Pereira, Carlos Domingues, Alexandre Aronne, 2022, Brazilian Business Review.

VICE: Variational Interpretable Concept Embeddings

Robert A. Vandermeulen, Patrick McClure, Francisco Pereira, 2022, NeurIPS.

Representation Learning of Rare Temporal Conditions for Travel Time Prediction

Francisco Pereira, Filipe Rodrigues, N. Petersen, 2022, 2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC).

Knowing what you know in brain segmentation using deep neural networks pdf

Satrajit S. Ghosh, Adam G. Thomas, Charles Y. Zheng, 2018, ArXiv.

Hume y la ficción de identidad personal

Francisco Pereira, 2013 .

Exploring predictive and reproducible modeling with the single‐subject FIAC dataset

Tom Mitchell, Francisco Pereira, Wayne Lee, 2006, Human brain mapping.

Machine learning classifiers and fMRI: A tutorial overview

Tom M. Mitchell, Francisco Pereira, Matthew Botvinick, 2009, NeuroImage.

Semantic projection: recovering human knowledge of multiple, distinct object features from word embeddings pdf

Francisco Pereira, Evelina Fedorenko, Idan Asher Blank, 2018, ArXiv.

Toward a universal decoder of linguistic meaning from brain activation

Nancy Kanwisher, Francisco Pereira, Evelina Fedorenko, 2018, Nature Communications.

Revealing interpretable object representations from human behavior pdf

Chris I. Baker, Martin N. Hebart, Charles Y. Zheng, 2019, ICLR.

Network, Anatomical, and Non-Imaging Measures for the Prediction of ADHD Diagnosis in Individual Subjects

Leo Grady, Francisco Pereira, Jason W. Bohland, 2012, Front. Syst. Neurosci..

Learning to Decode Cognitive States from Brain Images

Tom M. Mitchell, Radu Stefan Niculescu, Sharlene D. Newman, 2004, Machine Learning.

Generating descriptive text from functional brain images

Francisco Pereira, Matthew Botvinick, Greg Detre, 2011 .

Learning semantic features for fMRI data from definitional text

Francisco Pereira, Matthew Botvinick, Greg Detre, 2010, HLT-NAACL 2010.

Using Wikipedia to learn semantic feature representations of concrete concepts in neuroimaging experiments

Francisco Pereira, Matthew Botvinick, Greg Detre, 2013, Artif. Intell..

Information mapping with pattern classifiers: A comparative study

Francisco Pereira, Matthew Botvinick, M. Botvinick, 2011, NeuroImage.

Classifying Instantaneous Cognitive States from fMRI Data

Tom M. Mitchell, Radu Stefan Niculescu, Francisco Pereira, 2003, AMIA.

Applied metamodelling for ATM performance simulations

Carlos Lima Azevedo, Francisco Pereira, Francisco Antunes, 2023, ArXiv.

Graph Reinforcement Learning for Network Control via Bi-Level Optimization

M. Pavone, Francisco Pereira, Kaidi Yang, 2023, ICML.

Deep Evidential Learning for Bayesian Quantile Regression

Francisco Pereira, Frederik Boe Huttel, Filipe Rodrigues, 2023, ArXiv.

A Neural-embedded Choice Model: TasteNet-MNL Modeling Taste Heterogeneity with Flexibility and Interpretability pdf

Francisco C. Pereira, Moshe E. Ben-Akiva, Pericles Christopher Zegras, 2020, ArXiv.

A topographic latent source model for fMRI data

David M. Blei, Francisco Pereira, Samuel Gershman, 2011, NeuroImage.

The support vector decomposition machine

Geoffrey J. Gordon, Francisco Pereira, Francisco Pereira, 2006, ICML.

A systematic approach to extracting semantic information from functional MRI data

Francisco Pereira, Matthew Botvinick, M. Botvinick, 2012, NIPS.

Classification of functional magnetic resonance imaging data using informative pattern features

Francisco Pereira, Matthew Botvinick, M. Botvinick, 2011, KDD.

Learning and Generalizing Polynomials in Simulation Metamodeling

Francisco Pereira, Christoffer Riis, J. Hauch, 2023, ArXiv.

Simitar: Simplified Searching of Statistically Significant Similarity Structure

Francisco Pereira, Matthew Botvinick, M. Botvinick, 2013, 2013 International Workshop on Pattern Recognition in Neuroimaging.