M. A. Carreira-Perpiñán

发表

An isotropic Gaussian mixture can have more modes than components

Christopher K. I. Williams, M. A. Carreira-Perpiñán, 2003 .

Multiscale conditional random fields for image labeling

Miguel Á. Carreira-Perpiñán, Richard S. Zemel, Xuming He, 2004, Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004..

Multiscale conditional random fields for image labeling

R. Zemel, M. A. Carreira-Perpiñán, Xuming He, 2004, CVPR 2004.

Distributed optimization of deeply nested systems pdf

Miguel Á. Carreira-Perpiñán, Weiran Wang, M. A. Carreira-Perpiñán, 2012, AISTATS.

Cortical Columns

M. A. Carreira-Perpiñán, G. Goodhill, 2002 .

Linear-time training of nonlinear low-dimensional embeddings

Miguel Á. Carreira-Perpiñán, Max Vladymyrov, M. A. Carreira-Perpiñán, 2014, AISTATS.

The Elastic Embedding Algorithm for Dimensionality Reduction

Miguel Á. Carreira-Perpiñán, M. A. Carreira-Perpiñán, 2010, ICML.

Influence of lateral connections on the structure of cortical maps.

M. A. Carreira-Perpiñán, G. Goodhill, 2004, Journal of neurophysiology.

A computational model for the development of multiple maps in primary visual cortex.

M. A. Carreira-Perpiñán, G. Goodhill, R. Lister, 2005, Cerebral cortex.

On Contrastive Divergence Learning

Miguel Á. Carreira-Perpiñán, Geoffrey E. Hinton, M. A. Carreira-Perpiñán, 2005, AISTATS.

A Denoising View of Matrix Completion

Miguel Á. Carreira-Perpiñán, Zhengdong Lu, Weiran Wang, 2011, NIPS.

Hashing with binary autoencoders

Miguel Á. Carreira-Perpiñán, Ramin Raziperchikolaei, M. A. Carreira-Perpiñán, 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

Dimensionality reduction by unsupervised regression

Miguel Á. Carreira-Perpiñán, Zhengdong Lu, M. A. Carreira-Perpiñán, 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

People Tracking with the Laplacian Eigenmaps Latent Variable Model

Miguel Á. Carreira-Perpiñán, Cristian Sminchisescu, Zhengdong Lu, 2007, NIPS.

Continuous latent variable models for dimensionality reduction and sequential data reconstruction

Miguel Á. Carreira-Perpiñán, M. A. Carreira-Perpiñán, 2001 .

On the Number of Modes of a Gaussian Mixture

Miguel Á. Carreira-Perpiñán, Christopher K. I. Williams, M. A. Carreira-Perpiñán, 2003, Scale-Space.

The Laplacian Eigenmaps Latent Variable Model

Miguel Á. Carreira-Perpiñán, Zhengdong Lu, M. A. Carreira-Perpiñán, 2007, AISTATS.

Predicting tongue shapes from a few landmark locations

Miguel Á. Carreira-Perpiñán, Steve Renals, Alan Wrench, 2008, INTERSPEECH.

Differential Priors for Elastic Nets

Miguel Á. Carreira-Perpiñán, Peter Dayan, Geoffrey J. Goodhill, 2005, IDEAL.

Entropic Affinities: Properties and Efficient Numerical Computation

Miguel Á. Carreira-Perpiñán, Max Vladymyrov, M. A. Carreira-Perpiñán, 2013, ICML.

IGlasses: an automatic wearable speech supplementin face-to-face communication and classroom situations

Miguel Á. Carreira-Perpiñán, Dominic W. Massaro, Marcus Perlman, 2008, ICMI '08.

SUPERVISED BINARY HASHING : OPTIMIZATION VS DIVERSITY

M. A. Carreira-Perpiñán, Ramin Raziperchikolaei, 2017 .

PARMAC: DISTRIBUTED OPTIMISATION

M. A. Carreira-Perpiñán, Mehdi Alizadeh, 2019 .

Boosted Sparse Oblique Decision Trees

M. A. Carreira-Perpiñán, Magzhan Gabidolla, A. Zharmagambetov, 2020 .

Development of Columnar Structures in Visual Cortex

M. A. Carreira-Perpiñán, G. Goodhill, 2002 .

LEARNING SUPERVISED BINARY HASHING WITHOUT BINARY CODE OPTIMIZATION

M. A. Carreira-Perpiñán, Ramin Raziperchikolaei, 2017 .

Optimizing binary autoencoders using auxiliary coordinates , with application to learning binary hashing

M. A. Carreira-Perpiñán, Ramin Raziperchikolaei, 2016 .

Scale Space Methods in Computer Vision

Miguel Á. Carreira-Perpiñán, Christopher K. I. Williams, M. A. Carreira-Perpiñán, 2003 .

Non-parametric Image Registration Using Generalized Elastic Nets

Xubo B. Song, M. A. Carreira-Perpiñán, A. Myronenko, 2006 .

Clustering Methods Based on Kernel Density Estimators: Mean-Shift Algorithms

M. A. Carreira-Perpiñán, 2015 .

Dimensionality Reduction

Miguel A. Carreira-Perpinan, M. A. Carreira-Perpiñán, 2011 .

Reconstruction of sequential data with density models pdf

Miguel Á. Carreira-Perpiñán, M. A. Carreira-Perpiñán, 2011, ArXiv.

More General and Effective Model Compression via an Additive Combination of Compressions

Miguel Á. Carreira-Perpiñán, Yerlan Idelbayev, M. A. Carreira-Perpiñán, 2021, ECML/PKDD.

Low-Rank Compression of Neural Nets: Learning the Rank of Each Layer

Miguel Á. Carreira-Perpiñán, Yerlan Idelbayev, M. A. Carreira-Perpiñán, 2020, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

"Learning-Compression" Algorithms for Neural Net Pruning

Miguel Á. Carreira-Perpiñán, Yerlan Idelbayev, M. A. Carreira-Perpiñán, 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

Model compression as constrained optimization, with application to neural nets. Part II: quantization pdf

Miguel Á. Carreira-Perpiñán, Yerlan Idelbayev, M. A. Carreira-Perpiñán, 2017, ArXiv.

Generalised elastic nets pdf

Miguel Á. Carreira-Perpiñán, Geoffrey J. Goodhill, M. A. Carreira-Perpiñán, 2011, ArXiv.

Dimensionality reduction of electropalatographic data using latent variable models

Miguel Á. Carreira-Perpiñán, Steve Renals, M. A. Carreira-Perpiñán, 1998, Speech Commun..

Ensembles of Bagged TAO Trees Consistently Improve over Random Forests, AdaBoost and Gradient Boosting

Miguel Á. Carreira-Perpiñán, Arman Zharmagambetov, M. A. Carreira-Perpiñán, 2020, FODS.

Practical Identifiability of Finite Mixtures of Multivariate Bernoulli Distributions

Miguel Á. Carreira-Perpiñán, Steve Renals, M. A. Carreira-Perpiñán, 2000, Neural Computation.

Alternating optimization of decision trees, with application to learning sparse oblique trees

Miguel Á. Carreira-Perpiñán, Pooya Tavallali, M. A. Carreira-Perpiñán, 2018, NeurIPS.

Proximity Graphs for Clustering and Manifold Learning

Miguel Á. Carreira-Perpiñán, Richard S. Zemel, R. Zemel, 2004, NIPS.

A review of mean-shift algorithms for clustering pdf

Miguel Á. Carreira-Perpiñán, M. A. Carreira-Perpiñán, 2015, ArXiv.

A Review of Dimension Reduction Techniques

Miguel Á. Carreira-Perpiñán, M. A. Carreira-Perpiñán, 2009 .

Sampling the "Inverse Set" of a Neuron: An Approach to Understanding Neural Nets

Suryabhan Singh Hada, M. A. Carreira-Perpiñán, 2019, ArXiv.

Projection onto the probability simplex: An efficient algorithm with a simple proof, and an application pdf

Miguel Á. Carreira-Perpiñán, Weiran Wang, M. A. Carreira-Perpiñán, 2013, ArXiv.

LC: A Flexible, Extensible Open-Source Toolkit for Model Compression

Miguel Á. Carreira-Perpiñán, Yerlan Idelbayev, M. A. Carreira-Perpiñán, 2021, International Conference on Information and Knowledge Management.

Optimal Selection of Matrix Shape and Decomposition Scheme for Neural Network Compression

Miguel Á. Carreira-Perpiñán, Yerlan Idelbayev, M. A. Carreira-Perpiñán, 2021, ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

Neural Network Compression via Additive Combination of Reshaped, Low-Rank Matrices

Miguel Á. Carreira-Perpiñán, Yerlan Idelbayev, M. A. Carreira-Perpiñán, 2021, 2021 Data Compression Conference (DCC).

Model compression as constrained optimization, with application to neural nets. Part I: general framework pdf

Miguel Á. Carreira-Perpiñán, M. A. Carreira-Perpiñán, 2017, ArXiv.

Learning supervised binary hashing: Optimization vs diversity

Miguel Á. Carreira-Perpiñán, Ramin Raziperchikolaei, M. A. Carreira-Perpiñán, 2017, 2017 IEEE International Conference on Image Processing (ICIP).

An ensemble diversity approach to supervised binary hashing

Miguel Á. Carreira-Perpiñán, Ramin Raziperchikolaei, M. A. Carreira-Perpiñán, 2016, NIPS.

Locally Linear Landmarks for Large-Scale Manifold Learning

Miguel Á. Carreira-Perpiñán, Max Vladymyrov, M. A. Carreira-Perpiñán, 2013, ECML/PKDD.

Occupancy Modeling and Prediction for Building Energy Management

Miguel Á. Carreira-Perpiñán, Alberto E. Cerpa, Varick L. Erickson, 2014, ACM Trans. Sens. Networks.

Improving wireless link simulation using multilevel markov models

Miguel Á. Carreira-Perpiñán, Alberto Cerpa, Ankur Kamthe, 2013, ACM Trans. Sens. Networks.

ParMAC: distributed optimisation of nested functions, with application to learning binary autoencoders pdf

Miguel Á. Carreira-Perpiñán, Mehdi Alizadeh, M. A. Carreira-Perpiñán, 2016, MLSys.

ParMAC: distributed optimisation of nested functions, with application to binary autoencoders

Miguel Á. Carreira-Perpiñán, Mehdi Alizadeh, M. A. Carreira-Perpiñán, 2017 .

Constrained spectral clustering through affinity propagation

Miguel Á. Carreira-Perpiñán, Zhengdong Lu, M. A. Carreira-Perpiñán, 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

Compression neural networks for feature extraction: Application to human recognition from ear images

M. A. Carreira-Perpinan, M. A. Carreira-Perpiñán, 1995 .

Manifold blurring mean shift algorithms for manifold denoising

Miguel Á. Carreira-Perpiñán, Weiran Wang, M. A. Carreira-Perpiñán, 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

Gaussian Mean-Shift Is an EM Algorithm

Miguel Á. Carreira-Perpiñán, M. A. Carreira-Perpiñán, 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

Fast nonparametric clustering with Gaussian blurring mean-shift

Miguel Á. Carreira-Perpiñán, M. A. Carreira-Perpiñán, 2006, ICML.

Reconstruction of Sequential Data with Probabilistic Models and Continuity Constraints

Miguel Á. Carreira-Perpiñán, M. A. Carreira-Perpiñán, 1999, NIPS.

An empirical investigation of the nonuniqueness in the acoustic-to-articulatory mapping

Miguel Á. Carreira-Perpiñán, Chao Qin, M. A. Carreira-Perpiñán, 2007, INTERSPEECH.

Non-rigid point set registration: Coherent Point Drift

Miguel Á. Carreira-Perpiñán, Andriy Myronenko, Xubo B. Song, 2006, NIPS.

Generalised blurring mean-shift algorithms for nonparametric clustering

Miguel Á. Carreira-Perpiñán, M. A. Carreira-Perpiñán, 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

Mode-Finding for Mixtures of Gaussian Distributions

Miguel Á. Carreira-Perpiñán, M. A. Carreira-Perpiñán, 2000, IEEE Trans. Pattern Anal. Mach. Intell..

Reconstructing the full tongue contour from EMA/X-ray microbeam

Miguel Á. Carreira-Perpiñán, Chao Qin, M. A. Carreira-Perpiñán, 2010, 2010 IEEE International Conference on Acoustics, Speech and Signal Processing.

M&M: multi-level Markov model for wireless link simulations

Miguel Á. Carreira-Perpiñán, Alberto Cerpa, Ankur Kamthe, 2009, SenSys '09.

Dimensionality Reduction

Miguel Á. Carreira-Perpiñán, M. A. Carreira-Perpiñán, Michail Vlachos, 2011 .

Softmax Tree: An Accurate, Fast Classifier When the Number of Classes Is Large

Miguel Á. Carreira-Perpiñán, Arman Zharmagambetov, Magzhan Gabidolla, 2021, EMNLP.

The K-modes algorithm for clustering pdf

Miguel Á. Carreira-Perpiñán, Weiran Wang, M. A. Carreira-Perpiñán, 2013, ArXiv.

Manifold Learning and Missing Data Recovery through Unsupervised Regression

Miguel Á. Carreira-Perpiñán, Zhengdong Lu, M. A. Carreira-Perpiñán, 2011, 2011 IEEE 11th International Conference on Data Mining.

Improved Multiclass Adaboost For Image Classification: The Role Of Tree Optimization

Miguel Á. Carreira-Perpiñán, Arman Zharmagambetov, Magzhan Gabidolla, 2021, 2021 IEEE International Conference on Image Processing (ICIP).

Improved Boosted Regression Forests Through Non-Greedy Tree Optimization

Arman Zharmagambetov, Magzhan Gabidolla, Miguel Ê. Carreira-Perpiñán, 2021, 2021 International Joint Conference on Neural Networks (IJCNN).

Non-Greedy Algorithms for Decision Tree Optimization: An Experimental Comparison

Miguel Á. Carreira-Perpiñán, Arman Zharmagambetov, Suryabhan Singh Hada, 2021, 2021 International Joint Conference on Neural Networks (IJCNN).

Smaller, more accurate regression forests using tree alternating optimization

Miguel Á. Carreira-Perpiñán, Arman Zharmagambetov, M. A. Carreira-Perpiñán, 2020, ICML.

Learning a Tree of Neural Nets

Miguel Á. Carreira-Perpiñán, Arman Zharmagambetov, M. A. Carreira-Perpiñán, 2021, ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

Joint optimization of mapping and classifier using auxiliary coordinates

Miguel Á. Carreira-Perpiñán, Weiran Wang, M. A. Carreira-Perpiñán, 2014 .

The Laplacian K-modes algorithm for clustering pdf

Miguel Á. Carreira-Perpiñán, Weiran Wang, M. A. Carreira-Perpiñán, 2014, ArXiv.

Quick construction of data-driven models of the short-term behavior of wireless links

Miguel Á. Carreira-Perpiñán, Alberto Cerpa, Ankur Kamthe, 2013, 2013 Proceedings IEEE INFOCOM.

Trajectory inverse kinematics by conditional density modes

Chao Qin, Miguel Á. Carreira-Perpiñán, M. A. Carreira-Perpiñán, 2008, 2008 IEEE International Conference on Robotics and Automation.

Trajectory inverse kinematics by nonlinear, nongaussian tracking

Chao Qin, Miguel Á. Carreira-Perpiñán, M. A. Carreira-Perpiñán, 2008, 2008 IEEE International Conference on Acoustics, Speech and Signal Processing.

Articulatory inversion of American English /ô/ by conditional density modes

Miguel Á. Carreira-Perpiñán, Chao Qin, M. A. Carreira-Perpiñán, 2010 .

Articulatory inversion of american English /turnr/ by conditional density modes

Miguel Á. Carreira-Perpiñán, Chao Qin, M. A. Carreira-Perpiñán, 2010, INTERSPEECH.

A comparison of acoustic features for articulatory inversion

Miguel Á. Carreira-Perpiñán, Chao Qin, M. A. Carreira-Perpiñán, 2007, INTERSPEECH.

The Variational Nystrom method for large-scale spectral problems

Miguel Á. Carreira-Perpiñán, Max Vladymyrov, M. A. Carreira-Perpiñán, 2016, ICML.

Reduction of multi-dimensional laboratory data to a two-dimensional plot: a novel technique for the identification of laboratory error

Deniz Erdogmus, Todd K. Leen, Steven C. Kazmierczak, 2007, Clinical chemistry and laboratory medicine.

Dimensionality Reduction

M. A. Carreira-Perpiñán, 2011 .

LASS: A Simple Assignment Model with Laplacian Smoothing pdf

Miguel Á. Carreira-Perpiñán, Weiran Wang, M. A. Carreira-Perpiñán, 2014, AAAI.

Nonlinear low-dimensional regression using auxiliary coordinates

Miguel Á. Carreira-Perpiñán, Weiran Wang, M. A. Carreira-Perpiñán, 2012, AISTATS.

Parametric dimensionality reduction by unsupervised regression

Miguel Á. Carreira-Perpiñán, Zhengdong Lu, M. A. Carreira-Perpiñán, 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

Fast Training of Nonlinear Embedding Algorithms

Miguel Á. Carreira-Perpiñán, Max Vladymyrov, M. A. Carreira-Perpiñán, 2012, ICML.

OBSERVE: Occupancy-based system for efficient reduction of HVAC energy

Miguel Á. Carreira-Perpiñán, Alberto Cerpa, Varick L. Erickson, 2011, Proceedings of the 10th ACM/IEEE International Conference on Information Processing in Sensor Networks.

Influence of lateral connections on the structure of cortical maps.

M. A. Carreira-Perpiñán, G. Goodhill, 2004, Journal of neurophysiology.

Optimizing Visual Feature Perception for an Automatic Wearable Speech Supplement in Face-to-Face Communication and Classroom Situations

Miguel Á. Carreira-Perpiñán, Dominic W. Massaro, David J. Merrill, 2009, 2009 42nd Hawaii International Conference on System Sciences.

Plug-and-Play Irrigation Control at Scale

Miguel Á. Carreira-Perpiñán, Alberto Cerpa, Daniel A. Winkler, 2018, 2018 17th ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN).

Fast, accurate spectral clustering using locally linear landmarks

Miguel Á. Carreira-Perpiñán, Max Vladymyrov, M. A. Carreira-Perpiñán, 2017, 2017 International Joint Conference on Neural Networks (IJCNN).

A fast, universal algorithm to learn parametric nonlinear embeddings

Miguel Á. Carreira-Perpiñán, Max Vladymyrov, M. A. Carreira-Perpiñán, 2015, NIPS.

Structured Multi-Hashing for Model Compression

Elad Eban, Yair Movshovitz-Attias, Hao Wu, 2019, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).