Astrometry.net: Blind astrometric calibration of arbitrary astronomical images (2009)
Lang, Dustin, Hogg, David W., Mierle, Keir, Blanton, Michael, Roweis, Sam
We have built a reliable and robust system that takes as input an astronomical image, and returns as output the pointing, scale, and orientation of that image (the astrometric calibration or WCS...
Manifold Learning: The Price of Normalization (2009)
Yair Goldberg, Alon Zakai, Dan Kushnir, Sam Roweis
We analyze the performance of a class of manifold-learning algorithms that find their output by minimizing a quadratic form under some normalization constraints. This class consists of Locally Linear...
When is Clustering Hard? (2009)
Nathan Srebro, Gregory Shakhnarovich, Sam Roweis
We propose questions regarding the informational and computational limits of learning a mixture of Gaussians—the sample sizes necessary in order to recover the generating mixture with unbounded,...
Ruslan Salakhutdinov, Sam Roweis, Zoubin Ghahramani
We show a close relationship between bound optimization (BO) algorithms such as Expectation-Maximization and direct optimization (DO) algorithms such as gradient-based methods for parameter learning....
Segmental Hidden Markov Models with Random Effects for Waveform Modeling (2008)
Seyoung Kim, Padhraic Smyth, Sam Roweis
This paper proposes a general probabilistic framework for shape-based modeling and classification of waveform data. A segmental hidden Markov model (HMM) is used to characterize waveform shape and...
Russ Salakhutdinov, Sam Roweis
Unsupervised learning plays an important role in many areas of machine learning. Two major research fields in unsupervied learning are density estimation, where one seeks to find a descriptive model...
An Improved Photometric Calibration of the Sloan Digital Sky Survey Imaging Data (2008)
Padmanabhan, Nikhil, Schlegel, David J., Finkbeiner, Douglas P., Barentine, J.C., Blanton, Michael R., Brewington, Howard J., ...
We present an algorithm to photometrically calibrate wide field optical imaging surveys, that simultaneously solves for the calibration parameters and relative stellar fluxes using overlapping...
Blind Date: Using proper motions to determine the ages of historical images (2008)
Barron, Jonathan T., Hogg, David W., Lang, Dustin, Roweis, Sam
Astrometric calibration is based on patterns of cataloged stars and therefore effectively assumes a particular epoch, which can be substantially incorrect for historical images. With the known proper...
Choon Hui Teo, Sam Roweis, Amir Globerson, Alexander J. Smola
Incorporating invariances into a learning algorithm is a common problem in machine learning. We provide a convex formulation which can deal with arbitrary loss functions and arbitrary losses. In...
Edward Meeds, Sam Roweis, Edward Meeds, Edward Meeds
To obtain work in the field of quantitative finance, using statistical machine learning techniques in either a research or applied capacity.
Balázs Kégl, Yoshua Bengio, Jean-jules Brault, Sam Roweis, Lael Parrott
Modèles à noyaux à structure locale par
Adaptive Gaussian Kernel SVMs (2008)
We consider binary classification using Support Vector Machines with Gaussian kernels: KΣ(xi, xj) = e −(xi−xj) ′ Σ −1 (xi−xj) and address the problem of selecting a covariance matrix Σ...
We present an algorithm for learning a quadratic Gaussian metric (Mahalanobis distance) for use in classification tasks. Our method relies on the simple geometric intuition that a good metric is one...
Edward Meeds, Radford Neal, Zoubin Ghahramani, Sam Roweis
We introduce binary matrix factorization, a novel model for unsupervised matrix decomposition. The decomposition is learned by fitting a non-parametric Bayesian probabilistic model with binary latent...
We introduce a novel learning algorithm for binary pairwise similarity measurements on a set of objects. The algorithm delivers an embedding of the objects into a vector representation space that...
Edward Meeds, Radford Neal, Zoubin Ghahramani, Sam Roweis
We introduce binary matrix factorization, a novel model for unsupervised matrix decomposition. The decomposition is learned by fitting a non-parametric Bayesian probabilistic model with binary latent...
1 What Are HMMs? Hidden Markov Models (2008)
Hidden Markov Models (HMMs) are a class of models for mimicing the probability density of a sequence of observed symbols. They are essentially stochastic nite state machines which output asymbol...
We present an algorithm for learning a quadratic Gaussian metric (Mahalanobis distance) for use in classification tasks. Our method relies on the simple geometric intuition that a good metric is one...
Choon Hui Teo, Sam Roweis, Amir Globerson, Alexander J. Smola
Incorporating invariances into a learning algorithm is a common problem in machine learning. We provide a convex formulation which can deal with arbitrary loss functions and arbitrary losses. In...
Edward Meeds, Radford Neal, Zoubin Ghahramani, Sam Roweis
We introduce binary matrix factorization, a novel model for unsupervised matrix decomposition. The decomposition is learned by fitting a non-parametric Bayesian probabilistic model with binary latent...
Metric Learning by Collapsing Classes (2008)
Amir Globerson School, Amir Globerson, Sam Roweis
We present an algorithm for learning a quadratic Gaussian metric (Mahalanobis distance) for use in classification tasks. Our method relies on the simple geometric intuition that a good metric is one...
REVIEW Communicated by Steven Nowlan A Unifying Review of Linear Gaussian Models (2007)
Factor analysis, principal component analysis, mixtures of gaussian clusters, vector quantization, Kalman filter models, and hidden Markov models can all be unified as variations of unsupervised...
This lecture introduces Hopeld networks and covers the basics of two extensions: stochastic units and hidden units. If both of these extensions are added, we get a new architecture called a Boltzmann...
Have Had Wonderful, Sanjoy Mahajan, Carlos Brody, Craig Jin, Elisabeth Moyer, Sam Roweis, ...
I develop tools to amplify our mental senses: our intuition and reasoning abilities. The first five chapters---based on the Order of Magnitude Physics class taught at Caltech by Peter Goldreich and...
Have Had Wonderful, Sanjoy Mahajan, Carlos Brody, Craig Jin, Elisabeth Moyer, Sam Roweis, ...
I develop tools to amplify our mental senses: our intuition and reasoning abilities. The first five chapters---based on the Order of Magnitude Physics class taught at Caltech by Peter Goldreich and...
F53.36> a 0 which represents the null output -- in other words if the model generates the symbol a 0 it simply does not output anything. Each state s j has an output distribution defined by the...
Graphical Model for HMM/LDS (2007)
Sam Roweis, Mealy Machines (engineering, Psfrag Replacements
Generative models for time-series: Need noise and system state. internal state outputs noise sources
1.1 Current vs. Voltage Mode (2007)
The whole idea of building circuits is to take some input data and do some useful computation to give some output data. But how is this data presented to the circuit and collected from it afterwards?...
A Panoramic View of Yeast Noncoding RNA Processing (2007)
Wen-tao Peng, Mark D. Robinson, Sanie Mnaimneh, Nevan J. Krogan, Armaity P. Davierwala, Jörg Grigull, ...
that many currently uncharacterized yeast proteins are involved in biogenesis of noncoding RNA.
John J. Hopfield, Carlos D. Brody, Sam Roweis
Most computational engineering based loosely on biology uses continuous variables to represent neural activity. Yet most neurons communicate with action potentials. The engineering view is equivalent...
Sam Roweis, Erik Winfree, Richard Burgoyne, Nickolas V. Chelyapov, Myron F. Goodman, ...
We introduce a new model of molecular computation that we call the sticker model. Like many previous proposals it makes use of DNA strands as the physical substrate in which information is...
Gatsby Computational Neuroscience Unit (2007)
Ruslan Salakhutdinov, Sam Roweis, Zoubin Ghahramani
We show a close relationship between bound optimization (BO) algorithms such as Expectation-Maximization and direct optimization (DO) algorithms such as gradient-based methods for parameter learning....
atrix T of transistion probabilities defined so that T jk is the probability of moving into state s k if the model is currently in state s j . Note that rows of T must sum to unity (although columns...
Non-Negative Matrix Factorization. We provide (2007)
Ruslan Salakhutdinov, Sam Roweis
We study a class of overrelaxed bound optimization algorithms, and their relationship to standard bound optimizers, such as Expectation-Maximization, Iterative Scaling, CCCP and
Cleaning the USNO-B Catalog through automatic detection of optical artifacts (2007)
Barron, Jonathan T., Stumm, Christopher, Hogg, David W., Lang, Dustin, Roweis, Sam
The USNO-B Catalog contains spurious entries that are caused by diffraction spikes and circular reflection halos around bright stars in the original imaging data. These spurious entries appear in the...
Graduate Group Chairperson COPYRIGHT (2007)
Fei Sha, Prof Lawrence, K. Saul, Prof Rajeev Alur, Fei Sha, Li Deng, ...
To my wife Ping, and my son Lucas iii Acknowledgments First and foremost I would like to thank my advisor Dr. Lawrence K. Saul. I was very fortunate to have Lawrence as my mentor. I have benefited...
Scott Leishman, Sam Roweis, Classic Pattern
Optical Character Recognition (OCR) is the process by which digital images of textual symbols are translated into a machine-readable representation. 2
Ping Li, Trevor J. Hastie, Kenneth W. Church, Sam Roweis
For1 dimension reduction in the l1 norm, the method of Cauchy random projections multiplies the original data matrix A ∈ Rn×D with a random matrix R ∈ RD×k (k ≪ D) whose entries are i.i.d....
Nonparametric Bayesian Biclustering (2007)
Edward Meeds, Sam Roweis, Edward Meeds, Sam Roweis
Copyright c ○ Edward Meeds 2007. We present a probabilistic block-constant biclustering model that simultaneously clusters rows and columns of a data matrix. All entries with the same row cluster...
Dimensionality reduction of multimodal labeled data by local Fisher discriminant analysis (2007)
Reducing the dimensionality of data without losing intrinsic information is an important preprocessing step in high-dimensional data analysis. Fisher discriminant analysis (FDA) is a traditional...
K-corrections and filter transformations in the ultraviolet, optical, and near infrared (2006)
Blanton, Michael R., Roweis, Sam
Template fits to observed galaxy fluxes allow calculation of K-corrections and conversions among observations of galaxies at various wavelengths. We present a method for creating model-based template...
An investigation of computational and informational limits in gaussian mixture clustering (2006)
Nathan Srebro, Gregory Shakhnarovich, Sam Roweis, Nathan Srebro, Gregory Shkhnarovich, Sam Roweis
We investigate under what conditions clustering by learning a mixture of spherical Gaussians is (a) computationally tractable; and (b) statistically possible. We show that using principal component...
Graduate Group Chairperson COPYRIGHT (2006)
Fei Sha, Prof Lawrence, K. Saul, Prof Rajeev Alur, Fei Sha, Li Deng, ...
To my wife Ping, and my son Lucas iii Acknowledgments First and foremost I would like to thank my advisor Dr. Lawrence K. Saul. I was very fortunate to have Lawrence as my mentor. I have benefited...
Modeling human motion using binary latent variables (2006)
Graham W. Taylor, Geoffrey E. Hinton, Sam Roweis
We propose a non-linear generative model for human motion data that uses an undirected model with binary latent variables and real-valued “visible ” variables that represent joint angles. The...
An investigation of computational and informational limits in gaussian mixture clustering (2006)
Nathan Srebro, Gregory Shakhnarovich, Sam Roweis
We investigate under what conditions clustering by learning a mixture of spherical Gaussians is (a) computationally tractable; and (b) statistically possible. We show that using principal component...
Nightmare at test time: Robust learning by feature deletion (2006)
When constructing a classifier from labeled data, it is important not to assign too much weight to any single input feature, in order to increase the robustness of the classifier. This is...
An investigation of computational and informational limits in gaussian mixture clustering (2006)
Nathan Srebro, Gregory Shakhnarovich, Sam Roweis
We investigate under what conditions clustering by learning a mixture of spherical Gaussians is (a) computationally tractable; and (b) statistically possible. We show that using principal component...
Time-Varying Topic Models using Dependent Dirichlet Processes (2005)
Nathan Srebro, Sam Roweis, Nathan Srebro, Sam Roweis
We lay the ground for extending Dirichlet Processes based clustering and factor models to explicitly include variability as a function of time (or other known covariates) by integrating a Dependent...
A statistical learning approach to document image analysis (2005)
Kevin Laven, Scott Leishman, Sam Roweis
In the field of computer analysis of document images, the problems of physical and logical layout analysis have been approached through a variety of heuristic, rule-based, and grammar-based...
A Segment-Based Probabilistic Generative Model Of Speech (2005)
Kannan Achan Sam, Sam Roweis, Aaron Hertzmann, Brendan Frey
We present a purely time domain approach to speech processing which identifies waveform samples at the boundaries between glottal pulse periods (in voiced speech) or at the boundaries of unvoiced...
Hierarchical clustering of a mixture model (2005)
In this paper we propose an efficient algorithm for reducing a large mixture of Gaussians into a smaller mixture while still preserving the component structure of the original model; this is achieved...
A statistical learning approach to document image analysis (2005)
Kevin Laven, Scott Leishman, Sam Roweis
In the field of computer analysis of document images, the problems of physical and logical layout analysis have been approached through a variety of heuristic, rule-based, and grammar-based...
Neighbourhood components analysis (2004)
Goldberger, Jacob, Roweis, Sam, Hinton, Geoff, Salakhutdinov, Ruslan
In this paper we propose a novel method for learning a Mahalanobis distance measure to be used in the KNN classification algorithm. The algorithm directly maximizes a stochastic variant of the...
Hierarchical clustering of a mixture model (2004)
Goldberger, Jacob, Roweis, Sam
In this paper we propose an efficient algorithm for reducing a large mixture of Gaussians into a smaller mixture while still preserving the component structure of the original model; this is achieved...
Non-linear CCA and PCA by alignment of local models (2004)
Verbeek, Jakob, Roweis, Sam, Vlassis, Nikos
We propose a non-linear Canonical Correlation Analysis (CCA) method which works by coordinating or aligning mixtures of linear models. In the same way that CCA extends the idea of PCA, our work...
Neighbourhood components analysis (2004)
Jacob Goldberger, Sam Roweis, Geoff Hinton, Ruslan Salakhutdinov
In this paper we propose a novel method for learning a Mahalanobis distance measure to be used in the KNN classification algorithm. The algorithm directly maximizes a stochastic variant of the...
A Segmental HMM for Speech Waveforms (2004)
Kannan Achan, Kannan Achan, Sam Roweis, Sam Roweis, Sam Roweis, Brendan Frey, ...
We present a purely time domain approach to speech processing which identifies waveform samples at the boundaries between glottal pulse periods (in voiced speech) or at the boundaries between...
A Segmental HMM for Speech Waveforms (2004)
Kannan Achan, Kannan Achan, Sam Roweis, Sam Roweis, Aaron Hertzmann, Aaron Hertzmann, ...
We present a purely time domain approach to speech processing which identi es waveform samples at the boundaries between glottal pulse periods (in voiced speech) or at the boundaries between unvoiced...
A Segmental HMM for Speech Waveforms (2004)
Kannan Achan, Kannan Achan, Sam Roweis, Sam Roweis, Brendan Frey, Brendan Frey
We present a purely time domain approach to speech processing which identifies waveform samples at the boundaries between glottal pulse periods (in voiced speech) or at the boundaries between...
Neighbourhood components analysis (2004)
Jacob Goldberger, Sam Roweis, Geoff Hinton, Ruslan Salakhutdinov
In this paper we propose a novel method for learning a Mahalanobis distance measure to be used in the KNN classification algorithm. The algorithm directly maximizes a stochastic variant of the...
Ruslan Salakhutdinov, Sam Roweis, Zoubin Ghahramani
We present a close relationship between Expectation- Maximization algorithm and direct optimization approaches such as gradient-based methods for parameter learning. We show that the step EM takes in...
Adaptive overrelaxed bound optimization methods (2003)
Ruslan Salakhutdinov, Sam Roweis
We study a class of overrelaxed bound optimization algorithms, and their relationship to standard bound optimizers, such as ExpectationMaximization, Iterative Scaling, CCCP and Non-Negative Matrix...
On the convergence of bound optimization algorithms (2003)
Ruslan Salakhutdinov, Sam Roweis, Zoubin Ghahramani
Many practitioners who use EM and related algorithms complain that they are sometimes slow. When does this happen, and what can be done about it? In this paper, we study the general class of bound...
Optimization with em and expectation-conjugate-gradient (2003)
Ruslan Salakhutdinov, Sam Roweis, Zoubin Ghahramani
We show a close relationship between the Expectation- Maximization (EM) algorithm and direct optimization algorithms such as gradient-based methods for parameter learning. We identify analytic...
Stochastic Neighbor Embedding (2003)
We describe a probabilistic approach to the task of placing objects, described by high-dimensional vectors or by pairwise dissimilarities, in a low-dimensional space in a way that preserves neighbor...
Automatic alignment of local representations (2003)
We present an automatic alignment procedure which maps the disparate internal representations learned by several local dimensionality reduction experts into a single, coherent global coordinate...
On the convergence of bound optimization algorithms (2003)
Ruslan Salakhutdinov, Sam Roweis, Zoubin Ghahramani
Many practitioners who use EM and related algorithms complain that they are sometimes slow. When does this happen, and what can be done about it? In this paper, we study the general class of bound...
Automatic alignment of local representations (2003)
We present an automatic alignment procedure which maps the disparate internal representations learned by several local dimensionality reduction experts into a single, coherent global coordinate...
Stochastic Neighbor Embedding (2003)
We describe a probabilistic approach to the task of embedding highdimensional objects into a low-dimensional space in a way that preserves neighbor identities. A Gaussian is centered on each object...
Optimization with em and expectation-conjugate-gradient (2003)
Ruslan Salakhutdinov, Sam Roweis, Zoubin Ghahramani
We show a close relationship between the Expectation- Maximization (EM) algorithm and direct optimization algorithms such as gradientbased methods for parameter learning. We identify analytic...
Ruslan Salakhutdinov, Sam Roweis, Zoubin Ghahramani
We present a close relationship between Expectation- Maximization algorithm and direct optimization approaches such as gradient-based methods for parameter learning. We show that the step EM takes in...
To Eric, The Original Dr. Frankel, Steve Isard, Amos Storkey, Chris Williams, Sam Roweis, ...
Firstly, utmost thanks to Simon King for his generosity. This thesis would never have reached fruition without his insight and guidance, and I count myself very lucky to have had someone with such an...
On the convergence of bound optimization algorithms (2003)
Ruslan Salakhutdinov, Sam Roweis, Zoubin Ghahramani
Many practitioners who use EM and related algorithms complain that they are sometimes slow. When does this happen, and what can be done about it? In this paper, we study the general class of bound...
Stochastic Neighbor Embedding (2003)
We describe a probabilistic approach to the task of placing objects, described by high-dimensional vectors or by pairwise dissimilarities, in a low-dimensional space in a way that preserves neighbor...
Optimization with em and expectation-conjugate-gradient (2003)
Ruslan Salakhutdinov, Sam Roweis, Zoubin Ghahramani
We show a close relationship between the Expectation- Maximization (EM) algorithm and direct optimization algorithms such as gradientbased methods for parameter learning. We identify analytic...
Global coordination of local linear models (2002)
Sam Roweis, Geoffrey E. Hinton
High dimensional data that lies on or near a low dimensional manifold can be described by a collection of local linear models. Such a description, however, does not provide a global parameterization...
Global coordination of local linear models (2002)
Sam Roweis, Geoffrey E. Hinton
High dimensional data that lies on or near a low dimensional manifold can be described by a collection of local linear models. Such a description, however, does not provide a global parameterization...
Automatic Alignment of Local Representations (2002)
We present an automatic alignment procedure which maps the disparate internal representations learned by several local dimensionality reduction experts into a single, coherent global coordinate...
Global coordination of local linear models (2002)
Sam Roweis, Lawrence K. Saul, Geoffrey E. Hinton
High dimensional data that lies on or near a low dimensional manifold can be described by a collection of local linear models. Such a description, however, does not provide a global parameterization...
Global Coordination of Local Linear Models (2001)
Roweis, Sam, Saul, Lawrence K, Hinton, Geoffrey E
High dimensional data that lies on or near a low dimensional manifold can be described by a collection of local linear models. Such a description, however, does not provide a global parameterization...
Professional and research experience: (2000)
Nathan Srebro, Supervisor Prof, Sam Roweis, Supervisor Prof, Tommi Jaakkola, ...
I conducted research on alternatively spliced exons using the LEADS expressed sequence database.
An EM algorithm for identification of nonlinear dynamical systems (2000)
We provide a novel solution to the problem of simultaneously estimating the unknown parameters and hidden states of a nonlinear dynamical system. Our solution is based on the expectation-maximization...
Constrained Hidden Markov Models (2000)
By thinking of each state in a hidden Markov model as corresponding to some spatial region of a fictitious topology space it is possible to naturally define neighbouring states as those which are...
Constrained Hidden Markov Models (2000)
By thinking of each state in a hidden Markov model as corresponding to some spatial region of a fictitious topology space it is possible to naturally define neighbouring states as those which are...
A Unifying review of linear gaussian models (1999)
Roweis, Sam, Ghahramani, Zoubin
Factor analysis, principal component analysis, mixtures of gaussian clusters, vector quantization, Kalman filter models, and hidden Markov models can all be unified as variations of unsupervised...
Time Series Models: Hidden Markov Models & Linear Dynamical Systems (1999)
Sam Roweis, Mealy Machines (engineering
t this state path into a sequence of observable symbols or vectors P(y t = yjx = j) = A (y) Notes: { Even though hidden state seq. is 1st-order Markov, the output process is not Markov of any order...
Sam Roweis, Sam Roweis, Carlos Brody, Carlos Brody
This note gives a closed form expression for the linear transform computed by an optimally trained linear heteroencoder network of arbitrary topology trained to minimize squared error. The transform...
Constrained hidden Markov models (1999)
By thinking of each state in a hidden Markov model as corresponding to some spatial region of a fictitious topology space it is possible to naturally define neighbouring states as those which are...
A unifying review of linear Gaussian models (1999)
(Neural Computation, Vol. 11 No. 2, 1999) Factor analysis, principal component analysis (PCA), mixtures of Gaussian clusters, vector quantization (VQ), Kalman lter models and hidden Markov models can...
Goals of Unsupervised Learning (1999)
Imagine a machine or organism that experiences over its lifetime a series of sensory inputs: Supervised learning: The machine is also given desired outputs�����, and its goal is to...
D. M. Blei, A. Y. Ng, M. I. Jordan, Latent Dirichlet, T. Dietterich, ...
[4] G. Hinton and R.S. Zemel. Autoencoders, minimum description length, and Helmholtz free energy. In G. Tesauro
Imagine a machine or organism that experiences over its lifetime a series of sensory inputs: Supervised learning: The machine is also given desired outputs�¡¤£�¦¨£������, and...
Sam Roweis, Carlos Brody, Sam Roweis, Carlos Brody
This note gives a closed form expression for the linear transform computed by an optimally trained linear heteroencoder network of arbitrary topology trained to minimize squared error. The transform...
D. M. Blei, A. Y. Ng, M. I. Jordan, Latent Dirichlet, T. Dietterich, ...
[4] G. Hinton and R.S. Zemel. Autoencoders, minimum description length, and Helmholtz free energy. In G. Tesauro
Em algorithms for pca and spca (1998)
I present an expectation-maximization (EM) algorithm for principal component analysis (PCA). The algorithm allows a few eigenvectors and eigenvalues to be extracted from large collections of high...
Sanjoy Mahajan, Carlos Brody, Craig Jin, Elisabeth Moyer, Sam Roweis, Rahul Sarpeshkar
I develop tools to amplify our mental senses: our intuition and reasoning abilities. The first five chapters---based on the Order of Magnitude Physics class taught at Caltech by Peter Goldreich and...
Speech Processing Background (1998)
Introduction This note provides an extremely brief and necessarily incomplete introduction to speech processing by machines for those unfamiliar with the basics of the eld. It is clearly beyond the...
Computing with Action Potentials (1998)
John Hopfield Carlos, Carlos D. Brody, Sam Roweis
Most computational engineering based loosely on biology uses continuous variables to represent neural activity. Yet most neurons communicate with action potentials. The engineering view is equivalent...
Speech Processing Background (1998)
Introduction This note provides an extremely brief and necessarily incomplete introduction to speech processing by machines for those unfamiliar with the basics of the eld. It is clearly beyond the...
Signal Reconstruction from Zero-Crossings (1998)
Sam Roweis, Sanjoy Mahajan, John Hopfield
We present a method for recovering (to within a constant factor) periodic, octave band-limited signals given the times of the zero-crossings.
Em algorithms for pca and spca (1998)
I present an expectation-maximization (EM) algorithm for principal component analysis (PCA). The algorithm allows a few eigenvectors and eigenvalues to be extracted from large collections of high...
A Unifying Review of Linear Gaussian Models (1997)
Factor analysis, principal component analysis (PCA), mixtures of Gaussian clusters, vector quantization (VQ), Kalman filter models and hidden Markov models can all be unified as variations of...
EM Algorithms for PCA and Sensible PCA (1997)
I present an expectation-maximization (EM) algorithm for principal component analysis (PCA). The algorithm allows a few eigenvectors and eigenvalues to be extracted from large collections of high...
A Unifying Review of Linear Gaussian Models (1997)
Factor analysis, principal component analysis (PCA), mixtures of Gaussian clusters, vector quantization (VQ), Kalman filter models and hidden Markov models can all be unified as variations of...
On Applying Molecular Computation To The Data Encryption Standard (1996)
Leonard M. Adleman, Sam Roweis, Erik Winfree
this paper we consider the so called plaintext-ciphertext attack. Here the cryptanalyst obtains a plaintext and its corresponding ciphertext and wishes todeterminethekey used to perform the...
A sticker based model for DNA computation (1996)
Sam Roweis, Erik Winfree, Richard Burgoyne, Nickolas V. Chelyapov, Myron F. Goodman, ...
Weintroduce a new model of molecular computation thatwe call the sticker model. Likemany previous proposals it makes use of DNA strands as the physical substrate in which information is represented...
A sticker based model for DNA computation (1996)
Sam Roweis, Erik Winfree, Richard Burgoyne, Nickolas V. Chelyapov, Myron F. Goodman, ...
Weintroduce a new model of molecular computation thatwe call the sticker model. Likemany previous proposals it makes use of DNA strands as the physical substrateinwhichinformation is represented and...
On Applying Molecular Computation To The Data Encryption Standard
this paper we consider the so called plaintext-ciphertext attack. Here the cryptanalyst obtains a plaintext and its corresponding ciphertext and wishes to determine the key used to perform the...