Microsoft Technology Licensing, LLC

Brendan John Frey

Nebojsa Jojic

Alejandro Acero

Li Deng

David Earl Heckerman

Christopher A Meek

Ciprian Ioan Chelba

Ye-Yi Wang

Leon Wong

Vladimir Jojic

The subject invention provides systems and methods that facilitate AIDS vaccine cocktail assembly via machine learning algorithms such as a cost function, a greedy algorithm, an expectation-maximization (EM) algorithm, etc. Such assembly can be utilized to generate vaccine cocktails for species of pathogens that evolve quickly under immune pressure of the host. For example, the systems and methods of the subject invention can be utilized to facilitate design of T cell vaccines for pathogens such HIV. In addition, the systems and methods of the subject invention can be utilized in connection with other applications, such as, for example, sequence alignment, motif discovery, classification, and recombination hot spot detection. The novel techniques described herein can provide for improvements over traditional approaches to designing vaccines by constructing vaccine cocktails with higher epitope coverage, for example, in comparison with cocktails of consensi, tree nodes and random strains from data.

Systems and methods that utilize machine learning algorithms to facilitate assembly of aids vaccine cocktails

The present invention involves using one or more statistical classifiers in order to perform task classification on natural language inputs. In another embodiment, the statistical classifiers can be used in conjunction with a rule-based classifier to perform task classification.

System for using statistical classifiers for spoken language understanding

A simplified general model and an associated estimation algorithm is provided for modeling visual data such as a video sequence. Specifically, images or frames in a video sequence are represented as collections of flat moving objects that change their appearance and shape over time, and can occlude each other over time. A statistical generative model is defined for generating such visual data where parameters such as appearance bit maps and noise, shape bit-maps and variability in shape, etc., are known. Further, when unknown, these parameters are estimated from visual data without prior pre-processing by using a maximization algorithm. By parameter estimation and inference in the model, visual data is segmented into components which facilitates sophisticated applications in video or image editing, such as, for example, object removal or insertion, tracking and visual surveillance, video browsing, photo organization, video compositing, etc.

Generative models for constructing panoramas from an image sequence

Modeling reflections within an image sequence

Modeling variable illumination in an image sequence

The present invention provides a method of constructing recognition models. Under the method, a set of probabilities is estimated for values of a hidden variable. A Fourier transform is determined for the set of probabilities and is used to determine a Fourier transform of an estimated prototype pattern. The inverse Fourier transform is then determined for the Fourier transform of the estimated prototype pattern to form an estimated prototype pattern.

Method and apparatus for fast machine learning using probability maps and fourier transforms

Stabilization of objects within a video sequence

A method and computer-readable medium are provided for identifying clean signal feature vectors from noisy signal feature vectors. One aspect of the invention includes using an iterative approach to identify the clean signal feature vector. Another aspect of the invention includes using the variance of a set of noise feature vectors and/or channel distortion feature vectors when identifying the clean signal feature vectors.

Removing noise from feature vectors

A method and computer-readable medium are provided for identifying clean signal feature vectors from noisy signal feature vectors. Aspects of the invention use mixtures of distributions of noise feature vectors and/or channel distortion feature vectors when identifying the clean signal feature vectors.

A method and computer-readable medium are provided for identifying clean signal feature vectors from noisy signal feature vectors. The method is based on variational inference techniques. One aspect of the invention includes using an iterative approach to identify the clean signal feature vector. Another aspect of the invention includes using the variance of a set of noise feature vectors and/or channel distortion feature vectors when identifying the clean signal feature vectors. Further aspects of the invention use mixtures of distributions of noise feature vectors and/or channel distortion feature vectors when identifying the clean signal feature vectors. Additional aspects of the invention include using a variance for the noisy signal feature vector conditioned on fixed values of noise, channel transfer function, and clean speech, when identifying the clean signal feature vector.

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Mississauga, Canada

Brendan John Frey