On distributional autoregression and iterated transportation
Graph Chatbot
Chat with Graph Search
Ask any question about EPFL courses, lectures, exercises, research, news, etc. or try the example questions below.
DISCLAIMER: The Graph Chatbot is not programmed to provide explicit or categorical answers to your questions. Rather, it transforms your questions into API requests that are distributed across the various IT services officially administered by EPFL. Its purpose is solely to collect and recommend relevant references to content that you can explore to help you answer your questions.
This paper presents a novel algorithm for robust object recognition. We propose to model the visual appearance of objects via probability density functions. The algorithm consists of a fully connected Markov random field with energy function derived from r ...
A method is presented to construct object-related structure observables, such as size, mass, shape, and trajectories from two-dimensional plasma imaging data. The probability distributions of these observables, deduced from measurements of many realization ...
Models dealing directly with the raw acoustic speech signal are an alternative to conventional feature-based HMMs. A popular way to model the raw speech signal is by means of an autoregressive (AR) process. Being too simple to cope with the nonlinearity of ...
The problem of estimating and predicting Origin-Destination (OD) tables is known to be important and difficult. In the specific context of Intelligent Transportation Systems (ITS), the dynamic nature of the problem and the real-time requirements make it ev ...
Autoregressive modeling is applied for approximating the temporal evolution of spectral density in critical-band-sized sub-bands of a segment of speech signal. The generalized autocorrelation linear predictive technique allows for a compromise between fitt ...
An approach to analysis of time series of edge localized modes (ELMs) is proposed. It is based on the use of the autoregressive moving average model, which decomposes time series into deterministic and noise components. Despite the inclusion of nonlinearit ...
The temporal trajectories of the spectral energy in auditory critical bands over 250~ms segments are approximated by an all-pole model, the time-domain dual of conventional linear prediction. This quarter-second auditory spectro-temporal pattern is further ...
Autoregressive modeling is applied for approximating the temporal evolution of spectral density in critical-band-sized sub-bands of a segment of speech signal. The generalized autocorrelation linear predictive technique allows for a compromise between fitt ...
We present a general method for maintaining estimates of the distribution of parameters in arbitrary models. This is then applied to the estimation of probability distributions over actions in value-based reinforcement learning. While this approach is simi ...
This letter provides a unified mean-square performance analysis of the class of data reusing adaptive algorithms. The derivation relies on energy conservation arguments, and it does not restrict the regression data to being Gaussian. Simulation results sho ...