Naver Corporation

Jean-Marc Andréoli

Marc Dymetman

Tetiana Parshakova

Daniel Guggenheim

In a method for generating a normalized sequential model using a processor, a sequential energy-based model computed by a parameterized neural network is provided. The sequential energy-based model defines an unnormalized probability distribution over a target sequence for a context source. The normalized sequential model is generated by projecting the sequential energy-based model onto a target autoregressive model that approximates a normalized distribution associated with the sequential energy-based model.

Methods and systems for producing neural sequential models

A method is disclosed for estimating a trajectory of an object on a map given a sequence of traces for the moving object. Each trace of the object including information defining a position measured at a given time for the object, as well as information as to an area of accuracy around the measured position. The method processes pairs of successive traces, corresponding to two positions successive in time in the sequence of measured positions for the moving object. For each trace of a pair of successive traces, the method defines road segments on the map within the area of accuracy of the trace. For each road segment within the area of accuracy of a first trace of a pair of traces and each road segment within the area of accuracy of the second trace of the pair, the method determines at least one candidate path between the two road segments. A neural network and a neural graph model are used to compute the most probable sequence of candidate paths to estimate the trajectory of the object on the map.

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Meylan, France

Jean-Marc Andréoli