Learning Inverse Rig Mappings by Nonlinear Regression
نویسندگان
چکیده
منابع مشابه
Learning Inverse Mappings with Adversarial Criterion
We propose a flipped-Adversarial AutoEncoder (F-AAE) that simultaneously trains a generative model G that maps an arbitrary latent code distribution to a data distribution and an encoder E that embodies an “inverse mapping” that encodes a data sample into a latent code vector. Unlike previous hybrid approaches that leverage adversarial training criterion in constructing autoencoders, F-AAE mini...
متن کاملNonlinear Inverse Reinforcement Learning with Gaussian Processes
We present a probabilistic algorithm for nonlinear inverse reinforcement learning. The goal of inverse reinforcement learning is to learn the reward function in a Markov decision process from expert demonstrations. While most prior inverse reinforcement learning algorithms represent the reward as a linear combination of a set of features, we use Gaussian processes to learn the reward as a nonli...
متن کاملA General Regression Framework for Learning String-to-String Mappings
The problem of learning a mapping from strings to strings arises in many areas of text and speech processing. As an example, an important component of speech recognition or speech synthesis systems is a pronunciation model, which provides the possible phonemic transcriptions of a word, or a sequence of words. An accurate pronunciation model is crucial for the overall quality of such systems. An...
متن کاملLearning Inverse Dynamics by Gaussian Process Regression under the Multi-Task Learning Framework
In this chapter, dedicated to Dit-Yan’s mentor and friend George Bekey on the occasion of his 80th birthday, we investigate for the first time the feasibility of applying the multi-task learning (or called transfer learning) approach to the learning of inverse dynamics. Due to the difficulties of modeling the dynamics completely and accurately and solving the dynamics equations analytically to ...
متن کاملNonparametric Regression for Learning Nonlinear Transformations
Information processing in animals and artificial movement systems consists of a series of transformations that map sensory signals to intermediate representations, and finally to motor commands. Given the physical and neuroanatomical differences between individuals and the need for plasticity during development, it is highly likely that such transformations are learned rather than pre-programme...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: IEEE Transactions on Visualization and Computer Graphics
سال: 2017
ISSN: 1077-2626
DOI: 10.1109/tvcg.2016.2628036