Yilun Du Implicit Learning With Energy Based Models Nuro Technical Talks

Yilun Du Tedxmit In this talk, i’ll discuss how energy based models are a useful tool for constructing deep learning systems in embodied domains. first, i will illustr more. about the talk: deep learning has. These models aim to learn an energy function e(x) that assigns low energy values to inputs x in the data distribution and high energy values to other inputs. importantly, they allow the use of an implicit sample generation procedure, where sample x is found from x e e(x) through mcmc sampling.

Conditional Energy Based Models For Implicit Policies The Gap Between We highlight some unique capabilities of implicit generation such as compositionality and corrupt image reconstruction and inpainting. finally, we show that ebms are useful models across a wide variety of tasks, achieving state of the art out of distribution classification, adversarially robust classification, state of the art continual online. We study a new approach to learning energy based models (ebms) based on adversarial training (at). we show that (binary) at learns a special kind of energy function that models the support of the data distribution, and the learning process is. My work addresses this by constructing composable generative models using the idea of learning energy landscapes (ebms) as a means to generalize beyond the narrow amount of data that is available, and some of my early work on ebms led to the development of diffusion models in 2020. These models aim to learn an energy function e(x) that assigns low energy values to inputs x in the data distribution and high energy values to other inputs. importantly, they allow the use of an implicit sample generation procedure, where sample x is found from x e e(x) through mcmc sampling.

Yilun Du Harvard John A Paulson School Of Engineering And Applied My work addresses this by constructing composable generative models using the idea of learning energy landscapes (ebms) as a means to generalize beyond the narrow amount of data that is available, and some of my early work on ebms led to the development of diffusion models in 2020. These models aim to learn an energy function e(x) that assigns low energy values to inputs x in the data distribution and high energy values to other inputs. importantly, they allow the use of an implicit sample generation procedure, where sample x is found from x e e(x) through mcmc sampling. Yilun du (google scholar) is a graduate student at mit advised by professors leslie kaelbling, tomas lozano perez, and josh tenenbaum. he's interested in building robots that can understand the world like humans and construct world representations that enable task planning over long horizons. We present techniques to scale mcmc based ebm training on continuous neural networks, and we show its success on the high dimensional data domains of imagenet32x32, imagenet128x128, cifar 10, and robotic hand trajectories, achieving better samples than other likelihood models and nearing the performance of contemporary gan approaches, while. We have presented a series of techniques to scale up energy based model training to complex high dimensional datasets and showed that energy based models provide a number ben efits, such as much sharper generation than other likelihood models or image and robot trajectory domains. Model based planning holds great promise for improving both sample efficiency and generalization in reinforcement learning (rl). we show that energy based models (ebms) are a promising class of models to use for model based planning.

Shuang Li Yilun Du Gido Van De Ven Igor Mordatch Energy Based Yilun du (google scholar) is a graduate student at mit advised by professors leslie kaelbling, tomas lozano perez, and josh tenenbaum. he's interested in building robots that can understand the world like humans and construct world representations that enable task planning over long horizons. We present techniques to scale mcmc based ebm training on continuous neural networks, and we show its success on the high dimensional data domains of imagenet32x32, imagenet128x128, cifar 10, and robotic hand trajectories, achieving better samples than other likelihood models and nearing the performance of contemporary gan approaches, while. We have presented a series of techniques to scale up energy based model training to complex high dimensional datasets and showed that energy based models provide a number ben efits, such as much sharper generation than other likelihood models or image and robot trajectory domains. Model based planning holds great promise for improving both sample efficiency and generalization in reinforcement learning (rl). we show that energy based models (ebms) are a promising class of models to use for model based planning.