L
Li Fei-Fei
Researcher at Stanford University
Publications - 515
Citations - 199224
Li Fei-Fei is an academic researcher from Stanford University. The author has contributed to research in topics: Computer science & Medicine. The author has an hindex of 120, co-authored 420 publications receiving 145574 citations. Previous affiliations of Li Fei-Fei include Google & California Institute of Technology.
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Representation Learning with Statistical Independence to Mitigate Bias
Ehsan Adeli,Qingyu Zhao,Adolf Pfefferbaum,Edith V. Sullivan,Li Fei-Fei,Juan Carlos Niebles,Kilian M. Pohl +6 more
TL;DR: In this paper, the authors proposed a fair representation learning model based on adversarial training with two competing objectives to learn features that have maximum discriminative power with respect to the task and minimal statistical mean dependence with the protected (bias) variable(s).
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Making Sense of Vision and Touch: Self-Supervised Learning of Multimodal Representations for Contact-Rich Tasks
Michelle A. Lee,Yuke Zhu,Krishnan Srinivasan,Parth Shah,Silvio Savarese,Li Fei-Fei,Animesh Garg,Jeannette Bohg +7 more
TL;DR: In this paper, self-supervision is used to learn a compact and multimodal representation of sensory inputs, which can then be used to improve the sample efficiency of policy learning.
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Viewpoint Invariant 3D Human Pose Estimation with Recurrent Error Feedback
TL;DR: This work proposes a viewpoint invariant model for 3D human pose estimation from a single depth image that leverages a convolutional and recurrent network with a top-down error feedback mechanism to self-correct previous pose estimates in an end-to-end manner.
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Neural Task Programming: Learning to Generalize Across Hierarchical Tasks
TL;DR: In this article, a task specification (e.g., video demonstration of a task) and recursively decomposes it into finer sub-task specifications are fed to a hierarchical neural program, where bottom level programs are callable subroutines that interact with the environment.
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DualSMC: Tunneling Differentiable Filtering and Planning under Continuous POMDPs
TL;DR: An adversarial particle filter is introduced that leverages the adversarial relationship between its internal components and a planning algorithm is proposed that extends the previous SMC planning approach to continuous POMDPs with an uncertainty-dependent policy.