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pro vyhledávání: '"Pathak, Arkanath"'
Autor:
Dufour, Nicholas, Pathak, Arkanath, Samangouei, Pouya, Hariri, Nikki, Deshetti, Shashi, Dudfield, Andrew, Guess, Christopher, Escayola, Pablo Hernández, Tran, Bobby, Babakar, Mevan, Bregler, Christoph
The prevalence and harms of online misinformation is a perennial concern for internet platforms, institutions and society at large. Over time, information shared online has become more media-heavy and misinformation has readily adapted to these new m
Externí odkaz:
http://arxiv.org/abs/2405.11697
Autor:
Pathak, Arkanath, Dufour, Nicholas
Modern Generative Adversarial Networks (GANs) generate realistic images remarkably well. Previous work has demonstrated the feasibility of "GAN-classifiers" that are distinct from the co-trained discriminator, and operate on images generated from a f
Externí odkaz:
http://arxiv.org/abs/2303.15533
Predicting future video frames is extremely challenging, as there are many factors of variation that make up the dynamics of how frames change through time. Previously proposed solutions require complex inductive biases inside network architectures w
Externí odkaz:
http://arxiv.org/abs/1911.01655
Autor:
Yan, Xinchen, Hsu, Jasmine, Khansari, Mohi, Bai, Yunfei, Pathak, Arkanath, Gupta, Abhinav, Davidson, James, Lee, Honglak
This paper focuses on the problem of learning 6-DOF grasping with a parallel jaw gripper in simulation. We propose the notion of a geometry-aware representation in grasping based on the assumption that knowledge of 3D geometry is at the heart of inte
Externí odkaz:
http://arxiv.org/abs/1708.07303
We propose a new approach for extracting argument structure from natural language texts that contain an underlying argument. Our approach comprises of two phases: Score Assignment and Structure Prediction. The Score Assignment phase trains models to
Externí odkaz:
http://arxiv.org/abs/1612.05420
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