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pro vyhledávání: '"Jain, Arnav Kumar"'
Autor:
Jain, Arnav Kumar, Wiltzer, Harley, Farebrother, Jesse, Rish, Irina, Berseth, Glen, Choudhury, Sanjiban
In inverse reinforcement learning (IRL), an agent seeks to replicate expert demonstrations through interactions with the environment. Traditionally, IRL is treated as an adversarial game, where an adversary searches over reward models, and a learner
Externí odkaz:
http://arxiv.org/abs/2411.07007
Animals have a developed ability to explore that aids them in important tasks such as locating food, exploring for shelter, and finding misplaced items. These exploration skills necessarily track where they have been so that they can plan for finding
Externí odkaz:
http://arxiv.org/abs/2306.14808
Autor:
Jain, Arnav Kumar, Sujit, Shivakanth, Joshi, Shruti, Michalski, Vincent, Hafner, Danijar, Ebrahimi-Kahou, Samira
Learning world models from their sensory inputs enables agents to plan for actions by imagining their future outcomes. World models have previously been shown to improve sample-efficiency in simulated environments with few objects, but have not yet b
Externí odkaz:
http://arxiv.org/abs/2210.11698
Autor:
Samil, Hadia Mohmmed Osman Ahmed, Martin, Annabelle, Jain, Arnav Kumar, Amin, Susan, Kahou, Samira Ebrahimi
Locust infestation of some regions in the world, including Africa, Asia and Middle East has become a concerning issue that can affect the health and the lives of millions of people. In this respect, there have been attempts to resolve or reduce the s
Externí odkaz:
http://arxiv.org/abs/2011.14371
Contemporary deep learning based inpainting algorithms are mainly based on a hybrid dual stage training policy of supervised reconstruction loss followed by an unsupervised adversarial critic loss. However, there is a dearth of literature for a fully
Externí odkaz:
http://arxiv.org/abs/1908.05861
In this paper, we propose to improve the inference speed and visual quality of contemporary baseline of Generative Adversarial Networks (GAN) based unsupervised semantic inpainting. This is made possible with better initialization of the core iterati
Externí odkaz:
http://arxiv.org/abs/1908.04968
In this paper, we introduce Key-Value Memory Networks to a multimodal setting and a novel key-addressing mechanism to deal with sequence-to-sequence models. The proposed model naturally decomposes the problem of video captioning into vision and langu
Externí odkaz:
http://arxiv.org/abs/1611.06492
We integrate learning and motion planning for soccer playing differential drive robots using Bayesian optimisation. Trajectories generated using end-slope cubic Bezier splines are first optimised globally through Bayesian optimisation for a set of ca
Externí odkaz:
http://arxiv.org/abs/1611.01851
Autor:
Agarwalla, Abhinav, Jain, Arnav Kumar, Manohar, K V, Saxena, Arpit Tarang, Mukhopadhyay, Jayanta
Publikováno v:
ACM International Conference Proceeding Series; 1/11/2018, p88-97, 10p
