1 code implementation • NeurIPS 2021 • Swaminathan Gurumurthy, Shaojie Bai, Zachary Manchester, J. Zico Kolter
Many tasks in deep learning involve optimizing over the \emph{inputs} to a network to minimize or maximize some objective; examples include optimization over latent spaces in a generative model to match a target image, or adversarially perturbing an input to worsen classifier performance.
no code implementations • 11 Nov 2019 • Swaminathan Gurumurthy, Sumit Kumar, Katia Sycara
Meta-Reinforcement learning approaches aim to develop learning procedures that can adapt quickly to a distribution of tasks with the help of a few examples.
1 code implementation • 10 Aug 2018 • Akshat Agarwal, Swaminathan Gurumurthy, Vasu Sharma, Mike Lewis, Katia Sycara
The task of conducting visually grounded dialog involves learning goal-oriented cooperative dialog between autonomous agents who exchange information about a scene through several rounds of questions and answers in natural language.
no code implementations • 9 Jul 2018 • Swaminathan Gurumurthy, Shubham Agrawal
Experiments show that our algorithm is capable of successfully reconstructing point clouds with large missing regions with very high fidelity without having to rely on exemplar based database retrieval.
2 code implementations • CVPR 2017 • Swaminathan Gurumurthy, Ravi Kiran Sarvadevabhatla, Venkatesh Babu Radhakrishnan
A class of recent approaches for generating images, called Generative Adversarial Networks (GAN), have been used to generate impressively realistic images of objects, bedrooms, handwritten digits and a variety of other image modalities.