S
Satwik Kottur
Researcher at Facebook
Publications - 36
Citations - 4269
Satwik Kottur is an academic researcher from Facebook. The author has contributed to research in topics: Dialog box & Computer science. The author has an hindex of 16, co-authored 30 publications receiving 3351 citations. Previous affiliations of Satwik Kottur include Carnegie Mellon University.
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Deep Sets
Manzil Zaheer,Satwik Kottur,Siamak Ravanbakhsh,Barnabás Póczos,Ruslan Salakhutdinov,Alexander J. Smola +5 more
TL;DR: The main theorem characterizes the permutation invariant objective functions and provides a family of functions to which any permutation covariant objective function must belong, which enables the design of a deep network architecture that can operate on sets and which can be deployed on a variety of scenarios including both unsupervised and supervised learning tasks.
Proceedings Article
Visual Dialog
Abhishek Das,Satwik Kottur,Khushi Gupta,Avi Singh,Deshraj Yadav,Jose M. F. Moura,Devi Parikh,Dhruv Batra +7 more
TL;DR: In this article, the authors introduce the task of Visual Dialog, which requires an AI agent to hold a meaningful dialog with humans in natural, conversational language about visual content, given an image, a dialog history and a question about the image, the agent has to ground the question in image, infer context from history, and answer the question accurately.
Journal Article
Visual Dialog
Abhishek Das,Satwik Kottur,Khushi Gupta,Avi Singh,Deshraj Yadav,Stefan Lee,Jose M. F. Moura,Devi Parikh,Dhruv Batra +8 more
TL;DR: The authors introduced the task of Visual Dialog, which requires an AI agent to hold a meaningful dialog with humans in natural, conversational language about visual content, given an image, a dialog history and a question about the image, the agent has to ground the question in image, infer context from history, and answer the question accurately.
Proceedings Article
Deep Sets
Manzil Zaheer,Satwik Kottur,Siamak Ravanbakhsh,Barnabás Póczos,Ruslan Salakhutdinov,Alexander J. Smola +5 more
TL;DR: In this paper, the authors study the problem of designing models for machine learning tasks defined on sets and provide a family of functions to which any permutation invariant objective function must belong.
Proceedings ArticleDOI
Learning Cooperative Visual Dialog Agents with Deep Reinforcement Learning
TL;DR: This work poses a cooperative ‘image guessing’ game between two agents who communicate in natural language dialog so that Q-BOT can select an unseen image from a lineup of images and shows the emergence of grounded language and communication among ‘visual’ dialog agents with no human supervision.