no code implementations • 27 Feb 2024 • Arjun Gupta, Michelle Zhang, Rishik Sathua, Saurabh Gupta
In this work, we build an end-to-end system that enables a commodity mobile manipulator (Stretch RE2) to pull open cabinets and drawers in diverse previously unseen real world environments.
no code implementations • 11 Dec 2023 • Aditya Prakash, Arjun Gupta, Saurabh Gupta
Objects undergo varying amounts of perspective distortion as they move across a camera's field of view.
no code implementations • 2 Mar 2023 • Arjun Gupta, Max E. Shepherd, Saurabh Gupta
Our key insight is to cast it as a learning problem to leverage past experience of solving similar planning problems to directly predict motion plans for mobile manipulation tasks in novel situations at test time.
1 code implementation • 27 Sep 2022 • Jayakrishnan Vijayamohanan, Arjun Gupta, Oameed Noakoasteen, Sotirios Goudos, Christos Christodoulou
Radio source detection through conventional algorithms has been unreliable when trying to solve for large number of sources in the presence of low SINR and less number of snapshots.
no code implementations • ICLR 2022 • Matthew Chang, Arjun Gupta, Saurabh Gupta
We show that LAQ can recover value functions that have high correlation with value functions learned using ground truth actions.
no code implementations • 29 Sep 2021 • Liam H Fowl, Micah Goldblum, Arjun Gupta, Amr Sharaf, Tom Goldstein
We validate and deploy this metric on both images and text.
no code implementations • 29 Sep 2021 • Eitan Borgnia, Jonas Geiping, Valeriia Cherepanova, Liam H Fowl, Arjun Gupta, Amin Ghiasi, Furong Huang, Micah Goldblum, Tom Goldstein
Data poisoning and backdoor attacks manipulate training data to induce security breaches in a victim model.
1 code implementation • 13 Aug 2021 • Avi Schwarzschild, Eitan Borgnia, Arjun Gupta, Arpit Bansal, Zeyad Emam, Furong Huang, Micah Goldblum, Tom Goldstein
We describe new datasets for studying generalization from easy to hard examples.
1 code implementation • NeurIPS 2021 • Avi Schwarzschild, Eitan Borgnia, Arjun Gupta, Furong Huang, Uzi Vishkin, Micah Goldblum, Tom Goldstein
In this work, we show that recurrent networks trained to solve simple problems with few recurrent steps can indeed solve much more complex problems simply by performing additional recurrences during inference.
1 code implementation • 2 Mar 2021 • Eitan Borgnia, Jonas Geiping, Valeriia Cherepanova, Liam Fowl, Arjun Gupta, Amin Ghiasi, Furong Huang, Micah Goldblum, Tom Goldstein
The InstaHide method has recently been proposed as an alternative to DP training that leverages supposed privacy properties of the mixup augmentation, although without rigorous guarantees.
1 code implementation • ICLR 2022 • Avi Schwarzschild, Arjun Gupta, Amin Ghiasi, Micah Goldblum, Tom Goldstein
It is widely believed that deep neural networks contain layer specialization, wherein neural networks extract hierarchical features representing edges and patterns in shallow layers and complete objects in deeper layers.
2 code implementations • 18 Jan 2021 • Antoni Rosinol, Andrew Violette, Marcus Abate, Nathan Hughes, Yun Chang, Jingnan Shi, Arjun Gupta, Luca Carlone
This mental model captures geometric and semantic aspects of the scene, describes the environment at multiple levels of abstractions (e. g., objects, rooms, buildings), includes static and dynamic entities and their relations (e. g., a person is in a room at a given time).
no code implementations • 1 Jan 2021 • Avi Schwarzschild, Micah Goldblum, Arjun Gupta, John P Dickerson, Tom Goldstein
Data poisoning and backdoor attacks manipulate training data in order to cause models to fail during inference.
1 code implementation • 18 Nov 2020 • Eitan Borgnia, Valeriia Cherepanova, Liam Fowl, Amin Ghiasi, Jonas Geiping, Micah Goldblum, Tom Goldstein, Arjun Gupta
Data poisoning and backdoor attacks manipulate victim models by maliciously modifying training data.
no code implementations • 13 Oct 2020 • Liam Fowl, Micah Goldblum, Arjun Gupta, Amr Sharaf, Tom Goldstein
We validate and deploy this metric on both images and text.
2 code implementations • 22 Jun 2020 • Avi Schwarzschild, Micah Goldblum, Arjun Gupta, John P. Dickerson, Tom Goldstein
Data poisoning and backdoor attacks manipulate training data in order to cause models to fail during inference.
1 code implementation • NeurIPS 2020 • Matthew Chang, Arjun Gupta, Saurabh Gupta
Semantic cues and statistical regularities in real-world environment layouts can improve efficiency for navigation in novel environments.
no code implementations • 11 May 2020 • Arjun Gupta, Luca Carlone
We investigate the problem of online output monitoring for neural networks that estimate 3D human shapes and poses from images.
3 code implementations • 15 Feb 2020 • Antoni Rosinol, Arjun Gupta, Marcus Abate, Jingnan Shi, Luca Carlone
Our second contribution is to provide the first fully automatic Spatial PerceptIon eNgine(SPIN) to build a DSG from visual-inertial data.
1 code implementation • arXiv 2019 • Arjun Gupta, E. A. Huerta, Zhizhen Zhao, Issam Moussa
Myocardial infarction is the leading cause of death worldwide.
no code implementations • 14 Dec 2019 • Igor Gilitschenski, Guy Rosman, Arjun Gupta, Sertac Karaman, Daniela Rus
Our main contribution is the concept of learning context maps to improve the prediction task.