Zobrazeno 1 - 10
of 282
pro vyhledávání: '"Anandkumar, Animashree"'
Autor:
Li, Zhuofang, Kocielnik, Rafal, Linegar, Mitchell, Sambrano, Deshawn, Soltani, Fereshteh, Kim, Min, Naqvie, Nabiha, Cahill, Grant, Anandkumar, Animashree, Alvarez, R. Michael
Online competitive action games have flourished as a space for entertainment and social connections, yet they face challenges from a small percentage of players engaging in disruptive behaviors. This study delves into the under-explored realm of unde
Externí odkaz:
http://arxiv.org/abs/2411.01057
Autor:
Chen, Yilun, Yu, Zhiding, Chen, Yukang, Lan, Shiyi, Anandkumar, Animashree, Jia, Jiaya, Alvarez, Jose
False negatives (FN) in 3D object detection, {\em e.g.}, missing predictions of pedestrians, vehicles, or other obstacles, can lead to potentially dangerous situations in autonomous driving. While being fatal, this issue is understudied in many curre
Externí odkaz:
http://arxiv.org/abs/2308.04556
Autor:
Luo, Zelun, Zou, Yuliang, Yang, Yijin, Durante, Zane, Huang, De-An, Yu, Zhiding, Xiao, Chaowei, Fei-Fei, Li, Anandkumar, Animashree
In recent years, differential privacy has seen significant advancements in image classification; however, its application to video activity recognition remains under-explored. This paper addresses the challenges of applying differential privacy to vi
Externí odkaz:
http://arxiv.org/abs/2306.15742
Autor:
Jeong, Yoonwoo, Shin, Seungjoo, Lee, Junha, Choy, Christopher, Anandkumar, Animashree, Cho, Minsu, Park, Jaesik
The recent progress in implicit 3D representation, i.e., Neural Radiance Fields (NeRFs), has made accurate and photorealistic 3D reconstruction possible in a differentiable manner. This new representation can effectively convey the information of hun
Externí odkaz:
http://arxiv.org/abs/2208.11537
Autor:
Kurth, Thorsten, Subramanian, Shashank, Harrington, Peter, Pathak, Jaideep, Mardani, Morteza, Hall, David, Miele, Andrea, Kashinath, Karthik, Anandkumar, Animashree
Extreme weather amplified by climate change is causing increasingly devastating impacts across the globe. The current use of physics-based numerical weather prediction (NWP) limits accuracy due to high computational cost and strict time-to-solution l
Externí odkaz:
http://arxiv.org/abs/2208.05419
We propose a learning-based method to reconstruct the local terrain for locomotion with a mobile robot traversing urban environments. Using a stream of depth measurements from the onboard cameras and the robot's trajectory, the algorithm estimates th
Externí odkaz:
http://arxiv.org/abs/2206.08077
Autor:
Kiyasseh, Dani, Ma, Runzhuo, Haque, Taseen F., Nguyen, Jessica, Wagner, Christian, Anandkumar, Animashree, Hung, Andrew J.
Surgery is a high-stakes domain where surgeons must navigate critical anatomical structures and actively avoid potential complications while achieving the main task at hand. Such surgical activity has been shown to affect long-term patient outcomes.
Externí odkaz:
http://arxiv.org/abs/2205.03028
Autor:
Pathak, Jaideep, Subramanian, Shashank, Harrington, Peter, Raja, Sanjeev, Chattopadhyay, Ashesh, Mardani, Morteza, Kurth, Thorsten, Hall, David, Li, Zongyi, Azizzadenesheli, Kamyar, Hassanzadeh, Pedram, Kashinath, Karthik, Anandkumar, Animashree
FourCastNet, short for Fourier Forecasting Neural Network, is a global data-driven weather forecasting model that provides accurate short to medium-range global predictions at $0.25^{\circ}$ resolution. FourCastNet accurately forecasts high-resolutio
Externí odkaz:
http://arxiv.org/abs/2202.11214
Autor:
Mahajan, Anuj, Samvelyan, Mikayel, Mao, Lei, Makoviychuk, Viktor, Garg, Animesh, Kossaifi, Jean, Whiteson, Shimon, Zhu, Yuke, Anandkumar, Animashree
Publikováno v:
2nd Workshop on Quantum Tensor Networks in Machine Learning (NeurIPS 2021)
We present an extended abstract for the previously published work TESSERACT [Mahajan et al., 2021], which proposes a novel solution for Reinforcement Learning (RL) in large, factored action spaces using tensor decompositions. The goal of this abstrac
Externí odkaz:
http://arxiv.org/abs/2110.14538
Autor:
Jeong, Yoonwoo, Ahn, Seokjun, Choy, Christopher, Anandkumar, Animashree, Cho, Minsu, Park, Jaesik
In this work, we propose a camera self-calibration algorithm for generic cameras with arbitrary non-linear distortions. We jointly learn the geometry of the scene and the accurate camera parameters without any calibration objects. Our camera model co
Externí odkaz:
http://arxiv.org/abs/2108.13826