Zobrazeno 1 - 10
of 14 175
pro vyhledávání: '"Roy Chowdhury"'
Publikováno v:
BIO Web of Conferences, Vol 86, p 01057 (2024)
Modern economies are centered on electricity, which is also contributing to an increasing amount of energy services. As a result of increased household incomes, electrification of transport and rising demand for digital connected devices and air cond
Externí odkaz:
https://doaj.org/article/dff62dc80a674100b130f46249553224
Reinforcement Learning (RL) has enabled social robots to generate trajectories without human-designed rules or interventions, which makes it more effective than hard-coded systems for generalizing to complex real-world scenarios. However, social navi
Externí odkaz:
http://arxiv.org/abs/2407.17460
Autor:
Dutta, Arindam, Lal, Rohit, Garg, Yash, Ta, Calvin-Khang, Raychaudhuri, Dripta S., Cruz, Hannah Dela, Roy-Chowdhury, Amit K.
Existing algorithms for human body part segmentation have shown promising results on challenging datasets, primarily relying on end-to-end supervision. However, these algorithms exhibit severe performance drops in the face of domain shifts, leading t
Externí odkaz:
http://arxiv.org/abs/2407.03549
Autor:
Nabizadeh, Mohammad Sina, Roy-Chowdhury, Ritoban, Yin, Hang, Ramamoorthi, Ravi, Chern, Albert
We propose Coadjoint Orbit FLIP (CO-FLIP), a high order accurate, structure preserving fluid simulation method in the hybrid Eulerian-Lagrangian framework. We start with a Hamiltonian formulation of the incompressible Euler Equations, and then, using
Externí odkaz:
http://arxiv.org/abs/2406.01936
Autor:
Chakraborty, Trishna, Shayegani, Erfan, Cai, Zikui, Abu-Ghazaleh, Nael, Asif, M. Salman, Dong, Yue, Roy-Chowdhury, Amit K., Song, Chengyu
Recent studies reveal that integrating new modalities into Large Language Models (LLMs), such as Vision-Language Models (VLMs), creates a new attack surface that bypasses existing safety training techniques like Supervised Fine-tuning (SFT) and Reinf
Externí odkaz:
http://arxiv.org/abs/2406.02575
Autor:
Yao, Manyi, Aich, Abhishek, Suh, Yumin, Roy-Chowdhury, Amit, Shelton, Christian, Chandraker, Manmohan
Vision transformer based models bring significant improvements for image segmentation tasks. Although these architectures offer powerful capabilities irrespective of specific segmentation tasks, their use of computational resources can be taxing on d
Externí odkaz:
http://arxiv.org/abs/2404.15244
Autor:
Chang, Xiangyu, Ahmed, Sk Miraj, Krishnamurthy, Srikanth V., Guler, Basak, Swami, Ananthram, Oymak, Samet, Roy-Chowdhury, Amit K.
The key premise of federated learning (FL) is to train ML models across a diverse set of data-owners (clients), without exchanging local data. An overarching challenge to this date is client heterogeneity, which may arise not only from variations in
Externí odkaz:
http://arxiv.org/abs/2402.08769
Autor:
Chang, Xiangyu, Ahmed, Sk Miraj, Krishnamurthy, Srikanth V., Guler, Basak, Swami, Ananthram, Oymak, Samet, Roy-Chowdhury, Amit K.
Parameter-efficient tuning (PET) methods such as LoRA, Adapter, and Visual Prompt Tuning (VPT) have found success in enabling adaptation to new domains by tuning small modules within a transformer model. However, the number of domains encountered dur
Externí odkaz:
http://arxiv.org/abs/2401.04130
Autor:
Ahmed, Sk Miraj, Niloy, Fahim Faisal, Raychaudhuri, Dripta S., Oymak, Samet, Roy-Chowdhury, Amit K.
Test time adaptation is the process of adapting, in an unsupervised manner, a pre-trained source model to each incoming batch of the test data (i.e., without requiring a substantial portion of the test data to be available, as in traditional domain a
Externí odkaz:
http://arxiv.org/abs/2401.02561
Publikováno v:
International Journal of Applied Mechanics and Engineering, Vol 23, Iss 1, Pp 121-136 (2018)
The present study makes an effort to understand the damage of earthen dams under static and seismic loading condition. To make the investigation more realistic, behaviour of earthen dams considering the occurrence of a phreatic line indicating the su
Externí odkaz:
https://doaj.org/article/ebab2892384544a2bd3598cffbfb921c