Zobrazeno 1 - 9
of 9
pro vyhledávání: '"Bachu, Saketh"'
Autor:
Bachu, Saketh, Shayegani, Erfan, Chakraborty, Trishna, Lal, Rohit, Dutta, Arindam, Song, Chengyu, Dong, Yue, Abu-Ghazaleh, Nael, Roy-Chowdhury, Amit K.
Vision-language models (VLMs) have improved significantly in multi-modal tasks, but their more complex architecture makes their safety alignment more challenging than the alignment of large language models (LLMs). In this paper, we reveal an unfair d
Externí odkaz:
http://arxiv.org/abs/2411.04291
Autor:
Lal, Rohit, Bachu, Saketh, Garg, Yash, Dutta, Arindam, Ta, Calvin-Khang, Raychaudhuri, Dripta S., Cruz, Hannah Dela, Asif, M. Salman, Roy-Chowdhury, Amit K.
The capability to accurately estimate 3D human poses is crucial for diverse fields such as action recognition, gait recognition, and virtual/augmented reality. However, a persistent and significant challenge within this field is the accurate predicti
Externí odkaz:
http://arxiv.org/abs/2312.16221
Autor:
Vashishtha, Aniket, Reddy, Abbavaram Gowtham, Kumar, Abhinav, Bachu, Saketh, Balasubramanian, Vineeth N, Sharma, Amit
At the core of causal inference lies the challenge of determining reliable causal graphs solely based on observational data. Since the well-known backdoor criterion depends on the graph, any errors in the graph can propagate downstream to effect infe
Externí odkaz:
http://arxiv.org/abs/2310.15117
Autor:
B, Vimal K, Bachu, Saketh, Garg, Tanmay, Narasimhan, Niveditha Lakshmi, Konuru, Raghavan, Balasubramanian, Vineeth N
Estimating the transferability of publicly available pretrained models to a target task has assumed an important place for transfer learning tasks in recent years. Existing efforts propose metrics that allow a user to choose one model from a pool of
Externí odkaz:
http://arxiv.org/abs/2309.02429
Autor:
Reddy, Abbavaram Gowtham, Bachu, Saketh, Dash, Saloni, Sharma, Charchit, Sharma, Amit, Balasubramanian, Vineeth N
Counterfactual data augmentation has recently emerged as a method to mitigate confounding biases in the training data. These biases, such as spurious correlations, arise due to various observed and unobserved confounding variables in the data generat
Externí odkaz:
http://arxiv.org/abs/2305.18183
Autor:
Reddy, Abbavaram Gowtham, Bachu, Saketh, Pathak, Harsharaj, Godfrey, Benin L, Balasubramanian, Vineeth N., V, Varshaneya, Kar, Satya Narayanan
Recently, there has been a growing interest in learning and explaining causal effects within Neural Network (NN) models. By virtue of NN architectures, previous approaches consider only direct and total causal effects assuming independence among inpu
Externí odkaz:
http://arxiv.org/abs/2303.13850
Autor:
Menta, Tarun Ram, Jandial, Surgan, Patil, Akash, KB, Vimal, Bachu, Saketh, Krishnamurthy, Balaji, Balasubramanian, Vineeth N., Agarwal, Chirag, Sarkar, Mausoom
As transfer learning techniques are increasingly used to transfer knowledge from the source model to the target task, it becomes important to quantify which source models are suitable for a given target task without performing computationally expensi
Externí odkaz:
http://arxiv.org/abs/2301.06928
Autor:
Reddy, Abbavaram Gowtham, Bachu, Saketh, Dash, Saloni, Sharma, Charchit, Sharma, Amit, Balasubramanian, Vineeth N
Counterfactual data augmentation has recently emerged as a method to mitigate confounding biases in the training data for a machine learning model. These biases, such as spurious correlations, arise due to various observed and unobserved confounding
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::59c7972e3f94deb0f36ec3c7487c4cd5
http://arxiv.org/abs/2305.18183
http://arxiv.org/abs/2305.18183
Autor:
Reddy, Abbaavaram Gowtham, Bachu, Saketh, Pathak, Harsharaj, Godfrey, Benin L, Balasubramanian, Vineeth N., V, Varshaneya, Kar, Satya Narayanan
There has been a growing interest in capturing and maintaining causal relationships in Neural Network (NN) models in recent years. We study causal approaches to estimate and maintain input-output attributions in NN models in this work. In particular,
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6b14cbe9b0c76ca39d3f90f7da063f99