Zobrazeno 1 - 10
of 45
pro vyhledávání: '"Prasad, Ranjitha"'
Wireless embedded edge devices are ubiquitous in our daily lives, enabling them to gather immense data via onboard sensors and mobile applications. This offers an amazing opportunity to train machine learning (ML) models in the realm of wireless devi
Externí odkaz:
http://arxiv.org/abs/2312.08577
Autor:
Hussain, Aadil, Gundapu, Nitheesh, Drugkar, Sarang, Kiran, Suraj, Harshan, J., Prasad, Ranjitha
Reactive injection attacks are a class of security threats in wireless networks wherein adversaries opportunistically inject spoofing packets in the frequency band of a client thereby forcing the base-station to deploy impersonation-detection methods
Externí odkaz:
http://arxiv.org/abs/2311.06564
Autor:
Nanavati, Praharsh, Prasad, Ranjitha
Explainable AI is an evolving area that deals with understanding the decision making of machine learning models so that these models are more transparent, accountable, and understandable for humans. In particular, post-hoc model-agnostic interpretabl
Externí odkaz:
http://arxiv.org/abs/2307.00680
Privacy and bandwidth constraints have led to the use of federated learning (FL) in wireless systems, where training a machine learning (ML) model is accomplished collaboratively without sharing raw data. While using bandwidth-constrained uplink wire
Externí odkaz:
http://arxiv.org/abs/2211.03363
Causal structures for observational survival data provide crucial information regarding the relationships between covariates and time-to-event. We derive motivation from the information theoretic source coding argument, and show that incorporating th
Externí odkaz:
http://arxiv.org/abs/2111.01482
Autor:
Saini, Aditya, Prasad, Ranjitha
Albeit the tremendous performance improvements in designing complex artificial intelligence (AI) systems in data-intensive domains, the black-box nature of these systems leads to the lack of trustworthiness. Post-hoc interpretability methods explain
Externí odkaz:
http://arxiv.org/abs/2108.06907
Low-latency provenance embedding methods have received traction in vehicular networks for their ability to track the footprint of information flow. One such known method is based on Bloom filters wherein the nodes that forward the packets appropriate
Externí odkaz:
http://arxiv.org/abs/2105.12456
Autor:
Madan, Anish, Prasad, Ranjitha
There is a growing interest in the learning-to-learn paradigm, also known as meta-learning, where models infer on new tasks using a few training examples. Recently, meta-learning based methods have been widely used in few-shot classification, regress
Externí odkaz:
http://arxiv.org/abs/2101.00203
Autor:
Kumar, Sachin, Gupta, Garima, Prasad, Ranjitha, Chatterjee, Arnab, Vig, Lovekesh, Shroff, Gautam
Advertising channels have evolved from conventional print media, billboards and radio advertising to online digital advertising (ad), where the users are exposed to a sequence of ad campaigns via social networks, display ads, search etc. While advert
Externí odkaz:
http://arxiv.org/abs/2012.11403
Autor:
Sharma, Ankit, Gupta, Garima, Prasad, Ranjitha, Chatterjee, Arnab, Vig, Lovekesh, Shroff, Gautam
We address the problem of counterfactual regression using causal inference (CI) in observational studies consisting of high dimensional covariates and high cardinality treatments. Confounding bias, which leads to inaccurate treatment effect estimatio
Externí odkaz:
http://arxiv.org/abs/2008.09858