Zobrazeno 1 - 10
of 22 564
pro vyhledávání: '"Trivedi, P."'
Retrieval-based question answering systems often suffer from positional bias, leading to suboptimal answer generation. We propose LoRE (Logit-Ranked Retriever Ensemble), a novel approach that improves answer accuracy and relevance by mitigating posit
Externí odkaz:
http://arxiv.org/abs/2410.10042
We consider Axion-like particle (ALP) model to construct numerical spatially homogeneous anisotropic Kantowski-Sachs cosmological model. We present various analytical and numerical results in this regard, discussing the evolution of various important
Externí odkaz:
http://arxiv.org/abs/2410.09571
Autor:
Trivedi, Prapti, Gulati, Aditya, Molenschot, Oliver, Rajeev, Meghana Arakkal, Ramamurthy, Rajkumar, Stevens, Keith, Chaudhery, Tanveesh Singh, Jambholkar, Jahnavi, Zou, James, Rajani, Nazneen
LLM-as-a-judge models have been used for evaluating both human and AI generated content, specifically by providing scores and rationales. Rationales, in addition to increasing transparency, help models learn to calibrate its judgments. Enhancing a mo
Externí odkaz:
http://arxiv.org/abs/2410.05495
Autor:
Trivedi, Anupam, Chauhan, Dikshit
In this paper, an enhanced unified differential evolution algorithm, named UDE-III, is presented for real parameter-constrained optimization problems (COPs). The proposed UDE-III is a significantly enhanced version of the Improved UDE (i.e., IUDE or
Externí odkaz:
http://arxiv.org/abs/2410.03992
Detecting anomalies in brain MRI scans using supervised deep learning methods presents challenges due to anatomical diversity and labor-intensive requirement of pixel-level annotations. Generative models like Denoising Diffusion Probabilistic Model (
Externí odkaz:
http://arxiv.org/abs/2409.19623
As the laws have become more complicated and enormous, the role of software systems in navigating and understanding these intricacies has become more critical. Given their socio-economic and legally critical implications, ensuring software accountabi
Externí odkaz:
http://arxiv.org/abs/2409.16140
In recent years, multi-operator and multi-method algorithms have succeeded, encouraging their combination within single frameworks. Despite promising results, there remains room for improvement as only some evolutionary algorithms (EAs) consistently
Externí odkaz:
http://arxiv.org/abs/2409.15994
Autor:
Darabi, Nastaran, Jayasuriya, Dinithi, Naik, Devashri, Tulabandhula, Theja, Trivedi, Amit Ranjan
Adversarial attacks exploit vulnerabilities in a model's decision boundaries through small, carefully crafted perturbations that lead to significant mispredictions. In 3D vision, the high dimensionality and sparsity of data greatly expand the attack
Externí odkaz:
http://arxiv.org/abs/2409.12379
There has been much recent interest in designing symmetry-aware neural networks (NNs) exhibiting relaxed equivariance. Such NNs aim to interpolate between being exactly equivariant and being fully flexible, affording consistent performance benefits.
Externí odkaz:
http://arxiv.org/abs/2409.11772
We present an approach for systematically anticipating the actions and policies employed by \emph{oblivious} environments in concurrent stochastic games, while maximizing a reward function. Our main contribution lies in the synthesis of a finite \emp
Externí odkaz:
http://arxiv.org/abs/2409.11671