Zobrazeno 1 - 10
of 152 410
pro vyhledávání: '"Sai, A"'
Autor:
Balasubramanian, S, Subramaniam, M Sai, Talasu, Sai Sriram, Krishna, P Yedu, Sai, Manepalli Pranav Phanindra, Mukkamala, Ravi, Gera, Darshan
Deep neural networks (DNNS) excel at learning from static datasets but struggle with continual learning, where data arrives sequentially. Catastrophic forgetting, the phenomenon of forgetting previously learned knowledge, is a primary challenge. This
Externí odkaz:
http://arxiv.org/abs/2410.23751
Accurate localization in indoor environments is a challenge due to the Non Line of Sight (NLoS) nature of the signaling. In this paper, we explore the use of AI/ML techniques for positioning accuracy enhancement in Indoor Factory (InF) scenarios. The
Externí odkaz:
http://arxiv.org/abs/2410.19436
Classifier-free guidance (CFG) is widely used in diffusion models but often introduces over-contrast and over-saturation artifacts at higher guidance strengths. We present EP-CFG (Energy-Preserving Classifier-Free Guidance), which addresses these iss
Externí odkaz:
http://arxiv.org/abs/2412.09966
Foundation Models and Adaptive Feature Selection: A Synergistic Approach to Video Question Answering
Autor:
Rongali, Sai Bhargav, C, Mohamad Hassan N, Jha, Ankit, Bhargava, Neha, Prasad, Saurabh, Banerjee, Biplab
Publikováno v:
WACV2025
This paper tackles the intricate challenge of video question-answering (VideoQA). Despite notable progress, current methods fall short of effectively integrating questions with video frames and semantic object-level abstractions to create question-aw
Externí odkaz:
http://arxiv.org/abs/2412.09230
Deformation of material lines drives transport and dissipation in many industrial and natural flows. Here we report an exact Eulerian formula for the stretching rate of a material line, also known as the topological entropy, in a prototype two-dimens
Externí odkaz:
http://arxiv.org/abs/2412.08996
Autor:
Kumar, Kandula Eswara Sai, S, Supreeth B, Dalvi, Rajas, Mittal, Aman, Akhtar, Aakif, Bosco, Ferdin Don, Lineswala, Rut, Chopra, Abhishek
This article presents a comparative analysis of GPU-parallelized implementations of the quantum-inspired evolutionary optimization (QIEO) approach and one of the well-known classical metaheuristic techniques, the genetic algorithm (GA). The study ass
Externí odkaz:
http://arxiv.org/abs/2412.08992
Automatic syllable stress detection is a crucial component in Computer-Assisted Language Learning (CALL) systems for language learners. Current stress detection models are typically trained on clean speech, which may not be robust in real-world scena
Externí odkaz:
http://arxiv.org/abs/2412.08306
This paper proposes a graph neural network (GNN)-based space multiple-input multiple-output (MIMO) framework, named GSM, for direct-to-cell communications, aiming to achieve distributed coordinated beamforming for low Earth orbit (LEO) satellites. Fi
Externí odkaz:
http://arxiv.org/abs/2412.07555
Autor:
Sethi, Sahil, Reddy, Sai, Sakarvadia, Mansi, Serotte, Jordan, Nwaudo, Darlington, Maassen, Nicholas, Shi, Lewis
Bankart lesions, or anterior-inferior glenoid labral tears, are diagnostically challenging on standard MRIs due to their subtle imaging features-often necessitating invasive MRI arthrograms (MRAs). This study develops deep learning (DL) models to det
Externí odkaz:
http://arxiv.org/abs/2412.06717
Autor:
Naskar, Joydeep, Samal, Sai Satyam
Topological entanglement entropy (TEE) is an efficient way to detect topological order in the ground state of gapped Hamiltonians. The seminal work of Kitaev and Preskill~\cite{preskill-kitaev-tee} and simultaneously by Levin and Wen~\cite{levin-wen-
Externí odkaz:
http://arxiv.org/abs/2412.05484