Zobrazeno 1 - 10
of 5 192
pro vyhledávání: '"A Abhiram"'
Autor:
Manohara, Abhiram, Zehmakan, Ahad N.
Consider an undirected graph G, representing a social network, where each node is blue or red, corresponding to positive or negative opinion on a topic. In the voter model, in discrete time rounds, each node picks a neighbour uniformly at random and
Externí odkaz:
http://arxiv.org/abs/2411.04564
Autor:
Patel, Maitreya, Kusumba, Abhiram, Cheng, Sheng, Kim, Changhoon, Gokhale, Tejas, Baral, Chitta, Yang, Yezhou
Contrastive Language-Image Pretraining (CLIP) models maximize the mutual information between text and visual modalities to learn representations. This makes the nature of the training data a significant factor in the efficacy of CLIP for downstream t
Externí odkaz:
http://arxiv.org/abs/2411.02545
Traditional deep learning-based methods for classifying cellular features in microscopy images require time- and labor-intensive processes for training models. Among the current limitations are major time commitments from domain experts for accurate
Externí odkaz:
http://arxiv.org/abs/2411.02639
Sequential learning in deep models often suffers from challenges such as catastrophic forgetting and loss of plasticity, largely due to the permutation dependence of gradient-based algorithms, where the order of training data impacts the learning out
Externí odkaz:
http://arxiv.org/abs/2410.22695
Autor:
Sripat, Abhiram
Mycorrhizal fungi form vast subterranean networks that are critical for plant nutrient uptake, carbon sequestration, and ecosystem resilience. Despite their ecological importance, optimizing these networks for precision agriculture, forestry,and carb
Externí odkaz:
http://arxiv.org/abs/2410.18149
Autor:
Lim, Arisrei, Maddukuri, Abhiram
In recent years, reinforcement learning (RL) has gained popularity and has been applied to a wide range of tasks. One such popular domain where RL has been effective is resource management problems in systems. We look to extend work on RL for resourc
Externí odkaz:
http://arxiv.org/abs/2410.15492
Autor:
Chennuri, Prateek, Chi, Yiheng, Jiang, Enze, Godaliyadda, G. M. Dilshan, Gnanasambandam, Abhiram, Sheikh, Hamid R., Gyongy, Istvan, Chan, Stanley H.
Publikováno v:
European Conference on Computer Vision (ECCV) 2024
The proliferation of single-photon image sensors has opened the door to a plethora of high-speed and low-light imaging applications. However, data collected by these sensors are often 1-bit or few-bit, and corrupted by noise and strong motion. Conven
Externí odkaz:
http://arxiv.org/abs/2410.14994
Library learning is the process of building a library of common functionalities from a given set of programs. Typically, this process is applied in the context of aiding program synthesis: concise functions can help the synthesizer produce modularize
Externí odkaz:
http://arxiv.org/abs/2410.06438
Autor:
Sripat, Abhiram
We propose a novel hybrid quantum-classical framework that integrates the Quantum Approximate Optimization Algorithm (QAOA) and Quantum-enhanced Markov Chain Monte Carlo (QMCMC) with variational particle filters to tackle the computational challenges
Externí odkaz:
http://arxiv.org/abs/2410.03853
We present Vercel, a network verification and automatic fault rectification tool that is based on a computationally tractable, algorithmically expressive, and mathematically aesthetic domain of linear algebra. Vercel works on abstracting out packet h
Externí odkaz:
http://arxiv.org/abs/2409.14341