Zobrazeno 1 - 10
of 32
pro vyhledávání: '"Pal, Soumyasundar"'
Autor:
Zhou, Jiaming, Ghaddar, Abbas, Zhang, Ge, Ma, Liheng, Hu, Yaochen, Pal, Soumyasundar, Coates, Mark, Wang, Bin, Zhang, Yingxue, Hao, Jianye
Despite recent advances in training and prompting strategies for Large Language Models (LLMs), these models continue to face challenges with complex logical reasoning tasks that involve long reasoning chains. In this work, we explore the potential an
Externí odkaz:
http://arxiv.org/abs/2409.12437
The existing definitions of graph convolution, either from spatial or spectral perspectives, are inflexible and not unified. Defining a general convolution operator in the graph domain is challenging due to the lack of canonical coordinates, the pres
Externí odkaz:
http://arxiv.org/abs/2404.13604
Adaptive importance sampling (AIS) methods provide a useful alternative to Markov Chain Monte Carlo (MCMC) algorithms for performing inference of intractable distributions. Population Monte Carlo (PMC) algorithms constitute a family of AIS approaches
Externí odkaz:
http://arxiv.org/abs/2312.03857
The performance of transformers for time-series forecasting has improved significantly. Recent architectures learn complex temporal patterns by segmenting a time-series into patches and using the patches as tokens. The patch size controls the ability
Externí odkaz:
http://arxiv.org/abs/2311.04147
Publikováno v:
Signal Processing, Volume 192, March 2022, 108335
There has been an increased interest in applying machine learning techniques on relational structured-data based on an observed graph. Often, this graph is not fully representative of the true relationship amongst nodes. In these settings, building a
Externí odkaz:
http://arxiv.org/abs/2208.02435
Multiple Instance Learning (MIL) is a weakly supervised learning problem where the aim is to assign labels to sets or bags of instances, as opposed to traditional supervised learning where each instance is assumed to be independent and identically di
Externí odkaz:
http://arxiv.org/abs/2202.11132
Spatio-temporal forecasting has numerous applications in analyzing wireless, traffic, and financial networks. Many classical statistical models often fall short in handling the complexity and high non-linearity present in time-series data. Recent adv
Externí odkaz:
http://arxiv.org/abs/2106.06064
Adversarial attacks can affect the performance of existing deep learning models. With the increased interest in graph based machine learning techniques, there have been investigations which suggest that these models are also vulnerable to attacks. In
Externí odkaz:
http://arxiv.org/abs/2007.06704
Node classification in attributed graphs is an important task in multiple practical settings, but it can often be difficult or expensive to obtain labels. Active learning can improve the achieved classification performance for a given budget on the n
Externí odkaz:
http://arxiv.org/abs/2007.05003