Zobrazeno 1 - 10
of 232
pro vyhledávání: '"Lakshmanan, Laks V. S."'
Autor:
Zhang, Xiang, Li, Senyu, Shi, Ning, Hauer, Bradley, Wu, Zijun, Kondrak, Grzegorz, Abdul-Mageed, Muhammad, Lakshmanan, Laks V. S.
Recent developments in multimodal methodologies have marked the beginning of an exciting era for models adept at processing diverse data types, encompassing text, audio, and visual content. Models like GPT-4V, which merge computer vision with advance
Externí odkaz:
http://arxiv.org/abs/2411.09273
Influence maximization (IM) is a classic problem that aims to identify a small group of critical individuals, known as seeds, who can influence the largest number of users in a social network through word-of-mouth. This problem finds important applic
Externí odkaz:
http://arxiv.org/abs/2410.16603
The Transformer architecture excels in a variety of language modeling tasks, outperforming traditional neural architectures such as RNN and LSTM. This is partially due to its elimination of recurrent connections, which allows for parallel training an
Externí odkaz:
http://arxiv.org/abs/2409.09239
With the recent advancements in graph neural networks (GNNs), spectral GNNs have received increasing popularity by virtue of their specialty in capturing graph signals in the frequency domain, demonstrating promising capability in specific tasks. How
Externí odkaz:
http://arxiv.org/abs/2406.09675
In this paper, we study cascading failures in power grids through the lens of information diffusion models. Similar to the spread of rumors or influence in an online social network, it has been observed that failures (outages) in a power grid can spr
Externí odkaz:
http://arxiv.org/abs/2406.08522
Image classification is a fundamental building block for a majority of computer vision applications. With the growing popularity and capacity of machine learning models, people can easily access trained image classifiers as a service online or offlin
Externí odkaz:
http://arxiv.org/abs/2406.04508
Autor:
Ding, Dujian, Mallick, Ankur, Wang, Chi, Sim, Robert, Mukherjee, Subhabrata, Ruhle, Victor, Lakshmanan, Laks V. S., Awadallah, Ahmed Hassan
Large language models (LLMs) excel in most NLP tasks but also require expensive cloud servers for deployment due to their size, while smaller models that can be deployed on lower cost (e.g., edge) devices, tend to lag behind in terms of response qual
Externí odkaz:
http://arxiv.org/abs/2404.14618
Prior works on detoxification are scattered in the sense that they do not cover all aspects of detoxification needed in a real-world scenario. Notably, prior works restrict the task of developing detoxification models to only a seen subset of platfor
Externí odkaz:
http://arxiv.org/abs/2402.15951
In this work, we utilize Large Language Models (LLMs) for a novel use case: constructing Performance Predictors (PP) that estimate the performance of specific deep neural network architectures on downstream tasks. We create PP prompts for LLMs, compr
Externí odkaz:
http://arxiv.org/abs/2310.16712
Self-training (ST) has come to fruition in language understanding tasks by producing pseudo labels, which reduces the labeling bottleneck of language model fine-tuning. Nevertheless, in facilitating semi-supervised controllable language generation, S
Externí odkaz:
http://arxiv.org/abs/2306.10414