Tootfinder

VI-OOD: A Unified Representation Learning Framework for Textual Out-of-distribution Detection
Li-Ming Zhan, Bo Liu, Xiao-Ming Wu
https://arxiv.org/abs/2404.06217

VI-OOD: A Unified Representation Learning Framework for Textual Out-of-distribution Detection
Out-of-distribution (OOD) detection plays a crucial role in ensuring the safety and reliability of deep neural networks in various applications. While there has been a growing focus on OOD detection in visual data, the field of textual OOD detection has received less attention. Only a few attempts have been made to directly apply general OOD detection methods to natural language processing (NLP) tasks, without adequately considering the characteristics of textual data. In this paper, we delve i…

🔌 Utilizing BERT for Information Retrieval: Survey, Applications, Resources, and Challenges
#ai

Utilizing BERT for Information Retrieval: Survey, Applications, Resources, and Challenges
Recent years have witnessed a substantial increase in the use of deep learning to solve various natural language processing (NLP) problems. Early deep learning models were constrained by their sequential or unidirectional nature, such that they struggled to capture the contextual relationships across text inputs. The introduction of bidirectional encoder representations from transformers (BERT) leads to a robust encoder for the transformer model that can understand the broader context and deliv…

This https://arxiv.org/abs/2403.11894 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCL_…

From Explainable to Interpretable Deep Learning for Natural Language Processing in Healthcare: How Far from Reality?
Deep learning (DL) has substantially enhanced natural language processing (NLP) in healthcare research. However, the increasing complexity of DL-based NLP necessitates transparent model interpretability, or at least explainability, for reliable decision-making. This work presents a thorough scoping review of explainable and interpretable DL in healthcare NLP. The term "eXplainable and Interpretable Artificial Intelligence" (XIAI) is introduced to distinguish XAI from IAI. Different models are f…

ATOM: Asynchronous Training of Massive Models for Deep Learning in a Decentralized Environment
Xiaofeng Wu, Jia Rao, Wei Chen
https://arxiv.org/abs/2403.10504

ATOM: Asynchronous Training of Massive Models for Deep Learning in a Decentralized Environment
The advent of the Transformer architecture has propelled the growth of natural language processing (NLP) models, leading to remarkable achievements in numerous NLP tasks. Yet, the absence of specialized hardware like expansive GPU memory and high-speed interconnects poses challenges for training large-scale models. This makes it daunting for many users to experiment with pre-training and fine-tuning large language models (LLMs). In this study, we introduce \atom, a resilient distributed trainin…

Effect of Leaders Voice on Financial Market: An Empirical Deep Learning Expedition on NASDAQ, NSE, and Beyond
Arijit Das, Tanmoy Nandi, Prasanta Saha, Suman Das, Saronyo Mukherjee, Sudip Kumar Naskar, Diganta Saha
https://arxiv.org/abs/2403.12161

Effect of Leaders Voice on Financial Market: An Empirical Deep Learning Expedition on NASDAQ, NSE, and Beyond
Financial market like the price of stock, share, gold, oil, mutual funds are affected by the news and posts on social media. In this work deep learning based models are proposed to predict the trend of financial market based on NLP analysis of the twitter handles of leaders of different fields. There are many models available to predict financial market based on only the historical data of the financial component but combining historical data with news and posts of the social media like Twitter…

ATOM: Asynchronous Training of Massive Models for Deep Learning in a Decentralized Environment
Xiaofeng Wu, Jia Rao, Wei Chen
https://arxiv.org/abs/2403.10504

ATOM: Asynchronous Training of Massive Models for Deep Learning in a Decentralized Environment
The advent of the Transformer architecture has propelled the growth of natural language processing (NLP) models, leading to remarkable achievements in numerous NLP tasks. Yet, the absence of specialized hardware like expansive GPU memory and high-speed interconnects poses challenges for training large-scale models. This makes it daunting for many users to experiment with pre-training and fine-tuning large language models (LLMs). In this study, we introduce \atom, a resilient distributed trainin…

From explainable to interpretable deep learning for natural language processing in healthcare: how far from reality?
Guangming Huang, Yunfei Long, Yingya Li, Giorgos Papanastasiou
https://arxiv.org/abs/2403.11894

From explainable to interpretable deep learning for natural language processing in healthcare: how far from reality?
Deep learning (DL) has substantially enhanced healthcare research by addressing various natural language processing (NLP) tasks. Yet, the increasing complexity of DL-based NLP methods necessitates transparent model interpretability, or at least explainability, for reliable decision-making. This work presents a thorough scoping review on explainable and interpretable DL in healthcare NLP. The term "XIAI" (eXplainable and Interpretable Artificial Intelligence) was introduced to distinguish XAI fr…