Stock price prediction plays an important role in financial decision-making, enabling investors and analysts to make informed choices regarding trading and investment strategies. Traditional statistical methods have b...
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Stock price prediction plays an important role in financial decision-making, enabling investors and analysts to make informed choices regarding trading and investment strategies. Traditional statistical methods have been utilized for the prediction of stock price, but it is often difficult for them to capture complex patterns, adapt to changing market conditions, handle large datasets, and automatically extract relevant features. Recent advancements in machine learning and deep learning offer promising solutions to address these challenges. In this paper, we propose a new approach to enhance the stock price prediction by leveraging generative adversarial networks (GANs) and transformer-based attention mechanisms. GANs are utilized to generate synthetic stock price data, and incorporating market sentiment and volatility. Attention mechanisms will selectively concentrate on the important features and patterns in the data, which may do good to the identification of key market indicators which will impact stock prices. By integrating market social media news which can tell about the sentiment and volatility, our model aims to improve the accuracy and robustness of stock price forecasts. We also address the limitations of GANs and attention mechanisms separately used in stock price prediction, such as unrealistic data generation and overfitting, by employing regularization techniques and incorporating additional data sources. Experimental evaluations using real-world stock market data will be conducted to compare the performance of our proposed models with conventional approaches. The findings of this research have implications for investors, financial analysts, and other stakeholders who are engaged in the stock market ecosystem, providing valuable insights for the investment strategies.
In recent years, there has been a significant increase in published research on deploying various machine learning (ML) methods and algorithms in the development of chatbots for diverse applications. Substantial evide...
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The rapid advancements in large language models (LLMs) have significantly enhanced naturallanguageprocessing capabilities, facilitating the development of AudioLLMs that process and understand speech and audio input...
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ISBN:
(数字)9798350368741
ISBN:
(纸本)9798350368758
The rapid advancements in large language models (LLMs) have significantly enhanced naturallanguageprocessing capabilities, facilitating the development of AudioLLMs that process and understand speech and audio inputs alongside text. Existing AudioLLMs typically combine a pre-trained audio encoder with a pre-trained LLM, which are subsequently finetuned on specific audio tasks. However, the pre-trained audio encoder has constrained capacity to capture features for new tasks and datasets. To address this, we propose to incorporate mixtures of ‘weak’ encoders (MoWE) into the AudioLLM framework. MoWE supplements a base encoder with a pool of relatively lightweight encoders, selectively activated based on the audio input to enhance feature extraction without significantly increasing model size. Our empirical results demonstrate that MoWE effectively improves multi-task performance, broadening the applicability of AudioLLMs to more diverse audio tasks.
As large language models continue to scale up, distributed training systems have expanded beyond 10k nodes, intensifying the importance of fault tolerance. Checkpoint has emerged as the predominant fault tolerance str...
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ISBN:
(纸本)9798400710797
As large language models continue to scale up, distributed training systems have expanded beyond 10k nodes, intensifying the importance of fault tolerance. Checkpoint has emerged as the predominant fault tolerance strategy, with extensive studies dedicated to optimizing its efficiency. However, the advent of the sparse Mixture-of-Experts (MoE) model presents new challenges due to the substantial increase in model size, despite comparable computational demands to dense models. In this work, we propose the Mixture-of-Checkpoint System (MoC-System) to orchestrate the vast array of checkpoint shards produced in distributed training systems. MoC-System features a novel Partial Experts Checkpointing (PEC) mechanism, an algorithm-system co-design that strategically saves a selected subset of experts, effectively reducing the MoE checkpoint size to levels comparable with dense models. Incorporating hybrid parallel strategies, MoC-System involves fully sharded checkpointing strategies to evenly distribute the workload across distributed ranks. Furthermore, MoC-System introduces a two-level checkpointing management method that asynchronously handles in-memory snapshots and persistence processes. We build MoC-System upon the Megatron-DeepSpeed framework, achieving up to a 98.9% reduction in overhead for each checkpointing process compared to the original method, during MoE model training with ZeRO-2 data parallelism and expert parallelism. Additionally, extensive empirical analyses substantiate that our methods enhance efficiency while maintaining comparable model accuracy, even achieving an average accuracy increase of 1.08% on downstream tasks.
Class imbalance is a prevalent issue in real-world graph-structure data, such as social and citation networks, posing significant challenges for Graph Neural Networks (GNNs). Existing solutions often focus on balancin...
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The EU Bioeconomy Strategy underscores the importance of fostering a shared understanding of the shift to a bioeconomy and raising awareness of the diverse biomass demand. Since concrete is a globally prevalent materi...
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In the digital age, the explosive growth of information has led to significant challenges such as the burden of reading long texts, cross-language communication barriers, and information overload. To address these iss...
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ISBN:
(数字)9798331508456
ISBN:
(纸本)9798331508463
In the digital age, the explosive growth of information has led to significant challenges such as the burden of reading long texts, cross-language communication barriers, and information overload. To address these issues, this paper investigates the application and development of naturallanguageprocessing (NLP) algorithm systems in corpus linguistics. We employ two methods for automatic summary generation-extractive and generative-which identify key sentences or phrases and utilize Seq2Seq models to produce natural and fluent summaries. For machine translation, we build a multi-language parallel corpus and train a neural machine translation (NMT) model using statistical techniques and the Transformer architecture with attention mechanisms. This approach enhances translation accuracy and fluency. Additionally, we explore summarization technology to combat information overload, improving summary relevance and accuracy through multi-task learning and generative adversarial networks (GANs). Our Re3Sum model further guides text summary generation using real summaries as soft templates. That the Transformer-based model significantly outperforms traditional methods in accuracy, fluency, and information retention, achieving a maximum information retention score of 88.76 points. This reduces the burden of reading long texts and enhances cross-language communication efficiency. Overall, this study not only offers new research directions for the NLP field but also provides practical solutions to languageprocessing challenges.
With the rapid increase in the volume of scientific literature, researchers face challenges in keeping up with the latest advancements while summarizing the documents. Scientific document text summarization offers a s...
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ISBN:
(纸本)9783031837920;9783031837937
With the rapid increase in the volume of scientific literature, researchers face challenges in keeping up with the latest advancements while summarizing the documents. Scientific document text summarization offers a solution by providing concise and informative summaries that highlight the key contributions from original texts. This study introduces a novel method leveraging deep learning, specifically the sBERT model to summarize scientific documents. The proposed approach treats the extractive summarization as a classification problem using a dual BERT model setup. The methodology is evaluated using data set from CL-SciSumm. Results indicate that our approach significantly outperforms the existing methods in terms of ROUGE scores, demonstrating its effectiveness in generating accurate summaries of scientific literature.
The increasing workload of educators, particularly in manual question creation, poses a significant challenge in modern education. Manual question creation demands time, effort, and a deep understanding of the materia...
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Randomized Controlled Trials (RCTs) are rigorous clinical studies crucial for reliable decision-making, but their credibility can be compromised by bias. The Cochrane Risk of Bias tool (RoB 2) assesses this risk, yet ...
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