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The Representative Capacity of Transformer Language Models LMs with n-gram Language Models LMs: Capturing the Parallelizable Nature of n-gram LMs
Neural language models (LMs) have become popular due to their extensive theoretical work mostly focusing on representational capacity. An earlier study of representational capacity using Boolean sequential models helps in a proper understanding of its lower and upper bound and the potential of the transformer architecture. LMs have become the backbone of many NLP tasks,… →
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Advancing Time Series Forecasting: The Impact of Bi-Mamba4TS’s Bidirectional State Space Modeling on Long-Term Predictive Accuracy
Time series forecasting is increasingly vital across numerous sectors, such as meteorology, finance, and energy management. Its relevance has grown as organizations aim to predict future trends and patterns more accurately. This type of forecasting is instrumental in enhancing decision-making processes and optimizing resource allocation over long periods. However, making accurate long-term forecasts is complex… →
