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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...
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...
FlashSpeech: A Novel Speech Generation System that Significantly Reduces Computational Costs while Maintaining High-Quality Speech Output
In recent years, speech synthesis has undergone a profound transformation thanks to the emergence of large-scale generative models. This evolution has led to significant strides in zero-shot speech synthesis systems, including text-to-speech (TTS), voice conversion...
Mixture of Data Experts (MoDE) Transforms Vision-Language Models: Enhancing Accuracy and Efficiency through Specialized Data Experts in Noisy Environments
The interdisciplinary domain of vision-language representation seeks innovative methods to develop systems to understand the nuanced interactions between text and images. This area is pivotal as it enables machines to process and interpret the vast amount of digitally...
Neuromorphic Computing: Algorithms, Use Cases and Applications
Neuromorphic computing represents a transformative approach to artificial intelligence, seeking to emulate the human brain’s neural structures and processing methods. This computing paradigm offers significant advancements in efficiency and performance for specific...
SEED-X: A Unified and Versatile Foundation Model that can Model Multi-Granularity Visual Semantics for Comprehension and Generation Tasks
In artificial intelligence, a significant focus has been on developing models that simultaneously process and interpret multiple forms of data. These multimodal models are designed to analyze and synthesize information from various sources, such as text, images, and...
