6 accepted papers
While Large Language Models (LLMs) demonstrate immense potential for automating integrated circuit (IC) development, their practical deployment is fundamentally limited by restricted context windows. Existing context-extension methods struggle to achieve effective semantic modeling and thorough mult
Dual-arm robots promise greater efficiency but require planning for complex tasks with nonlinear sub-task dependencies. Current methods using Large Language Models (LLMs) suffer from a fundamental trade-off: generating linear sequences is efficient but fails to model parallelism and adapt to changes…
Spiking Neural Networks (SNNs), with their brain-inspired spatiotemporal dynamics and spike-driven computation, have emerged as promising energy-efficient alternatives to Artificial Neural Networks (ANNs). However, existing SNNs typically replicate inputs directly or aggregate them into frames at fi
Vision-language navigation (VLN) is a pivotal area within embodied intelligence, where agents must navigate based on natural language instructions. While traditional VLN research has focused on enhancing environmental comprehension and decision-making policy, these methods often reveal substantial p
Spiking Neural Networks (SNNs) have gained attention for their apparent energy efficiency and significant biological interpretability, although they also face significant challenges such as prolonged latency and suboptimal tracking accuracy. Recent studies have explored the application of SNNs in ob…
Text-to-Speech (TTS) systems have been evolving rapidly in recent years. With the great modelling power of deep neural networks, researchers have achieved end-to-end conversion from raw text to speech. It has been shown by various research projects that end-to-end TTS systems are able to generate sp…