9 accepted papers
Deploying large language models (LLMs) on untrusted hardware entails a risk of weight extraction, which can lead to unauthorized replication and misuse of the model. A practical approach is to leverage Trusted Execution Environments (TEEs) and protect model security by obfuscating model weights. How…
The local extremum is a crucial factor that affects the efficiency of online coverage path planning (CPP). Most online CPP methods generate coverage motions point by point in unknown environments. However, these solutions ignore efficient global coverage and probably result in local extremum. This l
This paper addresses the challenge of Velocity Vector Control (VVC) for fixed-wing UAVs using Reinforcement Learning (RL) in the presence of imperfect demonstrations. The multi-objective and long-horizon nature of VVC introduces significant spatial and temporal complexities, complicating RL's explor
Multi-goal long-horizon problems are prevalent in real-world applications. The additional goal space introduced by multi-goal problems intensifies the spatial complexity of exploration; meanwhile, the long interaction sequences in long-horizon problems exacerbate the temporal complexity of explorati…
The emergence of AIGC has brought attention to the issue of generating realistic deceptive content. While AIGC has the potential to revolutionize content creation, it also facilitates criminal activities. Specifically, the manipulation of speech has been exploited in tele-fraud and financial fraud s…
"Recent image-to-3D methods achieve impressive results with plausible 3D geometry due to the development of diffusion models and optimization techniques. However, existing image-to-3D methods suffer from texture deficiencies in novel views, including multi-view inconsistency and quality degradation.…
Dubins coverage has been extensively researched to address the coverage path planning (CPP) problem of a known environment for the curvature-constrained robot. However, its fixed-speed assumption prevents the robot from accelerating to reduce the time and limits its flexibility to avoid obstacles. T…
Mixture of Experts layers (MoEs) enable efficient scaling of language models through conditional computation. This paper presents a detailed empirical study of how autoregressive MoE language models scale in comparison with dense models in a wide range of settings: in- and out-of-domain language mod…
Complete coverage, which is the foundation of many robotic applications, aims to cover an area as quickly as possible. This study investigates the time-limited version of multi-robot complete coverage problem, that is, to find the least number of robots and allocate tasks properly to them such that…