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Found 6 papers, 2 papers with code
On the Empirical Complexity of Reasoning and Planning in LLMs
We found that if problems can be decomposed into a sequence of reasoning steps and learning to predict the next step has a low sample and computational complexity, explicitly outlining the reasoning chain with all necessary information for predicting the next step may improve performance.
A Virtual Reality Training System for Automotive Engines Assembly and Disassembly
A free-to-use executable file (Microsoft Windows) and open-source code are available at https://github. com/LadissonLai/SUSTech_VREngine for facilitating the development of VR systems in the automotive industry.
Differentiable Parsing and Visual Grounding of Natural Language Instructions for Object Placement
Natural language generally describes objects and spatial relations with compositionality and ambiguity, two major obstacles to effective language grounding.
Visual Semantic SLAM with Landmarks for Large-Scale Outdoor Environment
Semantic SLAM is an important field in autonomous driving and intelligent agents, which can enable robots to achieve high-level navigation tasks, obtain simple cognition or reasoning ability and achieve language-based human-robot-interaction.
Active Learning for Risk-Sensitive Inverse Reinforcement Learning
One typical assumption in inverse reinforcement learning (IRL) is that human experts act to optimize the expected utility of a stochastic cost with a fixed distribution.
DeepObfuscation: Securing the Structure of Convolutional Neural Networks via Knowledge Distillation
Our obfuscation approach is very effective to protect the critical structure of a deep learning model from being exposed to attackers.
Cryptography and Security
