274 citations · 274 across the 6 of their papers we have counts for
7 papers
MHWA: Multi-timescale Hierarchical World-Action Model
Pengcheng Pan, Guoqing Ma, Yuhan Zhang +4
Autoregressive world models have emerged as a powerful paradigm for sample efficient decision making, yet their open loop rollouts often exhibit increasing drift that leads to erro…
A neural network for modeling human concept formation, understanding and communication
Liangxuan Guo, Haoyang Chen, Yang Chen +2
A remarkable capability of the human brain is to form more abstract conceptual representations from sensorimotor experiences and flexibly apply them independent of direct sensory i…
Flexible Tool Selection through Low-dimensional Attribute Alignment of Vision and Language
Guangfu Hao, Haojie Wen, Liangxuan Guo +3
Flexible tool selection reflects a complex cognitive ability that distinguishes humans from other species, yet computational models that capture this ability remain underdeveloped.…
Efficient Reinforcement Learning Through Adaptively Pretrained Visual Encoder
Yuhan Zhang, Guoqing Ma, Guangfu Hao +3
While Reinforcement Learning (RL) agents can successfully learn to handle complex tasks, effectively generalizing acquired skills to unfamiliar settings remains a challenge. One of…
Out-of-distribution forgetting: vulnerability of continual learning to intra-class distribution shift
Liangxuan Guo, Yang Chen, Shan Yu
Continual learning (CL) is an important technique to allow artificial neural networks to work in open environments. CL enables a system to learn new tasks without severe interferen…
Emergence of Symbols in Neural Networks for Semantic Understanding and Communication
Yang Chen, Liangxuan Guo, Shan Yu
The capacity to generate meaningful symbols and effectively employ them for advanced cognitive processes, such as communication, reasoning, and planning, constitutes a fundamental…
Continual Learning of Context-dependent Processing in Neural Networks
Guanxiong Zeng, Yang Chen, Bo Cui +1
Deep neural networks (DNNs) are powerful tools in learning sophisticated but fixed mapping rules between inputs and outputs, thereby limiting their application in more complex and…