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Found 10 papers, 2 papers with code
Individual differences in the cognitive mechanisms of planning strategy discovery
While prior work has shown that MCRL models can learn new planning strategies and explain more participants' experience-driven discovery better than alternative mechanisms, it also revealed significant individual differences in metacognitive learning.
TSDW: A Tri-Stream Dynamic Weight Network for Cloth-Changing Person Re-Identification
This dynamic weighting network consists of three parallel feature streams: facial features, head-limb features, and global features.
Chumor 2.0: Towards Benchmarking Chinese Humor Understanding
Existing humor datasets and evaluations predominantly focus on English, leaving limited resources for culturally nuanced humor in non-English languages like Chinese.
Experience-driven discovery of planning strategies
In this work, we propose that new planning strategies are discovered through metacognitive reinforcement learning.
Your Semantic-Independent Watermark is Fragile: A Semantic Perturbation Attack against EaaS Watermark
Embedding-as-a-Service (EaaS) has emerged as a successful business pattern but faces significant challenges related to various forms of copyright infringement, particularly, the API misuse and model extraction attacks.
Chumor 1.0: A Truly Funny and Challenging Chinese Humor Understanding Dataset from Ruo Zhi Ba
Existing humor datasets and evaluations predominantly focus on English, lacking resources for culturally nuanced humor in non-English languages like Chinese.
Tables as Texts or Images: Evaluating the Table Reasoning Ability of LLMs and MLLMs
In this paper, we investigate the effectiveness of various LLMs in interpreting tabular data through different prompting strategies and data formats.
Structurally guided task decomposition in spatial navigation tasks
How are people able to plan so efficiently despite limited cognitive resources?
What are the mechanisms underlying metacognitive learning?
How is it that humans can solve complex planning tasks so efficiently despite limited cognitive resources?
Have I done enough planning or should I plan more?
People's decisions about how to allocate their limited computational resources are essential to human intelligence.
