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Saturday

  1. The Gradient

    Neural Algorithmic Reasoning: Teaching Neural Networks to Execute Classical Algorithms

    The Gradient published an article exploring neural algorithmic reasoning, that is, using deep neural networks to capture the computations of classical algorithms such as shortest paths and sorting. The article points out that MIT's "What Can Neural Networks Reason About?" demonstrates that the better the algorithmic alignment, the better the generalization; GNNs are naturally aligned with dynamic programming and outperform architectures with weaker inductive biases on DP execution benchmarks. The author also cautions that algorithmic alignment does not mean one can arbitrarily apply any highly expressive GNN, and out-of-distribution performance remains a key challenge.

    The Gradient published an article exploring neural algorithmic reasoning, that is, using deep neural networks to capture the computations of classical algorithms such as shortest paths and sorting. The article points out that MIT's "What Can Neural Networks Reason About?" demonstrates that the better the algorithmic alignment, the better the generalization; GNNs are naturally aligned with dynamic programming and outperform architectures with weaker inductive biases on DP execution benchmarks. Th

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    #ai-vertical#Papers/Research#Reasoning#English text is being prepared.

Wednesday

  1. People's Daily

    North Carolina State University team develops self-resetting soft ring jumping robot that jumps continuously over 1,000 times under constant illumination

    A research team at North Carolina State University in the United States has published a new self-resetting soft ring jumping robot in the Proceedings of the National Academy of Sciences. It requires no manual resetting or complex control system, relying solely on the "physical intelligence" of liquid crystal elastomer materials and a ring structure, and jumps continuously more than 1,000 times under constant infrared illumination, with a maximum vertical jump height of up to 80 times its own height. The robot can also be adapted for "jump—swim—jump again" amphibious locomotion, and is expected in the future to be used for sensing and monitoring in complex environments such as grasslands, gravel, and shallow water.

    A research team at North Carolina State University in the United States has published a new self-resetting soft ring jumping robot in the Proceedings of the National Academy of Sciences. It requires no manual resetting or complex control system, relying solely on the "physical intelligence" of liquid crystal elastomer materials and a ring structure, and jumps continuously more than 1,000 times under constant infrared illumination, with a maximum vertical jump height of up to 80 times its own hei

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    #chinese#Papers/Research#Embodied AI