5 accepted papers
Large Language Models have demonstrated superior reasoning capabilities by generating step-by-step reasoning in natural language before deriving the final answer. Recently, Geiping et al. introduced 3.5B-Huginn as an alternative to this paradigm, a depth-recurrent Transformer that increases computat…
Conceptual abstraction and analogy-making are crucial for human learning, reasoning, and adapting to unfamiliar domains. Recently, large language models (LLMs) have made the synthesis of analogical data possible, which, however, still heavily relies on extensive human efforts to be annotated. This p…
This paper establishes a continuous time approximation, a piece-wise continuous differential equation, for the discrete Heavy-Ball (HB) momentum method with explicit discretization error. Investigating continuous differential equations has been a promising approach for studying the discrete optimiza…
Many robotics tasks, such as path planning or trajectory optimization, are formulated as optimal control problems (OCPs). The key to obtaining high performance lies in the design of the OCP's objective function. In practice, the objective function consists of a set of individual components that must
As an important work in computer vision, some recent representative works such as Two-stream networks, 3D ConvNets, and Transformer-based networks have achieved outstanding performance. However, due to the high computational cost, the explosion of computation time and parameters, they cannot meet th…