Reducing parameter redundancies in neural network architectures is crucial for achieving feasible computational and memory requirements during training and inference phases.

To bridge this gap, we present Job-SDF, a dataset designed to train and benchmark job-skill demand forecasting models.

Compared to the clock-driven synchronous chip, the event-driven asynchronous chip achieves much lower energy consumption but only supports some specific network operations. Recently, a series of SNN projects have achieved tremendous success, significantly improving the SNN's performance. However, event-driven asynchronous chips do not support some of the proposed structures, making it impossible to integrate these SNNs into asynchronous hardware.

Current vision models typically maintain a fixed correspondence between their representation structure and image space. Each layer comprises a set of tokens arranged "on-the-grid," which biases patches or tokens to encode information at

We explore online learning in episodic Markov decision processes on non-stationary environments (changing losses and probability transitions).

Figure 1: Comparison between Lagrangian (left) and Eulerian (right) perspectives.