We present a novel self-supervised approach for hierarchical representation learning and segmentation of perceptual inputs in a streaming fashion.

Linear RNN architectures, like Mamba, can be competitive with Transformer models in language modeling while having advantageous deployment characteristics. Given the focus on training large-scale Transformer models, we consider the challenge of converting these pretrained models for deployment.

In the reminder of the paper we will try whenever possible to formally introduce functions by clearly stating domain and co-domain.

We propose a novel loss function called Energy Discrepancy (ED) which does not rely on the computation of scores or expensive Markov chain Monte Carlo.

Educational recommendation lies in constructing a recommend system that can process the interactions between students and questions.

The focus of our work is on diagnostic tasks in personalized learning, such as cognitive diagnosis and knowledge tracing. The goal of these tasks is to assess students'

The general recipe for each communication round is composed of three steps.