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PeerGPT: Probing the Roles of LLM-based Peer Agents as Team Moderators and Participants in Children's Collaborative Learning
In children's collaborative learning, effective peer conversations can significantly enhance the quality of children's collaborative interactions.
Bilevel Scheduled Sampling for Dialogue Generation
To enhance sampling diversity and improve the model's adaptability, we propose a smooth function that maps the combined result of sentence-level and word-level information to an appropriate range, and employ probabilistic sampling based on the mapped values instead of threshold truncation.
FLAME: A Self-Adaptive Auto-labeling System for Heterogeneous Mobile Processors
How to accurately and efficiently label data on a mobile device is critical for the success of training machine learning models on mobile devices.
Performance Analysis and Characterization of Training Deep Learning Models on Mobile Devices
In this paper, we perform a variety of experiments on a representative mobile device (the NVIDIA TX2) to study the performance of training deep learning models.
Runtime Concurrency Control and Operation Scheduling for High Performance Neural Network Training
These frameworks employ a dataflow model where the NN training is modeled as a directed graph composed of a set of nodes.
