**BRepNet** is a neural network for CAD applications. It is designed to operate directly on B-rep data structures, avoiding the need to approximate the model as meshes or point clouds. BRepNet defines convolutional kernels with respect to oriented coedges in the data structure. In the neighborhood of each coedge, a small collection of faces, edges and coedges can be identified and patterns in the feature vectors from these entities detected by specific learnable parameters.

**Dialogue-Adaptive Pre-training Objective (DAPO)** is a pre-training objective for dialogue adaptation, which is designed to measure qualities of dialogues from multiple important aspects, like Readability, Consistency and Fluency which have already been focused on by general LM pre-training objectives, and those also significant for assessing dialogues but ignored by general LM pre-training objectives, like Diversity and Specificity.

DAPO

Dialogue-adaptive Language Model Pre-training From Quality Estimation

BRepNet

BRepNet: A topological message passing system for solid models

**BLOOM** is a decoder-only Transformer language model that was trained on the ROOTS corpus, a dataset comprising hundreds of
sources in 46 natural and 13 programming languages (59 in total).

Source	BRepNet: A topological message passing system for solid models
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com