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Found 5 papers, 4 papers with code
Automatic Truss Design with Reinforcement Learning
Directly applying end-to-end reinforcement learning (RL) methods to truss layout design is infeasible either, since only a tiny portion of the entire layout space is valid under the physical constraints, leading to particularly sparse rewards for RL training.
NLP From Scratch Without Large-Scale Pretraining: A Simple and Efficient Framework
Pretrained language models have become the standard approach for many NLP tasks due to strong performance, but they are very expensive to train.
SimCSE: Simple Contrastive Learning of Sentence Embeddings
This paper presents SimCSE, a simple contrastive learning framework that greatly advances state-of-the-art sentence embeddings.
Ranked #3 on
Linear-Probe Classification
on SentEval
CogDL: A Comprehensive Library for Graph Deep Learning
In CogDL, we propose a unified design for the training and evaluation of GNN models for various graph tasks, making it unique among existing graph learning libraries.
A Semi-Supervised Framework for Automatic Pixel-Wise Breast Cancer Grading of Histological Images
The proposed EM approach is based on the collaborative filtering among the annotated and unannotated datasets.
