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Found 5 papers, 2 papers with code
Scalable Graph Self-Supervised Learning
Our experiments demonstrate that the cost associated with the loss computation can be reduced via node or dimension sampling without lowering the downstream performance.
WERank: Towards Rank Degradation Prevention for Self-Supervised Learning Using Weight Regularization
A common phenomena confining the representation quality in Self-Supervised Learning (SSL) is dimensional collapse (also known as rank degeneration), where the learned representations are mapped to a low dimensional subspace of the representation space.
Pro-KD: Progressive Distillation by Following the Footsteps of the Teacher
A case in point is that the best performing checkpoint of the teacher might not necessarily be the best teacher for training the student in KD.
Quantized Fisher Discriminant Analysis
This paper proposes a new subspace learning method, named Quantized Fisher Discriminant Analysis (QFDA), which makes use of both machine learning and information theory.
Artificial Counselor System for Stock Investment
This paper proposes a novel trading system which plays the role of an artificial counselor for stock investment.
Portfolio Optimization
Stock Prediction
+1
General Finance
Computational Engineering, Finance, and Science
General Economics
Economics
