no code implementations • 14 Feb 2024 • Ali Saheb Pasand, Reza Moravej, Mahdi Biparva, Raika Karimi, Ali Ghodsi
Our experiments demonstrate that the cost associated with the loss computation can be reduced via node or dimension sampling without lowering the downstream performance.
no code implementations • 14 Feb 2024 • Ali Saheb Pasand, Reza Moravej, Mahdi Biparva, Ali Ghodsi
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.
1 code implementation • 21 Sep 2021 • Mohammad Ali Alomrani, Reza Moravej, Elias B. Khalil
We present an end-to-end Reinforcement Learning framework for deriving better matching policies based on trial-and-error on historical data.