1 code implementation • 24 Oct 2023 • Alokendu Mazumder, Tirthajit Baruah, Bhartendu Kumar, Rishab Sharma, Vishwajeet Pattanaik, Punit Rathore
In LoRAE, we incorporated a low-rank regularizer to adaptively reconstruct a low-dimensional latent space while preserving the basic objective of an autoencoder.
1 code implementation • 28 Sep 2023 • Chirayu D. Athalye, Kunal N. Chaudhury, Bhartendu Kumar
The present work is motivated by the following questions: Can we relax the assumptions on the forward model?
no code implementations • 15 Sep 2023 • Alokendu Mazumder, Rishabh Sabharwal, Manan Tayal, Bhartendu Kumar, Punit Rathore
Lastly, (iii) we also demonstrate that our derived constant step size has better abilities in reducing the gradient norms, and empirically, we show that despite the accumulation of a few past gradients, the key driver for convergence in Adam is the non-increasing step sizes.