2 citations · 2 across the 14 of their papers we have counts for
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Learning Low-Rank Latent Spaces with Simple Deterministic Autoencoder: Theoretical and Empirical Insights
Alokendu Mazumder, Tirthajit Baruah, Bhartendu Kumar +3
The autoencoder is an unsupervised learning paradigm that aims to create a compact latent representation of data by minimizing the reconstruction loss. However, it tends to overloo…
A Theoretical and Empirical Study on the Convergence of Adam with an "Exact" Constant Step Size in Non-Convex Settings
Alokendu Mazumder, Rishabh Sabharwal, Manan Tayal +2
In neural network training, RMSProp and Adam remain widely favoured optimisation algorithms. One of the keys to their performance lies in selecting the correct step size, which can…
DeepVAT: A Self-Supervised Technique for Cluster Assessment in Image Datasets
Alokendu Mazumder, Tirthajit Baruah, Akash Kumar Singh +3
Estimating the number of clusters and cluster structures in unlabeled, complex, and high-dimensional datasets (like images) is challenging for traditional clustering algorithms. In…