1 citations · 2 across the 8 of their papers we have counts for
8 papers
Chain-of-Thought Shows the Path to a Tree: Realizing Branching Complexity
Debanjan Dutta, Anish Chakrabarty, Swagatam Das
Chain of Thought (CoT) lifts the expressive ceiling of bounded-depth Transformers, with characterizations tying the number of CoT steps to circuit complexity classes. What remains…
Relation-Aware Slicing in Cross-Domain Alignment
Dhruv Sarkar, Aprameyo Chakrabartty, Anish Chakrabarty +1
The Sliced Gromov-Wasserstein (SGW) distance, aiming to relieve the computational cost of solving a non-convex quadratic program that is the Gromov-Wasserstein distance, utilizes p…
On the Existence of Universal Simulators of Attention
Debanjan Dutta, Anish Chakrabarty, Faizanuddin Ansari +1
Previous work on the learnability of transformers \textemdash\ focused primarily on examining their ability to approximate specific algorithmic patterns through training \textemdas…
On Robust Cross Domain Alignment
Anish Chakrabarty, Arkaprabha Basu, Swagatam Das
The Gromov-Wasserstein (GW) distance is an effective measure of alignment between distributions supported on distinct ambient spaces. Calculating essentially the mutual departure f…
Fortifying Fully Convolutional Generative Adversarial Networks for Image Super-Resolution Using Divergence Measures
Arkaprabha Basu, Kushal Bose, Sankha Subhra Mullick +2
Super-Resolution (SR) is a time-hallowed image processing problem that aims to improve the quality of a Low-Resolution (LR) sample up to the standard of its High-Resolution (HR) co…
Concurrent Density Estimation with Wasserstein Autoencoders: Some Statistical Insights
Anish Chakrabarty, Arkaprabha Basu, Swagatam Das
Variational Autoencoders (VAEs) have been a pioneering force in the realm of deep generative models. Amongst its legions of progenies, Wasserstein Autoencoders (WAEs) stand out in…
Interval Bound Interpolation for Few-shot Learning with Few Tasks
Shounak Datta, Sankha Subhra Mullick, Anish Chakrabarty +1
Few-shot learning aims to transfer the knowledge acquired from training on a diverse set of tasks to unseen tasks from the same task distribution with a limited amount of labeled d…
Statistical Regeneration Guarantees of the Wasserstein Autoencoder with Latent Space Consistency
Anish Chakrabarty, Swagatam Das
The introduction of Variational Autoencoders (VAE) has been marked as a breakthrough in the history of representation learning models. Besides having several accolades of its own,…