13 citations · 13 across the 10 of their papers we have counts for
5 papers · 1 filter
Understanding Submodular Information Measure Based Objectives for Representation Learning: A Variance and Separation Perspective
Rishabh Iyer, Truong Pham, Anay Majee
Submodular Information Measures (SIMs) have recently emerged as a powerful framework for representation learning and multimodal learning. In particular, the SCORE framework~\cite{m…
SMA: Submodular Modality Aligner For Data Efficient Multimodal Learning
Truong Pham, Anay Majee, Rishabh Iyer
Despite the recent success of Multimodal Foundation Models (FMs), their reliance on massive paired datasets limits their applicability in low-data and rare-scenario settings where…
InSQuAD: In-Context Learning for Efficient Retrieval via Submodular Mutual Information to Enforce Quality and Diversity
Souradeep Nanda, Anay Majee, Rishabh Iyer
In this paper, we introduce InSQuAD, designed to enhance the performance of In-Context Learning (ICL) models through Submodular Mutual Information} (SMI) enforcing Quality and Dive…
TabGLM: Tabular Graph Language Model for Learning Transferable Representations Through Multi-Modal Consistency Minimization
Anay Majee, Maria Xenochristou, Wei-Peng Chen
Handling heterogeneous data in tabular datasets poses a significant challenge for deep learning models. While attention-based architectures and self-supervised learning have achiev…
SCoRe: Submodular Combinatorial Representation Learning
Anay Majee, Suraj Kothawade, Krishnateja Killamsetty +1
In this paper we introduce the SCoRe (Submodular Combinatorial Representation Learning) framework, a novel approach in representation learning that addresses inter-class bias and i…