5 papers
Block Disentanglement in CRL: Bridging Identifiability and Visual State Estimation
Emre Acartürk, Pranamya Kulkarni, Puranjay Datta +3
Causal representation learning (CRL) is the process of recovering causally-related latent variables from high-dimensional observations. As a label-free inference method, CRL is par…
ROPES: Robotic Pose Estimation via Score-Based Causal Representation Learning
Pranamya Kulkarni, Puranjay Datta, Burak Varıcı +3
Causal representation learning (CRL) has emerged as a powerful unsupervised framework that (i) disentangles the latent generative factors underlying high-dimensional data, and (ii)…
Compressing Many-Shots in In-Context Learning
Devvrit Khatri, Pranamya Kulkarni, Nilesh Gupta +9
Large Language Models (LLMs) have been shown to be able to learn different tasks without explicit finetuning when given many input-output examples / demonstrations through In-Conte…
Harnessing Shared Relations via Multimodal Mixup Contrastive Learning for Multimodal Classification
Raja Kumar, Raghav Singhal, Pranamya Kulkarni +2
Deep multimodal learning has shown remarkable success by leveraging contrastive learning to capture explicit one-to-one relations across modalities. However, real-world data often…
IndiBias: A Benchmark Dataset to Measure Social Biases in Language Models for Indian Context
Nihar Ranjan Sahoo, Pranamya Prashant Kulkarni, Narjis Asad +4
The pervasive influence of social biases in language data has sparked the need for benchmark datasets that capture and evaluate these biases in Large Language Models (LLMs). Existi…