3 papers
cs.LG2026
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…
cs.RO2025
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)…
cs.LG2025
Matryoshka Quantization
Pranav Nair, Puranjay Datta, Jeff Dean +2
Quantizing model weights is critical for reducing the communication and inference costs of large models. However, quantizing models -- especially to low precisions like int4 or int…