14 citations · 20 across the 7 of their papers we have counts for
7 papers
Towards Self-Supervised Covariance Estimation in Deep Heteroscedastic Regression
Megh Shukla, Aziz Shameem, Mathieu Salzmann +1
Deep heteroscedastic regression models the mean and covariance of the target distribution through neural networks. The challenge arises from heteroscedasticity, which implies that…
MotionMap: Representing Multimodality in Human Pose Forecasting
Reyhaneh Hosseininejad, Megh Shukla, Saeed Saadatnejad +2
Human pose forecasting is inherently multimodal since multiple futures exist for an observed pose sequence. However, evaluating multimodality is challenging since the task is ill-p…
TIC-TAC: A Framework for Improved Covariance Estimation in Deep Heteroscedastic Regression
Megh Shukla, Mathieu Salzmann, Alexandre Alahi
Deep heteroscedastic regression involves jointly optimizing the mean and covariance of the predicted distribution using the negative log-likelihood. However, recent works show that…
VL4Pose: Active Learning Through Out-Of-Distribution Detection For Pose Estimation
Megh Shukla, Roshan Roy, Pankaj Singh +2
Advances in computing have enabled widespread access to pose estimation, creating new sources of data streams. Unlike mock set-ups for data collection, tapping into these data stre…
A Mathematical Analysis of Learning Loss for Active Learning in Regression
Megh Shukla, Shuaib Ahmed
Active learning continues to remain significant in the industry since it is data efficient. Not only is it cost effective on a constrained budget, continuous refinement of the mode…
Bayesian Uncertainty and Expected Gradient Length -- Regression: Two Sides Of The Same Coin?
Megh Shukla
Active learning algorithms select a subset of data for annotation to maximize the model performance on a budget. One such algorithm is Expected Gradient Length, which as the name s…
LEt-SNE: A Hybrid Approach To Data Embedding and Visualization of Hyperspectral Imagery
Megh Shukla, Biplab Banerjee, Krishna Mohan Buddhiraju
Hyperspectral Imagery (and Remote Sensing in general) captured from UAVs or satellites are highly voluminous in nature due to the large spatial extent and wavelengths captured by t…