62 citations · 245 across the 42 of their papers we have counts for
7 papers · 1 filter
Invenio: Discovering Hidden Relationships Between Tasks/Domains Using Structured Meta Learning
Sameeksha Katoch, Kowshik Thopalli, Jayaraman J. Thiagarajan +2
Exploiting known semantic relationships between fine-grained tasks is critical to the success of recent model agnostic approaches. These approaches often rely on meta-optimization…
Product of Orthogonal Spheres Parameterization for Disentangled Representation Learning
Ankita Shukla, Sarthak Bhagat, Shagun Uppal +2
Learning representations that can disentangle explanatory attributes underlying the data improves interpretabilty as well as provides control on data generation. Various learning f…
Temporal Transformer Networks: Joint Learning of Invariant and Discriminative Time Warping
Suhas Lohit, Qiao Wang, Pavan Turaga
Many time-series classification problems involve developing metrics that are invariant to temporal misalignment. In human activity analysis, temporal misalignment arises due to var…
SALT: Subspace Alignment as an Auxiliary Learning Task for Domain Adaptation
Kowshik Thopalli, Jayaraman J. Thiagarajan, Rushil Anirudh +1
Unsupervised domain adaptation aims to transfer and adapt knowledge learned from a labeled source domain to an unlabeled target domain. Key components of unsupervised domain adapta…
PI-Net: A Deep Learning Approach to Extract Topological Persistence Images
Anirudh Som, Hongjun Choi, Karthikeyan Natesan Ramamurthy +2
Topological features such as persistence diagrams and their functional approximations like persistence images (PIs) have been showing substantial promise for machine learning and c…
Non-Parametric Priors For Generative Adversarial Networks
Rajhans Singh, Pavan Turaga, Suren Jayasuriya +2
The advent of generative adversarial networks (GAN) has enabled new capabilities in synthesis, interpolation, and data augmentation heretofore considered very challenging. However,…
Geometry of Deep Generative Models for Disentangled Representations
Ankita Shukla, Shagun Uppal, Sarthak Bhagat +2
Deep generative models like variational autoencoders approximate the intrinsic geometry of high dimensional data manifolds by learning low-dimensional latent-space variables and an…