35 citations · 66 across the 33 of their papers we have counts for
5 papers · 1 filter
Scalable Hybrid Learning Techniques for Scientific Data Compression
Tania Banerjee, Jong Choi, Jaemoon Lee +5
Data compression is becoming critical for storing scientific data because many scientific applications need to store large amounts of data and post process this data for scientific…
Expressing linear equality constraints in feedforward neural networks
Anand Rangarajan, Pan He, Jaemoon Lee +2
We seek to impose linear, equality constraints in feedforward neural networks. As top layer predictors are usually nonlinear, this is a difficult task if we seek to deploy standard…
Learning Canonical Embeddings for Unsupervised Shape Correspondence with Locally Linear Transformations
Pan He, Patrick Emami, Sanjay Ranka +1
We present a new approach to unsupervised shape correspondence learning between pairs of point clouds. We make the first attempt to adapt the classical locally linear embedding alg…
Towards Improving the Generation Quality of Autoregressive Slot VAEs
Patrick Emami, Pan He, Sanjay Ranka +1
Unconditional scene inference and generation are challenging to learn jointly with a single compositional model. Despite encouraging progress on models that extract object-centric…
Self-Supervised Robust Scene Flow Estimation via the Alignment of Probability Density Functions
Pan He, Patrick Emami, Sanjay Ranka +1
In this paper, we present a new self-supervised scene flow estimation approach for a pair of consecutive point clouds. The key idea of our approach is to represent discrete point c…