18 citations · 43 across the 12 of their papers we have counts for
5 papers · 1 filter
Prospective Learning in Retrospect
Yuxin Bai, Cecelia Shuai, Ashwin De Silva +3
In most real-world applications of artificial intelligence, the distributions of the data and the goals of the learners tend to change over time. The Probably Approximately Correct…
The Value of Out-of-Distribution Data
Ashwin De Silva, Rahul Ramesh, Carey E. Priebe +2
We expect the generalization error to improve with more samples from a similar task, and to deteriorate with more samples from an out-of-distribution (OOD) task. In this work, we s…
Simple Calibration via Geodesic Kernels
Jayanta Dey, Haoyin Xu, Ashwin De Silva +1
Deep discriminative approaches, such as decision forests and deep neural networks, have recently found applications in many important real-world scenarios. However, deploying these…
Prospective Learning: Principled Extrapolation to the Future
Ashwin De Silva, Rahul Ramesh, Lyle Ungar +40
Learning is a process which can update decision rules, based on past experience, such that future performance improves. Traditionally, machine learning is often evaluated under the…
A Joint Convolutional and Spatial Quad-Directional LSTM Network for Phase Unwrapping
Malsha V. Perera, Ashwin De Silva
Phase unwrapping is a classical ill-posed problem which aims to recover the true phase from wrapped phase. In this paper, we introduce a novel Convolutional Neural Network (CNN) th…