3 citations · 6 across the 3 of their papers we have counts for
5 papers
AutoNLU: Detecting, root-causing, and fixing NLU model errors
Pooja Sethi, Denis Savenkov, Forough Arabshahi +6
Improving the quality of Natural Language Understanding (NLU) models, and more specifically, task-oriented semantic parsing models, in production is a cumbersome task. In this work…
Localized Uncertainty Attacks
Ousmane Amadou Dia, Theofanis Karaletsos, Caner Hazirbas +3
The susceptibility of deep learning models to adversarial perturbations has stirred renewed attention in adversarial examples resulting in a number of attacks. However, most of the…
Robust Deep Learning with Active Noise Cancellation for Spatial Computing
Li Chen, David Yang, Purvi Goel +1
This paper proposes CANC, a Co-teaching Active Noise Cancellation method, applied in spatial computing to address deep learning trained with extreme noisy labels. Deep learning alg…
Multi-Task Learning with Incomplete Data for Healthcare
Xin J. Hunt, Saba Emrani, Ilknur Kaynar Kabul +1
Multi-task learning is a type of transfer learning that trains multiple tasks simultaneously and leverages the shared information between related tasks to improve the generalizatio…
RULLS: Randomized Union of Locally Linear Subspaces for Feature Engineering
Namita Lokare, Jorge Silva, Ilknur Kaynar Kabul
Feature engineering plays an important role in the success of a machine learning model. Most of the effort in training a model goes into data preparation and choosing the right rep…