163 citations · 212 across the 10 of their papers we have counts for
11 papers
Blind Biological Sequence Denoising with Self-Supervised Set Learning
Nathan Ng, Ji Won Park, Jae Hyeon Lee +3
Biological sequence analysis relies on the ability to denoise the imprecise output of sequencing platforms. We consider a common setting where a short sequence is read out repeated…
Tuning the Tail Latency of Distributed Queries Using Replication
Nathan Ng, Hung Le, Marco Serafini
Querying graph data with low latency is an important requirement in application domains such as social networks and knowledge graphs. Graph queries perform multiple hops between ve…
If Influence Functions are the Answer, Then What is the Question?
Juhan Bae, Nathan Ng, Alston Lo +2
Influence functions efficiently estimate the effect of removing a single training data point on a model's learned parameters. While influence estimates align well with leave-one-ou…
Predicting Out-of-Domain Generalization with Neighborhood Invariance
Nathan Ng, Neha Hulkund, Kyunghyun Cho +1
Developing and deploying machine learning models safely depends on the ability to characterize and compare their abilities to generalize to new environments. Although recent work h…
Improving Dialogue Breakdown Detection with Semi-Supervised Learning
Nathan Ng, Marzyeh Ghassemi, Narendran Thangarajan +2
Building user trust in dialogue agents requires smooth and consistent dialogue exchanges. However, agents can easily lose conversational context and generate irrelevant utterances.…
SSMBA: Self-Supervised Manifold Based Data Augmentation for Improving Out-of-Domain Robustness
Nathan Ng, Kyunghyun Cho, Marzyeh Ghassemi
Models that perform well on a training domain often fail to generalize to out-of-domain (OOD) examples. Data augmentation is a common method used to prevent overfitting and improve…