117 citations · 148 across the 16 of their papers we have counts for
6 papers · 1 filter
Active Covering
Heinrich Jiang, Afshin Rostamizadeh
We analyze the problem of active covering, where the learner is given an unlabeled dataset and can sequentially label query examples. The objective is to label query all of the pos…
SCARF: Self-Supervised Contrastive Learning using Random Feature Corruption
Dara Bahri, Heinrich Jiang, Yi Tay +1
Self-supervised contrastive representation learning has proved incredibly successful in the vision and natural language domains, enabling state-of-the-art performance with orders o…
Churn Reduction via Distillation
Heinrich Jiang, Harikrishna Narasimhan, Dara Bahri +2
In real-world systems, models are frequently updated as more data becomes available, and in addition to achieving high accuracy, the goal is to also maintain a low difference in pr…
MeanShift++: Extremely Fast Mode-Seeking With Applications to Segmentation and Object Tracking
Jennifer Jang, Heinrich Jiang
MeanShift is a popular mode-seeking clustering algorithm used in a wide range of applications in machine learning. However, it is known to be prohibitively slow, with quadratic run…
Label Smoothed Embedding Hypothesis for Out-of-Distribution Detection
Dara Bahri, Heinrich Jiang, Yi Tay +1
Detecting out-of-distribution (OOD) examples is critical in many applications. We propose an unsupervised method to detect OOD samples using a -NN density estimate with respect…
Locally Adaptive Label Smoothing for Predictive Churn
Dara Bahri, Heinrich Jiang
Training modern neural networks is an inherently noisy process that can lead to high \emph{prediction churn} -- disagreements between re-trainings of the same model due to factors…