12 citations · 59 across the 9 of their papers we have counts for
7 papers · 1 filter
Optimizing Data Collection for Machine Learning
Rafid Mahmood, James Lucas, Jose M. Alvarez +2
Modern deep learning systems require huge data sets to achieve impressive performance, but there is little guidance on how much or what kind of data to collect. Over-collecting dat…
Domain Adversarial Training: A Game Perspective
David Acuna, Marc T Law, Guojun Zhang +1
The dominant line of work in domain adaptation has focused on learning invariant representations using domain-adversarial training. In this paper, we interpret this approach from a…
f-Domain-Adversarial Learning: Theory and Algorithms
David Acuna, Guojun Zhang, Marc T. Law +1
Unsupervised domain adaptation is used in many machine learning applications where, during training, a model has access to unlabeled data in the target domain, and a related labele…
Low Budget Active Learning via Wasserstein Distance: An Integer Programming Approach
Rafid Mahmood, Sanja Fidler, Marc T. Law
Active learning is the process of training a model with limited labeled data by selecting a core subset of an unlabeled data pool to label. The large scale of data sets used in dee…
Ultrahyperbolic Representation Learning
Marc T. Law, Jos Stam
In machine learning, data is usually represented in a (flat) Euclidean space where distances between points are along straight lines. Researchers have recently considered more exot…
A Theoretical Analysis of the Number of Shots in Few-Shot Learning
Tianshi Cao, Marc Law, Sanja Fidler
Few-shot classification is the task of predicting the category of an example from a set of few labeled examples. The number of labeled examples per category is called the number of…
Centroid-based deep metric learning for speaker recognition
Jixuan Wang, Kuan-Chieh Wang, Marc Law +2
Speaker embedding models that utilize neural networks to map utterances to a space where distances reflect similarity between speakers have driven recent progress in the speaker re…