48 citations · 58 across the 6 of their papers we have counts for
6 papers
An Empirical Study on Clustering Pretrained Embeddings: Is Deep Strictly Better?
Tyler R. Scott, Ting Liu, Michael C. Mozer +1
Recent research in clustering face embeddings has found that unsupervised, shallow, heuristic-based methods -- including -means and hierarchical agglomerative clustering -- unde…
Online Unsupervised Learning of Visual Representations and Categories
Mengye Ren, Tyler R. Scott, Michael L. Iuzzolino +2
Real world learning scenarios involve a nonstationary distribution of classes with sequential dependencies among the samples, in contrast to the standard machine learning formulati…
von Mises-Fisher Loss: An Exploration of Embedding Geometries for Supervised Learning
Tyler R. Scott, Andrew C. Gallagher, Michael C. Mozer
Recent work has argued that classification losses utilizing softmax cross-entropy are superior not only for fixed-set classification tasks, but also by outperforming losses develop…
Unifying Few- and Zero-Shot Egocentric Action Recognition
Tyler R. Scott, Michael Shvartsman, Karl Ridgeway
Although there has been significant research in egocentric action recognition, most methods and tasks, including EPIC-KITCHENS, suppose a fixed set of action classes. Fixed-set cla…
Stochastic Prototype Embeddings
Tyler R. Scott, Karl Ridgeway, Michael C. Mozer
Supervised deep-embedding methods project inputs of a domain to a representational space in which same-class instances lie near one another and different-class instances lie far ap…
Adapted Deep Embeddings: A Synthesis of Methods for -Shot Inductive Transfer Learning
Tyler R. Scott, Karl Ridgeway, Michael C. Mozer
The focus in machine learning has branched beyond training classifiers on a single task to investigating how previously acquired knowledge in a source domain can be leveraged to fa…