367 citations · 798 across the 13 of their papers we have counts for
3 papers · 1 filter
Learning to Linearize Under Uncertainty
Ross Goroshin, Michael Mathieu, Yann LeCun
Training deep feature hierarchies to solve supervised learning tasks has achieved state of the art performance on many problems in computer vision. However, a principled way in whi…
Stacked What-Where Auto-encoders
Junbo Zhao, Michael Mathieu, Ross Goroshin +1
We present a novel architecture, the "stacked what-where auto-encoders" (SWWAE), which integrates discriminative and generative pathways and provides a unified approach to supervis…
Unsupervised Feature Learning from Temporal Data
Ross Goroshin, Joan Bruna, Jonathan Tompson +2
Current state-of-the-art classification and detection algorithms rely on supervised training. In this work we study unsupervised feature learning in the context of temporally coher…