65 citations · 233 across the 23 of their papers we have counts for
6 papers · 1 filter
Learning by Asking Questions
Ishan Misra, Ross Girshick, Rob Fergus +3
We introduce an interactive learning framework for the development and testing of intelligent visual systems, called learning-by-asking (LBA). We explore LBA in context of the Visu…
Log-DenseNet: How to Sparsify a DenseNet
Hanzhang Hu, Debadeepta Dey, Allison Del Giorno +2
Skip connections are increasingly utilized by deep neural networks to improve accuracy and cost-efficiency. In particular, the recent DenseNet is efficient in computation and param…
Predictive-State Decoders: Encoding the Future into Recurrent Networks
Arun Venkatraman, Nicholas Rhinehart, Wen Sun +5
Recurrent neural networks (RNNs) are a vital modeling technique that rely on internal states learned indirectly by optimization of a supervised, unsupervised, or reinforcement trai…
Ignoring Distractors in the Absence of Labels: Optimal Linear Projection to Remove False Positives During Anomaly Detection
Allison Del Giorno, J. Andrew Bagnell, Martial Hebert
In the anomaly detection setting, the native feature embedding can be a crucial source of bias. We present a technique, Feature Omission using Context in Unsupervised Settings (FOC…
Cut, Paste and Learn: Surprisingly Easy Synthesis for Instance Detection
Debidatta Dwibedi, Ishan Misra, Martial Hebert
A major impediment in rapidly deploying object detection models for instance detection is the lack of large annotated datasets. For example, finding a large labeled dataset contain…
Gradient Boosting on Stochastic Data Streams
Hanzhang Hu, Wen Sun, Arun Venkatraman +2
Boosting is a popular ensemble algorithm that generates more powerful learners by linearly combining base models from a simpler hypothesis class. In this work, we investigate the p…