382 citations · 978 across the 50 of their papers we have counts for
8 papers · 1 filter
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…
Learning Anytime Predictions in Neural Networks via Adaptive Loss Balancing
Hanzhang Hu, Debadeepta Dey, Martial Hebert +1
This work considers the trade-off between accuracy and test-time computational cost of deep neural networks (DNNs) via \emph{anytime} predictions from auxiliary predictions. Specif…
Shared Autonomy via Hindsight Optimization for Teleoperation and Teaming
Shervin Javdani, Henny Admoni, Stefania Pellegrinelli +2
In shared autonomy, a user and autonomous system work together to achieve shared goals. To collaborate effectively, the autonomous system must know the user's goal. As such, most p…
A Fast Stochastic Contact Model for Planar Pushing and Grasping: Theory and Experimental Validation
Jiaji Zhou, J. Andrew Bagnell, Matthew T. Mason
Based on the convex force-motion polynomial model for quasi-static sliding, we derive the kinematic contact model to determine the contact modes and instantaneous object motion on…
Deeply AggreVaTeD: Differentiable Imitation Learning for Sequential Prediction
Wen Sun, Arun Venkatraman, Geoffrey J. Gordon +2
Researchers have demonstrated state-of-the-art performance in sequential decision making problems (e.g., robotics control, sequential prediction) with deep neural network models. O…
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…