13 citations · 21 across the 8 of their papers we have counts for
8 papers
H-SAUR: Hypothesize, Simulate, Act, Update, and Repeat for Understanding Object Articulations from Interactions
Kei Ota, Hsiao-Yu Tung, Kevin A. Smith +5
The world is filled with articulated objects that are difficult to determine how to use from vision alone, e.g., a door might open inwards or outwards. Humans handle these objects…
InSeGAN: A Generative Approach to Segmenting Identical Instances in Depth Images
Anoop Cherian, Goncalo Dias Pais, Siddarth Jain +2
In this paper, we present InSeGAN, an unsupervised 3D generative adversarial network (GAN) for segmenting (nearly) identical instances of rigid objects in depth images. Using an an…
Data-Efficient Learning for Complex and Real-Time Physical Problem Solving using Augmented Simulation
Kei Ota, Devesh K. Jha, Diego Romeres +7
Humans quickly solve tasks in novel systems with complex dynamics, without requiring much interaction. While deep reinforcement learning algorithms have achieved tremendous success…
Verification of Very Low-Resolution Faces Using An Identity-Preserving Deep Face Super-Resolution Network
Esra Ataer-Cansizoglu, Michael Jones, Ziming Zhang +1
Face super-resolution methods usually aim at producing visually appealing results rather than preserving distinctive features for further face identification. In this work, we prop…
Equilibrated Recurrent Neural Network: Neuronal Time-Delayed Self-Feedback Improves Accuracy and Stability
Ziming Zhang, Anil Kag, Alan Sullivan +1
We propose a novel {\it Equilibrated Recurrent Neural Network} (ERNN) to combat the issues of inaccuracy and instability in conventional RNNs. Drawing upon the concept of autapse i…
Time-Delay Momentum: A Regularization Perspective on the Convergence and Generalization of Stochastic Momentum for Deep Learning
Ziming Zhang, Wenju Xu, Alan Sullivan
In this paper we study the problem of convergence and generalization error bound of stochastic momentum for deep learning from the perspective of regularization. To do so, we first…