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Neural Networks with Recurrent Generative Feedback
Neural networks are vulnerable to input perturbations such as additive n...
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Sample Efficient Graph-Based Optimization with Noisy Observations
We study sample complexity of optimizing "hill-climbing friendly" functi...
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Learning Near-optimal Convex Combinations of Basis Models with Generalization Guarantees
The problem of learning an optimal convex combination of basis models ha...
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Out-of-Distribution Detection Using Neural Rendering Generative Models
Out-of-distribution (OoD) detection is a natural downstream task for dee...
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Dual Dynamic Inference: Enabling More Efficient, Adaptive and Controllable Deep Inference
State-of-the-art convolutional neural networks (CNNs) yield record-break...
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Neural Rendering Model: Joint Generation and Prediction for Semi-Supervised Learning
Unsupervised and semi-supervised learning are important problems that ar...
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Semi-Supervised Learning with the Deep Rendering Mixture Model
Semi-supervised learning algorithms reduce the high cost of acquiring la...
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A Probabilistic Framework for Deep Learning
We develop a probabilistic framework for deep learning based on the Deep...
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BoxLib with Tiling: An AMR Software Framework
In this paper we introduce a block-structured adaptive mesh refinement (...
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A Probabilistic Theory of Deep Learning
A grand challenge in machine learning is the development of computationa...
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