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Tensor vs Matrix Methods: Robust Tensor Decomposition under Block Sparse Perturbations
Animashree Anandkumar, Prateek Jain, Yang Shi +1
Robust tensor CP decomposition involves decomposing a tensor into low rank and sparse components. We propose a novel non-convex iterative algorithm with guaranteed recovery. It alt…
Convolutional Dictionary Learning through Tensor Factorization
Furong Huang, Animashree Anandkumar
Tensor methods have emerged as a powerful paradigm for consistent learning of many latent variable models such as topic models, independent component analysis and dictionary learni…
A Scale Mixture Perspective of Multiplicative Noise in Neural Networks
Eric Nalisnick, Anima Anandkumar, Padhraic Smyth
Corrupting the input and hidden layers of deep neural networks (DNNs) with multiplicative noise, often drawn from the Bernoulli distribution (or 'dropout'), provides regularization…
Beating the Perils of Non-Convexity: Guaranteed Training of Neural Networks using Tensor Methods
Majid Janzamin, Hanie Sedghi, Anima Anandkumar
Training neural networks is a challenging non-convex optimization problem, and backpropagation or gradient descent can get stuck in spurious local optima. We propose a novel algori…
Fast and Guaranteed Tensor Decomposition via Sketching
Yining Wang, Hsiao-Yu Tung, Alexander Smola +1
Tensor CANDECOMP/PARAFAC (CP) decomposition has wide applications in statistical learning of latent variable models and in data mining. In this paper, we propose fast and randomize…
Multi-Object Classification and Unsupervised Scene Understanding Using Deep Learning Features and Latent Tree Probabilistic Models
Tejaswi Nimmagadda, Anima Anandkumar
Deep learning has shown state-of-art classification performance on datasets such as ImageNet, which contain a single object in each image. However, multi-object classification is f…
Learning Mixed Membership Community Models in Social Tagging Networks through Tensor Methods
Anima Anandkumar, Hanie Sedghi
Community detection in graphs has been extensively studied both in theory and in applications. However, detecting communities in hypergraphs is more challenging. In this paper, we…