1.1k citations · 7.8k across the 180 of their papers we have counts for
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Score Function Features for Discriminative Learning: Matrix and Tensor Framework
Majid Janzamin, Hanie Sedghi, Anima Anandkumar
Feature learning forms the cornerstone for tackling challenging learning problems in domains such as speech, computer vision and natural language processing. In this paper, we cons…
Score Function Features for Discriminative Learning
Majid Janzamin, Hanie Sedghi, Anima Anandkumar
Feature learning forms the cornerstone for tackling challenging learning problems in domains such as speech, computer vision and natural language processing. In this paper, we cons…
Provable Tensor Methods for Learning Mixtures of Generalized Linear Models
Hanie Sedghi, Majid Janzamin, Anima Anandkumar
We consider the problem of learning mixtures of generalized linear models (GLM) which arise in classification and regression problems. Typical learning approaches such as expectati…
Provable Methods for Training Neural Networks with Sparse Connectivity
Hanie Sedghi, Anima Anandkumar
We provide novel guaranteed approaches for training feedforward neural networks with sparse connectivity. We leverage on the techniques developed previously for learning linear net…
Analyzing Tensor Power Method Dynamics in Overcomplete Regime
Anima Anandkumar, Rong Ge, Majid Janzamin
We present a novel analysis of the dynamics of tensor power iterations in the overcomplete regime where the tensor CP rank is larger than the input dimension. Finding the CP decomp…
Sample Complexity Analysis for Learning Overcomplete Latent Variable Models through Tensor Methods
Animashree Anandkumar, Rong Ge, Majid Janzamin
We provide guarantees for learning latent variable models emphasizing on the overcomplete regime, where the dimensionality of the latent space can exceed the observed dimensionalit…