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Learning From Noisy Singly-labeled Data
Ashish Khetan, Zachary C. Lipton, Anima Anandkumar
Supervised learning depends on annotated examples, which are taken to be the \emph{ground truth}. But these labels often come from noisy crowdsourcing platforms, like Amazon Mechan…
StrassenNets: Deep Learning with a Multiplication Budget
Michael Tschannen, Aran Khanna, Anima Anandkumar
A large fraction of the arithmetic operations required to evaluate deep neural networks (DNNs) consists of matrix multiplications, in both convolution and fully connected layers. W…
Long-term Forecasting using Higher Order Tensor RNNs
Rose Yu, Stephan Zheng, Anima Anandkumar +1
We present Higher-Order Tensor RNN (HOT-RNN), a novel family of neural sequence architectures for multivariate forecasting in environments with nonlinear dynamics. Long-term foreca…
Tensor Regression Networks
Jean Kossaifi, Zachary C. Lipton, Arinbjorn Kolbeinsson +3
Convolutional neural networks typically consist of many convolutional layers followed by one or more fully connected layers. While convolutional layers map between high-order activ…
Compact Tensor Pooling for Visual Question Answering
Yang Shi, Tommaso Furlanello, Anima Anandkumar
Performing high level cognitive tasks requires the integration of feature maps with drastically different structure. In Visual Question Answering (VQA) image descriptors have spati…
Tensor Contraction Layers for Parsimonious Deep Nets
Jean Kossaifi, Aran Khanna, Zachary C. Lipton +2
Tensors offer a natural representation for many kinds of data frequently encountered in machine learning. Images, for example, are naturally represented as third order tensors, whe…
Experimental results : Reinforcement Learning of POMDPs using Spectral Methods
Kamyar Azizzadenesheli, Alessandro Lazaric, Animashree Anandkumar
We propose a new reinforcement learning algorithm for partially observable Markov decision processes (POMDP) based on spectral decomposition methods. While spectral methods have be…