Further, the $\ell_2^2$-regressor which minimizes the loss on the aggregated dataset has a loss within $\left(1 + o(1)\right)$-factor of the optimum on the original dataset w. p.
Our algorithm is versatile and can be used with many popular compression methods including pruning, low-rank factorization, and quantization.
To bridge the gap between these two lines of work, we first hypothesize and verify that while SB may not altogether preclude learning complex features, it amplifies simpler features over complex ones.
We consider the problem of OOD generalization, where the goal is to train a model that performs well on test distributions that are different from the training distribution.
In several real world applications, machine learning models are deployed to make predictions on data whose distribution changes gradually along time, leading to a drift between the train and test distributions.
In this paper, we propose a novel task - MIMOQA - Multimodal Input Multimodal Output Question Answering in which the output is also multimodal.
We introduce a novel formulation that takes advantage of the syntactic grammar rules and is independent of the base system.
Recent experiments in computer vision demonstrate texture bias as the primary reason for supreme results in models employing Convolutional Neural Networks (CNNs), conflicting with early works claiming that these networks identify objects using shape.