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Found 5 papers, 4 papers with code
Training for the Future: A Simple Gradient Interpolation Loss to Generalize Along Time
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.
MIMOQA: Multimodal Input Multimodal Output Question Answering
In this paper, we propose a novel task - MIMOQA - Multimodal Input Multimodal Output Question Answering in which the output is also multimodal.
Rule Augmented Unsupervised Constituency Parsing
We introduce a novel formulation that takes advantage of the syntactic grammar rules and is independent of the base system.
What if Neural Networks had SVDs?
We present an algorithm that is fast enough to speed up several matrix operations.
Teaching CNNs to mimic Human Visual Cognitive Process & regularise Texture-Shape bias
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.
