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Found 7 papers, 0 papers with code
Multimodal Representations for Teacher-Guided Compositional Visual Reasoning
Neural Module Networks (NMN) are a compelling method for visual question answering, enabling the translation of a question into a program consisting of a series of reasoning sub-tasks that are sequentially executed on the image to produce an answer.
Curriculum Learning for Compositional Visual Reasoning
Visual Question Answering (VQA) is a complex task requiring large datasets and expensive training.
Why is the prediction wrong? Towards underfitting case explanation via meta-classification
In this paper we present a heuristic method to provide individual explanations for those elements in a dataset (data points) which are wrongly predicted by a given classifier.
Efficient Autoprecoder-based deep learning for massive MU-MIMO Downlink under PA Non-Linearities
Specifically, we aim to design the PA-aware precoder and the receive decoder by leveraging the concept of autoprecoder, whereas the end-to-end massive multiuser (MU)-MIMO downlink is designed using a deep neural network (NN).
Global Vertices and the Noising Paradox
A theoretical and experimental analysis related to the identification of vertices of unknown shapes is presented.
Incremental Noising and its Fractal Behavior
This manuscript is about investigating this claim by introducing incremental noising, in a recursive deterministic manner, analogous to how smoothing is extended to progressive smoothing in similar tasks.
Noising versus Smoothing for Vertex Identification in Unknown Shapes
A method for identifying shape features of local nature on the shape's boundary, in a way that is facilitated by the presence of noise is presented.
