Recent advances in document summarization
The task of automatic document summarization aims at generating short summaries for
originally long documents. A good summary should cover the most important information of …
originally long documents. A good summary should cover the most important information of …
Determinantal point processes for machine learning
Determinantal point processes (DPPs) are elegant probabilistic models of repulsion that
arise in quantum physics and random matrix theory. In contrast to traditional structured …
arise in quantum physics and random matrix theory. In contrast to traditional structured …
Diverse sequential subset selection for supervised video summarization
Video summarization is a challenging problem with great application potential. Whereas
prior approaches, largely unsupervised in nature, focus on sampling useful frames and …
prior approaches, largely unsupervised in nature, focus on sampling useful frames and …
Summary transfer: Exemplar-based subset selection for video summarization
Video summarization has unprecedented importance to help us digest, browse, and search
today's ever-growing video collections. We propose a novel subset selection technique that …
today's ever-growing video collections. We propose a novel subset selection technique that …
Discovering diverse subset for unsupervised hyperspectral band selection
Y Yuan, X Zheng, X Lu - IEEE Transactions on Image …, 2016 - ieeexplore.ieee.org
Band selection, as a special case of the feature selection problem, tries to remove redundant
bands and select a few important bands to represent the whole image cube. This has …
bands and select a few important bands to represent the whole image cube. This has …
Differentiable subset pruning of transformer heads
J Li, R Cotterell, M Sachan - Transactions of the Association for …, 2021 - direct.mit.edu
Multi-head attention, a collection of several attention mechanisms that independently attend
to different parts of the input, is the key ingredient in the Transformer. Recent work has …
to different parts of the input, is the key ingredient in the Transformer. Recent work has …
Learning from the dark: boosting graph convolutional neural networks with diverse negative samples
Abstract Graph Convolutional Neural Networks (GCNs) have been generally accepted to be
an effective tool for node representations learning. An interesting way to understand GCNs …
an effective tool for node representations learning. An interesting way to understand GCNs …
Learning the parameters of determinantal point process kernels
Determinantal point processes (DPPs) are well-suited for modeling repulsion and have
proven useful in applications where diversity is desired. While DPPs have many appealing …
proven useful in applications where diversity is desired. While DPPs have many appealing …
Streaming non-monotone submodular maximization: Personalized video summarization on the fly
The need for real time analysis of rapidly producing data streams (eg, video and image
streams) motivated the design of streaming algorithms that can efficiently extract and …
streams) motivated the design of streaming algorithms that can efficiently extract and …
Expectation-maximization for learning determinantal point processes
A determinantal point process (DPP) is a probabilistic model of set diversity compactly
parameterized by a positive semi-definite kernel matrix. To fit a DPP to a given task, we …
parameterized by a positive semi-definite kernel matrix. To fit a DPP to a given task, we …