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Discrete Signal Processing on Graphs
TLDR
This paper extends to signals on graphs DSP and its basic tenets, including filters, convolution, z-transform, impulse response, spectral representation, Fourier transform, frequency response, and illustrates DSP on graphs by classifying blogs, linear predicting and compressing data from irregularly located weather stations, or predicting behavior of customers of a mobile service provider.
View on IEEE
Visual Dialog
TLDR
A retrieval-based evaluation protocol for Visual Dialog where the AI agent is asked to sort a set of candidate answers and evaluated on metrics such as mean-reciprocal-rank of human response, and a family of neural encoder-decoder models, which outperform a number of sophisticated baselines.
View on IEEE
Discrete Signal Processing on Graphs: Frequency Analysis
TLDR
A definition of total variation for graph signals that naturally leads to a frequency ordering on graphs is proposed and defines low-, high-, and band-pass graph signals and filters.
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Adversarial Multiple Source Domain Adaptation
TLDR
This paper proposes multisource domain adversarial networks (MDAN) that approach domain adaptation by optimizing task-adaptive generalization bounds and conducts extensive experiments showing superior adaptation performance on both classification and regression problems: sentiment analysis, digit classification, and vehicle counting.
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SPIRAL: Code Generation for DSP Transforms
TLDR
SPIRAL generates high-performance code for a broad set of DSP transforms, including the discrete Fourier transform, other trigonometric transforms, filter transforms, and discrete wavelet transforms.
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Adversarial Geometry-Aware Human Motion Prediction
TLDR
This work proposes a novel frame-wise geodesic loss as a geometrically meaningful, more precise distance measurement and presents a new learning procedure to simultaneously validate the sequence-level plausibility of the prediction and its coherence with the input sequence by introducing two global recurrent discriminators.
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Big Data Analysis with Signal Processing on Graphs: Representation and processing of massive data sets with irregular structure
TLDR
This article considers product graphs as a graph model that helps extend the application of DSPG methods to large data sets through efficient implementation based on parallelization and vectorization and relates the presented framework to existing methods for large-scale data processing.
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Distributed Parameter Estimation in Sensor Networks: Nonlinear Observation Models and Imperfect Communication
TLDR
This paper proves consistency (all sensors reach consensus almost surely and converge to the true parameter value), efficiency, and asymptotic unbiasedness, and provides convergence rate guarantees in distributed static parameter (vector) estimation in sensor networks with nonlinear observation models and noisy intersensor communication.
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Topology adaptive graph convolutional networks
TLDR
The proposed TAGCN not only inherits the properties of convolutions in CNN for grid-structured data, but it is also consistent with convolution as defined in graph signal processing, exhibiting better performance than existing graph-convolution-approximation methods on a number of data sets.
View PDF on arXiv
Natural Language Does Not Emerge ‘Naturally’ in Multi-Agent Dialog
TLDR
This paper presents a sequence of ‘negative’ results culminating in a ‘positive’ one – showing that while most agent-invented languages are effective, they are decidedly not interpretable or compositional.
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