Search Results for author: José M. F. Moura

Found 36 papers, 11 papers with code

Graph Signal Processing: History, Development, Impact, and Outlook

no code implementations21 Mar 2023 Geert Leus, Antonio G. Marques, José M. F. Moura, Antonio Ortega, David I Shuman

Graph signal processing (GSP) generalizes signal processing (SP) tasks to signals living on non-Euclidean domains whose structure can be captured by a weighted graph.

Graph Learning

Learning Gradients of Convex Functions with Monotone Gradient Networks

no code implementations25 Jan 2023 Shreyas Chaudhari, Srinivasa Pranav, José M. F. Moura

While much effort has been devoted to deriving and analyzing effective convex formulations of signal processing problems, the gradients of convex functions also have critical applications ranging from gradient-based optimization to optimal transport.

Networked Signal and Information Processing

no code implementations25 Oct 2022 Stefan Vlaski, Soummya Kar, Ali H. Sayed, José M. F. Moura

Moreover, and significantly, theory and applications show that networked agents, through cooperation and sharing, are able to match the performance of cloud or federated solutions, while offering the potential for improved privacy, increasing resilience, and saving resources.

Decision Making Inference Optimization

GSA-Forecaster: Forecasting Graph-Based Time-Dependent Data with Graph Sequence Attention

no code implementations13 Apr 2021 Yang Li, Di Wang, José M. F. Moura

This task is challenging as models need not only to capture spatial dependency and temporal dependency within the data, but also to leverage useful auxiliary information for accurate predictions.

Unsupervised Clustering of Time Series Signals using Neuromorphic Energy-Efficient Temporal Neural Networks

no code implementations18 Feb 2021 Shreyas Chaudhari, Harideep Nair, José M. F. Moura, John Paul Shen

Unsupervised time series clustering is a challenging problem with diverse industrial applications such as anomaly detection, bio-wearables, etc.

Anomaly Detection Clustering +2

Annotation-Efficient Untrimmed Video Action Recognition

no code implementations30 Nov 2020 Yixiong Zou, Shanghang Zhang, Guangyao Chen, Yonghong Tian, Kurt Keutzer, José M. F. Moura

In this paper, we target a new problem, Annotation-Efficient Video Recognition, to reduce the requirement of annotations for both large amount of samples and the action location.

Action Recognition Contrastive Learning +3

Revisiting Mid-Level Patterns for Cross-Domain Few-Shot Recognition

no code implementations7 Aug 2020 Yixiong Zou, Shanghang Zhang, JianPeng Yu, Yonghong Tian, José M. F. Moura

To solve this problem, cross-domain FSL (CDFSL) is proposed very recently to transfer knowledge from general-domain base classes to special-domain novel classes.

cross-domain few-shot learning

Graph Signal Processing and Deep Learning: Convolution, Pooling, and Topology

no code implementations4 Aug 2020 Mark Cheung, John Shi, Oren Wright, Lavender Y. Jiang, Xujin Liu, José M. F. Moura

Deep learning, particularly convolutional neural networks (CNNs), have yielded rapid, significant improvements in computer vision and related domains.

Compositional Few-Shot Recognition with Primitive Discovery and Enhancing

no code implementations12 May 2020 Yixiong Zou, Shanghang Zhang, Ke Chen, Yonghong Tian, Yao-Wei Wang, José M. F. Moura

Inspired by such capability of humans, to imitate humans' ability of learning visual primitives and composing primitives to recognize novel classes, we propose an approach to FSL to learn a feature representation composed of important primitives, which is jointly trained with two parts, i. e. primitive discovery and primitive enhancing.

Few-Shot Image Classification Few-Shot Learning +1

Evaluating and Aggregating Feature-based Model Explanations

no code implementations1 May 2020 Umang Bhatt, Adrian Weller, José M. F. Moura

A feature-based model explanation denotes how much each input feature contributes to a model's output for a given data point.

Pooling in Graph Convolutional Neural Networks

no code implementations7 Apr 2020 Mark Cheung, John Shi, Lavender Yao Jiang, Oren Wright, José M. F. Moura

Graph convolutional neural networks (GCNNs) are a powerful extension of deep learning techniques to graph-structured data problems.

General Classification Graph Classification

Distributed Gradient Methods for Nonconvex Optimization: Local and Global Convergence Guarantees

no code implementations23 Mar 2020 Brian Swenson, Soummya Kar, H. Vincent Poor, José M. F. Moura, Aaron Jaech

We discuss local minima convergence guarantees and explore the simple but critical role of the stable-manifold theorem in analyzing saddle-point avoidance.

Optimization and Control

Graph Fourier Transform: A Stable Approximation

no code implementations14 Jan 2020 João Domingos, José M. F. Moura

The (right) eigenvectors of the shift $A$ (graph spectral components) diagonalize $A$ and lead to a graph Fourier basis $F$ that provides a graph spectral representation of the graph signal.

Forecaster: A Graph Transformer for Forecasting Spatial and Time-Dependent Data

no code implementations9 Sep 2019 Yang Li, José M. F. Moura

Specifically, we start by learning the structure of the graph that parsimoniously represents the spatial dependency between the data at different locations.

Adversarial Multiple Source Domain Adaptation

no code implementations NeurIPS 2018 Han Zhao, Shanghang Zhang, Guanhang Wu, José M. F. Moura, Joao P. Costeira, Geoffrey J. Gordon

In this paper we propose new generalization bounds and algorithms under both classification and regression settings for unsupervised multiple source domain adaptation.

Classification Domain Adaptation +5

Single Index Latent Variable Models for Network Topology Inference

no code implementations28 Jun 2018 Jonathan Mei, José M. F. Moura

A semi-parametric, non-linear regression model in the presence of latent variables is applied towards learning network graph structure.



no code implementations5 Jun 2018 Jonathan Mei, José M. F. Moura

Algorithms for learning the time series of graphs $\left\{G_k\right\}$, deriving the eigennetworks, eigenfeatures and eigentrajectories, and detecting changepoints are presented.

Descriptive Time Series +1

Graph Signal Processing: Overview, Challenges and Applications

2 code implementations1 Dec 2017 Antonio Ortega, Pascal Frossard, Jelena Kovačević, José M. F. Moura, Pierre Vandergheynst

Research in Graph Signal Processing (GSP) aims to develop tools for processing data defined on irregular graph domains.

Signal Processing

FCN-rLSTM: Deep Spatio-Temporal Neural Networks for Vehicle Counting in City Cameras

1 code implementation ICCV 2017 Shanghang Zhang, Guanhang Wu, João P. Costeira, José M. F. Moura

To overcome limitations of existing methods and incorporate the temporal information of traffic video, we design a novel FCN-rLSTM network to jointly estimate vehicle density and vehicle count by connecting fully convolutional neural networks (FCN) with long short term memory networks (LSTM) in a residual learning fashion.

Natural Language Does Not Emerge 'Naturally' in Multi-Agent Dialog

3 code implementations26 Jun 2017 Satwik Kottur, José M. F. Moura, Stefan Lee, Dhruv Batra

A number of recent works have proposed techniques for end-to-end learning of communication protocols among cooperative multi-agent populations, and have simultaneously found the emergence of grounded human-interpretable language in the protocols developed by the agents, all learned without any human supervision!

Convergence analysis of belief propagation for pairwise linear Gaussian models

no code implementations12 Jun 2017 Jian Du, Shaodan Ma, Yik-Chung Wu, Soummya Kar, José M. F. Moura

Gaussian belief propagation (BP) has been widely used for distributed inference in large-scale networks such as the smart grid, sensor networks, and social networks, where local measurements/observations are scattered over a wide geographical area.

Multiple Source Domain Adaptation with Adversarial Training of Neural Networks

4 code implementations26 May 2017 Han Zhao, Shanghang Zhang, Guanhang Wu, João P. Costeira, José M. F. Moura, Geoffrey J. Gordon

As a step toward bridging the gap, we propose a new generalization bound for domain adaptation when there are multiple source domains with labeled instances and one target domain with unlabeled instances.

Domain Adaptation Sentiment Analysis

Convergence analysis of the information matrix in Gaussian belief propagation

no code implementations13 Apr 2017 Jian Du, Shaodan Ma, Yik-Chung Wu, Soummya Kar, José M. F. Moura

Gaussian belief propagation (BP) has been widely used for distributed estimation in large-scale networks such as the smart grid, communication networks, and social networks, where local measurements/observations are scattered over a wide geographical area.

Learning Cooperative Visual Dialog Agents with Deep Reinforcement Learning

6 code implementations ICCV 2017 Abhishek Das, Satwik Kottur, José M. F. Moura, Stefan Lee, Dhruv Batra

Specifically, we pose a cooperative 'image guessing' game between two agents -- Qbot and Abot -- who communicate in natural language dialog so that Qbot can select an unseen image from a lineup of images.

reinforcement-learning Reinforcement Learning (RL) +2

Understanding Traffic Density from Large-Scale Web Camera Data

1 code implementation CVPR 2017 Shanghang Zhang, Guanhang Wu, João P. Costeira, José M. F. Moura

Understanding traffic density from large-scale web camera (webcam) videos is a challenging problem because such videos have low spatial and temporal resolution, high occlusion and large perspective.


Visual Dialog

11 code implementations CVPR 2017 Abhishek Das, Satwik Kottur, Khushi Gupta, Avi Singh, Deshraj Yadav, José M. F. Moura, Devi Parikh, Dhruv Batra

We introduce the task of Visual Dialog, which requires an AI agent to hold a meaningful dialog with humans in natural, conversational language about visual content.

Chatbot Retrieval +1

Convergence Analysis of Distributed Inference with Vector-Valued Gaussian Belief Propagation

no code implementations7 Nov 2016 Jian Du, Shaodan Ma, Yik-Chung Wu, Soummya Kar, José M. F. Moura

A necessary and sufficient convergence condition for the belief mean vector to converge to the optimal centralized estimator is provided under the assumption that the message information matrix is initialized as a positive semidefinite matrix.

Visual Word2Vec (vis-w2v): Learning Visually Grounded Word Embeddings Using Abstract Scenes

1 code implementation CVPR 2016 Satwik Kottur, Ramakrishna Vedantam, José M. F. Moura, Devi Parikh

While word embeddings trained using text have been extremely successful, they cannot uncover notions of semantic relatedness implicit in our visual world.

Common Sense Reasoning Image Retrieval +3

Signal Processing on Graphs: Causal Modeling of Unstructured Data

no code implementations28 Feb 2015 Jonathan Mei, José M. F. Moura

Many applications collect a large number of time series, for example, the financial data of companies quoted in a stock exchange, the health care data of all patients that visit the emergency room of a hospital, or the temperature sequences continuously measured by weather stations across the US.

Time Series Time Series Analysis

Signal Recovery on Graphs: Variation Minimization

no code implementations26 Nov 2014 Siheng Chen, Aliaksei Sandryhaila, José M. F. Moura, Jelena Kovačević

We consider the problem of signal recovery on graphs as graphs model data with complex structure as signals on a graph.

Anomaly Detection General Classification +2

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