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Found 42 papers, 13 papers with code
Gradient Networks
Our analysis leads to two distinct GradNet architectures, GradNet-C and GradNet-M, and we describe the corresponding monotone versions, mGradNet-C and mGradNet-M. Our empirical results show that these architectures offer efficient parameterizations and outperform popular methods in gradient field learning tasks.
An Analytic Solution to Covariance Propagation in Neural Networks
Uncertainty quantification of neural networks is critical to measuring the reliability and robustness of deep learning systems.
Peer-to-Peer Learning + Consensus with Non-IID Data
Peer-to-peer deep learning algorithms are enabling distributed edge devices to collaboratively train deep neural networks without exchanging raw training data or relying on a central server.
Inferring the Graph of Networked Dynamical Systems under Partial Observability and Spatially Colored Noise
In a Networked Dynamical System (NDS), each node is a system whose dynamics are coupled with the dynamics of neighboring nodes.
Learning the Causal Structure of Networked Dynamical Systems under Latent Nodes and Structured Noise
To address the challenge of noise correlation and partial observability, we assign to each pair of nodes a feature vector computed from the time series data of observed nodes.
Peer-to-Peer Deep Learning for Beyond-5G IoT
We present P2PL, a practical multi-device peer-to-peer deep learning algorithm that, unlike the federated learning paradigm, does not require coordination from edge servers or the cloud.
Graph Signal Processing: History, Development, Impact, and Outlook
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.
Learning Gradients of Convex Functions with Monotone Gradient Networks
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
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.
Recovering the Graph Underlying Networked Dynamical Systems under Partial Observability: A Deep Learning Approach
This renders the features amenable to train a variety of classifiers to perform causal inference.
GSA-Forecaster: Forecasting Graph-Based Time-Dependent Data with Graph Sequence Attention
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
Unsupervised time series clustering is a challenging problem with diverse industrial applications such as anomaly detection, bio-wearables, etc.
Edge Entropy as an Indicator of the Effectiveness of GNNs over CNNs for Node Classification
Graph neural networks (GNNs) extend convolutional neural networks (CNNs) to graph-based data.
Annotation-Efficient Untrimmed Video Action Recognition
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.
Revisiting Mid-Level Patterns for Cross-Domain Few-Shot Recognition
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.
Graph Signal Processing and Deep Learning: Convolution, Pooling, and Topology
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
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.
Evaluating and Aggregating Feature-based Model Explanations
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
Graph convolutional neural networks (GCNNs) are a powerful extension of deep learning techniques to graph-structured data problems.
Distributed Gradient Methods for Nonconvex Optimization: Local and Global Convergence Guarantees
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
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.
Explainable Machine Learning in Deployment
Yet there is little understanding of how organizations use these methods in practice.
Forecaster: A Graph Transformer for Forecasting Spatial and Time-Dependent Data
Specifically, we start by learning the structure of the graph that parsimoniously represents the spatial dependency between the data at different locations.
CLEVR-Dialog: A Diagnostic Dataset for Multi-Round Reasoning in Visual Dialog
Specifically, we construct a dialog grammar that is grounded in the scene graphs of the images from the CLEVR dataset.
On Network Science and Mutual Information for Explaining Deep Neural Networks
In this paper, we present a new approach to interpret deep learning models.
Adversarial Multiple Source Domain Adaptation
In this paper we propose new generalization bounds and algorithms under both classification and regression settings for unsupervised multiple source domain adaptation.
Ranked #3 on
Domain Adaptation
on GTA5+Synscapes to Cityscapes
Visual Coreference Resolution in Visual Dialog using Neural Module Networks
Visual dialog entails answering a series of questions grounded in an image, using dialog history as context.
Ranked #1 on
Common Sense Reasoning
on Visual Dialog v0.9
Single Index Latent Variable Models for Network Topology Inference
A semi-parametric, non-linear regression model in the presence of latent variables is applied towards learning network graph structure.
EigenNetworks
Algorithms for learning the time series of graphs $\left\{G_k\right\}$, deriving the eigennetworks, eigenfeatures and eigentrajectories, and detecting changepoints are presented.
Graph Signal Processing: Overview, Challenges and Applications
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
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
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
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
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.
Convergence analysis of the information matrix in Gaussian belief propagation
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
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.
Understanding Traffic Density from Large-Scale Web Camera Data
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
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.
Ranked #15 on
Visual Dialog
on VisDial v0.9 val
Convergence Analysis of Distributed Inference with Vector-Valued Gaussian Belief Propagation
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
While word embeddings trained using text have been extremely successful, they cannot uncover notions of semantic relatedness implicit in our visual world.
Signal Processing on Graphs: Causal Modeling of Unstructured Data
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.
Signal Recovery on Graphs: Variation Minimization
We consider the problem of signal recovery on graphs as graphs model data with complex structure as signals on a graph.
