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Found 30 papers, 7 papers with code
Spatial-Temporal DAG Convolutional Networks for End-to-End Joint Effective Connectivity Learning and Resting-State fMRI Classification
To address these issues in an end-to-end manner, we model the brain network as a directed acyclic graph (DAG) to discover direct causal connections between brain regions and propose Spatial-Temporal DAG Convolutional Network (ST-DAGCN) to jointly infer effective connectivity and classify rs-fMRI time series by learning brain representations based on nonlinear structural equation model.
scBiGNN: Bilevel Graph Representation Learning for Cell Type Classification from Single-cell RNA Sequencing Data
A gene-level GNN is established to adaptively learn gene-gene interactions and cell representations via the self-attention mechanism, and a cell-level GNN builds on the cell-cell graph that is constructed from the cell representations generated by the gene-level GNN.
Deep Learning Enables Large Depth-of-Field Images for Sub-Diffraction-Limit Scanning Superlens Microscopy
Scanning electron microscopy (SEM) is indispensable in diverse applications ranging from microelectronics to food processing because it provides large depth-of-field images with a resolution beyond the optical diffraction limit.
Spatial-Temporal Enhanced Transformer Towards Multi-Frame 3D Object Detection
Specifically, to model the inter-object spatial interaction and complex temporal dependencies, we introduce the spatial-temporal graph attention network, which represents queries as nodes in a graph and enables effective modeling of object interactions within a social context.
Distributed Pruning Towards Tiny Neural Networks in Federated Learning
To address these challenges, we propose FedTiny, a distributed pruning framework for federated learning that generates specialized tiny models for memory- and computing-constrained devices.
FedZKT: Zero-Shot Knowledge Transfer towards Resource-Constrained Federated Learning with Heterogeneous On-Device Models
To achieve knowledge transfer across these heterogeneous on-device models, a zero-shot distillation approach is designed without any prerequisites for private on-device data, which is contrary to certain prior research based on a public dataset or a pre-trained data generator.
Server Averaging for Federated Learning
Federated learning allows distributed devices to collectively train a model without sharing or disclosing the local dataset with a central server.
ES Attack: Model Stealing against Deep Neural Networks without Data Hurdles
The experimental results reveal the severity of ES Attack: i) ES Attack successfully steals the victim model without data hurdles, and ES Attack even outperforms most existing model stealing attacks using auxiliary data in terms of model accuracy; ii) most countermeasures are ineffective in defending ES Attack; iii) ES Attack facilitates further attacks relying on the stolen model.
Distilled One-Shot Federated Learning
DOSFL serves as an inexpensive method to quickly converge on a performant pre-trained model with less than 0. 1% communication cost of traditional methods.
Asking Complex Questions with Multi-hop Answer-focused Reasoning
Asking questions from natural language text has attracted increasing attention recently, and several schemes have been proposed with promising results by asking the right question words and copy relevant words from the input to the question.
Connecting Web Event Forecasting with Anomaly Detection: A Case Study on Enterprise Web Applications Using Self-Supervised Neural Networks
In this paper, we present a web event forecasting approach, DeepEvent, in enterprise web applications for better anomaly detection.
PRI-VAE: Principle-of-Relevant-Information Variational Autoencoders
Although substantial efforts have been made to learn disentangled representations under the variational autoencoder (VAE) framework, the fundamental properties to the dynamics of learning of most VAE models still remain unknown and under-investigated.
Application of Deep Interpolation Network for Clustering of Physiologic Time Series
M Interpretation: In a heterogeneous cohort of hospitalized patients, a deep interpolation network extracted representations from vital sign data measured within six hours of hospital admission.
A Batch Normalized Inference Network Keeps the KL Vanishing Away
Variational Autoencoder (VAE) is widely used as a generative model to approximate a model's posterior on latent variables by combining the amortized variational inference and deep neural networks.
Curiosity-Driven Energy-Efficient Worker Scheduling in Vehicular Crowdsourcing: A Deep Reinforcement Learning Approach
Spatial crowdsourcing (SC) utilizes the potential of a crowd to accomplish certain location based tasks.
Improving Question Generation with Sentence-level Semantic Matching and Answer Position Inferring
Taking an answer and its context as input, sequence-to-sequence models have made considerable progress on question generation.
DeepAtom: A Framework for Protein-Ligand Binding Affinity Prediction
The cornerstone of computational drug design is the calculation of binding affinity between two biological counterparts, especially a chemical compound, i. e., a ligand, and a protein.
A Federated Filtering Framework for Internet of Medical Things
Based on the dominant paradigm, all the wearable IoT devices used in the healthcare sector also known as the internet of medical things (IoMT) are resource constrained in power and computational capabilities.
Networking and Internet Architecture
Turbo Learning for Captionbot and Drawingbot
We study in this paper the problems of both image captioning and text-to-image generation, and present a novel turbo learning approach to jointly training an image-to-text generator (a. k. a.
Hierarchically Structured Reinforcement Learning for Topically Coherent Visual Story Generation
We propose a hierarchically structured reinforcement learning approach to address the challenges of planning for generating coherent multi-sentence stories for the visual storytelling task.
Ranked #24 on
Visual Storytelling
on VIST
Attentive Tensor Product Learning
This paper proposes a new architecture - Attentive Tensor Product Learning (ATPL) - to represent grammatical structures in deep learning models.
Structured Memory based Deep Model to Detect as well as Characterize Novel Inputs
While deep learning has pushed the boundaries in various machine learning tasks, the current models are still far away from replicating many functions that a normal human brain can do.
A Neural-Symbolic Approach to Design of CAPTCHA
To address this, this paper promotes image/visual captioning based CAPTCHAs, which is robust against machine-learning-based attacks.
Tensor Product Generation Networks for Deep NLP Modeling
We present a new approach to the design of deep networks for natural language processing (NLP), based on the general technique of Tensor Product Representations (TPRs) for encoding and processing symbol structures in distributed neural networks.
Context-Aware Online Learning for Course Recommendation of MOOC Big Data
In this respect, the key challenge is how to realize personalized course recommendation as well as to reduce the computing and storage costs for the tremendous course data.
Differentially Private Online Learning for Cloud-Based Video Recommendation with Multimedia Big Data in Social Networks
In addition, none of them has considered both the privacy of users' contexts (e, g., social status, ages and hobbies) and video service vendors' repositories, which are extremely sensitive and of significant commercial value.
Joint Association Graph Screening and Decomposition for Large-scale Linear Dynamical Systems
In particular, our method can pre-determine and remove unnecessary edges based on the joint graphical structure, referred to as JAG screening, and can decompose a large network into smaller subnetworks in a robust manner, referred to as JAG decomposition.
Learning Topology and Dynamics of Large Recurrent Neural Networks
Large-scale recurrent networks have drawn increasing attention recently because of their capabilities in modeling a large variety of real-world phenomena and physical mechanisms.
Group Iterative Spectrum Thresholding for Super-Resolution Sparse Spectral Selection
Recently, sparsity-based algorithms are proposed for super-resolution spectrum estimation.
