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Found 48 papers, 11 papers with code
Personalized Entity Resolution with Dynamic Heterogeneous KnowledgeGraph Representations
We first build a cross-source heterogeneous knowledge graph from customer purchase history and product knowledge graph to jointly learn customer and product embeddings.
Simple Contrastive Graph Clustering
To solve this problem, we propose a Simple Contrastive Graph Clustering (SCGC) algorithm to improve the existing methods from the perspectives of network architecture, data augmentation, and objective function.
FedSynth: Gradient Compression via Synthetic Data in Federated Learning
Model compression is important in federated learning (FL) with large models to reduce communication cost.
Improved Dual Correlation Reduction Network
Deep graph clustering, which aims to reveal the underlying graph structure and divide the nodes into different clusters without human annotations, is a fundamental yet challenging task.
Deep Graph Clustering via Dual Correlation Reduction
To address this issue, we propose a novel self-supervised deep graph clustering method termed Dual Correlation Reduction Network (DCRN) by reducing information correlation in a dual manner.
Temporal epistasis inference from more than 3,500,000 SARS-CoV-2 Genomic Sequences
We use Direct Coupling Analysis to determine epistatic interactions between loci of variability of the SARS-CoV-2 virus, segmenting genomes by month of sampling.
Siamese Attribute-missing Graph Auto-encoder
First, we entangle the attribute embedding and structure embedding by introducing a siamese network structure to share the parameters learned by both processes, which allows the network training to benefit from more abundant and diverse information.
CSAW-M: An Ordinal Classification Dataset for Benchmarking Mammographic Masking of Cancer
Interval and large invasive breast cancers, which are associated with worse prognosis than other cancers, are usually detected at a late stage due to false negative assessments of screening mammograms.
Control of diffusion-driven pattern formation behind a wave of competency
In certain biological contexts, such as the plumage patterns of birds and stripes on certain species of fishes, pattern formation takes place behind a so-called "wave of competency".
AutoNLU: Detecting, root-causing, and fixing NLU model errors
Improving the quality of Natural Language Understanding (NLU) models, and more specifically, task-oriented semantic parsing models, in production is a cumbersome task.
Lagrangian Inference for Ranking Problems
Our proposed method aims to infer general ranking properties of the BTL model.
Mutation frequency time series reveal complex mixtures of clones in the world-wide SARS-CoV-2 viral population
We compute the allele frequencies of the alpha (B. 1. 1. 7), beta (B. 1. 351) and delta (B. 167. 2) variants of SARS-CoV-2 from almost two million genome sequences on the GISAID repository.
Blockchain-based Trustworthy Federated Learning Architecture
To enhance the accountability and fairness of federated learning systems, we present a blockchain-based trustworthy federated learning architecture.
Entity Resolution in Open-domain Conversations
In recent years, incorporating external knowledge for response generation in open-domain conversation systems has attracted great interest.
Optimizing NLU Reranking Using Entity Resolution Signals in Multi-domain Dialog Systems
In dialog systems, the Natural Language Understanding (NLU) component typically makes the interpretation decision (including domain, intent and slots) for an utterance before the mentioned entities are resolved.
Towards Demystifying Serverless Machine Learning Training
The appeal of serverless (FaaS) has triggered a growing interest on how to use it in data-intensive applications such as ETL, query processing, or machine learning (ML).
Option to survive or surrender: carbon asset management and optimization in thermal power enterprises from China
Considering the impact of price fluctuations of carbon emission right allowance, we investigate the operation of Chinese thermal power plant by modeling the decision-making with optimal stopping problem, which is established on the stochastic environment with carbon emission allowance price process simulated by geometric Brownian motion.
Personalized Entity Resolution with Dynamic Heterogeneous Knowledge Graph Representations
For example, with "add milk to my cart", a customer may refer to a certain organic product, while some customers may want to re-order products they regularly purchase.
Beyond Visual Attractiveness: Physically Plausible Single Image HDR Reconstruction for Spherical Panoramas
By the proposed physical regularization, our method can generate HDRs which are not only visually appealing but also physically plausible.
Deep Learning for Android Malware Defenses: a Systematic Literature Review
In this paper, we conducted a systematic literature review to search and analyze how deep learning approaches have been applied in the context of malware defenses in the Android environment.
A Local Method for Identifying Causal Relations under Markov Equivalence
We propose a local approach to identify whether a variable is a cause of a given target under the framework of causal graphical models of directed acyclic graphs (DAGs).
Electron heating mode transitions in radio-frequency driven micro atmospheric pressure plasma jets in He/O$_{2}$: A fluid dynamics approach
A two-dimensional fluid model is used to investigate the electron heating dynamics and the production of neutral species in a capacitively coupled radio-frequency micro atmospheric pressure helium plasma jet -- specifically the COST jet -- with a small oxygen admixture.
Plasma Physics
Out-of-Distribution Generalization Analysis via Influence Function
One of the definitions of OOD accuracy is worst-domain accuracy.
On Low Rank Directed Acyclic Graphs and Causal Structure Learning
Despite several important advances in recent years, learning causal structures represented by directed acyclic graphs (DAGs) remains a challenging task in high dimensional settings when the graphs to be learned are not sparse.
Electron pairing in the pseudogap state revealed by shot noise in copper-oxide junctions
In the quest to understand high-temperature superconductivity in copper oxides, a vigorous debate has been focused on the pseudogap - a partial gap that opens over portions of the Fermi surface in the 'normal' state above the bulk critical temperature ($T_{c}$).
Superconductivity Mesoscale and Nanoscale Physics Strongly Correlated Electrons
Reconstruction Condition of Quantized Signals in Unlimited Sampling Framework
The latest theoretical advances in the field of unlimited sampling framework (USF) show the potential to avoid clipping problems of analog-to-digital converters (ADC).
Deep Neural Network Approach for Annual Luminance Simulations
The proposed DNN model can faithfully predict high-quality annual panoramic luminance maps from one of the three options within 30 minutes training time: a) point-in-time luminance imagery spanning 5% of the year, when evenly distributed during daylight hours, b) one-month hourly imagery generated or collected continuously during daylight hours around the equinoxes (8% of the year); or c) 9 days of hourly data collected around the spring equinox, summer and winter solstices (2. 5% of the year) all suffice to predict the luminance maps for the rest of the year.
Blockchain-based Federated Learning for Failure Detection in Industrial IoT
Therefore, in this paper, we present a platform architecture of blockchain-based federated learning systems for failure detection in IIoT.
Video Moment Retrieval via Natural Language Queries
However, the inference time will not be a problem for our model since our model has a simple architecture which enables efficient training and inference.
Adding Seemingly Uninformative Labels Helps in Low Data Regimes
Evidence suggests that networks trained on large datasets generalize well not solely because of the numerous training examples, but also class diversity which encourages learning of enriched features.
Decoupling Inherent Risk and Early Cancer Signs in Image-based Breast Cancer Risk Models
With this in mind, we trained networks using three different criteria to select the positive training data (i. e. images from patients that will develop cancer): an inherent risk model trained on images with no visible signs of cancer, a cancer signs model trained on images containing cancer or early signs of cancer, and a conflated model trained on all images from patients with a cancer diagnosis.
Online NEAT for Credit Evaluation -- a Dynamic Problem with Sequential Data
In this paper, we describe application of Neuroevolution to a P2P lending problem in which a credit evaluation model is updated based on streaming data.
Risk Variance Penalization
The key of the out-of-distribution (OOD) generalization is to generalize invariance from training domains to target domains.
Low Rank Directed Acyclic Graphs and Causal Structure Learning
Despite several important advances in recent years, learning causal structures represented by directed acyclic graphs (DAGs) remains a challenging task in high dimensional settings when the graphs to be learned are not sparse.
Stable Prediction via Leveraging Seed Variable
By assuming the relationships between causal variables and response variable are invariant across data, to address this problem, we propose a conditional independence test based algorithm to separate those causal variables with a seed variable as priori, and adopt them for stable prediction.
Real-time Human Activity Recognition Using Conditionally Parametrized Convolutions on Mobile and Wearable Devices
For deep learning, improvements in performance have to heavily rely on increasing model size or capacity to scale to larger and larger datasets, which inevitably leads to the increase of operations.
RecipeGPT: Generative Pre-training Based Cooking Recipe Generation and Evaluation System
Interests in the automatic generation of cooking recipes have been growing steadily over the past few years thanks to a large amount of online cooking recipes.
Spots, strips, and spiral waves in models for static and motile cells
The polarization and motility of eukaryotic cells depends on assembly and contraction of the actin cytoskeleton and its regulation by proteins called GTPases.
Estimating Glycemic Impact of Cooking Recipes via Online Crowdsourcing and Machine Learning
Consumption of diets with low glycemic impact is highly recommended for diabetics and pre-diabetics as it helps maintain their blood glucose levels.
Characterizing and Predicting Repeat Food Consumption Behavior for Just-in-Time Interventions
Human beings are creatures of habit.
Causal Discovery by Kernel Intrinsic Invariance Measure
We show our algorithm can be reduced to an eigen-decomposition task on a kernel matrix measuring intrinsic deviance/invariance.
Cell size, mechanical tension, and GTPase signaling in the Single Cell
We further consider the feedback between mechanical tension, GTPase activation, and cell deformation in both static, growing, shrinking, and moving cells.
Exploring Stereovision-Based 3-D Scene Reconstruction for Augmented Reality
Three-dimensional (3-D) scene reconstruction is one of the key techniques in Augmented Reality (AR), which is related to the integration of image processing and display systems of complex information.
Seq2RDF: An end-to-end application for deriving Triples from Natural Language Text
Inspired by recent successes in neural machine translation, we treat the triples within a given knowledge graph as an independent graph language and propose an encoder-decoder framework with an attention mechanism that leverages knowledge graph embeddings.
Recurrent knowledge distillation
Knowledge distillation compacts deep networks by letting a small student network learn from a large teacher network.
Exploiting Task-Oriented Resources to Learn Word Embeddings for Clinical Abbreviation Expansion
In the medical domain, identifying and expanding abbreviations in clinical texts is a vital task for both better human and machine understanding.
Deformable 3D Fusion: From Partial Dynamic 3D Observations to Complete 4D Models
Capturing the 3D motion of dynamic, non-rigid objects has attracted significant attention in computer vision.
