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Found 54 papers, 12 papers with code
GeniePath: Graph Neural Networks with Adaptive Receptive Paths
We present, GeniePath, a scalable approach for learning adaptive receptive fields of neural networks defined on permutation invariant graph data.
A Semi-supervised Graph Attentive Network for Financial Fraud Detection
Additionally, among the network, only very few of the users are labelled, which also poses a great challenge for only utilizing labeled data to achieve a satisfied performance on fraud detection.
SpellGCN: Incorporating Phonological and Visual Similarities into Language Models for Chinese Spelling Check
This paper proposes to incorporate phonological and visual similarity knowledge into language models for CSC via a specialized graph convolutional network (SpellGCN).
Generative Adversarial User Model for Reinforcement Learning Based Recommendation System
There are great interests as well as many challenges in applying reinforcement learning (RL) to recommendation systems.
Generative Adversarial Network
Model-based Reinforcement Learning
+3
Efficient Probabilistic Logic Reasoning with Graph Neural Networks
In this paper, we explore the combination of MLNs and GNNs, and use graph neural networks for variational inference in MLN.
FuXi: A cascade machine learning forecasting system for 15-day global weather forecast
Over the past few years, due to the rapid development of machine learning (ML) models for weather forecasting, state-of-the-art ML models have shown superior performance compared to the European Centre for Medium-Range Weather Forecasts (ECMWF)'s high-resolution forecast (HRES) in 10-day forecasts at a spatial resolution of 0. 25 degree.
InfMLLM: A Unified Framework for Visual-Language Tasks
Our experiments demonstrate that preserving the positional information of visual embeddings through the pool-adapter is particularly beneficial for tasks like visual grounding.
Ranked #60 on
Visual Question Answering
on MM-Vet
Dual-Modal Attention-Enhanced Text-Video Retrieval with Triplet Partial Margin Contrastive Learning
We thereby present a new Triplet Partial Margin Contrastive Learning (TPM-CL) module to construct partial order triplet samples by automatically generating fine-grained hard negatives for matched text-video pairs.
Ranked #4 on
Video Retrieval
on MSR-VTT-1kA
Reinforcement Learning for Uplift Modeling
Uplift modeling aims to directly model the incremental impact of a treatment on an individual response.
EigenGP: Gaussian Process Models with Adaptive Eigenfunctions
In this paper, we propose a new Bayesian approach, EigenGP, that learns both basis dictionary elements--eigenfunctions of a GP prior--and prior precisions in a sparse finite model.
Bandit Samplers for Training Graph Neural Networks
However, due to the intractable computation of optimal sampling distribution, these sampling algorithms are suboptimal for GCNs and are not applicable to more general graph neural networks (GNNs) where the message aggregator contains learned weights rather than fixed weights, such as Graph Attention Networks (GAT).
Ranked #1 on
Node Property Prediction
on ogbn-proteins
Using Forests in Multivariate Regression Discontinuity Designs
We discuss estimating conditional treatment effects in regression discontinuity designs with multiple scores.
Asynchronous Distributed Variational Gaussian Processes for Regression
Gaussian processes (GPs) are powerful non-parametric function estimators.
Distributed Flexible Nonlinear Tensor Factorization
Tensor factorization is a powerful tool to analyse multi-way data.
DinTucker: Scaling up Gaussian process models on multidimensional arrays with billions of elements
To overcome this limitation, we present Distributed Infinite Tucker (DINTUCKER), a large-scale nonlinear tensor decomposition algorithm on MAPREDUCE.
Supervised Heterogeneous Multiview Learning for Joint Association Study and Disease Diagnosis
To unify these two tasks, we present a new sparse Bayesian approach for joint association study and disease diagnosis.
Latent Dirichlet Allocation for Internet Price War
Then we develop a variant of Latent Dirichlet Allocation (LDA) to infer latent variables under the current market environment, which represents the preferences of customers and strategies of competitors.
t-divergence Based Approximate Inference
Approximate inference is an important technique for dealing with large, intractable graphical models based on the exponential family of distributions.
EigenNet: A Bayesian hybrid of generative and conditional models for sparse learning
To overcome this limitation, we present a novel hybrid model, EigenNet, that uses the eigenstructures of data to guide variable selection.
Parallel Inference for Latent Dirichlet Allocation on Graphics Processing Units
Extensive experiments showed that our parallel inference methods consistently produced LDA models with the same predictive power as sequential training methods did but with 26x speedup for CGS and 196x speedup for CVB on a GPU with 30 multiprocessors; actually the speedup is almost linearly scalable with the number of multiprocessors available.
Double Neural Counterfactual Regret Minimization
Counterfactual Regret Minimization (CRF) is a fundamental and effective technique for solving Imperfect Information Games (IIG).
Value Propagation for Decentralized Networked Deep Multi-agent Reinforcement Learning
To the best of our knowledge, it is the first MARL algorithm with convergence guarantee in the control, off-policy and non-linear function approximation setting.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Cost-Effective Incentive Allocation via Structured Counterfactual Inference
We address a practical problem ubiquitous in modern marketing campaigns, in which a central agent tries to learn a policy for allocating strategic financial incentives to customers and observes only bandit feedback.
Can Graph Neural Networks Help Logic Reasoning?
Effectively combining logic reasoning and probabilistic inference has been a long-standing goal of machine learning: the former has the ability to generalize with small training data, while the latter provides a principled framework for dealing with noisy data.
TitAnt: Online Real-time Transaction Fraud Detection in Ant Financial
With the explosive growth of e-commerce and the booming of e-payment, detecting online transaction fraud in real time has become increasingly important to Fintech business.
How Much Can A Retailer Sell? Sales Forecasting on Tmall
By analyzing the data, we have two main observations, i. e., sales seasonality after we group different groups of retails and a Tweedie distribution after we transform the sales (target to forecast).
Uncovering Insurance Fraud Conspiracy with Network Learning
In order to detect and prevent fraudulent insurance claims, we developed a novel data-driven procedure to identify groups of organized fraudsters, one of the major contributions to financial losses, by learning network information.
Long Short-Term Sample Distillation
The long-term teacher draws on snapshots from several epochs ago in order to provide steadfast guidance and to guarantee teacher--student differences, while the short-term one yields more up-to-date cues with the goal of enabling higher-quality updates.
Graph Representation Learning for Merchant Incentive Optimization in Mobile Payment Marketing
In this paper, we present a graph representation learning method atop of transaction networks for merchant incentive optimization in mobile payment marketing.
Practical Privacy Preserving POI Recommendation
Meanwhile, the public data need to be accessed by all the users are kept by the recommender to reduce the storage costs of users' devices.
InfDetect: a Large Scale Graph-based Fraud Detection System for E-Commerce Insurance
The insurance industry has been creating innovative products around the emerging online shopping activities.
Local Contextual Attention with Hierarchical Structure for Dialogue Act Recognition
We revise the attention distribution to focus on the local and contextual semantic information by incorporating the relative position information between utterances.
RNE: A Scalable Network Embedding for Billion-scale Recommendation
However, due to the huge number of users and items, the diversity and dynamic property of the user interest, how to design a scalable recommendation system, which is able to efficiently produce effective and diverse recommendation results on billion-scale scenarios, is still a challenging and open problem for existing methods.
Generating Natural Language Adversarial Examples on a Large Scale with Generative Models
It can attack text classification models with a higher success rate than existing methods, and provide acceptable quality for humans in the meantime.
NetDP: An Industrial-Scale Distributed Network Representation Framework for Default Prediction in Ant Credit Pay
Considering the above challenges and the special scenario in Ant Financial, we try to incorporate default prediction with network information to alleviate the cold-start problem.
Variational Policy Propagation for Multi-agent Reinforcement Learning
We propose a \emph{collaborative} multi-agent reinforcement learning algorithm named variational policy propagation (VPP) to learn a \emph{joint} policy through the interactions over agents.
Multi-agent Reinforcement Learning
reinforcement-learning
+2
A Riemannian Primal-dual Algorithm Based on Proximal Operator and its Application in Metric Learning
In this paper, we consider optimizing a smooth, convex, lower semicontinuous function in Riemannian space with constraints.
Riemannian Proximal Policy Optimization
In this paper, We propose a general Riemannian proximal optimization algorithm with guaranteed convergence to solve Markov decision process (MDP) problems.
Modeling the Field Value Variations and Field Interactions Simultaneously for Fraud Detection
In this paper, we propose the Dual Importance-aware Factorization Machines (DIFM), which exploits the internal field information among users' behavior sequence from dual perspectives, i. e., field value variations and field interactions simultaneously for fraud detection.
Temporal Logic Point Processes
We propose a modeling framework for event data, which excels in small data regime with the ability to incorporate domain knowledge.
Question Directed Graph Attention Network for Numerical Reasoning over Text
Numerical reasoning over texts, such as addition, subtraction, sorting and counting, is a challenging machine reading comprehension task, since it requires both natural language understanding and arithmetic computation.
Ranked #1 on
Question Answering
on DROP Test
Memory Augmented Design of Graph Neural Networks
Specifically, we allow every node in the original graph to interact with a group of memory nodes.
Practical Locally Private Federated Learning with Communication Efficiency
We also initialize a discussion about the role of quantization and perturbation in FL algorithm design with privacy and communication constraints.
SHORING: Design Provable Conditional High-Order Interaction Network via Symbolic Testing
Deep learning provides a promising way to extract effective representations from raw data in an end-to-end fashion and has proven its effectiveness in various domains such as computer vision, natural language processing, etc.
Conditional Attention Networks for Distilling Knowledge Graphs in Recommendation
Knowledge graph is generally incorporated into recommender systems to improve overall performance.
Deep Interaction Processes for Time-Evolving Graphs
To model the dependency between latent dynamic representations of each node, we define a mixture of temporal cascades in which a node's neural representation depends on not only this node's previous representations but also the previous representations of related nodes that have interacted with this node.
The APC Algorithm of Solving Large-Scale Linear Systems: A Generalized Analysis
A new algorithm called accelerated projection-based consensus (APC) has recently emerged as a promising approach to solve large-scale systems of linear equations in a distributed fashion.
FuXi-Extreme: Improving extreme rainfall and wind forecasts with diffusion model
State-of-the-art ML-based weather forecast models, such as FuXi, have demonstrated superior statistical forecast performance in comparison to the high-resolution forecasts (HRES) of the European Centre for Medium-Range Weather Forecasts (ECMWF).
Enhancing the Reliability of Segment Anything Model for Auto-Prompting Medical Image Segmentation with Uncertainty Rectification
The Segment Anything Model (SAM) has recently emerged as a groundbreaking foundation model for prompt-driven image segmentation tasks.
SemiSAM: Exploring SAM for Enhancing Semi-Supervised Medical Image Segmentation with Extremely Limited Annotations
Semi-supervised learning has attracted much attention due to its less dependence on acquiring abundant annotations from experts compared to fully supervised methods, which is especially important for medical image segmentation which typically requires intensive pixel/voxel-wise labeling by domain experts.
PILLOW: Enhancing Efficient Instruction Fine-tuning via Prompt Matching
Instruction fine-tuning has conventionally been employed to adapt Large Language Models (LLMs) to a variety of tasks.
FuXi-S2S: An accurate machine learning model for global subseasonal forecasts
Skillful subseasonal forecasts beyond 2 weeks are crucial for a wide range of applications across various sectors of society.
PDETime: Rethinking Long-Term Multivariate Time Series Forecasting from the perspective of partial differential equations
Recent advancements in deep learning have led to the development of various models for long-term multivariate time-series forecasting (LMTF), many of which have shown promising results.
AI2Apps: A Visual IDE for Building LLM-based AI Agent Applications
We introduce AI2Apps, a Visual Integrated Development Environment (Visual IDE) with full-cycle capabilities that accelerates developers to build deployable LLM-based AI agent Applications.
