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Found 18 papers, 3 papers with code
Towards Stable and Storage-efficient Dataset Distillation: Matching Convexified Trajectory
To address these issues, we offer a new perspective on understanding the essence of Dataset Distillation and MTT through a simple transformation of the objective function, and introduce a novel method called Matching Convexified Trajectory (MCT), which aims to provide better guidance for the student trajectory.
Enhancing Multimodal Large Language Models with Multi-instance Visual Prompt Generator for Visual Representation Enrichment
Multimodal Large Language Models (MLLMs) have achieved SOTA performance in various visual language tasks by fusing the visual representations with LLMs leveraging some visual adapters.
Your decision path does matter in pre-training industrial recommenders with multi-source behaviors
Online service platforms offering a wide range of services through miniapps have become crucial for users who visit these platforms with clear intentions to find services they are interested in.
PathM3: A Multimodal Multi-Task Multiple Instance Learning Framework for Whole Slide Image Classification and Captioning
In the field of computational histopathology, both whole slide images (WSIs) and diagnostic captions provide valuable insights for making diagnostic decisions.
Towards Efficient Replay in Federated Incremental Learning
Then, the client trains the local model with both the cached samples and the samples from the new task.
Denoising Time Cycle Modeling for Recommendation
We define the subset of user behaviors that are irrelevant to the target item as noises, which limits the performance of target-related time cycle modeling and affect the recommendation performance.
End-to-end Learnable Clustering for Intent Learning in Recommendation
Concretely, we encode users' behavior sequences and initialize the cluster centers (latent intents) as learnable neurons.
MIVC: Multiple Instance Visual Component for Visual-Language Models
Vision-language models have been widely explored across a wide range of tasks and achieve satisfactory performance.
PEACE: Prototype lEarning Augmented transferable framework for Cross-domain rEcommendation
To help merchants/customers to provide/access a variety of services through miniapps, online service platforms have occupied a critical position in the effective content delivery, in which how to recommend items in the new domain launched by the service provider for customers has become more urgent.
Which Matters Most in Making Fund Investment Decisions? A Multi-granularity Graph Disentangled Learning Framework
In this paper, we highlight that both conformity and risk preference matter in making fund investment decisions beyond personal interest and seek to jointly characterize these aspects in a disentangled manner.
AntM$^{2}$C: A Large Scale Dataset For Multi-Scenario Multi-Modal CTR Prediction
3) AntM$^{2}$C provides 1 billion CTR data with 200 features, including 200 million users and 6 million items.
ChatGraph: Interpretable Text Classification by Converting ChatGPT Knowledge to Graphs
To tackle these limitations, we propose a novel framework that leverages the power of ChatGPT for specific tasks, such as text classification, while improving its interpretability.
COUPA: An Industrial Recommender System for Online to Offline Service Platforms
Aiming at helping users locally discovery retail services (e. g., entertainment and dinning), Online to Offline (O2O) service platforms have become popular in recent years, which greatly challenge current recommender systems.
Edge-cloud Collaborative Learning with Federated and Centralized Features
Federated learning (FL) is a popular way of edge computing that doesn't compromise users' privacy.
Prototypical Contrastive Learning and Adaptive Interest Selection for Candidate Generation in Recommendations
First, a user has multiple embeddings to reflect various interests, and such number is fixed.
Learning Graph Neural Networks with Approximate Gradient Descent
Two types of GNNs are investigated, depending on whether labels are attached to nodes or graphs.
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.
Fast Low-Rank Matrix Learning with Nonconvex Regularization
This allows the use of power method to approximate the SVD efficiently.
