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Found 8 papers, 3 papers with code
End-to-end training of Multimodal Model and ranking Model
In this paper, we propose an industrial multimodal recommendation framework named EM3: End-to-end training of Multimodal Model and ranking Model, which sufficiently utilizes multimodal information and allows personalized ranking tasks to directly train the core modules in the multimodal model to obtain more task-oriented content features, without overburdening resource consumption.
State Machine of Thoughts: Leveraging Past Reasoning Trajectories for Enhancing Problem Solving
Current Large Language Model-based agents reason within an exploration-evaluation framework, navigating problem-solving processes in a tree-like manner.
ProS: Prompting-to-simulate Generalized knowledge for Universal Cross-Domain Retrieval
Then, in Context-aware Simulator Learning stage, we train a Content-aware Prompt Simulator under a simulated test scenarios to produce the corresponding CaDP.
MoCoSA: Momentum Contrast for Knowledge Graph Completion with Structure-Augmented Pre-trained Language Models
However, they struggle with semantically rich real-world entities due to limited structural information and fail to generalize to unseen entities.
Ranked #1 on
Link Prediction
on WN18RR
Conductivity Imaging from Internal Measurements with Mixed Least-Squares Deep Neural Networks
In this work we develop a novel approach using deep neural networks to reconstruct the conductivity distribution in elliptic problems from one measurement of the solution over the whole domain.
Fashion Image Retrieval with Multi-Granular Alignment
In this paper, we propose a novel fashion image retrieval method leveraging both global and fine-grained features, dubbed Multi-Granular Alignment (MGA).
Ranked #3 on
Metric Learning
on In-Shop
Imaging Conductivity from Current Density Magnitude using Neural Networks
Conductivity imaging represents one of the most important tasks in medical imaging.
CADG: A Model Based on Cross Attention for Domain Generalization
In Domain Generalization (DG) tasks, models are trained by using only training data from the source domains to achieve generalization on an unseen target domain, this will suffer from the distribution shift problem.
Ranked #9 on
Domain Generalization
on VLCS
