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Found 15 papers, 2 papers with code
Unisolver: PDE-Conditional Transformers Are Universal PDE Solvers
This limits the generalization of neural solvers to diverse PDEs, impeding them from being practical surrogate models for numerical solvers.
RJUA-MedDQA: A Multimodal Benchmark for Medical Document Question Answering and Clinical Reasoning
Recent advancements in Large Language Models (LLMs) and Large Multi-modal Models (LMMs) have shown potential in various medical applications, such as Intelligent Medical Diagnosis.
Transolver: A Fast Transformer Solver for PDEs on General Geometries
Transformers have empowered many milestones across various fields and have recently been applied to solve partial differential equations (PDEs).
OrchMoE: Efficient Multi-Adapter Learning with Task-Skill Synergy
We advance the field of Parameter-Efficient Fine-Tuning (PEFT) with our novel multi-adapter method, OrchMoE, which capitalizes on modular skill architecture for enhanced forward transfer in neural networks.
SM$^3$: Self-Supervised Multi-task Modeling with Multi-view 2D Images for Articulated Objects
Reconstructing real-world objects and estimating their movable joint structures are pivotal technologies within the field of robotics.
GACE: Learning Graph-Based Cross-Page Ads Embedding For Click-Through Rate Prediction
In this paper, we proposed GACE, a graph-based cross-page ads embedding generation method.
Exploring Popularity Bias in Session-based Recommendation
Existing work has revealed that large-scale offline evaluation of recommender systems for user-item interactions is prone to bias caused by the deployed system itself, as a form of closed loop feedback.
Hypergraph-Guided Disentangled Spectrum Transformer Networks for Near-Infrared Facial Expression Recognition
With the strong robusticity on illumination variations, near-infrared (NIR) can be an effective and essential complement to visible (VIS) facial expression recognition in low lighting or complete darkness conditions.
Facial Expression Recognition
Facial Expression Recognition (FER)
Customizable Combination of Parameter-Efficient Modules for Multi-Task Learning
Modular and composable transfer learning is an emerging direction in the field of Parameter Efficient Fine-Tuning, as it enables neural networks to better organize various aspects of knowledge, leading to improved cross-task generalization.
GraNet: A Multi-Level Graph Network for 6-DoF Grasp Pose Generation in Cluttered Scenes
6-DoF object-agnostic grasping in unstructured environments is a critical yet challenging task in robotics.
From Beginner to Expert: Modeling Medical Knowledge into General LLMs
(3) Specifically for multi-choice questions in the medical domain, we propose a novel Verification-of-Choice approach for prompting engineering, which significantly enhances the reasoning ability of LLMs.
Uplift Modeling based on Graph Neural Network Combined with Causal Knowledge
Through uplift modeling, we can identify the treatment with the greatest benefit.
DTF-Net: Category-Level Pose Estimation and Shape Reconstruction via Deformable Template Field
We introduce a pose regression module that shares the deformation features and template codes from the fields to estimate the accurate 6D pose of each object in the scene.
RDFC-GAN: RGB-Depth Fusion CycleGAN for Indoor Depth Completion
Raw depth images captured in indoor scenarios frequently exhibit extensive missing values due to the inherent limitations of the sensors and environments.
RGB-Depth Fusion GAN for Indoor Depth Completion
In this paper, we design a novel two-branch end-to-end fusion network, which takes a pair of RGB and incomplete depth images as input to predict a dense and completed depth map.
