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Found 20 papers, 3 papers with code
DELTA: Direct Embedding Enhancement and Leverage Truncated Conscious Attention for Recommendation System
Click-Through Rate (CTR) prediction is the most critical task in product and content recommendation, and learning effective feature interaction is the key challenge to exploiting user preferences for products.
MathPrompter: Mathematical Reasoning using Large Language Models
Large Language Models (LLMs) have limited performance when solving arithmetic reasoning tasks and often provide incorrect answers.
Rethinking Precision of Pseudo Label: Test-Time Adaptation via Complementary Learning
In this work, we propose a novel complementary learning approach to enhance test-time adaptation (TTA), which has been proven to exhibit good performance on testing data with distribution shifts such as corruptions.
Repainting and Imitating Learning for Lane Detection
In this paper, we target at finding an enhanced feature space where the lane features are distinctive while maintaining a similar distribution of lanes in the wild.
AGO-Net: Association-Guided 3D Point Cloud Object Detection Network
A feasible method is investigated to construct conceptual scenes without external datasets.
The Devil is in the Task: Exploiting Reciprocal Appearance-Localization Features for Monocular 3D Object Detection
Low-cost monocular 3D object detection plays a fundamental role in autonomous driving, whereas its accuracy is still far from satisfactory.
SGM3D: Stereo Guided Monocular 3D Object Detection
Monocular 3D object detection aims to predict the object location, dimension and orientation in 3D space alongside the object category given only a monocular image.
A Data-Driven Democratized Control Architecture for Regional Transmission Operators
As probably the most complicated and critical infrastructure system, U. S. power grids become increasingly vulnerable to extreme events such as cyber-attacks and severe weather, as well as higher DER penetrations and growing information mismatch among system operators, utilities (transmission or generation owners), and end-users.
Multilingual Molecular Representation Learning via Contrastive Pre-training
Molecular representation learning plays an essential role in cheminformatics.
Random vector functional link neural network based ensemble deep learning for short-term load forecasting
Electricity load forecasting is crucial for the power systems' planning and maintenance.
Depth-conditioned Dynamic Message Propagation for Monocular 3D Object Detection
The objective of this paper is to learn context- and depth-aware feature representation to solve the problem of monocular 3D object detection.
Ranked #10 on
Monocular 3D Object Detection
on KITTI Cars Moderate
Manifold Adaptive Multiple Kernel K-Means for Clustering
Multiple kernel methods based on k-means aims to integrate a group of kernels to improve the performance of kernel k-means clustering.
Associate-3Ddet: Perceptual-to-Conceptual Association for 3D Point Cloud Object Detection
Object detection from 3D point clouds remains a challenging task, though recent studies pushed the envelope with the deep learning techniques.
3DCFS: Fast and Robust Joint 3D Semantic-Instance Segmentation via Coupled Feature Selection
We propose a novel fast and robust 3D point clouds segmentation framework via coupled feature selection, named 3DCFS, that jointly performs semantic and instance segmentation.
Visualization of Multi-Objective Switched Reluctance Machine Optimization at Multiple Operating Conditions with t-SNE
However, the number of objectives that would need to be optimized would significantly increase with the number of operating points considered in the optimization, thus posting a potential problem in regards to the visualization techniques currently in use, such as in the scatter plots of Pareto fronts, the parallel coordinates, and in the principal component analysis (PCA), inhibiting their ability to provide machine designers with intuitive and informative visualizations of all of the design candidates and their ability to pick a few for further fine-tuning with performance verification.
SSF-DAN: Separated Semantic Feature Based Domain Adaptation Network for Semantic Segmentation
Despite the great success achieved by supervised fully convolutional models in semantic segmentation, training the models requires a large amount of labor-intensive work to generate pixel-level annotations.
3D Recurrent Neural Networks with Context Fusion for Point Cloud Semantic Segmentation
Semantic segmentation of 3D unstructured point clouds remains an open research problem.
Cross-Age Face Verification by Coordinating With Cross-Face Age Verification
As shown in our experiments, the algorithm effectively balances feature sharing and feature exclusion between the two tasks; and, for face verification, the algorithm effectively removes distracting features used in age verification.
Unsupervised Feature Selection with Adaptive Structure Learning
Traditional unsupervised methods select the features which can faithfully preserve the intrinsic structures of data, where the intrinsic structures are estimated using all the input features of data.
Heterogeneous Metric Learning with Content-based Regularization for Software Artifact Retrieval
Specifically, this method can capture both the inherent information in the source codes and the semantic information hidden in the comments, descriptions, and identifiers of the source codes.
