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Found 13 papers, 5 papers with code
On Practical Diversified Recommendation with Controllable Category Diversity Framework
These neglected non-interaction preferences are especially important for broadening user's interests in alleviating echo chamber/filter bubble effects. Therefore, in this paper, we first define diversity as two distinct definitions, i. e., user-explicit diversity (U-diversity) and user-item non-interaction diversity (N-diversity) based on user historical behaviors.
Deep Evolutional Instant Interest Network for CTR Prediction in Trigger-Induced Recommendation
Recently, a new recommendation scenario, called Trigger-Induced Recommendation (TIR), where users are able to explicitly express their instant interests via trigger items, is emerging as an essential role in many e-commerce platforms, e. g., Alibaba. com and Amazon.
PNeRFLoc: Visual Localization with Point-based Neural Radiance Fields
In this paper, we propose a novel visual localization framework, \ie, PNeRFLoc, based on a unified point-based representation.
Distributed Multi-Time Slot Power Balancing Control of Power Systems with Energy Storage Devices
This paper studies a crucial problem in power system balancing control, i. e., the multi-time slot economic dispatch (MTSED) problem, for power grids with substantial renewables, synchronous generators (SGs), and energy storage devices (ESDs).
Dense RGB SLAM with Neural Implicit Maps
To reach this challenging goal without depth input, we introduce a hierarchical feature volume to facilitate the implicit map decoder.
SceneSqueezer: Learning To Compress Scene for Camera Relocalization
Standard visual localization methods build a priori 3D model of a scene which is used to establish correspondences against the 2D keypoints in a query image.
End-to-End Rotation Averaging With Multi-Source Propagation
These methods can suffer from bad initializations due to the noisy spanning tree or outliers in input relative rotations.
Multiplex Bipartite Network Embedding using Dual Hypergraph Convolutional Networks
A large number of network embedding methods exist to learn vectorial node representations from general graphs with both homogeneous and heterogeneous node and edge types, including some that can specifically model the distinct properties of bipartite networks.
Deep Multi-Interest Network for Click-through Rate Prediction
Then the Multi-Interest Extractor Layer is applied to extract multiple user interests.
Modeling Dynamic Heterogeneous Network for Link Prediction using Hierarchical Attention with Temporal RNN
It has achieved great success on many tasks of network analysis such as link prediction and node classification.
SANet: Scene Agnostic Network for Camera Localization
This paper presents a scene agnostic neural architecture for camera localization, where model parameters and scenes are independent from each other. Despite recent advancement in learning based methods, most approaches require training for each scene one by one, not applicable for online applications such as SLAM and robotic navigation, where a model must be built on-the-fly. Our approach learns to build a hierarchical scene representation and predicts a dense scene coordinate map of a query RGB image on-the-fly given an arbitrary scene.
Polarimetric Dense Monocular SLAM
This paper presents a novel polarimetric dense monocular SLAM (PDMS) algorithm based on a polarization camera.
Attribute Recognition from Adaptive Parts
Previous part-based attribute recognition approaches perform part detection and attribute recognition in separate steps.
