Search Results for author:
Found 14 papers, 3 papers with code
Towards Unified Modeling for Positive and Negative Preferences in Sign-Aware Recommendation
Recently, sign-aware graph recommendation has drawn much attention as it will learn users' negative preferences besides positive ones from both positive and negative interactions (i. e., links in a graph) with items.
Repeated Padding as Data Augmentation for Sequential Recommendation
Specifically, we use the original interaction sequences as the padding content and fill it to the padding positions during model training.
Stealthy Attack on Large Language Model based Recommendation
Recently, the powerful large language models (LLMs) have been instrumental in propelling the progress of recommender systems (RS).
ID Embedding as Subtle Features of Content and Structure for Multimodal Recommendation
In this paper, we revisit the value of ID embeddings for multimodal recommendation and conduct a thorough study regarding its semantics, which we recognize as subtle features of content and structures.
Video and Audio are Images: A Cross-Modal Mixer for Original Data on Video-Audio Retrieval
Cross-modal retrieval has become popular in recent years, particularly with the rise of multimedia.
Evaluating robustness of support vector machines with the Lagrangian dual approach
To this end, we propose a method to improve the verification performance for SVMs with nonlinear kernels.
Face2Exp: Combating Data Biases for Facial Expression Recognition
To combat the mismatch between FR and FER data, Meta-Face2Exp uses a circuit feedback mechanism, which improves the base network with the feedback from the adaptation network.
Gradient Information Matters in Policy Optimization by Back-propagating through Model
Then we proposed a two-model-based learning method to control the prediction error and the gradient error.
Learning Residue-Aware Correlation Filters and Refining Scale Estimates with the GrabCut for Real-Time UAV Tracking
Unmanned aerial vehicle (UAV)-based tracking is attracting increasing attention and developing rapidly in applications such as agriculture, aviation, navigation, transportation and public security.
The velocity dispersion function of early-type galaxies and its redshift evolution: the newest results from lens redshift test
The redshift distribution of galactic-scale lensing systems provides a laboratory to probe the velocity dispersion function (VDF) of early-type galaxies (ETGs) and measure the evolution of early-type galaxies at redshift z ~ 1.
Astrophysics of Galaxies Cosmology and Nongalactic Astrophysics
A model-independent constraint on the Hubble constant with gravitational waves from the Einstein Telescope
In this paper, we investigate the expected constraints on the Hubble constant from the gravitational-wave standard sirens, in a cosmological-model-independent way.
Cosmology and Nongalactic Astrophysics
Towards Unsupervised Crowd Counting via Regression-Detection Bi-knowledge Transfer
We formulate the mutual transformations between the outputs of regression- and detection-based models as two scene-agnostic transformers which enable knowledge distillation between the two models.
Path Space for Recurrent Neural Networks with ReLU Activations
Optimization algorithms like stochastic gradient descent that optimize the neural networks in the vector space of weights, which are not positively scale-invariant.
Point in, Box out: Beyond Counting Persons in Crowds
In the end, we propose a curriculum learning strategy to train the network from images of relatively accurate and easy pseudo ground truth first.
