Search Results for author:
Found 29 papers, 12 papers with code
Federated Continual Learning for Edge-AI: A Comprehensive Survey
Edge-AI, the convergence of edge computing and artificial intelligence (AI), has become a promising paradigm that enables the deployment of advanced AI models at the network edge, close to users.
CLaMP 2: Multimodal Music Information Retrieval Across 101 Languages Using Large Language Models
Challenges in managing linguistic diversity and integrating various musical modalities are faced by current music information retrieval systems.
S-MolSearch: 3D Semi-supervised Contrastive Learning for Bioactive Molecule Search
Virtual Screening is an essential technique in the early phases of drug discovery, aimed at identifying promising drug candidates from vast molecular libraries.
The Solution for the 5th GCAIAC Zero-shot Referring Expression Comprehension Challenge
Recent studies have enhanced the zero-shot performance of multimodal base models in referring expression comprehension tasks by introducing visual prompts.
A Subspace-Constrained Tyler's Estimator and its Applications to Structure from Motion
We present the subspace-constrained Tyler's estimator (STE) designed for recovering a low-dimensional subspace within a dataset that may be highly corrupted with outliers.
Theoretical Guarantees for the Subspace-Constrained Tyler's Estimator
It further shows that under the generalized haystack model, STE initialized by the Tyler's M-estimator (TME), can recover the subspace when the fraction of iniliers is too small for TME to handle.
A Bi-Pyramid Multimodal Fusion Method for the Diagnosis of Bipolar Disorders
Previous research on the diagnosis of Bipolar disorder has mainly focused on resting-state functional magnetic resonance imaging.
Hyperparameter Estimation for Sparse Bayesian Learning Models
Sparse Bayesian Learning (SBL) models are extensively used in signal processing and machine learning for promoting sparsity through hierarchical priors.
DTP-Net: Learning to Reconstruct EEG signals in Time-Frequency Domain by Multi-scale Feature Reuse
Finally, a Decoder layer is employed to reconstruct the artifact-reduced EEG signal.
Multi-Dimension-Embedding-Aware Modality Fusion Transformer for Psychiatric Disorder Clasification
In this work, we proposed a multi-dimension-embedding-aware modality fusion transformer (MFFormer) for schizophrenia and bipolar disorder classification using rs-fMRI and T1 weighted structural MRI (T1w sMRI).
Robust Regularized Low-Rank Matrix Models for Regression and Classification
While matrix variate regression models have been studied in many existing works, classical statistical and computational methods for the analysis of the regression coefficient estimation are highly affected by high dimensional and noisy matrix-valued predictors.
Symphony Generation with Permutation Invariant Language Model
In this work, we propose a permutation invariant language model, SymphonyNet, as a solution for symbolic symphony music generation.
Ranked #1 on
Audio Generation
on Symphony music
Vertical Federated Principal Component Analysis and Its Kernel Extension on Feature-wise Distributed Data
Despite enormous research interest and rapid application of federated learning (FL) to various areas, existing studies mostly focus on supervised federated learning under the horizontally partitioned local dataset setting.
An Efficient Network Design for Face Video Super-resolution
We construct a dataset consisting entirely of face video sequences for network training and evaluation, and conduct hyper-parameter optimization in our experiments.
ALMA: Alternating Minimization Algorithm for Clustering Mixture Multilayer Network
The paper considers a Mixture Multilayer Stochastic Block Model (MMLSBM), where layers can be partitioned into groups of similar networks, and networks in each group are equipped with a distinct Stochastic Block Model.
Disentangled Self-Attentive Neural Networks for Click-Through Rate Prediction
To better model complex feature interaction, in this paper we propose a novel DisentanglEd Self-atTentIve NEtwork (DESTINE) framework for CTR prediction that explicitly decouples the computation of unary feature importance from pairwise interaction.
Graph Contrastive Learning with Adaptive Augmentation
On the node attribute level, we corrupt node features by adding more noise to unimportant node features, to enforce the model to recognize underlying semantic information.
CAGNN: Cluster-Aware Graph Neural Networks for Unsupervised Graph Representation Learning
In CAGNN, we perform clustering on the node embeddings and update the model parameters by predicting the cluster assignments.
Disentangled Item Representation for Recommender Systems
In this way, the items are represented at the attribute level, which can provide fine-grained information of items in recommendation.
TFNet: Multi-Semantic Feature Interaction for CTR Prediction
The CTR (Click-Through Rate) prediction plays a central role in the domain of computational advertising and recommender systems.
Ranked #33 on
Click-Through Rate Prediction
on Criteo
Deep Graph Contrastive Representation Learning
Moreover, our unsupervised method even surpasses its supervised counterparts on transductive tasks, demonstrating its great potential in real-world applications.
Ranked #1 on
Node Classification
on DBLP
TAGNN: Target Attentive Graph Neural Networks for Session-based Recommendation
However, these methods compress a session into one fixed representation vector without considering the target items to be predicted.
Ranked #3 on
Session-Based Recommendations
on yoochoose1
Deep Interaction Machine: A Simple but Effective Model for High-order Feature Interactions
IM is an efficient and exact implementation of high-order FM, whose time complexity linearly grows with the order of interactions and the number of feature fields.
Ranked #2 on
Click-Through Rate Prediction
on KKBox
Deep Interaction Machine: A Simple but Effective Model for High-order Feature Interactions
IM is an efficient and exact implementation of high-order FM, whose time complexity linearly grows with the order of interactions and the number of feature fields.
An Algorithm for Graph-Fused Lasso Based on Graph Decomposition
In comparison, this work proposes to decompose the objective function into two components, where one component is the loss function plus part of the total variation penalty, and the other component is the remaining total variation penalty.
Optimization and Control Computation
Learning Longer-term Dependencies via Grouped Distributor Unit
Learning long-term dependencies still remains difficult for recurrent neural networks (RNNs) despite their success in sequence modeling recently.
ICE: Information Credibility Evaluation on Social Media via Representation Learning
In this paper, we propose a novel representation learning method, Information Credibility Evaluation (ICE), to learn representations of information credibility on social media.
A Convolutional Click Prediction Model
The explosion in online advertisement urges to better estimate the click prediction of ads.
A Convolutional Click Prediction Model
The explosion in online advertisement urges to better estimate the click prediction of ads.
