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Found 41 papers, 25 papers with code
Cross-modal Audio-visual Co-learning for Text-independent Speaker Verification
Visual speech (i. e., lip motion) is highly related to auditory speech due to the co-occurrence and synchronization in speech production.
Self-Supervised Temporal Graph learning with Temporal and Structural Intensity Alignment
To solve this issue, by extracting both temporal and structural information to learn more informative node representations, we propose a self-supervised method termed S2T for temporal graph learning.
A Survey of Knowledge Graph Reasoning on Graph Types: Static, Dynamic, and Multimodal
The early works in this domain mainly focus on static KGR and tend to directly apply general knowledge graph embedding models to the reasoning task.
COVID-19 Activity Risk Calculator as a Gamified Public Health Intervention Tool
Public health intervention techniques have been highly significant in reducing the negative impact of several epidemics and pandemics.
Coordinating Cross-modal Distillation for Molecular Property Prediction
The existing graph methods have demonstrated that 3D geometric information is significant for better performance in MPP.
DiffBP: Generative Diffusion of 3D Molecules for Target Protein Binding
Previous works usually generate atoms in an auto-regressive way, where element types and 3D coordinates of atoms are generated one by one.
I4U System Description for NIST SRE'20 CTS Challenge
This manuscript describes the I4U submission to the 2020 NIST Speaker Recognition Evaluation (SRE'20) Conversational Telephone Speech (CTS) Challenge.
FedVMR: A New Federated Learning method for Video Moment Retrieval
As the first that is explored in VMR field, the new task is defined as video moment retrieval with distributed data.
Gradient-Guided Importance Sampling for Learning Binary Energy-Based Models
the discrete data space to approximately construct the provably optimal proposal distribution, which is subsequently used by importance sampling to efficiently estimate the original ratio matching objective.
Deep Spectro-temporal Artifacts for Detecting Synthesized Speech
The Audio Deep Synthesis Detection (ADD) Challenge has been held to detect generated human-like speech.
Spoofing-Aware Attention based ASV Back-end with Multiple Enrollment Utterances and a Sampling Strategy for the SASV Challenge 2022
Current state-of-the-art automatic speaker verification (ASV) systems are vulnerable to presentation attacks, and several countermeasures (CMs), which distinguish bona fide trials from spoofing ones, have been explored to protect ASV.
Topological structure of complex predictions
Complex prediction models such as deep learning are the output from fitting machine learning, neural networks, or AI models to a set of training data.
GraphFM: Improving Large-Scale GNN Training via Feature Momentum
GraphFM-IB applies FM to in-batch sampled data, while GraphFM-OB applies FM to out-of-batch data that are 1-hop neighborhood of in-batch data.
Your Neighbors Are Communicating: Towards Powerful and Scalable Graph Neural Networks
Message passing graph neural networks (GNNs) are known to have their expressiveness upper-bounded by 1-dimensional Weisfeiler-Lehman (1-WL) algorithm.
Generating 3D Molecules for Target Protein Binding
Second, to preserve the desirable equivariance property, we select a local reference atom according to the designed auxiliary classifiers and then construct a local spherical coordinate system.
Neighbor2Seq: Deep Learning on Massive Graphs by Transforming Neighbors to Sequences
Therefore, our Neighbor2Seq naturally endows GNNs with the efficiency and advantages of deep learning operations on grid-like data by precomputing the Neighbor2Seq transformations.
Inductive Representation Learning in Temporal Networks via Mining Neighborhood and Community Influences
Therefore, we propose a new inductive network representation learning method called MNCI by mining neighborhood and community influences in temporal networks.
Molecule3D: A Benchmark for Predicting 3D Geometries from Molecular Graphs
Here, we propose to predict the ground-state 3D geometries from molecular graphs using machine learning methods.
Ranked #1 on
3D Geometry Prediction
on Molecule3D val
$m$-mix: Generating hard negatives via multiple samples mixing for contrastive learning
Negative pairs are essential in contrastive learning, which plays the role of avoiding degenerate solutions.
GraphEBM: Towards Permutation Invariant and Multi-Objective Molecular Graph Generation
In this work, we propose GraphEBM, a molecular graph generation method via energy-based models (EBMs), as an exploratory work to perform permutation invariant and multi-objective molecule generation.
Gradient-Guided Importance Sampling for Learning Discrete Energy-Based Models
In this study, we propose ratio matching with gradient-guided importance sampling (RMwGGIS) to alleviate the above limitations.
A Novel Patch Convolutional Neural Network for View-based 3D Model Retrieval
Thus, we tackle this problem from the perspective of exploiting the relationships between patch features to capture long-range associations among multi-view images.
Multigranular Visual-Semantic Embedding for Cloth-Changing Person Re-identification
To solve these issues, in this work, a novel multigranular visual-semantic embedding algorithm (MVSE) is proposed for cloth-changing person ReID, where visual semantic information and human attributes are embedded into the network, and the generalized features of human appearance can be well learned to effectively solve the problem of clothing changes.
Dynamic Modality Interaction Modeling for Image-Text Retrieval
To address these issues, we develop a novel modality interaction modeling network based upon the routing mechanism, which is the first unified and dynamic multimodal interaction framework towards image-text retrieval.
Multi-Modal Relational Graph for Cross-Modal Video Moment Retrieval
Toward this end, we contribute a multi-modal relational graph to capture the interactions among objects from the visual and textual content to identify the differences among similar video moment candidates.
Fast Quantum Property Prediction via Deeper 2D and 3D Graph Networks
We employ our methods to participate in the 2021 KDD Cup on OGB Large-Scale Challenge (OGB-LSC), which aims to predict the HOMO-LUMO energy gap of molecules.
Exploring Deep Learning for Joint Audio-Visual Lip Biometrics
Audio-visual (AV) lip biometrics is a promising authentication technique that leverages the benefits of both the audio and visual modalities in speech communication.
DIG: A Turnkey Library for Diving into Graph Deep Learning Research
Although there exist several libraries for deep learning on graphs, they are aiming at implementing basic operations for graph deep learning.
Spherical Message Passing for 3D Graph Networks
Based on such observations, we propose the spherical message passing (SMP) as a novel and powerful scheme for 3D molecular learning.
GraphEBM: Molecular Graph Generation with Energy-Based Models
We note that most existing approaches for molecular graph generation fail to guarantee the intrinsic property of permutation invariance, resulting in unexpected bias in generative models.
Advanced Graph and Sequence Neural Networks for Molecular Property Prediction and Drug Discovery
Here we develop a suite of comprehensive machine learning methods and tools spanning different computational models, molecular representations, and loss functions for molecular property prediction and drug discovery.
The Diversified Ensemble Neural Network
Ensemble is a general way of improving the accuracy and stability of learning models, especially for the generalization ability on small datasets.
Frame-wise Cross-modal Matching for Video Moment Retrieval
Another contribution is that we propose an additional predictor to utilize the internal frames in the model training to improve the localization accuracy.
Towards Deeper Graph Neural Networks
Based on our theoretical and empirical analysis, we propose Deep Adaptive Graph Neural Network (DAGNN) to adaptively incorporate information from large receptive fields.
Ranked #2 on
Node Classification
on AMZ Computers
Strongly local p-norm-cut algorithms for semi-supervised learning and local graph clustering
For this problem, we propose a novel generalization of random walk, diffusion, or smooth function methods in the literature to a convex p-norm cut function.
Non-Local Graph Neural Networks
Modern graph neural networks (GNNs) learn node embeddings through multilayer local aggregation and achieve great success in applications on assortative graphs.
NTIRE 2020 Challenge on Real Image Denoising: Dataset, Methods and Results
This challenge is based on a newly collected validation and testing image datasets, and hence, named SIDD+.
KGAT: Knowledge Graph Attention Network for Recommendation
To provide more accurate, diverse, and explainable recommendation, it is compulsory to go beyond modeling user-item interactions and take side information into account.
Ranked #2 on
Link Prediction
on Yelp
Cost-Sensitive Feature Selection by Optimizing F-Measures
Feature selection is beneficial for improving the performance of general machine learning tasks by extracting an informative subset from the high-dimensional features.
Assessment of central serous chorioretinopathy (CSC) depicted on color fundus photographs using deep Learning
To investigate whether and to what extent central serous chorioretinopathy (CSC) depicted on color fundus photographs can be assessed using deep learning technology.
Sparsely Grouped Multi-task Generative Adversarial Networks for Facial Attribute Manipulation
To overcome such a key limitation, we propose Sparsely Grouped Generative Adversarial Networks (SG-GAN) as a novel approach that can translate images on sparsely grouped datasets where only a few samples for training are labelled.
