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08/23/2023
Distribution-Aware Calibration for Object Detection with Noisy Bounding Boxes
Large-scale well-annotated datasets are of great importance for training...
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03/21/2023
Adaptive Negative Evidential Deep Learning for Open-set Semi-supervised Learning
Semi-supervised learning (SSL) methods assume that labeled data, unlabel...
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03/12/2023
Traj-MAE: Masked Autoencoders for Trajectory Prediction
Trajectory prediction has been a crucial task in building a reliable aut...
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03/12/2023
PointPatchMix: Point Cloud Mixing with Patch Scoring
Data augmentation is an effective regularization strategy for mitigating...
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03/06/2023
DR-Label: Improving GNN Models for Catalysis Systems by Label Deconstruction and Reconstruction
Attaining the equilibrium state of a catalyst-adsorbate system is key to...
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03/03/2023
Uncertainty Estimation by Fisher Information-based Evidential Deep Learning
Uncertainty estimation is a key factor that makes deep learning reliable...
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12/20/2022
RepMode: Learning to Re-parameterize Diverse Experts for Subcellular Structure Prediction
In subcellular biological research, fluorescence staining is a key techn...
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09/23/2022
Spatio-Temporal Contrastive Learning Enhanced GNNs for Session-based Recommendation
Session-based recommendation (SBR) systems aim to utilize the user's sho...
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09/15/2022
Multi-Task Mixture Density Graph Neural Networks for Predicting Cu-based Single-Atom Alloy Catalysts for CO2 Reduction Reaction
Graph neural networks (GNNs) have drawn more and more attention from mat...
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08/22/2022
MetaRF: Differentiable Random Forest for Reaction Yield Prediction with a Few Trails
Artificial intelligence has deeply revolutionized the field of medicinal...
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07/20/2022
Pseudo-label Guided Cross-video Pixel Contrast for Robotic Surgical Scene Segmentation with Limited Annotations
Surgical scene segmentation is fundamentally crucial for prompting cogni...
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04/12/2022
Explore More Guidance: A Task-aware Instruction Network for Sign Language Translation Enhanced with Data Augmentation
Sign language recognition and translation first uses a recognition modul...
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03/30/2022
Acknowledging the Unknown for Multi-label Learning with Single Positive Labels
Due to the difficulty of collecting exhaustive multi-label annotations, ...
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03/24/2022
LHNN: Lattice Hypergraph Neural Network for VLSI Congestion Prediction
Precise congestion prediction from a placement solution plays a crucial ...
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10/09/2021
Flattening Sharpness for Dynamic Gradient Projection Memory Benefits Continual Learning
The backpropagation networks are notably susceptible to catastrophic for...
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06/25/2021
Balancing Accuracy and Fairness for Interactive Recommendation with Reinforcement Learning
Fairness in recommendation has attracted increasing attention due to bia...
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12/10/2020
Beyond Class-Conditional Assumption: A Primary Attempt to Combat Instance-Dependent Label Noise
Supervised learning under label noise has seen numerous advances recentl...
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12/08/2020
Robustness of Accuracy Metric and its Inspirations in Learning with Noisy Labels
For multi-class classification under class-conditional label noise, we p...
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03/16/2020
A Rotation-Invariant Framework for Deep Point Cloud Analysis
Recently, many deep neural networks were designed to process 3D point cl...
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02/10/2020
Q-value Path Decomposition for Deep Multiagent Reinforcement Learning
Recently, deep multiagent reinforcement learning (MARL) has become a hig...
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02/10/2020
Qatten: A General Framework for Cooperative Multiagent Reinforcement Learning
In many real-world settings, a team of cooperative agents must learn to ...
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09/10/2019
Wasserstein Collaborative Filtering for Item Cold-start Recommendation
The item cold-start problem seriously limits the recommendation performa...
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09/10/2019
PMD: A New User Distance for Recommender Systems
Collaborative filtering, a widely-used recommendation technique, predict...
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07/21/2019
Spectral-based Graph Convolutional Network for Directed Graphs
Graph convolutional networks(GCNs) have become the most popular approach...
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06/22/2019
Alchemy: A Quantum Chemistry Dataset for Benchmarking AI Models
We introduce a new molecular dataset, named Alchemy, for developing mach...
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06/13/2019
A Meta Approach to Defend Noisy Labels by the Manifold Regularizer PSDR
Noisy labels are ubiquitous in real-world datasets, which poses a challe...
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06/13/2019
Utilizing Edge Features in Graph Neural Networks via Variational Information Maximization
Graph Neural Networks (GNNs) achieve an impressive performance on struct...
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06/06/2019
Understanding Adversarial Behavior of DNNs by Disentangling Non-Robust and Robust Components in Performance Metric
The vulnerability to slight input perturbations is a worrying yet intrig...
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05/27/2019
Disentangling Dynamics and Returns: Value Function Decomposition with Future Prediction
Value functions are crucial for model-free Reinforcement Learning (RL) t...
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05/15/2019
Rethinking the Usage of Batch Normalization and Dropout in the Training of Deep Neural Networks
In this work, we propose a novel technique to boost training efficiency ...
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05/13/2019
Understanding and Utilizing Deep Neural Networks Trained with Noisy Labels
Noisy labels are ubiquitous in real-world datasets, which poses a challe...
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01/13/2016
Online Prediction of Dyadic Data with Heterogeneous Matrix Factorization
Dyadic Data Prediction (DDP) is an important problem in many research ar...
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01/13/2016