research
∙
09/18/2023
LogShrink: Effective Log Compression by Leveraging Commonality and Variability of Log Data
Log data is a crucial resource for recording system events and states du...
share
research
∙
07/22/2023
Spatial Self-Distillation for Object Detection with Inaccurate Bounding Boxes
Object detection via inaccurate bounding boxes supervision has boosted a...
research
∙
04/20/2023
HyperTuner: A Cross-Layer Multi-Objective Hyperparameter Auto-Tuning Framework for Data Analytic Services
Hyper-parameters optimization (HPO) is vital for machine learning models...
research
∙
08/08/2022
Constructing Large-Scale Real-World Benchmark Datasets for AIOps
Recently, AIOps (Artificial Intelligence for IT Operations) has been wel...
research
∙
05/10/2022
Robust Medical Image Classification from Noisy Labeled Data with Global and Local Representation Guided Co-training
Deep neural networks have achieved remarkable success in a wide variety ...
research
∙
03/30/2022
Acknowledging the Unknown for Multi-label Learning with Single Positive Labels
Due to the difficulty of collecting exhaustive multi-label annotations, ...
research
∙
03/17/2022
Object Localization under Single Coarse Point Supervision
Point-based object localization (POL), which pursues high-performance ob...
research
∙
02/03/2021
Function Delivery Network: Extending Serverless Computing for Heterogeneous Platforms
Serverless computing has rapidly grown following the launch of Amazon's ...
research
∙
01/27/2021
Improving Graph Representation Learning by Contrastive Regularization
Graph representation learning is an important task with applications in ...
research
∙
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...
research
∙
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...
research
∙
08/05/2020
Integrated Traffic Simulation-Prediction System using Neural Networks with Application to the Los Angeles International Airport Road Network
Transportation networks are highly complex and the design of efficient t...
research
∙
06/22/2019
Alchemy: A Quantum Chemistry Dataset for Benchmarking AI Models
We introduce a new molecular dataset, named Alchemy, for developing mach...
research
∙
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...
research
∙
06/13/2019
Utilizing Edge Features in Graph Neural Networks via Variational Information Maximization
Graph Neural Networks (GNNs) achieve an impressive performance on struct...
research
∙
05/27/2019
Disentangling Dynamics and Returns: Value Function Decomposition with Future Prediction
Value functions are crucial for model-free Reinforcement Learning (RL) t...
research
∙
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 ...
research
∙
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...
research
∙
02/27/2017