85 citations · 263 across the 35 of their papers we have counts for
12 papers · 1 filter
Extensible Proxy for Efficient NAS
Yuhong Li, Jiajie Li, Cong Han +3
Neural Architecture Search (NAS) has become a de facto approach in the recent trend of AutoML to design deep neural networks (DNNs). Efficient or near-zero-cost NAS proxies are fur…
Generalization Guarantee of Training Graph Convolutional Networks with Graph Topology Sampling
Hongkang Li, Meng Wang, Sijia Liu +2
Graph convolutional networks (GCNs) have recently achieved great empirical success in learning graph-structured data. To address its scalability issue due to the recursive embeddin…
How does unlabeled data improve generalization in self-training? A one-hidden-layer theoretical analysis
Shuai Zhang, Meng Wang, Sijia Liu +2
Self-training, a semi-supervised learning algorithm, leverages a large amount of unlabeled data to improve learning when the labeled data are limited. Despite empirical successes,…
Graph Neural Network Training with Data Tiering
Seung Won Min, Kun Wu, Mert Hidayetoğlu +3
Graph Neural Networks (GNNs) have shown success in learning from graph-structured data, with applications to fraud detection, recommendation, and knowledge graph reasoning. However…
MLHarness: A Scalable Benchmarking System for MLCommons
Yen-Hsiang Chang, Jianhao Pu, Wen-mei Hwu +1
With the society's growing adoption of machine learning (ML) and deep learning (DL) for various intelligent solutions, it becomes increasingly imperative to standardize a common se…
Why Lottery Ticket Wins? A Theoretical Perspective of Sample Complexity on Pruned Neural Networks
Shuai Zhang, Meng Wang, Sijia Liu +2
The \textit{lottery ticket hypothesis} (LTH) states that learning on a properly pruned network (the \textit{winning ticket}) improves test accuracy over the original unpruned netwo…
Generic Neural Architecture Search via Regression
Yuhong Li, Cong Hao, Pan Li +2
Most existing neural architecture search (NAS) algorithms are dedicated to and evaluated by the downstream tasks, e.g., image classification in computer vision. However, extensive…
Heterogeneous Contrastive Learning
Lecheng Zheng, Jinjun Xiong, Yada Zhu +1
With the advent of big data across multiple high-impact applications, we are often facing the challenge of complex heterogeneity. The newly collected data usually consist of multip…
Fast Learning of Graph Neural Networks with Guaranteed Generalizability: One-hidden-layer Case
Shuai Zhang, Meng Wang, Sijia Liu +2
Although graph neural networks (GNNs) have made great progress recently on learning from graph-structured data in practice, their theoretical guarantee on generalizability remains…
On Interpretability of Artificial Neural Networks: A Survey
Fenglei Fan, Jinjun Xiong, Mengzhou Li +1
Deep learning as represented by the artificial deep neural networks (DNNs) has achieved great success in many important areas that deal with text, images, videos, graphs, and so on…
NAIS: Neural Architecture and Implementation Search and its Applications in Autonomous Driving
Cong Hao, Yao Chen, Xinheng Liu +9
The rapidly growing demands for powerful AI algorithms in many application domains have motivated massive investment in both high-quality deep neural network (DNN) models and high-…
Challenges and Pitfalls of Machine Learning Evaluation and Benchmarking
Cheng Li, Abdul Dakkak, Jinjun Xiong +1
An increasingly complex and diverse collection of Machine Learning (ML) models as well as hardware/software stacks, collectively referred to as "ML artifacts", are being proposed -…