726 citations · 2.2k across the 66 of their papers we have counts for
68 papers
LVM-Med: Learning Large-Scale Self-Supervised Vision Models for Medical Imaging via Second-order Graph Matching
Duy M. H. Nguyen, Hoang Nguyen, Nghiem T. Diep +9
Obtaining large pre-trained models that can be fine-tuned to new tasks with limited annotated samples has remained an open challenge for medical imaging data. While pre-trained dee…
Learning by Grouping: A Multilevel Optimization Framework for Improving Fairness in Classification without Losing Accuracy
Ramtin Hosseini, Li Zhang, Bhanu Garg +1
The integration of machine learning models in various real-world applications is becoming more prevalent to assist humans in their daily decision-making tasks as a result of recent…
A Comprehensive Survey on Pretrained Foundation Models: A History from BERT to ChatGPT
Ce Zhou, Qian Li, Chen Li +16
Pretrained Foundation Models (PFMs) are regarded as the foundation for various downstream tasks with different data modalities. A PFM (e.g., BERT, ChatGPT, and GPT-4) is trained on…
Learning Compiler Pass Orders using Coreset and Normalized Value Prediction
Youwei Liang, Kevin Stone, Ali Shameli +8
Finding the optimal pass sequence of compilation can lead to a significant reduction in program size and/or improvement in program efficiency. Prior works on compilation pass order…
DRG-Net: Interactive Joint Learning of Multi-lesion Segmentation and Classification for Diabetic Retinopathy Grading
Hasan Md Tusfiqur, Duy M. H. Nguyen, Mai T. N. Truong +8
Diabetic Retinopathy (DR) is a leading cause of vision loss in the world, and early DR detection is necessary to prevent vision loss and support an appropriate treatment. In this w…
Joint Self-Supervised Image-Volume Representation Learning with Intra-Inter Contrastive Clustering
Duy M. H. Nguyen, Hoang Nguyen, Mai T. N. Truong +7
Collecting large-scale medical datasets with fully annotated samples for training of deep networks is prohibitively expensive, especially for 3D volume data. Recent breakthroughs i…
Type Information Utilized Event Detection via Multi-Channel GNNs in Electrical Power Systems
Qian Li, Jianxin Li, Lihong Wang +6
Event detection in power systems aims to identify triggers and event types, which helps relevant personnel respond to emergencies promptly and facilitates the optimization of power…
Betty: An Automatic Differentiation Library for Multilevel Optimization
Sang Keun Choe, Willie Neiswanger, Pengtao Xie +1
Gradient-based multilevel optimization (MLO) has gained attention as a framework for studying numerous problems, ranging from hyperparameter optimization and meta-learning to neura…
Not All Patches are What You Need: Expediting Vision Transformers via Token Reorganizations
Youwei Liang, Chongjian Ge, Zhan Tong +3
Vision Transformers (ViTs) take all the image patches as tokens and construct multi-head self-attention (MHSA) among them. Complete leverage of these image tokens brings redundant…
Self-directed Machine Learning
Wenwu Zhu, Xin Wang, Pengtao Xie
Conventional machine learning (ML) relies heavily on manual design from machine learning experts to decide learning tasks, data, models, optimization algorithms, and evaluation met…
Learning from Mistakes based on Class Weighting with Application to Neural Architecture Search
Jay Gala, Pengtao Xie
Learning from mistakes is an effective learning approach widely used in human learning, where a learner pays greater focus on mistakes to circumvent them in the future to improve t…
Improving Differentiable Architecture Search with a Generative Model
Ruisi Zhang, Youwei Liang, Sai Ashish Somayajula +1
In differentiable neural architecture search (NAS) algorithms like DARTS, the training set used to update model weight and the validation set used to update model architectures are…
Learning from Mistakes -- A Framework for Neural Architecture Search
Bhanu Garg, Li Zhang, Pradyumna Sridhara +3
Learning from one's mistakes is an effective human learning technique where the learners focus more on the topics where mistakes were made, so as to deepen their understanding. In…
Learning by Examples Based on Multi-level Optimization
Shentong Mo, Pengtao Xie
Learning by examples, which learns to solve a new problem by looking into how similar problems are solved, is an effective learning method in human learning. When a student learns…
Self-supervised Regularization for Text Classification
Meng Zhou, Zechen Li, Pengtao Xie
Text classification is a widely studied problem and has broad applications. In many real-world problems, the number of texts for training classification models is limited, which re…
Interleaving Learning, with Application to Neural Architecture Search
Hao Ban, Pengtao Xie
Interleaving learning is a human learning technique where a learner interleaves the studies of multiple topics, which increases long-term retention and improves ability to transfer…
Learning by Teaching, with Application to Neural Architecture Search
Parth Sheth, Yueyu Jiang, Pengtao Xie
In human learning, an effective skill in improving learning outcomes is learning by teaching: a learner deepens his/her understanding of a topic by teaching this topic to others. I…
DSRNA: Differentiable Search of Robust Neural Architectures
Ramtin Hosseini, Xingyi Yang, Pengtao Xie
In deep learning applications, the architectures of deep neural networks are crucial in achieving high accuracy. Many methods have been proposed to search for high-performance neur…
Learning by Ignoring, with Application to Domain Adaptation
Xingchen Zhao, Xuehai He, Pengtao Xie
Learning by ignoring, which identifies less important things and excludes them from the learning process, is broadly practiced in human learning and has shown ubiquitous effectiven…
Learning by Self-Explanation, with Application to Neural Architecture Search
Ramtin Hosseini, Pengtao Xie
Learning by self-explanation is an effective learning technique in human learning, where students explain a learned topic to themselves for deepening their understanding of this to…
Small-Group Learning, with Application to Neural Architecture Search
Xuefeng Du, Pengtao Xie
In human learning, an effective learning methodology is small-group learning: a small group of students work together towards the same learning objective, where they express their…
Validate and Enable Machine Learning in Industrial AI
Hongbo Zou, Guangjing Chen, Pengtao Xie +10
Industrial Artificial Intelligence (Industrial AI) is an emerging concept which refers to the application of artificial intelligence to industry. Industrial AI promises more effici…
Skillearn: Machine Learning Inspired by Humans' Learning Skills
Pengtao Xie, Xuefeng Du, Hao Ban
Humans, as the most powerful learners on the planet, have accumulated a lot of learning skills, such as learning through tests, interleaving learning, self-explanation, active reca…
Learning by Passing Tests, with Application to Neural Architecture Search
Xuefeng Du, Haochen Zhang, Pengtao Xie
Learning through tests is a broadly used methodology in human learning and shows great effectiveness in improving learning outcome: a sequence of tests are made with increasing lev…