Search Results for author: Zifeng Wang

Found 29 papers, 18 papers with code

Robust Meta Learning for Image based tasks

no code implementations30 Jan 2023 Penghao Jiang, Xin Ke, Zifeng Wang, Chunxi Li

However, learning such a model is not possible in standard machine learning frameworks as the distribution of the test data is unknown.


Invariant Meta Learning for Out-of-Distribution Generalization

no code implementations26 Jan 2023 Penghao Jiang, Ke Xin, Zifeng Wang, Chunxi Li

Modern deep learning techniques have illustrated their excellent capabilities in many areas, but relies on large training data.

Meta-Learning Out-of-Distribution Generalization

QueryForm: A Simple Zero-shot Form Entity Query Framework

no code implementations14 Nov 2022 Zifeng Wang, Zizhao Zhang, Jacob Devlin, Chen-Yu Lee, Guolong Su, Hao Zhang, Jennifer Dy, Vincent Perot, Tomas Pfister

Zero-shot transfer learning for document understanding is a crucial yet under-investigated scenario to help reduce the high cost involved in annotating document entities.

Transfer Learning

MedCLIP: Contrastive Learning from Unpaired Medical Images and Text

1 code implementation18 Oct 2022 Zifeng Wang, Zhenbang Wu, Dinesh Agarwal, Jimeng Sun

Existing vision-text contrastive learning like CLIP aims to match the paired image and caption embeddings while pushing others apart, which improves representation transferability and supports zero-shot prediction.

Contrastive Learning Retrieval +1

PromptEHR: Conditional Electronic Healthcare Records Generation with Prompt Learning

1 code implementation11 Oct 2022 Zifeng Wang, Jimeng Sun

Accessing longitudinal multimodal Electronic Healthcare Records (EHRs) is challenging due to privacy concerns, which hinders the use of ML for healthcare applications.

Imputation Privacy Preserving

SparCL: Sparse Continual Learning on the Edge

1 code implementation20 Sep 2022 Zifeng Wang, Zheng Zhan, Yifan Gong, Geng Yuan, Wei Niu, Tong Jian, Bin Ren, Stratis Ioannidis, Yanzhi Wang, Jennifer Dy

SparCL achieves both training acceleration and accuracy preservation through the synergy of three aspects: weight sparsity, data efficiency, and gradient sparsity.

Continual Learning

Artificial Intelligence for In Silico Clinical Trials: A Review

no code implementations16 Sep 2022 Zifeng Wang, Chufan Gao, Lucas M. Glass, Jimeng Sun

In silico trials are clinical trials conducted digitally through simulation and modeling as an alternative to traditional clinical trials.

Trial2Vec: Zero-Shot Clinical Trial Document Similarity Search using Self-Supervision

1 code implementation29 Jun 2022 Zifeng Wang, Jimeng Sun

We propose a zero-shot clinical trial retrieval method, Trial2Vec, which learns through self-supervision without annotating similar clinical trials.

Clinical Knowledge Retrieval

Deep Learning on Multimodal Sensor Data at the Wireless Edge for Vehicular Network

1 code implementation12 Jan 2022 Batool Salehi, Guillem Reus-Muns, Debashri Roy, Zifeng Wang, Tong Jian, Jennifer Dy, Stratis Ioannidis, Kaushik Chowdhury

Beam selection for millimeter-wave links in a vehicular scenario is a challenging problem, as an exhaustive search among all candidate beam pairs cannot be assuredly completed within short contact times.


Learning to Prompt for Continual Learning

1 code implementation CVPR 2022 Zifeng Wang, Zizhao Zhang, Chen-Yu Lee, Han Zhang, Ruoxi Sun, Xiaoqi Ren, Guolong Su, Vincent Perot, Jennifer Dy, Tomas Pfister

The mainstream paradigm behind continual learning has been to adapt the model parameters to non-stationary data distributions, where catastrophic forgetting is the central challenge.

Continual Learning Image Classification

SurvTRACE: Transformers for Survival Analysis with Competing Events

1 code implementation2 Oct 2021 Zifeng Wang, Jimeng Sun

In medicine, survival analysis studies the time duration to events of interest such as mortality.

Multi-Task Learning Selection bias +1

PAC-Bayes Information Bottleneck

1 code implementation ICLR 2022 Zifeng Wang, Shao-Lun Huang, Ercan E. Kuruoglu, Jimeng Sun, Xi Chen, Yefeng Zheng

Then, we build an IIW-based information bottleneck on the trade-off between accuracy and information complexity of NNs, namely PIB.

Deep Bayesian Unsupervised Lifelong Learning

1 code implementation13 Jun 2021 Tingting Zhao, Zifeng Wang, Aria Masoomi, Jennifer Dy

We develop a fully Bayesian inference framework for ULL with a novel end-to-end Deep Bayesian Unsupervised Lifelong Learning (DBULL) algorithm, which can progressively discover new clusters without forgetting the past with unlabelled data while learning latent representations.

Bayesian Inference

Revisiting Hilbert-Schmidt Information Bottleneck for Adversarial Robustness

1 code implementation NeurIPS 2021 Zifeng Wang, Tong Jian, Aria Masoomi, Stratis Ioannidis, Jennifer Dy

We investigate the HSIC (Hilbert-Schmidt independence criterion) bottleneck as a regularizer for learning an adversarially robust deep neural network classifier.

Adversarial Robustness

Lifelong Learning based Disease Diagnosis on Clinical Notes

1 code implementation27 Feb 2021 Zifeng Wang, Yifan Yang, Rui Wen, Xi Chen, Shao-Lun Huang, Yefeng Zheng

Current deep learning based disease diagnosis systems usually fall short in catastrophic forgetting, i. e., directly fine-tuning the disease diagnosis model on new tasks usually leads to abrupt decay of performance on previous tasks.

Learn-Prune-Share for Lifelong Learning

1 code implementation13 Dec 2020 Zifeng Wang, Tong Jian, Kaushik Chowdhury, Yanzhi Wang, Jennifer Dy, Stratis Ioannidis

In lifelong learning, we wish to maintain and update a model (e. g., a neural network classifier) in the presence of new classification tasks that arrive sequentially.

Open-World Class Discovery with Kernel Networks

1 code implementation13 Dec 2020 Zifeng Wang, Batool Salehi, Andrey Gritsenko, Kaushik Chowdhury, Stratis Ioannidis, Jennifer Dy

We study an Open-World Class Discovery problem in which, given labeled training samples from old classes, we need to discover new classes from unlabeled test samples.

Instance-wise Feature Grouping

no code implementations NeurIPS 2020 Aria Masoomi, Chieh Wu, Tingting Zhao, Zifeng Wang, Peter Castaldi, Jennifer Dy

Moreover, the features that belong to each group, and the important feature groups may vary per sample.

General Classification

Finding Influential Instances for Distantly Supervised Relation Extraction

no code implementations COLING 2022 Zifeng Wang, Rui Wen, Xi Chen, Shao-Lun Huang, Ningyu Zhang, Yefeng Zheng

Distant supervision (DS) is a strong way to expand the datasets for enhancing relation extraction (RE) models but often suffers from high label noise.

Relation Extraction

Information Theoretic Counterfactual Learning from Missing-Not-At-Random Feedback

1 code implementation NeurIPS 2020 Zifeng Wang, Xi Chen, Rui Wen, Shao-Lun Huang, Ercan E. Kuruoglu, Yefeng Zheng

Counterfactual learning for dealing with missing-not-at-random data (MNAR) is an intriguing topic in the recommendation literature since MNAR data are ubiquitous in modern recommender systems.

Recommendation Systems

Online Disease Self-diagnosis with Inductive Heterogeneous Graph Convolutional Networks

no code implementations6 Sep 2020 Zifeng Wang, Rui Wen, Xi Chen, Shilei Cao, Shao-Lun Huang, Buyue Qian, Yefeng Zheng

We propose a Healthcare Graph Convolutional Network (HealGCN) to offer disease self-diagnosis service for online users based on Electronic Healthcare Records (EHRs).

Graph Representation Learning Retrieval

On the Fairness of Randomized Trials for Recommendation with Heterogeneous Demographics and Beyond

no code implementations25 Jan 2020 Zifeng Wang, Xi Chen, Rui Wen, Shao-Lun Huang

Observed events in recommendation are consequence of the decisions made by a policy, thus they are usually selectively labeled, namely the data are Missing Not At Random (MNAR), which often causes large bias to the estimate of true outcomes risk.


Less Is Better: Unweighted Data Subsampling via Influence Function

1 code implementation3 Dec 2019 Zifeng Wang, Hong Zhu, Zhenhua Dong, Xiuqiang He, Shao-Lun Huang

In the time of Big Data, training complex models on large-scale data sets is challenging, making it appealing to reduce data volume for saving computation resources by subsampling.

General Classification Image Classification +2

Streaming Adaptive Nonparametric Variational Autoencoder

no code implementations7 Jun 2019 Tingting Zhao, Zifeng Wang, Aria Masoomi, Jennifer G. Dy

We develop a data driven approach to perform clustering and end-to-end feature learning simultaneously for streaming data that can adaptively detect novel clusters in emerging data.

Feature Engineering Variational Inference

Collaborative Deep Reinforcement Learning for Multi-Object Tracking

no code implementations ECCV 2018 Liangliang Ren, Jiwen Lu, Zifeng Wang, Qi Tian, Jie zhou

To address this, we develop a deep prediction-decision network in our C-DRL, which simultaneously detects and predicts objects under a unified network via deep reinforcement learning.

Multi-Object Tracking reinforcement-learning +1

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