no code implementations • 30 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.
no code implementations • 26 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.
no code implementations • 14 Dec 2022 • Tooba Imtiaz, Morgan Kohler, Jared Miller, Zifeng Wang, Mario Sznaier, Octavia Camps, Jennifer Dy
Adversarial attacks hamper the decision-making ability of neural networks by perturbing the input signal.
no code implementations • 14 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.
1 code implementation • 18 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.
1 code implementation • 11 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.
1 code implementation • 9 Oct 2022 • Tong Jian, Zifeng Wang, Yanzhi Wang, Jennifer Dy, Stratis Ioannidis
Adversarial pruning compresses models while preserving robustness.
1 code implementation • 20 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.
no code implementations • 16 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.
1 code implementation • 29 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.
1 code implementation • 19 May 2022 • Zifeng Wang, Jimeng Sun
Can we leverage model pretraining on multiple distinct tables?
2 code implementations • 10 Apr 2022 • Zifeng Wang, Zizhao Zhang, Sayna Ebrahimi, Ruoxi Sun, Han Zhang, Chen-Yu Lee, Xiaoqi Ren, Guolong Su, Vincent Perot, Jennifer Dy, Tomas Pfister
Continual learning aims to enable a single model to learn a sequence of tasks without catastrophic forgetting.
1 code implementation • 12 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.
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.
1 code implementation • 2 Oct 2021 • Zifeng Wang, Jimeng Sun
In medicine, survival analysis studies the time duration to events of interest such as mortality.
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.
1 code implementation • 13 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.
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.
1 code implementation • 27 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.
1 code implementation • 13 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.
1 code implementation • 13 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.
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.
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.
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.
no code implementations • 6 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).
no code implementations • 25 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.
1 code implementation • 3 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.
no code implementations • 7 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.
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.