Fetching the paper…
Reading the bibliography…
Clinical trials are essential to drug development but time-consuming, costly, and prone to failure.
Evolutionary principles in self-referential learning, or on learning how to learn: the meta-meta-… hook
Jürgen Schmidhuber. 1987 · 1987
Earlier work this paper cites.
Learning to learn: Introduction and overview
Sebastian Thrun and Lorien Pratt. 1998 · 1998
Earlier work this paper cites.
Scikit-learn: Machine Learning in Python
F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay. 2011 · 2011
Earlier work this paper cites.
Basic principles of drug discovery and development
Benjamin E Blass. 2015 · 2015
Earlier work this paper cites.
Rupesh Kumar Srivastava, Klaus Greff, and Jürgen Schmidhuber. 2015 · 2015
Earlier work this paper cites.
RLˆ 2: Fast Reinforcement Learning via Slow Reinforcement Learning. In ICLR
Yan Duan, John Schulman, Xi Chen, Peter L Bartlett, Ilya Sutskever, and Pieter Abbeel. 2016 · 2016
Earlier work this paper cites.
A data-driven approach to predicting successes and failures of clinical trials
Kaitlyn M Gayvert, Neel S Madhukar, and Olivier Elemento. 2016 · 2016
Earlier work this paper cites.
Optimization as a model for few-shot learning. In ICLR
Sachin Ravi and Hugo Larochelle. 2016 · 2016
Earlier work this paper cites.
GRAM: graph-based attention model for healthcare representation learning. In Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining . 787–795
Edward Choi, Mohammad Taha Bahadori, Le Song, Walter F Stewart, and Jimeng Sun. 2017 · 2017
Earlier work this paper cites.
Model-agnostic meta-learning for fast adaptation of deep networks. In International Conference on Machine Learning . PMLR, 1126–1135
Chelsea Finn, Pieter Abbeel, and Sergey Levine. 2017 · 2017
Earlier work this paper cites.
Neural message passing for quantum chemistry. In International Conference on Machine Learning . PMLR, 1263–1272
Justin Gilmer, Samuel S Schoenholz, Patrick F Riley, Oriol Vinyals, and George E Dahl. 2017 · 2017
Cited alongside, same era.
Meta-SGD: Learning to learn quickly for few-shot learning
Zhenguo Li, Fengwei Zhou, Fei Chen, and Hang Li. 2017 · 2017
Cited alongside, same era.
Automating biomedical evidence synthesis: RobotReviewer. In Proceedings of the conference. Association for Computational Linguistics. Meeting , Vol. 2017. NIH Public Access, 7
Iain J Marshall, Joël Kuiper, Edward Banner, and Byron C Wallace. 2017 · 2017
Cited alongside, same era.
SMASH: One-Shot Model Architecture Search through HyperNetworks. In International Conference on Learning Representations
Andrew Brock, Theo Lim, JM Ritchie, and Nick Weston. 2018 · 2018
Cited alongside, same era.
Dynamic few-shot visual learning without forgetting. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition . 4367–4375
Machine learning with statistical imputation for predicting drug approval
Andrew W Lo, Kien Wei Siah, and Chi Heem Wong. 2019 · 2019
Later among the works it cites.
Predicting phase 3 clinical trial results by modeling phase 2 clinical trial subject level data using deep learning. In Machine Learning for Healthcare Conference . PMLR, 288–303
Youran Qi and Qi Tang. 2019 · 2019
Later among the works it cites.
A deep neural network approach to predicting clinical outcomes of neuroblastoma patients
Léon-Charles Tranchevent, Francisco Azuaje, and Jagath C Rajapakse. 2019 · 2019
Later among the works it cites.
Application of KPCA and AdaBoost algorithm in classification of functional magnetic resonance imaging of Alzheimer’s disease
Zhao Fan, Fanyu Xu, Cai Li, and Lili Yao. 2020 · 2020
Later among the works it cites.
COMPOSE: cross-modal pseudo-siamese network for patient trial matching. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining . 803–812
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Spyros Gidaris and Nikos Komodakis. 2018 · 2018
Cited alongside, same era.
Meta-reinforcement learning of structured exploration strategies
Abhishek Gupta, Russell Mendonca, YuXuan Liu, Pieter Abbeel, and Sergey Levine. 2018 · 2018
Cited alongside, same era.
DARTS: Differentiable Architecture Search. In International Conference on Learning Representations
Hanxiao Liu, Karen Simonyan, and Yiming Yang. 2018 · 2018
Cited alongside, same era.
On first-order meta-learning algorithms
Alex Nichol, Joshua Achiam, and John Schulman. 2018 · 2018
Cited alongside, same era.
Decoupling Representation and Classifier for Long-Tailed Recognition. In International Conference on Learning Representations
Bingyi Kang, Saining Xie, Marcus Rohrbach, Zhicheng Yan, Albert Gordo, Jiashi Feng, and Yannis Kalantidis. 2019 · 2019
Cited alongside, same era.
TransTab: Learning Transferable Tabular Transformers Across Tables. In Advances in Neural Information Processing Systems
Zifeng Wang and Jimeng Sun. 2022a
Cited in the paper.
Trial2Vec: Zero-Shot Clinical Trial Document Similarity Search using Self-Supervision. In Findings of EMNLP
Zifeng Wang and Jimeng Sun. 2022b
Cited in the paper.
Junyi Gao, Cao Xiao, Lucas M Glass, and Jimeng Sun. 2020 · 2020
Later among the works it cites.
DeepEnroll: patient-trial matching with deep embedding and entailment prediction. In Proceedings of The Web Conference 2020 . 1029–1037
Xingyao Zhang, Cao Xiao, Lucas M Glass, and Jimeng Sun. 2020 · 2020
Later among the works it cites.
Predicting drug approvals: the novartis data science and artificial intelligence challenge
Kien Wei Siah, Nicholas W Kelley, Steffen Ballerstedt, Björn Holzhauer, Tianmeng Lyu, David Mettler, Sophie Sun, Simon Wandel, Yang Zhong, Bin Zhou, et al · 2021
Later among the works it cites.
HINT: Hierarchical interaction network for clinical-trial-outcome predictions
Tianfan Fu, Kexin Huang, Cao Xiao, Lucas M Glass, and Jimeng Sun. 2022 · 2022
Later among the works it cites.
Artificial Intelligence for In Silico Clinical Trials: A Review
Zifeng Wang, Chufan Gao, Lucas M Glass, and Jimeng Sun. 2022 · 2022
Later among the works it cites.