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Machine learning models exhibit strong performance on datasets with abundant labeled samples.
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Genomics of Drug Sensitivity in Cancer (GDSC): a resource for therapeutic biomarker discovery in cancer cells
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Predicting parameters in deep learning
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Interpretable whole-brain prediction analysis with GraphNet
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Knowledge graph embedding by translating on hyperplanes
Zhen Wang, Jianwen Zhang, Jianlin Feng, and Zheng Chen · 2014
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High-throughput screening using patient-derived tumor xenografts to predict clinical trial drug response
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Network lasso: Clustering and optimization in large graphs
David Hallac, Jure Leskovec, and Stephen Boyd · 2015
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Deep neural decision forests
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A landscape of pharmacogenomic interactions in cancer
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GLUE: A multi-task benchmark and analysis platform for natural language understanding
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Graph-based semi-supervised learning: A review
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The tree ensemble layer: Differentiability meets conditional computation
Hussein Hazimeh, Natalia Ponomareva, Petros Mol, Zhenyu Tan, and Rahul Mazumder · 2020
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Open Graph Benchmark: Datasets for machine learning on graphs
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Strategies for pre-training graph neural networks
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TabTransformer: Tabular data modeling using contextual embeddings
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The Human Phenotype Ontology in 2017
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LightGBM: A highly efficient gradient boosting decision tree
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Diet networks: thin parameters for fat genomics
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A reference map of the human binary protein interactome
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Neural oblivious decision ensembles for deep learning on tabular data
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The STRING database in 2021: customizable protein–protein networks, and functional characterization of user-uploaded gene/measurement sets
Damian Szklarczyk, Annika L Gable, Katerina C Nastou, David Lyon, Rebecca Kirsch, Sampo Pyysalo, Nadezhda T Doncheva, Marc Legeay, Tao Fang, Peer Bork, Lars J Jensen, and Christian von Mering · 2020
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Feature selection using stochastic gates
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TabNet: Attentive interpretable tabular learning
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Injecting background knowledge into embedding models for predictive tasks on knowledge graphs
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Post-selection inference with HSIC-Lasso
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Revisiting deep learning models for tabular data
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The LHC Olympics 2020: a community challenge for anomaly detection in high energy physics
Gregor Kasieczka, Benjamin Nachman, David Shih, Oz Amram, Anders Andreassen, Kees Benkendorder, Blaz Bortolato, Gustaaf Broojimans, Florencia Canelli, Jack Collins, et al · 2021
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Self-attention between datapoints: Going beyond individual input-output pairs in deep learning
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Identification of disease treatment mechanisms through the multiscale interactome
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SAINT: Improved neural networks for tabular data via row attention and contrastive pre-training
Gowthami Somepalli, Micah Goldblum, Avi Schwarzschild, C Bayan Bruss, and Tom Goldstein · 2021
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Do transformers really perform badly for graph representation?
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Why do tree-based models still outperform deep learning on typical tabular data?
Leo Grinsztajn, Edouard Oyallon, and Gael Varoquaux · 2022
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Hierarchical text-conditional image generation with clip latents
Aditya Ramesh, Prafulla Dhariwal, Alex Nichol, Casey Chu, and Mark Chen · 2022
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Tabular data: Deep learning is not all you need
Ravid Shwartz-Ziv and Amitai Armon · 2022
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Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time
Mitchell Wortsman, Gabriel Ilharco, Samir Ya Gadre, Rebecca Roelofs, Raphael Gontijo-Lopes, Ari S Morcos, Hongseok Namkoong, Ali Farhadi, Yair Carmon, Simon Kornblith, et al · 2022
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