Fetching the paper…
Reading the bibliography…
Neural additive models (NAMs) enhance the transparency of deep neural networks by handling input features in separate additive sub-networks.
’In-Between’ Uncertainty in Bayesian Neural Networks, June 2019
Foong, A. Y. K., Li, Y., Hernández-Lobato, J. M., and Turner, R. E · 1906
Earlier work this paper cites.
InterpretML: A Unified Framework for Machine Learning Interpretability, September 2019
Nori, H., Jenkins, S., Koch, P., and Caruana, R · 1909
Earlier work this paper cites.
Bayesian functional ANOVA modeling using Gaussian process prior distributions
Kaufman, C. G. and Sain, S. R · 1931
Earlier work this paper cites.
Generalized Cross-Validation as a Method for Choosing a Good Ridge Parameter
Golub, G. H., Heath, M., and Wahba, G · 1979
Earlier work this paper cites.
Bayesian “Confidence Intervals” for the Cross-Validated Smoothing Spline
Wahba, G · 1983
Earlier work this paper cites.
Estimating Optimal Transformations for Multiple Regression and Correlation
Breiman, L. and Friedman, J. H · 1985
Earlier work this paper cites.
Approximation by superpositions of a sigmoidal function
Cybenko, G · 1989
Earlier work this paper cites.
Bayesian Model Comparison and Backprop Nets
MacKay, D. J. C · 1991
Earlier work this paper cites.
A Practical Bayesian Framework for Backpropagation Networks
MacKay, D. J. C · 1992
Earlier work this paper cites.
Bayesian Learning via Stochastic Dynamics
Neal, R. M · 1992
Earlier work this paper cites.
Thrombocytopenia in the Intensive Care Unit
Baughman, R. R., Lower, E. E., Flessa, H. C., and Tollerud, D. J · 1993
Earlier work this paper cites.
Bayesian Learning for Neural Networks
Neal, R. M · 1995
Earlier work this paper cites.
Bayesian Non-Linear Modeling for the Prediction Competition
MacKay, D. J. C · 1996
Earlier work this paper cites.
Generalized Additive Models
Hastie, T. and Tibshirani, R · 1999
Earlier work this paper cites.
Lower bounds for approximation by MLP neural networks
Maiorov, V. and Pinkus, A · 1999
Earlier work this paper cites.
Greedy Function Approximation: A Gradient Boosting Machine
Friedman, J. H · 2001
Earlier work this paper cites.
Sparse Bayesian Learning and the Relevance Vector Machine
Tipping, M. E · 2001
Earlier work this paper cites.
SpAM: Sparse Additive Models
Liu, H., Wasserman, L., Lafferty, J., and Ravikumar, P · 2007
Earlier work this paper cites.
Metabolic acidosis: Pathophysiology, diagnosis and management
Kraut, J. A. and Madias, N. E · 2010
Earlier work this paper cites.
Additive Gaussian Processes
Duvenaud, D. K., Nickisch, H., and Rasmussen, C · 2011
Earlier work this paper cites.
Practical Variational Inference for Neural Networks
Graves, A · 2011
Earlier work this paper cites.
MCMC Using Hamiltonian Dynamics
Neal, R. M · 2011
Earlier work this paper cites.
Scikit-learn: Machine Learning in Python
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., and Duchesnay, É · 2011
Earlier work this paper cites.
Bayesian learning via stochastic gradient langevin dynamics
Welling, M. and Teh, Y. W · 2011
Earlier work this paper cites.
Additive Covariance kernels for high-dimensional Gaussian Process modeling
Durrande, N., Ginsbourger, D., and Roustant, O · 2012
Earlier work this paper cites.
Treatment of acute metabolic acidosis: A pathophysiologic approach
Kraut, J. A. and Madias, N. E · 2012
Earlier work this paper cites.
Intelligible models for classification and regression
Lou, Y., Caruana, R., and Gehrke, J · 2012
Earlier work this paper cites.
Clinical and Biologic Features of Patients Suspected or Confirmed to Have Heparin-Induced Thrombocytopenia in a Cardiothoracic Surgical ICU
Trehel-Tursis, V., Louvain-Quintard, V., Zarrouki, Y., Imbert, A., Doubine, S., and Stéphan, F · 2012
Earlier work this paper cites.
Group sparse additive models
Yin, J., Chen, X., and Xing, E. P · 2012
Earlier work this paper cites.
Sparse Additive Machine
Zhao, T. and Liu, H · 2012
Earlier work this paper cites.
Accurate intelligible models with pairwise interactions
Lou, Y., Caruana, R., Gehrke, J., and Hooker, G · 2013
Earlier work this paper cites.
Weight Uncertainty in Neural Network
Blundell, C., Cornebise, J., Kavukcuoglu, K., and Wierstra, D · 2015
Earlier work this paper cites.
Intelligible Models for HealthCare: Predicting Pneumonia Risk and Hospital 30-day Readmission
Caruana, R., Lou, Y., Gehrke, J., Koch, P., Sturm, M., and Elhadad, N · 2015
Earlier work this paper cites.
Variational Dropout and the Local Reparameterization Trick
Kingma, D. P., Salimans, T., and Welling, M · 2015
Cited alongside, same era.
Optimizing Neural Networks with Kronecker-factored Approximate Curvature
Martens, J. and Grosse, R · 2015
Cited alongside, same era.
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning
Gal, Y. and Ghahramani, Z · 2016
Cited alongside, same era.
MIMIC-III, a freely accessible critical care database
Johnson, A. E. W., Pollard, T. J., Shen, L., Lehman, L.-w. H., Feng, M., Ghassemi, M., Moody, B., Szolovits, P., Anthony Celi, L., and Mark, R. G · 2016
Cited alongside, same era.
Structured and Efficient Variational Deep Learning with Matrix Gaussian Posteriors
Louizos, C. and Welling, M · 2016
Cited alongside, same era.
”Why Should I Trust You?”: Explaining the Predictions of Any Classifier
Exact Langevin Dynamics with Stochastic Gradients, February 2021
Garriga-Alonso, A. and Fortuin, V · 2021
Later among the works it cites.
What Are Bayesian Neural Network Posteriors Really Like?
Izmailov, P., Vikram, S., Hoffman, M. D., and Wilson, A. G. G · 2021
Later among the works it cites.
Adoption of Machine Learning Systems for Medical Diagnostics in Clinics: Qualitative Interview Study
Pumplun, L., Fecho, M., Wahl, N., Peters, F., and Buxmann, P · 2021
Later among the works it cites.
PACOH: Bayes-Optimal Meta-Learning with PAC-Guarantees
Rothfuss, J., Fortuin, V., Josifoski, M., and Krause, A · 2021
Later among the works it cites.
Lgpr: An interpretable non-parametric method for inferring covariate effects from longitudinal data
Timonen, J., Mannerström, H., Vehtari, A., and Lähdesmäki, H · 2021
Later among the works it cites.
Calibration tests beyond classification
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Ribeiro, M. T., Singh, S., and Guestrin, C · 2016
Cited alongside, same era.
Group Sparse Additive Machine
Chen, H., Wang, X., Deng, C., and Huang, H · 2017
Cited alongside, same era.
On Calibration of Modern Neural Networks
Guo, C., Pleiss, G., Sun, Y., and Weinberger, K. Q · 2017
Cited alongside, same era.
LightGBM: A Highly Efficient Gradient Boosting Decision Tree
Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., Ye, Q., and Liu, T.-Y · 2017
Cited alongside, same era.
Adam: A Method for Stochastic Optimization, January 2017
Kingma, D. P. and Ba, J · 2017
Cited alongside, same era.
Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles
Lakshminarayanan, B., Pritzel, A., and Blundell, C · 2017
Cited alongside, same era.
The Expressive Power of Neural Networks: A View from the Width
Lu, Z., Pu, H., Wang, F., Hu, Z., and Wang, L · 2017
Cited alongside, same era.
Widmann, D., Lindsten, F., and Zachariah, D · 2021
Later among the works it cites.
GAMI-Net: An explainable neural network based on generalized additive models with structured interactions
Yang, Z., Zhang, A., and Sudjianto, A · 2021
Later among the works it cites.
HiRID-ICU-Benchmark — A Comprehensive Machine Learning Benchmark on High-resolution ICU Data
Yèche, H., Kuznetsova, R., Zimmermann, M., Hüser, M., Lyu, X., Faltys, M., and Rätsch, G · 2021
Later among the works it cites.
Adapting the Linearised Laplace Model Evidence for Modern Deep Learning
Antoran, J., Janz, D., Allingham, J. U., Daxberger, E., Barbano, R. R., Nalisnick, E., and Hernandez-Lobato, J. M · 2022
Later among the works it cites.
Priors in Bayesian Deep Learning: A Review
Fortuin, V · 2022
Later among the works it cites.
Bayesian Neural Network Priors Revisited
Fortuin, V., Garriga-Alonso, A., Ober, S. W., Wenzel, F., Ratsch, G., Turner, R. E., van der Wilk, M., and Aitchison, L · 2022
Later among the works it cites.
Why do tree-based models still outperform deep learning on typical tabular data?
Grinsztajn, L., Oyallon, E., and Varoquaux, G · 2022
Later among the works it cites.
Better Uncertainty Calibration via Proper Scores for Classification and Beyond
Gruber, S. and Buettner, F · 2022
Later among the works it cites.
Probing as Quantifying Inductive Bias
Immer, A., Torroba Hennigen, L., Fortuin, V., and Cotterell, R · 2022
Later among the works it cites.
Hands-On Bayesian Neural Networks—A Tutorial for Deep Learning Users
Jospin, L. V., Laga, H., Boussaid, F., Buntine, W., and Bennamoun, M · 2022
Later among the works it cites.
Death by Round Numbers: Glass-Box Machine Learning Uncovers Biases in Medical Practice, November 2022
Lengerich, B. J., Caruana, R., Nunnally, M. E., and Kellis, M · 2022
Later among the works it cites.
Additive Gaussian Processes Revisited
Lu, X., Boukouvalas, A., and Hensman, J · 2022
Later among the works it cites.
Data augmentation in Bayesian neural networks and the cold posterior effect
Nabarro, S., Ganev, S., Garriga-Alonso, A., Fortuin, V., van der Wilk, M., and Aitchison, L · 2022
Later among the works it cites.
Neural Basis Models for Interpretability
Radenovic, P., Dubey, A., and Mahajan, D · 2022
Later among the works it cites.
Last Layer Marginal Likelihood for Invariance Learning
Schwöbel, P., Jørgensen, M., Ober, S. W., and Wilk, M. V. D · 2022
Later among the works it cites.
Learning invariant weights in neural networks
van der Ouderaa, T. F. A. and van der Wilk, M · 2022
Later among the works it cites.
A Primer on Bayesian Neural Networks: Review and Debates, September 2023
Arbel, J., Pitas, K., Vladimirova, M., and Fortuin, V · 2023
Closest in time.
Gaussian Error Linear Units (GELUs), June 2023
Hendrycks, D. and Gimpel, K · 2023
Closest in time.
Structural Neural Additive Models: Enhanced Interpretable Machine Learning, February 2023
Luber, M., Thielmann, A., and Säfken, B · 2023
Closest in time.
Scalable PAC-Bayesian Meta-Learning via the PAC-Optimal Hyper-Posterior: From Theory to Practice
Rothfuss, J., Josifoski, M., Fortuin, V., and Krause, A · 2023
Closest in time.
Incorporating Unlabelled Data into Bayesian Neural Networks, May 2023
Sharma, M., Rainforth, T., Teh, Y. W., and Fortuin, V · 2023
Closest in time.
Learning Layer-wise Equivariances Automatically using Gradients
van der Ouderaa, T., Immer, A., and van der Wilk, M · 2023
Closest in time.
Sparse Neural Additive Model: Interpretable Deep Learning with Feature Selection via Group Sparsity
Xu, S., Bu, Z., Chaudhari, P., and Barnett, I. J · 2023
Closest in time.
HiRID, a high time-resolution ICU dataset, May 2024
Faltys, M., Zimmermann, M., Lyu, X., Hüser, M., Hyland, S., Rätsch, G., and Merz, T · 2024
Closest in time.
On the Challenges and Opportunities in Generative AI, February 2024
Manduchi, L., Pandey, K., Bamler, R., Cotterell, R., Däubener, S., Fellenz, S., Fischer, A., Gärtner, T., Kirchler, M., Kloft, M., Li, Y., Lippert, C., de Melo, G., Nalisnick, E., Ommer, B., Ranganath, R., Rudolph, M., Ullrich, K., den Broeck, G. V., Vogt, J. E., Wang, Y., Wenzel, F., Wood, F., Mandt, S., and Fortuin, V · 2024
Closest in time.
Position Paper: Bayesian Deep Learning in the Age of Large-Scale AI, February 2024
Papamarkou, T., Skoularidou, M., Palla, K., Aitchison, L., Arbel, J., Dunson, D., Filippone, M., Fortuin, V., Hennig, P., Lobato, J. M. H., Hubin, A., Immer, A., Karaletsos, T., Khan, M. E., Kristiadi, A., Li, Y., Mandt, S., Nemeth, C., Osborne, M. A., Rudner, T. G. J., Rügamer, D., Teh, Y. W., Welling, M., Wilson, A. G., and Zhang, R · 2024
Closest in time.
Neural Additive Models for Location Scale and Shape: A Framework for Interpretable Neural Regression Beyond the Mean
Thielmann, A. F., Kruse, R.-M., Kneib, T., and Säfken, B · 2024
Closest in time.