Fetching the paper…
Reading the bibliography…
Nowadays, deep neural networks (DNNs) have become the main instrument for machine learning tasks within a wide range of domains, including vision, NLP, and speech.
Learning internal representations by error propagation
David E Rumelhart, Geoffrey E Hinton, and Ronald J Williams · 1985
Earlier work this paper cites.
Simple statistical gradient-following algorithms for connectionist reinforcement learning
Ronald J. Williams · 1992
Earlier work this paper cites.
Bottom-up induction of oblivious read-once decision graphs: strengths and limitations
Ron Kohavi · 1994
Earlier work this paper cites.
The random subspace method for constructing decision forests
Iñigo Barandiaran · 1998
Earlier work this paper cites.
Random forests
Leo Breiman · 2001
Earlier work this paper cites.
Greedy function approximation: a gradient boosting machine
Jerome H Friedman · 2001
Earlier work this paper cites.
Rectified linear units improve restricted boltzmann machines
Vinod Nair and Geoffrey E Hinton · 2010
Earlier work this paper cites.
Lessons from 2 million machine learning models on kaggle, 2015
Vasyl Harasymiv · 2015
Earlier work this paper cites.
Deep neural decision forests
Peter Kontschieder, Madalina Fiterau, Antonio Criminisi, and Samuel Rota Bulo · 2015
Earlier work this paper cites.
Xgboost: A scalable tree boosting system
Tianqi Chen and Carlos Guestrin · 2016
Earlier work this paper cites.
Deep learning
Ian Goodfellow, Yoshua Bengio, and Aaron Courville · 2016
Earlier work this paper cites.
Categorical reparameterization with gumbel-softmax
Eric Jang, Shixiang Gu, and Ben Poole · 2016
Cited alongside, same era.
From softmax to sparsemax: A sparse model of attention and multi-label classification
Andre Martins and Ramon Astudillo · 2016
Cited alongside, same era.
All you need is a good init
Dmytro Mishkin and Jiri Matas · 2016
Cited alongside, same era.
Densely connected convolutional networks
Gao Huang, Zhuang Liu, Laurens Van Der Maaten, and Kilian Q Weinberger · 2017
Cited alongside, same era.
Lightgbm: A highly efficient gradient boosting decision tree
Guolin Ke, Qi Meng, Thomas Finley, Taifeng Wang, Wei Chen, Weidong Ma, Qiwei Ye, and Tie-Yan Liu · 2017
Cited alongside, same era.
Bdt: Gradient boosted decision tables for high accuracy and scoring efficiency
Averaging weights leads to wider optima and better generalization
Pavel Izmailov, Dmitrii Podoprikhin, Timur Garipov, Dmitry Vetrov, and Andrew Gordon Wilson · 2018
Later among the works it cites.
Tabnn: A universal neural network solution for tabular data
Guolin Ke, Jia Zhang, Zhenhui Xu, Jiang Bian, and Tie-Yan Liu · 2018
Later among the works it cites.
Random hinge forest for differentiable learning
Nathan Lay, Adam P Harrison, Sharon Schreiber, Gitesh Dawer, and Adrian Barbu · 2018
Later among the works it cites.
Quasi-hyperbolic momentum and adam for deep learning
Jerry Ma and Denis Yarats · 2018
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Yin Lou and Mikhail Obukhov · 2017
Cited alongside, same era.
Forward thinking: building deep random forests
Kevin Miller, Chris Hettinger, Jeffrey Humpherys, Tyler Jarvis, and David Kartchner · 2017
Cited alongside, same era.
A regularized framework for sparse and structured neural attention
Vlad Niculae and Mathieu Blondel · 2017
Cited alongside, same era.
Deep forest: Towards an alternative to deep neural networks
Zhi-Hua Zhou and Ji Feng · 2017
Cited alongside, same era.
Multi-layered gradient boosting decision trees
Ji Feng, Yang Yu, and Zhi-Hua Zhou · 2018
Cited alongside, same era.
Benchmarking and optimization of gradient boosting decision tree algorithms
Andreea Anghel, Nikolaos Papandreou, Thomas Parnell, Alessandro de Palma, and Haralampos Pozidis
Cited in the paper.
Vlad Niculae, André FT Martins, Mathieu Blondel, and Claire Cardie · 2018
Later among the works it cites.
Catboost: unbiased boosting with categorical features
Liudmila Prokhorenkova, Gleb Gusev, Aleksandr Vorobev, Anna Veronika Dorogush, and Andrey Gulin · 2018
Later among the works it cites.
Regularization learning networks: Deep learning for tabular datasets
Ira Shavitt and Eran Segal · 2018
Later among the works it cites.
Yongxin Yang, Irene Garcia Morillo, and Timothy M Hospedales · 2018
Later among the works it cites.
Sparsemax and relaxed wasserstein for topic sparsity
Tianyi Lin, Zhiyue Hu, and Xin Guo · 2019
Closest in time.
Sparse sequence-to-sequence models
Ben Peters, Vlad Niculae, and André F. T. Martins · 2019
Closest in time.