Author | Title | Year | Topics |
---|---|---|---|

Weyn et al. | Can Machines Learn to Predict Weather | 2019 | weather forecasting, deep learning |

Raghu et al. | Transfusion Understanding Transfer Learning for Medical Imaging | 2019 | deep learning properties, transfer learning |

Caron et al. | Finding winning tickets with limited (or no) supervision | 2020 | sparse neural networks, lottery ticket hypothesis |

Liu et al. | An Intriguing Failing of Convolutional Neural Networks and the CoordConv Solution | 2018 | deep learning properties |

Gang et al. | Hamiltonian Neural Networks | 2019 | dynamical systems, deep learning properties |

Yoon et al. | Time-series Generative Adversarial Networks | 2019 | time series, generative adversarial networks |

Xu et al. | GAN-based Virtual Re-Staining A Promising Solution for Whole Slide Image Analysis | 2019 | generative adversarial networks, computational pathology |

Spector et al. | Google’s Hybrid Approach to Research | 2012 | research engineering processes |

Zhou et al. | Deconstructing Lottery Tickets: Zeros, Signs and Supermasks | 2019 | sparse neural networks, lottery ticket hypothesis, deep learning properties |

DJ Hand | Classifier Technology and the Illusion of Progress | 2006 | statistical analysis, machine learning |

Morcos et al. | One ticket to win them all: generalizing lottery ticket initializations across datasets and optimizers | 2019 | sparse neural networks, lottery ticket hypothesis |

Lee et al. | Harmonizing Maximum Likelihood with GANs for Multimodal Conditional Generation | 2019 | generative adversarial networks, deep learning properties |

Scher et al. | Weather and climate forecasting with neural networks using general circulation models (GCMs) with different complexity as a study ground | 2019 | weather systems, deep learning |

Yu et al. | Playing the lottery with rewards and multiple languages: lottery tickets in RL and NLP | 2020 | sparse neural networks, lottery ticket hypothesis, reinforcement learning, natural language processing |

Wang et al. | SAR-to-Optical Image Translation Using Supervised Cycle-Consistent Adversarial Networks | 2019 | generative adversarial networks, remote sensing |

Renda et al. | Comparing fine-tuning and rewinding in neural network prunning | 2020 | sparse neural networks, lottery ticket hypothesis |

Greydanus et al. | Hamiltonian Neural Networks | 2019 | dynamical systems, deep learning properties |

Franceschi et al. | Unsupervised Scalable Representation Learning for Multivariate Time Series | 2019 | self-supervised learning, time series |

Rasp et al. | WeatherBench A benchmark dataset for data-driven weather forecasting | 2019 | weather systems, deep learning |

Frankle et al. | The Lottery Ticket Hypothesis Finding Sparse, Trainable Neural Networks | 2019 | sparse neural networks, lottery ticket hypothesis |

Liu et al. | A comparison of deep learning performance against health-care professionals in detecting deseases from medical imaging: a systematic review and meta-analysis | 2019 | deep learning, medical review |

Kohl et al. | A Probabilistic U-Net for Segmentation of Ambiguous Images | 2018 | probabilistic deep learning, computer vision, uncertainty quantification |

Lee et al. | Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples | 2018 | deep learning properties |

Lucic et al. | Are GANs Created Equal? A Large-Scale Study | 2018 | ablation study, deep learning properties, generative adversarial networks |

Fruhwirt et al. | Towards better healthcare What could and should be automated | 2019 | medical review |

Huang et al. | Diversity-Aware Vehicle Motion Prediction via Latent Semantic Sampling | 2019 | probabilistic deep learning, uncertainty quantification |

Li et al. | Measuring the Intrinsic Dimension of Objective Landscapes | 2018 | deep learning properties |

Frankle et al. | Linear Mode Connectivity and The Lottery Ticket Hypothesis | 2019 | sparse neural networks, lottery ticket hypothesis |

Rivenson et al. | Virtual histological staining of unlabelled tissue | 2019 | generative adversarial networks, computational pathology |

Jiang et al. | Fantastic generalisation measures and where to find them | 2020 | ablation study, deep learning properties, causal inference |

Hagendorff | The Ethics of AI Ethics | 2018 | AI ethics |

Reyes et al. | Sar-to-optical image translation based on conditional generative adversarial networks—Optimization, opportunities and limits | 2019 | generative adversarial networks, remote sensing |

Kvamme et al. | Continuous and Discrete-Time Survival Prediction with Neural Networks | 2019 | deep learning, survival analysis |

Zhu et al. | Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks | 2018 | generative adversarial networks, computer vision |

Arbabi et al. | Data-driven modeling of strongly nonlinear chaotic systems with non-Gaussian statistics | 2019 | dynamical systems, weather systems, stochastic modeling |

Shafani et al. | Adversarially Robust Transfer Learning | 2020 | adversarial learning, transfer learning, deep learning properties |

Ben Haim et al. | Inundation Modeling in Data Scarce Regions | 2019 | hydrology, remote sensing, deep learning |

Dueben et al. | Challenges and design choices for global weather and climate models based on machine learning | 2020 | weather systems |

Hjelm et al. | Learning deep representations by mutual information estimation and maximisation | 2019 | self-supervised learning, deep learning |

Levy et al. | Preliminary Evaluation of the Utility of Deep Generative Histopathology Image Translation at a Mid-Sized NCI Cancer Center | 2020 | generative adversarial networks, computational pathology |

Scher et al. | Generalization properties of feed-forward neural networks trained on Lorenz systems | 2019 | dynamical systems, deep learning |

Sculley et al. | Hidden Technical Debt in Machine Learning Systems | 2015 | research engineering processes |

Hwang et al. | Improving Subseasonal Forecasting in the Western U.S. with Machine Learning | 2019 | weather systems, statistical machine learning |

Wang et al. | Picking winning tickets by preserving gradient flow | 2020 | sparse neural networks, lottery ticket hypothesis |

Lee et al. | Set Transformer A Framework for Attention-based Permutation-Invariant Neural Networks | 2019 | deep learning on sets, multiple instance learning |

Le et al. | Fastfood Approximating Kernel Expansion in Log Linear Time | 2013 | machine learning, kernel methods |

Frankle et al. | The Early Phase of Neural Networks Training | 2020 | deep learning ablation study, deep learning properties, sparse neural networks |

Weyn et al. | Improving data-driven global weather prediction using deep convolutional neural networks on a cubed sphere | 2020 | weather systems, deep learning |

Schmidt et al. | Distilling free-form natural laws from experimental data | 2009 | dynamical systems |

Groenke et al. | ClimAlign Unsupervised statistical downscaling of climate variables via normalizing flows | 2020 | normalizing flows, deep learning, climate modeling |

Ruthanne Huising | Can You Know Too Much About Your Ogranisation | 2019 | sociology, management |

Grover et al | AlignFlow Cycle Consistent Learning from Multiple Domains via Normalizing Flows | 2020 | normalizing flows |

Rezendle et al. | Variational Inference with Normalizing Flows | 2015 | normalizing flows |

Desai et al | VirTex Learning Visual Representations from Textual Annotations | 2020 | computer vision, image captioning, deep learning properties |

Tschannen et al. | On Mutual Information Maximization for Representation Learning | 2020 | self-supervised learning |

Radford et al. | Language Models are Unsupervised Multitask Learners | 2019 | deep learning, natural language processing |

Radford et al. | Improving Language Understanding by Generative Pre-Training | 2018 | deep learning, natural language processing |

Venkatesh Rao | A Big Little Idea Called Legibility | 2010 | sociology, antropology |

Ding et al. | Coherence-Aware Neural Topic Modeling | 2018 | natural language processing |

David Graeber | Bullshit jobs | 2013 | sociology |

Chapwufa et al. | Adversarial Time-to-Event Modeling | 2018 | survival analysis, generative adversarial networks |

Merity S. | Single Headed Attention RNN Stop Thinking With Your Head | 2019 | RNN, deep learning ablation study |

Shi et al. | Adapting Neural Networks for the Estimation of Treatment Effects | 2019 | deep learning, causal inference, potential outcomes |

Rosenbaum et al. | The central role of the propensity score in observational studies for causal effects | 1983 | potential outcomes, statistics |

GomezUribe et al. | The Netflix Recommender System Algorithms, Business Value, and Innovation | 2015 | recommender systems, causal inference, ml infrastructure |

Avati et al. | Countdown Regression Sharp and Calibrated Survival Predictions | 2019 | survival analysis |

Louizos et al. | Causal Effect Inference with Deep Latent-Variable Models | 2017 | causal inference, amortized variational inference |

Bernardi et al. | 150 successful Machine Learning models 6 lessons learned at Booking dot com | 2019 | ml infrastructure |

Diamontopoulos et al. | Engineering for a science-centric experimentation platform | 2020 | ml infrastructure |

Vartak et al. | A Meta-Learning Perspective on Cold-Start Recommendations for Items | 2017 | recommender systems, deep learning, meta learning |

Chapwufa et al. | Survival Analysis meets Counterfactual Inference | 2020 | causal inference, survival analysis, normalising flows |

Shalit et al. | Estimating individual treatment effect generalization bounds and algorithms | 2017 | deep learning, causal inference, potential outcomes |

Schuler et al. | Targeted Maximum Likelihood Estimation for Causal Inference in Observational Studies | 2016 | TMLE, potential outcomes, statistics |

DAmour et al. | Overlap in Observational Studies with High-Dimensional Covariates | 2020 | causal inference, statistics |

Gutierrez et al. | Causal Inference and Uplift Modelling A Review of the Literature | 2017 | uplift modeling, causal inference |

Jaskowski et al. | Uplift Modeling For Clinical Data | 2012 | uplift modeling |

Hu et al. | Estimating heterogeneous survival treatment effect in observational data using machine learning | 2020 | survival analysis, potential outcomes, ablation study |

Murdoch et al. | P-Values are Random Variables | 2008 | statistics |

Kaplan et al. | Scaling Laws for Neural Language Models | 2020 | deep learning, natural language processing |

Resnick et al. | Probing the State of the Art_ A Critical Look at Visual Representation Evaluation | 2020 | deep learning, representation learning |

Ilse et al. | Selecting Data Augmentation for Simulating Intervention | 2020 | causal inference, deep learning, data augmentation |

Hoel | The Overfitted Brain Dreams evolved to assist generalization | 2020 | neuro science |

Whitney et al. | Evaluating representations by the complexity of learning low-loss predictors | 2020 | deep learning properties |

Ilse et al. | Selecting Data Augmentation for Simulating Intervention | 2020 | deep learning, data augmentation, causal inference |

Luo et al. | Differentiable Learning-to-Normalize via Switchable Normalization | 2019 | deep learning properties, ablation study |

Rosenberg et al. | Astronomy in Everyday Life | 2014 | astronomy |

Northcutt et al. | Confident Learning Estimating Uncertainty in Dataset Labels | 2020 | label noise, uncertainty quantification |

Sengupta et al. | Ensembling geophysical models with Bayesian Neural Networks | 2020 | deep learning, uncertainty quantification, weather systems |

Liu et al. | SphereFace Deep Hypersphere Embedding for Face Recognition | 2017 | computer vision, facial recognition |

Wang et al. | Visual Commonsense R-CNN | 2020 | computer vision, deep learning, causal inference |

Castro et al. | Causality matters in medical imaging | 2019 | causal inference |

Johansson et al. | Learning Representations for Counterfactual Inference | 2018 | causal inference, deep learning |

Shi et al. | Adapting Neural Networks for the Estimation of Treatment Effects | 2019 | causal inference, deep learning |

Bottou et al. | Counterfactual Reasoning and Learning Systems The Example of Computational Advertising | 2013 | causal inference |

Joon Oh et al. | Modeling Uncertainty with Hedged Instance Embedding | 2019 | deep learning, uncertainty quantification |

Schuler et al. | Targeted Maximum Likelihood Estimation for Causal Inference in Observational Studies | 2016 | causal inference |

Alaa et al. | Validating Causal Inference Models via Influence Functions | 2019 | causal inference, model comparison |

Jesson et al. | Identifying Causal-Effect Inference Failure with Uncertainty-Aware Models | 2020 | uncertainty quantification, deep learning, causal inference |

Gutierrez et al. | Causal Inference and Uplift Modelling A Review of the Literature | 2017 | causal inference |

HernandezGonzalez et al. | Weak supervision and other non-standard classification problems a taxonomy | 2016 | weakly supervised learning, ablation study |

Raf E. | A Step Toward Quantifying Independently Reproducible Machine Learning Research | 2019 | statistical analysis, ablation study, machine learning |

Lu et al. | Deep Learning-based Computational Pathology Predicts Origins for Cancers of Unknown Primary | 2020 | deep learning, computational pathology |

Cheng et al. | Panoptic-DeepLab A Simple, Strong, and Fast Baseline for Bottom-Up Panoptic Segmentation | 2020 | deep learning, computer vision |

Choi et al. | StarGAN v2 Diverse Image Synthesis for Multiple Domains | 2020 | generative adversarial networks, computer vision |

Sirinukunwattana et al. | Image-based consensus molecular subtype (imCMS) classification of colorectal cancer using deep learning | 2020 | deep learning, computational pathology |

Christidis et al. | The increasing likelihood of temperature above 30 to 40 in the United Kingdom | 2020 | weather systems, climate modeling, statistical analysis |

Armstrong et al. | Exoplanet Validation with Machine Learning 50 new exoplanets | 2020 | astronomy, machine learning |

Brown et al. | Language Models are Few-Shot Learners | 2020 | deep learning, natural language processing |

Chen et al. | A general statistical framework for subgroup identification and comparative treatment scoring | 2017 | causal inference, potential outcomes, statistics |

Wojtas et al. | Feature Importance Ranking for Deep Learning | 2020 | feature selection, deep learning |

Lewis et al. | VOGUE Try On by StyleGAN Interpolation Optimization | 2020 | generative adversarial networks |

Goldstein et al. | XCAL Explicit Calibration for Survival Analysis | 2020 | survival analysis, uncertainty quantification |

请
登录后发表观点