Tracks research papers, articles and their topics read since July 2020 đź‘Ť, and some that were read prior to July 2020
Every badge is a topic with the count of articles associated with that topic. Colour represents if the number of read articles with that topic is below (red) or above (blue) average count.
| 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 |