|
Size: 615
Comment: Added 2 papers about wisdom of the crowds
|
Size: 2668
Comment:
|
| Deletions are marked like this. | Additions are marked like this. |
| Line 4: | Line 4: |
| * [[http://engineering.flipboard.com/2017/02/storyclustering|Clustering Similar Stories Using LDA]]: Good mash-up of ideas, including LDA (Latent Dirilecht Allocation), automatic dimensionality reduction, clustering. * [[https://openai.com/blog/adversarial-example-research/|Attacking machine learning with adversarial examples]]: Particular mention of image-classifying ANNs, which are especially prone to adversial noise that's imperceptible to humans. * [[https://hbr.org/2017/04/good-management-predicts-a-firms-success-better-than-it-rd-or-even-employee-skills|Good Management Predicts a Firm’s Success Better Than IT, R&D, or Even Employee Skills]]: An NBER study that appears to be done in R. * [[https://datawhatnow.com/simhash-question-deduplicatoin/|SimHash for question deduplication]]: Very easy intro to !SimHash. See also [[https://en.wikipedia.org/wiki/SimHash|the Wikipedia entry]]. * [[https://www.autodeskresearch.com/publications/samestats|Same Stats, Different Graphs: Generating Datasets with Varied Appearance and Identical Statistics through Simulated Annealing]] * [[http://www.gasmodel.com/|Generalized Autoregressive Score models]]: a way to fit time-series with a variety of distributions * [[https://arxiv.org/abs/1705.03633|Inferring and Executing Programs for Visual Reasoning]]: ML programs generating other ML programs * [[https://arxiv.org/abs/1706.03741|Deep reinforcement learning from human preferences]]: Aims to minimize how much time a human must give feedback to the system for the system to train itself correctly * [[https://arxiv.org/abs/1708.00630|ProjectionNet: Learning Efficient On-Device Deep Networks Using Neural Projections]]: Trains a simpler ANN "next to" a more traditional ANN for image recognition, getting good results from the simpler ANN with reduced memory requirements. * [[https://www.theatlantic.com/business/archive/2012/05/when-correlation-is-not-causation-but-something-much-more-screwy/256918/|When Correlation Is Not Causation, But Something Much More Screwy]] |
Papers for discussion
An algorithm that finds truth even if most people are wrong [Prelec]: "Crowd" predictions are not necessarily good, but analyzing meta-knowledge of individual predictors can help you pick out the best predictors in the crowd.
Extracting the Wisdom of Crowds When Information is Shared [Palley]: Like Prelec's paper, but uses prediction of crowd's average as proxy for meta-knowledge, instead of prediction of crowd that would agree with you.
Clustering Similar Stories Using LDA: Good mash-up of ideas, including LDA (Latent Dirilecht Allocation), automatic dimensionality reduction, clustering.
Attacking machine learning with adversarial examples: Particular mention of image-classifying ANNs, which are especially prone to adversial noise that's imperceptible to humans.
Good Management Predicts a Firm’s Success Better Than IT, R&D, or Even Employee Skills: An NBER study that appears to be done in R.
SimHash for question deduplication: Very easy intro to SimHash. See also the Wikipedia entry.
Generalized Autoregressive Score models: a way to fit time-series with a variety of distributions
Inferring and Executing Programs for Visual Reasoning: ML programs generating other ML programs
Deep reinforcement learning from human preferences: Aims to minimize how much time a human must give feedback to the system for the system to train itself correctly
ProjectionNet: Learning Efficient On-Device Deep Networks Using Neural Projections: Trains a simpler ANN "next to" a more traditional ANN for image recognition, getting good results from the simpler ANN with reduced memory requirements.
When Correlation Is Not Causation, But Something Much More Screwy