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.

• Same Stats, Different Graphs: Generating Datasets with Varied Appearance and Identical Statistics through Simulated Annealing

• 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

• Deep Image Prior

• Lessons from Optics, The Other Deep Learning: Phenomena noticed in training deep ANNs, with an analogy to optics.

• AI2 (Abstract Interpretation for AI Safety): Using absint to guard against adversarial attacks.

• INFERNO: Inference-Aware Neural Optimisation

• A New Angle on L2 Regularization

• KiloGrams: Very Large N-Grams for Malware Classification: Clever use of multi-passing over a large dataset with an approximating first pass, to reduce computational and memory requirements.