Blog debunks seven common myths in machine learning.
problem Misconceptions about machine learning practices and datasets.
method Analysis of common myths in machine learning research.
result Myths about TensorFlow, image datasets, validation, and neural networks are debunked.
Study examines fake news as modern myths using AI.
problem Misinformation and propaganda in fake news.
method Machine learning to generate fake articles.
result Details of fake news generation pipeline.
The Unified Growth Theory is a puzzling collection of myths based on illusions created by hyperbolic distributions. Some of these myths are discussed. The examination of data shows that the three stages of growth (Malthusian Regime, Post-Malthusian Regime and Modern Growth Regime) did not exist and that Industrial Revo…
This article provides an overview of relative strengths of polynomial invariants of knots and links, such as the Alexander, Jones, Homflypt, Kaufman two-variable polynomial, and Khovanov polynomial.
This article provides an overview of relative strengths of polynomial invariants of knots and links, such as the Alexander, Jones, Homflypt, and Kaufman two-variable polynomial, Khovanov homology, factorizability of the polynomials, and knot primeness detection.
Machine learning basics: key principles and limitations.
problem Understanding machine learning principles and their limitations.
method Analysis of machine learning families, performance comparison, and model interpretation.
result Interpretable models are often sufficient and deep learning doesn't always outperform others.
Automated feature engineering improves interpretable models without manual work.
problem Lack of interpretability in complex models causes trust and stability issues.
method Use elastic black-box models to create simpler, interpretable glass-box models.
result Extracted features from complex models improve linear model performance.
The contradiction between physical and economical sciences concerning the growth of the production/consumption mechanism is analyzed. It is then shown that if one wishes to keep the security level stable or to enhance it in a growing economy the cost of security grows faster than the gross wealth. The result is a typic…
It is often stated in papers tackling the task of inferring Bayesian network structures from data that there are these two distinct approaches: (i) Apply conditional independence tests when testing for the presence or otherwise of edges; (ii) Search the model space using a scoring metric. Here I argue that for complete…
New categorization of community detection methods to avoid pitfalls.
problem Misuse of community detection methods in practice.
method Descriptive vs. inferential approaches.
result Inferential methods provide clearer insights into network formation.
The paper analyzes the intrinsic exploration terms in policy-gradient algorithms.
problem Exploration in policy-gradient algorithms and its impact on policy optimization.
method Numerical optimization criteria and stochastic gradient analysis.
result Exploration techniques improve policy optimization by smoothing the learning objective and modifying gradient estimates.
The independence of Central Banks is shown to be a myth.
problem The independence of Central Banks is questioned as a flawed concept.
method Analyzes the arguments for Central Bank independence and demonstrates their logical flaws.
result The independence of Central Banks is found to be a chimera.
This work compares and evaluates various sampling methods for neural language models.
problem Lack of systematic comparison and myths about sampling methods.
method Monte Carlo sampling, importance sampling, compensated partial summation, noise contrastive estimation.
result All sampling methods can perform equally well if posterior probabilities are corrected.
Quantum game theory, whatever opinions may be held due to its abstract physical formalism, have already found various applications even outside the orthodox physics domain. In this paper we introduce the concept of a quantum auction, its advantages and drawbacks. Then we describe the models that have already been put f…
The prevalent view in the economics literature is that a high level of infrastructure investment is a precursor to economic growth. China is especially held up as a model to emulate. Based on the largest dataset of its kind, this paper punctures the twin myths that, first, infrastructure creates economic value, and, se…
Study challenges the Gaussian pre-activations assumption in neural networks.
problem Challenges the assumption that pre-activations are Gaussian in neural networks.
method Constructs pairs of activation functions and initialization distributions to ensure Gaussian pre-activations.
result Discovered constraints for ensuring Gaussian pre-activations in neural networks.
Paper argues the bear case for Bitcoin is bounded and terminal states are neutral to positive.
problem The identity of Bitcoin's creator and the associated overhang risk.
method Quantitative analysis of Satoshi's 1.148 million BTC position, considering various preference sets.
result The terminal states most consistent with observed behavior are neutral to slightly positive for Bitcoin's effective supply.
The paper reviews machine learning safety techniques for autonomous vehicles.
problem Challenges in machine learning safety for autonomous vehicles.
method Organizes practical safety techniques to complement engineering safety.
result Enhances dependability and safety of machine learning algorithms in autonomous vehicles.
This paper surveys optimization methods in machine learning.
problem Challenges in optimization methods due to growing data and model complexity.
method Systematic review of optimization methods from machine learning perspective.
result Guidance for optimization and machine learning research.
Optimal control theory applied to machine learning adversarial attacks.
problem Adversarial machine learning threats and detection challenges.
method Optimal control theory applied to machine learning systems.
result Advances in control theory and reinforcement learning can enhance adversarial machine learning research.
Optimization techniques for machine learning explained.
problem Improving machine learning models' performance.
method Course notes and tutorials on optimization methods.
result Comprehensive coverage of optimization techniques.
Machine learning aids in clinical prediction tasks.
problem Improving accuracy in clinical predictions.
method Introduction to machine learning concepts and algorithms, followed by practical application to clinical datasets.
result Demonstrated the application of machine learning models to clinical prediction problems.
We conduct an empirical study of machine learning functionalities provided by major cloud service providers, which we call machine learning clouds. Machine learning clouds hold the promise of hiding all the sophistication of running large-scale machine learning: Instead of specifying how to run a machine learning task,…
This paper surveys informed machine learning, integrating prior knowledge into ML.
problem Machine learning's limitations with insufficient data.
method Taxonomy and survey of informed machine learning approaches.
result A taxonomy classifies informed machine learning approaches based on knowledge source, representation, and integration.
Survey on techniques to make machine learning models understandable.
problem Humans cannot understand complex machine learning model decisions.
method Survey of existing techniques to increase interpretability.
result Challenges and achievements in interpretable machine learning need further exploration.
The current processes for building machine learning systems require practitioners with deep knowledge of machine learning. This significantly limits the number of machine learning systems that can be created and has led to a mismatch between the demand for machine learning systems and the ability for organizations to b…
Optimal Transport enhances machine learning with new methods.
problem Comparing and manipulating probability distributions in machine learning.
method Probabilistic framework rooted in rich history and theory.
result New solutions in generative modeling and transfer learning.
Automated machine learning simplifies model selection and tuning.
problem Manual tuning of machine learning models by data scientists is time-consuming and requires extensive expertise.
method Review of AutoML techniques including automated feature engineering, model learning, and deep learning.
result Current AutoML techniques can significantly reduce the burden of manual tuning.
Market incentivizes parties to share high-quality data for collaborative machine learning tasks.
problem Fair revenue distribution and data replication threats in collaborative machine learning markets.
method Introduces a novel payment division function robust to replication and customized output models.
result Validated assumptions and showed approximate satisfaction for commonly used models.
DoubleML implements machine learning for causal inference in R.
problem Estimating causal effects in regression models with high-dimensional data.
method Double machine learning framework with Neyman orthogonality and sample splitting.
result Valid inference on causal parameters using machine learning methods.
Quantum computers can speed up machine learning optimization problems.
problem Long computation times and high resource requirements for classical optimization algorithms in machine learning.
method Developed a mathematical model to leverage quantum parallelism for machine learning.
result Quantum machine learning applied to a 3D time-varying image demonstrated significant speedup.
New machine learning algorithms inspired by ecological principles.
problem Improving machine learning performance.
method Inspired by ecological dynamics, developed new online SVM algorithms.
result New algorithms outperform traditional methods on the MNIST dataset.
This paper reviews quantum machine learning from NISQ to fault tolerance.
problem The challenges and opportunities in quantum machine learning.
method Comprehensive review of quantum machine learning concepts.
result Coverage of NISQ and fault-tolerant quantum computing approaches.
pystacked combines machine learning models for improved predictions.
problem Improving machine learning model performance through stacking.
method Stacked generalization using Python's scikit-learn with various base learners.
result Enhanced predictive models through combining multiple machine learning algorithms.
Machine learning explores symmetries in field theory and algebra.
problem Understanding symmetries in field theory and algebra.
method Using neural networks to analyze conformal field theory and Lie algebra representation theory.
result Recent advances in machine learning have uncovered new symmetries.
Explains how machine learning models can be biased and presents interactive plots to visualize bias.
problem Bias in machine learning models can lead to unfair decisions.
method Develops interactive plots to visualize bias in machine learning models.
result Demonstrates that machine learning models can learn and propagate bias from the data.
Survey on distributed machine learning to handle large data.
problem Training large models requires vast amounts of data.
method Distribute workload across multiple machines.
result Efficient parallelization and coherent model creation.
This paper explores causal analysis in machine learning for better interpretability.
problem The lack of causality in traditional interpretable machine learning models.
method An overview of causal approaches for interpretable machine learning.
result Causal analysis improves the interpretability of machine learning models.
R package for machine learning in survival analysis.
problem Limited machine learning interfaces for survival analysis.
method Provides a comprehensive machine learning interface for survival analysis.
result Systematic infrastructure for survival modeling and evaluation.
A new process model for machine learning applications with quality assurance.
problem Lack of standard process model for machine learning applications.
method Six-phase process model with quality assurance methodology.
result Proposes a new process model for machine learning applications.
Julia accelerates machine learning in various fields with balance of efficiency and simplicity.
problem Efficiency and simplicity in machine learning algorithms.
method Developed and applied Julia language in machine learning.
result Julia balances efficiency and simplicity for machine learning.
New theory challenges traditional machine learning assumptions.
problem Traditional machine learning theories are critiqued.
method A new theory is proposed and discussed.
result Learning true probabilities is not equivalent to other learning goals.
Matched Machine Learning combines machine learning and matching for causal inference.
problem Non-interpretable methods for causal inference.
method Combines machine learning and matching for interpretable causal inference.
result Performs as well as black-box machine learning methods and better than existing matching methods.
Machine learning boosts physics research, especially at high energy experiments.
problem Finding new fundamental physics in high energy experiments.
method Review of machine learning methods and applications in high energy physics.
result Modern machine learning techniques have expanded the scope of physics research.
Study examines challenges and applications of machine learning in finance.
problem Challenges in applying machine learning to financial research due to market idiosyncrasies and methodological differences.
method Discussion of adjustments needed to conventional machine learning methodology to account for financial market peculiarities.
result Machine learning can be unified with financial research as a robust complement to econometric methods.
Probabilistic ML improves healthcare data analysis.
problem Insufficient understanding and incomplete data in healthcare.
method Examination of probabilistic machine learning models for healthcare challenges.
result Probabilistic models enhance healthcare data analysis and model building.
AI can learn true probabilities if data and assumptions align.
problem Understanding when AI models can accurately represent true objective probabilities.
method Proved conditions under which AI can learn true probabilities.
result Conditions for learning true probabilities are identified.
Machine learning aids epidemiologists in analyzing big data.
problem Handling large, complex data in epidemiology.
method Explains principles and methods of supervised and unsupervised learning.
result Develops strategies for model evaluation and hyperparameter optimization.