Characterizes surfaces enveloped by rotating cones for CNC machining.
problem Characterizing surfaces enveloped by rotating cones for CNC machining.
method Derives higher-order nonlinear PDEs for enveloping surfaces, including local approximations.
result Characterizations generalize previous work on developable and ruled surfaces.
New method uses deep learning to solve inverse problems with provable guarantees.
problem Solving inverse problems with high-quality results and provable guarantees.
method Convex-Nonconvex (CNC) framework with input weakly convex neural network (IWCNN).
result The method provides provably convergent regularization for inverse problems.
In manufacture, steel and other metals are mainly cut and shaped during the fabrication process by computer numerical control (CNC) machines. To keep high productivity and efficiency of the fabrication process, engineers need to monitor the real-time process of CNC machines, and the lifetime management of machine tools…
A new method helps escape saddle points in non-convex optimization.
problem Escaping saddle points in non-convex optimization problems.
method CNC-SCSG method using a separate SGD step to help escape from strict saddle points.
result The method converges to a second-order stationary point with a rate of O(ε−2log(1/ε)). Method improves few-shot one-class classification.
problem Learning binary classifier with data from only one class.
method Modified MAML algorithm to learn initialization for few-shot OCC.
result Method leads to better results than classical approaches.
We examined the use of three conventional anomaly detection methods and assess their potential for on-line tool wear monitoring. Through efficient data processing and transformation of the algorithm proposed here, in a real-time environment, these methods were tested for fast evaluation of cutting tools on CNC machines…
Smooth curves from polygonal chains with vertex preservation and explicit curvature control.
problem Preserving vertices while smoothing polygonal chains to C∞ curves. method Directional mollification operator for polygonal chains.
result Smooth curves that intersect original vertices and maintain explicit curvature bounds.
Approximating complex curves with simple parametric curves is widely used in CAGD, CG, and CNC. This paper presents an algorithm to compute a certified approximation to a given parametric space curve with cubic B-spline curves. By certified, we mean that the approximation can approximate the given curve to any given pr…
Reservoir Memory Machines solve benchmark tasks faster than Neural Turing Machines.
problem Training Neural Turing Machines is hard and limits their applicability.
method Proposes Reservoir Memory Machines, combining neural network flexibility with Turing machine capabilities, but with faster training via alignment and linear regression.
result Reservoir Memory Machines solve benchmark tasks as well as Neural Turing Machines but are much faster to train.
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…
We generalize a support vector machine to a support spinor machine by using the mathematical structure of wedge product over vector machine in order to extend field from vector field to spinor field. The separated hyperplane is extended to Kolmogorov space in time series data which allow us to extend a structure of sup…
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.
The preceding paper constructed tangle machines as diagrammatic models, and illustrated their utility with a number of examples. The information content of a tangle machine is contained in characteristic quantities associated to equivalence classes of tangle machines, which are called invariants. This paper constructs …
Application of fuzzy support vector machine in stock price forecast. Support vector machine is a new type of machine learning method proposed in 1990s. It can deal with classification and regression problems very successfully. Due to the excellent learning performance of support vector machine, the technology has becom…
RBM and DBM are represented as 2D tensor networks, revealing their expressive power and efficiency.
problem Understanding and optimizing RBM and DBM models.
method Representing RBM and DBM as 2D tensor networks and developing an efficient tensor network contraction algorithm.
result The proposed algorithm for computing partition functions is more accurate than state-of-the-art methods.
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,…
A new machine learning model uses score matching to estimate probability densities efficiently.
problem Estimating probability density functions is challenging.
method Introduced a product Jacobi-Theta Boltzmann machine (pJTBM) and used score matching for efficient fitting.
result The pJTBM can fit probability densities more efficiently than the RTBM using score matching.
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.
Machine learning develops rapidly, which has made many theoretical breakthroughs and is widely applied in various fields. Optimization, as an important part of machine learning, has attracted much attention of researchers. With the exponential growth of data amount and the increase of model complexity, optimization met…
In this chapter, we provide a brief overview of applying machine learning techniques for clinical prediction tasks. We begin with a quick introduction to the concepts of machine learning and outline some of the most common machine learning algorithms. Next, we demonstrate how to apply the algorithms with appropriate to…
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.
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.
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.
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.
Product Kanerva Machines dynamically combine smaller models for better memory organization.
problem Limited organization in the Kanerva Machine.
method Introducing Product Kanerva Machines that dynamically combine multiple smaller Kanerva Machines.
result Product Kanerva Machines can discover spatial tunings that approximately factorize simple images by object.
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.
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.
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.
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.
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.
This guide introduces machine learning for medical data analysis.
problem Handling large, complex datasets in healthcare.
method Explains common machine learning algorithms and tasks.
result Illustrates machine learning applications in healthcare.
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.
Missing responses is a missing data format in which outcomes are not always observed. In this work we develop kernel machines that can handle missing responses. First, we propose a kernel machine family that uses mainly the complete cases. For the quadratic loss, we then propose a family of doubly-robust kernel machine…
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 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.
R package psvmSDR simplifies SDR computation for machine learning.
problem Sufficient dimension reduction in machine learning.
method Principal machine (PM) generalized from PSVM.
result Efficient computation of SDR estimators in various scenarios.
This paper explores how machine learning can improve life insurance risk assessment.
problem Limited use of machine learning in life insurance due to statistical models' efficiency.
method Review and extension of traditional actuarial methodologies with machine learning techniques.
result Developed Python library for life insurance data, improving risk modeling.
Machine learning impacts computational math, offering new functions approximations.
problem Machine learning's black box nature hinders further progress in computational math.
method Analyzes machine learning's impact on computational math and vice versa.
result Integrating computational math with machine learning can enhance both fields.
Machine learning workflow development is anecdotally regarded to be an iterative process of trial-and-error with humans-in-the-loop. However, we are not aware of quantitative evidence corroborating this popular belief. A quantitative characterization of iteration can serve as a benchmark for machine learning workflow d…
The paper proposes a method to adapt machine learning models to changing conditions.
problem Machine learning models need to adapt to new conditions in a constantly changing environment.
method Reuse knowledge from existing models to train future generations.
result The proposed method allows machine learning models to adapt and survive in a dynamic environment.
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.
Machine learning improves solving inverse problems and integrating data.
problem Solving complex inverse problems and integrating data effectively.
method Integrates machine learning techniques with inverse problems and data assimilation.
result Demonstrates machine learning's potential to enhance these fields.
Quantum machine learning models can approximate any continuous function.
problem Theoretical understanding of quantum feature maps in machine learning.
method Proving universal approximation property of quantum machine learning models in quantum-enhanced feature spaces.
result Quantum machine learning models are universal approximators of continuous functions.
Humans are the final decision makers in critical tasks that involve ethical and legal concerns, ranging from recidivism prediction, to medical diagnosis, to fighting against fake news. Although machine learning models can sometimes achieve impressive performance in these tasks, these tasks are not amenable to full auto…
Developed mlf-core for deterministic machine learning.
problem Ensuring machine learning models are deterministic for verification.
method Formulated requirements, developed mlf-core ecosystem, tested various models.
result Demonstrated deterministic models in biomedical fields.
Study examines explainable machine learning for monotonic models, finding Integrated gradients better for strong monotonicity.
problem Applying explainable machine learning to science-informed models.
method Proposed axioms for monotonicity, tested Shapley value and Integrated gradients methods.
result Integrated gradients provides better explanations for strong monotonicity.