Nonlinear MCMC improves Bayesian machine learning sampling.
problem Sampling problems in Bayesian machine learning.
method Nonlinear MCMC technique with convergence guarantees.
result Improves sampling in Bayesian neural networks.
On-device research index
arXiv research
A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
168,742 papers · 148 categories
Trend · papers per month
48 results for “Neural Sampling Machines”
Efficiently samples sequences without replacement for machine learning models.
problem Generating diverse outputs from sequential models without duplicates.
method Incremental sampling procedure for randomized programs, including neural models.
result Efficacy and flexibility of incremental sampling for large output spaces.
A Review of the Gumbel-max Trick and its Extensions for Discrete Stochasticity in Machine Learningcs.LG
The Gumbel-max trick and its extensions simplify sampling from categorical distributions in machine learning.
problem Sampling from categorical distributions with unnormalized probabilities.
method Extensions of the Gumbel-max trick for various applications.
result Simplified and efficient methods for sampling and gradient estimation.
New sampling scheme improves ML accuracy in physics simulations.
problem Improving accuracy of ML models in physics simulations.
method Taylor-based data sampling scheme for DNNs.
result Reduces error in DNN solutions of ODE systems.
This study shows neural nets can approximate Turing machines with meaningful statistical properties.
problem Theoretical limitations in approximating Turing machines with neural networks.
method Formal definition of statistically meaningful approximation, analysis of boolean circuits and Turing machines using neural nets.
result Transformers can statistically meaningfully approximate Turing machines with polynomial sample complexity.
Invariant polynomials improve machine learning performance.
problem Improving machine learning algorithms using invariant polynomials.
method Developed and implemented Lorentz- and permutation-invariant polynomial generators in neural networks.
result Reduction in loss on training and validation data with Hironaka decompositions.
Paper analyzes sample complexity of polynomial neural networks.
problem Understanding the sample complexity of polynomial neural networks.
method Extends previous literature to polynomial neural networks and analyzes sample complexity.
result Obtains novel results on sample complexity of polynomial neural networks.
Shapley Homology measures sample influence on neural networks' manifold topology.
problem Assumption of iid samples simplifies manifold analysis in machine learning.
method Shapley Homology framework quantifies sample influence on neural networks' manifold topology.
result Higher influence scores correlate with greater impact on neural network accuracy.
Igeood detects out-of-distribution samples using information geometry.
problem Out-of-distribution (OOD) detection in machine learning systems.
method Igeood uses the Fisher-Rao geodesic distance to detect OOD samples from any pre-trained neural network.
result Igeood outperforms state-of-the-art methods on various network architectures and datasets.
The emergence of a concept in shallow neural networkscond-mat.dis-nn
RBMs learn archetypes when trained on blurred copies of them, revealing a critical sample size.
problem Determining the critical sample size for RBMs to learn archetypes.
method Formal equivalence between RBMs and Hopfield networks, statistical-mechanics of disordered systems, Monte Carlo simulations.
result A phase diagram highlights regions where learning can be accomplished.
D-Wave computers struggle with sampling Boltzmann distributions efficiently.
problem Sampling Boltzmann distributions efficiently on D-Wave computers.
method Exploring various obstacles and remaining difficulties.
result Challenges remain in using D-Wave computers for efficient sampling.
A theorem for debiasing machine learning with finite sample guarantees.
problem Calculating confidence intervals for machine learning functionals.
method Debiased machine learning based on bias correction and sample splitting.
result Nonasymptotic debiased machine learning theorem with finite sample guarantees.
Detects poisoned training samples in deep neural networks.
problem Data poisoning attacks on deep neural networks.
method Two approaches: parametric probability distributions and Bayesian deep neural networks.
result Uncertainty estimates from trained models can discriminate clean from poisoned samples.
Active Importance Sampling for Variational Objectives Dominated by Rare Events: Consequences for Optimization and Generalizationphysics.data-an
New method uses active importance sampling for rare event optimization in high-dimensional problems.
problem Optimizing complex, high-dimensional functions with rare events.
method Combines rare events sampling with neural network optimization.
result Importance sampling reduces asymptotic variance, improving generalization.
TF-GNN simplifies graph neural networks in TensorFlow.
problem Handling rich heterogeneous graph data in machine learning.
method A scalable library with a Keras message passing API.
result Enables low-code solutions for broader developers.
New method uses CNNs to estimate graph means.
problem Estimating the mean of graph-valued data.
method Convolutional Neural Networks (CNNs) for graph morphology learning.
result CNNs reliably recover the sample Frechet mean.
EuLearn creates diverse 3D topological datasets for machine learning.
problem Training machine learning systems to discern topological features.
method Developed novel sampling and neural network architectures for graph and manifold data.
result Incorporating topological information improves deep learning performance on EuLearn datasets.
Study improves prediction accuracy and uncertainty for mobile sensor data using randomized neural networks.
problem Improving prediction accuracy and uncertainty for mobile sensor data.
method Cross-validation and uncertainty determination for randomized neural networks.
result Improved out-of-sample performance and confidence intervals for prediction error.
Imbalanced data classification problem has always been a popular topic in the field of machine learning research. In order to balance the samples between majority and minority class. Oversampling algorithm is used to synthesize new minority class samples, but it could bring in noise. Pointing to the noise problems, thi…
A technique to quickly fix mistakes in neural networks.
problem Fixing model errors in neural networks quickly and without affecting other samples.
method Editable Training, a model-agnostic training technique.
result Effectiveness demonstrated on large-scale image classification and machine translation tasks.
Study rare-event simulation for neural networks and random forests.
problem Safety evaluation and robustness quantification of machine learning models.
method Importance sampling scheme integrating large deviations and sequential mixed integer programming.
result Efficiency guarantees and numerical demonstrations for various neural network architectures.
With the advent of GPU-assisted hardware and maturing high-efficiency software platforms such as TensorFlow and PyTorch, Bayesian posterior sampling for neural networks becomes plausible. In this article we discuss Bayesian parametrization in machine learning based on Markov Chain Monte Carlo methods, specifically disc…
A real-time federated neural architecture search approach reduces costs and improves performance.
problem High communication and computational demands in federated learning for large models.
method Evolutionary approach with double-sampling technique to optimize model performance and reduce costs.
result Effective real-time federated neural architecture search for deep models on edge devices.
Proposes a new information-theoretic framework for analyzing deep neural networks.
problem Difficulty in analyzing deep neural networks using existing theoretical frameworks.
method Introduces an information-theoretic framework with new notions of regret and sample complexity.
result Establishes sample complexity bounds for deep neural networks that are width-independent and linear in depth.
New algorithms sample from complex path measures using neural networks.
problem Sampling from posterior path measures under a general prior process.
method Combines controlled equilibrium dynamics and optimization in infinite-dimensional probability space.
result The algorithms can be integrated with neural networks for learning target trajectory ensembles.
Bayesian optimization sped up with importance sampling.
problem Efficiently tuning hyperparameters for neural networks.
method Bayesian optimization with importance sampling.
result Significantly improved runtime and validation error.
NSMs use always-on stochasticity to normalize activations, improving convergence and performance.
problem Improving the robustness and generalizability of deep neural networks.
method Developed Neural Sampling Machines (NSMs) using always-on multiplicative stochasticity and simple threshold neurons.
result NSMs exhibit self-normalizing properties similar to Weight Normalization, speeding up convergence and preventing internal covariate shift.
Anisotropic neural network selects relevant features from datasets.
problem Reduction of irrelevant features improves model interpretability and performance.
method General Regression Neural Network with an anisotropic Gaussian Kernel.
result The method robustly selects features from simulated and real-world datasets.
Unbiased Monte Carlo Cluster Updates with Autoregressive Neural Networkscond-mat.stat-mech
New method makes machine learning approximations unbiased and efficient.
problem Efficient sampling of complex probability distributions.
method Uses autoregressive neural networks with cluster updates and physical symmetries.
result Shows unbiased and low-variance approximations for phase transitions.
New methods improve uncertainty in machine learning predictions for asset returns.
problem Uncertainty in machine learning predictions for asset returns.
method Developed new methods to construct forecast confidence intervals for expected returns from neural networks.
result Neural network forecasts of expected returns have the same asymptotic distribution as classic nonparametric methods, enabling standard error calculation.
Thermally Averaged Magnetic Anisotropy Tensors via Machine Learning Based on Gaussian Momentsphysics.comp-ph
Machine learning models accurately predict molecular magnetic anisotropy tensors.
problem Accurately modeling molecular magnetic anisotropy tensors.
method Gaussian-moment neural-network approach for machine learning.
result Achieved accuracy of 0.3--0.4 cm for magnetic anisotropy tensor predictions.
This paper introduces a neural sampler for scalable sampling from complex distributions.
problem Efficiently sampling from high-dimensional un-normalized distributions.
method Neural implicit sampler trained with KL and Fisher divergence methods.
result The neural sampler generates large batches of samples with low computational costs.
This study compares machine learning methods for high-cardinality categorical variables.
problem Machine learning struggles with high-cardinality categorical variables.
method Empirical comparison of tree-boosting, deep neural networks, and linear mixed effects models.
result Tree-boosting with random effects outperforms deep neural networks with random effects.
We analyze how kernel machines and neural networks learn different frequency modes of the target function as data size increases.
problem Understanding how kernel machines and neural networks learn different frequency modes of the target function as data size increases.
method Theoretical methods from Gaussian processes and statistical physics, combined with simulations on synthetic data and MNIST dataset.
result Kernel machines and neural networks fit successively higher spectral modes of the target function as the size of the training set grows.
New classical algorithm outperforms quantum in neural network subnetwork selection.
problem Selecting sparse subnetworks from large neural networks efficiently.
method Quantum-inspired classical algorithm using ridgelet transform sampling.
result Runs in polynomial time, outperforming naive classical methods.
Paper introduces a neural network training algorithm for noisy data that achieves optimal parameters and replicates real-world behaviors.
problem Theoretical gap between universal approximation theorems and practical machine learning with noisy data.
method Randomized training algorithm for neural networks trained on noisy data samples.
result Trained neural networks achieve optimal parameters and exhibit real-world behaviors like sub-linear complexity and interpolation.
GPU-accelerated particle methods outperform neural samplers in LFT benchmarks.
problem High-dimensional multimodal sampling problems in lattice field theory.
method GPU-accelerated particle Monte Carlo methods (Sequential Monte Carlo and nested sampling).
result These methods match or outperform neural samplers in sample quality and wall-clock time.
EBR improves NMT by re-ranking samples drawn from MLE-trained models.
problem Discrepancy between MLE and BLEU score in neural machine translation.
method Train an energy-based model to mimic BLEU score, then use it for re-ranking.
result EBR consistently improves NMT performance by +4 BLEU points on IWSLT'14 German-English.
Survey of diffusion and optimal transport methods in machine learning.
problem Design and analysis of time-evolving probability distributions in machine learning.
method Switch from Eulerian to Lagrangian representation through vector fields.
result Both diffusion methods and optimal transport offer computational advantages.
Deep neural networks correct Mie scattering in FTIR spectra of biological samples.
problem Mie scattering obscures biochemically relevant spectral information in FTIR spectra of biological samples.
method Deep neural networks to approximate the preprocessing function that removes Mie scattering.
result The model is faster and more generalizable across different tissue types.
New method corrects ML for informative sampling in time-series treatment outcomes.
problem Informative sampling in irregularly observed data hinders accurate treatment outcome forecasting.
method Formalized as covariate shift, proposed inverse intensity-weighting framework, TESAR-CDE.
result TESAR-CDE effectively learns treatment outcomes under informative sampling.
The paper argues that machine learning is a falsificationist process.
problem The role of falsification in machine learning is underexplored.
method The paper presents a falsificationist account of artificial neural networks, emphasizing empirical risk minimization and implicit regularization.
result Artificial neural networks can be seen as a falsificationist process, rejecting inadequate prediction rules.
New method trains neural samplers to sample from multi-modal distributions efficiently.
problem Mode-seeking behavior of reverse KL divergence hinders effective sampling from multi-modal target distributions.
method Minimizing reverse diffusive KL divergence along diffusion trajectories of model and target densities.
result Demonstrated enhanced sampling performance across various multi-modal distributions.
Adversarial machine learning in the context of image processing and related applications has received a large amount of attention. However, adversarial machine learning, especially adversarial deep learning, in the context of malware detection has received much less attention despite its apparent importance. In this pa…
This paper provides a tutorial on Boltzmann Machines and Deep Belief Networks.
problem Understanding and applying Boltzmann Machines and Deep Belief Networks.
method Explains the structures, conditional distributions, Gibbs sampling, training methods, and deep belief networks of RBMs.
result Comprehensive overview of RBMs and DBNs, useful in various fields.
Efficiently tests machine learning models with minimal labeled data.
problem Guaranteeing the performance of machine learning models and preventing failures.
method Proposes a novel framework using Bayesian neural networks and data augmentation for efficient testing.
result Metrics estimations by the proposed method are significantly better than existing baselines.
MDNS generates samples from complex discrete distributions efficiently.
problem Learning neural samplers for discrete state spaces with multi-modal distributions.
method A novel framework using stochastic optimal control of continuous-time Markov chains.
result MDNS outperforms other methods in generating accurate samples from high-dimensional, multi-modal distributions.
Study on consistency of ML methods for moving objects in non-stationary environments.
problem Consistency of machine learning methods for moving objects in non-stationary environments.
method Least squares, ridge regression, and -penalized least squares methods under non-stationary spatial-temporal sampling.
result Consistency and asymptotic normality of the estimates under weak conditions.