In this paper, we build on the biquasiles and dual graph diagrams introduced in arXiv:1610.06969. We introduce \textit{biquasile Boltzmann weights} that enhance the previous knot coloring invariant defined in terms of finite biquasiles and provide examples differentiating links with the same counting invariant, demonst…
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.
169,181 papers · 148 categories
Trend · papers per month
48 results for “Boltzmann enhancements”
Invariants from biquasile colorings distinguish surface-links.
problem Counting and distinguishing surface-links.
method Coloring oriented surface-links using biquasiles and marked graph diagrams.
result Invariants can distinguish closed surface-links and cobordisms.
Transferable Boltzmann Generators learn to sample unseen molecules efficiently.
problem Generating equilibrium samples of molecular systems.
method Boltzmann Generators using normalizing flows to learn transformations.
result Transferable Boltzmann Generators can predict zero-shot Boltzmann distributions for unseen molecules.
Enhanced coloring invariant distinguishes folded molecular chain topologies.
problem Apparent indistinguishability of folded chain topologies using current coloring invariants.
method Introduced Boltzmann weights to improve the resolving power of quandle colorings.
result Improved resolution in distinguishing folded chain topologies.
Enhances hashing for fast retrieval with correlated bits.
problem Fast retrieval and small memory footprint for large-scale information retrieval.
method Employing Boltzmann machine distribution as variational posterior to model correlations among hash code bits.
result Significant performance gains achieved by effectively modeling correlations among hash code bits.
CG-BGs combine flow-based models with PMFs to sample large systems efficiently.
problem Sampling equilibrium molecular configurations from the Boltzmann distribution is challenging.
method Coarse-grained Boltzmann Generators (CG-BGs) use flow-based models and learned PMFs for efficient sampling.
result CG-BGs provide a practical route for sampling larger molecular systems efficiently.
In this letter, we propose enhanced factored three way restricted Boltzmann machines (EFTW-RBMs) for speech detection. The proposed model incorporates conditional feature learning by multiplying the dynamical state of the third unit, which allows a modulation over the visible-hidden node pairs. Instead of stacking prev…
This work analyzes centered binary Restricted Boltzmann Machines (RBMs) and binary Deep Boltzmann Machines (DBMs), where centering is done by subtracting offset values from visible and hidden variables. We show analytically that (i) centering results in a different but equivalent parameterization for artificial neural …
Enhances DyBM for better financial time-series prediction.
problem Limitations of Gaussian DyBM in financial applications.
method Extends DyBM to handle second-order moments and generalized Gaussian distributions.
result Significant performance improvement in predicting financial time-series data.
Neural-Network Quantum States connect to Tensor-Network states, enhancing quantum state representation.
problem Describing complex quantum wave functions efficiently.
method Introducing Neural-Network Quantum States and showing their connections to Tensor-Network states.
result Neural-Network Quantum States and String-Bond States can approximate chiral topological states with better accuracy.
ASBS improves sampling from Boltzmann distributions without importance weighting.
problem Sampling from Boltzmann distributions with known energies but unknown samples.
method Adjoint Schrödinger Bridge Sampler using kinetic-optimal transportation.
result ASBS achieves scalable and efficient sampling without importance weighting.
Statistical Mechanics Calculations Using Variational Autoregressive Networks and Quantum Annealingcond-mat.dis-nn
New method uses quantum annealing and VAN for better statistical mechanics calculations.
problem Difficulty in computing partition function in statistical mechanics.
method Combines quantum annealing samples with variational autoregressive networks.
result Enhanced accuracy in finite-size Sherrington-Kirkpatrick model.
This paper improves DQN agents' robustness to adversarial perturbations.
problem Improving DQN agents' robustness to adversarial perturbations.
method Adversarial training and a novel AGE mechanism based on -greedy and Boltzmann exploration.
result AGE mechanism enhances DQN agents' robustness to adversarial perturbations.
Paper proposes efficient training for normalizing flows in Boltzmann generators.
problem Training normalizing flows for Boltzmann generators is computationally challenging and unstable.
method Regression Training of Normalizing Flows (RegFlow) using -regression.
result RegFlow enables efficient and stable training of normalizing flows for Boltzmann generators.
New method tackles incomplete data in RBM inverse Ising problems.
problem Computing data and model expectations in inverse Ising problems with missing observations.
method Combines mean-field approximation, persistent contrastive divergence, and spatial Monte Carlo integration.
result Effective and accurate tuning of model parameters compared to conventional methods.
Conditional probabilities modeled using Riemann-Theta Boltzmann Machines.
problem Modeling conditional probabilities in Boltzmann machines.
method Deriving conditional density functions from Riemann-Theta Boltzmann machines.
result Conditional densities can be directly inferred from Riemann-Theta Boltzmann machines.
Develops a data-driven model for porous media flow simulations.
problem Capturing flow field and permeability in digital porous media.
method Data-driven approach using Lattice Boltzmann simulation data.
result Accurately predicts flow solutions with reduced computational time.
Active learning of Boltzmann samplers and potential energies with quantum mechanical accuracyphysics.chem-ph
New method combines deep learning and quantum mechanics for efficient molecular statistics.
problem Computational expense in extracting statistics from molecular systems.
method Adaptive Markov chain Monte Carlo with Normalizing Flow and MLP for quantum accuracy.
result Rapid convergence to Boltzmann distribution and accurate thermodynamic observables.
TBMs learn Gibbs distribution adaptively from data.
problem Combinatorial explosion in Boltzmann machines.
method Adaptive construction of minimum required sample space.
result TBMs outperform other Boltzmann machines in efficiency and effectiveness.
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.
Quantum Boltzmann Machines trained on quantum annealers produce noisy synthetic data.
problem Training quantum Boltzmann machines on quantum annealers for financial data generation.
method Used D-Wave Advantage 4.1 quantum annealer to train QBMs and compare with classical RBMs.
result Quantum Boltzmann Machines trained on quantum annealers are noisier and less effective than classical RBMs.
Low-rank combinatorial optimization and statistical learning by spatial photonic Ising machinecond-mat.dis-nn
New model enhances SPIM for solving low-rank combinatorial optimization and statistical learning problems.
problem Solving large-scale combinatorial optimization problems efficiently.
method Proposed a new computing model for SPIM that can handle low-rank interaction matrices.
result Demonstrated efficient learning, classification, and sampling of MNIST images using the model.
Improved discrete VAEs using relaxed Boltzmann priors for better performance.
problem Training discrete VAEs with tighter importance-weighted bounds.
method Two approaches for relaxing Boltzmann machines to continuous distributions, based on generalized overlapping transformations and the Gaussian integral trick.
result These relaxations outperform previous discrete VAEs with Boltzmann priors on MNIST and OMNIGLOT datasets.
We introduce a new method for training deep Boltzmann machines jointly. Prior methods require an initial learning pass that trains the deep Boltzmann machine greedily, one layer at a time, or do not perform well on classifi- cation tasks.
Boltzmann machines as two-dimensional tensor networkscond-mat.stat-mech
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.
A new method speeds up sampling of Boltzmann distribution in high-dimensional systems.
problem High computational cost of obtaining Jacobian of flow-based models in high dimensions.
method Flow perturbation method that incorporates stochastic perturbations and reweighting.
result Achieves unbiased sampling of Boltzmann distribution with orders of magnitude speedup.
We show that deep narrow Boltzmann machines are universal approximators of probability distributions on the activities of their visible units, provided they have sufficiently many hidden layers, each containing the same number of units as the visible layer. We show that, within certain parameter domains, deep Boltzmann…
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.
Restricted Boltzmann Machines (RBM) are a neural network model used in deep learning.
problem Representing probability distributions with hidden variables.
method Mathematical analysis and geometric study of RBM structures.
result Geometry of probability distributions in RBM models.
Neural Boltzmann Machines improve on CRBMs for modeling data.
problem Limited expressivity of CRBMs with noisy data.
method Convert CRBM parameters to neural networks.
result NBMs can approximate data likelihood better.
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.
New method trains Boltzmann machines without supervision.
problem Training unsupervised learning models.
method Mixed binary quadratic feasibility problem formulation.
result Theory validated on XOR patterns.
CAP-BM learns complex-valued data's amplitude and phase distributions.
problem Learning from complex-valued data with amplitude variation.
method Complex Amplitude-Phase Boltzmann machine (CAP-BM) with Gibbs sampling.
result Necessity of amplitude-amplitude coupling term in CAP-BM.
In 1870s, L. Boltzmann proved the famous -theorem for the Boltzmann equation in the kinetic theory of gas and gave the statistical interpretation of the thermodynamic entropy. In 2002, G. Perelman introduced the notion of -entropy and proved the -entropy formula for the Ricci flow. This plays a crucial role in…
We propose a relaxation-based approximate inference algorithm that samples near-MAP configurations of a binary pairwise Markov random field. We experiment on MAP inference tasks in several restricted Boltzmann machines. We also use our underlying sampler to estimate the log-partition function of restricted Boltzmann ma…
Reduced hidden units in RBM without performance loss.
problem Maintaining performance with fewer hidden units in RBM.
method Proposed algorithm to decrease hidden units while keeping performance constant.
result Demonstrated through numerical simulations that fewer hidden units can be used without performance degradation.
Can Boltzmann Machines Discover Cluster Updates ?physics.comp-ph
Boltzmann machines are physics informed generative models with wide applications in machine learning. They can learn the probability distribution from an input dataset and generate new samples accordingly. Applying them back to physics, the Boltzmann machines are ideal recommender systems to accelerate Monte Carlo simu…
Enhances gradient-based discrete samplers with parallel tempering for multimodal distributions.
problem Local minima in high-dimensional, multimodal discrete distributions.
method Combines parallel tempering with discrete Langevin proposal, using Metropolis criterion for swaps.
result Significantly faster mixing and better sampling from complex distributions.
Introduces a Boltzmann machine with Riemann-Theta functions for continuous and discrete states.
problem Modeling continuous and discrete states in neural networks.
method Develops a Boltzmann machine with continuous visible and discrete hidden states, solving probability density and conditional expectation analytically.
result Derives a novel parametric density function involving Riemann-Theta functions and uses it as an activation function in a feedforward neural network.
Meta-learning algorithms prepare quantum Gibbs states efficiently for NISQ devices.
problem Efficiently preparing quantum Gibbs states for NISQ devices.
method Meta-Variational Quantum Thermalizer (Meta-VQT) and Neural Network Meta-VQT (NN-Meta VQT) algorithms.
result Meta-learned parameters significantly outperform random initializations in optimization tasks.
A new sampler and temperature estimation method enable efficient learning of Boltzmann Machines.
problem Efficient learning of Boltzmann Machines (BMs) is challenging due to high training costs and difficulty in parallelization.
method Proposed a new Boltzmann sampler (Langevin SB, LSB) and an efficient method (Conditional Expectation Matching, CEM) for estimating inverse temperature.
result Established an efficient learning framework (Sampler-Adaptive Learning, SAL) for BMs with greater expressive power than Restricted Boltzmann Machines (RBMs).
EDG generates Boltzmann samples from latent variables efficiently.
problem Sampling from complex energy functions in high dimensions.
method Combines variational autoencoders and diffusion models; uses a decoder and diffusion-based encoder.
result EDG outperforms existing methods in various sampling tasks.
A Gaussian restricted Boltzmann machine (GRBM) is a Boltzmann machine defined on a bipartite graph and is an extension of usual restricted Boltzmann machines. A GRBM consists of two different layers: a visible layer composed of continuous visible variables and a hidden layer composed of discrete hidden variables. In th…
BNEM improves Boltzmann sampler efficiency.
problem Generating IID samples from Boltzmann distributions efficiently.
method Bootstrapped Noised Energy Matching (NEM) combined with diffusion-based learning and bootstrapping.
result BNEM achieves state-of-the-art performance with improved robustness.
Inverse problem solved for relativistic Boltzmann equation on spacetime.
problem Determining spacetime from causal measurements.
method Using the nonlinearity of the Boltzmann equation to uniquely determine the spacetime.
result The spacetime is uniquely determined up to isometry in the causal set .
Graph clustering improved using Boltzmann machine heuristics.
problem Graph clustering to form densely connected clusters.
method Two mathematical programming formulations, two variations of Boltzmann machine heuristic.
result Boltzmann machine provides superior solutions and faster computation times.
The Riemann-Theta Boltzmann machine's visible sector is sampled using a discrete multi-variate Gaussian.
problem Sampling the visible sector of the Riemann-Theta Boltzmann machine.
method Discrete multi-variate Gaussian over the hidden state space.
result The visible sector probability density function is an infinite mixture of multi-variate Gaussians.
Since learning is typically very slow in Boltzmann machines, there is a need to restrict connections within hidden layers. However, the resulting states of hidden units exhibit statistical dependencies. Based on this observation, we propose using regularization upon the activation possibilities of hidden unit…