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.
Lean 2-layer RBMs achieve similar representational power as single-layer RBMs with fewer parameters.
Lean Copilot uses LLMs to assist theorem proving in Lean, improving efficiency and automation.
Report on formalizing differential geometry in Lean.
Improved RBM training speed using parallel computing.
Reformulates RBM for unified linear and nonlinear dimensionality reduction.
Formalizes synthetic differential geometry in Lean.
A restricted Boltzmann machine (RBM) is a generative neural-network model with many novel applications such as collaborative filtering and acoustic modeling. An RBM lacks the capacity to retain memory, making it inappropriate for dynamic data modeling as in time-series analysis. In this paper we address this issue by p…
A restricted Boltzmann machine (RBM) is an undirected graphical model constructed for discrete or continuous random variables, with two layers, one hidden and one visible, and no conditional dependency within a layer. In recent years, RBMs have risen to prominence due to their connection to deep learning. By treating a…
Quantum annealer speeds up RBM training for image classification.
A new RBM model handles both linear and log-amplitude spectrograms.
Lean 4 formalizes Stokes' theorem for smooth singular cubes.
The restricted Boltzmann machine (RBM) is one of the fundamental building blocks of deep learning. RBM finds wide applications in dimensional reduction, feature extraction, and recommender systems via modeling the probability distributions of a variety of input data including natural images, speech signals, and custome…
Lean 4 library formalizes mathematical finance, verifying over 200 theorems.
Restricted Boltzmann Machine (RBM) is a bipartite graphical model that is used as the building block in energy-based deep generative models. Due to numerical stability and quantifiability of the likelihood, RBM is commonly used with Bernoulli units. Here, we consider an alternative member of exponential family RBM with…
New algorithm for learning RBMs with sparse latent variables.
Hardware-accelerated RBM solves large combinatorial problems and integer factorization.
This paper provides a tutorial on Boltzmann Machines and Deep Belief Networks.
Study proposes an RBM with multivalued hidden variables to improve generalization.
We propose a novel online learning algorithm for Restricted Boltzmann Machines (RBM), namely, the Online Generative Discriminative Restricted Boltzmann Machine (OGD-RBM), that provides the ability to build and adapt the network architecture of RBM according to the statistics of streaming data. The OGD-RBM is trained in…
Study extends deep learning theory to long-range spin systems.
Scores political leanings in Web3 betting markets.
RBM learns in high dimensions via AMP and GD, reaching optimal weak recovery.
We propose a Laplace approximation that creates a stochastic unit from any smooth monotonic activation function, using only Gaussian noise. This paper investigates the application of this stochastic approximation in training a family of Restricted Boltzmann Machines (RBM) that are closely linked to Bregman divergences.…
Restricted Boltzmann Machines (RBMs) are a class of generative neural network that are typically trained to maximize a log-likelihood objective function. We argue that likelihood-based training strategies may fail because the objective does not sufficiently penalize models that place a high probability in regions where…
New method uses RBM flows to find critical temperatures in Ising models.
LeanML reduces machine learning project waste by estimating best performance without training models.
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 …
Extracting automatically the complex set of features composing real high-dimensional data is crucial for achieving high performance in machine--learning tasks. Restricted Boltzmann Machines (RBM) are empirically known to be efficient for this purpose, and to be able to generate distributed and graded representations of…
Improved sampling quality with RBM-Flow and D-Flow models.
New algorithm embeds crime events using RBMs with feature selection.
Restricted Boltzmann machines~(RBMs) and conditional RBMs~(CRBMs) are popular models for a wide range of applications. In previous work, learning on such models has been dominated by contrastive divergence~(CD) and its variants. Belief propagation~(BP) algorithms are believed to be slow for structured prediction on con…
Theoretical understanding of how deep neural network (DNN) extracts features from input images is still unclear, but it is widely believed that the extraction is performed hierarchically through a process of coarse-graining. It reminds us of the basic concept of renormalization group (RG) in statistical physics. In ord…
Approximate Message Passing (AMP) has been shown to be an excellent statistical approach to signal inference and compressed sensing problem. The AMP framework provides modularity in the choice of signal prior; here we propose a hierarchical form of the Gauss-Bernouilli prior which utilizes a Restricted Boltzmann Machin…
A study ranks critical Lean Six Sigma tools for implementation in Portuguese companies.
RBM and DBM are represented as 2D tensor networks, revealing their expressive power and efficiency.
This paper formalizes -learning and linear TD convergence using Lean 4.
The Restricted Boltzmann Machines (RBM) can be used either as classifiers or as generative models. The quality of the generative RBM is measured through the average log-likelihood on test data. Due to the high computational complexity of evaluating the partition function, exact calculation of test log-likelihood is ver…
Formalizes integral curves on Banach manifolds in Lean.
New method transfers instances between domains using Gibbs Sampling and RBM.
Restricted Boltzmann Machines (RBMs) are one of the fundamental building blocks of deep learning. Approximate maximum likelihood training of RBMs typically necessitates sampling from these models. In many training scenarios, computationally efficient Gibbs sampling procedures are crippled by poor mixing. In this work w…
Quantum machine learns faster by reverse annealing on AQCs.
Restricted Boltzmann machines (RBMs) are powerful machine learning models, but learning and some kinds of inference in the model require sampling-based approximations, which, in classical digital computers, are implemented using expensive MCMC. Physical computation offers the opportunity to reduce the cost of sampling …
RBMs model binary interactions with hidden node activation effects.
Lean 4 library formalizes mathematical finance, verifying over 200 theorems.
Estimating the log-likelihood gradient with respect to the parameters of a Restricted Boltzmann Machine (RBM) typically requires sampling using Markov Chain Monte Carlo (MCMC) techniques. To save computation time, the Markov chains are only run for a small number of steps, which leads to a biased estimate. This bias ca…
We reveal connections between RBMs and Bosons, explaining symmetry breaking in their energy landscapes.
New method combines RBMs to solve complex combinatorial optimization problems.
Stochastic gradient descent based algorithms are typically used as the general optimization tools for most deep learning models. A Restricted Boltzmann Machine (RBM) is a probabilistic generative model that can be stacked to construct deep architectures. For RBM with Bernoulli inputs, non-Euclidean algorithm such as st…