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.
Repulsive ensembles improve uncertainty estimates in PINNs for differential equations.
Repulsive deep ensembles improve diversity and Bayesian inference.
FoRDE uses input gradients to improve neural network ensembles.
Paper improves particle variational inference by optimizing generalization error bound.
A new model improves clustering by reducing redundancy in mixture of local EPCAs.
Study shows global invertibility in nonlinear elasticity with vanishing self-repulsion term.
Neighbor embeddings balance attraction and repulsion to visualize data.
ARS visualization improves t-SNE dynamics with tunable attraction and repulsion.
We propose a new Stein self-repulsive dynamics for obtaining diversified samples from intractable un-normalized distributions. Our idea is to introduce Stein variational gradient as a repulsive force to push the samples of Langevin dynamics away from the past trajectories. This simple idea allows us to significantly de…
Computer experiments reveal complex knots that don't simplify.
Study identifies stable configurations of intertwined threads with repulsive interactions.
This work shows dimension regularization can replace skip-gram negative sampling for graph embeddings, improving efficiency and performance.
Shielded LMC samples from non-convex spaces with repulsive drift.
New approach improves multi-head attention by making heads less similar.
Research examines correlations of complex logarithms of lattice points, showing level repulsion and Poissonian behavior.
Generative adversarial nets (GANs) are widely used to learn the data sampling process and their performance may heavily depend on the loss functions, given a limited computational budget. This study revisits MMD-GAN that uses the maximum mean discrepancy (MMD) as the loss function for GAN and makes two contributions. F…
RePULSe improves language model alignment by reducing undesired outputs without sacrificing overall performance.
The convergence speed of stochastic gradient descent (SGD) can be improved by actively selecting mini-batches. We explore sampling schemes where similar data points are less likely to be selected in the same mini-batch. In particular, we prove that such repulsive sampling schemes lowers the variance of the gradient est…
This paper introduces repulsive Monte Carlo methods for computing the sliced Wasserstein distance.
New algorithm detects anomalies by forcing samples to displace mass in low-density regions.
We consider the problem of diversity enhancing clustering, i.e, developing clustering methods which produce clusters that favour diversity with respect to a set of protected attributes such as race, sex, age, etc. In the context of fair clustering, diversity plays a major role when fairness is understood as demographic…
New Gaussian DPP model reveals directionality in data.
We propose a unifying view of two different Bayesian inference algorithms, Stochastic Gradient Markov Chain Monte Carlo (SG-MCMC) and Stein Variational Gradient Descent (SVGD), leading to improved and efficient novel sampling schemes. We show that SVGD combined with a noise term can be framed as a multiple chain SG-MCM…
Continuous control tasks in reinforcement learning are important because they provide an important framework for learning in high-dimensional state spaces with deceptive rewards, where the agent can easily become trapped into suboptimal solutions. One way to avoid local optima is to use a population of agents to ensure…
The study examines correlations of logarithms of integers at different scalings.
The paper proves a Lojasiewicz inequality for maps from the 2-sphere to itself.
Feature normalization prevents collapse in non-contrastive learning dynamics.
Paper proposes ARB-Loss to improve classification precision in imbalanced datasets.
Determinantal point processes (DPPs) are random point processes well-suited for modeling repulsion. In machine learning, the focus of DPP-based models has been on diverse subset selection from a discrete and finite base set. This discrete setting admits an efficient sampling algorithm based on the eigendecomposition of…
Study on capillarity minimizers with nonlocal repulsion and gravity, proving existence and nonexistence.
Determinantal point processes (DPPs) enable the modeling of repulsion: they provide diverse sets of points. The repulsion is encoded in a kernel that can be seen as a matrix storing the similarity between points. The diversity comes from the fact that the inclusion probability of a subset is equal to the determinan…
The standard loss function used to train neural network classifiers, categorical cross-entropy (CCE), seeks to maximize accuracy on the training data; building useful representations is not a necessary byproduct of this objective. In this work, we propose clustering-oriented representation learning (COREL) as an altern…
Image partitioning, or segmentation without semantics, is the task of decomposing an image into distinct segments, or equivalently to detect closed contours. Most prior work either requires seeds, one per segment; or a threshold; or formulates the task as multicut / correlation clustering, an NP-hard problem. Here, we …
Stein variational gradient descent (SVGD) is a recently proposed particle-based Bayesian inference method, which has attracted a lot of interest due to its remarkable approximation ability and particle efficiency compared to traditional variational inference and Markov Chain Monte Carlo methods. However, we observed th…
Enhances contrastive learning for better representation learning on wild images.
Learning of low dimensional structure in multidimensional data is a canonical problem in machine learning. One common approach is to suppose that the observed data are close to a lower-dimensional smooth manifold. There are a rich variety of manifold learning methods available, which allow mapping of data points to the…
Study the limits of discrete DPPs to continuous DPPs as set size grows.
New models explain heavy-tailed behavior in neural networks.
New insights into surface energy reduction.
We confirm universal behaviors such as eigenvalue distribution and spacings predicted by Random Matrix Theory (RMT) for the cross correlation matrix of the daily stock prices of Tokyo Stock Exchange from 1993 to 2001, which have been reported for New York Stock Exchange in previous studies. It is shown that the random …
New energy model avoids self-intersections in curve optimization.
We consider the problem of inferring the interactions between a set of N binary variables from the knowledge of their frequencies and pairwise correlations. The inference framework is based on the Hopfield model, a special case of the Ising model where the interaction matrix is defined through a set of patterns in the …
In the present work, torsion energy is defined. Its law of conservation is given. It is shown that this type of energy gives rise to a repulsive force which can be used to interpret supernovae type Ia observations, and consequently the accelerating expansion of the Universe. This interpretation is a pure geometric one …
Determinantal point processes (DPPs) have attracted substantial attention as an elegant probabilistic model that captures the balance between quality and diversity within sets. DPPs are conventionally parameterized by a positive semi-definite kernel matrix, and this symmetric kernel encodes only repulsive interactions …
We apply the potential force estimation method to artificial time series of market price produced by a deterministic dealer model. We find that dealers' feedback of linear prediction of market price based on the latest mean price changes plays the central role in the market's potential force. When markets are dominated…
Determinantal Point Processes (DPPs) are popular models for point processes with repulsion. They appear in numerous contexts, from physics to graph theory, and display appealing theoretical properties. On the more practical side of things, since DPPs tend to select sets of points that are some distance apart (repulsion…
Constructs surfaces with conical singularities using variational methods.
Stochastic subgradient descent avoids critical points in definable functions.