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.
Novel method decorrelates neurons for better deep learning model generalization.
Deep neural networks are widely used in various domains. However, the nature of computations at each layer of the deep networks is far from being well understood. Increasing the interpretability of deep neural networks is thus important. Here, we construct a mean-field framework to understand how compact representation…
In this paper, we propose novel strategies for neutral vector variable decorrelation. Two fundamental invertible transformations, namely serial nonlinear transformation and parallel nonlinear transformation, are proposed to carry out the decorrelation. For a neutral vector variable, which is not multivariate Gaussian d…
Improves causal graph learning on dependent binary data.
This work explores how feature decorrelation improves self-supervised learning.
Optimizes signal detection in particle physics by decorrelating classifiers.
A new distributed algorithm for fitting sparse additive models with feature division and decorrelation.
PFDL improves deep learning models' OOD generalization by decorrelating feature embeddings.
Proposes a method to improve Lasso stability via variable decorrelation.
New theory shows how multi-head attention reduces variance and decorrelates outputs.
Novel method decorrelates batches of triplets for active metric learning.
We describe a strategy for constructing a neural network jet substructure tagger which powerfully discriminates boosted decay signals while remaining largely uncorrelated with the jet mass. This reduces the impact of systematic uncertainties in background modeling while enhancing signal purity, resulting in improved di…
SGD reduces test error by decorrelating updates.
Learning an effective representation for high-dimensional data is a challenging problem in reinforcement learning (RL). Deep reinforcement learning (DRL) such as Deep Q networks (DQN) achieves remarkable success in computer games by learning deeply encoded representation from convolution networks. In this paper, we pro…
For many machine learning algorithms, two main assumptions are required to guarantee performance. One is that the test data are drawn from the same distribution as the training data, and the other is that the model is correctly specified. In real applications, however, we often have little prior knowledge on the test d…
A new algorithm removes unexpected correlations in biased data for better clustering.
Paper proposes methods to improve graph domain adaptation by decorrelating node features.
Unified framework for information-theoretic bounds on learning algorithms.
We introduce the blind subspace deconvolution (BSSD) problem, which is the extension of both the blind source deconvolution (BSD) and the independent subspace analysis (ISA) tasks. We examine the case of the undercomplete BSSD (uBSSD). Applying temporal concatenation we reduce this problem to ISA. The associated `high …
VAE improves anomaly detection for jet tagging at the LHC.
This paper proposes a decorrelation-based approach to test hypotheses and construct confidence intervals for the low dimensional component of high dimensional proportional hazards models. Motivated by the geometric projection principle, we propose new decorrelated score, Wald and partial likelihood ratio statistics. Wi…
Introduces a new feature importance measure using Gram-Schmidt decorrelation.
FreST Loss decorrelates spatio-temporal dependencies in graph signals.
Two methods use simulation to improve anomaly detection in particle physics.
Proposes SVI for covariate-shift generalization with sparse variable independence.
This study shows how social insects and machine learning methods share a common mathematical framework.
Fitting statistical models is computationally challenging when the sample size or the dimension of the dataset is huge. An attractive approach for down-scaling the problem size is to first partition the dataset into subsets and then fit using distributed algorithms. The dataset can be partitioned either horizontally (i…
We consider the problem of uncertainty assessment for low dimensional components in high dimensional models. Specifically, we propose a decorrelated score function to handle the impact of high dimensional nuisance parameters. We consider both hypothesis tests and confidence regions for generic penalized M-estimators. U…
Batch Normalization (BN) is capable of accelerating the training of deep models by centering and scaling activations within mini-batches. In this work, we propose Decorrelated Batch Normalization (DBN), which not just centers and scales activations but whitens them. We explore multiple whitening techniques, and find th…
Adam is shown not being able to converge to the optimal solution in certain cases. Researchers recently propose several algorithms to avoid the issue of non-convergence of Adam, but their efficiency turns out to be unsatisfactory in practice. In this paper, we provide new insight into the non-convergence issue of Adam …
Sparse codes improve optimal control tasks with correlated inputs.
Simplified non-contrastive learning avoids representation collapse.
A crucial problem in learning disentangled image representations is controlling the degree of disentanglement during image editing, while preserving the identity of objects. In this work, we propose a simple yet effective model with the encoder-decoder architecture to address this challenge. To encourage disentanglemen…
Estimators computed from adaptively collected data do not behave like their non-adaptive brethren. Rather, the sequential dependence of the collection policy can lead to severe distributional biases that persist even in the infinite data limit. We develop a general method -- -decorrelation -- for transformi…
Generalizes PCA and ICA for continuous-time signals using neural networks.
Machine learning models predict EUR/USD currency direction with 58.52% accuracy.
The paper develops methods for high-dimensional inference in Markov random fields.
Learning good representations is a long standing problem in reinforcement learning (RL). One of the conventional ways to achieve this goal in the supervised setting is through regularization of the parameters. Extending some of these ideas to the RL setting has not yielded similar improvements in learning. In this pape…
Describes explaining neurons in deep representations using compositional logical concepts.
SeReNe prunes neurons with low sensitivity to reduce network size.
Under-parameterized networks can either copy or average teacher weights, leading to universal optimal solutions.
We develop Neuron Shapley as a new framework to quantify the contribution of individual neurons to the prediction and performance of a deep network. By accounting for interactions across neurons, Neuron Shapley is more effective in identifying important filters compared to common approaches based on activation patterns…
Modeling hidden neurons in SNNs using mesoscopic approximations.
Topological methods improve neuron analysis and tracer injection summary.
Based on a recent theorem due to the authors, it is shown how the extreme tail dependence between an asset and a factor or index or between two assets can be easily calibrated. Portfolios constructed with stocks with minimal tail dependence with the market exhibit a remarkable degree of decorrelation with the market at…
Inspired by complexity and diversity of biological neurons, our group proposed quadratic neurons by replacing the inner product in current artificial neurons with a quadratic operation on input data, thereby enhancing the capability of an individual neuron. Along this direction, we are motivated to evaluate the power o…
Losaw improves FI scores by decorrelating features in ML models.
We propose a new generic type of stochastic neurons, called -neurons, that considers activation functions based on Jackson's -derivatives with stochastic parameters . Our generalization of neural network architectures with -neurons is shown to be both scalable and very easy to implement. We demonstrate expe…