arXiv research

Paper designs optimal ECOCs using IP for robust multiclass classification.

In econometrics and finance, the vector error correction model (VECM) is an important time series model for cointegration analysis, which is used to estimate the long-run equilibrium variable relationships. The traditional analysis and estimation methodologies assume the underlying Gaussian distribution but, in practic…

An associative memory is a framework of content-addressable memory that stores a collection of message vectors (or a dataset) over a neural network while enabling a neurally feasible mechanism to recover any message in the dataset from its noisy version. Designing an associative memory requires addressing two main task…

Bayesian method for high-dimensional VECM analysis of cointegration.

Certain theoretical aspects of vector autoregression (VAR) as tools to model economic time series are revised, in particular their capacity to include both short term and long term information. The VAR model, in its error correction form, is derived and the permanent-transitory decomposition of factors proposed by Gonz…

A model corrects Lithuanian grammatical errors.

The study examines methods to correct measurement error in nutritional epidemiology studies.

A new method for math reasoning that allows for iterative correction.

Study loop corrections in random feature models affecting training and test errors.

The paper proposes a new model to analyze directed networks and accurately estimate community memberships.

Proposes a model combining difference-attention and error-correction LSTMs for improved time series prediction.

New method combines machine learning with data assimilation for model error correction.

This paper examines interest rates and market efficiency in DeFi loanable funds protocols.

SCaSML improves PDE solvers by correcting errors efficiently.

This paper establishes non-asymptotic oracle inequalities for the prediction error and estimation accuracy of the LASSO in stationary vector autoregressive models. These inequalities are used to establish consistency of the LASSO even when the number of parameters is of a much larger order of magnitude than the sample …

Paper analyzes iterates in high-dimensional linear models and proposes estimators for their generalization error.

Bias correction needed after deep learning regression training.

The paper corrects for node degree in spectral clustering using random walk Laplacian.

A fast model estimates future prices from orderbook data.

Market forecasts converge to true values if some agents are correct.

Corrected samplers reduce discretization error in discrete flow models without additional computational cost.

Khovanov homology helps create quantum error-correcting codes.

This paper proposes a representational model for grid cells. In this model, the 2D self-position of the agent is represented by a high-dimensional vector, and the 2D self-motion or displacement of the agent is represented by a matrix that transforms the vector. Each component of the vector is a unit or a cell. The mode…

In this work, we present a family of vector quantization schemes \emph{vqSGD} (Vector-Quantized Stochastic Gradient Descent) that provide an asymptotic reduction in the communication cost with convergence guarantees in first-order distributed optimization. In the process we derive the following fundamental information …

Improves ASR accuracy for domain mismatch using machine translation.

In this paper, the problem of one-bit compressed sensing (OBCS) is formulated as a problem in probably approximately correct (PAC) learning. It is shown that the Vapnik-Chervonenkis (VC-) dimension of the set of half-spaces in $\mathbb{R}^n$ generated by $k$-sparse vectors is bounded below by $k \lg (n/k)$ and above by…

New method corrects seasonal Arctic sea ice predictions with probabilistic models.

A new framework for fair representation learning using correction vectors.

Correction for Error estimates for binomial approximations of game options [math.PR/0607123]

Fault-tolerant neural networks inspired by biological error correction codes.

ECNNs improve stock market predictions by correcting errors from previous steps.

Quantum codes on hyperbolic lattices outperform Euclidean ones with higher rates and lower overhead.

Neural operators correct PDE residuals to improve BIP solutions.

CRC improves multivariate forecasting accuracy without risking performance degradation.

This paper corrects an error in [Keller-Ressel, M. and Steiner T. "Yield curve shapes and the asymptotic short rate distribution in affine one-factor models." Finance and Stochastics 12.2 (2008): 149-172]. The error concerns the correct expression for the boundary between normal and humped yield curve behavior in affin…

The paper tackles model misspecification in reinforcement learning through a bootstrapped neural network and error correction.

LLMs struggle with zero-shot annotation tasks due to model-internalized priors.

Autonomy and adaptation of machines requires that they be able to measure their own errors. We consider the advantages and limitations of such an approach when a machine has to measure the error in a regression task. How can a machine measure the error of regression sub-components when it does not have the ground truth…

Theoretical work shows integrating coherent reasoning improves LLM performance and error correction.

We introduce the speculate-correct method to derive error bounds for local classifiers. Using it, we show that k nearest neighbor classifiers, in spite of their famously fractured decision boundaries, have exponential error bounds with O(sqrt((k + ln n) / n)) error bound range for n in-sample examples.

Corrects errors in ILA for Bayesian inference in LGMs.

Artificial Intelligence (AI) systems sometimes make errors and will make errors in the future, from time to time. These errors are usually unexpected, and can lead to dramatic consequences. Intensive development of AI and its practical applications makes the problem of errors more important. Total re-engineering of the…

A transformer model improves spell correction with hierarchical attention.

In this note we point out an error in the above paper and refer to some papers where this error is corrected and a more general theorem is proved.

Generative AI decodes quantum codes without labeled data.

Improved model error correction online with neural networks in 4D-Var.

This work presents a technique for statistically modeling errors introduced by reduced-order models. The method employs Gaussian-process regression to construct a mapping from a small number of computationally inexpensive `error indicators' to a distribution over the true error. The variance of this distribution can be…

Paper corrects Max-Margin loss for multi-label tasks.