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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.

168,657 papers · 148 categories

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4 results for “channel-mixing”

Enhancing Multivariate Time Series Forecasting with Mutual Information-driven Cross-Variable and Temporal Modelingcs.LG

New framework improves multivariate time series forecasting by minimizing redundant information.

problem Improving multivariate time series forecasting with deep learning techniques.

method Cross-variable Decorrelation Aware feature Modeling (CDAM) and Temporal correlation Aware Modeling (TAM) to refine Channel-mixing and exploit temporal correlations.

result Significantly surpasses existing models in comprehensive tests.

multivariate time series forecasting deep learning channel-mixing cross-variable decorrelation temporal correlation

2024-03-01abs ↗pdf ↗

Hierarchical Associative Memory, Parallelized MLP-Mixer, and Symmetry Breakingcs.LG

This paper proposes a new approach to Transformers by integrating hierarchical associative memory with MetaFormers.

problem Theoretical framework for Transformers and MLP-Mixers remains underdeveloped.

method Integrating hierarchical associative memory with MetaFormers to create a parallelized MLP-Mixer.

result Symmetry-breaking effects improve the performance of the MLP-Mixer in image recognition tasks.

Transformers MLP-Mixers Hierarchical Associative Memory Symmetry Breaking Attention Mechanisms

2024-06-18abs ↗pdf ↗

Learning Channel Importance for High Content Imaging with Interpretable Deep Input Channel Mixingcs.CV

DCMIX learns channel importance for high content imaging.

problem Lack of channel importance information in deep learning-based image analysis.

method Image blending concepts with alpha compositing for arbitrary channels.

result DCMIX learns biologically relevant channel importance without sacrificing prediction performance.

deep learning high content imaging channel importance image blending alpha compositing

2023-08-31abs ↗pdf ↗

Tokenizing Stock Prices for Enhanced Multi-Step Forecast and Predictioncs.CE

Model predicts stock price changes and forecasts using tokenized data.

problem Challenges in stock price forecasting and prediction due to dynamic data and statistical differences.

method Introduces PCIE model with tokenization to handle both forecasting and prediction.

result PCIE model outperforms state-of-the-art models in forecast and prediction tasks.

stock price forecasting stock price prediction tokenization multi-step prediction log-normal distribution

2025-04-24abs ↗pdf ↗