On-device research index

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.

168,695 papers · 148 categories

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4 results for “PTB-XL”

Deep Learning for ECG Analysis: Benchmarks and Insights from PTB-XLcs.LG

Deep learning benchmarks ECG analysis with strong performance.

problem Lack of appropriate datasets and evaluation procedures for ECG interpretation.

method Benchmarking on PTB-XL and ICBEB2018 datasets using convolutional neural networks.

result Convolutional neural networks, especially resnet- and inception-based architectures, outperform feature-based algorithms.

ECG analysis deep learning PTB-XL ICBEB2018 benchmarking

2020-04-28abs ↗pdf ↗

Sequence Modeling with Multiresolution Convolutional Memorycs.LG

New model captures long-range patterns in sequences efficiently.

problem Efficiently capturing long-range patterns in sequential data.

method Inspired by wavelet multiresolution analysis, introduces MultiresLayer with multiresolution convolution.

result State-of-the-art performance on sequence classification and autoregressive density estimation tasks.

sequence modeling multiresolution convolution wavelet analysis convolutional memory CIFAR-10

2023-05-02abs ↗pdf ↗

Considerations for Distribution Shift Robustness of Diagnostic Models in Healthcarestat.ML

Study highlights robustness issues in healthcare diagnostic models due to distribution shifts.

problem Robustness of diagnostic models in healthcare is compromised by distribution shifts.

method Theoretical analysis and simulation studies to understand and mitigate shortcuts learned by models.

result Ignoring covariates or using invariant learning approaches leads to non-robust predictors.

distribution shifts healthcare diagnostics causal inference shortcut learning robustness

2024-10-25abs ↗pdf ↗

Once-for-All Channel Mixers (HYPERTINYPW): Generative Compression for TinyMLcs.LG

Generative compression technique reduces neural network size and improves performance on microcontrollers.

problem Memory constraints on microcontrollers limit the size of neural networks, especially for 1x1 pointwise (PW) mixers.

method HYPER-TINYPW uses a shared micro-MLP to generate PW kernels from tiny per-layer codes, reducing memory usage.

result HYPER-TINYPW achieves comparable performance to larger models while being significantly smaller (225 kB vs 1.4 MB).

compression microcontrollers neural networks 1x1 pointwise generative models

2026-03-26abs ↗pdf ↗