arXiv AI
Aug 18
Pushing the Limits of High-Resolution Weather Forecasting through Data Scaling
arXiv:2608. 14652v1 Announce Type: cross Abstract: The development of 0.
On What We Can Learn from Low-Resolution Data
The paper investigates the impact of low‑resolution data on machine learning models, especially when high‑resolution data is scarce. It provides a theoretical analysis using Kullback‑Leibler divergence to quantify how data resolution affects a datapoint’s influence and derives bounds on information loss from downsampling. Empirical results with a vision transformer and a convolutional neural network show that incorporating low‑resolution data consistently improves performance in such settings.
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arXiv Machine Learning
Aug 20
An Empirical Benchmark of Deep Time-Series Models for Smart Meter Energy Forecasting
The paper presents an empirical benchmark of nine modern deep‑learning models for time‑series forecasting of smart‑meter energy consumption, evaluated on two publicly available datasets. It examines how historical input length, prediction horizon, and model architecture affect accuracy, finding that longer historical context improves performance up to a saturation point and that accuracy declines with longer horizons. The study also compares computational complexity, showing that lightweight architectures achieve similar performance to heavier models, and notes that model choice has limited impact across most demographic and household subgroups.
Hugging Face Trending Papers
Aug 19
An Empirical Benchmark of Deep Time-Series Models for Smart Meter Energy Forecasting
The paper presents an empirical benchmark of nine deep learning models for smart meter energy forecasting, evaluating them on two public datasets. It examines how historical input length, prediction horizon, and model architecture affect accuracy, finding that longer historical context improves performance up to a saturation point while accuracy declines with longer horizons. The study also compares computational cost, showing lightweight models achieve similar accuracy to heavier ones, and notes that model choice matters less across most population segments.
arXiv Machine Learning
Jul 13
Data-Efficient Deep Learning: Empirical Guidelines for Training Set Size Estimation in Inertial Sensor Classification
arXiv:2607. 09402v1 Announce Type: new Abstract: Deep learning models dependency on large-scale inertial datasets presents a significant bottleneck in inertial sensor-based classification tasks, such as human activity recognition and smartphone location recognition.
arXiv AI
Aug 26
msData: A Millisecond-Resolution Network Dataset for Advancing Time Series Foundation Models
arXiv:2603.16497v3 Announce Type: replace-cross Abstract: Time series foundation models (TSFMs) require diverse, real-world datasets to adapt across varying domains and temporal frequencies. However,...
arXiv Machine Learning
Jun 15
Can Deep Neural Networks Improve Compression of Very Large Scientific Data?
arXiv:2606. 14353v1 Announce Type: new Abstract: Error-bounded lossy compression is a fundamental technique for managing the rapidly growing volumes of scientific data produced by modern simulations and observational instruments.
arXiv Machine Learning
Jun 17
Does the Data Processing Inequality Reflect Practice? On the Utility of Low-Level Tasks
arXiv:2512. 21315v2 Announce Type: replace Abstract: The data processing inequality is an information-theoretic principle stating that the information content of a signal cannot be increased by processing the observations.
arXiv Machine Learning
Jun 4
Rethinking Incompleteness: Formalizing Protocol Divergence and Train-Once Learning for Robust IMVC
arXiv:2606. 04857v1 Announce Type: new Abstract: Standard IMVC evaluation retrains separate models for different missing-data configurations.
arXiv Statistics ML
Sep 10
Theoretical Analysis of Measure Consistency Regularization for Partially Observed Data
arXiv:2602.01437v2 Announce Type: replace-cross Abstract: The problem of corrupted data, missing features, or missing modalities continues to plague the modern machine learning landscape. To address...
arXiv Machine Learning
Jul 28
On-Device Inference versus Wireless Streaming: Energy-Efficient Multi-Modal Deep Learning for Wearable Cardiovascular Patches
arXiv:2510. 18668v4 Announce Type: replace Abstract: Wearable cardiovascular sensor patches promise continuous, unobtrusive monitoring, but their tight energy, memory, and compute budgets make it unclear whether physiological signals should be analyzed on the device or streamed to the cloud for processing.
arXiv AI
Jun 4
An Empirical Study of Data Scale, Model Complexity, and Input Modalities in Visual Generalization
arXiv:2606. 04409v1 Announce Type: cross Abstract: Modern deep neural networks usually have large parameter scales and nonlinear hierarchical structures, and they have achieved strong performance in computer vision.
arXiv Computer Vision
Sep 24
Information Capacity of Generative Video Compression: Quantifying the Rate-Compute Exchange at Identical Quality
The paper introduces the concept of Information Capacity (IC) to quantify how much bandwidth savings a unit of decoder compute can achieve in generative video compression (GVC). By modeling reconstruction quality as a two‑factor power law in data rate and compute, the authors fit measured DISTS of two GVC decoders with high accuracy and define IC as the negative logarithmic slope along an iso‑quality contour. IC is dimensionless, enabling architecture‑agnostic comparisons and revealing that a 14B decoder trades compute for rate far more efficiently than a 1.3B decoder, with significant variation across datasets.