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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.

168,742 papers · 148 categories

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12 results for subnets

Derives a size premium from automated market makers in decentralized AI subnets.

problem Determining the profitability and risk of decentralized AI subnets.
method Analyzes daily data on 128 subnets, tests the size premium, and calculates transaction costs.
result The size premium is reduced by a halving of token emissions but remains profitable only below a certain asset threshold.

Click-through rate (CTR) prediction is a critical task in online advertising systems. Most existing methods mainly model the feature-CTR relationship and suffer from the data sparsity issue. In this paper, we propose DeepMCP, which models other types of relationships in order to learn more informative and statistically…

2019-06-11abs ↗pdf ↗

IST trains neural networks locally, reducing memory and communication costs.

problem Challenges in distributed learning due to mandatory data separation.
method Independent subnet training (IST) decomposes the network into narrow subnetworks.
result IST reduces training times compared to common distributed learning approaches.

Derives token price process for AMM tokens, finds leverage effect and pricing discrepancies.

problem Derives token price process for AMM tokens.
method Derives CEV process for token price, derives closed-form option prices, introduces liquidity-adjusted Greeks.
result Token price process is CEV, with leverage effect and pricing discrepancies.

A geodesic net with 4 boundary vertices and 25 balanced vertices is constructed.

problem Constructing geodesic nets with specific vertex types and properties.
method Novel approach to increase the number of balanced vertices from 16 to 25.
result First net with four boundary vertices and 25 balanced vertices, including non-symmetric balanced vertices.

Paper presents UrbanFM and UrbanPy models for inferring fine-grained urban flows.

problem Reduce cost of urban flow monitoring while maintaining data accuracy and granularity.
method Develops UrbanFM and UrbanPy models to infer fine-grained urban flows from coarse-grained observations.
result UrbanPy model demonstrates favorable performance for larger-scale inference tasks.

Paper presents a CNN-RNN method for multi-dimensional emotion recognition in-the-wild.

problem Dimensional emotion recognition in real-world scenarios.
method Pre-training with Aff-Wild and Aff-Wild2, extracting low-, mid-, and high-level features, using RNN subnets in a multi-task framework, and fusion of networks.
result Our approach outperformed state-of-the-art methods using only visual information.