6 papers
SILVA Networks as Structured Implicit Layers and Vector Attractors via Dynamic Interaction Fields
Jose Luis Lima de Jesus Silva
Many learning problems require representations that reconcile direct input, nearby structure, and broader context. In implicit neural layers, these influences are usually absorbed…
A Symbolic Neural CPU for Quantization-Simulated Writeback and Interpretable Program Execution
Jose Luis Lima de Jesus Silva
Neural networks can learn algorithmic input-output mappings, but trusting a learned executor requires more than a correct final answer because the state transitions that produce it…
Safety-Contract Graph Multi-Agent Reinforcement Learning for Autonomous Network Security Response
Jose Luis Lima de Jesus Silva
Autonomous network-security response systems promise to reduce Security Operations Centre (SOC) reaction latency, but reward-only multi-agent reinforcement learning (MARL) can impr…
Weakly supervised multimodal segmentation of acoustic borehole images with depth-aware cross-attention
Jose Luis Lima de Jesus Silva
Acoustic borehole images provide high-resolution borehole-wall structure, but large-scale interpretation remains difficult because dense expert annotations are rarely available and…
Optimizing Information Asset Investment Strategies in the Exploratory Phase of the Oil and Gas Industry: A Reinforcement Learning Approach
Paulo Roberto de Melo Barros Junior, Monica Alexandra Vilar Ribeiro De Meireles, Jose Luis Lima de Jesus Silva
Our work investigates the economic efficiency of the prevailing "ladder-step" investment strategy in oil and gas exploration, which advocates for the incremental acquisition of geo…
Hybrid Context-Fusion Attention (CFA) U-Net and Clustering for Robust Seismic Horizon Interpretation
Jose Luis Lima de Jesus Silva, Joao Pedro Gomes, Paulo Roberto de Melo Barros Junior +2
Interpreting seismic horizons is a critical task for characterizing subsurface structures in hydrocarbon exploration. Recent advances in deep learning, particularly U-Net-based arc…