Field notes
2,343 notes from my AI research vault, mapped by topic. Pick a topic to see its notes, or search.
Click the web, then scroll to zoom
Every note is in the list below. The web of topics draws here once the page has loaded.
37 of 2,343 notes match.
- Question answered
- Source note
- Topic
- Shared notes
Notes on deep learning
37 notes, page 1 of 2
Interpretable AI predicts a 2026 summer dry anomaly in central China
Researchers have developed an AI model that predicts a dry anomaly in central China during the summer of 2026. This prediction is based on atmospheric circulation patterns, particularly a persistent cyclonic…
UniTAC: Universal Task-Aware Compression via Weighted Distortion Measures
Researchers propose UniTAC, a universal image compression system for physical AI systems like autonomous vehicles and robots. The system can adapt to changing tasks without retraining, using task-specific importance…
From Passive Mirrors to Active Agents: Holonic Digital Twins for Physical AI over Networks
The paper proposes a framework called Holonic Digital Twins Networks (HDT-Nets) to improve the performance of AI in physical systems like robots and vehicles. The framework aims to deliver real-time physical AI…
NSF-HRPT: Neural Semantic Field meets Hierarchical Risk Perception Tree for Safety-Critical Scenario Assessment
Researchers propose NSF-HRPT, a framework that uses a Neural Semantic Field (NSF) to model scene semantics, trajectory predictions, and probabilistic Time-to-Collision (TTC) distributions from simulation data. During…
CoCaRS: Correlation Calibration-Based Redundancy Suppression for Heterogeneous Knowledge Distillation
The research introduces Correlation Calibration-based Redundancy Suppression (CoCaRS), a method for knowledge distillation in heterogeneous settings where teacher and student models have different architectures. The…
DLAM: Distributional Latent Actions with Temporal Constraints
The paper introduces DLAM, a distributional latent-action model that aims to overcome the limitations of existing models by representing each transition as a diagonal Gaussian. This model is designed to learn more…
Multimodal fusion of visual and morphometric features for avian bone classification
Researchers developed a framework that combines convolutional neural network-based image analysis with standardized morphometric data for classifying bird bones. The model achieved 86% accuracy in identifying skeletal…
Agent-Guided Relational Concept Discovery: Toward Interpretable Surgical Margin Assessment
Researchers propose a new framework called Agent-Guided Concept Discovery to improve the clinical adoption of deep learning models for surgical margin assessment using Rapid Evaporative Ionization Mass Spectrometry…
Agent-Guided Relational Concept Discovery: Toward Interpretable Surgical Margin Assessment
Researchers propose Agent-Guided Concept Discovery, a framework that helps deep learning models interpret REIMS data for surgical margin assessment by learning meaningful concepts directly from data without predefined…
Agent-Guided Relational Concept Discovery: Toward Interpretable Surgical Margin Assessment
Researchers propose a new framework called Agent-Guided Concept Discovery for improving deep learning models used in surgical margin assessment. The model learns meaningful concepts directly from data without requiring…
AREX: Towards a Recursively Self-Improving Agent for Deep Research
The paper introduces a new type of research agent called AREX, which can recursively improve its answers by verifying intermediate results and using the partially verified state to guide subsequent refinement. The…
Compact Latent Coordination for Autonomous Vehicles at Unsignalized Intersections
A new approach called Master-Agent Proto-plan System (MAPS) is proposed to improve the coordination of autonomous vehicles at unsignalized intersections. MAPS uses a hierarchical deep reinforcement learning…
Compact Latent Coordination for Autonomous Vehicles at Unsignalized Intersections
Researchers present MAPS, a deep reinforcement learning architecture for coordinating autonomous vehicles at unsignalized intersections. The system uses a central Master agent that generates a global coordination…
Synthetic data generation framework for quality control automation in gravure printing
Researchers have developed a synthetic data generation framework to help automate the quality control process in gravure printing. The framework generates high-fidelity images of common printing defects and uses them…
Multi-modal transformer for signal classification in nanopore blockade experiments
Researchers present a deep learning architecture for analyzing complex signals from nanopore blockade experiments. The model, which combines multiple signal representations, outperforms existing methods on a peptide…
PhaseAware: Interpretable Human-in-the-Loop Rehabilitation Scoring with Boundary Monitoring
The study introduces PhaseAware, a framework designed for continuous evaluation of rehabilitation quality during physical therapy sessions. It combines temporal data with phase-specific and body part descriptors to…
Taming the Security-Energy Paradox: A Green AI Approach to Optimized Android Malware Detection
Researchers have developed an energy-efficient method for Android malware detection using deep learning models, aiming to balance performance and battery life. They used quantized neural networks (QNNs) with reduced…
Test Case Prioritization for DNNs via Neural Collapse Instability
A new method, Neural-Collapse-Inspired Prioritization (NCIP), is proposed to improve test case prioritization for deep neural networks (DNNs) in safety-critical domains where testing budgets are limited. The method…
GigaPath-Flash and GigaTIME-Flash: Efficient Pathology Foundation Models for Whole-Slide and Tumor Microenvironment Analysis
Researchers have developed two efficient deep learning models named GigaPath-Flash and GigaTIME-Flash for whole-slide pathology analysis and spatial proteomics prediction. These models are based on a compact ViT-S tile…
A Learning-Rate-Gated Failure of GRPO in a Small Language and Vision-Language Model Web Agent: A Controlled Null and Its Mechanism
The study investigates the impact of Reinforcement learning with verifiable rewards and Group Relative Policy Optimization (GRPO) on a small language and vision-language model web agent. The research found that GRPO…
A Learning-Rate-Gated Failure of GRPO in a Small Language and Vision-Language Model Web Agent: A Controlled Null and Its Mechanism
The research investigates the effectiveness of Reinforcement learning with verifiable rewards and Group Relative Policy Optimization (GRPO) on a small language and vision-language model web agent at scales ranging from…
Self-Adaptive Anomaly Detection with Reinforcement Learning and Human Feedback in Connected Vehicles
The paper presents an online anomaly detection framework for connected vehicles that uses a deep Q-network with self-attention to select suitable detectors, an ensemble of statistical drift detectors to monitor input…
Self-Adaptive Anomaly Detection with Reinforcement Learning and Human Feedback in Connected Vehicles
The paper proposes an online anomaly detection framework for autonomous cyber-physical systems (CPS), specifically connected vehicles, that combines three coordinated mechanisms: a deep Q-network with self-attention to…
SLORR: Simple and Efficient In-Training Low-Rank Regularization
The paper introduces SLORR, a simple and efficient in-training low-rank regularization framework for neural networks. It aims to improve compressibility without significant accuracy loss, while avoiding the limitations…
An AI research agent drafts these notes from public papers and posts, and each one keeps its source link. Only the folders I choose to publish leave my Obsidian vault.
