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TinyNS: Platform-aware Neurosymbolic Auto Tiny Machine Learning

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Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1002/rob.21918openalex-snapshot
A survey of deep learning techniques for autonomous driving
inferredcites →https://doi.org/10.1007/978-1-4419-7719-9_6openalex-snapshot
Boost C++ Libraries
inferredcites →https://doi.org/10.1007/978-3-030-58548-8_28openalex-snapshot
Tracking Objects as Points
inferredcites →https://doi.org/10.1007/978-3-030-63710-1_13openalex-snapshot
Time Series Encodings with Temporal Convolutional Networks
inferredcites →https://doi.org/10.1007/978-3-031-20047-2_1openalex-snapshot
ByteTrack: Multi-object Tracking by Associating Every Detection Box
inferredcites →https://doi.org/10.1007/978-3-319-49130-1_25openalex-snapshot
Learning and Reasoning with Logic Tensor Networks
inferredcites →https://doi.org/10.1007/978-3-319-49409-8_7openalex-snapshot
Temporal Convolutional Networks: A Unified Approach to Action Segmentation
inferredcites →https://doi.org/10.1007/978-3-540-30185-1_1openalex-snapshot
Neuro-symbolic System for Business Internal Control
inferredcites →https://doi.org/10.1007/bf00130011openalex-snapshot
Connectionist AI, symbolic AI, and the brain
inferredcites →https://doi.org/10.1007/bfb0020217openalex-snapshot
Kernel principal component analysis
inferredcites →https://doi.org/10.1007/s10994-022-06142-7openalex-snapshot
Detect, Understand, Act: A Neuro-symbolic Hierarchical Reinforcement Learning Framework
inferredcites →https://doi.org/10.1016/0165-1684(94)90029-9openalex-snapshot
Independent component analysis, A new concept?
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A survey on wearable sensor modality centred human activity recognition in health care
inferredcites →https://doi.org/10.1016/j.eswa.2022.117876openalex-snapshot
AutoTinyML for microcontrollers: Dealing with black-box deployability
inferredcites →https://doi.org/10.1016/j.iot.2021.100461openalex-snapshot
TinyML Meets IoT: A Comprehensive Survey
inferredcites →https://doi.org/10.1016/j.jksuci.2021.11.019openalex-snapshot
A review on TinyML: State-of-the-art and prospects
inferredcites →https://doi.org/10.1016/j.neucom.2019.11.004openalex-snapshot
Dealing with categorical and integer-valued variables in Bayesian Optimization with Gaussian processes
inferredcites →https://doi.org/10.1038/44565openalex-snapshot
Learning the parts of objects by non-negative matrix factorization
inferredcites →https://doi.org/10.1038/nature14539openalex-snapshot
Deep learning
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Discovering faster matrix multiplication algorithms with reinforcement learning
inferredcites →https://doi.org/10.1038/s42254-021-00314-5openalex-snapshot
Physics-informed machine learning
inferredcites →https://doi.org/10.1093/bioinformatics/btx275openalex-snapshot
Neuro-symbolic representation learning on biological knowledge graphs
inferredcites →https://doi.org/10.1093/bioinformatics/btx624openalex-snapshot
DeepGO: predicting protein functions from sequence and interactions using a deep ontology-aware classifier
inferredcites →https://doi.org/10.1093/nsr/nwac035openalex-snapshot
Neuro-symbolic approaches in artificial intelligence
inferredcites →https://doi.org/10.1098/rsta.2019.0155openalex-snapshot
PULP-NN: accelerating quantized neural networks on parallel ultra-low-power RISC-V processors
inferredcites →https://doi.org/10.1109/cogmi52975.2021.00033openalex-snapshot
Addressing the Limitations of AI/ML in creating Cognitive Solutions
inferredcites →https://doi.org/10.1109/cogmi52975.2021.00041openalex-snapshot
Enabling Hyperparameter Tuning of Machine Learning Classifiers in Production
inferredcites →https://doi.org/10.1109/cvpr.2016.90openalex-snapshot
Deep Residual Learning for Image Recognition
inferredcites →https://doi.org/10.1109/cvpr.2018.00474openalex-snapshot
MobileNetV2: Inverted Residuals and Linear Bottlenecks
inferredcites →https://doi.org/10.1109/dac18074.2021.9586232openalex-snapshot
TinyML: Current Progress, Research Challenges, and Future Roadmap
inferredcites →https://doi.org/10.1109/icassp40776.2020.9054609openalex-snapshot
Mango: A Python Library for Parallel Hyperparameter Tuning
inferredcites →https://doi.org/10.1109/icra40945.2020.9196860openalex-snapshot
RoNIN: Robust Neural Inertial Navigation in the Wild: Benchmark, Evaluations, & New Methods
inferredcites →https://doi.org/10.1109/icra48891.2023.10161351openalex-snapshot
Neural-Kalman GNSS/INS Navigation for Precision Agriculture
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Proceedings 2006 IEEE International Conference on Robotics and Automation, 2006. ICRA 2006.
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Proceedings of the International Joint Conference on Neural Networks (IJCNN) 2005
inferredcites →https://doi.org/10.1109/ijcnn52387.2021.9533927openalex-snapshot
TinyOL: TinyML with Online-Learning on Microcontrollers
inferredcites →https://doi.org/10.1109/iros.1994.407417openalex-snapshot
Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS'94)
inferredcites →https://doi.org/10.1109/iros.2017.8206066openalex-snapshot
Unscented Kalman filtering on Lie groups
inferredcites →https://doi.org/10.1109/jiot.2020.2966773openalex-snapshot
Deep-Learning-Based Pedestrian Inertial Navigation: Methods, Data Set, and On-Device Inference
inferredcites →https://doi.org/10.1109/jiot.2021.3064342openalex-snapshot
Glow in the Dark: Smartphone Inertial Odometry for Vehicle Tracking in GPS Blocked Environments
inferredcites →https://doi.org/10.1109/jsen.2022.3210773openalex-snapshot
Machine Learning for Microcontroller-Class Hardware: A Review
inferredcites →https://doi.org/10.1109/lca.2019.2907539openalex-snapshot
Orbital Edge Computing: Machine Inference in Space
inferredcites →https://doi.org/10.1109/mcse.2010.118openalex-snapshot
Cython: The Best of Both Worlds
inferredcites →https://doi.org/10.1109/percomworkshops53856.2022.9767250openalex-snapshot
TinyFedTL: Federated Transfer Learning on Ubiquitous Tiny IoT Devices
inferredcites →https://doi.org/10.1109/sai.2014.6918213openalex-snapshot
A survey of feature selection and feature extraction techniques in machine learning
inferredcites →https://doi.org/10.1109/siitme50350.2020.9292199openalex-snapshot
Investigating the performance of MicroPython and C on ESP32 and STM32 microcontrollers
inferredcites →https://doi.org/10.1109/tac.2004.834121openalex-snapshot
Kalman Filtering With Intermittent Observations
inferredcites →https://doi.org/10.1109/taes.2002.1008998openalex-snapshot
Direct Kalman filtering approach for GPS/INS integration
inferredcites →https://doi.org/10.1109/tcsii.2020.2983648openalex-snapshot
CMix-NN: Mixed Low-Precision CNN Library for Memory-Constrained Edge Devices
inferredcites →https://doi.org/10.1109/tim.2007.908635openalex-snapshot
Analysis and Modeling of Inertial Sensors Using Allan Variance
inferredcites →https://doi.org/10.1109/time.2004.1314402openalex-snapshot
Proceedings. 11th International Symposium on Temporal Representation and Reasoning (TIME 2004)
inferredcites →https://doi.org/10.1109/tit.2011.2182033openalex-snapshot
Information-Theoretic Regret Bounds for Gaussian Process Optimization in the Bandit Setting
inferredcites →https://doi.org/10.1109/tits.2019.2909064openalex-snapshot
AbolDeepIO: A Novel Deep Inertial Odometry Network for Autonomous Vehicles
inferredcites →https://doi.org/10.1109/tkde.2019.2946162openalex-snapshot
A Survey on Data Collection for Machine Learning: A Big Data - AI Integration Perspective
inferredcites →https://doi.org/10.1109/tnn.2008.2005605openalex-snapshot
The Graph Neural Network Model
inferredcites →https://doi.org/10.1109/tvcg.2019.2944182openalex-snapshot
Toward a Quantitative Survey of Dimension Reduction Techniques
inferredcites →https://doi.org/10.1109/waspaa.2019.8937164openalex-snapshot
ToyADMOS: A Dataset of Miniature-Machine Operating Sounds for Anomalous Sound Detection
inferredcites →https://doi.org/10.1142/s0129065711002833openalex-snapshot
HYBRID NEURAL INTELLIGENT SYSTEM TO PREDICT BUSINESS FAILURE IN SMALL-TO-MEDIUM-SIZE ENTERPRISES
inferredcites →https://doi.org/10.1145/195470.195557openalex-snapshot
Compiler optimizations for improving data locality
inferredcites →https://doi.org/10.1145/3180155.3180219openalex-snapshot
Neuro-symbolic program corrector for introductory programming assignments
inferredcites →https://doi.org/10.1145/3241036openalex-snapshot
The seven tools of causal inference, with reflections on machine learning
inferredcites →https://doi.org/10.1145/3297858.3304011openalex-snapshot
Intelligence Beyond the Edge
inferredcites →https://doi.org/10.1145/3308558.3313426openalex-snapshot
STFNets: Learning Sensing Signals from the Time-Frequency Perspective with Short-Time Fourier Neural Networks
inferredcites →https://doi.org/10.1145/3314221.3314597openalex-snapshot
Compiling KB-sized machine learning models to tiny IoT devices
inferredcites →https://doi.org/10.1145/3314221.3314642openalex-snapshot
Gen: a general-purpose probabilistic programming system with programmable inference
inferredcites →https://doi.org/10.1145/3316781.3317757openalex-snapshot
Accuracy vs. Efficiency
inferredcites →https://doi.org/10.1145/3356250openalex-snapshot
Proceedings of the 17th Conference on Embedded Networked Sensor Systems
inferredcites →https://doi.org/10.1145/3358189openalex-snapshot
TF-Net
inferredcites →https://doi.org/10.1145/3358198openalex-snapshot
dMazeRunner
inferredcites →https://doi.org/10.1145/3381831openalex-snapshot
Green AI
inferredcites →https://doi.org/10.1145/3384419.3431158openalex-snapshot
Neuroplex
inferredcites →https://doi.org/10.1145/3417313.3429382openalex-snapshot
Learning in the Wild
inferredcites →https://doi.org/10.1145/3437984.3458836openalex-snapshot
μNAS
inferredcites →https://doi.org/10.1145/3440016openalex-snapshot
Compressing RNNs to Kilobyte Budget for IoT Devices Using Kronecker Products
inferredcites →https://doi.org/10.1145/3447582openalex-snapshot
A Comprehensive Survey of Neural Architecture Search
inferredcites →https://doi.org/10.1145/3465480.3466928openalex-snapshot
The synergy of complex event processing and tiny machine learning in industrial IoT
inferredcites →https://doi.org/10.1145/3476995openalex-snapshot
Intermittent-Aware Neural Architecture Search
inferredcites →https://doi.org/10.1145/3490099.3511157openalex-snapshot
Better Together? An Evaluation of AI-Supported Code Translation
inferredcites →https://doi.org/10.1145/3503914openalex-snapshot
Toward verified artificial intelligence
inferredcites →https://doi.org/10.1145/3510850openalex-snapshot
Camaroptera: A Long-range Image Sensor with Local Inference for Remote Sensing Applications
inferredcites →https://doi.org/10.1145/3534586openalex-snapshot
Auritus
inferredcites →https://doi.org/10.1145/3534594openalex-snapshot
TinyOdom
inferredcites →https://doi.org/10.1145/3560905.3568504openalex-snapshot
Capricorn
inferredcites →https://doi.org/10.1177/0278364915620033openalex-snapshot
The EuRoC micro aerial vehicle datasets
inferredcites →https://doi.org/10.1177/0278364919883346openalex-snapshot
AQUALOC: An underwater dataset for visual–inertial–pressure localization
inferredcites →https://doi.org/10.1186/s40317-021-00245-zopenalex-snapshot
Dead-reckoning animal movements in R: a reappraisal using Gundog.Tracks
inferredcites →https://doi.org/10.1201/9781420009712-10openalex-snapshot
Nonlinear Programming
inferredcites →https://doi.org/10.1207/s15516709cog0402_2openalex-snapshot
Physical Symbol Systems*
inferredcites →https://doi.org/10.1609/aaai.v32i1.12102openalex-snapshot
IONet: Learning to Cure the Curse of Drift in Inertial Odometry
inferredcites →https://doi.org/10.1609/aaai.v33i01.33013027openalex-snapshot
ATOMIC: An Atlas of Machine Commonsense for If-Then Reasoning
inferredcites →https://doi.org/10.1609/aaai.v36i11.21711openalex-snapshot
PYLON: A PyTorch Framework for Learning with Constraints
inferredcites →https://doi.org/10.1609/aimag.v43i1.19122openalex-snapshot
The Third AI Summer: AAAI Robert S. Engelmore Memorial Lecture
inferredcites →https://doi.org/10.18653/v1/d19-6003openalex-snapshot
Towards Generalizable Neuro-Symbolic Systems for Commonsense Question Answering
inferredcites →https://doi.org/10.18653/v1/p19-1470openalex-snapshot
COMET: Commonsense Transformers for Automatic Knowledge Graph Construction
inferredcites →https://doi.org/10.21236/ada164453openalex-snapshot
Learning Internal Representations by Error Propagation
inferredcites →https://doi.org/10.2514/1.43270openalex-snapshot
Engineless Unmanned Aerial Vehicle Propulsion by Dynamic Soaring
inferredcites →https://doi.org/10.3233/aic-210084openalex-snapshot
Neuro-symbolic artificial intelligence
inferredcites →https://doi.org/10.3390/drones5040127openalex-snapshot
Energy-Efficient Inference on the Edge Exploiting TinyML Capabilities for UAVs
inferredcites →https://doi.org/10.3390/mi13060851openalex-snapshot
TinyML: Enabling of Inference Deep Learning Models on Ultra-Low-Power IoT Edge Devices for AI Applications
inferredcites →https://doi.org/10.3390/s151229858openalex-snapshot
Physical Human Activity Recognition Using Wearable Sensors
inferredcites →https://doi.org/10.4230/oasics.dx.2024.10openalex-snapshot
Leveraging Answer Set Programming for Continuous Monitoring, Fault Detection, and Explanation of Automated and Autonomous Driving Systems
inferredcites →https://doi.org/10.4230/oasics.ng-res.2025.4openalex-snapshot
CMSIS-NN: Efficient Neural Network Kernels for Arm Cortex-M CPUs
inferredcites →https://doi.org/10.48550/arxiv.1206.2944openalex-snapshot
Practical Bayesian Optimization of Machine Learning Algorithms
inferredcites →https://doi.org/10.48550/arxiv.1510.00149openalex-snapshot
Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding
inferredcites →https://doi.org/10.48550/arxiv.1602.07360openalex-snapshot
SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size
inferredcites →https://doi.org/10.48550/arxiv.1603.00831openalex-snapshot
MOT16: A Benchmark for Multi-Object Tracking
inferredcites →https://doi.org/10.48550/arxiv.1611.01578openalex-snapshot
Neural Architecture Search with Reinforcement Learning
inferredcites →https://doi.org/10.48550/arxiv.1611.02167openalex-snapshot
Designing Neural Network Architectures using Reinforcement Learning
inferredcites →https://doi.org/10.48550/arxiv.1704.04861openalex-snapshot
MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
inferredcites →https://doi.org/10.48550/arxiv.1708.06374openalex-snapshot
On a Formal Model of Safe and Scalable Self-driving Cars
inferredcites →https://doi.org/10.48550/arxiv.1711.03902openalex-snapshot
Neural-Symbolic Learning and Reasoning: A Survey and Interpretation
inferredcites →https://doi.org/10.48550/arxiv.1804.03209openalex-snapshot
Google Speech Commands-Musan test set
inferredcites →https://doi.org/10.48550/arxiv.1805.10872openalex-snapshot
DeepProbLog: Neural Probabilistic Logic Programming
inferredcites →https://doi.org/10.48550/arxiv.1810.02338openalex-snapshot
Neural-Symbolic VQA: Disentangling Reasoning from Vision and Language Understanding
inferredcites →https://doi.org/10.48550/arxiv.1901.02358openalex-snapshot
FastGRNN: A Fast, Accurate, Stable and Tiny Kilobyte Sized Gated Recurrent Neural Network
inferredcites →https://doi.org/10.48550/arxiv.1905.06088openalex-snapshot
Neural-Symbolic Computing: An Effective Methodology for Principled Integration of Machine Learning and Reasoning
inferredcites →https://doi.org/10.48550/arxiv.1905.12107openalex-snapshot
SpArSe: Sparse Architecture Search for CNNs on Resource-Constrained Microcontrollers
inferredcites →https://doi.org/10.48550/arxiv.1906.01563openalex-snapshot
Hamiltonian Neural Networks
inferredcites →https://doi.org/10.48550/arxiv.1906.05721openalex-snapshot
Visual Wake Words Dataset
inferredcites →https://doi.org/10.48550/arxiv.1912.01703openalex-snapshot
PyTorch: An Imperative Style, High-Performance Deep Learning Library
inferredcites →https://doi.org/10.48550/arxiv.2002.11921openalex-snapshot
RNNPool: Efficient Non-linear Pooling for RAM Constrained Inference
inferredcites →https://doi.org/10.48550/arxiv.2006.08852openalex-snapshot
Counterexample-Guided Learning of Monotonic Neural Networks
inferredcites →https://doi.org/10.48550/arxiv.2006.14032openalex-snapshot
Compositional Explanations of Neurons
inferredcites →https://doi.org/10.48550/arxiv.2007.10319openalex-snapshot
MCUNet: Tiny Deep Learning on IoT Devices
inferredcites →https://doi.org/10.48550/arxiv.2007.12101openalex-snapshot
Learning Differentiable Programs with Admissible Neural Heuristics
inferredcites →https://doi.org/10.48550/arxiv.2009.14385openalex-snapshot
AttendNets: Tiny Deep Image Recognition Neural Networks for the Edge via Visual Attention Condensers
inferredcites →https://doi.org/10.48550/arxiv.2010.08678openalex-snapshot
TensorFlow Lite Micro: Embedded Machine Learning on TinyML Systems
inferredcites →https://doi.org/10.48550/arxiv.2010.11267openalex-snapshot
MicroNets: Neural Network Architectures for Deploying TinyML Applications on Commodity Microcontrollers
inferredcites →https://doi.org/10.48550/arxiv.2106.07487openalex-snapshot
pix2rule: End-to-end Neuro-symbolic Rule Learning
inferredcites →https://doi.org/10.48550/arxiv.2201.05842openalex-snapshot
UDC: Unified DNAS for Compressible TinyML Models
inferredcites →https://doi.org/10.48550/arxiv.2212.03332openalex-snapshot
Edge Impulse: An MLOps Platform for Tiny Machine Learning
inferredcites →https://doi.org/10.5555/1953048.2078195openalex-snapshot
Scikit-learn: Machine Learning in Python
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Neural architecture search
inferredcites →https://doi.org/10.57702/zp44cu3gopenalex-snapshot
Learning Multiple Layers of Features from Tiny Images

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.1007/978-3-032-19038-3_23openalex-snapshot
TinyML: Small Models Making a Big Impact
inferred← citeshttps://doi.org/10.1007/978-981-97-5157-0_63openalex-snapshot
Dependability Framework for Smart Tiny Edge Devices
inferred← citeshttps://doi.org/10.1007/s13369-025-10887-3openalex-snapshot
A Comprehensive Review of Neuro-symbolic AI for Robustness, Uncertainty Quantification, and Intervenability
inferred← citeshttps://doi.org/10.1007/s13721-025-00710-2openalex-snapshot
A bidirectional neuro-symbolic framework for clinical decision support via dynamic integration of deep learning and symbolic reasoning
inferred← citeshttps://doi.org/10.1016/j.future.2025.108310openalex-snapshot
On-device explainable artificial intelligence for the semantic web of everything
inferred← citeshttps://doi.org/10.1016/j.neucom.2025.130746openalex-snapshot
Emerging trends and strategic opportunities in tiny machine learning: A comprehensive thematic analysis
inferred← citeshttps://doi.org/10.1038/s41598-025-90764-zopenalex-snapshot
MicroNAS for memory and latency constrained hardware aware neural architecture search in time series classification on microcontrollers
inferred← citeshttps://doi.org/10.1109/biosensors65002.2025.11239078openalex-snapshot
Vertical Analog Frontend with Machine Learning Core for On-Sensor Sleep Stage Monitoring
inferred← citeshttps://doi.org/10.1109/dsd64264.2024.00074openalex-snapshot
HW/SW Collaborative Techniques for Accelerating TinyML Inference Time at No Cost
inferred← citeshttps://doi.org/10.1109/embc58623.2025.11251861openalex-snapshot
On-Device, Continuous, Cuffless, and Accelerometer-Based Blood Pressure Monitoring
inferred← citeshttps://doi.org/10.1109/ic-eeta66496.2025.11548394openalex-snapshot
Optimising Cloud Resource Allocation using a Neurosymbolic Machine Learning Approach
inferred← citeshttps://doi.org/10.1109/icoecit68303.2026.11497919openalex-snapshot
Integrating Deep Learning with Symbolic Reasoning: A NeuroSymbolic Framework for Trustworthy Medical Diagnosis
inferred← citeshttps://doi.org/10.1109/ipsn61024.2024.00005openalex-snapshot
Intelligence Beyond the Edge using Hyperdimensional Computing
inferred← citeshttps://doi.org/10.1109/iros58592.2024.10802379openalex-snapshot
A Neurosymbolic Approach to Adaptive Feature Extraction in SLAM
inferred← citeshttps://doi.org/10.1109/jiot.2024.3376529openalex-snapshot
Design of Tiny Contrastive Learning Network With Noise Tolerance for Unauthorized Device Identification in Internet of UAVs
inferred← citeshttps://doi.org/10.1109/lsens.2023.3327914openalex-snapshot
AutoML for On-Sensor Tiny Machine Learning
inferred← citeshttps://doi.org/10.1109/tmc.2025.3600533openalex-snapshot
$\pi$-eLight: Learning Interpretable Programmatic Policies for Effective Traffic Signal Control
inferred← citeshttps://doi.org/10.21203/rs.3.rs-6844035/v1openalex-snapshot
Machine Learning on Microcontrollers for Biological Sensing: A Systematic Review
inferred← citeshttps://doi.org/10.3390/chips2020008openalex-snapshot
A Quantitative Review of Automated Neural Search and On-Device Learning for Tiny Devices
inferred← citeshttps://doi.org/10.3390/sci8010010openalex-snapshot
A Review of the Transition from Industry 4.0 to Industry 5.0: Unlocking the Potential of TinyML in Industrial IoT Systems
inferred← citeshttps://doi.org/10.38124/ijsrmt.v4i5.531openalex-snapshot
Energy Efficient Neural Architectures for TinyML Applications
inferred← citeshttps://doi.org/10.4018/979-8-3373-0442-7.ch032openalex-snapshot
Revolutionizing Human Resources for Safer Automotive Work Environments
inferred← citeshttps://doi.org/10.5772/intechopen.1011918openalex-snapshot
Neuro-Symbolic Methods for Times Series Analysis for Edge Computing
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