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Found 6 papers, 3 papers with code
Optimizing IoT-Based Asset and Utilization Tracking: Efficient Activity Classification with MiniRocket on Resource-Constrained Devices
Known for its accuracy, scalability, and fast training for time-series classification, in this paper, it is proposed as a TinyML algorithm for inference on resource-constrained IoT devices.
Gemtelligence: Accelerating Gemstone classification with Deep Learning
The value of luxury goods, particularly investment-grade gemstones, is greatly influenced by their origin and authenticity, sometimes resulting in differences worth millions of dollars.
Controllable Neural Symbolic Regression
In symbolic regression, the goal is to find an analytical expression that accurately fits experimental data with the minimal use of mathematical symbols such as operators, variables, and constants.
Dynaformer: A Deep Learning Model for Ageing-aware Battery Discharge Prediction
Electrochemical batteries are ubiquitous devices in our society.
Neural Symbolic Regression that Scales
We procedurally generate an unbounded set of equations, and simultaneously pre-train a Transformer to predict the symbolic equation from a corresponding set of input-output-pairs.
A Seq2Seq approach to Symbolic Regression
Deep neural networks have proved to be powerful function approximators.
