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Found 7 papers, 0 papers with code
Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone
We introduce phi-3-mini, a 3. 8 billion parameter language model trained on 3. 3 trillion tokens, whose overall performance, as measured by both academic benchmarks and internal testing, rivals that of models such as Mixtral 8x7B and GPT-3. 5 (e. g., phi-3-mini achieves 69% on MMLU and 8. 38 on MT-bench), despite being small enough to be deployed on a phone.
Underground Diagnosis Based on GPR and Learning in the Model Space
In this paper, a GPR B-scan image diagnosis method based on learning in the model space is proposed.
Improving the Anomaly Detection in GPR Images by Fine-Tuning CNNs with Synthetic Data
In this paper, a novel method is proposed to improve the subsurface anomaly detection from GPR B-scan images.
Mapping the Buried Cable by Ground Penetrating Radar and Gaussian-Process Regression
On the basis of the established coordinate system and the derived points on the cable, the clustering method and cable fitting algorithm based on Gaussian-process regression are proposed to find the most likely locations of the underground cables.
Estimating the Direction and Radius of Pipe from GPR Image by Ellipse Inversion Model
By minimizing the sum of the algebraic distances from the extracted point set to the inverted ellipse, the most likely pipeline's direction and radius are determined.
Towards robust and domain agnostic reinforcement learning competitions
Reinforcement learning competitions have formed the basis for standard research benchmarks, galvanized advances in the state-of-the-art, and shaped the direction of the field.
Rethink AI-based Power Grid Control: Diving Into Algorithm Design
Recently, deep reinforcement learning (DRL)-based approach has shown promisein solving complex decision and control problems in power engineering domain. In this paper, we present an in-depth analysis of DRL-based voltage control fromaspects of algorithm selection, state space representation, and reward engineering. To resolve observed issues, we propose a novel imitation learning-based approachto directly map power grid operating points to effective actions without any interimreinforcement learning process.
