GSoC 2026 DeepLense test submission: Common Test I classification and Test VI.A/VI.B super-resolution on simulated and real HSC/HST lensing data.
Implementation of ML4SCI evaluation tasks for GSoC 2025, featuring deep learning approaches for gravitational lensing analysis including multi-class classification, lens finding with class imbalance and generative models for synthetic lensing image creation.
ML4Sci GSoC 2026 - QMLHEP Evaluation Tasks
ML4SCI Genie GSoC 2026 Tasks
ML4Sci GSoC 2026 Evaluation (FASEROH): Training Seq2Seq architectures (LSTM & Transformer) to compute symbolic Taylor series expansions.
Jet classification using Autoencoder, GNN and Contrastive Learning for ML4SCI GSoC 2026
Evaluation pipeline for the ML4SCI DeepLense GSoC 2026 project, focusing on dark matter morphology and Equivariant Neural Networks.
Unified Python/C++ library for jet observables, HEPSIM-ready workflows, and Monte Carlo validation.
Quantum Machine Learning portfolio for ML4SCI QMLHEP, featuring QGANs, QGNNs, QRL, EQNNs, and Vision Transformers built with TensorFlow Quantum and Cirq.
GSoC 2026 — ML4SCI DeepLense Evaluation Test: Multi-Class Classification + Gravitational Lens Finding
tasks for Q-MAML - Quantum Model-Agnostic Meta-Learning for Variational Quantum Algorithms for High Energy Physics Analysis at the LHC
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