nmaher/repro-on-the-theory-of-continual-learning-with-gradient-descent-for-neural-networks-artifacts 13.6 kB
Running Reproduction: Active Continual Learning with Metaplastic Binary Bayesian Neural Networks 🎯 Explore experiment logs and traces in an interactive workspace
Running Reproduction: CausalProfiler: Generating Synthetic Benchmarks for Rigorous and Transparent Evaluation of Causal Machine Learning 🎯 Explore experiment logs, traces, and workspace in a web UI
Running Reproduction: Divide and Learn: Multi-Objective Combinatorial Optimization at Scale 🎯 Explore and manage experiment logs and traces
Running Reproduction: From Muon to Gluon: Bridging Theory and Practice of LMO-based Optimizers for LLMs 🎯 Explore project logs and traces in a web interface
Running Reproduction: On the Theory of Continual Learning with Gradient Descent for Neural Networks 🎯 Explore experiment logs and collaborate with an AI agent
Running Reproduction: Tackling Fake Forgetting through Uncertainty Quantification 🎯 Explore experiment logs, code, and traces in a web workspace
nmaher/repro-on-the-theory-of-continual-learning-with-gradient-descent-for-neural-networks-traces Traces • Updated 3 days ago • 1 • 47