end-to-end training
End-to-end training is a methodology where an entire model, comprising multiple components, is trained simultaneously, optimizing all parameters jointly to enhance learning efficiency and model performance.
- CTSketch: Compositional Tensor Sketching for Scalable Neurosymbolic Learning
- Contextual Tokenization for Graph Inverted Indices
- Enforcing convex constraints in Graph Neural Networks
- ExGra-Med: Extended Context Graph Alignment for Medical Vision-Language Models
- Exploiting Task Relationships in Continual Learning via Transferability-Aware Task Embeddings
- FSNet: Feasibility-Seeking Neural Network for Constrained Optimization with Guarantees
- From Cradle to Cane: A Two-Pass Framework for High-Fidelity Lifespan Face Aging
- Generalizable Reasoning through Compositional Energy Minimization
- Hierarchical Shortest-Path Graph Kernel Network
- Learning to Route: Per-Sample Adaptive Routing for Multimodal Multitask Prediction
- Quartet: Native FP4 Training Can Be Optimal for Large Language Models
- Sparse Meets Dense: Unified Generative Recommendations with Cascaded Sparse-Dense Representations