comprehensive experiments
Comprehensive experiments in AI research involve thorough testing and validation of models across multiple scenarios, datasets, and evaluation metrics to ensure robustness and generalizability.
- Accelerating Optimization via Differentiable Stopping Time
- An Effective Levelling Paradigm for Unlabeled Scenarios
- Beyond Token Probes: Hallucination Detection via Activation Tensors with ACT-ViT
- Flow Field Reconstruction with Sensor Placement Policy Learning
- Gated Attention for Large Language Models: Non-linearity, Sparsity, and Attention-Sink-Free
- Hybrid Boundary Physics-Informed Neural Networks for Solving Navier-Stokes Equations with Complex Boundary
- Let Me Think! A Long Chain of Thought Can Be Worth Exponentially Many Short Ones
- MARS-VFL: A Unified Benchmark for Vertical Federated Learning with Realistic Evaluation
- Panacea: Mitigating Harmful Fine-tuning for Large Language Models via Post-fine-tuning Perturbation
- RayFusion: Ray Fusion Enhanced Collaborative Visual Perception
- Risk Bounds For Distributional Regression
- STaRFormer: Semi-Supervised Task-Informed Representation Learning via Dynamic Attention-Based Regional Masking for Sequential Data
- Towards Unsupervised Open-Set Graph Domain Adaptation via Dual Reprogramming