Adaptive Importance
Adaptive importance focuses on dynamically adjusting resource allocation or model parameters based on learned importance scores or contextual information, aiming to improve efficiency and performance in various machine learning tasks. Current research emphasizes adaptive sampling techniques, hyperparameter optimization strategies (like Loss Conditional Training), and the development of novel architectures such as Mixture-of-Experts models and adaptive low-rank adaptations to achieve this goal. This field is significant because it addresses critical challenges in scalability, efficiency, and robustness across diverse applications, including federated learning, reinforcement learning, and real-time processing on resource-constrained devices.
Papers - Page 4
RODEO: Robust Outlier Detection via Exposing Adaptive Out-of-Distribution Samples
Hossein Mirzaei, Mohammad Jafari, Hamid Reza Dehbashi, Ali Ansari, Sepehr Ghobadi, Masoud Hadi, Arshia Soltani Moakhar+3Heterogeneity-aware Personalized Federated Learning via Adaptive Dual-Agent Reinforcement Learning
Xi Chen, Qin Li, Haibin Cai, Ting WangAdaSemSeg: An Adaptive Few-shot Semantic Segmentation of Seismic Facies
Surojit Saha, Ross Whitaker
Towards Robust Multimodal Open-set Test-time Adaptation via Adaptive Entropy-aware Optimization
Hao Dong, Eleni Chatzi, Olga FinkMinimizing Queue Length Regret for Arbitrarily Varying Channels
G Krishnakumar, Abhishek SinhaAdaptive Few-Shot Learning (AFSL): Tackling Data Scarcity with Stability, Robustness, and Versatility
Rishabh Agrawal
GATE: Adaptive Learning with Working Memory by Information Gating in Multi-lamellar Hippocampal Formation
Yuechen Liu, Zishun Wang, Chen Qiao, Zongben XuFedGrAINS: Personalized SubGraph Federated Learning with Adaptive Neighbor Sampling
Emir Ceyani, Han Xie, Baturalp Buyukates, Carl Yang, Salman Avestimehr