Energy
Riemannian Projection-free Online Learning
In Euclidean space, OCO boasts a robust theoretical foundation and numerous real-world applications, such as online load balancing (Molinaro, 2017), optimal control (Li et al., 2019), revenue maximization (Lin et al., 2019), and portfolio management (Jรฉzรฉquel et al., 2022).
Inference-Time Intervention: Eliciting Truthful Answers from a Language Model Kenneth Li
This intervention significantly improves the performance of LLaMA models on the TruthfulQA benchmark.
Idiographic Personality Gaussian Process for Psychological Assessment
However, existing frameworks face several interrelated limitations.
NeuralFuse: Learning to Recover the Accuracy of Access-Limited Neural Network Inference in Low-Voltage Regimes Hao-Lun Sun
Energy-efficient computing is of primary importance to the effective deployment of deep neural networks (DNNs), particularly in edge devices and in on-chip AI systems. Increasing DNN computation's energy efficiency and lowering its carbon footprint require iterative efforts from both chip designers and algorithm developers.
