The authors introduce a novel approach to hyper-parameter optimization in machine learning by focusing on the strong convexity of the loss and its flatness. They aim to find hyper-parameter configurations…
Continue readingThe paper discusses the challenges in evaluating large language models, including data contamination, prompt sensitivity, and benchmark creation cost. To address these, the authors propose a lossless data compression-based evaluation…
Continue readingThis research focuses on overcoming the language barriers in recommendation systems by using Transformer Leveraged Document Representations (TLDRs) to represent documents across languages. The team evaluated four multilingual pre-trained transformer…
Continue readingThe article presents a new framework for single-source domain generalization in lane detection. This is important for autonomous vehicles to navigate and localize their position on the road. The framework…
Continue readingThe article presents a novel adversarial defense method leveraging sample distribution shifts and pretrained diffusion models. The proposed method exploits the discrepancy between normal and adversarial sample distributions to counter…
Continue readingThis paper investigates the potential for enhancing cross-lingual generalization in large language models (LLMs), particularly for low-resource languages. It explores the possibility of explicitly aligning conceptual correspondence between languages. The…
Continue readingLooks like you're using an ad blocker. Please support us by disabling these ad blocker.
