Author: Evelyn Hill

Authors: Imran Nasim、Joaõ Lucas de Sousa Almeida Paper: https://arxiv.org/abs/2408.10720 Introduction Scientific Machine Learning (SciML) has emerged as a transformative approach in traditional engineering industries, enhancing the efficiency of existing technologies and accelerating innovation. One of the critical areas where SciML has shown significant promise is in modeling chemical reactions. Chemical reactions are fundamental across various industries, providing insights crucial for innovation, product quality, and environmental management. However, modeling these reactions is non-trivial, often requiring the solution of stiff ordinary differential equations (ODEs) within Computational Fluid Dynamics (CFD) simulations. Stiff chemical kinetics are computationally expensive to solve within CFD simulations, prompting…

Authors: Manon Revel、Matteo Cargnelutti、Tyna Eloundou、Greg Leppert Paper: https://arxiv.org/abs/2408.10270 Introduction Background Reinforcement Learning from Human Feedback (RLHF) is a critical technique used to align language models (LMs) with human values. This process involves training reward models (RMs) on human preferences and using these RMs to fine-tune the base LMs. Despite its significance, the internal mechanisms of RLHF are not well understood. This paper introduces new metrics to evaluate the effectiveness of modeling and aligning human values, aiming to provide deeper insights into the RLHF process. Problem Statement The primary challenge addressed in this study is the lack of understanding of how…

Authors: Xin Wang、Xiaoyu Liu、Peng Huang、Pu Huang、Shu Hu、Hongtu Zhu Paper: https://arxiv.org/abs/2408.08881 Introduction Background Medical image segmentation is a critical task in clinical practice, aiding in precise medicine, therapeutic outcome assessment, and disease diagnosis. It involves delineating organ boundaries and pathological regions, which enhances anatomical understanding and abnormality detection. Traditional segmentation models often struggle with the complex patterns and minute variations in medical images, which are crucial for clinical diagnosis. Problem Statement Despite the advancements in foundation models like MedSAM, accurately assessing the uncertainty of their predictions remains a significant challenge. Understanding and managing this uncertainty is essential for improving the robustness…

Authors: Zhili Cheng、Zhitong Wang、Jinyi Hu、Shengding Hu、An Liu、Yuge Tu、Pengkai Li、Lei Shi、Zhiyuan Liu、Maosong Sun Paper: https://arxiv.org/abs/2404.18243 Introduction In recent years, advancements in Large Language Models (LLMs) and Large Multimodal Models (LMMs) have showcased impressive capabilities in understanding and generating human-like text and realistic images. However, their application in embodied AI, where agents interact in physical or simulated environments, remains limited. This gap hinders the performance of complex real-life tasks in physical environments. Existing integrations often lack open-source access, which restricts collective progress in this field. To address these challenges, researchers from Tsinghua University have introduced LEGENT, an open and scalable platform for…

Authors: Kyle Moore、Jesse Roberts、Thao Pham、Douglas Fisher Paper: https://arxiv.org/abs/2408.08651 Introduction In the realm of cognitive reasoning, Kahneman’s “Thinking Fast and Slow” introduces two distinct modes of thinking: System 1 (heuristic) and System 2 (deliberative). This dichotomy is useful for understanding the relationship between behavior and computation, especially in the context of Large Language Models (LLMs). LLMs can exhibit both System 1 and System 2 behaviors, with Counterfactual (CF) prompting typically used to achieve the former. This study investigates the impact of biases on answer choice preferences in the Massive Multi-Task Language Understanding (MMLU) task and introduces novel methods to mitigate these…

Authors: Po-Yu Liang、Xueting Huang、Tibo Duran、Andrew J. Wiemer、Jun Bai Paper: https://arxiv.org/abs/2408.08341 Introduction Peptides play a crucial role in various biological processes and have significant applications in drug discovery and biotechnology. Traditional methods for peptide discovery, such as sequence-based and structure-based approaches, often require extensive datasets, which can limit their effectiveness. This study proposes a novel method that utilizes autoencoder-shaped models to explore the protein embedding space and generate novel peptide analogs using protein language models. This method requires only a single sequence of interest, avoiding the need for large datasets. Related Research Lab Experiment Based Method Traditional methods for identifying and…

Authors: Weimin Yin、Bin Chen adn Chunzhao Xie、Zhenhao Tan Paper: https://arxiv.org/abs/2408.08084 Introduction In the realm of artificial intelligence, the challenge of catastrophic forgetting—where a model forgets previously learned information upon learning new tasks—has been a significant hurdle. This paper introduces a novel method, Weight Balancing Replay (WBR), to address this issue in class-incremental learning using pre-trained models. The proposed method aims to maintain a balance between the weights of new and old tasks, thereby mitigating the effects of catastrophic forgetting. Related Work The paper categorizes existing approaches to tackle catastrophic forgetting into four main types: Parameter Regularization-Based Methods: These methods protect…

Authors: Yili Li、Jing Yu、Keke Gai、Gang Xiong Paper: https://arxiv.org/abs/2408.07989 Introduction Knowledge-based visual question answering (VQA) is a challenging task that requires integrating visual content with external knowledge to answer questions accurately. Traditional methods often focus on modeling inter-modal and intra-modal correlations, which can lead to complex multimodal clues being entangled in implicit embeddings, thus lacking interpretability and generalization ability. This paper introduces Independent Inference Units (IIU) for fine-grained multimodal reasoning, aiming to decompose intra-modal information through functionally independent units. By reusing each processing unit, the model’s generalization ability to handle diverse data is enhanced. Related Work Visual Question Answering The VQA…

Authors: Miao Zhang、Sherif Abdulatif、Benedikt Loesch、Marco Altmann、Marius Schwarz、Bin Yang Paper: https://arxiv.org/abs/2408.06772 Introduction The rapid advancement of deep learning technology has significantly enhanced autonomous driving systems, particularly in 3D perception using multimodal sensors. While cameras and lidar sensors are commonly used, they face challenges under adverse weather and varying illumination conditions. Radar sensors, however, offer greater robustness and cost-effectiveness in such scenarios. This study investigates the domain shift in 4D radar-based object detection, focusing on how different environmental conditions, such as weather patterns and road types, impact 3D object detection performance. The findings underscore the importance of diverse data collection to address…