Author: Olivia Robinson

#1 Explaining Reinforcement Learning Agents through Counterfactual Action Outcomes [PDF9]  Authors: Yotam Amitai ; Yael Septon ; Ofra Amir Explainable reinforcement learning (XRL) methods aim to help elucidate agent policies and decision-making processes. The majority of XRL approaches focus on local explanations, seeking to shed light on the reasons an agent acts the way it does at a specific world state. While such explanations are both useful and necessary, they typically do not portray the outcomes of the agent’s selected choice of action. In this work, we propose “COViz”, a new local explanation method that visually compares the outcome of an agent’s chosen action to a counterfactual…

Authors: Dona Arabi、Jafar Bakhshaliyev、Ayse Coskuner、Kiran Madhusudhanan、Kami Serdar Uckardes Paper: https://arxiv.org/abs/2408.10951 Introduction Time series forecasting (TSF) is a critical task in various domains such as finance, healthcare, and manufacturing. Accurate predictions can significantly impact decision-making processes. However, the scarcity of real-world data often hampers the performance of machine learning models. Data augmentation, which involves generating synthetic data to expand the training dataset, is a common solution. Traditional augmentation methods, however, often fail to maintain the temporal coherence necessary for TSF. This study introduces two novel augmentation techniques, Wavelet Masking (WaveMask) and Wavelet Mixing (WaveMix), leveraging the discrete wavelet transform (DWT) to…

Authors: Padmanaba Srinivasan、William Knottenbelt Paper: https://arxiv.org/abs/2408.10713 Offline Model-Based Reinforcement Learning with Anti-Exploration: A Detailed Interpretive Blog Introduction Reinforcement Learning (RL) has made significant strides in developing policies for sequential decision-making tasks, aiming to maximize expected rewards. Traditional online RL methods alternate between interacting with the environment to collect new data and improving the policy using previously collected data. However, offline RL, where the policy cannot interact with the real environment and can only access a static dataset of trajectories, presents unique challenges. These datasets often have limited coverage and quality, making it difficult to generalize and extrapolate beyond the provided…

Authors: Federico Simonetta、Rishav Mondal、Luca Andrea Ludovico、Stavros Ntalampiras Paper: https://arxiv.org/abs/2408.10260 Introduction The Ricordi Archive, known as Archivio Storico Ricordi, is a treasure trove of historical documents amassed by the Italian publisher Ricordi. This archive is particularly renowned for its digitized manuscripts of distinguished opera composers such as Donizetti, Verdi, and Puccini. The digitization of these manuscripts has opened new avenues for musicological and historical research, making it easier to access and analyze these priceless documents. In this study, the primary aim is to annotate the entire Ricordi Archive database with pertinent musical symbols, thereby improving the accessibility and discoverability of these…

Authors: Subrat Prasad Panda、Blaise Genest、Arvind Easwaran、Ponnuthurai Nagaratnam Suganthan Paper: https://arxiv.org/abs/2408.09135 Vanilla Gradient Descent for Oblique Decision Trees: A Detailed Exploration Introduction Decision Trees (DTs) are a cornerstone of machine learning, particularly valued for their performance on tabular data. Despite their popularity, learning accurate DTs, especially oblique DTs, is a complex and time-consuming task. Traditional DTs often suffer from overfitting and poor generalization in regression tasks. Recent advancements have aimed to make DTs differentiable, enabling the use of gradient descent algorithms for training. This paper introduces DTSemNet, a novel architecture that encodes oblique DTs as Neural Networks (NNs), allowing for efficient…

Authors: Cyril Shih-Huan Hsu、Danny De Vleeschauwer、Chrysa Papagianni Paper: https://arxiv.org/abs/2408.08968 Introduction The advent of the fifth generation (5G) of mobile communication technology has revolutionized the way networks are designed and managed. One of the key innovations in 5G is network slicing, which allows the creation of multiple End-to-End (E2E) logical networks on a shared infrastructure. These slices are tailored to meet specific service requirements, which are defined in Service Level Agreements (SLAs). SLAs are contracts between service providers and tenants, specifying the expected Quality of Service (QoS) through measurable benchmarks known as Service-Level Objectives (SLOs). In a multi-domain network slice, each…

Authors: Zunjie Xiao、Xiaoqing Zhang、Risa Higashita、Jiang Liu Paper: https://arxiv.org/abs/2408.08600 Introduction Ophthalmic image segmentation is a crucial step in the diagnosis of ocular diseases. Traditional image processing algorithms, such as Canny edge detection, have been used for segmentation but often require complex preprocessing. With the advent of deep learning, Convolutional Neural Networks (CNNs) have shown significant progress in ophthalmic segmentation tasks. However, CNNs are limited in capturing long-range dependencies due to their local receptive fields. Transformer architectures, such as ViTs, TransUNet, and UTNet, have been introduced to address these limitations by leveraging self-attention mechanisms. Despite their success, transformers come with substantial computational…

Authors: Zhihao Lin、Zhen Tian、Qi Zhang、Ziyang Ye、Hanyang Zhuang、Jianglin Lan Paper: https://arxiv.org/abs/2408.08242 Introduction Roundabouts have become a significant feature in urban roadways, enhancing vehicle distribution and road capacity. However, the safety and efficiency of autonomous vehicles (AVs) in roundabouts, especially in mixed traffic with human-driven vehicles (HDVs), remain a challenge. This paper introduces a learning-based algorithm designed to ensure safe and efficient driving behaviors in roundabouts. The proposed system employs a deep Q-learning network (DQN) enhanced by a Kolmogorov-Arnold Network (KAN) to robustly learn and execute driving strategies. Additionally, an action inspector and a route planner are integrated to avoid collisions and…

Authors: Jialiang Wang、Shimin Di、Hanmo Liu、Zhili Wang、Jiachuan Wang、Lei Chen、Xiaofang Zhou Paper: https://arxiv.org/abs/2408.06717 Graph Neural Networks (GNNs) have emerged as powerful tools for graph representation learning, but designing optimal GNN architectures remains a complex and resource-intensive task. The paper “Computation-friendly Graph Neural Network Design by Accumulating Knowledge on Large Language Models” proposes a novel framework, DesiGNN, to streamline this process by leveraging Large Language Models (LLMs) to accumulate and apply specialized knowledge in GNN design. This blog post provides a detailed interpretation of the paper, explaining its methodology, contributions, and experimental results. Introduction Graph Neural Networks (GNNs) have proven effective in modeling…

Authors: Yuyang Xue、Junyu Yan、Raman Dutt、Fasih Haider、Jingshuai Liu、Steven McDonagh、Sotirios A. Tsaftaris Paper: https://arxiv.org/abs/2408.06890 Achieving Fairness in Machine Learning with BMFT: A Detailed Exploration In the realm of machine learning, ensuring fairness is crucial, especially in sensitive domains like medical diagnosis. The paper “BMFT: Achieving Fairness via Bias-based Weight Masking Fine-tuning” by Yuyang Xue et al. introduces an innovative post-processing method aimed at enhancing the fairness of pre-trained models without requiring access to the original training data. This blog post delves into the methodology, experiments, and results presented in the paper. Introduction Machine learning models often exhibit biases due to various factors…