categories
string | doi
string | id
string | year
float64 | venue
string | link
string | updated
string | published
string | title
string | abstract
string | authors
list |
|---|---|---|---|---|---|---|---|---|---|---|
null | null | 2404.11171 | null | null | http://arxiv.org/pdf/2404.11171v2 | 2024-05-11T18:15:15Z | 2024-04-17T08:40:54Z | Personalized Heart Disease Detection via ECG Digital Twin Generation | Heart diseases rank among the leading causes of global mortality, demonstrating a crucial need for early diagnosis and intervention. Most traditional electrocardiogram (ECG) based automated diagnosis methods are trained at population level, neglecting the customization of personalized ECGs to enhance individual healthcare management. A potential solution to address this limitation is to employ digital twins to simulate symptoms of diseases in real patients. In this paper, we present an innovative prospective learning approach for personalized heart disease detection, which generates digital twins of healthy individuals' anomalous ECGs and enhances the model sensitivity to the personalized symptoms. In our approach, a vector quantized feature separator is proposed to locate and isolate the disease symptom and normal segments in ECG signals with ECG report guidance. Thus, the ECG digital twins can simulate specific heart diseases used to train a personalized heart disease detection model. Experiments demonstrate that our approach not only excels in generating high-fidelity ECG signals but also improves personalized heart disease detection. Moreover, our approach ensures robust privacy protection, safeguarding patient data in model development. | [
"['Yaojun Hu' 'Jintai Chen' 'Lianting Hu' 'Dantong Li' 'Jiahuan Yan'\n 'Haochao Ying' 'Huiying Liang' 'Jian Wu']"
]
|
null | null | 2404.11172 | null | null | http://arxiv.org/pdf/2404.11172v2 | 2024-04-18T11:17:43Z | 2024-04-17T08:42:42Z | Deep Neural Networks via Complex Network Theory: a Perspective | Deep Neural Networks (DNNs) can be represented as graphs whose links and vertices iteratively process data and solve tasks sub-optimally. Complex Network Theory (CNT), merging statistical physics with graph theory, provides a method for interpreting neural networks by analysing their weights and neuron structures. However, classic works adapt CNT metrics that only permit a topological analysis as they do not account for the effect of the input data. In addition, CNT metrics have been applied to a limited range of architectures, mainly including Fully Connected neural networks. In this work, we extend the existing CNT metrics with measures that sample from the DNNs' training distribution, shifting from a purely topological analysis to one that connects with the interpretability of deep learning. For the novel metrics, in addition to the existing ones, we provide a mathematical formalisation for Fully Connected, AutoEncoder, Convolutional and Recurrent neural networks, of which we vary the activation functions and the number of hidden layers. We show that these metrics differentiate DNNs based on the architecture, the number of hidden layers, and the activation function. Our contribution provides a method rooted in physics for interpreting DNNs that offers insights beyond the traditional input-output relationship and the CNT topological analysis. | [
"['Emanuele La Malfa' 'Gabriele La Malfa' 'Giuseppe Nicosia' 'Vito Latora']"
]
|
null | null | 2404.11181 | null | null | http://arxiv.org/pdf/2404.11181v2 | 2024-04-19T14:28:00Z | 2024-04-17T08:53:59Z | KI-GAN: Knowledge-Informed Generative Adversarial Networks for Enhanced
Multi-Vehicle Trajectory Forecasting at Signalized Intersections | Reliable prediction of vehicle trajectories at signalized intersections is crucial to urban traffic management and autonomous driving systems. However, it presents unique challenges, due to the complex roadway layout at intersections, involvement of traffic signal controls, and interactions among different types of road users. To address these issues, we present in this paper a novel model called Knowledge-Informed Generative Adversarial Network (KI-GAN), which integrates both traffic signal information and multi-vehicle interactions to predict vehicle trajectories accurately. Additionally, we propose a specialized attention pooling method that accounts for vehicle orientation and proximity at intersections. Based on the SinD dataset, our KI-GAN model is able to achieve an Average Displacement Error (ADE) of 0.05 and a Final Displacement Error (FDE) of 0.12 for a 6-second observation and 6-second prediction cycle. When the prediction window is extended to 9 seconds, the ADE and FDE values are further reduced to 0.11 and 0.26, respectively. These results demonstrate the effectiveness of the proposed KI-GAN model in vehicle trajectory prediction under complex scenarios at signalized intersections, which represents a significant advancement in the target field. | [
"['Chuheng Wei' 'Guoyuan Wu' 'Matthew J. Barth' 'Amr Abdelraouf'\n 'Rohit Gupta' 'Kyungtae Han']"
]
|
null | null | 2404.11207 | null | null | http://arxiv.org/pdf/2404.11207v1 | 2024-04-17T09:39:07Z | 2024-04-17T09:39:07Z | Exploring the Transferability of Visual Prompting for Multimodal Large
Language Models | Although Multimodal Large Language Models (MLLMs) have demonstrated promising versatile capabilities, their performance is still inferior to specialized models on downstream tasks, which makes adaptation necessary to enhance their utility. However, fine-tuning methods require independent training for every model, leading to huge computation and memory overheads. In this paper, we propose a novel setting where we aim to improve the performance of diverse MLLMs with a group of shared parameters optimized for a downstream task. To achieve this, we propose Transferable Visual Prompting (TVP), a simple and effective approach to generate visual prompts that can transfer to different models and improve their performance on downstream tasks after trained on only one model. We introduce two strategies to address the issue of cross-model feature corruption of existing visual prompting methods and enhance the transferability of the learned prompts, including 1) Feature Consistency Alignment: which imposes constraints to the prompted feature changes to maintain task-agnostic knowledge; 2) Task Semantics Enrichment: which encourages the prompted images to contain richer task-specific semantics with language guidance. We validate the effectiveness of TVP through extensive experiments with 6 modern MLLMs on a wide variety of tasks ranging from object recognition and counting to multimodal reasoning and hallucination correction. | [
"['Yichi Zhang' 'Yinpeng Dong' 'Siyuan Zhang' 'Tianzan Min' 'Hang Su'\n 'Jun Zhu']"
]
|
null | null | 2404.11216 | null | null | http://arxiv.org/pdf/2404.11216v1 | 2024-04-17T10:00:56Z | 2024-04-17T10:00:56Z | Position Engineering: Boosting Large Language Models through Positional
Information Manipulation | The performance of large language models (LLMs) is significantly influenced by the quality of the prompts provided. In response, researchers have developed enormous prompt engineering strategies aimed at modifying the prompt text to enhance task performance. In this paper, we introduce a novel technique termed position engineering, which offers a more efficient way to guide large language models. Unlike prompt engineering, which requires substantial effort to modify the text provided to LLMs, position engineering merely involves altering the positional information in the prompt without modifying the text itself. We have evaluated position engineering in two widely-used LLM scenarios: retrieval-augmented generation (RAG) and in-context learning (ICL). Our findings show that position engineering substantially improves upon the baseline in both cases. Position engineering thus represents a promising new strategy for exploiting the capabilities of large language models. | [
"['Zhiyuan He' 'Huiqiang Jiang' 'Zilong Wang' 'Yuqing Yang' 'Luna Qiu'\n 'Lili Qiu']"
]
|
null | null | 2404.11224 | null | null | http://arxiv.org/pdf/2404.11224v2 | 2024-05-08T15:50:31Z | 2024-04-17T10:16:20Z | Analytical results for uncertainty propagation through trained machine
learning regression models | Machine learning (ML) models are increasingly being used in metrology applications. However, for ML models to be credible in a metrology context they should be accompanied by principled uncertainty quantification. This paper addresses the challenge of uncertainty propagation through trained/fixed machine learning (ML) regression models. Analytical expressions for the mean and variance of the model output are obtained/presented for certain input data distributions and for a variety of ML models. Our results cover several popular ML models including linear regression, penalised linear regression, kernel ridge regression, Gaussian Processes (GPs), support vector machines (SVMs) and relevance vector machines (RVMs). We present numerical experiments in which we validate our methods and compare them with a Monte Carlo approach from a computational efficiency point of view. We also illustrate our methods in the context of a metrology application, namely modelling the state-of-health of lithium-ion cells based upon Electrical Impedance Spectroscopy (EIS) data | [
"['Andrew Thompson']"
]
|
null | null | 2404.11269 | null | null | http://arxiv.org/pdf/2404.11269v2 | 2024-07-11T12:04:13Z | 2024-04-17T11:20:14Z | DACAD: Domain Adaptation Contrastive Learning for Anomaly Detection in
Multivariate Time Series | In time series anomaly detection (TSAD), the scarcity of labeled data poses a challenge to the development of accurate models. Unsupervised domain adaptation (UDA) offers a solution by leveraging labeled data from a related domain to detect anomalies in an unlabeled target domain. However, existing UDA methods assume consistent anomalous classes across domains. To address this limitation, we propose a novel Domain Adaptation Contrastive learning model for Anomaly Detection in multivariate time series (DACAD), combining UDA with contrastive learning. DACAD utilizes an anomaly injection mechanism that enhances generalization across unseen anomalous classes, improving adaptability and robustness. Additionally, our model employs supervised contrastive loss for the source domain and self-supervised contrastive triplet loss for the target domain, ensuring comprehensive feature representation learning and domain-invariant feature extraction. Finally, an effective Centre-based Entropy Classifier (CEC) accurately learns normal boundaries in the source domain. Extensive evaluations on multiple real-world datasets and a synthetic dataset highlight DACAD's superior performance in transferring knowledge across domains and mitigating the challenge of limited labeled data in TSAD. | [
"['Zahra Zamanzadeh Darban' 'Yiyuan Yang' 'Geoffrey I. Webb'\n 'Charu C. Aggarwal' 'Qingsong Wen' 'Mahsa Salehi']"
]
|
null | null | 2404.11277 | null | null | http://arxiv.org/pdf/2404.11277v1 | 2024-04-17T11:34:14Z | 2024-04-17T11:34:14Z | Quantum-inspired Techniques in Tensor Networks for Industrial Contexts | In this paper we present a study of the applicability and feasibility of quantum-inspired algorithms and techniques in tensor networks for industrial environments and contexts, with a compilation of the available literature and an analysis of the use cases that may be affected by such methods. In addition, we explore the limitations of such techniques in order to determine their potential scalability. | [
"['Alejandro Mata Ali' 'Iñigo Perez Delgado' 'Aitor Moreno Fdez. de Leceta']"
]
|
null | null | 2404.11299 | null | null | http://arxiv.org/pdf/2404.11299v1 | 2024-04-17T12:12:48Z | 2024-04-17T12:12:48Z | Learning from Unlabelled Data with Transformers: Domain Adaptation for
Semantic Segmentation of High Resolution Aerial Images | Data from satellites or aerial vehicles are most of the times unlabelled. Annotating such data accurately is difficult, requires expertise, and is costly in terms of time. Even if Earth Observation (EO) data were correctly labelled, labels might change over time. Learning from unlabelled data within a semi-supervised learning framework for segmentation of aerial images is challenging. In this paper, we develop a new model for semantic segmentation of unlabelled images, the Non-annotated Earth Observation Semantic Segmentation (NEOS) model. NEOS performs domain adaptation as the target domain does not have ground truth semantic segmentation masks. The distribution inconsistencies between the target and source domains are due to differences in acquisition scenes, environment conditions, sensors, and times. Our model aligns the learned representations of the different domains to make them coincide. The evaluation results show that NEOS is successful and outperforms other models for semantic segmentation of unlabelled data. | [
"['Nikolaos Dionelis' 'Francesco Pro' 'Luca Maiano' 'Irene Amerini'\n 'Bertrand Le Saux']"
]
|
null | null | 2404.11302 | null | null | http://arxiv.org/pdf/2404.11302v2 | 2024-05-23T11:30:05Z | 2024-04-17T12:13:18Z | A Semantic Segmentation-guided Approach for Ground-to-Aerial Image
Matching | Nowadays the accurate geo-localization of ground-view images has an important role across domains as diverse as journalism, forensics analysis, transports, and Earth Observation. This work addresses the problem of matching a query ground-view image with the corresponding satellite image without GPS data. This is done by comparing the features from a ground-view image and a satellite one, innovatively leveraging the corresponding latter's segmentation mask through a three-stream Siamese-like network. The proposed method, Semantic Align Net (SAN), focuses on limited Field-of-View (FoV) and ground panorama images (images with a FoV of 360{deg}). The novelty lies in the fusion of satellite images in combination with their semantic segmentation masks, aimed at ensuring that the model can extract useful features and focus on the significant parts of the images. This work shows how SAN through semantic analysis of images improves the performance on the unlabelled CVUSA dataset for all the tested FoVs. | [
"['Francesco Pro' 'Nikolaos Dionelis' 'Luca Maiano' 'Bertrand Le Saux'\n 'Irene Amerini']"
]
|
null | null | 2404.11311 | null | null | http://arxiv.org/abs/2404.11311v1 | 2024-04-17T12:22:54Z | 2024-04-17T12:22:54Z | Use of Parallel Explanatory Models to Enhance Transparency of Neural
Network Configurations for Cell Degradation Detection | In a previous paper, we have shown that a recurrent neural network (RNN) can be used to detect cellular network radio signal degradations accurately. We unexpectedly found, though, that accuracy gains diminished as we added layers to the RNN. To investigate this, in this paper, we build a parallel model to illuminate and understand the internal operation of neural networks, such as the RNN, which store their internal state in order to process sequential inputs. This model is widely applicable in that it can be used with any input domain where the inputs can be represented by a Gaussian mixture. By looking at the RNN processing from a probability density function perspective, we are able to show how each layer of the RNN transforms the input distributions to increase detection accuracy. At the same time we also discover a side effect acting to limit the improvement in accuracy. To demonstrate the fidelity of the model we validate it against each stage of RNN processing as well as the output predictions. As a result, we have been able to explain the reasons for the RNN performance limits with useful insights for future designs for RNNs and similar types of neural network. | [
"['David Mulvey' 'Chuan Heng Foh' 'Muhammad Ali Imran' 'Rahim Tafazolli']"
]
|
null | null | 2404.11330 | null | null | http://arxiv.org/pdf/2404.11330v1 | 2024-04-17T12:45:59Z | 2024-04-17T12:45:59Z | Toward Understanding the Disagreement Problem in Neural Network Feature
Attribution | In recent years, neural networks have demonstrated their remarkable ability to discern intricate patterns and relationships from raw data. However, understanding the inner workings of these black box models remains challenging, yet crucial for high-stake decisions. Among the prominent approaches for explaining these black boxes are feature attribution methods, which assign relevance or contribution scores to each input variable for a model prediction. Despite the plethora of proposed techniques, ranging from gradient-based to backpropagation-based methods, a significant debate persists about which method to use. Various evaluation metrics have been proposed to assess the trustworthiness or robustness of their results. However, current research highlights disagreement among state-of-the-art methods in their explanations. Our work addresses this confusion by investigating the explanations' fundamental and distributional behavior. Additionally, through a comprehensive simulation study, we illustrate the impact of common scaling and encoding techniques on the explanation quality, assess their efficacy across different effect sizes, and demonstrate the origin of inconsistency in rank-based evaluation metrics. | [
"['Niklas Koenen' 'Marvin N. Wright']"
]
|
null | null | 2404.11335 | null | null | http://arxiv.org/pdf/2404.11335v1 | 2024-04-17T12:53:45Z | 2024-04-17T12:53:45Z | SoccerNet Game State Reconstruction: End-to-End Athlete Tracking and
Identification on a Minimap | Tracking and identifying athletes on the pitch holds a central role in collecting essential insights from the game, such as estimating the total distance covered by players or understanding team tactics. This tracking and identification process is crucial for reconstructing the game state, defined by the athletes' positions and identities on a 2D top-view of the pitch, (i.e. a minimap). However, reconstructing the game state from videos captured by a single camera is challenging. It requires understanding the position of the athletes and the viewpoint of the camera to localize and identify players within the field. In this work, we formalize the task of Game State Reconstruction and introduce SoccerNet-GSR, a novel Game State Reconstruction dataset focusing on football videos. SoccerNet-GSR is composed of 200 video sequences of 30 seconds, annotated with 9.37 million line points for pitch localization and camera calibration, as well as over 2.36 million athlete positions on the pitch with their respective role, team, and jersey number. Furthermore, we introduce GS-HOTA, a novel metric to evaluate game state reconstruction methods. Finally, we propose and release an end-to-end baseline for game state reconstruction, bootstrapping the research on this task. Our experiments show that GSR is a challenging novel task, which opens the field for future research. Our dataset and codebase are publicly available at https://github.com/SoccerNet/sn-gamestate. | [
"['Vladimir Somers' 'Victor Joos' 'Anthony Cioppa' 'Silvio Giancola'\n 'Seyed Abolfazl Ghasemzadeh' 'Floriane Magera' 'Baptiste Standaert'\n 'Amir Mohammad Mansourian' 'Xin Zhou' 'Shohreh Kasaei' 'Bernard Ghanem'\n 'Alexandre Alahi' 'Marc Van Droogenbroeck' 'Christophe De Vleeschouwer']"
]
|
null | null | 2404.11341 | null | null | http://arxiv.org/pdf/2404.11341v1 | 2024-04-17T13:00:52Z | 2024-04-17T13:00:52Z | The Causal Chambers: Real Physical Systems as a Testbed for AI
Methodology | In some fields of AI, machine learning and statistics, the validation of new methods and algorithms is often hindered by the scarcity of suitable real-world datasets. Researchers must often turn to simulated data, which yields limited information about the applicability of the proposed methods to real problems. As a step forward, we have constructed two devices that allow us to quickly and inexpensively produce large datasets from non-trivial but well-understood physical systems. The devices, which we call causal chambers, are computer-controlled laboratories that allow us to manipulate and measure an array of variables from these physical systems, providing a rich testbed for algorithms from a variety of fields. We illustrate potential applications through a series of case studies in fields such as causal discovery, out-of-distribution generalization, change point detection, independent component analysis, and symbolic regression. For applications to causal inference, the chambers allow us to carefully perform interventions. We also provide and empirically validate a causal model of each chamber, which can be used as ground truth for different tasks. All hardware and software is made open source, and the datasets are publicly available at causalchamber.org or through the Python package causalchamber. | [
"['Juan L. Gamella' 'Jonas Peters' 'Peter Bühlmann']"
]
|
null | null | 2404.11350 | null | null | http://arxiv.org/pdf/2404.11350v1 | 2024-04-17T13:08:26Z | 2024-04-17T13:08:26Z | Calibrating Bayesian Learning via Regularization, Confidence
Minimization, and Selective Inference | The application of artificial intelligence (AI) models in fields such as engineering is limited by the known difficulty of quantifying the reliability of an AI's decision. A well-calibrated AI model must correctly report its accuracy on in-distribution (ID) inputs, while also enabling the detection of out-of-distribution (OOD) inputs. A conventional approach to improve calibration is the application of Bayesian ensembling. However, owing to computational limitations and model misspecification, practical ensembling strategies do not necessarily enhance calibration. This paper proposes an extension of variational inference (VI)-based Bayesian learning that integrates calibration regularization for improved ID performance, confidence minimization for OOD detection, and selective calibration to ensure a synergistic use of calibration regularization and confidence minimization. The scheme is constructed successively by first introducing calibration-regularized Bayesian learning (CBNN), then incorporating out-of-distribution confidence minimization (OCM) to yield CBNN-OCM, and finally integrating also selective calibration to produce selective CBNN-OCM (SCBNN-OCM). Selective calibration rejects inputs for which the calibration performance is expected to be insufficient. Numerical results illustrate the trade-offs between ID accuracy, ID calibration, and OOD calibration attained by both frequentist and Bayesian learning methods. Among the main conclusions, SCBNN-OCM is seen to achieve best ID and OOD performance as compared to existing state-of-the-art approaches at the cost of rejecting a sufficiently large number of inputs. | [
"['Jiayi Huang' 'Sangwoo Park' 'Osvaldo Simeone']"
]
|
null | null | 2404.11354 | null | null | http://arxiv.org/pdf/2404.11354v1 | 2024-04-17T13:09:33Z | 2024-04-17T13:09:33Z | Distributed Fractional Bayesian Learning for Adaptive Optimization | This paper considers a distributed adaptive optimization problem, where all agents only have access to their local cost functions with a common unknown parameter, whereas they mean to collaboratively estimate the true parameter and find the optimal solution over a connected network. A general mathematical framework for such a problem has not been studied yet. We aim to provide valuable insights for addressing parameter uncertainty in distributed optimization problems and simultaneously find the optimal solution. Thus, we propose a novel Prediction while Optimization scheme, which utilizes distributed fractional Bayesian learning through weighted averaging on the log-beliefs to update the beliefs of unknown parameters, and distributed gradient descent for renewing the estimation of the optimal solution. Then under suitable assumptions, we prove that all agents' beliefs and decision variables converge almost surely to the true parameter and the optimal solution under the true parameter, respectively. We further establish a sublinear convergence rate for the belief sequence. Finally, numerical experiments are implemented to corroborate the theoretical analysis. | [
"['Yaqun Yang' 'Jinlong Lei' 'Guanghui Wen' 'Yiguang Hong']"
]
|
null | null | 2404.11374 | null | null | http://arxiv.org/pdf/2404.11374v1 | 2024-04-17T13:32:05Z | 2024-04-17T13:32:05Z | Tensor Factorisation for Polypharmacy Side Effect Prediction | Adverse reactions caused by drug combinations are an increasingly common phenomenon, making their accurate prediction an important challenge in modern medicine. However, the polynomial nature of this problem renders lab-based identification of adverse reactions insufficient. Dozens of computational approaches have therefore been proposed for the task in recent years, with varying degrees of success. One group of methods that has seemingly been under-utilised in this area is tensor factorisation, despite their clear applicability to this type of data. In this work, we apply three such models to a benchmark dataset in order to compare them against established techniques. We find, in contrast to previous reports, that for this task tensor factorisation models are competitive with state-of-the-art graph neural network models and we recommend that future work in this field considers cheaper methods with linear complexity before running costly deep learning processes. | [
"['Oliver Lloyd' 'Yi Liu' 'Tom R. Gaunt']"
]
|
null | null | 2404.11384 | null | null | http://arxiv.org/pdf/2404.11384v1 | 2024-04-17T13:44:29Z | 2024-04-17T13:44:29Z | Exploring Key Point Analysis with Pairwise Generation and Graph
Partitioning | Key Point Analysis (KPA), the summarization of multiple arguments into a concise collection of key points, continues to be a significant and unresolved issue within the field of argument mining. Existing models adapt a two-stage pipeline of clustering arguments or generating key points for argument clusters. This approach rely on semantic similarity instead of measuring the existence of shared key points among arguments. Additionally, it only models the intra-cluster relationship among arguments, disregarding the inter-cluster relationship between arguments that do not share key points. To address these limitations, we propose a novel approach for KPA with pairwise generation and graph partitioning. Our objective is to train a generative model that can simultaneously provide a score indicating the presence of shared key point between a pair of arguments and generate the shared key point. Subsequently, to map generated redundant key points to a concise set of key points, we proceed to construct an arguments graph by considering the arguments as vertices, the generated key points as edges, and the scores as edge weights. We then propose a graph partitioning algorithm to partition all arguments sharing the same key points to the same subgraph. Notably, our experimental findings demonstrate that our proposed model surpasses previous models when evaluated on both the ArgKP and QAM datasets. | [
"['Xiao Li' 'Yong Jiang' 'Shen Huang' 'Pengjun Xie' 'Gong Cheng'\n 'Fei Huang']"
]
|
null | null | 2404.11422 | null | null | http://arxiv.org/pdf/2404.11422v2 | 2024-04-22T11:21:31Z | 2024-04-17T14:27:45Z | Short-term wind speed forecasting model based on an attention-gated
recurrent neural network and error correction strategy | The accurate wind speed series forecast is very pivotal to security of grid dispatching and the application of wind power. Nevertheless, on account of their nonlinear and non-stationary nature, their short-term forecast is extremely challenging. Therefore, this dissertation raises one short-term wind speed forecast pattern on the foundation of attention with an improved gated recurrent neural network (AtGRU) and a tactic of error correction. That model uses the AtGRU model as the preliminary predictor and the GRU model as the error corrector. At the beginning, SSA (singular spectrum analysis) is employed in previous wind speed series for lessening the noise. Subsequently, historical wind speed series is going to be used for the predictor training. During this process, the prediction can have certain errors. The sequence of these errors processed by variational modal decomposition (VMD) is used to train the corrector of error. The eventual forecast consequence is just the sum of predictor forecast and error corrector. The proposed SSA-AtGRU-VMD-GRU model outperforms the compared models in three case studies on Woodburn, St. Thomas, and Santa Cruz. It is indicated that the model evidently enhances the correction of the wind speed forecast. | [
"['Haojian Huang']"
]
|
null | null | 2404.11428 | null | null | http://arxiv.org/pdf/2404.11428v1 | 2024-04-17T14:34:35Z | 2024-04-17T14:34:35Z | Explainable Lung Disease Classification from Chest X-Ray Images
Utilizing Deep Learning and XAI | Lung diseases remain a critical global health concern, and it's crucial to have accurate and quick ways to diagnose them. This work focuses on classifying different lung diseases into five groups: viral pneumonia, bacterial pneumonia, COVID, tuberculosis, and normal lungs. Employing advanced deep learning techniques, we explore a diverse range of models including CNN, hybrid models, ensembles, transformers, and Big Transfer. The research encompasses comprehensive methodologies such as hyperparameter tuning, stratified k-fold cross-validation, and transfer learning with fine-tuning.Remarkably, our findings reveal that the Xception model, fine-tuned through 5-fold cross-validation, achieves the highest accuracy of 96.21%. This success shows that our methods work well in accurately identifying different lung diseases. The exploration of explainable artificial intelligence (XAI) methodologies further enhances our understanding of the decision-making processes employed by these models, contributing to increased trust in their clinical applications. | [
"['Tanzina Taher Ifty' 'Saleh Ahmed Shafin' 'Shoeb Mohammad Shahriar'\n 'Tashfia Towhid']"
]
|
null | null | 2404.11449 | null | null | http://arxiv.org/pdf/2404.11449v1 | 2024-04-17T14:55:27Z | 2024-04-17T14:55:27Z | AI-Enhanced Cognitive Behavioral Therapy: Deep Learning and Large
Language Models for Extracting Cognitive Pathways from Social Media Texts | Cognitive Behavioral Therapy (CBT) is an effective technique for addressing the irrational thoughts stemming from mental illnesses, but it necessitates precise identification of cognitive pathways to be successfully implemented in patient care. In current society, individuals frequently express negative emotions on social media on specific topics, often exhibiting cognitive distortions, including suicidal behaviors in extreme cases. Yet, there is a notable absence of methodologies for analyzing cognitive pathways that could aid psychotherapists in conducting effective interventions online. In this study, we gathered data from social media and established the task of extracting cognitive pathways, annotating the data based on a cognitive theoretical framework. We initially categorized the task of extracting cognitive pathways as a hierarchical text classification with four main categories and nineteen subcategories. Following this, we structured a text summarization task to help psychotherapists quickly grasp the essential information. Our experiments evaluate the performance of deep learning and large language models (LLMs) on these tasks. The results demonstrate that our deep learning method achieved a micro-F1 score of 62.34% in the hierarchical text classification task. Meanwhile, in the text summarization task, GPT-4 attained a Rouge-1 score of 54.92 and a Rouge-2 score of 30.86, surpassing the experimental deep learning model's performance. However, it may suffer from an issue of hallucination. We have made all models and codes publicly available to support further research in this field. | [
"['Meng Jiang' 'Yi Jing Yu' 'Qing Zhao' 'Jianqiang Li' 'Changwei Song'\n 'Hongzhi Qi' 'Wei Zhai' 'Dan Luo' 'Xiaoqin Wang' 'Guanghui Fu'\n 'Bing Xiang Yang']"
]
|
null | null | 2404.11461 | null | null | http://arxiv.org/pdf/2404.11461v2 | 2024-06-23T10:38:22Z | 2024-04-17T15:09:31Z | Using Game Engines and Machine Learning to Create Synthetic Satellite
Imagery for a Tabletop Verification Exercise | Satellite imagery is regarded as a great opportunity for citizen-based monitoring of activities of interest. Relevant imagery may however not be available at sufficiently high resolution, quality, or cadence -- let alone be uniformly accessible to open-source analysts. This limits an assessment of the true long-term potential of citizen-based monitoring of nuclear activities using publicly available satellite imagery. In this article, we demonstrate how modern game engines combined with advanced machine-learning techniques can be used to generate synthetic imagery of sites of interest with the ability to choose relevant parameters upon request; these include time of day, cloud cover, season, or level of activity onsite. At the same time, resolution and off-nadir angle can be adjusted to simulate different characteristics of the satellite. While there are several possible use-cases for synthetic imagery, here we focus on its usefulness to support tabletop exercises in which simple monitoring scenarios can be examined to better understand verification capabilities enabled by new satellite constellations and very short revisit times. | [
"['Johannes Hoster' 'Sara Al-Sayed' 'Felix Biessmann' 'Alexander Glaser'\n 'Kristian Hildebrand' 'Igor Moric' 'Tuong Vy Nguyen']"
]
|
null | null | 2404.11470 | null | null | http://arxiv.org/pdf/2404.11470v1 | 2024-04-17T15:23:12Z | 2024-04-17T15:23:12Z | A Federated Learning Approach to Privacy Preserving Offensive Language
Identification | The spread of various forms of offensive speech online is an important concern in social media. While platforms have been investing heavily in ways of coping with this problem, the question of privacy remains largely unaddressed. Models trained to detect offensive language on social media are trained and/or fine-tuned using large amounts of data often stored in centralized servers. Since most social media data originates from end users, we propose a privacy preserving decentralized architecture for identifying offensive language online by introducing Federated Learning (FL) in the context of offensive language identification. FL is a decentralized architecture that allows multiple models to be trained locally without the need for data sharing hence preserving users' privacy. We propose a model fusion approach to perform FL. We trained multiple deep learning models on four publicly available English benchmark datasets (AHSD, HASOC, HateXplain, OLID) and evaluated their performance in detail. We also present initial cross-lingual experiments in English and Spanish. We show that the proposed model fusion approach outperforms baselines in all the datasets while preserving privacy. | [
"['Marcos Zampieri' 'Damith Premasiri' 'Tharindu Ranasinghe']"
]
|
null | null | 2404.11476 | null | null | http://arxiv.org/pdf/2404.11476v1 | 2024-04-17T15:32:56Z | 2024-04-17T15:32:56Z | Taxonomy to Regulation: A (Geo)Political Taxonomy for AI Risks and
Regulatory Measures in the EU AI Act | Technological innovations have shown remarkable capabilities to benefit and harm society alike. AI constitutes a democratized sophisticated technology accessible to large parts of society, including malicious actors. This work proposes a taxonomy focusing on on (geo)political risks associated with AI. It identifies 12 risks in total divided into four categories: (1) Geopolitical Pressures, (2) Malicious Usage, (3) Environmental, Social, and Ethical Risks, and (4) Privacy and Trust Violations. Incorporating a regulatory side, this paper conducts a policy assessment of the EU AI Act. Adopted in March 2023, the landmark regulation has the potential to have a positive top-down impact concerning AI risk reduction but needs regulatory adjustments to mitigate risks more comprehensively. Regulatory exceptions for open-source models, excessively high parameters for the classification of GPAI models as a systemic risk, and the exclusion of systems designed exclusively for military purposes from the regulation's obligations leave room for future action. | [
"['Sinan Arda']"
]
|
null | null | 2404.11477 | null | null | http://arxiv.org/pdf/2404.11477v3 | 2024-07-03T14:47:09Z | 2024-04-17T15:32:58Z | Discovering Nuclear Models from Symbolic Machine Learning | Numerous phenomenological nuclear models have been proposed to describe specific observables within different regions of the nuclear chart. However, developing a unified model that describes the complex behavior of all nuclei remains an open challenge. Here, we explore whether novel symbolic Machine Learning (ML) can rediscover traditional nuclear physics models or identify alternatives with improved simplicity, fidelity, and predictive power. To address this challenge, we developed a Multi-objective Iterated Symbolic Regression approach that handles symbolic regressions over multiple target observables, accounts for experimental uncertainties and is robust against high-dimensional problems. As a proof of principle, we applied this method to describe the nuclear binding energies and charge radii of light and medium mass nuclei. Our approach identified simple analytical relationships based on the number of protons and neutrons, providing interpretable models with precision comparable to state-of-the-art nuclear models. Additionally, we integrated this ML-discovered model with an existing complementary model to estimate the limits of nuclear stability. These results highlight the potential of symbolic ML to develop accurate nuclear models and guide our description of complex many-body problems. | [
"['Jose M. Munoz' 'Silviu M. Udrescu' 'Ronald F. Garcia Ruiz']"
]
|
null | null | 2404.11483 | null | null | http://arxiv.org/pdf/2404.11483v1 | 2024-04-17T15:40:45Z | 2024-04-17T15:40:45Z | AgentKit: Flow Engineering with Graphs, not Coding | We propose an intuitive LLM prompting framework (AgentKit) for multifunctional agents. AgentKit offers a unified framework for explicitly constructing a complex "thought process" from simple natural language prompts. The basic building block in AgentKit is a node, containing a natural language prompt for a specific subtask. The user then puts together chains of nodes, like stacking LEGO pieces. The chains of nodes can be designed to explicitly enforce a naturally structured "thought process". For example, for the task of writing a paper, one may start with the thought process of 1) identify a core message, 2) identify prior research gaps, etc. The nodes in AgentKit can be designed and combined in different ways to implement multiple advanced capabilities including on-the-fly hierarchical planning, reflection, and learning from interactions. In addition, due to the modular nature and the intuitive design to simulate explicit human thought process, a basic agent could be implemented as simple as a list of prompts for the subtasks and therefore could be designed and tuned by someone without any programming experience. Quantitatively, we show that agents designed through AgentKit achieve SOTA performance on WebShop and Crafter. These advances underscore AgentKit's potential in making LLM agents effective and accessible for a wider range of applications. https://github.com/holmeswww/AgentKit | [
"['Yue Wu' 'Yewen Fan' 'So Yeon Min' 'Shrimai Prabhumoye' 'Stephen McAleer'\n 'Yonatan Bisk' 'Ruslan Salakhutdinov' 'Yuanzhi Li' 'Tom Mitchell']"
]
|
null | null | 2404.11492 | null | null | http://arxiv.org/pdf/2404.11492v1 | 2024-04-17T15:47:26Z | 2024-04-17T15:47:26Z | arcjetCV: an open-source software to analyze material ablation | arcjetCV is an open-source Python software designed to automate time-resolved measurements of heatshield material recession and recession rates from arcjet test video footage. This new automated and accessible capability greatly exceeds previous manual extraction methods, enabling rapid and detailed characterization of material recession for any sample with a profile video. arcjetCV automates the video segmentation process using machine learning models, including a one-dimensional (1D) Convolutional Neural Network (CNN) to infer the time-window of interest, a two-dimensional (2D) CNN for image and edge segmentation, and a Local Outlier Factor (LOF) for outlier filtering. A graphical user interface (GUI) simplifies the user experience and an application programming interface (API) allows users to call the core functions from scripts, enabling video batch processing. arcjetCV's capability to measure time-resolved recession in turn enables characterization of non-linear processes (shrinkage, swelling, melt flows, etc.), contributing to higher fidelity validation and improved modeling of heatshield material performance. The source code associated with this article can be found at https://github.com/magnus-haw/arcjetCV. | [
"['Alexandre Quintart' 'Magnus Haw' 'Federico Semeraro']"
]
|
null | null | 2404.11509 | null | null | http://arxiv.org/pdf/2404.11509v2 | 2024-07-07T19:32:17Z | 2024-04-17T16:05:03Z | VC Theory for Inventory Policies | Advances in computational power and AI have increased interest in reinforcement learning approaches to inventory management. This paper provides a theoretical foundation for these approaches and investigates the benefits of restricting to policy structures that are well-established by inventory theory. In particular, we prove generalization guarantees for learning several well-known classes of inventory policies, including base-stock and (s, S) policies, by leveraging the celebrated Vapnik-Chervonenkis (VC) theory. We apply the Pseudo-dimension and Fat-shattering dimension from VC theory to determine the generalization error of inventory policies, that is, the difference between an inventory policy's performance on training data and its expected performance on unseen data. We focus on a classical setting without contexts, but allow for an arbitrary distribution over demand sequences and do not make any assumptions such as independence over time. We corroborate our supervised learning results using numerical simulations. Managerially, our theory and simulations translate to the following insights. First, there is a principle of ``learning less is more'' in inventory management: depending on the amount of data available, it may be beneficial to restrict oneself to a simpler, albeit suboptimal, class of inventory policies to minimize overfitting errors. Second, the number of parameters in a policy class may not be the correct measure of overfitting error: in fact, the class of policies defined by T time-varying base-stock levels exhibits a generalization error an order of magnitude lower than that of the two-parameter (s, S) policy class. Finally, our research suggests situations in which it could be beneficial to incorporate the concepts of base-stock and inventory position into black-box learning machines, instead of having these machines directly learn the order quantity actions. | [
"['Yaqi Xie' 'Will Ma' 'Linwei Xin']"
]
|
null | null | 2404.11526 | null | null | http://arxiv.org/pdf/2404.11526v3 | 2024-04-23T16:08:17Z | 2024-04-17T16:16:50Z | A Comparison of Traditional and Deep Learning Methods for Parameter
Estimation of the Ornstein-Uhlenbeck Process | We consider the Ornstein-Uhlenbeck (OU) process, a stochastic process widely used in finance, physics, and biology. Parameter estimation of the OU process is a challenging problem. Thus, we review traditional tracking methods and compare them with novel applications of deep learning to estimate the parameters of the OU process. We use a multi-layer perceptron to estimate the parameters of the OU process and compare its performance with traditional parameter estimation methods, such as the Kalman filter and maximum likelihood estimation. We find that the multi-layer perceptron can accurately estimate the parameters of the OU process given a large dataset of observed trajectories and, on average, outperforms traditional parameter estimation methods. | [
"['Jacob Fein-Ashley']"
]
|
null | null | 2404.11534 | null | null | http://arxiv.org/pdf/2404.11534v1 | 2024-04-17T16:28:08Z | 2024-04-17T16:28:08Z | Decomposing and Editing Predictions by Modeling Model Computation | How does the internal computation of a machine learning model transform inputs into predictions? In this paper, we introduce a task called component modeling that aims to address this question. The goal of component modeling is to decompose an ML model's prediction in terms of its components -- simple functions (e.g., convolution filters, attention heads) that are the "building blocks" of model computation. We focus on a special case of this task, component attribution, where the goal is to estimate the counterfactual impact of individual components on a given prediction. We then present COAR, a scalable algorithm for estimating component attributions; we demonstrate its effectiveness across models, datasets, and modalities. Finally, we show that component attributions estimated with COAR directly enable model editing across five tasks, namely: fixing model errors, ``forgetting'' specific classes, boosting subpopulation robustness, localizing backdoor attacks, and improving robustness to typographic attacks. We provide code for COAR at https://github.com/MadryLab/modelcomponents . | [
"['Harshay Shah' 'Andrew Ilyas' 'Aleksander Madry']"
]
|
null | null | 2404.11536 | null | null | http://arxiv.org/pdf/2404.11536v2 | 2024-04-28T11:11:16Z | 2024-04-17T16:30:06Z | FedPFT: Federated Proxy Fine-Tuning of Foundation Models | Adapting Foundation Models (FMs) for downstream tasks through Federated Learning (FL) emerges a promising strategy for protecting data privacy and valuable FMs. Existing methods fine-tune FM by allocating sub-FM to clients in FL, however, leading to suboptimal performance due to insufficient tuning and inevitable error accumulations of gradients. In this paper, we propose Federated Proxy Fine-Tuning (FedPFT), a novel method enhancing FMs adaptation in downstream tasks through FL by two key modules. First, the sub-FM construction module employs a layer-wise compression approach, facilitating comprehensive FM fine-tuning across all layers by emphasizing those crucial neurons. Second, the sub-FM alignment module conducts a two-step distillations-layer-level and neuron-level-before and during FL fine-tuning respectively, to reduce error of gradient by accurately aligning sub-FM with FM under theoretical guarantees. Experimental results on seven commonly used datasets (i.e., four text and three vision) demonstrate the superiority of FedPFT. | [
"['Zhaopeng Peng' 'Xiaoliang Fan' 'Yufan Chen' 'Zheng Wang' 'Shirui Pan'\n 'Chenglu Wen' 'Ruisheng Zhang' 'Cheng Wang']"
]
|
null | null | 2404.11538 | null | null | http://arxiv.org/pdf/2404.11538v1 | 2024-04-17T16:32:13Z | 2024-04-17T16:32:13Z | GenFighter: A Generative and Evolutive Textual Attack Removal | Adversarial attacks pose significant challenges to deep neural networks (DNNs) such as Transformer models in natural language processing (NLP). This paper introduces a novel defense strategy, called GenFighter, which enhances adversarial robustness by learning and reasoning on the training classification distribution. GenFighter identifies potentially malicious instances deviating from the distribution, transforms them into semantically equivalent instances aligned with the training data, and employs ensemble techniques for a unified and robust response. By conducting extensive experiments, we show that GenFighter outperforms state-of-the-art defenses in accuracy under attack and attack success rate metrics. Additionally, it requires a high number of queries per attack, making the attack more challenging in real scenarios. The ablation study shows that our approach integrates transfer learning, a generative/evolutive procedure, and an ensemble method, providing an effective defense against NLP adversarial attacks. | [
"['Md Athikul Islam' 'Edoardo Serra' 'Sushil Jajodia']"
]
|
null | null | 2404.11553 | null | null | http://arxiv.org/pdf/2404.11553v2 | 2024-06-16T08:24:32Z | 2024-04-17T16:53:16Z | Quantifying Multilingual Performance of Large Language Models Across
Languages | The development of Large Language Models (LLMs) relies on extensive text corpora, which are often unevenly distributed across languages. This imbalance results in LLMs performing significantly better on high-resource languages like English, German, and French, while their capabilities in low-resource languages remain inadequate. Currently, there is a lack of quantitative methods to evaluate the performance of LLMs in these low-resource languages. To address this gap, we propose the Language Ranker, an intrinsic metric designed to benchmark and rank languages based on LLM performance using internal representations. By comparing the LLM's internal representation of various languages against a baseline derived from English, we can assess the model's multilingual capabilities in a robust and language-agnostic manner. Our analysis reveals that high-resource languages exhibit higher similarity scores with English, demonstrating superior performance, while low-resource languages show lower similarity scores, underscoring the effectiveness of our metric in assessing language-specific capabilities. Besides, the experiments show that there is a strong correlation between the LLM's performance in different languages and the proportion of those languages in its pre-training corpus. These insights underscore the efficacy of the Language Ranker as a tool for evaluating LLM performance across different languages, particularly those with limited resources. | [
"['Zihao Li' 'Yucheng Shi' 'Zirui Liu' 'Fan Yang' 'Ali Payani'\n 'Ninghao Liu' 'Mengnan Du']"
]
|
null | null | 2404.11568 | null | null | http://arxiv.org/pdf/2404.11568v3 | 2024-05-02T02:13:36Z | 2024-04-17T17:11:31Z | On the Scalability of GNNs for Molecular Graphs | Scaling deep learning models has been at the heart of recent revolutions in language modelling and image generation. Practitioners have observed a strong relationship between model size, dataset size, and performance. However, structure-based architectures such as Graph Neural Networks (GNNs) are yet to show the benefits of scale mainly due to the lower efficiency of sparse operations, large data requirements, and lack of clarity about the effectiveness of various architectures. We address this drawback of GNNs by studying their scaling behavior. Specifically, we analyze message-passing networks, graph Transformers, and hybrid architectures on the largest public collection of 2D molecular graphs. For the first time, we observe that GNNs benefit tremendously from the increasing scale of depth, width, number of molecules, number of labels, and the diversity in the pretraining datasets. We further demonstrate strong finetuning scaling behavior on 38 highly competitive downstream tasks, outclassing previous large models. This gives rise to MolGPS, a new graph foundation model that allows to navigate the chemical space, outperforming the previous state-of-the-arts on 26 out the 38 downstream tasks. We hope that our work paves the way for an era where foundational GNNs drive pharmaceutical drug discovery. | [
"['Maciej Sypetkowski' 'Frederik Wenkel' 'Farimah Poursafaei' 'Nia Dickson'\n 'Karush Suri' 'Philip Fradkin' 'Dominique Beaini']"
]
|
null | null | 2404.11569 | null | null | http://arxiv.org/pdf/2404.11569v1 | 2024-04-17T17:11:47Z | 2024-04-17T17:11:47Z | Simple Image Signal Processing using Global Context Guidance | In modern smartphone cameras, the Image Signal Processor (ISP) is the core element that converts the RAW readings from the sensor into perceptually pleasant RGB images for the end users. The ISP is typically proprietary and handcrafted and consists of several blocks such as white balance, color correction, and tone mapping. Deep learning-based ISPs aim to transform RAW images into DSLR-like RGB images using deep neural networks. However, most learned ISPs are trained using patches (small regions) due to computational limitations. Such methods lack global context, which limits their efficacy on full-resolution images and harms their ability to capture global properties such as color constancy or illumination. First, we propose a novel module that can be integrated into any neural ISP to capture the global context information from the full RAW images. Second, we propose an efficient and simple neural ISP that utilizes our proposed module. Our model achieves state-of-the-art results on different benchmarks using diverse and real smartphone images. | [
"['Omar Elezabi' 'Marcos V. Conde' 'Radu Timofte']"
]
|
null | null | 2404.11577 | null | null | http://arxiv.org/pdf/2404.11577v2 | 2024-06-12T08:04:58Z | 2024-04-17T17:20:27Z | Towards Reliable Empirical Machine Unlearning Evaluation: A
Game-Theoretic View | Machine unlearning is the process of updating machine learning models to remove the information of specific training data samples, in order to comply with data protection regulations that allow individuals to request the removal of their personal data. Despite the recent development of numerous unlearning algorithms, reliable evaluation of these algorithms remains an open research question. In this work, we focus on membership inference attack (MIA) based evaluation, one of the most common approaches for evaluating unlearning algorithms, and address various pitfalls of existing evaluation metrics that lack reliability. Specifically, we propose a game-theoretic framework that formalizes the evaluation process as a game between unlearning algorithms and MIA adversaries, measuring the data removal efficacy of unlearning algorithms by the capability of the MIA adversaries. Through careful design of the game, we demonstrate that the natural evaluation metric induced from the game enjoys provable guarantees that the existing evaluation metrics fail to satisfy. Furthermore, we propose a practical and efficient algorithm to estimate the evaluation metric induced from the game, and demonstrate its effectiveness through both theoretical analysis and empirical experiments. This work presents a novel and reliable approach to empirically evaluating unlearning algorithms, paving the way for the development of more effective unlearning techniques. | [
"['Yiwen Tu' 'Pingbang Hu' 'Jiaqi Ma']"
]
|
null | null | 2404.11578 | null | null | http://arxiv.org/pdf/2404.11578v2 | 2024-05-24T22:57:06Z | 2024-04-17T17:24:44Z | LTL-Constrained Policy Optimization with Cycle Experience Replay | Linear Temporal Logic (LTL) offers a precise means for constraining the behavior of reinforcement learning agents. However, in many tasks, LTL is insufficient for task specification; LTL-constrained policy optimization, where the goal is to optimize a scalar reward under LTL constraints, is needed. Prior methods for this constrained problem are restricted to finite state spaces. In this work, we present Cycle Experience Replay (CyclER), a reward-shaping approach to this problem that allows continuous state and action spaces and the use of function approximations. CyclER guides a policy towards satisfaction by encouraging partial behaviors compliant with the LTL constraint, using the structure of the constraint. In doing so, it addresses the optimization challenges stemming from the sparse nature of LTL satisfaction. We evaluate CyclER in three continuous control domains. On these tasks, CyclER outperforms existing reward-shaping methods at finding performant and LTL-satisfying policies. | [
"['Ameesh Shah' 'Cameron Voloshin' 'Chenxi Yang' 'Abhinav Verma'\n 'Swarat Chaudhuri' 'Sanjit A. Seshia']"
]
|
null | null | 2404.11589 | null | null | http://arxiv.org/abs/2404.11589v1 | 2024-04-17T17:38:56Z | 2024-04-17T17:38:56Z | Prompt Optimizer of Text-to-Image Diffusion Models for Abstract Concept
Understanding | The rapid evolution of text-to-image diffusion models has opened the door of generative AI, enabling the translation of textual descriptions into visually compelling images with remarkable quality. However, a persistent challenge within this domain is the optimization of prompts to effectively convey abstract concepts into concrete objects. For example, text encoders can hardly express "peace", while can easily illustrate olive branches and white doves. This paper introduces a novel approach named Prompt Optimizer for Abstract Concepts (POAC) specifically designed to enhance the performance of text-to-image diffusion models in interpreting and generating images from abstract concepts. We propose a Prompt Language Model (PLM), which is initialized from a pre-trained language model, and then fine-tuned with a curated dataset of abstract concept prompts. The dataset is created with GPT-4 to extend the abstract concept to a scene and concrete objects. Our framework employs a Reinforcement Learning (RL)-based optimization strategy, focusing on the alignment between the generated images by a stable diffusion model and optimized prompts. Through extensive experiments, we demonstrate that our proposed POAC significantly improves the accuracy and aesthetic quality of generated images, particularly in the description of abstract concepts and alignment with optimized prompts. We also present a comprehensive analysis of our model's performance across diffusion models under different settings, showcasing its versatility and effectiveness in enhancing abstract concept representation. | [
"['Zezhong Fan' 'Xiaohan Li' 'Chenhao Fang' 'Topojoy Biswas' 'Kaushiki Nag'\n 'Jianpeng Xu' 'Kannan Achan']"
]
|
null | null | 2404.11597 | null | null | http://arxiv.org/pdf/2404.11597v2 | 2024-06-10T17:04:10Z | 2024-04-17T17:49:38Z | Explainable Artificial Intelligence Techniques for Accurate Fault
Detection and Diagnosis: A Review | As the manufacturing industry advances with sensor integration and automation, the opaque nature of deep learning models in machine learning poses a significant challenge for fault detection and diagnosis. And despite the related predictive insights Artificial Intelligence (AI) can deliver, advanced machine learning engines often remain a black box. This paper reviews the eXplainable AI (XAI) tools and techniques in this context. We explore various XAI methodologies, focusing on their role in making AI decision-making transparent, particularly in critical scenarios where humans are involved. We also discuss current limitations and potential future research that aims to balance explainability with model performance while improving trustworthiness in the context of AI applications for critical industrial use cases. | [
"['Ahmed Maged' 'Salah Haridy' 'Herman Shen']"
]
|
null | null | 2404.11599 | null | null | http://arxiv.org/pdf/2404.11599v1 | 2024-04-17T17:50:24Z | 2024-04-17T17:50:24Z | Variational Bayesian Last Layers | We introduce a deterministic variational formulation for training Bayesian last layer neural networks. This yields a sampling-free, single-pass model and loss that effectively improves uncertainty estimation. Our variational Bayesian last layer (VBLL) can be trained and evaluated with only quadratic complexity in last layer width, and is thus (nearly) computationally free to add to standard architectures. We experimentally investigate VBLLs, and show that they improve predictive accuracy, calibration, and out of distribution detection over baselines across both regression and classification. Finally, we investigate combining VBLL layers with variational Bayesian feature learning, yielding a lower variance collapsed variational inference method for Bayesian neural networks. | [
"['James Harrison' 'John Willes' 'Jasper Snoek']"
]
|
null | null | 2404.11606 | null | null | http://arxiv.org/pdf/2404.11606v1 | 2024-04-17T17:55:17Z | 2024-04-17T17:55:17Z | Learning to Solve the Constrained Most Probable Explanation Task in
Probabilistic Graphical Models | We propose a self-supervised learning approach for solving the following constrained optimization task in log-linear models or Markov networks. Let $f$ and $g$ be two log-linear models defined over the sets $mathbf{X}$ and $mathbf{Y}$ of random variables respectively. Given an assignment $mathbf{x}$ to all variables in $mathbf{X}$ (evidence) and a real number $q$, the constrained most-probable explanation (CMPE) task seeks to find an assignment $mathbf{y}$ to all variables in $mathbf{Y}$ such that $f(mathbf{x}, mathbf{y})$ is maximized and $g(mathbf{x}, mathbf{y})leq q$. In our proposed self-supervised approach, given assignments $mathbf{x}$ to $mathbf{X}$ (data), we train a deep neural network that learns to output near-optimal solutions to the CMPE problem without requiring access to any pre-computed solutions. The key idea in our approach is to use first principles and approximate inference methods for CMPE to derive novel loss functions that seek to push infeasible solutions towards feasible ones and feasible solutions towards optimal ones. We analyze the properties of our proposed method and experimentally demonstrate its efficacy on several benchmark problems. | [
"['Shivvrat Arya' 'Tahrima Rahman' 'Vibhav Gogate']"
]
|
null | null | 2404.11624 | null | null | http://arxiv.org/pdf/2404.11624v1 | 2024-04-11T15:56:06Z | 2024-04-11T15:56:06Z | Token Space: A Category Theory Framework for AI Computations | This paper introduces the Token Space framework, a novel mathematical construct designed to enhance the interpretability and effectiveness of deep learning models through the application of category theory. By establishing a categorical structure at the Token level, we provide a new lens through which AI computations can be understood, emphasizing the relationships between tokens, such as grouping, order, and parameter types. We explore the foundational methodologies of the Token Space, detailing its construction, the role of construction operators and initial categories, and its application in analyzing deep learning models, specifically focusing on attention mechanisms and Transformer architectures. The integration of category theory into AI research offers a unified framework to describe and analyze computational structures, enabling new research paths and development possibilities. Our investigation reveals that the Token Space framework not only facilitates a deeper theoretical understanding of deep learning models but also opens avenues for the design of more efficient, interpretable, and innovative models, illustrating the significant role of category theory in advancing computational models. | [
"['Wuming Pan']"
]
|
null | null | 2404.11661 | null | null | http://arxiv.org/abs/2404.11661v1 | 2024-04-17T18:00:46Z | 2024-04-17T18:00:46Z | Designing an Intelligent Parcel Management System using IoT & Machine
Learning | Parcels delivery is a critical activity in railways. More importantly, each parcel must be thoroughly checked and sorted according to its destination address. We require an efficient and robust IoT system capable of doing all of these tasks with great precision and minimal human interaction. This paper discusses, We created a fully-fledged solution using IoT and machine learning to assist trains in performing this operation efficiently. In this study, we covered the product, which consists mostly of two phases. Scanning is the first step, followed by sorting. During the scanning process, the parcel will be passed through three scanners that will look for explosives, drugs, and any dangerous materials in the parcel and will trash it if any of the tests fail. When the scanning step is over, the parcel moves on to the sorting phase, where we use QR codes to retrieve the details of the parcels and sort them properly. The simulation of the system is done using the blender software. Our research shows that our procedure significantly improves accuracy as well as the assessment of cutting-edge technology and existing techniques. | [
"['Mohit Gupta' 'Nitesh Garg' 'Jai Garg' 'Vansh Gupta' 'Devraj Gautam']"
]
|
null | null | 2404.11665 | null | null | http://arxiv.org/pdf/2404.11665v1 | 2024-04-17T18:03:12Z | 2024-04-17T18:03:12Z | Exploring DNN Robustness Against Adversarial Attacks Using Approximate
Multipliers | Deep Neural Networks (DNNs) have advanced in many real-world applications, such as healthcare and autonomous driving. However, their high computational complexity and vulnerability to adversarial attacks are ongoing challenges. In this letter, approximate multipliers are used to explore DNN robustness improvement against adversarial attacks. By uniformly replacing accurate multipliers for state-of-the-art approximate ones in DNN layer models, we explore the DNNs robustness against various adversarial attacks in a feasible time. Results show up to 7% accuracy drop due to approximations when no attack is present while improving robust accuracy up to 10% when attacks applied. | [
"['Mohammad Javad Askarizadeh' 'Ebrahim Farahmand' 'Jorge Castro-Godinez'\n 'Ali Mahani' 'Laura Cabrera-Quiros' 'Carlos Salazar-Garcia']"
]
|
null | null | 2404.11667 | null | null | http://arxiv.org/pdf/2404.11667v1 | 2024-04-17T18:04:37Z | 2024-04-17T18:04:37Z | Deep Dependency Networks and Advanced Inference Schemes for Multi-Label
Classification | We present a unified framework called deep dependency networks (DDNs) that combines dependency networks and deep learning architectures for multi-label classification, with a particular emphasis on image and video data. The primary advantage of dependency networks is their ease of training, in contrast to other probabilistic graphical models like Markov networks. In particular, when combined with deep learning architectures, they provide an intuitive, easy-to-use loss function for multi-label classification. A drawback of DDNs compared to Markov networks is their lack of advanced inference schemes, necessitating the use of Gibbs sampling. To address this challenge, we propose novel inference schemes based on local search and integer linear programming for computing the most likely assignment to the labels given observations. We evaluate our novel methods on three video datasets (Charades, TACoS, Wetlab) and three image datasets (MS-COCO, PASCAL VOC, NUS-WIDE), comparing their performance with (a) basic neural architectures and (b) neural architectures combined with Markov networks equipped with advanced inference and learning techniques. Our results demonstrate the superiority of our new DDN methods over the two competing approaches. | [
"['Shivvrat Arya' 'Yu Xiang' 'Vibhav Gogate']"
]
|
null | null | 2404.11674 | null | null | http://arxiv.org/pdf/2404.11674v1 | 2024-04-17T18:17:14Z | 2024-04-17T18:17:14Z | Practical applications of machine-learned flows on gauge fields | Normalizing flows are machine-learned maps between different lattice theories which can be used as components in exact sampling and inference schemes. Ongoing work yields increasingly expressive flows on gauge fields, but it remains an open question how flows can improve lattice QCD at state-of-the-art scales. We discuss and demonstrate two applications of flows in replica exchange (parallel tempering) sampling, aimed at improving topological mixing, which are viable with iterative improvements upon presently available flows. | [
"['Ryan Abbott' 'Michael S. Albergo' 'Denis Boyda' 'Daniel C. Hackett'\n 'Gurtej Kanwar' 'Fernando Romero-López' 'Phiala E. Shanahan'\n 'Julian M. Urban']"
]
|
null | null | 2404.11735 | null | null | http://arxiv.org/pdf/2404.11735v2 | 2024-06-19T10:17:54Z | 2024-04-17T20:37:29Z | Learning with 3D rotations, a hitchhiker's guide to SO(3) | Many settings in machine learning require the selection of a rotation representation. However, choosing a suitable representation from the many available options is challenging. This paper acts as a survey and guide through rotation representations. We walk through their properties that harm or benefit deep learning with gradient-based optimization. By consolidating insights from rotation-based learning, we provide a comprehensive overview of learning functions with rotation representations. We provide guidance on selecting representations based on whether rotations are in the model's input or output and whether the data primarily comprises small angles. | [
"['A. René Geist' 'Jonas Frey' 'Mikel Zobro' 'Anna Levina' 'Georg Martius']"
]
|
null | null | 2404.11753 | null | null | http://arxiv.org/pdf/2404.11753v1 | 2024-04-17T21:11:12Z | 2024-04-17T21:11:12Z | Virtual Foundry Graphnet for Metal Sintering Deformation Prediction | Metal Sintering is a necessary step for Metal Injection Molded parts and binder jet such as HP's metal 3D printer. The metal sintering process introduces large deformation varying from 25 to 50% depending on the green part porosity. In this paper, we use a graph-based deep learning approach to predict the part deformation, which can speed up the deformation simulation substantially at the voxel level. Running a well-trained Metal Sintering inferencing engine only takes a range of seconds to obtain the final sintering deformation value. The tested accuracy on example complex geometry achieves 0.7um mean deviation for a 63mm testing part. | [
"['Rachel' 'Chen' 'Juheon Lee' 'Chuang Gan' 'Zijiang Yang'\n 'Mohammad Amin Nabian' 'Jun Zeng']"
]
|
null | null | 2404.11754 | null | null | http://arxiv.org/pdf/2404.11754v3 | 2024-05-27T23:20:52Z | 2024-04-17T21:17:48Z | Improved Generalization Bounds for Communication Efficient Federated
Learning | This paper focuses on reducing the communication cost of federated learning by exploring generalization bounds and representation learning. We first characterize a tighter generalization bound for one-round federated learning based on local clients' generalizations and heterogeneity of data distribution (non-iid scenario). We also characterize a generalization bound in R-round federated learning and its relation to the number of local updates (local stochastic gradient descents (SGDs)). Then, based on our generalization bound analysis and our representation learning interpretation of this analysis, we show for the first time that less frequent aggregations, hence more local updates, for the representation extractor (usually corresponds to initial layers) leads to the creation of more generalizable models, particularly for non-iid scenarios. We design a novel Federated Learning with Adaptive Local Steps (FedALS) algorithm based on our generalization bound and representation learning analysis. FedALS employs varying aggregation frequencies for different parts of the model, so reduces the communication cost. The paper is followed with experimental results showing the effectiveness of FedALS. | [
"['Peyman Gholami' 'Hulya Seferoglu']"
]
|
null | null | 2404.11760 | null | null | http://arxiv.org/pdf/2404.11760v1 | 2024-04-17T21:36:33Z | 2024-04-17T21:36:33Z | Predictive Model Development to Identify Failed Healing in Patients
after Non-Union Fracture Surgery | Bone non-union is among the most severe complications associated with trauma surgery, occurring in 10-30% of cases after long bone fractures. Treating non-unions requires a high level of surgical expertise and often involves multiple revision surgeries, sometimes even leading to amputation. Thus, more accurate prognosis is crucial for patient well-being. Recent advances in machine learning (ML) hold promise for developing models to predict non-union healing, even when working with smaller datasets, a commonly encountered challenge in clinical domains. To demonstrate the effectiveness of ML in identifying candidates at risk of failed non-union healing, we applied three ML models (logistic regression, support vector machine, and XGBoost) to the clinical dataset TRUFFLE, which includes 797 patients with long bone non-union. The models provided prediction results with 70% sensitivity, and the specificities of 66% (XGBoost), 49% (support vector machine), and 43% (logistic regression). These findings offer valuable clinical insights because they enable early identification of patients at risk of failed non-union healing after the initial surgical revision treatment protocol. | [
"['Cedric Donié' 'Marie K. Reumann' 'Tony Hartung' 'Benedikt J. Braun'\n 'Tina Histing' 'Satoshi Endo' 'Sandra Hirche']"
]
|
null | null | 2404.11766 | null | null | http://arxiv.org/pdf/2404.11766v2 | 2024-04-28T16:01:21Z | 2024-04-17T21:49:45Z | End-to-End Mesh Optimization of a Hybrid Deep Learning Black-Box PDE
Solver | Deep learning has been widely applied to solve partial differential equations (PDEs) in computational fluid dynamics. Recent research proposed a PDE correction framework that leverages deep learning to correct the solution obtained by a PDE solver on a coarse mesh. However, end-to-end training of such a PDE correction model over both solver-dependent parameters such as mesh parameters and neural network parameters requires the PDE solver to support automatic differentiation through the iterative numerical process. Such a feature is not readily available in many existing solvers. In this study, we explore the feasibility of end-to-end training of a hybrid model with a black-box PDE solver and a deep learning model for fluid flow prediction. Specifically, we investigate a hybrid model that integrates a black-box PDE solver into a differentiable deep graph neural network. To train this model, we use a zeroth-order gradient estimator to differentiate the PDE solver via forward propagation. Although experiments show that the proposed approach based on zeroth-order gradient estimation underperforms the baseline that computes exact derivatives using automatic differentiation, our proposed method outperforms the baseline trained with a frozen input mesh to the solver. Moreover, with a simple warm-start on the neural network parameters, we show that models trained by these zeroth-order algorithms achieve an accelerated convergence and improved generalization performance. | [
"['Shaocong Ma' 'James Diffenderfer' 'Bhavya Kailkhura' 'Yi Zhou']"
]
|
null | null | 2404.11768 | null | null | http://arxiv.org/pdf/2404.11768v1 | 2024-04-17T21:51:03Z | 2024-04-17T21:51:03Z | Tensor-Networks-based Learning of Probabilistic Cellular Automata
Dynamics | Algorithms developed to solve many-body quantum problems, like tensor networks, can turn into powerful quantum-inspired tools to tackle problems in the classical domain. In this work, we focus on matrix product operators, a prominent numerical technique to study many-body quantum systems, especially in one dimension. It has been previously shown that such a tool can be used for classification, learning of deterministic sequence-to-sequence processes and of generic quantum processes. We further develop a matrix product operator algorithm to learn probabilistic sequence-to-sequence processes and apply this algorithm to probabilistic cellular automata. This new approach can accurately learn probabilistic cellular automata processes in different conditions, even when the process is a probabilistic mixture of different chaotic rules. In addition, we find that the ability to learn these dynamics is a function of the bit-wise difference between the rules and whether one is much more likely than the other. | [
"['Heitor P. Casagrande' 'Bo Xing' 'William J. Munro' 'Chu Guo'\n 'Dario Poletti']"
]
|
null | null | 2404.11769 | null | null | http://arxiv.org/pdf/2404.11769v2 | 2024-04-19T16:50:05Z | 2024-04-17T21:52:21Z | QGen: On the Ability to Generalize in Quantization Aware Training | Quantization lowers memory usage, computational requirements, and latency by utilizing fewer bits to represent model weights and activations. In this work, we investigate the generalization properties of quantized neural networks, a characteristic that has received little attention despite its implications on model performance. In particular, first, we develop a theoretical model for quantization in neural networks and demonstrate how quantization functions as a form of regularization. Second, motivated by recent work connecting the sharpness of the loss landscape and generalization, we derive an approximate bound for the generalization of quantized models conditioned on the amount of quantization noise. We then validate our hypothesis by experimenting with over 2000 models trained on CIFAR-10, CIFAR-100, and ImageNet datasets on convolutional and transformer-based models. | [
"['MohammadHossein AskariHemmat' 'Ahmadreza Jeddi' 'Reyhane Askari Hemmat'\n 'Ivan Lazarevich' 'Alexander Hoffman' 'Sudhakar Sah' 'Ehsan Saboori'\n 'Yvon Savaria' 'Jean-Pierre David']"
]
|
null | null | 2404.11776 | null | null | http://arxiv.org/abs/2404.11776v1 | 2024-04-17T21:57:29Z | 2024-04-17T21:57:29Z | 3D object quality prediction for Metal Jet Printer with Multimodal
thermal encoder | With the advancements in 3D printing technologies, it is extremely important that the quality of 3D printed objects, and dimensional accuracies should meet the customer's specifications. Various factors during metal printing affect the printed parts' quality, including the power quality, the printing stage parameters, the print part's location inside the print bed, the curing stage parameters, and the metal sintering process. With the large data gathered from HP's MetJet printing process, AI techniques can be used to analyze, learn, and effectively infer the printed part quality metrics, as well as assist in improving the print yield. In-situ thermal sensing data captured by printer-installed thermal sensors contains the part thermal signature of fusing layers. Such part thermal signature contains a convoluted impact from various factors. In this paper, we use a multimodal thermal encoder network to fuse data of a different nature including the video data vectorized printer control data, and exact part thermal signatures with a trained encoder-decoder module. We explored the data fusing techniques and stages for data fusing, the optimized end-to-end model architecture indicates an improved part quality prediction accuracy. | [
"['Rachel' 'Chen' 'Wenjia Zheng' 'Sandeep Jalui' 'Pavan Suri' 'Jun Zeng']"
]
|
null | null | 2404.11782 | null | null | http://arxiv.org/pdf/2404.11782v1 | 2024-04-17T22:12:41Z | 2024-04-17T22:12:41Z | REQUAL-LM: Reliability and Equity through Aggregation in Large Language
Models | The extensive scope of large language models (LLMs) across various domains underscores the critical importance of responsibility in their application, beyond natural language processing. In particular, the randomized nature of LLMs, coupled with inherent biases and historical stereotypes in data, raises critical concerns regarding reliability and equity. Addressing these challenges are necessary before using LLMs for applications with societal impact. Towards addressing this gap, we introduce REQUAL-LM, a novel method for finding reliable and equitable LLM outputs through aggregation. Specifically, we develop a Monte Carlo method based on repeated sampling to find a reliable output close to the mean of the underlying distribution of possible outputs. We formally define the terms such as reliability and bias, and design an equity-aware aggregation to minimize harmful bias while finding a highly reliable output. REQUAL-LM does not require specialized hardware, does not impose a significant computing load, and uses LLMs as a blackbox. This design choice enables seamless scalability alongside the rapid advancement of LLM technologies. Our system does not require retraining the LLMs, which makes it deployment ready and easy to adapt. Our comprehensive experiments using various tasks and datasets demonstrate that REQUAL- LM effectively mitigates bias and selects a more equitable response, specifically the outputs that properly represents minority groups. | [
"['Sana Ebrahimi' 'Nima Shahbazi' 'Abolfazl Asudeh']"
]
|
null | null | 2404.11788 | null | null | http://arxiv.org/pdf/2404.11788v2 | 2024-04-24T17:58:45Z | 2024-04-17T22:44:22Z | NonGEMM Bench: Understanding the Performance Horizon of the Latest ML
Workloads with NonGEMM Workloads | Machine Learning (ML) operators are the building blocks to design ML models with various target applications. GEneral Matrix Multiplication (GEMM) operators are the backbone of ML models. They are notorious for being computationally expensive requiring billions of multiply-and-accumulate. Therefore, significant effort has been put to study and optimize the GEMM operators in order to speed up the execution of ML models. GPUs and accelerators are widely deployed to accelerate ML workloads by optimizing the execution of GEMM operators. Nonetheless, the performance of NonGEMM operators have not been studied as thoroughly as GEMMs. Therefore, this paper describes bench, a benchmark to study NonGEMM operators. We first construct bench using popular ML workloads from different domains, then perform case studies on various grade GPU platforms to analyze the behavior of NonGEMM operators in GPU accelerated systems. Finally, we present some key takeaways to bridge the gap between GEMM and NonGEMM operators and to offer the community with potential new optimization directions. | [
"['Rachid Karami' 'Hemanth Kota' 'Sheng-Chun Kao' 'Hyoukjun Kwon']"
]
|
null | null | 2404.11795 | null | null | http://arxiv.org/pdf/2404.11795v1 | 2024-04-17T23:10:11Z | 2024-04-17T23:10:11Z | Prompt-Driven Feature Diffusion for Open-World Semi-Supervised Learning | In this paper, we present a novel approach termed Prompt-Driven Feature Diffusion (PDFD) within a semi-supervised learning framework for Open World Semi-Supervised Learning (OW-SSL). At its core, PDFD deploys an efficient feature-level diffusion model with the guidance of class-specific prompts to support discriminative feature representation learning and feature generation, tackling the challenge of the non-availability of labeled data for unseen classes in OW-SSL. In particular, PDFD utilizes class prototypes as prompts in the diffusion model, leveraging their class-discriminative and semantic generalization ability to condition and guide the diffusion process across all the seen and unseen classes. Furthermore, PDFD incorporates a class-conditional adversarial loss for diffusion model training, ensuring that the features generated via the diffusion process can be discriminatively aligned with the class-conditional features of the real data. Additionally, the class prototypes of the unseen classes are computed using only unlabeled instances with confident predictions within a semi-supervised learning framework. We conduct extensive experiments to evaluate the proposed PDFD. The empirical results show PDFD exhibits remarkable performance enhancements over many state-of-the-art existing methods. | [
"['Marzi Heidari' 'Hanping Zhang' 'Yuhong Guo']"
]
|
null | null | 2404.11797 | null | null | http://arxiv.org/pdf/2404.11797v1 | 2024-04-17T23:30:48Z | 2024-04-17T23:30:48Z | When are Foundation Models Effective? Understanding the Suitability for
Pixel-Level Classification Using Multispectral Imagery | Foundation models, i.e., very large deep learning models, have demonstrated impressive performances in various language and vision tasks that are otherwise difficult to reach using smaller-size models. The major success of GPT-type of language models is particularly exciting and raises expectations on the potential of foundation models in other domains including satellite remote sensing. In this context, great efforts have been made to build foundation models to test their capabilities in broader applications, and examples include Prithvi by NASA-IBM, Segment-Anything-Model, ViT, etc. This leads to an important question: Are foundation models always a suitable choice for different remote sensing tasks, and when or when not? This work aims to enhance the understanding of the status and suitability of foundation models for pixel-level classification using multispectral imagery at moderate resolution, through comparisons with traditional machine learning (ML) and regular-size deep learning models. Interestingly, the results reveal that in many scenarios traditional ML models still have similar or better performance compared to foundation models, especially for tasks where texture is less useful for classification. On the other hand, deep learning models did show more promising results for tasks where labels partially depend on texture (e.g., burn scar), while the difference in performance between foundation models and deep learning models is not obvious. The results conform with our analysis: The suitability of foundation models depend on the alignment between the self-supervised learning tasks and the real downstream tasks, and the typical masked autoencoder paradigm is not necessarily suitable for many remote sensing problems. | [
"['Yiqun Xie' 'Zhihao Wang' 'Weiye Chen' 'Zhili Li' 'Xiaowei Jia'\n 'Yanhua Li' 'Ruichen Wang' 'Kangyang Chai' 'Ruohan Li' 'Sergii Skakun']"
]
|
null | null | 2404.11803 | null | null | http://arxiv.org/pdf/2404.11803v1 | 2024-04-17T23:49:00Z | 2024-04-17T23:49:00Z | TempBEV: Improving Learned BEV Encoders with Combined Image and BEV
Space Temporal Aggregation | Autonomous driving requires an accurate representation of the environment. A strategy toward high accuracy is to fuse data from several sensors. Learned Bird's-Eye View (BEV) encoders can achieve this by mapping data from individual sensors into one joint latent space. For cost-efficient camera-only systems, this provides an effective mechanism to fuse data from multiple cameras with different views. Accuracy can further be improved by aggregating sensor information over time. This is especially important in monocular camera systems to account for the lack of explicit depth and velocity measurements. Thereby, the effectiveness of developed BEV encoders crucially depends on the operators used to aggregate temporal information and on the used latent representation spaces. We analyze BEV encoders proposed in the literature and compare their effectiveness, quantifying the effects of aggregation operators and latent representations. While most existing approaches aggregate temporal information either in image or in BEV latent space, our analyses and performance comparisons suggest that these latent representations exhibit complementary strengths. Therefore, we develop a novel temporal BEV encoder, TempBEV, which integrates aggregated temporal information from both latent spaces. We consider subsequent image frames as stereo through time and leverage methods from optical flow estimation for temporal stereo encoding. Empirical evaluation on the NuScenes dataset shows a significant improvement by TempBEV over the baseline for 3D object detection and BEV segmentation. The ablation uncovers a strong synergy of joint temporal aggregation in the image and BEV latent space. These results indicate the overall effectiveness of our approach and make a strong case for aggregating temporal information in both image and BEV latent spaces. | [
"['Thomas Monninger' 'Vandana Dokkadi' 'Md Zafar Anwar' 'Steffen Staab']"
]
|
null | null | 2404.11809 | null | null | http://arxiv.org/pdf/2404.11809v1 | 2024-04-18T00:05:02Z | 2024-04-18T00:05:02Z | Sharing Parameter by Conjugation for Knowledge Graph Embeddings in
Complex Space | A Knowledge Graph (KG) is the directed graphical representation of entities and relations in the real world. KG can be applied in diverse Natural Language Processing (NLP) tasks where knowledge is required. The need to scale up and complete KG automatically yields Knowledge Graph Embedding (KGE), a shallow machine learning model that is suffering from memory and training time consumption issues. To mitigate the computational load, we propose a parameter-sharing method, i.e., using conjugate parameters for complex numbers employed in KGE models. Our method improves memory efficiency by 2x in relation embedding while achieving comparable performance to the state-of-the-art non-conjugate models, with faster, or at least comparable, training time. We demonstrated the generalizability of our method on two best-performing KGE models $5^{bigstar}mathrm{E}$ and $mathrm{ComplEx}$ on five benchmark datasets. | [
"['Xincan Feng' 'Zhi Qu' 'Yuchang Cheng' 'Taro Watanabe' 'Nobuhiro Yugami']"
]
|
null | null | 2404.11811 | null | null | http://arxiv.org/pdf/2404.11811v1 | 2024-04-18T00:17:01Z | 2024-04-18T00:17:01Z | Physics-informed active learning for accelerating quantum chemical
simulations | Quantum chemical simulations can be greatly accelerated by constructing machine learning potentials, which is often done using active learning (AL). The usefulness of the constructed potentials is often limited by the high effort required and their insufficient robustness in the simulations. Here we introduce the end-to-end AL for constructing robust data-efficient potentials with affordable investment of time and resources and minimum human interference. Our AL protocol is based on the physics-informed sampling of training points, automatic selection of initial data, and uncertainty quantification. The versatility of this protocol is shown in our implementation of quasi-classical molecular dynamics for simulating vibrational spectra, conformer search of a key biochemical molecule, and time-resolved mechanism of the Diels-Alder reaction. These investigations took us days instead of weeks of pure quantum chemical calculations on a high-performance computing cluster. | [
"['Yi-Fan Hou' 'Lina Zhang' 'Quanhao Zhang' 'Fuchun Ge' 'Pavlo O. Dral']"
]
|
null | null | 2404.11816 | null | null | http://arxiv.org/pdf/2404.11816v1 | 2024-04-18T00:26:43Z | 2024-04-18T00:26:43Z | Tailoring Generative Adversarial Networks for Smooth Airfoil Design | In the realm of aerospace design, achieving smooth curves is paramount, particularly when crafting objects such as airfoils. Generative Adversarial Network (GAN), a widely employed generative AI technique, has proven instrumental in synthesizing airfoil designs. However, a common limitation of GAN is the inherent lack of smoothness in the generated airfoil surfaces. To address this issue, we present a GAN model featuring a customized loss function built to produce seamlessly contoured airfoil designs. Additionally, our model demonstrates a substantial increase in design diversity compared to a conventional GAN augmented with a post-processing smoothing filter. | [
"['Joyjit Chattoraj' 'Jian Cheng Wong' 'Zhang Zexuan' 'Manna Dai'\n 'Xia Yingzhi' 'Li Jichao' 'Xu Xinxing' 'Ooi Chin Chun' 'Yang Feng'\n 'Dao My Ha' 'Liu Yong']"
]
|
null | null | 2404.11825 | null | null | http://arxiv.org/pdf/2404.11825v1 | 2024-04-18T01:14:50Z | 2024-04-18T01:14:50Z | Hypergraph Self-supervised Learning with Sampling-efficient Signals | Self-supervised learning (SSL) provides a promising alternative for representation learning on hypergraphs without costly labels. However, existing hypergraph SSL models are mostly based on contrastive methods with the instance-level discrimination strategy, suffering from two significant limitations: (1) They select negative samples arbitrarily, which is unreliable in deciding similar and dissimilar pairs, causing training bias. (2) They often require a large number of negative samples, resulting in expensive computational costs. To address the above issues, we propose SE-HSSL, a hypergraph SSL framework with three sampling-efficient self-supervised signals. Specifically, we introduce two sampling-free objectives leveraging the canonical correlation analysis as the node-level and group-level self-supervised signals. Additionally, we develop a novel hierarchical membership-level contrast objective motivated by the cascading overlap relationship in hypergraphs, which can further reduce membership sampling bias and improve the efficiency of sample utilization. Through comprehensive experiments on 7 real-world hypergraphs, we demonstrate the superiority of our approach over the state-of-the-art method in terms of both effectiveness and efficiency. | [
"['Fan Li' 'Xiaoyang Wang' 'Dawei Cheng' 'Wenjie Zhang' 'Ying Zhang'\n 'Xuemin Lin']"
]
|
null | null | 2404.11834 | null | null | http://arxiv.org/pdf/2404.11834v1 | 2024-04-18T01:27:31Z | 2024-04-18T01:27:31Z | Actor-Critic Reinforcement Learning with Phased Actor | Policy gradient methods in actor-critic reinforcement learning (RL) have become perhaps the most promising approaches to solving continuous optimal control problems. However, the trial-and-error nature of RL and the inherent randomness associated with solution approximations cause variations in the learned optimal values and policies. This has significantly hindered their successful deployment in real life applications where control responses need to meet dynamic performance criteria deterministically. Here we propose a novel phased actor in actor-critic (PAAC) method, aiming at improving policy gradient estimation and thus the quality of the control policy. Specifically, PAAC accounts for both $Q$ value and TD error in its actor update. We prove qualitative properties of PAAC for learning convergence of the value and policy, solution optimality, and stability of system dynamics. Additionally, we show variance reduction in policy gradient estimation. PAAC performance is systematically and quantitatively evaluated in this study using DeepMind Control Suite (DMC). Results show that PAAC leads to significant performance improvement measured by total cost, learning variance, robustness, learning speed and success rate. As PAAC can be piggybacked onto general policy gradient learning frameworks, we select well-known methods such as direct heuristic dynamic programming (dHDP), deep deterministic policy gradient (DDPG) and their variants to demonstrate the effectiveness of PAAC. Consequently we provide a unified view on these related policy gradient algorithms. | [
"['Ruofan Wu' 'Junmin Zhong' 'Jennie Si']"
]
|
null | null | 2404.11843 | null | null | http://arxiv.org/pdf/2404.11843v2 | 2024-04-19T01:45:02Z | 2024-04-18T01:46:31Z | Computer-Aided Diagnosis of Thoracic Diseases in Chest X-rays using
hybrid CNN-Transformer Architecture | Medical imaging has been used for diagnosis of various conditions, making it one of the most powerful resources for effective patient care. Due to widespread availability, low cost, and low radiation, chest X-ray is one of the most sought after radiology examination for the diagnosis of various thoracic diseases. Due to advancements in medical imaging technologies and increasing patient load, current radiology workflow faces various challenges including increasing backlogs, working long hours, and increase in diagnostic errors. An automated computer-aided diagnosis system that can interpret chest X-rays to augment radiologists by providing actionable insights has potential to provide second opinion to radiologists, highlight relevant regions in the image, in turn expediting clinical workflow, reducing diagnostic errors, and improving patient care. In this study, we applied a novel architecture augmenting the DenseNet121 Convolutional Neural Network (CNN) with multi-head self-attention mechanism using transformer, namely SA-DenseNet121, that can identify multiple thoracic diseases in chest X-rays. We conducted experiments on four of the largest chest X-ray datasets, namely, ChestX-ray14, CheXpert, MIMIC-CXR-JPG, and IU-CXR. Experimental results in terms of area under the receiver operating characteristics (AUC-ROC) shows that augmenting CNN with self-attention has potential in diagnosing different thoracic diseases from chest X-rays. The proposed methodology has the potential to support the reading workflow, improve efficiency, and reduce diagnostic errors. | [
"['Sonit Singh']"
]
|
null | null | 2404.11868 | null | null | http://arxiv.org/pdf/2404.11868v3 | 2024-05-12T03:15:07Z | 2024-04-18T02:59:48Z | OPTiML: Dense Semantic Invariance Using Optimal Transport for
Self-Supervised Medical Image Representation | Self-supervised learning (SSL) has emerged as a promising technique for medical image analysis due to its ability to learn without annotations. However, despite the promising potential, conventional SSL methods encounter limitations, including challenges in achieving semantic alignment and capturing subtle details. This leads to suboptimal representations, which fail to accurately capture the underlying anatomical structures and pathological details. In response to these constraints, we introduce a novel SSL framework OPTiML, employing optimal transport (OT), to capture the dense semantic invariance and fine-grained details, thereby enhancing the overall effectiveness of SSL in medical image representation learning. The core idea is to integrate OT with a cross-viewpoint semantics infusion module (CV-SIM), which effectively captures complex, fine-grained details inherent in medical images across different viewpoints. In addition to the CV-SIM module, OPTiML imposes the variance and covariance regularizations within OT framework to force the model focus on clinically relevant information while discarding less informative features. Through these, the proposed framework demonstrates its capacity to learn semantically rich representations that can be applied to various medical imaging tasks. To validate its effectiveness, we conduct experimental studies on three publicly available datasets from chest X-ray modality. Our empirical results reveal OPTiML's superiority over state-of-the-art methods across all evaluated tasks. | [
"['Azad Singh' 'Vandan Gorade' 'Deepak Mishra']"
]
|
null | null | 2404.11869 | null | null | http://arxiv.org/pdf/2404.11869v2 | 2024-06-24T08:45:52Z | 2024-04-18T03:03:37Z | Node-like as a Whole: Structure-aware Searching and Coarsening for Graph
Classification | Graph Transformers (GTs) have made remarkable achievements in graph-level tasks. However, most existing works regard graph structures as a form of guidance or bias for enhancing node representations, which focuses on node-central perspectives and lacks explicit representations of edges and structures. One natural question is, can we treat graph structures node-like as a whole to learn high-level features? Through experimental analysis, we explore the feasibility of this assumption. Based on our findings, we propose a novel multi-view graph representation learning model via structure-aware searching and coarsening (GRLsc) on GT architecture for graph classification. Specifically, we build three unique views, original, coarsening, and conversion, to learn a thorough structural representation. We compress loops and cliques via hierarchical heuristic graph coarsening and restrict them with well-designed constraints, which builds the coarsening view to learn high-level interactions between structures. We also introduce line graphs for edge embeddings and switch to edge-central perspective to construct the conversion view. Experiments on eight real-world datasets demonstrate the improvements of GRLsc over 28 baselines from various architectures. | [
"['Xiaorui Qi' 'Qijie Bai' 'Yanlong Wen' 'Haiwei Zhang' 'Xiaojie Yuan']"
]
|
null | null | 2404.11870 | null | null | http://arxiv.org/pdf/2404.11870v1 | 2024-04-18T03:03:46Z | 2024-04-18T03:03:46Z | Enhancing Length Extrapolation in Sequential Models with
Pointer-Augmented Neural Memory | We propose Pointer-Augmented Neural Memory (PANM) to help neural networks understand and apply symbol processing to new, longer sequences of data. PANM integrates an external neural memory that uses novel physical addresses and pointer manipulation techniques to mimic human and computer symbol processing abilities. PANM facilitates pointer assignment, dereference, and arithmetic by explicitly using physical pointers to access memory content. Remarkably, it can learn to perform these operations through end-to-end training on sequence data, powering various sequential models. Our experiments demonstrate PANM's exceptional length extrapolating capabilities and improved performance in tasks that require symbol processing, such as algorithmic reasoning and Dyck language recognition. PANM helps Transformer achieve up to 100% generalization accuracy in compositional learning tasks and significantly better results in mathematical reasoning, question answering and machine translation tasks. | [
"['Hung Le' 'Dung Nguyen' 'Kien Do' 'Svetha Venkatesh' 'Truyen Tran']"
]
|
null | null | 2404.11874 | null | null | http://arxiv.org/pdf/2404.11874v1 | 2024-04-18T03:17:45Z | 2024-04-18T03:17:45Z | Using a Local Surrogate Model to Interpret Temporal Shifts in Global
Annual Data | This paper focuses on explaining changes over time in globally-sourced, annual temporal data, with the specific objective of identifying pivotal factors that contribute to these temporal shifts. Leveraging such analytical frameworks can yield transformative impacts, including the informed refinement of public policy and the identification of key drivers affecting a country's economic evolution. We employ Local Interpretable Model-agnostic Explanations (LIME) to shed light on national happiness indices, economic freedom, and population metrics, spanning variable time frames. Acknowledging the presence of missing values, we employ three imputation approaches to generate robust multivariate time-series datasets apt for LIME's input requirements. Our methodology's efficacy is substantiated through a series of empirical evaluations involving multiple datasets. These evaluations include comparative analyses against random feature selection, correlation with real-world events as elucidated by LIME, and validation through Individual Conditional Expectation (ICE) plots, a state-of-the-art technique proficient in feature importance detection. | [
"['Shou Nakano' 'Yang Liu']"
]
|
null | null | 2404.11888 | null | null | http://arxiv.org/pdf/2404.11888v1 | 2024-04-18T04:25:21Z | 2024-04-18T04:25:21Z | The Dog Walking Theory: Rethinking Convergence in Federated Learning | Federated learning (FL) is a collaborative learning paradigm that allows different clients to train one powerful global model without sharing their private data. Although FL has demonstrated promising results in various applications, it is known to suffer from convergence issues caused by the data distribution shift across different clients, especially on non-independent and identically distributed (non-IID) data. In this paper, we study the convergence of FL on non-IID data and propose a novel emph{Dog Walking Theory} to formulate and identify the missing element in existing research. The Dog Walking Theory describes the process of a dog walker leash walking multiple dogs from one side of the park to the other. The goal of the dog walker is to arrive at the right destination while giving the dogs enough exercise (i.e., space exploration). In FL, the server is analogous to the dog walker while the clients are analogous to the dogs. This analogy allows us to identify one crucial yet missing element in existing FL algorithms: the leash that guides the exploration of the clients. To address this gap, we propose a novel FL algorithm emph{FedWalk} that leverages an external easy-to-converge task at the server side as a emph{leash task} to guide the local training of the clients. We theoretically analyze the convergence of FedWalk with respect to data heterogeneity (between server and clients) and task discrepancy (between the leash and the original tasks). Experiments on multiple benchmark datasets demonstrate the superiority of FedWalk over state-of-the-art FL methods under both IID and non-IID settings. | [
"['Kun Zhai' 'Yifeng Gao' 'Xingjun Ma' 'Difan Zou' 'Guangnan Ye'\n 'Yu-Gang Jiang']"
]
|
null | null | 2404.11890 | null | null | http://arxiv.org/pdf/2404.11890v1 | 2024-04-18T04:30:18Z | 2024-04-18T04:30:18Z | FCNCP: A Coupled Nonnegative CANDECOMP/PARAFAC Decomposition Based on
Federated Learning | In the field of brain science, data sharing across servers is becoming increasingly challenging due to issues such as industry competition, privacy security, and administrative procedure policies and regulations. Therefore, there is an urgent need to develop new methods for data analysis and processing that enable scientific collaboration without data sharing. In view of this, this study proposes to study and develop a series of efficient non-negative coupled tensor decomposition algorithm frameworks based on federated learning called FCNCP for the EEG data arranged on different servers. It combining the good discriminative performance of tensor decomposition in high-dimensional data representation and decomposition, the advantages of coupled tensor decomposition in cross-sample tensor data analysis, and the features of federated learning for joint modelling in distributed servers. The algorithm utilises federation learning to establish coupling constraints for data distributed across different servers. In the experiments, firstly, simulation experiments are carried out using simulated data, and stable and consistent decomposition results are obtained, which verify the effectiveness of the proposed algorithms in this study. Then the FCNCP algorithm was utilised to decompose the fifth-order event-related potential (ERP) tensor data collected by applying proprioceptive stimuli on the left and right hands. It was found that contralateral stimulation induced more symmetrical components in the activation areas of the left and right hemispheres. The conclusions drawn are consistent with the interpretations of related studies in cognitive neuroscience, demonstrating that the method can efficiently process higher-order EEG data and that some key hidden information can be preserved. | [
"['Yukai Cai' 'Hang Liu' 'Xiulin Wang' 'Hongjin Li' 'Ziyi Wang'\n 'Chuanshuai Yang' 'Fengyu Cong']"
]
|
null | null | 2404.11905 | null | null | http://arxiv.org/pdf/2404.11905v1 | 2024-04-18T05:10:05Z | 2024-04-18T05:10:05Z | FedMID: A Data-Free Method for Using Intermediate Outputs as a Defense
Mechanism Against Poisoning Attacks in Federated Learning | Federated learning combines local updates from clients to produce a global model, which is susceptible to poisoning attacks. Most previous defense strategies relied on vectors derived from projections of local updates on a Euclidean space; however, these methods fail to accurately represent the functionality and structure of local models, resulting in inconsistent performance. Here, we present a new paradigm to defend against poisoning attacks in federated learning using functional mappings of local models based on intermediate outputs. Experiments show that our mechanism is robust under a broad range of computing conditions and advanced attack scenarios, enabling safer collaboration among data-sensitive participants via federated learning. | [
"['Sungwon Han' 'Hyeonho Song' 'Sungwon Park' 'Meeyoung Cha']"
]
|
null | null | 2404.11912 | null | null | http://arxiv.org/pdf/2404.11912v2 | 2024-04-23T03:38:13Z | 2024-04-18T05:25:54Z | TriForce: Lossless Acceleration of Long Sequence Generation with
Hierarchical Speculative Decoding | With large language models (LLMs) widely deployed in long content generation recently, there has emerged an increasing demand for efficient long-sequence inference support. However, key-value (KV) cache, which is stored to avoid re-computation, has emerged as a critical bottleneck by growing linearly in size with the sequence length. Due to the auto-regressive nature of LLMs, the entire KV cache will be loaded for every generated token, resulting in low utilization of computational cores and high latency. While various compression methods for KV cache have been proposed to alleviate this issue, they suffer from degradation in generation quality. We introduce TriForce, a hierarchical speculative decoding system that is scalable to long sequence generation. This approach leverages the original model weights and dynamic sparse KV cache via retrieval as a draft model, which serves as an intermediate layer in the hierarchy and is further speculated by a smaller model to reduce its drafting latency. TriForce not only facilitates impressive speedups for Llama2-7B-128K, achieving up to 2.31$times$ on an A100 GPU but also showcases scalability in handling even longer contexts. For the offloading setting on two RTX 4090 GPUs, TriForce achieves 0.108s/token$unicode{x2014}$only half as slow as the auto-regressive baseline on an A100, which attains 7.78$times$ on our optimized offloading system. Additionally, TriForce performs 4.86$times$ than DeepSpeed-Zero-Inference on a single RTX 4090 GPU. TriForce's robustness is highlighted by its consistently outstanding performance across various temperatures. The code is available at https://github.com/Infini-AI-Lab/TriForce. | [
"['Hanshi Sun' 'Zhuoming Chen' 'Xinyu Yang' 'Yuandong Tian' 'Beidi Chen']"
]
|
null | null | 2404.11917 | null | null | http://arxiv.org/pdf/2404.11917v1 | 2024-04-18T05:48:15Z | 2024-04-18T05:48:15Z | Expected Coordinate Improvement for High-Dimensional Bayesian
Optimization | Bayesian optimization (BO) algorithm is very popular for solving low-dimensional expensive optimization problems. Extending Bayesian optimization to high dimension is a meaningful but challenging task. One of the major challenges is that it is difficult to find good infill solutions as the acquisition functions are also high-dimensional. In this work, we propose the expected coordinate improvement (ECI) criterion for high-dimensional Bayesian optimization. The proposed ECI criterion measures the potential improvement we can get by moving the current best solution along one coordinate. The proposed approach selects the coordinate with the highest ECI value to refine in each iteration and covers all the coordinates gradually by iterating over the coordinates. The greatest advantage of the proposed ECI-BO (expected coordinate improvement based Bayesian optimization) algorithm over the standard BO algorithm is that the infill selection problem of the proposed algorithm is always a one-dimensional problem thus can be easily solved. Numerical experiments show that the proposed algorithm can achieve significantly better results than the standard BO algorithm and competitive results when compared with five state-of-the-art high-dimensional BOs. This work provides a simple but efficient approach for high-dimensional Bayesian optimization. | [
"['Dawei Zhan']"
]
|
null | null | 2404.11922 | null | null | http://arxiv.org/pdf/2404.11922v1 | 2024-04-18T05:59:28Z | 2024-04-18T05:59:28Z | Redefining the Shortest Path Problem Formulation of the Linear
Non-Gaussian Acyclic Model: Pairwise Likelihood Ratios, Prior Knowledge, and
Path Enumeration | Effective causal discovery is essential for learning the causal graph from observational data. The linear non-Gaussian acyclic model (LiNGAM) operates under the assumption of a linear data generating process with non-Gaussian noise in determining the causal graph. Its assumption of unmeasured confounders being absent, however, poses practical limitations. In response, empirical research has shown that the reformulation of LiNGAM as a shortest path problem (LiNGAM-SPP) addresses this limitation. Within LiNGAM-SPP, mutual information is chosen to serve as the measure of independence. A challenge is introduced - parameter tuning is now needed due to its reliance on kNN mutual information estimators. The paper proposes a threefold enhancement to the LiNGAM-SPP framework. First, the need for parameter tuning is eliminated by using the pairwise likelihood ratio in lieu of kNN-based mutual information. This substitution is validated on a general data generating process and benchmark real-world data sets, outperforming existing methods especially when given a larger set of features. The incorporation of prior knowledge is then enabled by a node-skipping strategy implemented on the graph representation of all causal orderings to eliminate violations based on the provided input of relative orderings. Flexibility relative to existing approaches is achieved. Last among the three enhancements is the utilization of the distribution of paths in the graph representation of all causal orderings. From this, crucial properties of the true causal graph such as the presence of unmeasured confounders and sparsity may be inferred. To some extent, the expected performance of the causal discovery algorithm may be predicted. The refinements above advance the practicality and performance of LiNGAM-SPP, showcasing the potential of graph-search-based methodologies in advancing causal discovery. | [
"['Hans Jarett J. Ong' 'Brian Godwin S. Lim']"
]
|
null | null | 2404.11925 | null | null | http://arxiv.org/pdf/2404.11925v1 | 2024-04-18T06:02:54Z | 2024-04-18T06:02:54Z | EdgeFusion: On-Device Text-to-Image Generation | The intensive computational burden of Stable Diffusion (SD) for text-to-image generation poses a significant hurdle for its practical application. To tackle this challenge, recent research focuses on methods to reduce sampling steps, such as Latent Consistency Model (LCM), and on employing architectural optimizations, including pruning and knowledge distillation. Diverging from existing approaches, we uniquely start with a compact SD variant, BK-SDM. We observe that directly applying LCM to BK-SDM with commonly used crawled datasets yields unsatisfactory results. It leads us to develop two strategies: (1) leveraging high-quality image-text pairs from leading generative models and (2) designing an advanced distillation process tailored for LCM. Through our thorough exploration of quantization, profiling, and on-device deployment, we achieve rapid generation of photo-realistic, text-aligned images in just two steps, with latency under one second on resource-limited edge devices. | [
"['Thibault Castells' 'Hyoung-Kyu Song' 'Tairen Piao' 'Shinkook Choi'\n 'Bo-Kyeong Kim' 'Hanyoung Yim' 'Changgwun Lee' 'Jae Gon Kim' 'Tae-Ho Kim']"
]
|
null | null | 2404.11936 | null | null | http://arxiv.org/pdf/2404.11936v1 | 2024-04-18T06:35:37Z | 2024-04-18T06:35:37Z | LD-Pruner: Efficient Pruning of Latent Diffusion Models using
Task-Agnostic Insights | Latent Diffusion Models (LDMs) have emerged as powerful generative models, known for delivering remarkable results under constrained computational resources. However, deploying LDMs on resource-limited devices remains a complex issue, presenting challenges such as memory consumption and inference speed. To address this issue, we introduce LD-Pruner, a novel performance-preserving structured pruning method for compressing LDMs. Traditional pruning methods for deep neural networks are not tailored to the unique characteristics of LDMs, such as the high computational cost of training and the absence of a fast, straightforward and task-agnostic method for evaluating model performance. Our method tackles these challenges by leveraging the latent space during the pruning process, enabling us to effectively quantify the impact of pruning on model performance, independently of the task at hand. This targeted pruning of components with minimal impact on the output allows for faster convergence during training, as the model has less information to re-learn, thereby addressing the high computational cost of training. Consequently, our approach achieves a compressed model that offers improved inference speed and reduced parameter count, while maintaining minimal performance degradation. We demonstrate the effectiveness of our approach on three different tasks: text-to-image (T2I) generation, Unconditional Image Generation (UIG) and Unconditional Audio Generation (UAG). Notably, we reduce the inference time of Stable Diffusion (SD) by 34.9% while simultaneously improving its FID by 5.2% on MS-COCO T2I benchmark. This work paves the way for more efficient pruning methods for LDMs, enhancing their applicability. | [
"['Thibault Castells' 'Hyoung-Kyu Song' 'Bo-Kyeong Kim' 'Shinkook Choi']"
]
|
null | null | 2404.11944 | null | null | http://arxiv.org/pdf/2404.11944v2 | 2024-05-10T06:20:22Z | 2024-04-18T06:47:30Z | Trusted Multi-view Learning with Label Noise | Multi-view learning methods often focus on improving decision accuracy while neglecting the decision uncertainty, which significantly restricts their applications in safety-critical applications. To address this issue, researchers propose trusted multi-view methods that learn the class distribution for each instance, enabling the estimation of classification probabilities and uncertainty. However, these methods heavily rely on high-quality ground-truth labels. This motivates us to delve into a new generalized trusted multi-view learning problem: how to develop a reliable multi-view learning model under the guidance of noisy labels? We propose a trusted multi-view noise refining method to solve this problem. We first construct view-opinions using evidential deep neural networks, which consist of belief mass vectors and uncertainty estimates. Subsequently, we design view-specific noise correlation matrices that transform the original opinions into noisy opinions aligned with the noisy labels. Considering label noises originating from low-quality data features and easily-confused classes, we ensure that the diagonal elements of these matrices are inversely proportional to the uncertainty, while incorporating class relations into the off-diagonal elements. Finally, we aggregate the noisy opinions and employ a generalized maximum likelihood loss on the aggregated opinion for model training, guided by the noisy labels. We empirically compare TMNR with state-of-the-art trusted multi-view learning and label noise learning baselines on 5 publicly available datasets. Experiment results show that TMNR outperforms baseline methods on accuracy, reliability and robustness. The code and appendix are released at https://github.com/YilinZhang107/TMNR. | [
"['Cai Xu' 'Yilin Zhang' 'Ziyu Guan' 'Wei Zhao']"
]
|
null | null | 2404.11947 | null | null | http://arxiv.org/pdf/2404.11947v2 | 2024-04-21T06:36:08Z | 2024-04-18T06:59:40Z | VCC-INFUSE: Towards Accurate and Efficient Selection of Unlabeled
Examples in Semi-supervised Learning | Despite the progress of Semi-supervised Learning (SSL), existing methods fail to utilize unlabeled data effectively and efficiently. Many pseudo-label-based methods select unlabeled examples based on inaccurate confidence scores from the classifier. Most prior work also uses all available unlabeled data without pruning, making it difficult to handle large amounts of unlabeled data. To address these issues, we propose two methods: Variational Confidence Calibration (VCC) and Influence-Function-based Unlabeled Sample Elimination (INFUSE). VCC is an universal plugin for SSL confidence calibration, using a variational autoencoder to select more accurate pseudo labels based on three types of consistency scores. INFUSE is a data pruning method that constructs a core dataset of unlabeled examples under SSL. Our methods are effective in multiple datasets and settings, reducing classification errors rates and saving training time. Together, VCC-INFUSE reduces the error rate of FlexMatch on the CIFAR-100 dataset by 1.08% while saving nearly half of the training time. | [
"['Shijie Fang' 'Qianhan Feng' 'Tong Lin']"
]
|
null | null | 2404.11949 | null | null | http://arxiv.org/pdf/2404.11949v1 | 2024-04-18T07:07:38Z | 2024-04-18T07:07:38Z | Sketch-guided Image Inpainting with Partial Discrete Diffusion Process | In this work, we study the task of sketch-guided image inpainting. Unlike the well-explored natural language-guided image inpainting, which excels in capturing semantic details, the relatively less-studied sketch-guided inpainting offers greater user control in specifying the object's shape and pose to be inpainted. As one of the early solutions to this task, we introduce a novel partial discrete diffusion process (PDDP). The forward pass of the PDDP corrupts the masked regions of the image and the backward pass reconstructs these masked regions conditioned on hand-drawn sketches using our proposed sketch-guided bi-directional transformer. The proposed novel transformer module accepts two inputs -- the image containing the masked region to be inpainted and the query sketch to model the reverse diffusion process. This strategy effectively addresses the domain gap between sketches and natural images, thereby, enhancing the quality of inpainting results. In the absence of a large-scale dataset specific to this task, we synthesize a dataset from the MS-COCO to train and extensively evaluate our proposed framework against various competent approaches in the literature. The qualitative and quantitative results and user studies establish that the proposed method inpaints realistic objects that fit the context in terms of the visual appearance of the provided sketch. To aid further research, we have made our code publicly available at https://github.com/vl2g/Sketch-Inpainting . | [
"['Nakul Sharma' 'Aditay Tripathi' 'Anirban Chakraborty' 'Anand Mishra']"
]
|
null | null | 2404.11962 | null | null | http://arxiv.org/pdf/2404.11962v1 | 2024-04-18T07:48:00Z | 2024-04-18T07:48:00Z | ©Plug-in Authorization for Human Content Copyright Protection
in Text-to-Image Model | This paper addresses the contentious issue of copyright infringement in images generated by text-to-image models, sparking debates among AI developers, content creators, and legal entities. State-of-the-art models create high-quality content without crediting original creators, causing concern in the artistic community. To mitigate this, we propose the copyright Plug-in Authorization framework, introducing three operations: addition, extraction, and combination. Addition involves training a copyright plug-in for specific copyright, facilitating proper credit attribution. Extraction allows creators to reclaim copyright from infringing models, and combination enables users to merge different copyright plug-ins. These operations act as permits, incentivizing fair use and providing flexibility in authorization. We present innovative approaches,"Reverse LoRA" for extraction and "EasyMerge" for seamless combination. Experiments in artist-style replication and cartoon IP recreation demonstrate copyright plug-ins' effectiveness, offering a valuable solution for human copyright protection in the age of generative AIs. | [
"['Chao Zhou' 'Huishuai Zhang' 'Jiang Bian' 'Weiming Zhang' 'Nenghai Yu']"
]
|
null | null | 2404.11965 | null | null | http://arxiv.org/pdf/2404.11965v1 | 2024-04-18T07:52:12Z | 2024-04-18T07:52:12Z | Multi-fidelity Gaussian process surrogate modeling for regression
problems in physics | One of the main challenges in surrogate modeling is the limited availability of data due to resource constraints associated with computationally expensive simulations. Multi-fidelity methods provide a solution by chaining models in a hierarchy with increasing fidelity, associated with lower error, but increasing cost. In this paper, we compare different multi-fidelity methods employed in constructing Gaussian process surrogates for regression. Non-linear autoregressive methods in the existing literature are primarily confined to two-fidelity models, and we extend these methods to handle more than two levels of fidelity. Additionally, we propose enhancements for an existing method incorporating delay terms by introducing a structured kernel. We demonstrate the performance of these methods across various academic and real-world scenarios. Our findings reveal that multi-fidelity methods generally have a smaller prediction error for the same computational cost as compared to the single-fidelity method, although their effectiveness varies across different scenarios. | [
"['Kislaya Ravi' 'Vladyslav Fediukov' 'Felix Dietrich' 'Tobias Neckel'\n 'Fabian Buse' 'Michael Bergmann' 'Hans-Joachim Bungartz']"
]
|
null | null | 2404.11993 | null | null | http://arxiv.org/pdf/2404.11993v1 | 2024-04-18T08:39:52Z | 2024-04-18T08:39:52Z | Knowledge-Aware Multi-Intent Contrastive Learning for Multi-Behavior
Recommendation | Multi-behavioral recommendation optimizes user experiences by providing users with more accurate choices based on their diverse behaviors, such as view, add to cart, and purchase. Current studies on multi-behavioral recommendation mainly explore the connections and differences between multi-behaviors from an implicit perspective. Specifically, they directly model those relations using black-box neural networks. In fact, users' interactions with items under different behaviors are driven by distinct intents. For instance, when users view products, they tend to pay greater attention to information such as ratings and brands. However, when it comes to the purchasing phase, users become more price-conscious. To tackle this challenge and data sparsity problem in the multi-behavioral recommendation, we propose a novel model: Knowledge-Aware Multi-Intent Contrastive Learning (KAMCL) model. This model uses relationships in the knowledge graph to construct intents, aiming to mine the connections between users' multi-behaviors from the perspective of intents to achieve more accurate recommendations. KAMCL is equipped with two contrastive learning schemes to alleviate the data scarcity problem and further enhance user representations. Extensive experiments on three real datasets demonstrate the superiority of our model. | [
"['Shunpan Liang' 'Junjie Zhao' 'Chen Li' 'Yu Lei']"
]
|
null | null | 2404.12010 | null | null | http://arxiv.org/abs/2404.12010v1 | 2024-04-18T09:02:45Z | 2024-04-18T09:02:45Z | ParaFusion: A Large-Scale LLM-Driven English Paraphrase Dataset Infused
with High-Quality Lexical and Syntactic Diversity | Paraphrase generation is a pivotal task in natural language processing (NLP). Existing datasets in the domain lack syntactic and lexical diversity, resulting in paraphrases that closely resemble the source sentences. Moreover, these datasets often contain hate speech and noise, and may unintentionally include non-English language sentences. This research introduces ParaFusion, a large-scale, high-quality English paraphrase dataset developed using Large Language Models (LLM) to address these challenges. ParaFusion augments existing datasets with high-quality data, significantly enhancing both lexical and syntactic diversity while maintaining close semantic similarity. It also mitigates the presence of hate speech and reduces noise, ensuring a cleaner and more focused English dataset. Results show that ParaFusion offers at least a 25% improvement in both syntactic and lexical diversity, measured across several metrics for each data source. The paper also aims to set a gold standard for paraphrase evaluation as it contains one of the most comprehensive evaluation strategies to date. The results underscore the potential of ParaFusion as a valuable resource for improving NLP applications. | [
"['Lasal Jayawardena' 'Prasan Yapa']"
]
|
null | null | 2404.12025 | null | null | http://arxiv.org/abs/2404.12025v1 | 2024-04-18T09:22:08Z | 2024-04-18T09:22:08Z | PID Tuning using Cross-Entropy Deep Learning: a Lyapunov Stability
Analysis | Underwater Unmanned Vehicles (UUVs) have to constantly compensate for the external disturbing forces acting on their body. Adaptive Control theory is commonly used there to grant the control law some flexibility in its response to process variation. Today, learning-based (LB) adaptive methods are leading the field where model-based control structures are combined with deep model-free learning algorithms. This work proposes experiments and metrics to empirically study the stability of such a controller. We perform this stability analysis on a LB adaptive control system whose adaptive parameters are determined using a Cross-Entropy Deep Learning method. | [
"['Hector Kohler' 'Benoit Clement' 'Thomas Chaffre' 'Gilles Le Chenadec']"
]
|
null | null | 2404.12063 | null | null | http://arxiv.org/pdf/2404.12063v1 | 2024-04-18T10:21:28Z | 2024-04-18T10:21:28Z | FastVPINNs: Tensor-Driven Acceleration of VPINNs for Complex Geometries | Variational Physics-Informed Neural Networks (VPINNs) utilize a variational loss function to solve partial differential equations, mirroring Finite Element Analysis techniques. Traditional hp-VPINNs, while effective for high-frequency problems, are computationally intensive and scale poorly with increasing element counts, limiting their use in complex geometries. This work introduces FastVPINNs, a tensor-based advancement that significantly reduces computational overhead and improves scalability. Using optimized tensor operations, FastVPINNs achieve a 100-fold reduction in the median training time per epoch compared to traditional hp-VPINNs. With proper choice of hyperparameters, FastVPINNs surpass conventional PINNs in both speed and accuracy, especially in problems with high-frequency solutions. Demonstrated effectiveness in solving inverse problems on complex domains underscores FastVPINNs' potential for widespread application in scientific and engineering challenges, opening new avenues for practical implementations in scientific machine learning. | [
"['Thivin Anandh' 'Divij Ghose' 'Himanshu Jain' 'Sashikumaar Ganesan']"
]
|
null | null | 2404.12064 | null | null | http://arxiv.org/pdf/2404.12064v2 | 2024-05-14T06:56:53Z | 2024-04-18T10:23:10Z | PureForest: A Large-Scale Aerial Lidar and Aerial Imagery Dataset for
Tree Species Classification in Monospecific Forests | Knowledge of tree species distribution is fundamental to managing forests. New deep learning approaches promise significant accuracy gains for forest mapping, and are becoming a critical tool for mapping multiple tree species at scale. To advance the field, deep learning researchers need large benchmark datasets with high-quality annotations. To this end, we present the PureForest dataset: a large-scale, open, multimodal dataset designed for tree species classification from both Aerial Lidar Scanning (ALS) point clouds and Very High Resolution (VHR) aerial images. Most current public Lidar datasets for tree species classification have low diversity as they only span a small area of a few dozen annotated hectares at most. In contrast, PureForest has 18 tree species grouped into 13 semantic classes, and spans 339 km$^2$ across 449 distinct monospecific forests, and is to date the largest and most comprehensive Lidar dataset for the identification of tree species. By making PureForest publicly available, we hope to provide a challenging benchmark dataset to support the development of deep learning approaches for tree species identification from Lidar and/or aerial imagery. In this data paper, we describe the annotation workflow, the dataset, the recommended evaluation methodology, and establish a baseline performance from both 3D and 2D modalities. | [
"['Charles Gaydon' 'Floryne Roche']"
]
|
null | null | 2404.12070 | null | null | http://arxiv.org/pdf/2404.12070v1 | 2024-04-18T10:45:47Z | 2024-04-18T10:45:47Z | Towards an Approximation Theory of Observable Operator Models | Observable operator models (OOMs) offer a powerful framework for modelling stochastic processes, surpassing the traditional hidden Markov models (HMMs) in generality and efficiency. However, using OOMs to model infinite-dimensional processes poses significant theoretical challenges. This article explores a rigorous approach to developing an approximation theory for OOMs of infinite-dimensional processes. Building upon foundational work outlined in an unpublished tutorial [Jae98], an inner product structure on the space of future distributions is rigorously established and the continuity of observable operators with respect to the associated 2-norm is proven. The original theorem proven in this thesis describes a fundamental obstacle in making an infinite-dimensional space of future distributions into a Hilbert space. The presented findings lay the groundwork for future research in approximating observable operators of infinite-dimensional processes, while a remedy to the encountered obstacle is suggested. | [
"['Wojciech Anyszka']"
]
|
null | null | 2404.12077 | null | null | http://arxiv.org/pdf/2404.12077v1 | 2024-04-18T10:59:54Z | 2024-04-18T10:59:54Z | TIMIT Speaker Profiling: A Comparison of Multi-task learning and
Single-task learning Approaches | This study employs deep learning techniques to explore four speaker profiling tasks on the TIMIT dataset, namely gender classification, accent classification, age estimation, and speaker identification, highlighting the potential and challenges of multi-task learning versus single-task models. The motivation for this research is twofold: firstly, to empirically assess the advantages and drawbacks of multi-task learning over single-task models in the context of speaker profiling; secondly, to emphasize the undiminished significance of skillful feature engineering for speaker recognition tasks. The findings reveal challenges in accent classification, and multi-task learning is found advantageous for tasks of similar complexity. Non-sequential features are favored for speaker recognition, but sequential ones can serve as starting points for complex models. The study underscores the necessity of meticulous experimentation and parameter tuning for deep learning models. | [
"['Rong Wang' 'Kun Sun']"
]
|
null | null | 2404.12096 | null | null | http://arxiv.org/pdf/2404.12096v2 | 2024-04-25T02:26:15Z | 2024-04-18T11:29:23Z | LongEmbed: Extending Embedding Models for Long Context Retrieval | Embedding models play a pivot role in modern NLP applications such as IR and RAG. While the context limit of LLMs has been pushed beyond 1 million tokens, embedding models are still confined to a narrow context window not exceeding 8k tokens, refrained from application scenarios requiring long inputs such as legal contracts. This paper explores context window extension of existing embedding models, pushing the limit to 32k without requiring additional training. First, we examine the performance of current embedding models for long context retrieval on our newly constructed LongEmbed benchmark. LongEmbed comprises two synthetic tasks and four carefully chosen real-world tasks, featuring documents of varying length and dispersed target information. Benchmarking results underscore huge room for improvement in these models. Based on this, comprehensive experiments show that training-free context window extension strategies like position interpolation can effectively extend the context window of existing embedding models by several folds, regardless of their original context being 512 or beyond 4k. Furthermore, for models employing absolute position encoding (APE), we show the possibility of further fine-tuning to harvest notable performance gains while strictly preserving original behavior for short inputs. For models using rotary position embedding (RoPE), significant enhancements are observed when employing RoPE-specific methods, such as NTK and SelfExtend, indicating RoPE's superiority over APE for context window extension. To facilitate future research, we release E5-Base-4k and E5-RoPE-Base, along with the LongEmbed benchmark. | [
"['Dawei Zhu' 'Liang Wang' 'Nan Yang' 'Yifan Song' 'Wenhao Wu' 'Furu Wei'\n 'Sujian Li']"
]
|
null | null | 2404.12097 | null | null | http://arxiv.org/pdf/2404.12097v1 | 2024-04-18T11:29:43Z | 2024-04-18T11:29:43Z | MPC of Uncertain Nonlinear Systems with Meta-Learning for Fast
Adaptation of Neural Predictive Models | In this paper, we consider the problem of reference tracking in uncertain nonlinear systems. A neural State-Space Model (NSSM) is used to approximate the nonlinear system, where a deep encoder network learns the nonlinearity from data, and a state-space component captures the temporal relationship. This transforms the nonlinear system into a linear system in a latent space, enabling the application of model predictive control (MPC) to determine effective control actions. Our objective is to design the optimal controller using limited data from the textit{target system} (the system of interest). To this end, we employ an implicit model-agnostic meta-learning (iMAML) framework that leverages information from textit{source systems} (systems that share similarities with the target system) to expedite training in the target system and enhance its control performance. The framework consists of two phases: the (offine) meta-training phase learns a aggregated NSSM using data from source systems, and the (online) meta-inference phase quickly adapts this aggregated model to the target system using only a few data points and few online training iterations, based on local loss function gradients. The iMAML algorithm exploits the implicit function theorem to exactly compute the gradient during training, without relying on the entire optimization path. By focusing solely on the optimal solution, rather than the path, we can meta-train with less storage complexity and fewer approximations than other contemporary meta-learning algorithms. We demonstrate through numerical examples that our proposed method can yield accurate predictive models by adaptation, resulting in a downstream MPC that outperforms several baselines. | [
"['Jiaqi Yan' 'Ankush Chakrabarty' 'Alisa Rupenyan' 'John Lygeros']"
]
|
null | null | 2404.12104 | null | null | http://arxiv.org/pdf/2404.12104v1 | 2024-04-18T11:38:25Z | 2024-04-18T11:38:25Z | Ethical-Lens: Curbing Malicious Usages of Open-Source Text-to-Image
Models | The burgeoning landscape of text-to-image models, exemplified by innovations such as Midjourney and DALLE 3, has revolutionized content creation across diverse sectors. However, these advancements bring forth critical ethical concerns, particularly with the misuse of open-source models to generate content that violates societal norms. Addressing this, we introduce Ethical-Lens, a framework designed to facilitate the value-aligned usage of text-to-image tools without necessitating internal model revision. Ethical-Lens ensures value alignment in text-to-image models across toxicity and bias dimensions by refining user commands and rectifying model outputs. Systematic evaluation metrics, combining GPT4-V, HEIM, and FairFace scores, assess alignment capability. Our experiments reveal that Ethical-Lens enhances alignment capabilities to levels comparable with or superior to commercial models like DALLE 3, ensuring user-generated content adheres to ethical standards while maintaining image quality. This study indicates the potential of Ethical-Lens to ensure the sustainable development of open-source text-to-image tools and their beneficial integration into society. Our code is available at https://github.com/yuzhu-cai/Ethical-Lens. | [
"['Yuzhu Cai' 'Sheng Yin' 'Yuxi Wei' 'Chenxin Xu' 'Weibo Mao'\n 'Felix Juefei-Xu' 'Siheng Chen' 'Yanfeng Wang']"
]
|
null | null | 2404.12130 | null | null | http://arxiv.org/pdf/2404.12130v1 | 2024-04-18T12:31:48Z | 2024-04-18T12:31:48Z | One-Shot Sequential Federated Learning for Non-IID Data by Enhancing
Local Model Diversity | Traditional federated learning mainly focuses on parallel settings (PFL), which can suffer significant communication and computation costs. In contrast, one-shot and sequential federated learning (SFL) have emerged as innovative paradigms to alleviate these costs. However, the issue of non-IID (Independent and Identically Distributed) data persists as a significant challenge in one-shot and SFL settings, exacerbated by the restricted communication between clients. In this paper, we improve the one-shot sequential federated learning for non-IID data by proposing a local model diversity-enhancing strategy. Specifically, to leverage the potential of local model diversity for improving model performance, we introduce a local model pool for each client that comprises diverse models generated during local training, and propose two distance measurements to further enhance the model diversity and mitigate the effect of non-IID data. Consequently, our proposed framework can improve the global model performance while maintaining low communication costs. Extensive experiments demonstrate that our method exhibits superior performance to existing one-shot PFL methods and achieves better accuracy compared with state-of-the-art one-shot SFL methods on both label-skew and domain-shift tasks (e.g., 6%+ accuracy improvement on the CIFAR-10 dataset). | [
"['Naibo Wang' 'Yuchen Deng' 'Wenjie Feng' 'Shichen Fan' 'Jianwei Yin'\n 'See-Kiong Ng']"
]
|
null | null | 2404.12141 | null | null | http://arxiv.org/pdf/2404.12141v4 | 2024-05-28T03:48:38Z | 2024-04-18T12:43:39Z | MolCRAFT: Structure-Based Drug Design in Continuous Parameter Space | Generative models for structure-based drug design (SBDD) have shown promising results in recent years. Existing works mainly focus on how to generate molecules with higher binding affinity, ignoring the feasibility prerequisites for generated 3D poses and resulting in false positives. We conduct thorough studies on key factors of ill-conformational problems when applying autoregressive methods and diffusion to SBDD, including mode collapse and hybrid continuous-discrete space. In this paper, we introduce MolCRAFT, the first SBDD model that operates in the continuous parameter space, together with a novel noise reduced sampling strategy. Empirical results show that our model consistently achieves superior performance in binding affinity with more stable 3D structure, demonstrating our ability to accurately model interatomic interactions. To our best knowledge, MolCRAFT is the first to achieve reference-level Vina Scores (-6.59 kcal/mol) with comparable molecular size, outperforming other strong baselines by a wide margin (-0.84 kcal/mol). Code is available at https://github.com/AlgoMole/MolCRAFT. | [
"['Yanru Qu' 'Keyue Qiu' 'Yuxuan Song' 'Jingjing Gong' 'Jiawei Han'\n 'Mingyue Zheng' 'Hao Zhou' 'Wei-Ying Ma']"
]
|
null | null | 2404.12142 | null | null | http://arxiv.org/pdf/2404.12142v1 | 2024-04-17T16:50:14Z | 2024-04-17T16:50:14Z | SDIP: Self-Reinforcement Deep Image Prior Framework for Image Processing | Deep image prior (DIP) proposed in recent research has revealed the inherent trait of convolutional neural networks (CNN) for capturing substantial low-level image statistics priors. This framework efficiently addresses the inverse problems in image processing and has induced extensive applications in various domains. However, as the whole algorithm is initialized randomly, the DIP algorithm often lacks stability. Thus, this method still has space for further improvement. In this paper, we propose the self-reinforcement deep image prior (SDIP) as an improved version of the original DIP. We observed that the changes in the DIP networks' input and output are highly correlated during each iteration. SDIP efficiently utilizes this trait in a reinforcement learning manner, where the current iteration's output is utilized by a steering algorithm to update the network input for the next iteration, guiding the algorithm toward improved results. Experimental results across multiple applications demonstrate that our proposed SDIP framework offers improvement compared to the original DIP method and other state-of-the-art methods. | [
"['Ziyu Shu' 'Zhixin Pan']"
]
|
null | null | 2404.12150 | null | null | http://arxiv.org/pdf/2404.12150v1 | 2024-04-18T12:55:18Z | 2024-04-18T12:55:18Z | Aligning language models with human preferences | Language models (LMs) trained on vast quantities of text data can acquire sophisticated skills such as generating summaries, answering questions or generating code. However, they also manifest behaviors that violate human preferences, e.g., they can generate offensive content, falsehoods or perpetuate social biases. In this thesis, I explore several approaches to aligning LMs with human preferences. First, I argue that aligning LMs can be seen as Bayesian inference: conditioning a prior (base, pretrained LM) on evidence about human preferences (Chapter 2). Conditioning on human preferences can be implemented in numerous ways. In Chapter 3, I investigate the relation between two approaches to finetuning pretrained LMs using feedback given by a scoring function: reinforcement learning from human feedback (RLHF) and distribution matching. I show that RLHF can be seen as a special case of distribution matching but distributional matching is strictly more general. In chapter 4, I show how to extend the distribution matching to conditional language models. Finally, in chapter 5 I explore a different root: conditioning an LM on human preferences already during pretraining. I show that involving human feedback from the very start tends to be more effective than using it only during supervised finetuning. Overall, these results highlight the room for alignment techniques different from and complementary to RLHF. | [
"['Tomasz Korbak']"
]
|
null | null | 2404.12172 | null | null | http://arxiv.org/pdf/2404.12172v2 | 2024-06-10T10:05:01Z | 2024-04-18T13:27:29Z | How to Benchmark Vision Foundation Models for Semantic Segmentation? | Recent vision foundation models (VFMs) have demonstrated proficiency in various tasks but require supervised fine-tuning to perform the task of semantic segmentation effectively. Benchmarking their performance is essential for selecting current models and guiding future model developments for this task. The lack of a standardized benchmark complicates comparisons. Therefore, the primary objective of this paper is to study how VFMs should be benchmarked for semantic segmentation. To do so, various VFMs are fine-tuned under various settings, and the impact of individual settings on the performance ranking and training time is assessed. Based on the results, the recommendation is to fine-tune the ViT-B variants of VFMs with a 16x16 patch size and a linear decoder, as these settings are representative of using a larger model, more advanced decoder and smaller patch size, while reducing training time by more than 13 times. Using multiple datasets for training and evaluation is also recommended, as the performance ranking across datasets and domain shifts varies. Linear probing, a common practice for some VFMs, is not recommended, as it is not representative of end-to-end fine-tuning. The benchmarking setup recommended in this paper enables a performance analysis of VFMs for semantic segmentation. The findings of such an analysis reveal that pretraining with promptable segmentation is not beneficial, whereas masked image modeling (MIM) with abstract representations is crucial, even more important than the type of supervision used. The code for efficiently fine-tuning VFMs for semantic segmentation can be accessed through the project page at: https://tue-mps.github.io/benchmark-vfm-ss/. | [
"['Tommie Kerssies' 'Daan de Geus' 'Gijs Dubbelman']"
]
|
null | null | 2404.12186 | null | null | http://arxiv.org/pdf/2404.12186v3 | 2024-06-06T21:02:21Z | 2024-04-18T13:49:07Z | Privacy-Preserving UCB Decision Process Verification via zk-SNARKs | With the increasingly widespread application of machine learning, how to strike a balance between protecting the privacy of data and algorithm parameters and ensuring the verifiability of machine learning has always been a challenge. This study explores the intersection of reinforcement learning and data privacy, specifically addressing the Multi-Armed Bandit (MAB) problem with the Upper Confidence Bound (UCB) algorithm. We introduce zkUCB, an innovative algorithm that employs the Zero-Knowledge Succinct Non-Interactive Argument of Knowledge (zk-SNARKs) to enhance UCB. zkUCB is carefully designed to safeguard the confidentiality of training data and algorithmic parameters, ensuring transparent UCB decision-making. Experiments highlight zkUCB's superior performance, attributing its enhanced reward to judicious quantization bit usage that reduces information entropy in the decision-making process. zkUCB's proof size and verification time scale linearly with the execution steps of zkUCB. This showcases zkUCB's adept balance between data security and operational efficiency. This approach contributes significantly to the ongoing discourse on reinforcing data privacy in complex decision-making processes, offering a promising solution for privacy-sensitive applications. | [
"['Xikun Jiang' 'He Lyu' 'Chenhao Ying' 'Yibin Xu' 'Boris Düdder'\n 'Yuan Luo']"
]
|
null | null | 2404.12187 | null | null | http://arxiv.org/pdf/2404.12187v1 | 2024-04-18T13:49:09Z | 2024-04-18T13:49:09Z | Stability-informed Bayesian Optimization for MPC Cost Function Learning | Designing predictive controllers towards optimal closed-loop performance while maintaining safety and stability is challenging. This work explores closed-loop learning for predictive control parameters under imperfect information while considering closed-loop stability. We employ constrained Bayesian optimization to learn a model predictive controller's (MPC) cost function parametrized as a feedforward neural network, optimizing closed-loop behavior as well as minimizing model-plant mismatch. Doing so offers a high degree of freedom and, thus, the opportunity for efficient and global optimization towards the desired and optimal closed-loop behavior. We extend this framework by stability constraints on the learned controller parameters, exploiting the optimal value function of the underlying MPC as a Lyapunov candidate. The effectiveness of the proposed approach is underlined in simulations, highlighting its performance and safety capabilities. | [
"['Sebastian Hirt' 'Maik Pfefferkorn' 'Ali Mesbah' 'Rolf Findeisen']"
]
|
null | null | 2404.12190 | null | null | http://arxiv.org/abs/2404.12190v2 | 2024-05-09T10:07:47Z | 2024-04-18T13:53:32Z | Estimating the Hessian Matrix of Ranking Objectives for Stochastic
Learning to Rank with Gradient Boosted Trees | Stochastic learning to rank (LTR) is a recent branch in the LTR field that concerns the optimization of probabilistic ranking models. Their probabilistic behavior enables certain ranking qualities that are impossible with deterministic models. For example, they can increase the diversity of displayed documents, increase fairness of exposure over documents, and better balance exploitation and exploration through randomization. A core difficulty in LTR is gradient estimation, for this reason, existing stochastic LTR methods have been limited to differentiable ranking models (e.g., neural networks). This is in stark contrast with the general field of LTR where Gradient Boosted Decision Trees (GBDTs) have long been considered the state-of-the-art. In this work, we address this gap by introducing the first stochastic LTR method for GBDTs. Our main contribution is a novel estimator for the second-order derivatives, i.e., the Hessian matrix, which is a requirement for effective GBDTs. To efficiently compute both the first and second-order derivatives simultaneously, we incorporate our estimator into the existing PL-Rank framework, which was originally designed for first-order derivatives only. Our experimental results indicate that stochastic LTR without the Hessian has extremely poor performance, whilst the performance is competitive with the current state-of-the-art with our estimated Hessian. Thus, through the contribution of our novel Hessian estimation method, we have successfully introduced GBDTs to stochastic LTR. | [
"['Jingwei Kang' 'Maarten de Rijke' 'Harrie Oosterhuis']"
]
|
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