categories
string | doi
string | id
string | year
float64 | venue
string | link
string | updated
string | published
string | title
string | abstract
string | authors
sequence |
|---|---|---|---|---|---|---|---|---|---|---|
null | null | 2406.03504 | null | null | http://arxiv.org/pdf/2406.03504v1 | 2024-06-03T19:40:06Z | 2024-06-03T19:40:06Z | A New Branch-and-Bound Pruning Framework for $\ell_0$-Regularized
Problems | We consider the resolution of learning problems involving $ell_0$-regularization via Branch-and-Bound (BnB) algorithms. These methods explore regions of the feasible space of the problem and check whether they do not contain solutions through "pruning tests". In standard implementations, evaluating a pruning test requires to solve a convex optimization problem, which may result in computational bottlenecks. In this paper, we present an alternative to implement pruning tests for some generic family of $ell_0$-regularized problems. Our proposed procedure allows the simultaneous assessment of several regions and can be embedded in standard BnB implementations with a negligible computational overhead. We show through numerical simulations that our pruning strategy can improve the solving time of BnB procedures by several orders of magnitude for typical problems encountered in machine-learning applications. | [
"['Theo Guyard' 'Cédric Herzet' 'Clément Elvira' 'Ayşe-Nur Arslan']"
] |
null | null | 2406.03505 | null | null | http://arxiv.org/pdf/2406.03505v1 | 2024-06-04T20:32:14Z | 2024-06-04T20:32:14Z | Dynamic and Adaptive Feature Generation with LLM | The representation of feature space is a crucial environment where data points get vectorized and embedded for upcoming modeling. Thus the efficacy of machine learning (ML) algorithms is closely related to the quality of feature engineering. As one of the most important techniques, feature generation transforms raw data into an optimized feature space conducive to model training and further refines the space. Despite the advancements in automated feature engineering and feature generation, current methodologies often suffer from three fundamental issues: lack of explainability, limited applicability, and inflexible strategy. These shortcomings frequently hinder and limit the deployment of ML models across varied scenarios. Our research introduces a novel approach adopting large language models (LLMs) and feature-generating prompts to address these challenges. We propose a dynamic and adaptive feature generation method that enhances the interpretability of the feature generation process. Our approach broadens the applicability across various data types and tasks and draws advantages over strategic flexibility. A broad range of experiments showcases that our approach is significantly superior to existing methods. | [
"['Xinhao Zhang' 'Jinghan Zhang' 'Banafsheh Rekabdar' 'Yuanchun Zhou'\n 'Pengfei Wang' 'Kunpeng Liu']"
] |
null | null | 2406.03506 | null | null | http://arxiv.org/pdf/2406.03506v3 | 2024-06-18T09:36:13Z | 2024-06-04T20:33:35Z | Fuzzy Convolution Neural Networks for Tabular Data Classification | Recently, convolution neural networks (CNNs) have attracted a great deal of attention due to their remarkable performance in various domains, particularly in image and text classification tasks. However, their application to tabular data classification remains underexplored. There are many fields such as bioinformatics, finance, medicine where nonimage data are prevalent. Adaption of CNNs to classify nonimage data remains highly challenging. This paper investigates the efficacy of CNNs for tabular data classification, aiming to bridge the gap between traditional machine learning approaches and deep learning techniques. We propose a novel framework fuzzy convolution neural network (FCNN) tailored specifically for tabular data to capture local patterns within feature vectors. In our approach, we map feature values to fuzzy memberships. The fuzzy membership vectors are converted into images that are used to train the CNN model. The trained CNN model is used to classify unknown feature vectors. To validate our approach, we generated six complex noisy data sets. We used randomly selected seventy percent samples from each data set for training and thirty percent for testing. The data sets were also classified using the state-of-the-art machine learning algorithms such as the decision tree (DT), support vector machine (SVM), fuzzy neural network (FNN), Bayes classifier, and Random Forest (RF). Experimental results demonstrate that our proposed model can effectively learn meaningful representations from tabular data, achieving competitive or superior performance compared to existing methods. Overall, our finding suggests that the proposed FCNN model holds promise as a viable alternative for tabular data classification tasks, offering a fresh prospective and potentially unlocking new opportunities for leveraging deep learning in structured data analysis. | [
"['Arun D. Kulkarni']"
] |
null | null | 2406.03507 | null | null | http://arxiv.org/pdf/2406.03507v1 | 2024-06-05T06:28:39Z | 2024-06-05T06:28:39Z | Robust Prediction Model for Multidimensional and Unbalanced Datasets | Data Mining is a promising field and is applied in multiple domains for its predictive capabilities. Data in the real world cannot be readily used for data mining as it suffers from the problems of multidimensionality, unbalance and missing values. It is difficult to use its predictive capabilities by novice users. It is difficult for a beginner to find the relevant set of attributes from a large pool of data available. The paper presents a Robust Prediction Model that finds a relevant set of attributes; resolves the problems of unbalanced and multidimensional real-life datasets and helps in finding patterns for informed decision making. Model is tested upon five different datasets in the domain of Health Sector, Education, Business and Fraud Detection. The results showcase the robust behaviour of the model and its applicability in various domains. | [
"['Pooja Thakar' 'Anil Mehta' 'Manisha']"
] |
null | null | 2406.03508 | null | null | http://arxiv.org/pdf/2406.03508v2 | 2024-06-11T06:11:36Z | 2024-06-05T07:27:15Z | Mutual Information Guided Backdoor Mitigation for Pre-trained Encoders | Self-supervised learning (SSL) is increasingly attractive for pre-training encoders without requiring labeled data. Downstream tasks built on top of those pre-trained encoders can achieve nearly state-of-the-art performance. The pre-trained encoders by SSL, however, are vulnerable to backdoor attacks as demonstrated by existing studies. Numerous backdoor mitigation techniques are designed for downstream task models. However, their effectiveness is impaired and limited when adapted to pre-trained encoders, due to the lack of label information when pre-training. To address backdoor attacks against pre-trained encoders, in this paper, we innovatively propose a mutual information guided backdoor mitigation technique, named MIMIC. MIMIC treats the potentially backdoored encoder as the teacher net and employs knowledge distillation to distill a clean student encoder from the teacher net. Different from existing knowledge distillation approaches, MIMIC initializes the student with random weights, inheriting no backdoors from teacher nets. Then MIMIC leverages mutual information between each layer and extracted features to locate where benign knowledge lies in the teacher net, with which distillation is deployed to clone clean features from teacher to student. We craft the distillation loss with two aspects, including clone loss and attention loss, aiming to mitigate backdoors and maintain encoder performance at the same time. Our evaluation conducted on two backdoor attacks in SSL demonstrates that MIMIC can significantly reduce the attack success rate by only utilizing <5% of clean data, surpassing seven state-of-the-art backdoor mitigation techniques. | [
"['Tingxu Han' 'Weisong Sun' 'Ziqi Ding' 'Chunrong Fang' 'Hanwei Qian'\n 'Jiaxun Li' 'Zhenyu Chen' 'Xiangyu Zhang']"
] |
null | null | 2406.03511 | null | null | http://arxiv.org/pdf/2406.03511v1 | 2024-06-05T10:06:07Z | 2024-06-05T10:06:07Z | MagiNet: Mask-Aware Graph Imputation Network for Incomplete Traffic Data | Due to detector malfunctions and communication failures, missing data is ubiquitous during the collection of traffic data. Therefore, it is of vital importance to impute the missing values to facilitate data analysis and decision-making for Intelligent Transportation System (ITS). However, existing imputation methods generally perform zero pre-filling techniques to initialize missing values, introducing inevitable noises. Moreover, we observe prevalent over-smoothing interpolations, falling short in revealing the intrinsic spatio-temporal correlations of incomplete traffic data. To this end, we propose Mask-Aware Graph imputation Network: MagiNet. Our method designs an adaptive mask spatio-temporal encoder to learn the latent representations of incomplete data, eliminating the reliance on pre-filling missing values. Furthermore, we devise a spatio-temporal decoder that stacks multiple blocks to capture the inherent spatial and temporal dependencies within incomplete traffic data, alleviating over-smoothing imputation. Extensive experiments demonstrate that our method outperforms state-of-the-art imputation methods on five real-world traffic datasets, yielding an average improvement of 4.31% in RMSE and 3.72% in MAPE. | [
"['Jianping Zhou' 'Bin Lu' 'Zhanyu Liu' 'Siyu Pan' 'Xuejun Feng' 'Hua Wei'\n 'Guanjie Zheng' 'Xinbing Wang' 'Chenghu Zhou']"
] |
null | null | 2406.03516 | null | null | http://arxiv.org/pdf/2406.03516v1 | 2024-06-05T16:39:32Z | 2024-06-05T16:39:32Z | Buffered Asynchronous Secure Aggregation for Cross-Device Federated
Learning | Asynchronous federated learning (AFL) is an effective method to address the challenge of device heterogeneity in cross-device federated learning. However, AFL is usually incompatible with existing secure aggregation protocols used to protect user privacy in federated learning because most existing secure aggregation protocols are based on synchronous aggregation. To address this problem, we propose a novel secure aggregation protocol named buffered asynchronous secure aggregation (BASA) in this paper. Compared with existing protocols, BASA is fully compatible with AFL and provides secure aggregation under the condition that each user only needs one round of communication with the server without relying on any synchronous interaction among users. Based on BASA, we propose the first AFL method which achieves secure aggregation without extra requirements on hardware. We empirically demonstrate that BASA outperforms existing secure aggregation protocols for cross-device federated learning in terms of training efficiency and scalability. | [
"['Kun Wang' 'Yi-Rui Yang' 'Wu-Jun Li']"
] |
null | null | 2406.03519 | null | null | http://arxiv.org/pdf/2406.03519v1 | 2024-06-05T17:41:42Z | 2024-06-05T17:41:42Z | Noise-Aware Algorithm for Heterogeneous Differentially Private Federated
Learning | High utility and rigorous data privacy are of the main goals of a federated learning (FL) system, which learns a model from the data distributed among some clients. The latter has been tried to achieve by using differential privacy in FL (DPFL). There is often heterogeneity in clients privacy requirements, and existing DPFL works either assume uniform privacy requirements for clients or are not applicable when server is not fully trusted (our setting). Furthermore, there is often heterogeneity in batch and/or dataset size of clients, which as shown, results in extra variation in the DP noise level across clients model updates. With these sources of heterogeneity, straightforward aggregation strategies, e.g., assigning clients aggregation weights proportional to their privacy parameters will lead to lower utility. We propose Robust-HDP, which efficiently estimates the true noise level in clients model updates and reduces the noise-level in the aggregated model updates considerably. Robust-HDP improves utility and convergence speed, while being safe to the clients that may maliciously send falsified privacy parameter to server. Extensive experimental results on multiple datasets and our theoretical analysis confirm the effectiveness of Robust-HDP. Our code can be found here. | [
"['Saber Malekmohammadi' 'Yaoliang Yu' 'Yang Cao']"
] |
null | null | 2406.03520 | null | null | http://arxiv.org/pdf/2406.03520v1 | 2024-06-05T17:53:55Z | 2024-06-05T17:53:55Z | VideoPhy: Evaluating Physical Commonsense for Video Generation | Recent advances in internet-scale video data pretraining have led to the development of text-to-video generative models that can create high-quality videos across a broad range of visual concepts and styles. Due to their ability to synthesize realistic motions and render complex objects, these generative models have the potential to become general-purpose simulators of the physical world. However, it is unclear how far we are from this goal with the existing text-to-video generative models. To this end, we present VideoPhy, a benchmark designed to assess whether the generated videos follow physical commonsense for real-world activities (e.g. marbles will roll down when placed on a slanted surface). Specifically, we curate a list of 688 captions that involve interactions between various material types in the physical world (e.g., solid-solid, solid-fluid, fluid-fluid). We then generate videos conditioned on these captions from diverse state-of-the-art text-to-video generative models, including open models (e.g., VideoCrafter2) and closed models (e.g., Lumiere from Google, Pika). Further, our human evaluation reveals that the existing models severely lack the ability to generate videos adhering to the given text prompts, while also lack physical commonsense. Specifically, the best performing model, Pika, generates videos that adhere to the caption and physical laws for only 19.7% of the instances. VideoPhy thus highlights that the video generative models are far from accurately simulating the physical world. Finally, we also supplement the dataset with an auto-evaluator, VideoCon-Physics, to assess semantic adherence and physical commonsense at scale. | [
"['Hritik Bansal' 'Zongyu Lin' 'Tianyi Xie' 'Zeshun Zong' 'Michal Yarom'\n 'Yonatan Bitton' 'Chenfanfu Jiang' 'Yizhou Sun' 'Kai-Wei Chang'\n 'Aditya Grover']"
] |
null | null | 2406.03537 | null | null | http://arxiv.org/pdf/2406.03537v1 | 2024-06-05T18:00:02Z | 2024-06-05T18:00:02Z | A Geometric View of Data Complexity: Efficient Local Intrinsic Dimension
Estimation with Diffusion Models | High-dimensional data commonly lies on low-dimensional submanifolds, and estimating the local intrinsic dimension (LID) of a datum -- i.e. the dimension of the submanifold it belongs to -- is a longstanding problem. LID can be understood as the number of local factors of variation: the more factors of variation a datum has, the more complex it tends to be. Estimating this quantity has proven useful in contexts ranging from generalization in neural networks to detection of out-of-distribution data, adversarial examples, and AI-generated text. The recent successes of deep generative models present an opportunity to leverage them for LID estimation, but current methods based on generative models produce inaccurate estimates, require more than a single pre-trained model, are computationally intensive, or do not exploit the best available deep generative models, i.e. diffusion models (DMs). In this work, we show that the Fokker-Planck equation associated with a DM can provide a LID estimator which addresses all the aforementioned deficiencies. Our estimator, called FLIPD, is compatible with all popular DMs, and outperforms existing baselines on LID estimation benchmarks. We also apply FLIPD on natural images where the true LID is unknown. Compared to competing estimators, FLIPD exhibits a higher correlation with non-LID measures of complexity, better matches a qualitative assessment of complexity, and is the only estimator to remain tractable with high-resolution images at the scale of Stable Diffusion. | [
"['Hamidreza Kamkari' 'Brendan Leigh Ross' 'Rasa Hosseinzadeh'\n 'Jesse C. Cresswell' 'Gabriel Loaiza-Ganem']"
] |
null | null | 2406.03548 | null | null | http://arxiv.org/pdf/2406.03548v1 | 2024-06-05T18:00:09Z | 2024-06-05T18:00:09Z | Robust Communication and Computation using Deep Learning via Joint
Uncertainty Injection | The convergence of communication and computation, along with the integration of machine learning and artificial intelligence, stand as key empowering pillars for the sixth-generation of communication systems (6G). This paper considers a network of one base station serving a number of devices simultaneously using spatial multiplexing. The paper then presents an innovative deep learning-based approach to simultaneously manage the transmit and computing powers, alongside computation allocation, amidst uncertainties in both channel and computing states information. More specifically, the paper aims at proposing a robust solution that minimizes the worst-case delay across the served devices subject to computation and power constraints. The paper uses a deep neural network (DNN)-based solution that maps estimated channels and computation requirements to optimized resource allocations. During training, uncertainty samples are injected after the DNN output to jointly account for both communication and computation estimation errors. The DNN is then trained via backpropagation using the robust utility, thus implicitly learning the uncertainty distributions. Our results validate the enhanced robust delay performance of the joint uncertainty injection versus the classical DNN approach, especially in high channel and computational uncertainty regimes. | [
"['Robert-Jeron Reifert' 'Hayssam Dahrouj' 'Alaa Alameer Ahmad'\n 'Haris Gacanin' 'Aydin Sezgin']"
] |
null | null | 2406.03562 | null | null | http://arxiv.org/pdf/2406.03562v1 | 2024-06-05T18:17:33Z | 2024-06-05T18:17:33Z | Neural empirical interpolation method for nonlinear model reduction | In this paper, we introduce the neural empirical interpolation method (NEIM), a neural network-based alternative to the discrete empirical interpolation method for reducing the time complexity of computing the nonlinear term in a reduced order model (ROM) for a parameterized nonlinear partial differential equation. NEIM is a greedy algorithm which accomplishes this reduction by approximating an affine decomposition of the nonlinear term of the ROM, where the vector terms of the expansion are given by neural networks depending on the ROM solution, and the coefficients are given by an interpolation of some "optimal" coefficients. Because NEIM is based on a greedy strategy, we are able to provide a basic error analysis to investigate its performance. NEIM has the advantages of being easy to implement in models with automatic differentiation, of being a nonlinear projection of the ROM nonlinearity, of being efficient for both nonlocal and local nonlinearities, and of relying solely on data and not the explicit form of the ROM nonlinearity. We demonstrate the effectiveness of the methodology on solution-dependent and solution-independent nonlinearities, a nonlinear elliptic problem, and a nonlinear parabolic model of liquid crystals. | [
"['Max Hirsch' 'Federico Pichi' 'Jan S. Hesthaven']"
] |
null | null | 2406.03569 | null | null | http://arxiv.org/pdf/2406.03569v1 | 2024-06-05T18:31:37Z | 2024-06-05T18:31:37Z | GFN: A graph feedforward network for resolution-invariant reduced
operator learning in multifidelity applications | This work presents a novel resolution-invariant model order reduction strategy for multifidelity applications. We base our architecture on a novel neural network layer developed in this work, the graph feedforward network, which extends the concept of feedforward networks to graph-structured data by creating a direct link between the weights of a neural network and the nodes of a mesh, enhancing the interpretability of the network. We exploit the method's capability of training and testing on different mesh sizes in an autoencoder-based reduction strategy for parametrised partial differential equations. We show that this extension comes with provable guarantees on the performance via error bounds. The capabilities of the proposed methodology are tested on three challenging benchmarks, including advection-dominated phenomena and problems with a high-dimensional parameter space. The method results in a more lightweight and highly flexible strategy when compared to state-of-the-art models, while showing excellent generalisation performance in both single fidelity and multifidelity scenarios. | [
"['Oisín M. Morrison' 'Federico Pichi' 'Jan S. Hesthaven']"
] |
null | null | 2406.03574 | null | null | http://arxiv.org/pdf/2406.03574v1 | 2024-06-05T18:39:28Z | 2024-06-05T18:39:28Z | A Simple Learning-Augmented Algorithm for Online Packing with Concave
Objectives | Learning-augmented algorithms has been extensively studied recently in the computer-science community, due to the potential of using machine learning predictions in order to improve the performance of algorithms. Predictions are especially useful for online algorithms making irrevocable decisions without knowledge of the future. Such learning-augmented algorithms aim to overcome the limitations of classical online algorithms when the predictions are accurate, and still perform comparably when the predictions are inaccurate. A common approach is to adapt existing online algorithms to the particular advice notion employed, which often involves understanding previous sophisticated algorithms and their analyses. However, ideally, one would simply use previous online solutions in a black-box fashion, without much loss in the approximation guarantees. Such clean solutions that avoid opening up black-boxes are often rare, and may be even missed the first time around. For example, Grigorescu et al. (NeurIPS 22) proposed a learning-augmented algorithms for online covering linear programs, but it later turned out that their results can be subsumed by a natural approach that switches between the advice and an online algorithm given as a black-box, as noted in their paper. In this work, we introduce and analyze a simple learning-augmented algorithm for online packing problems with linear constraints and concave objectives. We exhibit several direct applications of our framework including online packing linear programming, knapsack, resource management benefit, throughput maximization, and network utility maximization. We further raise the problem of understanding necessary and sufficient conditions for when such simple black-box solutions may be optimal. We believe this is an important direction of research that would unify many ad-hoc approaches from the literature. | [
"['Elena Grigorescu' 'Young-San Lin' 'Maoyuan Song']"
] |
null | null | 2406.03575 | null | null | http://arxiv.org/pdf/2406.03575v1 | 2024-06-05T18:43:46Z | 2024-06-05T18:43:46Z | Reconciling Heterogeneous Effects in Causal Inference | In this position and problem pitch paper, we offer a solution to the reference class problem in causal inference. We apply the Reconcile algorithm for model multiplicity in machine learning to reconcile heterogeneous effects in causal inference. Discrepancy between conditional average treatment effect (CATE) estimators of heterogeneous effects poses the reference class problem, where estimates for individual predictions differ by choice of reference class. By adopting the individual to group framework for interpreting probability, we can recognize that the reference class problem -- which appears across fields such as philosophy of science and causal inference -- is equivalent to the model multiplicity problem in computer science. We then apply the Reconcile Algorithm to reconcile differences in estimates of individual probability among CATE estimators. Because the reference class problem manifests in contexts of individual probability prediction using group-based evidence, our results have tangible implications for ensuring fair outcomes in high-stakes such as healthcare, insurance, and housing, especially for marginalized communities. By highlighting the importance of mitigating disparities in predictive modeling, our work invites further exploration into interdisciplinary strategies that combine technical rigor with a keen awareness of social implications. Ultimately, our findings advocate for a holistic approach to algorithmic fairness, underscoring the critical role of thoughtful, well-rounded solutions in achieving the broader goals of equity and access. | [
"['Audrey Chang' 'Emily Diana' 'Alexander Williams Tolbert']"
] |
null | null | 2406.03585 | null | null | http://arxiv.org/pdf/2406.03585v1 | 2024-06-05T19:01:43Z | 2024-06-05T19:01:43Z | A Comparison of Recent Algorithms for Symbolic Regression to Genetic
Programming | Symbolic regression is a machine learning method with the goal to produce interpretable results. Unlike other machine learning methods such as, e.g. random forests or neural networks, which are opaque, symbolic regression aims to model and map data in a way that can be understood by scientists. Recent advancements, have attempted to bridge the gap between these two fields; new methodologies attempt to fuse the mapping power of neural networks and deep learning techniques with the explanatory power of symbolic regression. In this paper, we examine these new emerging systems and test the performance of an end-to-end transformer model for symbolic regression versus the reigning traditional methods based on genetic programming that have spearheaded symbolic regression throughout the years. We compare these systems on novel datasets to avoid bias to older methods who were improved on well-known benchmark datasets. Our results show that traditional GP methods as implemented e.g., by Operon still remain superior to two recently published symbolic regression methods. | [
"['Yousef A. Radwan' 'Gabriel Kronberger' 'Stephan Winkler']"
] |
null | null | 2406.03586 | null | null | http://arxiv.org/pdf/2406.03586v2 | 2024-06-10T12:09:37Z | 2024-06-05T19:05:08Z | CountCLIP -- [Re] Teaching CLIP to Count to Ten | Large vision-language models (VLMs) are shown to learn rich joint image-text representations enabling high performances in relevant downstream tasks. However, they fail to showcase their quantitative understanding of objects, and they lack good counting-aware representation. This paper conducts a reproducibility study of 'Teaching CLIP to Count to Ten' (Paiss et al., 2023), which presents a method to finetune a CLIP model (Radford et al., 2021) to improve zero-shot counting accuracy in an image while maintaining the performance for zero-shot classification by introducing a counting-contrastive loss term. We improve the model's performance on a smaller subset of their training data with lower computational resources. We verify these claims by reproducing their study with our own code. The implementation can be found at https://github.com/SforAiDl/CountCLIP. | [
"['Harshvardhan Mestha' 'Tejas Agrawal' 'Karan Bania' 'Shreyas V'\n 'Yash Bhisikar']"
] |
null | null | 2406.03591 | null | null | http://arxiv.org/pdf/2406.03591v1 | 2024-06-05T19:20:34Z | 2024-06-05T19:20:34Z | BVE + EKF: A viewpoint estimator for the estimation of the object's
position in the 3D task space using Extended Kalman Filters | RGB-D sensors face multiple challenges operating under open-field environments because of their sensitivity to external perturbations such as radiation or rain. Multiple works are approaching the challenge of perceiving the 3D position of objects using monocular cameras. However, most of these works focus mainly on deep learning-based solutions, which are complex, data-driven, and difficult to predict. So, we aim to approach the problem of predicting the 3D objects' position using a Gaussian viewpoint estimator named best viewpoint estimator (BVE) powered by an extended Kalman filter (EKF). The algorithm proved efficient on the tasks and reached a maximum average Euclidean error of about 32 mm. The experiments were deployed and evaluated in MATLAB using artificial Gaussian noise. Future work aims to implement the system in a robotic system. | [
"['Sandro Costa Magalhães' 'António Paulo Moreira'\n 'Filipe Neves dos Santos' 'Jorge Dias']"
] |
null | null | 2406.03594 | null | null | http://arxiv.org/abs/2406.03594v1 | 2024-06-05T19:31:19Z | 2024-06-05T19:31:19Z | Why is "Problems" Predictive of Positive Sentiment? A Case Study of
Explaining Unintuitive Features in Sentiment Classification | Explainable AI (XAI) algorithms aim to help users understand how a machine learning model makes predictions. To this end, many approaches explain which input features are most predictive of a target label. However, such explanations can still be puzzling to users (e.g., in product reviews, the word "problems" is predictive of positive sentiment). If left unexplained, puzzling explanations can have negative impacts. Explaining unintuitive associations between an input feature and a target label is an underexplored area in XAI research. We take an initial effort in this direction using unintuitive associations learned by sentiment classifiers as a case study. We propose approaches for (1) automatically detecting associations that can appear unintuitive to users and (2) generating explanations to help users understand why an unintuitive feature is predictive. Results from a crowdsourced study (N=300) found that our proposed approaches can effectively detect and explain predictive but unintuitive features in sentiment classification. | [
"['Jiaming Qu' 'Jaime Arguello' 'Yue Wang']"
] |
null | null | 2406.03599 | null | null | http://arxiv.org/pdf/2406.03599v1 | 2024-06-05T19:45:10Z | 2024-06-05T19:45:10Z | Hi5: 2D Hand Pose Estimation with Zero Human Annotation | We propose a new large synthetic hand pose estimation dataset, Hi5, and a novel inexpensive method for collecting high-quality synthetic data that requires no human annotation or validation. Leveraging recent advancements in computer graphics, high-fidelity 3D hand models with diverse genders and skin colors, and dynamic environments and camera movements, our data synthesis pipeline allows precise control over data diversity and representation, ensuring robust and fair model training. We generate a dataset with 583,000 images with accurate pose annotation using a single consumer PC that closely represents real-world variability. Pose estimation models trained with Hi5 perform competitively on real-hand benchmarks while surpassing models trained with real data when tested on occlusions and perturbations. Our experiments show promising results for synthetic data as a viable solution for data representation problems in real datasets. Overall, this paper provides a promising new approach to synthetic data creation and annotation that can reduce costs and increase the diversity and quality of data for hand pose estimation. | [
"['Masum Hasan' 'Cengiz Ozel' 'Nina Long' 'Alexander Martin'\n 'Samuel Potter' 'Tariq Adnan' 'Sangwu Lee' 'Amir Zadeh' 'Ehsan Hoque']"
] |
null | null | 2406.03603 | null | null | http://arxiv.org/pdf/2406.03603v1 | 2024-06-05T19:55:45Z | 2024-06-05T19:55:45Z | Alignment Calibration: Machine Unlearning for Contrastive Learning under
Auditing | Machine unlearning provides viable solutions to revoke the effect of certain training data on pre-trained model parameters. Existing approaches provide unlearning recipes for classification and generative models. However, a category of important machine learning models, i.e., contrastive learning (CL) methods, is overlooked. In this paper, we fill this gap by first proposing the framework of Machine Unlearning for Contrastive learning (MUC) and adapting existing methods. Furthermore, we observe that several methods are mediocre unlearners and existing auditing tools may not be sufficient for data owners to validate the unlearning effects in contrastive learning. We thus propose a novel method called Alignment Calibration (AC) by explicitly considering the properties of contrastive learning and optimizing towards novel auditing metrics to easily verify unlearning. We empirically compare AC with baseline methods on SimCLR, MoCo and CLIP. We observe that AC addresses drawbacks of existing methods: (1) achieving state-of-the-art performance and approximating exact unlearning (retraining); (2) allowing data owners to clearly visualize the effect caused by unlearning through black-box auditing. | [
"['Yihan Wang' 'Yiwei Lu' 'Guojun Zhang' 'Franziska Boenisch'\n 'Adam Dziedzic' 'Yaoliang Yu' 'Xiao-Shan Gao']"
] |
null | null | 2406.03611 | null | null | http://arxiv.org/pdf/2406.03611v1 | 2024-06-05T20:06:59Z | 2024-06-05T20:06:59Z | FedPylot: Navigating Federated Learning for Real-Time Object Detection
in Internet of Vehicles | The Internet of Vehicles (IoV) emerges as a pivotal component for autonomous driving and intelligent transportation systems (ITS), by enabling low-latency big data processing in a dense interconnected network that comprises vehicles, infrastructures, pedestrians and the cloud. Autonomous vehicles are heavily reliant on machine learning (ML) and can strongly benefit from the wealth of sensory data generated at the edge, which calls for measures to reconcile model training with preserving the privacy of sensitive user data. Federated learning (FL) stands out as a promising solution to train sophisticated ML models in vehicular networks while protecting the privacy of road users and mitigating communication overhead. This paper examines the federated optimization of the cutting-edge YOLOv7 model to tackle real-time object detection amid data heterogeneity, encompassing unbalancedness, concept drift, and label distribution skews. To this end, we introduce FedPylot, a lightweight MPI-based prototype to simulate federated object detection experiments on high-performance computing (HPC) systems, where we safeguard server-client communications using hybrid encryption. Our study factors in accuracy, communication cost, and inference speed, thereby presenting a balanced approach to the challenges faced by autonomous vehicles. We demonstrate promising results for the applicability of FL in IoV and hope that FedPylot will provide a basis for future research into federated real-time object detection. The source code is available at https://github.com/cyprienquemeneur/fedpylot. | [
"['Cyprien Quéméneur' 'Soumaya Cherkaoui']"
] |
null | null | 2406.03614 | null | null | http://arxiv.org/pdf/2406.03614v1 | 2024-06-05T20:19:09Z | 2024-06-05T20:19:09Z | Advancing Anomaly Detection: Non-Semantic Financial Data Encoding with
LLMs | Detecting anomalies in general ledger data is of utmost importance to ensure trustworthiness of financial records. Financial audits increasingly rely on machine learning (ML) algorithms to identify irregular or potentially fraudulent journal entries, each characterized by a varying number of transactions. In machine learning, heterogeneity in feature dimensions adds significant complexity to data analysis. In this paper, we introduce a novel approach to anomaly detection in financial data using Large Language Models (LLMs) embeddings. To encode non-semantic categorical data from real-world financial records, we tested 3 pre-trained general purpose sentence-transformer models. For the downstream classification task, we implemented and evaluated 5 optimized ML models including Logistic Regression, Random Forest, Gradient Boosting Machines, Support Vector Machines, and Neural Networks. Our experiments demonstrate that LLMs contribute valuable information to anomaly detection as our models outperform the baselines, in selected settings even by a large margin. The findings further underscore the effectiveness of LLMs in enhancing anomaly detection in financial journal entries, particularly by tackling feature sparsity. We discuss a promising perspective on using LLM embeddings for non-semantic data in the financial context and beyond. | [
"['Alexander Bakumenko' 'Kateřina Hlaváčková-Schindler' 'Claudia Plant'\n 'Nina C. Hubig']"
] |
null | null | 2406.03616 | null | null | http://arxiv.org/pdf/2406.03616v1 | 2024-06-05T20:23:52Z | 2024-06-05T20:23:52Z | BEACON: A Bayesian Optimization Strategy for Novelty Search in Expensive
Black-Box Systems | Novelty search (NS) refers to a class of exploration algorithms that automatically uncover diverse system behaviors through simulations or experiments. Systematically obtaining diverse outcomes is a key component in many real-world design problems such as material and drug discovery, neural architecture search, reinforcement learning, and robot navigation. Since the relationship between the inputs and outputs (i.e., behaviors) of these complex systems is typically not available in closed form, NS requires a black-box perspective. Consequently, popular NS algorithms rely on evolutionary optimization and other meta-heuristics that require intensive sampling of the input space, which is impractical when the system is expensive to evaluate. We propose a Bayesian optimization inspired algorithm for sample-efficient NS that is specifically designed for such expensive black-box systems. Our approach models the input-to-behavior mapping with multi-output Gaussian processes (MOGP) and selects the next point to evaluate by maximizing a novelty metric that depends on a posterior sample drawn from the MOGP that promotes both exploration and exploitation. By leveraging advances in efficient posterior sampling and high-dimensional Gaussian process modeling, we discuss how our approach can be made scalable with respect to both amount of data and number of inputs. We test our approach on ten synthetic benchmark problems and eight real-world problems (with up to 2133 inputs) including new applications such as discovery of diverse metal organic frameworks for use in clean energy technology. We show that our approach greatly outperforms existing NS algorithms by finding substantially larger sets of diverse behaviors under limited sample budgets. | [
"['Wei-Ting Tang' 'Ankush Chakrabarty' 'Joel A. Paulson']"
] |
null | null | 2406.03619 | null | null | http://arxiv.org/pdf/2406.03619v1 | 2024-06-05T20:38:30Z | 2024-06-05T20:38:30Z | Symmetry Discovery Beyond Affine Transformations | Symmetry detection has been shown to improve various machine learning tasks. In the context of continuous symmetry detection, current state of the art experiments are limited to the detection of affine transformations. Under the manifold assumption, we outline a framework for discovering continuous symmetry in data beyond the affine transformation group. We also provide a similar framework for discovering discrete symmetry. We experimentally compare our method to an existing method known as LieGAN and show that our method is competitive at detecting affine symmetries for large sample sizes and superior than LieGAN for small sample sizes. We also show our method is able to detect continuous symmetries beyond the affine group and is generally more computationally efficient than LieGAN. | [
"['Ben Shaw' 'Abram Magner' 'Kevin R. Moon']"
] |
null | null | 2406.03620 | null | null | http://arxiv.org/pdf/2406.03620v1 | 2024-06-05T20:43:05Z | 2024-06-05T20:43:05Z | Private Online Learning via Lazy Algorithms | We study the problem of private online learning, specifically, online prediction from experts (OPE) and online convex optimization (OCO). We propose a new transformation that transforms lazy online learning algorithms into private algorithms. We apply our transformation for differentially private OPE and OCO using existing lazy algorithms for these problems. Our final algorithms obtain regret, which significantly improves the regret in the high privacy regime $varepsilon ll 1$, obtaining $sqrt{T log d} + T^{1/3} log(d)/varepsilon^{2/3}$ for DP-OPE and $sqrt{T} + T^{1/3} sqrt{d}/varepsilon^{2/3}$ for DP-OCO. We also complement our results with a lower bound for DP-OPE, showing that these rates are optimal for a natural family of low-switching private algorithms. | [
"['Hilal Asi' 'Tomer Koren' 'Daogao Liu' 'Kunal Talwar']"
] |
null | null | 2406.03628 | null | null | http://arxiv.org/pdf/2406.03628v1 | 2024-06-05T21:24:26Z | 2024-06-05T21:24:26Z | Synthetic Oversampling: Theory and A Practical Approach Using LLMs to
Address Data Imbalance | Imbalanced data and spurious correlations are common challenges in machine learning and data science. Oversampling, which artificially increases the number of instances in the underrepresented classes, has been widely adopted to tackle these challenges. In this article, we introduce OPAL (textbf{O}versamtextbf{P}ling with textbf{A}rtificial textbf{L}LM-generated data), a systematic oversampling approach that leverages the capabilities of large language models (LLMs) to generate high-quality synthetic data for minority groups. Recent studies on synthetic data generation using deep generative models mostly target prediction tasks. Our proposal differs in that we focus on handling imbalanced data and spurious correlations. More importantly, we develop a novel theory that rigorously characterizes the benefits of using the synthetic data, and shows the capacity of transformers in generating high-quality synthetic data for both labels and covariates. We further conduct intensive numerical experiments to demonstrate the efficacy of our proposed approach compared to some representative alternative solutions. | [
"['Ryumei Nakada' 'Yichen Xu' 'Lexin Li' 'Linjun Zhang']"
] |
null | null | 2406.03630 | null | null | http://arxiv.org/pdf/2406.03630v1 | 2024-06-05T21:29:05Z | 2024-06-05T21:29:05Z | Active ML for 6G: Towards Efficient Data Generation, Acquisition, and
Annotation | This paper explores the integration of active machine learning (ML) for 6G networks, an area that remains under-explored yet holds potential. Unlike passive ML systems, active ML can be made to interact with the network environment. It actively selects informative and representative data points for training, thereby reducing the volume of data needed while accelerating the learning process. While active learning research mainly focuses on data annotation, we call for a network-centric active learning framework that considers both annotation (i.e., what is the label) and data acquisition (i.e., which and how many samples to collect). Moreover, we explore the synergy between generative artificial intelligence (AI) and active learning to overcome existing limitations in both active learning and generative AI. This paper also features a case study on a mmWave throughput prediction problem to demonstrate the practical benefits and improved performance of active learning for 6G networks. Furthermore, we discuss how the implications of active learning extend to numerous 6G network use cases. We highlight the potential of active learning based 6G networks to enhance computational efficiency, data annotation and acquisition efficiency, adaptability, and overall network intelligence. We conclude with a discussion on challenges and future research directions for active learning in 6G networks, including development of novel query strategies, distributed learning integration, and inclusion of human- and machine-in-the-loop learning. | [
"['Omar Alhussein' 'Ning Zhang' 'Sami Muhaidat' 'Weihua Zhuang']"
] |
null | null | 2406.03631 | null | null | http://arxiv.org/pdf/2406.03631v1 | 2024-06-05T21:29:09Z | 2024-06-05T21:29:09Z | Discovering Bias in Latent Space: An Unsupervised Debiasing Approach | The question-answering (QA) capabilities of foundation models are highly sensitive to prompt variations, rendering their performance susceptible to superficial, non-meaning-altering changes. This vulnerability often stems from the model's preference or bias towards specific input characteristics, such as option position or superficial image features in multi-modal settings. We propose to rectify this bias directly in the model's internal representation. Our approach, SteerFair, finds the bias direction in the model's representation space and steers activation values away from it during inference. Specifically, we exploit the observation that bias often adheres to simple association rules, such as the spurious association between the first option and correctness likelihood. Next, we construct demonstrations of these rules from unlabeled samples and use them to identify the bias directions. We empirically show that SteerFair significantly reduces instruction-tuned model performance variance across prompt modifications on three benchmark tasks. Remarkably, our approach surpasses a supervised baseline with 100 labels by an average of 10.86% accuracy points and 12.95 score points and matches the performance with 500 labels. | [
"['Dyah Adila' 'Shuai Zhang' 'Boran Han' 'Yuyang Wang']"
] |
null | null | 2406.03636 | null | null | http://arxiv.org/pdf/2406.03636v3 | 2024-06-29T20:24:23Z | 2024-06-05T22:16:19Z | Synthetic Programming Elicitation and Repair for Text-to-Code in Very
Low-Resource Programming Languages | Recent advances in large language models (LLMs) for code applications have demonstrated remarkable zero-shot fluency and instruction following on challenging code related tasks ranging from test case generation to self-repair. Unsurprisingly, however, models struggle to compose syntactically valid programs in programming languages unrepresented in pre-training, referred to as very low-resource Programming Languages (VLPLs). VLPLs appear in crucial settings, including domain-specific languages for internal tools and tool-chains for legacy languages. Inspired by an HCI technique called natural program elicitation, we propose designing an intermediate language that LLMs ``naturally'' know how to use and which can be automatically compiled to a target VLPL. When LLMs generate code that lies outside of this intermediate language, we use compiler techniques to repair the code into programs in the intermediate language. Overall, we introduce emph{synthetic programming elicitation and compilation} (SPEAC), an approach that enables LLMs to generate syntactically valid code even for VLPLs. We empirically evaluate the performance of SPEAC in a case study and find that, compared to existing retrieval and fine-tuning baselines, SPEAC produces syntactically correct programs significantly more frequently without sacrificing semantic correctness. | [
"['Federico Mora' 'Justin Wong' 'Haley Lepe' 'Sahil Bhatia'\n 'Karim Elmaaroufi' 'George Varghese' 'Joseph E. Gonzalez'\n 'Elizabeth Polgreen' 'Sanjit A. Seshia']"
] |
null | null | 2406.03637 | null | null | http://arxiv.org/pdf/2406.03637v1 | 2024-06-05T22:17:47Z | 2024-06-05T22:17:47Z | Style Mixture of Experts for Expressive Text-To-Speech Synthesis | Recent advances in style transfer text-to-speech (TTS) have improved the expressiveness of synthesized speech. Despite these advancements, encoding stylistic information from diverse and unseen reference speech remains challenging. This paper introduces StyleMoE, an approach that divides the embedding space, modeled by the style encoder, into tractable subsets handled by style experts. The proposed method replaces the style encoder in a TTS system with a Mixture of Experts (MoE) layer. By utilizing a gating network to route reference speeches to different style experts, each expert specializes in aspects of the style space during optimization. Our experiments objectively and subjectively demonstrate the effectiveness of our proposed method in increasing the coverage of the style space for diverse and unseen styles. This approach can enhance the performance of existing state-of-the-art style transfer TTS models, marking the first study of MoE in style transfer TTS to our knowledge. | [
"['Ahad Jawaid' 'Shreeram Suresh Chandra' 'Junchen Lu' 'Berrak Sisman']"
] |
null | null | 2406.03642 | null | null | http://arxiv.org/pdf/2406.03642v1 | 2024-06-05T22:35:17Z | 2024-06-05T22:35:17Z | Is Free Self-Alignment Possible? | Aligning pretrained language models (LMs) is a complex and resource-intensive process, often requiring access to large amounts of ground-truth preference data and substantial compute. Are these costs necessary? That is, it is possible to align using only inherent model knowledge and without additional training? We tackle this challenge with AlignEZ, a novel approach that uses (1) self-generated preference data and (2) representation editing to provide nearly cost-free alignment. During inference, AlignEZ modifies LM representations to reduce undesirable and boost desirable components using subspaces identified via self-generated preference pairs. Our experiments reveal that this nearly cost-free procedure significantly narrows the gap between base pretrained and tuned models by an average of 31.6%, observed across six datasets and three model architectures. Additionally, we explore the potential of using AlignEZ as a means of expediting more expensive alignment procedures. Our experiments show that AlignEZ improves DPO models tuned only using a small subset of ground-truth preference data. Lastly, we study the conditions under which improvement using AlignEZ is feasible, providing valuable insights into its effectiveness. | [
"['Dyah Adila' 'Changho Shin' 'Yijing Zhang' 'Frederic Sala']"
] |
null | null | 2406.03647 | null | null | http://arxiv.org/pdf/2406.03647v2 | 2024-06-10T00:53:40Z | 2024-06-05T22:52:27Z | Decision-focused Graph Neural Networks for Combinatorial Optimization | In recent years, there has been notable interest in investigating combinatorial optimization (CO) problems by neural-based framework. An emerging strategy to tackle these challenging problems involves the adoption of graph neural networks (GNNs) as an alternative to traditional algorithms, a subject that has attracted considerable attention. Despite the growing popularity of GNNs and traditional algorithm solvers in the realm of CO, there is limited research on their integrated use and the correlation between them within an end-to-end framework. The primary focus of our work is to formulate a more efficient and precise framework for CO by employing decision-focused learning on graphs. Additionally, we introduce a decision-focused framework that utilizes GNNs to address CO problems with auxiliary support. To realize an end-to-end approach, we have designed two cascaded modules: (a) an unsupervised trained graph predictive model, and (b) a solver for quadratic binary unconstrained optimization. Empirical evaluations are conducted on various classical tasks, including maximum cut, maximum independent set, and minimum vertex cover. The experimental results on classical CO problems (i.e. MaxCut, MIS, and MVC) demonstrate the superiority of our method over both the standalone GNN approach and classical methods. | [
"['Yang Liu' 'Chuan Zhou' 'Peng Zhang' 'Shirui Pan' 'Zhao Li'\n 'Hongyang Chen']"
] |
null | null | 2406.03651 | null | null | http://arxiv.org/pdf/2406.03651v1 | 2024-06-05T23:06:48Z | 2024-06-05T23:06:48Z | Inductive Generalization in Reinforcement Learning from Specifications | We present a novel inductive generalization framework for RL from logical specifications. Many interesting tasks in RL environments have a natural inductive structure. These inductive tasks have similar overarching goals but they differ inductively in low-level predicates and distributions. We present a generalization procedure that leverages this inductive relationship to learn a higher-order function, a policy generator, that generates appropriately adapted policies for instances of an inductive task in a zero-shot manner. An evaluation of the proposed approach on a set of challenging control benchmarks demonstrates the promise of our framework in generalizing to unseen policies for long-horizon tasks. | [
"['Vignesh Subramanian' 'Rohit Kushwah' 'Subhajit Roy' 'Suguman Bansal']"
] |
null | null | 2406.03652 | null | null | http://arxiv.org/pdf/2406.03652v1 | 2024-06-05T23:08:57Z | 2024-06-05T23:08:57Z | Ensembling Portfolio Strategies for Long-Term Investments: A
Distribution-Free Preference Framework for Decision-Making and Algorithms | This paper investigates the problem of ensembling multiple strategies for sequential portfolios to outperform individual strategies in terms of long-term wealth. Due to the uncertainty of strategies' performances in the future market, which are often based on specific models and statistical assumptions, investors often mitigate risk and enhance robustness by combining multiple strategies, akin to common approaches in collective learning prediction. However, the absence of a distribution-free and consistent preference framework complicates decisions of combination due to the ambiguous objective. To address this gap, we introduce a novel framework for decision-making in combining strategies, irrespective of market conditions, by establishing the investor's preference between decisions and then forming a clear objective. Through this framework, we propose a combinatorial strategy construction, free from statistical assumptions, for any scale of component strategies, even infinite, such that it meets the determined criterion. Finally, we test the proposed strategy along with its accelerated variant and some other multi-strategies. The numerical experiments show results in favor of the proposed strategies, albeit with small tradeoffs in their Sharpe ratios, in which their cumulative wealths eventually exceed those of the best component strategies while the accelerated strategy significantly improves performance. | [
"['Duy Khanh Lam']"
] |
null | null | 2406.03653 | null | null | http://arxiv.org/pdf/2406.03653v1 | 2024-06-05T23:35:37Z | 2024-06-05T23:35:37Z | Equivalence Set Restricted Latent Class Models (ESRLCM) | Latent Class Models (LCMs) are used to cluster multivariate categorical data, commonly used to interpret survey responses. We propose a novel Bayesian model called the Equivalence Set Restricted Latent Class Model (ESRLCM). This model identifies clusters who have common item response probabilities, and does so more generically than traditional restricted latent attribute models. We verify the identifiability of ESRLCMs, and demonstrate the effectiveness in both simulations and real-world applications. | [
"['Jesse Bowers' 'Steve Culpepper']"
] |
null | null | 2406.03662 | null | null | http://arxiv.org/pdf/2406.03662v1 | 2024-06-06T00:28:49Z | 2024-06-06T00:28:49Z | The Missing Curve Detectors of InceptionV1: Applying Sparse Autoencoders
to InceptionV1 Early Vision | Recent work on sparse autoencoders (SAEs) has shown promise in extracting interpretable features from neural networks and addressing challenges with polysemantic neurons caused by superposition. In this paper, we apply SAEs to the early vision layers of InceptionV1, a well-studied convolutional neural network, with a focus on curve detectors. Our results demonstrate that SAEs can uncover new interpretable features not apparent from examining individual neurons, including additional curve detectors that fill in previous gaps. We also find that SAEs can decompose some polysemantic neurons into more monosemantic constituent features. These findings suggest SAEs are a valuable tool for understanding InceptionV1, and convolutional neural networks more generally. | [
"['Liv Gorton']"
] |
null | null | 2406.03663 | null | null | http://arxiv.org/pdf/2406.03663v1 | 2024-06-06T00:30:51Z | 2024-06-06T00:30:51Z | A Hybrid Deep Learning Classification of Perimetric Glaucoma Using
Peripapillary Nerve Fiber Layer Reflectance and Other OCT Parameters from
Three Anatomy Regions | Precis: A hybrid deep-learning model combines NFL reflectance and other OCT parameters to improve glaucoma diagnosis. Objective: To investigate if a deep learning model could be used to combine nerve fiber layer (NFL) reflectance and other OCT parameters for glaucoma diagnosis. Patients and Methods: This is a prospective observational study where of 106 normal subjects and 164 perimetric glaucoma (PG) patients. Peripapillary NFL reflectance map, NFL thickness map, optic head analysis of disc, and macular ganglion cell complex thickness were obtained using spectral domain OCT. A hybrid deep learning model combined a fully connected network (FCN) and a convolution neural network (CNN) to develop and combine those OCT maps and parameters to distinguish normal and PG eyes. Two deep learning models were compared based on whether the NFL reflectance map was used as part of the input or not. Results: The hybrid deep learning model with reflectance achieved 0.909 sensitivity at 99% specificity and 0.926 at 95%. The overall accuracy was 0.948 with 0.893 sensitivity and 1.000 specificity, and the AROC was 0.979, which is significantly better than the logistic regression models (p < 0.001). The second best model is the hybrid deep learning model w/o reflectance, which also had significantly higher AROC than logistic regression models (p < 0.001). Logistic regression with reflectance model had slightly higher AROC or sensitivity than the other logistic regression model without reflectance (p = 0.024). Conclusions: Hybrid deep learning model significantly improved the diagnostic accuracy, without or without NFL reflectance. Hybrid deep learning model, combining reflectance/NFL thickness/GCC thickness/ONH parameter, may be a practical model for glaucoma screen purposes. | [
"['Ou Tan' 'David S. Greenfield' 'Brian A. Francis' 'Rohit Varma'\n 'Joel S. Schuman' 'David Huang' 'Dongseok Choi']"
] |
null | null | 2406.03665 | null | null | http://arxiv.org/pdf/2406.03665v1 | 2024-06-06T00:50:22Z | 2024-06-06T00:50:22Z | Towards Dynamic Trend Filtering through Trend Point Detection with
Reinforcement Learning | Trend filtering simplifies complex time series data by applying smoothness to filter out noise while emphasizing proximity to the original data. However, existing trend filtering methods fail to reflect abrupt changes in the trend due to `approximateness,' resulting in constant smoothness. This approximateness uniformly filters out the tail distribution of time series data, characterized by extreme values, including both abrupt changes and noise. In this paper, we propose Trend Point Detection formulated as a Markov Decision Process (MDP), a novel approach to identifying essential points that should be reflected in the trend, departing from approximations. We term these essential points as Dynamic Trend Points (DTPs) and extract trends by interpolating them. To identify DTPs, we utilize Reinforcement Learning (RL) within a discrete action space and a forecasting sum-of-squares loss function as a reward, referred to as the Dynamic Trend Filtering network (DTF-net). DTF-net integrates flexible noise filtering, preserving critical original subsequences while removing noise as required for other subsequences. We demonstrate that DTF-net excels at capturing abrupt changes compared to other trend filtering algorithms and enhances forecasting performance, as abrupt changes are predicted rather than smoothed out. | [
"['Jihyeon Seong' 'Sekwang Oh' 'Jaesik Choi']"
] |
null | null | 2406.03671 | null | null | http://arxiv.org/pdf/2406.03671v1 | 2024-06-06T01:14:24Z | 2024-06-06T01:14:24Z | PANDA: Expanded Width-Aware Message Passing Beyond Rewiring | Recent research in the field of graph neural network (GNN) has identified a critical issue known as "over-squashing," resulting from the bottleneck phenomenon in graph structures, which impedes the propagation of long-range information. Prior works have proposed a variety of graph rewiring concepts that aim at optimizing the spatial or spectral properties of graphs to promote the signal propagation. However, such approaches inevitably deteriorate the original graph topology, which may lead to a distortion of information flow. To address this, we introduce an expanded width-aware (PANDA) message passing, a new message passing paradigm where nodes with high centrality, a potential source of over-squashing, are selectively expanded in width to encapsulate the growing influx of signals from distant nodes. Experimental results show that our method outperforms existing rewiring methods, suggesting that selectively expanding the hidden state of nodes can be a compelling alternative to graph rewiring for addressing the over-squashing. | [
"['Jeongwhan Choi' 'Sumin Park' 'Hyowon Wi' 'Sung-Bae Cho' 'Noseong Park']"
] |
null | null | 2406.03678 | null | null | http://arxiv.org/pdf/2406.03678v1 | 2024-06-06T01:46:49Z | 2024-06-06T01:46:49Z | Reflective Policy Optimization | On-policy reinforcement learning methods, like Trust Region Policy Optimization (TRPO) and Proximal Policy Optimization (PPO), often demand extensive data per update, leading to sample inefficiency. This paper introduces Reflective Policy Optimization (RPO), a novel on-policy extension that amalgamates past and future state-action information for policy optimization. This approach empowers the agent for introspection, allowing modifications to its actions within the current state. Theoretical analysis confirms that policy performance is monotonically improved and contracts the solution space, consequently expediting the convergence procedure. Empirical results demonstrate RPO's feasibility and efficacy in two reinforcement learning benchmarks, culminating in superior sample efficiency. The source code of this work is available at https://github.com/Edgargan/RPO. | [
"['Yaozhong Gan' 'Renye Yan' 'Zhe Wu' 'Junliang Xing']"
] |
null | null | 2406.03679 | null | null | http://arxiv.org/pdf/2406.03679v3 | 2024-06-13T13:31:05Z | 2024-06-06T01:49:29Z | On the Effects of Data Scale on Computer Control Agents | Autonomous agents that control computer interfaces to accomplish human tasks are emerging. Leveraging LLMs to power such agents has been of special interest, but unless fine-tuned on human-collected task demonstrations, performance is still relatively low. In this work we study whether fine-tuning alone is a viable approach for building real-world computer control agents. In particularly, we investigate how performance measured on both high and low-level tasks in domain and out of domain scales as more training data is collected. To this end we collect and release a new dataset, AndroidControl, consisting of 15,283 demonstrations of everyday tasks with Android apps. Compared to existing datasets, each AndroidControl task instance includes both high and low-level human-generated instructions, allowing us to explore the level of task complexity an agent can handle. Moreover, AndroidControl is the most diverse computer control dataset to date, including 15,283 unique tasks over 833 Android apps, thus allowing us to conduct in-depth analysis of the model performance in and out of the domain of the training data. Using the dataset, we find that when tested in domain fine-tuned models outperform zero and few-shot baselines and scale in such a way that robust performance might feasibly be obtained simply by collecting more data. Out of domain, performance scales significantly more slowly and suggests that in particular for high-level tasks, fine-tuning on more data alone may be insufficient for achieving robust out-of-domain performance. | [
"['Wei Li' 'William Bishop' 'Alice Li' 'Chris Rawles'\n 'Folawiyo Campbell-Ajala' 'Divya Tyamagundlu' 'Oriana Riva']"
] |
null | null | 2406.03680 | null | null | http://arxiv.org/pdf/2406.03680v1 | 2024-06-06T01:50:01Z | 2024-06-06T01:50:01Z | Meta-learning for Positive-unlabeled Classification | We propose a meta-learning method for positive and unlabeled (PU) classification, which improves the performance of binary classifiers obtained from only PU data in unseen target tasks. PU learning is an important problem since PU data naturally arise in real-world applications such as outlier detection and information retrieval. Existing PU learning methods require many PU data, but sufficient data are often unavailable in practice. The proposed method minimizes the test classification risk after the model is adapted to PU data by using related tasks that consist of positive, negative, and unlabeled data. We formulate the adaptation as an estimation problem of the Bayes optimal classifier, which is an optimal classifier to minimize the classification risk. The proposed method embeds each instance into a task-specific space using neural networks. With the embedded PU data, the Bayes optimal classifier is estimated through density-ratio estimation of PU densities, whose solution is obtained as a closed-form solution. The closed-form solution enables us to efficiently and effectively minimize the test classification risk. We empirically show that the proposed method outperforms existing methods with one synthetic and three real-world datasets. | [
"['Atsutoshi Kumagai' 'Tomoharu Iwata' 'Yasuhiro Fujiwara']"
] |
null | null | 2406.03682 | null | null | http://arxiv.org/pdf/2406.03682v2 | 2024-06-10T05:40:59Z | 2024-06-06T01:52:09Z | A Universal Class of Sharpness-Aware Minimization Algorithms | Recently, there has been a surge in interest in developing optimization algorithms for overparameterized models as achieving generalization is believed to require algorithms with suitable biases. This interest centers on minimizing sharpness of the original loss function; the Sharpness-Aware Minimization (SAM) algorithm has proven effective. However, most literature only considers a few sharpness measures, such as the maximum eigenvalue or trace of the training loss Hessian, which may not yield meaningful insights for non-convex optimization scenarios like neural networks. Additionally, many sharpness measures are sensitive to parameter invariances in neural networks, magnifying significantly under rescaling parameters. Motivated by these challenges, we introduce a new class of sharpness measures in this paper, leading to new sharpness-aware objective functions. We prove that these measures are textit{universally expressive}, allowing any function of the training loss Hessian matrix to be represented by appropriate hyperparameters. Furthermore, we show that the proposed objective functions explicitly bias towards minimizing their corresponding sharpness measures, and how they allow meaningful applications to models with parameter invariances (such as scale-invariances). Finally, as instances of our proposed general framework, we present textit{Frob-SAM} and textit{Det-SAM}, which are specifically designed to minimize the Frobenius norm and the determinant of the Hessian of the training loss, respectively. We also demonstrate the advantages of our general framework through extensive experiments. | [
"['Behrooz Tahmasebi' 'Ashkan Soleymani' 'Dara Bahri' 'Stefanie Jegelka'\n 'Patrick Jaillet']"
] |
null | null | 2406.03683 | null | null | http://arxiv.org/pdf/2406.03683v1 | 2024-06-06T01:52:28Z | 2024-06-06T01:52:28Z | Bayesian Power Steering: An Effective Approach for Domain Adaptation of
Diffusion Models | We propose a Bayesian framework for fine-tuning large diffusion models with a novel network structure called Bayesian Power Steering (BPS). We clarify the meaning behind adaptation from a textit{large probability space} to a textit{small probability space} and explore the task of fine-tuning pre-trained models using learnable modules from a Bayesian perspective. BPS extracts task-specific knowledge from a pre-trained model's learned prior distribution. It efficiently leverages large diffusion models, differentially intervening different hidden features with a head-heavy and foot-light configuration. Experiments highlight the superiority of BPS over contemporary methods across a range of tasks even with limited amount of data. Notably, BPS attains an FID score of 10.49 under the sketch condition on the COCO17 dataset. | [
"['Ding Huang' 'Ting Li' 'Jian Huang']"
] |
null | null | 2406.03686 | null | null | http://arxiv.org/pdf/2406.03686v1 | 2024-06-06T02:10:50Z | 2024-06-06T02:10:50Z | BindGPT: A Scalable Framework for 3D Molecular Design via Language
Modeling and Reinforcement Learning | Generating novel active molecules for a given protein is an extremely challenging task for generative models that requires an understanding of the complex physical interactions between the molecule and its environment. In this paper, we present a novel generative model, BindGPT which uses a conceptually simple but powerful approach to create 3D molecules within the protein's binding site. Our model produces molecular graphs and conformations jointly, eliminating the need for an extra graph reconstruction step. We pretrain BindGPT on a large-scale dataset and fine-tune it with reinforcement learning using scores from external simulation software. We demonstrate how a single pretrained language model can serve at the same time as a 3D molecular generative model, conformer generator conditioned on the molecular graph, and a pocket-conditioned 3D molecule generator. Notably, the model does not make any representational equivariance assumptions about the domain of generation. We show how such simple conceptual approach combined with pretraining and scaling can perform on par or better than the current best specialized diffusion models, language models, and graph neural networks while being two orders of magnitude cheaper to sample. | [
"['Artem Zholus' 'Maksim Kuznetsov' 'Roman Schutski' 'Rim Shayakhmetov'\n 'Daniil Polykovskiy' 'Sarath Chandar' 'Alex Zhavoronkov']"
] |
null | null | 2406.03696 | null | null | http://arxiv.org/pdf/2406.03696v1 | 2024-06-06T02:26:14Z | 2024-06-06T02:26:14Z | Discrete error dynamics of mini-batch gradient descent for least squares
regression | We study the discrete dynamics of mini-batch gradient descent for least squares regression when sampling without replacement. We show that the dynamics and generalization error of mini-batch gradient descent depends on a sample cross-covariance matrix $Z$ between the original features $X$ and a set of new features $widetilde{X}$, in which each feature is modified by the mini-batches that appear before it during the learning process in an averaged way. Using this representation, we rigorously establish that the dynamics of mini-batch and full-batch gradient descent agree up to leading order with respect to the step size using the linear scaling rule. We also study discretization effects that a continuous-time gradient flow analysis cannot detect, and show that mini-batch gradient descent converges to a step-size dependent solution, in contrast with full-batch gradient descent. Finally, we investigate the effects of batching, assuming a random matrix model, by using tools from free probability theory to numerically compute the spectrum of $Z$. | [
"['Jackie Lok' 'Rishi Sonthalia' 'Elizaveta Rebrova']"
] |
null | null | 2406.03703 | null | null | http://arxiv.org/pdf/2406.03703v1 | 2024-06-06T02:52:45Z | 2024-06-06T02:52:45Z | Synthesizing Conversations from Unlabeled Documents using Automatic
Response Segmentation | In this study, we tackle the challenge of inadequate and costly training data that has hindered the development of conversational question answering (ConvQA) systems. Enterprises have a large corpus of diverse internal documents. Instead of relying on a searching engine, a more compelling approach for people to comprehend these documents is to create a dialogue system. In this paper, we propose a robust dialog synthesising method. We learn the segmentation of data for the dialog task instead of using segmenting at sentence boundaries. The synthetic dataset generated by our proposed method achieves superior quality when compared to WikiDialog, as assessed through machine and human evaluations. By employing our inpainted data for ConvQA retrieval system pre-training, we observed a notable improvement in performance across OR-QuAC benchmarks. | [
"['Fanyou Wu' 'Weijie Xu' 'Chandan K. Reddy' 'Srinivasan H. Sengamedu']"
] |
null | null | 2406.03704 | null | null | http://arxiv.org/pdf/2406.03704v1 | 2024-06-06T02:55:16Z | 2024-06-06T02:55:16Z | Excluding the Irrelevant: Focusing Reinforcement Learning through
Continuous Action Masking | Continuous action spaces in reinforcement learning (RL) are commonly defined as interval sets. While intervals usually reflect the action boundaries for tasks well, they can be challenging for learning because the typically large global action space leads to frequent exploration of irrelevant actions. Yet, little task knowledge can be sufficient to identify significantly smaller state-specific sets of relevant actions. Focusing learning on these relevant actions can significantly improve training efficiency and effectiveness. In this paper, we propose to focus learning on the set of relevant actions and introduce three continuous action masking methods for exactly mapping the action space to the state-dependent set of relevant actions. Thus, our methods ensure that only relevant actions are executed, enhancing the predictability of the RL agent and enabling its use in safety-critical applications. We further derive the implications of the proposed methods on the policy gradient. Using Proximal Policy Optimization (PPO), we evaluate our methods on three control tasks, where the relevant action set is computed based on the system dynamics and a relevant state set. Our experiments show that the three action masking methods achieve higher final rewards and converge faster than the baseline without action masking. | [
"['Roland Stolz' 'Hanna Krasowski' 'Jakob Thumm' 'Michael Eichelbeck'\n 'Philipp Gassert' 'Matthias Althoff']"
] |
null | null | 2406.03707 | null | null | http://arxiv.org/pdf/2406.03707v1 | 2024-06-06T03:06:46Z | 2024-06-06T03:06:46Z | What Should Embeddings Embed? Autoregressive Models Represent Latent
Generating Distributions | Autoregressive language models have demonstrated a remarkable ability to extract latent structure from text. The embeddings from large language models have been shown to capture aspects of the syntax and semantics of language. But what {em should} embeddings represent? We connect the autoregressive prediction objective to the idea of constructing predictive sufficient statistics to summarize the information contained in a sequence of observations, and use this connection to identify three settings where the optimal content of embeddings can be identified: independent identically distributed data, where the embedding should capture the sufficient statistics of the data; latent state models, where the embedding should encode the posterior distribution over states given the data; and discrete hypothesis spaces, where the embedding should reflect the posterior distribution over hypotheses given the data. We then conduct empirical probing studies to show that transformers encode these three kinds of latent generating distributions, and that they perform well in out-of-distribution cases and without token memorization in these settings. | [
"['Liyi Zhang' 'Michael Y. Li' 'Thomas L. Griffiths']"
] |
null | null | 2406.03710 | null | null | http://arxiv.org/pdf/2406.03710v2 | 2024-07-14T14:55:16Z | 2024-06-06T03:14:23Z | TwinS: Revisiting Non-Stationarity in Multivariate Time Series
Forecasting | Recently, multivariate time series forecasting tasks have garnered increasing attention due to their significant practical applications, leading to the emergence of various deep forecasting models. However, real-world time series exhibit pronounced non-stationary distribution characteristics. These characteristics are not solely limited to time-varying statistical properties highlighted by non-stationary Transformer but also encompass three key aspects: nested periodicity, absence of periodic distributions, and hysteresis among time variables. In this paper, we begin by validating this theory through wavelet analysis and propose the Transformer-based TwinS model, which consists of three modules to address the non-stationary periodic distributions: Wavelet Convolution, Period-Aware Attention, and Channel-Temporal Mixed MLP. Specifically, The Wavelet Convolution models nested periods by scaling the convolution kernel size like wavelet transform. The Period-Aware Attention guides attention computation by generating period relevance scores through a convolutional sub-network. The Channel-Temporal Mixed MLP captures the overall relationships between time series through channel-time mixing learning. TwinS achieves SOTA performance compared to mainstream TS models, with a maximum improvement in MSE of 25.8% over PatchTST. | [
"['Jiaxi Hu' 'Qingsong Wen' 'Sijie Ruan' 'Li Liu' 'Yuxuan Liang']"
] |
null | null | 2406.03712 | null | null | http://arxiv.org/pdf/2406.03712v1 | 2024-06-06T03:15:13Z | 2024-06-06T03:15:13Z | A Survey on Medical Large Language Models: Technology, Application,
Trustworthiness, and Future Directions | Large language models (LLMs), such as GPT series models, have received substantial attention due to their impressive capabilities for generating and understanding human-level language. More recently, LLMs have emerged as an innovative and powerful adjunct in the medical field, transforming traditional practices and heralding a new era of enhanced healthcare services. This survey provides a comprehensive overview of Medical Large Language Models (Med-LLMs), outlining their evolution from general to the medical-specific domain (i.e, Technology and Application), as well as their transformative impact on healthcare (e.g., Trustworthiness and Safety). Concretely, starting from the fundamental history and technology of LLMs, we first delve into the progressive adaptation and refinements of general LLM models in the medical domain, especially emphasizing the advanced algorithms that boost the LLMs' performance in handling complicated medical environments, including clinical reasoning, knowledge graph, retrieval-augmented generation, human alignment, and multi-modal learning. Secondly, we explore the extensive applications of Med-LLMs across domains such as clinical decision support, report generation, and medical education, illustrating their potential to streamline healthcare services and augment patient outcomes. Finally, recognizing the imperative and responsible innovation, we discuss the challenges of ensuring fairness, accountability, privacy, and robustness in Med-LLMs applications. Finally, we conduct a concise discussion for anticipating possible future trajectories of Med-LLMs, identifying avenues for the prudent expansion of Med-LLMs. By consolidating above-mentioned insights, this review seeks to provide a comprehensive investigation of the potential strengths and limitations of Med-LLMs for professionals and researchers, ensuring a responsible landscape in the healthcare setting. | [
"['Lei Liu' 'Xiaoyan Yang' 'Junchi Lei' 'Xiaoyang Liu' 'Yue Shen'\n 'Zhiqiang Zhang' 'Peng Wei' 'Jinjie Gu' 'Zhixuan Chu' 'Zhan Qin'\n 'Kui Ren']"
] |
null | null | 2406.03722 | null | null | http://arxiv.org/pdf/2406.03722v1 | 2024-06-06T03:35:09Z | 2024-06-06T03:35:09Z | Offline Multi-Objective Optimization | Offline optimization aims to maximize a black-box objective function with a static dataset and has wide applications. In addition to the objective function being black-box and expensive to evaluate, numerous complex real-world problems entail optimizing multiple conflicting objectives, i.e., multi-objective optimization (MOO). Nevertheless, offline MOO has not progressed as much as offline single-objective optimization (SOO), mainly due to the lack of benchmarks like Design-Bench for SOO. To bridge this gap, we propose a first benchmark for offline MOO, covering a range of problems from synthetic to real-world tasks. This benchmark provides tasks, datasets, and open-source examples, which can serve as a foundation for method comparisons and advancements in offline MOO. Furthermore, we analyze how the current related methods can be adapted to offline MOO from four fundamental perspectives, including data, model architecture, learning algorithm, and search algorithm. Empirical results show improvements over the best value of the training set, demonstrating the effectiveness of offline MOO methods. As no particular method stands out significantly, there is still an open challenge in further enhancing the effectiveness of offline MOO. We finally discuss future challenges for offline MOO, with the hope of shedding some light on this emerging field. Our code is available at url{https://github.com/lamda-bbo/offline-moo}. | [
"['Ke Xue' 'Rong-Xi Tan' 'Xiaobin Huang' 'Chao Qian']"
] |
null | null | 2406.03726 | null | null | http://arxiv.org/pdf/2406.03726v1 | 2024-06-06T03:49:34Z | 2024-06-06T03:49:34Z | Efficient Graph Encoder Embedding for Large Sparse Graphs in Python | Graph is a ubiquitous representation of data in various research fields, and graph embedding is a prevalent machine learning technique for capturing key features and generating fixed-sized attributes. However, most state-of-the-art graph embedding methods are computationally and spatially expensive. Recently, the Graph Encoder Embedding (GEE) has been shown as the fastest graph embedding technique and is suitable for a variety of network data applications. As real-world data often involves large and sparse graphs, the huge sparsity usually results in redundant computations and storage. To address this issue, we propose an improved version of GEE, sparse GEE, which optimizes the calculation and storage of zero entries in sparse matrices to enhance the running time further. Our experiments demonstrate that the sparse version achieves significant speedup compared to the original GEE with Python implementation for large sparse graphs, and sparse GEE is capable of processing millions of edges within minutes on a standard laptop. | [
"['Xihan Qin' 'Cencheng Shen']"
] |
null | null | 2406.03729 | null | null | http://arxiv.org/pdf/2406.03729v1 | 2024-06-06T04:05:12Z | 2024-06-06T04:05:12Z | Enhancing Sign Language Detection through Mediapipe and Convolutional
Neural Networks (CNN) | This research combines MediaPipe and CNNs for the efficient and accurate interpretation of ASL dataset for the real-time detection of sign language. The system presented here captures and processes hands' gestures in real time. the intended purpose was to create a very easy, accurate, and fast way of entering commands without the necessity of touching something.MediaPipe supports one of the powerful frameworks in real-time hand tracking capabilities for the ability to capture and preprocess hand movements, which increases the accuracy of the gesture recognition system. Actually, the integration of CNN with the MediaPipe results in higher efficiency in using the model of real-time processing.The accuracy achieved by the model on ASL datasets is 99.12%.The model was tested using American Sign Language (ASL) datasets. The results were then compared to those of existing methods to evaluate how well it performed, using established evaluation techniques. The system will have applications in the communication, education, and accessibility domains. Making systems such as described in this paper even better will assist people with hearing impairment and make things accessible to them. We tested the recognition and translation performance on an ASL dataset and achieved better accuracy over previous models.It is meant to the research is to identify the characters that American signs recognize using hand images taken from a web camera by based on mediapipe and CNNs | [
"['Aditya Raj Verma' 'Gagandeep Singh' 'Karnim Meghwal' 'Banawath Ramji'\n 'Praveen Kumar Dadheech']"
] |
null | null | 2406.03730 | null | null | http://arxiv.org/pdf/2406.03730v1 | 2024-06-06T04:05:54Z | 2024-06-06T04:05:54Z | FastGAS: Fast Graph-based Annotation Selection for In-Context Learning | In-context learning (ICL) empowers large language models (LLMs) to tackle new tasks by using a series of training instances as prompts. Since generating the prompts needs to sample from a vast pool of instances and annotate them (e.g., add labels in classification task), existing methods have proposed to select a subset of unlabeled examples for annotation, thus enhancing the quality of prompts and concurrently mitigating annotation costs. However, these methods often require a long time to select instances due to their complexity, hindering their practical viability. To address this limitation, we propose a graph-based selection method, FastGAS, designed to efficiently identify high-quality instances while minimizing computational overhead. Initially, we construct a data similarity graph based on instance similarities. Subsequently, employing a graph partitioning algorithm, we partition the graph into pieces. Within each piece (i.e., subgraph), we adopt a greedy approach to pick the most representative nodes. By aggregating nodes from diverse pieces and annotating the corresponding instances, we identify a set of diverse and representative instances for ICL. Compared to prior approaches, our method not only exhibits superior performance on different tasks but also significantly reduces selection time. In addition, we demonstrate the efficacy of our approach in LLMs of larger sizes. | [
"['Zihan Chen' 'Song Wang' 'Cong Shen' 'Jundong Li']"
] |
null | null | 2406.03731 | null | null | http://arxiv.org/pdf/2406.03731v1 | 2024-06-06T04:06:00Z | 2024-06-06T04:06:00Z | Quality-Diversity with Limited Resources | Quality-Diversity (QD) algorithms have emerged as a powerful optimization paradigm with the aim of generating a set of high-quality and diverse solutions. To achieve such a challenging goal, QD algorithms require maintaining a large archive and a large population in each iteration, which brings two main issues, sample and resource efficiency. Most advanced QD algorithms focus on improving the sample efficiency, while the resource efficiency is overlooked to some extent. Particularly, the resource overhead during the training process has not been touched yet, hindering the wider application of QD algorithms. In this paper, we highlight this important research question, i.e., how to efficiently train QD algorithms with limited resources, and propose a novel and effective method called RefQD to address it. RefQD decomposes a neural network into representation and decision parts, and shares the representation part with all decision parts in the archive to reduce the resource overhead. It also employs a series of strategies to address the mismatch issue between the old decision parts and the newly updated representation part. Experiments on different types of tasks from small to large resource consumption demonstrate the excellent performance of RefQD: it not only uses significantly fewer resources (e.g., 16% GPU memories on QDax and 3.7% on Atari) but also achieves comparable or better performance compared to sample-efficient QD algorithms. Our code is available at url{https://github.com/lamda-bbo/RefQD}. | [
"['Ren-Jian Wang' 'Ke Xue' 'Cong Guan' 'Chao Qian']"
] |
null | null | 2406.03733 | null | null | http://arxiv.org/pdf/2406.03733v2 | 2024-06-21T22:48:12Z | 2024-06-06T04:12:57Z | Credit Card Fraud Detection Using Advanced Transformer Model | With the proliferation of various online and mobile payment systems, credit card fraud has emerged as a significant threat to financial security. This study focuses on innovative applications of the latest Transformer models for more robust and precise fraud detection. To ensure the reliability of the data, we meticulously processed the data sources, balancing the dataset to address the issue of data sparsity significantly. We also selected highly correlated vectors to strengthen the training process.To guarantee the reliability and practicality of the new Transformer model, we conducted performance comparisons with several widely adopted models, including Support Vector Machine (SVM), Random Forest, Neural Network, and Logistic Regression. We rigorously compared these models using metrics such as Precision, Recall, and F1 Score. Through these detailed analyses and comparisons, we present to the readers a highly efficient and powerful anti-fraud mechanism with promising prospects. The results demonstrate that the Transformer model not only excels in traditional applications but also shows great potential in niche areas like fraud detection, offering a substantial advancement in the field. | [
"['Chang Yu' 'Yongshun Xu' 'Jin Cao' 'Ye Zhang' 'Yinxin Jin' 'Mengran Zhu']"
] |
null | null | 2406.03735 | null | null | http://arxiv.org/pdf/2406.03735v1 | 2024-06-06T04:19:55Z | 2024-06-06T04:19:55Z | Phase-Amplitude Reduction-Based Imitation Learning | In this study, we propose the use of the phase-amplitude reduction method to construct an imitation learning framework. Imitating human movement trajectories is recognized as a promising strategy for generating a range of human-like robot movements. Unlike previous dynamical system-based imitation learning approaches, our proposed method allows the robot not only to imitate a limit cycle trajectory but also to replicate the transient movement from the initial or disturbed state to the limit cycle. Consequently, our method offers a safer imitation learning approach that avoids generating unpredictable motions immediately after disturbances or from a specified initial state. We first validated our proposed method by reconstructing a simple limit-cycle attractor. We then compared the proposed approach with a conventional method on a lemniscate trajectory tracking task with a simulated robot arm. Our findings confirm that our proposed method can more accurately generate transient movements to converge on a target periodic attractor compared to the previous standard approach. Subsequently, we applied our method to a real robot arm to imitate periodic human movements. | [
"['Satoshi Yamamori' 'Jun Morimoto']"
] |
null | null | 2406.03736 | null | null | http://arxiv.org/pdf/2406.03736v2 | 2024-07-06T14:40:08Z | 2024-06-06T04:22:11Z | Your Absorbing Discrete Diffusion Secretly Models the Conditional
Distributions of Clean Data | Discrete diffusion models with absorbing processes have shown promise in language modeling. The key quantities to be estimated are the ratios between the marginal probabilities of two transitive states at all timesteps, called the concrete score. In this paper, we reveal that the concrete score in absorbing diffusion can be expressed as conditional probabilities of clean data, multiplied by a time-dependent scalar in an analytic form. Motivated by this finding, we propose reparameterized absorbing discrete diffusion (RADD), a dedicated diffusion model without time-condition that characterizes the time-independent conditional probabilities. Besides its simplicity, RADD can reduce the number of function evaluations (NFEs) by caching the output of the time-independent network when the noisy sample remains unchanged in a sampling interval. Empirically, RADD is up to 3.5 times faster while achieving similar performance with the strongest baseline. Built upon the new perspective of conditional distributions, we further unify absorbing discrete diffusion and any-order autoregressive models (AO-ARMs), showing that the upper bound on the negative log-likelihood for the diffusion model can be interpreted as an expected negative log-likelihood for AO-ARMs. Further, our RADD models achieve SOTA performance among diffusion models on 5 zero-shot language modeling benchmarks (measured by perplexity) at the GPT-2 scale. Our code is available at https://github.com/ML-GSAI/RADD. | [
"['Jingyang Ou' 'Shen Nie' 'Kaiwen Xue' 'Fengqi Zhu' 'Jiacheng Sun'\n 'Zhenguo Li' 'Chongxuan Li']"
] |
null | null | 2406.03744 | null | null | http://arxiv.org/pdf/2406.03744v2 | 2024-06-07T03:26:18Z | 2024-06-06T04:44:10Z | ReDistill: Residual Encoded Distillation for Peak Memory Reduction | The expansion of neural network sizes and the enhancement of image resolution through modern camera sensors result in heightened memory and power demands for neural networks. Reducing peak memory, which is the maximum memory consumed during the execution of a neural network, is critical to deploy neural networks on edge devices with limited memory budget. A naive approach to reducing peak memory is aggressive down-sampling of feature maps via pooling with large stride, which often results in unacceptable degradation in network performance. To mitigate this problem, we propose residual encoded distillation (ReDistill) for peak memory reduction in a teacher-student framework, in which a student network with less memory is derived from the teacher network using aggressive pooling. We apply our distillation method to multiple problems in computer vision including image classification and diffusion based image generation. For image classification, our method yields 2x-3.2x measured peak memory on an edge GPU with negligible degradation in accuracy for most CNN based architectures. Additionally, our method yields improved test accuracy for tiny vision transformer (ViT) based models distilled from large CNN based teacher architectures. For diffusion-based image generation, our proposed distillation method yields a denoising network with 4x lower theoretical peak memory while maintaining decent diversity and fidelity for image generation. Experiments demonstrate our method's superior performance compared to other feature-based and response-based distillation methods. | [
"['Fang Chen' 'Gourav Datta' 'Mujahid Al Rafi' 'Hyeran Jeon' 'Meng Tang']"
] |
null | null | 2406.03747 | null | null | http://arxiv.org/pdf/2406.03747v1 | 2024-06-06T04:57:29Z | 2024-06-06T04:57:29Z | Instance Segmentation and Teeth Classification in Panoramic X-rays | Teeth segmentation and recognition are critical in various dental applications and dental diagnosis. Automatic and accurate segmentation approaches have been made possible by integrating deep learning models. Although teeth segmentation has been studied in the past, only some techniques were able to effectively classify and segment teeth simultaneously. This article offers a pipeline of two deep learning models, U-Net and YOLOv8, which results in BB-UNet, a new architecture for the classification and segmentation of teeth on panoramic X-rays that is efficient and reliable. We have improved the quality and reliability of teeth segmentation by utilising the YOLOv8 and U-Net capabilities. The proposed networks have been evaluated using the mean average precision (mAP) and dice coefficient for YOLOv8 and BB-UNet, respectively. We have achieved a 3% increase in mAP score for teeth classification compared to existing methods, and a 10-15% increase in dice coefficient for teeth segmentation compared to U-Net across different categories of teeth. A new Dental dataset was created based on UFBA-UESC dataset with Bounding-Box and Polygon annotations of 425 dental panoramic X-rays. The findings of this research pave the way for a wider adoption of object detection models in the field of dental diagnosis. | [
"['Devichand Budagam' 'Ayush Kumar' 'Sayan Ghosh' 'Anuj Shrivastav'\n 'Azamat Zhanatuly Imanbayev' 'Iskander Rafailovich Akhmetov'\n 'Dmitrii Kaplun' 'Sergey Antonov' 'Artem Rychenkov' 'Gleb Cyganov'\n 'Aleksandr Sinitca']"
] |
null | null | 2406.03751 | null | null | http://arxiv.org/pdf/2406.03751v1 | 2024-06-06T05:27:33Z | 2024-06-06T05:27:33Z | Adaptive Multi-Scale Decomposition Framework for Time Series Forecasting | Transformer-based and MLP-based methods have emerged as leading approaches in time series forecasting (TSF). While Transformer-based methods excel in capturing long-range dependencies, they suffer from high computational complexities and tend to overfit. Conversely, MLP-based methods offer computational efficiency and adeptness in modeling temporal dynamics, but they struggle with capturing complex temporal patterns effectively. To address these challenges, we propose a novel MLP-based Adaptive Multi-Scale Decomposition (AMD) framework for TSF. Our framework decomposes time series into distinct temporal patterns at multiple scales, leveraging the Multi-Scale Decomposable Mixing (MDM) block to dissect and aggregate these patterns in a residual manner. Complemented by the Dual Dependency Interaction (DDI) block and the Adaptive Multi-predictor Synthesis (AMS) block, our approach effectively models both temporal and channel dependencies and utilizes autocorrelation to refine multi-scale data integration. Comprehensive experiments demonstrate that our AMD framework not only overcomes the limitations of existing methods but also consistently achieves state-of-the-art performance in both long-term and short-term forecasting tasks across various datasets, showcasing superior efficiency. Code is available at url{https://github.com/TROUBADOUR000/AMD} | [
"['Yifan Hu' 'Peiyuan Liu' 'Peng Zhu' 'Dawei Cheng' 'Tao Dai']"
] |
null | null | 2406.03757 | null | null | http://arxiv.org/pdf/2406.03757v1 | 2024-06-06T05:41:47Z | 2024-06-06T05:41:47Z | RoboCoder: Robotic Learning from Basic Skills to General Tasks with
Large Language Models | The emergence of Large Language Models (LLMs) has improved the prospects for robotic tasks. However, existing benchmarks are still limited to single tasks with limited generalization capabilities. In this work, we introduce a comprehensive benchmark and an autonomous learning framework, RoboCoder aimed at enhancing the generalization capabilities of robots in complex environments. Unlike traditional methods that focus on single-task learning, our research emphasizes the development of a general-purpose robotic coding algorithm that enables robots to leverage basic skills to tackle increasingly complex tasks. The newly proposed benchmark consists of 80 manually designed tasks across 7 distinct entities, testing the models' ability to learn from minimal initial mastery. Initial testing revealed that even advanced models like GPT-4 could only achieve a 47% pass rate in three-shot scenarios with humanoid entities. To address these limitations, the RoboCoder framework integrates Large Language Models (LLMs) with a dynamic learning system that uses real-time environmental feedback to continuously update and refine action codes. This adaptive method showed a remarkable improvement, achieving a 36% relative improvement. Our codes will be released. | [
"['Jingyao Li' 'Pengguang Chen' 'Sitong Wu' 'Chuanyang Zheng' 'Hong Xu'\n 'Jiaya Jia']"
] |
null | null | 2406.03766 | null | null | http://arxiv.org/pdf/2406.03766v1 | 2024-06-06T06:12:15Z | 2024-06-06T06:12:15Z | Privacy Preserving Semi-Decentralized Mean Estimation over
Intermittently-Connected Networks | We consider the problem of privately estimating the mean of vectors distributed across different nodes of an unreliable wireless network, where communications between nodes can fail intermittently. We adopt a semi-decentralized setup, wherein to mitigate the impact of intermittently connected links, nodes can collaborate with their neighbors to compute a local consensus, which they relay to a central server. In such a setting, the communications between any pair of nodes must ensure that the privacy of the nodes is rigorously maintained to prevent unauthorized information leakage. We study the tradeoff between collaborative relaying and privacy leakage due to the data sharing among nodes and, subsequently, propose PriCER: Private Collaborative Estimation via Relaying -- a differentially private collaborative algorithm for mean estimation to optimize this tradeoff. The privacy guarantees of PriCER arise (i) implicitly, by exploiting the inherent stochasticity of the flaky network connections, and (ii) explicitly, by adding Gaussian perturbations to the estimates exchanged by the nodes. Local and central privacy guarantees are provided against eavesdroppers who can observe different signals, such as the communications amongst nodes during local consensus and (possibly multiple) transmissions from the relays to the central server. We substantiate our theoretical findings with numerical simulations. Our implementation is available at https://github.com/rajarshisaha95/private-collaborative-relaying. | [
"['Rajarshi Saha' 'Mohamed Seif' 'Michal Yemini' 'Andrea J. Goldsmith'\n 'H. Vincent Poor']"
] |
null | null | 2406.03768 | null | null | http://arxiv.org/pdf/2406.03768v1 | 2024-06-06T06:15:35Z | 2024-06-06T06:15:35Z | Enhancing In-Context Learning Performance with just SVD-Based Weight
Pruning: A Theoretical Perspective | Pre-trained large language models (LLMs) based on Transformer have demonstrated striking in-context learning (ICL) abilities. With a few demonstration input-label pairs, they can predict the label for an unseen input without any parameter updates. In this paper, we show an exciting phenomenon that SVD-based weight pruning can enhance ICL performance, and more surprising, pruning weights in deep layers often results in more stable performance improvements in shallow layers. However, the underlying mechanism of those findings still remains an open question. To reveal those findings, we conduct an in-depth theoretical analysis by presenting the implicit gradient descent (GD) trajectories of ICL and giving the mutual information based generalization bounds of ICL via full implicit GD trajectories. This helps us reasonably explain the surprising experimental findings. Besides, based on all our experimental and theoretical insights, we intuitively propose a simple, model-compression and derivative-free algorithm for downstream tasks in enhancing ICL inference. Experiments on benchmark datasets and open source LLMs display the method effectivenessfootnote{The code is available at url{https://github.com/chen123CtrlS/EnhancingICL_SVDPruning}}. | [
"['Xinhao Yao' 'Xiaolin Hu' 'Shenzhi Yang' 'Yong Liu']"
] |
null | null | 2406.03769 | null | null | http://arxiv.org/pdf/2406.03769v1 | 2024-06-06T06:17:27Z | 2024-06-06T06:17:27Z | DeepRacer on Physical Track: Parameters Exploration and Performance
Evaluation | This paper focuses on the physical racetrack capabilities of AWS DeepRacer. Two separate experiments were conducted. The first experiment (Experiment I) focused on evaluating the impact of hyperparameters on the physical environment. Hyperparameters such as gradient descent batch size and loss type were changed systematically as well as training time settings. The second experiment (Experiment II) focused on exploring AWS DeepRacer object avoidance in the physical environment. It was uncovered that in the simulated environment, models with a higher gradient descent batch size had better performance than models with a lower gradient descent batch size. Alternatively, in the physical environment, a gradient descent batch size of 128 appears to be preferable. It was found that models using the loss type of Huber outperformed models that used the loss type of MSE in both the simulated and physical environments. Finally, object avoidance in the simulated environment appeared to be effective; however, when bringing these models to the physical environment, there was a pronounced challenge to avoid objects. Therefore, object avoidance in the physical environment remains an open challenge. | [
"['Sinan Koparan' 'Bahman Javadi']"
] |
null | null | 2406.03777 | null | null | http://arxiv.org/pdf/2406.03777v2 | 2024-06-13T17:00:47Z | 2024-06-06T06:41:53Z | Empirical Guidelines for Deploying LLMs onto Resource-constrained Edge
Devices | The scaling laws have become the de facto guidelines for designing large language models (LLMs), but they were studied under the assumption of unlimited computing resources for both training and inference. As LLMs are increasingly used as personalized intelligent assistants, their customization (i.e., learning through fine-tuning) and deployment onto resource-constrained edge devices will become more and more prevalent. An urging but open question is how a resource-constrained computing environment would affect the design choices for a personalized LLM. We study this problem empirically in this work. In particular, we consider the tradeoffs among a number of key design factors and their intertwined impacts on learning efficiency and accuracy. The factors include the learning methods for LLM customization, the amount of personalized data used for learning customization, the types and sizes of LLMs, the compression methods of LLMs, the amount of time afforded to learn, and the difficulty levels of the target use cases. Through extensive experimentation and benchmarking, we draw a number of surprisingly insightful guidelines for deploying LLMs onto resource-constrained devices. For example, an optimal choice between parameter learning and RAG may vary depending on the difficulty of the downstream task, the longer fine-tuning time does not necessarily help the model, and a compressed LLM may be a better choice than an uncompressed LLM to learn from limited personalized data. | [
"['Ruiyang Qin' 'Dancheng Liu' 'Zheyu Yan' 'Zhaoxuan Tan' 'Zixuan Pan'\n 'Zhenge Jia' 'Meng Jiang' 'Ahmed Abbasi' 'Jinjun Xiong' 'Yiyu Shi']"
] |
null | null | 2406.03787 | null | null | http://arxiv.org/pdf/2406.03787v1 | 2024-06-06T06:56:56Z | 2024-06-06T06:56:56Z | Projection-Free Variance Reduction Methods for Stochastic Constrained
Multi-Level Compositional Optimization | This paper investigates projection-free algorithms for stochastic constrained multi-level optimization. In this context, the objective function is a nested composition of several smooth functions, and the decision set is closed and convex. Existing projection-free algorithms for solving this problem suffer from two limitations: 1) they solely focus on the gradient mapping criterion and fail to match the optimal sample complexities in unconstrained settings; 2) their analysis is exclusively applicable to non-convex functions, without considering convex and strongly convex objectives. To address these issues, we introduce novel projection-free variance reduction algorithms and analyze their complexities under different criteria. For gradient mapping, our complexities improve existing results and match the optimal rates for unconstrained problems. For the widely-used Frank-Wolfe gap criterion, we provide theoretical guarantees that align with those for single-level problems. Additionally, by using a stage-wise adaptation, we further obtain complexities for convex and strongly convex functions. Finally, numerical experiments on different tasks demonstrate the effectiveness of our methods. | [
"['Wei Jiang' 'Sifan Yang' 'Wenhao Yang' 'Yibo Wang' 'Yuanyu Wan'\n 'Lijun Zhang']"
] |
null | null | 2406.03789 | null | null | http://arxiv.org/pdf/2406.03789v1 | 2024-06-06T07:01:36Z | 2024-06-06T07:01:36Z | Enhancing Graph U-Nets for Mesh-Agnostic Spatio-Temporal Flow Prediction | This study aims to overcome the conventional deep-learning approaches based on convolutional neural networks, whose applicability to complex geometries and unstructured meshes is limited due to their inherent mesh dependency. We propose novel approaches to improve mesh-agnostic spatio-temporal prediction of transient flow fields using graph U-Nets, enabling accurate prediction on diverse mesh configurations. Key enhancements to the graph U-Net architecture, including the Gaussian mixture model convolutional operator and noise injection approaches, provide increased flexibility in modeling node dynamics: the former reduces prediction error by 95% compared to conventional convolutional operators, while the latter improves long-term prediction robustness, resulting in an error reduction of 86%. We also investigate transductive and inductive-learning perspectives of graph U-Nets with proposed improvements. In the transductive setting, they effectively predict quantities for unseen nodes within the trained graph. In the inductive setting, they successfully perform in mesh scenarios with different vortex-shedding periods, showing 98% improvement in predicting the future flow fields compared to a model trained without the inductive settings. It is found that graph U-Nets without pooling operations, i.e. without reducing and restoring the node dimensionality of the graph data, perform better in inductive settings due to their ability to learn from the detailed structure of each graph. Meanwhile, we also discover that the choice of normalization technique significantly impacts graph U-Net performance. | [
"['Sunwoong Yang' 'Ricardo Vinuesa' 'Namwoo Kang']"
] |
null | null | 2406.03791 | null | null | http://arxiv.org/pdf/2406.03791v1 | 2024-06-06T07:03:28Z | 2024-06-06T07:03:28Z | Speed of Light Exact Greedy Decoding for RNN-T Speech Recognition Models
on GPU | The vast majority of inference time for RNN Transducer (RNN-T) models today is spent on decoding. Current state-of-the-art RNN-T decoding implementations leave the GPU idle ~80% of the time. Leveraging a new CUDA 12.4 feature, CUDA graph conditional nodes, we present an exact GPU-based implementation of greedy decoding for RNN-T models that eliminates this idle time. Our optimizations speed up a 1.1 billion parameter RNN-T model end-to-end by a factor of 2.5x. This technique can applied to the "label looping" alternative greedy decoding algorithm as well, achieving 1.7x and 1.4x end-to-end speedups when applied to 1.1 billion parameter RNN-T and Token and Duration Transducer models respectively. This work enables a 1.1 billion parameter RNN-T model to run only 16% slower than a similarly sized CTC model, contradicting the common belief that RNN-T models are not suitable for high throughput inference. The implementation is available in NVIDIA NeMo. | [
"['Daniel Galvez' 'Vladimir Bataev' 'Hainan Xu' 'Tim Kaldewey']"
] |
null | null | 2406.03793 | null | null | http://arxiv.org/pdf/2406.03793v1 | 2024-06-06T07:05:20Z | 2024-06-06T07:05:20Z | Low-Rank Similarity Mining for Multimodal Dataset Distillation | Though dataset distillation has witnessed rapid development in recent years, the distillation of multimodal data, e.g., image-text pairs, poses unique and under-explored challenges. Unlike unimodal data, image-text contrastive learning (ITC) data lack inherent categorization and should instead place greater emphasis on modality correspondence. In this work, we propose Low-Rank Similarity Mining (LoRS) for multimodal dataset distillation, that concurrently distills a ground truth similarity matrix with image-text pairs, and leverages low-rank factorization for efficiency and scalability. The proposed approach brings significant improvement to the existing algorithms, marking a significant contribution to the field of visual-language dataset distillation. We advocate adopting LoRS as a foundational synthetic data setup for image-text dataset distillation. Our code is available at https://github.com/silicx/LoRS_Distill. | [
"['Yue Xu' 'Zhilin Lin' 'Yusong Qiu' 'Cewu Lu' 'Yong-Lu Li']"
] |
null | null | 2406.03794 | null | null | http://arxiv.org/pdf/2406.03794v1 | 2024-06-06T07:05:58Z | 2024-06-06T07:05:58Z | Infusing Self-Consistency into Density Functional Theory Hamiltonian
Prediction via Deep Equilibrium Models | In this study, we introduce a unified neural network architecture, the Deep Equilibrium Density Functional Theory Hamiltonian (DEQH) model, which incorporates Deep Equilibrium Models (DEQs) for predicting Density Functional Theory (DFT) Hamiltonians. The DEQH model inherently captures the self-consistency nature of Hamiltonian, a critical aspect often overlooked by traditional machine learning approaches for Hamiltonian prediction. By employing DEQ within our model architecture, we circumvent the need for DFT calculations during the training phase to introduce the Hamiltonian's self-consistency, thus addressing computational bottlenecks associated with large or complex systems. We propose a versatile framework that combines DEQ with off-the-shelf machine learning models for predicting Hamiltonians. When benchmarked on the MD17 and QH9 datasets, DEQHNet, an instantiation of the DEQH framework, has demonstrated a significant improvement in prediction accuracy. Beyond a predictor, the DEQH model is a Hamiltonian solver, in the sense that it uses the fixed-point solving capability of the deep equilibrium model to iteratively solve for the Hamiltonian. Ablation studies of DEQHNet further elucidate the network's effectiveness, offering insights into the potential of DEQ-integrated networks for Hamiltonian learning. | [
"['Zun Wang' 'Chang Liu' 'Nianlong Zou' 'He Zhang' 'Xinran Wei' 'Lin Huang'\n 'Lijun Wu' 'Bin Shao']"
] |
null | null | 2406.03808 | null | null | http://arxiv.org/pdf/2406.03808v1 | 2024-06-06T07:30:27Z | 2024-06-06T07:30:27Z | Cross-variable Linear Integrated ENhanced Transformer for Photovoltaic
power forecasting | Photovoltaic (PV) power forecasting plays a crucial role in optimizing the operation and planning of PV systems, thereby enabling efficient energy management and grid integration. However, un certainties caused by fluctuating weather conditions and complex interactions between different variables pose significant challenges to accurate PV power forecasting. In this study, we propose PV-Client (Cross-variable Linear Integrated ENhanced Transformer for Photovoltaic power forecasting) to address these challenges and enhance PV power forecasting accuracy. PV-Client employs an ENhanced Transformer module to capture complex interactions of various features in PV systems, and utilizes a linear module to learn trend information in PV power. Diverging from conventional time series-based Transformer models that use cross-time Attention to learn dependencies between different time steps, the Enhanced Transformer module integrates cross-variable Attention to capture dependencies between PV power and weather factors. Furthermore, PV-Client streamlines the embedding and position encoding layers by replacing the Decoder module with a projection layer. Experimental results on three real-world PV power datasets affirm PV-Client's state-of-the-art (SOTA) performance in PV power forecasting. Specifically, PV-Client surpasses the second-best model GRU by 5.3% in MSE metrics and 0.9% in accuracy metrics at the Jingang Station. Similarly, PV-Client outperforms the second-best model SVR by 10.1% in MSE metrics and 0.2% in accuracy metrics at the Xinqingnian Station, and PV-Client exhibits superior performance compared to the second-best model SVR with enhancements of 3.4% in MSE metrics and 0.9% in accuracy metrics at the Hongxing Station. | [
"['Jiaxin Gao' 'Qinglong Cao' 'Yuntian Chen' 'Dongxiao Zhang']"
] |
null | null | 2406.03810 | null | null | http://arxiv.org/pdf/2406.03810v1 | 2024-06-06T07:34:58Z | 2024-06-06T07:34:58Z | Spherinator and HiPSter: Representation Learning for Unbiased Knowledge
Discovery from Simulations | Simulations are the best approximation to experimental laboratories in astrophysics and cosmology. However, the complexity, richness, and large size of their outputs severely limit the interpretability of their predictions. We describe a new, unbiased, and machine learning based approach to obtaining useful scientific insights from a broad range of simulations. The method can be used on today's largest simulations and will be essential to solve the extreme data exploration and analysis challenges posed by the Exascale era. Furthermore, this concept is so flexible, that it will also enable explorative access to observed data. Our concept is based on applying nonlinear dimensionality reduction to learn compact representations of the data in a low-dimensional space. The simulation data is projected onto this space for interactive inspection, visual interpretation, sample selection, and local analysis. We present a prototype using a rotational invariant hyperspherical variational convolutional autoencoder, utilizing a power distribution in the latent space, and trained on galaxies from IllustrisTNG simulation. Thereby, we obtain a natural Hubble tuning fork like similarity space that can be visualized interactively on the surface of a sphere by exploiting the power of HiPS tilings in Aladin Lite. | [
"['Kai L. Polsterer' 'Bernd Doser' 'Andreas Fehlner'\n 'Sebastian Trujillo-Gomez']"
] |
null | null | 2406.03812 | null | null | http://arxiv.org/pdf/2406.03812v1 | 2024-06-06T07:37:42Z | 2024-06-06T07:37:42Z | How to Scale Inverse RL to Large State Spaces? A Provably Efficient
Approach | In online Inverse Reinforcement Learning (IRL), the learner can collect samples about the dynamics of the environment to improve its estimate of the reward function. Since IRL suffers from identifiability issues, many theoretical works on online IRL focus on estimating the entire set of rewards that explain the demonstrations, named the feasible reward set. However, none of the algorithms available in the literature can scale to problems with large state spaces. In this paper, we focus on the online IRL problem in Linear Markov Decision Processes (MDPs). We show that the structure offered by Linear MDPs is not sufficient for efficiently estimating the feasible set when the state space is large. As a consequence, we introduce the novel framework of rewards compatibility, which generalizes the notion of feasible set, and we develop CATY-IRL, a sample efficient algorithm whose complexity is independent of the cardinality of the state space in Linear MDPs. When restricted to the tabular setting, we demonstrate that CATY-IRL is minimax optimal up to logarithmic factors. As a by-product, we show that Reward-Free Exploration (RFE) enjoys the same worst-case rate, improving over the state-of-the-art lower bound. Finally, we devise a unifying framework for IRL and RFE that may be of independent interest. | [
"['Filippo Lazzati' 'Mirco Mutti' 'Alberto Maria Metelli']"
] |
null | null | 2406.03818 | null | null | http://arxiv.org/pdf/2406.03818v1 | 2024-06-06T07:49:02Z | 2024-06-06T07:49:02Z | Amortized Equation Discovery in Hybrid Dynamical Systems | Hybrid dynamical systems are prevalent in science and engineering to express complex systems with continuous and discrete states. To learn the laws of systems, all previous methods for equation discovery in hybrid systems follow a two-stage paradigm, i.e. they first group time series into small cluster fragments and then discover equations in each fragment separately through methods in non-hybrid systems. Although effective, these methods do not fully take advantage of the commonalities in the shared dynamics of multiple fragments that are driven by the same equations. Besides, the two-stage paradigm breaks the interdependence between categorizing and representing dynamics that jointly form hybrid systems. In this paper, we reformulate the problem and propose an end-to-end learning framework, i.e. Amortized Equation Discovery (AMORE), to jointly categorize modes and discover equations characterizing the dynamics of each mode by all segments of the mode. Experiments on four hybrid and six non-hybrid systems show that our method outperforms previous methods on equation discovery, segmentation, and forecasting. | [
"['Yongtuo Liu' 'Sara Magliacane' 'Miltiadis Kofinas' 'Efstratios Gavves']"
] |
null | null | 2406.03819 | null | null | http://arxiv.org/pdf/2406.03819v1 | 2024-06-06T07:49:11Z | 2024-06-06T07:49:11Z | Subspace Clustering in Wavelet Packets Domain | Subspace clustering (SC) algorithms utilize the union of subspaces model to cluster data points according to the subspaces from which they are drawn. To better address separability of subspaces and robustness to noise we propose a wavelet packet (WP) based transform domain subspace clustering. Depending on the number of resolution levels, WP yields several representations instantiated in terms of subbands. The first approach combines original and subband data into one complementary multi-view representation. Afterward, we formulate joint representation learning as a low-rank MERA tensor network approximation problem. That is motivated by the strong representation power of the MERA network to capture complex intra/inter-view dependencies in corresponding self-representation tensor. In the second approach, we use a self-stopping computationally efficient method to select the subband with the smallest clustering error on the validation set. When existing SC algorithms are applied to the chosen subband, their performance is expected to improve. Consequently, both approaches enable the re-use of SC algorithms developed so far. Improved clustering performance is due to the dual nature of subbands as representations and filters, which is essential for noise suppression. We exemplify the proposed WP domain approach to SC on the MERA tensor network and eight other well-known linear SC algorithms using six well-known image datasets representing faces, digits, and objects. Although WP domain-based SC is a linear method, it achieved clustering performance comparable with some best deep SC algorithms and outperformed many other deep SC algorithms by a significant margin. That is in particular case for the WP MERA SC algorithm. On the COIL100 dataset, it achieves an accuracy of 87.45% and outperforms the best deep SC competitor in the amount of 14.75%. | [
"['Ivica Kopriva' 'Damir Sersic']"
] |
null | null | 2406.03820 | null | null | http://arxiv.org/pdf/2406.03820v2 | 2024-06-21T14:43:41Z | 2024-06-06T07:55:30Z | A Survey on Intelligent Internet of Things: Applications, Security,
Privacy, and Future Directions | The rapid advances in the Internet of Things (IoT) have promoted a revolution in communication technology and offered various customer services. Artificial intelligence (AI) techniques have been exploited to facilitate IoT operations and maximize their potential in modern application scenarios. In particular, the convergence of IoT and AI has led to a new networking paradigm called Intelligent IoT (IIoT), which has the potential to significantly transform businesses and industrial domains. This paper presents a comprehensive survey of IIoT by investigating its significant applications in mobile networks, as well as its associated security and privacy issues. Specifically, we explore and discuss the roles of IIoT in a wide range of key application domains, from smart healthcare and smart cities to smart transportation and smart industries. Through such extensive discussions, we investigate important security issues in IIoT networks, where network attacks, confidentiality, integrity, and intrusion are analyzed, along with a discussion of potential countermeasures. Privacy issues in IIoT networks were also surveyed and discussed, including data, location, and model privacy leakage. Finally, we outline several key challenges and highlight potential research directions in this important area. | [
"['Ons Aouedi' 'Thai-Hoc Vu' 'Alessio Sacco' 'Dinh C. Nguyen'\n 'Kandaraj Piamrat' 'Guido Marchetto' 'Quoc-Viet Pham']"
] |
null | null | 2406.03824 | null | null | http://arxiv.org/pdf/2406.03824v1 | 2024-06-06T07:59:19Z | 2024-06-06T07:59:19Z | Predictability Analysis of Regression Problems via Conditional Entropy
Estimations | In the field of machine learning, regression problems are pivotal due to their ability to predict continuous outcomes. Traditional error metrics like mean squared error, mean absolute error, and coefficient of determination measure model accuracy. The model accuracy is the consequence of the selected model and the features, which blurs the analysis of contribution. Predictability, in the other hand, focus on the predictable level of a target variable given a set of features. This study introduces conditional entropy estimators to assess predictability in regression problems, bridging this gap. We enhance and develop reliable conditional entropy estimators, particularly the KNIFE-P estimator and LMC-P estimator, which offer under- and over-estimation, providing a practical framework for predictability analysis. Extensive experiments on synthesized and real-world datasets demonstrate the robustness and utility of these estimators. Additionally, we extend the analysis to the coefficient of determination (R^2 ), enhancing the interpretability of predictability. The results highlight the effectiveness of KNIFE-P and LMC-P in capturing the achievable performance and limitations of feature sets, providing valuable tools in the development of regression models. These indicators offer a robust framework for assessing the predictability for regression problems. | [
"['Yu-Hsueh Fang' 'Chia-Yen Lee']"
] |
null | null | 2406.03833 | null | null | http://arxiv.org/pdf/2406.03833v1 | 2024-06-06T08:08:01Z | 2024-06-06T08:08:01Z | Exploiting Global Graph Homophily for Generalized Defense in Graph
Neural Networks | Graph neural network (GNN) models play a pivotal role in numerous tasks involving graph-related data analysis. Despite their efficacy, similar to other deep learning models, GNNs are susceptible to adversarial attacks. Even minor perturbations in graph data can induce substantial alterations in model predictions. While existing research has explored various adversarial defense techniques for GNNs, the challenge of defending against adversarial attacks on real-world scale graph data remains largely unresolved. On one hand, methods reliant on graph purification and preprocessing tend to excessively emphasize local graph information, leading to sub-optimal defensive outcomes. On the other hand, approaches rooted in graph structure learning entail significant time overheads, rendering them impractical for large-scale graphs. In this paper, we propose a new defense method named Talos, which enhances the global, rather than local, homophily of graphs as a defense. Experiments show that the proposed approach notably outperforms state-of-the-art defense approaches, while imposing little computational overhead. | [
"['Duanyu Li' 'Huijun Wu' 'Min Xie' 'Xugang Wu' 'Zhenwei Wu' 'Wenzhe Zhang']"
] |
null | null | 2406.03845 | null | null | http://arxiv.org/pdf/2406.03845v1 | 2024-06-06T08:23:22Z | 2024-06-06T08:23:22Z | Open Problem: Active Representation Learning | In this work, we introduce the concept of Active Representation Learning, a novel class of problems that intertwines exploration and representation learning within partially observable environments. We extend ideas from Active Simultaneous Localization and Mapping (active SLAM), and translate them to scientific discovery problems, exemplified by adaptive microscopy. We explore the need for a framework that derives exploration skills from representations that are in some sense actionable, aiming to enhance the efficiency and effectiveness of data collection and model building in the natural sciences. | [
"['Nikola Milosevic' 'Gesine Müller' 'Jan Huisken' 'Nico Scherf']"
] |
null | null | 2406.03848 | null | null | http://arxiv.org/pdf/2406.03848v2 | 2024-06-09T04:22:21Z | 2024-06-06T08:29:29Z | OceanCastNet: A Deep Learning Ocean Wave Model with Energy Conservation | Traditional wave forecasting models, although based on energy conservation equations, are computationally expensive. On the other hand, existing deep learning geophysical fluid models, while computationally efficient, often suffer from issues such as energy dissipation in long-term forecasts. This paper proposes a novel energy-balanced deep learning wave forecasting model called OceanCastNet (OCN). By incorporating wind fields at the current, previous, and future time steps, as well as wave fields at the current and previous time steps as input variables, OCN maintains energy balance within the model. Furthermore, the model employs adaptive Fourier operators as its core components and designs a masked loss function to better handle the impact of land-sea boundaries. A series of experiments on the ERA5 dataset demonstrate that OCN can achieve short-term forecast accuracy comparable to traditional models while exhibiting an understanding of the wave generation process. In comparative experiments under both normal and extreme conditions, OCN consistently outperforms the widely used WaveWatch III model in the industry. Even after long-term forecasting, OCN maintains a stable and energy-rich state. By further constructing a simple meteorological model, OCN-wind, which considers energy balance, this paper confirms the importance of energy constraints for improving the long-term forecast performance of deep learning meteorological models. This finding provides new ideas for future research on deep learning geophysical fluid models. | [
"['Ziliang Zhang' 'Huaming Yu' 'Danqin Ren']"
] |
null | null | 2406.03849 | null | null | http://arxiv.org/pdf/2406.03849v1 | 2024-06-06T08:31:52Z | 2024-06-06T08:31:52Z | A Noise-robust Multi-head Attention Mechanism for Formation Resistivity
Prediction: Frequency Aware LSTM | The prediction of formation resistivity plays a crucial role in the evaluation of oil and gas reservoirs, identification and assessment of geothermal energy resources, groundwater detection and monitoring, and carbon capture and storage. However, traditional well logging techniques fail to measure accurate resistivity in cased boreholes, and the transient electromagnetic method for cased borehole resistivity logging encounters challenges of high-frequency disaster (the problem of inadequate learning by neural networks in high-frequency features) and noise interference, badly affecting accuracy. To address these challenges, frequency-aware framework and temporal anti-noise block are proposed to build frequency aware LSTM (FAL). The frequency-aware framework implements a dual-stream structure through wavelet transformation, allowing the neural network to simultaneously handle high-frequency and low-frequency flows of time-series data, thus avoiding high-frequency disaster. The temporal anti-noise block integrates multiple attention mechanisms and soft-threshold attention mechanisms, enabling the model to better distinguish noise from redundant features. Ablation experiments demonstrate that the frequency-aware framework and temporal anti-noise block contribute significantly to performance improvement. FAL achieves a 24.3% improvement in R2 over LSTM, reaching the highest value of 0.91 among all models. In robustness experiments, the impact of noise on FAL is approximately 1/8 of the baseline, confirming the noise resistance of FAL. The proposed FAL effectively reduces noise interference in predicting formation resistivity from cased transient electromagnetic well logging curves, better learns high-frequency features, and thereby enhances the prediction accuracy and noise resistance of the neural network model. | [
"['Yongan Zhang' 'Junfeng Zhao' 'Jian Li' 'Xuanran Wang' 'Youzhuang Sun'\n 'Yuntian Chen' 'Dongxiao Zhang']"
] |
null | null | 2406.03852 | null | null | http://arxiv.org/pdf/2406.03852v1 | 2024-06-06T08:36:21Z | 2024-06-06T08:36:21Z | Why the Metric Backbone Preserves Community Structure | The metric backbone of a weighted graph is the union of all-pairs shortest paths. It is obtained by removing all edges $(u,v)$ that are not the shortest path between $u$ and $v$. In networks with well-separated communities, the metric backbone tends to preserve many inter-community edges, because these edges serve as bridges connecting two communities, but tends to delete many intra-community edges because the communities are dense. This suggests that the metric backbone would dilute or destroy the community structure of the network. However, this is not borne out by prior empirical work, which instead showed that the metric backbone of real networks preserves the community structure of the original network well. In this work, we analyze the metric backbone of a broad class of weighted random graphs with communities, and we formally prove the robustness of the community structure with respect to the deletion of all the edges that are not in the metric backbone. An empirical comparison of several graph sparsification techniques confirms our theoretical finding and shows that the metric backbone is an efficient sparsifier in the presence of communities. | [
"['Maximilien Dreveton' 'Charbel Chucri' 'Matthias Grossglauser'\n 'Patrick Thiran']"
] |
null | null | 2406.03857 | null | null | http://arxiv.org/pdf/2406.03857v1 | 2024-06-06T08:42:36Z | 2024-06-06T08:42:36Z | MuJo: Multimodal Joint Feature Space Learning for Human Activity
Recognition | Human Activity Recognition is a longstanding problem in AI with applications in a broad range of areas: from healthcare, sports and fitness, security, and human computer interaction to robotics. The performance of HAR in real-world settings is strongly dependent on the type and quality of the input signal that can be acquired. Given an unobstructed, high-quality camera view of a scene, computer vision systems, in particular in conjunction with foundational models (e.g., CLIP), can today fairly reliably distinguish complex activities. On the other hand, recognition using modalities such as wearable sensors (which are often more broadly available, e.g, in mobile phones and smartwatches) is a more difficult problem, as the signals often contain less information and labeled training data is more difficult to acquire. In this work, we show how we can improve HAR performance across different modalities using multimodal contrastive pretraining. Our approach MuJo (Multimodal Joint Feature Space Learning), learns a multimodal joint feature space with video, language, pose, and IMU sensor data. The proposed approach combines contrastive and multitask learning methods and analyzes different multitasking strategies for learning a compact shared representation. A large dataset with parallel video, language, pose, and sensor data points is also introduced to support the research, along with an analysis of the robustness of the multimodal joint space for modal-incomplete and low-resource data. On the MM-Fit dataset, our model achieves an impressive Macro F1-Score of up to 0.992 with only 2% of the train data and 0.999 when using all available training data for classification tasks. Moreover, in the scenario where the MM-Fit dataset is unseen, we demonstrate a generalization performance of up to 0.638. | [
"['Stefan Gerd Fritsch' 'Cennet Oguz' 'Vitor Fortes Rey' 'Lala Ray'\n 'Maximilian Kiefer-Emmanouilidis' 'Paul Lukowicz']"
] |
null | null | 2406.03862 | null | null | http://arxiv.org/pdf/2406.03862v1 | 2024-06-06T08:49:51Z | 2024-06-06T08:49:51Z | Behavior-Targeted Attack on Reinforcement Learning with Limited Access
to Victim's Policy | This study considers the attack on reinforcement learning agents where the adversary aims to control the victim's behavior as specified by the adversary by adding adversarial modifications to the victim's state observation. While some attack methods reported success in manipulating the victim agent's behavior, these methods often rely on environment-specific heuristics. In addition, all existing attack methods require white-box access to the victim's policy. In this study, we propose a novel method for manipulating the victim agent in the black-box (i.e., the adversary is allowed to observe the victim's state and action only) and no-box (i.e., the adversary is allowed to observe the victim's state only) setting without requiring environment-specific heuristics. Our attack method is formulated as a bi-level optimization problem that is reduced to a distribution matching problem and can be solved by an existing imitation learning algorithm in the black-box and no-box settings. Empirical evaluations on several reinforcement learning benchmarks show that our proposed method has superior attack performance to baselines. | [
"['Shojiro Yamabe' 'Kazuto Fukuchi' 'Ryoma Senda' 'Jun Sakuma']"
] |
null | null | 2406.03864 | null | null | http://arxiv.org/pdf/2406.03864v1 | 2024-06-06T08:50:16Z | 2024-06-06T08:50:16Z | PairNet: Training with Observed Pairs to Estimate Individual Treatment
Effect | Given a dataset of individuals each described by a covariate vector, a treatment, and an observed outcome on the treatment, the goal of the individual treatment effect (ITE) estimation task is to predict outcome changes resulting from a change in treatment. A fundamental challenge is that in the observational data, a covariate's outcome is observed only under one treatment, whereas we need to infer the difference in outcomes under two different treatments. Several existing approaches address this issue through training with inferred pseudo-outcomes, but their success relies on the quality of these pseudo-outcomes. We propose PairNet, a novel ITE estimation training strategy that minimizes losses over pairs of examples based on their factual observed outcomes. Theoretical analysis for binary treatments reveals that PairNet is a consistent estimator of ITE risk, and achieves smaller generalization error than baseline models. Empirical comparison with thirteen existing methods across eight benchmarks, covering both discrete and continuous treatments, shows that PairNet achieves significantly lower ITE error compared to the baselines. Also, it is model-agnostic and easy to implement. | [
"['Lokesh Nagalapatti' 'Pranava Singhal' 'Avishek Ghosh' 'Sunita Sarawagi']"
] |
null | null | 2406.03873 | null | null | http://arxiv.org/pdf/2406.03873v1 | 2024-06-06T09:04:48Z | 2024-06-06T09:04:48Z | Quantum Implicit Neural Representations | Implicit neural representations have emerged as a powerful paradigm to represent signals such as images and sounds. This approach aims to utilize neural networks to parameterize the implicit function of the signal. However, when representing implicit functions, traditional neural networks such as ReLU-based multilayer perceptrons face challenges in accurately modeling high-frequency components of signals. Recent research has begun to explore the use of Fourier Neural Networks (FNNs) to overcome this limitation. In this paper, we propose Quantum Implicit Representation Network (QIREN), a novel quantum generalization of FNNs. Furthermore, through theoretical analysis, we demonstrate that QIREN possesses a quantum advantage over classical FNNs. Lastly, we conducted experiments in signal representation, image superresolution, and image generation tasks to show the superior performance of QIREN compared to state-of-the-art (SOTA) models. Our work not only incorporates quantum advantages into implicit neural representations but also uncovers a promising application direction for Quantum Neural Networks. | [
"['Jiaming Zhao' 'Wenbo Qiao' 'Peng Zhang' 'Hui Gao']"
] |
null | null | 2406.03879 | null | null | http://arxiv.org/pdf/2406.03879v1 | 2024-06-06T09:14:32Z | 2024-06-06T09:14:32Z | Decay Pruning Method: Smooth Pruning With a Self-Rectifying Procedure | Current structured pruning methods often result in considerable accuracy drops due to abrupt network changes and loss of information from pruned structures. To address these issues, we introduce the Decay Pruning Method (DPM), a novel smooth pruning approach with a self-rectifying mechanism. DPM consists of two key components: (i) Smooth Pruning: It converts conventional single-step pruning into multi-step smooth pruning, gradually reducing redundant structures to zero over N steps with ongoing optimization. (ii) Self-Rectifying: This procedure further enhances the aforementioned process by rectifying sub-optimal pruning based on gradient information. Our approach demonstrates strong generalizability and can be easily integrated with various existing pruning methods. We validate the effectiveness of DPM by integrating it with three popular pruning methods: OTOv2, Depgraph, and Gate Decorator. Experimental results show consistent improvements in performance compared to the original pruning methods, along with further reductions of FLOPs in most scenarios. | [
"['Minghao Yang' 'Linlin Gao' 'Pengyuan Li' 'Wenbo Li' 'Yihong Dong'\n 'Zhiying Cui']"
] |
null | null | 2406.03880 | null | null | http://arxiv.org/pdf/2406.03880v1 | 2024-06-06T09:17:40Z | 2024-06-06T09:17:40Z | Memorization in deep learning: A survey | Deep Learning (DL) powered by Deep Neural Networks (DNNs) has revolutionized various domains, yet understanding the intricacies of DNN decision-making and learning processes remains a significant challenge. Recent investigations have uncovered an interesting memorization phenomenon in which DNNs tend to memorize specific details from examples rather than learning general patterns, affecting model generalization, security, and privacy. This raises critical questions about the nature of generalization in DNNs and their susceptibility to security breaches. In this survey, we present a systematic framework to organize memorization definitions based on the generalization and security/privacy domains and summarize memorization evaluation methods at both the example and model levels. Through a comprehensive literature review, we explore DNN memorization behaviors and their impacts on security and privacy. We also introduce privacy vulnerabilities caused by memorization and the phenomenon of forgetting and explore its connection with memorization. Furthermore, we spotlight various applications leveraging memorization and forgetting mechanisms, including noisy label learning, privacy preservation, and model enhancement. This survey offers the first-in-kind understanding of memorization in DNNs, providing insights into its challenges and opportunities for enhancing AI development while addressing critical ethical concerns. | [
"['Jiaheng Wei' 'Yanjun Zhang' 'Leo Yu Zhang' 'Ming Ding' 'Chao Chen'\n 'Kok-Leong Ong' 'Jun Zhang' 'Yang Xiang']"
] |
null | null | 2406.03886 | null | null | http://arxiv.org/pdf/2406.03886v1 | 2024-06-06T09:24:21Z | 2024-06-06T09:24:21Z | BiomedBench: A benchmark suite of TinyML biomedical applications for
low-power wearables | The design of low-power wearables for the biomedical domain has received a lot of attention in recent decades, as technological advances in chip manufacturing have allowed real-time monitoring of patients using low-complexity ML within the mW range. Despite advances in application and hardware design research, the domain lacks a systematic approach to hardware evaluation. In this work, we propose BiomedBench, a new benchmark suite composed of complete end-to-end TinyML biomedical applications for real-time monitoring of patients using wearable devices. Each application presents different requirements during typical signal acquisition and processing phases, including varying computational workloads and relations between active and idle times. Furthermore, our evaluation of five state-of-the-art low-power platforms in terms of energy efficiency shows that modern platforms cannot effectively target all types of biomedical applications. BiomedBench will be released as an open-source suite to enable future improvements in the entire domain of bioengineering systems and TinyML application design. | [
"['Dimitrios Samakovlis' 'Stefano Albini' 'Rubén Rodríguez Álvarez'\n 'Denisa-Andreea Constantinescu' 'Pasquale Davide Schiavone'\n 'Miguel Peón Quirós' 'David Atienza']"
] |
null | null | 2406.03890 | null | null | http://arxiv.org/pdf/2406.03890v1 | 2024-06-06T09:26:02Z | 2024-06-06T09:26:02Z | Exploring Pessimism and Optimism Dynamics in Deep Reinforcement Learning | Off-policy actor-critic algorithms have shown promise in deep reinforcement learning for continuous control tasks. Their success largely stems from leveraging pessimistic state-action value function updates, which effectively address function approximation errors and improve performance. However, such pessimism can lead to under-exploration, constraining the agent's ability to explore/refine its policies. Conversely, optimism can counteract under-exploration, but it also carries the risk of excessive risk-taking and poor convergence if not properly balanced. Based on these insights, we introduce Utility Soft Actor-Critic (USAC), a novel framework within the actor-critic paradigm that enables independent control over the degree of pessimism/optimism for both the actor and the critic via interpretable parameters. USAC adapts its exploration strategy based on the uncertainty of critics through a utility function that allows us to balance between pessimism and optimism separately. By going beyond binary choices of optimism and pessimism, USAC represents a significant step towards achieving balance within off-policy actor-critic algorithms. Our experiments across various continuous control problems show that the degree of pessimism or optimism depends on the nature of the task. Furthermore, we demonstrate that USAC can outperform state-of-the-art algorithms for appropriately configured pessimism/optimism parameters. | [
"['Bahareh Tasdighi' 'Nicklas Werge' 'Yi-Shan Wu' 'Melih Kandemir']"
] |
null | null | 2406.03892 | null | null | http://arxiv.org/pdf/2406.03892v1 | 2024-06-06T09:26:48Z | 2024-06-06T09:26:48Z | Polyhedral Conic Classifier for CTR Prediction | This paper introduces a novel approach for click-through rate (CTR) prediction within industrial recommender systems, addressing the inherent challenges of numerical imbalance and geometric asymmetry. These challenges stem from imbalanced datasets, where positive (click) instances occur less frequently than negatives (non-clicks), and geometrically asymmetric distributions, where positive samples exhibit visually coherent patterns while negatives demonstrate greater diversity. To address these challenges, we have used a deep neural network classifier that uses the polyhedral conic functions. This classifier is similar to the one-class classifiers in spirit and it returns compact polyhedral acceptance regions to separate the positive class samples from the negative samples that have diverse distributions. Extensive experiments have been conducted to test the proposed approach using state-of-the-art (SOTA) CTR prediction models on four public datasets, namely Criteo, Avazu, MovieLens and Frappe. The experimental evaluations highlight the superiority of our proposed approach over Binary Cross Entropy (BCE) Loss, which is widely used in CTR prediction tasks. | [
"['Beyza Turkmen' 'Ramazan Tarik Turksoy' 'Hasan Saribas' 'Hakan Cevikalp']"
] |
null | null | 2406.03894 | null | null | http://arxiv.org/pdf/2406.03894v1 | 2024-06-06T09:29:40Z | 2024-06-06T09:29:40Z | Transductive Off-policy Proximal Policy Optimization | Proximal Policy Optimization (PPO) is a popular model-free reinforcement learning algorithm, esteemed for its simplicity and efficacy. However, due to its inherent on-policy nature, its proficiency in harnessing data from disparate policies is constrained. This paper introduces a novel off-policy extension to the original PPO method, christened Transductive Off-policy PPO (ToPPO). Herein, we provide theoretical justification for incorporating off-policy data in PPO training and prudent guidelines for its safe application. Our contribution includes a novel formulation of the policy improvement lower bound for prospective policies derived from off-policy data, accompanied by a computationally efficient mechanism to optimize this bound, underpinned by assurances of monotonic improvement. Comprehensive experimental results across six representative tasks underscore ToPPO's promising performance. | [
"['Yaozhong Gan' 'Renye Yan' 'Xiaoyang Tan' 'Zhe Wu' 'Junliang Xing']"
] |
null | null | 2406.03896 | null | null | http://arxiv.org/pdf/2406.03896v1 | 2024-06-06T09:36:05Z | 2024-06-06T09:36:05Z | Data-driven discovery of self-similarity using neural networks | Finding self-similarity is a key step for understanding the governing law behind complex physical phenomena. Traditional methods for identifying self-similarity often rely on specific models, which can introduce significant bias. In this paper, we present a novel neural network-based approach that discovers self-similarity directly from observed data, without presupposing any models. The presence of self-similar solutions in a physical problem signals that the governing law contains a function whose arguments are given by power-law monomials of physical parameters, which are characterized by power-law exponents. The basic idea is to enforce such particular forms structurally in a neural network in a parametrized way. We train the neural network model using the observed data, and when the training is successful, we can extract the power exponents that characterize scale-transformation symmetries of the physical problem. We demonstrate the effectiveness of our method with both synthetic and experimental data, validating its potential as a robust, model-independent tool for exploring self-similarity in complex systems. | [
"['Ryota Watanabe' 'Takanori Ishii' 'Yuji Hirono' 'Hirokazu Maruoka']"
] |
null | null | 2406.03901 | null | null | http://arxiv.org/abs/2406.03901v1 | 2024-06-06T09:37:46Z | 2024-06-06T09:37:46Z | Polyp and Surgical Instrument Segmentation with Double Encoder-Decoder
Networks | This paper describes a solution for the MedAI competition, in which participants were required to segment both polyps and surgical instruments from endoscopic images. Our approach relies on a double encoder-decoder neural network which we have previously applied for polyp segmentation, but with a series of enhancements: a more powerful encoder architecture, an improved optimization procedure, and the post-processing of segmentations based on tempered model ensembling. Experimental results show that our method produces segmentations that show a good agreement with manual delineations provided by medical experts. | [
"['Adrian Galdran']"
] |
null | null | 2406.03903 | null | null | http://arxiv.org/pdf/2406.03903v1 | 2024-06-06T09:38:06Z | 2024-06-06T09:38:06Z | Data-Centric Label Smoothing for Explainable Glaucoma Screening from Eye
Fundus Images | As current computing capabilities increase, modern machine learning and computer vision system tend to increase in complexity, mostly by means of larger models and advanced optimization strategies. Although often neglected, in many problems there is also much to be gained by considering potential improvements in understanding and better leveraging already-available training data, including annotations. This so-called data-centric approach can lead to substantial performance increases, sometimes beyond what can be achieved by larger models. In this paper we adopt such an approach for the task of justifiable glaucoma screening from retinal images. In particular, we focus on how to combine information from multiple annotators of different skills into a tailored label smoothing scheme that allows us to better employ a large collection of fundus images, instead of discarding samples suffering from inter-rater variability. Internal validation results indicate that our bespoke label smoothing approach surpasses the performance of a standard resnet50 model and also the same model trained with conventional label smoothing techniques, in particular for the multi-label scenario of predicting clinical reasons of glaucoma likelihood in a highly imbalanced screening context. Our code is made available at github.com/agaldran/justraigs . | [
"['Adrian Galdran' 'Miguel A. González Ballester']"
] |
null | null | 2406.03912 | null | null | http://arxiv.org/pdf/2406.03912v1 | 2024-06-06T09:51:30Z | 2024-06-06T09:51:30Z | GenSafe: A Generalizable Safety Enhancer for Safe Reinforcement Learning
Algorithms Based on Reduced Order Markov Decision Process Model | Although deep reinforcement learning has demonstrated impressive achievements in controlling various autonomous systems, e.g., autonomous vehicles or humanoid robots, its inherent reliance on random exploration raises safety concerns in their real-world applications. To improve system safety during the learning process, a variety of Safe Reinforcement Learning (SRL) algorithms have been proposed, which usually incorporate safety constraints within the Constrained Markov Decision Process (CMDP) framework. However, the efficacy of these SRL algorithms often relies on accurate function approximations, a task that is notably challenging to accomplish in the early learning stages due to data insufficiency. To address this problem, we introduce a Genralizable Safety enhancer (GenSafe) in this work. Leveraging model order reduction techniques, we first construct a Reduced Order Markov Decision Process (ROMDP) as a low-dimensional proxy for the original cost function in CMDP. Then, by solving ROMDP-based constraints that are reformulated from the original cost constraints, the proposed GenSafe refines the actions taken by the agent to enhance the possibility of constraint satisfaction. Essentially, GenSafe acts as an additional safety layer for SRL algorithms, offering broad compatibility across diverse SRL approaches. The performance of GenSafe is examined on multiple SRL benchmark problems. The results show that, it is not only able to improve the safety performance, especially in the early learning phases, but also to maintain the task performance at a satisfactory level. | [
"['Zhehua Zhou' 'Xuan Xie' 'Jiayang Song' 'Zhan Shu' 'Lei Ma']"
] |
null | null | 2406.03914 | null | null | http://arxiv.org/pdf/2406.03914v1 | 2024-06-06T09:52:56Z | 2024-06-06T09:52:56Z | Neuro-Symbolic Temporal Point Processes | Our goal is to $textit{efficiently}$ discover a compact set of temporal logic rules to explain irregular events of interest. We introduce a neural-symbolic rule induction framework within the temporal point process model. The negative log-likelihood is the loss that guides the learning, where the explanatory logic rules and their weights are learned end-to-end in a $textit{differentiable}$ way. Specifically, predicates and logic rules are represented as $textit{vector embeddings}$, where the predicate embeddings are fixed and the rule embeddings are trained via gradient descent to obtain the most appropriate compositional representations of the predicate embeddings. To make the rule learning process more efficient and flexible, we adopt a $textit{sequential covering algorithm}$, which progressively adds rules to the model and removes the event sequences that have been explained until all event sequences have been covered. All the found rules will be fed back to the models for a final rule embedding and weight refinement. Our approach showcases notable efficiency and accuracy across synthetic and real datasets, surpassing state-of-the-art baselines by a wide margin in terms of efficiency. | [
"['Yang Yang' 'Chao Yang' 'Boyang Li' 'Yinghao Fu' 'Shuang Li']"
] |
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