Split (2)

train · 433k rows

id stringlengths 9 16	title stringlengths 4 278	abstract stringlengths 3 4.08k	cs.HC bool 2 classes	cs.CE bool 2 classes	cs.SD bool 2 classes	cs.SI bool 2 classes	cs.AI bool 2 classes	cs.IR bool 2 classes	cs.LG bool 2 classes	cs.RO bool 2 classes	cs.CL bool 2 classes	cs.IT bool 2 classes	cs.SY bool 2 classes	cs.CV bool 2 classes	cs.CR bool 2 classes	cs.CY bool 2 classes	cs.MA bool 2 classes	cs.NE bool 2 classes	cs.DB bool 2 classes	Other bool 2 classes	__index_level_0__ int64 0 541k
1902.06285	Exploiting Unlabeled Data in CNNs by Self-supervised Learning to Rank	For many applications the collection of labeled data is expensive laborious. Exploitation of unlabeled data during training is thus a long pursued objective of machine learning. Self-supervised learning addresses this by positing an auxiliary task (different, but related to the supervised task) for which data is abunda...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	121,729
1606.01352	Implementation of real-time moving horizon estimation for robust air data sensor fault diagnosis in the RECONFIGURE benchmark	This paper presents robust fault diagnosis and estimation for the calibrated airspeed and angle-of-attack sensor faults in the RECONFIGURE benchmark. We adopt a low-order longitudinal model augmented with wind dynamics. In order to enhance sensitivity to faults in the presence of winds, we propose a constrained residua...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	56,795
2007.16054	Learning to Learn to Compress	In this paper we present an end-to-end meta-learned system for image compression. Traditional machine learning based approaches to image compression train one or more neural network for generalization performance. However, at inference time, the encoder or the latent tensor output by the encoder can be optimized for ea...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	true	189,836
2410.00332	Vision Language Models Know Law of Conservation without Understanding More-or-Less	Conservation is a critical milestone of cognitive development considered to be supported by both the understanding of quantitative concepts and the reversibility of operations. To assess whether this critical component of human intelligence has emerged in Vision Language Models, we have curated the ConserveBench, a bat...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	493,322
1901.02620	Fast CNN-Based Object Tracking Using Localization Layers and Deep Features Interpolation	Object trackers based on Convolution Neural Network (CNN) have achieved state-of-the-art performance on recent tracking benchmarks, while they suffer from slow computational speed. The high computational load arises from the extraction of the feature maps of the candidate and training patches in every video frame. The ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	118,251
2401.08688	Automated Answer Validation using Text Similarity	Automated answer validation can help improve learning outcomes by providing appropriate feedback to learners, and by making question answering systems and online learning solutions more widely available. There have been some works in science question answering which show that information retrieval methods outperform ne...	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	421,990
2107.10731	Neural Variational Gradient Descent	Particle-based approximate Bayesian inference approaches such as Stein Variational Gradient Descent (SVGD) combine the flexibility and convergence guarantees of sampling methods with the computational benefits of variational inference. In practice, SVGD relies on the choice of an appropriate kernel function, which impa...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	247,387
2011.11191	Socially Aware Crowd Navigation with Multimodal Pedestrian Trajectory Prediction for Autonomous Vehicles	Seamlessly operating an autonomous vehicle in a crowded pedestrian environment is a very challenging task. This is because human movement and interactions are very hard to predict in such environments. Recent work has demonstrated that reinforcement learning-based methods have the ability to learn to drive in crowds. H...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	207,748
2404.15005	Scandium Aluminum Nitride Overmoded Bulk Acoustic Resonators for Future Wireless Communication	This work reports on the modeling, fabrication, and experimental characterization of a 13 GHz 30% Scandium-doped Aluminum Nitride (ScAlN) Overmoded Bulk Acoustic Resonator (OBAR) for high-frequency Radio Frequency (RF) applications, notably in 5G technology and beyond. The Finite Element Analysis (FEA) optimization pro...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	448,912
2108.03576	BeatNet: CRNN and Particle Filtering for Online Joint Beat Downbeat and Meter Tracking	The online estimation of rhythmic information, such as beat positions, downbeat positions, and meter, is critical for many real-time music applications. Musical rhythm comprises complex hierarchical relationships across time, rendering its analysis intrinsically challenging and at times subjective. Furthermore, systems...	false	false	true	false	true	true	true	false	false	false	false	false	false	false	false	false	false	false	249,708
2405.16148	Accelerating Transformers with Spectrum-Preserving Token Merging	Increasing the throughput of the Transformer architecture, a foundational component used in numerous state-of-the-art models for vision and language tasks (e.g., GPT, LLaVa), is an important problem in machine learning. One recent and effective strategy is to merge token representations within Transformer models, aimin...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	457,283
2209.11024	Google Coral-based edge computing person reidentification using human parsing combined with analytical method	Person reidentification (re-ID) is becoming one of the most significant application areas of computer vision due to its importance for science and social security. Due to enormous size and scale of camera systems it is beneficial to develop edge computing re-ID applications where at least part of the analysis could be ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	319,052
2012.02076	SSGD: A safe and efficient method of gradient descent	With the vigorous development of artificial intelligence technology, various engineering technology applications have been implemented one after another. The gradient descent method plays an important role in solving various optimization problems, due to its simple structure, good stability and easy implementation. In ...	false	false	false	false	true	false	true	false	false	false	false	true	true	false	false	false	false	true	209,637
2403.00880	CIDGMed: Causal Inference-Driven Medication Recommendation with Enhanced Dual-Granularity Learning	Medication recommendation aims to integrate patients' long-term health records to provide accurate and safe medication combinations for specific health states. Existing methods often fail to deeply explore the true causal relationships between diseases/procedures and medications, resulting in biased recommendations. Ad...	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	false	false	434,171
2409.13624	Safe stabilization using generalized Lyapunov barrier function	This paper addresses the safe stabilization problem, focusing on controlling the system state to the origin while avoiding entry into unsafe state sets. The current methods for solving this issue rely on smooth Lyapunov and barrier functions, which do not always ensure the existence of an effective controller even when...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	490,073
2103.06168	Towards automated brain aneurysm detection in TOF-MRA: open data, weak labels, and anatomical knowledge	Brain aneurysm detection in Time-Of-Flight Magnetic Resonance Angiography (TOF-MRA) has undergone drastic improvements with the advent of Deep Learning (DL). However, performances of supervised DL models heavily rely on the quantity of labeled samples, which are extremely costly to obtain. Here, we present a DL model f...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	224,215
2306.04422	A Gamified Interaction with a Humanoid Robot to explain Therapeutic Procedures in Pediatric Asthma	In chronic diseases, obtaining a correct diagnosis and providing the most appropriate treatments often is not enough to guarantee an improvement of the clinical condition of a patient. Poor adherence to medical prescriptions constitutes one of the main causes preventing achievement of therapeutic goals. This is general...	true	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	false	371,735
2102.08183	Comparison of semi-supervised deep learning algorithms for audio classification	In this article, we adapted five recent SSL methods to the task of audio classification. The first two methods, namely Deep Co-Training (DCT) and Mean Teacher (MT), involve two collaborative neural networks. The three other algorithms, called MixMatch (MM), ReMixMatch (RMM), and FixMatch (FM), are single-model methods ...	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	220,370
1308.3956	Target Assignment in Robotic Networks: Distance Optimality Guarantees and Hierarchical Strategies	We study the problem of multi-robot target assignment to minimize the total distance traveled by the robots until they all reach an equal number of static targets. In the first half of the paper, we present a necessary and sufficient condition under which true distance optimality can be achieved for robots with limited...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	26,515
2211.04698	Unsupervised Extractive Summarization with Heterogeneous Graph Embeddings for Chinese Document	In the scenario of unsupervised extractive summarization, learning high-quality sentence representations is essential to select salient sentences from the input document. Previous studies focus more on employing statistical approaches or pre-trained language models (PLMs) to extract sentence embeddings, while ignoring ...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	329,326
2104.09116	TransCrowd: weakly-supervised crowd counting with transformers	The mainstream crowd counting methods usually utilize the convolution neural network (CNN) to regress a density map, requiring point-level annotations. However, annotating each person with a point is an expensive and laborious process. During the testing phase, the point-level annotations are not considered to evaluate...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	231,120
2311.04710	The Quest for Content: A Survey of Search-Based Procedural Content Generation for Video Games	Video games demand is constantly increasing, which requires the costly production of large amounts of content. Towards this challenge, researchers have developed Search-Based Procedural Content Generation (SBPCG), that is, the (semi-)automated creation of content through search algorithms. We survey the current state o...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	406,321
2408.01839	Complexity of Minimizing Projected-Gradient-Dominated Functions with Stochastic First-order Oracles	This work investigates the performance limits of projected stochastic first-order methods for minimizing functions under the $(\alpha,\tau,\mathcal{X})$-projected-gradient-dominance property, that asserts the sub-optimality gap $F(\mathbf{x})-\min_{\mathbf{x}'\in \mathcal{X}}F(\mathbf{x}')$ is upper-bounded by $\tau\cd...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	478,388
1601.04595	Multi-Processor Approximate Message Passing Using Lossy Compression	In this paper, a communication-efficient multi-processor compressed sensing framework based on the approximate message passing algorithm is proposed. We perform lossy compression on the data being communicated between processors, resulting in a reduction in communication costs with a minor degradation in recovery quali...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	51,039
2412.12621	Jailbreaking? One Step Is Enough!	Large language models (LLMs) excel in various tasks but remain vulnerable to jailbreak attacks, where adversaries manipulate prompts to generate harmful outputs. Examining jailbreak prompts helps uncover the shortcomings of LLMs. However, current jailbreak methods and the target model's defenses are engaged in an indep...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	517,951
2307.15588	OAFuser: Towards Omni-Aperture Fusion for Light Field Semantic Segmentation	Light field cameras are capable of capturing intricate angular and spatial details. This allows for acquiring complex light patterns and details from multiple angles, significantly enhancing the precision of image semantic segmentation. However, two significant issues arise: (1) The extensive angular information of lig...	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	382,312
1910.01847	Dual Learning Algorithm for Delayed Conversions	In display advertising, predicting the conversion rate (CVR), meaning the probability that a user takes a predefined action on an advertiser's website, is a fundamental task for estimating the value of displaying an advertisement to a user. There are two main challenges in CVR prediction due to delayed feedback. First,...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	148,055
2406.12412	A Novel Algorithm for Community Detection in Networks using Rough Sets and Consensus Clustering	Complex networks, such as those in social, biological, and technological systems, often present challenges to the task of community detection. Our research introduces a novel rough clustering based consensus community framework (RC-CCD) for effective structure identification of network communities. The RC-CCD method em...	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	false	false	false	465,393
2312.11043	TDeLTA: A Light-weight and Robust Table Detection Method based on Learning Text Arrangement	The diversity of tables makes table detection a great challenge, leading to existing models becoming more tedious and complex. Despite achieving high performance, they often overfit to the table style in training set, and suffer from significant performance degradation when encountering out-of-distribution tables in ot...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	416,428
cs/0008004	Comparing two trainable grammatical relations finders	Grammatical relationships (GRs) form an important level of natural language processing, but different sets of GRs are useful for different purposes. Therefore, one may often only have time to obtain a small training corpus with the desired GR annotations. On such a small training corpus, we compare two systems. They us...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	537,176
0810.0154	Optimization of sequences in CDMA systems: a statistical-mechanics approach	Statistical mechanics approach is useful not only in analyzing macroscopic system performance of wireless communication systems, but also in discussing design problems of wireless communication systems. In this paper, we discuss a design problem of spreading sequences in code-division multiple-access (CDMA) systems, as...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	2,438
2108.06027	PAIR: Leveraging Passage-Centric Similarity Relation for Improving Dense Passage Retrieval	Recently, dense passage retrieval has become a mainstream approach to finding relevant information in various natural language processing tasks. A number of studies have been devoted to improving the widely adopted dual-encoder architecture. However, most of the previous studies only consider query-centric similarity r...	false	false	false	false	true	true	false	false	true	false	false	false	false	false	false	false	false	false	250,483
2108.05524	Silhouette based View embeddings for Gait Recognition under Multiple Views	Gait recognition under multiple views is an important computer vision and pattern recognition task. In the emerging convolutional neural network based approaches, the information of view angle is ignored to some extent. Instead of direct view estimation and training view-specific recognition models, we propose a compat...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	250,322
2404.15608	Understanding and Improving CNNs with Complex Structure Tensor: A Biometrics Study	Our study provides evidence that CNNs struggle to effectively extract orientation features. We show that the use of Complex Structure Tensor, which contains compact orientation features with certainties, as input to CNNs consistently improves identification accuracy compared to using grayscale inputs alone. Experiments...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	449,158
2203.06823	SKM-TEA: A Dataset for Accelerated MRI Reconstruction with Dense Image Labels for Quantitative Clinical Evaluation	Magnetic resonance imaging (MRI) is a cornerstone of modern medical imaging. However, long image acquisition times, the need for qualitative expert analysis, and the lack of (and difficulty extracting) quantitative indicators that are sensitive to tissue health have curtailed widespread clinical and research studies. W...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	285,233
1311.6810	Identification of geometrical and elastostatic parameters of heavy industrial robots	The paper focuses on the stiffness modeling of heavy industrial robots with gravity compensators. The main attention is paid to the identification of geometrical and elastostatic parameters and calibration accuracy. To reduce impact of the measurement errors, the set of manipulator configurations for calibration experi...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	28,688
1811.03157	Forensic Discrimination between Traditional and Compressive Imaging Systems	Compressive sensing is a new technology for modern computational imaging systems. In comparison to widespread conventional image sensing, the compressive imaging paradigm requires specific forensic analysis techniques and tools. In this regards, one of basic scenarios in image forensics is to distinguish traditionally ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	112,765
2005.10881	Revisiting Membership Inference Under Realistic Assumptions	We study membership inference in settings where some of the assumptions typically used in previous research are relaxed. First, we consider skewed priors, to cover cases such as when only a small fraction of the candidate pool targeted by the adversary are actually members and develop a PPV-based metric suitable for th...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	178,308
2010.07892	Robotic Pick-and-Place With Uncertain Object Instance Segmentation and Shape Completion	We consider robotic pick-and-place of partially visible, novel objects, where goal placements are non-trivial, e.g., tightly packed into a bin. One approach is (a) use object instance segmentation and shape completion to model the objects and (b) use a regrasp planner to decide grasps and places displacing the models t...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	200,981
2407.10077	Transferable 3D Adversarial Shape Completion using Diffusion Models	Recent studies that incorporate geometric features and transformers into 3D point cloud feature learning have significantly improved the performance of 3D deep-learning models. However, their robustness against adversarial attacks has not been thoroughly explored. Existing attack methods primarily focus on white-box sc...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	472,830
2205.02450	Pessimism meets VCG: Learning Dynamic Mechanism Design via Offline Reinforcement Learning	Dynamic mechanism design has garnered significant attention from both computer scientists and economists in recent years. By allowing agents to interact with the seller over multiple rounds, where agents' reward functions may change with time and are state-dependent, the framework is able to model a rich class of real-...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	294,944
2310.07361	Domain Generalization Guided by Gradient Signal to Noise Ratio of Parameters	Overfitting to the source domain is a common issue in gradient-based training of deep neural networks. To compensate for the over-parameterized models, numerous regularization techniques have been introduced such as those based on dropout. While these methods achieve significant improvements on classical benchmarks suc...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	398,944
2005.07960	Data Driven Aircraft Trajectory Prediction with Deep Imitation Learning	The current Air Traffic Management (ATM) system worldwide has reached its limits in terms of predictability, efficiency and cost effectiveness. Different initiatives worldwide propose trajectory-oriented transformations that require high fidelity aircraft trajectory planning and prediction capabilities, supporting the ...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	177,452
1805.08079	Faster Neural Network Training with Approximate Tensor Operations	We propose a novel technique for faster deep neural network training which systematically applies sample-based approximation to the constituent tensor operations, i.e., matrix multiplications and convolutions. We introduce new sampling techniques, study their theoretical properties, and prove that they provide the same...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	true	false	false	98,039
2408.04382	Judgment2vec: Apply Graph Analytics to Searching and Recommendation of Similar Judgments	In court practice, legal professionals rely on their training to provide opinions that resolve cases, one of the most crucial aspects being the ability to identify similar judgments from previous courts efficiently. However, finding a similar case is challenging and often depends on experience, legal domain knowledge, ...	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	false	false	479,367
1602.01003	Using Node Centrality and Optimal Control to Maximize Information Diffusion in Social Networks	We model information dissemination as a susceptible-infected epidemic process and formulate a problem to jointly optimize seeds for the epidemic and time varying resource allocation over the period of a fixed duration campaign running on a social network with a given adjacency matrix. Individuals in the network are gro...	false	false	false	true	false	false	false	false	false	false	true	false	false	false	true	false	false	false	51,645
2006.16189	DOME: Recommendations for supervised machine learning validation in biology	Modern biology frequently relies on machine learning to provide predictions and improve decision processes. There have been recent calls for more scrutiny on machine learning performance and possible limitations. Here we present a set of community-wide recommendations aiming to help establish standards of supervised ma...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	184,740
2012.04726	Edited Media Understanding: Reasoning About Implications of Manipulated Images	Multimodal disinformation, from `deepfakes' to simple edits that deceive, is an important societal problem. Yet at the same time, the vast majority of media edits are harmless -- such as a filtered vacation photo. The difference between this example, and harmful edits that spread disinformation, is one of intent. Recog...	false	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	210,544
2012.02978	Design and Implementation of Path Trackers for Ackermann Drive based Vehicles	This article is an overview of the various literature on path tracking methods and their implementation in simulation and realistic operating environments.The scope of this study includes analysis, implementation,tuning, and comparison of some selected path tracking methods commonly used in practice for trajectory trac...	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	209,939
2112.02498	Consistent Training and Decoding For End-to-end Speech Recognition Using Lattice-free MMI	Recently, End-to-End (E2E) frameworks have achieved remarkable results on various Automatic Speech Recognition (ASR) tasks. However, Lattice-Free Maximum Mutual Information (LF-MMI), as one of the discriminative training criteria that show superior performance in hybrid ASR systems, is rarely adopted in E2E ASR framewo...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	269,861
2409.16953	Path-adaptive Spatio-Temporal State Space Model for Event-based Recognition with Arbitrary Duration	Event cameras are bio-inspired sensors that capture the intensity changes asynchronously and output event streams with distinct advantages, such as high temporal resolution. To exploit event cameras for object/action recognition, existing methods predominantly sample and aggregate events in a second-level duration at e...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	491,592
2404.02930	What Blocks My Blockchain's Throughput? Developing a Generalizable Approach for Identifying Bottlenecks in Permissioned Blockchains	Permissioned blockchains have been proposed for a variety of use cases that require decentralization yet address enterprise requirements that permissionless blockchains to date cannot satisfy -- particularly in terms of performance. However, popular permissioned blockchains still exhibit a relatively low maximum throug...	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	true	false	444,056
1708.04423	Distributed Weighted Sum-Rate Maximization in Multicell MU-MIMO OFDMA Downlink	This paper considers distributed linear beamforming in downlink multicell multiuser orthogonal frequency-division multiple access networks. A fast convergent solution maximizing the weighted sum- rate with per base station (BS) transmiting power constraint is formulated. We approximate the non- convex weighted sum-rate...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	78,944
2407.00979	Cross-Modal Attention Alignment Network with Auxiliary Text Description for zero-shot sketch-based image retrieval	In this paper, we study the problem of zero-shot sketch-based image retrieval (ZS-SBIR). The prior methods tackle the problem in a two-modality setting with only category labels or even no textual information involved. However, the growing prevalence of Large-scale pre-trained Language Models (LLMs), which have demonst...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	469,092
2011.11880	Effective Parallelism for Equation and Jacobian Evaluation in Power Flow Calculation	This letter investigates parallelism approaches for equation and Jacobian evaluations in large-scale power flow calculation. Two levels of parallelism are proposed and analyzed: inter-model parallelism, which evaluates models in parallel, and intra-model parallelism, which evaluates calculations within each model in pa...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	207,977
2207.12647	Cross-Modal Causal Relational Reasoning for Event-Level Visual Question Answering	Existing visual question answering methods often suffer from cross-modal spurious correlations and oversimplified event-level reasoning processes that fail to capture event temporality, causality, and dynamics spanning over the video. In this work, to address the task of event-level visual question answering, we propos...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	310,074
2201.01415	Problem-dependent attention and effort in neural networks with applications to image resolution and model selection	This paper introduces two new ensemble-based methods to reduce the data and computation costs of image classification. They can be used with any set of classifiers and do not require additional training. In the first approach, data usage is reduced by only analyzing a full-sized image if the model has low confidence in...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	274,244
1510.00783	Trilateral Large-Scale OSN Account Linkability Study	In the last decade, Online Social Networks (OSNs) have taken the world by storm. They range from superficial to professional, from focused to general-purpose, and, from free-form to highly structured. Numerous people have multiple accounts within the same OSN and even more people have an account on more than one OSN. S...	false	false	false	true	false	false	false	false	false	false	false	false	true	true	false	false	false	false	47,547
2105.02470	Generalized Multimodal ELBO	Multiple data types naturally co-occur when describing real-world phenomena and learning from them is a long-standing goal in machine learning research. However, existing self-supervised generative models approximating an ELBO are not able to fulfill all desired requirements of multimodal models: their posterior approx...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	233,829
2402.12608	Patient-Centric Knowledge Graphs: A Survey of Current Methods, Challenges, and Applications	Patient-Centric Knowledge Graphs (PCKGs) represent an important shift in healthcare that focuses on individualized patient care by mapping the patient's health information in a holistic and multi-dimensional way. PCKGs integrate various types of health data to provide healthcare professionals with a comprehensive under...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	430,909
2401.16430	An Information Retrieval and Extraction Tool for Covid-19 Related Papers	Background: The COVID-19 pandemic has caused severe impacts on health systems worldwide. Its critical nature and the increased interest of individuals and organizations to develop countermeasures to the problem has led to a surge of new studies in scientific journals. Objetive: We sought to develop a tool that incorpor...	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	424,824
2011.11188	Integrating Deep Learning in Domain Sciences at Exascale	This paper presents some of the current challenges in designing deep learning artificial intelligence (AI) and integrating it with traditional high-performance computing (HPC) simulations. We evaluate existing packages for their ability to run deep learning models and applications on large-scale HPC systems efficiently...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	207,746
2103.00704	FedPower: Privacy-Preserving Distributed Eigenspace Estimation	Eigenspace estimation is fundamental in machine learning and statistics, which has found applications in PCA, dimension reduction, and clustering, among others. The modern machine learning community usually assumes that data come from and belong to different organizations. The low communication power and the possible p...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	222,368
2401.15508	Proto-MPC: An Encoder-Prototype-Decoder Approach for Quadrotor Control in Challenging Winds	Quadrotors are increasingly used in the evolving field of aerial robotics for their agility and mechanical simplicity. However, inherent uncertainties, such as aerodynamic effects coupled with quadrotors' operation in dynamically changing environments, pose significant challenges for traditional, nominal model-based co...	false	false	false	false	false	false	true	true	false	false	true	false	false	false	false	false	false	false	424,477
1804.08875	Data-driven Summarization of Scientific Articles	Data-driven approaches to sequence-to-sequence modelling have been successfully applied to short text summarization of news articles. Such models are typically trained on input-summary pairs consisting of only a single or a few sentences, partially due to limited availability of multi-sentence training data. Here, we p...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	95,856
1503.01655	Studying the Wikipedia Hyperlink Graph for Relatedness and Disambiguation	Hyperlinks and other relations in Wikipedia are a extraordinary resource which is still not fully understood. In this paper we study the different types of links in Wikipedia, and contrast the use of the full graph with respect to just direct links. We apply a well-known random walk algorithm on two tasks, word related...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	40,857
2202.01288	Imitation Learning by Estimating Expertise of Demonstrators	Many existing imitation learning datasets are collected from multiple demonstrators, each with different expertise at different parts of the environment. Yet, standard imitation learning algorithms typically treat all demonstrators as homogeneous, regardless of their expertise, absorbing the weaknesses of any suboptima...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	278,427
2211.15992	MoDA: Map style transfer for self-supervised Domain Adaptation of embodied agents	We propose a domain adaptation method, MoDA, which adapts a pretrained embodied agent to a new, noisy environment without ground-truth supervision. Map-based memory provides important contextual information for visual navigation, and exhibits unique spatial structure mainly composed of flat walls and rectangular obstac...	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	333,477
1806.03645	Deep Curiosity Loops in Social Environments	Inspired by infants' intrinsic motivation to learn, which values informative sensory channels contingent on their immediate social environment, we developed a deep curiosity loop (DCL) architecture. The DCL is composed of a learner, which attempts to learn a forward model of the agent's state-action transition, and a n...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	100,050
2410.14158	A Mirror Descent Perspective of Smoothed Sign Descent	Recent work by Woodworth et al. (2020) shows that the optimization dynamics of gradient descent for overparameterized problems can be viewed as low-dimensional dual dynamics induced by a mirror map, explaining the implicit regularization phenomenon from the mirror descent perspective. However, the methodology does not ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	499,900
1105.2902	A Multi-Purpose Scenario-based Simulator for Smart House Environments	Developing smart house systems has been a great challenge for researchers and engineers in this area because of the high cost of implementation and evaluation process of these systems, while being very time consuming. Testing a designed smart house before actually building it is considered as an obstacle towards an eff...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	10,372
2007.07876	Upper Counterfactual Confidence Bounds: a New Optimism Principle for Contextual Bandits	The principle of optimism in the face of uncertainty is one of the most widely used and successful ideas in multi-armed bandits and reinforcement learning. However, existing optimistic algorithms (primarily UCB and its variants) often struggle to deal with general function classes and large context spaces. In this pape...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	187,460
1710.11272	Empirical analysis of non-linear activation functions for Deep Neural Networks in classification tasks	We provide an overview of several non-linear activation functions in a neural network architecture that have proven successful in many machine learning applications. We conduct an empirical analysis on the effectiveness of using these function on the MNIST classification task, with the aim of clarifying which functions...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	83,557
2403.11447	Motion-aware 3D Gaussian Splatting for Efficient Dynamic Scene Reconstruction	3D Gaussian Splatting (3DGS) has become an emerging tool for dynamic scene reconstruction. However, existing methods focus mainly on extending static 3DGS into a time-variant representation, while overlooking the rich motion information carried by 2D observations, thus suffering from performance degradation and model r...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	438,701
2309.15604	Entropic Matching for Expectation Propagation of Markov Jump Processes	This paper addresses the problem of statistical inference for latent continuous-time stochastic processes, which is often intractable, particularly for discrete state space processes described by Markov jump processes. To overcome this issue, we propose a new tractable inference scheme based on an entropic matching fra...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	395,037
2106.14446	Approximately Envy-Free Budget-Feasible Allocation	In the budget-feasible allocation problem, a set of items with varied sizes and values are to be allocated to a group of agents. Each agent has a budget constraint on the total size of items she can receive. The goal is to compute a feasible allocation that is envy-free (EF), in which the agents do not envy each other ...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	true	243,415
2307.13470	Combinatorial Auctions and Graph Neural Networks for Local Energy Flexibility Markets	This paper proposes a new combinatorial auction framework for local energy flexibility markets, which addresses the issue of prosumers' inability to bundle multiple flexibility time intervals. To solve the underlying NP-complete winner determination problems, we present a simple yet powerful heterogeneous tri-partite g...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	381,598
1705.09966	Attribute-Guided Face Generation Using Conditional CycleGAN	We are interested in attribute-guided face generation: given a low-res face input image, an attribute vector that can be extracted from a high-res image (attribute image), our new method generates a high-res face image for the low-res input that satisfies the given attributes. To address this problem, we condition the ...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	74,310
2303.03378	PaLM-E: An Embodied Multimodal Language Model	Large language models excel at a wide range of complex tasks. However, enabling general inference in the real world, e.g., for robotics problems, raises the challenge of grounding. We propose embodied language models to directly incorporate real-world continuous sensor modalities into language models and thereby establ...	false	false	false	false	true	false	true	true	false	false	false	false	false	false	false	false	false	false	349,706
1104.0651	Meaningful Clustered Forest: an Automatic and Robust Clustering Algorithm	We propose a new clustering technique that can be regarded as a numerical method to compute the proximity gestalt. The method analyzes edge length statistics in the MST of the dataset and provides an a contrario cluster detection criterion. The approach is fully parametric on the chosen distance and can detect arbitrar...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	9,864
1606.04930	Deep Learning for Music	Our goal is to be able to build a generative model from a deep neural network architecture to try to create music that has both harmony and melody and is passable as music composed by humans. Previous work in music generation has mainly been focused on creating a single melody. More recent work on polyphonic music mode...	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	57,326
2310.02113	FLEDGE: Ledger-based Federated Learning Resilient to Inference and Backdoor Attacks	Federated learning (FL) is a distributed learning process that uses a trusted aggregation server to allow multiple parties (or clients) to collaboratively train a machine learning model without having them share their private data. Recent research, however, has demonstrated the effectiveness of inference and poisoning ...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	true	396,713
2202.09597	STAR-RIS-NOMA Networks: An Error Performance Perspective	This letter investigates the bit error rate (BER) performance of simultaneous transmitting and reflecting reconfigurable intelligent surfaces (STAR-RISs) in non-orthogonal multiple access (NOMA) networks. In the investigated network, a STAR-RIS serves multiple non-orthogonal users located on either side of the surface ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	281,253
2312.09439	Smart Roads: Roadside Perception, Vehicle-Road Cooperation and Business Model	Smart roads have become an essential component of intelligent transportation systems (ITS). The roadside perception technology, a critical aspect of smart roads, utilizes various sensors, roadside units (RSUs), and edge computing devices to gather real-time traffic data for vehicle-road cooperation. However, the full p...	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	415,721
2410.16027	ComPO: Community Preferences for Language Model Personalization	Conventional algorithms for training language models (LMs) with human feedback rely on preferences that are assumed to account for an "average" user, disregarding subjectivity and finer-grained variations. Recent studies have raised concerns that aggregating such diverse and often contradictory human feedback to finetu...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	500,846
1904.06807	Multi-Channel Attention Selection GAN with Cascaded Semantic Guidance for Cross-View Image Translation	Cross-view image translation is challenging because it involves images with drastically different views and severe deformation. In this paper, we propose a novel approach named Multi-Channel Attention SelectionGAN (SelectionGAN) that makes it possible to generate images of natural scenes in arbitrary viewpoints, based ...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	true	127,636
1004.3549	Signature Region of Interest using Auto cropping	A new approach for signature region of interest pre-processing was presented. It used new auto cropping preparation on the basis of the image content, where the intensity value of pixel is the source of cropping. This approach provides both the possibility of improving the performance of security systems based on signa...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	6,220
2409.01768	Mapping Safe Zones for Co-located Human-UAV Interaction	Recent advances in robotics bring us closer to the reality of living, co-habiting, and sharing personal spaces with robots. However, it is not clear how close a co-located robot can be to a human in a shared environment without making the human uncomfortable or anxious. This research aims to map safe and comfortable zo...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	485,462
1510.01006	Monitoring Potential Drug Interactions and Reactions via Network Analysis of Instagram User Timelines	Much recent research aims to identify evidence for Drug-Drug Interactions (DDI) and Adverse Drug reactions (ADR) from the biomedical scientific literature. In addition to this "Bibliome", the universe of social media provides a very promising source of large-scale data that can help identify DDI and ADR in ways that ha...	false	false	false	true	false	true	false	false	false	false	false	false	false	true	false	false	false	false	47,565
1511.00573	From random walks to distances on unweighted graphs	Large unweighted directed graphs are commonly used to capture relations between entities. A fundamental problem in the analysis of such networks is to properly define the similarity or dissimilarity between any two vertices. Despite the significance of this problem, statistical characterization of the proposed metrics ...	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	false	false	false	48,423
2208.09978	Bayesian Complementary Kernelized Learning for Multidimensional Spatiotemporal Data	Probabilistic modeling of multidimensional spatiotemporal data is critical to many real-world applications. As real-world spatiotemporal data often exhibits complex dependencies that are nonstationary and nonseparable, developing effective and computationally efficient statistical models to accommodate nonstationary/no...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	313,905
2411.08375	Developing an Effective Training Dataset to Enhance the Performance of AI-based Speaker Separation Systems	This paper addresses the challenge of speaker separation, which remains an active research topic despite the promising results achieved in recent years. These results, however, often degrade in real recording conditions due to the presence of noise, echo, and other interferences. This is because neural models are typic...	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	507,866
2207.00975	Understanding Tieq Viet with Deep Learning Models	Deep learning is a powerful approach in recovering lost information as well as harder inverse function computation problems. When applied in natural language processing, this approach is essentially making use of context as a mean to recover information through likelihood maximization. Not long ago, a linguistic study ...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	305,983
2210.05558	Causal and Counterfactual Views of Missing Data Models	It is often said that the fundamental problem of causal inference is a missing data problem -- the comparison of responses to two hypothetical treatment assignments is made difficult because for every experimental unit only one potential response is observed. In this paper, we consider the implications of the converse ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	322,904
2403.18402	On Spectrogram Analysis in a Multiple Classifier Fusion Framework for Power Grid Classification Using Electric Network Frequency	The Electric Network Frequency (ENF) serves as a unique signature inherent to power distribution systems. Here, a novel approach for power grid classification is developed, leveraging ENF. Spectrograms are generated from audio and power recordings across different grids, revealing distinctive ENF patterns that aid in g...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	441,923
1803.08970	State measurement error-to-state stability results based on approximate discrete-time models	Digital controller design for nonlinear systems may be complicated by the fact that an exact discrete-time plant model is not known. One existing approach employs approximate discrete-time models for stability analysis and control design, and ensures different types of closedloop stability properties based on the appro...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	93,375
2010.05545	Local Search for Policy Iteration in Continuous Control	We present an algorithm for local, regularized, policy improvement in reinforcement learning (RL) that allows us to formulate model-based and model-free variants in a single framework. Our algorithm can be interpreted as a natural extension of work on KL-regularized RL and introduces a form of tree search for continuou...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	200,180
1401.0892	Optimum Trade-offs Between the Error Exponent and the Excess-Rate Exponent of Variable-Rate Slepian-Wolf Coding	We analyze the optimal trade-off between the error exponent and the excess-rate exponent for variable-rate Slepian-Wolf codes. In particular, we first derive upper (converse) bounds on the optimal error and excess-rate exponents, and then lower (achievable) bounds, via a simple class of variable-rate codes which assign...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	29,605
2311.06009	Polar-Net: A Clinical-Friendly Model for Alzheimer's Disease Detection in OCTA Images	Optical Coherence Tomography Angiography (OCTA) is a promising tool for detecting Alzheimer's disease (AD) by imaging the retinal microvasculature. Ophthalmologists commonly use region-based analysis, such as the ETDRS grid, to study OCTA image biomarkers and understand the correlation with AD. However, existing studie...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	406,786
1809.10491	On the Regret Minimization of Nonconvex Online Gradient Ascent for Online PCA	In this paper we focus on the problem of Online Principal Component Analysis in the regret minimization framework. For this problem, all existing regret minimization algorithms for the fully-adversarial setting are based on a positive semidefinite convex relaxation, and hence require quadratic memory and SVD computatio...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	108,924

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