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
1805.11780	Detecting Data Leakage from Databases on Android Apps with Concept Drift	Mobile databases are the statutory backbones of many applications on smartphones, and they store a lot of sensitive information. However, vulnerabilities in the operating system or the app logic can lead to sensitive data leakage by giving the adversaries unauthorized access to the app's database. In this paper, we stu...	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	true	false	99,010
2301.06681	Cross-domain Self-supervised Framework for Photoacoustic Computed Tomography Image Reconstruction	Accurate image reconstruction is crucial for photoacoustic (PA) computed tomography (PACT). Recently, deep learning has been used to reconstruct the PA image with a supervised scheme, which requires high-quality images as ground truth labels. In practice, there are inevitable trade-offs between cost and performance sin...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	340,711
2010.00048	Creative Captioning: An AI Grand Challenge Based on the Dixit Board Game	We propose a new class of "grand challenge" AI problems that we call creative captioning---generating clever, interesting, or abstract captions for images, as well as understanding such captions. Creative captioning draws on core AI research areas of vision, natural language processing, narrative reasoning, and social ...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	198,163
1909.10801	WATTNet: Learning to Trade FX via Hierarchical Spatio-Temporal Representation of Highly Multivariate Time Series	Finance is a particularly challenging application area for deep learning models due to low noise-to-signal ratio, non-stationarity, and partial observability. Non-deliverable-forwards (NDF), a derivatives contract used in foreign exchange (FX) trading, presents additional difficulty in the form of long-term planning re...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	146,634
cs/0505018	Temporal and Spatial Data Mining with Second-Order Hidden Models	In the frame of designing a knowledge discovery system, we have developed stochastic models based on high-order hidden Markov models. These models are capable to map sequences of data into a Markov chain in which the transitions between the states depend on the \texttt{n} previous states according to the order of the m...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	538,706
1812.03412	Learning Multiplication-free Linear Transformations	In this paper, we propose several dictionary learning algorithms for sparse representations that also impose specific structures on the learned dictionaries such that they are numerically efficient to use: reduced number of addition/multiplications and even avoiding multiplications altogether. We base our work on facto...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	116,007
1702.08798	Unsupervised Triplet Hashing for Fast Image Retrieval	Hashing has played a pivotal role in large-scale image retrieval. With the development of Convolutional Neural Network (CNN), hashing learning has shown great promise. But existing methods are mostly tuned for classification, which are not optimized for retrieval tasks, especially for instance-level retrieval. In this ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	69,069
2309.05444	Pushing Mixture of Experts to the Limit: Extremely Parameter Efficient MoE for Instruction Tuning	The Mixture of Experts (MoE) is a widely known neural architecture where an ensemble of specialized sub-models optimizes overall performance with a constant computational cost. However, conventional MoEs pose challenges at scale due to the need to store all experts in memory. In this paper, we push MoE to the limit. We...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	391,084
2012.13391	I like fish, especially dolphins: Addressing Contradictions in Dialogue Modeling	To quantify how well natural language understanding models can capture consistency in a general conversation, we introduce the DialoguE COntradiction DEtection task (DECODE) and a new conversational dataset containing both human-human and human-bot contradictory dialogues. We then compare a structured utterance-based a...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	213,211
2011.04457	Binary Matrix Factorisation via Column Generation	Identifying discrete patterns in binary data is an important dimensionality reduction tool in machine learning and data mining. In this paper, we consider the problem of low-rank binary matrix factorisation (BMF) under Boolean arithmetic. Due to the hardness of this problem, most previous attempts rely on heuristic tec...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	205,594
2310.05589	DRIN: Dynamic Relation Interactive Network for Multimodal Entity Linking	Multimodal Entity Linking (MEL) is a task that aims to link ambiguous mentions within multimodal contexts to referential entities in a multimodal knowledge base. Recent methods for MEL adopt a common framework: they first interact and fuse the text and image to obtain representations of the mention and entity respectiv...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	true	398,213
2406.15541	Cyclic Scheduler Design for Minimizing Age of Information in Massive Scale Networks Susceptible to Packet Errors	In multi-source status update systems, sources need to be scheduled appropriately to maintain timely communication between each of the sources and the monitor. A cyclic schedule is an age-agnostic schedule in which the sources are served according to a fixed finite transmission pattern, which upon completion, repeats i...	false	false	false	false	false	false	false	false	false	true	true	false	false	false	false	false	false	true	466,773
cmp-lg/9407027	Parsing as Tree Traversal	This paper presents a unified approach to parsing, in which top-down, bottom-up and left-corner parsers are related to preorder, postorder and inorder tree traversals. It is shown that the simplest bottom-up and left-corner parsers are left recursive and must be converted using an extended Greibach normal form. With fu...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	536,146
2502.08209	Equivariant Masked Position Prediction for Efficient Molecular Representation	Graph neural networks (GNNs) have shown considerable promise in computational chemistry. However, the limited availability of molecular data raises concerns regarding GNNs' ability to effectively capture the fundamental principles of physics and chemistry, which constrains their generalization capabilities. To address ...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	532,935
2102.11584	Enhancing Model Robustness By Incorporating Adversarial Knowledge Into Semantic Representation	Despite that deep neural networks (DNNs) have achieved enormous success in many domains like natural language processing (NLP), they have also been proven to be vulnerable to maliciously generated adversarial examples. Such inherent vulnerability has threatened various real-world deployed DNNs-based applications. To st...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	221,474
2308.06100	Diffusion-based Visual Counterfactual Explanations -- Towards Systematic Quantitative Evaluation	Latest methods for visual counterfactual explanations (VCE) harness the power of deep generative models to synthesize new examples of high-dimensional images of impressive quality. However, it is currently difficult to compare the performance of these VCE methods as the evaluation procedures largely vary and often boil...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	385,032
1802.04365	Learning a Neural-network-based Representation for Open Set Recognition	Open set recognition problems exist in many domains. For example in security, new malware classes emerge regularly; therefore malware classification systems need to identify instances from unknown classes in addition to discriminating between known classes. In this paper we present a neural network based representation...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	90,202
2103.07392	Temporal Logic for Social Networks	This paper introduces a logic with a class of social network models that is based on standard Linear Temporal Logic (LTL), leveraging the power of existing model checkers for the analysis of social networks. We provide a short literature overview, and then define our logic and its axiomatization, present some simple mo...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	true	224,582
2311.14006	High-resolution Population Maps Derived from Sentinel-1 and Sentinel-2	Detailed population maps play an important role in diverse fields ranging from humanitarian action to urban planning. Generating such maps in a timely and scalable manner presents a challenge, especially in data-scarce regions. To address it we have developed POPCORN, a population mapping method whose only inputs are f...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	409,960
2501.14183	VarDrop: Enhancing Training Efficiency by Reducing Variate Redundancy in Periodic Time Series Forecasting	Variate tokenization, which independently embeds each variate as separate tokens, has achieved remarkable improvements in multivariate time series forecasting. However, employing self-attention with variate tokens incurs a quadratic computational cost with respect to the number of variates, thus limiting its training e...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	527,012
2004.00930	Neuronal Sequence Models for Bayesian Online Inference	Sequential neuronal activity underlies a wide range of processes in the brain. Neuroscientific evidence for neuronal sequences has been reported in domains as diverse as perception, motor control, speech, spatial navigation and memory. Consequently, different dynamical principles have been proposed as possible sequence...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	true	false	false	170,776
2402.05054	LGM: Large Multi-View Gaussian Model for High-Resolution 3D Content Creation	3D content creation has achieved significant progress in terms of both quality and speed. Although current feed-forward models can produce 3D objects in seconds, their resolution is constrained by the intensive computation required during training. In this paper, we introduce Large Multi-View Gaussian Model (LGM), a no...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	427,705
2209.08665	Allocation Schemes in Analytic Evaluation: Applicant-Centric Holistic or Attribute-Centric Segmented?	Many applications such as hiring and university admissions involve evaluation and selection of applicants. These tasks are fundamentally difficult, and require combining evidence from multiple different aspects (what we term "attributes"). In these applications, the number of applicants is often large, and a common pra...	true	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	318,214
2409.20302	OM4OV: Leveraging Ontology Matching for Ontology Versioning	Due to the dynamic nature of the semantic web, ontology version control is required to capture time-varying information, most importantly for widely-used ontologies. Despite the long-standing recognition of ontology versioning (OV) as a crucial component for efficient ontology management, the growing size of ontologies...	false	false	false	false	true	true	false	false	true	false	false	false	false	false	false	false	false	false	493,078
2011.05543	An ensemble-based approach by fine-tuning the deep transfer learning models to classify pneumonia from chest X-ray images	Pneumonia is caused by viruses, bacteria, or fungi that infect the lungs, which, if not diagnosed, can be fatal and lead to respiratory failure. More than 250,000 individuals in the United States, mainly adults, are diagnosed with pneumonia each year, and 50,000 die from the disease. Chest Radiography (X-ray) is widely...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	205,954
2411.16475	NonSysId: A nonlinear system identification package with improved model term selection for NARMAX models	System identification involves constructing mathematical models of dynamic systems using input-output data, enabling analysis and prediction of system behaviour in both time and frequency domains. This approach can model the entire system or capture specific dynamics within it. For meaningful analysis, it is essential ...	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	511,038
2010.01650	Supporting large-scale image recognition with out-of-domain samples	This article presents an efficient end-to-end method to perform instance-level recognition employed to the task of labeling and ranking landmark images. In a first step, we embed images in a high dimensional feature space using convolutional neural networks trained with an additive angular margin loss and classify imag...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	198,722
2203.08075	Things not Written in Text: Exploring Spatial Commonsense from Visual Signals	Spatial commonsense, the knowledge about spatial position and relationship between objects (like the relative size of a lion and a girl, and the position of a boy relative to a bicycle when cycling), is an important part of commonsense knowledge. Although pretrained language models (PLMs) succeed in many NLP tasks, the...	false	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	285,670
2411.16816	SplatAD: Real-Time Lidar and Camera Rendering with 3D Gaussian Splatting for Autonomous Driving	Ensuring the safety of autonomous robots, such as self-driving vehicles, requires extensive testing across diverse driving scenarios. Simulation is a key ingredient for conducting such testing in a cost-effective and scalable way. Neural rendering methods have gained popularity, as they can build simulation environment...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	511,203
2407.21016	Add-SD: Rational Generation without Manual Reference	Diffusion models have exhibited remarkable prowess in visual generalization. Building on this success, we introduce an instruction-based object addition pipeline, named Add-SD, which automatically inserts objects into realistic scenes with rational sizes and positions. Different from layout-conditioned methods, Add-SD ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	477,356
2406.01309	REvolve: Reward Evolution with Large Language Models using Human Feedback	Designing effective reward functions is crucial to training reinforcement learning (RL) algorithms. However, this design is non-trivial, even for domain experts, due to the subjective nature of certain tasks that are hard to quantify explicitly. In recent works, large language models (LLMs) have been used for reward ge...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	false	false	460,260
2002.06532	Active Bayesian Assessment for Black-Box Classifiers	Recent advances in machine learning have led to increased deployment of black-box classifiers across a wide variety of applications. In many such situations there is a critical need to both reliably assess the performance of these pre-trained models and to perform this assessment in a label-efficient manner (given that...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	164,222
2203.07897	Magnetic Field Prediction Using Generative Adversarial Networks	Plenty of scientific and real-world applications are built on magnetic fields and their characteristics. To retrieve the valuable magnetic field information in high resolution, extensive field measurements are required, which are either time-consuming to conduct or even not feasible due to physical constraints. To alle...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	285,599
2203.00597	Path sampling of recurrent neural networks by incorporating known physics	Recurrent neural networks have seen widespread use in modeling dynamical systems in varied domains such as weather prediction, text prediction and several others. Often one wishes to supplement the experimentally observed dynamics with prior knowledge or intuition about the system. While the recurrent nature of these n...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	283,060
2210.13129	Facial Soft Biometrics for Recognition in the Wild: Recent Works, Annotation, and COTS Evaluation	The role of soft biometrics to enhance person recognition systems in unconstrained scenarios has not been extensively studied. Here, we explore the utility of the following modalities: gender, ethnicity, age, glasses, beard, and moustache. We consider two assumptions: 1) manual estimation of soft biometrics and 2) auto...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	326,056
2309.00357	Discrete Versus Continuous Algorithms in Dynamics of Affective Decision Making	The dynamics of affective decision making is considered for an intelligent network composed of agents with different types of memory: long-term and short-term memory. The consideration is based on probabilistic affective decision theory, which takes into account the rational utility of alternatives as well as the emoti...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	389,282
2111.11720	Gait Identification under Surveillance Environment based on Human Skeleton	As an emerging biological identification technology, vision-based gait identification is an important research content in biometrics. Most existing gait identification methods extract features from gait videos and identify a probe sample by a query in the gallery. However, video data contains redundant information and ...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	267,753
2312.08932	Influence of Prompting Strategies on Segment Anything Model (SAM) for Short-axis Cardiac MRI segmentation	The Segment Anything Model (SAM) has recently emerged as a significant breakthrough in foundation models, demonstrating remarkable zero-shot performance in object segmentation tasks. While SAM is designed for generalization, it exhibits limitations in handling specific medical imaging tasks that require fine-structure ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	415,523
1607.06525	CGMOS: Certainty Guided Minority OverSampling	Handling imbalanced datasets is a challenging problem that if not treated correctly results in reduced classification performance. Imbalanced datasets are commonly handled using minority oversampling, whereas the SMOTE algorithm is a successful oversampling algorithm with numerous extensions. SMOTE extensions do not ha...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	58,902
1404.2571	RANCOR: Non-Linear Image Registration with Total Variation Regularization	Optimization techniques have been widely used in deformable registration, allowing for the incorporation of similarity metrics with regularization mechanisms. These regularization mechanisms are designed to mitigate the effects of trivial solutions to ill-posed registration problems and to otherwise ensure the resultin...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	32,221
2411.07075	Transformer verbatim in-context retrieval across time and scale	To predict upcoming text, language models must in some cases retrieve in-context information verbatim. In this report, we investigated how the ability of language models to retrieve arbitrary in-context nouns developed during training (across time) and as language models trained on the same dataset increase in size (ac...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	507,381
2408.05819	On the Convergence of a Federated Expectation-Maximization Algorithm	Data heterogeneity has been a long-standing bottleneck in studying the convergence rates of Federated Learning algorithms. In order to better understand the issue of data heterogeneity, we study the convergence rate of the Expectation-Maximization (EM) algorithm for the Federated Mixture of $K$ Linear Regressions model...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	479,955
2102.12733	Distributed Online Learning with Multiple Kernels	We consider the problem of learning a nonlinear function over a network of learners in a fully decentralized fashion. Online learning is additionally assumed, where every learner receives continuous streaming data locally. This learning model is called a fully distributed online learning (or a fully decentralized onlin...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	221,832
2412.02807	Learning Koopman-based Stability Certificates for Unknown Nonlinear Systems	Koopman operator theory has gained significant attention in recent years for identifying discrete-time nonlinear systems by embedding them into an infinite-dimensional linear vector space. However, providing stability guarantees while learning the continuous-time dynamics, especially under conditions of relatively low ...	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	513,686
2211.13185	BaRe-ESA: A Riemannian Framework for Unregistered Human Body Shapes	We present Basis Restricted Elastic Shape Analysis (BaRe-ESA), a novel Riemannian framework for human body scan representation, interpolation and extrapolation. BaRe-ESA operates directly on unregistered meshes, i.e., without the need to establish prior point to point correspondences or to assume a consistent mesh stru...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	332,377
2407.03379	missForestPredict -- Missing data imputation for prediction settings	Prediction models are used to predict an outcome based on input variables. Missing data in input variables often occurs at model development and at prediction time. The missForestPredict R package proposes an adaptation of the missForest imputation algorithm that is fast, user-friendly and tailored for prediction setti...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	470,136
1407.8337	A New Model of Array Grammar for generating Connected Patterns on an Image Neighborhood	Study of patterns on images is recognized as an important step in characterization and classification of image. The ability to efficiently analyze and describe image patterns is thus of fundamental importance. The study of syntactic methods of describing pictures has been of interest for researchers. Array Grammars can...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	35,026
0711.0574	Singular Curves in the Joint Space and Cusp Points of 3-RPR parallel manipulators	This paper investigates the singular curves in the joint space of a family of planar parallel manipulators. It focuses on special points, referred to as cusp points, which may appear on these curves. Cusp points play an important role in the kinematic behavior of parallel manipulators since they make possible a nonsing...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	867
2403.06487	Multilingual Turn-taking Prediction Using Voice Activity Projection	This paper investigates the application of voice activity projection (VAP), a predictive turn-taking model for spoken dialogue, on multilingual data, encompassing English, Mandarin, and Japanese. The VAP model continuously predicts the upcoming voice activities of participants in dyadic dialogue, leveraging a cross-att...	false	false	true	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	436,477
2308.15364	Heterogeneous Multi-Task Gaussian Cox Processes	This paper presents a novel extension of multi-task Gaussian Cox processes for modeling multiple heterogeneous correlated tasks jointly, e.g., classification and regression, via multi-output Gaussian processes (MOGP). A MOGP prior over the parameters of the dedicated likelihoods for classification, regression and point...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	388,658
2406.12123	ChatEMG: Synthetic Data Generation to Control a Robotic Hand Orthosis for Stroke	Intent inferral on a hand orthosis for stroke patients is challenging due to the difficulty of data collection. Additionally, EMG signals exhibit significant variations across different conditions, sessions, and subjects, making it hard for classifiers to generalize. Traditional approaches require a large labeled datas...	false	false	false	false	true	false	true	true	false	false	false	false	false	false	false	false	false	false	465,233
2211.01592	Try to Avoid Attacks: A Federated Data Sanitization Defense for Healthcare IoMT Systems	Healthcare IoMT systems are becoming intelligent, miniaturized, and more integrated into daily life. As for the distributed devices in the IoMT, federated learning has become a topical area with cloud-based training procedures when meeting data security. However, the distribution of IoMT has the risk of protection from...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	true	328,284
1406.3949	A Fusion of Labeled-Grid Shape Descriptors with Weighted Ranking Algorithm for Shapes Recognition	Retrieving similar images from a large dataset based on the image content has been a very active research area and is a very challenging task. Studies have shown that retrieving similar images based on their shape is a very effective method. For this purpose a large number of methods exist in literature. The combinatio...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	33,893
1710.03326	Inspiration, Captivation, and Misdirection: Emergent Properties in Networks of Online Navigation	The World Wide Web (WWW) has fundamentally changed the ways billions of people are able to access information. Thus, understanding how people seek information online is an important issue of study. Wikipedia is a hugely important part of information provision on the web, with hundreds of millions of users browsing and ...	false	false	false	true	false	false	false	false	false	false	false	false	false	true	false	false	false	false	82,308
2003.11959	Pedestrian Models for Autonomous Driving Part II: High-Level Models of Human Behavior	Autonomous vehicles (AVs) must share space with pedestrians, both in carriageway cases such as cars at pedestrian crossings and off-carriageway cases such as delivery vehicles navigating through crowds on pedestrianized high-streets. Unlike static obstacles, pedestrians are active agents with complex, interactive motio...	true	false	false	false	false	false	true	true	false	false	true	false	false	false	false	false	false	true	169,770
2207.04108	ReFinED: An Efficient Zero-shot-capable Approach to End-to-End Entity Linking	We introduce ReFinED, an efficient end-to-end entity linking model which uses fine-grained entity types and entity descriptions to perform linking. The model performs mention detection, fine-grained entity typing, and entity disambiguation for all mentions within a document in a single forward pass, making it more than...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	307,075
2311.00527	A Leakage-based Method for Mitigation of Faulty Reconfigurable Intelligent Surfaces	Reconfigurable Intelligent Surfaces (RISs) are expected to be massively deployed in future beyond-5th generation wireless networks, thanks to their ability to programmatically alter the propagation environment, inherent low-cost and low-maintenance nature. Indeed, they are envisioned to be implemented on the facades of...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	404,686
2408.14689	Federated User Preference Modeling for Privacy-Preserving Cross-Domain Recommendation	Cross-domain recommendation (CDR) aims to address the data-sparsity problem by transferring knowledge across domains. Existing CDR methods generally assume that the user-item interaction data is shareable between domains, which leads to privacy leakage. Recently, some privacy-preserving CDR (PPCDR) models have been pro...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	483,631
1508.05116	Resolving Weak Sources within a Dense Array using a Network Approach	A non-parametric technique to identify weak sources within dense sensor arrays is developed using a network approach. No knowledge about the propagation medium is needed except that signal strengths decay to insignificant levels within a scale that is shorter than the aperture. We then reinterpret the spatial covarianc...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	46,201
2006.05646	Scalable Backdoor Detection in Neural Networks	Recently, it has been shown that deep learning models are vulnerable to Trojan attacks, where an attacker can install a backdoor during training time to make the resultant model misidentify samples contaminated with a small trigger patch. Current backdoor detection methods fail to achieve good detection performance and...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	181,151
2210.14434	A formal process of hierarchical functional requirements development for Set-Based Design	The design of complex systems is typically uncertain and ambiguous at early stages. Set-Based Design is a promising approach to complex systems design as it supports alternative exploration and gradual uncertainty reduction. When designing a complex system, functional requirements decomposition is a common and effectiv...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	326,545
1604.06182	The THUMOS Challenge on Action Recognition for Videos "in the Wild"	Automatically recognizing and localizing wide ranges of human actions has crucial importance for video understanding. Towards this goal, the THUMOS challenge was introduced in 2013 to serve as a benchmark for action recognition. Until then, video action recognition, including THUMOS challenge, had focused primarily on ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	54,908
2103.00683	Decision Making in Monopoly using a Hybrid Deep Reinforcement Learning Approach	Learning to adapt and make real-time informed decisions in a dynamic and complex environment is a challenging problem. Monopoly is a popular strategic board game that requires players to make multiple decisions during the game. Decision-making in Monopoly involves many real-world elements such as strategizing, luck, an...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	222,360
2210.12035	BlanketGen - A synthetic blanket occlusion augmentation pipeline for MoCap datasets	Human motion analysis has seen drastic improvements recently, however, due to the lack of representative datasets, for clinical in-bed scenarios it is still lagging behind. To address this issue, we implemented BlanketGen, a pipeline that augments videos with synthetic blanket occlusions. With this pipeline, we generat...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	325,556
2309.13599	From Cluster Assumption to Graph Convolution: Graph-based Semi-Supervised Learning Revisited	Graph-based semi-supervised learning (GSSL) has long been a hot research topic. Traditional methods are generally shallow learners, based on the cluster assumption. Recently, graph convolutional networks (GCNs) have become the predominant techniques for their promising performance. In this paper, we theoretically discu...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	394,276
2203.14961	A Deep Learning Approach for Thermal Plume Prediction of Groundwater Heat Pumps	Climate control of buildings makes up a significant portion of global energy consumption, with groundwater heat pumps providing a suitable alternative. To prevent possibly negative interactions between heat pumps throughout a city, city planners have to optimize their layouts in the future. We develop a novel data-driv...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	288,194
2402.01765	LLMs Simulate Big Five Personality Traits: Further Evidence	An empirical investigation into the simulation of the Big Five personality traits by large language models (LLMs), namely Llama2, GPT4, and Mixtral, is presented. We analyze the personality traits simulated by these models and their stability. This contributes to the broader understanding of the capabilities of LLMs to...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	426,205
2102.06610	Enhancing into the codec: Noise Robust Speech Coding with Vector-Quantized Autoencoders	Audio codecs based on discretized neural autoencoders have recently been developed and shown to provide significantly higher compression levels for comparable quality speech output. However, these models are tightly coupled with speech content, and produce unintended outputs in noisy conditions. Based on VQ-VAE autoenc...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	219,816
2102.05374	Enhancing Reading Strategies by Exploring A Theme-based Approach to Literature Surveys	Searching large digital repositories can be extremely frustrating, as common list-based formats encourage users to adopt a convenience-sampling approach that favours chance discovery and random search, over meaningful exploration. We have designed a methodology that allows users to visually and thematically explore cor...	true	false	false	false	false	true	false	false	false	false	false	false	false	true	false	false	false	false	219,417
2305.15265	Winner-Take-All Column Row Sampling for Memory Efficient Adaptation of Language Model	With the rapid growth in model size, fine-tuning the large pre-trained language model has become increasingly difficult due to its extensive memory usage. Previous works usually focus on reducing the number of trainable parameters in the network. While the model parameters do contribute to memory usage, the primary mem...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	367,537
2311.14494	MVControl: Adding Conditional Control to Multi-view Diffusion for Controllable Text-to-3D Generation	We introduce MVControl, a novel neural network architecture that enhances existing pre-trained multi-view 2D diffusion models by incorporating additional input conditions, e.g. edge maps. Our approach enables the generation of controllable multi-view images and view-consistent 3D content. To achieve controllable multi-...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	410,129
2403.07134	COMQ: A Backpropagation-Free Algorithm for Post-Training Quantization	Post-training quantization (PTQ) has emerged as a practical approach to compress large neural networks, making them highly efficient for deployment. However, effectively reducing these models to their low-bit counterparts without compromising the original accuracy remains a key challenge. In this paper, we propose an i...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	436,750
2301.05873	Opponent-aware Role-based Learning in Team Competitive Markov Games	Team competition in multi-agent Markov games is an increasingly important setting for multi-agent reinforcement learning, due to its general applicability in modeling many real-life situations. Multi-agent actor-critic methods are the most suitable class of techniques for learning optimal policies in the team competiti...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	340,481
2308.16687	DictaBERT: A State-of-the-Art BERT Suite for Modern Hebrew	We present DictaBERT, a new state-of-the-art pre-trained BERT model for modern Hebrew, outperforming existing models on most benchmarks. Additionally, we release three fine-tuned versions of the model, designed to perform three specific foundational tasks in the analysis of Hebrew texts: prefix segmentation, morphologi...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	389,079
2008.09497	Single-Image Depth Prediction Makes Feature Matching Easier	Good local features improve the robustness of many 3D re-localization and multi-view reconstruction pipelines. The problem is that viewing angle and distance severely impact the recognizability of a local feature. Attempts to improve appearance invariance by choosing better local feature points or by leveraging outside...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	192,732
2110.05775	Quantifying Cognitive Factors in Lexical Decline	We adopt an evolutionary view on language change in which cognitive factors (in addition to social ones) affect the fitness of words and their success in the linguistic ecosystem. Specifically, we propose a variety of psycholinguistic factors -- semantic, distributional, and phonological -- that we hypothesize are pred...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	260,399
2302.14728	Semantically Consistent Person Image Generation	We propose a data-driven approach for context-aware person image generation. Specifically, we attempt to generate a person image such that the synthesized instance can blend into a complex scene. In our method, the position, scale, and appearance of the generated person are semantically conditioned on the existing pers...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	348,414
2301.13545	Holistic Graph-based Motion Prediction	Motion prediction for automated vehicles in complex environments is a difficult task that is to be mastered when automated vehicles are to be used in arbitrary situations. Many factors influence the future motion of traffic participants starting with traffic rules and reaching from the interaction between each other to...	false	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	342,953
2105.03162	Adv-Makeup: A New Imperceptible and Transferable Attack on Face Recognition	Deep neural networks, particularly face recognition models, have been shown to be vulnerable to both digital and physical adversarial examples. However, existing adversarial examples against face recognition systems either lack transferability to black-box models, or fail to be implemented in practice. In this paper, w...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	234,061
2409.02648	Creating a Microstructure Latent Space with Rich Material Information for Multiphase Alloy Design	The intricate microstructure serves as the cornerstone for the composition/processing-structure-property (CPSP) connection in multiphase alloys. Traditional alloy design methods often overlook microstructural details, which diminishes the reliability and effectiveness of the outcomes. This study introduces an improved ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	485,781
2008.08989	Towards Inferring Queries from Simple and Partial Provenance Examples	The field of query-by-example aims at inferring queries from output examples given by non-expert users, by finding the underlying logic that binds the examples. However, for a very small set of examples, it is difficult to correctly infer such logic. To bridge this gap, previous work suggested attaching explanations to...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	192,577
1605.06940	Elastic Solver: Balancing Solution Time and Energy Consumption	Combinatorial decision problems arise in many different domains such as scheduling, routing, packing, bioinformatics, and many more. Despite recent advances in developing scalable solvers, there are still many problems which are often very hard to solve. Typically the most advanced solvers include elements which are st...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	56,217
2209.10585	Grape Cold Hardiness Prediction via Multi-Task Learning	Cold temperatures during fall and spring have the potential to cause frost damage to grapevines and other fruit plants, which can significantly decrease harvest yields. To help prevent these losses, farmers deploy expensive frost mitigation measures such as sprinklers, heaters, and wind machines when they judge that da...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	318,906
2004.08814	Graph-Structured Referring Expression Reasoning in The Wild	Grounding referring expressions aims to locate in an image an object referred to by a natural language expression. The linguistic structure of a referring expression provides a layout of reasoning over the visual contents, and it is often crucial to align and jointly understand the image and the referring expression. I...	false	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	173,180
2001.08662	The INTERSPEECH 2020 Deep Noise Suppression Challenge: Datasets, Subjective Speech Quality and Testing Framework	The INTERSPEECH 2020 Deep Noise Suppression Challenge is intended to promote collaborative research in real-time single-channel Speech Enhancement aimed to maximize the subjective (perceptual) quality of the enhanced speech. A typical approach to evaluate the noise suppression methods is to use objective metrics on the...	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	161,349
2202.09275	Rethinking Pareto Frontier for Performance Evaluation of Deep Neural Networks	Performance optimization of deep learning models is conducted either manually or through automatic architecture search, or a combination of both. On the other hand, their performance strongly depends on the target hardware and how successfully the models were trained. We propose to use a multi-dimensional Pareto fronti...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	true	false	false	281,139
2310.04461	AI-based automated active learning for discovery of hidden dynamic processes: A use case in light microscopy	In the biomedical environment, experiments assessing dynamic processes are primarily performed by a human acquisition supervisor. Contemporary implementations of such experiments frequently aim to acquire a maximum number of relevant events from sometimes several hundred parallel, non-synchronous processes. Since in so...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	397,668
2009.01119	Self-driving car safety quantification via component-level analysis	In this paper, we present a rigorous modular statistical approach for arguing safety or its insufficiency of an autonomous vehicle through a concrete illustrative example. The methodology relies on making appropriate quantitative studies of the performance of constituent components. We explain the importance of suffici...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	194,226
2404.16552	Efficient Solution of Point-Line Absolute Pose	We revisit certain problems of pose estimation based on 3D--2D correspondences between features which may be points or lines. Specifically, we address the two previously-studied minimal problems of estimating camera extrinsics from $p \in \{ 1, 2 \}$ point--point correspondences and $l=3-p$ line--line correspondences. ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	449,538
1406.1134	Local Decorrelation For Improved Detection	Even with the advent of more sophisticated, data-hungry methods, boosted decision trees remain extraordinarily successful for fast rigid object detection, achieving top accuracy on numerous datasets. While effective, most boosted detectors use decision trees with orthogonal (single feature) splits, and the topology of ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	33,603
2007.15209	Depressive, Drug Abusive, or Informative: Knowledge-aware Study of News Exposure during COVID-19 Outbreak	The COVID-19 pandemic is having a serious adverse impact on the lives of people across the world. COVID-19 has exacerbated community-wide depression, and has led to increased drug abuse brought about by isolation of individuals as a result of lockdown. Further, apart from providing informative content to the public, th...	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	false	189,606
2303.05768	Learning Global-Local Correspondence with Semantic Bottleneck for Logical Anomaly Detection	This paper presents a novel framework, named Global-Local Correspondence Framework (GLCF), for visual anomaly detection with logical constraints. Visual anomaly detection has become an active research area in various real-world applications, such as industrial anomaly detection and medical disease diagnosis. However, m...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	350,595
2402.16486	Intelligent Known and Novel Aircraft Recognition -- A Shift from Classification to Similarity Learning for Combat Identification	Precise aircraft recognition in low-resolution remote sensing imagery is a challenging yet crucial task in aviation, especially combat identification. This research addresses this problem with a novel, scalable, and AI-driven solution. The primary hurdle in combat identification in remote sensing imagery is the accurat...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	432,592
1904.05712	Reconstructing Network Inputs with Additive Perturbation Signatures	In this work, we present preliminary results demonstrating the ability to recover a significant amount of information about secret model inputs given only very limited access to model outputs and the ability evaluate the model on additive perturbations to the input.	false	false	false	false	false	false	false	false	false	false	false	true	true	false	false	false	false	false	127,389
2004.11205	Optimized Quantum Compilation for Near-Term Algorithms with OpenPulse	Quantum computers are traditionally operated by programmers at the granularity of a gate-based instruction set. However, the actual device-level control of a quantum computer is performed via analog pulses. We introduce a compiler that exploits direct control at this microarchitectural level to achieve significant impr...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	173,848
0905.4656	Quantization Errors of fGn and fBm Signals	In this Letter, we show that under the assumption of high resolution, the quantization errors of fGn and fBm signals with uniform quantizer can be treated as uncorrelated white noises.	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	3,789
1704.04155	Timely Updates over an Erasure Channel	Using an age of information (AoI) metric, we examine the transmission of coded updates through a binary erasure channel to a monitor/receiver. We start by deriving the average status update age of an infinite incremental redundancy (IIR) system in which the transmission of a k-symbol update continuesuntil k symbols are...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	71,759
1902.00936	New Constellation Design and Bit Mapping for Dual Mode OFDM-IM	Dual mode orthogonal frequency division multiplexing with index modulation (DM-OFDM-IM) is recently proposed, which modulates all subcarriers to eliminate the limits of spectrum efficiency in OFDM with index modulation (OFDM-IM). In DM-OFDM-IM, the subcarriers within each subblock are divided into two groups, which are...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	120,548
2404.17367	An Optimised Brushless DC Motor Control Scheme for Robotics Applications	This work aims to develop an integrated control strategy for Brushless Direct Current Motors for a wide range of applications in robotics systems. The controller is suited for both high torque - low speed and high-speed control of the motors. Hardware validation is done by developing a custom BLDC drive system, and the...	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	449,835
1303.2048	Finding Zeros: Greedy Detection of Holes	In this paper, motivated by the setting of white-space detection [1], we present theoretical and empirical results for detection of the zero-support E of x \in Cp (xi = 0 for i \in E) with reduced-dimension linear measurements. We propose two low- complexity algorithms based on one-step thresholding [2] for this purpos...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	22,785

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