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
1506.03645	Contact patterns in a high school: a comparison between data collected using wearable sensors, contact diaries and friendship surveys	Given their importance in shaping social networks and determining how information or diseases propagate in a population, human interactions are the subject of many data collection efforts. To this aim, different methods are commonly used, from diaries and surveys to wearable sensors. These methods show advantages and l...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	44,078
1511.01258	Learn on Source, Refine on Target:A Model Transfer Learning Framework with Random Forests	We propose novel model transfer-learning methods that refine a decision forest model M learned within a "source" domain using a training set sampled from a "target" domain, assumed to be a variation of the source. We present two random forest transfer algorithms. The first algorithm searches greedily for locally optima...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	48,484
2501.05483	Human Grasp Generation for Rigid and Deformable Objects with Decomposed VQ-VAE	Generating realistic human grasps is crucial yet challenging for object manipulation in computer graphics and robotics. Current methods often struggle to generate detailed and realistic grasps with full finger-object interaction, as they typically rely on encoding the entire hand and estimating both posture and positio...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	true	523,613
2204.01390	A Comprehensive Survey on Automated Machine Learning for Recommendations	Deep recommender systems (DRS) are critical for current commercial online service providers, which address the issue of information overload by recommending items that are tailored to the user's interests and preferences. They have unprecedented feature representations effectiveness and the capacity of modeling the non...	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	false	false	289,590
2408.12840	HGNAS: Hardware-Aware Graph Neural Architecture Search for Edge Devices	Graph Neural Networks (GNNs) are becoming increasingly popular for graph-based learning tasks such as point cloud processing due to their state-of-the-art (SOTA) performance. Nevertheless, the research community has primarily focused on improving model expressiveness, lacking consideration of how to design efficient GN...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	482,914
2303.05286	Euler Characteristic Transform Based Topological Loss for Reconstructing 3D Images from Single 2D Slices	The computer vision task of reconstructing 3D images, i.e., shapes, from their single 2D image slices is extremely challenging, more so in the regime of limited data. Deep learning models typically optimize geometric loss functions, which may lead to poor reconstructions as they ignore the structural properties of the ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	350,408
2101.09495	Granular conditional entropy-based attribute reduction for partially labeled data with proxy labels	Attribute reduction is one of the most important research topics in the theory of rough sets, and many rough sets-based attribute reduction methods have thus been presented. However, most of them are specifically designed for dealing with either labeled data or unlabeled data, while many real-world applications come in...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	216,616
1811.01065	Real-time Magnetometer Disturbance Estimation via Online Nonlinear Programming	Magnetometer is a significant sensor for integrated navigation. However, it suffers from many kinds of unknown dynamic magnetic disturbances. We study the problem of online estimating such disturbances via a nonlinear optimization aided by intermediate quaternion estimation from inertial fusion. The proposed optimizati...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	112,259
2406.02456	Offline Bayesian Aleatoric and Epistemic Uncertainty Quantification and Posterior Value Optimisation in Finite-State MDPs	We address the challenge of quantifying Bayesian uncertainty and incorporating it in offline use cases of finite-state Markov Decision Processes (MDPs) with unknown dynamics. Our approach provides a principled method to disentangle epistemic and aleatoric uncertainty, and a novel technique to find policies that optimis...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	460,773
2501.18838	Partially Rewriting a Transformer in Natural Language	The greatest ambition of mechanistic interpretability is to completely rewrite deep neural networks in a format that is more amenable to human understanding, while preserving their behavior and performance. In this paper, we attempt to partially rewrite a large language model using simple natural language explanations....	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	528,881
2205.10727	Residual regularization path-following methods for linear complementarity problems	In this article, we consider the residual regularization path-following method with the trust-region updating strategy for the linear complementarity problem. This time-stepping selection based on the trust-region updating strategy overcomes the shortcoming of the line search method, which consumes the unnecessary tria...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	297,834
1603.08705	ROOT13: Spotting Hypernyms, Co-Hyponyms and Randoms	In this paper, we describe ROOT13, a supervised system for the classification of hypernyms, co-hyponyms and random words. The system relies on a Random Forest algorithm and 13 unsupervised corpus-based features. We evaluate it with a 10-fold cross validation on 9,600 pairs, equally distributed among the three classes a...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	53,821
2210.07032	Prompt-based Connective Prediction Method for Fine-grained Implicit Discourse Relation Recognition	Due to the absence of connectives, implicit discourse relation recognition (IDRR) is still a challenging and crucial task in discourse analysis. Most of the current work adopted multi-task learning to aid IDRR through explicit discourse relation recognition (EDRR) or utilized dependencies between discourse relation lab...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	323,551
2106.09352	Large Scale Private Learning via Low-rank Reparametrization	We propose a reparametrization scheme to address the challenges of applying differentially private SGD on large neural networks, which are 1) the huge memory cost of storing individual gradients, 2) the added noise suffering notorious dimensional dependence. Specifically, we reparametrize each weight matrix with two \e...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	241,635
1909.07182	Distance Assessment and Hypothesis Testing of High-Dimensional Samples using Variational Autoencoders	Given two distinct datasets, an important question is if they have arisen from the the same data generating function or alternatively how their data generating functions diverge from one another. In this paper, we introduce an approach for measuring the distance between two datasets with high dimensionality using varia...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	145,614
2401.15444	Towards Causal Classification: A Comprehensive Study on Graph Neural Networks	The exploration of Graph Neural Networks (GNNs) for processing graph-structured data has expanded, particularly their potential for causal analysis due to their universal approximation capabilities. Anticipated to significantly enhance common graph-based tasks such as classification and prediction, the development of a...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	424,446
2408.00949	Equivariant neural networks and piecewise linear representation theory	Equivariant neural networks are neural networks with symmetry. Motivated by the theory of group representations, we decompose the layers of an equivariant neural network into simple representations. The nonlinear activation functions lead to interesting nonlinear equivariant maps between simple representations. For exa...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	478,041
2206.09977	Thompson Sampling Efficiently Learns to Control Diffusion Processes	Diffusion processes that evolve according to linear stochastic differential equations are an important family of continuous-time dynamic decision-making models. Optimal policies are well-studied for them, under full certainty about the drift matrices. However, little is known about data-driven control of diffusion proc...	false	false	false	false	true	false	true	false	false	false	true	false	false	false	false	false	false	false	303,747
1408.4576	Introduction to Clustering Algorithms and Applications	Data clustering is the process of identifying natural groupings or clusters within multidimensional data based on some similarity measure. Clustering is a fundamental process in many different disciplines. Hence, researchers from different fields are actively working on the clustering problem. This paper provides an ov...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	35,470
1810.02565	Continuous-time Models for Stochastic Optimization Algorithms	We propose new continuous-time formulations for first-order stochastic optimization algorithms such as mini-batch gradient descent and variance-reduced methods. We exploit these continuous-time models, together with simple Lyapunov analysis as well as tools from stochastic calculus, in order to derive convergence bound...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	109,619
1207.4825	A new algorithm for extracting a small representative subgraph from a very large graph	Many real-world networks are prohibitively large for data retrieval, storage and analysis of all of its nodes and links. Understanding the structure and dynamics of these networks entails creating a smaller representative sample of the full graph while preserving its relevant topological properties. In this report, we ...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	true	17,673
2306.14306	Adaptive Sharpness-Aware Pruning for Robust Sparse Networks	Robustness and compactness are two essential attributes of deep learning models that are deployed in the real world. The goals of robustness and compactness may seem to be at odds, since robustness requires generalization across domains, while the process of compression exploits specificity in one domain. We introduce ...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	375,631
1912.12076	An Efficient CSI Acquisition Method for Intelligent Reflecting Surface-assisted mmWave Networks	Millimeter-wave (mmWave) communication is one of the key enablers of the fifth-generation cellular networks (5G). However, one of the fundamental challenges of mmWave communication is the susceptibility to blockage effects. One way to alleviate this effect is the use of Intelligent Reflecting Surface (IRS). Nevertheles...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	158,747
2209.11972	Ground then Navigate: Language-guided Navigation in Dynamic Scenes	We investigate the Vision-and-Language Navigation (VLN) problem in the context of autonomous driving in outdoor settings. We solve the problem by explicitly grounding the navigable regions corresponding to the textual command. At each timestamp, the model predicts a segmentation mask corresponding to the intermediate o...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	319,367
2302.09413	New Dualities in Linear Systems and Optimal Output Control under Bounded Disturbances	In this paper, we introduce novel equations that are dual to the ones of the well-known invariant ellipsoids method. These equations yield ellipsoids with newly established geometrical interpretations and connections to linear system norms. The established duality leads to the optimal synthesis results for state-feedba...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	346,415
2010.11132	Sentence Boundary Augmentation For Neural Machine Translation Robustness	Neural Machine Translation (NMT) models have demonstrated strong state of the art performance on translation tasks where well-formed training and evaluation data are provided, but they remain sensitive to inputs that include errors of various types. Specifically, in the context of long-form speech translation systems, ...	false	false	true	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	202,132
1302.7096	Using Artificial Intelligence Models in System Identification	Artificial Intelligence (AI) techniques are known for its ability in tackling problems found to be unyielding to traditional mathematical methods. A recent addition to these techniques are the Computational Intelligence (CI) techniques which, in most cases, are nature or biologically inspired techniques. Different CI t...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	true	false	false	22,507
1507.02037	Sparse Time-Frequency decomposition for multiple signals with same frequencies	In this paper, we consider multiple signals sharing same instantaneous frequencies. This kind of data is very common in scientific and engineering problems. To take advantage of this special structure, we modify our data-driven time-frequency analysis by updating the instantaneous frequencies simultaneously. Moreover, ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	44,935
2101.06228	Task-driven Self-supervised Bi-channel Networks for Diagnosis of Breast Cancers with Mammography	Deep learning can promote the mammography-based computer-aided diagnosis (CAD) for breast cancers, but it generally suffers from the small sample size problem. Self-supervised learning (SSL) has shown its effectiveness in medical image analysis with limited training samples. However, the network model sometimes cannot ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	215,649
2304.02595	Bayesian neural networks via MCMC: a Python-based tutorial	Bayesian inference provides a methodology for parameter estimation and uncertainty quantification in machine learning and deep learning methods. Variational inference and Markov Chain Monte-Carlo (MCMC) sampling methods are used to implement Bayesian inference. In the past three decades, MCMC sampling methods have face...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	356,477
2002.11039	A study of resting-state EEG biomarkers for depression recognition	Background: Depression has become a major health burden worldwide, and effective detection depression is a great public-health challenge. This Electroencephalography (EEG)-based research is to explore the effective biomarkers for depression recognition. Methods: Resting state EEG data was collected from 24 major depres...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	165,591
1903.09992	Coded trace reconstruction	Motivated by average-case trace reconstruction and coding for portable DNA-based storage systems, we initiate the study of \emph{coded trace reconstruction}, the design and analysis of high-rate efficiently encodable codes that can be efficiently decoded with high probability from few reads (also called \emph{traces}) ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	125,179
1606.01623	Position-Indexed Formulations for Kidney Exchange	A kidney exchange is an organized barter market where patients in need of a kidney swap willing but incompatible donors. Determining an optimal set of exchanges is theoretically and empirically hard. Traditionally, exchanges took place in cycles, with each participating patient-donor pair both giving and receiving a ki...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	56,843
2502.02391	FewTopNER: Integrating Few-Shot Learning with Topic Modeling and Named Entity Recognition in a Multilingual Framework	We introduce FewTopNER, a novel framework that integrates few-shot named entity recognition (NER) with topic-aware contextual modeling to address the challenges of cross-lingual and low-resource scenarios. FewTopNER leverages a shared multilingual encoder based on XLM-RoBERTa, augmented with language-specific calibrati...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	530,296
1911.05329	Knowledge Representing: Efficient, Sparse Representation of Prior Knowledge for Knowledge Distillation	Despite the recent works on knowledge distillation (KD) have achieved a further improvement through elaborately modeling the decision boundary as the posterior knowledge, their performance is still dependent on the hypothesis that the target network has a powerful capacity (representation ability). In this paper, we pr...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	153,231
1803.09967	Reinforcement Learning for Fair Dynamic Pricing	Unfair pricing policies have been shown to be one of the most negative perceptions customers can have concerning pricing, and may result in long-term losses for a company. Despite the fact that dynamic pricing models help companies maximize revenue, fairness and equality should be taken into account in order to avoid u...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	93,621
2205.09416	A Weakly-Supervised Iterative Graph-Based Approach to Retrieve COVID-19 Misinformation Topics	The COVID-19 pandemic has been accompanied by an `infodemic' -- of accurate and inaccurate health information across social media. Detecting misinformation amidst dynamically changing information landscape is challenging; identifying relevant keywords and posts is arduous due to the large amount of human effort require...	true	false	false	true	false	false	false	false	true	false	false	false	false	false	false	false	false	false	297,264
2202.01421	Characterization of Semantic Segmentation Models on Mobile Platforms for Self-Navigation in Disaster-Struck Zones	The role of unmanned vehicles for searching and localizing the victims in disaster impacted areas such as earthquake-struck zones is getting more important. Self-navigation on an earthquake zone has a unique challenge of detecting irregularly shaped obstacles such as road cracks, debris on the streets, and water puddle...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	278,477
1206.5265	Consensus ranking under the exponential model	We analyze the generalized Mallows model, a popular exponential model over rankings. Estimating the central (or consensus) ranking from data is NP-hard. We obtain the following new results: (1) We show that search methods can estimate both the central ranking pi0 and the model parameters theta exactly. The search is n!...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	16,802
1804.10428	Localized Traffic Sign Detection with Multi-scale Deconvolution Networks	Autonomous driving is becoming a future practical lifestyle greatly driven by deep learning. Specifically, an effective traffic sign detection by deep learning plays a critical role for it. However, different countries have different sets of traffic signs, making localized traffic sign recognition model training a tedi...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	96,159
2304.09527	Single-View View Synthesis with Self-Rectified Pseudo-Stereo	Synthesizing novel views from a single view image is a highly ill-posed problem. We discover an effective solution to reduce the learning ambiguity by expanding the single-view view synthesis problem to a multi-view setting. Specifically, we leverage the reliable and explicit stereo prior to generate a pseudo-stereo vi...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	359,083
2407.08464	TLDR: Unsupervised Goal-Conditioned RL via Temporal Distance-Aware Representations	Unsupervised goal-conditioned reinforcement learning (GCRL) is a promising paradigm for developing diverse robotic skills without external supervision. However, existing unsupervised GCRL methods often struggle to cover a wide range of states in complex environments due to their limited exploration and sparse or noisy ...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	472,175
2407.20660	What makes for good morphology representations for spatial omics?	Spatial omics has transformed our understanding of tissue architecture by preserving spatial context of gene expression patterns. Simultaneously, advances in imaging AI have enabled extraction of morphological features describing the tissue. The intersection of spatial omics and imaging AI presents opportunities for a ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	477,237
2007.12859	Physical Layer Security of Large Reflecting Surface Aided Communications with Phase Errors	The physical layer security (PLS) performance of a wireless communication link through a large reflecting surface (LRS) with phase errors is analyzed. Leveraging recent results that express the \ac{LRS}-based composite channel as an equivalent scalar fading channel, we show that the eavesdropper's link is Rayleigh dist...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	188,944
2201.03168	Nukhada USV: a Robot for Autonomous Surveying and Support to Underwater Operations	The Technology Innovation Institute in Abu Dhabi, United Arab Emirates, has recently finished the production and testing of a new unmanned surface vehicle, called Nukhada, specifically designed for autonomous survey, inspection, and support to underwater operations. This manuscript describes the main characteristics of...	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	274,770
2403.15522	Medical Image Data Provenance for Medical Cyber-Physical System	Continuous advancements in medical technology have led to the creation of affordable mobile imaging devices suitable for telemedicine and remote monitoring. However, the rapid examination of large populations poses challenges, including the risk of fraudulent practices by healthcare professionals and social workers exc...	false	false	false	false	false	false	false	false	false	false	false	true	true	false	false	false	false	false	440,624
2310.17216	Three-dimensional Bone Image Synthesis with Generative Adversarial Networks	Medical image processing has been highlighted as an area where deep learning-based models have the greatest potential. However, in the medical field in particular, problems of data availability and privacy are hampering research progress and thus rapid implementation in clinical routine. The generation of synthetic dat...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	403,048
2208.03934	Inflating 2D Convolution Weights for Efficient Generation of 3D Medical Images	The generation of three-dimensional (3D) medical images has great application potential since it takes into account the 3D anatomical structure. Two problems prevent effective training of a 3D medical generative model: (1) 3D medical images are expensive to acquire and annotate, resulting in an insufficient number of t...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	311,944
1712.06713	Game-Theoretic Electric Vehicle Charging Management Resilient to Non-Ideal User Behavior	In this paper, an electric vehicle (EV) charging competition, among EV aggregators that perform coordinated EV charging, is explored while taking into consideration potential non-ideal actions of the aggregators. In the coordinated EV charging strategy presented in this paper, each aggregator determines EV charging sta...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	true	86,923
1403.2111	Protograph-Based Raptor-Like LDPC Codes	This paper proposes a class of rate-compatible LDPC codes, called protograph-based Raptor-like (PBRL) codes. The construction is focused on binary codes for BI-AWGN channels. As with the Raptor codes, additional parity bits are produced by exclusive-OR operations on the precoded bits, providing extensive rate compatibi...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	31,460
2209.08237	Understanding the Impact of Image Quality and Distance of Objects to Object Detection Performance	Deep learning has made great strides for object detection in images. The detection accuracy and computational cost of object detection depend on the spatial resolution of an image, which may be constrained by both the camera and storage considerations. Compression is often achieved by reducing either spatial or amplitu...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	318,044
2401.15615	Mitigating the Impact of Noisy Edges on Graph-Based Algorithms via Adversarial Robustness Evaluation	Given that no existing graph construction method can generate a perfect graph for a given dataset, graph-based algorithms are often affected by redundant and erroneous edges present within the constructed graphs. In this paper, we view these noisy edges as adversarial attack and propose to use a spectral adversarial ro...	false	false	false	false	false	false	true	false	false	false	false	true	true	false	false	false	false	false	424,517
1111.0067	A General Theory of Additive State Space Abstractions	Informally, a set of abstractions of a state space S is additive if the distance between any two states in S is always greater than or equal to the sum of the corresponding distances in the abstract spaces. The first known additive abstractions, called disjoint pattern databases, were experimentally demonstrated to pro...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	12,857
2206.15464	Practical Black Box Hamiltonian Learning	We study the problem of learning the parameters for the Hamiltonian of a quantum many-body system, given limited access to the system. In this work, we build upon recent approaches to Hamiltonian learning via derivative estimation. We propose a protocol that improves the scaling dependence of prior works, particularly ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	305,596
2010.14227	Efficient, Simple and Automated Negative Sampling for Knowledge Graph Embedding	Negative sampling, which samples negative triplets from non-observed ones in knowledge graph (KG), is an essential step in KG embedding. Recently, generative adversarial network (GAN), has been introduced in negative sampling. By sampling negative triplets with large gradients, these methods avoid the problem of vanish...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	true	false	203,379
1801.01486	Deep Cross Polarimetric Thermal-to-visible Face Recognition	In this paper, we present a deep coupled learning frame- work to address the problem of matching polarimetric ther- mal face photos against a gallery of visible faces. Polariza- tion state information of thermal faces provides the miss- ing textural and geometrics details in the thermal face im- agery which exist in vi...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	87,738
2210.10162	Computational pathology in renal disease: a comprehensive perspective	Computational pathology is a field that has complemented various subspecialties of diagnostic pathology over the last few years. In this article a brief analyzis the different applications in nephrology is developed. To begin, an overview of the different forms of image production is provided. To continue, the most fre...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	324,805
2408.03616	Distillation Learning Guided by Image Reconstruction for One-Shot Medical Image Segmentation	Traditional one-shot medical image segmentation (MIS) methods use registration networks to propagate labels from a reference atlas or rely on comprehensive sampling strategies to generate synthetic labeled data for training. However, these methods often struggle with registration errors and low-quality synthetic images...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	479,089
2103.08439	S-AT GCN: Spatial-Attention Graph Convolution Network based Feature Enhancement for 3D Object Detection	3D object detection plays a crucial role in environmental perception for autonomous vehicles, which is the prerequisite of decision and control. This paper analyses partition-based methods' inherent drawbacks. In the partition operation, a single instance such as a pedestrian is sliced into several pieces, which we cal...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	224,903
1806.04589	Computation Rate Maximization in UAV-Enabled Wireless Powered Mobile-Edge Computing Systems	Mobile edge computing (MEC) and wireless power transfer (WPT) are two promising techniques to enhance the computation capability and to prolong the operational time of low-power wireless devices that are ubiquitous in Internet of Things. However, the computation performance and the harvested energy are significantly im...	false	true	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	100,274
2402.18383	Robust Quantification of Percent Emphysema on CT via Domain Attention: the Multi-Ethnic Study of Atherosclerosis (MESA) Lung Study	Robust quantification of pulmonary emphysema on computed tomography (CT) remains challenging for large-scale research studies that involve scans from different scanner types and for translation to clinical scans. Existing studies have explored several directions to tackle this challenge, including density correction, n...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	433,410
1707.07608	Vision-Based Fallen Person Detection for the Elderly	Falls are serious and costly for elderly people. The Centers for Disease Control and Prevention of the US reports that millions of older people, 65 and older, fall each year at least once. Serious injuries such as; hip fractures, broken bones or head injury, are caused by 20% of the falls. The time it takes to respond ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	77,664
2112.09329	Point2Cyl: Reverse Engineering 3D Objects from Point Clouds to Extrusion Cylinders	We propose Point2Cyl, a supervised network transforming a raw 3D point cloud to a set of extrusion cylinders. Reverse engineering from a raw geometry to a CAD model is an essential task to enable manipulation of the 3D data in shape editing software and thus expand their usages in many downstream applications. Particul...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	272,111
2405.09086	Chaos-based reinforcement learning with TD3	Chaos-based reinforcement learning (CBRL) is a method in which the agent's internal chaotic dynamics drives exploration. This approach offers a model for considering how the biological brain can create variability in its behavior and learn in an exploratory manner. At the same time, it is a learning model that has the ...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	true	false	false	454,299
1908.05600	Diffusive Mobile MC with Absorbing Receivers: Stochastic Analysis and Applications	This paper presents a stochastic analysis of the time-variant channel impulse response (CIR) of a three dimensional diffusive mobile molecular communication (MC) system where the transmitter, the absorbing receiver, and the molecules can freely diffuse. In our analysis, we derive the mean, variance, probability density...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	141,755
2309.10729	PAMS: Platform for Artificial Market Simulations	This paper presents a new artificial market simulation platform, PAMS: Platform for Artificial Market Simulations. PAMS is developed as a Python-based simulator that is easily integrated with deep learning and enabling various simulation that requires easy users' modification. In this paper, we demonstrate PAMS effecti...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	true	false	false	false	393,132
2006.12726	Prediction of fitness in bacteria with causal jump dynamic mode decomposition	In this paper, we consider the problem of learning a predictive model for population cell growth dynamics as a function of the media conditions. We first introduce a generic data-driven framework for training operator-theoretic models to predict cell growth rate. We then introduce the experimental design and data gener...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	183,688
2001.03787	Attitude Determination and Estimation using Vector Observations: Review, Challenges and Comparative Results	This paper concerns the problem of attitude determination and estimation. The early applications considered algebraic methods of attitude determination. Attitude determination algorithms were supplanted by the Gaussian attitude estimation filters (which continue to be widely used in commercial applications). However, t...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	160,059
2405.14082	Exclusively Penalized Q-learning for Offline Reinforcement Learning	Constraint-based offline reinforcement learning (RL) involves policy constraints or imposing penalties on the value function to mitigate overestimation errors caused by distributional shift. This paper focuses on a limitation in existing offline RL methods with penalized value function, indicating the potential for und...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	456,237
2001.00813	Computing L1 Straight-Line Fits to Data (Part 1)	The initial remarks in this technical report are primarily for those not familiar with the properties of L1 approximation, but the remainder of the report should also interest readers who are already acquainted with the inner workings of L1 algorithms.	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	159,331
2309.15770	Generating Transferable Adversarial Simulation Scenarios for Self-Driving via Neural Rendering	Self-driving software pipelines include components that are learned from a significant number of training examples, yet it remains challenging to evaluate the overall system's safety and generalization performance. Together with scaling up the real-world deployment of autonomous vehicles, it is of critical importance t...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	395,107
2405.06800	LLM-Generated Black-box Explanations Can Be Adversarially Helpful	Large Language Models (LLMs) are becoming vital tools that help us solve and understand complex problems by acting as digital assistants. LLMs can generate convincing explanations, even when only given the inputs and outputs of these problems, i.e., in a ``black-box'' approach. However, our research uncovers a hidden r...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	453,456
1612.01452	ImageNet pre-trained models with batch normalization	Convolutional neural networks (CNN) pre-trained on ImageNet are the backbone of most state-of-the-art approaches. In this paper, we present a new set of pre-trained models with popular state-of-the-art architectures for the Caffe framework. The first release includes Residual Networks (ResNets) with generation script a...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	65,088
1408.3725	Object Structure from Manipulation via Particle Filter and Robot-based Active Learning	To learn object models for robotic manipulation, unsupervised methods cannot provide accurate object structural information and supervised methods require a large amount of manually labeled training samples, thus interactive object segmentation is developed to automate object modeling. In this article, we formulate a n...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	35,402
2202.08159	Domain Adaptive Fake News Detection via Reinforcement Learning	With social media being a major force in information consumption, accelerated propagation of fake news has presented new challenges for platforms to distinguish between legitimate and fake news. Effective fake news detection is a non-trivial task due to the diverse nature of news domains and expensive annotation costs....	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	false	false	280,784
2411.14779	New families of non-Reed-Solomon MDS codes	MDS codes have garnered significant attention due to their wide applications in practice. To date, most known MDS codes are equivalent to Reed-Solomon codes. The construction of non-Reed-Solomon (non-RS) type MDS codes has emerged as an intriguing and important problem in both coding theory and finite geometry. Althoug...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	510,321
2305.13831	ZET-Speech: Zero-shot adaptive Emotion-controllable Text-to-Speech Synthesis with Diffusion and Style-based Models	Emotional Text-To-Speech (TTS) is an important task in the development of systems (e.g., human-like dialogue agents) that require natural and emotional speech. Existing approaches, however, only aim to produce emotional TTS for seen speakers during training, without consideration of the generalization to unseen speaker...	false	false	true	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	366,709
2105.04156	ReLU Deep Neural Networks from the Hierarchical Basis Perspective	We study ReLU deep neural networks (DNNs) by investigating their connections with the hierarchical basis method in finite element methods. First, we show that the approximation schemes of ReLU DNNs for $x^2$ and $xy$ are composition versions of the hierarchical basis approximation for these two functions. Based on this...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	234,408
2107.05045	Positive-Unlabeled Classification under Class-Prior Shift: A Prior-invariant Approach Based on Density Ratio Estimation	Learning from positive and unlabeled (PU) data is an important problem in various applications. Most of the recent approaches for PU classification assume that the class-prior (the ratio of positive samples) in the training unlabeled dataset is identical to that of the test data, which does not hold in many practical c...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	245,649
2004.08522	Super-Resolution-based Snake Model -- An Unsupervised Method for Large-Scale Building Extraction using Airborne LiDAR Data and Optical Image	Automatic extraction of buildings in urban and residential scenes has become a subject of growing interest in the domain of photogrammetry and remote sensing, particularly since mid-1990s. Active contour model, colloquially known as snake model, has been studied to extract buildings from aerial and satellite imagery. H...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	173,083
2405.17139	Synergy and Diversity in CLIP: Enhancing Performance Through Adaptive Backbone Ensembling	Contrastive Language-Image Pretraining (CLIP) stands out as a prominent method for image representation learning. Various architectures, from vision transformers (ViTs) to convolutional networks (ResNets) have been trained with CLIP to serve as general solutions to diverse vision tasks. This paper explores the differen...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	457,782
1711.07624	A deep learning-based method for relative location prediction in CT scan images	Relative location prediction in computed tomography (CT) scan images is a challenging problem. In this paper, a regression model based on one-dimensional convolutional neural networks is proposed to determine the relative location of a CT scan image both robustly and precisely. A public dataset is employed to validate ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	85,030
1910.00617	Predicting materials properties without crystal structure: Deep representation learning from stoichiometry	Machine learning has the potential to accelerate materials discovery by accurately predicting materials properties at a low computational cost. However, the model inputs remain a key stumbling block. Current methods typically use descriptors constructed from knowledge of either the full crystal structure -- therefore o...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	147,711
2406.09906	Label-Efficient Semantic Segmentation of LiDAR Point Clouds in Adverse Weather Conditions	Adverse weather conditions can severely affect the performance of LiDAR sensors by introducing unwanted noise in the measurements. Therefore, differentiating between noise and valid points is crucial for the reliable use of these sensors. Current approaches for detecting adverse weather points require large amounts of ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	464,137
2011.13077	Functional Time Series Forecasting: Functional Singular Spectrum Analysis Approaches	In this paper, we propose two nonparametric methods used in the forecasting of functional time-dependent data, namely functional singular spectrum analysis recurrent forecasting and vector forecasting. Both algorithms utilize the results of functional singular spectrum analysis and past observations in order to predict...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	208,358
2112.04330	Estimation in Rotationally Invariant Generalized Linear Models via Approximate Message Passing	We consider the problem of signal estimation in generalized linear models defined via rotationally invariant design matrices. Since these matrices can have an arbitrary spectral distribution, this model is well suited for capturing complex correlation structures which often arise in applications. We propose a novel fam...	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	270,492
2312.05786	Deep Learning for Joint Design of Pilot, Channel Feedback, and Hybrid Beamforming in FDD Massive MIMO-OFDM Systems	This letter considers the transceiver design in frequency division duplex (FDD) massive multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems for high-quality data transmission. We propose a novel deep learning based framework where the procedures of pilot design, channel feedb...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	414,237
1811.01282	Partitions of Matrix Spaces With an Application to $q$-Rook Polynomials	We study the row-space partition and the pivot partition on the matrix space $\mathbb{F}_q^{n \times m}$. We show that both these partitions are reflexive and that the row-space partition is self-dual. Moreover, using various combinatorial methods, we explicitly compute the Krawtchouk coefficients associated with these...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	112,319
2008.04652	Scientific Article Recommendation: Exploiting Common Author Relations and Historical Preferences	Scientific article recommender systems are playing an increasingly important role for researchers in retrieving scientific articles of interest in the coming era of big scholarly data. Most existing studies have designed unified methods for all target researchers and hence the same algorithms are run to generate recomm...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	true	191,289
2102.10905	Joint Intent Detection And Slot Filling Based on Continual Learning Model	Slot filling and intent detection have become a significant theme in the field of natural language understanding. Even though slot filling is intensively associated with intent detection, the characteristics of the information required for both tasks are different while most of those approaches may not fully aware of t...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	221,257
1905.13164	Hierarchical Transformers for Multi-Document Summarization	In this paper, we develop a neural summarization model which can effectively process multiple input documents and distill Transformer architecture with the ability to encode documents in a hierarchical manner. We represent cross-document relationships via an attention mechanism which allows to share information as oppo...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	133,015
2412.04637	Semantic Retrieval at Walmart	In product search, the retrieval of candidate products before re-ranking is more critical and challenging than other search like web search, especially for tail queries, which have a complex and specific search intent. In this paper, we present a hybrid system for e-commerce search deployed at Walmart that combines tra...	false	false	false	false	true	true	true	false	false	false	false	false	false	false	false	false	false	false	514,492
2103.12992	Non-Compression Auto-Encoder for Detecting Road Surface Abnormality via Vehicle Driving Noise	Road accident can be triggered by wet road because it decreases skid resistance. To prevent the road accident, detecting road surface abnomality is highly useful. In this paper, we propose the deep learning based cost-effective real-time anomaly detection architecture, naming with non-compression auto-encoder (NCAE). T...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	226,351
2407.13873	Keypoint Aware Masked Image Modelling	SimMIM is a widely used method for pretraining vision transformers using masked image modeling. However, despite its success in fine-tuning performance, it has been shown to perform sub-optimally when used for linear probing. We propose an efficient patch-wise weighting derived from keypoint features which captures the...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	474,547
1809.02709	Exploiting Edge Features in Graph Neural Networks	Edge features contain important information about graphs. However, current state-of-the-art neural network models designed for graph learning, e.g. graph convolutional networks (GCN) and graph attention networks (GAT), adequately utilize edge features, especially multi-dimensional edge features. In this paper, we build...	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	false	false	107,107
2410.11861	Investigating Role of Big Five Personality Traits in Audio-Visual Rapport Estimation	Automatic rapport estimation in social interactions is a central component of affective computing. Recent reports have shown that the estimation performance of rapport in initial interactions can be improved by using the participant's personality traits as the model's input. In this study, we investigate whether this f...	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	498,750
1506.08472	Solving the power flow equations: a monotone operator approach	The AC power flow equations underlie all operational aspects of power systems. They are solved routinely in operational practice using the Newton-Raphson method and its variants. These methods work well given a good initial "guess" for the solution, which is always available in normal system operations. However, with t...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	44,619
1811.10433	Compact and Efficient Encodings for Planning in Factored State and Action Spaces with Learned Binarized Neural Network Transition Models	In this paper, we leverage the efficiency of Binarized Neural Networks (BNNs) to learn complex state transition models of planning domains with discretized factored state and action spaces. In order to directly exploit this transition structure for planning, we present two novel compilations of the learned factored pla...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	114,491
cs/0611046	Analytic Tableaux Calculi for KLM Logics of Nonmonotonic Reasoning	We present tableau calculi for some logics of nonmonotonic reasoning, as defined by Kraus, Lehmann and Magidor. We give a tableau proof procedure for all KLM logics, namely preferential, loop-cumulative, cumulative and rational logics. Our calculi are obtained by introducing suitable modalities to interpret conditional...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	539,867
2006.09117	End-to-End Real-time Catheter Segmentation with Optical Flow-Guided Warping during Endovascular Intervention	Accurate real-time catheter segmentation is an important pre-requisite for robot-assisted endovascular intervention. Most of the existing learning-based methods for catheter segmentation and tracking are only trained on small-scale datasets or synthetic data due to the difficulties of ground-truth annotation. Furthermo...	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	182,434

Subsets and Splits

No community queries yet

The top public SQL queries from the community will appear here once available.