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
2112.07571	Epigenomic language models powered by Cerebras	Large scale self-supervised pre-training of Transformer language models has advanced the field of Natural Language Processing and shown promise in cross-application to the biological `languages' of proteins and DNA. Learning effective representations of DNA sequences using large genomic sequence corpuses may accelerate...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	271,524
2502.00737	Scalable Sobolev IPM for Probability Measures on a Graph	We investigate the Sobolev IPM problem for probability measures supported on a graph metric space. Sobolev IPM is an important instance of integral probability metrics (IPM), and is obtained by constraining a critic function within a unit ball defined by the Sobolev norm. In particular, it has been used to compare prob...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	529,527
1306.3764	Bounding ground state energy of Hopfield models	In this paper we look at a class of random optimization problems that arise in the forms typically known as Hopfield models. We view two scenarios which we term as the positive Hopfield form and the negative Hopfield form. For both of these scenarios we define the binary optimization problems that essentially emulate w...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	25,242
2110.10815	Convergence Analysis and Implicit Regularization of Feedback Alignment for Deep Linear Networks	We theoretically analyze the Feedback Alignment (FA) algorithm, an efficient alternative to backpropagation for training neural networks. We provide convergence guarantees with rates for deep linear networks for both continuous and discrete dynamics. Additionally, we study incremental learning phenomena for shallow lin...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	262,267
2211.03148	UATTA-ENS: Uncertainty Aware Test Time Augmented Ensemble for PIRC Diabetic Retinopathy Detection	Deep Ensemble Convolutional Neural Networks has become a methodology of choice for analyzing medical images with a diagnostic performance comparable to a physician, including the diagnosis of Diabetic Retinopathy. However, commonly used techniques are deterministic and are therefore unable to provide any estimate of pr...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	328,842
2009.06370	Transparency and granularity in the SP Theory of Intelligence and its realisation in the SP Computer Model	This chapter describes how the SP System, meaning the SP Theory of Intelligence, and its realisation as the SP Computer Model, may promote transparency and granularity in AI, and some other areas of application. The chapter describes how transparency in the workings and output of the SP Computer Model may be achieved v...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	195,615
1701.01858	Automatic Wikipedia Link Generation Based On Interlanguage Links	This paper presents a new way to increase interconnectivity in small Wikipedias (fewer than a 100,000 articles), by automatically linking articles based on interlanguage links. Many small Wikipedias have many articles with very few links, this is mainly due to the short article length. This makes it difficult to naviga...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	66,460
0901.1866	Capacity Achieving Codes From Randomness Condensers	We establish a general framework for construction of small ensembles of capacity achieving linear codes for a wide range of (not necessarily memoryless) discrete symmetric channels, and in particular, the binary erasure and symmetric channels. The main tool used in our constructions is the notion of randomness extracto...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	2,943
2203.17269	A Closer Look at Rehearsal-Free Continual Learning	Continual learning is a setting where machine learning models learn novel concepts from continuously shifting training data, while simultaneously avoiding degradation of knowledge on previously seen classes which may disappear from the training data for extended periods of time (a phenomenon known as the catastrophic f...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	289,092
1307.1289	Further results on dissimilarity spaces for hyperspectral images RF-CBIR	Content-Based Image Retrieval (CBIR) systems are powerful search tools in image databases that have been little applied to hyperspectral images. Relevance feedback (RF) is an iterative process that uses machine learning techniques and user's feedback to improve the CBIR systems performance. We pursued to expand previou...	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	false	25,613
2110.13649	An algorithm for the computation of joint Hawkes moments with exponential kernel	The purpose of this paper is to present a recursive algorithm and its implementation in Maple and Mathematica for the computation of joint moments and cumulants of Hawkes processes with exponential kernels. Numerical results and computation times are also discussed. Obtaining closed form expressions can be computationa...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	263,263
1103.1741	Mitigation of Malicious Attacks on Networks	Terrorist attacks on transportation networks have traumatized modern societies. With a single blast, it has become possible to paralyze airline traffic, electric power supply, ground transportation or Internet communication. How and at which cost can one restructure the network such that it will become more robust agai...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	9,541
2305.03963	Beyond the Model: Data Pre-processing Attack to Deep Learning Models in Android Apps	The increasing popularity of deep learning (DL) models and the advantages of computing, including low latency and bandwidth savings on smartphones, have led to the emergence of intelligent mobile applications, also known as DL apps, in recent years. However, this technological development has also given rise to several...	false	false	false	false	true	false	false	false	false	false	false	true	true	false	false	false	false	false	362,581
1604.07427	NRSSPrioritize: Associating Protein Complex and Disease Similarity Information to Prioritize Disease Candidate Genes	The identification of disease-associated genes has recently gathered much attention for uncovering disease complex mechanisms that could lead to new insights into the treatment of diseases. For exploring disease-susceptible genes, not only experimental approaches such as genome-wide association studies (GWAS) have been...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	55,089
1807.00083	Topology classification with deep learning to improve real-time event selection at the LHC	We show how event topology classification based on deep learning could be used to improve the purity of data samples selected in real time at at the Large Hadron Collider. We consider different data representations, on which different kinds of multi-class classifiers are trained. Both raw data and high-level features a...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	101,760
2408.04221	Connective Viewpoints of Signal-to-Noise Diffusion Models	Diffusion models (DM) have become fundamental components of generative models, excelling across various domains such as image creation, audio generation, and complex data interpolation. Signal-to-Noise diffusion models constitute a diverse family covering most state-of-the-art diffusion models. While there have been se...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	true	false	false	479,299
2010.07830	Semi-Supervised Semantic Segmentation in Earth Observation: The MiniFrance Suite, Dataset Analysis and Multi-task Network Study	The development of semi-supervised learning techniques is essential to enhance the generalization capacities of machine learning algorithms. Indeed, raw image data are abundant while labels are scarce, therefore it is crucial to leverage unlabeled inputs to build better models. The availability of large databases have ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	200,958
1105.1930	Emerging multidisciplinary research across database management systems	The database community is exploring more and more multidisciplinary avenues: Data semantics overlaps with ontology management; reasoning tasks venture into the domain of artificial intelligence; and data stream management and information retrieval shake hands, e.g., when processing Web click-streams. These new research...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	10,313
2006.06856	BanditPAM: Almost Linear Time $k$-Medoids Clustering via Multi-Armed Bandits	Clustering is a ubiquitous task in data science. Compared to the commonly used $k$-means clustering, $k$-medoids clustering requires the cluster centers to be actual data points and support arbitrary distance metrics, which permits greater interpretability and the clustering of structured objects. Current state-of-the-...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	181,576
1308.3847	Exploiting Binary Floating-Point Representations for Constraint Propagation: The Complete Unabridged Version	Floating-point computations are quickly finding their way in the design of safety- and mission-critical systems, despite the fact that designing floating-point algorithms is significantly more difficult than designing integer algorithms. For this reason, verification and validation of floating-point computations is a h...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	26,507
2305.03487	HD2Reg: Hierarchical Descriptors and Detectors for Point Cloud Registration	Feature Descriptors and Detectors are two main components of feature-based point cloud registration. However, little attention has been drawn to the explicit representation of local and global semantics in the learning of descriptors and detectors. In this paper, we present a framework that explicitly extracts dual-lev...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	362,410
2202.00813	A Graph Based Neural Network Approach to Immune Profiling of Multiplexed Tissue Samples	Multiplexed immunofluorescence provides an unprecedented opportunity for studying specific cell-to-cell and cell microenvironment interactions. We employ graph neural networks to combine features obtained from tissue morphology with measurements of protein expression to profile the tumour microenvironment associated wi...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	278,270
2402.12370	AnaloBench: Benchmarking the Identification of Abstract and Long-context Analogies	Humans regularly engage in analogical thinking, relating personal experiences to current situations (X is analogous to Y because of Z). Analogical thinking allows humans to solve problems in creative ways, grasp difficult concepts, and articulate ideas more effectively. Can language models (LMs) do the same? To answer ...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	430,820
2112.09546	Complex Functional Maps : a Conformal Link Between Tangent Bundles	In this paper, we introduce complex functional maps, which extend the functional map framework to conformal maps between tangent vector fields on surfaces. A key property of these maps is their orientation awareness. More specifically, we demonstrate that unlike regular functional maps that link functional spaces of tw...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	272,180
2303.16160	One-Stage 3D Whole-Body Mesh Recovery with Component Aware Transformer	Whole-body mesh recovery aims to estimate the 3D human body, face, and hands parameters from a single image. It is challenging to perform this task with a single network due to resolution issues, i.e., the face and hands are usually located in extremely small regions. Existing works usually detect hands and faces, enla...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	354,761
2103.07552	Few-Shot Text Classification with Triplet Networks, Data Augmentation, and Curriculum Learning	Few-shot text classification is a fundamental NLP task in which a model aims to classify text into a large number of categories, given only a few training examples per category. This paper explores data augmentation -- a technique particularly suitable for training with limited data -- for this few-shot, highly-multicl...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	224,619
1712.00433	Single-Shot Object Detection with Enriched Semantics	We propose a novel single shot object detection network named Detection with Enriched Semantics (DES). Our motivation is to enrich the semantics of object detection features within a typical deep detector, by a semantic segmentation branch and a global activation module. The segmentation branch is supervised by weak se...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	85,897
2409.16495	Flight: A FaaS-Based Framework for Complex and Hierarchical Federated Learning	Federated Learning (FL) is a decentralized machine learning paradigm where models are trained on distributed devices and are aggregated at a central server. Existing FL frameworks assume simple two-tier network topologies where end devices are directly connected to the aggregation server. While this is a practical ment...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	491,368
1908.08652	MTCNET: Multi-task Learning Paradigm for Crowd Count Estimation	We propose a Multi-Task Learning (MTL) paradigm based deep neural network architecture, called MTCNet (Multi-Task Crowd Network) for crowd density and count estimation. Crowd count estimation is challenging due to the non-uniform scale variations and the arbitrary perspective of an individual image. The proposed model ...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	142,613
2410.08938	KinDEL: DNA-Encoded Library Dataset for Kinase Inhibitors	DNA-Encoded Libraries (DEL) are combinatorial small molecule libraries that offer an efficient way to characterize diverse chemical spaces. Selection experiments using DELs are pivotal to drug discovery efforts, enabling high-throughput screens for hit finding. However, limited availability of public DEL datasets hinde...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	497,354
2409.05086	Exploring the Optimal Size of Grid-forming Energy Storage in an Off-grid Renewable P2H System under Multi-timescale Energy Management	Utility-scale off-grid renewable power-to-hydrogen systems (OReP2HSs) typically include photovoltaic plants, wind turbines, electrolyzers (ELs), and energy storage systems. As an island system, OReP2HS requires at least one component, generally the battery energy storage system (BESS), that operates for grid-forming co...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	486,631
1906.04509	BasisConv: A method for compressed representation and learning in CNNs	It is well known that Convolutional Neural Networks (CNNs) have significant redundancy in their filter weights. Various methods have been proposed in the literature to compress trained CNNs. These include techniques like pruning weights, filter quantization and representing filters in terms of a basis functions. Our ap...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	134,740
1809.03568	Improving Question Answering by Commonsense-Based Pre-Training	Although neural network approaches achieve remarkable success on a variety of NLP tasks, many of them struggle to answer questions that require commonsense knowledge. We believe the main reason is the lack of commonsense \mbox{connections} between concepts. To remedy this, we provide a simple and effective method that ...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	107,355
2402.18095	Exergetic Port-Hamiltonian Systems for Multibody Dynamics	Multibody dynamics simulation plays an important role in various fields, including mechanical engineering, robotics, and biomechanics. Setting up computational models however becomes increasingly challenging as systems grow in size and complexity. Especially the consistent combination of models across different physica...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	433,279
2306.05698	JABBERWOCK: A Tool for WebAssembly Dataset Generation and Its Application to Malicious Website Detection	Machine learning is often used for malicious website detection, but an approach incorporating WebAssembly as a feature has not been explored due to a limited number of samples, to the best of our knowledge. In this paper, we propose JABBERWOCK (JAvascript-Based Binary EncodeR by WebAssembly Optimization paCKer), a tool...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	372,301
2206.12417	Deep embedded clustering algorithm for clustering PACS repositories	Creating large datasets of medical radiology images from several sources can be challenging because of the differences in the acquisition and storage standards. One possible way of controlling and/or assessing the image selection process is through medical image clustering. This, however, requires an efficient method f...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	304,595
2404.11578	LTL-Constrained Policy Optimization with Cycle Experience Replay	Linear Temporal Logic (LTL) offers a precise means for constraining the behavior of reinforcement learning agents. However, in many tasks, LTL is insufficient for task specification; LTL-constrained policy optimization, where the goal is to optimize a scalar reward under LTL constraints, is needed. Prior methods for th...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	true	447,545
1811.11358	Future Segmentation Using 3D Structure	Predicting the future to anticipate the outcome of events and actions is a critical attribute of autonomous agents; particularly for agents which must rely heavily on real time visual data for decision making. Working towards this capability, we address the task of predicting future frame segmentation from a stream of ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	114,756
2009.03938	Distributed Economic Model Predictive Control -- Addressing Non-convexity Using Social Hierarchies	This paper introduces a novel concept for addressing non-convexity in the cost functions of distributed economic model predictive control (DEMPC) systems. Specifically, the proposed algorithm enables agents to self-organize into a hierarchy which determines the order in which control decisions are made. This concept is...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	194,923
2203.03091	Modeling Field-level Factor Interactions for Fashion Recommendation	Personalized fashion recommendation aims to explore patterns from historical interactions between users and fashion items and thereby predict the future ones. It is challenging due to the sparsity of the interaction data and the diversity of user preference in fashion. To tackle the challenge, this paper investigates m...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	true	283,963
1903.00958	End-to-End Game-Focused Learning of Adversary Behavior in Security Games	Stackelberg security games are a critical tool for maximizing the utility of limited defense resources to protect important targets from an intelligent adversary. Motivated by green security, where the defender may only observe an adversary's response to defense on a limited set of targets, we study the problem of lear...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	123,149
1907.05494	Entropy Estimation of Physically Unclonable Functions via Chow Parameters	A physically unclonable function (PUF) is an electronic circuit that produces an intrinsic identifier in response to a challenge. These identifiers depend on uncontrollable variations of the manufacturing process, which make them hard to predict or to replicate. Various security protocols leverage on such intrinsic ran...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	138,377
2111.04071	DVS: Deep Visibility Series and its Application in Construction Cost Index Forecasting	Time series forecasting is a hot spot in recent years. Visibility Graph (VG) algorithm is used for time series forecasting in previous research, but the forecasting effect is not as good as deep learning prediction methods such as methods based on Artificial Neural Network (ANN), Convolutional Neural Network (CNN) and ...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	265,370
2406.10311	CHiSafetyBench: A Chinese Hierarchical Safety Benchmark for Large Language Models	With the profound development of large language models(LLMs), their safety concerns have garnered increasing attention. However, there is a scarcity of Chinese safety benchmarks for LLMs, and the existing safety taxonomies are inadequate, lacking comprehensive safety detection capabilities in authentic Chinese scenario...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	464,356
2501.11097	Unit Region Encoding: A Unified and Compact Geometry-aware Representation for Floorplan Applications	We present the Unit Region Encoding of floorplans, which is a unified and compact geometry-aware encoding representation for various applications, ranging from interior space planning, floorplan metric learning to floorplan generation tasks. The floorplans are represented as the latent encodings on a set of boundary-ad...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	525,782
2106.03505	Self-supervised Depth Estimation Leveraging Global Perception and Geometric Smoothness Using On-board Videos	Self-supervised depth estimation has drawn much attention in recent years as it does not require labeled data but image sequences. Moreover, it can be conveniently used in various applications, such as autonomous driving, robotics, realistic navigation, and smart cities. However, extracting global contextual informatio...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	239,347
2409.20053	GUNDAM: Aligning Large Language Models with Graph Understanding	Large Language Models (LLMs) have achieved impressive results in processing text data, which has sparked interest in applying these models beyond textual data, such as graphs. In the field of graph learning, there is a growing interest in harnessing LLMs to comprehend and manipulate graph-structured data. Existing rese...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	492,975
1312.6533	A General, Fast, and Robust Implementation of the Time-Optimal Path Parameterization Algorithm	Finding the Time-Optimal Parameterization of a given Path (TOPP) subject to kinodynamic constraints is an essential component in many robotic theories and applications. The objective of this article is to provide a general, fast and robust implementation of this component. For this, we give a complete solution to the i...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	29,372
1202.3748	Conditional Restricted Boltzmann Machines for Structured Output Prediction	Conditional Restricted Boltzmann Machines (CRBMs) are rich probabilistic models that have recently been applied to a wide range of problems, including collaborative filtering, classification, and modeling motion capture data. While much progress has been made in training non-conditional RBMs, these algorithms are not a...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	14,420
1812.02035	Stochastic Model Pruning via Weight Dropping Away and Back	Deep neural networks have dramatically achieved great success on a variety of challenging tasks. However, most successful DNNs have an extremely complex structure, leading to extensive research on model compression.As a significant area of progress in model compression, traditional gradual pruning approaches involve an...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	115,666
1304.6172	Outage Probability in Arbitrarily-Shaped Finite Wireless Networks	This paper analyzes the outage performance in finite wireless networks. Unlike most prior works, which either assumed a specific network shape or considered a special location of the reference receiver, we propose two general frameworks for analytically computing the outage probability at any arbitrary location of an a...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	24,153
1610.02357	Xception: Deep Learning with Depthwise Separable Convolutions	We present an interpretation of Inception modules in convolutional neural networks as being an intermediate step in-between regular convolution and the depthwise separable convolution operation (a depthwise convolution followed by a pointwise convolution). In this light, a depthwise separable convolution can be underst...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	62,085
2411.17605	Distractor-free Generalizable 3D Gaussian Splatting	We present DGGS, a novel framework addressing the previously unexplored challenge of Distractor-free Generalizable 3D Gaussian Splatting (3DGS). It accomplishes two key objectives: fortifying generalizable 3DGS against distractor-laden data during both training and inference phases, while successfully extending cross-s...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	511,514
1706.01014	Nonconvex penalties with analytical solutions for one-bit compressive sensing	One-bit measurements widely exist in the real world, and they can be used to recover sparse signals. This task is known as the problem of learning halfspaces in learning theory and one-bit compressive sensing (1bit-CS) in signal processing. In this paper, we propose novel algorithms based on both convex and nonconvex s...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	74,735
2203.14768	Pruning In Time (PIT): A Lightweight Network Architecture Optimizer for Temporal Convolutional Networks	Temporal Convolutional Networks (TCNs) are promising Deep Learning models for time-series processing tasks. One key feature of TCNs is time-dilated convolution, whose optimization requires extensive experimentation. We propose an automatic dilation optimizer, which tackles the problem as a weight pruning on the time-ax...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	288,126
2304.07650	Understanding Developers Privacy Concerns Through Reddit Thread Analysis	With the growing global emphasis on regulating the protection of personal information and increasing user expectation of the same, developing with privacy in mind is becoming ever more important. In this paper, we study the concerns, questions, and solutions developers discuss on Reddit forums to enhance our understand...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	true	358,425
1304.0954	Labeling and Retrieval of Emotionally-Annotated Images using WordNet	Repositories of images with semantic and emotion content descriptions are valuable tools in many areas such as Affective Computing and Human-Computer Interaction, but they are also important in the development of multimodal searchable online databases. Ever growing number of image documents available on the Internet co...	true	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	23,426
2104.00273	Perspective, Survey and Trends: Public Driving Datasets and Toolsets for Autonomous Driving Virtual Test	Owing to the merits of early safety and reliability guarantee, autonomous driving virtual testing has recently gains increasing attention compared with closed-loop testing in real scenarios. Although the availability and quality of autonomous driving datasets and toolsets are the premise to diagnose the autonomous driv...	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	false	227,940
1503.04371	Uniform Random Number Generation from Markov Chains: Non-Asymptotic and Asymptotic Analyses	In this paper, we derive non-asymptotic achievability and converse bounds on the random number generation with/without side-information. Our bounds are efficiently computable in the sense that the computational complexity does not depend on the block length. We also characterize the asymptotic behaviors of the large de...	false	false	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	41,153
2407.20248	LAPIS: Language Model-Augmented Police Investigation System	Crime situations are race against time. An AI-assisted criminal investigation system, providing prompt but precise legal counsel is in need for police officers. We introduce LAPIS (Language Model Augmented Police Investigation System), an automated system that assists police officers to perform rational and legal inves...	false	false	false	false	true	false	false	false	true	false	false	false	false	true	false	false	false	false	477,106
2004.10572	It is time for Factor Graph Optimization for GNSS/INS Integration: Comparison between FGO and EKF	The recently proposed factor graph optimization (FGO) is adopted to integrate GNSS/INS attracted lots of attention and improved the performance over the existing EKF-based GNSS/INS integrations. However, a comprehensive comparison of those two GNSS/INS integration schemes in the urban canyon is not available. Moreover,...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	173,672
2103.16299	Generalized $b$-symbol weights of Linear Codes and $b$-symbol MDS Codes	Generalized pair weights of linear codes are generalizations of minimum symbol-pair weights, which were introduced by Liu and Pan \cite{LP} recently. Generalized pair weights can be used to characterize the ability of protecting information in the symbol-pair read wire-tap channels of type II. In this paper, we introdu...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	227,534
1310.7682	Contextualizing concepts using a mathematical generalization of the quantum formalism	We outline the rationale and preliminary results of using the state context property (SCOP) formalism, originally developed as a generalization of quantum mechanics, to describe the contextual manner in which concepts are evoked, used and combined to generate meaning. The quantum formalism was developed to cope with pr...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	28,053
2008.07948	Adaptive Distillation for Decentralized Learning from Heterogeneous Clients	This paper addresses the problem of decentralized learning to achieve a high-performance global model by asking a group of clients to share local models pre-trained with their own data resources. We are particularly interested in a specific case where both the client model architectures and data distributions are diver...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	192,266
2406.13625	Enhance the Image: Super Resolution using Artificial Intelligence in MRI	This chapter provides an overview of deep learning techniques for improving the spatial resolution of MRI, ranging from convolutional neural networks, generative adversarial networks, to more advanced models including transformers, diffusion models, and implicit neural representations. Our exploration extends beyond th...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	465,923
2103.00172	A Survey on Physarum Polycephalum Intelligent Foraging Behaviour and Bio-Inspired Applications	In recent years, research on Physarum polycephalum has become more popular after Nakagaki et al. (2000) performed their famous experiment showing that Physarum was able to find the shortest route through a maze. Subsequent researches have confirmed the ability of Physarum-inspired algorithms to solve a wide range of NP...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	222,174
2105.07837	An Experimental Analysis of Work-Life Balance Among The Employees using Machine Learning Classifiers	Researchers today have found out the importance of Artificial Intelligence, and Machine Learning in our daily lives, as well as they can be used to improve the quality of our lives as well as the cities and nations alike. An example of this is that it is currently speculated that ML can provide ways to relieve workers ...	false	false	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	235,572
2010.07037	Soil moisture map construction using microwave remote sensors and sequential data assimilation	Microwave remote sensors mounted on center pivot irrigation systems provide a feasible approach to obtain soil moisture information, in the form of water content maps, for the implementation of closed-loop irrigation. Major challenges such as significant time delays in the soil moisture measurements, the inability of t...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	200,689
1704.03169	Later-stage Minimum Bayes-Risk Decoding for Neural Machine Translation	For extended periods of time, sequence generation models rely on beam search algorithm to generate output sequence. However, the correctness of beam search degrades when the a model is over-confident about a suboptimal prediction. In this paper, we propose to perform minimum Bayes-risk (MBR) decoding for some extra ste...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	71,583
1310.4656	Maximizing Barber's bipartite modularity is also hard	Modularity introduced by Newman and Girvan [Phys. Rev. E 69, 026113 (2004)] is a quality function for community detection. Numerous methods for modularity maximization have been developed so far. In 2007, Barber [Phys. Rev. E 76, 066102 (2007)] introduced a variant of modularity called bipartite modularity which is app...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	true	27,830
2205.07337	Output-Feedback Path Planning with Robustness to State-Dependent Errors	We consider the problem of sample-based feedback motion planning from measurements affected by systematic errors. Our previous work presented output feedback controllers that use measurements from landmarks in the environment to navigate through a cell-decomposable environment using duality, Control Lyapunov and Barrie...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	296,565
2412.11106	Unpaired Multi-Domain Histopathology Virtual Staining using Dual Path Prompted Inversion	Virtual staining leverages computer-aided techniques to transfer the style of histochemically stained tissue samples to other staining types. In virtual staining of pathological images, maintaining strict structural consistency is crucial, as these images emphasize structural integrity more than natural images. Even sl...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	517,261
1111.1136	Universal MMSE Filtering With Logarithmic Adaptive Regret	We consider the problem of online estimation of a real-valued signal corrupted by oblivious zero-mean noise using linear estimators. The estimator is required to iteratively predict the underlying signal based on the current and several last noisy observations, and its performance is measured by the mean-square-error. ...	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	12,913
1808.08309	Trajectory Tracking Control of a Flexible Spine Robot, With and Without a Reference Input	The Underactuated Lightweight Tensegrity Robotic Assistive Spine (ULTRA Spine) project is an ongoing effort to develop a flexible, actuated backbone for quadruped robots. In this work, model-predictive control is used to track a trajectory in the robot's state space, in simulation. The state trajectory used here corres...	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	105,915
2008.09474	Deep Phase Correlation for End-to-End Heterogeneous Sensor Measurements Matching	The crucial step for localization is to match the current observation to the map. When the two sensor modalities are significantly different, matching becomes challenging. In this paper, we present an end-to-end deep phase correlation network (DPCN) to match heterogeneous sensor measurements. In DPCN, the primary compo...	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	192,725
2201.03614	SpectraNet: Learned Recognition of Artificial Satellites From High Contrast Spectroscopic Imagery	Effective space traffic management requires positive identification of artificial satellites. Current methods for extracting object identification from observed data require spatially resolved imagery which limits identification to objects in low earth orbits. Most artificial satellites, however, operate in geostationa...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	274,889
2405.15165	A Solution-based LLM API-using Methodology for Academic Information Seeking	Applying large language models (LLMs) for academic API usage shows promise in reducing researchers' academic information seeking efforts. However, current LLM API-using methods struggle with complex API coupling commonly encountered in academic queries. To address this, we introduce SoAy, a solution-based LLM API-using...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	true	456,781
2312.10101	A Review of Repository Level Prompting for LLMs	As coding challenges become more complex, recent advancements in Large Language Models (LLMs) have led to notable successes, such as achieving a 94.6\% solve rate on the HumanEval benchmark. Concurrently, there is an increasing commercial push for repository-level inline code completion tools, such as GitHub Copilot an...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	true	416,012
2202.08429	CHEX: Multiversion Replay with Ordered Checkpoints	In scientific computing and data science disciplines, it is often necessary to share application workflows and repeat results. Current tools containerize application workflows, and share the resulting container for repeating results. These tools, due to containerization, do improve sharing of results. However, they do ...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	280,870
2402.16459	Defending LLMs against Jailbreaking Attacks via Backtranslation	Although many large language models (LLMs) have been trained to refuse harmful requests, they are still vulnerable to jailbreaking attacks which rewrite the original prompt to conceal its harmful intent. In this paper, we propose a new method for defending LLMs against jailbreaking attacks by ``backtranslation''. Speci...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	432,581
1510.07176	On the Effect of Fronthaul Latency on ARQ in C-RAN Systems	In the Cloud Radio Access Network (C-RAN) architecture, a Control Unit (CU) implements the baseband processing functionalities of a cluster of Base Stations (BSs), which are connected to it through a fronthaul network. This architecture enables centralized processing at the CU, and hence the implementation of enhanced ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	48,176
1809.01622	Ranking RDF Instances in Degree-decoupled RDF Graphs	In the last decade, RDF emerged as a new kind of standardized data model, and a sizable body of knowledge from fields such as Information Retrieval was adapted to RDF graphs. One common task in graph databases is to define an importance score for nodes based on centrality measures, such as PageRank and HITS. The majori...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	106,854
2409.10081	Messy Code Makes Managing ML Pipelines Difficult? Just Let LLMs Rewrite the Code!	Machine learning (ML) applications that learn from data are increasingly used to automate impactful decisions. Unfortunately, these applications often fall short of adequately managing critical data and complying with upcoming regulations. A technical reason for the persistence of these issues is that the data pipeline...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	488,608
2406.02746	RATT: A Thought Structure for Coherent and Correct LLM Reasoning	Large Language Models (LLMs) gain substantial reasoning and decision-making capabilities from thought structures. However, existing methods such as Tree of Thought and Retrieval Augmented Thoughts often fall short in complex tasks due to the limitations of insufficient local retrieval of factual knowledge and inadequat...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	460,917
2008.07085	Multi-Task Learning for Interpretable Weakly Labelled Sound Event Detection	Weakly Labelled learning has garnered lot of attention in recent years due to its potential to scale Sound Event Detection (SED) and is formulated as Multiple Instance Learning (MIL) problem. This paper proposes a Multi-Task Learning (MTL) framework for learning from Weakly Labelled Audio data which encompasses the tra...	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	191,995
1508.01521	Automatic 3D Liver Segmentation Using Sparse Representation of Global and Local Image Information via Level Set Formulation	In this paper, a novel framework for automated liver segmentation via a level set formulation is presented. A sparse representation of both global (region-based) and local (voxel-wise) image information is embedded in a level set formulation to innovate a new cost function. Two dictionaries are build: A region-based fe...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	45,791
1912.10903	Spectral embedding of regularized block models	Spectral embedding is a popular technique for the representation of graph data. Several regularization techniques have been proposed to improve the quality of the embedding with respect to downstream tasks like clustering. In this paper, we explain on a simple block model the impact of the complete graph regularization...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	158,443
1811.04747	Reimplementation and Reinterpretation of the Copycat Project	We present the reinterpreted and reimplemented Copycat project, an architecture solving letter analogy domain problems. To support a flexible implementation change and rigor testing process, we propose a implementation method in DrRacket by using functional abstraction, naming system, initialization, and structural ref...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	113,161
1401.2838	GPS-ABC: Gaussian Process Surrogate Approximate Bayesian Computation	Scientists often express their understanding of the world through a computationally demanding simulation program. Analyzing the posterior distribution of the parameters given observations (the inverse problem) can be extremely challenging. The Approximate Bayesian Computation (ABC) framework is the standard statistical...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	29,787
2202.09648	Echofilter: A Deep Learning Segmentation Model Improves the Automation, Standardization, and Timeliness for Post-Processing Echosounder Data in Tidal Energy Streams	Understanding the abundance and distribution of fish in tidal energy streams is important to assess risks presented by introducing tidal energy devices to the habitat. However tidal current flows suitable for tidal energy are often highly turbulent, complicating the interpretation of echosounder data. The portion of th...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	281,265
1703.10518	Design of Soft Viterbi Algorithm Decoder Enhanced With Non-Transmittable Codewords for Storage Media	Viterbi Algorithm Decoder Enhanced with Non-transmittable Codewords is one of the best decoding algorithm which effectively improves forward error correction performance. HoweverViterbi decoder enhanced with NTCs is not yet designed to work in storage media devices. Currently Reed Solomon (RS) Algorithm is almost the d...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	70,925
2203.06906	PERT: Pre-training BERT with Permuted Language Model	Pre-trained Language Models (PLMs) have been widely used in various natural language processing (NLP) tasks, owing to their powerful text representations trained on large-scale corpora. In this paper, we propose a new PLM called PERT for natural language understanding (NLU). PERT is an auto-encoding model (like BERT) t...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	285,266
2102.09666	Dynamic curriculum learning via data parameters for noise robust keyword spotting	We propose dynamic curriculum learning via data parameters for noise robust keyword spotting. Data parameter learning has recently been introduced for image processing, where weight parameters, so-called data parameters, for target classes and instances are introduced and optimized along with model parameters. The data...	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	220,844
1701.01945	A Framework for Wasserstein-1-Type Metrics	We propose a unifying framework for generalising the Wasserstein-1 metric to a discrepancy measure between nonnegative measures of different mass. This generalization inherits the convexity and computational efficiency from the Wasserstein-1 metric, and it includes several previous approaches from the literature as spe...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	66,481
2401.08559	Multi-Track Timeline Control for Text-Driven 3D Human Motion Generation	Recent advances in generative modeling have led to promising progress on synthesizing 3D human motion from text, with methods that can generate character animations from short prompts and specified durations. However, using a single text prompt as input lacks the fine-grained control needed by animators, such as compos...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	true	421,935
1511.05610	Bounded Stability in Networked Systems with Parameter Mismatch and Adaptive Decentralized Estimation	Here, we study the ultimately bounded stability of network of mismatched systems using Lyapunov direct method. The upper bound on the error of oscillators from the center of the neighborhood is derived. Then the performance of an adaptive compensation via decentralized control is analyzed. Finally, the analytical resul...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	49,065
2011.11579	The Interconnectivity Vector: A Finite-Dimensional Vector Representation of Persistent Homology	Persistent Homology (PH) is a useful tool to study the underlying structure of a data set. Persistence Diagrams (PDs), which are 2D multisets of points, are a concise summary of the information found by studying the PH of a data set. However, PDs are difficult to incorporate into a typical machine learning workflow. To...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	207,870
2109.02054	Sensor Data Augmentation by Resampling for Contrastive Learning in Human Activity Recognition	While deep learning has contributed to the advancement of sensor-based Human Activity Recognition (HAR), it is usually a costly and challenging supervised task with the needs of a large amount of labeled data. To alleviate this issue, contrastive learning has been applied for sensor-based HAR. Data augmentation is an e...	true	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	253,619
1907.06022	Multiscale Principle of Relevant Information for Hyperspectral Image Classification	This paper proposes a novel architecture, termed multiscale principle of relevant information (MPRI), to learn discriminative spectral-spatial features for hyperspectral image (HSI) classification. MPRI inherits the merits of the principle of relevant information (PRI) to effectively extract multiscale information embe...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	138,503
2310.04813	Age of Information Guaranteed Scheduling for Asynchronous Status Updates in Collaborative Perception	We consider collaborative perception (CP) systems where a fusion center monitors various regions by multiple sources. The center has different age of information (AoI) constraints for different regions. Multi-view sensing data for a region generated by sources can be fused by the center for a reliable representation of...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	397,832

Subsets and Splits

No community queries yet

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