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
2007.14118	DeScarGAN: Disease-Specific Anomaly Detection with Weak Supervision	Anomaly detection and localization in medical images is a challenging task, especially when the anomaly exhibits a change of existing structures, e.g., brain atrophy or changes in the pleural space due to pleural effusions. In this work, we present a weakly supervised and detail-preserving method that is able to detect...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	189,310
1906.03994	Smart IoT Cameras for Crowd Analysis based on augmentation for automatic pedestrian detection, simulation and annotation	Smart video sensors for applications related to surveillance and security are IOT-based as they use Internet for various purposes. Such applications include crowd behaviour monitoring and advanced decision support systems operating and transmitting information over internet. The analysis of crowd and pedestrian behavio...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	134,564
1908.03544	Slepian-Bangs formula and Cramer Rao bound for circular and non-circular complex elliptical symmetric distributions	This paper is mainly dedicated to an extension of the Slepian-Bangs formula to non-circular complex elliptical symmetric (NC-CES) distributions, which is derived from a new stochastic representation theorem. This formula includes the non-circular complex Gaussian and the circular CES (CCES) distributions. Some general ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	141,257
2407.17875	Overcoming Binary Adversarial Optimisation with Competitive Coevolution	Co-evolutionary algorithms (CoEAs), which pair candidate designs with test cases, are frequently used in adversarial optimisation, particularly for binary test-based problems where designs and tests yield binary outcomes. The effectiveness of designs is determined by their performance against tests, and the value of te...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	476,157
2403.14679	Continual Learning by Three-Phase Consolidation	TPC (Three-Phase Consolidation) is here introduced as a simple but effective approach to continually learn new classes (and/or instances of known classes) while controlling forgetting of previous knowledge. Each experience (a.k.a. task) is learned in three phases characterized by different rules and learning dynamics, ...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	440,194
2309.13078	LPML: LLM-Prompting Markup Language for Mathematical Reasoning	In utilizing large language models (LLMs) for mathematical reasoning, addressing the errors in the reasoning and calculation present in the generated text by LLMs is a crucial challenge. In this paper, we propose a novel framework that integrates the Chain-of-Thought (CoT) method with an external tool (Python REPL). We...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	true	394,048
2501.14607	ReferDINO: Referring Video Object Segmentation with Visual Grounding Foundations	Referring video object segmentation (RVOS) aims to segment target objects throughout a video based on a text description. Despite notable progress in recent years, current RVOS models remain struggle to handle complicated object descriptions due to their limited video-language understanding. To address this limitation,...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	527,184
2412.17098	DreamOmni: Unified Image Generation and Editing	Currently, the success of large language models (LLMs) illustrates that a unified multitasking approach can significantly enhance model usability, streamline deployment, and foster synergistic benefits across different tasks. However, in computer vision, while text-to-image (T2I) models have significantly improved gene...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	519,828
2404.08371	Code Generation and Performance Engineering for Matrix-Free Finite Element Methods on Hybrid Tetrahedral Grids	This paper introduces a code generator designed for node-level optimized, extreme-scalable, matrix-free finite element operators on hybrid tetrahedral grids. It optimizes the local evaluation of bilinear forms through various techniques including tabulation, relocation of loop invariants, and inter-element vectorizatio...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	446,216
2110.09147	BEAMetrics: A Benchmark for Language Generation Evaluation Evaluation	Natural language processing (NLP) systems are increasingly trained to generate open-ended text rather than classifying between responses. This makes research on evaluation metrics for generated language -- functions that score system output given the context and/or human reference responses -- of critical importance. H...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	261,697
2312.06592	Flexible visual prompts for in-context learning in computer vision	In this work, we address in-context learning (ICL) for the task of image segmentation, introducing a novel approach that adapts a modern Video Object Segmentation (VOS) technique for visual in-context learning. This adaptation is inspired by the VOS method's ability to efficiently and flexibly learn objects from a few ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	414,586
1909.03792	Tehran Stock Exchange Prediction Using Sentiment Analysis of Online Textual Opinions	In this paper, we investigate the impact of the social media data in predicting the Tehran Stock Exchange (TSE) variables for the first time. We consider the closing price and daily return of three different stocks for this investigation. We collected our social media data from Sahamyab.com/stocktwits for about three m...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	144,603
2302.13848	ELITE: Encoding Visual Concepts into Textual Embeddings for Customized Text-to-Image Generation	In addition to the unprecedented ability in imaginary creation, large text-to-image models are expected to take customized concepts in image generation. Existing works generally learn such concepts in an optimization-based manner, yet bringing excessive computation or memory burden. In this paper, we instead propose a ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	348,074
2012.10191	Reconstructing a single-head formula to facilitate logical forgetting	Logical forgetting may take exponential time in general, but it does not when its input is a single-head propositional definite Horn formula. Single-head means that no variable is the head of multiple clauses. An algorithm to make a formula single-head if possible is shown. It improves over a previous one by being comp...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	212,282
2106.09207	On the Power of Preconditioning in Sparse Linear Regression	Sparse linear regression is a fundamental problem in high-dimensional statistics, but strikingly little is known about how to efficiently solve it without restrictive conditions on the design matrix. We consider the (correlated) random design setting, where the covariates are independently drawn from a multivariate Gau...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	241,575
2109.00202	Federated Learning: Issues in Medical Application	Since the federated learning, which makes AI learning possible without moving local data around, was introduced by google in 2017 it has been actively studied particularly in the field of medicine. In fact, the idea of machine learning in AI without collecting data from local clients is very attractive because data rem...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	253,043
2008.06063	Full-Duplex Amplify-and-Forward MIMO Relaying: Impairments Aware Design and Performance Analysis	Full-Duplex (FD) Amplify-and-Forward (AF) Multiple-Input Multiple-Output (MIMO) relaying has been the focus of several recent studies, due to the potential for achieving a higher spectral efficiency and lower latency, together with inherent processing simplicity. However, when the impact of hardware distortions are con...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	191,686
1804.06896	A Multi-task Selected Learning Approach for Solving 3D Flexible Bin Packing Problem	A 3D flexible bin packing problem (3D-FBPP) arises from the process of warehouse packing in e-commerce. An online customer's order usually contains several items and needs to be packed as a whole before shipping. In particular, 5% of tens of millions of packages are using plastic wrapping as outer packaging every day, ...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	95,406
2212.02011	PointCaM: Cut-and-Mix for Open-Set Point Cloud Learning	Point cloud learning is receiving increasing attention, however, most existing point cloud models lack the practical ability to deal with the unavoidable presence of unknown objects. This paper mainly discusses point cloud learning under open-set settings, where we train the model without data from unknown classes and ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	334,647
2009.06375	EdinburghNLP at WNUT-2020 Task 2: Leveraging Transformers with Generalized Augmentation for Identifying Informativeness in COVID-19 Tweets	Twitter and, in general, social media has become an indispensable communication channel in times of emergency. The ubiquitousness of smartphone gadgets enables people to declare an emergency observed in real-time. As a result, more agencies are interested in programmatically monitoring Twitter (disaster relief organiza...	false	false	false	true	false	true	true	false	true	false	false	false	false	false	false	false	false	false	195,619
2112.02303	An Annotated Video Dataset for Computing Video Memorability	Using a collection of publicly available links to short form video clips of an average of 6 seconds duration each, 1,275 users manually annotated each video multiple times to indicate both long-term and short-term memorability of the videos. The annotations were gathered as part of an online memory game and measured a ...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	269,805
1703.04529	Task-based End-to-end Model Learning in Stochastic Optimization	With the increasing popularity of machine learning techniques, it has become common to see prediction algorithms operating within some larger process. However, the criteria by which we train these algorithms often differ from the ultimate criteria on which we evaluate them. This paper proposes an end-to-end approach fo...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	69,915
2202.02095	Fixed-Point Code Synthesis For Neural Networks	Over the last few years, neural networks have started penetrating safety critical systems to take decisions in robots, rockets, autonomous driving car, etc. A problem is that these critical systems often have limited computing resources. Often, they use the fixed-point arithmetic for its many advantages (rapidity, comp...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	278,695
2406.04680	MTS-Net: Dual-Enhanced Positional Multi-Head Self-Attention for 3D CT Diagnosis of May-Thurner Syndrome	May-Thurner Syndrome (MTS), also known as iliac vein compression syndrome or Cockett's syndrome, is a condition potentially impacting over 20 percent of the population, leading to an increased risk of iliofemoral deep venous thrombosis. In this paper, we present a 3D-based deep learning approach called MTS-Net for diag...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	461,793
2311.05137	Differentiable Fluid Physics Parameter Identification Via Stirring	Fluid interactions permeate daily human activities, with properties like density and viscosity playing pivotal roles in household tasks. While density estimation is straightforward through Archimedes' principle, viscosity poses a more intricate challenge, especially given the varied behaviors of Newtonian and non-Newto...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	406,485
1901.09916	Clustered Millimeter Wave Networks with Non-Orthogonal Multiple Access	We introduce clustered millimeter wave networks with invoking non-orthogonal multiple access~(NOMA) techniques, where the NOMA users are modeled as Poisson cluster processes and each cluster contains a base station (BS) located at the center. To provide realistic directional beamforming, an actual antenna array pattern...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	119,879
2104.09396	Continual Learning in Sensor-based Human Activity Recognition: an Empirical Benchmark Analysis	Sensor-based human activity recognition (HAR), i.e., the ability to discover human daily activity patterns from wearable or embedded sensors, is a key enabler for many real-world applications in smart homes, personal healthcare, and urban planning. However, with an increasing number of applications being deployed, an i...	true	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	231,224
1905.03304	Deep Closest Point: Learning Representations for Point Cloud Registration	Point cloud registration is a key problem for computer vision applied to robotics, medical imaging, and other applications. This problem involves finding a rigid transformation from one point cloud into another so that they align. Iterative Closest Point (ICP) and its variants provide simple and easily-implemented iter...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	130,171
2407.16803	C3T: Cross-modal Transfer Through Time for Human Action Recognition	In order to unlock the potential of diverse sensors, we investigate a method to transfer knowledge between modalities using the structure of a unified multimodal representation space for Human Action Recognition (HAR). We formalize and explore an understudied cross-modal transfer setting we term Unsupervised Modality A...	true	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	475,727
1710.11341	Global Rank Estimation	In real world complex networks, the importance of a node depends on two important parameters: 1. characteristics of the node, and 2. the context of the given application. The current literature contains several centrality measures that have been defined to measure the importance of a node based on the given application...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	83,574
2201.04796	CFNet: Learning Correlation Functions for One-Stage Panoptic Segmentation	Recently, there is growing attention on one-stage panoptic segmentation methods which aim to segment instances and stuff jointly within a fully convolutional pipeline efficiently. However, most of the existing works directly feed the backbone features to various segmentation heads ignoring the demands for semantic and ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	275,196
1712.09867	Future Frame Prediction for Anomaly Detection -- A New Baseline	Anomaly detection in videos refers to the identification of events that do not conform to expected behavior. However, almost all existing methods tackle the problem by minimizing the reconstruction errors of training data, which cannot guarantee a larger reconstruction error for an abnormal event. In this paper, we pro...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	87,420
1802.06079	Global-scale phylogenetic linguistic inference from lexical resources	Automatic phylogenetic inference plays an increasingly important role in computational historical linguistics. Most pertinent work is currently based on expert cognate judgments. This limits the scope of this approach to a small number of well-studied language families. We used machine learning techniques to compile da...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	90,585
2402.11573	BGE Landmark Embedding: A Chunking-Free Embedding Method For Retrieval Augmented Long-Context Large Language Models	Large language models (LLMs) call for extension of context to handle many critical applications. However, the existing approaches are prone to expensive costs and inferior quality of context extension. In this work, we proposeExtensible Embedding, which realizes high-quality extension of LLM's context with strong flexi...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	430,468
2111.02767	RLDS: an Ecosystem to Generate, Share and Use Datasets in Reinforcement Learning	We introduce RLDS (Reinforcement Learning Datasets), an ecosystem for recording, replaying, manipulating, annotating and sharing data in the context of Sequential Decision Making (SDM) including Reinforcement Learning (RL), Learning from Demonstrations, Offline RL or Imitation Learning. RLDS enables not only reproducib...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	264,966
2006.12040	Clinical Predictive Keyboard using Statistical and Neural Language Modeling	A language model can be used to predict the next word during authoring, to correct spelling or to accelerate writing (e.g., in sms or emails). Language models, however, have only been applied in a very small scale to assist physicians during authoring (e.g., discharge summaries or radiology reports). But along with the...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	183,456
1912.00651	An Investigation of Biases in Web Search Engine Query Suggestions	Survey-based studies suggest that search engines are trusted more than social media or even traditional news, although cases of false information or defamation are known. In this study, we analyze query suggestion features of three search engines to see if these features introduce some bias into the query and search pr...	false	false	false	false	false	true	false	false	false	false	false	false	false	true	false	false	false	false	155,846
2409.13544	Graph Similarity Regularized Softmax for Semi-Supervised Node Classification	Graph Neural Networks (GNNs) are powerful deep learning models designed for graph-structured data, demonstrating effectiveness across a wide range of applications.The softmax function is the most commonly used classifier for semi-supervised node classification. However, the softmax function lacks spatial information of...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	490,040
1904.02614	Sampling Limits for Electron Tomography with Sparsity-exploiting Reconstructions	Electron tomography (ET) has become a standard technique for 3D characterization of materials at the nano-scale. Traditional reconstruction algorithms such as weighted back projection suffer from disruptive artifacts with insufficient projections. Popularized by compressed sensing, sparsity-exploiting algorithms have b...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	126,473
2102.05793	Lenient Regret and Good-Action Identification in Gaussian Process Bandits	In this paper, we study the problem of Gaussian process (GP) bandits under relaxed optimization criteria stating that any function value above a certain threshold is "good enough". On the theoretical side, we study various {\em lenient regret} notions in which all near-optimal actions incur zero penalty, and provide up...	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	219,547
2406.02171	A Multipurpose Interface for Close- and Far-Proximity Control of Mobile Collaborative Robots	This letter introduces an innovative visuo-haptic interface to control Mobile Collaborative Robots (MCR). Thanks to a passive detachable mechanism, the interface can be attached/detached from a robot, offering two control modes: local control (attached) and teleoperation (detached). These modes are integrated with a ro...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	460,650
1909.04002	The Trumpiest Trump? Identifying a Subject's Most Characteristic Tweets	The sequence of documents produced by any given author varies in style and content, but some documents are more typical or representative of the source than others. We quantify the extent to which a given short text is characteristic of a specific person, using a dataset of tweets from fifteen celebrities. Such analysi...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	144,670
2311.09269	NormNet: Scale Normalization for 6D Pose Estimation in Stacked Scenarios	Existing Object Pose Estimation (OPE) methods for stacked scenarios are not robust to changes in object scale. This paper proposes a new 6DoF OPE network (NormNet) for different scale objects in stacked scenarios. Specifically, each object's scale is first learned with point-wise regression. Then, all objects in the st...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	408,078
2312.16752	Relationships Between Necessary Conditions for Feedback Stabilizability	The author's extensions of Brockett's and Coron's necessary conditions for stabilizability are shown to be independent in the fiber bundle picture of control, but the latter is shown to be stronger in the vector bundle picture if the state space is orientable and the Cech-Euler characteristic of the set to be stabilize...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	418,494
2304.11744	SketchXAI: A First Look at Explainability for Human Sketches	This paper, for the very first time, introduces human sketches to the landscape of XAI (Explainable Artificial Intelligence). We argue that sketch as a ``human-centred'' data form, represents a natural interface to study explainability. We focus on cultivating sketch-specific explainability designs. This starts by iden...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	359,950
2303.02407	Local Path Planning among Pushable Objects based on Reinforcement Learning	In this paper, we introduce a method to deal with the problem of robot local path planning among pushable objects -- an open problem in robotics. In particular, we achieve that by training multiple agents simultaneously in a physics-based simulation environment, utilizing an Advantage Actor-Critic algorithm coupled wit...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	349,344
2401.02445	Social and Economic Impact Analysis of Solar Mini-Grids in Rural Africa: A Cohort Study from Kenya and Nigeria	This study presents the first comprehensive analysis of the social and economic effects of solar mini-grids in rural African settings, specifically in Kenya and Nigeria. A group of 2,658 household heads and business owners connected to mini-grids over the last five years were interviewed both before and one year after ...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	419,724
2007.15217	Key Frame Proposal Network for Efficient Pose Estimation in Videos	Human pose estimation in video relies on local information by either estimating each frame independently or tracking poses across frames. In this paper, we propose a novel method combining local approaches with global context. We introduce a light weighted, unsupervised, key frame proposal network (K-FPN) to select inf...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	189,608
2312.15809	A Closed-Loop Multi-perspective Visual Servoing Approach with Reinforcement Learning	Traditional visual servoing methods suffer from serving between scenes from multiple perspectives, which humans can complete with visual signals alone. In this paper, we investigated how multi-perspective visual servoing could be solved under robot-specific constraints, including self-collision, singularity problems. W...	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	false	418,135
2310.07821	Non-autoregressive Text Editing with Copy-aware Latent Alignments	Recent work has witnessed a paradigm shift from Seq2Seq to Seq2Edit in the field of text editing, with the aim of addressing the slow autoregressive inference problem posed by the former. Despite promising results, Seq2Edit approaches still face several challenges such as inflexibility in generation and difficulty in g...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	399,138
1002.2755	Multibiometrics Belief Fusion	This paper proposes a multimodal biometric system through Gaussian Mixture Model (GMM) for face and ear biometrics with belief fusion of the estimated scores characterized by Gabor responses and the proposed fusion is accomplished by Dempster-Shafer (DS) decision theory. Face and ear images are convolved with Gabor wav...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	5,699
1510.06375	Towards Direct Medical Image Analysis without Segmentation	Direct methods have recently emerged as an effective and efficient tool in automated medical image analysis and become a trend to solve diverse challenging tasks in clinical practise. Compared to traditional methods, direct methods are of much more clinical significance by straightly targeting to the final clinical goa...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	48,105
2302.14035	The ROOTS Search Tool: Data Transparency for LLMs	ROOTS is a 1.6TB multilingual text corpus developed for the training of BLOOM, currently the largest language model explicitly accompanied by commensurate data governance efforts. In continuation of these efforts, we present the ROOTS Search Tool: a search engine over the entire ROOTS corpus offering both fuzzy and exa...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	348,134
1712.01235	On the Real-time Vehicle Placement Problem	Motivated by ride-sharing platforms' efforts to reduce their riders' wait times for a vehicle, this paper introduces a novel problem of placing vehicles to fulfill real-time pickup requests in a spatially and temporally changing environment. The real-time nature of this problem makes it fundamentally different from oth...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	86,069
2404.01154	Uncovering the Text Embedding in Text-to-Image Diffusion Models	The correspondence between input text and the generated image exhibits opacity, wherein minor textual modifications can induce substantial deviations in the generated image. While, text embedding, as the pivotal intermediary between text and images, remains relatively underexplored. In this paper, we address this resea...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	443,280
2111.09353	Case study of SARS-CoV-2 transmission risk assessment in indoor environments using cloud computing resources	Complex flow simulations are conventionally performed on HPC clusters. However, the limited availability of HPC resources and steep learning curve of executing on traditional supercomputer infrastructure has drawn attention towards deploying flow simulation software on the cloud. We showcase how a complex computational...	false	true	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	true	266,984
2010.03957	Transformers for Modeling Physical Systems	Transformers are widely used in natural language processing due to their ability to model longer-term dependencies in text. Although these models achieve state-of-the-art performance for many language related tasks, their applicability outside of the natural language processing field has been minimal. In this work, we ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	199,570
2410.11627	Tokenization and Morphology in Multilingual Language Models: A Comparative Analysis of mT5 and ByT5	Morphology is a crucial factor for multilingual language modeling as it poses direct challenges for tokenization. Here, we seek to understand how tokenization influences the morphological knowledge encoded in multilingual language models. Specifically, we capture the impact of tokenization by contrasting two multilingu...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	498,649
1703.02419	Probabilistic learning of nonlinear dynamical systems using sequential Monte Carlo	Probabilistic modeling provides the capability to represent and manipulate uncertainty in data, models, predictions and decisions. We are concerned with the problem of learning probabilistic models of dynamical systems from measured data. Specifically, we consider learning of probabilistic nonlinear state-space models....	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	69,551
1609.08868	Reliability of universal decoding based on vector-quantized codewords	Motivated by applications of biometric identification and content identification systems, we consider the problem of random coding for channels, where each codeword undergoes lossy compression (vector quantization), and where the decoder bases its decision only on the compressed codewords and the channel output, which ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	61,645
2102.08867	Hypergraph Laplacians in Diffusion Framework	Networks are important structures used to model complex systems where interactions take place. In a basic network model, entities are represented as nodes, and interaction and relations among them are represented as edges. However, in a complex system, we cannot describe all relations as pairwise interactions, rather s...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	220,595
2311.15896	Data Generation for Post-OCR correction of Cyrillic handwriting	This paper introduces a novel approach to post-Optical Character Recognition Correction (POC) for handwritten Cyrillic text, addressing a significant gap in current research methodologies. This gap is due to the lack of large text corporas that provide OCR errors for further training of language-based POC models, which...	false	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	410,676
2308.00331	Target Search and Navigation in Heterogeneous Robot Systems with Deep Reinforcement Learning	Collaborative heterogeneous robot systems can greatly improve the efficiency of target search and navigation tasks. In this paper, we design a heterogeneous robot system consisting of a UAV and a UGV for search and rescue missions in unknown environments. The system is able to search for targets and navigate to them in...	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	false	382,907
2202.12448	Deep neural networks for fine-grained surveillance of overdose mortality	Surveillance of drug overdose deaths relies on death certificates for identification of the substances that caused death. Drugs and drug classes can be identified through the International Classification of Diseases, 10th Revision (ICD-10) codes present on death certificates. However, ICD-10 codes do not always provide...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	282,244
2310.09766	Pseudo-Bayesian Optimization	Bayesian Optimization is a popular approach for optimizing expensive black-box functions. Its key idea is to use a surrogate model to approximate the objective and, importantly, quantify the associated uncertainty that allows a sequential search of query points that balance exploitation-exploration. Gaussian process (G...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	399,938
2311.17243	PHG-Net: Persistent Homology Guided Medical Image Classification	Modern deep neural networks have achieved great successes in medical image analysis. However, the features captured by convolutional neural networks (CNNs) or Transformers tend to be optimized for pixel intensities and neglect key anatomical structures such as connected components and loops. In this paper, we propose a...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	411,217
2502.01654	Predicting concentration levels of air pollutants by transfer learning and recurrent neural network	Air pollution (AP) poses a great threat to human health, and people are paying more attention than ever to its prediction. Accurate prediction of AP helps people to plan for their outdoor activities and aids protecting human health. In this paper, long-short term memory (LSTM) recurrent neural networks (RNNs) have been...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	true	false	false	529,960
2305.00911	SRPT vs Smith Predictor for Vehicle Teleoperation	Vehicle teleoperation has potential applications in fallback solutions for autonomous vehicles, remote delivery services, and hazardous operations. However, network delays and limited situational awareness can compromise teleoperation performance and increase the cognitive workload of human operators. To address these ...	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	361,483
1607.06439	Mobility-Aware Modeling and Analysis of Dense Cellular Networks with C-plane/U-plane Split Architecture	The unrelenting increase in the population of mobile users and their traffic demands drive cellular network operators to densify their network infrastructure. Network densification shrinks the footprint of base stations (BSs) and reduces the number of users associated with each BS, leading to an improved spatial freque...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	58,896
2309.06801	Defensive Alliances in Signed Networks	The analysis of (social) networks and multi-agent systems is a central theme in Artificial Intelligence. Some line of research deals with finding groups of agents that could work together to achieve a certain goal. To this end, different notions of so-called clusters or communities have been introduced in the literatur...	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	false	false	true	391,550
1804.02176	Monocular Semantic Occupancy Grid Mapping with Convolutional Variational Encoder-Decoder Networks	In this work, we research and evaluate end-to-end learning of monocular semantic-metric occupancy grid mapping from weak binocular ground truth. The network learns to predict four classes, as well as a camera to bird's eye view mapping. At the core, it utilizes a variational encoder-decoder network that encodes the fro...	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	94,356
2208.03569	Constrained self-supervised method with temporal ensembling for fiber bundle detection on anatomic tracing data	Anatomic tracing data provides detailed information on brain circuitry essential for addressing some of the common errors in diffusion MRI tractography. However, automated detection of fiber bundles on tracing data is challenging due to sectioning distortions, presence of noise and artifacts and intensity/contrast vari...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	311,827
2405.19121	Spatio-Spectral Graph Neural Networks	Spatial Message Passing Graph Neural Networks (MPGNNs) are widely used for learning on graph-structured data. However, key limitations of l-step MPGNNs are that their "receptive field" is typically limited to the l-hop neighborhood of a node and that information exchange between distant nodes is limited by over-squashi...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	458,760
2105.12202	Context-Sensitive Visualization of Deep Learning Natural Language Processing Models	The introduction of Transformer neural networks has changed the landscape of Natural Language Processing (NLP) during the last years. So far, none of the visualization systems has yet managed to examine all the facets of the Transformers. This gave us the motivation of the current work. We propose a new NLP Transformer...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	236,932
2110.14355	Transfer learning with causal counterfactual reasoning in Decision Transformers	The ability to adapt to changes in environmental contingencies is an important challenge in reinforcement learning. Indeed, transferring previously acquired knowledge to environments with unseen structural properties can greatly enhance the flexibility and efficiency by which novel optimal policies may be constructed. ...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	263,509
2010.00312	On two conjectures about the intersection distribution	Recently, S. Li and A. Pott\cite{LP} proposed a new concept of intersection distribution concerning the interaction between the graph $\{(x,f(x))~\|~x\in\F_{q}\}$ of $f$ and the lines in the classical affine plane $AG(2,q)$. Later, G. Kyureghyan, et al.\cite{KLP} proceeded to consider the next simplest case and derive t...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	198,250
1903.01612	Defense Against Adversarial Images using Web-Scale Nearest-Neighbor Search	A plethora of recent work has shown that convolutional networks are not robust to adversarial images: images that are created by perturbing a sample from the data distribution as to maximize the loss on the perturbed example. In this work, we hypothesize that adversarial perturbations move the image away from the image...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	123,297
2011.06146	Learning Models for Actionable Recourse	As machine learning models are increasingly deployed in high-stakes domains such as legal and financial decision-making, there has been growing interest in post-hoc methods for generating counterfactual explanations. Such explanations provide individuals adversely impacted by predicted outcomes (e.g., an applicant deni...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	206,145
2412.08947	Selective Visual Prompting in Vision Mamba	Pre-trained Vision Mamba (Vim) models have demonstrated exceptional performance across various computer vision tasks in a computationally efficient manner, attributed to their unique design of selective state space models. To further extend their applicability to diverse downstream vision tasks, Vim models can be adapt...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	516,291
1503.07940	Competitive Distribution Estimation	Estimating an unknown distribution from its samples is a fundamental problem in statistics. The common, min-max, formulation of this goal considers the performance of the best estimator over all distributions in a class. It shows that with $n$ samples, distributions over $k$ symbols can be learned to a KL divergence th...	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	true	41,530
2202.03202	One-Year In: COVID-19 Research at the International Level in CORD-19 Data	The appearance of a novel coronavirus in late 2019 radically changed the community of researchers working on coronaviruses since the 2002 SARS epidemic. In 2020, coronavirus-related publications grew by 20 times over the previous two years, with 130,000 more researchers publishing on related topics. The United States, ...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	true	279,116
cmp-lg/9607009	Semantic-based Transfer	This article presents a new semantic-based transfer approach developed and applied within the Verbmobil Machine Translation project. We give an overview of the declarative transfer formalism together with its procedural realization. Our approach is discussed and compared with several other approaches from the MT litera...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	536,607
2405.19570	Distributed Online Planning for Min-Max Problems in Networked Markov Games	Min-max problems are important in multi-agent sequential decision-making because they improve the performance of the worst-performing agent in the network. However, solving the multi-agent min-max problem is challenging. We propose a modular, distributed, online planning-based algorithm that is able to approximate the ...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	458,954
2207.14408	Deep learning for understanding multilabel imbalanced Chest X-ray datasets	Over the last few years, convolutional neural networks (CNNs) have dominated the field of computer vision thanks to their ability to extract features and their outstanding performance in classification problems, for example in the automatic analysis of X-rays. Unfortunately, these neural networks are considered black-b...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	310,573
2311.06368	The AeroSonicDB (YPAD-0523) Dataset for Acoustic Detection and Classification of Aircraft	The time and expense required to collect and label audio data has been a prohibitive factor in the availability of domain specific audio datasets. As the predictive specificity of a classifier depends on the specificity of the labels it is trained on, it follows that finely-labelled datasets are crucial for advances in...	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	406,908
1202.5477	Analyzing Tag Distributions in Folksonomies for Resource Classification	Recent research has shown the usefulness of social tags as a data source to feed resource classification. Little is known about the effect of settings on folksonomies created on social tagging systems. In this work, we consider the settings of social tagging systems to further understand tag distributions in folksonomi...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	true	14,559
2305.05113	Object-Centric Alignments	Processes tend to interact with other processes and operate on various objects of different types. These objects can influence each other creating dependencies between sub-processes. Analyzing the conformance of such complex processes challenges traditional conformance-checking approaches because they assume a single-c...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	363,011
2402.12331	Generating Survival Interpretable Trajectories and Data	A new model for generating survival trajectories and data based on applying an autoencoder of a specific structure is proposed. It solves three tasks. First, it provides predictions in the form of the expected event time and the survival function for a new generated feature vector on the basis of the Beran estimator. S...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	430,805
2305.05485	Resilient Temporal Logic Planning in the Presence of Robot Failures	Several task and motion planning algorithms have been proposed recently to design paths for mobile robot teams with collaborative high-level missions specified using formal languages, such as Linear Temporal Logic (LTL). However, the designed paths often lack reactivity to failures of robot capabilities (e.g., sensing,...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	363,169
2309.16228	Brand Network Booster: A new system for improving brand connectivity	This paper presents a new decision support system offered for an in-depth analysis of semantic networks, which can provide insights for a better exploration of a brand's image and the improvement of its connectivity. In terms of network analysis, we show that this goal is achieved by solving an extended version of the ...	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	false	false	true	395,273
2201.06783	Label-dependent and event-guided interpretable disease risk prediction using EHRs	Electronic health records (EHRs) contain patients' heterogeneous data that are collected from medical providers involved in the patient's care, including medical notes, clinical events, laboratory test results, symptoms, and diagnoses. In the field of modern healthcare, predicting whether patients would experience any ...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	275,834
2010.05436	Leveraging the Capabilities of Connected and Autonomous Vehicles and Multi-Agent Reinforcement Learning to Mitigate Highway Bottleneck Congestion	Active Traffic Management strategies are often adopted in real-time to address such sudden flow breakdowns. When queuing is imminent, Speed Harmonization (SH), which adjusts speeds in upstream traffic to mitigate traffic showckwaves downstream, can be applied. However, because SH depends on driver awareness and complia...	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	200,130
2406.19756	Structure-aware World Model for Probe Guidance via Large-scale Self-supervised Pre-train	The complex structure of the heart leads to significant challenges in echocardiography, especially in acquisition cardiac ultrasound images. Successful echocardiography requires a thorough understanding of the structures on the two-dimensional plane and the spatial relationships between planes in three-dimensional spac...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	468,551
2202.06406	Visual Sound Localization in the Wild by Cross-Modal Interference Erasing	The task of audio-visual sound source localization has been well studied under constrained scenes, where the audio recordings are clean. However, in real-world scenarios, audios are usually contaminated by off-screen sound and background noise. They will interfere with the procedure of identifying desired sources and b...	false	false	true	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	280,208
2106.05769	End-to-End Transmission Analysis of Simultaneous Wireless Information and Power Transfer using Resonant Beam	Integrating the wireless power transfer (WPT) technology into the wireless communication system has been important for operational cost saving and power-hungry problem solving of electronic devices. In this paper, we propose a resonant beam simultaneous wireless information and power transfer (RB-SWIPT) system, which u...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	240,222
1907.05316	Computing sharp recovery structures for Locally Recoverable codes	A locally recoverable code is an error-correcting code such that any erasure in a single coordinate of a codeword can be recovered from a small subset of other coordinates. In this article we develop an algorithm that computes a recovery structure as concise posible for an arbitrary linear code $\mathcal{C}$ and a reco...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	138,333
2303.11909	The Multiscale Surface Vision Transformer	Surface meshes are a favoured domain for representing structural and functional information on the human cortex, but their complex topology and geometry pose significant challenges for deep learning analysis. While Transformers have excelled as domain-agnostic architectures for sequence-to-sequence learning, the quadra...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	353,058
2407.00888	Papez: Resource-Efficient Speech Separation with Auditory Working Memory	Transformer-based models recently reached state-of-the-art single-channel speech separation accuracy; However, their extreme computational load makes it difficult to deploy them in resource-constrained mobile or IoT devices. We thus present Papez, a lightweight and computation-efficient single-channel speech separation...	false	false	true	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	469,040
2412.01113	Think-to-Talk or Talk-to-Think? When LLMs Come Up with an Answer in Multi-Step Reasoning	This study investigates the internal reasoning mechanism of language models during symbolic multi-step reasoning, motivated by the question of whether chain-of-thought (CoT) outputs are faithful to the model's internals. Specifically, we inspect when they internally determine their answers, particularly before or after...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	512,960
1612.01650	Closed-Chain Manipulation of Large Objects by Multi-Arm Robotic Systems	Closed kinematic chains are created whenever multiple robot arms concurrently manipulate a single object. The closed-chain constraint, when coupled with robot joint limits, dramatically changes the connectivity of the configuration space. We propose a regrasping move, termed "IK-switch", which allows efficiently bridgi...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	65,123

Subsets and Splits

No community queries yet

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