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
2202.12350	DoCoGen: Domain Counterfactual Generation for Low Resource Domain Adaptation	Natural language processing (NLP) algorithms have become very successful, but they still struggle when applied to out-of-distribution examples. In this paper we propose a controllable generation approach in order to deal with this domain adaptation (DA) challenge. Given an input text example, our DoCoGen algorithm gene...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	282,196
2011.02744	Deep learning for biomedical photoacoustic imaging: A review	Photoacoustic imaging (PAI) is a promising emerging imaging modality that enables spatially resolved imaging of optical tissue properties up to several centimeters deep in tissue, creating the potential for numerous exciting clinical applications. However, extraction of relevant tissue parameters from the raw data requ...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	205,021
2501.05033	Towards High-Performance Network Coding: FPGA Acceleration With Bounded-value Generators	Network coding enhances performance in network communications and distributed storage by increasing throughput and robustness while reducing latency. Batched Sparse (BATS) codes are a class of capacity-achieving network codes, but their practical applications are hindered by their structure, computational intensity, an...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	523,441
cs/9907003	Annotation graphs as a framework for multidimensional linguistic data analysis	In recent work we have presented a formal framework for linguistic annotation based on labeled acyclic digraphs. These `annotation graphs' offer a simple yet powerful method for representing complex annotation structures incorporating hierarchy and overlap. Here, we motivate and illustrate our approach using discourse-...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	540,534
2310.14883	Non-autoregressive Streaming Transformer for Simultaneous Translation	Simultaneous machine translation (SiMT) models are trained to strike a balance between latency and translation quality. However, training these models to achieve high quality while maintaining low latency often leads to a tendency for aggressive anticipation. We argue that such issue stems from the autoregressive archi...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	402,061
2309.11576	Examining the Limitations of Computational Rumor Detection Models Trained on Static Datasets	A crucial aspect of a rumor detection model is its ability to generalize, particularly its ability to detect emerging, previously unknown rumors. Past research has indicated that content-based (i.e., using solely source posts as input) rumor detection models tend to perform less effectively on unseen rumors. At the sam...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	393,453
1711.00905	Sparse-View X-Ray CT Reconstruction Using $\ell_1$ Prior with Learned Transform	A major challenge in X-ray computed tomography (CT) is reducing radiation dose while maintaining high quality of reconstructed images. To reduce the radiation dose, one can reduce the number of projection views (sparse-view CT); however, it becomes difficult to achieve high-quality image reconstruction as the number of...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	83,796
2006.03224	Scalable Plug-and-Play ADMM with Convergence Guarantees	Plug-and-play priors (PnP) is a broadly applicable methodology for solving inverse problems by exploiting statistical priors specified as denoisers. Recent work has reported the state-of-the-art performance of PnP algorithms using pre-trained deep neural nets as denoisers in a number of imaging applications. However, c...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	180,254
2206.06213	Symbolic Regression for Space Applications: Differentiable Cartesian Genetic Programming Powered by Multi-objective Memetic Algorithms	Interpretable regression models are important for many application domains, as they allow experts to understand relations between variables from sparse data. Symbolic regression addresses this issue by searching the space of all possible free form equations that can be constructed from elementary algebraic functions. W...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	302,292
2403.16343	Percentile Optimization in Wireless Networks- Part II: Beamforming for Cell-Edge Throughput Maximization	Part I of this two-part paper focused on the formulation of percentile problems, complexity analysis, and development of power control algorithms via the quadratic fractional transform (QFT) and logarithmic fractional transform (LFT) for sum-least-qth-percentile (SLqP) rate maximization problems. In this second part, w...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	440,985
2305.13309	Evaluating Factual Consistency of Texts with Semantic Role Labeling	Automated evaluation of text generation systems has recently seen increasing attention, particularly checking whether generated text stays truthful to input sources. Existing methods frequently rely on an evaluation using task-specific language models, which in turn allows for little interpretability of generated score...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	366,454
2006.03586	Novel Object Viewpoint Estimation through Reconstruction Alignment	The goal of this paper is to estimate the viewpoint for a novel object. Standard viewpoint estimation approaches generally fail on this task due to their reliance on a 3D model for alignment or large amounts of class-specific training data and their corresponding canonical pose. We overcome those limitations by learnin...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	180,358
2203.00088	Virtual Reference Feedback Tuning for linear discrete-time systems with robust stability guarantees based on Set Membership	In this paper we propose a novel methodology that allows to design, in a purely data-based fashion and for linear single-input and single-output systems, both robustly stable and performing control systems for tracking piecewise constant reference signals. The approach uses both (i) Virtual Reference Feedback Tuning fo...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	282,862
2410.09890	Large-Scale 3D Medical Image Pre-training with Geometric Context Priors	The scarcity of annotations poses a significant challenge in medical image analysis. Large-scale pre-training has emerged as a promising label-efficient solution, owing to the utilization of large-scale data, large models, and advanced pre-training techniques. However, its development in medical images remains underexp...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	497,817
2207.05080	Learning an evolved mixture model for task-free continual learning	Recently, continual learning (CL) has gained significant interest because it enables deep learning models to acquire new knowledge without forgetting previously learnt information. However, most existing works require knowing the task identities and boundaries, which is not realistic in a real context. In this paper, w...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	307,418
1407.4062	The friendship paradox in scale-free networks	Our friends have more friends than we do. That is the basis of the friendship paradox. In mathematical terms, the mean number of friends of friends is higher than the mean number of friends. In the present study, we analyzed the relationship between the mean degree of vertices (individuals), <k>, and the mean number of...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	34,678
2107.04562	The Bayesian Learning Rule	We show that many machine-learning algorithms are specific instances of a single algorithm called the \emph{Bayesian learning rule}. The rule, derived from Bayesian principles, yields a wide-range of algorithms from fields such as optimization, deep learning, and graphical models. This includes classical algorithms suc...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	245,499
1509.02668	A graph theoretic approach to input-to-state stability of switched systems	This article deals with input-to-state stability (ISS) of discrete-time switched systems. Given a family of nonlinear systems with exogenous inputs, we present a class of switching signals under which the resulting switched system is ISS. We allow non-ISS systems in the family and our analysis involves graph-theoretic ...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	46,759
2307.16643	Improving grapheme-to-phoneme conversion by learning pronunciations from speech recordings	The Grapheme-to-Phoneme (G2P) task aims to convert orthographic input into a discrete phonetic representation. G2P conversion is beneficial to various speech processing applications, such as text-to-speech and speech recognition. However, these tend to rely on manually-annotated pronunciation dictionaries, which are of...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	382,676
1809.08410	Entropy-Assisted Multi-Modal Emotion Recognition Framework Based on Physiological Signals	As the result of the growing importance of the Human Computer Interface system, understanding human's emotion states has become a consequential ability for the computer. This paper aims to improve the performance of emotion recognition by conducting the complexity analysis of physiological signals. Based on AMIGOS data...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	108,500
1601.02733	Deep Learning of Part-based Representation of Data Using Sparse Autoencoders with Nonnegativity Constraints	We demonstrate a new deep learning autoencoder network, trained by a nonnegativity constraint algorithm (NCAE), that learns features which show part-based representation of data. The learning algorithm is based on constraining negative weights. The performance of the algorithm is assessed based on decomposing data into...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	50,856
2502.13105	Enhanced uncertainty quantification variational autoencoders for the solution of Bayesian inverse problems	Among other uses, neural networks are a powerful tool for solving deterministic and Bayesian inverse problems in real-time. In the Bayesian framework, variational autoencoders, a specialized type of neural network, enable the estimation of model parameters and their distribution based on observational data allowing to ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	535,202
2208.04591	Stronger Privacy Amplification by Shuffling for R\'enyi and Approximate Differential Privacy	The shuffle model of differential privacy has gained significant interest as an intermediate trust model between the standard local and central models [EFMRTT19; CSUZZ19]. A key result in this model is that randomly shuffling locally randomized data amplifies differential privacy guarantees. Such amplification implies ...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	true	312,163
2207.14387	Model Reduction for Nonlinear Systems by Balanced Truncation of State and Gradient Covariance	Data-driven reduced-order models often fail to make accurate forecasts of high-dimensional nonlinear dynamical systems that are sensitive along coordinates with low-variance because such coordinates are often truncated, e.g., by proper orthogonal decomposition, kernel principal component analysis, and autoencoders. Suc...	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	310,564
2406.01970	The Crystal Ball Hypothesis in diffusion models: Anticipating object positions from initial noise	Diffusion models have achieved remarkable success in text-to-image generation tasks; however, the role of initial noise has been rarely explored. In this study, we identify specific regions within the initial noise image, termed trigger patches, that play a key role for object generation in the resulting images. Notabl...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	460,553
2312.07434	Multi-Modal Conformal Prediction Regions with Simple Structures by Optimizing Convex Shape Templates	Conformal prediction is a statistical tool for producing prediction regions for machine learning models that are valid with high probability. A key component of conformal prediction algorithms is a \emph{non-conformity score function} that quantifies how different a model's prediction is from the unknown ground truth v...	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	414,923
1209.4246	Distributed Bayesian Detection Under Unknown Observation Statistics	In this paper, distributed Bayesian detection problems with unknown prior probabilities of hypotheses are considered. The sensors obtain observations which are conditionally dependent across sensors and their probability density functions (pdf) are not exactly known. The observations are quantized and are sent to the f...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	18,631
2412.03343	Improving Linguistic Diversity of Large Language Models with Possibility Exploration Fine-Tuning	While Large Language Models (LLMs) have made significant strides in replicating human-like abilities, there are concerns about a reduction in the linguistic diversity of their outputs. This results in the homogenization of viewpoints and perspectives, as well as the underrepresentation of specific demographic groups. A...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	513,926
2212.08423	Context Label Learning: Improving Background Class Representations in Semantic Segmentation	Background samples provide key contextual information for segmenting regions of interest (ROIs). However, they always cover a diverse set of structures, causing difficulties for the segmentation model to learn good decision boundaries with high sensitivity and precision. The issue concerns the highly heterogeneous natu...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	336,750
1911.11265	Internet of things-based (IoT) inventory monitoring refrigerator using arduino sensor network	This study presents a system that combines a conventional refrigerator, microcontrollers and a smart phone to create an inventory monitoring that can monitor the stocks inside the refrigerator wirelessly by accessing an Android application. The developed refrigerator uses a sensor network system that is installed in a ...	false	false	false	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	155,065
2310.18614	Hierarchical Mutual Information Analysis: Towards Multi-view Clustering in The Wild	Multi-view clustering (MVC) can explore common semantics from unsupervised views generated by different sources, and thus has been extensively used in applications of practical computer vision. Due to the spatio-temporal asynchronism, multi-view data often suffer from view missing and are unaligned in real-world applic...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	403,621
2304.04819	Recent Advancements in Machine Learning For Cybercrime Prediction	Cybercrime is a growing threat to organizations and individuals worldwide, with criminals using sophisticated techniques to breach security systems and steal sensitive data. This paper aims to comprehensively survey the latest advancements in cybercrime prediction, highlighting the relevant research. For this purpose, ...	false	false	false	false	true	false	true	false	false	false	false	true	true	false	false	false	false	false	357,372
1410.7660	Non-convex Robust PCA	We propose a new method for robust PCA -- the task of recovering a low-rank matrix from sparse corruptions that are of unknown value and support. Our method involves alternating between projecting appropriate residuals onto the set of low-rank matrices, and the set of sparse matrices; each projection is {\em non-convex...	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	37,091
2011.02842	Depth Self-Optimized Learning Toward Data Science	We propose a two-stage model called Depth Self-Optimized Learning (DSOL), which aims to realize ANN depth self-configuration, self-optimization as well as ANN training without manual intervention. In the first stage of DSOL, it will configure ANN of specific depth according to a specific dataset. In the second stage, D...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	205,056
2402.16786	Political Compass or Spinning Arrow? Towards More Meaningful Evaluations for Values and Opinions in Large Language Models	Much recent work seeks to evaluate values and opinions in large language models (LLMs) using multiple-choice surveys and questionnaires. Most of this work is motivated by concerns around real-world LLM applications. For example, politically-biased LLMs may subtly influence society when they are used by millions of peop...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	432,693
2108.08762	Dynamic Difficulty Adjustment in Virtual Reality Exergames through Experience-driven Procedural Content Generation	Virtual Reality (VR) games that feature physical activities have been shown to increase players' motivation to do physical exercise. However, for such exercises to have a positive healthcare effect, they have to be repeated several times a week. To maintain player motivation over longer periods of time, games often emp...	true	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	true	251,373
1812.11856	Latent Variable Modeling for Generative Concept Representations and Deep Generative Models	Latent representations are the essence of deep generative models and determine their usefulness and power. For latent representations to be useful as generative concept representations, their latent space must support latent space interpolation, attribute vectors and concept vectors, among other things. We investigate ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	117,643
2109.00025	Sense representations for Portuguese: experiments with sense embeddings and deep neural language models	Sense representations have gone beyond word representations like Word2Vec, GloVe and FastText and achieved innovative performance on a wide range of natural language processing tasks. Although very useful in many applications, the traditional approaches for generating word embeddings have a strict drawback: they produc...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	252,979
2308.06931	FusionPlanner: A Multi-task Motion Planner for Mining Trucks via Multi-sensor Fusion	In recent years, significant achievements have been made in motion planning for intelligent vehicles. However, as a typical unstructured environment, open-pit mining attracts limited attention due to its complex operational conditions and adverse environmental factors. A comprehensive paradigm for unmanned transportati...	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	false	385,328
2502.08449	CordViP: Correspondence-based Visuomotor Policy for Dexterous Manipulation in Real-World	Achieving human-level dexterity in robots is a key objective in the field of robotic manipulation. Recent advancements in 3D-based imitation learning have shown promising results, providing an effective pathway to achieve this goal. However, obtaining high-quality 3D representations presents two key problems: (1) the q...	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	false	533,017
2004.11999	Syntactic Data Augmentation Increases Robustness to Inference Heuristics	Pretrained neural models such as BERT, when fine-tuned to perform natural language inference (NLI), often show high accuracy on standard datasets, but display a surprising lack of sensitivity to word order on controlled challenge sets. We hypothesize that this issue is not primarily caused by the pretrained model's lim...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	174,094
1906.02439	Should Adversarial Attacks Use Pixel p-Norm?	Adversarial attacks aim to confound machine learning systems, while remaining virtually imperceptible to humans. Attacks on image classification systems are typically gauged in terms of $p$-norm distortions in the pixel feature space. We perform a behavioral study, demonstrating that the pixel $p$-norm for any $0\le p ...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	134,061
2212.02955	Solving Rearrangement Puzzles using Path Defragmentation in Factored State Spaces	Rearrangement puzzles are variations of rearrangement problems in which the elements of a problem are potentially logically linked together. To efficiently solve such puzzles, we develop a motion planning approach based on a new state space that is logically factored, integrating the capabilities of the robot through f...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	334,947
2005.00100	Linguistic Typology Features from Text: Inferring the Sparse Features of World Atlas of Language Structures	The use of linguistic typological resources in natural language processing has been steadily gaining more popularity. It has been observed that the use of typological information, often combined with distributed language representations, leads to significantly more powerful models. While linguistic typology representat...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	175,124
2405.19298	Adaptive Image Quality Assessment via Teaching Large Multimodal Model to Compare	While recent advancements in large multimodal models (LMMs) have significantly improved their abilities in image quality assessment (IQA) relying on absolute quality rating, how to transfer reliable relative quality comparison outputs to continuous perceptual quality scores remains largely unexplored. To address this g...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	458,831
2406.00489	Efficient Sign-Based Optimization: Accelerating Convergence via Variance Reduction	Sign stochastic gradient descent (signSGD) is a communication-efficient method that transmits only the sign of stochastic gradients for parameter updating. Existing literature has demonstrated that signSGD can achieve a convergence rate of $\mathcal{O}(d^{1/2}T^{-1/4})$, where $d$ represents the dimension and $T$ is th...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	459,876
1812.05313	When Semi-Supervised Learning Meets Transfer Learning: Training Strategies, Models and Datasets	Semi-Supervised Learning (SSL) has been proved to be an effective way to leverage both labeled and unlabeled data at the same time. Recent semi-supervised approaches focus on deep neural networks and have achieved promising results on several benchmarks: CIFAR10, CIFAR100 and SVHN. However, most of their experiments ar...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	116,394
2209.09432	CofeNet: Context and Former-Label Enhanced Net for Complicated Quotation Extraction	Quotation extraction aims to extract quotations from written text. There are three components in a quotation: source refers to the holder of the quotation, cue is the trigger word(s), and content is the main body. Existing solutions for quotation extraction mainly utilize rule-based approaches and sequence labeling mod...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	318,506
2407.11205	Impact on clinical guideline adherence of Orient-COVID, a CDSS based on dynamic medical decision trees for COVID19 management: a randomized simulation trial	Background: The adherence of clinicians to clinical practice guidelines is known to be low, including for the management of COVID-19, due to their difficult use at the point of care and their complexity. Clinical decision support systems have been proposed to implement guidelines and improve adherence. One approach is ...	true	false	false	false	true	false	false	false	false	false	false	false	false	true	false	false	false	false	473,341
1909.01315	Deep Graph Library: A Graph-Centric, Highly-Performant Package for Graph Neural Networks	Advancing research in the emerging field of deep graph learning requires new tools to support tensor computation over graphs. In this paper, we present the design principles and implementation of Deep Graph Library (DGL). DGL distills the computational patterns of GNNs into a few generalized sparse tensor operations su...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	143,872
2401.01010	Unsupervised Continual Anomaly Detection with Contrastively-learned Prompt	Unsupervised Anomaly Detection (UAD) with incremental training is crucial in industrial manufacturing, as unpredictable defects make obtaining sufficient labeled data infeasible. However, continual learning methods primarily rely on supervised annotations, while the application in UAD is limited due to the absence of s...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	419,190
2206.08774	Spectral-Efficiency of Cell-Free Massive MIMO with Multicarrier-Division Duplex	A multicarrier-division duplex (MDD)-based cell-free (CF) scheme, namely MDD-CF, is proposed, which enables downlink (DL) data and uplink (UL) data or pilots to be concurrently transmitted on mutually orthogonal subcarriers in distributed CF massive MIMO (mMIMO) systems. To demonstrate the advantages of MDD-CF, we firs...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	303,290
2109.05095	Stochastic Adversarial Koopman Model for Dynamical Systems	Dynamical systems are ubiquitous and are often modeled using a non-linear system of governing equations. Numerical solution procedures for many dynamical systems have existed for several decades, but can be slow due to high-dimensional state space of the dynamical system. Thus, deep learning-based reduced order models ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	254,653
2207.08467	Segmenting white matter hyperintensities on isotropic three-dimensional Fluid Attenuated Inversion Recovery magnetic resonance images: Assessing deep learning tools on norwegian imaging database	Automated segmentation of white matter hyperintensities (WMHs) is an essential step in neuroimaging analysis of Magnetic Resonance Imaging (MRI). Fluid Attenuated Inversion Recovery (FLAIR-weighted) is an MRI contrast that is particularly useful to visualize and quantify WMHs, a hallmark of cerebral small vessel diseas...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	308,601
1802.01212	Non-Gaussian information from weak lensing data via deep learning	Weak lensing maps contain information beyond two-point statistics on small scales. Much recent work has tried to extract this information through a range of different observables or via nonlinear transformations of the lensing field. Here we train and apply a 2D convolutional neural network to simulated noiseless lensi...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	89,560
2403.04114	Closing the Visual Sim-to-Real Gap with Object-Composable NeRFs	Deep learning methods for perception are the cornerstone of many robotic systems. Despite their potential for impressive performance, obtaining real-world training data is expensive, and can be impractically difficult for some tasks. Sim-to-real transfer with domain randomization offers a potential workaround, but ofte...	false	false	false	false	false	false	true	true	false	false	false	true	false	false	false	false	false	false	435,457
2501.05014	UAV-VLA: Vision-Language-Action System for Large Scale Aerial Mission Generation	The UAV-VLA (Visual-Language-Action) system is a tool designed to facilitate communication with aerial robots. By integrating satellite imagery processing with the Visual Language Model (VLM) and the powerful capabilities of GPT, UAV-VLA enables users to generate general flight paths-and-action plans through simple tex...	false	false	false	false	true	false	true	true	false	false	false	true	false	false	false	false	false	false	523,433
1605.06597	Adaptive Algorithm and Platform Selection for Visual Detection and Tracking	Computer vision algorithms are known to be extremely sensitive to the environmental conditions in which the data is captured, e.g., lighting conditions and target density. Tuning of parameters or choosing a completely new algorithm is often needed to achieve a certain performance level, especially when there is a limit...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	56,159
2406.04236	Understanding Information Storage and Transfer in Multi-modal Large Language Models	Understanding the mechanisms of information storage and transfer in Transformer-based models is important for driving model understanding progress. Recent work has studied these mechanisms for Large Language Models (LLMs), revealing insights on how information is stored in a model's parameters and how information flows...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	461,578
2202.00380	Machine-learning-enhanced quantum sensors for accurate magnetic field imaging	Local detection of magnetic fields is crucial for characterizing nano- and micro-materials and has been implemented using various scanning techniques or even diamond quantum sensors. Diamond nanoparticles (nanodiamonds) offer an attractive opportunity to chieve high spatial resolution because they can easily be close t...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	278,118
2410.23329	Variable Resolution Sampling and Deep Learning Image Recovery for Accelerated Multi-Spectral MRI Near Metal Implants	Purpose: This study presents a variable resolution (VR) sampling and deep learning reconstruction approach for multi-spectral MRI near metal implants, aiming to reduce scan times while maintaining image quality. Background: The rising use of metal implants has increased MRI scans affected by metal artifacts. Multi-spec...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	504,005
2305.10160	Stop Uploading Test Data in Plain Text: Practical Strategies for Mitigating Data Contamination by Evaluation Benchmarks	Data contamination has become prevalent and challenging with the rise of models pretrained on large automatically-crawled corpora. For closed models, the training data becomes a trade secret, and even for open models, it is not trivial to detect contamination. Strategies such as leaderboards with hidden answers, or usi...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	364,936
2410.13696	Efficient Function Placement in Virtual Networks: An Online Learning Approach	We propose a model for the virtual function placement problem and several novel algorithms using ideas based on multi-armed bandits. We prove that these algorithms learn the optimal placement policy rapidly, and their regret grows at a rate at most $O( N M \sqrt{T\ln T} )$ while respecting the feasibility constraints w...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	499,643
2312.02251	Fine-Tuning Language Models for Context-Specific SQL Query Generation	The ability to generate SQL queries from natural language has significant implications for making data accessible to non-specialists. This paper presents a novel approach to fine-tuning open-source large language models (LLMs) for the task of transforming natural language into SQL queries within the retail domain. We i...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	true	false	412,797
2103.03755	Leveraging Recursive Processing for Neural-Symbolic Affect-Target Associations	Explaining the outcome of deep learning decisions based on affect is challenging but necessary if we expect social companion robots to interact with users on an emotional level. In this paper, we present a commonsense approach that utilizes an interpretable hybrid neural-symbolic system to associate extracted targets, ...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	223,401
1411.7450	Worst-Case Linear Discriminant Analysis as Scalable Semidefinite Feasibility Problems	In this paper, we propose an efficient semidefinite programming (SDP) approach to worst-case linear discriminant analysis (WLDA). Compared with the traditional LDA, WLDA considers the dimensionality reduction problem from the worst-case viewpoint, which is in general more robust for classification. However, the origina...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	37,922
1811.01220	Sharp worst-case evaluation complexity bounds for arbitrary-order nonconvex optimization with inexpensive constraints	We provide sharp worst-case evaluation complexity bounds for nonconvex minimization problems with general inexpensive constraints, i.e.\ problems where the cost of evaluating/enforcing of the (possibly nonconvex or even disconnected) constraints, if any, is negligible compared to that of evaluating the objective functi...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	112,306
2405.02175	Hoaxpedia: A Unified Wikipedia Hoax Articles Dataset	Hoaxes are a recognised form of disinformation created deliberately, with potential serious implications in the credibility of reference knowledge resources such as Wikipedia. What makes detecting Wikipedia hoaxes hard is that they often are written according to the official style guidelines. In this work, we first pro...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	451,652
1712.06760	Mining Point Cloud Local Structures by Kernel Correlation and Graph Pooling	Unlike on images, semantic learning on 3D point clouds using a deep network is challenging due to the naturally unordered data structure. Among existing works, PointNet has achieved promising results by directly learning on point sets. However, it does not take full advantage of a point's local neighborhood that contai...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	86,929
2407.12403	Reliability Function of Classical-Quantum Channels	We study the reliability function of general classical-quantum channels, which describes the optimal exponent of the decay of decoding error when the communication rate is below the capacity. As the main result, we prove a lower bound, in terms of the quantum Renyi information in Petz's form, for the reliability functi...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	473,913
1906.04567	CVPR19 Tracking and Detection Challenge: How crowded can it get?	Standardized benchmarks are crucial for the majority of computer vision applications. Although leaderboards and ranking tables should not be over-claimed, benchmarks often provide the most objective measure of performance and are therefore important guides for research. The benchmark for Multiple Object Tracking, MOT...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	134,758
2309.01202	MAGMA: Music Aligned Generative Motion Autodecoder	Mapping music to dance is a challenging problem that requires spatial and temporal coherence along with a continual synchronization with the music's progression. Taking inspiration from large language models, we introduce a 2-step approach for generating dance using a Vector Quantized-Variational Autoencoder (VQ-VAE) t...	false	false	true	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	389,591
2206.02066	PIDNet: A Real-time Semantic Segmentation Network Inspired by PID Controllers	Two-branch network architecture has shown its efficiency and effectiveness in real-time semantic segmentation tasks. However, direct fusion of high-resolution details and low-frequency context has the drawback of detailed features being easily overwhelmed by surrounding contextual information. This overshoot phenomenon...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	300,731
2010.11487	Faithful Euclidean Distance Field from Log-Gaussian Process Implicit Surfaces	In this letter, we introduce the Log-Gaussian Process Implicit Surface (Log-GPIS), a novel continuous and probabilistic mapping representation suitable for surface reconstruction and local navigation. Our key contribution is the realisation that the regularised Eikonal equation can be simply solved by applying the loga...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	202,275
cmp-lg/9606028	Maximizing Top-down Constraints for Unification-based Systems	A left-corner parsing algorithm with top-down filtering has been reported to show very efficient performance for unification-based systems. However, due to the nontermination of parsing with left-recursive grammars, top-down constraints must be weakened. In this paper, a general method of maximizing top-down constraint...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	536,594
2202.00185	LayoutEnhancer: Generating Good Indoor Layouts from Imperfect Data	We address the problem of indoor layout synthesis, which is a topic of continuing research interest in computer graphics. The newest works made significant progress using data-driven generative methods; however, these approaches rely on suitable datasets. In practice, desirable layout properties may not exist in a data...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	true	278,061
2310.17247	Grokking Beyond Neural Networks: An Empirical Exploration with Model Complexity	In some settings neural networks exhibit a phenomenon known as \textit{grokking}, where they achieve perfect or near-perfect accuracy on the validation set long after the same performance has been achieved on the training set. In this paper, we discover that grokking is not limited to neural networks but occurs in othe...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	403,063
2410.11500	On Rank-Dependent Generalisation Error Bounds for Transformers	In this paper, we introduce various covering number bounds for linear function classes, each subject to different constraints on input and matrix norms. These bounds are contingent on the rank of each class of matrices. We then apply these bounds to derive generalization errors for single layer transformers. Our result...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	498,592
2005.13523	Emotion-robust EEG Classification for Motor Imagery	Developments in Brain Computer Interfaces (BCIs) are empowering those with severe physical afflictions through their use in assistive systems. Common methods of achieving this is via Motor Imagery (MI), which maps brain signals to code for certain commands. Electroencephalogram (EEG) is preferred for recording brain si...	true	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	179,033
2312.06660	EdgeSAM: Prompt-In-the-Loop Distillation for On-Device Deployment of SAM	This paper presents EdgeSAM, an accelerated variant of the Segment Anything Model (SAM), optimized for efficient execution on edge devices with minimal compromise in performance. Our approach involves distilling the original ViT-based SAM image encoder into a purely CNN-based architecture, better suited for edge device...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	414,619
2409.09042	Semantic Communication for Cooperative Perception using HARQ	Cooperative perception, offering a wider field of view than standalone perception, is becoming increasingly crucial in autonomous driving. This perception is enabled through vehicle-to-vehicle (V2V) communication, allowing connected automated vehicles (CAVs) to exchange sensor data, such as light detection and ranging ...	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	false	false	488,147
1808.01452	Traits & Transferability of Adversarial Examples against Instance Segmentation & Object Detection	Despite the recent advancements in deploying neural networks for image classification, it has been found that adversarial examples are able to fool these models leading them to misclassify the images. Since these models are now being widely deployed, we provide an insight on the threat of these adversarial examples by ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	104,569
1306.3890	Big data and the SP theory of intelligence	This article is about how the "SP theory of intelligence" and its realisation in the "SP machine" may, with advantage, be applied to the management and analysis of big data. The SP system -- introduced in the article and fully described elsewhere -- may help to overcome the problem of variety in big data: it has potent...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	25,262
1708.04299	Emotion Detection on TV Show Transcripts with Sequence-based Convolutional Neural Networks	While there have been significant advances in detecting emotions from speech and image recognition, emotion detection on text is still under-explored and remained as an active research field. This paper introduces a corpus for text-based emotion detection on multiparty dialogue as well as deep neural models that outper...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	78,913
1810.08217	Deep Learning Methods for Reynolds-Averaged Navier-Stokes Simulations of Airfoil Flows	With this study we investigate the accuracy of deep learning models for the inference of Reynolds-Averaged Navier-Stokes solutions. We focus on a modernized U-net architecture, and evaluate a large number of trained neural networks with respect to their accuracy for the calculation of pressure and velocity distribution...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	110,779
1110.1091	A simulation of the Neolithic transition in the Indus valley	The Indus Valley Civilization (IVC) was one of the first great civilizations in prehistory. This bronze age civilization flourished from the end of the fourth millennium BC. It disintegrated during the second millennium BC; despite much research effort, this decline is not well understood. Less research has been devote...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	12,502
1707.05635	Spherical Paragraph Model	Representing texts as fixed-length vectors is central to many language processing tasks. Most traditional methods build text representations based on the simple Bag-of-Words (BoW) representation, which loses the rich semantic relations between words. Recent advances in natural language processing have shown that semant...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	77,265
1601.06497	Quegel: A General-Purpose Query-Centric Framework for Querying Big Graphs	Pioneered by Google's Pregel, many distributed systems have been developed for large-scale graph analytics. These systems expose the user-friendly "think like a vertex" programming interface to users, and exhibit good horizontal scalability. However, these systems are designed for tasks where the majority of graph vert...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	true	51,297
2010.10681	Deep Learning Frameworks for Pavement Distress Classification: A Comparative Analysis	Automatic detection and classification of pavement distresses is critical in timely maintaining and rehabilitating pavement surfaces. With the evolution of deep learning and high performance computing, the feasibility of vision-based pavement defect assessments has significantly improved. In this study, the authors dep...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	201,959
2108.02569	Data Streaming and Traffic Gathering in Mesh-based NoC for Deep Neural Network Acceleration	The increasing popularity of deep neural network (DNN) applications demands high computing power and efficient hardware accelerator architecture. DNN accelerators use a large number of processing elements (PEs) and on-chip memory for storing weights and other parameters. As the communication backbone of a DNN accelerat...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	249,373
2111.15519	Gram Barcodes for Histopathology Tissue Texture Retrieval	Recent advances in digital pathology have led to the need for Histopathology Image Retrieval (HIR) systems that search through databases of biopsy images to find similar cases to a given query image. These HIR systems allow pathologists to effortlessly and efficiently access thousands of previously diagnosed cases in o...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	268,959
1903.01656	Visual-Thermal Landmarks and Inertial Fusion for Navigation in Degraded Visual Environments	With an ever-widening domain of aerial robotic applications, including many mission critical tasks such as disaster response operations, search and rescue missions and infrastructure inspections taking place in GPS-denied environments, the need for reliable autonomous operation of aerial robots has become crucial. Oper...	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	123,308
2104.08718	CLIPScore: A Reference-free Evaluation Metric for Image Captioning	Image captioning has conventionally relied on reference-based automatic evaluations, where machine captions are compared against captions written by humans. This is in contrast to the reference-free manner in which humans assess caption quality. In this paper, we report the surprising empirical finding that CLIP (Rad...	false	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	230,936
2401.12317	Software Engineering for Robotics: Future Research Directions; Report from the 2023 Workshop on Software Engineering for Robotics	Robots are experiencing a revolution as they permeate many aspects of our daily lives, from performing house maintenance to infrastructure inspection, from efficiently warehousing goods to autonomous vehicles, and more. This technical progress and its impact are astounding. This revolution, however, is outstripping the...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	true	423,335
2308.02203	An Add-on Model Predictive Control Strategy for the Energy Management of Hybrid Electric Tractors	The hybridization process has recently touched also the world of agricultural vehicles. Within this context, we develop an Energy Management Strategy (EMS) aiming at optimizing fuel consumption, while maintaining the battery state of charge. A typical feature of agricultural machines is that their internal combustion e...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	383,538
2303.05755	A tight bound on the stepsize of the decentralized gradient descent	In this paper, we consider the decentralized gradinet descent (DGD) given by \begin{equation} x_i (t+1) = \sum_{j=1}^m w_{ij} x_j (t) - \alpha (t) \nabla f_i (x_i (t)). \end{equation} We find a sharp range of the stepsize $\alpha (t)>0$ such that the sequence $\{x_i (t)\}$ is uniformly bounded when the aggregate cost...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	350,588
2405.13526	Understanding Virtual Nodes: Oversmoothing, Oversquashing, and Node Heterogeneity	Message passing neural networks (MPNNs) have been shown to have limitations in terms of expressivity and modeling long-range interactions. Augmenting MPNNs with a virtual node (VN) removes the locality constraint of the layer aggregation and has been found to improve performance on a range of benchmarks. We provide a c...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	455,970
1607.02829	Hypergraph Modelling for Geometric Model Fitting	In this paper, we propose a novel hypergraph based method (called HF) to fit and segment multi-structural data. The proposed HF formulates the geometric model fitting problem as a hypergraph partition problem based on a novel hypergraph model. In the hypergraph model, vertices represent data points and hyperedges denot...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	58,427
2312.08470	Best practices for machine learning in antibody discovery and development	Over the past 40 years, the discovery and development of therapeutic antibodies to treat disease has become common practice. However, as therapeutic antibody constructs are becoming more sophisticated (e.g., multi-specifics), conventional approaches to optimisation are increasingly inefficient. Machine learning (ML) pr...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	415,311
2212.12757	An optimized fuzzy logic model for proactive maintenance	Fuzzy logic has been proposed in previous studies for machine diagnosis, to overcome different drawbacks of the traditional diagnostic approaches used. Among these approaches Failure Mode and Effect Critical Analysis method(FMECA) attempts to identify potential modes and treat failures before they occur based on subjec...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	338,130

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