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
2405.06917	Design Requirements for Human-Centered Graph Neural Network Explanations	Graph neural networks (GNNs) are powerful graph-based machine-learning models that are popular in various domains, e.g., social media, transportation, and drug discovery. However, owing to complex data representations, GNNs do not easily allow for human-intelligible explanations of their predictions, which can decrease...	true	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	453,503
2407.11492	MMSD-Net: Towards Multi-modal Stuttering Detection	Stuttering is a common speech impediment that is caused by irregular disruptions in speech production, affecting over 70 million people across the world. Standard automatic speech processing tools do not take speech ailments into account and are thereby not able to generate meaningful results when presented with stutte...	false	false	true	false	false	false	false	false	true	false	false	false	false	false	false	false	false	true	473,484
2007.04169	An exploration of the influence of path choice in game-theoretic attribution algorithms	We compare machine learning explainability methods based on the theory of atomic (Shapley, 1953) and infinitesimal (Aumann and Shapley, 1974) games, in a theoretical and experimental investigation into how the model and choice of integration path can influence the resulting feature attributions. To gain insight into di...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	186,272
2312.10585	ESDMR-Net: A Lightweight Network With Expand-Squeeze and Dual Multiscale Residual Connections for Medical Image Segmentation	Segmentation is an important task in a wide range of computer vision applications, including medical image analysis. Recent years have seen an increase in the complexity of medical image segmentation approaches based on sophisticated convolutional neural network architectures. This progress has led to incremental enhan...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	416,232
1509.00836	Energy Harvesting Transmitters that Heat Up: Throughput Maximization under Temperature Constraints	Motivated by damage due to heating in sensor operation, we consider the throughput optimal offline data scheduling problem in an energy harvesting transmitter such that the resulting temperature increase remains below a critical level. We model the temperature dynamics of the transmitter as a linear system and determin...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	46,539
2012.03143	Majority Opinion Diffusion in Social Networks: An Adversarial Approach	We introduce and study a novel majority-based opinion diffusion model. Consider a graph $G$, which represents a social network. Assume that initially a subset of nodes, called seed nodes or early adopters, are colored either black or white, which correspond to positive or negative opinion regarding a consumer product o...	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	false	false	true	210,005
2403.09700	Shapley Values-Powered Framework for Fair Reward Split in Content Produced by GenAI	It is evident that, currently, generative models are surpassed in quality by human professionals. However, with the advancements in Artificial Intelligence, this gap will narrow, leading to scenarios where individuals who have dedicated years of their lives to mastering a skill become obsolete due to their high costs, ...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	437,869
cs/9809113	Improving Tagging Performance by Using Voting Taggers	We present a bootstrapping method to develop an annotated corpus, which is specially useful for languages with few available resources. The method is being applied to develop a corpus of Spanish of over 5Mw. The method consists on taking advantage of the collaboration of two different POS taggers. The cases in which bo...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	540,415
1206.6877	Inference in Hybrid Bayesian Networks Using Mixtures of Gaussians	The main goal of this paper is to describe a method for exact inference in general hybrid Bayesian networks (BNs) (with a mixture of discrete and continuous chance variables). Our method consists of approximating general hybrid Bayesian networks by a mixture of Gaussians (MoG) BNs. There exists a fast algorithm by Laur...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	17,101
1711.10521	A Recursive Bayesian Approach To Describe Retinal Vasculature Geometry	Demographic studies suggest that changes in the retinal vasculature geometry, especially in vessel width, are associated with the incidence or progression of eye-related or systemic diseases. To date, the main information source for width estimation from fundus images has been the intensity profile between vessel edges...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	85,615
2212.14736	PRISM: Privacy Preserving Healthcare Internet of Things Security Management	Consumer healthcare Internet of Things (IoT) devices are gaining popularity in our homes and hospitals. These devices provide continuous monitoring at a low cost and can be used to augment high-precision medical equipment. However, major challenges remain in applying pre-trained global models for anomaly detection on s...	false	false	false	false	false	false	false	false	false	false	true	false	true	false	false	false	false	false	338,723
2203.01994	Fast Neural Architecture Search for Lightweight Dense Prediction Networks	We present LDP, a lightweight dense prediction neural architecture search (NAS) framework. Starting from a pre-defined generic backbone, LDP applies the novel Assisted Tabu Search for efficient architecture exploration. LDP is fast and suitable for various dense estimation problems, unlike previous NAS methods that are...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	283,590
2208.09418	SAFARI: Versatile and Efficient Evaluations for Robustness of Interpretability	Interpretability of Deep Learning (DL) is a barrier to trustworthy AI. Despite great efforts made by the Explainable AI (XAI) community, explanations lack robustness -- indistinguishable input perturbations may lead to different XAI results. Thus, it is vital to assess how robust DL interpretability is, given an XAI me...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	313,694
2411.00004	RapidDock: Unlocking Proteome-scale Molecular Docking	Accelerating molecular docking -- the process of predicting how molecules bind to protein targets -- could boost small-molecule drug discovery and revolutionize medicine. Unfortunately, current molecular docking tools are too slow to screen potential drugs against all relevant proteins, which often results in missed dr...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	504,393
1212.3996	Increasing Air Traffic: What is the Problem?	Nowadays, huge efforts are made to modernize the air traffic management systems to cope with uncertainty, complexity and sub-optimality. An answer is to enhance the information sharing between the stakeholders. This paper introduces a framework that bridges the gap between air traffic management and air traffic control...	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	false	false	20,448
2302.08058	Learning Non-Local Spatial-Angular Correlation for Light Field Image Super-Resolution	Exploiting spatial-angular correlation is crucial to light field (LF) image super-resolution (SR), but is highly challenging due to its non-local property caused by the disparities among LF images. Although many deep neural networks (DNNs) have been developed for LF image SR and achieved continuously improved performan...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	345,922
1410.0640	Term-Weighting Learning via Genetic Programming for Text Classification	This paper describes a novel approach to learning term-weighting schemes (TWSs) in the context of text classification. In text mining a TWS determines the way in which documents will be represented in a vector space model, before applying a classifier. Whereas acceptable performance has been obtained with standard TWSs...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	true	false	false	36,491
2210.07703	Hybrid Decentralized Optimization: Leveraging Both First- and Zeroth-Order Optimizers for Faster Convergence	Distributed optimization is the standard way of speeding up machine learning training, and most of the research in the area focuses on distributed first-order, gradient-based methods. Yet, there are settings where some computationally-bounded nodes may not be able to implement first-order, gradient-based optimization, ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	323,831
1810.05456	Modeling Varying Camera-IMU Time Offset in Optimization-Based Visual-Inertial Odometry	Combining cameras and inertial measurement units (IMUs) has been proven effective in motion tracking, as these two sensing modalities offer complementary characteristics that are suitable for fusion. While most works focus on global-shutter cameras and synchronized sensor measurements, consumer-grade devices are mostly...	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	110,234
2110.09291	Reconfigurable Intelligent Surface-Enhanced OFDM Communications via Delay Adjustable Metasurface	Reconfigurable intelligent surface (RIS) is a promising technology for establishing spectral- and energy-efficient wireless networks. In this paper, we study RIS-enhanced orthogonal frequency division multiplexing (OFDM) communications, which generalize the existing RIS-driven context focusing only on frequency-flat ch...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	261,757
1806.02081	Distributed vs. Centralized Scheduling in D2D-enabled Cellular Networks	Employing channel adaptive resource allocation can yield to a large enhancement in almost any performance metric of Device-to-Device (D2D) communications. We observe that D2D users are able to estimate their local Channel State Information (CSI), however the base station needs some signaling exchange to acquire this in...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	99,704
1802.02498	Spectral Learning of Binomial HMMs for DNA Methylation Data	We consider learning parameters of Binomial Hidden Markov Models, which may be used to model DNA methylation data. The standard algorithm for the problem is EM, which is computationally expensive for sequences of the scale of the mammalian genome. Recently developed spectral algorithms can learn parameters of latent va...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	89,779
1708.02300	Reinforced Video Captioning with Entailment Rewards	Sequence-to-sequence models have shown promising improvements on the temporal task of video captioning, but they optimize word-level cross-entropy loss during training. First, using policy gradient and mixed-loss methods for reinforcement learning, we directly optimize sentence-level task-based metrics (as rewards), ac...	false	false	false	false	true	false	true	false	true	false	false	true	false	false	false	false	false	false	78,563
2208.04313	AUTOSHAPE: An Autoencoder-Shapelet Approach for Time Series Clustering	Time series shapelets are discriminative subsequences that have been recently found effective for time series clustering (TSC). The shapelets are convenient for interpreting the clusters. Thus, the main challenge for TSC is to discover high-quality variable-length shapelets to discriminate different clusters. In this p...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	312,067
2407.07550	Evaluating the method reproducibility of deep learning models in the biodiversity domain	Artificial Intelligence (AI) is revolutionizing biodiversity research by enabling advanced data analysis, species identification, and habitats monitoring, thereby enhancing conservation efforts. Ensuring reproducibility in AI-driven biodiversity research is crucial for fostering transparency, verifying results, and pro...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	471,808
2112.09631	Sublinear Time Approximation of Text Similarity Matrices	We study algorithms for approximating pairwise similarity matrices that arise in natural language processing. Generally, computing a similarity matrix for $n$ data points requires $\Omega(n^2)$ similarity computations. This quadratic scaling is a significant bottleneck, especially when similarities are computed via exp...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	272,204
2003.05410	How Powerful Are Randomly Initialized Pointcloud Set Functions?	We study random embeddings produced by untrained neural set functions, and show that they are powerful representations which well capture the input features for downstream tasks such as classification, and are often linearly separable. We obtain surprising results that show that random set functions can often obtain cl...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	167,848
2103.14529	Real-Time and Accurate Object Detection in Compressed Video by Long Short-term Feature Aggregation	Video object detection is a fundamental problem in computer vision and has a wide spectrum of applications. Based on deep networks, video object detection is actively studied for pushing the limits of detection speed and accuracy. To reduce the computation cost, we sparsely sample key frames in video and treat the rest...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	226,884
2301.05919	Efficient Evaluation Methods for Neural Architecture Search: A Survey	Neural Architecture Search (NAS) has received increasing attention because of its exceptional merits in automating the design of Deep Neural Network (DNN) architectures. However, the performance evaluation process, as a key part of NAS, often requires training a large number of DNNs. This inevitably makes NAS computati...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	340,492
2301.11422	RMSim: Controlled Respiratory Motion Simulation on Static Patient Scans	This work aims to generate realistic anatomical deformations from static patient scans. Specifically, we present a method to generate these deformations/augmentations via deep learning driven respiratory motion simulation that provides the ground truth for validating deformable image registration (DIR) algorithms and d...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	342,135
1508.00691	Deterministic Differential Search Algorithm for Distributed Sensor/Relay Networks	For distributed sensor/relay networks, high reliability and power efficiency are often required. However, several implementation issues arise in practice. One such problem is that all the distributed transmitters have limited power supply since the power source of the transmitters cannot be recharged continually. To re...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	45,704
2205.05192	Social Inclusion in Curated Contexts: Insights from Museum Practices	Artificial intelligence literature suggests that minority and fragile communities in society can be negatively impacted by machine learning algorithms due to inherent biases in the design process, which lead to socially exclusive decisions and policies. Faced with similar challenges in dealing with an increasingly dive...	false	false	false	false	true	false	true	false	false	false	false	false	false	true	false	false	false	false	295,863
2410.14185	Combining Hough Transform and Deep Learning Approaches to Reconstruct ECG Signals From Printouts	This work presents our team's (SignalSavants) winning contribution to the 2024 George B. Moody PhysioNet Challenge. The Challenge had two goals: reconstruct ECG signals from printouts and classify them for cardiac diseases. Our focus was the first task. Despite many ECGs being digitally recorded today, paper ECGs remai...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	499,920
2006.04996	Implicit Class-Conditioned Domain Alignment for Unsupervised Domain Adaptation	We present an approach for unsupervised domain adaptation---with a strong focus on practical considerations of within-domain class imbalance and between-domain class distribution shift---from a class-conditioned domain alignment perspective. Current methods for class-conditioned domain alignment aim to explicitly minim...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	180,889
2202.03951	On Sibson's $\alpha$-Mutual Information	We explore a family of information measures that stems from R\'enyi's $\alpha$-Divergences with $\alpha<0$. In particular, we extend the definition of Sibson's $\alpha$-Mutual Information to negative values of $\alpha$ and show several properties of these objects. Moreover, we highlight how this family of information m...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	279,400
2306.10739	COLE: A Column-based Learned Storage for Blockchain Systems	Blockchain systems suffer from high storage costs as every node needs to store and maintain the entire blockchain data. After investigating Ethereum's storage, we find that the storage cost mostly comes from the index, i.e., Merkle Patricia Trie (MPT). To support provenance queries, MPT persists the index nodes during ...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	374,344
2502.03200	CORTEX: A Cost-Sensitive Rule and Tree Extraction Method	Tree-based and rule-based machine learning models play pivotal roles in explainable artificial intelligence (XAI) due to their unique ability to provide explanations in the form of tree or rule sets that are easily understandable and interpretable, making them essential for applications in which trust in model decision...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	530,624
1808.00197	MaxMin Linear Initialization for Fuzzy C-Means	Clustering is an extensive research area in data science. The aim of clustering is to discover groups and to identify interesting patterns in datasets. Crisp (hard) clustering considers that each data point belongs to one and only one cluster. However, it is inadequate as some data points may belong to several clusters...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	true	false	104,332
2303.05936	Learning Decoupled Multi-touch Force Estimation, Localization and Stretch for Soft Capacitive E-skin	Distributed sensor arrays capable of detecting multiple spatially distributed stimuli are considered an important element in the realisation of exteroceptive and proprioceptive soft robots. This paper expands upon the previously presented idea of decoupling the measurements of pressure and location of a local indentati...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	350,639
2309.11735	FleXstage: Lightweight Magnetically Levitated Precision Stage with Over-Actuation towards High-Throughput IC Manufacturing	Precision motion stages play a critical role in various manufacturing and inspection equipment, for example, the wafer/reticle scanning in photolithography scanners and positioning stages in wafer inspection systems. To meet the growing demand for higher throughput in chip manufacturing and inspection, it is critical t...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	393,522
2005.07031	Temporal signals to images: Monitoring the condition of industrial assets with deep learning image processing algorithms	The ability to detect anomalies in time series is considered highly valuable in numerous application domains. The sequential nature of time series objects is responsible for an additional feature complexity, ultimately requiring specialized approaches in order to solve the task. Essential characteristics of time series...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	177,171
1711.07566	Neural 3D Mesh Renderer	For modeling the 3D world behind 2D images, which 3D representation is most appropriate? A polygon mesh is a promising candidate for its compactness and geometric properties. However, it is not straightforward to model a polygon mesh from 2D images using neural networks because the conversion from a mesh to an image, o...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	85,015
1811.00430	GA Based Q-Attack on Community Detection	Community detection plays an important role in social networks, since it can help to naturally divide the network into smaller parts so as to simplify network analysis. However, on the other hand, it arises the concern that individual information may be over-mined, and the concept community deception thus is proposed t...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	112,099
2412.11506	Glimpse: Enabling White-Box Methods to Use Proprietary Models for Zero-Shot LLM-Generated Text Detection	Advanced large language models (LLMs) can generate text almost indistinguishable from human-written text, highlighting the importance of LLM-generated text detection. However, current zero-shot techniques face challenges as white-box methods are restricted to use weaker open-source LLMs, and black-box methods are limit...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	517,447
2210.05022	Dynamic Gap: Safe Gap-based Navigation in Dynamic Environments	This paper extends the family of gap-based local planners to unknown dynamic environments through generating provable collision-free properties for hierarchical navigation systems. Existing perception-informed local planners that operate in dynamic environments rely on emergent or empirical robustness for collision avo...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	322,666
2102.03613	Linear Matrix Inequality Approaches to Koopman Operator Approximation	The regression problem associated with finding a matrix approximation of the Koopman operator from data is considered. The regression problem is formulated as a convex optimization problem subject to linear matrix inequality (LMI) constraints. Doing so allows for additional LMI constraints to be incorporated into the r...	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	218,811
1703.04103	Detection of Human Rights Violations in Images: Can Convolutional Neural Networks help?	After setting the performance benchmarks for image, video, speech and audio processing, deep convolutional networks have been core to the greatest advances in image recognition tasks in recent times. This raises the question of whether there are any benefit in targeting these remarkable deep architectures with the unat...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	69,837
2208.00050	Generating Multiple 4D Expression Transitions by Learning Face Landmark Trajectories	In this work, we address the problem of 4D facial expressions generation. This is usually addressed by animating a neutral 3D face to reach an expression peak, and then get back to the neutral state. In the real world though, people show more complex expressions, and switch from one expression to another. We thus propo...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	310,725
2009.03162	Improving colonoscopy lesion classification using semi-supervised deep learning	While data-driven approaches excel at many image analysis tasks, the performance of these approaches is often limited by a shortage of annotated data available for training. Recent work in semi-supervised learning has shown that meaningful representations of images can be obtained from training with large quantities of...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	194,760
2004.08947	Desmoking laparoscopy surgery images using an image-to-image translation guided by an embedded dark channel	In laparoscopic surgery, the visibility in the image can be severely degraded by the smoke caused by the $CO_2$ injection, and dissection tools, thus reducing the visibility of organs and tissues. This lack of visibility increases the surgery time and even the probability of mistakes conducted by the surgeon, then prod...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	173,217
2301.08146	What's happening in your neighborhood? A Weakly Supervised Approach to Detect Local News	Local news articles are a subset of news that impact users in a geographical area, such as a city, county, or state. Detecting local news (Step 1) and subsequently deciding its geographical location as well as radius of impact (Step 2) are two important steps towards accurate local news recommendation. Naive rule-based...	false	false	false	false	false	true	true	false	true	false	false	false	false	false	false	false	false	false	341,115
2203.04430	The Impact of Heavy-Duty Vehicle Electrification on Large Power Grids: a Synthetic Texas Case Study	The electrification of heavy-duty vehicles (HDEVs) is a nascent and rapidly emerging avenue for decarbonization of the transportation sector. In this paper, we examine the impacts of increased vehicle electrification on the power grid infrastructure, with particular focus on HDEVs. We utilize a synthetic representation...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	284,464
1603.08616	Submodular Variational Inference for Network Reconstruction	In real-world and online social networks, individuals receive and transmit information in real time. Cascading information transmissions (e.g. phone calls, text messages, social media posts) may be understood as a realization of a diffusion process operating on the network, and its branching path can be represented by ...	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	false	true	53,806
1902.09884	Assume, Augment and Learn: Unsupervised Few-Shot Meta-Learning via Random Labels and Data Augmentation	The field of few-shot learning has been laboriously explored in the supervised setting, where per-class labels are available. On the other hand, the unsupervised few-shot learning setting, where no labels of any kind are required, has seen little investigation. We propose a method, named Assume, Augment and Learn or AA...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	122,537
2405.03541	RepVGG-GELAN: Enhanced GELAN with VGG-STYLE ConvNets for Brain Tumour Detection	Object detection algorithms particularly those based on YOLO have demonstrated remarkable efficiency in balancing speed and accuracy. However, their application in brain tumour detection remains underexplored. This study proposes RepVGG-GELAN, a novel YOLO architecture enhanced with RepVGG, a reparameterized convolutio...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	452,220
2311.12992	FollowMe: a Robust Person Following Framework Based on Re-Identification and Gestures	Human-robot interaction (HRI) has become a crucial enabler in houses and industries for facilitating operational flexibility. When it comes to mobile collaborative robots, this flexibility can be further increased due to the autonomous mobility and navigation capacity of the robotic agents, expanding their workspace an...	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	409,579
1712.04182	A Generic Model for Swarm Intelligence and Its Validations	The modeling of emergent swarm intelligence constitutes a major challenge and it has been tackled in a number of different ways. However, existing approaches fail to capture the nature of swarm intelligence and they are either too abstract for practical application or not generic enough to describe the various types of...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	86,569
2103.03729	Data-Driven Short-Term Voltage Stability Assessment Based on Spatial-Temporal Graph Convolutional Network	Post-fault dynamics of short-term voltage stability (SVS) present spatial-temporal characteristics, but the existing data-driven methods for online SVS assessment fail to incorporate such characteristics into their models effectively. Confronted with this dilemma, this paper develops a novel spatial-temporal graph conv...	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	223,393
2304.08235	A Platform-Agnostic Deep Reinforcement Learning Framework for Effective Sim2Real Transfer towards Autonomous Driving	Deep Reinforcement Learning (DRL) has shown remarkable success in solving complex tasks across various research fields. However, transferring DRL agents to the real world is still challenging due to the significant discrepancies between simulation and reality. To address this issue, we propose a robust DRL framework th...	false	false	false	false	true	false	true	true	false	false	false	false	false	false	false	false	false	false	358,635
2102.07337	Machine Learning on Camera Images for Fast mmWave Beamforming	Perfect alignment in chosen beam sectors at both transmit- and receive-nodes is required for beamforming in mmWave bands. Current 802.11ad WiFi and emerging 5G cellular standards spend up to several milliseconds exploring different sector combinations to identify the beam pair with the highest SNR. In this paper, we pr...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	220,073
1604.07952	Zero-shot object prediction using semantic scene knowledge	This work focuses on the semantic relations between scenes and objects for visual object recognition. Semantic knowledge can be a powerful source of information especially in scenarios with few or no annotated training samples. These scenarios are referred to as zero-shot or few-shot recognition and often build on visu...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	55,152
2303.07295	Meet in the Middle: A New Pre-training Paradigm	Most language models (LMs) are trained and applied in an autoregressive left-to-right fashion, assuming that the next token only depends on the preceding ones. However, this assumption ignores the potential benefits of using the full sequence information during training, and the possibility of having context from both ...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	351,206
1705.01332	LiDAR-based Control of Autonomous Rotorcraft for the Inspection of Pier-like Structures: Proofs	This is a complementary document to the paper presented in [1], to provide more detailed proofs for some results. The main paper addresses the problem of trajectory tracking control of autonomous rotorcraft in operation scenarios where only relative position measurements obtained from LiDAR sensors are possible. The pr...	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	72,828
2406.01206	On the Stability of Networked Nonlinear Negative Imaginary Systems with Applications to Electrical Power Systems	In the transition to achieving net zero emissions, it has been suggested that a substantial expansion of electric power grids will be necessary to support emerging renewable energy zones. In this paper, we propose employing battery-based feedback control and nonlinear negative imaginary (NI) systems theory to reduce th...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	460,221
2412.20682	Learning to Rank Pre-trained Vision-Language Models for Downstream Tasks	Vision language models (VLMs) like CLIP show stellar zero-shot capability on classification benchmarks. However, selecting the VLM with the highest performance on the unlabeled downstream task is non-trivial. Existing VLM selection methods focus on the class-name-only setting, relying on a supervised large-scale datase...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	521,317
1909.03582	Clickbait? Sensational Headline Generation with Auto-tuned Reinforcement Learning	Sensational headlines are headlines that capture people's attention and generate reader interest. Conventional abstractive headline generation methods, unlike human writers, do not optimize for maximal reader attention. In this paper, we propose a model that generates sensational headlines without labeled data. We firs...	true	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	144,535
2404.00394	Analysis of Fairness-promoting Optimization Schemes of Photovoltaic Curtailments for Voltage Regulation in Power Distribution Networks	Active power curtailment of photovoltaic (PV) generation is commonly exercised to mitigate over-voltage issues in power distribution networks. However, fairness concerns arise as certain PV plants may experience more significant curtailments than others depending on their locations within the network. Existing literatu...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	442,900
2408.03872	Inter-Series Transformer: Attending to Products in Time Series Forecasting	Time series forecasting is an important task in many fields ranging from supply chain management to weather forecasting. Recently, Transformer neural network architectures have shown promising results in forecasting on common time series benchmark datasets. However, application to supply chain demand forecasting, which...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	479,177
0901.1408	A Message-Passing Approach for Joint Channel Estimation, Interference Mitigation and Decoding	Channel uncertainty and co-channel interference are two major challenges in the design of wireless systems such as future generation cellular networks. This paper studies receiver design for a wireless channel model with both time-varying Rayleigh fading and strong co-channel interference of similar form as the desired...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	2,921
2003.10381	Ambiguity in Sequential Data: Predicting Uncertain Futures with Recurrent Models	Ambiguity is inherently present in many machine learning tasks, but especially for sequential models seldom accounted for, as most only output a single prediction. In this work we propose an extension of the Multiple Hypothesis Prediction (MHP) model to handle ambiguous predictions with sequential data, which is of spe...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	169,319
1710.07723	Generalized linear mixing model accounting for endmember variability	Endmember variability is an important factor for accurately unveiling vital information relating the pure materials and their distribution in hyperspectral images. Recently, the extended linear mixing model (ELMM) has been proposed as a modification of the linear mixing model (LMM) to consider endmember variability eff...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	82,970
2208.06117	Facial Expression Recognition and Image Description Generation in Vietnamese	This paper discusses a facial expression recognition model and a description generation model to build descriptive sentences for images and facial expressions of people in images. Our study shows that YOLOv5 achieves better results than a traditional CNN for all emotions on the KDEF dataset. In particular, the accuraci...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	312,604
math/0603155	Vers une commande multivariable sans mod\`ele	A control strategy without any precise mathematical model is derived for linear or nonlinear systems which are assumed to be finite-dimensional. Two convincing numerical simulations are provided.	false	true	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	540,712
2305.11854	Multimodal Web Navigation with Instruction-Finetuned Foundation Models	The progress of autonomous web navigation has been hindered by the dependence on billions of exploratory interactions via online reinforcement learning, and domain-specific model designs that make it difficult to leverage generalization from rich out-of-domain data. In this work, we study data-driven offline training f...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	365,721
1610.09995	Generating Sentiment Lexicons for German Twitter	Despite a substantial progress made in developing new sentiment lexicon generation (SLG) methods for English, the task of transferring these approaches to other languages and domains in a sound way still remains open. In this paper, we contribute to the solution of this problem by systematically comparing semi-automati...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	63,146
2111.01590	Detect-and-Segment: a Deep Learning Approach to Automate Wound Image Segmentation	Chronic wounds significantly impact quality of life. If not properly managed, they can severely deteriorate. Image-based wound analysis could aid in objectively assessing the wound status by quantifying important features that are related to healing. However, the high heterogeneity of the wound types, image background ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	264,603
2303.06468	Accurate Prediction of Global Mean Temperature through Data Transformation Techniques	It is important to predict how the Global Mean Temperature (GMT) will evolve in the next few decades. The ability to predict historical data is a necessary first step toward the actual goal of making long-range forecasts. This paper examines the advantage of statistical and simpler Machine Learning (ML) methods instead...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	350,857
1105.5545	Competing activation mechanisms in epidemics on networks	In contrast to previous common wisdom that epidemic activity in heterogeneous networks is dominated by the hubs with the largest number of connections, recent research has pointed out the role that the innermost, dense core of the network plays in sustaining epidemic processes. Here we show that the mechanism responsib...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	10,547
2011.03372	FDNAS: Improving Data Privacy and Model Diversity in AutoML	To prevent the leakage of private information while enabling automated machine intelligence, there is an emerging trend to integrate federated learning and Neural Architecture Search (NAS). Although promising as it may seem, the coupling of difficulties from both two tenets makes the algorithm development quite challen...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	205,229
2112.13595	Depth estimation of endoscopy using sim-to-real transfer	In order to use the navigation system effectively, distance information sensors such as depth sensors are essential. Since depth sensors are difficult to use in endoscopy, many groups propose a method using convolutional neural networks. In this paper, the ground truth of the depth image and the endoscopy image is gene...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	273,302
2001.03067	Domain-independent Extraction of Scientific Concepts from Research Articles	We examine the novel task of domain-independent scientific concept extraction from abstracts of scholarly articles and present two contributions. First, we suggest a set of generic scientific concepts that have been identified in a systematic annotation process. This set of concepts is utilised to annotate a corpus of ...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	true	159,876
2307.02106	SoK: Privacy-Preserving Data Synthesis	As the prevalence of data analysis grows, safeguarding data privacy has become a paramount concern. Consequently, there has been an upsurge in the development of mechanisms aimed at privacy-preserving data analyses. However, these approaches are task-specific; designing algorithms for new tasks is a cumbersome process....	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	true	false	377,591
2103.12338	Consistency Analysis of the Closed-loop SRIVC Estimator	The Consistency of the Closed-Loop Simplified Refined Instrumental Variable method for Continuous-time system (CLSRIVC) is analysed based on sampled data. It is proven that the CLSRIVC estimator is not consistent when a continuous-time controller is used in the closed-loop.	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	226,137
1409.6941	Individual risk in mean-field control models for decentralized control, with application to automated demand response	Flexibility of energy consumption can be harnessed for the purposes of ancillary services in a large power grid. In prior work by the authors a randomized control architecture is introduced for individual loads for this purpose. In examples it is shown that the control architecture can be designed so that control of th...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	36,282
1906.10973	Defending Adversarial Attacks by Correcting logits	Generating and eliminating adversarial examples has been an intriguing topic in the field of deep learning. While previous research verified that adversarial attacks are often fragile and can be defended via image-level processing, it remains unclear how high-level features are perturbed by such attacks. We investigate...	false	false	false	false	false	false	true	false	false	false	false	true	true	false	false	false	false	false	136,559
1610.06067	Fairness as a Program Property	We explore the following question: Is a decision-making program fair, for some useful definition of fairness? First, we describe how several algorithmic fairness questions can be phrased as program verification problems. Second, we discuss an automated verification technique for proving or disproving fairness of decisi...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	62,601
2310.04041	Observation-Guided Diffusion Probabilistic Models	We propose a novel diffusion-based image generation method called the observation-guided diffusion probabilistic model (OGDM), which effectively addresses the tradeoff between quality control and fast sampling. Our approach reestablishes the training objective by integrating the guidance of the observation process with...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	397,516
2010.12363	Regret in Online Recommendation Systems	This paper proposes a theoretical analysis of recommendation systems in an online setting, where items are sequentially recommended to users over time. In each round, a user, randomly picked from a population of $m$ users, requests a recommendation. The decision-maker observes the user and selects an item from a catalo...	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	false	202,668
2412.17142	AI-Based Teat Shape and Skin Condition Prediction for Dairy Management	Dairy owners spend significant effort to keep their animals healthy. There is good reason to hope that technologies such as computer vision and artificial intelligence (AI) could reduce these costs, yet obstacles arise when adapting advanced tools to farming environments. In this work, we adapt AI tools to dairy cow te...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	519,846
2210.11513	Learning Sample Reweighting for Accuracy and Adversarial Robustness	There has been great interest in enhancing the robustness of neural network classifiers to defend against adversarial perturbations through adversarial training, while balancing the trade-off between robust accuracy and standard accuracy. We propose a novel adversarial training framework that learns to reweight the los...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	325,341
1807.04020	Improved SVD-based Initialization for Nonnegative Matrix Factorization using Low-Rank Correction	Due to the iterative nature of most nonnegative matrix factorization (\textsc{NMF}) algorithms, initialization is a key aspect as it significantly influences both the convergence and the final solution obtained. Many initialization schemes have been proposed for NMF, among which one of the most popular class of methods...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	102,652
2310.11569	When Rigidity Hurts: Soft Consistency Regularization for Probabilistic Hierarchical Time Series Forecasting	Probabilistic hierarchical time-series forecasting is an important variant of time-series forecasting, where the goal is to model and forecast multivariate time-series that have underlying hierarchical relations. Most methods focus on point predictions and do not provide well-calibrated probabilistic forecasts distribu...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	400,686
2310.02568	Stand for Something or Fall for Everything: Predict Misinformation Spread with Stance-Aware Graph Neural Networks	Although pervasive spread of misinformation on social media platforms has become a pressing challenge, existing platform interventions have shown limited success in curbing its dissemination. In this study, we propose a stance-aware graph neural network (stance-aware GNN) that leverages users' stances to proactively pr...	false	false	false	true	true	false	true	false	false	false	false	false	false	true	false	false	false	false	396,907
2210.16099	An Empirical Evaluation of Zeroth-Order Optimization Methods on AI-driven Molecule Optimization	Molecule optimization is an important problem in chemical discovery and has been approached using many techniques, including generative modeling, reinforcement learning, genetic algorithms, and much more. Recent work has also applied zeroth-order (ZO) optimization, a subset of gradient-free optimization that solves pro...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	327,222
1801.01179	Inferring propagation paths for sparsely observed perturbations on complex networks	In a complex system, perturbations propagate by following paths on the network of interactions among the system's units. In contrast to what happens with the spreading of epidemics, observations of general perturbations are often very sparse in time (there is a single observation of the perturbed system) and in "space"...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	87,687
2311.10359	FIKIT: Priority-Based Real-time GPU Multi-tasking Scheduling with Kernel Identification	Highly parallelized workloads like machine learning training, inferences and general HPC tasks are greatly accelerated using GPU devices. In a cloud computing cluster, serving a GPU's computation power through multi-tasks sharing is highly demanded since there are always more task requests than the number of GPU availa...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	408,506
1906.06931	Of Cores: A Partial-Exploration Framework for Markov Decision Processes	We introduce a framework for approximate analysis of Markov decision processes (MDP) with bounded-, unbounded-, and infinite-horizon properties. The main idea is to identify a "core" of an MDP, i.e., a subsystem where we provably remain with high probability, and to avoid computation on the less relevant rest of the st...	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	false	true	135,467
1808.01102	Hallucinating Agnostic Images to Generalize Across Domains	The ability to generalize across visual domains is crucial for the robustness of artificial recognition systems. Although many training sources may be available in real contexts, the access to even unlabeled target samples cannot be taken for granted, which makes standard unsupervised domain adaptation methods inapplic...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	104,509
1602.03814	Enabling Basic Normative HRI in a Cognitive Robotic Architecture	Collaborative human activities are grounded in social and moral norms, which humans consciously and subconsciously use to guide and constrain their decision-making and behavior, thereby strengthening their interactions and preventing emotional and physical harm. This type of norm-based processing is also critical for r...	true	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	false	52,055
2205.07872	ScAN: Suicide Attempt and Ideation Events Dataset	Suicide is an important public health concern and one of the leading causes of death worldwide. Suicidal behaviors, including suicide attempts (SA) and suicide ideations (SI), are leading risk factors for death by suicide. Information related to patients' previous and current SA and SI are frequently documented in the ...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	296,751

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