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
2009.11892	PK-GCN: Prior Knowledge Assisted Image Classification using Graph Convolution Networks	Deep learning has gained great success in various classification tasks. Typically, deep learning models learn underlying features directly from data, and no underlying relationship between classes are included. Similarity between classes can influence the performance of classification. In this article, we propose a met...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	197,271
2408.04284	LLM-DetectAIve: a Tool for Fine-Grained Machine-Generated Text Detection	The ease of access to large language models (LLMs) has enabled a widespread of machine-generated texts, and now it is often hard to tell whether a piece of text was human-written or machine-generated. This raises concerns about potential misuse, particularly within educational and academic domains. Thus, it is importan...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	479,329
2409.09568	Thesis proposal: Are We Losing Textual Diversity to Natural Language Processing?	This thesis argues that the currently widely used Natural Language Processing algorithms possibly have various limitations related to the properties of the texts they handle and produce. With the wide adoption of these tools in rapid progress, we must ask what these limitations are and what are the possible implication...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	488,373
2307.06079	Better bounds on the minimal Lee distance	This paper provides new and improved Singleton-like bounds for Lee metric codes over integer residue rings. We derive the bounds using various novel definitions of generalized Lee weights based on different notions of a support of a linear code. In this regard, we introduce three main different support types for codes ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	378,958
2203.00555	DeepNet: Scaling Transformers to 1,000 Layers	In this paper, we propose a simple yet effective method to stabilize extremely deep Transformers. Specifically, we introduce a new normalization function (DeepNorm) to modify the residual connection in Transformer, accompanying with theoretically derived initialization. In-depth theoretical analysis shows that model up...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	283,049
2302.14777	VQA with Cascade of Self- and Co-Attention Blocks	The use of complex attention modules has improved the performance of the Visual Question Answering (VQA) task. This work aims to learn an improved multi-modal representation through dense interaction of visual and textual modalities. The proposed model has an attention block containing both self-attention and co-attent...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	348,434
2208.13412	Distributed Cooperative Control and Optimization of Connected Automated Vehicles Platoon Against Cut-in Behaviors of Social Drivers	Connected automated vehicles (CAVs) have brought new opportunities to improve traffic throughput and reduce energy consumption. However, the uncertain lane-change behaviors (LCBs) of surrounding vehicles (SVs) as an uncontrollable factor significantly threaten the driving safety and the consistent movement of a group o...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	315,050
2401.07756	Joint Probability Selection and Power Allocation for Federated Learning	In this paper, we study the performance of federated learning over wireless networks, where devices with a limited energy budget train a machine learning model. The federated learning performance depends on the selection of the clients participating in the learning at each round. Most existing studies suggest determini...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	421,645
2405.10266	A Tale of Two Languages: Large-Vocabulary Continuous Sign Language Recognition from Spoken Language Supervision	In this work, our goals are two fold: large-vocabulary continuous sign language recognition (CSLR), and sign language retrieval. To this end, we introduce a multi-task Transformer model, CSLR2, that is able to ingest a signing sequence and output in a joint embedding space between signed language and spoken language te...	false	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	454,699
1908.10486	Exploiting Global Camera Network Constraints for Unsupervised Video Person Re-identification	Many unsupervised approaches have been proposed recently for the video-based re-identification problem since annotations of samples across cameras are time-consuming. However, higher-order relationships across the entire camera network are ignored by these methods, leading to contradictory outputs when matching results...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	143,128
2412.02768	Quaternion-based Unscented Kalman Filter for 6-DoF Vision-based Inertial Navigation in GPS-denied Regions	This paper investigates the orientation, position, and linear velocity estimation problem of a rigid-body moving in three-dimensional (3D) space with six degrees-of-freedom (6 DoF). The highly nonlinear navigation kinematics are formulated to ensure global representation of the navigation problem. A computationally eff...	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	513,668
2310.14749	Exploring hierarchical framework of nonlinear sparse Bayesian learning algorithm through numerical investigations	Sparse Bayesian learning (SBL) has been extensively utilized in data-driven modeling to combat the issue of overfitting. While SBL excels in linear-in-parameter models, its direct applicability is limited in models where observations possess nonlinear relationships with unknown parameters. Recently, a semi-analytical B...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	402,003
2207.10286	Comparative Study on Supervised versus Semi-supervised Machine Learning for Anomaly Detection of In-vehicle CAN Network	As the central nerve of the intelligent vehicle control system, the in-vehicle network bus is crucial to the security of vehicle driving. One of the best standards for the in-vehicle network is the Controller Area Network (CAN bus) protocol. However, the CAN bus is designed to be vulnerable to various attacks due to it...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	309,203
1309.5605	Stochastic Bound Majorization	Recently a majorization method for optimizing partition functions of log-linear models was proposed alongside a novel quadratic variational upper-bound. In the batch setting, it outperformed state-of-the-art first- and second-order optimization methods on various learning tasks. We propose a stochastic version of this ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	27,182
2407.02816	Large and Small Deviations for Statistical Sequence Matching	We revisit the problem of statistical sequence matching between two databases of sequences initiated by Unnikrishnan (TIT 2015) and derive theoretical performance guarantees for the generalized likelihood ratio test (GLRT). We first consider the case where the number of matched pairs of sequences between the databases ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	469,896
2408.07404	Efficient Edge AI: Deploying Convolutional Neural Networks on FPGA with the Gemmini Accelerator	The growing concerns regarding energy consumption and privacy have prompted the development of AI solutions deployable on the edge, circumventing the substantial CO2 emissions associated with cloud servers and mitigating risks related to sharing sensitive data. But deploying Convolutional Neural Networks (CNNs) on non-...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	480,570
2407.14386	Frontiers of Deep Learning: From Novel Application to Real-World Deployment	Deep learning continues to re-shape numerous fields, from natural language processing and imaging to data analytics and recommendation systems. This report studies two research papers that represent recent progress on deep learning from two largely different aspects: The first paper applied the transformer networks, wh...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	474,766
1904.01555	Active Learning for Network Intrusion Detection	Network operators are generally aware of common attack vectors that they defend against. For most networks the vast majority of traffic is legitimate. However new attack vectors are continually designed and attempted by bad actors which bypass detection and go unnoticed due to low volume. One strategy for finding such ...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	126,170
2302.03648	Class-Incremental Learning: A Survey	Deep models, e.g., CNNs and Vision Transformers, have achieved impressive achievements in many vision tasks in the closed world. However, novel classes emerge from time to time in our ever-changing world, requiring a learning system to acquire new knowledge continually. Class-Incremental Learning (CIL) enables the lear...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	344,417
1709.02243	Towards a Dedicated Computer Vision Tool set for Crowd Simulation Models	As the population of world is increasing, and even more concentrated in urban areas, ensuring public safety is becoming a taunting job for security personnel and crowd managers. Mass events like sports, festivals, concerts, political gatherings attract thousand of people in a constraint environment,therefore adequate s...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	80,225
2105.14615	On the Controllers Based on Time Delay Estimation for Robotic Manipulators	Assurance of asymptotic trajectory tracking in robotic manipulators with a smooth control law in the presence of unmodeled dynamics or external disturbance is a challenging problem. Recently, it is asserted that it is achieved via a rigorous proof by designing a traditional model-free controller together with time dela...	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	237,741
2306.02351	RSSOD-Bench: A large-scale benchmark dataset for Salient Object Detection in Optical Remote Sensing Imagery	We present the RSSOD-Bench dataset for salient object detection (SOD) in optical remote sensing imagery. While SOD has achieved success in natural scene images with deep learning, research in SOD for remote sensing imagery (RSSOD) is still in its early stages. Existing RSSOD datasets have limitations in terms of scale,...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	370,868
1805.11366	Advancement of MSA-technique for stiffness modeling of serial and parallel robotic manipulators	The paper presents advancement of the matrix structural analysis technique (MSA) for stiffness modeling of robotic manipulators. In contrast to the classical MSA, it can be applied to both parallel and serial manipulators composed of flexible and rigid links connected by rigid, passive or elastic joints with multiple e...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	98,908
1909.05507	Effective training of deep convolutional neural networks for hyperspectral image classification through artificial labeling	Hyperspectral imaging is a rich source of data, allowing for multitude of effective applications. However, such imaging remains challenging because of large data dimension and, typically, small pool of available training examples. While deep learning approaches have been shown to be successful in providing effective cl...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	true	false	false	145,120
1910.01808	Estimating Lower Limb Kinematics using a Lie Group Constrained EKF and a Reduced Wearable IMU Count	This paper presents an algorithm that makes novel use of a Lie group representation of position and orientation alongside a constrained extended Kalman filter (CEKF) to accurately estimate pelvis, thigh, and shank kinematics during walking using only three wearable inertial sensors. The algorithm iterates through the p...	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	148,044
2403.17839	ReMamber: Referring Image Segmentation with Mamba Twister	Referring Image Segmentation~(RIS) leveraging transformers has achieved great success on the interpretation of complex visual-language tasks. However, the quadratic computation cost makes it resource-consuming in capturing long-range visual-language dependencies. Fortunately, Mamba addresses this with efficient linear ...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	441,652
2307.00936	OpenAPMax: Abnormal Patterns-based Model for Real-World Alzheimer's Disease Diagnosis	Alzheimer's disease (AD) cannot be reversed, but early diagnosis will significantly benefit patients' medical treatment and care. In recent works, AD diagnosis has the primary assumption that all categories are known a prior -- a closed-set classification problem, which contrasts with the open-set recognition problem. ...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	377,186
2101.11427	One Model to Serve All: Star Topology Adaptive Recommender for Multi-Domain CTR Prediction	Traditional industrial recommenders are usually trained on a single business domain and then serve for this domain. However, in large commercial platforms, it is often the case that the recommenders need to make click-through rate (CTR) predictions for multiple business domains. Different domains have overlapping user ...	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	false	217,267
2012.05901	Robust Consistent Video Depth Estimation	We present an algorithm for estimating consistent dense depth maps and camera poses from a monocular video. We integrate a learning-based depth prior, in the form of a convolutional neural network trained for single-image depth estimation, with geometric optimization, to estimate a smooth camera trajectory as well as d...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	210,926
2405.13378	FedCache 2.0: Federated Edge Learning with Knowledge Caching and Dataset Distillation	Federated Edge Learning (FEL) has emerged as a promising approach for enabling edge devices to collaboratively train machine learning models while preserving data privacy. Despite its advantages, practical FEL deployment faces significant challenges related to device constraints and device-server interactions, necessit...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	455,919
2307.14010	ESSAformer: Efficient Transformer for Hyperspectral Image Super-resolution	Single hyperspectral image super-resolution (single-HSI-SR) aims to restore a high-resolution hyperspectral image from a low-resolution observation. However, the prevailing CNN-based approaches have shown limitations in building long-range dependencies and capturing interaction information between spectral features. Th...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	381,785
2410.14283	Takin-ADA: Emotion Controllable Audio-Driven Animation with Canonical and Landmark Loss Optimization	Existing audio-driven facial animation methods face critical challenges, including expression leakage, ineffective subtle expression transfer, and imprecise audio-driven synchronization. We discovered that these issues stem from limitations in motion representation and the lack of fine-grained control over facial expre...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	499,972
1903.00107	GAN Based Image Deblurring Using Dark Channel Prior	A conditional general adversarial network (GAN) is proposed for image deblurring problem. It is tailored for image deblurring instead of just applying GAN on the deblurring problem. Motivated by that, dark channel prior is carefully picked to be incorporated into the loss function for network training. To make it more ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	122,942
2401.13327	Generating Synthetic Health Sensor Data for Privacy-Preserving Wearable Stress Detection	Smartwatch health sensor data are increasingly utilized in smart health applications and patient monitoring, including stress detection. However, such medical data often comprise sensitive personal information and are resource-intensive to acquire for research purposes. In response to this challenge, we introduce the p...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	423,693
1901.02514	Autoencoders and Generative Adversarial Networks for Imbalanced Sequence Classification	Generative Adversarial Networks (GANs) have been used in many different applications to generate realistic synthetic data. We introduce a novel GAN with Autoencoder (GAN-AE) architecture to generate synthetic samples for variable length, multi-feature sequence datasets. In this model, we develop a GAN architecture with...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	118,222
2407.00129	Multimodal Learning and Cognitive Processes in Radiology: MedGaze for Chest X-ray Scanpath Prediction	Predicting human gaze behavior within computer vision is integral for developing interactive systems that can anticipate user attention, address fundamental questions in cognitive science, and hold implications for fields like human-computer interaction (HCI) and augmented/virtual reality (AR/VR) systems. Despite metho...	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	468,741
2304.04757	A new perspective on building efficient and expressive 3D equivariant graph neural networks	Geometric deep learning enables the encoding of physical symmetries in modeling 3D objects. Despite rapid progress in encoding 3D symmetries into Graph Neural Networks (GNNs), a comprehensive evaluation of the expressiveness of these networks through a local-to-global analysis lacks today. In this paper, we propose a l...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	357,345
2403.02607	MEBS: Multi-task End-to-end Bid Shading for Multi-slot Display Advertising	Online bidding and auction are crucial aspects of the online advertising industry. Conventionally, there is only one slot for ad display and most current studies focus on it. Nowadays, multi-slot display advertising is gradually becoming popular where many ads could be displayed in a list and shown as a whole to users....	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	434,860
1811.02797	Deep Neural Networks for ECG-free Cardiac Phase and End-Diastolic Frame Detection on Coronary Angiographies	Invasive coronary angiography (ICA) is the gold standard in Coronary Artery Disease (CAD) imaging. Detection of the end-diastolic frame (EDF) and, in general, cardiac phase detection on each temporal frame of a coronary angiography acquisition is of significant importance for the anatomical and non-invasive functional ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	112,692
1601.06362	Progress on High-rate MSR Codes: Enabling Arbitrary Number of Helper Nodes	This paper presents a construction for high-rate MDS codes that enable bandwidth-efficient repair of a single node. Such MDS codes are also referred to as the minimum storage regenerating (MSR) codes in the distributed storage literature. The construction presented in this paper generates MSR codes for all possible num...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	51,268
2202.04494	A Circle Grid-based Approach for Obstacle Avoidance Motion Planning of Unmanned Surface Vehicles	Aiming at an obstacle avoidance problem with dynamic constraints for Unmanned Surface Vehicle (USV), a method based on Circle Grid Trajectory Cell (CGTC) is proposed. Firstly, the ship model and standardization rules are constructed to develop and constrain the trajectory, respectively. Secondly, by analyzing the prope...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	279,567
2007.07878	Quantifying and Reducing Bias in Maximum Likelihood Estimation of Structured Anomalies	Anomaly estimation, or the problem of finding a subset of a dataset that differs from the rest of the dataset, is a classic problem in machine learning and data mining. In both theoretical work and in applications, the anomaly is assumed to have a specific structure defined by membership in an $\textit{anomaly family}$...	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	187,461
1802.09681	On Euler Emulation of Observer-Based Stabilizers for Nonlinear Time-Delay Systems	In this paper, we deal with the problem of the stabilization in the sample-and-hold sense, by emulation of continuous-time, observer-based, global stabilizers. Fully nonlinear time-delay systems are studied. Sufficient conditions are provided such that the Euler approximation of continuous-time, observer-based, global ...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	91,368
2501.12594	A 3-Step Optimization Framework with Hybrid Models for a Humanoid Robot's Jump Motion	High dynamic jump motions are challenging tasks for humanoid robots to achieve environment adaptation and obstacle crossing. The trajectory optimization is a practical method to achieve high-dynamic and explosive jumping. This paper proposes a 3-step trajectory optimization framework for generating a jump motion for a ...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	526,369
2111.13585	Evaluating importance of nodes in complex networks with local volume information dimension	How to evaluate the importance of nodes is essential in research of complex network. There are many methods proposed for solving this problem, but they still have room to be improved. In this paper, a new approach called local volume information dimension is proposed. In this method, the sum of degree of nodes within d...	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	false	false	false	268,329
2210.08265	Bearing-based Relative Localization for Robotic Swarm with Partially Mutual Observations	Mutual localization provides a consensus of reference frame as an essential basis for cooperation in multirobot systems. Previous works have developed certifiable and robust solvers for relative transformation estimation between each pair of robots. However, recovering relative poses for robotic swarm with partially mu...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	324,066
2101.02500	Bridging In- and Out-of-distribution Samples for Their Better Discriminability	This paper proposes a method for OOD detection. Questioning the premise of previous studies that ID and OOD samples are separated distinctly, we consider samples lying in the intermediate of the two and use them for training a network. We generate such samples using multiple image transformations that corrupt inputs in...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	214,649
1901.10435	A Deep Learning Framework for Assessing Physical Rehabilitation Exercises	Computer-aided assessment of physical rehabilitation entails evaluation of patient performance in completing prescribed rehabilitation exercises, based on processing movement data captured with a sensory system. Despite the essential role of rehabilitation assessment toward improved patient outcomes and reduced healthc...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	120,019
2406.15639	Low Fidelity Visuo-Tactile Pretraining Improves Vision-Only Manipulation Performance	Tactile perception is a critical component of solving real-world manipulation tasks, but tactile sensors for manipulation have barriers to use such as fragility and cost. In this work, we engage a robust, low-cost tactile sensor, BeadSight, as an alternative to precise pre-calibrated sensors for a pretraining approach ...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	466,802
2410.04652	Multimodal 3D Fusion and In-Situ Learning for Spatially Aware AI	Seamless integration of virtual and physical worlds in augmented reality benefits from the system semantically "understanding" the physical environment. AR research has long focused on the potential of context awareness, demonstrating novel capabilities that leverage the semantics in the 3D environment for various obje...	true	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	495,379
1910.13439	Learning to Manipulate Deformable Objects without Demonstrations	In this paper we tackle the problem of deformable object manipulation through model-free visual reinforcement learning (RL). In order to circumvent the sample inefficiency of RL, we propose two key ideas that accelerate learning. First, we propose an iterative pick-place action space that encodes the conditional relati...	false	false	false	false	false	false	true	true	false	false	false	true	false	false	false	false	false	false	151,391
2203.11702	BERT-ASC: Auxiliary-Sentence Construction for Implicit Aspect Learning in Sentiment Analysis	Aspect-based sentiment analysis (ABSA) aims to associate a text with a set of aspects and infer their respective sentimental polarities. State-of-the-art approaches are built on fine-tuning pre-trained language models, focusing on learning aspect-specific representations from the corpus. However, aspects are often expr...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	287,010
2212.04074	Cross-view Geo-localization via Learning Disentangled Geometric Layout Correspondence	Cross-view geo-localization aims to estimate the location of a query ground image by matching it to a reference geo-tagged aerial images database. As an extremely challenging task, its difficulties root in the drastic view changes and different capturing time between two views. Despite these difficulties, recent works ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	335,314
2304.14500	SRCNet: Seminal Representation Collaborative Network for Marine Oil Spill Segmentation	Effective oil spill segmentation in Synthetic Aperture Radar (SAR) images is critical for marine oil pollution cleanup, and proper image representation is helpful for accurate image segmentation. In this paper, we propose an effective oil spill image segmentation network named SRCNet by leveraging SAR image representat...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	360,988
2312.16427	Learning to Embed Time Series Patches Independently	Masked time series modeling has recently gained much attention as a self-supervised representation learning strategy for time series. Inspired by masked image modeling in computer vision, recent works first patchify and partially mask out time series, and then train Transformers to capture the dependencies between patc...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	418,381
2104.08305	Membership Inference Attack Susceptibility of Clinical Language Models	Deep Neural Network (DNN) models have been shown to have high empirical privacy leakages. Clinical language models (CLMs) trained on clinical data have been used to improve performance in biomedical natural language processing tasks. In this work, we investigate the risks of training-data leakage through white-box or b...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	230,739
2402.18553	Selection of appropriate multispectral camera exposure settings and radiometric calibration methods for applications in phenotyping and precision agriculture	Radiometric accuracy of data is crucial in quantitative precision agriculture, to produce reliable and repeatable data for modeling and decision making. The effect of exposure time and gain settings on the radiometric accuracy of multispectral images was not explored enough. The goal of this study was to determine if h...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	433,467
1909.00025	Meta-Learning with Warped Gradient Descent	Learning an efficient update rule from data that promotes rapid learning of new tasks from the same distribution remains an open problem in meta-learning. Typically, previous works have approached this issue either by attempting to train a neural network that directly produces updates or by attempting to learn better i...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	true	false	false	143,499
2303.13773	Graph Neural Networks for the Offline Nanosatellite Task Scheduling Problem	This study investigates how to schedule nanosatellite tasks more efficiently using Graph Neural Networks (GNNs). In the Offline Nanosatellite Task Scheduling (ONTS) problem, the goal is to find the optimal schedule for tasks to be carried out in orbit while taking into account Quality-of-Service (QoS) considerations su...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	353,815
1707.00587	Automatic Cardiac Disease Assessment on cine-MRI via Time-Series Segmentation and Domain Specific Features	Cardiac magnetic resonance imaging improves on diagnosis of cardiovascular diseases by providing images at high spatiotemporal resolution. Manual evaluation of these time-series, however, is expensive and prone to biased and non-reproducible outcomes. In this paper, we present a method that addresses named limitations ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	76,377
2305.14587	Contextualized Topic Coherence Metrics	The recent explosion in work on neural topic modeling has been criticized for optimizing automated topic evaluation metrics at the expense of actual meaningful topic identification. But human annotation remains expensive and time-consuming. We propose LLM-based methods inspired by standard human topic evaluations, in a...	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	367,112
2012.02360	Research Progress of News Recommendation Methods	Due to researchers'aim to study personalized recommendations for different business fields, the summary of recommendation methods in specific fields is of practical significance. News recommendation systems were the earliest research field regarding recommendation systems, and were also the earliest recommendation fiel...	false	false	false	false	true	true	false	false	true	false	false	false	false	false	false	false	false	false	209,742
2411.05193	Q-SFT: Q-Learning for Language Models via Supervised Fine-Tuning	Value-based reinforcement learning (RL) can in principle learn effective policies for a wide range of multi-turn problems, from games to dialogue to robotic control, including via offline RL from static previously collected datasets. However, despite the widespread use of policy gradient methods to train large language...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	506,568
2410.14929	Water quality polluted by total suspended solids classified within an Artificial Neural Network approach	This study investigates the application of an artificial neural network framework for analysing water pollution caused by solids. Water pollution by suspended solids poses significant environmental and health risks. Traditional methods for assessing and predicting pollution levels are often time-consuming and resource-...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	500,267
2411.14797	Continual SFT Matches Multimodal RLHF with Negative Supervision	Multimodal RLHF usually happens after supervised finetuning (SFT) stage to continually improve vision-language models' (VLMs) comprehension. Conventional wisdom holds its superiority over continual SFT during this preference alignment stage. In this paper, we observe that the inherent value of multimodal RLHF lies in i...	false	false	false	false	true	false	true	false	true	false	false	true	false	false	false	false	false	false	510,332
2007.15646	Rewriting a Deep Generative Model	A deep generative model such as a GAN learns to model a rich set of semantic and physical rules about the target distribution, but up to now, it has been obscure how such rules are encoded in the network, or how a rule could be changed. In this paper, we introduce a new problem setting: manipulation of specific rules e...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	true	189,721
2402.06436	Improving 2D-3D Dense Correspondences with Diffusion Models for 6D Object Pose Estimation	Estimating 2D-3D correspondences between RGB images and 3D space is a fundamental problem in 6D object pose estimation. Recent pose estimators use dense correspondence maps and Point-to-Point algorithms to estimate object poses. The accuracy of pose estimation depends heavily on the quality of the dense correspondence ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	428,297
2310.04102	Nash Welfare and Facility Location	We consider the problem of locating a facility to serve a set of agents located along a line. The Nash welfare objective function, defined as the product of the agents' utilities, is known to provide a compromise between fairness and efficiency in resource allocation problems. We apply this welfare notion to the facili...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	true	false	false	true	397,534
2202.01246	PolarDenseNet: A Deep Learning Model for CSI Feedback in MIMO Systems	In multiple-input multiple-output (MIMO) systems, the high-resolution channel information (CSI) is required at the base station (BS) to ensure optimal performance, especially in the case of multi-user MIMO (MU-MIMO) systems. In the absence of channel reciprocity in frequency division duplex (FDD) systems, the user need...	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	false	false	278,409
2405.15006	A rescaling-invariant Lipschitz bound based on path-metrics for modern ReLU network parameterizations	Lipschitz bounds on neural network parameterizations are important to establish generalization, quantization or pruning guarantees, as they control the robustness of the network with respect to parameter changes. Yet, there are few Lipschitz bounds with respect to parameters in the literature, and existing ones only ap...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	456,695
2412.05566	Dif4FF: Leveraging Multimodal Diffusion Models and Graph Neural Networks for Accurate New Fashion Product Performance Forecasting	In the fast-fashion industry, overproduction and unsold inventory create significant environmental problems. Precise sales forecasts for unreleased items could drastically improve the efficiency and profits of industries. However, predicting the success of entirely new styles is difficult due to the absence of past dat...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	514,879
2411.00186	Self-Healing Machine Learning: A Framework for Autonomous Adaptation in Real-World Environments	Real-world machine learning systems often encounter model performance degradation due to distributional shifts in the underlying data generating process (DGP). Existing approaches to addressing shifts, such as concept drift adaptation, are limited by their reason-agnostic nature. By choosing from a pre-defined set of a...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	504,473
2009.07983	Strategy Proof Mechanisms for Facility Location in Euclidean and Manhattan Space	We study the impact on mechanisms for facility location of moving from one dimension to two (or more) dimensions and Euclidean or Manhattan distances. We consider three fundamental axiomatic properties: anonymity which is a basic fairness property, Pareto optimality which is one of the most important efficiency propert...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	196,092
1402.0563	Evaluating Indirect Strategies for Chinese-Spanish Statistical Machine Translation	Although, Chinese and Spanish are two of the most spoken languages in the world, not much research has been done in machine translation for this language pair. This paper focuses on investigating the state-of-the-art of Chinese-to-Spanish statistical machine translation (SMT), which nowadays is one of the most popular ...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	30,579
2406.09323	Master of Disaster: A Disaster-Related Event Monitoring System From News Streams	The need for a disaster-related event monitoring system has arisen due to the societal and economic impact caused by the increasing number of severe disaster events. An event monitoring system should be able to extract event-related information from texts, and discriminates event instances. We demonstrate our open-sour...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	463,871
2401.12798	Gradient Flow of Energy: A General and Efficient Approach for Entity Alignment Decoding	Entity alignment (EA), a pivotal process in integrating multi-source Knowledge Graphs (KGs), seeks to identify equivalent entity pairs across these graphs. Most existing approaches regard EA as a graph representation learning task, concentrating on enhancing graph encoders. However, the decoding process in EA - essenti...	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	423,499
1910.05485	A preference learning framework for multiple criteria sorting with diverse additive value models and valued assignment examples	We present a preference learning framework for multiple criteria sorting. We consider sorting procedures applying an additive value model with diverse types of marginal value functions (including linear, piecewise-linear, splined, and general monotone ones) under a unified analytical framework. Differently from the exi...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	149,080
1709.05475	Order-Preserving Abstractive Summarization for Spoken Content Based on Connectionist Temporal Classification	Connectionist temporal classification (CTC) is a powerful approach for sequence-to-sequence learning, and has been popularly used in speech recognition. The central ideas of CTC include adding a label "blank" during training. With this mechanism, CTC eliminates the need of segment alignment, and hence has been applied ...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	80,882
2309.11880	Polynomial growth in degree-dependent first passage percolation on spatial random graphs	In this paper we study a version of (non-Markovian) first passage percolation on graphs, where the transmission time between two connected vertices is non-iid, but increases by a penalty factor polynomial in their expected degrees. Based on the exponent of the penalty-polynomial, this makes it increasingly harder to tr...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	393,576
2309.13394	Smart City Digital Twin Framework for Real-Time Multi-Data Integration and Wide Public Distribution	Digital Twins are digital replica of real entities and are becoming fundamental tools to monitor and control the status of entities, predict their future evolutions, and simulate alternative scenarios to understand the impact of changes. Thanks to the large deployment of sensors, with the increasing information it is p...	false	false	false	false	true	false	false	false	false	false	false	false	false	true	false	false	false	false	394,188
2407.20987	PIXELMOD: Improving Soft Moderation of Visual Misleading Information on Twitter	Images are a powerful and immediate vehicle to carry misleading or outright false messages, yet identifying image-based misinformation at scale poses unique challenges. In this paper, we present PIXELMOD, a system that leverages perceptual hashes, vector databases, and optical character recognition (OCR) to efficiently...	false	false	false	false	false	false	false	false	false	false	false	true	false	true	false	false	false	false	477,347
2101.06800	Heterogeneous Similarity Graph Neural Network on Electronic Health Records	Mining Electronic Health Records (EHRs) becomes a promising topic because of the rich information they contain. By learning from EHRs, machine learning models can be built to help human experts to make medical decisions and thus improve healthcare quality. Recently, many models based on sequential or graph models are p...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	215,836
2302.13088	Kahramanmaras-Gaziantep, Turkiye Mw 7.8 Earthquake on February 6, 2023: Preliminary Report on Strong Ground Motion and Building Response Estimations	The effects on structures of the earthquake with magnitude 7.8 on the Richter scale (moment magnitude scale) which took place in Pazarcik, Kahramanmaras, Turkiye at 04:17 a.m. local time (01:17 UTC) on February 6, 2023, are investigated by processing suitable seismic records using the open-source software OpenSeismoMat...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	347,806
2207.13280	On-Device CPU Scheduling for Sense-React Systems	Sense-react systems (e.g. robotics and AR/VR) have to take highly responsive real-time actions, driven by complex decisions involving a pipeline of sensing, perception, planning, and reaction tasks. These tasks must be scheduled on resource-constrained devices such that the performance goals and the requirements of the...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	310,250
1205.0908	Weighted Patterns as a Tool for Improving the Hopfield Model	We generalize the standard Hopfield model to the case when a weight is assigned to each input pattern. The weight can be interpreted as the frequency of the pattern occurrence at the input of the network. In the framework of the statistical physics approach we obtain the saddle-point equation allowing us to examine the...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	true	false	false	15,793
2401.16911	Generalized Reed-Muller codes: A new construction of information sets	In [2] we show how to construct information sets for Reed-Muller codes only in terms of their basic parameters. In this work we deal with the corresponding problem for q-ary Generalized Reed-Muller codes of first and second order. We see that for first-order codes the result for binary Reed-Muller codes is also valid, ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	425,027
2201.11066	Server-Side Stepsizes and Sampling Without Replacement Provably Help in Federated Optimization	We present a theoretical study of server-side optimization in federated learning. Our results are the first to show that the widely popular heuristic of scaling the client updates with an extra parameter is very useful in the context of Federated Averaging (FedAvg) with local passes over the client data. Each local pas...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	277,173
1809.10271	Batch-normalized Recurrent Highway Networks	Gradient control plays an important role in feed-forward networks applied to various computer vision tasks. Previous work has shown that Recurrent Highway Networks minimize the problem of vanishing or exploding gradients. They achieve this by setting the eigenvalues of the temporal Jacobian to 1 across the time steps. ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	108,871
2406.06056	Synth-SBDH: A Synthetic Dataset of Social and Behavioral Determinants of Health for Clinical Text	Social and behavioral determinants of health (SBDH) play a crucial role in health outcomes and are frequently documented in clinical text. Automatically extracting SBDH information from clinical text relies on publicly available good-quality datasets. However, existing SBDH datasets exhibit substantial limitations in t...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	462,422
2203.09679	Modeling Intensification for Sign Language Generation: A Computational Approach	End-to-end sign language generation models do not accurately represent the prosody in sign language. A lack of temporal and spatial variations leads to poor-quality generated presentations that confuse human interpreters. In this paper, we aim to improve the prosody in generated sign languages by modeling intensificati...	false	false	false	false	true	false	false	false	true	false	false	true	false	false	false	false	false	false	286,243
1810.06118	Learning to fail: Predicting fracture evolution in brittle material models using recurrent graph convolutional neural networks	We propose a machine learning approach to address a key challenge in materials science: predicting how fractures propagate in brittle materials under stress, and how these materials ultimately fail. Our methods use deep learning and train on simulation data from high-fidelity models, emulating the results of these mode...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	110,380
1704.08815	Generator polynomials and generator matrix for quasi cyclic codes	Quasi-cyclic (QC) codes form an important generalization of cyclic codes. It is well know that QC codes of length $s\ell$ with index $s$ over the finite field $\mathbb{F}$ are $\mathbb{F}[y]$-submodules of the ring $\frac{\mathbb{F}[x,y]}{< x^s-1,y^{\ell}-1 >}$. The aim of the present paper, is to study QC codes of len...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	72,575
2008.00542	Efficient Deep Learning of Non-local Features for Hyperspectral Image Classification	Deep learning based methods, such as Convolution Neural Network (CNN), have demonstrated their efficiency in hyperspectral image (HSI) classification. These methods can automatically learn spectral-spatial discriminative features within local patches. However, for each pixel in an HSI, it is not only related to its nea...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	190,040
2211.11087	Conceptor-Aided Debiasing of Large Language Models	Pre-trained large language models (LLMs) reflect the inherent social biases of their training corpus. Many methods have been proposed to mitigate this issue, but they often fail to debias or they sacrifice model accuracy. We use conceptors--a soft projection method--to identify and remove the bias subspace in LLMs such...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	331,576
1609.02911	Entropy of the Sum of Two Independent, Non-Identically-Distributed Exponential Random Variables	In this letter, we give a concise, closed-form expression for the differential entropy of the sum of two independent, non-identically-distributed exponential random variables. The derivation is straightforward, but such a concise entropy has not been previously given in the literature. The usefulness of the expression ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	60,796
1403.6952	Optimal pricing control in distribution networks with time-varying supply and demand	This paper studies the problem of optimal flow control in dynamic inventory systems. A dynamic optimal distribution problem, including time-varying supply and demand, capacity constraints on the transportation lines, and convex flow cost functions of Legendre-type, is formalized and solved. The time-varying optimal flo...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	31,854
2211.16291	On Controller Reduction in Linear Quadratic Gaussian Control with Performance Bounds	The problem of controller reduction has a rich history in control theory. Yet, many questions remain open. In particular, there exist very few results on the order reduction of general non-observer based controllers and the subsequent quantification of the closed-loop performance. Recent developments in model-free poli...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	333,589
2411.06518	Causal Representation Learning from Multimodal Biological Observations	Prevalent in biological applications (e.g., human phenotype measurements), multimodal datasets can provide valuable insights into the underlying biological mechanisms. However, current machine learning models designed to analyze such datasets still lack interpretability and theoretical guarantees, which are essential t...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	507,146
1808.03227	Identifying Protein-Protein Interaction using Tree LSTM and Structured Attention	Identifying interactions between proteins is important to understand underlying biological processes. Extracting a protein-protein interaction (PPI) from the raw text is often very difficult. Previous supervised learning methods have used handcrafted features on human-annotated data sets. In this paper, we propose a no...	false	false	false	false	false	true	true	false	true	false	false	false	false	false	false	false	false	false	104,894
1903.10605	Q-Learning for Continuous Actions with Cross-Entropy Guided Policies	Off-Policy reinforcement learning (RL) is an important class of methods for many problem domains, such as robotics, where the cost of collecting data is high and on-policy methods are consequently intractable. Standard methods for applying Q-learning to continuous-valued action domains involve iteratively sampling the ...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	125,312

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