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
2308.08167	A Quantum Approximation Scheme for k-Means	We give a quantum approximation scheme (i.e., $(1 + \varepsilon)$-approximation for every $\varepsilon > 0$) for the classical $k$-means clustering problem in the QRAM model with a running time that has only polylogarithmic dependence on the number of data points. More specifically, given a dataset $V$ with $N$ points ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	385,795
2407.04061	Detect Closer Surfaces that can be Seen: New Modeling and Evaluation in Cross-domain 3D Object Detection	The performance of domain adaptation technologies has not yet reached an ideal level in the current 3D object detection field for autonomous driving, which is mainly due to significant differences in the size of vehicles, as well as the environments they operate in when applied across domains. These factors together hi...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	470,409
2402.09650	Foul prediction with estimated poses from soccer broadcast video	Recent advances in computer vision have made significant progress in tracking and pose estimation of sports players. However, there have been fewer studies on behavior prediction with pose estimation in sports, in particular, the prediction of soccer fouls is challenging because of the smaller image size of each player...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	429,613
1809.09747	Analyzing CDR/IPDR data to find People Network from Encrypted Messaging Services	Criminals are increasingly using mobile based communication applications, like WhatsApp, that have end-to-end encryption to connect to each other. This makes traditional analysis of call graphs, or traffic analysis, virtually impossible and so is a hindrance for law enforcement personnel. Old methods of traffic analysi...	false	false	false	true	false	false	false	false	false	false	false	false	true	false	false	false	false	false	108,763
2410.24200	Length-Induced Embedding Collapse in Transformer-based Models	Text embeddings enable various applications, but their performance deteriorates on longer texts. In this paper, we find that the performance degradation is due to a phenomenon called Length Collapse, where longer text embeddings collapse into a narrow space. This collapse results in a distributional inconsistency betwe...	false	false	false	false	true	true	false	false	true	false	false	false	false	false	false	false	false	false	504,373
2308.09285	RFDforFin: Robust Deep Forgery Detection for GAN-generated Fingerprint Images	With the rapid development of the image generation technologies, the malicious abuses of the GAN-generated fingerprint images poses a significant threat to the public safety in certain circumstances. Although the existing universal deep forgery detection approach can be applied to detect the fake fingerprint images, th...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	386,221
1507.03729	Optimization of Code Rates in SISOME Wiretap Channels	We propose a new framework for determining the wiretap code rates of single-input single-output multi-antenna eavesdropper (SISOME) wiretap channels when the capacity of the eavesdropper's channel is not available at the transmitter. In our framework we introduce the effective secrecy throughput (EST) as a new performa...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	45,099
2405.15778	Investigation of Energy-efficient AI Model Architectures and Compression Techniques for "Green" Fetal Brain Segmentation	Artificial intelligence have contributed to advancements across various industries. However, the rapid growth of artificial intelligence technologies also raises concerns about their environmental impact, due to associated carbon footprints to train computational models. Fetal brain segmentation in medical imaging is c...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	true	457,094
2301.01087	Neural Point Catacaustics for Novel-View Synthesis of Reflections	View-dependent effects such as reflections pose a substantial challenge for image-based and neural rendering algorithms. Above all, curved reflectors are particularly hard, as they lead to highly non-linear reflection flows as the camera moves. We introduce a new point-based representation to compute Neural Point Catac...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	339,126
1403.2871	Shape-Based Plagiarism Detection for Flowchart Figures in Texts	Plagiarism detection is well known phenomenon in the academic arena. Copying other people is considered as serious offence that needs to be checked. There are many plagiarism detection systems such as turn-it-in that has been developed to provide this checks. Most, if not all, discard the figures and charts before chec...	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	false	31,516
1805.08349	A Solvable High-Dimensional Model of GAN	We present a theoretical analysis of the training process for a single-layer GAN fed by high-dimensional input data. The training dynamics of the proposed model at both microscopic and macroscopic scales can be exactly analyzed in the high-dimensional limit. In particular, we prove that the macroscopic quantities measu...	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	98,115
2111.10302	Instance-Adaptive Video Compression: Improving Neural Codecs by Training on the Test Set	We introduce a video compression algorithm based on instance-adaptive learning. On each video sequence to be transmitted, we finetune a pretrained compression model. The optimal parameters are transmitted to the receiver along with the latent code. By entropy-coding the parameter updates under a suitable mixture model ...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	267,280
1805.03110	Secret Key Generation for Minimally Connected Hypergraphical Sources	This paper investigates the secret key generation in the multiterminal source model, where users observing correlated sources discuss interactively under limited rates to agree on a secret key. We focus on a class of sources representable by minimally connected hypergraphs. For such sources, we give a single-letter exp...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	96,988
2012.12265	Generative Interventions for Causal Learning	We introduce a framework for learning robust visual representations that generalize to new viewpoints, backgrounds, and scene contexts. Discriminative models often learn naturally occurring spurious correlations, which cause them to fail on images outside of the training distribution. In this paper, we show that we can...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	212,875
2304.10984	IBBT: Informed Batch Belief Trees for Motion Planning Under Uncertainty	In this work, we propose the Informed Batch Belief Trees (IBBT) algorithm for motion planning under motion and sensing uncertainties. The original stochastic motion planning problem is divided into a deterministic motion planning problem and a graph search problem. We solve the deterministic planning problem using samp...	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	false	359,625
2210.05839	SEAL : Interactive Tool for Systematic Error Analysis and Labeling	With the advent of Transformers, large language models (LLMs) have saturated well-known NLP benchmarks and leaderboards with high aggregate performance. However, many times these models systematically fail on tail data or rare groups not obvious in aggregate evaluation. Identifying such problematic data groups is even ...	true	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	323,022
2206.12464	Motion Estimation for Large Displacements and Deformations	Large displacement optical flow is an integral part of many computer vision tasks. Variational optical flow techniques based on a coarse-to-fine scheme interpolate sparse matches and locally optimize an energy model conditioned on colour, gradient and smoothness, making them sensitive to noise in the sparse matches, de...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	304,608
2104.07878	Histopathology WSI Encoding based on GCNs for Scalable and Efficient Retrieval of Diagnostically Relevant Regions	Content-based histopathological image retrieval (CBHIR) has become popular in recent years in the domain of histopathological image analysis. CBHIR systems provide auxiliary diagnosis information for pathologists by searching for and returning regions that are contently similar to the region of interest (ROI) from a pr...	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	false	230,582
2501.02410	JammingSnake: A follow-the-leader continuum robot with variable stiffness based on fiber jamming	Follow-the-leader (FTL) motion is essential for continuum robots operating in fragile and confined environments. It allows the robot to exert minimal force on its surroundings, reducing the risk of damage. This paper presents a novel design of a snake-like robot capable of achieving FTL motion by integrating fiber jamm...	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	522,471
2102.00461	Multilingual Email Zoning	The segmentation of emails into functional zones (also dubbed email zoning) is a relevant preprocessing step for most NLP tasks that deal with emails. However, despite the multilingual character of emails and their applications, previous literature regarding email zoning corpora and systems was developed essentially fo...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	217,790
2202.00817	Do Differentiable Simulators Give Better Policy Gradients?	Differentiable simulators promise faster computation time for reinforcement learning by replacing zeroth-order gradient estimates of a stochastic objective with an estimate based on first-order gradients. However, it is yet unclear what factors decide the performance of the two estimators on complex landscapes that inv...	false	false	false	false	true	false	true	true	false	false	false	false	false	false	false	false	false	false	278,271
2301.03167	Machining feature recognition using descriptors with range constraints for mechanical 3D models	In machining feature recognition, geometric elements generated in a three-dimensional computer-aided design model are identified. This technique is used in manufacturability evaluation, process planning, and tool path generation. Here, we propose a method of recognizing 16 types of machining features using descriptors,...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	339,720
2207.03435	Sociable and Ergonomic Human-Robot Collaboration through Action Recognition and Augmented Hierarchical Quadratic Programming	The recognition of actions performed by humans and the anticipation of their intentions are important enablers to yield sociable and successful collaboration in human-robot teams. Meanwhile, robots should have the capacity to deal with multiple objectives and constraints, arising from the collaborative task or the huma...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	306,844
1809.05890	Semantic Interoperability Middleware Architecture for Heterogeneous Environmental Data Sources	Data heterogeneity hampers the effort to integrate and infer knowledge from vast heterogeneous data sources. An application case study is described, in which the objective was to semantically represent and integrate structured data from sensor devices with unstructured data in the form of local indigenous knowledge. Ho...	false	false	false	false	true	true	false	false	false	false	false	false	false	true	false	false	false	false	107,904
2012.03386	SoK: Training Machine Learning Models over Multiple Sources with Privacy Preservation	Nowadays, gathering high-quality training data from multiple data sources with privacy preservation is a crucial challenge to training high-performance machine learning models. The potential solutions could break the barriers among isolated data corpus, and consequently enlarge the range of data available for processin...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	210,087
1706.01406	NullHop: A Flexible Convolutional Neural Network Accelerator Based on Sparse Representations of Feature Maps	Convolutional neural networks (CNNs) have become the dominant neural network architecture for solving many state-of-the-art (SOA) visual processing tasks. Even though Graphical Processing Units (GPUs) are most often used in training and deploying CNNs, their power efficiency is less than 10 GOp/s/W for single-frame run...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	true	false	false	74,797
2401.15739	SegmentAnyTree: A sensor and platform agnostic deep learning model for tree segmentation using laser scanning data	This research advances individual tree crown (ITC) segmentation in lidar data, using a deep learning model applicable to various laser scanning types: airborne (ULS), terrestrial (TLS), and mobile (MLS). It addresses the challenge of transferability across different data characteristics in 3D forest scene analysis. The...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	424,567
2008.06255	WAN: Watermarking Attack Network	Multi-bit watermarking (MW) has been developed to improve robustness against signal processing operations and geometric distortions. To this end, benchmark tools that test robustness by applying simulated attacks on watermarked images are available. However, limitations in these general attacks exist since they cannot ...	false	false	false	false	false	false	false	false	false	false	false	true	true	false	false	false	false	true	191,750
1709.02435	An Analysis of ISO 26262: Using Machine Learning Safely in Automotive Software	Machine learning (ML) plays an ever-increasing role in advanced automotive functionality for driver assistance and autonomous operation; however, its adequacy from the perspective of safety certification remains controversial. In this paper, we analyze the impacts that the use of ML as an implementation approach has on...	false	false	false	false	true	false	true	false	false	false	true	false	false	false	false	false	false	true	80,260
2207.04384	Sparse and Safe Frequency Regulation for Inverter Intensive Microgrids	This paper developed a novel control approach for the sparse and safe frequency regulation for inverter intensive microgrids (MGs). In the scenario, the inverters and external grids are expected to reach a synchronized desired frequency under regulations. To this end, the active power set-point acting as a control from...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	307,183
2208.00031	Paddy Leaf diseases identification on Infrared Images based on Convolutional Neural Networks	Agriculture is the mainstay of human society because it is an essential need for every organism. Paddy cultivation is very significant so far as humans are concerned, largely in the Asian continent, and it is one of the staple foods. However, plant diseases in agriculture lead to depletion in productivity. Plant diseas...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	310,719
2304.10770	DEIR: Efficient and Robust Exploration through Discriminative-Model-Based Episodic Intrinsic Rewards	Exploration is a fundamental aspect of reinforcement learning (RL), and its effectiveness is a deciding factor in the performance of RL algorithms, especially when facing sparse extrinsic rewards. Recent studies have shown the effectiveness of encouraging exploration with intrinsic rewards estimated from novelties in o...	false	false	false	false	true	false	true	false	false	true	false	false	false	false	false	false	false	false	359,553
2408.08379	Towards Realistic Synthetic User-Generated Content: A Scaffolding Approach to Generating Online Discussions	The emergence of synthetic data represents a pivotal shift in modern machine learning, offering a solution to satisfy the need for large volumes of data in domains where real data is scarce, highly private, or difficult to obtain. We investigate the feasibility of creating realistic, large-scale synthetic datasets of u...	false	false	false	false	false	true	true	false	true	false	false	false	false	false	false	false	false	false	480,970
2012.10053	Instance Space Analysis for the Car Sequencing Problem	We investigate an important research question for solving the car sequencing problem, that is, which characteristics make an instance hard to solve? To do so, we carry out an instance space analysis for the car sequencing problem, by extracting a vector of problem features to characterize an instance. In order to visua...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	212,243
2408.11920	Modular Hypernetworks for Scalable and Adaptive Deep MIMO Receivers	Deep neural networks (DNNs) were shown to facilitate the operation of uplink multiple-input multiple-output (MIMO) receivers, with emerging architectures augmenting modules of classic receiver processing. Current designs consider static DNNs, whose architecture is fixed and weights are pre-trained. This induces a notab...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	482,504
2306.11548	Graph-Based Conditions for Feedback Stabilization of Switched and LPV Systems	This paper presents novel stabilizability conditions for switched linear systems with arbitrary and uncontrollable underlying switching signals. We distinguish and study two particular settings: i) the \emph{robust} case, in which the active mode is completely unknown and unobservable, and ii) the \emph{mode-dependent}...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	374,635
2012.11582	HyperSeg: Patch-wise Hypernetwork for Real-time Semantic Segmentation	We present a novel, real-time, semantic segmentation network in which the encoder both encodes and generates the parameters (weights) of the decoder. Furthermore, to allow maximal adaptivity, the weights at each decoder block vary spatially. For this purpose, we design a new type of hypernetwork, composed of a nested U...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	212,678
2211.11656	SIFU: Sequential Informed Federated Unlearning for Efficient and Provable Client Unlearning in Federated Optimization	Machine Unlearning (MU) is an increasingly important topic in machine learning safety, aiming at removing the contribution of a given data point from a training procedure. Federated Unlearning (FU) consists in extending MU to unlearn a given client's contribution from a federated training routine. While several FU meth...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	331,822
2005.03776	Mapping Natural Language Instructions to Mobile UI Action Sequences	We present a new problem: grounding natural language instructions to mobile user interface actions, and create three new datasets for it. For full task evaluation, we create PIXELHELP, a corpus that pairs English instructions with actions performed by people on a mobile UI emulator. To scale training, we decouple the l...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	176,251
2502.10417	Evolutionary Power-Aware Routing in VANETs using Monte-Carlo Simulation	This work addresses the reduction of power consumption of the AODV routing protocol in vehicular networks as an optimization problem. Nowadays, network designers focus on energy-aware communication protocols, specially to deploy wireless networks. Here, we introduce an automatic method to search for energy-efficient AO...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	false	true	533,862
2211.07096	Alternating Implicit Projected SGD and Its Efficient Variants for Equality-constrained Bilevel Optimization	Stochastic bilevel optimization, which captures the inherent nested structure of machine learning problems, is gaining popularity in many recent applications. Existing works on bilevel optimization mostly consider either unconstrained problems or constrained upper-level problems. This paper considers the stochastic bil...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	330,126
2407.11894	Deep Learning without Global Optimization by Random Fourier Neural Networks	We introduce a new training algorithm for variety of deep neural networks that utilize random complex exponential activation functions. Our approach employs a Markov Chain Monte Carlo sampling procedure to iteratively train network layers, avoiding global and gradient-based optimization while maintaining error control....	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	473,662
1802.07623	Explanations based on the Missing: Towards Contrastive Explanations with Pertinent Negatives	In this paper we propose a novel method that provides contrastive explanations justifying the classification of an input by a black box classifier such as a deep neural network. Given an input we find what should be %necessarily and minimally and sufficiently present (viz. important object pixels in an image) to justif...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	90,932
2204.12786	Machines of finite depth: towards a formalization of neural networks	We provide a unifying framework where artificial neural networks and their architectures can be formally described as particular cases of a general mathematical construction--machines of finite depth. Unlike neural networks, machines have a precise definition, from which several properties follow naturally. Machines of...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	293,603
1703.03773	Evolutionary Image Composition Using Feature Covariance Matrices	Evolutionary algorithms have recently been used to create a wide range of artistic work. In this paper, we propose a new approach for the composition of new images from existing ones, that retain some salient features of the original images. We introduce evolutionary algorithms that create new images based on a fitness...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	69,776
2105.04301	ADASYN-Random Forest Based Intrusion Detection Model	Intrusion detection has been a key topic in the field of cyber security, and the common network threats nowadays have the characteristics of varieties and variation. Considering the serious imbalance of intrusion detection datasets will result in low classification performance on attack behaviors of small sample size a...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	234,466
1606.09222	Penambahan emosi menggunakan metode manipulasi prosodi untuk sistem text to speech bahasa Indonesia	Adding an emotions using prosody manipulation method for Indonesian text to speech system. Text To Speech (TTS) is a system that can convert text in one language into speech, accordance with the reading of the text in the language used. The focus of this research is a natural sounding concept, the make "humanize" for t...	false	false	true	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	57,965
2204.11488	Mining and searching association relation of scientific papers based on deep learning	There is a complex correlation among the data of scientific papers. The phenomenon reveals the data characteristics, laws, and correlations contained in the data of scientific and technological papers in specific fields, which can realize the analysis of scientific and technological big data and help to design applicat...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	true	293,172
1512.03501	ClusPath: A Temporal-driven Clustering to Infer Typical Evolution Paths	We propose ClusPath, a novel algorithm for detecting general evolution tendencies in a population of entities. We show how abstract notions, such as the Swedish socio-economical model (in a political dataset) or the companies fiscal optimization (in an economical dataset) can be inferred from low-level descriptive feat...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	true	50,044
2108.01504	Ontology Modeling for Decentralized Household Energy Systems	In a decentralized household energy system consisting of various devices such as washing machines, heat pumps, and solar panels, understanding the electric energy consumption and production data at the granularity of the device helps end-users be closer to the system and further achieve the sustainability of energy use...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	249,048
2112.07082	DeepDiffusion: Unsupervised Learning of Retrieval-adapted Representations via Diffusion-based Ranking on Latent Feature Manifold	Unsupervised learning of feature representations is a challenging yet important problem for analyzing a large collection of multimedia data that do not have semantic labels. Recently proposed neural network-based unsupervised learning approaches have succeeded in obtaining features appropriate for classification of mul...	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	false	271,367
2304.08024	Smart farming using iot for efficient crop growth	In general. automated farming systems make decisions based on static models built from the properties of the plant. in the contrast, irrigation decisions in our suggested method are dynamically changing environmental conditions. the model"s learning process reveals the mathematical links between the environmental facto...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	358,565
2305.17323	Some Primal-Dual Theory for Subgradient Methods for Strongly Convex Optimization	We consider (stochastic) subgradient methods for strongly convex but potentially nonsmooth non-Lipschitz optimization. We provide new equivalent dual descriptions (in the style of dual averaging) for the classic subgradient method, the proximal subgradient method, and the switching subgradient method. These equivalence...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	368,522
2105.08534	PNLSS Toolbox 1.0	This is a demonstration of the PNLSS Toolbox 1.0. The toolbox is designed to identify polynomial nonlinear state-space models from data. Nonlinear state-space models can describe a wide range of nonlinear systems. An illustration is provided on experimental data of an electrical system mimicking the forced Duffing osci...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	235,787
2311.11193	Assessing AI Impact Assessments: A Classroom Study	Artificial Intelligence Impact Assessments ("AIIAs"), a family of tools that provide structured processes to imagine the possible impacts of a proposed AI system, have become an increasingly popular proposal to govern AI systems. Recent efforts from government or private-sector organizations have proposed many diverse ...	true	false	false	false	true	false	false	false	false	false	false	false	false	true	false	false	false	false	408,840
2012.11195	Personalized fall detection monitoring system based on learning from the user movements	Personalized fall detection system is shown to provide added and more benefits compare to the current fall detection system. The personalized model can also be applied to anything where one class of data is hard to gather. The results show that adapting to the user needs, improve the overall accuracy of the system. Fut...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	212,568
1804.00863	Deep Appearance Maps	We propose a deep representation of appearance, i. e., the relation of color, surface orientation, viewer position, material and illumination. Previous approaches have useddeep learning to extract classic appearance representationsrelating to reflectance model parameters (e. g., Phong) orillumination (e. g., HDR enviro...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	94,135
2307.06189	Cooperative Localization for Autonomous Underwater Vehicles -- a comprehensive review	Cooperative localization is an important technique in environments devoid of GPS-based localization, more so in underwater scenarios, where none of the terrestrial localization techniques based on radio frequency or optics are suitable due to severe attenuation. Given the large swaths of oceans and seas where autonomou...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	378,997
2312.11476	The geometry of flow: Advancing predictions of river geometry with multi-model machine learning	Hydraulic geometry parameters describing river hydrogeomorphic is important for flood forecasting. Although well-established, power-law hydraulic geometry curves have been widely used to understand riverine systems and mapping flooding inundation worldwide for the past 70 years, we have become increasingly aware of the...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	416,584
1309.1349	Ergodic Randomized Algorithms and Dynamics over Networks	Algorithms and dynamics over networks often involve randomization, and randomization may result in oscillating dynamics which fail to converge in a deterministic sense. In this paper, we observe this undesired feature in three applications, in which the dynamics is the randomized asynchronous counterpart of a well-beha...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	26,855
2311.12052	MagicPose: Realistic Human Poses and Facial Expressions Retargeting with Identity-aware Diffusion	In this work, we propose MagicPose, a diffusion-based model for 2D human pose and facial expression retargeting. Specifically, given a reference image, we aim to generate a person's new images by controlling the poses and facial expressions while keeping the identity unchanged. To this end, we propose a two-stage train...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	409,180
2201.10739	Infrared and visible image fusion based on Multi-State Contextual Hidden Markov Model	The traditional two-state hidden Markov model divides the high frequency coefficients only into two states (large and small states). Such scheme is prone to produce an inaccurate statistical model for the high frequency subband and reduces the quality of fusion result. In this paper, a fine-grained multi-state contextu...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	277,085
2407.13766	Visual Haystacks: A Vision-Centric Needle-In-A-Haystack Benchmark	Large Multimodal Models (LMMs) have made significant strides in visual question-answering for single images. Recent advancements like long-context LMMs have allowed them to ingest larger, or even multiple, images. However, the ability to process a large number of visual tokens does not guarantee effective retrieval and...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	474,514
2209.15036	Large-Scale Spatial Cross-Calibration of Hinode/SOT-SP and SDO/HMI	We investigate the cross-calibration of the Hinode/SOT-SP and SDO/HMI instrument meta-data, specifically the correspondence of the scaling and pointing information. Accurate calibration of these datasets gives the correspondence needed by inter-instrument studies and learning-based magnetogram systems, and is required ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	320,437
2006.14109	Scalable Data Classification for Security and Privacy	Content based data classification is an open challenge. Traditional Data Loss Prevention (DLP)-like systems solve this problem by fingerprinting the data in question and monitoring endpoints for the fingerprinted data. With a large number of constantly changing data assets in Facebook, this approach is both not scalabl...	false	false	false	false	true	false	false	false	false	false	false	false	true	true	false	false	false	false	184,126
1905.12698	Leveraging Latent Features for Local Explanations	As the application of deep neural networks proliferates in numerous areas such as medical imaging, video surveillance, and self driving cars, the need for explaining the decisions of these models has become a hot research topic, both at the global and local level. Locally, most explanation methods have focused on ident...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	132,844
cmp-lg/9606005	Part-of-Speech-Tagging using morphological information	This paper presents the results of an experiment to decide the question of authenticity of the supposedly spurious Rhesus - a attic tragedy sometimes credited to Euripides. The experiment involves use of statistics in order to test whether significant deviations in the distribution of word categories between Rhesus and...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	536,571
2306.12700	Accelerated Training via Incrementally Growing Neural Networks using Variance Transfer and Learning Rate Adaptation	We develop an approach to efficiently grow neural networks, within which parameterization and optimization strategies are designed by considering their effects on the training dynamics. Unlike existing growing methods, which follow simple replication heuristics or utilize auxiliary gradient-based local optimization, we...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	375,038
2209.08982	How to Adapt Pre-trained Vision-and-Language Models to a Text-only Input?	Current language models have been criticised for learning language from text alone without connection between words and their meaning. Consequently, multimodal training has been proposed as a way for creating models with better language understanding by providing the lacking connection. We focus on pre-trained multimod...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	318,332
2211.11191	Correlative Preference Transfer with Hierarchical Hypergraph Network for Multi-Domain Recommendation	Advanced recommender systems usually involve multiple domains (such as scenarios or categories) for various marketing strategies, and users interact with them to satisfy diverse demands. The goal of multi-domain recommendation (MDR) is to improve the recommendation performance of all domains simultaneously. Conventiona...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	331,628
2406.03746	Efficient Knowledge Infusion via KG-LLM Alignment	To tackle the problem of domain-specific knowledge scarcity within large language models (LLMs), knowledge graph-retrievalaugmented method has been proven to be an effective and efficient technique for knowledge infusion. However, existing approaches face two primary challenges: knowledge mismatch between public availa...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	461,373
1510.01116	Centrality metrics and localization in core-periphery networks	Two concepts of centrality have been defined in complex networks. The first considers the centrality of a node and many different metrics for it has been defined (e.g. eigenvector centrality, PageRank, non-backtracking centrality, etc). The second is related to a large scale organization of the network, the core-periph...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	47,582
1102.1963	On quantum limit of optical communications: concatenated codes and joint-detection receivers	When classical information is sent over a channel with quantum-state modulation alphabet, such as the free-space optical (FSO) channel, attaining the ultimate (Holevo) limit to channel capacity requires the receiver to make joint measurements over long codeword blocks. In recent work, we showed a receiver for a pure-st...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	9,096
2001.01506	Deceiving Image-to-Image Translation Networks for Autonomous Driving with Adversarial Perturbations	Deep neural networks (DNNs) have achieved impressive performance on handling computer vision problems, however, it has been found that DNNs are vulnerable to adversarial examples. For such reason, adversarial perturbations have been recently studied in several respects. However, most previous works have focused on imag...	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	159,500
2102.05960	Comparative Analysis of Machine Learning Approaches to Analyze and Predict the Covid-19 Outbreak	Background. Forecasting the time of forthcoming pandemic reduces the impact of diseases by taking precautionary steps such as public health messaging and raising the consciousness of doctors. With the continuous and rapid increase in the cumulative incidence of COVID-19, statistical and outbreak prediction models inclu...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	219,592
2007.08095	Synthesize, Execute and Debug: Learning to Repair for Neural Program Synthesis	The use of deep learning techniques has achieved significant progress for program synthesis from input-output examples. However, when the program semantics become more complex, it still remains a challenge to synthesize programs that are consistent with the specification. In this work, we propose SED, a neural program ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	187,522
2407.17216	Reduced-Space Iteratively Reweighted Second-Order Methods for Nonconvex Sparse Regularization	This paper explores a specific type of nonconvex sparsity-promoting regularization problems, namely those involving $\ell_p$-norm regularization, in conjunction with a twice continuously differentiable loss function. We propose a novel second-order algorithm designed to effectively address this class of challenging non...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	475,890
2307.00181	Influence maximization on temporal networks: a review	Influence maximization (IM) is an important topic in network science where a small seed set is chosen to maximize the spread of influence on a network. Recently, this problem has attracted attention on temporal networks where the network structure changes with time. IM on such dynamically varying networks is the topic ...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	376,903
1304.0018	Statistical inference framework for source detection of contagion processes on arbitrary network structures	In this paper we introduce a statistical inference framework for estimating the contagion source from a partially observed contagion spreading process on an arbitrary network structure. The framework is based on a maximum likelihood estimation of a partial epidemic realization and involves large scale simulation of con...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	23,358
1910.03875	How Well Do WGANs Estimate the Wasserstein Metric?	Generative modelling is often cast as minimizing a similarity measure between a data distribution and a model distribution. Recently, a popular choice for the similarity measure has been the Wasserstein metric, which can be expressed in the Kantorovich duality formulation as the optimum difference of the expected value...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	148,616
2311.01993	Active Exploration in Iterative Gaussian Process Regression for Uncertainty Modeling in Autonomous Racing	Autonomous racing creates challenging control problems, but Model Predictive Control (MPC) has made promising steps toward solving both the minimum lap-time problem and head-to-head racing. Yet, accurate models of the system are necessary for model-based control, including models of vehicle dynamics and opponent behavi...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	405,255
2211.03267	Prompter: Utilizing Large Language Model Prompting for a Data Efficient Embodied Instruction Following	Embodied Instruction Following (EIF) studies how autonomous mobile manipulation robots should be controlled to accomplish long-horizon tasks described by natural language instructions. While much research on EIF is conducted in simulators, the ultimate goal of the field is to deploy the agents in real life. This is one...	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	328,885
2102.08314	Tighter Bounds on the Log Marginal Likelihood of Gaussian Process Regression Using Conjugate Gradients	We propose a lower bound on the log marginal likelihood of Gaussian process regression models that can be computed without matrix factorisation of the full kernel matrix. We show that approximate maximum likelihood learning of model parameters by maximising our lower bound retains many of the sparse variational approac...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	220,408
2211.01484	Data Level Lottery Ticket Hypothesis for Vision Transformers	The conventional lottery ticket hypothesis (LTH) claims that there exists a sparse subnetwork within a dense neural network and a proper random initialization method called the winning ticket, such that it can be trained from scratch to almost as good as the dense counterpart. Meanwhile, the research of LTH in vision t...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	328,241
1903.11207	Information Maximizing Visual Question Generation	Though image-to-sequence generation models have become overwhelmingly popular in human-computer communications, they suffer from strongly favoring safe generic questions ("What is in this picture?"). Generating uninformative but relevant questions is not sufficient or useful. We argue that a good question is one that h...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	125,451
1904.09609	TiK-means: $K$-means clustering for skewed groups	The $K$-means algorithm is extended to allow for partitioning of skewed groups. Our algorithm is called TiK-Means and contributes a $K$-means type algorithm that assigns observations to groups while estimating their skewness-transformation parameters. The resulting groups and transformation reveal general-structured cl...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	128,424
2002.07033	Towards an Appropriate Query, Key, and Value Computation for Knowledge Tracing	Knowledge tracing, the act of modeling a student's knowledge through learning activities, is an extensively studied problem in the field of computer-aided education. Although models with attention mechanism have outperformed traditional approaches such as Bayesian knowledge tracing and collaborative filtering, they sha...	false	false	false	false	true	false	true	false	false	false	false	false	false	true	false	false	false	false	164,364
1903.01864	Frustum ConvNet: Sliding Frustums to Aggregate Local Point-Wise Features for Amodal 3D Object Detection	In this work, we propose a novel method termed \emph{Frustum ConvNet (F-ConvNet)} for amodal 3D object detection from point clouds. Given 2D region proposals in an RGB image, our method first generates a sequence of frustums for each region proposal, and uses the obtained frustums to group local points. F-ConvNet aggre...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	123,355
2410.01731	ComfyGen: Prompt-Adaptive Workflows for Text-to-Image Generation	The practical use of text-to-image generation has evolved from simple, monolithic models to complex workflows that combine multiple specialized components. While workflow-based approaches can lead to improved image quality, crafting effective workflows requires significant expertise, owing to the large number of availa...	false	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	true	493,915
2406.03141	Floating Anchor Diffusion Model for Multi-motif Scaffolding	Motif scaffolding seeks to design scaffold structures for constructing proteins with functions derived from the desired motif, which is crucial for the design of vaccines and enzymes. Previous works approach the problem by inpainting or conditional generation. Both of them can only scaffold motifs with fixed positions,...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	461,107
2401.10773	Multilevel lattice codes from Hurwitz quaternion integers	This work presents an extension of the Construction $\pi_A$ lattices proposed in \cite{huang2017construction}, to Hurwitz quaternion integers. This construction is provided by using an isomorphism from a version of the Chinese remainder theorem applied to maximal orders in contrast to natural orders in prior works. Exp...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	422,768
1908.05567	Deep reinforcement learning in World-Earth system models to discover sustainable management strategies	Increasingly complex, non-linear World-Earth system models are used for describing the dynamics of the biophysical Earth system and the socio-economic and socio-cultural World of human societies and their interactions. Identifying pathways towards a sustainable future in these models for informing policy makers and the...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	141,749
2006.00814	Online Versus Offline NMT Quality: An In-depth Analysis on English-German and German-English	We conduct in this work an evaluation study comparing offline and online neural machine translation architectures. Two sequence-to-sequence models: convolutional Pervasive Attention (Elbayad et al. 2018) and attention-based Transformer (Vaswani et al. 2017) are considered. We investigate, for both architectures, the im...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	179,577
2103.05255	Improving Generalizability in Limited-Angle CT Reconstruction with Sinogram Extrapolation	Computed tomography (CT) reconstruction from X-ray projections acquired within a limited angle range is challenging, especially when the angle range is extremely small. Both analytical and iterative models need more projections for effective modeling. Deep learning methods have gained prevalence due to their excellent ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	223,923
2411.00208	Using Large Language Models for a standard assessment mapping for sustainable communities	This paper presents a new approach to urban sustainability assessment through the use of Large Language Models (LLMs) to streamline the use of the ISO 37101 framework to automate and standardise the assessment of urban initiatives against the six "sustainability purposes" and twelve "issues" outlined in the standard. T...	false	false	false	false	true	false	false	false	false	false	false	false	false	true	false	false	false	false	504,484
1712.03073	DeepWear: Adaptive Local Offloading for On-Wearable Deep Learning	Due to their on-body and ubiquitous nature, wearables can generate a wide range of unique sensor data creating countless opportunities for deep learning tasks. We propose DeepWear, a deep learning (DL) framework for wearable devices to improve the performance and reduce the energy footprint. DeepWear strategically offl...	false	false	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	86,390
1706.05572	Information Structure Design in Team Decision Problems	We consider a problem of information structure design in team decision problems and team games. We propose simple, scalable greedy algorithms for adding a set of extra information links to optimize team performance and resilience to non-cooperative and adversarial agents. We show via a simple counterexample that the se...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	75,535
2311.09172	Enhancing AmBC Systems with Deep Learning for Joint Channel Estimation and Signal Detection	The era of ubiquitous, affordable wireless connectivity has opened doors to countless practical applications. In this context, ambient backscatter communication (AmBC) stands out, utilizing passive tags to establish connections with readers by harnessing reflected ambient radio frequency (RF) signals. However, conventi...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	408,016
0906.0744	Ergodic Fading Interference Channels: Sum-Capacity and Separability	The sum-capacity for specific sub-classes of ergodic fading Gaussian two-user interference channels (IFCs) is developed under the assumption of perfect channel state information at all transmitters and receivers. For the sub-classes of uniformly strong (every fading state is strong) and ergodic very strong two-sided IF...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	3,825
2202.12575	Mining Naturally-occurring Corrections and Paraphrases from Wikipedia's Revision History	Naturally-occurring instances of linguistic phenomena are important both for training and for evaluating automatic processes on text. When available in large quantities, they also prove interesting material for linguistic studies. In this article, we present a new resource built from Wikipedia's revision history, calle...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	282,294

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