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
2310.19289	AMLNet: Adversarial Mutual Learning Neural Network for Non-AutoRegressive Multi-Horizon Time Series Forecasting	Multi-horizon time series forecasting, crucial across diverse domains, demands high accuracy and speed. While AutoRegressive (AR) models excel in short-term predictions, they suffer speed and error issues as the horizon extends. Non-AutoRegressive (NAR) models suit long-term predictions but struggle with interdependenc...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	403,936
2410.17401	AdvWeb: Controllable Black-box Attacks on VLM-powered Web Agents	Vision Language Models (VLMs) have revolutionized the creation of generalist web agents, empowering them to autonomously complete diverse tasks on real-world websites, thereby boosting human efficiency and productivity. However, despite their remarkable capabilities, the safety and security of these agents against mali...	false	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	501,442
1607.03084	Kernel-based methods for bandit convex optimization	We consider the adversarial convex bandit problem and we build the first $\mathrm{poly}(T)$-time algorithm with $\mathrm{poly}(n) \sqrt{T}$-regret for this problem. To do so we introduce three new ideas in the derivative-free optimization literature: (i) kernel methods, (ii) a generalization of Bernoulli convolutions, ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	58,457
2210.07143	Performance Evaluation of Query Plan Recommendation with Apache Hadoop and Apache Spark	Access plan recommendation is a query optimization approach that executes new queries using prior created query execution plans (QEPs). The query optimizer divides the query space into clusters in the mentioned method. However, traditional clustering algorithms take a significant amount of execution time for clustering...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	true	false	323,591
2208.08865	Lessons from a Space Lab -- An Image Acquisition Perspective	The use of Deep Learning (DL) algorithms has improved the performance of vision-based space applications in recent years. However, generating large amounts of annotated data for training these DL algorithms has proven challenging. While synthetically generated images can be used, the DL models trained on synthetic data...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	313,510
2412.20412	Multi-Objective Large Language Model Unlearning	Machine unlearning in the domain of large language models (LLMs) has attracted great attention recently, which aims to effectively eliminate undesirable behaviors from LLMs without full retraining from scratch. In this paper, we explore the Gradient Ascent (GA) approach in LLM unlearning, which is a proactive way to de...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	521,216
2210.02562	Dueling Convex Optimization with General Preferences	We address the problem of \emph{convex optimization with dueling feedback}, where the goal is to minimize a convex function given a weaker form of \emph{dueling} feedback. Each query consists of two points and the dueling feedback returns a (noisy) single-bit binary comparison of the function values of the two queried ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	321,684
1507.01894	On pore-scale modeling and simulation of reactive transport in 3D geometries	Pore-scale modeling and simulation of reactive flow in porous media has a range of diverse applications, and poses a number of research challenges. It is known that the morphology of a porous medium has significant influence on the local flow rate, which can have a substantial impact on the rate of chemical reactions. ...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	44,918
2310.03808	A Safe First-Order Method for Pricing-Based Resource Allocation in Safety-Critical Networks	We introduce a novel algorithm for solving network utility maximization (NUM) problems that arise in resource allocation schemes over networks with known safety-critical constraints, where the constraints form an arbitrary convex and compact feasible set. Inspired by applications where customers' demand can only be aff...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	397,427
1608.04917	Cohesion and Coalition Formation in the European Parliament: Roll-Call Votes and Twitter Activities	We study the cohesion within and the coalitions between political groups in the Eighth European Parliament (2014--2019) by analyzing two entirely different aspects of the behavior of the Members of the European Parliament (MEPs) in the policy-making processes. On one hand, we analyze their co-voting patterns and, on th...	false	false	false	true	false	false	false	false	true	false	false	false	false	true	false	false	false	false	59,902
2305.19216	Translation-Enhanced Multilingual Text-to-Image Generation	Research on text-to-image generation (TTI) still predominantly focuses on the English language due to the lack of annotated image-caption data in other languages; in the long run, this might widen inequitable access to TTI technology. In this work, we thus investigate multilingual TTI (termed mTTI) and the current pote...	false	false	false	false	true	false	true	false	true	false	false	true	false	false	false	false	false	false	369,428
2501.03571	AADNet: Exploring EEG Spatiotemporal Information for Fast and Accurate Orientation and Timbre Detection of Auditory Attention Based on A Cue-Masked Paradigm	Auditory attention decoding from electroencephalogram (EEG) could infer to which source the user is attending in noisy environments. Decoding algorithms and experimental paradigm designs are crucial for the development of technology in practical applications. To simulate real-world scenarios, this study proposed a cue-...	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	522,923
1709.09014	Implementation of Fuzzy Inference Engine for equilibrium and roll-angle tracking of riderless bicycle	In this paper, a Fuzzy Inference System (FIS) is fabricated on a riderless bicycle. The Fuzzy Inference System is based on a rule base inherited from human experience of bicycle riding. The steady turning motion and roll-angle tracking controls for the riderless bicycle were achieved by using fuzzy concept. Collection ...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	81,562
2005.00068	On Existence of Equilibria, Voltage Balancing, and Current Sharing in Consensus-Based DC Microgrids	In this work, we present new secondary regulators for current sharing and voltage balancing in DC microgrids, composed of distributed generation units, dynamic RLC lines, and nonlinear ZIP (constant impedance, constant current, and constant power) loads. The proposed controllers sit atop a primary voltage control layer...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	175,113
2108.02651	Next-Gen Gas Network Simulation	To overcome many-query optimization, control, or uncertainty quantification work loads in reliable gas and energy network operations, model order reduction is the mathematical technology of choice. To this end, we enhance the model, solver and reductor components of the "morgen" platform, introduced in Himpe et al [J.~...	false	true	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	249,401
0906.4805	A Trivial Observation related to Sparse Recovery	We make a trivial modification to the elegant analysis of Garg and Khandekar (\emph{Gradient Descent with Sparsification} ICML 2009) that replaces the standard Restricted Isometry Property (RIP), with another RIP-type property (which could be simpler than the RIP, but we are not sure; it could be as hard as the RIP t...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	3,972
2107.05707	Computational modelling and data-driven homogenisation of knitted membranes	Knitting is an effective technique for producing complex three-dimensional surfaces owing to the inherent flexibility of interlooped yarns and recent advances in manufacturing providing better control of local stitch patterns. Fully yarn-level modelling of large-scale knitted membranes is not feasible. Therefore, we us...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	245,857
2502.05320	Towards Fine-grained Renal Vasculature Segmentation: Full-Scale Hierarchical Learning with FH-Seg	Accurate fine-grained segmentation of the renal vasculature is critical for nephrological analysis, yet it faces challenges due to diverse and insufficiently annotated images. Existing methods struggle to accurately segment intricate regions of the renal vasculature, such as the inner and outer walls, arteries and lesi...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	531,550
2106.09564	Knowledge distillation from multi-modal to mono-modal segmentation networks	The joint use of multiple imaging modalities for medical image segmentation has been widely studied in recent years. The fusion of information from different modalities has demonstrated to improve the segmentation accuracy, with respect to mono-modal segmentations, in several applications. However, acquiring multiple m...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	241,706
1911.05946	A Scalable Approach for Facial Action Unit Classifier Training UsingNoisy Data for Pre-Training	Machine learning systems are being used to automate many types of laborious labeling tasks. Facial actioncoding is an example of such a labeling task that requires copious amounts of time and a beyond average level of human domain expertise. In recent years, the use of end-to-end deep neural networks has led to signifi...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	153,419
1609.04904	Long-Term Trends in the Public Perception of Artificial Intelligence	Analyses of text corpora over time can reveal trends in beliefs, interest, and sentiment about a topic. We focus on views expressed about artificial intelligence (AI) in the New York Times over a 30-year period. General interest, awareness, and discussion about AI has waxed and waned since the field was founded in 1956...	false	false	false	false	true	false	false	false	true	false	false	false	false	true	false	false	false	false	61,052
2407.12376	Deep Learning-based Sentiment Analysis of Olympics Tweets	Sentiment analysis (SA), is an approach of natural language processing (NLP) for determining a text's emotional tone by analyzing subjective information such as views, feelings, and attitudes toward specific topics, products, services, events, or experiences. This study attempts to develop an advanced deep learning (DL...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	473,897
2502.00063	A Multi-Layered Large Language Model Framework for Disease Prediction	Social telehealth has revolutionized healthcare by enabling patients to share symptoms and receive medical consultations remotely. Users frequently post symptoms on social media and online health platforms, generating a vast repository of medical data that can be leveraged for disease classification and symptom severit...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	529,199
1908.05757	Debiasing Personal Identities in Toxicity Classification	As Machine Learning models continue to be relied upon for making automated decisions, the issue of model bias becomes more and more prevalent. In this paper, we approach training a text classifica-tion model and optimize on bias minimization by measuring not only the models performance on our dataset as a whole, but al...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	141,799
2207.07268	Lightweight Vision Transformer with Cross Feature Attention	Recent advances in vision transformers (ViTs) have achieved great performance in visual recognition tasks. Convolutional neural networks (CNNs) exploit spatial inductive bias to learn visual representations, but these networks are spatially local. ViTs can learn global representations with their self-attention mechanis...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	308,152
2501.16099	An Air-Gap Element for the Isogeometric Space-Time-Simulation of Electric Machines	Space-time methods promise more efficient time-domain simulations, in particular of electrical machines. However, most approaches require the motion to be known in advance so that it can be included in the space-time mesh. To overcome this problem, this paper proposes to use the well-known air-gap element for the rotor...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	527,815
2003.04219	Application of Support Vector Machines for Seismogram Analysis and Differentiation	Support Vector Machines (SVM) is a computational technique which has been used in various fields of sciences as a classifier with k-class classification capability, k being 2,3,4, etc. Seismograms of volcanic tremors often contain noises which can prove harmful for correct interpretation. The PCAB station (located in t...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	167,489
2011.07440	Quantifying Community Resilience Based on Fluctuations in Visits to Point-of-Interest from Digital Trace Data	This study aims to quantify community resilience based on fluctuations in the visits to various Point-of-Interest (POIs) locations. Visit to POIs is an essential indicator of human activities and captures the combined effects of perturbations in people lifestyles, built environment conditions, and businesses status. Th...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	206,556
2307.00347	Spatial-Temporal Graph Enhanced DETR Towards Multi-Frame 3D Object Detection	The Detection Transformer (DETR) has revolutionized the design of CNN-based object detection systems, showcasing impressive performance. However, its potential in the domain of multi-frame 3D object detection remains largely unexplored. In this paper, we present STEMD, a novel end-to-end framework that enhances the DET...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	376,973
1807.03923	Generative Adversarial Networks with Decoder-Encoder Output Noise	In recent years, research on image generation methods has been developing fast. The auto-encoding variational Bayes method (VAEs) was proposed in 2013, which uses variational inference to learn a latent space from the image database and then generates images using the decoder. The generative adversarial networks (GANs)...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	102,634
2111.15193	Shunted Self-Attention via Multi-Scale Token Aggregation	Recent Vision Transformer~(ViT) models have demonstrated encouraging results across various computer vision tasks, thanks to their competence in modeling long-range dependencies of image patches or tokens via self-attention. These models, however, usually designate the similar receptive fields of each token feature wit...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	268,855
2110.10474	R4: A Framework for Route Representation and Route Recommendation	Route recommendation is significant in navigation service. Two major challenges for route recommendation are route representation and user representation. Different from items that can be identified by unique IDs in traditional recommendation, routes are combinations of links (i.e., a road segment and its following act...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	262,168
1803.08602	Maximum Consensus Parameter Estimation by Reweighted $\ell_1$ Methods	Robust parameter estimation in computer vision is frequently accomplished by solving the maximum consensus (MaxCon) problem. Widely used randomized methods for MaxCon, however, can only produce {random} approximate solutions, while global methods are too slow to exercise on realistic problem sizes. Here we analyse MaxC...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	93,292
2412.11434	Auto-bidding in real-time auctions via Oracle Imitation Learning (OIL)	Online advertising has become one of the most successful business models of the internet era. Impression opportunities are typically allocated through real-time auctions, where advertisers bid to secure advertisement slots. Deciding the best bid for an impression opportunity is challenging, due to the stochastic nature...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	517,405
2502.12048	A Survey on Bridging EEG Signals and Generative AI: From Image and Text to Beyond	Integration of Brain-Computer Interfaces (BCIs) and Generative Artificial Intelligence (GenAI) has opened new frontiers in brain signal decoding, enabling assistive communication, neural representation learning, and multimodal integration. BCIs, particularly those leveraging Electroencephalography (EEG), provide a non-...	true	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	534,659
2202.13312	Sampling in Dirichlet Process Mixture Models for Clustering Streaming Data	Practical tools for clustering streaming data must be fast enough to handle the arrival rate of the observations. Typically, they also must adapt on the fly to possible lack of stationarity; i.e., the data statistics may be time-dependent due to various forms of drifts, changes in the number of clusters, etc. The Diric...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	282,558
2104.13786	Unsupervised Detection of Cancerous Regions in Histology Imagery using Image-to-Image Translation	Detection of visual anomalies refers to the problem of finding patterns in different imaging data that do not conform to the expected visual appearance and is a widely studied problem in different domains. Due to the nature of anomaly occurrences and underlying generating processes, it is hard to characterize them and ...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	232,612
2104.07692	Higgs analysis with quantum classifiers	We have developed two quantum classifier models for the $t\bar{t}H(b\bar{b})$ classification problem, both of which fall into the category of hybrid quantum-classical algorithms for Noisy Intermediate Scale Quantum devices (NISQ). Our results, along with other studies, serve as a proof of concept that Quantum Machine L...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	230,511
2408.12476	Predicting Solar Energy Generation with Machine Learning based on AQI and Weather Features	This paper addresses the pressing need for an accurate solar energy prediction model, which is crucial for efficient grid integration. We explore the influence of the Air Quality Index and weather features on solar energy generation, employing advanced Machine Learning and Deep Learning techniques. Our methodology uses...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	482,750
0810.1253	Dynamic Rate Allocation in Fading Multiple-access Channels	We consider the problem of rate allocation in a fading Gaussian multiple-access channel (MAC) with fixed transmission powers. Our goal is to maximize a general concave utility function of transmission rates over the throughput capacity region. In contrast to earlier works in this context that propose solutions where a ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	2,467
2403.16582	In the Search for Optimal Multi-view Learning Models for Crop Classification with Global Remote Sensing Data	Studying and analyzing cropland is a difficult task due to its dynamic and heterogeneous growth behavior. Usually, diverse data sources can be collected for its estimation. Although deep learning models have proven to excel in the crop classification task, they face substantial challenges when dealing with multiple inp...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	441,110
1904.01624	Lessons from Building Acoustic Models with a Million Hours of Speech	This is a report of our lessons learned building acoustic models from 1 Million hours of unlabeled speech, while labeled speech is restricted to 7,000 hours. We employ student/teacher training on unlabeled data, helping scale out target generation in comparison to confidence model based methods, which require a decoder...	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	126,188
1812.01584	Detect-to-Retrieve: Efficient Regional Aggregation for Image Search	Retrieving object instances among cluttered scenes efficiently requires compact yet comprehensive regional image representations. Intuitively, object semantics can help build the index that focuses on the most relevant regions. However, due to the lack of bounding-box datasets for objects of interest among retrieval be...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	115,555
1803.02562	Energy Efficiency of an Unlicensed Wireless Network in the Presence of Retransmissions	This paper analysis the energy efficiency of an unlicensed wireless network in which retransmission is possible if the transmitted message is decoded in outage. A wireless sensor network is considered in which the sensor nodes are unlicensed users of a wireless network which transmit its data in the uplink channel used...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	92,089
2402.02631	Learning to Understand: Identifying Interactions via the M\"obius Transform	One of the key challenges in machine learning is to find interpretable representations of learned functions. The M\"obius transform is essential for this purpose, as its coefficients correspond to unique importance scores for sets of input variables. This transform is closely related to widely used game-theoretic notio...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	426,657
1810.09739	Bayesian Deconvolution of Scanning Electron Microscopy Images Using Point-spread Function Estimation and Non-local Regularization	Microscopy is one of the most essential imaging techniques in life sciences. High-quality images are required in order to solve (potentially life-saving) biomedical research problems. Many microscopy techniques do not achieve sufficient resolution for these purposes, being limited by physical diffraction and hardware d...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	111,122
2502.08611	Robustly Learning Monotone Generalized Linear Models via Data Augmentation	We study the task of learning Generalized Linear models (GLMs) in the agnostic model under the Gaussian distribution. We give the first polynomial-time algorithm that achieves a constant-factor approximation for \textit{any} monotone Lipschitz activation. Prior constant-factor GLM learners succeed for a substantially s...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	533,080
2210.13326	Clean Text and Full-Body Transformer: Microsoft's Submission to the WMT22 Shared Task on Sign Language Translation	This paper describes Microsoft's submission to the first shared task on sign language translation at WMT 2022, a public competition tackling sign language to spoken language translation for Swiss German sign language. The task is very challenging due to data scarcity and an unprecedented vocabulary size of more than 20...	false	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	326,114
2402.07037	Recursive Model-agnostic Inverse Dynamics of Serial Soft-Rigid Robots	Robotics is shifting from rigid, articulated systems to more sophisticated and heterogeneous mechanical structures. Soft robots, for example, have continuously deformable elements capable of large deformations. The flourishing of control techniques developed for this class of systems is fueling the need of efficient pr...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	428,543
2212.05034	SmartBrush: Text and Shape Guided Object Inpainting with Diffusion Model	Generic image inpainting aims to complete a corrupted image by borrowing surrounding information, which barely generates novel content. By contrast, multi-modal inpainting provides more flexible and useful controls on the inpainted content, \eg, a text prompt can be used to describe an object with richer attributes, an...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	335,655
1605.08513	Stochastic Online Control for Energy-Harvesting Wireless Networks with Battery Imperfections	In energy harvesting (EH) network, the energy storage devices (i.e., batteries) are usually not perfect. In this paper, we consider a practical battery model with finite battery capacity, energy (dis-)charging loss, and energy dissipation. Taking into account such battery imperfections, we rely on the Lyapunov optimiza...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	56,452
1610.01066	Sparsity-based Color Image Super Resolution via Exploiting Cross Channel Constraints	Sparsity constrained single image super-resolution (SR) has been of much recent interest. A typical approach involves sparsely representing patches in a low-resolution (LR) input image via a dictionary of example LR patches, and then using the coefficients of this representation to generate the high-resolution (HR) out...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	61,918
2207.09663	Streamable Neural Fields	Neural fields have emerged as a new data representation paradigm and have shown remarkable success in various signal representations. Since they preserve signals in their network parameters, the data transfer by sending and receiving the entire model parameters prevents this emerging technology from being used in many ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	308,972
2305.16982	TranSFormer: Slow-Fast Transformer for Machine Translation	Learning multiscale Transformer models has been evidenced as a viable approach to augmenting machine translation systems. Prior research has primarily focused on treating subwords as basic units in developing such systems. However, the incorporation of fine-grained character-level features into multiscale Transformer h...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	368,356
2102.07057	Learning Intents behind Interactions with Knowledge Graph for Recommendation	Knowledge graph (KG) plays an increasingly important role in recommender systems. A recent technical trend is to develop end-to-end models founded on graph neural networks (GNNs). However, existing GNN-based models are coarse-grained in relational modeling, failing to (1) identify user-item relation at a fine-grained l...	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	false	false	219,971
2004.06627	An Application of Deep Reinforcement Learning to Algorithmic Trading	This scientific research paper presents an innovative approach based on deep reinforcement learning (DRL) to solve the algorithmic trading problem of determining the optimal trading position at any point in time during a trading activity in stock markets. It proposes a novel DRL trading strategy so as to maximise the r...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	172,559
2009.01776	HiFiSinger: Towards High-Fidelity Neural Singing Voice Synthesis	High-fidelity singing voices usually require higher sampling rate (e.g., 48kHz) to convey expression and emotion. However, higher sampling rate causes the wider frequency band and longer waveform sequences and throws challenges for singing voice synthesis (SVS) in both frequency and time domains. Conventional SVS syste...	false	false	true	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	194,389
1805.10997	Adversarial Examples in Remote Sensing	This paper considers attacks against machine learning algorithms used in remote sensing applications, a domain that presents a suite of challenges that are not fully addressed by current research focused on natural image data such as ImageNet. In particular, we present a new study of adversarial examples in the context...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	98,819
1912.07101	Efficient Bitmap-based Indexing and Retrieval of Similarity Search Image Queries	Finding similar images is a necessary operation in many multimedia applications. Images are often represented and stored as a set of high-dimensional features, which are extracted using localized feature extraction algorithms. Locality Sensitive Hashing is one of the most popular approximate processing techniques for f...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	true	157,512
2305.15067	Not All Metrics Are Guilty: Improving NLG Evaluation by Diversifying References	Most research about natural language generation (NLG) relies on evaluation benchmarks with limited references for a sample, which may result in poor correlations with human judgements. The underlying reason is that one semantic meaning can actually be expressed in different forms, and the evaluation with a single or fe...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	367,432
2407.14649	The Collection of a Human Robot Collaboration Dataset for Cooperative Assembly in Glovebox Environments	Industry 4.0 introduced AI as a transformative solution for modernizing manufacturing processes. Its successor, Industry 5.0, envisions humans as collaborators and experts guiding these AI-driven manufacturing solutions. Developing these techniques necessitates algorithms capable of safe, real-time identification of hu...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	474,853
2004.13932	CoronaVis: A Real-time COVID-19 Tweets Data Analyzer and Data Repository	Due to the nature of the data and public interaction, twitter is becoming more and more useful to understand and model various events. The goal of CoronaVis is to use tweets as the information shared by the people to visualize topic modeling, study subjectivity, and to model the human emotions during the COVID-19 pande...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	174,718
2501.01835	ASKCOS: an open source software suite for synthesis planning	The advancement of machine learning and the availability of large-scale reaction datasets have accelerated the development of data-driven models for computer-aided synthesis planning (CASP) in the past decade. Here, we detail the newest version of ASKCOS, an open source software suite for synthesis planning that makes ...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	522,235
2309.14240	Learning to Abstain From Uninformative Data	Learning and decision-making in domains with naturally high noise-to-signal ratio, such as Finance or Healthcare, is often challenging, while the stakes are very high. In this paper, we study the problem of learning and acting under a general noisy generative process. In this problem, the data distribution has a signif...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	394,518
2403.20331	Unsolvable Problem Detection: Evaluating Trustworthiness of Vision Language Models	This paper introduces a novel and significant challenge for Vision Language Models (VLMs), termed Unsolvable Problem Detection (UPD). UPD examines the VLM's ability to withhold answers when faced with unsolvable problems in the context of Visual Question Answering (VQA) tasks. UPD encompasses three distinct settings: A...	false	false	false	false	true	false	true	false	true	false	false	true	false	false	false	false	false	false	442,714
2007.01282	Leveraging Passage Retrieval with Generative Models for Open Domain Question Answering	Generative models for open domain question answering have proven to be competitive, without resorting to external knowledge. While promising, this approach requires to use models with billions of parameters, which are expensive to train and query. In this paper, we investigate how much these models can benefit from ret...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	185,382
1903.04340	Prescribed Performance Control Guided Policy Improvement for Satisfying Signal Temporal Logic Tasks	Signal temporal logic (STL) provides a user-friendly interface for defining complex tasks for robotic systems. Recent efforts aim at designing control laws or using reinforcement learning methods to find policies which guarantee satisfaction of these tasks. While the former suffer from the trade-off between task specif...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	123,957
2206.15378	Mastering the Game of Stratego with Model-Free Multiagent Reinforcement Learning	We introduce DeepNash, an autonomous agent capable of learning to play the imperfect information game Stratego from scratch, up to a human expert level. Stratego is one of the few iconic board games that Artificial Intelligence (AI) has not yet mastered. This popular game has an enormous game tree on the order of $10^{...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	true	false	false	true	305,566
2006.04296	Randomised Gaussian Process Upper Confidence Bound for Bayesian Optimisation	In order to improve the performance of Bayesian optimisation, we develop a modified Gaussian process upper confidence bound (GP-UCB) acquisition function. This is done by sampling the exploration-exploitation trade-off parameter from a distribution. We prove that this allows the expected trade-off parameter to be alter...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	180,641
2012.05640	Asymptotic study of stochastic adaptive algorithm in non-convex landscape	This paper studies some asymptotic properties of adaptive algorithms widely used in optimization and machine learning, and among them Adagrad and Rmsprop, which are involved in most of the blackbox deep learning algorithms. Our setup is the non-convex landscape optimization point of view, we consider a one time scale p...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	210,845
2405.11336	UPAM: Unified Prompt Attack in Text-to-Image Generation Models Against Both Textual Filters and Visual Checkers	Text-to-Image (T2I) models have raised security concerns due to their potential to generate inappropriate or harmful images. In this paper, we propose UPAM, a novel framework that investigates the robustness of T2I models from the attack perspective. Unlike most existing attack methods that focus on deceiving textual d...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	455,097
2209.06381	GES Model :Combining Pearson Correlation Coefficient Analysis with Multilayer Perceptron	With the development of technological progress, mining on asteroids is becoming a reality. This paper focuses on how to distribute asteroid mineral resources in a reasonable way to ensure global equity. To distribute asteroid resources fairly, 7 primary indicators and 20 secondary indicators are introduced to build a m...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	317,376
2401.09101	PIN-SLAM: LiDAR SLAM Using a Point-Based Implicit Neural Representation for Achieving Global Map Consistency	Accurate and robust localization and mapping are essential components for most autonomous robots. In this paper, we propose a SLAM system for building globally consistent maps, called PIN-SLAM, that is based on an elastic and compact point-based implicit neural map representation. Taking range measurements as input, ou...	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	422,152
2402.17242	Scalable Community Search with Accuracy Guarantee on Attributed Graphs	Given an attributed graph $G$ and a query node $q$, \underline{C}ommunity \underline{S}earch over \underline{A}ttributed \underline{G}raphs (CS-AG) aims to find a structure- and attribute-cohesive subgraph from $G$ that contains $q$. Although CS-AG has been widely studied, they still face three challenges. (1) Exact me...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	false	432,908
2004.12331	Neural Topic Modeling with Bidirectional Adversarial Training	Recent years have witnessed a surge of interests of using neural topic models for automatic topic extraction from text, since they avoid the complicated mathematical derivations for model inference as in traditional topic models such as Latent Dirichlet Allocation (LDA). However, these models either typically assume im...	false	false	false	false	false	true	true	false	true	false	false	false	false	false	false	false	false	false	174,213
cs/0612068	Interactive Configuration by Regular String Constraints	A product configurator which is complete, backtrack free and able to compute the valid domains at any state of the configuration can be constructed by building a Binary Decision Diagram (BDD). Despite the fact that the size of the BDD is exponential in the number of variables in the worst case, BDDs have proved to work...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	539,964
2404.13804	Adaptive Heterogeneous Client Sampling for Federated Learning over Wireless Networks	Federated learning (FL) algorithms usually sample a fraction of clients in each round (partial participation) when the number of participants is large and the server's communication bandwidth is limited. Recent works on the convergence analysis of FL have focused on unbiased client sampling, e.g., sampling uniformly at...	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	true	448,447
2302.11868	A2S-NAS: Asymmetric Spectral-Spatial Neural Architecture Search For Hyperspectral Image Classification	Existing deep learning-based hyperspectral image (HSI) classification works still suffer from the limitation of the fixed-sized receptive field, leading to difficulties in distinctive spectral-spatial features for ground objects with various sizes and arbitrary shapes. Meanwhile, plenty of previous works ignore asymmet...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	347,344
1506.04869	Modeling and Computation of Mean Field Equilibria in Producers' Game with Emission Permits Trading	In this paper, we present a mean field game to model the production behaviors of a very large number of producers, whose carbon emissions are regulated by government. Especially, an emission permits trading scheme is considered in our model, in which each enterprise can trade its own permits flexibly. By means of the m...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	44,225
2201.07886	THz-Empowered UAVs in 6G: Opportunities, Challenges, and Trade-Offs	Envisioned use cases of unmanned aerial vehicles (UAVs) impose new service requirements in terms of data rate, latency, and sensing accuracy, to name a few. If such requirements are satisfactorily met, it can create novel applications and enable highly reliable and harmonized integration of UAVs in the 6G network ecosy...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	276,155
1709.06158	Matterport3D: Learning from RGB-D Data in Indoor Environments	Access to large, diverse RGB-D datasets is critical for training RGB-D scene understanding algorithms. However, existing datasets still cover only a limited number of views or a restricted scale of spaces. In this paper, we introduce Matterport3D, a large-scale RGB-D dataset containing 10,800 panoramic views from 194,4...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	81,039
2208.03571	Transformer-based assignment decision network for multiple object tracking	Data association is a crucial component for any multiple object tracking (MOT) method that follows the tracking-by-detection paradigm. To generate complete trajectories such methods employ a data association process to establish assignments between detections and existing targets during each timestep. Recent data assoc...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	311,828
1506.00669	Concentration and regularization of random graphs	This paper studies how close random graphs are typically to their expectations. We interpret this question through the concentration of the adjacency and Laplacian matrices in the spectral norm. We study inhomogeneous Erd\"os-R\'enyi random graphs on $n$ vertices, where edges form independently and possibly with differ...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	43,694
2410.08567	Diffusion-Based Depth Inpainting for Transparent and Reflective Objects	Transparent and reflective objects, which are common in our everyday lives, present a significant challenge to 3D imaging techniques due to their unique visual and optical properties. Faced with these types of objects, RGB-D cameras fail to capture the real depth value with their accurate spatial information. To addres...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	497,173
2406.10111	GaussianSR: 3D Gaussian Super-Resolution with 2D Diffusion Priors	Achieving high-resolution novel view synthesis (HRNVS) from low-resolution input views is a challenging task due to the lack of high-resolution data. Previous methods optimize high-resolution Neural Radiance Field (NeRF) from low-resolution input views but suffer from slow rendering speed. In this work, we base our met...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	464,228
2002.00215	Palpatine: Mining Frequent Sequences for Data Prefetching in NoSQL Distributed Key-Value Stores	This paper presents PALPATINE, the first in-memory application-level cache for Distributed Key-Value (DKV) data stores, capable of prefetching data that is likely to be accessed in an immediate future. To predict data accesses, PALPATINE continuously captures frequent access patterns to the back store by means of data ...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	true	162,295
2001.09769	Stock Price Prediction Using Convolutional Neural Networks on a Multivariate Timeseries	Prediction of future movement of stock prices has been a subject matter of many research work. In this work, we propose a hybrid approach for stock price prediction using machine learning and deep learning-based methods. We select the NIFTY 50 index values of the National Stock Exchange of India, over a period of four ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	161,664
1906.08587	REBEC: Robust Evolutionary-based Calibration Approach for the Numerical Wind Wave Model	The adaptation of numerical wind wave models to the local time-spatial conditions is a problem that can be solved by using various calibration techniques. However, the obtained sets of physical parameters become over-tuned to specific events if there is a lack of observations. In this paper, we propose a robust evoluti...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	true	false	false	135,910
2406.12366	Structured Prediction in Online Learning	We study a theoretical and algorithmic framework for structured prediction in the online learning setting. The problem of structured prediction, i.e. estimating function where the output space lacks a vectorial structure, is well studied in the literature of supervised statistical learning. We show that our algorithm i...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	465,365
2201.08512	Vertical Federated Edge Learning with Distributed Integrated Sensing and Communication	This letter studies a vertical federated edge learning (FEEL) system for collaborative objects/human motion recognition by exploiting the distributed integrated sensing and communication (ISAC). In this system, distributed edge devices first send wireless signals to sense targeted objects/human, and then exchange inter...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	276,361
2203.01738	Machine learning model to project the impact of Ukraine crisis	Russia's attack on Ukraine on Thursday 24 February 2022 hitched financial markets and the increased geopolitical crisis. In this paper, we select some main economic indexes, such as Gold, Oil (WTI), NDAQ, and known currency which are involved in this crisis and try to find the quantitative effect of this war on them. T...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	283,499
2210.07573	Model-based Safe Deep Reinforcement Learning via a Constrained Proximal Policy Optimization Algorithm	During initial iterations of training in most Reinforcement Learning (RL) algorithms, agents perform a significant number of random exploratory steps. In the real world, this can limit the practicality of these algorithms as it can lead to potentially dangerous behavior. Hence safe exploration is a critical issue in ap...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	323,777
2212.10515	CausalDialogue: Modeling Utterance-level Causality in Conversations	Despite their widespread adoption, neural conversation models have yet to exhibit natural chat capabilities with humans. In this research, we examine user utterances as causes and generated responses as effects, recognizing that changes in a cause should produce a different effect. To further explore this concept, we h...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	337,496
2301.13476	An investigation of challenges encountered when specifying training data and runtime monitors for safety critical ML applications	Context and motivation: The development and operation of critical software that contains machine learning (ML) models requires diligence and established processes. Especially the training data used during the development of ML models have major influences on the later behaviour of the system. Runtime monitors are used ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	342,932
2405.18110	Individual Contributions as Intrinsic Exploration Scaffolds for Multi-agent Reinforcement Learning	In multi-agent reinforcement learning (MARL), effective exploration is critical, especially in sparse reward environments. Although introducing global intrinsic rewards can foster exploration in such settings, it often complicates credit assignment among agents. To address this difficulty, we propose Individual Contrib...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	true	false	false	false	458,280
2207.13479	AutoTransition: Learning to Recommend Video Transition Effects	Video transition effects are widely used in video editing to connect shots for creating cohesive and visually appealing videos. However, it is challenging for non-professionals to choose best transitions due to the lack of cinematographic knowledge and design skills. In this paper, we present the premier work on perfor...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	310,322
2202.07720	Active Uncertainty Reduction for Human-Robot Interaction: An Implicit Dual Control Approach	The ability to accurately predict human behavior is central to the safety and efficiency of robot autonomy in interactive settings. Unfortunately, robots often lack access to key information on which these predictions may hinge, such as people's goals, attention, and willingness to cooperate. Dual control theory addres...	false	false	false	false	false	false	true	true	false	false	true	false	false	false	false	false	false	false	280,631
2007.03074	Kernel Stein Generative Modeling	We are interested in gradient-based Explicit Generative Modeling where samples can be derived from iterative gradient updates based on an estimate of the score function of the data distribution. Recent advances in Stochastic Gradient Langevin Dynamics (SGLD) demonstrates impressive results with energy-based models on h...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	185,939
2312.00043	Who is leading in AI? An analysis of industry AI research	AI research is increasingly industry-driven, making it crucial to understand company contributions to this field. We compare leading AI companies by research publications, citations, size of training runs, and contributions to algorithmic innovations. Our analysis reveals the substantial role played by Google, OpenAI a...	false	false	false	false	true	false	false	false	false	false	false	false	false	true	false	false	false	false	411,870
2309.11357	3D Face Reconstruction: the Road to Forensics	3D face reconstruction algorithms from images and videos are applied to many fields, from plastic surgery to the entertainment sector, thanks to their advantageous features. However, when looking at forensic applications, 3D face reconstruction must observe strict requirements that still make its possible role in bring...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	393,378

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