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
2404.14107	PGNAA Spectral Classification of Aluminium and Copper Alloys with Machine Learning	In this paper, we explore the optimization of metal recycling with a focus on real-time differentiation between alloys of copper and aluminium. Spectral data, obtained through Prompt Gamma Neutron Activation Analysis (PGNAA), is utilized for classification. The study compares data from two detectors, cerium bromide (Ce...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	448,574
1001.1968	A Topological derivative based image segmentation for sign language recognition system using isotropic filter	The need of sign language is increasing radically especially to hearing impaired community. Only few research groups try to automatically recognize sign language from video, colored gloves and etc. Their approach requires a valid segmentation of the data that is used for training and of the data that is used to be reco...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	5,338
1503.07387	Vectorized VByte Decoding	We consider the ubiquitous technique of VByte compression, which represents each integer as a variable length sequence of bytes. The low 7 bits of each byte encode a portion of the integer, and the high bit of each byte is reserved as a continuation flag. This flag is set to 1 for all bytes except the last, and the dec...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	41,469
2110.15145	Deep Learning Aided Packet Routing in Aeronautical Ad-Hoc Networks Relying on Real Flight Data: From Single-Objective to Near-Pareto Multi-Objective Optimization	Data packet routing in aeronautical ad-hoc networks (AANETs) is challenging due to their high-dynamic topology. In this paper, we invoke deep learning (DL) to assist routing in AANETs. We set out from the single objective of minimizing the end-to-end (E2E) delay. Specifically, a deep neural network (DNN) is conceived f...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	true	263,779
2004.01422	Learning synchronous context-free grammars with multiple specialised non-terminals for hierarchical phrase-based translation	Translation models based on hierarchical phrase-based statistical machine translation (HSMT) have shown better performances than the non-hierarchical phrase-based counterparts for some language pairs. The standard approach to HSMT learns and apply a synchronous context-free grammar with a single non-terminal. The hypot...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	170,907
1808.08933	Unsupervised Multilingual Word Embeddings	Multilingual Word Embeddings (MWEs) represent words from multiple languages in a single distributional vector space. Unsupervised MWE (UMWE) methods acquire multilingual embeddings without cross-lingual supervision, which is a significant advantage over traditional supervised approaches and opens many new possibilities...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	106,067
2109.11839	Frequency Pooling: Shift-Equivalent and Anti-Aliasing Downsampling	Convolution utilizes a shift-equivalent prior of images, thus leading to great success in image processing tasks. However, commonly used poolings in convolutional neural networks (CNNs), such as max-pooling, average-pooling, and strided-convolution, are not shift-equivalent. Thus, the shift-equivalence of CNNs is destr...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	257,077
2409.14972	Deep Reinforcement Learning-based Obstacle Avoidance for Robot Movement in Warehouse Environments	At present, in most warehouse environments, the accumulation of goods is complex, and the management personnel in the control of goods at the same time with the warehouse mobile robot trajectory interaction, the traditional mobile robot can not be very good on the goods and pedestrians to feed back the correct obstacle...	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	false	490,704
2304.11116	Graph-ToolFormer: To Empower LLMs with Graph Reasoning Ability via Prompt Augmented by ChatGPT	In this paper, we aim to develop a large language model (LLM) with the reasoning ability on complex graph data. Currently, LLMs have achieved very impressive performance on various natural language learning tasks, extensions of which have also been applied to study the vision tasks with multi-modal data. However, when ...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	359,689
2302.05548	On Safety of Passengers Entering a Bus Rapid Transit System from Scheduled Stops	In this paper, we address the vehicle scheduling problem for improving passenger safety in bus rapid transit systems. Our focus is on passengers waiting at street stops to enter terminal stations. To enhance their safety, we minimize deviations from the proposed timetable, thereby minimizing passengers' initial waiting...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	345,083
2310.05166	A Corrected Expected Improvement Acquisition Function Under Noisy Observations	Sequential maximization of expected improvement (EI) is one of the most widely used policies in Bayesian optimization because of its simplicity and ability to handle noisy observations. In particular, the improvement function often uses the best posterior mean as the best incumbent in noisy settings. However, the uncer...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	398,016
2502.05494	Multi-scale Masked Autoencoder for Electrocardiogram Anomaly Detection	Electrocardiogram (ECG) analysis is a fundamental tool for diagnosing cardiovascular conditions, yet anomaly detection in ECG signals remains challenging due to their inherent complexity and variability. We propose Multi-scale Masked Autoencoder for ECG anomaly detection (MMAE-ECG), a novel end-to-end framework that ef...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	531,639
2101.03839	On information projections between multivariate elliptical and location-scale families	We study information projections with respect to statistical $f$-divergences between any two location-scale families. We consider a multivariate generalization of the location-scale families which includes the elliptical and the spherical subfamilies. By using the action of the multivariate location-scale group, we sho...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	215,007
2308.01010	Point Anywhere: Directed Object Estimation from Omnidirectional Images	One of the intuitive instruction methods in robot navigation is a pointing gesture. In this study, we propose a method using an omnidirectional camera to eliminate the user/object position constraint and the left/right constraint of the pointing arm. Although the accuracy of skeleton and object detection is low due to ...	true	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	383,110
1605.07960	Multi-Object Tracking and Identification over Sets	The ability for an autonomous agent or robot to track and identify potentially multiple objects in a dynamic environment is essential for many applications, such as automated surveillance, traffic monitoring, human-robot interaction, etc. The main challenge is due to the noisy and incomplete perception including inevit...	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	56,366
2107.08248	Learning De-identified Representations of Prosody from Raw Audio	We propose a method for learning de-identified prosody representations from raw audio using a contrastive self-supervised signal. Whereas prior work has relied on conditioning models on bottlenecks, we introduce a set of inductive biases that exploit the natural structure of prosody to minimize timbral information and ...	false	false	true	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	246,665
2307.00495	STG4Traffic: A Survey and Benchmark of Spatial-Temporal Graph Neural Networks for Traffic Prediction	Traffic prediction has been an active research topic in the domain of spatial-temporal data mining. Accurate real-time traffic prediction is essential to improve the safety, stability, and versatility of smart city systems, i.e., traffic control and optimal routing. The complex and highly dynamic spatial-temporal depen...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	377,028
2212.02659	Continual learning on deployment pipelines for Machine Learning Systems	Following the development of digitization, a growing number of large Original Equipment Manufacturers (OEMs) are adapting computer vision or natural language processing in a wide range of applications such as anomaly detection and quality inspection in plants. Deployment of such a system is becoming an extremely import...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	334,838
2005.02124	Realization of MIMO Channel Model for Spatial Diversity with Capacity and SNR Multiplexing Gains	Multiple input multiple output (MIMO) system transmission is a popular diversity technique to improve the reliability of a communication system where transmitter, communication channel and receiver are the important elements. Data transmission reliability can be ensured when the bit error rate is very low. Normally, mu...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	175,766
2310.10289	Moving Object Localization based on the Fusion of Ultra-WideBand and LiDAR with a Mobile Robot	Localization of objects is vital for robot-object interaction. Light Detection and Ranging (LiDAR) application in robotics is an emerging and widely used object localization technique due to its accurate distance measurement, long-range, wide field of view, and robustness in different conditions. However, LiDAR is unab...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	400,160
1303.5675	Markov random walk under constraint for discovering overlapping communities in complex networks	Detection of overlapping communities in complex networks has motivated recent research in the relevant fields. Aiming this problem, we propose a Markov dynamics based algorithm, called UEOC, which means, 'unfold and extract overlapping communities'. In UEOC, when identifying each natural community that overlaps, a Mark...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	23,145
2109.04954	Saliency Guided Experience Packing for Replay in Continual Learning	Artificial learning systems aspire to mimic human intelligence by continually learning from a stream of tasks without forgetting past knowledge. One way to enable such learning is to store past experiences in the form of input examples in episodic memory and replay them when learning new tasks. However, performance of ...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	254,607
2103.00785	Detection and Rectification of Arbitrary Shaped Scene Texts by using Text Keypoints and Links	Detection and recognition of scene texts of arbitrary shapes remain a grand challenge due to the super-rich text shape variation in text line orientations, lengths, curvatures, etc. This paper presents a mask-guided multi-task network that detects and rectifies scene texts of arbitrary shapes reliably. Three types of k...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	222,400
2011.07997	Comparative Probing of Lexical Semantics Theories for Cognitive Plausibility and Technological Usefulness	Lexical semantics theories differ in advocating that the meaning of words is represented as an inference graph, a feature mapping or a vector space, thus raising the question: is it the case that one of these approaches is superior to the others in representing lexical semantics appropriately? Or in its non antagonisti...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	206,737
2005.05586	Existence of structured perfect Bayesian equilibrium in dynamic games of asymmetric information	In~[1],authors considered a general finite horizon model of dynamic game of asymmetric information, where N players have types evolving as independent Markovian process, where each player observes its own type perfectly and actions of all players. The authors present a sequential decomposition algorithm to find all str...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	true	176,775
2207.09721	Feature Representation Learning for Unsupervised Cross-domain Image Retrieval	Current supervised cross-domain image retrieval methods can achieve excellent performance. However, the cost of data collection and labeling imposes an intractable barrier to practical deployment in real applications. In this paper, we investigate the unsupervised cross-domain image retrieval task, where class labels a...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	309,004
2205.08099	Dimensionality Reduced Training by Pruning and Freezing Parts of a Deep Neural Network, a Survey	State-of-the-art deep learning models have a parameter count that reaches into the billions. Training, storing and transferring such models is energy and time consuming, thus costly. A big part of these costs is caused by training the network. Model compression lowers storage and transfer costs, and can further make tr...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	296,821
2410.14682	ET-Plan-Bench: Embodied Task-level Planning Benchmark Towards Spatial-Temporal Cognition with Foundation Models	Recent advancements in Large Language Models (LLMs) have spurred numerous attempts to apply these technologies to embodied tasks, particularly focusing on high-level task planning and task decomposition. To further explore this area, we introduce a new embodied task planning benchmark, ET-Plan-Bench, which specifically...	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	false	500,142
2408.08642	The Power of Bias: Optimizing Client Selection in Federated Learning with Heterogeneous Differential Privacy	To preserve the data privacy, the federated learning (FL) paradigm emerges in which clients only expose model gradients rather than original data for conducting model training. To enhance the protection of model gradients in FL, differentially private federated learning (DPFL) is proposed which incorporates differentia...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	481,082
1202.3698	Extended Lifted Inference with Joint Formulas	The First-Order Variable Elimination (FOVE) algorithm allows exact inference to be applied directly to probabilistic relational models, and has proven to be vastly superior to the application of standard inference methods on a grounded propositional model. Still, FOVE operators can be applied under restricted condition...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	14,370
2103.10919	Robustness via Cross-Domain Ensembles	We present a method for making neural network predictions robust to shifts from the training data distribution. The proposed method is based on making predictions via a diverse set of cues (called 'middle domains') and ensembling them into one strong prediction. The premise of the idea is that predictions made via diff...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	225,605
1911.13087	Kurdish (Sorani) Speech to Text: Presenting an Experimental Dataset	We present an experimental dataset, Basic Dataset for Sorani Kurdish Automatic Speech Recognition (BD-4SK-ASR), which we used in the first attempt in developing an automatic speech recognition for Sorani Kurdish. The objective of the project was to develop a system that automatically could recognize simple sentences ba...	false	false	true	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	155,583
1905.06220	Cluster, Classify, Regress: A General Method For Learning Discountinous Functions	This paper presents a method for solving the supervised learning problem in which the output is highly nonlinear and discontinuous. It is proposed to solve this problem in three stages: (i) cluster the pairs of input-output data points, resulting in a label for each point; (ii) classify the data, where the correspondin...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	130,924
2307.16622	Detecting diabetic retinopathy severity through fundus images using an ensemble of classifiers	Diabetic retinopathy is an ocular condition that affects individuals with diabetes mellitus. It is a common complication of diabetes that can impact the eyes and lead to vision loss. One method for diagnosing diabetic retinopathy is the examination of the fundus of the eye. An ophthalmologist examines the back part of ...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	382,671
2404.00622	OpenMines: A Light and Comprehensive Mining Simulation Environment for Truck Dispatching	Mine fleet management algorithms can significantly reduce operational costs and enhance productivity in mining systems. Most current fleet management algorithms are evaluated based on self-implemented or proprietary simulation environments, posing challenges for replication and comparison. This paper models the simulat...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	443,030
2303.14865	Revisiting Multimodal Representation in Contrastive Learning: From Patch and Token Embeddings to Finite Discrete Tokens	Contrastive learning-based vision-language pre-training approaches, such as CLIP, have demonstrated great success in many vision-language tasks. These methods achieve cross-modal alignment by encoding a matched image-text pair with similar feature embeddings, which are generated by aggregating information from visual p...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	354,268
2411.16289	Utilizing Uncertainty in 2D Pose Detectors for Probabilistic 3D Human Mesh Recovery	Monocular 3D human pose and shape estimation is an inherently ill-posed problem due to depth ambiguities, occlusions, and truncations. Recent probabilistic approaches learn a distribution over plausible 3D human meshes by maximizing the likelihood of the ground-truth pose given an image. We show that this objective fun...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	510,973
1208.2429	Linear model predictive control based on polyhedral control Lyapunov functions: theory and applications	Polyhedral control Lyapunov functions (PCLFs) are exploited in finite-horizon linear model predictive control formulations in order to guarantee the maximal domain of attraction (DoA), in contrast to traditional formulations based on quadratic control Lyapunov functions. In particular, the terminal region is chosen as ...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	18,042
2006.08816	Signed Graph Metric Learning via Gershgorin Disc Perfect Alignment	Given a convex and differentiable objective $Q(\M)$ for a real symmetric matrix $\M$ in the positive definite (PD) cone -- used to compute Mahalanobis distances -- we propose a fast general metric learning framework that is entirely projection-free. We first assume that $\M$ resides in a space $\cS$ of generalized grap...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	182,314
1507.04844	Learning Robust Deep Face Representation	With the development of convolution neural network, more and more researchers focus their attention on the advantage of CNN for face recognition task. In this paper, we propose a deep convolution network for learning a robust face representation. The deep convolution net is constructed by 4 convolution layers, 4 max po...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	45,222
1911.12036	Discriminative Adversarial Domain Adaptation	Given labeled instances on a source domain and unlabeled ones on a target domain, unsupervised domain adaptation aims to learn a task classifier that can well classify target instances. Recent advances rely on domain-adversarial training of deep networks to learn domain-invariant features. However, due to an issue of m...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	155,298
1404.0850	Application of Ontologies in Identifying Requirements Patterns in Use Cases	Use case specifications have successfully been used for requirements description. They allow joining, in the same modeling space, the expectations of the stakeholders as well as the needs of the software engineer and analyst involved in the process. While use cases are not meant to describe a system's implementation, b...	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	true	32,056
1906.07689	Expressing Visual Relationships via Language	Describing images with text is a fundamental problem in vision-language research. Current studies in this domain mostly focus on single image captioning. However, in various real applications (e.g., image editing, difference interpretation, and retrieval), generating relational captions for two images, can also be very...	false	false	false	false	false	false	true	false	true	false	false	true	false	false	false	false	false	false	135,663
1103.0920	Reduction of Many-valued into Two-valued Modal Logics	In this paper we develop a 2-valued reduction of many-valued logics, into 2-valued multi-modal logics. Such an approach is based on the contextualization of many-valued logics with the introduction of higher-order Herbrand interpretation types, where we explicitly introduce the coexistence of a set of algebraic truth v...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	9,478
2007.01465	Deep-PowerX: A Deep Learning-Based Framework for Low-Power Approximate Logic Synthesis	This paper aims at integrating three powerful techniques namely Deep Learning, Approximate Computing, and Low Power Design into a strategy to optimize logic at the synthesis level. We utilize advances in deep learning to guide an approximate logic synthesis engine to minimize the dynamic power consumption of a given di...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	185,437
2107.02963	Disentangle Your Dense Object Detector	Deep learning-based dense object detectors have achieved great success in the past few years and have been applied to numerous multimedia applications such as video understanding. However, the current training pipeline for dense detectors is compromised to lots of conjunctions that may not hold. In this paper, we inves...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	244,995
2412.02415	Knowledge-Enhanced Conversational Recommendation via Transformer-based Sequential Modelling	In conversational recommender systems (CRSs), conversations usually involve a set of items and item-related entities or attributes, e.g., director is a related entity of a movie. These items and item-related entities are often mentioned along the development of a dialog, leading to potential sequential dependencies amo...	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	false	false	513,532
2302.10918	Level set-based multiscale topology optimization for a thermal cloak design problem using the homogenization method	Artificially designed composite materials consist of microstructures, that exhibit various thermal properties depending on their shapes, such as anisotropic thermal conductivity. One of the representative applications of such composite materials for thermal control is the thermal cloak. This study proposed a topology o...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	347,024
2310.06427	TANGO: Time-Reversal Latent GraphODE for Multi-Agent Dynamical Systems	Learning complex multi-agent system dynamics from data is crucial across many domains, such as in physical simulations and material modeling. Extended from purely data-driven approaches, existing physics-informed approaches such as Hamiltonian Neural Network strictly follow energy conservation law to introduce inductiv...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	398,574
2405.07277	Mining Influential Spreaders in Complex Networks by an Effective Combination of the Degree and K-Shell	Graph mining is an important technique that used in many applications such as predicting and understanding behaviors and information dissemination within networks. One crucial aspect of graph mining is the identification and ranking of influential nodes, which has applications in various fields including marketing, soc...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	453,645
1907.10993	Weakly Supervised Recognition of Surgical Gestures	Kinematic trajectories recorded from surgical robots contain information about surgical gestures and potentially encode cues about surgeon's skill levels. Automatic segmentation of these trajectories into meaningful action units could help to develop new metrics for surgical skill assessment as well as to simplify surg...	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	139,758
2004.08731	Enhancing Pharmacovigilance with Drug Reviews and Social Media	This paper explores whether the use of drug reviews and social media could be leveraged as potential alternative sources for pharmacovigilance of adverse drug reactions (ADRs). We examined the performance of BERT alongside two variants that are trained on biomedical papers, BioBERT7, and clinical notes, Clinical BERT8....	false	false	false	false	false	true	true	false	true	false	false	false	false	false	false	false	false	false	173,151
2205.04366	Effectively Using Long and Short Sessions for Multi-Session-based Recommendations	It is not accurate to make recommendations only based one single current session. Therefore, multi-session-based recommendation(MSBR) is a solution for the problem. Compared with the previous MSBR models, we have made three improvements in this paper. First, the previous work choose to use all the history sessions of t...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	295,622
2010.12230	Coping with Label Shift via Distributionally Robust Optimisation	The label shift problem refers to the supervised learning setting where the train and test label distributions do not match. Existing work addressing label shift usually assumes access to an \emph{unlabelled} test sample. This sample may be used to estimate the test label distribution, and to then train a suitably re-w...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	202,615
1708.04538	Artistic style transfer for videos and spherical images	Manually re-drawing an image in a certain artistic style takes a professional artist a long time. Doing this for a video sequence single-handedly is beyond imagination. We present two computational approaches that transfer the style from one image (for example, a painting) to a whole video sequence. In our first approa...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	78,967
2502.08922	Self-Consistency of the Internal Reward Models Improves Self-Rewarding Language Models	Aligning Large Language Models (LLMs) with human preferences is crucial for their deployment in real-world applications. Recent advancements in Self-Rewarding Language Models suggest that an LLM can use its internal reward models (such as LLM-as-a-Judge) \cite{yuanself} to generate preference data, improving alignment ...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	533,224
2006.04248	Learning Convex Optimization Models	A convex optimization model predicts an output from an input by solving a convex optimization problem. The class of convex optimization models is large, and includes as special cases many well-known models like linear and logistic regression. We propose a heuristic for learning the parameters in a convex optimization m...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	180,623
1607.04439	A Networked Swarm Model for UAV Deployment in the Assessment of Forest Environments	Autonomous Unmanned Aerial Vehicles (UAVs) have gained popularity due to their many potential application fields. Alongside sophisticated sensors, UAVs can be equipped with communication adaptors aimed for inter-UAV communication. Inter-communication of UAVs to form a UAV swarm raises questions on how to manage its com...	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	true	58,619
2306.06359	NeRFool: Uncovering the Vulnerability of Generalizable Neural Radiance Fields against Adversarial Perturbations	Generalizable Neural Radiance Fields (GNeRF) are one of the most promising real-world solutions for novel view synthesis, thanks to their cross-scene generalization capability and thus the possibility of instant rendering on new scenes. While adversarial robustness is essential for real-world applications, little study...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	372,569
1910.03638	Bregman Proximal Framework for Deep Linear Neural Networks	A typical assumption for the analysis of first order optimization methods is the Lipschitz continuity of the gradient of the objective function. However, for many practical applications this assumption is violated, including loss functions in deep learning. To overcome this issue, certain extensions based on generalize...	false	false	false	false	false	true	true	false	false	false	false	true	false	false	false	false	false	false	148,541
1401.5390	Learning to Win by Reading Manuals in a Monte-Carlo Framework	Domain knowledge is crucial for effective performance in autonomous control systems. Typically, human effort is required to encode this knowledge into a control algorithm. In this paper, we present an approach to language grounding which automatically interprets text in the context of a complex control application, suc...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	30,196
2308.09106	Optimal Closed Loop Control of G2V/V2G Action Using Model Predictive Controller	This paper has developed a closed-loop control algorithm to operate the G2V/V2G action, tested under varying battery voltage conditions and load and source power differences. Under V2G action, to maintain total harmonic distortion under minimum level and grid frequency under the standard limit, a Model predictive contr...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	386,158
2307.02819	Trends in Machine Learning and Electroencephalogram (EEG): A Review for Undergraduate Researchers	This paper presents a systematic literature review on Brain-Computer Interfaces (BCIs) in the context of Machine Learning. Our focus is on Electroencephalography (EEG) research, highlighting the latest trends as of 2023. The objective is to provide undergraduate researchers with an accessible overview of the BCI field,...	true	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	377,826
2306.04803	Privately generating tabular data using language models	Privately generating synthetic data from a table is an important brick of a privacy-first world. We propose and investigate a simple approach of treating each row in a table as a sentence and training a language model with differential privacy. We show this approach obtains competitive results in modelling tabular data...	false	false	false	false	false	false	true	false	true	false	false	false	true	false	false	false	false	false	371,903
2103.03457	IOT: Instance-wise Layer Reordering for Transformer Structures	With sequentially stacked self-attention, (optional) encoder-decoder attention, and feed-forward layers, Transformer achieves big success in natural language processing (NLP), and many variants have been proposed. Currently, almost all these models assume that the layer order is fixed and kept the same across data samp...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	223,290
2009.13668	Parameter Critic: a Model Free Variance Reduction Method Through Imperishable Samples	We consider the problem of finding a policy that maximizes an expected reward throughout the trajectory of an agent that interacts with an unknown environment. Frequently denoted Reinforcement Learning, this framework suffers from the need of large amount of samples in each step of the learning process. To this end, we...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	197,800
2302.08638	Low Latency Video Denoising for Online Conferencing Using CNN Architectures	In this paper, we propose a pipeline for real-time video denoising with low runtime cost and high perceptual quality. The vast majority of denoising studies focus on image denoising. However, a minority of research works focusing on video denoising do so with higher performance costs to obtain higher quality while main...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	346,116
2108.07127	Active Learning for Massively Parallel Translation of Constrained Text into Low Resource Languages	We translate a closed text that is known in advance and available in many languages into a new and severely low resource language. Most human translation efforts adopt a portion-based approach to translate consecutive pages/chapters in order, which may not suit machine translation. We compare the portion-based approach...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	250,836
2002.12312	Advances in Collaborative Filtering and Ranking	In this dissertation, we cover some recent advances in collaborative filtering and ranking. In chapter 1, we give a brief introduction of the history and the current landscape of collaborative filtering and ranking; chapter 2 we first talk about pointwise collaborative filtering problem with graph information, and how ...	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	false	165,984
2208.11464	FactMix: Using a Few Labeled In-domain Examples to Generalize to Cross-domain Named Entity Recognition	Few-shot Named Entity Recognition (NER) is imperative for entity tagging in limited resource domains and thus received proper attention in recent years. Existing approaches for few-shot NER are evaluated mainly under in-domain settings. In contrast, little is known about how these inherently faithful models perform in ...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	314,435
1906.05781	A Low-Power Domino Logic Architecture for Memristor-Based Neuromorphic Computing	We propose a domino logic architecture for memristor-based neuromorphic computing. The design uses the delay of memristor RC circuits to represent synaptic computations and a simple binary neuron activation function. Synchronization schemes are proposed for communicating information between neural network layers, and a...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	135,119
2303.09946	An Adaptive Fuzzy Reinforcement Learning Cooperative Approach for the Autonomous Control of Flock Systems	The flock-guidance problem enjoys a challenging structure where multiple optimization objectives are solved simultaneously. This usually necessitates different control approaches to tackle various objectives, such as guidance, collision avoidance, and cohesion. The guidance schemes, in particular, have long suffered fr...	false	false	false	false	false	false	true	true	false	false	true	false	false	false	true	false	false	false	352,249
2210.06551	MotionBERT: A Unified Perspective on Learning Human Motion Representations	We present a unified perspective on tackling various human-centric video tasks by learning human motion representations from large-scale and heterogeneous data resources. Specifically, we propose a pretraining stage in which a motion encoder is trained to recover the underlying 3D motion from noisy partial 2D observati...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	323,335
2411.00836	DynaMath: A Dynamic Visual Benchmark for Evaluating Mathematical Reasoning Robustness of Vision Language Models	The rapid advancements in Vision-Language Models (VLMs) have shown great potential in tackling mathematical reasoning tasks that involve visual context. Unlike humans who can reliably apply solution steps to similar problems with minor modifications, we found that SOTA VLMs like GPT-4o can consistently fail in these sc...	false	false	false	false	true	false	false	false	true	false	false	true	false	false	false	false	false	false	504,775
2312.03005	Few-Shot Anomaly Detection with Adversarial Loss for Robust Feature Representations	Anomaly detection is a critical and challenging task that aims to identify data points deviating from normal patterns and distributions within a dataset. Various methods have been proposed using a one-class-one-model approach, but these techniques often face practical problems such as memory inefficiency and the requir...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	413,086
2407.07603	iiANET: Inception Inspired Attention Hybrid Network for efficient Long-Range Dependency	The recent emergence of hybrid models has introduced another transformative approach to solving computer vision tasks, slowly shifting away from conventional CNN (Convolutional Neural Network) and ViT (Vision Transformer). However, not enough effort has been made to efficiently combine these two approaches to improve c...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	471,827
2101.11863	Exploiting the Hidden Tasks of GANs: Making Implicit Subproblems Explicit	We present an alternative perspective on the training of generative adversarial networks (GANs), showing that the training step for a GAN generator decomposes into two implicit subproblems. In the first, the discriminator provides new target data to the generator in the form of "inverse examples" produced by approximat...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	217,411
1005.5337	Using a Kernel Adatron for Object Classification with RCS Data	Rapid identification of object from radar cross section (RCS) signals is important for many space and military applications. This identification is a problem in pattern recognition which either neural networks or support vector machines should prove to be high-speed. Bayesian networks would also provide value but requi...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	6,597
1901.02780	Sentiment Analysis of Czech Texts: An Algorithmic Survey	In the area of online communication, commerce and transactions, analyzing sentiment polarity of texts written in various natural languages has become crucial. While there have been a lot of contributions in resources and studies for the English language, "smaller" languages like Czech have not received much attention. ...	false	false	false	false	false	true	true	false	true	false	false	false	false	false	false	false	false	false	118,275
2406.14765	ChatGPT as Research Scientist: Probing GPT's Capabilities as a Research Librarian, Research Ethicist, Data Generator and Data Predictor	How good a research scientist is ChatGPT? We systematically probed the capabilities of GPT-3.5 and GPT-4 across four central components of the scientific process: as a Research Librarian, Research Ethicist, Data Generator, and Novel Data Predictor, using psychological science as a testing field. In Study 1 (Research Li...	false	false	false	false	true	true	true	false	true	false	false	false	false	true	false	false	false	false	466,455
2302.09590	Accelerated Video Annotation driven by Deep Detector and Tracker	Annotating object ground truth in videos is vital for several downstream tasks in robot perception and machine learning, such as for evaluating the performance of an object tracker or training an image-based object detector. The accuracy of the annotated instances of the moving objects on every image frame in a video i...	true	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	346,489
1912.00634	Meta-Path Constrained Random Walk Inference for Large-Scale Heterogeneous Information Networks	Heterogeneous information network (HIN) has shown its power of modeling real world data as a multi-typed entity-relation graph. Meta-path is the key contributor to this power since it enables inference by capturing the proximities between entities via rich semantic links. Previous HIN studies ask users to provide eithe...	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	false	false	155,840
2408.11935	Explainable Anomaly Detection: Counterfactual driven What-If Analysis	There exists three main areas of study inside of the field of predictive maintenance: anomaly detection, fault diagnosis, and remaining useful life prediction. Notably, anomaly detection alerts the stakeholder that an anomaly is occurring. This raises two fundamental questions: what is causing the fault and how can we ...	true	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	482,508
1802.10405	Massive MIMO for Ultra-reliable Communications with Constellations for Dual Coherent-noncoherent Detection	The stringent requirements of ultra-reliable low-latency communications (URLLC) require rethinking of the physical layer transmission techniques. Massive antenna arrays are seen as an enabler of the emerging $5^\text{th}$ generation systems, due to increases in spectral efficiency and degrees of freedom for transmissio...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	91,516
2101.06069	Mining Data Impressions from Deep Models as Substitute for the Unavailable Training Data	Pretrained deep models hold their learnt knowledge in the form of model parameters. These parameters act as "memory" for the trained models and help them generalize well on unseen data. However, in absence of training data, the utility of a trained model is merely limited to either inference or better initialization to...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	215,601
2405.05908	Multimodal Super-Resolution: Discovering hidden physics and its application to fusion plasmas	A non-linear system governed by multi-spatial and multi-temporal physics scales cannot be fully understood with a single diagnostic, as each provides only a partial view, leading to information loss. Combining multiple diagnostics may also result in incomplete projections of the system's physics. By identifying hidden ...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	453,107
2212.14453	Learning Multimodal Data Augmentation in Feature Space	The ability to jointly learn from multiple modalities, such as text, audio, and visual data, is a defining feature of intelligent systems. While there have been promising advances in designing neural networks to harness multimodal data, the enormous success of data augmentation currently remains limited to single-modal...	false	false	false	false	false	false	true	false	true	false	false	true	false	false	false	false	false	false	338,620
2203.13959	Robust Fuzzy Q-Learning-Based Strictly Negative Imaginary Tracking Controllers for the Uncertain Quadrotor Systems	Quadrotors are one of the popular unmanned aerial vehicles (UAVs) due to their versatility and simple design. However, the tuning of gains for quadrotor flight controllers can be laborious, and accurately stable control of trajectories can be difficult to maintain under exogenous disturbances and uncertain system param...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	287,824
1811.11818	Disease phenotyping using deep learning: A diabetes case study	Characterization of a patient clinical phenotype is central to biomedical informatics. ICD codes, assigned to inpatient encounters by coders, is important for population health and cohort discovery when clinical information is limited. While ICD codes are assigned to patients by professionals trained and certified in c...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	114,858
2405.02161	Simulating the Economic Impact of Rationality through Reinforcement Learning and Agent-Based Modelling	Agent-based models (ABMs) are simulation models used in economics to overcome some of the limitations of traditional frameworks based on general equilibrium assumptions. However, agents within an ABM follow predetermined 'bounded rational' behavioural rules which can be cumbersome to design and difficult to justify. He...	false	true	false	false	true	false	true	false	false	false	false	false	false	false	true	false	false	false	451,648
2205.12760	Guiding Vector Fields for Following Occluded Paths	Accurately following a geometric desired path in a two-dimensional space is a fundamental task for many engineering systems, in particular mobile robots. When the desired path is occluded by obstacles, it is necessary and crucial to temporarily deviate from the path for obstacle/collision avoidance. In this paper, we d...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	298,704
2402.00412	Hidding the Ghostwriters: An Adversarial Evaluation of AI-Generated Student Essay Detection	Large language models (LLMs) have exhibited remarkable capabilities in text generation tasks. However, the utilization of these models carries inherent risks, including but not limited to plagiarism, the dissemination of fake news, and issues in educational exercises. Although several detectors have been proposed to ad...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	425,602
2009.14509	Towards Target-Driven Visual Navigation in Indoor Scenes via Generative Imitation Learning	We present a target-driven navigation system to improve mapless visual navigation in indoor scenes. Our method takes a multi-view observation of a robot and a target as inputs at each time step to provide a sequence of actions that move the robot to the target without relying on odometry or GPS at runtime. The system i...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	198,061
2408.10596	Fast Collective Evasion in Self-Localized Swarms of Unmanned Aerial Vehicles	A novel approach for achieving fast evasion in self-localized swarms of Unmanned Aerial Vehicles (UAVs) threatened by an intruding moving object is presented in this paper. Motivated by natural self-organizing systems, the presented approach of fast and collective evasion enables the UAV swarm to avoid dynamic objects ...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	481,929
2309.11888	High-order Joint Constituency and Dependency Parsing	This work revisits the topic of jointly parsing constituency and dependency trees, i.e., to produce compatible constituency and dependency trees simultaneously for input sentences, which is attractive considering that the two types of trees are complementary in representing syntax. The original work of Zhou and Zhao (2...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	393,580
1803.08679	Learning Spatial-Temporal Regularized Correlation Filters for Visual Tracking	Discriminative Correlation Filters (DCF) are efficient in visual tracking but suffer from unwanted boundary effects. Spatially Regularized DCF (SRDCF) has been suggested to resolve this issue by enforcing spatial penalty on DCF coefficients, which, inevitably, improves the tracking performance at the price of increasin...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	93,318
2008.00932	AUTSL: A Large Scale Multi-modal Turkish Sign Language Dataset and Baseline Methods	Sign language recognition is a challenging problem where signs are identified by simultaneous local and global articulations of multiple sources, i.e. hand shape and orientation, hand movements, body posture, and facial expressions. Solving this problem computationally for a large vocabulary of signs in real life setti...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	190,154
2404.07584	UltraEval: A Lightweight Platform for Flexible and Comprehensive Evaluation for LLMs	Evaluation is pivotal for refining Large Language Models (LLMs), pinpointing their capabilities, and guiding enhancements. The rapid development of LLMs calls for a lightweight and easy-to-use framework for swift evaluation deployment. However, considering various implementation details, developing a comprehensive eval...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	445,897
2502.04937	Data-driven Modality Fusion: An AI-enabled Framework for Large-Scale Sensor Network Management	The development and operation of smart cities relyheavily on large-scale Internet-of-Things (IoT) networks and sensor infrastructures that continuously monitor various aspects of urban environments. These networks generate vast amounts of data, posing challenges related to bandwidth usage, energy consumption, and syste...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	true	531,374
2208.06623	Granular Directed Rough Sets, Concept Organization and Soft Clustering	Up-directed rough sets are introduced and studied by the present author in earlier papers. This is extended by her in two different granular directions in this research, with a surprising algebraic semantics. The granules are based on ideas of generalized closure under up-directedness that may be read as a form of weak...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	312,777

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