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
2410.16314	Steering Large Language Models using Conceptors: Improving Addition-Based Activation Engineering	Large language models have transformed AI, yet reliably controlling their outputs remains a challenge. This paper explores activation engineering, where outputs of pre-trained LLMs are controlled by manipulating their activations at inference time. Unlike traditional methods using a single steering vector, we introduce...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	true	false	false	500,977
1907.11546	Compressing deep quaternion neural networks with targeted regularization	In recent years, hyper-complex deep networks (such as complex-valued and quaternion-valued neural networks) have received a renewed interest in the literature. They find applications in multiple fields, ranging from image reconstruction to 3D audio processing. Similar to their real-valued counterparts, quaternion neura...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	139,878
2202.09609	A Lightweight Dual-Domain Attention Framework for Sparse-View CT Reconstruction	Computed Tomography (CT) plays an essential role in clinical diagnosis. Due to the adverse effects of radiation on patients, the radiation dose is expected to be reduced as low as possible. Sparse sampling is an effective way, but it will lead to severe artifacts on the reconstructed CT image, thus sparse-view CT image...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	281,256
2311.03381	Separating and Learning Latent Confounders to Enhancing User Preferences Modeling	Recommender models aim to capture user preferences from historical feedback and then predict user-specific feedback on candidate items. However, the presence of various unmeasured confounders causes deviations between the user preferences in the historical feedback and the true preferences, resulting in models not meet...	false	false	false	false	true	true	true	false	false	false	false	false	false	false	false	false	false	false	405,829
2501.02114	Relaxation-assisted reverse annealing on nonnegative/binary matrix factorization	Quantum annealing has garnered significant attention as meta-heuristics inspired by quantum physics for combinatorial optimization problems. Among its many applications, nonnegative/binary matrix factorization stands out for its complexity and relevance in unsupervised machine learning. The use of reverse annealing, a ...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	522,354
2205.06242	Graph Fourier transform based on singular value decomposition of directed Laplacian	Graph Fourier transform (GFT) is a fundamental concept in graph signal processing. In this paper, based on singular value decomposition of Laplacian, we introduce a novel definition of GFT on directed graphs, and use singular values of Laplacian to carry the notion of graph frequencies. % of the proposed GFT. The propo...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	296,184
2202.10716	HRel: Filter Pruning based on High Relevance between Activation Maps and Class Labels	This paper proposes an Information Bottleneck theory based filter pruning method that uses a statistical measure called Mutual Information (MI). The MI between filters and class labels, also called \textit{Relevance}, is computed using the filter's activation maps and the annotations. The filters having High Relevance ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	281,636
2306.16621	Assessing the Performance of 1D-Convolution Neural Networks to Predict Concentration of Mixture Components from Raman Spectra	An emerging application of Raman spectroscopy is monitoring the state of chemical reactors during biologic drug production. Raman shift intensities scale linearly with the concentrations of chemical species and thus can be used to analytically determine real-time concentrations using non-destructive light irradiation i...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	376,419
2201.10860	A deep learning method based on patchwise training for reconstructing temperature field	Physical field reconstruction is highly desirable for the measurement and control of engineering systems. The reconstruction of the temperature field from limited observation plays a crucial role in thermal management for electronic equipment. Deep learning has been employed in physical field reconstruction, whereas th...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	277,123
2203.03883	Online Dynamic Parameter Estimation of an Alkaline Electrolysis System Based on Bayesian Inference	When directly coupled with fluctuating energy sources such as wind and photovoltage power, the alkaline electrolysis (AEL) in a power-to-hydrogen (P2H) system is required to operate flexibly by dynamically adjusting its hydrogen production rate. The flex-ibility characteristics, e.g., loading range and ramping rate, of...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	284,261
1902.01769	Dungeon Crawl Stone Soup as an Evaluation Domain for Artificial Intelligence	Dungeon Crawl Stone Soup is a popular, single-player, free and open-source rogue-like video game with a sufficiently complex decision space that makes it an ideal testbed for research in cognitive systems and, more generally, artificial intelligence. This paper describes the properties of Dungeon Crawl Stone Soup that ...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	120,728
1912.10803	Discriminative Robust Deep Dictionary Learning for Hyperspectral Image Classification	This work proposes a new framework for deep learning that has been particularly tailored for hyperspectral image classification. We learn multiple levels of dictionaries in a robust fashion. The last layer is discriminative that learns a linear classifier. The training proceeds greedily, at a time a single level of dic...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	158,411
2302.02186	Perimeter Defense using a Turret with Finite Range and Service Times	We consider a perimeter defense problem in a planar conical environment comprising a single turret that has a finite range and non-zero service time. The turret seeks to defend a concentric perimeter against $N\geq 2$ intruders. Upon release, each intruder moves radially towards the perimeter with a fixed speed. To cap...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	343,899
2003.06615	Medical Image Enhancement Using Histogram Processing and Feature Extraction for Cancer Classification	MRI (Magnetic Resonance Imaging) is a technique used to analyze and diagnose the problem defined by images like cancer or tumor in a brain. Physicians require good contrast images for better treatment purpose as it contains maximum information of the disease. MRI images are low contrast images which make diagnoses diff...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	168,171
2207.08656	Towards High-Fidelity Single-view Holistic Reconstruction of Indoor Scenes	We present a new framework to reconstruct holistic 3D indoor scenes including both room background and indoor objects from single-view images. Existing methods can only produce 3D shapes of indoor objects with limited geometry quality because of the heavy occlusion of indoor scenes. To solve this, we propose an instanc...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	308,658
2009.02516	Generalization on the Enhancement of Layerwise Relevance Interpretability of Deep Neural Network	The practical application of deep neural networks are still limited by their lack of transparency. One of the efforts to provide explanation for decisions made by artificial intelligence (AI) is the use of saliency or heat maps highlighting relevant regions that contribute significantly to its prediction. A layer-wise ...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	194,560
2502.11953	Refined PAC-Bayes Bounds for Offline Bandits	In this paper, we present refined probabilistic bounds on empirical reward estimates for off-policy learning in bandit problems. We build on the PAC-Bayesian bounds from Seldin et al. (2010) and improve on their results using a new parameter optimization approach introduced by Rodr\'iguez et al. (2024). This technique ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	534,619
2010.01041	Homography Estimation with Convolutional Neural Networks Under Conditions of Variance	Planar homography estimation is foundational to many computer vision problems, such as Simultaneous Localization and Mapping (SLAM) and Augmented Reality (AR). However, conditions of high variance confound even the state-of-the-art algorithms. In this report, we analyze the performance of two recently published methods...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	198,492
1910.03937	New and Explicit Constructions of Unbalanced Ramanujan Bipartite Graphs	The objectives of this article are three-fold. Firstly, we present for the first time explicit constructions of an infinite family of \textit{unbalanced} Ramanujan bigraphs. Secondly, we revisit some of the known methods for constructing Ramanujan graphs and discuss the computational work required in actually implement...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	148,635
2202.06997	Cross-Modality Neuroimage Synthesis: A Survey	Multi-modality imaging improves disease diagnosis and reveals distinct deviations in tissues with anatomical properties. The existence of completely aligned and paired multi-modality neuroimaging data has proved its effectiveness in brain research. However, collecting fully aligned and paired data is expensive or even ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	280,390
1912.05629	Large-scale Kernel Methods and Applications to Lifelong Robot Learning	As the size and richness of available datasets grow larger, the opportunities for solving increasingly challenging problems with algorithms learning directly from data grow at the same pace. Consequently, the capability of learning algorithms to work with large amounts of data has become a crucial scientific and techno...	false	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	157,148
1911.00658	Global Adaptive Generative Adjustment	Many traditional signal recovery approaches can behave well basing on the penalized likelihood. However, they have to meet with the difficulty in the selection of hyperparameters or tuning parameters in the penalties. In this article, we propose a global adaptive generative adjustment (GAGA) algorithm for signal recove...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	151,879
1805.09966	Prestige drives epistemic inequality in the diffusion of scientific ideas	The spread of ideas in the scientific community is often viewed as a competition, in which good ideas spread further because of greater intrinsic fitness, and publication venue and citation counts correlate with importance and impact. However, relatively little is known about how structural factors influence the spread...	false	false	false	true	false	false	false	false	false	false	false	false	false	true	false	false	false	false	98,543
2311.01197	AiluRus: A Scalable ViT Framework for Dense Prediction	Vision transformers (ViTs) have emerged as a prevalent architecture for vision tasks owing to their impressive performance. However, when it comes to handling long token sequences, especially in dense prediction tasks that require high-resolution input, the complexity of ViTs increases significantly. Notably, dense pre...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	404,949
2409.12161	Generalized compression and compressive search of large datasets	The Big Data explosion has necessitated the development of search algorithms that scale sub-linearly in time and memory. While compression algorithms and search algorithms do exist independently, few algorithms offer both, and those which do are domain-specific. We present panCAKES, a novel approach to compressive ...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	true	489,463
2006.15274	Deep Generative Modeling for Mechanistic-based Learning and Design of Metamaterial Systems	Metamaterials are emerging as a new paradigmatic material system to render unprecedented and tailorable properties for a wide variety of engineering applications. However, the inverse design of metamaterial and its multiscale system is challenging due to high-dimensional topological design space, multiple local optima,...	false	true	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	184,457
1701.00008	Deep Neural Networks to Enable Real-time Multimessenger Astrophysics	Gravitational wave astronomy has set in motion a scientific revolution. To further enhance the science reach of this emergent field, there is a pressing need to increase the depth and speed of the gravitational wave algorithms that have enabled these groundbreaking discoveries. To contribute to this effort, we introduc...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	66,214
2012.04290	Channel Gain Cartography via Mixture of Experts	In order to estimate the channel gain (CG) between the locations of an arbitrary transceiver pair across a geographic area of interest, CG maps can be constructed from spatially distributed sensor measurements. Most approaches to build such spectrum maps are location-based, meaning that the input variable to the estima...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	210,410
2103.13298	Deep Reinforcement Learning with Symmetric Prior for Predictive Power Allocation to Mobile Users	Deep reinforcement learning has been applied for a variety of wireless tasks, which is however known with high training and inference complexity. In this paper, we resort to deep deterministic policy gradient (DDPG) algorithm to optimize predictive power allocation among K mobile users requesting video streaming, which...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	226,447
1908.06612	Deep neural network or dermatologist?	Deep learning techniques have proven high accuracy for identifying melanoma in digitised dermoscopic images. A strength is that these methods are not constrained by features that are pre-defined by human semantics. A down-side is that it is difficult to understand the rationale of the model predictions and to identify ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	142,064
2004.07202	Entities as Experts: Sparse Memory Access with Entity Supervision	We focus on the problem of capturing declarative knowledge about entities in the learned parameters of a language model. We introduce a new model - Entities as Experts (EAE) - that can access distinct memories of the entities mentioned in a piece of text. Unlike previous efforts to integrate entity knowledge into seque...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	172,721
2209.07240	Neural Stochastic Control	Control problems are always challenging since they arise from the real-world systems where stochasticity and randomness are of ubiquitous presence. This naturally and urgently calls for developing efficient neural control policies for stabilizing not only the deterministic equations but the stochastic systems as well. ...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	317,674
2406.03808	Cross-variable Linear Integrated ENhanced Transformer for Photovoltaic power forecasting	Photovoltaic (PV) power forecasting plays a crucial role in optimizing the operation and planning of PV systems, thereby enabling efficient energy management and grid integration. However, un certainties caused by fluctuating weather conditions and complex interactions between different variables pose significant chall...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	461,399
1501.05636	Quantum Markov chains, sufficiency of quantum channels, and Renyi information measures	A short quantum Markov chain is a tripartite state $\rho_{ABC}$ such that system $A$ can be recovered perfectly by acting on system $C$ of the reduced state $\rho_{BC}$. Such states have conditional mutual information $I(A;B\|C)$ equal to zero and are the only states with this property. A quantum channel $\mathcal{N}$ i...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	39,508
2102.07017	Mitigating Negative Side Effects via Environment Shaping	Agents operating in unstructured environments often produce negative side effects (NSE), which are difficult to identify at design time. While the agent can learn to mitigate the side effects from human feedback, such feedback is often expensive and the rate of learning is sensitive to the agent's state representation....	false	false	false	false	true	false	false	true	false	false	false	false	false	false	true	false	false	false	219,958
1904.12743	End-to-end Cloud Segmentation in High-Resolution Multispectral Satellite Imagery Using Deep Learning	Segmenting clouds in high-resolution satellite images is an arduous and challenging task due to the many types of geographies and clouds a satellite can capture. Therefore, it needs to be automated and optimized, specially for those who regularly process great amounts of satellite images, such as governmental instituti...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	129,214
1310.0602	Iterated Variable Neighborhood Search for the resource constrained multi-mode multi-project scheduling problem	The resource constrained multi-mode multi-project scheduling problem (RCMMMPSP) is a notoriously difficult combinatorial optimization problem. For a given set of activities, feasible execution mode assignments and execution starting times must be found such that some optimization function, e.g. the makespan, is optimiz...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	27,505
2105.11432	Design to automate the detection and counting of Tuberculosis(TB) bacilli	Tuberculosis is a contagious disease which is one of the leading causes of death, globally. The general diagnosis methods for tuberculosis include microscopic examination, tuberculin skin test, culture method, enzyme linked immunosorbent assay (ELISA) and electronic nose system. World Health Organization (WHO) recommen...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	236,700
2411.09874	A Hybrid Artificial Intelligence System for Automated EEG Background Analysis and Report Generation	Electroencephalography (EEG) plays a crucial role in the diagnosis of various neurological disorders. However, small hospitals and clinics often lack advanced EEG signal analysis systems and are prone to misinterpretation in manual EEG reading. This study proposes an innovative hybrid artificial intelligence (AI) syste...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	508,402
1912.08924	The Iterated Local Directed Transitivity Model for Social Networks	We introduce a new directed graph model for social networks, based on the transitivity of triads. In the Iterated Local Directed Transitivity (ILDT) model, new nodes are born over discrete time-steps, and inherit the link structure of their parent nodes. The ILDT model may be viewed as a directed analogue of the ILT mo...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	157,947
2102.01538	A Matrix-based Distance of Pythagorean Fuzzy Set and its Application in Medical Diagnosis	The pythagorean fuzzy set (PFS) which is developed based on intuitionistic fuzzy set, is more efficient in elaborating and disposing uncertainties in indeterminate situations, which is a very reason of that PFS is applied in various kinds of fields. How to measure the distance between two pythagorean fuzzy sets is stil...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	218,146
2502.08323	Contextual Compression Encoding for Large Language Models: A Novel Framework for Multi-Layered Parameter Space Pruning	Context-aware compression techniques have gained increasing attention as model sizes continue to grow, introducing computational bottlenecks that hinder efficient deployment. A structured encoding approach was proposed to selectively eliminate redundant parameter groups while ensuring that representational fidelity was...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	532,976
1802.04903	Molecular Structure Extraction From Documents Using Deep Learning	Chemical structure extraction from documents remains a hard problem due to both false positive identification of structures during segmentation and errors in the predicted structures. Current approaches rely on handcrafted rules and subroutines that perform reasonably well generally, but still routinely encounter situa...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	90,332
2412.14762	A General Control Method for Human-Robot Integration	This paper introduces a new generalized control method designed for multi-degrees-of-freedom devices to help people with limited motion capabilities in their daily activities. The challenge lies in finding the most adapted strategy for the control interface to effectively map user's motions in a low-dimensional space t...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	518,851
1503.00778	Simple, Efficient, and Neural Algorithms for Sparse Coding	Sparse coding is a basic task in many fields including signal processing, neuroscience and machine learning where the goal is to learn a basis that enables a sparse representation of a given set of data, if one exists. Its standard formulation is as a non-convex optimization problem which is solved in practice by heuri...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	true	false	true	40,745
2004.00188	Improving Perceptual Quality of Drum Transcription with the Expanded Groove MIDI Dataset	We introduce the Expanded Groove MIDI dataset (E-GMD), an automatic drum transcription (ADT) dataset that contains 444 hours of audio from 43 drum kits, making it an order of magnitude larger than similar datasets, and the first with human-performed velocity annotations. We use E-GMD to optimize classifiers for use in ...	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	170,545
2408.11371	Solving Decision Theory Problems with Probabilistic Answer Set Programming	Solving a decision theory problem usually involves finding the actions, among a set of possible ones, which optimize the expected reward, possibly accounting for the uncertainty of the environment. In this paper, we introduce the possibility to encode decision theory problems with Probabilistic Answer Set Programming u...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	482,263
2101.05974	Inductive Representation Learning in Temporal Networks via Causal Anonymous Walks	Temporal networks serve as abstractions of many real-world dynamic systems. These networks typically evolve according to certain laws, such as the law of triadic closure, which is universal in social networks. Inductive representation learning of temporal networks should be able to capture such laws and further be appl...	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	false	false	215,572
1311.4486	Discriminative Density-ratio Estimation	The covariate shift is a challenging problem in supervised learning that results from the discrepancy between the training and test distributions. An effective approach which recently drew a considerable attention in the research community is to reweight the training samples to minimize that discrepancy. In specific, m...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	28,497
2006.00859	Nonlinear observability algorithms with known and unknown inputs: analysis and implementation	The observability of a dynamical system is affected by the presence of external inputs, either known (such as control actions) or unknown (disturbances). Inputs of unknown magnitude are especially detrimental for observability, and they also complicate its analysis. Hence the availability of computational tools capable...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	true	179,592
1703.09474	Robust Depth-based Person Re-identification	Person re-identification (re-id) aims to match people across non-overlapping camera views. So far the RGB-based appearance is widely used in most existing works. However, when people appeared in extreme illumination or changed clothes, the RGB appearance-based re-id methods tended to fail. To overcome this problem, we ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	70,756
2102.01302	Stability and Generalization of the Decentralized Stochastic Gradient Descent	The stability and generalization of stochastic gradient-based methods provide valuable insights into understanding the algorithmic performance of machine learning models. As the main workhorse for deep learning, stochastic gradient descent has received a considerable amount of studies. Nevertheless, the community paid ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	218,063
2110.09856	Network Science Predicts Who Dies Next in Game of Thrones	Social network analysis and machine learning have found countless applications in recent years. As an example, this short project was carried out in 2017 and was followed by some media attention, with the following goal: to bring network science and predictive modeling together on the subject of the popular TV and book...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	261,951
2101.03618	Network clique cover approximation to analyze complex contagions through group interactions	Contagion processes have been proven to fundamentally depend on the structural properties of the interaction networks conveying them. Many real networked systems are characterized by clustered substructures representing either collections of all-to-all pair-wise interactions (cliques) and/or group interactions, involvi...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	214,954
math/0307196	Convolutional Codes with Maximum Distance Profile	Maximum distance profile codes are characterized by the property that two trajectories which start at the same state and proceed to a different state will have the maximum possible distance from each other relative to any other convolutional code of the same rate and degree. In this paper we use methods from systems ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	540,662
2102.08430	Multi-Stage Transmission Line Flow Control Using Centralized and Decentralized Reinforcement Learning Agents	Planning future operational scenarios of bulk power systems that meet security and economic constraints typically requires intensive labor efforts in performing massive simulations. To automate this process and relieve engineers' burden, a novel multi-stage control approach is presented in this paper to train centraliz...	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	220,447
1505.04307	Ergodic Diffusion Control of Multiclass Multi-Pool Networks in the Halfin-Whitt Regime	We consider Markovian multiclass multi-pool networks with heterogeneous server pools, each consisting of many statistically identical parallel servers, where the bipartite graph of customer classes and server pools forms a tree. Customers form their own queue and are served in the first-come first-served discipline, an...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	43,172
2410.17741	Efficient Neural Implicit Representation for 3D Human Reconstruction	High-fidelity digital human representations are increasingly in demand in the digital world, particularly for interactive telepresence, AR/VR, 3D graphics, and the rapidly evolving metaverse. Even though they work well in small spaces, conventional methods for reconstructing 3D human motion frequently require the use o...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	501,595
2109.09232	UPV at CheckThat! 2021: Mitigating Cultural Differences for Identifying Multilingual Check-worthy Claims	Identifying check-worthy claims is often the first step of automated fact-checking systems. Tackling this task in a multilingual setting has been understudied. Encoding inputs with multilingual text representations could be one approach to solve the multilingual check-worthiness detection. However, this approach could ...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	256,205
2103.03539	Extend the FFmpeg Framework to Analyze Media Content	This paper introduces a new set of video analytics plugins developed for the FFmpeg framework. Multimedia applications that increasingly utilize the FFmpeg media features for its comprehensive media encoding, decoding, muxing, and demuxing capabilities can now additionally analyze the video content based on AI models. ...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	223,319
2007.03874	Fine-grained Vibration Based Sensing Using a Smartphone	Recognizing surfaces based on their vibration signatures is useful as it can enable tagging of different locations without requiring any additional hardware such as Near Field Communication (NFC) tags. However, previous vibration based surface recognition schemes either use custom hardware for creating and sensing vibr...	true	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	186,189
2409.15051	Scaling Laws of Decoder-Only Models on the Multilingual Machine Translation Task	Recent studies have showcased remarkable capabilities of decoder-only models in many NLP tasks, including translation. Yet, the machine translation field has been largely dominated by encoder-decoder models based on the Transformer architecture. As a consequence, scaling laws of encoder-decoder models for neural machin...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	490,735
2411.19408	SoGraB: A Visual Method for Soft Grasping Benchmarking and Evaluation	Recent years have seen soft robotic grippers gain increasing attention due to their ability to robustly grasp soft and fragile objects. However, a commonly available standardised evaluation protocol has not yet been developed to assess the performance of varying soft robotic gripper designs. This work introduces a nove...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	512,229
1712.00684	GAGAN: Geometry-Aware Generative Adversarial Networks	Deep generative models learned through adversarial training have become increasingly popular for their ability to generate naturalistic image textures. However, aside from their texture, the visual appearance of objects is significantly influenced by their shape geometry; information which is not taken into account by ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	85,957
0906.3036	Mnesors for automatic control	Mnesors are defined as elements of a semimodule over the min-plus integers. This two-sorted structure is able to merge graduation properties of vectors and idempotent properties of boolean numbers, which makes it appropriate for hybrid systems. We apply it to the control of an inverted pendulum and design a full logica...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	3,896
1703.04699	A fully end-to-end deep learning approach for real-time simultaneous 3D reconstruction and material recognition	This paper addresses the problem of simultaneous 3D reconstruction and material recognition and segmentation. Enabling robots to recognise different materials (concrete, metal etc.) in a scene is important for many tasks, e.g. robotic interventions in nuclear decommissioning. Previous work on 3D semantic reconstruction...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	69,934
2310.04044	Graph-based 3D Collision-distance Estimation Network with Probabilistic Graph Rewiring	We aim to solve the problem of data-driven collision-distance estimation given 3-dimensional (3D) geometries. Conventional algorithms suffer from low accuracy due to their reliance on limited representations, such as point clouds. In contrast, our previous graph-based model, GraphDistNet, achieves high accuracy using e...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	397,519
2211.11761	From Node Interaction to Hop Interaction: New Effective and Scalable Graph Learning Paradigm	Existing Graph Neural Networks (GNNs) follow the message-passing mechanism that conducts information interaction among nodes iteratively. While considerable progress has been made, such node interaction paradigms still have the following limitation. First, the scalability limitation precludes the broad application of G...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	331,870
1912.07942	Analyzing Information Leakage of Updates to Natural Language Models	To continuously improve quality and reflect changes in data, machine learning applications have to regularly retrain and update their core models. We show that a differential analysis of language model snapshots before and after an update can reveal a surprising amount of detailed information about changes in the train...	false	false	false	false	false	false	true	false	true	false	false	false	true	false	false	false	false	false	157,726
2209.02854	Video Restoration with a Deep Plug-and-Play Prior	This paper presents a novel method for restoring digital videos via a Deep Plug-and-Play (PnP) approach. Under a Bayesian formalism, the method consists in using a deep convolutional denoising network in place of the proximal operator of the prior in an alternating optimization scheme. We distinguish ourselves from pri...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	316,314
2409.03239	DiffGrad for Physics-Informed Neural Networks	Physics-Informed Neural Networks (PINNs) are regarded as state-of-the-art tools for addressing highly nonlinear problems based on partial differential equations. Despite their broad range of applications, PINNs encounter several performance challenges, including issues related to efficiency, minimization of computation...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	485,977
2008.09824	Self-Competitive Neural Networks	Deep Neural Networks (DNNs) have improved the accuracy of classification problems in lots of applications. One of the challenges in training a DNN is its need to be fed by an enriched dataset to increase its accuracy and avoid it suffering from overfitting. One way to improve the generalization of DNNs is to augment th...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	192,826
1705.06614	Approximate Bayesian inference as a gauge theory	In a published paper [Sengupta, 2016], we have proposed that the brain (and other self-organized biological and artificial systems) can be characterized via the mathematical apparatus of a gauge theory. The picture that emerges from this approach suggests that any biological system (from a neuron to an organism) can be...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	73,660
2406.03152	Dynamic Spectral Clustering with Provable Approximation Guarantee	This paper studies clustering algorithms for dynamically evolving graphs $\{G_t\}$, in which new edges (and potential new vertices) are added into a graph, and the underlying cluster structure of the graph can gradually change. The paper proves that, under some mild condition on the cluster-structure, the clusters of t...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	461,116
1504.07426	A New Approach to Linear Estimation Problem in Multi-user Massive MIMO Systems	A novel approach for solving linear estimation problem in multi-user massive MIMO systems is proposed. In this approach, the difficulty of matrix inversion is attributed to the incomplete definition of the dot product. The general definition of dot product implies that the columns of channel matrix are always orthogona...	false	false	false	false	false	false	false	false	false	true	true	false	false	false	false	false	false	false	42,535
2404.16840	Biometrics Employing Neural Network	Biometrics involves using unique human traits, both physical and behavioral, for the digital identification of individuals to provide access to systems, devices, or information. Within the field of computer science, it acts as a method for identifying and verifying individuals and controlling access. While the conventi...	false	false	false	false	true	false	false	false	false	false	false	false	true	false	false	false	false	false	449,638
2109.07342	Sequential Point Cloud Prediction in Interactive Scenarios: A Survey	Point cloud has been widely used in the field of autonomous driving since it can provide a more comprehensive three-dimensional representation of the environment than 2D images. Point-wise prediction based on point cloud sequence (PCS) is an essential part of environment understanding, which can assist in the decision-...	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	255,486
2409.03183	Bypassing DARCY Defense: Indistinguishable Universal Adversarial Triggers	Neural networks (NN) classification models for Natural Language Processing (NLP) are vulnerable to the Universal Adversarial Triggers (UAT) attack that triggers a model to produce a specific prediction for any input. DARCY borrows the "honeypot" concept to bait multiple trapdoors, effectively detecting the adversarial ...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	485,949
2410.06804	The Clear Sky Corridor: Insights Towards Aerosol Formation in Exoplanets Using An AI-based Survey of Exoplanet Atmospheres	Producing optimized and accurate transmission spectra of exoplanets from telescope data has traditionally been a manual and labor-intensive procedure. Here we present the results of the first attempt to improve and standardize this procedure using artificial intelligence (AI) based processing of light curves and spectr...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	496,353
2301.02885	SCOREH+: A High-Order Node Proximity Spectral Clustering on Ratios-of-Eigenvectors Algorithm for Community Detection	The research on complex networks has achieved significant progress in revealing the mesoscopic features of networks. Community detection is an important aspect of understanding real-world complex systems. We present in this paper a High-order node proximity Spectral Clustering on Ratios-of-Eigenvectors (SCOREH+) algori...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	339,622
1804.08890	Segmentation of Scanning Tunneling Microscopy Images Using Variational Methods and Empirical Wavelets	In the fields of nanoscience and nanotechnology, it is important to be able to functionalize surfaces chemically for a wide variety of applications. Scanning tunneling microscopes (STMs) are important instruments in this area used to measure the surface structure and chemistry with better than molecular resolution. Sel...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	95,860
2404.10757	Deep Learning and LLM-based Methods Applied to Stellar Lightcurve Classification	Light curves serve as a valuable source of information on stellar formation and evolution. With the rapid advancement of machine learning techniques, it can be effectively processed to extract astronomical patterns and information. In this study, we present a comprehensive evaluation of deep-learning and large language...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	447,235
2310.01770	A simple connection from loss flatness to compressed representations in neural networks	The generalization capacity of deep neural networks has been studied in a variety of ways, including at least two distinct categories of approaches: one based on the shape of the loss landscape in parameter space, and the other based on the structure of the representation manifold in feature space (that is, in the spac...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	396,565
1805.08060	Channel Estimation for Visible Light Communications Using Neural Networks	Visible light communications (VLC) is an emerging field in technology and research. Estimating the channel taps is a major requirement for designing reliable communication systems. Due to the nonlinear characteristics of the VLC channel those parameters cannot be derived easily. They can be calculated by means of softw...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	98,036
1905.13736	Unlabeled Data Improves Adversarial Robustness	We demonstrate, theoretically and empirically, that adversarial robustness can significantly benefit from semisupervised learning. Theoretically, we revisit the simple Gaussian model of Schmidt et al. that shows a sample complexity gap between standard and robust classification. We prove that unlabeled data bridges thi...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	133,231
2409.03412	TG-LMM: Enhancing Medical Image Segmentation Accuracy through Text-Guided Large Multi-Modal Model	We propose TG-LMM (Text-Guided Large Multi-Modal Model), a novel approach that leverages textual descriptions of organs to enhance segmentation accuracy in medical images. Existing medical image segmentation methods face several challenges: current medical automatic segmentation models do not effectively utilize prior ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	486,035
2305.01515	MTrainS: Improving DLRM training efficiency using heterogeneous memories	Recommendation models are very large, requiring terabytes (TB) of memory during training. In pursuit of better quality, the model size and complexity grow over time, which requires additional training data to avoid overfitting. This model growth demands a large number of resources in data centers. Hence, training effic...	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	true	361,698
2401.00168	Multiform Evolution for High-Dimensional Problems with Low Effective Dimensionality	In this paper, we scale evolutionary algorithms to high-dimensional optimization problems that deceptively possess a low effective dimensionality (certain dimensions do not significantly affect the objective function). To this end, an instantiation of the multiform optimization paradigm is presented, where multiple low...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	418,907
2204.12953	Market Integration of Excess Heat	Excess heat will be an important heat source in future carbon-neutral district heating systems. A barrier to excess heat integration is the lack of appropriate scheduling and pricing systems for these producers, which generally have small capacity and limited flexibility. In this work, we formulate and analyze two meth...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	293,668
1507.05717	An End-to-End Trainable Neural Network for Image-based Sequence Recognition and Its Application to Scene Text Recognition	Image-based sequence recognition has been a long-standing research topic in computer vision. In this paper, we investigate the problem of scene text recognition, which is among the most important and challenging tasks in image-based sequence recognition. A novel neural network architecture, which integrates feature ext...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	45,316
1709.02285	Monocular Navigation in Large Scale Dynamic Environments	We present a processing technique for a robust reconstruction of motion properties for single points in large scale, dynamic environments. We assume that the acquisition camera is moving and that there are other independently moving agents in a large environment, like road scenarios. The separation of direction and mag...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	80,242
1705.07562	On the diffusion approximation of nonconvex stochastic gradient descent	We study the Stochastic Gradient Descent (SGD) method in nonconvex optimization problems from the point of view of approximating diffusion processes. We prove rigorously that the diffusion process can approximate the SGD algorithm weakly using the weak form of master equation for probability evolution. In the small ste...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	73,862
1804.09691	Surveillance Face Recognition Challenge	Face recognition (FR) is one of the most extensively investigated problems in computer vision. Significant progress in FR has been made due to the recent introduction of the larger scale FR challenges, particularly with constrained social media web images, e.g. high-resolution photos of celebrity faces taken by profess...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	96,024
2101.08607	Path Loss Modeling and Measurements for Reconfigurable Intelligent Surfaces in the Millimeter-Wave Frequency Band	Reconfigurable intelligent surfaces (RISs) provide an interface between the electromagnetic world of wireless propagation environments and the digital world of information science. Simple yet sufficiently accurate path loss models for RISs are an important basis for theoretical analysis and optimization of RIS-assisted...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	216,366
2311.07203	Optical Quantum Sensing for Agnostic Environments via Deep Learning	Optical quantum sensing promises measurement precision beyond classical sensors termed the Heisenberg limit (HL). However, conventional methodologies often rely on prior knowledge of the target system to achieve HL, presenting challenges in practical applications. Addressing this limitation, we introduce an innovative ...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	407,239
2206.04910	NAGphormer: A Tokenized Graph Transformer for Node Classification in Large Graphs	The graph Transformer emerges as a new architecture and has shown superior performance on various graph mining tasks. In this work, we observe that existing graph Transformers treat nodes as independent tokens and construct a single long sequence composed of all node tokens so as to train the Transformer model, causing...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	301,821
2001.00624	Analytic Continued Fractions for Regression: A Memetic Algorithm Approach	We present an approach for regression problems that employs analytic continued fractions as a novel representation. Comparative computational results using a memetic algorithm are reported in this work. Our experiments included fifteen other different machine learning approaches including five genetic programming metho...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	true	false	false	159,277
1609.06323	Automated Visual Fin Identification of Individual Great White Sharks	This paper discusses the automated visual identification of individual great white sharks from dorsal fin imagery. We propose a computer vision photo ID system and report recognition results over a database of thousands of unconstrained fin images. To the best of our knowledge this line of work establishes the first fu...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	61,266
1809.11143	Duality between source coding with quantum side information and c-q channel coding	In this paper, we establish an interesting duality between two different quantum information-processing tasks, namely, classical source coding with quantum side information, and channel coding over c-q channels. The duality relates the optimal error exponents of these two tasks, generalizing the classical results of Ah...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	109,064
2302.09624	Breaking the Communication-Privacy-Accuracy Tradeoff with $f$-Differential Privacy	We consider a federated data analytics problem in which a server coordinates the collaborative data analysis of multiple users with privacy concerns and limited communication capability. The commonly adopted compression schemes introduce information loss into local data while improving communication efficiency, and it ...	false	false	false	false	false	false	true	false	false	true	false	false	true	false	false	false	false	false	346,500

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