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
2305.05991	DMNR: Unsupervised De-noising of Point Clouds Corrupted by Airborne Particles	LiDAR sensors are critical for autonomous driving and robotics applications due to their ability to provide accurate range measurements and their robustness to lighting conditions. However, airborne particles, such as fog, rain, snow, and dust, will degrade its performance and it is inevitable to encounter these inclem...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	363,367
2204.01713	Exemplar Learning for Medical Image Segmentation	Medical image annotation typically requires expert knowledge and hence incurs time-consuming and expensive data annotation costs. To alleviate this burden, we propose a novel learning scenario, Exemplar Learning (EL), to explore automated learning processes for medical image segmentation with a single annotated image e...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	289,703
1305.6402	From Parametric Model-based Optimization to robust PID Gain Scheduling	In chemical process applications, model predictive control effectively deals with input and state constraints during transient operations. However, industrial PID controllers directly manipulates the actuators, so they play the key role in small perturbation robustness. This paper considers the problem of augmenting th...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	24,838
0904.1446	Concavity of entropy under thinning	Building on the recent work of Johnson (2007) and Yu (2008), we prove that entropy is a concave function with respect to the thinning operation T_a. That is, if X and Y are independent random variables on Z_+ with ultra-log-concave probability mass functions, then H(T_a X+T_{1-a} Y)>= a H(X)+(1-a)H(Y), 0 <= a <= 1, whe...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	3,514
2108.09891	Multi-Expert Adversarial Attack Detection in Person Re-identification Using Context Inconsistency	The success of deep neural networks (DNNs) has promoted the widespread applications of person re-identification (ReID). However, ReID systems inherit the vulnerability of DNNs to malicious attacks of visually inconspicuous adversarial perturbations. Detection of adversarial attacks is, therefore, a fundamental requirem...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	251,731
1305.0412	Filter Design with Secrecy Constraints: The MIMO Gaussian Wiretap Channel	This paper considers the problem of filter design with secrecy constraints, where two legitimate parties (Alice and Bob) communicate in the presence of an eavesdropper (Eve), over a Gaussian multiple-input-multiple-output (MIMO) wiretap channel. This problem involves designing, subject to a power constraint, the transm...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	24,346
2006.06921	Maximum $w$-cyclic holely group divisible packings with block size three and applications to optical orthogonal codes	In this paper we investigate combinatorial constructions for $w$-cyclic holely group divisible packings with block size three (briefly by $3$-HGDPs). For any positive integers $u,v,w$ with $u\equiv0,1~(\bmod~3)$, the exact number of base blocks of a maximum $w$-cyclic $3$-HGDP of type $(u,w^v)$ is determined. This resu...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	181,613
2203.15250	Analysis of EEG frequency bands for Envisioned Speech Recognition	The use of Automatic speech recognition (ASR) interfaces have become increasingly popular in daily life for use in interaction and control of electronic devices. The interfaces currently being used are not feasible for a variety of users such as those suffering from a speech disorder, locked-in syndrome, paralysis or p...	false	false	true	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	288,309
2305.10609	Unsourced Massive Access-Based Digital Over-the-Air Computation for Efficient Federated Edge Learning	Over-the-air computation (OAC) is a promising technique to achieve fast model aggregation across multiple devices in federated edge learning (FEEL). In addition to the analog schemes, one-bit digital aggregation (OBDA) scheme was proposed to adapt OAC to modern digital wireless systems. However, one-bit quantization in...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	365,139
2107.12571	CFLOW-AD: Real-Time Unsupervised Anomaly Detection with Localization via Conditional Normalizing Flows	Unsupervised anomaly detection with localization has many practical applications when labeling is infeasible and, moreover, when anomaly examples are completely missing in the train data. While recently proposed models for such data setup achieve high accuracy metrics, their complexity is a limiting factor for real-tim...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	247,933
2204.02944	"The Pedestrian next to the Lamppost" Adaptive Object Graphs for Better Instantaneous Mapping	Estimating a semantically segmented bird's-eye-view (BEV) map from a single image has become a popular technique for autonomous control and navigation. However, they show an increase in localization error with distance from the camera. While such an increase in error is entirely expected - localization is harder at dis...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	290,131
2201.06972	Representation Learning on Heterostructures via Heterogeneous Anonymous Walks	Capturing structural similarity has been a hot topic in the field of network embedding recently due to its great help in understanding the node functions and behaviors. However, existing works have paid very much attention to learning structures on homogeneous networks while the related study on heterogeneous networks ...	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	false	false	false	275,894
2410.01083	Deep Nets with Subsampling Layers Unwittingly Discard Useful Activations at Test-Time	Subsampling layers play a crucial role in deep nets by discarding a portion of an activation map to reduce its spatial dimensions. This encourages the deep net to learn higher-level representations. Contrary to this motivation, we hypothesize that the discarded activations are useful and can be incorporated on the fly ...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	493,597
1808.03935	Fine-grained visual recognition with salient feature detection	Computer vision based fine-grained recognition has received great attention in recent years. Existing works focus on discriminative part localization and feature learning. In this paper, to improve the performance of fine-grained recognition, we try to precisely locate as many salient parts of object as possible at fir...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	105,035
2403.05783	Large Generative Model Assisted 3D Semantic Communication	Semantic Communication (SC) is a novel paradigm for data transmission in 6G. However, there are several challenges posed when performing SC in 3D scenarios: 1) 3D semantic extraction; 2) Latent semantic redundancy; and 3) Uncertain channel estimation. To address these issues, we propose a Generative AI Model assisted 3...	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	436,152
2010.08534	Latent Vector Recovery of Audio GANs	Advanced Generative Adversarial Networks (GANs) are remarkable in generating intelligible audio from a random latent vector. In this paper, we examine the task of recovering the latent vector of both synthesized and real audio. Previous works recovered latent vectors of given audio through an auto-encoder inspired tech...	false	false	true	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	201,207
2106.08424	Design and analysis of deployable clustered tensegrity cable domes	This study presents the design and analysis of deployable cable domes based on the clustered tensegrity structures (CTS). In this paper, the statics and dynamics equations of the CTS are first given. Using a traditional Levy cable dome as an example, we show the approach to modify the Levy dome to a deployable CTS one....	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	241,285
1612.07106	The Global Dynamical Complexity of the Human Brain Network	How much information do large brain networks integrate as a whole over the sum of their parts? Can the dynamical complexity of such networks be globally quantified in an information-theoretic way and be meaningfully coupled to brain function? Recently, measures of dynamical complexity such as integrated information hav...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	65,896
2410.06913	Utilize the Flow before Stepping into the Same River Twice: Certainty Represented Knowledge Flow for Refusal-Aware Instruction Tuning	Refusal-Aware Instruction Tuning (RAIT) enables Large Language Models (LLMs) to refuse to answer unknown questions. By modifying responses of unknown questions in the training data to refusal responses such as "I don't know", RAIT enhances the reliability of LLMs and reduces their hallucination. Generally, RAIT modifie...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	496,392
2102.11603	SeqNet: Learning Descriptors for Sequence-based Hierarchical Place Recognition	Visual Place Recognition (VPR) is the task of matching current visual imagery from a camera to images stored in a reference map of the environment. While initial VPR systems used simple direct image methods or hand-crafted visual features, recent work has focused on learning more powerful visual features and further im...	false	false	false	false	true	true	true	true	false	false	false	true	false	false	false	false	false	false	221,481
2203.01937	BoMD: Bag of Multi-label Descriptors for Noisy Chest X-ray Classification	Deep learning methods have shown outstanding classification accuracy in medical imaging problems, which is largely attributed to the availability of large-scale datasets manually annotated with clean labels. However, given the high cost of such manual annotation, new medical imaging classification problems may need to ...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	283,576
2211.09817	On the Effect of Pre-training for Transformer in Different Modality on Offline Reinforcement Learning	We empirically investigate how pre-training on data of different modalities, such as language and vision, affects fine-tuning of Transformer-based models to Mujoco offline reinforcement learning tasks. Analysis of the internal representation reveals that the pre-trained Transformers acquire largely different representa...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	331,116
2010.14666	Equivariant Filter (EqF)	The kinematics of many systems encountered in robotics, mechatronics, and avionics are naturally posed on homogeneous spaces, that is, their state lies in a smooth manifold equipped with a transitive Lie group symmetry. This paper proposes a novel filter, the Equivariant Filter (EqF), by posing the observer state on th...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	203,526
1802.06299	Robotic design choice overview using co-simulation	Rapid robotic system development sets a demand for multi-disciplinary methods and tools to explore and compare design alternatives. In this paper, we present collaborative modeling that combines discrete-event models of controller software with continuous-time models of physical robot components. The presented co-model...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	90,638
2110.06400	CyTran: A Cycle-Consistent Transformer with Multi-Level Consistency for Non-Contrast to Contrast CT Translation	We propose a novel approach to translate unpaired contrast computed tomography (CT) scans to non-contrast CT scans and the other way around. Solving this task has two important applications: (i) to automatically generate contrast CT scans for patients for whom injecting contrast substance is not an option, and (ii) to ...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	260,603
2407.20584	Pruning Large Language Models with Semi-Structural Adaptive Sparse Training	The remarkable success of Large Language Models (LLMs) relies heavily on their substantial scale, which poses significant challenges during model deployment in terms of latency and memory consumption. Recently, numerous studies have attempted to compress LLMs using one-shot pruning methods. However, these methods often...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	477,210
2310.04892	Commercialized Generative AI: A Critical Study of the Feasibility and Ethics of Generating Native Advertising Using Large Language Models in Conversational Web Search	How will generative AI pay for itself? Unless charging users for access, selling advertising is the only alternative. Especially in the multi-billion dollar web search market with ads as the main source of revenue, the introduction of a subscription model seems unlikely. The recent disruption of search by generative la...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	397,871
2405.17625	Matrix Low-Rank Trust Region Policy Optimization	Most methods in reinforcement learning use a Policy Gradient (PG) approach to learn a parametric stochastic policy that maps states to actions. The standard approach is to implement such a mapping via a neural network (NN) whose parameters are optimized using stochastic gradient descent. However, PG methods are prone t...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	458,029
1902.06838	SC-FEGAN: Face Editing Generative Adversarial Network with User's Sketch and Color	We present a novel image editing system that generates images as the user provides free-form mask, sketch and color as an input. Our system consist of a end-to-end trainable convolutional network. Contrary to the existing methods, our system wholly utilizes free-form user input with color and shape. This allows the sys...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	121,854
2106.01078	Physics-Guided Discovery of Highly Nonlinear Parametric Partial Differential Equations	Partial differential equations (PDEs) that fit scientific data can represent physical laws with explainable mechanisms for various mathematically-oriented subjects, such as physics and finance. The data-driven discovery of PDEs from scientific data thrives as a new attempt to model complex phenomena in nature, but the ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	238,374
2212.01538	Multi-resolution Monocular Depth Map Fusion by Self-supervised Gradient-based Composition	Monocular depth estimation is a challenging problem on which deep neural networks have demonstrated great potential. However, depth maps predicted by existing deep models usually lack fine-grained details due to the convolution operations and the down-samplings in networks. We find that increasing input resolution is h...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	334,465
1908.06820	Are You for Real? Detecting Identity Fraud via Dialogue Interactions	Identity fraud detection is of great importance in many real-world scenarios such as the financial industry. However, few studies addressed this problem before. In this paper, we focus on identity fraud detection in loan applications and propose to solve this problem with a novel interactive dialogue system which consi...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	142,121
1508.03329	Multi-Task Learning with Group-Specific Feature Space Sharing	When faced with learning a set of inter-related tasks from a limited amount of usable data, learning each task independently may lead to poor generalization performance. Multi-Task Learning (MTL) exploits the latent relations between tasks and overcomes data scarcity limitations by co-learning all these tasks simultane...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	45,984
2412.16468	The Road to Artificial SuperIntelligence: A Comprehensive Survey of Superalignment	The emergence of large language models (LLMs) has sparked the possibility of about Artificial Superintelligence (ASI), a hypothetical AI system surpassing human intelligence. However, existing alignment paradigms struggle to guide such advanced AI systems. Superalignment, the alignment of AI systems with human values a...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	519,534
2008.13426	Self-supervised Video Representation Learning by Uncovering Spatio-temporal Statistics	This paper proposes a novel pretext task to address the self-supervised video representation learning problem. Specifically, given an unlabeled video clip, we compute a series of spatio-temporal statistical summaries, such as the spatial location and dominant direction of the largest motion, the spatial location and do...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	193,843
1904.13030	SeqLPD: Sequence Matching Enhanced Loop-Closure Detection Based on Large-Scale Point Cloud Description for Self-Driving Vehicles	Place recognition and loop-closure detection are main challenges in the localization, mapping and navigation tasks of self-driving vehicles. In this paper, we solve the loop-closure detection problem by incorporating the deep-learning based point cloud description method and the coarse-to-fine sequence matching strateg...	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	129,282
2109.03413	YouRefIt: Embodied Reference Understanding with Language and Gesture	We study the understanding of embodied reference: One agent uses both language and gesture to refer to an object to another agent in a shared physical environment. Of note, this new visual task requires understanding multimodal cues with perspective-taking to identify which object is being referred to. To tackle this p...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	254,055
2409.02817	Obsidian: Cooperative State-Space Exploration for Performant Inference on Secure ML Accelerators	Trusted execution environments (TEEs) for machine learning accelerators are indispensable in secure and efficient ML inference. Optimizing workloads through state-space exploration for the accelerator architectures improves performance and energy consumption. However, such explorations are expensive and slow due to the...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	485,839
2402.01122	Generalized Multi-Speed Dubins Motion Model	The paper develops a novel motion model, called Generalized Multi-Speed Dubins Motion Model (GMDM), which extends the Dubins model by considering multiple speeds. While the Dubins model produces time-optimal paths under a constant-speed constraint, these paths could be suboptimal if this constraint is relaxed to includ...	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	425,871
2006.14744	Graph Optimal Transport for Cross-Domain Alignment	Cross-domain alignment between two sets of entities (e.g., objects in an image, words in a sentence) is fundamental to both computer vision and natural language processing. Existing methods mainly focus on designing advanced attention mechanisms to simulate soft alignment, with no training signals to explicitly encoura...	false	false	false	false	false	false	true	false	true	false	false	true	false	false	false	false	false	false	184,311
2108.06763	Two Eyes Are Better Than One: Exploiting Binocular Correlation for Diabetic Retinopathy Severity Grading	Diabetic retinopathy (DR) is one of the most common eye conditions among diabetic patients. However, vision loss occurs primarily in the late stages of DR, and the symptoms of visual impairment, ranging from mild to severe, can vary greatly, adding to the burden of diagnosis and treatment in clinical practice. Deep lea...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	250,714
2411.19092	Neural Window Decoder for SC-LDPC Codes	In this paper, we propose a neural window decoder (NWD) for spatially coupled low-density parity-check (SC-LDPC) codes. The proposed NWD retains the conventional window decoder (WD) process but incorporates trainable neural weights. To train the weights of NWD, we introduce two novel training strategies. First, we rest...	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	512,115
1504.07278	Optimal Convergence Rate in Feed Forward Neural Networks using HJB Equation	A control theoretic approach is presented in this paper for both batch and instantaneous updates of weights in feed-forward neural networks. The popular Hamilton-Jacobi-Bellman (HJB) equation has been used to generate an optimal weight update law. The remarkable contribution in this paper is that closed form solutions ...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	42,517
2204.08515	Multi-dimensional extensions of the Hegselmann-Krause model	In this paper, we consider two multi-dimensional Hagselmann-Krause (HK) models for opinion dynamics. The two models describe how individuals adjust their opinions on multiple topics, based on the influence of their peers. The models differ in the criterion according to which individuals decide whom they want to be infl...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	292,112
2408.14135	Foodfusion: A Novel Approach for Food Image Composition via Diffusion Models	Food image composition requires the use of existing dish images and background images to synthesize a natural new image, while diffusion models have made significant advancements in image generation, enabling the construction of end-to-end architectures that yield promising results. However, existing diffusion models f...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	483,430
2407.20432	Neural Surrogate HMC: Accelerated Hamiltonian Monte Carlo with a Neural Network Surrogate Likelihood	Bayesian Inference with Markov Chain Monte Carlo requires efficient computation of the likelihood function. In some scientific applications, the likelihood must be computed by numerically solving a partial differential equation, which can be prohibitively expensive. We demonstrate that some such problems can be made tr...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	477,158
2212.02701	On the Discredibility of Membership Inference Attacks	With the wide-spread application of machine learning models, it has become critical to study the potential data leakage of models trained on sensitive data. Recently, various membership inference (MI) attacks are proposed to determine if a sample was part of the training set or not. The question is whether these attack...	false	false	false	false	true	false	false	false	false	false	false	false	true	false	false	false	false	false	334,850
2401.09979	False Discovery Rate Control for Gaussian Graphical Models via Neighborhood Screening	Gaussian graphical models emerge in a wide range of fields. They model the statistical relationships between variables as a graph, where an edge between two variables indicates conditional dependence. Unfortunately, well-established estimators, such as the graphical lasso or neighborhood selection, are known to be susc...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	422,446
1805.12291	Empirical Evaluation of Character-Based Model on Neural Named-Entity Recognition in Indonesian Conversational Texts	Despite the long history of named-entity recognition (NER) task in the natural language processing community, previous work rarely studied the task on conversational texts. Such texts are challenging because they contain a lot of word variations which increase the number of out-of-vocabulary (OOV) words. The high numbe...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	99,132
1809.06993	Deep-learning models improve on community-level diagnosis for common congenital heart disease lesions	Prenatal diagnosis of tetralogy of Fallot (TOF) and hypoplastic left heart syndrome (HLHS), two serious congenital heart defects, improves outcomes and can in some cases facilitate in utero interventions. In practice, however, the fetal diagnosis rate for these lesions is only 30-50 percent in community settings. Impro...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	108,182
1912.08578	Taming an autonomous surface vehicle for path following and collision avoidance using deep reinforcement learning	In this article, we explore the feasibility of applying proximal policy optimization, a state-of-the-art deep reinforcement learning algorithm for continuous control tasks, on the dual-objective problem of controlling an underactuated autonomous surface vehicle to follow an a priori known path while avoiding collisions...	false	false	false	false	true	false	true	true	false	false	false	false	false	false	false	false	false	false	157,873
2102.10200	A High Performance, Low Complexity Algorithm for Multi-Player Bandits Without Collision Sensing Information	Motivated by applications in cognitive radio networks, we consider the decentralized multi-player multi-armed bandit problem, without collision nor sensing information. We propose Randomized Selfish KL-UCB, an algorithm with very low computational complexity, inspired by the Selfish KL-UCB algorithm, which has been aba...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	221,002
1909.02225	POD: Practical Object Detection with Scale-Sensitive Network	Scale-sensitive object detection remains a challenging task, where most of the existing methods could not learn it explicitly and are not robust to scale variance. In addition, the most existing methods are less efficient during training or slow during inference, which are not friendly to real-time applications. In thi...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	144,137
1909.03423	Pose Estimation for Ground Robots: On Manifold Representation, Integration, Re-Parameterization, and Optimization	In this paper, we focus on motion estimation dedicated for non-holonomic ground robots, by probabilistically fusing measurements from the wheel odometer and exteroceptive sensors. For ground robots, the wheel odometer is widely used in pose estimation tasks, especially in applications under planar-scene based environme...	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	144,475
2109.11888	Robustness and Sensitivity of BERT Models Predicting Alzheimer's Disease from Text	Understanding robustness and sensitivity of BERT models predicting Alzheimer's disease from text is important for both developing better classification models and for understanding their capabilities and limitations. In this paper, we analyze how a controlled amount of desired and undesired text alterations impacts per...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	257,091
1805.04007	A Unified Knowledge Representation and Context-aware Recommender System in Internet of Things	Within the rapidly developing Internet of Things (IoT), numerous and diverse physical devices, Edge devices, Cloud infrastructure, and their quality of service requirements (QoS), need to be represented within a unified specification in order to enable rapid IoT application development, monitoring, and dynamic reconfig...	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	false	true	97,155
2210.06709	Categorizing Semantic Representations for Neural Machine Translation	Modern neural machine translation (NMT) models have achieved competitive performance in standard benchmarks. However, they have recently been shown to suffer limitation in compositional generalization, failing to effectively learn the translation of atoms (e.g., words) and their semantic composition (e.g., modification...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	323,406
1511.04868	A Neural Transducer	Sequence-to-sequence models have achieved impressive results on various tasks. However, they are unsuitable for tasks that require incremental predictions to be made as more data arrives or tasks that have long input sequences and output sequences. This is because they generate an output sequence conditioned on an enti...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	true	false	false	48,960
2003.07307	Metrics for Evaluating the Efficiency of Compressing Sensing Techniques	Compressive sensing has been receiving a great deal of interest from researchers in many areas because of its ability in speeding up data acquisition. This framework allows fast signal acquisition and compression when signals are sparse in some domains. It extracts the main information from high dimensional sparse sign...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	168,382
2105.10883	Byzantine-Resilient Federated Machine Learning via Over-the-Air Computation	Federated learning (FL) is recognized as a key enabling technology to provide intelligent services for future wireless networks and industrial systems with delay and privacy guarantees. However, the performance of wireless FL can be significantly degraded by Byzantine attack, such as data poisoning attack, model poison...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	236,529
1511.06941	Local Multipath Model Parameters for Generating 5G Millimeter-Wave 3GPP-like Channel Impulse Response	This paper presents 28 GHz and 73 GHz empirically-derived large-scale and small-scale channel model parameters that characterize average temporal and angular properties of multipaths. Omnidirectional azimuth scans at both the transmitter and receiver used high gain directional antennas, from which global 3GPP modeling ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	49,354
2203.15332	Balanced Multimodal Learning via On-the-fly Gradient Modulation	Multimodal learning helps to comprehensively understand the world, by integrating different senses. Accordingly, multiple input modalities are expected to boost model performance, but we actually find that they are not fully exploited even when the multimodal model outperforms its uni-modal counterpart. Specifically, i...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	288,351
2406.10295	Robustness of Structured Data Extraction from In-plane Rotated Documents using Multi-Modal Large Language Models (LLM)	Multi-modal large language models (LLMs) have shown remarkable performance in various natural language processing tasks, including data extraction from documents. However, the accuracy of these models can be significantly affected by document in-plane rotation, also known as skew, a common issue in real-world scenarios...	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	464,345
2004.00858	Projection Neural Network for a Class of Sparse Regression Problems with Cardinality Penalty	In this paper, we consider a class of sparse regression problems, whose objective function is the summation of a convex loss function and a cardinality penalty. By constructing a smoothing function for the cardinality function, we propose a projected neural network and design a correction method for solving this proble...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	170,759
2209.14196	Quantitative Khintchine in Simultaneous Approximation	In a ground-breaking work \cite{BY}, Beresnevich and Yang recently proved Khintchine's theorem in simultaneous Diophantine approximation for nondegenerate manifolds resolving a long-standing problem in the theory of Diophantine approximation. In this paper, we prove an effective version of their result.	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	320,168
2404.04818	DWE+: Dual-Way Matching Enhanced Framework for Multimodal Entity Linking	Multimodal entity linking (MEL) aims to utilize multimodal information (usually textual and visual information) to link ambiguous mentions to unambiguous entities in knowledge base. Current methods facing main issues: (1)treating the entire image as input may contain redundant information. (2)the insufficient utilizati...	false	false	false	false	true	false	false	false	true	false	false	true	false	false	false	false	false	false	444,802
2301.02934	Advancing 3D finger knuckle recognition via deep feature learning	Contactless 3D finger knuckle patterns have emerged as an effective biometric identifier due to its discriminativeness, visibility from a distance, and convenience. Recent research has developed a deep feature collaboration network which simultaneously incorporates intermediate features from deep neural networks with m...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	339,636
1910.01716	False Data Injection Attacks in Internet of Things and Deep Learning enabled Predictive Analytics	Industry 4.0 is the latest industrial revolution primarily merging automation with advanced manufacturing to reduce direct human effort and resources. Predictive maintenance (PdM) is an industry 4.0 solution, which facilitates predicting faults in a component or a system powered by state-of-the-art machine learning (ML...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	148,010
2209.09963	Learning Acceptance Regions for Many Classes with Anomaly Detection	Set-valued classification, a new classification paradigm that aims to identify all the plausible classes that an observation belongs to, can be obtained by learning the acceptance regions for all classes. Many existing set-valued classification methods do not consider the possibility that a new class that never appeare...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	318,687
2303.06440	Xformer: Hybrid X-Shaped Transformer for Image Denoising	In this paper, we present a hybrid X-shaped vision Transformer, named Xformer, which performs notably on image denoising tasks. We explore strengthening the global representation of tokens from different scopes. In detail, we adopt two types of Transformer blocks. The spatial-wise Transformer block performs fine-graine...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	350,849
2008.04165	Proof-Carrying Plans: a Resource Logic for AI Planning	Recent trends in AI verification and Explainable AI have raised the question of whether AI planning techniques can be verified. In this paper, we present a novel resource logic, the Proof Carrying Plans (PCP) logic that can be used to verify plans produced by AI planners. The PCP logic takes inspiration from existing r...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	191,157
2407.19877	Language-driven Grasp Detection with Mask-guided Attention	Grasp detection is an essential task in robotics with various industrial applications. However, traditional methods often struggle with occlusions and do not utilize language for grasping. Incorporating natural language into grasp detection remains a challenging task and largely unexplored. To address this gap, we prop...	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	476,966
2206.03354	cViL: Cross-Lingual Training of Vision-Language Models using Knowledge Distillation	Vision-and-language tasks are gaining popularity in the research community, but the focus is still mainly on English. We propose a pipeline that utilizes English-only vision-language models to train a monolingual model for a target language. We propose to extend OSCAR+, a model which leverages object tags as anchor poi...	false	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	301,247
2408.05373	Evolutionary mechanisms that promote cooperation may not promote social welfare	Understanding the emergence of prosocial behaviours among self-interested individuals is an important problem in many scientific disciplines. Various mechanisms have been proposed to explain the evolution of such behaviours, primarily seeking the conditions under which a given mechanism can induce highest levels of coo...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	true	false	false	true	479,757
2406.01162	Conditional Gumbel-Softmax for constrained feature selection with application to node selection in wireless sensor networks	In this paper, we introduce Conditional Gumbel-Softmax as a method to perform end-to-end learning of the optimal feature subset for a given task and deep neural network (DNN) model, while adhering to certain pairwise constraints between the features. We do this by conditioning the selection of each feature in the subse...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	460,197
2206.03396	Group privacy for personalized federated learning	Federated learning (FL) is a type of collaborative machine learning where participating peers/clients process their data locally, sharing only updates to the collaborative model. This enables to build privacy-aware distributed machine learning models, among others. The goal is the optimization of a statistical model's ...	false	false	false	false	true	false	true	false	false	false	false	false	true	false	false	false	false	false	301,269
2102.04503	VS-Quant: Per-vector Scaled Quantization for Accurate Low-Precision Neural Network Inference	Quantization enables efficient acceleration of deep neural networks by reducing model memory footprint and exploiting low-cost integer math hardware units. Quantization maps floating-point weights and activations in a trained model to low-bitwidth integer values using scale factors. Excessive quantization, reducing pre...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	219,129
2403.07704	Symmetric Q-learning: Reducing Skewness of Bellman Error in Online Reinforcement Learning	In deep reinforcement learning, estimating the value function to evaluate the quality of states and actions is essential. The value function is often trained using the least squares method, which implicitly assumes a Gaussian error distribution. However, a recent study suggested that the error distribution for training...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	436,990
1210.1266	Nonanticipative Rate Distortion Function and Relations to Filtering Theory	The relation between nonanticipative Rate Distortion Function (RDF) and filtering theory is discussed on abstract spaces. The relation is established by imposing a realizability constraint on the reconstruction conditional distribution of the classical RDF. Existence of the extremum solution of the nonanticipative RDF ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	18,934
2106.15115	Neural Machine Translation for Low-Resource Languages: A Survey	Neural Machine Translation (NMT) has seen a tremendous spurt of growth in less than ten years, and has already entered a mature phase. While considered as the most widely used solution for Machine Translation, its performance on low-resource language pairs still remains sub-optimal compared to the high-resource counter...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	243,617
2202.01877	Stackelberg Strategic Guidance for Heterogeneous Robots Collaboration	In this study, we explore the application of game theory, in particular Stackelberg games, to address the issue of effective coordination strategy generation for heterogeneous robots with one-way communication. To that end, focusing on the task of multi-object rearrangement, we develop a theoretical and algorithmic fra...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	278,616
2108.00397	BORM: Bayesian Object Relation Model for Indoor Scene Recognition	Scene recognition is a fundamental task in robotic perception. For human beings, scene recognition is reasonable because they have abundant object knowledge of the real world. The idea of transferring prior object knowledge from humans to scene recognition is significant but still less exploited. In this paper, we prop...	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	248,706
2106.15319	Serial-EMD: Fast Empirical Mode Decomposition Method for Multi-dimensional Signals Based on Serialization	Empirical mode decomposition (EMD) has developed into a prominent tool for adaptive, scale-based signal analysis in various fields like robotics, security and biomedical engineering. Since the dramatic increase in amount of data puts forward higher requirements for the capability of real-time signal analysis, it is dif...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	243,707
2105.12786	cofga: A Dataset for Fine Grained Classification of Objects from Aerial Imagery	Detection and classification of objects in overhead images are two important and challenging problems in computer vision. Among various research areas in this domain, the task of fine-grained classification of objects in overhead images has become ubiquitous in diverse real-world applications, due to recent advances in...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	237,092
1211.3711	Sequence Transduction with Recurrent Neural Networks	Many machine learning tasks can be expressed as the transformation---or \emph{transduction}---of input sequences into output sequences: speech recognition, machine translation, protein secondary structure prediction and text-to-speech to name but a few. One of the key challenges in sequence transduction is learning to ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	true	false	false	19,750
1311.2971	Approximate Inference in Continuous Determinantal Point Processes	Determinantal point processes (DPPs) are random point processes well-suited for modeling repulsion. In machine learning, the focus of DPP-based models has been on diverse subset selection from a discrete and finite base set. This discrete setting admits an efficient sampling algorithm based on the eigendecomposition of...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	28,370
0704.3316	Vocabulary growth in collaborative tagging systems	We analyze a large-scale snapshot of del.icio.us and investigate how the number of different tags in the system grows as a function of a suitably defined notion of time. We study the temporal evolution of the global vocabulary size, i.e. the number of distinct tags in the entire system, as well as the evolution of loca...	false	false	false	false	false	true	false	false	false	false	false	false	false	true	false	false	false	false	93
1506.02751	Guaranteed Blind Sparse Spikes Deconvolution via Lifting and Convex Optimization	Neural recordings, returns from radars and sonars, images in astronomy and single-molecule microscopy can be modeled as a linear superposition of a small number of scaled and delayed copies of a band-limited or diffraction-limited point spread function, which is either determined by the nature or designed by the users;...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	43,969
1905.12243	Vision-to-Language Tasks Based on Attributes and Attention Mechanism	Vision-to-language tasks aim to integrate computer vision and natural language processing together, which has attracted the attention of many researchers. For typical approaches, they encode image into feature representations and decode it into natural language sentences. While they neglect high-level semantic concepts...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	132,706
2208.13825	Differentiable Programming for Earth System Modeling	Earth System Models (ESMs) are the primary tools for investigating future Earth system states at time scales from decades to centuries, especially in response to anthropogenic greenhouse gas release. State-of-the-art ESMs can reproduce the observational global mean temperature anomalies of the last 150 years. Neverthel...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	315,145
1409.7165	Heterogeneous Metric Learning with Content-based Regularization for Software Artifact Retrieval	The problem of software artifact retrieval has the goal to effectively locate software artifacts, such as a piece of source code, in a large code repository. This problem has been traditionally addressed through the textual query. In other words, information retrieval techniques will be exploited based on the textual s...	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	true	36,294
2103.04813	Boosting Semi-supervised Image Segmentation with Global and Local Mutual Information Regularization	The scarcity of labeled data often impedes the application of deep learning to the segmentation of medical images. Semi-supervised learning seeks to overcome this limitation by exploiting unlabeled examples in the learning process. In this paper, we present a novel semi-supervised segmentation method that leverages mut...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	223,773
1301.6691	Hybrid Probabilistic Programs: Algorithms and Complexity	Hybrid Probabilistic Programs (HPPs) are logic programs that allow the programmer to explicitly encode his knowledge of the dependencies between events being described in the program. In this paper, we classify HPPs into three classes called HPP_1,HPP_2 and HPP_r,r>= 3. For these classes, we provide three types of resu...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	21,485
1804.08044	Predicting User Performance and Bitcoin Price Using Block Chain Transaction Network	This work is organized as follows. In the first section we review the prior work and we have obtained our data. Next, we will look at address reuse in the Bitcoin network. We show that a great portion of users reuse their addresses which could enable us to cluster the addresses and attribute them to single users. Next,...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	95,672
2203.08252	Wind energy forecasting with missing values within a fully conditional specification framework	Wind power forecasting is essential to power system operation and electricity markets. As abundant data became available thanks to the deployment of measurement infrastructures and the democratization of meteorological modelling, extensive data-driven approaches have been developed within both point and probabilistic f...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	285,725
1609.05081	Asymptotic Analysis of Equivalences and Core-Structures in Kronecker-Style Graph Models	Growing interest in modeling large, complex networks has spurred significant research into generative graph models. Kronecker-style models (SKG and R-MAT) are often used due to their scalability and ability to mimic key properties of real-world networks. Although a few papers theoretically establish these models' behav...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	61,068
2502.04963	Fast Adaptive Anti-Jamming Channel Access via Deep Q Learning and Coarse-Grained Spectrum Prediction	This paper investigates the anti-jamming channel access problem in complex and unknown jamming environments, where the jammer could dynamically adjust its strategies to target different channels. Traditional channel hopping anti-jamming approaches using fixed patterns are ineffective against such dynamic jamming attack...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	531,383
2308.04870	Decorrelating neurons using persistence	We propose a novel way to improve the generalisation capacity of deep learning models by reducing high correlations between neurons. For this, we present two regularisation terms computed from the weights of a minimum spanning tree of the clique whose vertices are the neurons of a given network (or a sample of those), ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	384,592
2207.09902	Bayesian Hyperparameter Optimization for Deep Neural Network-Based Network Intrusion Detection	Traditional network intrusion detection approaches encounter feasibility and sustainability issues to combat modern, sophisticated, and unpredictable security attacks. Deep neural networks (DNN) have been successfully applied for intrusion detection problems. The optimal use of DNN-based classifiers requires careful tu...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	309,065
2107.14342	Real-Time Anchor-Free Single-Stage 3D Detection with IoU-Awareness	In this report, we introduce our winning solution to the Real-time 3D Detection and also the "Most Efficient Model" in the Waymo Open Dataset Challenges at CVPR 2021. Extended from our last year's award-winning model AFDet, we have made a handful of modifications to the base model, to improve the accuracy and at the sa...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	248,433

Subsets and Splits

No community queries yet

The top public SQL queries from the community will appear here once available.