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
2209.04945	Unsupervised Learning of 3D Scene Flow with 3D Odometry Assistance	Scene flow represents the 3D motion of each point in the scene, which explicitly describes the distance and the direction of each point's movement. Scene flow estimation is used in various applications such as autonomous driving fields, activity recognition, and virtual reality fields. As it is challenging to annotate ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	316,935
2409.15501	Adenocarcinoma Segmentation Using Pre-trained Swin-UNet with Parallel Cross-Attention for Multi-Domain Imaging	Computer aided pathological analysis has been the gold standard for tumor diagnosis, however domain shift is a significant problem in histopathology. It may be caused by variability in anatomical structures, tissue preparation, and imaging processes challenges the robustness of segmentation models. In this work, we pre...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	490,934
2304.07421	Peer-to-Peer Federated Continual Learning for Naturalistic Driving Action Recognition	Naturalistic driving action recognition (NDAR) has proven to be an effective method for detecting driver distraction and reducing the risk of traffic accidents. However, the intrusive design of in-cabin cameras raises concerns about driver privacy. To address this issue, we propose a novel peer-to-peer (P2P) federated ...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	358,334
1801.00857	Optimal Bayesian Transfer Learning	Transfer learning has recently attracted significant research attention, as it simultaneously learns from different source domains, which have plenty of labeled data, and transfers the relevant knowledge to the target domain with limited labeled data to improve the prediction performance. We propose a Bayesian transfer...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	87,636
1712.09043	Neural Collaborative Autoencoder	In recent years, deep neural networks have yielded state-of-the-art performance on several tasks. Although some recent works have focused on combining deep learning with recommendation, we highlight three issues of existing models. First, these models cannot work on both explicit and implicit feedback, since the networ...	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	false	87,301
1702.05870	Cosine Normalization: Using Cosine Similarity Instead of Dot Product in Neural Networks	Traditionally, multi-layer neural networks use dot product between the output vector of previous layer and the incoming weight vector as the input to activation function. The result of dot product is unbounded, thus increases the risk of large variance. Large variance of neuron makes the model sensitive to the change o...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	68,490
1507.00131	Wireless networks of bounded capacity	We consider a noncoherent wireless network, where the transmitters and receivers are cognizant of the statistics of the fading coefficients, but are ignorant of their realizations. We demonstrate that if the nodes do not cooperate, if they transmit symbols that all follow the same distribution, and if the variances of ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	44,722
2401.11085	Adaptive Global-Local Representation Learning and Selection for Cross-Domain Facial Expression Recognition	Domain shift poses a significant challenge in Cross-Domain Facial Expression Recognition (CD-FER) due to the distribution variation across different domains. Current works mainly focus on learning domain-invariant features through global feature adaptation, while neglecting the transferability of local features. Additi...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	422,866
2207.00004	Correction Algorithm of Sampling Effect and Its Application	The sampling effect of the imaging acquisition device is long considered to be a modulation process of the input signal, introducing additional error into the signal acquisition process. This paper proposes a correction algorithm for the modulation process that solves the sampling effect with high accuracy. We examine ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	305,608
cmp-lg/9411007	The Linguistic Relevance of Quasi-Trees	We discuss two constructions (long scrambling and ECM verbs) which challenge most syntactic theories (including traditional TAG approaches) since they seem to require exceptional mechanisms and postulates. We argue that these constructions should in fact be analyzed in a similar manner, namely as involving a verb which...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	536,223
2308.10627	Polarimetric Information for Multi-Modal 6D Pose Estimation of Photometrically Challenging Objects with Limited Data	6D pose estimation pipelines that rely on RGB-only or RGB-D data show limitations for photometrically challenging objects with e.g. textureless surfaces, reflections or transparency. A supervised learning-based method utilising complementary polarisation information as input modality is proposed to overcome such limita...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	386,813
2401.06644	SeizNet: An AI-enabled Implantable Sensor Network System for Seizure Prediction	In this paper, we introduce SeizNet, a closed-loop system for predicting epileptic seizures through the use of Deep Learning (DL) method and implantable sensor networks. While pharmacological treatment is effective for some epilepsy patients (with ~65M people affected worldwide), one out of three suffer from drug-resis...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	421,226
2108.13525	Identifying optimal cycles in quantum thermal machines with reinforcement-learning	The optimal control of open quantum systems is a challenging task but has a key role in improving existing quantum information processing technologies. We introduce a general framework based on Reinforcement Learning to discover optimal thermodynamic cycles that maximize the power of out-of-equilibrium quantum heat eng...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	252,822
2409.12941	Fact, Fetch, and Reason: A Unified Evaluation of Retrieval-Augmented Generation	Large Language Models (LLMs) have demonstrated significant performance improvements across various cognitive tasks. An emerging application is using LLMs to enhance retrieval-augmented generation (RAG) capabilities. These systems require LLMs to understand user queries, retrieve relevant information, and synthesize coh...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	489,777
2202.06127	Multicasting in NOMA-Based UAV Networks: Path Design and Throughput Maximization	In this paper, we propose a new resource allocation framework for unmanned aerial vehicle (UAV) assisted multicast wireless networks in which the network users according to their request are divided into several multicast groups. We adopt power domain non-orthogonal multiple access (PD-NOMA) as the transmission technol...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	280,101
2501.12016	Are Traditional Deep Learning Model Approaches as Effective as a Retinal-Specific Foundation Model for Ocular and Systemic Disease Detection?	Background: RETFound, a self-supervised, retina-specific foundation model (FM), showed potential in downstream applications. However, its comparative performance with traditional deep learning (DL) models remains incompletely understood. This study aimed to evaluate RETFound against three ImageNet-pretrained supervised...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	526,131
2205.14032	Ontology Design Facilitating Wikibase Integration -- and a Worked Example for Historical Data	Wikibase -- which is the software underlying Wikidata -- is a powerful platform for knowledge graph creation and management. However, it has been developed with a crowd-sourced knowledge graph creation scenario in mind, which in particular means that it has not been designed for use case scenarios in which a tightly co...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	299,185
2211.00483	From Information to Affirmation: An Investigation on the Echo Chamber Effect from YouTube Comments under Technology Product Reviews	Social media may create echo chambers that reaffirm users' beliefs and opinions through repeated exposure of similar notions. Whilst the formation and effect of echo chambers have been intensively examined in thread-based platforms such as Twitter, Facebook and Reddit, we shift our focus on product review discussions o...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	327,885
2405.17258	$\textit{Trans-LoRA}$: towards data-free Transferable Parameter Efficient Finetuning	Low-rank adapters (LoRA) and their variants are popular parameter-efficient fine-tuning (PEFT) techniques that closely match full model fine-tune performance while requiring only a small number of additional parameters. These additional LoRA parameters are specific to the base model being adapted. When the base model n...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	457,831
1305.7393	Performance of Single User vs. Multiuser Modulation in Wireless Multicarrier (MC) Communications	The main objective of this paper is to compare block transmission system performance analytically for Code Divisional Multiple Access (CDMA) modulations in Generalized Multicarrier environment against linear modulation techniques for both single user and multiuser. The effectivity of GMC-CDMA for multiuser will also be...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	24,900
1710.11350	Grammar Induction for Minimalist Grammars using Variational Bayesian Inference : A Technical Report	The following technical report presents a formal approach to probabilistic minimalist grammar parameter estimation. We describe a formalization of a minimalist grammar. We then present an algorithm for the application of variational Bayesian inference to this formalization.	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	83,579
1811.02617	Category Trees	This paper presents a batch classifier that has been improved from the earlier version and fixed a mistake in the earlier paper. Two important changes have been made. Each category is represented by a classifier, where each classifier classifies its own subset of data rows, using batch input values to represent the cen...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	112,629
2405.00791	Obtaining Favorable Layouts for Multiple Object Generation	Large-scale text-to-image models that can generate high-quality and diverse images based on textual prompts have shown remarkable success. These models aim ultimately to create complex scenes, and addressing the challenge of multi-subject generation is a critical step towards this goal. However, the existing state-of-t...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	451,082
1410.8149	Detecting Structural Irregularity in Electronic Dictionaries Using Language Modeling	Dictionaries are often developed using tools that save to Extensible Markup Language (XML)-based standards. These standards often allow high-level repeating elements to represent lexical entries, and utilize descendants of these repeating elements to represent the structure within each lexical entry, in the form of an ...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	37,139
2404.18876	A Multilevel Strategy to Improve People Tracking in a Real-World Scenario	The Pal\'acio do Planalto, office of the President of Brazil, was invaded by protesters on January 8, 2023. Surveillance videos taken from inside the building were subsequently released by the Brazilian Supreme Court for public scrutiny. We used segments of such footage to create the UFPR-Planalto801 dataset for people...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	450,421
2010.09482	Diving Deep into Context-Aware Neural Machine Translation	Context-aware neural machine translation (NMT) is a promising direction to improve the translation quality by making use of the additional context, e.g., document-level translation, or having meta-information. Although there exist various architectures and analyses, the effectiveness of different context-aware NMT mode...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	201,558
1405.6161	Real-Time Estimation of the Distribution of Brake Response Times for an Individual Driver Using Vehicular Ad Hoc Network	Adapting the functioning of the collision warning systems to the specific drivers' characteristics is of great benefit to drivers. For example, by customizing collision warning algorithms we can minimize false alarms, thereby reducing injuries and deaths in highway traffic accidents. In order to take the behaviors of i...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	true	33,346
2306.05179	M3Exam: A Multilingual, Multimodal, Multilevel Benchmark for Examining Large Language Models	Despite the existence of various benchmarks for evaluating natural language processing models, we argue that human exams are a more suitable means of evaluating general intelligence for large language models (LLMs), as they inherently demand a much wider range of abilities such as language understanding, domain knowled...	false	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	372,088
2312.01313	Observer-based Periodic Event-triggered and Self-triggered Boundary Control of a Class of Parabolic PDEs	This paper introduces the first observer-based periodic event-triggered control (PETC) and self-triggered control (STC) for boundary control of a class of parabolic PDEs using PDE backstepping control. We introduce techniques to convert a certain class of continuous-time event-triggered control into PETC and STC, elimi...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	412,404
2406.17559	Minimal Interaction Edge Tuning: A New Paradigm for Visual Adaptation	The rapid scaling of large vision pretrained models makes fine-tuning tasks more and more difficult on edge devices with low computational resources. We explore a new visual adaptation paradigm called edge tuning, which treats large pretrained models as standalone feature extractors that run on powerful cloud servers. ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	467,622
1511.00562	Distance Spectrum of Fixed-Rate Raptor Codes with Linear Random Precoders	Raptor code ensembles with linear random outer codes in a fixed-rate setting are considered. An expression for the average distance spectrum is derived and this expression is used to obtain the asymptotic exponent of the weight distribution. The asymptotic growth rate analysis is then exploited to develop a necessary a...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	48,422
2412.08019	Ask1: Development and Reinforcement Learning-Based Control of a Custom Quadruped Robot	In this work, we present the design, development, and experimental validation of a custom-built quadruped robot, Ask1. The Ask1 robot shares similar morphology with the Unitree Go1, but features custom hardware components and a different control architecture. We transfer and extend previous reinforcement learning (RL)-...	false	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	515,902
2306.01584	Learning from Partially Annotated Data: Example-aware Creation of Gap-filling Exercises for Language Learning	Since performing exercises (including, e.g., practice tests) forms a crucial component of learning, and creating such exercises requires non-trivial effort from the teacher, there is a great value in automatic exercise generation in digital tools in education. In this paper, we particularly focus on automatic creation ...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	370,507
2502.12562	SEA: Low-Resource Safety Alignment for Multimodal Large Language Models via Synthetic Embeddings	Multimodal Large Language Models (MLLMs) have serious security vulnerabilities.While safety alignment using multimodal datasets consisting of text and data of additional modalities can effectively enhance MLLM's security, it is costly to construct these datasets. Existing low-resource security alignment methods, includ...	false	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	true	534,939
2110.02858	Distribution Preserving Multiple Hypotheses Prediction for Uncertainty Modeling	Many supervised machine learning tasks, such as future state prediction in dynamical systems, require precise modeling of a forecast's uncertainty. The Multiple Hypotheses Prediction (MHP) approach addresses this problem by providing several hypotheses that represent possible outcomes. Unfortunately, with the common $l...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	259,273
1609.03756	Correlations of consumption patterns in social-economic networks	We analyze a coupled anonymized dataset collecting the mobile phone communication and bank transactions history of a large number of individuals. After mapping the social structure and introducing indicators of socioeconomic status, demographic features, and purchasing habits of individuals we show that typical consump...	false	false	false	true	false	false	false	false	false	false	false	false	false	true	false	false	false	false	60,924
1811.12889	Systematic Generalization: What Is Required and Can It Be Learned?	Numerous models for grounded language understanding have been recently proposed, including (i) generic models that can be easily adapted to any given task and (ii) intuitively appealing modular models that require background knowledge to be instantiated. We compare both types of models in how much they lend themselves ...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	115,119
2309.03048	Exploring Semantic Consistency in Unpaired Image Translation to Generate Data for Surgical Applications	In surgical computer vision applications, obtaining labeled training data is challenging due to data-privacy concerns and the need for expert annotation. Unpaired image-to-image translation techniques have been explored to automatically generate large annotated datasets by translating synthetic images to the realistic ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	390,250
1911.11490	Outage Duration in Poisson Networks and its Application to Erasure Codes	We derive the probability distribution of the link outage duration at a typical receiver in a wireless network with Poisson distributed interferers sending messages with slotted random access over a Rayleigh fading channel. This result is used to analyze the performance of random linear network coding, showing that the...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	155,143
2204.03864	Multi-scale temporal network for continuous sign language recognition	Continuous Sign Language Recognition (CSLR) is a challenging research task due to the lack of accurate annotation on the temporal sequence of sign language data. The recent popular usage is a hybrid model based on "CNN + RNN" for CSLR. However, when extracting temporal features in these works, most of the methods using...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	290,458
2403.00024	FlowCyt: A Comparative Study of Deep Learning Approaches for Multi-Class Classification in Flow Cytometry Benchmarking	This paper presents FlowCyt, the first comprehensive benchmark for multi-class single-cell classification in flow cytometry data. The dataset comprises bone marrow samples from 30 patients, with each cell characterized by twelve markers. Ground truth labels identify five hematological cell types: T lymphocytes, B lymph...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	433,833
1809.06943	Argumentation Mining: Exploiting Multiple Sources and Background Knowledge	The field of Argumentation Mining has arisen from the need of determining the underlying causes from an expressed opinion and the urgency to develop the established fields of Opinion Mining and Sentiment Analysis. The recent progress in the wider field of Artificial Intelligence in combination with the available data t...	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	false	108,166
2304.11846	Grad-PU: Arbitrary-Scale Point Cloud Upsampling via Gradient Descent with Learned Distance Functions	Most existing point cloud upsampling methods have roughly three steps: feature extraction, feature expansion and 3D coordinate prediction. However,they usually suffer from two critical issues: (1)fixed upsampling rate after one-time training, since the feature expansion unit is customized for each upsampling rate; (2)o...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	359,996
2206.11461	Towards Better User Studies in Computer Graphics and Vision	Online crowdsourcing platforms have made it increasingly easy to perform evaluations of algorithm outputs with survey questions like "which image is better, A or B?", leading to their proliferation in vision and graphics research papers. Results of these studies are often used as quantitative evidence in support of a p...	true	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	304,269
2005.01075	Autoencoders for strategic decision support	In the majority of executive domains, a notion of normality is involved in most strategic decisions. However, few data-driven tools that support strategic decision-making are available. We introduce and extend the use of autoencoders to provide strategically relevant granular feedback. A first experiment indicates that...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	175,481
2104.01318	Efficient DETR: Improving End-to-End Object Detector with Dense Prior	The recently proposed end-to-end transformer detectors, such as DETR and Deformable DETR, have a cascade structure of stacking 6 decoder layers to update object queries iteratively, without which their performance degrades seriously. In this paper, we investigate that the random initialization of object containers, whi...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	228,304
2412.08321	TGOSPA Metric Parameters Selection and Evaluation for Visual Multi-object Tracking	Multi-object tracking algorithms are deployed in various applications, each with unique performance requirements. For example, track switches pose significant challenges for offline scene understanding, as they hinder the accuracy of data interpretation. Conversely, in online surveillance applications, their impact is ...	false	false	false	false	false	false	false	false	false	false	true	true	false	false	false	false	false	false	516,044
1807.09937	HiDDeN: Hiding Data With Deep Networks	Recent work has shown that deep neural networks are highly sensitive to tiny perturbations of input images, giving rise to adversarial examples. Though this property is usually considered a weakness of learned models, we explore whether it can be beneficial. We find that neural networks can learn to use invisible pertu...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	103,832
1205.5443	Filter-and-Forward Transparent Relay Design for OFDM Systems	In this paper, the filter-and-forward (FF) relay design for orthogonal frequency-division multiplexing (OFDM) transmission systems is considered to improve the system performance over simple amplify-and-forward (AF) relaying. Unlike conventional OFDM relays performing OFDM demodulation and remodulation, to reduce proce...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	16,161
2406.19583	Interference Cancellation Information Geometry Approach for Massive MIMO Channel Estimation	In this paper, the interference cancellation information geometry approaches (IC-IGAs) for massive MIMO channel estimation are proposed. The proposed algorithms are low-complexity approximations of the minimum mean square error (MMSE) estimation. To illustrate the proposed algorithms, a unified framework of the informa...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	468,478
2203.02351	Uncertainty Estimation for Heatmap-based Landmark Localization	Automatic anatomical landmark localization has made great strides by leveraging deep learning methods in recent years. The ability to quantify the uncertainty of these predictions is a vital component needed for these methods to be adopted in clinical settings, where it is imperative that erroneous predictions are caug...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	283,717
2008.08272	Compiling ONNX Neural Network Models Using MLIR	Deep neural network models are becoming increasingly popular and have been used in various tasks such as computer vision, speech recognition, and natural language processing. Machine learning models are commonly trained in a resource-rich environment and then deployed in a distinct environment such as high availability...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	192,371
2408.11006	Security Attacks on LLM-based Code Completion Tools	The rapid development of large language models (LLMs) has significantly advanced code completion capabilities, giving rise to a new generation of LLM-based Code Completion Tools (LCCTs). Unlike general-purpose LLMs, these tools possess unique workflows, integrating multiple information sources as input and prioritizing...	false	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	482,115
2209.12010	Spiking SiamFC++: Deep Spiking Neural Network for Object Tracking	Spiking neural network (SNN) is a biologically-plausible model and exhibits advantages of high computational capability and low power consumption. While the training of deep SNN is still an open problem, which limits the real-world applications of deep SNN. Here we propose a deep SNN architecture named Spiking SiamFC++...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	319,380
2309.09976	Des-q: a quantum algorithm to provably speedup retraining of decision trees	Decision trees are widely adopted machine learning models due to their simplicity and explainability. However, as training data size grows, standard methods become increasingly slow, scaling polynomially with the number of training examples. In this work, we introduce Des-q, a novel quantum algorithm to construct and r...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	392,814
2403.13648	Priority-based Energy Allocation in Buildings through Distributed Model Predictive Control	Many countries are facing energy shortage today and most of the global energy is consumed by HVAC systems in buildings. For the scenarios where the energy system is not sufficiently supplied to HVAC systems, a priority-based allocation scheme based on distributed model predictive control is proposed in this paper, whic...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	439,713
2108.05454	Extracting Semantics from Maintenance Records	Rapid progress in natural language processing has led to its utilization in a variety of industrial and enterprise settings, including in its use for information extraction, specifically named entity recognition and relation extraction, from documents such as engineering manuals and field maintenance reports. While nam...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	250,304
2001.02915	Towards Coding for Human and Machine Vision: A Scalable Image Coding Approach	The past decades have witnessed the rapid development of image and video coding techniques in the era of big data. However, the signal fidelity-driven coding pipeline design limits the capability of the existing image/video coding frameworks to fulfill the needs of both machine and human vision. In this paper, we come ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	159,842
1407.1069	Control of nonlinear systems: a model inversion approach	A novel control design approach for general nonlinear systems is presented in this paper. The approach is based on the identification of a polynomial model of the system to control and on the on-line inversion of this model. An efficient technique is developed to perform the inversion, which allows an effective control...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	34,392
2207.12877	Representing Random Utility Choice Models with Neural Networks	Motivated by the successes of deep learning, we propose a class of neural network-based discrete choice models, called RUMnets, inspired by the random utility maximization (RUM) framework. This model formulates the agents' random utility function using a sample average approximation. We show that RUMnets sharply approx...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	310,144
2401.16922	Learning Properties of Quantum States Without the I.I.D. Assumption	We develop a framework for learning properties of quantum states beyond the assumption of independent and identically distributed (i.i.d.) input states. We prove that, given any learning problem (under reasonable assumptions), an algorithm designed for i.i.d. input states can be adapted to handle input states of any na...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	425,032
1904.01933	Personalized Bundle List Recommendation	Product bundling, offering a combination of items to customers, is one of the marketing strategies commonly used in online e-commerce and offline retailers. A high-quality bundle generalizes frequent items of interest, and diversity across bundles boosts the user-experience and eventually increases transaction volume. ...	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	false	false	126,286
1506.00981	Swiveled R\'enyi entropies	This paper introduces "swiveled Renyi entropies" as an alternative to the Renyi entropic quantities put forward in [Berta et al., Phys. Rev. A 91, 022333 (2015)]. What distinguishes the swiveled Renyi entropies from the prior proposal of Berta et al. is that there is an extra degree of freedom: an optimization over uni...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	43,736
1910.02411	Transforming the output of GANs by fine-tuning them with features from different datasets	In this work we present a method for fine-tuning pre-trained GANs with features from different datasets, resulting in the transformation of the output distribution into a new distribution with novel characteristics. The weights of the generator are updated using the weighted sum of the losses from a cross-dataset class...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	148,236
2409.17560	Dynamic Subframe Splitting and Spatio-Temporal Motion Entangled Sparse Attention for RGB-E Tracking	Event-based bionic camera asynchronously captures dynamic scenes with high temporal resolution and high dynamic range, offering potential for the integration of events and RGB under conditions of illumination degradation and fast motion. Existing RGB-E tracking methods model event characteristics utilising attention me...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	491,866
2401.17780	A Policy Gradient Primal-Dual Algorithm for Constrained MDPs with Uniform PAC Guarantees	We study a primal-dual (PD) reinforcement learning (RL) algorithm for online constrained Markov decision processes (CMDPs). Despite its widespread practical use, the existing theoretical literature on PD-RL algorithms for this problem only provides sublinear regret guarantees and fails to ensure convergence to optimal ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	425,318
2402.03694	ServeFlow: A Fast-Slow Model Architecture for Network Traffic Analysis	Network traffic analysis increasingly uses complex machine learning models as the internet consolidates and traffic gets more encrypted. However, over high-bandwidth networks, flows can easily arrive faster than model inference rates. The temporal nature of network flows limits simple scale-out approaches leveraged in ...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	427,138
2303.11646	Vehicle Sequencing at Signal-Free Intersections: Analytical Performance Guarantees Based on PDMP Formulation	Signal-free intersections are a representative application of smart and connected vehicle technologies. Although extensive results have been developed for trajectory planning and autonomous driving, the formulation and evaluation of vehicle sequencing have not been well understood.In this paper, we consider theoretical...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	352,941
2305.03512	Building Multimodal AI Chatbots	This work aims to create a multimodal AI system that chats with humans and shares relevant photos. While earlier works were limited to dialogues about specific objects or scenes within images, recent works have incorporated images into open-domain dialogues. However, their response generators are unimodal, accepting te...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	362,423
1801.02377	AUV Optimal Path for Leak Detection	This paper studies an optimal autonomous underwater vehicule (AUV) path planning method for both reducing average delay before pollutants detection in underwater mining, oil or gas fields and reducing AUV occupancy time. The proposed technique, based on the bayesian search theory framework and multi-objective optimizat...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	87,922
2308.15905	Thermodynamic Computing via Autonomous Quantum Thermal Machines	We develop a physics-based model for classical computation based on autonomous quantum thermal machines. These machines consist of few interacting quantum bits (qubits) connected to several environments at different temperatures. Heat flows through the machine are here exploited for computing. The process starts by set...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	388,845
1302.2017	Cooperative Environmental Monitoring for PTZ Visual Sensor Networks: A Payoff-based Learning Approach	This paper investigates cooperative environmental monitoring for Pan-Tilt-Zoom (PTZ) visual sensor networks. We first present a novel formulation of the optimal environmental monitoring problem, whose objective function is intertwined with the uncertain state of the environment. In addition, due to the large volume of ...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	21,909
2107.08909	MEGEX: Data-Free Model Extraction Attack against Gradient-Based Explainable AI	The advance of explainable artificial intelligence, which provides reasons for its predictions, is expected to accelerate the use of deep neural networks in the real world like Machine Learning as a Service (MLaaS) that returns predictions on queried data with the trained model. Deep neural networks deployed in MLaaS f...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	246,874
1407.5947	Modulation Formats and Waveforms for the Physical Layer of 5G Wireless Networks: Who Will be the Heir of OFDM?	5G cellular communications promise to deliver the gigabit experience to mobile users, with a capacity increase of up to three orders of magnitude with respect to current LTE systems. There is widespread agreement that such an ambitious goal will be realized through a combination of innovative techniques involving diffe...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	34,818
2407.09429	Open (Clinical) LLMs are Sensitive to Instruction Phrasings	Instruction-tuned Large Language Models (LLMs) can perform a wide range of tasks given natural language instructions to do so, but they are sensitive to how such instructions are phrased. This issue is especially concerning in healthcare, as clinicians are unlikely to be experienced prompt engineers and the potential c...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	472,567
2403.10861	FedQNN: Federated Learning using Quantum Neural Networks	In this study, we explore the innovative domain of Quantum Federated Learning (QFL) as a framework for training Quantum Machine Learning (QML) models via distributed networks. Conventional machine learning models frequently grapple with issues about data privacy and the exposure of sensitive information. Our proposed F...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	438,402
2203.09167	Unsigned Distance Field as an Accurate 3D Scene Representation for Neural Scene Completion	Scene Completion is the task of completing missing geometry from a partial scan of a scene. Most previous methods compute an implicit representation from range data using a Truncated Signed Distance Function (T-SDF) computed on a 3D grid as input to neural networks. The truncation decreases but does not remove the bord...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	286,056
2104.10262	Network Defense is Not a Game	Research seeks to apply Artificial Intelligence (AI) to scale and extend the capabilities of human operators to defend networks. A fundamental problem that hinders the generalization of successful AI approaches -- i.e., beating humans at playing games -- is that network defense cannot be defined as a single game with a...	false	false	false	false	true	false	false	false	false	false	false	false	true	false	false	false	false	true	231,511
2011.07931	Do Offline Metrics Predict Online Performance in Recommender Systems?	Recommender systems operate in an inherently dynamical setting. Past recommendations influence future behavior, including which data points are observed and how user preferences change. However, experimenting in production systems with real user dynamics is often infeasible, and existing simulation-based approaches hav...	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	false	206,707
2302.03242	Combating Online Misinformation Videos: Characterization, Detection, and Future Directions	With information consumption via online video streaming becoming increasingly popular, misinformation video poses a new threat to the health of the online information ecosystem. Though previous studies have made much progress in detecting misinformation in text and image formats, video-based misinformation brings new a...	false	false	false	true	false	false	false	false	false	false	false	true	false	false	false	false	false	true	344,272
1804.10987	Feedforward Architectures for Decentralized Precoding in Massive MU-MIMO Systems	Massive multi-user multiple-input multiple-output (MU-MIMO) enables significant gains in spectral efficiency and link reliability compared to conventional small-scale MIMO technology. Furthermore, linear precoders, e.g., using zero forcing or Wiener filter (WF) precoding, are sufficient to achieve excellent error-rate ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	96,274
2406.03283	Enhancing Repository-Level Code Generation with Integrated Contextual Information	Large language models (LLMs) have demonstrated remarkable capabilities in code generation tasks. However, repository-level code generation presents unique challenges, particularly due to the need to utilize information spread across multiple files within a repository. Existing retrieval-based approaches sometimes fall ...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	461,181
1107.6027	Minimax-Optimal Bounds for Detectors Based on Estimated Prior Probabilities	In many signal detection and classification problems, we have knowledge of the distribution under each hypothesis, but not the prior probabilities. This paper is aimed at providing theory to quantify the performance of detection via estimating prior probabilities from either labeled or unlabeled training data. The erro...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	11,512
2410.11612	Federated Learning framework for LoRaWAN-enabled IIoT communication: A case study	The development of intelligent Industrial Internet of Things (IIoT) systems promises to revolutionize operational and maintenance practices, driving improvements in operational efficiency. Anomaly detection within IIoT architectures plays a crucial role in preventive maintenance and spotting irregularities in industria...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	498,643
2001.10474	Coagent Networks Revisited	Coagent networks formalize the concept of arbitrary networks of stochastic agents that collaborate to take actions in a reinforcement learning environment. Prominent examples of coagent networks in action include approaches to hierarchical reinforcement learning (HRL), such as those using options, which attempt to addr...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	161,832
2102.09973	Discriminant Dynamic Mode Decomposition for Labeled Spatio-Temporal Data Collections	Extracting coherent patterns is one of the standard approaches towards understanding spatio-temporal data. Dynamic mode decomposition (DMD) is a powerful tool for extracting coherent patterns, but the original DMD and most of its variants do not consider label information, which is often available as side information o...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	true	220,944
1809.03400	A Moral Framework for Understanding of Fair ML through Economic Models of Equality of Opportunity	We map the recently proposed notions of algorithmic fairness to economic models of Equality of opportunity (EOP)---an extensively studied ideal of fairness in political philosophy. We formally show that through our conceptual mapping, many existing definition of algorithmic fairness, such as predictive value parity and...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	107,317
1809.04324	Poster Abstract: LPWA-MAC - a Low Power Wide Area network MAC protocol for cyber-physical systems	Low-Power Wide-Area Networks (LPWANs) are being successfully used for the monitoring of large-scale systems that are delay-tolerant and which have low-bandwidth requirements. The next step would be instrumenting these for the control of Cyber-Physical Systems (CPSs) distributed over large areas which require more bandw...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	true	107,539
2010.14599	Stereo Frustums: A Siamese Pipeline for 3D Object Detection	The paper proposes a light-weighted stereo frustums matching module for 3D objection detection. The proposed framework takes advantage of a high-performance 2D detector and a point cloud segmentation network to regress 3D bounding boxes for autonomous driving vehicles. Instead of performing traditional stereo matching ...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	203,498
2410.06536	Learning Recommender Systems with Soft Target: A Decoupled Perspective	Learning recommender systems with multi-class optimization objective is a prevalent setting in recommendation. However, as observed user feedback often accounts for a tiny fraction of the entire item pool, the standard Softmax loss tends to ignore the difference between potential positive feedback and truly negative fe...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	496,251
2403.09367	DF4LCZ: A SAM-Empowered Data Fusion Framework for Scene-Level Local Climate Zone Classification	Recent advancements in remote sensing (RS) technologies have shown their potential in accurately classifying local climate zones (LCZs). However, traditional scene-level methods using convolutional neural networks (CNNs) often struggle to integrate prior knowledge of ground objects effectively. Moreover, commonly utili...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	437,740
2206.10095	Pyramid Region-based Slot Attention Network for Temporal Action Proposal Generation	It has been found that temporal action proposal generation, which aims to discover the temporal action instances within the range of the start and end frames in the untrimmed videos, can largely benefit from proper temporal and semantic context exploitation. The latest efforts were dedicated to considering the temporal...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	303,791
1611.07403	Low-Dimensional Stochastic Modeling of the Electrical Properties of Biological Tissues	Uncertainty quantification plays an important role in biomedical engineering as measurement data is often unavailable and literature data shows a wide variability. Using state-of-the-art methods one encounters difficulties when the number of random inputs is large. This is the case, e.g., when using composite Cole-Cole...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	64,349
1911.08555	Exploiting Oxide Based Resistive RAM Variability for Bayesian Neural Network Hardware Design	Uncertainty plays a key role in real-time machine learning. As a significant shift from standard deep networks, which does not consider any uncertainty formulation during its training or inference, Bayesian deep networks are being currently investigated where the network is envisaged as an ensemble of plausible models ...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	true	154,218
1808.02668	An Occam's Razor View on Learning Audiovisual Emotion Recognition with Small Training Sets	This paper presents a light-weight and accurate deep neural model for audiovisual emotion recognition. To design this model, the authors followed a philosophy of simplicity, drastically limiting the number of parameters to learn from the target datasets, always choosing the simplest earning methods: i) transfer learnin...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	true	false	false	104,814
1904.09366	Reward Potentials for Planning with Learned Neural Network Transition Models	Optimal planning with respect to learned neural network (NN) models in continuous action and state spaces using mixed-integer linear programming (MILP) is a challenging task for branch-and-bound solvers due to the poor linear relaxation of the underlying MILP model. For a given set of features, potential heuristics pro...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	128,353
1812.03183	A hybrid machine-learning algorithm for designing quantum experiments	We introduce a hybrid machine-learning algorithm for designing quantum optics experiments that produce specific quantum states. Our algorithm successfully found experimental schemes to produce all 5 states we asked it to, including Schr\"odinger cat states and cubic phase states, all to a fidelity of over $96\%$. Here ...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	true	false	false	115,943
1811.08152	How You See Me	Convolution Neural Networks is one of the most powerful tools in the present era of science. There has been a lot of research done to improve their performance and robustness while their internal working was left unexplored to much extent. They are often defined as black boxes that can map non-linear data very effectiv...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	113,970
2211.03007	A Geometrically Constrained Point Matching based on View-invariant Cross-ratios, and Homography	In computer vision, finding point correspondence among images plays an important role in many applications, such as image stitching, image retrieval, visual localization, etc. Most of the research worksfocus on the matching of local feature before a sampling method is employed, such as RANSAC, to verify initial matchin...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	328,786
1909.02391	Data-driven simulation for general purpose multibody dynamics using deep neural networks	In this paper, a machine learning-based simulation framework of general-purpose multibody dynamics is introduced. The aim of the framework is to generate a well-trained meta-model of multibody dynamics (MBD) systems. To this end, deep neural network (DNN) is employed to the framework so as to construct data-based meta-...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	144,183

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