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
1903.02188	Bidirectional Attentive Memory Networks for Question Answering over Knowledge Bases	When answering natural language questions over knowledge bases (KBs), different question components and KB aspects play different roles. However, most existing embedding-based methods for knowledge base question answering (KBQA) ignore the subtle inter-relationships between the question and the KB (e.g., entity types, ...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	123,449
cs/0609066	Building and displaying name relations using automatic unsupervised analysis of newspaper articles	We present a tool that, from automatically recognised names, tries to infer inter-person relations in order to present associated people on maps. Based on an in-house Named Entity Recognition tool, applied on clusters of an average of 15,000 news articles per day, in 15 different languages, we build a knowledge base th...	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	539,696
2208.04491	Improving Vaccine Stance Detection by Combining Online and Offline Data	Differing opinions about COVID-19 have led to various online discourses regarding vaccines. Due to the detrimental effects and the scale of the COVID-19 pandemic, detecting vaccine stance has become especially important and is attracting increasing attention. Communication during the pandemic is typically done via onli...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	312,126
1912.09216	Semantic Segmentation from Remote Sensor Data and the Exploitation of Latent Learning for Classification of Auxiliary Tasks	In this paper we address three different aspects of semantic segmentation from remote sensor data using deep neural networks. Firstly, we focus on the semantic segmentation of buildings from remote sensor data and propose ICT-Net. The proposed network has been tested on the INRIA and AIRS benchmark datasets and is show...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	158,023
2402.13587	A Multimodal In-Context Tuning Approach for E-Commerce Product Description Generation	In this paper, we propose a new setting for generating product descriptions from images, augmented by marketing keywords. It leverages the combined power of visual and textual information to create descriptions that are more tailored to the unique features of products. For this setting, previous methods utilize visual ...	false	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	431,324
2408.00768	Comparing Optical Flow and Deep Learning to Enable Computationally Efficient Traffic Event Detection with Space-Filling Curves	Gathering data and identifying events in various traffic situations remains an essential challenge for the systematic evaluation of a perception system's performance. Analyzing large-scale, typically unstructured, multi-modal, time series data obtained from video, radar, and LiDAR is computationally demanding, particul...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	477,969
1004.2870	Nurse Rostering with Genetic Algorithms	In recent years genetic algorithms have emerged as a useful tool for the heuristic solution of complex discrete optimisation problems. In particular there has been considerable interest in their use in tackling problems arising in the areas of scheduling and timetabling. However, the classical genetic algorithm paradig...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	false	false	6,189
1910.05986	An Efficient Tensor Completion Method via New Latent Nuclear Norm	In tensor completion, the latent nuclear norm is commonly used to induce low-rank structure, while substantially failing to capture the global information due to the utilization of unbalanced unfolding scheme. To overcome this drawback, a new latent nuclear norm equipped with a more balanced unfolding scheme is defined...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	149,228
1809.01887	Travel Speed Prediction with a Hierarchical Convolutional Neural Network and Long Short-Term Memory Model Framework	Advanced travel information and warning, if provided accurately, can help road users avoid traffic congestion through dynamic route planning and behavior change. It also enables traffic control centres mitigate the impact of congestion by activating Intelligent Transport System (ITS) proactively. Deep learning has beco...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	106,914
1509.03000	Full-Duplex Transceiver for Future Cellular Network: A Smart Antenna Approach	In this paper, we propose a transceiver architecture for full-duplex (FD) eNodeB (eNB) and FD user equipment (UE) transceiver. For FD communication,.i.e., simultaneous in-band uplink and downlink operation, same subcarriers can be allocated to UE in both uplink and downlink. Hence, contrary to traditional LTE, we propo...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	46,787
2308.00284	CLAMS: A Cluster Ambiguity Measure for Estimating Perceptual Variability in Visual Clustering	Visual clustering is a common perceptual task in scatterplots that supports diverse analytics tasks (e.g., cluster identification). However, even with the same scatterplot, the ways of perceiving clusters (i.e., conducting visual clustering) can differ due to the differences among individuals and ambiguous cluster boun...	true	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	382,887
1209.3824	Interference Mitigation via Interference-Aware Successive Decoding	In modern wireless networks, interference is no longer negligible since each cell becomes smaller to support high throughput. The reduced size of each cell forces to install many cells, and consequently causes to increase inter-cell interference at many cell edge areas. This paper considers a practical way of mitigatin...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	18,604
1705.07183	Large System Analysis of Power Normalization Techniques in Massive MIMO	Linear precoding has been widely studied in the context of Massive multiple-input-multiple-output (MIMO) together with two common power normalization techniques, namely, matrix normalization (MN) and vector normalization (VN). Despite this, their effect on the performance of Massive MIMO systems has not been thoroughly...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	73,769
1808.02289	Predicting Visual Context for Unsupervised Event Segmentation in Continuous Photo-streams	Segmenting video content into events provides semantic structures for indexing, retrieval, and summarization. Since motion cues are not available in continuous photo-streams, and annotations in lifelogging are scarce and costly, the frames are usually clustered into events by comparing the visual features between them ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	104,745
2112.13408	Perlin Noise Improve Adversarial Robustness	Adversarial examples are some special input that can perturb the output of a deep neural network, in order to make produce intentional errors in the learning algorithms in the production environment. Most of the present methods for generating adversarial examples require gradient information. Even universal perturbatio...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	273,233
2302.10805	Repeated Bilateral Trade Against a Smoothed Adversary	We study repeated bilateral trade where an adaptive $\sigma$-smooth adversary generates the valuations of sellers and buyers. We provide a complete characterization of the regret regimes for fixed-price mechanisms under different feedback models in the two cases where the learner can post either the same or different p...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	346,956
1509.02604	Asynchronous Distributed ADMM for Large-Scale Optimization- Part II: Linear Convergence Analysis and Numerical Performance	The alternating direction method of multipliers (ADMM) has been recognized as a versatile approach for solving modern large-scale machine learning and signal processing problems efficiently. When the data size and/or the problem dimension is large, a distributed version of ADMM can be used, which is capable of distribu...	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	true	46,745
2404.07122	Driver Attention Tracking and Analysis	We propose a novel method to estimate a driver's points-of-gaze using a pair of ordinary cameras mounted on the windshield and dashboard of a car. This is a challenging problem due to the dynamics of traffic environments with 3D scenes of unknown depths. This problem is further complicated by the volatile distance betw...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	445,722
2008.08523	Scene Text Detection with Selected Anchor	Object proposal technique with dense anchoring scheme for scene text detection were applied frequently to achieve high recall. It results in the significant improvement in accuracy but waste of computational searching, regression and classification. In this paper, we propose an anchor selection-based region proposal ne...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	192,445
1912.09528	Randomized Reactive Redundancy for Byzantine Fault-Tolerance in Parallelized Learning	This report considers the problem of Byzantine fault-tolerance in synchronous parallelized learning that is founded on the parallelized stochastic gradient descent (parallelized-SGD) algorithm. The system comprises a master, and $n$ workers, where up to $f$ of the workers are Byzantine faulty. Byzantine workers need no...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	158,088
2304.03894	A multifidelity approach to continual learning for physical systems	We introduce a novel continual learning method based on multifidelity deep neural networks. This method learns the correlation between the output of previously trained models and the desired output of the model on the current training dataset, limiting catastrophic forgetting. On its own the multifidelity continual lea...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	356,981
0905.4605	Techniques for Securing Data Exchange between a Database Server and a Client Program	The goal of the presented work is to illustrate a method by which the data exchange between a standalone computer software and a shared database server can be protected of unauthorized interceptation of the traffic in Internet network, a transport network for data managed by those two systems, interceptation by which a...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	3,786
2211.02574	Pushing AI to Wireless Network Edge: An Overview on Integrated Sensing, Communication, and Computation towards 6G	Pushing artificial intelligence (AI) from central cloud to network edge has reached board consensus in both industry and academia for materializing the vision of artificial intelligence of things (AIoT) in the sixth-generation (6G) era. This gives rise to an emerging research area known as edge intelligence, which conc...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	328,618
2404.06659	Leveraging Interesting Facts to Enhance User Engagement with Conversational Interfaces	Conversational Task Assistants (CTAs) guide users in performing a multitude of activities, such as making recipes. However, ensuring that interactions remain engaging, interesting, and enjoyable for CTA users is not trivial, especially for time-consuming or challenging tasks. Grounded in psychological theories of human...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	445,540
1403.3100	Engaging with Massive Online Courses	The Web has enabled one of the most visible recent developments in education---the deployment of massive open online courses. With their global reach and often staggering enrollments, MOOCs have the potential to become a major new mechanism for learning. Despite this early promise, however, MOOCs are still relatively u...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	31,540
2310.18998	A 0.21-ps FOM Capacitor-Less Analog LDO with Dual-Range Load Current for Biomedical Applications	This paper presents an output capacitor-less low-dropout regulator (LDO) with a bias switching scheme for biomedical applications with dual-range load currents. Power optimization is crucial for systems with multiple activation modes such as neural interfaces, IoT and edge devices with varying load currents. To enable ...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	403,802
2411.15008	Evolutionary Automata and Deep Evolutionary Computation	Evolution by natural selection, which is one of the most compelling themes of modern science, brought forth evolutionary algorithms and evolutionary computation, applying mechanisms of evolution in nature to various problems solved by computers. In this paper we concentrate on evolutionary automata that constitute an a...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	true	false	false	510,405
2403.10252	Region-aware Distribution Contrast: A Novel Approach to Multi-Task Partially Supervised Learning	In this study, we address the intricate challenge of multi-task dense prediction, encompassing tasks such as semantic segmentation, depth estimation, and surface normal estimation, particularly when dealing with partially annotated data (MTPSL). The complexity arises from the absence of complete task labels for each tr...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	438,118
2406.04093	Scaling and evaluating sparse autoencoders	Sparse autoencoders provide a promising unsupervised approach for extracting interpretable features from a language model by reconstructing activations from a sparse bottleneck layer. Since language models learn many concepts, autoencoders need to be very large to recover all relevant features. However, studying the pr...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	461,515
2502.04045	Comparing privacy notions for protection against reconstruction attacks in machine learning	Within the machine learning community, reconstruction attacks are a principal concern and have been identified even in federated learning (FL), which was designed with privacy preservation in mind. In response to these threats, the privacy community recommends the use of differential privacy (DP) in the stochastic grad...	false	false	false	false	false	false	true	false	false	true	false	false	true	false	false	false	false	false	530,963
2404.16047	From "AI" to Probabilistic Automation: How Does Anthropomorphization of Technical Systems Descriptions Influence Trust?	This paper investigates the influence of anthropomorphized descriptions of so-called "AI" (artificial intelligence) systems on people's self-assessment of trust in the system. Building on prior work, we define four categories of anthropomorphization (1. Properties of a cognizer, 2. Agency, 3. Biological metaphors, and ...	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	449,333
2307.15245	A Practical Recipe for Federated Learning Under Statistical Heterogeneity Experimental Design	Federated Learning (FL) has been an area of active research in recent years. There have been numerous studies in FL to make it more successful in the presence of data heterogeneity. However, despite the existence of many publications, the state of progress in the field is unknown. Many of the works use inconsistent exp...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	382,198
1710.04782	Multimodal and Multiscale Deep Neural Networks for the Early Diagnosis of Alzheimer's Disease using structural MR and FDG-PET images	Alzheimer's Disease (AD) is a progressive neurodegenerative disease. Amnestic mild cognitive impairment (MCI) is a common first symptom before the conversion to clinical impairment where the individual becomes unable to perform activities of daily living independently. Although there is currently no treatment available...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	82,530
2407.21656	Comgra: A Tool for Analyzing and Debugging Neural Networks	Neural Networks are notoriously difficult to inspect. We introduce comgra, an open source python library for use with PyTorch. Comgra extracts data about the internal activations of a model and organizes it in a GUI (graphical user interface). It can show both summary statistics and individual data points, compare earl...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	477,624
2407.15070	GPHM: Gaussian Parametric Head Model for Monocular Head Avatar Reconstruction	Creating high-fidelity 3D human head avatars is crucial for applications in VR/AR, digital human, and film production. Recent advances have leveraged morphable face models to generate animated head avatars from easily accessible data, representing varying identities and expressions within a low-dimensional parametric s...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	475,020
2411.19870	DeMo: Decoupled Momentum Optimization	Training large neural networks typically requires sharing gradients between accelerators through specialized high-speed interconnects. Drawing from the signal processing principles of frequency decomposition and energy compaction, we demonstrate that synchronizing full optimizer states and model parameters during train...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	512,414
2105.05847	Learning to Generate Novel Scene Compositions from Single Images and Videos	Training GANs in low-data regimes remains a challenge, as overfitting often leads to memorization or training divergence. In this work, we introduce One-Shot GAN that can learn to generate samples from a training set as little as one image or one video. We propose a two-branch discriminator, with content and layout bra...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	234,939
2410.14423	Integrating Deep Learning with Fundus and Optical Coherence Tomography for Cardiovascular Disease Prediction	Early identification of patients at risk of cardiovascular diseases (CVD) is crucial for effective preventive care, reducing healthcare burden, and improving patients' quality of life. This study demonstrates the potential of retinal optical coherence tomography (OCT) imaging combined with fundus photographs for identi...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	500,023
2001.08861	Encoding Physical Constraints in Differentiable Newton-Euler Algorithm	The recursive Newton-Euler Algorithm (RNEA) is a popular technique for computing the dynamics of robots. RNEA can be framed as a differentiable computational graph, enabling the dynamics parameters of the robot to be learned from data via modern auto-differentiation toolboxes. However, the dynamics parameters learned i...	false	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	161,410
2408.08918	Supervised and Unsupervised Alignments for Spoofing Behavioral Biometrics	Biometric recognition systems are security systems based on intrinsic properties of their users, usually encoded in high dimension representations called embeddings, which potential theft would represent a greater threat than a temporary password or a replaceable key. To study the threat of embedding theft, we perform ...	false	false	false	false	true	false	false	false	false	false	false	false	true	false	false	false	false	false	481,210
1912.12171	So2Sat LCZ42: A Benchmark Dataset for Global Local Climate Zones Classification	Access to labeled reference data is one of the grand challenges in supervised machine learning endeavors. This is especially true for an automated analysis of remote sensing images on a global scale, which enables us to address global challenges such as urbanization and climate change using state-of-the-art machine lea...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	158,786
1703.06914	Applying Deep Machine Learning for psycho-demographic profiling of Internet users using O.C.E.A.N. model of personality	In the modern era, each Internet user leaves enormous amounts of auxiliary digital residuals (footprints) by using a variety of on-line services. All this data is already collected and stored for many years. In recent works, it was demonstrated that it's possible to apply simple machine learning methods to analyze coll...	false	false	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	70,298
2311.01195	Batch Bayesian Optimization for Replicable Experimental Design	Many real-world experimental design problems (a) evaluate multiple experimental conditions in parallel and (b) replicate each condition multiple times due to large and heteroscedastic observation noise. Given a fixed total budget, this naturally induces a trade-off between evaluating more unique conditions while replic...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	404,947
2309.08744	Personalized Food Image Classification: Benchmark Datasets and New Baseline	Food image classification is a fundamental step of image-based dietary assessment, enabling automated nutrient analysis from food images. Many current methods employ deep neural networks to train on generic food image datasets that do not reflect the dynamism of real-life food consumption patterns, in which food images...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	392,303
2308.11649	Exploring the Power of Creative AI Tools and Game-Based Methodologies for Interactive Web-Based Programming	In recent years, the fields of artificial intelligence and web-based programming have seen tremendous advancements, enabling developers to create dynamic and interactive websites and applications. At the forefront of these advancements, creative AI tools and game-based methodologies have emerged as potent instruments, ...	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	387,225
2201.07902	Evaluating Machine Common Sense via Cloze Testing	Language models (LMs) show state of the art performance for common sense (CS) question answering, but whether this ability implies a human-level mastery of CS remains an open question. Understanding the limitations and strengths of LMs can help researchers improve these models, potentially by developing novel ways of i...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	276,162
2501.02180	Phase Retrieval by Quaternionic Reweighted Amplitude Flow on Image Reconstruction	Quaternionic signal processing provides powerful tools for efficiently managing color signals by preserving the intrinsic correlations among signal dimensions through quaternion algebra. In this paper, we address the quaternionic phase retrieval problem by systematically developing novel algorithms based on an amplitud...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	522,381
2312.12541	Blood Glucose Level Prediction: A Graph-based Explainable Method with Federated Learning	In the UK, approximately 400,000 people with type 1 diabetes (T1D) rely on insulin delivery due to insufficient pancreatic insulin production. Managing blood glucose (BG) levels is crucial, with continuous glucose monitoring (CGM) playing a key role. CGM, tracking BG every 5 minutes, enables effective blood glucose lev...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	416,998
1902.02829	Low-cost Measurement of Industrial Shock Signals via Deep Learning Calibration	Special high-end sensors with expensive hardware are usually needed to measure shock signals with high accuracy. In this paper, we show that cheap low-end sensors calibrated by deep neural networks are also capable to measure high-g shocks accurately. Firstly we perform drop shock tests to collect a dataset of shock si...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	120,955
1810.01018	Simultaneously Optimizing Weight and Quantizer of Ternary Neural Network using Truncated Gaussian Approximation	In the past years, Deep convolution neural network has achieved great success in many artificial intelligence applications. However, its enormous model size and massive computation cost have become the main obstacle for deployment of such powerful algorithm in the low power and resource-limited mobile systems. As the c...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	109,311
2006.11890	Graph Backdoor	One intriguing property of deep neural networks (DNNs) is their inherent vulnerability to backdoor attacks -- a trojan model responds to trigger-embedded inputs in a highly predictable manner while functioning normally otherwise. Despite the plethora of prior work on DNNs for continuous data (e.g., images), the vulnera...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	183,403
2409.11828	Model-Free Generic Robust Control for Servo-Driven Actuation Mechanisms with Layered Insight into Energy Conversions	To advance theoretical solutions and address limitations in modeling complex servo-driven actuation systems experiencing high non-linearity and load disturbances, this paper aims to design a practical model-free generic robust control (GRC) framework for these mechanisms. This framework is intended to be applicable acr...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	489,334
2112.06721	PM-MMUT: Boosted Phone-Mask Data Augmentation using Multi-Modeling Unit Training for Phonetic-Reduction-Robust E2E Speech Recognition	Consonant and vowel reduction are often encountered in speech, which might cause performance degradation in automatic speech recognition (ASR). Our recently proposed learning strategy based on masking, Phone Masking Training (PMT), alleviates the impact of such phenomenon in Uyghur ASR. Although PMT achieves remarkably...	false	false	true	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	271,273
2410.08642	More than Memes: A Multimodal Topic Modeling Approach to Conspiracy Theories on Telegram	Research on conspiracy theories and related content online has traditionally focused on textual data. To address the increasing prevalence of (audio-)visual data on social media, and to capture the evolving and dynamic nature of this communication, researchers have begun to explore the potential of unsupervised approac...	false	false	false	true	false	false	false	false	true	false	false	true	false	false	false	false	false	true	497,210
2111.12880	Active Learning at the ImageNet Scale	Active learning (AL) algorithms aim to identify an optimal subset of data for annotation, such that deep neural networks (DNN) can achieve better performance when trained on this labeled subset. AL is especially impactful in industrial scale settings where data labeling costs are high and practitioners use every tool a...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	268,109
2306.08422	X-Detect: Explainable Adversarial Patch Detection for Object Detectors in Retail	Object detection models, which are widely used in various domains (such as retail), have been shown to be vulnerable to adversarial attacks. Existing methods for detecting adversarial attacks on object detectors have had difficulty detecting new real-life attacks. We present X-Detect, a novel adversarial patch detector...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	373,409
2410.21119	A Unified Solution to Diverse Heterogeneities in One-shot Federated Learning	One-shot federated learning (FL) limits the communication between the server and clients to a single round, which largely decreases the privacy leakage risks in traditional FLs requiring multiple communications. However, we find existing one-shot FL frameworks are vulnerable to distributional heterogeneity due to their...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	503,095
2211.03250	Uplink Sensing Using CSI Ratio in Perceptive Mobile Networks	Uplink sensing in perceptive mobile networks (PMNs), which uses uplink communication signals for sensing the environment around a base station, faces challenging issues of clock asynchronism and the requirement of a line-of-sight (LOS) path between transmitters and receivers. The channel state information (CSI) ratio h...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	328,879
2011.13527	TaylorGAN: Neighbor-Augmented Policy Update for Sample-Efficient Natural Language Generation	Score function-based natural language generation (NLG) approaches such as REINFORCE, in general, suffer from low sample efficiency and training instability problems. This is mainly due to the non-differentiable nature of the discrete space sampling and thus these methods have to treat the discriminator as a black box a...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	208,503
1608.00191	New MDS codes with small sub-packetization and near-optimal repair bandwidth	An $(n, M)$ vector code $\mathcal{C} \subseteq \mathbb{F}^n$ is a collection of $M$ codewords where $n$ elements (from the field $\mathbb{F}$) in each of the codewords are referred to as code blocks. Assuming that $\mathbb{F} \cong \mathbb{B}^{\ell}$, the code blocks are treated as $\ell$-length vectors over the base f...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	59,240
2205.13697	FedFormer: Contextual Federation with Attention in Reinforcement Learning	A core issue in multi-agent federated reinforcement learning is defining how to aggregate insights from multiple agents. This is commonly done by taking the average of each participating agent's model weights into one common model (FedAvg). We instead propose FedFormer, a novel federation strategy that utilizes Transfo...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	true	false	false	false	299,042
2311.10054	When "A Helpful Assistant" Is Not Really Helpful: Personas in System Prompts Do Not Improve Performances of Large Language Models	Prompting serves as the major way humans interact with Large Language Models (LLM). Commercial AI systems commonly define the role of the LLM in system prompts. For example, ChatGPT uses ``You are a helpful assistant'' as part of its default system prompt. Despite current practices of adding personas to system prompts,...	true	false	false	false	true	false	true	false	true	false	false	false	false	true	false	false	false	false	408,400
2104.04485	A Data-Driven Approach to Full-Field Damage and Failure Pattern Prediction in Microstructure-Dependent Composites using Deep Learning	An image-based deep learning framework is developed in this paper to predict damage and failure in microstructure-dependent composite materials. The work is motivated by the complexity and computational cost of high-fidelity simulations of such materials. The proposed deep learning framework predicts the post-failure f...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	229,403
2103.02396	$S^3$: Learnable Sparse Signal Superdensity for Guided Depth Estimation	Dense depth estimation plays a key role in multiple applications such as robotics, 3D reconstruction, and augmented reality. While sparse signal, e.g., LiDAR and Radar, has been leveraged as guidance for enhancing dense depth estimation, the improvement is limited due to its low density and imbalanced distribution. To ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	222,952
2211.00147	A Machine Learning Tutorial for Operational Meteorology, Part II: Neural Networks and Deep Learning	Over the past decade the use of machine learning in meteorology has grown rapidly. Specifically neural networks and deep learning have been used at an unprecedented rate. In order to fill the dearth of resources covering neural networks with a meteorological lens, this paper discusses machine learning methods in a plai...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	327,766
1102.1261	Symmetry in behavior of complex social systems - discussion of models of crowd evacuation organized in agreement with symmetry conditions	The evacuation of football stadium scenarios are discussed as model realizing ordered states, described as movements of individuals according to fields of displacements, calculated correspondingly to given scenario. The symmetry of the evacuation space is taken into account in calculation of displacements field - the d...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	9,058
2406.11353	$\texttt{MoE-RBench}$: Towards Building Reliable Language Models with Sparse Mixture-of-Experts	Mixture-of-Experts (MoE) has gained increasing popularity as a promising framework for scaling up large language models (LLMs). However, the reliability assessment of MoE lags behind its surging applications. Moreover, when transferred to new domains such as in fine-tuning MoE models sometimes underperform their dense ...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	464,858
1801.09946	"23andMe confirms: I'm super white" -- Analyzing Twitter Discourse On Genetic Testing	Recent progress in genomics is bringing genetic testing to the masses. Participatory public initiatives are underway to sequence the genome of millions of volunteers, and a new market is booming with a number of companies like 23andMe and AncestryDNA offering affordable tests directly to consumers. Consequently, news, ...	false	false	false	true	false	false	false	false	false	false	false	false	false	true	false	false	false	false	89,201
1909.02027	An Evaluation Dataset for Intent Classification and Out-of-Scope Prediction	Task-oriented dialog systems need to know when a query falls outside their range of supported intents, but current text classification corpora only define label sets that cover every example. We introduce a new dataset that includes queries that are out-of-scope---i.e., queries that do not fall into any of the system's...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	144,069
2003.00639	Learning from Easy to Complex: Adaptive Multi-curricula Learning for Neural Dialogue Generation	Current state-of-the-art neural dialogue systems are mainly data-driven and are trained on human-generated responses. However, due to the subjectivity and open-ended nature of human conversations, the complexity of training dialogues varies greatly. The noise and uneven complexity of query-response pairs impede the lea...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	166,360
2211.04031	Hilbert Distillation for Cross-Dimensionality Networks	3D convolutional neural networks have revealed superior performance in processing volumetric data such as video and medical imaging. However, the competitive performance by leveraging 3D networks results in huge computational costs, which are far beyond that of 2D networks. In this paper, we propose a novel Hilbert cur...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	329,113
2501.04766	Decoding rank metric Reed-Muller codes	In this article, we investigate the decoding of the rank metric Reed--Muller codes introduced by Augot, Couvreur, Lavauzelle and Neri in 2021. We propose a polynomial time algorithm that rests on the structure of Dickson matrices, works on any such code and corrects up to half the minimum distance.	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	523,340
2108.06281	Modal-Adaptive Gated Recoding Network for RGB-D Salient Object Detection	The multi-modal salient object detection model based on RGB-D information has better robustness in the real world. However, it remains nontrivial to better adaptively balance effective multi-modal information in the feature fusion phase. In this letter, we propose a novel gated recoding network (GRNet) to evaluate the ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	250,560
2302.13498	Pretraining De-Biased Language Model with Large-scale Click Logs for Document Ranking	Pre-trained language models have achieved great success in various large-scale information retrieval tasks. However, most of pretraining tasks are based on counterfeit retrieval data where the query produced by the tailored rule is assumed as the user's issued query on the given document or passage. Therefore, we explo...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	347,971
2004.08439	Scaling the training of particle classification on simulated MicroBooNE events to multiple GPUs	Measurements in Liquid Argon Time Projection Chamber (LArTPC) neutrino detectors, such as the MicroBooNE detector at Fermilab, feature large, high fidelity event images. Deep learning techniques have been extremely successful in classification tasks of photographs, but their application to LArTPC event images is challe...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	173,062
2407.20876	Automatic Die Studies for Ancient Numismatics	Die studies are fundamental to quantifying ancient monetary production, providing insights into the relationship between coinage, politics, and history. The process requires tedious manual work, which limits the size of the corpora that can be studied. Few works have attempted to automate this task, and none have been ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	477,315
2502.05679	Federated Learning with Reservoir State Analysis for Time Series Anomaly Detection	With a growing data privacy concern, federated learning has emerged as a promising framework to train machine learning models without sharing locally distributed data. In federated learning, local model training by multiple clients and model integration by a server are repeated only through model parameter sharing. Mos...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	531,721
1302.0324	A New Constructive Method to Optimize Neural Network Architecture and Generalization	In this paper, after analyzing the reasons of poor generalization and overfitting in neural networks, we consider some noise data as a singular value of a continuous function - jump discontinuity point. The continuous part can be approximated with the simplest neural networks, which have good generalization performance...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	21,708
1909.12321	Variational point-obstacle avoidance on Riemannian manifolds	In this letter we study variational obstacle avoidance problems on complete Riemannian manifolds. The problem consists of minimizing an energy functional depending on the velocity, covariant acceleration and a repulsive potential function used to avoid a static obstacle on the manifold, among a set of admissible curves...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	147,090
2306.12795	Learning Unseen Modality Interaction	Multimodal learning assumes all modality combinations of interest are available during training to learn cross-modal correspondences. In this paper, we challenge this modality-complete assumption for multimodal learning and instead strive for generalization to unseen modality combinations during inference. We pose the ...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	true	375,068
2207.03692	Mining Discriminative Food Regions for Accurate Food Recognition	Automatic food recognition is the very first step towards passive dietary monitoring. In this paper, we address the problem of food recognition by mining discriminative food regions. Taking inspiration from Adversarial Erasing, a strategy that progressively discovers discriminative object regions for weakly supervised ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	306,948
1601.07678	Extremal Relations Between Shannon Entropy and $\ell_{\alpha}$-Norm	The paper examines relationships between the Shannon entropy and the $\ell_{\alpha}$-norm for $n$-ary probability vectors, $n \ge 2$. More precisely, we investigate the tight bounds of the $\ell_{\alpha}$-norm with a fixed Shannon entropy, and vice versa. As applications of the results, we derive the tight bounds betwe...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	51,455
0805.4560	Rock mechanics modeling based on soft granulation theory	This paper describes application of information granulation theory, on the design of rock engineering flowcharts. Firstly, an overall flowchart, based on information granulation theory has been highlighted. Information granulation theory, in crisp (non-fuzzy) or fuzzy format, can take into account engineering experienc...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	1,847
1809.11158	Universal and Dynamic Locally Repairable Codes with Maximal Recoverability via Sum-Rank Codes	Locally repairable codes (LRCs) are considered with equal or unequal localities, local distances and local field sizes. An explicit two-layer architecture with a sum-rank outer code is obtained, having disjoint local groups and achieving maximal recoverability (MR) for all families of local linear codes (MDS or not) si...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	109,067
2209.14245	Framework for Highway Traffic Profiling using Connected Vehicle Data	The connected vehicle (CV) data could potentially revolutionize the traffic monitoring landscape as a new source of CV data that are collected exclusively from original equipment manufactures (OEMs) have emerged in the commercial market in recent years. Compared to existing CV data that are used by agencies, the new-ge...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	true	320,185
cs/9904001	A Proposal for the Establishment of Review Boards - a flexible approach to the selection of academic knowledge	Paper journals use a small number of trusted academics to select information on behalf of all their readers. This inflexibility in the selection was justified due to the expense of publishing. The advent of cheap distribution via the internet allows a new trade-off between time and expense and the flexibility of the se...	false	false	false	false	false	true	false	false	false	false	false	false	false	true	false	false	false	true	540,490
2111.00873	Probabilistic prediction of the heave motions of a semi-submersible by a deep learning problem model	The real-time motion prediction of a floating offshore platform refers to forecasting its motions in the following one- or two-wave cycles, which helps improve the performance of a motion compensation system and provides useful early warning information. In this study, we extend a deep learning (DL) model, which could ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	264,369
2404.18972	Impact of whole-body vibrations on electrovibration perception varies with target stimulus duration	This study explores the impact of whole-body vibrations induced by external vehicle perturbations, such as aircraft turbulence, on the perception of electrovibration displayed on touchscreens. Electrovibration holds promise as a technology for providing tactile feedback on future touchscreens, addressing usability chal...	true	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	450,461
1508.01585	Applying Deep Learning to Answer Selection: A Study and An Open Task	We apply a general deep learning framework to address the non-factoid question answering task. Our approach does not rely on any linguistic tools and can be applied to different languages or domains. Various architectures are presented and compared. We create and release a QA corpus and setup a new QA task in the insur...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	45,800
1104.1745	Multi-User Diversity with Random Number of Users	Multi-user diversity is considered when the number of users in the system is random. The complete monotonicity of the error rate as a function of the (deterministic) number of users is established and it is proved that randomization of the number of users always leads to deterioration of average system performance at a...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	9,929
2306.01264	Convex and Non-convex Optimization Under Generalized Smoothness	Classical analysis of convex and non-convex optimization methods often requires the Lipshitzness of the gradient, which limits the analysis to functions bounded by quadratics. Recent work relaxed this requirement to a non-uniform smoothness condition with the Hessian norm bounded by an affine function of the gradient n...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	370,375
2308.02339	Improving Scene Graph Generation with Superpixel-Based Interaction Learning	Recent advances in Scene Graph Generation (SGG) typically model the relationships among entities utilizing box-level features from pre-defined detectors. We argue that an overlooked problem in SGG is the coarse-grained interactions between boxes, which inadequately capture contextual semantics for relationship modeling...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	383,580
1707.07605	Share your Model instead of your Data: Privacy Preserving Mimic Learning for Ranking	Deep neural networks have become a primary tool for solving problems in many fields. They are also used for addressing information retrieval problems and show strong performance in several tasks. Training these models requires large, representative datasets and for most IR tasks, such data contains sensitive informatio...	false	false	false	false	true	true	true	false	true	false	false	false	false	false	false	false	false	false	77,663
2104.06548	Solving weakly supervised regression problem using low-rank manifold regularization	We solve a weakly supervised regression problem. Under "weakly" we understand that for some training points the labels are known, for some unknown, and for others uncertain due to the presence of random noise or other reasons such as lack of resources. The solution process requires to optimize a certain objective funct...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	230,108
1601.07446	A First Attempt to Cloud-Based User Verification in Distributed System	In this paper, the idea of client verification in distributed systems is presented. The proposed solution presents a sample system where client verification through cloud resources using input signature is discussed. For different signatures the proposed method has been examined. Research results are presented and disc...	false	false	false	false	true	false	false	false	false	false	false	false	true	false	false	true	false	true	51,430
2303.03786	Stability of the personal relationship networks in a longitudinal study of middle school students	The personal network of relationships is structured in circles of friendships, that go from the most intense relationships to the least intense ones. While this is a well established result, little is known about the stability of those circles and their evolution in time. To shed light on this issue, we study the tempo...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	349,852
1904.04084	ContextDesc: Local Descriptor Augmentation with Cross-Modality Context	Most existing studies on learning local features focus on the patch-based descriptions of individual keypoints, whereas neglecting the spatial relations established from their keypoint locations. In this paper, we go beyond the local detail representation by introducing context awareness to augment off-the-shelf local ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	126,936
2411.17863	LongKey: Keyphrase Extraction for Long Documents	In an era of information overload, manually annotating the vast and growing corpus of documents and scholarly papers is increasingly impractical. Automated keyphrase extraction addresses this challenge by identifying representative terms within texts. However, most existing methods focus on short documents (up to 512 t...	false	false	false	false	true	true	true	false	true	false	false	false	false	false	false	false	false	false	511,637
1905.10720	Gated Group Self-Attention for Answer Selection	Answer selection (answer ranking) is one of the key steps in many kinds of question answering (QA) applications, where deep models have achieved state-of-the-art performance. Among these deep models, recurrent neural network (RNN) based models are most popular, typically with better performance than convolutional neura...	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	132,171
2406.20044	Electrostatics-based particle sampling and approximate inference	A new particle-based sampling and approximate inference method, based on electrostatics and Newton mechanics principles, is introduced with theoretical ground, algorithm design and experimental validation. This method simulates an interacting particle system (IPS) where particles, i.e. the freely-moving negative charge...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	468,646

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