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
1802.09069	Active Learning with Logged Data	We consider active learning with logged data, where labeled examples are drawn conditioned on a predetermined logging policy, and the goal is to learn a classifier on the entire population, not just conditioned on the logging policy. Prior work addresses this problem either when only logged data is available, or purely...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	91,257
2203.10681	Online Continual Learning for Embedded Devices	Real-time on-device continual learning is needed for new applications such as home robots, user personalization on smartphones, and augmented/virtual reality headsets. However, this setting poses unique challenges: embedded devices have limited memory and compute capacity and conventional machine learning models suffer...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	286,640
2410.05863	Enhancing Playback Performance in Video Recommender Systems with an On-Device Gating and Ranking Framework	Video recommender systems (RSs) have gained increasing attention in recent years. Existing mainstream RSs focus on optimizing the matching function between users and items. However, we noticed that users frequently encounter playback issues such as slow loading or stuttering while browsing the videos, especially in wea...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	495,951
2105.09571	The impact of virtual mirroring on customer satisfaction	We investigate the impact of a novel method called "virtual mirroring" to promote employee self-reflection and impact customer satisfaction. The method is based on measuring communication patterns, through social network and semantic analysis, and mirroring them back to the individual. Our goal is to demonstrate that s...	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	false	false	false	236,109
2207.04591	Hybrid iLQR Model Predictive Control for Contact Implicit Stabilization on Legged Robots	Model Predictive Control (MPC) is a popular strategy for controlling robots but is difficult for systems with contact due to the complex nature of hybrid dynamics. To implement MPC for systems with contact, dynamic models are often simplified or contact sequences fixed in time in order to plan trajectories efficiently....	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	307,248
2406.03730	FastGAS: Fast Graph-based Annotation Selection for In-Context Learning	In-context learning (ICL) empowers large language models (LLMs) to tackle new tasks by using a series of training instances as prompts. Since generating the prompts needs to sample from a vast pool of instances and annotate them (e.g., add labels in classification task), existing methods have proposed to select a subse...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	461,365
1904.01681	Augmented Neural ODEs	We show that Neural Ordinary Differential Equations (ODEs) learn representations that preserve the topology of the input space and prove that this implies the existence of functions Neural ODEs cannot represent. To address these limitations, we introduce Augmented Neural ODEs which, in addition to being more expressive...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	126,206
1610.06856	Automated Big Text Security Classification	In recent years, traditional cybersecurity safeguards have proven ineffective against insider threats. Famous cases of sensitive information leaks caused by insiders, including the WikiLeaks release of diplomatic cables and the Edward Snowden incident, have greatly harmed the U.S. government's relationship with other g...	false	false	false	false	true	false	false	false	true	false	false	false	true	true	false	false	false	false	62,708
2405.07495	MacBehaviour: An R package for behavioural experimentation on large language models	There has been increasing interest in investigating the behaviours of large language models (LLMs) and LLM-powered chatbots by treating an LLM as a participant in a psychological experiment. We therefore developed an R package called "MacBehaviour" that aims to interact with more than 60 language models in one package ...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	453,739
1308.4801	The Mapping of Simulated Climate-Dependent Building Innovations	Performances of building energy innovations are most of the time dependent on the external climate conditions. This means a high performance of a specific innovation in a certain part of Europe, does not imply the same performances in other regions. The mapping of simulated building performances at the EU scale could p...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	26,567
2310.17523	Adaptive Resource Management for Edge Network Slicing using Incremental Multi-Agent Deep Reinforcement Learning	Multi-access edge computing provides local resources in mobile networks as the essential means for meeting the demands of emerging ultra-reliable low-latency communications. At the edge, dynamic computing requests require advanced resource management for adaptive network slicing, including resource allocations, functio...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	403,169
2404.15978	Learning deep Koopman operators with convex stability constraints	In this paper, we present a novel sufficient condition for the stability of discrete-time linear systems that can be represented as a set of piecewise linear constraints, which make them suitable for quadratic programming optimization problems. More specifically, we tackle the problem of imposing asymptotic stability t...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	449,303
2007.05864	Bayesian Deep Ensembles via the Neural Tangent Kernel	We explore the link between deep ensembles and Gaussian processes (GPs) through the lens of the Neural Tangent Kernel (NTK): a recent development in understanding the training dynamics of wide neural networks (NNs). Previous work has shown that even in the infinite width limit, when NNs become GPs, there is no GP poste...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	186,807
2212.01325	Resource Allocation for Augmented Reality Empowered Vehicular Edge Metaverse	Metaverse is considered to be the evolution of the next-generation networks, providing users with experience sharing at the intersection between physical and digital. Augmented reality (AR) is one of the primary supporting technologies in the Metaverse, which can seamlessly integrate real-world information with virtual...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	334,384
2203.10159	Discovering Objects that Can Move	This paper studies the problem of object discovery -- separating objects from the background without manual labels. Existing approaches utilize appearance cues, such as color, texture, and location, to group pixels into object-like regions. However, by relying on appearance alone, these methods fail to separate objects...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	286,420
2404.02512	Towards Large Language Model driven Reference-less Translation Evaluation for English and Indian Languages	With the primary focus on evaluating the effectiveness of large language models for automatic reference-less translation assessment, this work presents our experiments on mimicking human direct assessment to evaluate the quality of translations in English and Indian languages. We constructed a translation evaluation ta...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	443,888
1505.02910	Permutational Rademacher Complexity: a New Complexity Measure for Transductive Learning	Transductive learning considers situations when a learner observes $m$ labelled training points and $u$ unlabelled test points with the final goal of giving correct answers for the test points. This paper introduces a new complexity measure for transductive learning called Permutational Rademacher Complexity (PRC) and ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	43,016
2311.07692	On The Truthfulness of 'Surprisingly Likely' Responses of Large Language Models	The principle of rewarding a crowd for surprisingly common answers has been used in the literature for designing a number of truthful information elicitation mechanisms. A related method has also been proposed in the literature for better aggregation of crowd wisdom. Drawing a comparison between crowd based collective ...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	true	407,435
2106.05545	Super-Resolution Image Reconstruction Based on Self-Calibrated Convolutional GAN	With the effective application of deep learning in computer vision, breakthroughs have been made in the research of super-resolution images reconstruction. However, many researches have pointed out that the insufficiency of the neural network extraction on image features may bring the deteriorating of newly reconstruct...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	240,140
2106.00314	Dual Graph enhanced Embedding Neural Network for CTR Prediction	CTR prediction, which aims to estimate the probability that a user will click an item, plays a crucial role in online advertising and recommender system. Feature interaction modeling based and user interest mining based methods are the two kinds of most popular techniques that have been extensively explored for many ye...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	238,078
2309.09465	Active anomaly detection based on deep one-class classification	Active learning has been utilized as an efficient tool in building anomaly detection models by leveraging expert feedback. In an active learning framework, a model queries samples to be labeled by experts and re-trains the model with the labeled data samples. It unburdens in obtaining annotated datasets while improving...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	392,619
2210.12922	BARS: A Benchmark for Airport Runway Segmentation	Airport runway segmentation can effectively reduce the accident rate during the landing phase, which has the largest risk of flight accidents. With the rapid development of deep learning (DL), related methods achieve good performance on segmentation tasks and can be well adapted to complex scenes. However, the lack of ...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	325,969
2304.11901	Unsupervised Machine Learning to Classify the Confinement of Waves in Periodic Superstructures	We employ unsupervised machine learning to enhance the accuracy of our recently presented scaling method for wave confinement analysis [1]. We employ the standard k-means++ algorithm as well as our own model-based algorithm. We investigate cluster validity indices as a means to find the correct number of confinement di...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	360,013
2304.03103	Retention Is All You Need	Skilled employees are the most important pillars of an organization. Despite this, most organizations face high attrition and turnover rates. While several machine learning models have been developed to analyze attrition and its causal factors, the interpretations of those models remain opaque. In this paper, we propos...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	356,668
2003.08537	HOSVD-Based Algorithm for Weighted Tensor Completion	Matrix completion, the problem of completing missing entries in a data matrix with low dimensional structure (such as rank), has seen many fruitful approaches and analyses. Tensor completion is the tensor analog, that attempts to impute missing tensor entries from similar low-rank type assumptions. In this paper, we st...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	168,769
2408.12491	AI in radiological imaging of soft-tissue and bone tumours: a systematic review evaluating against CLAIM and FUTURE-AI guidelines	Soft-tissue and bone tumours (STBT) are rare, diagnostically challenging lesions with variable clinical behaviours and treatment approaches. This systematic review provides an overview of Artificial Intelligence (AI) methods using radiological imaging for diagnosis and prognosis of these tumours, highlighting challenge...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	482,757
2202.05302	Trust in AI: Interpretability is not necessary or sufficient, while black-box interaction is necessary and sufficient	The problem of human trust in artificial intelligence is one of the most fundamental problems in applied machine learning. Our processes for evaluating AI trustworthiness have substantial ramifications for ML's impact on science, health, and humanity, yet confusion surrounds foundational concepts. What does it mean to ...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	279,837
1712.07040	The NarrativeQA Reading Comprehension Challenge	Reading comprehension (RC)---in contrast to information retrieval---requires integrating information and reasoning about events, entities, and their relations across a full document. Question answering is conventionally used to assess RC ability, in both artificial agents and children learning to read. However, existin...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	true	false	false	86,978
2206.10390	The SPACE THEA Project	In some situations, no professional human contact can be available. Accordingly, one remains alone with one's problems and fears. A manned Mars flight is certainly such a situation. A voice assistant that shows empathy and assists the astronauts could be a solution. In the SPACE THEA project, a prototype with such capa...	true	false	false	false	true	false	false	true	false	false	false	false	false	true	false	false	false	false	303,895
2403.05256	DuDoUniNeXt: Dual-domain unified hybrid model for single and multi-contrast undersampled MRI reconstruction	Multi-contrast (MC) Magnetic Resonance Imaging (MRI) reconstruction aims to incorporate a reference image of auxiliary modality to guide the reconstruction process of the target modality. Known MC reconstruction methods perform well with a fully sampled reference image, but usually exhibit inferior performance, compare...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	435,935
1504.07967	Improved repeatability measures for evaluating performance of feature detectors	The most frequently employed measure for performance characterisation of local feature detectors is repeatability, but it has been observed that this does not necessarily mirror actual performance. Presented are improved repeatability formulations which correlate much better with the true performance of feature detecto...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	42,597
2211.01025	DynamicLight: Two-Stage Dynamic Traffic Signal Timing	Reinforcement learning (RL) is gaining popularity as an effective approach for traffic signal control (TSC) and is increasingly applied in this domain. However, most existing RL methodologies are confined to a single-stage TSC framework, primarily focusing on selecting an appropriate traffic signal phase at fixed actio...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	328,085
2202.02277	Quality Assessment of Low Light Restored Images: A Subjective Study and an Unsupervised Model	The quality assessment (QA) of restored low light images is an important tool for benchmarking and improving low light restoration (LLR) algorithms. While several LLR algorithms exist, the subjective perception of the restored images has been much less studied. Challenges in capturing aligned low light and well-lit ima...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	278,747
2302.05666	Jaccard Metric Losses: Optimizing the Jaccard Index with Soft Labels	Intersection over Union (IoU) losses are surrogates that directly optimize the Jaccard index. Leveraging IoU losses as part of the loss function have demonstrated superior performance in semantic segmentation tasks compared to optimizing pixel-wise losses such as the cross-entropy loss alone. However, we identify a lac...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	345,123
2005.07814	Low Complexity Sequential Search with Size-Dependent Measurement Noise	This paper considers a target localization problem where at any given time an agent can choose a region to query for the presence of the target in that region. The measurement noise is assumed to be increasing with the size of the query region the agent chooses. Motivated by practical applications such as initial beam ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	177,398
2406.17289	Hyperbolic Knowledge Transfer in Cross-Domain Recommendation System	Cross-Domain Recommendation (CDR) seeks to utilize knowledge from different domains to alleviate the problem of data sparsity in the target recommendation domain, and it has been gaining more attention in recent years. Although there have been notable advancements in this area, most current methods represent users and ...	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	false	false	467,508
1701.09007	Research Directions for Principles of Data Management (Dagstuhl Perspectives Workshop 16151)	In April 2016, a community of researchers working in the area of Principles of Data Management (PDM) joined in a workshop at the Dagstuhl Castle in Germany. The workshop was organized jointly by the Executive Committee of the ACM Symposium on Principles of Database Systems (PODS) and the Council of the International Co...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	67,573
2112.01702	Localized Feature Aggregation Module for Semantic Segmentation	We propose a new information aggregation method which called Localized Feature Aggregation Module based on the similarity between the feature maps of an encoder and a decoder. The proposed method recovers positional information by emphasizing the similarity between decoder's feature maps with superior semantic informat...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	269,578
2401.01107	CityPulse: Fine-Grained Assessment of Urban Change with Street View Time Series	Urban transformations have profound societal impact on both individuals and communities at large. Accurately assessing these shifts is essential for understanding their underlying causes and ensuring sustainable urban planning. Traditional measurements often encounter constraints in spatial and temporal granularity, fa...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	419,230
1602.02133	Mining Software Quality from Software Reviews: Research Trends and Open Issues	Software review text fragments have considerably valuable information about users experience. It includes a huge set of properties including the software quality. Opinion mining or sentiment analysis is concerned with analyzing textual user judgments. The application of sentiment analysis on software reviews can find a...	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	51,798
2103.11902	Autocorrelation-Driven Synthesis of Antenna Arrays -- The Case of DS-Based Planar Isophoric Thinned Arrays	A new methodology for the design of isophoric thinned arrays with a priori controlled pattern features is introduced. A fully analytical and general (i.e., valid for any lattice and set of weights) relationship between the autocorrelation of the array excitations and the power pattern samples is first derived. Binary 2...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	225,997
1808.06241	Spatio-temporal prediction of crimes using network analytic approach	It is quite evident that majority of the population lives in urban area today than in any time of the human history. This trend seems to increase in coming years. A study [5] says that nearly 80.7% of total population in USA stays in urban area. By 2030 nearly 60% of the population in the world will live in or move to ...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	105,494
2305.01783	Fairness and representation in satellite-based poverty maps: Evidence of urban-rural disparities and their impacts on downstream policy	Poverty maps derived from satellite imagery are increasingly used to inform high-stakes policy decisions, such as the allocation of humanitarian aid and the distribution of government resources. Such poverty maps are typically constructed by training machine learning algorithms on a relatively modest amount of ``ground...	false	false	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	361,799
2409.04720	A Comprehensive Survey on Evidential Deep Learning and Its Applications	Reliable uncertainty estimation has become a crucial requirement for the industrial deployment of deep learning algorithms, particularly in high-risk applications such as autonomous driving and medical diagnosis. However, mainstream uncertainty estimation methods, based on deep ensembling or Bayesian neural networks, g...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	486,476
2208.01749	Analysis of the Spatio-temporal Dynamics of COVID-19 in Massachusetts via Spectral Graph Wavelet Theory	The rapid spread of COVID-19 disease has had a significant impact on the world. In this paper, we study COVID-19 data interpretation and visualization using open-data sources for 351 cities and towns in Massachusetts from December 6, 2020 to September 25, 2021. Because cities are embedded in rather complex transportati...	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	false	false	311,251
2011.08439	A variational characterisation of projective spherical designs over the quaternions	We give an inequality on the packing of vectors/lines in quaternionic Hilbert space $\Hd$, which generalises those of Sidelnikov and Welch for unit vectors in $\Rd$ and $\Cd$. This has a parameter $t$, and depends only on the vectors up to projective unitary equivalence. The sequences of vectors in ${\mathbb{F}}^d={\ma...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	206,873
2106.06008	On the Bound of Energy Consumption in Cellular IoT Networks	Billions of sensors are expected to be connected to the Internet through the emerging Internet of Things (IoT) technologies. Many of these sensors will primarily be connected using wireless technologies powered using batteries as their sole energy source which makes it paramount to optimize their energy consumption. In...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	240,310
2311.05239	Towards Quantum-Native Communication Systems: State-of-the-Art, Trends, and Challenges	The potential synergy between quantum communications and future wireless communication systems is explored. By proposing a quantum-native or quantum-by-design philosophy, the survey examines technologies such as quantumdomain (QD) multi-input multi-output, QD non-orthogonal multiple access, quantum secure direct commun...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	406,521
1905.11128	Learning Multiple Markov Chains via Adaptive Allocation	We study the problem of learning the transition matrices of a set of Markov chains from a single stream of observations on each chain. We assume that the Markov chains are ergodic but otherwise unknown. The learner can sample Markov chains sequentially to observe their states. The goal of the learner is to sequentially...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	132,335
1012.3198	Network MIMO with Linear Zero-Forcing Beamforming: Large System Analysis, Impact of Channel Estimation and Reduced-Complexity Scheduling	We consider the downlink of a multi-cell system with multi-antenna base stations and single-antenna user terminals, arbitrary base station cooperation clusters, distance-dependent propagation pathloss, and general "fairness" requirements. Base stations in the same cooperation cluster employ joint transmission with line...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	8,539
2411.04662	Enhancing Trust in Clinically Significant Prostate Cancer Prediction with Multiple Magnetic Resonance Imaging Modalities	In the United States, prostate cancer is the second leading cause of deaths in males with a predicted 35,250 deaths in 2024. However, most diagnoses are non-lethal and deemed clinically insignificant which means that the patient will likely not be impacted by the cancer over their lifetime. As a result, numerous resear...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	506,357
2408.02685	Artificial Neural Networks for Photonic Applications: From Algorithms to Implementation	This tutorial-review on applications of artificial neural networks in photonics targets a broad audience, ranging from optical research and engineering communities to computer science and applied mathematics. We focus here on the research areas at the interface between these disciplines, attempting to find the right ba...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	478,720
2404.18469	Error-Resilient Weakly Constrained Coding via Row-by-Row Coding	A weakly constrained code is a collection of finite-length strings over a finite alphabet in which certain substrings or patterns occur according to some prescribed frequencies. Buzaglo and Siegel (ITW 2017) gave a construction of weakly constrained codes based on row-by-row coding, that achieved the capacity of the we...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	450,281
2003.14292	Graph Enhanced Representation Learning for News Recommendation	With the explosion of online news, personalized news recommendation becomes increasingly important for online news platforms to help their users find interesting information. Existing news recommendation methods achieve personalization by building accurate news representations from news content and user representations...	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	170,458
1602.03400	On the Noise Robustness of Simultaneous Orthogonal Matching Pursuit	In this paper, the joint support recovery of several sparse signals whose supports present similarities is examined. Each sparse signal is acquired using the same noisy linear measurement process, which returns fewer observations than the dimension of the sparse signals. The measurement noise is assumed additive, Gauss...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	51,993
2408.05705	TC-KANRecon: High-Quality and Accelerated MRI Reconstruction via Adaptive KAN Mechanisms and Intelligent Feature Scaling	Magnetic Resonance Imaging (MRI) has become essential in clinical diagnosis due to its high resolution and multiple contrast mechanisms. However, the relatively long acquisition time limits its broader application. To address this issue, this study presents an innovative conditional guided diffusion model, named as TC-...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	479,898
1504.08200	Predicting People's 3D Poses from Short Sequences	We propose an efficient approach to exploiting motion information from consecutive frames of a video sequence to recover the 3D pose of people. Instead of computing candidate poses in individual frames and then linking them, as is often done, we regress directly from a spatio-temporal block of frames to a 3D pose in th...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	42,628
2003.07292	Using context to adapt to sensor drift	Lifelong development allows animals and machines to adapt to changes in the environment as well as in their own systems, such as wear and tear in sensors and actuators. An important use case of such adaptation is industrial odor-sensing. Metal-oxide-based sensors can be used to detect gaseous compounds in the air; howe...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	true	false	false	168,377
1608.00758	Exploiting the Bipartite Structure of Entity Grids for Document Coherence and Retrieval	Document coherence describes how much sense text makes in terms of its logical organisation and discourse flow. Even though coherence is a relatively difficult notion to quantify precisely, it can be approximated automatically. This type of coherence modelling is not only interesting in itself, but also useful for a nu...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	59,332
2005.06334	The JuliaConnectoR: a functionally oriented interface for integrating Julia in R	Like many groups considering the new programming language Julia, we faced the challenge of accessing the algorithms that we develop in Julia from R. Therefore, we developed the R package JuliaConnectoR, available from the CRAN repository and GitHub (https://github.com/stefan-m-lenz/JuliaConnectoR), in particular for ma...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	176,978
1209.0841	Constructing the L2-Graph for Robust Subspace Learning and Subspace Clustering	Under the framework of graph-based learning, the key to robust subspace clustering and subspace learning is to obtain a good similarity graph that eliminates the effects of errors and retains only connections between the data points from the same subspace (i.e., intra-subspace data points). Recent works achieve good pe...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	18,394
2307.10719	LLM Censorship: A Machine Learning Challenge or a Computer Security Problem?	Large language models (LLMs) have exhibited impressive capabilities in comprehending complex instructions. However, their blind adherence to provided instructions has led to concerns regarding risks of malicious use. Existing defence mechanisms, such as model fine-tuning or output censorship using LLMs, have proven to ...	false	false	false	false	true	false	true	false	true	false	false	false	true	false	false	false	false	false	380,665
2008.00138	Vulnerability Under Adversarial Machine Learning: Bias or Variance?	Prior studies have unveiled the vulnerability of the deep neural networks in the context of adversarial machine learning, leading to great recent attention into this area. One interesting question that has yet to be fully explored is the bias-variance relationship of adversarial machine learning, which can potentially ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	true	false	false	189,913
1206.3189	A New Representation for the Symbol Error Rate	The symbol error rate of the minimum distance detector for an arbitrary multi-dimensional constellation impaired by additive white Gaussian noise is characterized as the product of a completely monotone function with a non-negative power of the signal to noise ratio. This representation is also shown to apply to cases ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	16,488
1806.10830	Grassmannian Discriminant Maps (GDM) for Manifold Dimensionality Reduction with Application to Image Set Classification	In image set classification, a considerable progress has been made by representing original image sets on Grassmann manifolds. In order to extend the advantages of the Euclidean based dimensionality reduction methods to the Grassmann Manifold, several methods have been suggested recently which jointly perform dimension...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	101,611
2108.10980	Physics-Based Causal Lifting Linearization of Nonlinear Control Systems Underpinned by the Koopman Operator	Methods for constructing causal linear models from nonlinear dynamical systems through lifting linearization underpinned by Koopman operator and physical system modeling theory are presented. Outputs of a nonlinear control system, called observables, may be functions of state and input, $\phi(x,u)$. These input-depende...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	252,051
2308.04198	Communication-Efficient Cooperative Multi-Agent PPO via Regulated Segment Mixture in Internet of Vehicles	Multi-Agent Reinforcement Learning (MARL) has become a classic paradigm to solve diverse, intelligent control tasks like autonomous driving in Internet of Vehicles (IoV). However, the widely assumed existence of a central node to implement centralized federated learning-assisted MARL might be impractical in highly dyna...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	384,325
2212.08973	Enhancing Cyber Resilience of Networked Microgrids using Vertical Federated Reinforcement Learning	This paper presents a novel federated reinforcement learning (Fed-RL) methodology to enhance the cyber resiliency of networked microgrids. We formulate a resilient reinforcement learning (RL) training setup which (a) generates episodic trajectories injecting adversarial actions at primary control reference signals of t...	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	336,940
2210.07179	MAPL: Parameter-Efficient Adaptation of Unimodal Pre-Trained Models for Vision-Language Few-Shot Prompting	Large pre-trained models have proved to be remarkable zero- and (prompt-based) few-shot learners in unimodal vision and language tasks. We propose MAPL, a simple and parameter-efficient method that reuses frozen pre-trained unimodal models and leverages their strong generalization capabilities in multimodal vision-lang...	false	false	false	false	true	false	true	false	true	false	false	true	false	false	false	false	false	false	323,599
2010.11425	Differentially-Private Federated Linear Bandits	The rapid proliferation of decentralized learning systems mandates the need for differentially-private cooperative learning. In this paper, we study this in context of the contextual linear bandit: we consider a collection of agents cooperating to solve a common contextual bandit, while ensuring that their communicatio...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	true	false	false	false	202,252
2203.12570	Your "Attention" Deserves Attention: A Self-Diversified Multi-Channel Attention for Facial Action Analysis	Visual attention has been extensively studied for learning fine-grained features in both facial expression recognition (FER) and Action Unit (AU) detection. A broad range of previous research has explored how to use attention modules to localize detailed facial parts (e,g. facial action units), learn discriminative fea...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	287,312
1705.10819	Surface Networks	We study data-driven representations for three-dimensional triangle meshes, which are one of the prevalent objects used to represent 3D geometry. Recent works have developed models that exploit the intrinsic geometry of manifolds and graphs, namely the Graph Neural Networks (GNNs) and its spectral variants, which learn...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	74,474
2310.10071	ZoomTrack: Target-aware Non-uniform Resizing for Efficient Visual Tracking	Recently, the transformer has enabled the speed-oriented trackers to approach state-of-the-art (SOTA) performance with high-speed thanks to the smaller input size or the lighter feature extraction backbone, though they still substantially lag behind their corresponding performance-oriented versions. In this paper, we d...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	400,074
cs/0612128	SASE: Complex Event Processing over Streams	RFID technology is gaining adoption on an increasing scale for tracking and monitoring purposes. Wide deployments of RFID devices will soon generate an unprecedented volume of data. Emerging applications require the RFID data to be filtered and correlated for complex pattern detection and transformed to events that pro...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	539,997
quant-ph/0610153	Subsystem Codes	We investigate various aspects of operator quantum error-correcting codes or, as we prefer to call them, subsystem codes. We give various methods to derive subsystem codes from classical codes. We give a proof for the existence of subsystem codes using a counting argument similar to the quantum Gilbert-Varshamov bound....	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	540,904
1506.07704	AttentionNet: Aggregating Weak Directions for Accurate Object Detection	We present a novel detection method using a deep convolutional neural network (CNN), named AttentionNet. We cast an object detection problem as an iterative classification problem, which is the most suitable form of a CNN. AttentionNet provides quantized weak directions pointing a target object and the ensemble of iter...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	44,552
2207.14545	A One-Shot Reparameterization Method for Reducing the Loss of Tile Pruning on DNNs	Recently, tile pruning has been widely studied to accelerate the inference of deep neural networks (DNNs). However, we found that the loss due to tile pruning, which can eliminate important elements together with unimportant elements, is large on trained DNNs. In this study, we propose a one-shot reparameterization met...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	310,625
2107.09853	EMG Pattern Recognition via Bayesian Inference with Scale Mixture-Based Stochastic Generative Models	Electromyogram (EMG) has been utilized to interface signals for prosthetic hands and information devices owing to its ability to reflect human motion intentions. Although various EMG classification methods have been introduced into EMG-based control systems, they do not fully consider the stochastic characteristics of ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	247,145
2003.10167	Performance Evaluation of Low-Cost Machine Vision Cameras for Image-Based Grasp Verification	Grasp verification is advantageous for autonomous manipulation robots as they provide the feedback required for higher level planning components about successful task completion. However, a major obstacle in doing grasp verification is sensor selection. In this paper, we propose a vision based grasp verification system...	false	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	169,259
2410.22307	SVIP: Towards Verifiable Inference of Open-source Large Language Models	Open-source Large Language Models (LLMs) have recently demonstrated remarkable capabilities in natural language understanding and generation, leading to widespread adoption across various domains. However, their increasing model sizes render local deployment impractical for individual users, pushing many to rely on com...	false	false	false	false	true	false	true	false	true	false	false	false	true	false	false	false	false	false	503,575
2103.14273	LightSAL: Lightweight Sign Agnostic Learning for Implicit Surface Representation	Recently, several works have addressed modeling of 3D shapes using deep neural networks to learn implicit surface representations. Up to now, the majority of works have concentrated on reconstruction quality, paying little or no attention to model size or training time. This work proposes LightSAL, a novel deep convolu...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	226,801
1810.06230	No Place to Hide: Catching Fraudulent Entities in Tensors	Many approaches focus on detecting dense blocks in the tensor of multimodal data to prevent fraudulent entities (e.g., accounts, links) from retweet boosting, hashtag hijacking, link advertising, etc. However, no existing method is effective to find the dense block if it only possesses high density on a subset of all d...	false	false	false	true	false	false	false	false	false	false	false	false	true	false	false	false	false	true	110,401
1909.10090	Towards Explainability for a Civilian UAV Fleet Management using an Agent-based Approach	This paper presents an initial design concept and specification of a civilian Unmanned Aerial Vehicle (UAV) management simulation system that focuses on explainability for the human-in-the-loop control of semi-autonomous UAVs. The goal of the system is to facilitate the operator intervention in critical scenarios (e.g....	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	146,444
2406.07329	Cinematic Gaussians: Real-Time HDR Radiance Fields with Depth of Field	Radiance field methods represent the state of the art in reconstructing complex scenes from multi-view photos. However, these reconstructions often suffer from one or both of the following limitations: First, they typically represent scenes in low dynamic range (LDR), which restricts their use to evenly lit environment...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	463,002
2110.13989	Revisiting Batch Norm Initialization	Batch normalization (BN) is comprised of a normalization component followed by an affine transformation and has become essential for training deep neural networks. Standard initialization of each BN in a network sets the affine transformation scale and shift to 1 and 0, respectively. However, after training we have obs...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	263,369
2403.08838	Predictive Clustering of Vessel Behavior Based on Hierarchical Trajectory Representation	Vessel trajectory clustering, which aims to find similar trajectory patterns, has been widely leveraged in overwater applications. Most traditional methods use predefined rules and thresholds to identify discrete vessel behaviors. They aim for high-quality clustering and conduct clustering on entire sequences, whether ...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	437,509
2406.08473	Strategies for Pretraining Neural Operators	Pretraining for partial differential equation (PDE) modeling has recently shown promise in scaling neural operators across datasets to improve generalizability and performance. Despite these advances, our understanding of how pretraining affects neural operators is still limited; studies generally propose tailored arch...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	463,506
2109.13226	BigSSL: Exploring the Frontier of Large-Scale Semi-Supervised Learning for Automatic Speech Recognition	We summarize the results of a host of efforts using giant automatic speech recognition (ASR) models pre-trained using large, diverse unlabeled datasets containing approximately a million hours of audio. We find that the combination of pre-training, self-training and scaling up model size greatly increases data efficien...	false	false	true	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	257,571
1902.04980	Recurrent Neural Networks with Stochastic Layers for Acoustic Novelty Detection	In this paper, we adapt Recurrent Neural Networks with Stochastic Layers, which are the state-of-the-art for generating text, music and speech, to the problem of acoustic novelty detection. By integrating uncertainty into the hidden states, this type of network is able to learn the distribution of complex sequences. Be...	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	121,449
2502.07135	One-Shot Learning for k-SAT	Consider a $k$-SAT formula $\Phi$ where every variable appears at most $d$ times, and let $\sigma$ be a satisfying assignment of $\Phi$ sampled proportionally to $e^{\beta m(\sigma)}$ where $m(\sigma)$ is the number of variables set to true and $\beta$ is a real parameter. Given $\Phi$ and $\sigma$, can we learn the va...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	532,438
2411.03500	{\lambda}-Tune: Harnessing Large Language Models for Automated Database System Tuning	We introduce {\lambda}-Tune, a framework that leverages Large Language Models (LLMs) for automated database system tuning. The design of {\lambda}-Tune is motivated by the capabilities of the latest generation of LLMs. Different from prior work, leveraging LLMs to extract tuning hints for single parameters, {\lambda}-T...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	505,920
2501.19398	Do LLMs Strategically Reveal, Conceal, and Infer Information? A Theoretical and Empirical Analysis in The Chameleon Game	Large language model-based (LLM-based) agents have become common in settings that include non-cooperative parties. In such settings, agents' decision-making needs to conceal information from their adversaries, reveal information to their cooperators, and infer information to identify the other agents' characteristics. ...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	true	529,148
2405.05741	Can large language models understand uncommon meanings of common words?	Large language models (LLMs) like ChatGPT have shown significant advancements across diverse natural language understanding (NLU) tasks, including intelligent dialogue and autonomous agents. Yet, lacking widely acknowledged testing mechanisms, answering `whether LLMs are stochastic parrots or genuinely comprehend the w...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	453,035
2104.03763	Detection of Message Injection Attacks onto the CAN Bus using Similarity of Successive Messages-Sequence Graphs	The smart features of modern cars are enabled by a number of Electronic Control Units (ECUs) components that communicate through an in-vehicle network, known as Controller Area Network (CAN) bus. The fundamental challenge is the security of the communication link where an attacker can inject messages (e.g., increase th...	false	false	false	false	false	false	false	false	false	false	true	false	true	false	false	false	false	false	229,164
1707.07819	Detecting Semantic Parts on Partially Occluded Objects	In this paper, we address the task of detecting semantic parts on partially occluded objects. We consider a scenario where the model is trained using non-occluded images but tested on occluded images. The motivation is that there are infinite number of occlusion patterns in real world, which cannot be fully covered in ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	77,701
2303.05352	Extracting Accurate Materials Data from Research Papers with Conversational Language Models and Prompt Engineering	There has been a growing effort to replace manual extraction of data from research papers with automated data extraction based on natural language processing, language models, and recently, large language models (LLMs). Although these methods enable efficient extraction of data from large sets of research papers, they ...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	350,434
1804.04241	Capsules for Object Segmentation	Convolutional neural networks (CNNs) have shown remarkable results over the last several years for a wide range of computer vision tasks. A new architecture recently introduced by Sabour et al., referred to as a capsule networks with dynamic routing, has shown great initial results for digit recognition and small image...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	94,791
1910.00730	Potential Key Technologies for 6G Mobile Communications	The standard development of 5G wireless communication culminated between 2017 and 2019, followed by the worldwide deployment of 5G networks, which is expected to result in very high data rate for enhanced mobile broadband, support ultra-reliable and low-latency services and accommodate massive number of connections. Re...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	147,748
1909.10414	Informing a BDI Player Model for an Interactive Narrative	This work focuses on studying players behaviour in interactive narratives with the aim to simulate their choices. Besides sub-optimal player behaviour due to limited knowledge about the environment, the difference in each player's style and preferences represents a challenge when trying to make an intelligent system mi...	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	146,539
2103.00055	Image-Based Trajectory Tracking through Unknown Environments without Absolute Positioning	This paper describes a stereo image-based visual servoing system for trajectory tracking by a non-holonomic robot without externally derived pose information nor a known visual map of the environment. It is called trajectory servoing. The critical component is a feature-based, indirect Simultaneous Localization And Map...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	222,129

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