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
2110.00542	RLO-MPC: Robust Learning-Based Output Feedback MPC for Improving the Performance of Uncertain Systems in Iterative Tasks	In this work we address the problem of performing a repetitive task when we have uncertain observations and dynamics. We formulate this problem as an iterative infinite horizon optimal control problem with output feedback. Previously, this problem was solved for linear time-invariant (LTI) system for the case when nois...	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	258,432
0909.2379	Implementation of Rule Based Algorithm for Sandhi-Vicheda Of Compound Hindi Words	Sandhi means to join two or more words to coin new word. Sandhi literally means `putting together' or combining (of sounds), It denotes all combinatory sound-changes effected (spontaneously) for ease of pronunciation. Sandhi-vicheda describes [5] the process by which one letter (whether single or cojoined) is broken to...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	4,487
2502.08324	Decentralised multi-agent coordination for real-time railway traffic management	The real-time Railway Traffic Management Problem (rtRTMP) is a challenging optimisation problem in railway transportation. It involves the efficient management of train movements while minimising delay propagation caused by unforeseen perturbations due to, e.g, temporary speed limitations or signal failures. This paper...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	532,977
2201.05899	Unobserved Local Structures Make Compositional Generalization Hard	While recent work has convincingly showed that sequence-to-sequence models struggle to generalize to new compositions (termed compositional generalization), little is known on what makes compositional generalization hard on a particular test instance. In this work, we investigate what are the factors that make generali...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	275,538
2001.01796	Fair Active Learning	Machine learning (ML) is increasingly being used in high-stakes applications impacting society. Therefore, it is of critical importance that ML models do not propagate discrimination. Collecting accurate labeled data in societal applications is challenging and costly. Active learning is a promising approach to build an...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	159,574
1502.06256	Spaced seeds improve k-mer-based metagenomic classification	Metagenomics is a powerful approach to study genetic content of environmental samples that has been strongly promoted by NGS technologies. To cope with massive data involved in modern metagenomic projects, recent tools [4, 39] rely on the analysis of k-mers shared between the read to be classified and sampled reference...	false	true	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	40,478
1904.11469	The Zero Resource Speech Challenge 2019: TTS without T	We present the Zero Resource Speech Challenge 2019, which proposes to build a speech synthesizer without any text or phonetic labels: hence, TTS without T (text-to-speech without text). We provide raw audio for a target voice in an unknown language (the Voice dataset), but no alignment, text or labels. Participants mus...	false	false	true	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	128,863
2404.00752	On the True Distribution Approximation of Minimum Bayes-Risk Decoding	Minimum Bayes-risk (MBR) decoding has recently gained renewed attention in text generation. MBR decoding considers texts sampled from a model as pseudo-references and selects the text with the highest similarity to the others. Therefore, sampling is one of the key elements of MBR decoding, and previous studies reported...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	443,092
1804.10846	Data science is science's second chance to get causal inference right: A classification of data science tasks	Causal inference from observational data is the goal of many data analyses in the health and social sciences. However, academic statistics has often frowned upon data analyses with a causal objective. The introduction of the term "data science" provides a historic opportunity to redefine data analysis in such a way tha...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	96,242
math/0701419	Strategies for prediction under imperfect monitoring	We propose simple randomized strategies for sequential prediction under imperfect monitoring, that is, when the forecaster does not have access to the past outcomes but rather to a feedback signal. The proposed strategies are consistent in the sense that they achieve, asymptotically, the best possible average reward. I...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	540,740
2211.08025	FedTune: A Deep Dive into Efficient Federated Fine-Tuning with Pre-trained Transformers	Federated Learning (FL) is an emerging paradigm that enables distributed users to collaboratively and iteratively train machine learning models without sharing their private data. Motivated by the effectiveness and robustness of self-attention-based architectures, researchers are turning to using pre-trained Transforme...	false	false	false	false	false	false	true	false	true	false	false	true	false	false	false	false	false	false	330,453
2309.06377	Adversarial attacks on hybrid classical-quantum Deep Learning models for Histopathological Cancer Detection	We present an effective application of quantum machine learning in histopathological cancer detection. The study here emphasizes two primary applications of hybrid classical-quantum Deep Learning models. The first application is to build a classification model for histopathological cancer detection using the quantum tr...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	391,401
2005.07493	History for Visual Dialog: Do we really need it?	Visual Dialog involves "understanding" the dialog history (what has been discussed previously) and the current question (what is asked), in addition to grounding information in the image, to generate the correct response. In this paper, we show that co-attention models which explicitly encode dialog history outperform ...	false	false	false	false	true	false	true	false	true	false	false	true	false	false	false	false	false	false	177,297
1809.06130	A Deep Learning Framework for Unsupervised Affine and Deformable Image Registration	Image registration, the process of aligning two or more images, is the core technique of many (semi-)automatic medical image analysis tasks. Recent studies have shown that deep learning methods, notably convolutional neural networks (ConvNets), can be used for image registration. Thus far training of ConvNets for regis...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	107,966
2205.14497	BadDet: Backdoor Attacks on Object Detection	Deep learning models have been deployed in numerous real-world applications such as autonomous driving and surveillance. However, these models are vulnerable in adversarial environments. Backdoor attack is emerging as a severe security threat which injects a backdoor trigger into a small portion of training data such t...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	299,386
1810.02780	A Relaxation-based Network Decomposition Algorithm for Parallel Transient Stability Simulation with Improved Convergence	Transient stability simulation of a large-scale and interconnected electric power system involves solving a large set of differential algebraic equations (DAEs) at every simulation time-step. With the ever-growing size and complexity of power grids, dynamic simulation becomes more time-consuming and computationally dif...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	true	109,658
1904.11088	D-VAE: A Variational Autoencoder for Directed Acyclic Graphs	Graph structured data are abundant in the real world. Among different graph types, directed acyclic graphs (DAGs) are of particular interest to machine learning researchers, as many machine learning models are realized as computations on DAGs, including neural networks and Bayesian networks. In this paper, we study dee...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	128,773
2010.12071	Translating Recursive Probabilistic Programs to Factor Graph Grammars	It is natural for probabilistic programs to use conditionals to express alternative substructures in models, and loops (recursion) to express repeated substructures in models. Thus, probabilistic programs with conditionals and recursion motivate ongoing interest in efficient and general inference. A factor graph gramma...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	202,546
2409.02635	Modelling, Design Optimization and Prototype development of Knee Exoskeleton	This study focuses on enhancing the design of an existing knee exoskeleton by addressing limitations in the range of motion (ROM) during Sit-to-Stand (STS) motions. While current knee exoskeletons emphasize toughness and rehabilitation, their closed-loop mechanisms hinder optimal ROM, which is crucial for effective reh...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	485,774
2306.01253	Mixture Proportion Estimation Beyond Irreducibility	The task of mixture proportion estimation (MPE) is to estimate the weight of a component distribution in a mixture, given observations from both the component and mixture. Previous work on MPE adopts the irreducibility assumption, which ensures identifiablity of the mixture proportion. In this paper, we propose a more ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	370,371
2411.04551	Measure-to-measure interpolation using Transformers	Transformers are deep neural network architectures that underpin the recent successes of large language models. Unlike more classical architectures that can be viewed as point-to-point maps, a Transformer acts as a measure-to-measure map implemented as specific interacting particle system on the unit sphere: the input ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	506,313
2206.13714	Generalized Policy Improvement Algorithms with Theoretically Supported Sample Reuse	We develop a new class of model-free deep reinforcement learning algorithms for data-driven, learning-based control. Our Generalized Policy Improvement algorithms combine the policy improvement guarantees of on-policy methods with the efficiency of sample reuse, addressing a trade-off between two important deployment r...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	305,056
2012.02670	Unleashing the Tiger: Inference Attacks on Split Learning	We investigate the security of Split Learning -- a novel collaborative machine learning framework that enables peak performance by requiring minimal resources consumption. In the present paper, we expose vulnerabilities of the protocol and demonstrate its inherent insecurity by introducing general attack strategies tar...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	209,843
2205.15146	Batch Normalization Is Blind to the First and Second Derivatives of the Loss	In this paper, we prove the effects of the BN operation on the back-propagation of the first and second derivatives of the loss. When we do the Taylor series expansion of the loss function, we prove that the BN operation will block the influence of the first-order term and most influence of the second-order term of the...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	299,623
1802.09897	Multiple structural transitions in interacting networks	Many real-world systems can be modeled as interconnected multilayer networks, namely a set of networks interacting with each other. Here we present a perturbative approach to study the properties of a general class of interconnected networks as inter-network interactions are established. We reveal multiple structural t...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	91,411
1311.7038	Group Coding with Complex Isometries	We investigate group coding for arbitrary finite groups acting linearly on a vector space. These yield robust codes based on real or complex matrix groups. We give necessary and sufficient conditions for correct subgroup decoding using geometric notions of minimal length coset representatives. The infinite family of co...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	28,704
2409.00501	Leaky Wave Antenna-Equipped RF Chipless Tags for Orientation Estimation	Accurate orientation estimation of an object in a scene is critical in robotics, aerospace, augmented reality, and medicine, as it supports scene understanding. This paper introduces a novel orientation estimation approach leveraging radio frequency (RF) sensing technology and leaky-wave antennas (LWAs). Specifically, ...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	484,948
2006.15757	Exploring Optimal Control With Observations at a Cost	There has been a current trend in reinforcement learning for healthcare literature, where in order to prepare clinical datasets, researchers will carry forward the last results of the non-administered test known as the last-observation-carried-forward (LOCF) value to fill in gaps, assuming that it is still an accurate ...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	184,616
2201.13395	Neural Collaborative Filtering Bandits via Meta Learning	Contextual multi-armed bandits provide powerful tools to solve the exploitation-exploration dilemma in decision making, with direct applications in the personalized recommendation. In fact, collaborative effects among users carry the significant potential to improve the recommendation. In this paper, we introduce and s...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	277,972
2305.11000	SpeechGPT: Empowering Large Language Models with Intrinsic Cross-Modal Conversational Abilities	Multi-modal large language models are regarded as a crucial step towards Artificial General Intelligence (AGI) and have garnered significant interest with the emergence of ChatGPT. However, current speech-language models typically adopt the cascade paradigm, preventing inter-modal knowledge transfer. In this paper, we ...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	365,330
2210.16525	Spectral Representation Learning for Conditional Moment Models	Many problems in causal inference and economics can be formulated in the framework of conditional moment models, which characterize the target function through a collection of conditional moment restrictions. For nonparametric conditional moment models, efficient estimation often relies on preimposed conditions on vari...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	327,367
2204.07059	Machine Learning-based Anomaly Detection in Optical Fiber Monitoring	Secure and reliable data communication in optical networks is critical for high-speed Internet. However, optical fibers, serving as the data transmission medium providing connectivity to billons of users worldwide, are prone to a variety of anomalies resulting from hard failures (e.g., fiber cuts) and malicious physica...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	291,548
2404.09232	MAP: Model Aggregation and Personalization in Federated Learning with Incomplete Classes	In some real-world applications, data samples are usually distributed on local devices, where federated learning (FL) techniques are proposed to coordinate decentralized clients without directly sharing users' private data. FL commonly follows the parameter server architecture and contains multiple personalization and ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	446,590
2403.02178	Masked Thought: Simply Masking Partial Reasoning Steps Can Improve Mathematical Reasoning Learning of Language Models	In reasoning tasks, even a minor error can cascade into inaccurate results, leading to suboptimal performance of large language models in such domains. Earlier fine-tuning approaches sought to mitigate this by leveraging more precise supervisory signals from human labeling, larger models, or self-sampling, although at ...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	434,720
2307.01582	IAdet: Simplest human-in-the-loop object detection	This work proposes a strategy for training models while annotating data named Intelligent Annotation (IA). IA involves three modules: (1) assisted data annotation, (2) background model training, and (3) active selection of the next datapoints. Under this framework, we open-source the IAdet tool, which is specific for s...	true	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	377,403
2208.13341	Artificial Neural Networks for Finger Vein Recognition: A Survey	Finger vein recognition is an emerging biometric recognition technology. Different from the other biometric features on the body surface, the venous vascular tissue of the fingers is buried deep inside the skin. Due to this advantage, finger vein recognition is highly stable and private. They are almost impossible to b...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	315,034
1708.05466	Large-Scale Domain Adaptation via Teacher-Student Learning	High accuracy speech recognition requires a large amount of transcribed data for supervised training. In the absence of such data, domain adaptation of a well-trained acoustic model can be performed, but even here, high accuracy usually requires significant labeled data from the target domain. In this work, we propose ...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	79,134
2501.04784	Leveraging Registers in Vision Transformers for Robust Adaptation	Vision Transformers (ViTs) have shown success across a variety of tasks due to their ability to capture global image representations. Recent studies have identified the existence of high-norm tokens in ViTs, which can interfere with unsupervised object discovery. To address this, the use of "registers" which are additi...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	523,343
1511.05201	The capacity of Bernoulli nonadaptive group testing	We consider nonadaptive group testing with Bernoulli tests, where each item is placed in each test independently with some fixed probability. We give a tight threshold on the maximum number of tests required to find the defective set under optimal Bernoulli testing. Achievability is given by a result of Scarlett and Ce...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	49,008
2303.05735	Hardware Acceleration of Neural Graphics	Rendering and inverse-rendering algorithms that drive conventional computer graphics have recently been superseded by neural representations (NR). NRs have recently been used to learn the geometric and the material properties of the scenes and use the information to synthesize photorealistic imagery, thereby promising ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	350,579
1503.00687	A review of mean-shift algorithms for clustering	A natural way to characterize the cluster structure of a dataset is by finding regions containing a high density of data. This can be done in a nonparametric way with a kernel density estimate, whose modes and hence clusters can be found using mean-shift algorithms. We describe the theory and practice behind clustering...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	40,733
2203.04564	Region-Aware Face Swapping	This paper presents a novel Region-Aware Face Swapping (RAFSwap) network to achieve identity-consistent harmonious high-resolution face generation in a local-global manner: \textbf{1)} Local Facial Region-Aware (FRA) branch augments local identity-relevant features by introducing the Transformer to effectively model mi...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	284,513
1903.06994	Visual Query Answering by Entity-Attribute Graph Matching and Reasoning	Visual Query Answering (VQA) is of great significance in offering people convenience: one can raise a question for details of objects, or high-level understanding about the scene, over an image. This paper proposes a novel method to address the VQA problem. In contrast to prior works, our method that targets single sce...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	124,506
2401.01511	Enhancing Multilingual Information Retrieval in Mixed Human Resources Environments: A RAG Model Implementation for Multicultural Enterprise	The advent of Large Language Models has revolutionized information retrieval, ushering in a new era of expansive knowledge accessibility. While these models excel in providing open-world knowledge, effectively extracting answers in diverse linguistic environments with varying levels of literacy remains a formidable cha...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	419,386
2110.02513	UGV-assisted Wireless Powered Backscatter Communications for Large-Scale IoT Networks	Wireless powered backscatter communications (WPBC) is capable of implementing ultra-low-power communication, thus promising in the Internet of Things (IoT) networks. In practice, however, it is challenging to apply WPBC in large-scale IoT networks because of its short communication range. To address this challenge, thi...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	259,153
2003.02681	Stochastic Linear Contextual Bandits with Diverse Contexts	In this paper, we investigate the impact of context diversity on stochastic linear contextual bandits. As opposed to the previous view that contexts lead to more difficult bandit learning, we show that when the contexts are sufficiently diverse, the learner is able to utilize the information obtained during exploitatio...	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	167,011
2203.13238	Open-set Recognition via Augmentation-based Similarity Learning	The primary assumption of conventional supervised learning or classification is that the test samples are drawn from the same distribution as the training samples, which is called closed set learning or classification. In many practical scenarios, this is not the case because there are unknowns or unseen class samples ...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	287,551
2409.12741	Fine Tuning Large Language Models for Medicine: The Role and Importance of Direct Preference Optimization	Large Language Model (LLM) fine tuning is underutilized in the field of medicine. Two of the most common methods of fine tuning are Supervised Fine Tuning (SFT) and Direct Preference Optimization (DPO), but there is little guidance informing users when to use either technique. In this investigation, we compare the perf...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	489,706
cs/0312047	Mapping weblog communities	Websites of a particular class form increasingly complex networks, and new tools are needed to map and understand them. A way of visualizing this complex network is by mapping it. A map highlights which members of the community have similar interests, and reveals the underlying social network. In this paper, we will ma...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	538,070
2302.11524	Slim U-Net: Efficient Anatomical Feature Preserving U-net Architecture for Ultrasound Image Segmentation	We investigate the applicability of U-Net based models for segmenting Urinary Bladder (UB) in male pelvic view UltraSound (US) images. The segmentation of UB in the US image aids radiologists in diagnosing the UB. However, UB in US images has arbitrary shapes, indistinct boundaries and considerably large inter- and int...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	347,238
2312.17024	Selective Run-Length Encoding	Run-Length Encoding (RLE) is one of the most fundamental tools in data compression. However, its compression power drops significantly if there lacks consecutive elements in the sequence. In extreme cases, the output of the encoder may require more space than the input (aka size inflation). To alleviate this issue, usi...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	418,597
1909.03227	A Novel Cascade Binary Tagging Framework for Relational Triple Extraction	Extracting relational triples from unstructured text is crucial for large-scale knowledge graph construction. However, few existing works excel in solving the overlapping triple problem where multiple relational triples in the same sentence share the same entities. In this work, we introduce a fresh perspective to revi...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	144,408
2408.16530	A Comprehensive Review of 3D Object Detection in Autonomous Driving: Technological Advances and Future Directions	In recent years, 3D object perception has become a crucial component in the development of autonomous driving systems, providing essential environmental awareness. However, as perception tasks in autonomous driving evolve, their variants have increased, leading to diverse insights from industry and academia. Currently,...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	484,365
1106.3759	Frequency Theorem for discrete time stochastic system with multiplicative noise	In this paper we consider the problem of minimizing a quadratic functional for a discrete-time linear stochastic system with multiplicative noise, on a standard probability space, in infinite time horizon. We show that the necessary and sufficient conditions for the existence of the optimal control can be formulated as...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	10,910
1811.08069	Representation Learning of Pedestrian Trajectories Using Actor-Critic Sequence-to-Sequence Autoencoder	Representation learning of pedestrian trajectories transforms variable-length timestamp-coordinate tuples of a trajectory into a fixed-length vector representation that summarizes spatiotemporal characteristics. It is a crucial technique to connect feature-based data mining with trajectory data. Trajectory representati...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	113,947
2310.17219	Scalable Verification of Strategy Logic through Three-valued Abstraction	The model checking problem for multi-agent systems against Strategy Logic specifications is known to be non-elementary. On this logic several fragments have been defined to tackle this issue but at the expense of expressiveness. In this paper, we propose a three-valued semantics for Strategy Logic upon which we define ...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	403,051
2403.01534	Conditional normality and finite-state dimensions revisited	The notion of a normal bit sequence was introduced by Borel in 1909; it was the first definition of an individual random object. Normality is a weak notion of randomness requiring only that all $2^n$ factors (substrings) of arbitrary length~$n$ appear with the same limit frequency $2^{-n}$. Later many stronger definiti...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	434,468
2410.04708	Tight Stability, Convergence, and Robustness Bounds for Predictive Coding Networks	Energy-based learning algorithms, such as predictive coding (PC), have garnered significant attention in the machine learning community due to their theoretical properties, such as local operations and biologically plausible mechanisms for error correction. In this work, we rigorously analyze the stability, robustness,...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	true	false	false	495,407
1807.05245	Performance of Humans in Iris Recognition: The Impact of Iris Condition and Annotation-driven Verification	This paper advances the state of the art in human examination of iris images by (1) assessing the impact of different iris conditions in identity verification, and (2) introducing an annotation step that improves the accuracy of people's decisions. In a first experimental session, 114 subjects were asked to decide if p...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	102,885
2306.13872	Learning from Pixels with Expert Observations	In reinforcement learning (RL), sparse rewards can present a significant challenge. Fortunately, expert actions can be utilized to overcome this issue. However, acquiring explicit expert actions can be costly, and expert observations are often more readily available. This paper presents a new approach that uses expert ...	false	false	false	false	true	false	true	true	false	false	false	false	false	false	false	false	false	false	375,433
2402.16641	Towards Open-ended Visual Quality Comparison	Comparative settings (e.g. pairwise choice, listwise ranking) have been adopted by a wide range of subjective studies for image quality assessment (IQA), as it inherently standardizes the evaluation criteria across different observers and offer more clear-cut responses. In this work, we extend the edge of emerging larg...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	432,636
2208.02343	Improvements to enhance robustness of third-order scale-independent WENO-Z schemes	Although there are many improvements to WENO3-Z that target the achievement of optimal order in the occurrence of the first-order critical point (CP1), they mainly address resolution performance, while the robustness of schemes is of less concern and lacks understanding accordingly. In light of our analysis considering...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	311,438
2501.06226	asanAI: In-Browser, No-Code, Offline-First Machine Learning Toolkit	Machine learning (ML) has become crucial in modern life, with growing interest from researchers and the public. Despite its potential, a significant entry barrier prevents widespread adoption, making it challenging for non-experts to understand and implement ML techniques. The increasing desire to leverage ML is counte...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	true	523,888
1904.07190	Explicit Spatial Encoding for Deep Local Descriptors	We propose a kernelized deep local-patch descriptor based on efficient match kernels of neural network activations. Response of each receptive field is encoded together with its spatial location using explicit feature maps. Two location parametrizations, Cartesian and polar, are used to provide robustness to a differen...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	127,727
2501.11613	Conversation Routines: A Prompt Engineering Framework for Task-Oriented Dialog Systems	This study introduces Conversation Routines (CR), a structured prompt engineering framework for developing task-oriented dialog systems using Large Language Models (LLMs). While LLMs demonstrate remarkable natural language understanding capabilities, engineering them to reliably execute complex business workflows remai...	true	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	true	525,982
2107.00966	Data-driven model predictive control: closed-loop guarantees and experimental results	We provide a comprehensive review and practical implementation of a recently developed model predictive control (MPC) framework for controlling unknown systems using only measured data and no explicit model knowledge. Our approach relies on an implicit system parametrization from behavioral systems theory based on one ...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	244,335
2201.09049	LTC-SUM: Lightweight Client-driven Personalized Video Summarization Framework Using 2D CNN	This paper proposes a novel lightweight thumbnail container-based summarization (LTC-SUM) framework for full feature-length videos. This framework generates a personalized keyshot summary for concurrent users by using the computational resource of the end-user device. State-of-the-art methods that acquire and process e...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	276,532
1912.08776	Frequency-Aware Reconstruction of Fluid Simulations with Generative Networks	Convolutional neural networks were recently employed to fully reconstruct fluid simulation data from a set of reduced parameters. However, since (de-)convolutions traditionally trained with supervised L1-loss functions do not discriminate between low and high frequencies in the data, the error is not minimized efficien...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	157,908
2406.06755	Optimal Federated Learning for Nonparametric Regression with Heterogeneous Distributed Differential Privacy Constraints	This paper studies federated learning for nonparametric regression in the context of distributed samples across different servers, each adhering to distinct differential privacy constraints. The setting we consider is heterogeneous, encompassing both varying sample sizes and differential privacy constraints across serv...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	462,757
cs/0504022	A Matter of Opinion: Sentiment Analysis and Business Intelligence (position paper)	A general-audience introduction to the area of "sentiment analysis", the computational treatment of subjective, opinion-oriented language (an example application is determining whether a review is "thumbs up" or "thumbs down"). Some challenges, applications to business-intelligence tasks, and potential future direction...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	538,647
2407.15734	TaskGen: A Task-Based, Memory-Infused Agentic Framework using StrictJSON	TaskGen is an open-sourced agentic framework which uses an Agent to solve an arbitrary task by breaking them down into subtasks. Each subtask is mapped to an Equipped Function or another Agent to execute. In order to reduce verbosity (and hence token usage), TaskGen uses StrictJSON that ensures JSON output from the Lar...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	true	false	false	false	475,308
1912.03456	Optimal Electricity Storage Sharing Mechanism for Single Peaked Time-of-Use Pricing Scheme	Sharing economy has disrupted many industries. We foresee that electricity storage systems could be the enabler for sharing economy in electricity sector, though its implementation is a delicate task. Unlike in the 2-tier Time-of-Use (ToU) pricing, where greedy arbitrage policy can achieve the maximal electricity bill ...	false	true	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	156,597
2404.04319	SpatialTracker: Tracking Any 2D Pixels in 3D Space	Recovering dense and long-range pixel motion in videos is a challenging problem. Part of the difficulty arises from the 3D-to-2D projection process, leading to occlusions and discontinuities in the 2D motion domain. While 2D motion can be intricate, we posit that the underlying 3D motion can often be simple and low-dim...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	444,605
2404.19126	Compositional Factorization of Visual Scenes with Convolutional Sparse Coding and Resonator Networks	We propose a system for visual scene analysis and recognition based on encoding the sparse, latent feature-representation of an image into a high-dimensional vector that is subsequently factorized to parse scene content. The sparse feature representation is learned from image statistics via convolutional sparse coding,...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	true	false	false	450,514
2209.02285	High Dynamic Range Image Quality Assessment Based on Frequency Disparity	In this paper, a novel and effective image quality assessment (IQA) algorithm based on frequency disparity for high dynamic range (HDR) images is proposed, termed as local-global frequency feature-based model (LGFM). Motivated by the assumption that the human visual system is highly adapted for extracting structural in...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	316,161
2412.01822	VLsI: Verbalized Layers-to-Interactions from Large to Small Vision Language Models	The recent surge in high-quality visual instruction tuning samples from closed-source vision-language models (VLMs) such as GPT-4V has accelerated the release of open-source VLMs across various model sizes. However, scaling VLMs to improve performance using larger models brings significant computational challenges, esp...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	513,279
2111.09648	Backswimmer Inspired Miniature Robot with Buoyancy Auto-Regulation through Controlled Nucleation and Release of Microbubbles	The backswimmer fly is an aquatic insect, capable of regulating its buoyancy underwater. Its abdomen is covered with hemoglobin cells, used to bind and release oxygen, reversibly. Upon entering water, the fly entraps an air bubble in a superhydrophobic hairy structure on its abdomen for respiration. This bubble, howeve...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	267,074
2004.05648	A Comparative Analysis of Knowledge Graph Query Performance	As Knowledge Graphs (KGs) continue to gain widespread momentum for use in different domains, storing the relevant KG content and efficiently executing queries over them are becoming increasingly important. A range of Data Management Systems (DMSs) have been employed to process KGs. This paper aims to provide an in-dept...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	172,263
1704.01416	Emergence of Time in a Participatory Universe	After stating the measurement problem, physicists usually assume the problem to be coming from the measurement part. Since classical probabilities also collapse when updating information, there is nothing special about quantum state collapse. I believe the problem comes from the unitary evolution part of quantum theory...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	71,257
2407.21497	Mitral Regurgitation Recognition based on Unsupervised Out-of-Distribution Detection with Residual Diffusion Amplification	Mitral regurgitation (MR) is a serious heart valve disease. Early and accurate diagnosis of MR via ultrasound video is critical for timely clinical decision-making and surgical intervention. However, manual MR diagnosis heavily relies on the operator's experience, which may cause misdiagnosis and inter-observer variabi...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	477,563
cs/0607029	A Coding Theorem Characterizing Renyi's Entropy through Variable-to-Fixed Length Codes	This paper has been withdrawn	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	539,569
1610.00580	Flint Water Crisis: Data-Driven Risk Assessment Via Residential Water Testing	Recovery from the Flint Water Crisis has been hindered by uncertainty in both the water testing process and the causes of contamination. In this work, we develop an ensemble of predictive models to assess the risk of lead contamination in individual homes and neighborhoods. To train these models, we utilize a wide rang...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	61,852
2011.04267	A Broad Dataset is All You Need for One-Shot Object Detection	Is it possible to detect arbitrary objects from a single example? A central problem of all existing attempts at one-shot object detection is the generalization gap: Object categories used during training are detected much more reliably than novel ones. We here show that this generalization gap can be nearly closed by i...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	205,532
2208.06953	Any strongly controllable group system or group shift or any linear block code is isomorphic to a generator group	Consider any sequence of finite groups $A^t$, where $t$ takes values in an integer index set $\mathbf{Z}$. A group system $A$ is a set of sequences with components in $A^t$ that forms a group under componentwise addition in $A^t$, for each $t\in\mathbf{Z}$. As shown previously, any strongly controllable complete group ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	312,878
2209.09120	A Closer Look at Novel Class Discovery from the Labeled Set	Novel class discovery (NCD) aims to infer novel categories in an unlabeled dataset leveraging prior knowledge of a labeled set comprising disjoint but related classes. Existing research focuses primarily on utilizing the labeled set at the methodological level, with less emphasis on the analysis of the labeled set itse...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	318,393
2005.02934	Learning Adaptive Exploration Strategies in Dynamic Environments Through Informed Policy Regularization	We study the problem of learning exploration-exploitation strategies that effectively adapt to dynamic environments, where the task may change over time. While RNN-based policies could in principle represent such strategies, in practice their training time is prohibitive and the learning process often converges to poor...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	176,010
2109.03783	Egocentric View Hand Action Recognition by Leveraging Hand Surface and Hand Grasp Type	We introduce a multi-stage framework that uses mean curvature on a hand surface and focuses on learning interaction between hand and object by analyzing hand grasp type for hand action recognition in egocentric videos. The proposed method does not require 3D information of objects including 6D object poses which are di...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	254,166
1306.0816	A Critical Assessment of Cost-Based Nash Methods for Demand Scheduling in Smart Grids	Demand-side management (DSM) is becoming an increasingly important component of the envisioned smart grid. The ability to improve the efficiency of energy use in the power system by altering demand is widely viewed as being not merely promising but in fact essential. However, while the advantages of DSM are clear, arri...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	24,992
2412.10717	HITgram: A Platform for Experimenting with n-gram Language Models	Large language models (LLMs) are powerful but resource intensive, limiting accessibility. HITgram addresses this gap by offering a lightweight platform for n-gram model experimentation, ideal for resource-constrained environments. It supports unigrams to 4-grams and incorporates features like context sensitive weightin...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	517,071
2412.03710	CIKAN: Constraint Informed Kolmogorov-Arnold Networks for Autonomous Spacecraft Rendezvous using Time Shift Governor	The paper considers a Constrained-Informed Neural Network (CINN) approximation for the Time Shift Governor (TSG), which is an add-on scheme to the nominal closed-loop system used to enforce constraints by time-shifting the reference trajectory in spacecraft rendezvous applications. We incorporate Kolmogorov-Arnold Netw...	false	false	false	false	true	false	true	false	false	false	true	false	false	false	false	false	false	false	514,072
1510.05879	What's the point? Frame-wise Pointing Gesture Recognition with Latent-Dynamic Conditional Random Fields	We use Latent-Dynamic Conditional Random Fields to perform skeleton-based pointing gesture classification at each time instance of a video sequence, where we achieve a frame-wise pointing accuracy of roughly 83%. Subsequently, we determine continuous time sequences of arbitrary length that form individual pointing gest...	true	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	48,061
1702.02265	Neural Machine Translation with Source-Side Latent Graph Parsing	This paper presents a novel neural machine translation model which jointly learns translation and source-side latent graph representations of sentences. Unlike existing pipelined approaches using syntactic parsers, our end-to-end model learns a latent graph parser as part of the encoder of an attention-based neural mac...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	67,952
1905.11046	Thresholding Bandit with Optimal Aggregate Regret	We consider the thresholding bandit problem, whose goal is to find arms of mean rewards above a given threshold $\theta$, with a fixed budget of $T$ trials. We introduce LSA, a new, simple and anytime algorithm that aims to minimize the aggregate regret (or the expected number of mis-classified arms). We prove that our...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	132,316
1911.04620	Identifying Hidden Buyers in Darknet Markets via Dirichlet Hawkes Process	The darknet markets are notorious black markets in cyberspace, which involve selling or brokering drugs, weapons, stolen credit cards, and other illicit goods. To combat illicit transactions in the cyberspace, it is important to analyze the behaviors of participants in darknet markets. Currently, many studies focus on ...	false	false	false	true	false	false	true	false	false	false	false	false	false	true	false	false	false	false	153,033
1601.07768	Effective Capacity of Retransmission Schemes - A Recurrence Relation Approach	We consider the effective capacity performance measure of persistent- and truncated-retransmission schemes that can involve any combination of multiple transmissions per packet, multiple communication modes, or multiple packet communication. We present a structured unified analytical approach, based on a random walk mo...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	51,464
1703.05298	Neural Networks for Beginners. A fast implementation in Matlab, Torch, TensorFlow	This report provides an introduction to some Machine Learning tools within the most common development environments. It mainly focuses on practical problems, skipping any theoretical introduction. It is oriented to both students trying to approach Machine Learning and experts looking for new frameworks.	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	true	70,053
1603.06652	Tangles and the Mona Lisa	We show how an image can, in principle, be described by the tangles of the graph of its pixels. The tangle-tree theorem provides a nested set of separations that efficiently distinguish all the distinguishable tangles in a graph. This translates to a small data set from which the image can be reconstructed. The tan...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	53,520
1801.09238	Performance Analysis of Robust Stable PID Controllers Using Dominant Pole Placement for SOPTD Process Models	This paper derives new formulations for designing dominant pole placement based proportional-integral-derivative (PID) controllers to handle second order processes with time delays (SOPTD). Previously, similar attempts have been made for pole placement in delay-free systems. The presence of the time delay term manifest...	false	false	false	false	false	false	false	false	false	false	true	true	false	false	false	false	false	false	89,067
1909.08961	Acoustic scene analysis with multi-head attention networks	Acoustic Scene Classification (ASC) is a challenging task, as a single scene may involve multiple events that contain complex sound patterns. For example, a cooking scene may contain several sound sources including silverware clinking, chopping, frying, etc. What complicates ASC more is that classes of different activi...	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	146,101
1902.08557	Skew Constacyclic and LCD Codes over $ \mathbb{F}_{q}+v \mathbb{F}_{q} $	The aim of this paper is to give conditions for the equivalency between skew constacyclic codes, skew cyclic codes and skew negacyclic codes defined over semi-local rings. Also, we provide construction and an enumeration of Euclidean and Hermitian skew LCD cyclic codes over $ \mathbb{F}_{p^{t}}+ v \mathbb{F}_{p^{t}} $....	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	122,217

Subsets and Splits

No community queries yet

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