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
2407.04503	When LLMs Play the Telephone Game: Cumulative Changes and Attractors in Iterated Cultural Transmissions	As large language models (LLMs) start interacting with each other and generating an increasing amount of text online, it becomes crucial to better understand how information is transformed as it passes from one LLM to the next. While significant research has examined individual LLM behaviors, existing studies have larg...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	true	false	false	false	470,584
2301.00984	Protein-Ligand Complex Generator & Drug Screening via Tiered Tensor Transform	The generation of small molecule candidate (ligand) binding poses in its target protein pocket is important for computer-aided drug discovery. Typical rigid-body docking methods ignore the pocket flexibility of protein, while the more accurate pose generation using molecular dynamics is hindered by slow protein dynamic...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	339,095
2307.12659	A Model for Every User and Budget: Label-Free and Personalized Mixed-Precision Quantization	Recent advancement in Automatic Speech Recognition (ASR) has produced large AI models, which become impractical for deployment in mobile devices. Model quantization is effective to produce compressed general-purpose models, however such models may only be deployed to a restricted sub-domain of interest. We show that AS...	false	false	true	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	381,337
1904.02021	Unsupervised Progressive Learning and the STAM Architecture	We first pose the Unsupervised Progressive Learning (UPL) problem: an online representation learning problem in which the learner observes a non-stationary and unlabeled data stream, learning a growing number of features that persist over time even though the data is not stored or replayed. To solve the UPL problem we ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	126,305
1401.3444	On the Qualitative Comparison of Decisions Having Positive and Negative Features	Making a decision is often a matter of listing and comparing positive and negative arguments. In such cases, the evaluation scale for decisions should be considered bipolar, that is, negative and positive values should be explicitly distinguished. That is what is done, for example, in Cumulative Prospect Theory. Howeve...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	29,855
2408.08185	Data-driven identification of latent port-Hamiltonian systems	Conventional physics-based modeling techniques involve high effort, e.g., time and expert knowledge, while data-driven methods often lack interpretability, structure, and sometimes reliability. To mitigate this, we present a data-driven system identification framework that derives models in the port-Hamiltonian (pH) fo...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	480,889
2403.19014	Thelxino\"e: Recognizing Human Emotions Using Pupillometry and Machine Learning	In this study, we present a method for emotion recognition in Virtual Reality (VR) using pupillometry. We analyze pupil diameter responses to both visual and auditory stimuli via a VR headset and focus on extracting key features in the time-domain, frequency-domain, and time-frequency domain from VR generated data. Our...	true	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	442,165
2410.00343	RRT-CBF Based Motion Planning	Control barrier functions (CBF) are widely explored to enforce the safety-critical constraints on nonlinear systems recently. There are many researchers incorporating the control barrier functions into path planning algorithms to find a safe path, but these methods involve huge computational complexity or unidirectiona...	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	493,326
2410.22392	CBAM-EfficientNetV2 for Histopathology Image Classification using Transfer Learning and Dual Attention Mechanisms	Breast cancer histopathology image classification is critical for early detection and improved patient outcomes. 1 This study introduces a novel approach leveraging EfficientNetV2 models, to improve feature extraction and focus on relevant tissue regions. The proposed models were evaluated on the BreakHis dataset acros...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	503,626
1705.10500	Exploiting Restricted Boltzmann Machines and Deep Belief Networks in Compressed Sensing	This paper proposes a CS scheme that exploits the representational power of restricted Boltzmann machines and deep learning architectures to model the prior distribution of the sparsity pattern of signals belonging to the same class. The determined probability distribution is then used in a maximum a posteriori (MAP) a...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	74,413
2307.16694	Investigating and Improving Latent Density Segmentation Models for Aleatoric Uncertainty Quantification in Medical Imaging	Data uncertainties, such as sensor noise, occlusions or limitations in the acquisition method can introduce irreducible ambiguities in images, which result in varying, yet plausible, semantic hypotheses. In Machine Learning, this ambiguity is commonly referred to as aleatoric uncertainty. In image segmentation, latent ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	382,700
1706.05814	Inactivation Decoding of LT and Raptor Codes: Analysis and Code Design	In this paper we analyze LT and Raptor codes under inactivation decoding. A first order analysis is introduced, which provides the expected number of inactivations for an LT code, as a function of the output distribution, the number of input symbols and the decoding overhead. The analysis is then extended to the calcul...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	75,581
1707.09916	Robust Private Information Retrieval on Coded Data	We consider the problem of designing PIR scheme on coded data when certain nodes are unresponsive. We provide the construction of $\nu$-robust PIR schemes that can tolerate up to $\nu$ unresponsive nodes. These schemes are adaptive and universally optimal in the sense of achieving (asymptotically) optimal download cost...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	78,110
1604.04660	Why Artificial Intelligence Needs a Task Theory --- And What It Might Look Like	The concept of "task" is at the core of artificial intelligence (AI): Tasks are used for training and evaluating AI systems, which are built in order to perform and automatize tasks we deem useful. In other fields of engineering theoretical foundations allow thorough evaluation of designs by methodical manipulation of ...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	54,679
2203.04236	A Complete Characterization of Linear Estimators for Offline Policy Evaluation	Offline policy evaluation is a fundamental statistical problem in reinforcement learning that involves estimating the value function of some decision-making policy given data collected by a potentially different policy. In order to tackle problems with complex, high-dimensional observations, there has been significant ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	284,396
2410.18301	LEO-based Positioning: Foundations, Signal Design, and Receiver Enhancements for 6G NTN	The integration of non-terrestrial networks (NTN) into 5G new radio (NR) has opened up the possibility of developing a new positioning infrastructure using NR signals from Low-Earth Orbit (LEO) satellites. LEO-based cellular positioning offers several advantages, such as a superior link budget, higher operating bandwid...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	501,827
2409.13083	FedAT: Federated Adversarial Training for Distributed Insider Threat Detection	Insider threats usually occur from within the workplace, where the attacker is an entity closely associated with the organization. The sequence of actions the entities take on the resources to which they have access rights allows us to identify the insiders. Insider Threat Detection (ITD) using Machine Learning (ML)-ba...	false	false	false	false	true	false	false	false	false	false	false	false	true	false	false	false	false	true	489,840
2402.09246	Who Plays First? Optimizing the Order of Play in Stackelberg Games with Many Robots	We consider the multi-agent spatial navigation problem of computing the socially optimal order of play, i.e., the sequence in which the agents commit to their decisions, and its associated equilibrium in an N-player Stackelberg trajectory game. We model this problem as a mixed-integer optimization problem over the spac...	false	false	false	false	true	false	false	true	false	false	true	false	false	false	false	false	false	false	429,433
1808.03550	Atmospheric turbulence mitigation for sequences with moving objects using recursive image fusion	This paper describes a new method for mitigating the effects of atmospheric distortion on observed sequences that include large moving objects. In order to provide accurate detail from objects behind the distorting layer, we solve the space-variant distortion problem using recursive image fusion based on the Dual Tree ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	104,951
1612.08333	Text Summarization using Deep Learning and Ridge Regression	We develop models and extract relevant features for automatic text summarization and investigate the performance of different models on the DUC 2001 dataset. Two different models were developed, one being a ridge regressor and the other one was a multi-layer perceptron. The hyperparameters were varied and their perform...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	66,058
2412.07883	On Faster Marginalization with Squared Circuits via Orthonormalization	Squared tensor networks (TNs) and their generalization as parameterized computational graphs -- squared circuits -- have been recently used as expressive distribution estimators in high dimensions. However, the squaring operation introduces additional complexity when marginalizing variables or computing the partition f...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	515,842
2203.12138	A Search-Based Framework for Automatic Generation of Testing Environments for Cyber-Physical Systems	Many modern cyber physical systems incorporate computer vision technologies, complex sensors and advanced control software, allowing them to interact with the environment autonomously. Testing such systems poses numerous challenges: not only should the system inputs be varied, but also the surrounding environment shoul...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	287,155
2105.11657	Dynamic Dual Sampling Module for Fine-Grained Semantic Segmentation	Representation of semantic context and local details is the essential issue for building modern semantic segmentation models. However, the interrelationship between semantic context and local details is not well explored in previous works. In this paper, we propose a Dynamic Dual Sampling Module (DDSM) to conduct dynam...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	236,778
2205.10955	Investigating classification learning curves for automatically generated and labelled plant images	In the context of supervised machine learning a learning curve describes how a model's performance on unseen data relates to the amount of samples used to train the model. In this paper we present a dataset of plant images with representatives of crops and weeds common to the Manitoba prairies at different growth stage...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	297,940
2409.11257	To What Extent do Open-loop and Feedback Nash Equilibria Diverge in General-Sum Linear Quadratic Dynamic Games?	Dynamic games offer a versatile framework for modeling the evolving interactions of strategic agents, whose steady-state behavior can be captured by the Nash equilibria of the games. Nash equilibria are often computed in feedback, with policies depending on the state at each time, or in open-loop, with policies dependi...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	489,078
2404.05447	Pansharpening of PRISMA products for archaeological prospection	Hyperspectral data recorded from satellite platforms are often ill-suited for geo-archaeological prospection due to low spatial resolution. The established potential of hyperspectral data from airborne sensors in identifying archaeological features has, on the other side, generated increased interest in enhancing hyper...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	445,090
2006.05575	Deep Learning-based Aerial Image Segmentation with Open Data for Disaster Impact Assessment	Satellite images are an extremely valuable resource in the aftermath of natural disasters such as hurricanes and tsunamis where they can be used for risk assessment and disaster management. In order to provide timely and actionable information for disaster response, in this paper a framework utilising segmentation neur...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	181,115
2411.13506	Bezier Reachable Polytopes: Efficient Certificates for Robust Motion Planning with Layered Architectures	Control architectures are often implemented in a layered fashion, combining independently designed blocks to achieve complex tasks. Providing guarantees for such hierarchical frameworks requires considering the capabilities and limitations of each layer and their interconnections at design time. To address this holisti...	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	509,803
2408.02683	Improving Machine Learning Based Sepsis Diagnosis Using Heart Rate Variability	The early and accurate diagnosis of sepsis is critical for enhancing patient outcomes. This study aims to use heart rate variability (HRV) features to develop an effective predictive model for sepsis detection. Critical HRV features are identified through feature engineering methods, including statistical bootstrapping...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	478,718
1210.4905	Latent Composite Likelihood Learning for the Structured Canonical Correlation Model	Latent variable models are used to estimate variables of interest quantities which are observable only up to some measurement error. In many studies, such variables are known but not precisely quantifiable (such as "job satisfaction" in social sciences and marketing, "analytical ability" in educational testing, or "inf...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	19,229
2203.05174	Assessing Phenotype Definitions for Algorithmic Fairness	Disease identification is a core, routine activity in observational health research. Cohorts impact downstream analyses, such as how a condition is characterized, how patient risk is defined, and what treatments are studied. It is thus critical to ensure that selected cohorts are representative of all patients, indepen...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	284,730
1705.00969	The Problem of Coincidence in A Theory of Temporal Multiple Recurrence	Logical theories have been developed which have allowed temporal reasoning about eventualities (a la Galton) such as states, processes, actions, events, processes and complex eventualities such as sequences and recurrences of other eventualities. This paper presents the problem of coincidence within the framework of a ...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	72,775
1907.02824	Visual Appearance Analysis of Forest Scenes for Monocular SLAM	Monocular simultaneous localisation and mapping (SLAM) is a cheap and energy efficient way to enable Unmanned Aerial Vehicles (UAVs) to safely navigate managed forests and gather data crucial for monitoring tree health. SLAM research, however, has mostly been conducted in structured human environments, and as such is p...	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	137,695
1205.0326	Performance Analysis of Decode-and-Forward Relaying in Gamma-Gamma Fading Channels	Decode-and-forward (DF) cooperative communication based on free space optical (FSO) links is studied in this letter. We analyze performance of the DF protocol in the FSO links following the Gamma-Gamma distribution. The cumulative distribution function (CDF) and probability density function (PDF) of a random variable c...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	15,761
2406.13725	Tree-Sliced Wasserstein Distance on a System of Lines	Sliced Wasserstein (SW) distance in Optimal Transport (OT) is widely used in various applications thanks to its statistical effectiveness and computational efficiency. On the other hand, Tree Wassenstein (TW) and Tree-sliced Wassenstein (TSW) are instances of OT for probability measures where its ground cost is a tree ...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	465,975
2108.06866	Receding Horizon Iterative Learning Control for Continuously Operated Systems	This paper presents an iterative learning control (ILC) scheme for continuously operated repetitive systems for which no initial condition reset exists. To accomplish this, we develop a lifted system representation that accounts for the effect of the initial conditions on dynamics and projects the dynamics over multipl...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	250,753
1907.02633	On the Convergence of Model Free Learning in Mean Field Games	Learning by experience in Multi-Agent Systems (MAS) is a difficult and exciting task, due to the lack of stationarity of the environment, whose dynamics evolves as the population learns. In order to design scalable algorithms for systems with a large population of interacting agents (e.g. swarms), this paper focuses on...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	137,646
2205.05015	Robust Optimization for Local Differential Privacy	We consider the setting of publishing data without leaking sensitive information. We do so in the framework of Robust Local Differential Privacy (RLDP). This ensures privacy for all distributions of the data in an uncertainty set. We formulate the problem of finding the optimal data release protocol as a robust optimiz...	false	false	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	295,813
2303.09645	Development of a Voice Controlled Robotic Arm	This paper describes a robotic arm with 5 degrees-of-freedom (DOF) which is controlled by human voice and has been developed in the Mechatronics Laboratory, CUET. This robotic arm is interfaced with a PC by serial communication (RS-232). Users' voice command is captured by a microphone, and this voice is processed by s...	false	false	true	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	352,125
2311.04664	Speech language models lack important brain-relevant semantics	Despite known differences between reading and listening in the brain, recent work has shown that text-based language models predict both text-evoked and speech-evoked brain activity to an impressive degree. This poses the question of what types of information language models truly predict in the brain. We investigate t...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	406,309
2207.01584	Classification of Alzheimer's Disease Using the Convolutional Neural Network (CNN) with Transfer Learning and Weighted Loss	Alzheimer's disease is a progressive neurodegenerative disorder that gradually deprives the patient of cognitive function and can end in death. With the advancement of technology today, it is possible to detect Alzheimer's disease through Magnetic Resonance Imaging (MRI) scans. So that MRI is the technique most often u...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	306,234
2303.06068	EEG Synthetic Data Generation Using Probabilistic Diffusion Models	Electroencephalography (EEG) plays a significant role in the Brain Computer Interface (BCI) domain, due to its non-invasive nature, low cost, and ease of use, making it a highly desirable option for widespread adoption by the general public. This technology is commonly used in conjunction with deep learning techniques,...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	350,687
2410.10409	SMART-TRACK: A Novel Kalman Filter-Guided Sensor Fusion For Robust UAV Object Tracking in Dynamic Environments	In the field of sensor fusion and state estimation for object detection and localization, ensuring accurate tracking in dynamic environments poses significant challenges. Traditional methods like the Kalman Filter (KF) often fail when measurements are intermittent, leading to rapid divergence in state estimations. To a...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	498,064
1910.06799	Federated Learning for Coalition Operations	Machine Learning in coalition settings requires combining insights available from data assets and knowledge repositories distributed across multiple coalition partners. In tactical environments, this requires sharing the assets, knowledge and models in a bandwidth-constrained environment, while staying in conformance w...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	149,449
1606.00313	Improved Regret Bounds for Oracle-Based Adversarial Contextual Bandits	We give an oracle-based algorithm for the adversarial contextual bandit problem, where either contexts are drawn i.i.d. or the sequence of contexts is known a priori, but where the losses are picked adversarially. Our algorithm is computationally efficient, assuming access to an offline optimization oracle, and enjoys ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	56,656
2412.04832	WRF-GS: Wireless Radiation Field Reconstruction with 3D Gaussian Splatting	Wireless channel modeling plays a pivotal role in designing, analyzing, and optimizing wireless communication systems. Nevertheless, developing an effective channel modeling approach has been a longstanding challenge. This issue has been escalated due to the denser network deployment, larger antenna arrays, and wider b...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	true	514,588
1608.02060	Weighted diffusion LMP algorithm for distributed estimation in non-uniform noise conditions	This letter presents an improved version of diffusion least mean ppower (LMP) algorithm for distributed estimation. Instead of sum of mean square errors, a weighted sum of mean square error is defined as the cost function for global and local cost functions of a network of sensors. The weight coefficients are updated b...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	59,505
2301.01764	UniHD at TSAR-2022 Shared Task: Is Compute All We Need for Lexical Simplification?	Previous state-of-the-art models for lexical simplification consist of complex pipelines with several components, each of which requires deep technical knowledge and fine-tuned interaction to achieve its full potential. As an alternative, we describe a frustratingly simple pipeline based on prompted GPT-3 responses, be...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	339,314
2008.03673	Feature Space Augmentation for Long-Tailed Data	Real-world data often follow a long-tailed distribution as the frequency of each class is typically different. For example, a dataset can have a large number of under-represented classes and a few classes with more than sufficient data. However, a model to represent the dataset is usually expected to have reasonably ho...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	190,990
2408.09583	Convolutional Conditional Neural Processes	Neural processes are a family of models which use neural networks to directly parametrise a map from data sets to predictions. Directly parametrising this map enables the use of expressive neural networks in small-data problems where neural networks would traditionally overfit. Neural processes can produce well-calibra...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	481,501
1902.07906	Wasserstein Adversarial Examples via Projected Sinkhorn Iterations	A rapidly growing area of work has studied the existence of adversarial examples, datapoints which have been perturbed to fool a classifier, but the vast majority of these works have focused primarily on threat models defined by $\ell_p$ norm-bounded perturbations. In this paper, we propose a new threat model for adver...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	122,090
2109.14213	On the One-sided Convergence of Adam-type Algorithms in Non-convex Non-concave Min-max Optimization	Adam-type methods, the extension of adaptive gradient methods, have shown great performance in the training of both supervised and unsupervised machine learning models. In particular, Adam-type optimizers have been widely used empirically as the default tool for training generative adversarial networks (GANs). On the t...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	257,900
1705.01721	Evolutionary learning of fire fighting strategies	The dynamic problem of enclosing an expanding fire can be modelled by a discrete variant in a grid graph. While the fire expands to all neighbouring cells in any time step, the fire fighter is allowed to block $c$ cells in the average outside the fire in the same time interval. It was shown that the success of the fire...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	72,879
2102.03551	Jointly Improving Language Understanding and Generation with Quality-Weighted Weak Supervision of Automatic Labeling	Neural natural language generation (NLG) and understanding (NLU) models are data-hungry and require massive amounts of annotated data to be competitive. Recent frameworks address this bottleneck with generative models that synthesize weak labels at scale, where a small amount of training labels are expert-curated and t...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	218,790
1704.03503	UC Merced Submission to the ActivityNet Challenge 2016	This notebook paper describes our system for the untrimmed classification task in the ActivityNet challenge 2016. We investigate multiple state-of-the-art approaches for action recognition in long, untrimmed videos. We exploit hand-crafted motion boundary histogram features as well feature activations from deep network...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	71,638
2212.12327	Linear features segmentation from aerial images	The rapid development of remote sensing technologies have gained significant attention due to their ability to accurately localize, classify, and segment objects from aerial images. These technologies are commonly used in unmanned aerial vehicles (UAVs) equipped with high-resolution cameras or sensors to capture data o...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	338,017
2202.10542	Cell-Free Massive MIMO with Finite Fronthaul Capacity: A Stochastic Geometry Perspective	In this work, we analyze the downlink performance of a cell-free massive multiple-input-multiple-output system with finite capacity fronthaul links between the centralized baseband units and the access point (APs). Conditioned on the user and AP locations, we first derive an achievable rate for a randomly selected user...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	281,553
2201.07289	Sparsification of Decomposable Submodular Functions	Submodular functions are at the core of many machine learning and data mining tasks. The underlying submodular functions for many of these tasks are decomposable, i.e., they are sum of several simple submodular functions. In many data intensive applications, however, the number of underlying submodular functions in the...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	275,983
2401.12873	Improving Machine Translation with Human Feedback: An Exploration of Quality Estimation as a Reward Model	Insufficient modeling of human preferences within the reward model is a major obstacle for leveraging human feedback to improve translation quality. Fortunately, quality estimation (QE), which predicts the quality of a given translation without reference, has achieved impressive alignment with human evaluations in the ...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	423,525
2012.11797	Time Series Domain Adaptation via Sparse Associative Structure Alignment	Domain adaptation on time series data is an important but challenging task. Most of the existing works in this area are based on the learning of the domain-invariant representation of the data with the help of restrictions like MMD. However, such extraction of the domain-invariant representation is a non-trivial task f...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	212,734
1903.04081	Redditors in Recovery: Text Mining Reddit to Investigate Transitions into Drug Addiction	Increasing rates of opioid drug abuse and heightened prevalence of online support communities underscore the necessity of employing data mining techniques to better understand drug addiction using these rapidly developing online resources. In this work, we obtain data from Reddit, an online collection of forums, to gat...	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	false	false	false	123,894
1709.02589	A geometric approach for learning compliant motions from demonstration	This paper proposes a method to learn from human demonstration compliant contact motions, which take advantage of interaction forces between workpieces to align them, even when contact force may occur from different directions on different instances of reproduction. To manage the uncertainty in unstructured conditions,...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	80,302
2201.05629	Zero-Shot Machine Unlearning	Modern privacy regulations grant citizens the right to be forgotten by products, services and companies. In case of machine learning (ML) applications, this necessitates deletion of data not only from storage archives but also from ML models. Due to an increasing need for regulatory compliance required for ML applicati...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	275,439
1607.02317	Power-Availability-Aware Cell Association for Energy-Harvesting Small-Cell Base Stations	Energy harvesting brings a key solution to the increasing energy bill and environmental concerns but, at the same time, the network availability may be deteriorated due to potential energy shortage. In this paper, we analyze the performance of off-grid small-cell base stations (scBS) with finite battery capacity and de...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	58,331
2410.09699	Honest AI: Fine-Tuning "Small" Language Models to Say "I Don't Know", and Reducing Hallucination in RAG	Hallucination is a key roadblock for applications of Large Language Models (LLMs), particularly for enterprise applications that are sensitive to information accuracy. To address this issue, two general approaches have been explored: Retrieval-Augmented Generation (RAG) to supply LLMs with updated information as contex...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	497,729
2206.03487	Formalization of the principles of brain Programming (Brain Principles Programming)	In the monograph "Strong artificial intelligence. On the Approaches to Superintelligence" contains an overview of general artificial intelligence (AGI). As an anthropomorphic research area, it includes Brain Principles Programming (BPP) -- the formalization of universal mechanisms (principles) of the brain work with in...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	301,305
1812.02335	Layer Flexible Adaptive Computational Time	Deep recurrent neural networks perform well on sequence data and are the model of choice. However, it is a daunting task to decide the structure of the networks, i.e. the number of layers, especially considering different computational needs of a sequence. We propose a layer flexible recurrent neural network with adapt...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	115,733
2009.12517	QuatRE: Relation-Aware Quaternions for Knowledge Graph Embeddings	We propose a simple yet effective embedding model to learn quaternion embeddings for entities and relations in knowledge graphs. Our model aims to enhance correlations between head and tail entities given a relation within the Quaternion space with Hamilton product. The model achieves this goal by further associating e...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	197,448
2406.13281	ECAFormer: Low-light Image Enhancement using Cross Attention	Low-light image enhancement (LLIE) is critical in computer vision. Existing LLIE methods often fail to discover the underlying relationships between different sub-components, causing the loss of complementary information between multiple modules and network layers, ultimately resulting in the loss of image details. To ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	465,786
2401.04345	RomniStereo: Recurrent Omnidirectional Stereo Matching	Omnidirectional stereo matching (OSM) is an essential and reliable means for $360^{\circ}$ depth sensing. However, following earlier works on conventional stereo matching, prior state-of-the-art (SOTA) methods rely on a 3D encoder-decoder block to regularize the cost volume, causing the whole system complicated and sub...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	420,400
1406.1224	XTQ: A Declarative Functional XML Query Language	Various query languages have been proposed to extract and restructure information in XML documents. These languages, usually claiming to be declarative, mainly consider the conjunctive relationships among data elements. In order to present the operations where the hierarchical and the disjunctive relationships need to ...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	true	33,612
2211.15328	A Survey on Conversational Search and Applications in Biomedicine	This paper aims to provide a radical rundown on Conversation Search (ConvSearch), an approach to enhance the information retrieval method where users engage in a dialogue for the information-seeking tasks. In this survey, we predominantly focused on the human interactive characteristics of the ConvSearch systems, highl...	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	333,203
2502.00003	Defending Compute Thresholds Against Legal Loopholes	Existing legal frameworks on AI rely on training compute thresholds as a proxy to identify potentially-dangerous AI models and trigger increased regulatory attention. In the United States, Section 4.2(a) of Executive Order 14110 instructs the Secretary of Commerce to require extensive reporting from developers of AI mo...	false	false	false	false	true	false	false	false	false	false	false	false	false	true	false	false	false	false	529,156
2308.00715	Automated COVID-19 CT Image Classification using Multi-head Channel Attention in Deep CNN	The rapid spread of COVID-19 has necessitated efficient and accurate diagnostic methods. Computed Tomography (CT) scan images have emerged as a valuable tool for detecting the disease. In this article, we present a novel deep learning approach for automated COVID-19 CT scan classification where a modified Xception mode...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	383,027
2305.16935	Gender Lost In Translation: How Bridging The Gap Between Languages Affects Gender Bias in Zero-Shot Multilingual Translation	Neural machine translation (NMT) models often suffer from gender biases that harm users and society at large. In this work, we explore how bridging the gap between languages for which parallel data is not available affects gender bias in multilingual NMT, specifically for zero-shot directions. We evaluate translation b...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	368,329
2012.09349	Dynamic Modeling and Real-time Management of a System of EV Fast-charging Stations	Demand for electric vehicles (EVs), and thus EV charging, has steadily increased over the last decade. However, there is limited fast-charging infrastructure in most parts of the world to support EV travel, especially long-distance trips. The goal of this study is to develop a stochastic dynamic simulation modeling fra...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	212,039
1801.07132	SecSens: Secure State Estimation with Application to Localization and Time Synchronization	Research evidence in Cyber-Physical Systems (CPS) shows that the introduced tight coupling of information technology with physical sensing and actuation leads to more vulnerability and security weaknesses. But, the traditional security protection mechanisms of CPS focus on data encryption while neglecting the sensors w...	false	false	false	false	false	false	false	true	false	false	true	false	true	false	false	false	false	false	88,727
2311.17126	Reason out Your Layout: Evoking the Layout Master from Large Language Models for Text-to-Image Synthesis	Recent advancements in text-to-image (T2I) generative models have shown remarkable capabilities in producing diverse and imaginative visuals based on text prompts. Despite the advancement, these diffusion models sometimes struggle to translate the semantic content from the text into images entirely. While conditioning ...	false	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	411,186
1912.11165	High Utility Interval-Based Sequences	Sequential pattern mining is an interesting research area with broad range of applications. Most prior research on sequential pattern mining has considered point-based data where events occur instantaneously. However, in many application domains, events persist over intervals of time of varying lengths. Furthermore, tr...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	158,494
2306.02841	CTRL: Connect Collaborative and Language Model for CTR Prediction	Traditional click-through rate (CTR) prediction models convert the tabular data into one-hot vectors and leverage the collaborative relations among features for inferring the user's preference over items. This modeling paradigm discards essential semantic information. Though some works like P5 and CTR-BERT have explore...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	371,076
2002.08907	Second-order Conditional Gradient Sliding	Constrained second-order convex optimization algorithms are the method of choice when a high accuracy solution to a problem is needed, due to their local quadratic convergence. These algorithms require the solution of a constrained quadratic subproblem at every iteration. We present the \emph{Second-Order Conditional G...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	164,897
2305.00767	RViDeformer: Efficient Raw Video Denoising Transformer with a Larger Benchmark Dataset	In recent years, raw video denoising has garnered increased attention due to the consistency with the imaging process and well-studied noise modeling in the raw domain. However, two problems still hinder the denoising performance. Firstly, there is no large dataset with realistic motions for supervised raw video denois...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	361,445
2103.16654	A Systematic Literature Review on Process-Aware Recommender Systems	Considering processes of a business in a recommender system is highly advantageous. Although most studies in the business process analysis domain are of descriptive and predictive nature, the feasibility of constructing a process-aware recommender system is assessed in a few works. One reason can be the lack of knowled...	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	false	227,654
2205.12923	Domain Adaptation for Object Detection using SE Adaptors and Center Loss	Despite growing interest in object detection, very few works address the extremely practical problem of cross-domain robustness especially for automative applications. In order to prevent drops in performance due to domain shift, we introduce an unsupervised domain adaptation method built on the foundation of faster-RC...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	298,750
1303.6088	Graphical Analysis of Social Group Dynamics	Identifying communities in social networks becomes an increasingly important research problem. Several methods for identifying such groups have been developed, however, qualitative analysis (taking into account the scale of the problem) still poses serious problems. This paper describes a tool for facilitating such an ...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	23,242
1905.01035	Secure Integration of Electric Vehicles with the Power Grid	This paper focuses on the secure integration of distributed energy resources (DERs), especially pluggable electric vehicles (EVs), with the power grid. We consider the vehicle-to-grid (V2G) system where EVs are connected to the power grid through an aggregator. In this paper, we propose a novel Cyber-Physical Anomaly D...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	129,626
2105.01755	Reinforcement Learning for Scalable Logic Optimization with Graph Neural Networks	Logic optimization is an NP-hard problem commonly approached through hand-engineered heuristics. We propose to combine graph convolutional networks with reinforcement learning and a novel, scalable node embedding method to learn which local transforms should be applied to the logic graph. We show that this method achie...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	233,611
1810.08747	Temporal Proximity induces Attributes Similarity	Users consume their favorite content in temporal proximity of consumption bundles according to their preferences and tastes. Thus, the underlying attributes of items implicitly match user preferences, however, current recommender systems largely ignore this fundamental driver in identifying matching items. In this work...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	110,904
2310.16368	Transformer-based Live Update Generation for Soccer Matches from Microblog Posts	It has been known to be difficult to generate adequate sports updates from a sequence of vast amounts of diverse live tweets, although the live sports viewing experience with tweets is gaining the popularity. In this paper, we focus on soccer matches and work on building a system to generate live updates for soccer mat...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	402,698
2110.09829	Towards Social Situation Awareness in Support Agents	Artificial agents that support people in their daily activities (e.g., virtual coaches and personal assistants) are increasingly prevalent. Since many daily activities are social in nature, support agents should understand a user's social situation to offer comprehensive support. However, there are no systematic approa...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	261,945
1509.06921	End-to-end delay in two hop relay MANETs with limited buffer	Despite lots of literature has been dedicated to researching the delay performance in two-hop relay (2HR) mobile ad hoc networks (MANETs), however, they usually assume the buffer size of each node is infinite, so these studies are not applicable to and thus may not reflect the real delay performance of a practical MANE...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	47,207
2412.06205	Applying Machine Learning Tools for Urban Resilience Against Floods	Floods are among the most prevalent and destructive natural disasters, often leading to severe social and economic impacts in urban areas due to the high concentration of assets and population density. In Iran, particularly in Tehran, recurring flood events underscore the urgent need for robust urban resilience strateg...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	515,148
2312.13327	In-Context Reinforcement Learning for Variable Action Spaces	Recently, it has been shown that transformers pre-trained on diverse datasets with multi-episode contexts can generalize to new reinforcement learning tasks in-context. A key limitation of previously proposed models is their reliance on a predefined action space size and structure. The introduction of a new action spac...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	417,282
2005.05957	Flowtron: an Autoregressive Flow-based Generative Network for Text-to-Speech Synthesis	In this paper we propose Flowtron: an autoregressive flow-based generative network for text-to-speech synthesis with control over speech variation and style transfer. Flowtron borrows insights from IAF and revamps Tacotron in order to provide high-quality and expressive mel-spectrogram synthesis. Flowtron is optimized ...	false	false	true	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	176,879
1809.09925	Every Node Counts: Self-Ensembling Graph Convolutional Networks for Semi-Supervised Learning	Graph convolutional network (GCN) provides a powerful means for graph-based semi-supervised tasks. However, as a localized first-order approximation of spectral graph convolution, the classic GCN can not take full advantage of unlabeled data, especially when the unlabeled node is far from labeled ones. To capitalize on...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	108,792
2112.10101	ArcFace Knows the Gender, Too!	The main idea of this paper is that if a model can recognize a person, of course, it must be able to know the gender of that person, too. Therefore, instead of defining a new model for gender classification, this paper uses ArcFace features to determine gender, based on the facial features. A face image is given to Arc...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	272,346
2110.12925	CoProtector: Protect Open-Source Code against Unauthorized Training Usage with Data Poisoning	Github Copilot, trained on billions of lines of public code, has recently become the buzzword in the computer science research and practice community. Although it is designed to help developers implement safe and effective code with powerful intelligence, practitioners and researchers raise concerns about its ethical a...	false	false	false	false	true	false	false	false	false	false	false	false	true	false	false	false	false	true	263,015
2407.10683	Addressing Image Hallucination in Text-to-Image Generation through Factual Image Retrieval	Text-to-image generation has shown remarkable progress with the emergence of diffusion models. However, these models often generate factually inconsistent images, failing to accurately reflect the factual information and common sense conveyed by the input text prompts. We refer to this issue as Image hallucination. Dra...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	473,078
2311.01138	AeroPath: An airway segmentation benchmark dataset with challenging pathology	To improve the prognosis of patients suffering from pulmonary diseases, such as lung cancer, early diagnosis and treatment are crucial. The analysis of CT images is invaluable for diagnosis, whereas high quality segmentation of the airway tree are required for intervention planning and live guidance during bronchoscopy...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	404,928
1911.04250	Methods for Stabilizing Models across Large Samples of Projects (with case studies on Predicting Defect and Project Health)	Despite decades of research, SE lacks widely accepted models (that offer precise quantitative stable predictions) about what factors most influence software quality. This paper provides a promising result showing such stable models can be generated using a new transfer learning framework called "STABILIZER". Given a tr...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	152,940

Subsets and Splits

No community queries yet

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