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
1310.6511	Simultaneous Information and Energy Transfer in Large-Scale Networks with/without Relaying	Energy harvesting (EH) from ambient radio-frequency (RF) electromagnetic waves is an efficient solution for fully autonomous and sustainable communication networks. Most of the related works presented in the literature are based on specific (and small-scale) network structures, which although give useful insights on th...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	27,971
1409.0797	Feature Engineering for Map Matching of Low-Sampling-Rate GPS Trajectories in Road Network	Map matching of GPS trajectories from a sequence of noisy observations serves the purpose of recovering the original routes in a road network. In this work in progress, we attempt to share our experience of feature construction in a spatial database by reporting our ongoing experiment of feature extrac-tion in Conditio...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	35,764
2210.15235	SSD: Towards Better Text-Image Consistency Metric in Text-to-Image Generation	Generating consistent and high-quality images from given texts is essential for visual-language understanding. Although impressive results have been achieved in generating high-quality images, text-image consistency is still a major concern in existing GAN-based methods. Particularly, the most popular metric $R$-precis...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	326,871
2501.08471	Benchmarking Classical, Deep, and Generative Models for Human Activity Recognition	Human Activity Recognition (HAR) has gained significant importance with the growing use of sensor-equipped devices and large datasets. This paper evaluates the performance of three categories of models : classical machine learning, deep learning architectures, and Restricted Boltzmann Machines (RBMs) using five key ben...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	524,777
1608.01793	Sparse Subspace Clustering via Diffusion Process	Subspace clustering refers to the problem of clustering high-dimensional data that lie in a union of low-dimensional subspaces. State-of-the-art subspace clustering methods are based on the idea of expressing each data point as a linear combination of other data points while regularizing the matrix of coefficients with...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	59,467
2104.07919	An expressiveness hierarchy of Behavior Trees and related architectures	In this paper we provide a formal framework for comparing the expressive power of Behavior Trees (BTs) to other action selection architectures. Taking inspiration from the analogous comparisons of structural programming methodologies, we formalise the concept of `expressiveness'. This leads us to an expressiveness hier...	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	false	230,599
2210.11173	Mathematical Justification of Hard Negative Mining via Isometric Approximation Theorem	In deep metric learning, the Triplet Loss has emerged as a popular method to learn many computer vision and natural language processing tasks such as facial recognition, object detection, and visual-semantic embeddings. One issue that plagues the Triplet Loss is network collapse, an undesirable phenomenon where the net...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	325,209
2502.12213	Spatiotemporal-aware Trend-Seasonality Decomposition Network for Traffic Flow Forecasting	Traffic prediction is critical for optimizing travel scheduling and enhancing public safety, yet the complex spatial and temporal dynamics within traffic data present significant challenges for accurate forecasting. In this paper, we introduce a novel model, the Spatiotemporal-aware Trend-Seasonality Decomposition Netw...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	534,759
2403.14796	Planning and Acting While the Clock Ticks	Standard temporal planning assumes that planning takes place offline and then execution starts at time 0. Recently, situated temporal planning was introduced, where planning starts at time 0 and execution occurs after planning terminates. Situated temporal planning reflects a more realistic scenario where time passes d...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	440,259
2306.08147	Multi-market Energy Optimization with Renewables via Reinforcement Learning	This paper introduces a deep reinforcement learning (RL) framework for optimizing the operations of power plants pairing renewable energy with storage. The objective is to maximize revenue from energy markets while minimizing storage degradation costs and renewable curtailment. The framework handles complexities such a...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	373,295
1410.4445	Patterns in the English Language: Phonological Networks, Percolation and Assembly Models	In this paper we provide a quantitative framework for the study of phonological networks (PNs) for the English language by carrying out principled comparisons to null models, either based on site percolation, randomization techniques, or network growth models. In contrast to previous work, we mainly focus on null model...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	36,807
1911.09281	Event Detection in Noisy Streaming Data with Combination of Corroborative and Probabilistic Sources	Global physical event detection has traditionally relied on dense coverage of physical sensors around the world; while this is an expensive undertaking, there have not been alternatives until recently. The ubiquity of social networks and human sensors in the field provides a tremendous amount of real-time, live data ab...	false	false	false	true	false	false	true	false	false	false	true	false	false	false	false	false	false	false	154,462
0911.0645	Bayes estimators for phylogenetic reconstruction	Tree reconstruction methods are often judged by their accuracy, measured by how close they get to the true tree. Yet most reconstruction methods like ML do not explicitly maximize this accuracy. To address this problem, we propose a Bayesian solution. Given tree samples, we propose finding the tree estimate which is cl...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	4,855
cond-mat/0602183	Nonlinear parametric model for Granger causality of time series	We generalize a previously proposed approach for nonlinear Granger causality of time series, based on radial basis function. The proposed model is not constrained to be additive in variables from the two time series and can approximate any function of these variables, still being suitable to evaluate causality. Usefuln...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	536,973
2207.04049	Learning Causal Effects on Hypergraphs	Hypergraphs provide an effective abstraction for modeling multi-way group interactions among nodes, where each hyperedge can connect any number of nodes. Different from most existing studies which leverage statistical dependencies, we study hypergraphs from the perspective of causality. Specifically, in this paper, we ...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	307,059
2108.08476	Proceedings of the 1st International Workshop on Adaptive Cyber Defense	The 1st International Workshop on Adaptive Cyber Defense was held as part of the 2021 International Joint Conference on Artificial Intelligence. This workshop was organized to share research that explores unique applications of Artificial Intelligence (AI) and Machine Learning (ML) as foundational capabilities for the ...	false	false	false	false	true	false	false	false	false	false	false	false	true	false	false	false	false	false	251,263
2312.16470	ReSynthDetect: A Fundus Anomaly Detection Network with Reconstruction and Synthetic Features	Detecting anomalies in fundus images through unsupervised methods is a challenging task due to the similarity between normal and abnormal tissues, as well as their indistinct boundaries. The current methods have limitations in accurately detecting subtle anomalies while avoiding false positives. To address these challe...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	418,393
2111.01035	A Unified View of cGANs with and without Classifiers	Conditional Generative Adversarial Networks (cGANs) are implicit generative models which allow to sample from class-conditional distributions. Existing cGANs are based on a wide range of different discriminator designs and training objectives. One popular design in earlier works is to include a classifier during traini...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	264,441
1704.03507	Unsupervised Learning of Parsimonious General-Purpose Embeddings for User and Location Modelling	Many social network applications depend on robust representations of spatio-temporal data. In this work, we present an embedding model based on feed-forward neural networks which transforms social media check-ins into dense feature vectors encoding geographic, temporal, and functional aspects for modelling places, neig...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	71,639
2402.17595	Implicit Regularization via Spectral Neural Networks and Non-linear Matrix Sensing	The phenomenon of implicit regularization has attracted interest in recent years as a fundamental aspect of the remarkable generalizing ability of neural networks. In a nutshell, it entails that gradient descent dynamics in many neural nets, even without any explicit regularizer in the loss function, converges to the s...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	true	false	false	433,063
1206.3602	Robust and Efficient Distributed Compression for Cloud Radio Access Networks	This work studies distributed compression for the uplink of a cloud radio access network where multiple multi-antenna base stations (BSs) are connected to a central unit, also referred to as cloud decoder, via capacity-constrained backhaul links. Since the signals received at different BSs are correlated, distributed s...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	16,580
2110.09764	Detecting Blurred Ground-based Sky/Cloud Images	Ground-based whole sky imagers (WSIs) are being used by researchers in various fields to study the atmospheric events. These ground-based sky cameras capture visible-light images of the sky at regular intervals of time. Owing to the atmospheric interference and camera sensor noise, the captured images often exhibit noi...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	261,915
1810.10151	AUNet: Attention-guided dense-upsampling networks for breast mass segmentation in whole mammograms	Mammography is one of the most commonly applied tools for early breast cancer screening. Automatic segmentation of breast masses in mammograms is essential but challenging due to the low signal-to-noise ratio and the wide variety of mass shapes and sizes. Existing methods deal with these challenges mainly by extracting...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	111,214
1904.06064	AI-IMU Dead-Reckoning	In this paper we propose a novel accurate method for dead-reckoning of wheeled vehicles based only on an Inertial Measurement Unit (IMU). In the context of intelligent vehicles, robust and accurate dead-reckoning based on the IMU may prove useful to correlate feeds from imaging sensors, to safely navigate through obstr...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	127,473
1510.01713	Chatter Avoidance in Delayed Feedback Attitude Control with MRP Shadow Set Switching	The chattering response at the MRP shadow set switching point for the controlled attitude dynamics of a rigid tumbling spacecraft using delayed state feedback control with MRPs is investigated, where the time delay is assumed to be in the measurement of the state. In addition, a strategy to reduce or completely avoid t...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	47,651
2309.01664	Fine-grained Affective Processing Capabilities Emerging from Large Language Models	Large language models, in particular generative pre-trained transformers (GPTs), show impressive results on a wide variety of language-related tasks. In this paper, we explore ChatGPT's zero-shot ability to perform affective computing tasks using prompting alone. We show that ChatGPT a) performs meaningful sentiment an...	true	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	389,761
2411.15046	On Feasible Rewards in Multi-Agent Inverse Reinforcement Learning	In multi-agent systems, agent behavior is driven by utility functions that encapsulate their individual goals and interactions. Inverse Reinforcement Learning (IRL) seeks to uncover these utilities by analyzing expert behavior, offering insights into the underlying decision-making processes. However, multi-agent settin...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	510,419
2306.01148	Addressing Discrepancies in Semantic and Visual Alignment in Neural Networks	For the task of image classification, neural networks primarily rely on visual patterns. In robust networks, we would expect for visually similar classes to be represented similarly. We consider the problem of when semantically similar classes are visually dissimilar, and when visual similarity is present among non-sim...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	370,312
1608.08305	Utilizing Large Scale Vision and Text Datasets for Image Segmentation from Referring Expressions	Image segmentation from referring expressions is a joint vision and language modeling task, where the input is an image and a textual expression describing a particular region in the image; and the goal is to localize and segment the specific image region based on the given expression. One major difficulty to train suc...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	60,341
1908.08609	Song Hit Prediction: Predicting Billboard Hits Using Spotify Data	In this work, we attempt to solve the Hit Song Science problem, which aims to predict which songs will become chart-topping hits. We constructed a dataset with approximately 1.8 million hit and non-hit songs and extracted their audio features using the Spotify Web API. We test four models on our dataset. Our best model...	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	false	142,598
2304.08186	Human Pose Estimation in Monocular Omnidirectional Top-View Images	Human pose estimation (HPE) with convolutional neural networks (CNNs) for indoor monitoring is one of the major challenges in computer vision. In contrast to HPE in perspective views, an indoor monitoring system can consist of an omnidirectional camera with a field of view of 180{\deg} to detect the pose of a person wi...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	358,622
1803.04354	Topical Community Detection in Event-based Social Network	Event-based services have recently witnessed a rapid growth driving the way people explore and share information of interest. They host a huge amount of users' activities including explicit RSVP, shared photos, comments and social connections. Exploiting these activities to detect communities of similar users is a chal...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	true	92,443
2409.04104	MixNet: Joining Force of Classical and Modern Approaches Toward the Comprehensive Pipeline in Motor Imagery EEG Classification	Recent advances in deep learning (DL) have significantly impacted motor imagery (MI)-based brain-computer interface (BCI) systems, enhancing the decoding of electroencephalography (EEG) signals. However, most studies struggle to identify discriminative patterns across subjects during MI tasks, limiting MI classificatio...	true	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	486,293
2203.11268	Domain Knowledge Aids in Signal Disaggregation; the Example of the Cumulative Water Heater	In this article we present an unsupervised low-frequency method aimed at detecting and disaggregating the power used by Cumulative Water Heaters (CWH) in residential homes. Our model circumvents the inherent difficulty of unsupervised signal disaggregation by using both the shape of a power spike and its time of occurr...	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	286,852
2206.12449	OPERA: Harmonizing Task-Oriented Dialogs and Information Seeking Experience	Existing studies in conversational AI mostly treat task-oriented dialog (TOD) and question answering (QA) as separate tasks. Towards the goal of constructing a conversational agent that can complete user tasks and support information seeking, it is important to build a system that handles both TOD and QA with access to...	false	false	false	false	true	true	false	false	true	false	false	false	false	false	false	false	false	false	304,602
1212.2519	CLP(BN): Constraint Logic Programming for Probabilistic Knowledge	We present CLP(BN), a novel approach that aims at expressing Bayesian networks through the constraint logic programming framework. Arguably, an important limitation of traditional Bayesian networks is that they are propositional, and thus cannot represent relations between multiple similar objects in multiple contexts....	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	20,322
2402.17249	Deep Learning-Based Speech and Vision Synthesis to Improve Phishing Attack Detection through a Multi-layer Adaptive Framework	The ever-evolving ways attacker continues to im prove their phishing techniques to bypass existing state-of-the-art phishing detection methods pose a mountain of challenges to researchers in both industry and academia research due to the inability of current approaches to detect complex phishing attack. Thus, current a...	false	false	false	false	true	false	true	false	false	false	false	true	true	false	false	false	false	false	432,913
2407.00945	Efficient Expert Pruning for Sparse Mixture-of-Experts Language Models: Enhancing Performance and Reducing Inference Costs	The rapid advancement of large language models (LLMs) has led to architectures with billions to trillions of parameters, posing significant deployment challenges due to their substantial demands on memory, processing power, and energy consumption. Sparse Mixture-of-Experts (SMoE) architectures have emerged as a solutio...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	469,074
2205.13616	Towards A Proactive ML Approach for Detecting Backdoor Poison Samples	Adversaries can embed backdoors in deep learning models by introducing backdoor poison samples into training datasets. In this work, we investigate how to detect such poison samples to mitigate the threat of backdoor attacks. First, we uncover a post-hoc workflow underlying most prior work, where defenders passively al...	false	false	false	false	false	false	true	false	false	false	false	true	true	false	false	false	false	false	299,007
1912.02640	Cryptographically Strong Permutations from the Butterfly Structure	In this paper, we present infinite families of permutations of $\mathbb{F}_{2^{2n}}$ with high nonlinearity and boomerang uniformity $4$ from generalized butterfly structures. Both open and closed butterfly structures are considered. It appears, according to experiment results, that open butterflies do not produce pe...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	156,401
2208.01113	On the Evaluation of User Privacy in Deep Neural Networks using Timing Side Channel	Recent Deep Learning (DL) advancements in solving complex real-world tasks have led to its widespread adoption in practical applications. However, this opportunity comes with significant underlying risks, as many of these models rely on privacy-sensitive data for training in a variety of applications, making them an ov...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	311,068
1711.03398	Data Fusion and Machine Learning Integration for Transformer Loss of Life Estimation	Rapid growth of machine learning methodologies and their applications offer new opportunity for improved transformer asset management. Accordingly, power system operators are currently looking for data-driven methods to make better-informed decisions in terms of network management. In this paper, machine learning and d...	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	84,203
1809.10789	An Empirical Comparison of Syllabuses for Curriculum Learning	Syllabuses for curriculum learning have been developed on an ad-hoc, per task basis and little is known about the relative performance of different syllabuses. We identify a number of syllabuses used in the literature. We compare the identified syllabuses based on their effect on the speed of learning and generalizatio...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	108,981
2101.03134	Explainable Systematic Analysis for Synthetic Aperture Sonar Imagery	In this work, we present an in-depth and systematic analysis using tools such as local interpretable model-agnostic explanations (LIME) (arXiv:1602.04938) and divergence measures to analyze what changes lead to improvement in performance in fine tuned models for synthetic aperture sonar (SAS) data. We examine the sensi...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	214,825
2104.10788	Defining and Detecting Toxicity on Social Media: Context and Knowledge are Key	Online platforms have become an increasingly prominent means of communication. Despite the obvious benefits to the expanded distribution of content, the last decade has resulted in disturbing toxic communication, such as cyberbullying and harassment. Nevertheless, detecting online toxicity is challenging due to its mul...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	231,710
2312.11306	Human-machine cooperation: optimization of drug retrieval sequencing in automated drug dispensing systems	Automated drug dispensing systems (ADDSs) are increasingly in demand in today's pharmacies, primarily driven by the growing ageing population. Recognizing the practical challenges faced by pharmacies implementing ADDSs, this study aims to optimize the layout design and sequencing issues within a human-machine cooperati...	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	416,509
2112.13724	Double Critic Deep Reinforcement Learning for Mapless 3D Navigation of Unmanned Aerial Vehicles	This paper presents a novel deep reinforcement learning-based system for 3D mapless navigation for Unmanned Aerial Vehicles (UAVs). Instead of using a image-based sensing approach, we propose a simple learning system that uses only a few sparse range data from a distance sensor to train a learning agent. We based our a...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	273,337
1812.08434	Graph Neural Networks: A Review of Methods and Applications	Lots of learning tasks require dealing with graph data which contains rich relation information among elements. Modeling physics systems, learning molecular fingerprints, predicting protein interface, and classifying diseases demand a model to learn from graph inputs. In other domains such as learning from non-structur...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	117,001
2310.18946	Video Frame Interpolation with Many-to-many Splatting and Spatial Selective Refinement	In this work, we first propose a fully differentiable Many-to-Many (M2M) splatting framework to interpolate frames efficiently. Given a frame pair, we estimate multiple bidirectional flows to directly forward warp the pixels to the desired time step before fusing overlapping pixels. In doing so, each source pixel rende...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	403,782
2210.09540	Contact-Implicit Planning and Control for Non-Prehensile Manipulation Using State-Triggered Constraints	We present a contact-implicit planning approach that can generate contact-interaction trajectories for non-prehensile manipulation problems without tuning or a tailored initial guess and with high success rates. This is achieved by leveraging the concept of state-triggered constraints (STCs) to capture the hybrid dynam...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	324,575
2408.08145	Model-based Workflow for the Automated Generation of PDDL Descriptions	Manually creating Planning Domain Definition Language (PDDL) descriptions is difficult, error-prone, and requires extensive expert knowledge. However, this knowledge is already embedded in engineering models and can be reused. Therefore, this contribution presents a comprehensive workflow for the automated generation o...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	480,877
2110.05477	Predicting the spread of COVID-19 in Delhi, India using Deep Residual Recurrent Neural Networks	Detecting the spread of coronavirus will go a long way toward reducing human and economic loss. Unfortunately, existing Epidemiological models used for COVID 19 prediction models are too slow and fail to capture the COVID-19 development in detail. This research uses Partial Differential Equations to improve the process...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	260,293
2008.10449	Data Dissemination Using Interest Tree in Socially Aware Networking	Socially aware networking (SAN) exploits social characteristics of mobile users to streamline data dissemination protocols in opportunistic environments. Existing protocols in this area utilized various social features such as user interests, social similarity, and community structure to improve the performance of data...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	true	193,005
2208.02252	GROWN+UP: A Graph Representation Of a Webpage Network Utilizing Pre-training	Large pre-trained neural networks are ubiquitous and critical to the success of many downstream tasks in natural language processing and computer vision. However, within the field of web information retrieval, there is a stark contrast in the lack of similarly flexible and powerful pre-trained models that can properly ...	false	false	false	true	true	true	true	false	false	false	false	false	false	false	false	false	false	false	311,419
1507.06106	The dynamic of information-driven coordination phenomena: a transfer entropy analysis	Data from social media are providing unprecedented opportunities to investigate the processes that rule the dynamics of collective social phenomena. Here, we consider an information theoretical approach to define and measure the temporal and structural signatures typical of collective social events as they arise and ga...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	45,362
2205.08588	Sampling with replacement vs Poisson sampling: a comparative study in optimal subsampling	Faced with massive data, subsampling is a commonly used technique to improve computational efficiency, and using nonuniform subsampling probabilities is an effective approach to improve estimation efficiency. For computational efficiency, subsampling is often implemented with replacement or through Poisson subsampling....	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	296,985
1902.01838	How to "DODGE" Complex Software Analytics?	Machine learning techniques applied to software engineering tasks can be improved by hyperparameter optimization, i.e., automatic tools that find good settings for a learner's control parameters. We show that such hyperparameter optimization can be unnecessarily slow, particularly when the optimizers waste time explo...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	true	false	true	120,742
2310.00816	Sharingan: A Transformer-based Architecture for Gaze Following	Gaze is a powerful form of non-verbal communication and social interaction that humans develop from an early age. As such, modeling this behavior is an important task that can benefit a broad set of application domains ranging from robotics to sociology. In particular, Gaze Following is defined as the prediction of the...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	396,160
2402.13496	HetTree: Heterogeneous Tree Graph Neural Network	The recent past has seen an increasing interest in Heterogeneous Graph Neural Networks (HGNNs) since many real-world graphs are heterogeneous in nature, from citation graphs to email graphs. However, existing methods ignore a tree hierarchy among metapaths, which is naturally constituted by different node types and rel...	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	false	false	431,273
0708.3936	Working and Assembly Modes of the Agile Eye	This paper deals with the in-depth kinematic analysis of a special spherical parallel wrist, called the Agile Eye. The Agile Eye is a three-legged spherical parallel robot with revolute joints in which all pairs of adjacent joint axes are orthogonal. Its most peculiar feature, demonstrated in this paper for the first t...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	610
1706.01740	Label-Dependencies Aware Recurrent Neural Networks	In the last few years, Recurrent Neural Networks (RNNs) have proved effective on several NLP tasks. Despite such great success, their ability to model \emph{sequence labeling} is still limited. This lead research toward solutions where RNNs are combined with models which already proved effective in this domain, such as...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	74,854
1802.09975	Mono-Camera 3D Multi-Object Tracking Using Deep Learning Detections and PMBM Filtering	Monocular cameras are one of the most commonly used sensors in the automotive industry for autonomous vehicles. One major drawback using a monocular camera is that it only makes observations in the two dimensional image plane and can not directly measure the distance to objects. In this paper, we aim at filling this ga...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	91,434
2403.12207	Synthetic Image Generation in Cyber Influence Operations: An Emergent Threat?	The evolution of artificial intelligence (AI) has catalyzed a transformation in digital content generation, with profound implications for cyber influence operations. This report delves into the potential and limitations of generative deep learning models, such as diffusion models, in fabricating convincing synthetic i...	false	false	false	false	true	false	false	false	false	false	false	true	false	true	false	false	false	false	439,077
2009.02400	The Area Under the ROC Curve as a Measure of Clustering Quality	The Area Under the the Receiver Operating Characteristics (ROC) Curve, referred to as AUC, is a well-known performance measure in the supervised learning domain. Due to its compelling features, it has been employed in a number of studies to evaluate and compare the performance of different classifiers. In this work, we...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	194,533
1512.04975	MIMO CDMA-based Optical SATCOMs: A New Solution	A new scheme for MIMO CDMA-based optical satellite communications (OSATCOMs) is presented. Three independent problems are described for up-link and down- link in terms of two distinguished optimization problems. At first, in up-link, Pulse-width optimization is proposed to reduce dispersions over fibers as the terrestr...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	50,179
2402.08753	Forecasting for Swap Regret for All Downstream Agents	We study the problem of making predictions so that downstream agents who best respond to them will be guaranteed diminishing swap regret, no matter what their utility functions are. It has been known since Foster and Vohra (1997) that agents who best-respond to calibrated forecasts have no swap regret. Unfortunately, t...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	429,218
1907.00168	The CUED's Grammatical Error Correction Systems for BEA-2019	We describe two entries from the Cambridge University Engineering Department to the BEA 2019 Shared Task on grammatical error correction. Our submission to the low-resource track is based on prior work on using finite state transducers together with strong neural language models. Our system for the restricted track is ...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	136,953
2310.06217	Federated Multi-Level Optimization over Decentralized Networks	Multi-level optimization has gained increasing attention in recent years, as it provides a powerful framework for solving complex optimization problems that arise in many fields, such as meta-learning, multi-player games, reinforcement learning, and nested composition optimization. In this paper, we study the problem o...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	398,473
1909.12561	Data-Driven Robust Stabilization with RobustDomain of Attraction Estimate for Nonlinear Discrete-Time Systems	Nonlinear robust control is pursued by overcoming the drawback of linear robust control that it ignores available information about existing nonlinearities and the resulting controllers may be too conservative, especially when the nonlinearities are significant. However, most existing nonlinear robust control approache...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	147,165
1810.01588	Interpreting Layered Neural Networks via Hierarchical Modular Representation	Interpreting the prediction mechanism of complex models is currently one of the most important tasks in the machine learning field, especially with layered neural networks, which have achieved high predictive performance with various practical data sets. To reveal the global structure of a trained neural network in an ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	109,430
2004.13371	3D Solid Spherical Bispectrum CNNs for Biomedical Texture Analysis	Locally Rotation Invariant (LRI) operators have shown great potential in biomedical texture analysis where patterns appear at random positions and orientations. LRI operators can be obtained by computing the responses to the discrete rotation of local descriptors, such as Local Binary Patterns (LBP) or the Scale Invari...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	174,534
2207.11670	Training Stronger Spiking Neural Networks with Biomimetic Adaptive Internal Association Neurons	As the third generation of neural networks, spiking neural networks (SNNs) are dedicated to exploring more insightful neural mechanisms to achieve near-biological intelligence. Intuitively, biomimetic mechanisms are crucial to understanding and improving SNNs. For example, the associative long-term potentiation (ALTP) ...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	true	false	false	309,729
2408.17354	Forget to Flourish: Leveraging Machine-Unlearning on Pretrained Language Models for Privacy Leakage	Fine-tuning large language models on private data for downstream applications poses significant privacy risks in potentially exposing sensitive information. Several popular community platforms now offer convenient distribution of a large variety of pre-trained models, allowing anyone to publish without rigorous verific...	false	false	false	false	true	false	true	false	false	false	false	false	true	false	false	false	false	false	484,674
cs/0701194	Menzerath-Altmann Law for Syntactic Structures in Ukrainian	In the paper, the definition of clause suitable for an automated processing of a Ukrainian text is proposed. The Menzerath-Altmann law is verified on the sentence level and the parameters for the dependences of the clause length counted in words and syllables on the sentence length counted in clauses are calculated for...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	540,120
2011.00594	Random Fourier Features based SLAM	This work is dedicated to simultaneous continuous-time trajectory estimation and mapping based on Gaussian Processes (GP). State-of-the-art GP-based models for Simultaneous Localization and Mapping (SLAM) are computationally efficient but can only be used with a restricted class of kernel functions. This paper provides...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	204,287
2407.15588	Unsupervised Robust Cross-Lingual Entity Alignment via Neighbor Triple Matching with Entity and Relation Texts	Cross-lingual entity alignment (EA) enables the integration of multiple knowledge graphs (KGs) across different languages, providing users with seamless access to diverse and comprehensive knowledge. Existing methods, mostly supervised, face challenges in obtaining labeled entity pairs. To address this, recent studies ...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	475,242
2307.06714	Asymptotic SEP Analysis and Optimization of Linear-Quantized Precoding in Massive MIMO Systems	A promising approach to deal with the high hardware cost and energy consumption of massive MIMO transmitters is to use low-resolution digital-to-analog converters (DACs) at each antenna element. This leads to a transmission scheme where the transmitted signals are restricted to a finite set of voltage levels. This pape...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	379,158
2303.13262	Noise impact on recurrent neural network with linear activation function	In recent years, more and more researchers in the field of neural networks are interested in creating hardware implementations where neurons and the connection between them are realized physically. The physical implementation of ANN fundamentally changes the features of noise influence. In the case hardware ANNs, there...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	353,601
1809.01341	Embedding Multimodal Relational Data for Knowledge Base Completion	Representing entities and relations in an embedding space is a well-studied approach for machine learning on relational data. Existing approaches, however, primarily focus on simple link structure between a finite set of entities, ignoring the variety of data types that are often used in knowledge bases, such as text, ...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	106,780
2405.18237	Unveiling the Cycloid Trajectory of EM Iterations in Mixed Linear Regression	We study the trajectory of iterations and the convergence rates of the Expectation-Maximization (EM) algorithm for two-component Mixed Linear Regression (2MLR). The fundamental goal of MLR is to learn the regression models from unlabeled observations. The EM algorithm finds extensive applications in solving the mixture...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	458,333
2412.15948	Trust Calibration in IDEs: Paving the Way for Widespread Adoption of AI Refactoring	In the software industry, the drive to add new features often overshadows the need to improve existing code. Large Language Models (LLMs) offer a new approach to improving codebases at an unprecedented scale through AI-assisted refactoring. However, LLMs come with inherent risks such as braking changes and the introduc...	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	519,319
1912.08337	A Bivariate Dead Band Process Adjustment Policy	A bivariate extension to Box and Jenkins (1963) feedback adjustment problem is presented in this paper. The model balances the fixed cost of making an adjustment, which is assumed independent of the magnitude of the adjustments, with the cost of running the process off-target, which is assumed quadratic. It is also ass...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	157,807
2301.00792	The Undesirable Dependence on Frequency of Gender Bias Metrics Based on Word Embeddings	Numerous works use word embedding-based metrics to quantify societal biases and stereotypes in texts. Recent studies have found that word embeddings can capture semantic similarity but may be affected by word frequency. In this work we study the effect of frequency when measuring female vs. male gender bias with word e...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	339,024
2110.12114	Dense Dual-Attention Network for Light Field Image Super-Resolution	Light field (LF) images can be used to improve the performance of image super-resolution (SR) because both angular and spatial information is available. It is challenging to incorporate distinctive information from different views for LF image SR. Moreover, the long-term information from the previous layers can be weak...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	262,717
2407.01437	Needle in the Haystack for Memory Based Large Language Models	Current large language models (LLMs) often perform poorly on simple fact retrieval tasks. Here we investigate if coupling a dynamically adaptable external memory to a LLM can alleviate this problem. For this purpose, we test Larimar, a recently proposed language model architecture which uses an external associative mem...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	469,298
2111.00966	VPFNet: Voxel-Pixel Fusion Network for Multi-class 3D Object Detection	Many LiDAR-based methods for detecting large objects, single-class object detection, or under easy situations were claimed to perform quite well. However, their performances of detecting small objects or under hard situations did not surpass those of the fusion-based ones due to failure to leverage the image semantics....	false	false	false	false	true	false	true	true	false	false	false	true	false	false	false	false	false	false	264,410
2007.08556	InfoFocus: 3D Object Detection for Autonomous Driving with Dynamic Information Modeling	Real-time 3D object detection is crucial for autonomous cars. Achieving promising performance with high efficiency, voxel-based approaches have received considerable attention. However, previous methods model the input space with features extracted from equally divided sub-regions without considering that point cloud i...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	187,662
2110.04831	Feature Imitating Networks	In this paper, we introduce a novel approach to neural learning: the Feature-Imitating-Network (FIN). A FIN is a neural network with weights that are initialized to reliably approximate one or more closed-form statistical features, such as Shannon's entropy. In this paper, we demonstrate that FINs (and FIN ensembles) p...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	260,057
2311.12793	ShareGPT4V: Improving Large Multi-Modal Models with Better Captions	In the realm of large multi-modal models (LMMs), efficient modality alignment is crucial yet often constrained by the scarcity of high-quality image-text data. To address this bottleneck, we introduce the ShareGPT4V dataset, a pioneering large-scale resource featuring 1.2 million highly descriptive captions, which surp...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	409,472
2409.12789	Reinforcement Learning-based Model Predictive Control for Greenhouse Climate Control	Greenhouse climate control is concerned with maximizing performance in terms of crop yield and resource efficiency. One promising approach is model predictive control (MPC), which leverages a model of the system to optimize the control inputs, while enforcing physical constraints. However, prediction models for greenho...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	489,720
1606.06992	Smart Grid Security: Threats, Challenges, and Solutions	The cyber-physical nature of the smart grid has rendered it vulnerable to a multitude of attacks that can occur at its communication, networking, and physical entry points. Such cyber-physical attacks can have detrimental effects on the operation of the grid as exemplified by the recent attack which caused a blackout o...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	57,638
1805.10082	A Novel High-Rate Polar-Staircase Coding Scheme	The long-haul communication systems can offer ultra high-speed data transfer rates but suffer from burst errors. The high-rate and high-performance staircase codes provide an efficient way for long-haul transmission. The staircase coding scheme is a concatenation structure, which provides the opportunity to improve the...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	98,578
1911.03199	Wind Turbines Partial Load Power Regulation Using a Fast MPC Approach	In this paper, the highly acknowledged advantages of the Model Predictive Control (MPC) approach are utilized to regulate the wind turbines' output power in the partial load region. In this region, the purpose of the designed controller is to capture maximum power from the wind. When the wind speed is above rated wind ...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	152,575
2305.19872	Spectral Heterogeneous Graph Convolutions via Positive Noncommutative Polynomials	Heterogeneous Graph Neural Networks (HGNNs) have gained significant popularity in various heterogeneous graph learning tasks. However, most existing HGNNs rely on spatial domain-based methods to aggregate information, i.e., manually selected meta-paths or some heuristic modules, lacking theoretical guarantees. Furtherm...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	369,719
2409.18209	A Unified View on Learning Unnormalized Distributions via Noise-Contrastive Estimation	This paper studies a family of estimators based on noise-contrastive estimation (NCE) for learning unnormalized distributions. The main contribution of this work is to provide a unified perspective on various methods for learning unnormalized distributions, which have been independently proposed and studied in separate...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	492,142
1910.11459	A Robot's Expressive Language Affects Human Strategy and Perceptions in a Competitive Game	As robots are increasingly endowed with social and communicative capabilities, they will interact with humans in more settings, both collaborative and competitive. We explore human-robot relationships in the context of a competitive Stackelberg Security Game. We vary humanoid robot expressive language (in the form of "...	true	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	150,780
1203.3376	Learning, Social Intelligence and the Turing Test - why an "out-of-the-box" Turing Machine will not pass the Turing Test	The Turing Test (TT) checks for human intelligence, rather than any putative general intelligence. It involves repeated interaction requiring learning in the form of adaption to the human conversation partner. It is a macro-level post-hoc test in contrast to the definition of a Turing Machine (TM), which is a prior mic...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	14,905
2202.06197	Web-Based File Clustering and Indexing for Mindoro State University	The Web Based File Clustering and Indexing for Mindoro State University aim to organize data circulated over the Web into groups or collections to facilitate data availability and access and at the same time meet user preferences. The main benefits include increasing Web information accessibility, understanding users n...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	280,129
2010.00202	Heteroscedastic Bayesian Optimisation for Stochastic Model Predictive Control	Model predictive control (MPC) has been successful in applications involving the control of complex physical systems. This class of controllers leverages the information provided by an approximate model of the system's dynamics to simulate the effect of control actions. MPC methods also present a few hyper-parameters w...	false	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	198,218
1207.5542	LT Codes For Efficient and Reliable Distributed Storage Systems Revisited	LT codes and digital fountain techniques have received significant attention from both academics and industry in the past few years. There have also been extensive interests in applying LT code techniques to distributed storage systems such as cloud data storage in recent years. However, Plank and Thomason's experiment...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	17,728

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