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
2108.05876	An Early Look at the Gettr Social Network	This paper presents the first data-driven analysis of Gettr, a new social network platform launched by former US President Donald Trump's team. Among other things, we find that users on the platform heavily discuss politics, with a focus on the Trump campaign in the US and Bolsonaro's in Brazil. Activity on the platfor...	false	false	false	true	false	false	false	false	false	false	false	false	false	true	false	false	false	false	250,440
2302.09240	Beamforming and Phase Shift Design for HR-IRS-aided Directional Modulation Network with a Malicious Attacker	In this paper, we propose to use hybrid relay-intelligent reflecting surface (HR-IRS) to improve the security performance of directional modulation (DM) system. In particular, the eavesdropper in this system works in full-duplex (FD) mode and he will eavesdrop on the confidential message (CM) as well as send malicious ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	346,336
2501.04155	MM-GEN: Enhancing Task Performance Through Targeted Multimodal Data Curation	Vision-language models (VLMs) are highly effective but often underperform on specialized tasks; for example, Llava-1.5 struggles with chart and diagram understanding due to scarce task-specific training data. Existing training data, sourced from general-purpose datasets, fails to capture the nuanced details needed for ...	false	false	false	false	false	false	true	false	true	false	false	true	false	false	false	false	false	false	523,111
2106.13500	TableSense: Spreadsheet Table Detection with Convolutional Neural Networks	Spreadsheet table detection is the task of detecting all tables on a given sheet and locating their respective ranges. Automatic table detection is a key enabling technique and an initial step in spreadsheet data intelligence. However, the detection task is challenged by the diversity of table structures and table layo...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	243,095
2105.07961	Joint Optimization of Hadamard Sensing and Reconstruction in Compressed Sensing Fluorescence Microscopy	Compressed sensing fluorescence microscopy (CS-FM) proposes a scheme whereby less measurements are collected during sensing and reconstruction is performed to recover the image. Much work has gone into optimizing the sensing and reconstruction portions separately. We propose a method of jointly optimizing both sensing ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	235,612
2407.11353	Preconditioned Gradient Descent Finds Over-Parameterized Neural Networks with Sharp Generalization for Nonparametric Regression	We consider nonparametric regression by an over-parameterized two-layer neural network trained by gradient descent (GD) or its variant in this paper. We show that, if the neural network is trained with a novel Preconditioned Gradient Descent (PGD) with early stopping and the target function has spectral bias widely stu...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	473,408
2104.08540	DWUG: A large Resource of Diachronic Word Usage Graphs in Four Languages	Word meaning is notoriously difficult to capture, both synchronically and diachronically. In this paper, we describe the creation of the largest resource of graded contextualized, diachronic word meaning annotation in four different languages, based on 100,000 human semantic proximity judgments. We thoroughly describe ...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	230,842
1904.02390	Interaction-aware Multi-agent Tracking and Probabilistic Behavior Prediction via Adversarial Learning	In order to enable high-quality decision making and motion planning of intelligent systems such as robotics and autonomous vehicles, accurate probabilistic predictions for surrounding interactive objects is a crucial prerequisite. Although many research studies have been devoted to making predictions on a single entity...	false	false	false	false	true	false	true	true	false	false	false	false	false	false	false	false	false	false	126,419
2205.07043	Naturalistic Causal Probing for Morpho-Syntax	Probing has become a go-to methodology for interpreting and analyzing deep neural models in natural language processing. However, there is still a lack of understanding of the limitations and weaknesses of various types of probes. In this work, we suggest a strategy for input-level intervention on naturalistic sentence...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	296,455
2406.07455	Reinforcement Learning from Human Feedback without Reward Inference: Model-Free Algorithm and Instance-Dependent Analysis	In this paper, we study reinforcement learning from human feedback (RLHF) under an episodic Markov decision process with a general trajectory-wise reward model. We developed a model-free RLHF best policy identification algorithm, called $\mathsf{BSAD}$, without explicit reward model inference, which is a critical inter...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	463,054
2501.08795	Heat transfer simulation of window frames with SPHinXsys	Maintaining a comfortable temperature inside a building requires appropriate thermal insulation of windows, which can be optimised iteratively with numerical simulation. Smoothed particle hydrodynamics(SPH) is a fully Lagrangian method widely used for simulating multi-physics applications with high computational effici...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	524,898
1907.08049	Towards $k$-connectivity in Heterogeneous Sensor Networks under Pairwise Key Predistribution	We study the secure and reliable connectivity of wireless sensor networks under the heterogeneous pairwise key predistribution scheme. This scheme was recently introduced as an extension of the random pairwise key predistribution scheme of Chan et al. to accommodate networks where the constituent sensors have different...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	139,018
2003.11266	Auto-Ensemble: An Adaptive Learning Rate Scheduling based Deep Learning Model Ensembling	Ensembling deep learning models is a shortcut to promote its implementation in new scenarios, which can avoid tuning neural networks, losses and training algorithms from scratch. However, it is difficult to collect sufficient accurate and diverse models through once training. This paper proposes Auto-Ensemble (AE) to c...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	169,563
2111.14651	Multi-objective Explanations of GNN Predictions	Graph Neural Network (GNN) has achieved state-of-the-art performance in various high-stake prediction tasks, but multiple layers of aggregations on graphs with irregular structures make GNN a less interpretable model. Prior methods use simpler subgraphs to simulate the full model, or counterfactuals to identify the cau...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	268,662
2010.08365	Toward Accurate Person-level Action Recognition in Videos of Crowded Scenes	Detecting and recognizing human action in videos with crowded scenes is a challenging problem due to the complex environment and diversity events. Prior works always fail to deal with this problem in two aspects: (1) lacking utilizing information of the scenes; (2) lacking training data in the crowd and complex scenes....	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	201,161
2402.06064	Formalizing Automated Market Makers in the Lean 4 Theorem Prover	Automated Market Makers (AMMs) are an integral component of the decentralized finance (DeFi) ecosystem, as they allow users to exchange crypto-assets without the need for trusted authorities or external price oracles. Although these protocols are based on relatively simple mechanisms, e.g., to algorithmically determine...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	428,145
2405.10051	MarkLLM: An Open-Source Toolkit for LLM Watermarking	LLM watermarking, which embeds imperceptible yet algorithmically detectable signals in model outputs to identify LLM-generated text, has become crucial in mitigating the potential misuse of large language models. However, the abundance of LLM watermarking algorithms, their intricate mechanisms, and the complex evaluati...	false	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	454,633
1912.01629	A Simulation Model for Pedestrian Crowd Evacuation Based on Various AI Techniques	This paper attempts to design an intelligent simulation model for pedestrian crowd evacuation. For this purpose, the cellular automata(CA) was fully integrated with fuzzy logic, the kth nearest neighbors (KNN), and some statistical equations. In this model, each pedestrian was assigned a specific speed, according to hi...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	true	false	false	false	156,131
2003.09887	Evaluation of Parameterized Quantum Circuits: on the relation between classification accuracy, expressibility and entangling capability	An active area of investigation in the search for quantum advantage is Quantum Machine Learning. Quantum Machine Learning, and Parameterized Quantum Circuits in a hybrid quantum-classical setup in particular, could bring advancements in accuracy by utilizing the high dimensionality of the Hilbert space as feature space...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	169,173
1904.03855	Evolved embodied phase coordination enables robust quadruped robot locomotion	Overcoming robotics challenges in the real world requires resilient control systems capable of handling a multitude of environments and unforeseen events. Evolutionary optimization using simulations is a promising way to automatically design such control systems, however, if the disparity between simulation and the rea...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	126,861
2102.10557	Contrastive Self-supervised Neural Architecture Search	This paper proposes a novel cell-based neural architecture search algorithm (NAS), which completely alleviates the expensive costs of data labeling inherited from supervised learning. Our algorithm capitalizes on the effectiveness of self-supervised learning for image representations, which is an increasingly crucial t...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	221,141
1508.02428	FactorBase: SQL for Learning A Multi-Relational Graphical Model	We describe FactorBase, a new SQL-based framework that leverages a relational database management system to support multi-relational model discovery. A multi-relational statistical model provides an integrated analysis of the heterogeneous and interdependent data resources in the database. We adopt the BayesStore desig...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	true	false	45,900
2206.06722	Specification sketching for Linear Temporal Logic	Virtually all verification and synthesis techniques assume that the formal specifications are readily available, functionally correct, and fully match the engineer's understanding of the given system. However, this assumption is often unrealistic in practice: formalizing system requirements is notoriously difficult, er...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	302,476
2404.15213	Automatic Classification of Subjective Time Perception Using Multi-modal Physiological Data of Air Traffic Controllers	In high-pressure environments where human individuals must simultaneously monitor multiple entities, communicate effectively, and maintain intense focus, the perception of time becomes a critical factor influencing performance and well-being. One indicator of well-being can be the person's subjective time perception. I...	true	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	448,991
2003.13109	Scene-Aware Error Modeling of LiDAR/Visual Odometry for Fusion-based Vehicle Localization	Localization is an essential technique in mobile robotics. In a complex environment, it is necessary to fuse different localization modules to obtain more robust results, in which the error model plays a paramount role. However, exteroceptive sensor-based odometries (ESOs), such as LiDAR/visual odometry, often deliver ...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	170,110
2304.05174	Electricity Demand Forecasting with Hybrid Statistical and Machine Learning Algorithms: Case Study of Ukraine	This article presents a novel hybrid approach using statistics and machine learning to forecast the national demand of electricity. As investment and operation of future energy systems require long-term electricity demand forecasts with hourly resolution, our mathematical model fills a gap in energy forecasting. The pr...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	357,525
2207.05228	Uncertainty-Aware Online Merge Planning with Learned Driver Behavior	Safe and reliable autonomy solutions are a critical component of next-generation intelligent transportation systems. Autonomous vehicles in such systems must reason about complex and dynamic driving scenes in real time and anticipate the behavior of nearby drivers. Human driving behavior is highly nuanced and specific ...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	307,455
2104.11408	Neural Mean Discrepancy for Efficient Out-of-Distribution Detection	Various approaches have been proposed for out-of-distribution (OOD) detection by augmenting models, input examples, training sets, and optimization objectives. Deviating from existing work, we have a simple hypothesis that standard off-the-shelf models may already contain sufficient information about the training set d...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	231,899
2012.11761	Bounding the Complexity of Formally Verifying Neural Networks: A Geometric Approach	In this paper, we consider the computational complexity of formally verifying the behavior of Rectified Linear Unit (ReLU) Neural Networks (NNs), where verification entails determining whether the NN satisfies convex polytopic specifications. Specifically, we show that for two different NN architectures -- shallow NNs ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	212,718
2110.04704	3D Object Detection Combining Semantic and Geometric Features from Point Clouds	In this paper, we investigate the combination of voxel-based methods and point-based methods, and propose a novel end-to-end two-stage 3D object detector named SGNet for point clouds scenes. The voxel-based methods voxelize the scene to regular grids, which can be processed with the current advanced feature learning fr...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	260,005
1301.3867	Fast Planning in Stochastic Games	Stochastic games generalize Markov decision processes (MDPs) to a multiagent setting by allowing the state transitions to depend jointly on all player actions, and having rewards determined by multiplayer matrix games at each state. We consider the problem of computing Nash equilibria in stochastic games, the analogue ...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	21,179
1406.1547	Arbitrage-free exchange rate ensembles over a general trade network	It is assumed that under suitable economic and information-theoretic conditions, market exchange rates are free from arbitrage. Commodity markets in which trades occur over a complete graph are shown to be trivial. We therefore examine the vector space of no-arbitrage exchange rate ensembles over an arbitrary connected...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	33,645
2102.10513	CheckSoft : A Scalable Event-Driven Software Architecture for Keeping Track of People and Things in People-Centric Spaces	We present CheckSoft, a scalable event-driven software architecture for keeping track of people-object interactions in people-centric applications such as airport checkpoint security areas, automated retail stores, smart libraries, and so on. The architecture works off the video data generated in real time by a network...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	221,120
2208.14161	Identifiable Latent Causal Content for Domain Adaptation under Latent Covariate Shift	Multi-source domain adaptation (MSDA) addresses the challenge of learning a label prediction function for an unlabeled target domain by leveraging both the labeled data from multiple source domains and the unlabeled data from the target domain. Conventional MSDA approaches often rely on covariate shift or conditional s...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	315,239
2401.09763	CLIP Model for Images to Textual Prompts Based on Top-k Neighbors	Text-to-image synthesis, a subfield of multimodal generation, has gained significant attention in recent years. We propose a cost-effective approach for image-to-prompt generation that leverages generative models to generate textual prompts without the need for large amounts of annotated data. We divide our method into...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	422,376
2410.15623	Guardians of Discourse: Evaluating LLMs on Multilingual Offensive Language Detection	Identifying offensive language is essential for maintaining safety and sustainability in the social media era. Though large language models (LLMs) have demonstrated encouraging potential in social media analytics, they lack thorough evaluation when in offensive language detection, particularly in multilingual environme...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	500,642
2406.16496	Recent advancements on MPC for tracking: periodic and harmonic formulations	The main benefit of model predictive control (MPC) is its ability to steer the system to a given reference without violating the constraints while minimizing some objective. Furthermore, a suitably designed MPC controller guarantees asymptotic stability of the closed-loop system to the given reference as long as its op...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	467,153
1805.08707	A syllogistic system for propositions with intermediate quantifiers	This paper describes a formalism that subsumes Peterson's intermediate quantifier syllogistic system, and extends the ideas by van Eijck on Aristotle's logic. Syllogisms are expressed in a concise form making use of and extending the Monotonicity Calculus. Contradictory and contrary relationships are added so that dedu...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	true	98,231
2007.05169	Detecting Malicious Accounts in Permissionless Blockchains using Temporal Graph Properties	The temporal nature of modeling accounts as nodes and transactions as directed edges in a directed graph -- for a blockchain, enables us to understand the behavior (malicious or benign) of the accounts. Predictive classification of accounts as malicious or benign could help users of the permissionless blockchain platfo...	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	false	false	186,589
2407.01468	Active Shadowing (ASD): Manipulating Visual Perception of Robotics Behaviors via Implicit Communication	Explicit communication is often valued for its directness during interaction. Implicit communication, on the other hand, is indirect in that its communicative content must be inferred. Implicit communication is considered more desirable in teaming situations that requires reduced interruptions for improved fluency. In ...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	469,311
1910.00985	A Blueprint for Interoperable Blockchains	Research in blockchain systems has mainly focused on improving security and bridging the performance gaps between blockchains and databases. Despite many promising results, we observe a worrying trend that the blockchain landscape is fragmented in which many systems exist in silos. Apart from a handful of general-purpo...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	true	147,816
2406.11290	Iterative Utility Judgment Framework via LLMs Inspired by Relevance in Philosophy	Utility and topical relevance are critical measures in information retrieval (IR), reflecting system and user perspectives, respectively. While topical relevance has long been emphasized, utility is a higher standard of relevance and is more useful for facilitating downstream tasks, e.g., in Retrieval-Augmented Generat...	false	false	false	false	true	true	true	false	true	false	false	false	false	false	false	false	false	false	464,825
1812.00602	Examining Deep Learning Architectures for Crime Classification and Prediction	In this paper, a detailed study on crime classification and prediction using deep learning architectures is presented. We examine the effectiveness of deep learning algorithms on this domain and provide recommendations for designing and training deep learning systems for predicting crime areas, using open data from pol...	false	false	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	115,310
2404.05966	THOUGHTSCULPT: Reasoning with Intermediate Revision and Search	We present THOUGHTSCULPT, a general reasoning and search method for tasks with outputs that can be decomposed into components. THOUGHTSCULPT explores a search tree of potential solutions using Monte Carlo Tree Search (MCTS), building solutions one action at a time and evaluating according to any domain-specific heurist...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	445,278
2005.11106	On the suitability of generalized regression neural networks for GNSS position time series prediction for geodetic applications in geodesy and geophysics	In this paper, the generalized regression neural network is used to predict the GNSS position time series. Using the IGS 24-hour final solution data for Bad Hamburg permanent GNSS station in Germany, it is shown that the larger the training of the network, the higher the accuracy is, regardless of the time span of the ...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	178,388
2402.02552	Neur2BiLO: Neural Bilevel Optimization	Bilevel optimization deals with nested problems in which a leader takes the first decision to minimize their objective function while accounting for a follower's best-response reaction. Constrained bilevel problems with integer variables are particularly notorious for their hardness. While exact solvers have been propo...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	426,616
2404.15786	Rethinking Model Prototyping through the MedMNIST+ Dataset Collection	The integration of deep learning based systems in clinical practice is often impeded by challenges rooted in limited and heterogeneous medical datasets. In addition, prioritization of marginal performance improvements on a few, narrowly scoped benchmarks over clinical applicability has slowed down meaningful algorithmi...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	449,239
2203.05842	Multiple Inputs Neural Networks for Medicare fraud Detection	Medicare fraud results in considerable losses for governments and insurance companies and results in higher premiums from clients. Medicare fraud costs around 13 billion euros in Europe and between 21 billion and 71 billion US dollars per year in the United States. This study aims to use artificial neural network based...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	284,933
2209.04924	Meta-Reinforcement Learning via Language Instructions	Although deep reinforcement learning has recently been very successful at learning complex behaviors, it requires a tremendous amount of data to learn a task. One of the fundamental reasons causing this limitation lies in the nature of the trial-and-error learning paradigm of reinforcement learning, where the agent com...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	316,925
1909.09565	Automatic Table completion using Knowledge Base	Table is a popular data format to organize and present relational information. Users often have to manually compose tables when gathering their desiderate information (e.g., entities and their attributes) for decision making. In this work, we propose to resolve a new type of heterogeneous query viz: tabular query, whic...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	146,293
2407.04168	Learning Interpretable Differentiable Logic Networks	The ubiquity of neural networks (NNs) in real-world applications, from healthcare to natural language processing, underscores their immense utility in capturing complex relationships within high-dimensional data. However, NNs come with notable disadvantages, such as their "black-box" nature, which hampers interpretabil...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	470,449
1708.00850	Towards Semantic Modeling of Contradictions and Disagreements: A Case Study of Medical Guidelines	We introduce a formal distinction between contradictions and disagreements in natural language texts, motivated by the need to formally reason about contradictory medical guidelines. This is a novel and potentially very useful distinction, and has not been discussed so far in NLP and logic. We also describe a NLP syste...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	78,283
2202.07856	The NLP Task Effectiveness of Long-Range Transformers	Transformer models cannot easily scale to long sequences due to their O(N^2) time and space complexity. This has led to Transformer variants seeking to lower computational complexity, such as Longformer and Performer. While such models have theoretically greater efficiency, their effectiveness on real NLP tasks has not...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	280,684
2502.11380	Exploring the Small World of Word Embeddings: A Comparative Study on Conceptual Spaces from LLMs of Different Scales	A conceptual space represents concepts as nodes and semantic relatedness as edges. Word embeddings, combined with a similarity metric, provide an effective approach to constructing such a space. Typically, embeddings are derived from traditional distributed models or encoder-only pretrained models, whose objectives dir...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	534,334
2308.15881	Interpretability-guided Data Augmentation for Robust Segmentation in Multi-centre Colonoscopy Data	Multi-centre colonoscopy images from various medical centres exhibit distinct complicating factors and overlays that impact the image content, contingent on the specific acquisition centre. Existing Deep Segmentation networks struggle to achieve adequate generalizability in such data sets, and the currently available d...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	388,831
2410.06957	Support Vector Boosting Machine (SVBM): Enhancing Classification Performance with AdaBoost and Residual Connections	In traditional boosting algorithms, the focus on misclassified training samples emphasizes their importance based on difficulty during the learning process. While using a standard Support Vector Machine (SVM) as a weak learner in an AdaBoost framework can enhance model performance by concentrating on error samples, thi...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	496,407
2201.04343	An Efficient and Adaptive Granular-ball Generation Method in Classification Problem	Granular-ball computing is an efficient, robust, and scalable learning method for granular computing. The basis of granular-ball computing is the granular-ball generation method. This paper proposes a method for accelerating the granular-ball generation using the division to replace $k$-means. It can greatly improve th...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	275,079
1705.04138	Fast Stochastic Variance Reduced ADMM for Stochastic Composition Optimization	We consider the stochastic composition optimization problem proposed in \cite{wang2017stochastic}, which has applications ranging from estimation to statistical and machine learning. We propose the first ADMM-based algorithm named com-SVR-ADMM, and show that com-SVR-ADMM converges linearly for strongly convex and Lipsc...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	73,285
1905.10671	DIANet: Dense-and-Implicit Attention Network	Attention networks have successfully boosted the performance in various vision problems. Previous works lay emphasis on designing a new attention module and individually plug them into the networks. Our paper proposes a novel-and-simple framework that shares an attention module throughout different network layers to en...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	132,141
2101.02046	TextBox: A Unified, Modularized, and Extensible Framework for Text Generation	In this paper, we release an open-source library, called TextBox, to provide a unified, modularized, and extensible text generation framework. TextBox aims to support a broad set of text generation tasks and models. In our library, we implement 21 text generation models on 9 benchmark datasets, covering the categories ...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	214,514
2501.09081	Inferring Transition Dynamics from Value Functions	In reinforcement learning, the value function is typically trained to solve the Bellman equation, which connects the current value to future values. This temporal dependency hints that the value function may contain implicit information about the environment's transition dynamics. By rearranging the Bellman equation, w...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	525,007
1611.00675	emgr - The Empirical Gramian Framework	System Gramian matrices are a well-known encoding for properties of input-output systems such as controllability, observability or minimality. These so-called system Gramians were developed in linear system theory for applications such as model order reduction of control systems. Empirical Gramian are an extension to t...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	true	63,265
1811.08006	Non-invasive thermal comfort perception based on subtleness magnification and deep learning for energy efficiency	Human thermal comfort measurement plays a critical role in giving feedback signals for building energy efficiency. A non-invasive measuring method based on subtleness magnification and deep learning (NIDL) was designed to achieve a comfortable, energy efficient built environment. The method relies on skin feature data,...	true	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	113,917
2404.04221	How Lexical is Bilingual Lexicon Induction?	In contemporary machine learning approaches to bilingual lexicon induction (BLI), a model learns a mapping between the embedding spaces of a language pair. Recently, retrieve-and-rank approach to BLI has achieved state of the art results on the task. However, the problem remains challenging in low-resource settings, du...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	444,550
2012.09112	Interoperability and computational framework for simulating open channel hydraulics: application to sensitivity analysis and calibration of Gironde Estuary model	Water resource management is of crucial societal and economic importance, requiring a strong capacity for anticipating environmental change. Progress in physical process knowledge, numerical methods and computational power, allows us to address hydro-environmental problems of growing complexity. Modeling of river and m...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	211,965
2411.00691	Leveraging Large Language Models for Code-Mixed Data Augmentation in Sentiment Analysis	Code-mixing (CM), where speakers blend languages within a single expression, is prevalent in multilingual societies but poses challenges for natural language processing due to its complexity and limited data. We propose using a large language model to generate synthetic CM data, which is then used to enhance the perfor...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	504,703
2003.03063	Quantum Adiabatic Theorem Revisited	In 2004 Ambainis and Regev formulated a certain form of quantum adiabatic theorem and provided an elementary proof which is especially accessible to computer scientists. Their result is achieved by discretizing the total adiabatic evolution into a sequence of unitary transformations acting on the quantum system. Here w...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	167,116
1404.2229	Towards the Safety of Human-in-the-Loop Robotics: Challenges and Opportunities for Safety Assurance of Robotic Co-Workers	The success of the human-robot co-worker team in a flexible manufacturing environment where robots learn from demonstration heavily relies on the correct and safe operation of the robot. How this can be achieved is a challenge that requires addressing both technical as well as human-centric research questions. In this ...	false	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	32,191
math/0510276	An algorithmic and a geometric characterization of Coarsening At Random	We show that the class of conditional distributions satisfying the coarsening at Random (CAR) property for discrete data has a simple and robust algorithmic description based on randomized uniform multicovers: combinatorial objects generalizing the notion of partition of a set. However, the complexity of a given CAR me...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	540,704
1810.01719	A Puff of Steem: Security Analysis of Decentralized Content Curation	Decentralized content curation is the process through which uploaded posts are ranked and filtered based exclusively on users' feedback. Platforms such as the blockchain-based Steemit employ this type of curation while providing monetary incentives to promote the visibility of high quality posts according to the percep...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	109,446
1604.03498	GPU-FV: Realtime Fisher Vector and Its Applications in Video Monitoring	Fisher vector has been widely used in many multimedia retrieval and visual recognition applications with good performance. However, the computation complexity prevents its usage in real-time video monitoring. In this work, we proposed and implemented GPU-FV, a fast Fisher vector extraction method with the help of moder...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	54,515
2311.17937	Unlocking Spatial Comprehension in Text-to-Image Diffusion Models	We propose CompFuser, an image generation pipeline that enhances spatial comprehension and attribute assignment in text-to-image generative models. Our pipeline enables the interpretation of instructions defining spatial relationships between objects in a scene, such as `An image of a gray cat on the left of an orange ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	411,476
1710.08070	Accelerated Reinforcement Learning	Policy gradient methods are widely used in reinforcement learning algorithms to search for better policies in the parameterized policy space. They do gradient search in the policy space and are known to converge very slowly. Nesterov developed an accelerated gradient search algorithm for convex optimization problems. T...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	83,035
2306.02083	Efficient Text-Guided 3D-Aware Portrait Generation with Score Distillation Sampling on Distribution	Text-to-3D is an emerging task that allows users to create 3D content with infinite possibilities. Existing works tackle the problem by optimizing a 3D representation with guidance from pre-trained diffusion models. An apparent drawback is that they need to optimize from scratch for each prompt, which is computationall...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	370,754
2303.00983	Using simulation to quantify the performance of automotive perception systems	The design and evaluation of complex systems can benefit from a software simulation - sometimes called a digital twin. The simulation can be used to characterize system performance or to test its performance under conditions that are difficult to measure (e.g., nighttime for automotive perception systems). We describe ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	348,787
2304.08639	pgmpy: A Python Toolkit for Bayesian Networks	Bayesian Networks (BNs) are used in various fields for modeling, prediction, and decision making. pgmpy is a python package that provides a collection of algorithms and tools to work with BNs and related models. It implements algorithms for structure learning, parameter estimation, approximate and exact inference, caus...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	358,773
2502.10412	Identifying relevant indicators for monitoring a National Artificial Intelligence Strategy	How can a National Artificial Intelligence Strategy be effectively monitored? To address this question, we propose a methodology consisting of two key components. First, it involves identifying relevant indicators within national AI strategies. Second, it assesses the alignment between these indicators and the strategi...	false	false	false	false	true	false	false	false	false	false	false	false	false	true	false	false	false	false	533,859
2406.17131	Bayesian temporal biclustering with applications to multi-subject neuroscience studies	We consider the problem of analyzing multivariate time series collected on multiple subjects, with the goal of identifying groups of subjects exhibiting similar trends in their recorded measurements over time as well as time-varying groups of associated measurements. To this end, we propose a Bayesian model for tempora...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	467,432
1911.05732	Antithetic integral feedback for the robust control of monostable and oscillatory biomolecular circuits	Biomolecular feedback systems are now a central application area of interest within control theory. While classical control techniques provide invaluable insight into the function and design of both natural and synthetic biomolecular systems, there are certain aspects of biological control that have proven difficult to...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	153,354
1702.07386	Toward Streaming Synapse Detection with Compositional ConvNets	Connectomics is an emerging field in neuroscience that aims to reconstruct the 3-dimensional morphology of neurons from electron microscopy (EM) images. Recent studies have successfully demonstrated the use of convolutional neural networks (ConvNets) for segmenting cell membranes to individuate neurons. However, there ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	68,772
2312.11816	A Dual-way Enhanced Framework from Text Matching Point of View for Multimodal Entity Linking	Multimodal Entity Linking (MEL) aims at linking ambiguous mentions with multimodal information to entity in Knowledge Graph (KG) such as Wikipedia, which plays a key role in many applications. However, existing methods suffer from shortcomings, including modality impurity such as noise in raw image and ambiguous textua...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	416,724
2404.17892	Shared learning of powertrain control policies for vehicle fleets	Emerging data-driven approaches, such as deep reinforcement learning (DRL), aim at on-the-field learning of powertrain control policies that optimize fuel economy and other performance metrics. Indeed, they have shown great potential in this regard for individual vehicles on specific routes or drive cycles. However, fo...	false	false	false	false	true	false	true	false	false	false	true	false	false	false	false	false	false	false	450,047
2002.11061	Ground Texture Based Localization Using Compact Binary Descriptors	Ground texture based localization is a promising approach to achieve high-accuracy positioning of vehicles. We present a self-contained method that can be used for global localization as well as for subsequent local localization updates, i.e. it allows a robot to localize without any knowledge of its current whereabout...	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	165,600
2501.04108	TrojanDec: Data-free Detection of Trojan Inputs in Self-supervised Learning	An image encoder pre-trained by self-supervised learning can be used as a general-purpose feature extractor to build downstream classifiers for various downstream tasks. However, many studies showed that an attacker can embed a trojan into an encoder such that multiple downstream classifiers built based on the trojaned...	false	false	false	false	true	false	false	false	false	false	false	false	true	false	false	false	false	false	523,099
2002.12589	Deep Learning Enabled Optimization of Downlink Beamforming Under Per-Antenna Power Constraints: Algorithms and Experimental Demonstration	This paper studies fast downlink beamforming algorithms using deep learning in multiuser multiple-input-single-output systems where each transmit antenna at the base station has its own power constraint. We focus on the signal-to-interference-plus-noise ratio (SINR) balancing problem which is quasi-convex but there is ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	166,083
2412.03582	Exploring Non-Linear Effects of Built Environment on Travel Using an Integrated Machine Learning and Inferential Modeling Approach: A Three-Wave Repeated Cross-Sectional Study	This study investigates the dynamic relationship between the built environment and travel in Austin, Texas, over a 20-year period. Using three waves of household travel surveys from 1997, 2006, and 2017, the research employs a repeated cross-sectional approach to address the limitations of traditional longitudinal and ...	false	false	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	514,021
2306.04375	Learning via Wasserstein-Based High Probability Generalisation Bounds	Minimising upper bounds on the population risk or the generalisation gap has been widely used in structural risk minimisation (SRM) -- this is in particular at the core of PAC-Bayesian learning. Despite its successes and unfailing surge of interest in recent years, a limitation of the PAC-Bayesian framework is that mos...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	371,722
2208.12809	Incrementality Bidding and Attribution	The causal effect of showing an ad to a potential customer versus not, commonly referred to as "incrementality", is the fundamental question of advertising effectiveness. In digital advertising three major puzzle pieces are central to rigorously quantifying advertising incrementality: ad buying/bidding/pricing, attribu...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	314,856
2312.11580	PlaNet-S: Automatic Semantic Segmentation of Placenta	[Purpose] To develop a fully automated semantic placenta segmentation model that integrates the U-Net and SegNeXt architectures through ensemble learning. [Methods] A total of 218 pregnant women with suspected placental anomalies who underwent magnetic resonance imaging (MRI) were enrolled, yielding 1090 annotated imag...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	416,656
2211.12977	Linear Programming Hierarchies in Coding Theory: Dual Solutions	The rate vs. distance problem is a long-standing open problem in coding theory. Recent papers have suggested a new way to tackle this problem by appealing to a new hierarchy of linear programs. If one can find good dual solutions to these LPs, this would result in improved upper bounds for the rate vs. distance problem...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	332,306
1707.05929	Learning Unified Embedding for Apparel Recognition	In apparel recognition, specialized models (e.g. models trained for a particular vertical like dresses) can significantly outperform general models (i.e. models that cover a wide range of verticals). Therefore, deep neural network models are often trained separately for different verticals. However, using specialized m...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	77,318
2108.02104	Point Discriminative Learning for Data-efficient 3D Point Cloud Analysis	3D point cloud analysis has drawn a lot of research attention due to its wide applications. However, collecting massive labelled 3D point cloud data is both time-consuming and labor-intensive. This calls for data-efficient learning methods. In this work we propose PointDisc, a point discriminative learning method to le...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	249,222
1903.05749	Inferring 3D Shapes of Unknown Rigid Objects in Clutter through Inverse Physics Reasoning	We present a probabilistic approach for building, on the fly, 3-D models of unknown objects while being manipulated by a robot. We specifically consider manipulation tasks in piles of clutter that contain previously unseen objects. Most manipulation algorithms for performing such tasks require known geometric models of...	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	124,221
2308.10606	Analyzing Complex Systems with Cascades Using Continuous-Time Bayesian Networks	Interacting systems of events may exhibit cascading behavior where events tend to be temporally clustered. While the cascades themselves may be obvious from the data, it is important to understand which states of the system trigger them. For this purpose, we propose a modeling framework based on continuous-time Bayesia...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	386,804
2412.12538	A Scalable Approach to Benchmarking the In-Conversation Differential Diagnostic Accuracy of a Health AI	Diagnostic errors in healthcare persist as a critical challenge, with increasing numbers of patients turning to online resources for health information. While AI-powered healthcare chatbots show promise, there exists no standardized and scalable framework for evaluating their diagnostic capabilities. This study introdu...	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	517,912
2212.01076	Are Straight-Through gradients and Soft-Thresholding all you need for Sparse Training?	Turning the weights to zero when training a neural network helps in reducing the computational complexity at inference. To progressively increase the sparsity ratio in the network without causing sharp weight discontinuities during training, our work combines soft-thresholding and straight-through gradient estimation t...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	334,302
2410.13566	360U-Former: HDR Illumination Estimation with Panoramic Adapted Vision Transformers	Recent illumination estimation methods have focused on enhancing the resolution and improving the quality and diversity of the generated textures. However, few have explored tailoring the neural network architecture to the Equirectangular Panorama (ERP) format utilised in image-based lighting. Consequently, high dynami...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	499,584
2202.08772	A Survey of Knowledge-Intensive NLP with Pre-Trained Language Models	With the increasing of model capacity brought by pre-trained language models, there emerges boosting needs for more knowledgeable natural language processing (NLP) models with advanced functionalities including providing and making flexible use of encyclopedic and commonsense knowledge. The mere pre-trained language mo...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	280,980
2412.13533	Language-guided Medical Image Segmentation with Target-informed Multi-level Contrastive Alignments	Medical image segmentation is crucial in modern medical image analysis, which can aid into diagnosis of various disease conditions. Recently, language-guided segmentation methods have shown promising results in automating image segmentation where text reports are incorporated as guidance. These text reports, containing...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	518,327
1807.02754	Model-Free Optimization Using Eagle Perching Optimizer	The paper proposes a novel nature-inspired technique of optimization. It mimics the perching nature of eagles and uses mathematical formulations to introduce a new addition to metaheuristic algorithms. The nature of the proposed algorithm is based on exploration and exploitation. The proposed algorithm is developed int...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	102,342

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