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
1904.08745	edGNN: a Simple and Powerful GNN for Directed Labeled Graphs	The ability of a graph neural network (GNN) to leverage both the graph topology and graph labels is fundamental to building discriminative node and graph embeddings. Building on previous work, we theoretically show that edGNN, our model for directed labeled graphs, is as powerful as the Weisfeiler-Lehman algorithm for ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	128,165
2211.15521	G^3: Geolocation via Guidebook Grounding	We demonstrate how language can improve geolocation: the task of predicting the location where an image was taken. Here we study explicit knowledge from human-written guidebooks that describe the salient and class-discriminative visual features humans use for geolocation. We propose the task of Geolocation via Guideboo...	false	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	333,304
2302.13475	Elementwise Language Representation	We propose a new technique for computational language representation called elementwise embedding, in which a material (semantic unit) is abstracted into a horizontal concatenation of lower-dimensional element (character) embeddings. While elements are always characters, materials are arbitrary levels of semantic units...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	347,962
2410.18371	Gibberish is All You Need for Membership Inference Detection in Contrastive Language-Audio Pretraining	Audio can disclose PII, particularly when combined with related text data. Therefore, it is essential to develop tools to detect privacy leakage in Contrastive Language-Audio Pretraining(CLAP). Existing MIAs need audio as input, risking exposure of voiceprint and requiring costly shadow models. We first propose PRMID, ...	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	501,854
2007.06284	Artificial Neural Networks Jamming on the Beat	This paper addresses the issue of long-scale correlations that is characteristic for symbolic music and is a challenge for modern generative algorithms. It suggests a very simple workaround for this challenge, namely, generation of a drum pattern that could be further used as a foundation for melody generation. The pap...	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	186,968
2103.06766	Stable Tuple Embeddings for Dynamic Databases	We study the problem of computing an embedding of the tuples of a relational database in a manner that is extensible to dynamic changes of the database. In this problem, the embedding should be stable in the sense that it should not change on the existing tuples due to the embedding of newly inserted tuples (as databas...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	224,402
2412.03681	Acquired TASTE: Multimodal Stance Detection with Textual and Structural Embeddings	Stance detection plays a pivotal role in enabling an extensive range of downstream applications, from discourse parsing to tracing the spread of fake news and the denial of scientific facts. While most stance classification models rely on textual representation of the utterance in question, prior work has demonstrated ...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	514,060
2409.13951	Deep learning for fast segmentation and critical dimension metrology & characterization enabling AR/VR design and fabrication	Quantitative analysis of microscopy images is essential in the design and fabrication of components used in augmented reality/virtual reality (AR/VR) modules. However, segmenting regions of interest (ROIs) from these complex images and extracting critical dimensions (CDs) requires novel techniques, such as deep learnin...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	490,251
2302.03978	Structural hierarchical learning for energy networks	Many sectors nowadays require accurate and coherent predictions across their organization to effectively operate. Otherwise, decision-makers would be planning using disparate views of the future, resulting in inconsistent decisions across their sectors. To secure coherency across hierarchies, recent research has put fo...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	344,543
2205.07182	Fair Bayes-Optimal Classifiers Under Predictive Parity	Increasing concerns about disparate effects of AI have motivated a great deal of work on fair machine learning. Existing works mainly focus on independence- and separation-based measures (e.g., demographic parity, equality of opportunity, equalized odds), while sufficiency-based measures such as predictive parity are m...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	296,508
2110.09904	Learning Robotic Manipulation Skills Using an Adaptive Force-Impedance Action Space	Intelligent agents must be able to think fast and slow to perform elaborate manipulation tasks. Reinforcement Learning (RL) has led to many promising results on a range of challenging decision-making tasks. However, in real-world robotics, these methods still struggle, as they require large amounts of expensive interac...	false	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	261,968
2501.19399	Scalable-Softmax Is Superior for Attention	The maximum element of the vector output by the Softmax function approaches zero as the input vector size increases. Transformer-based language models rely on Softmax to compute attention scores, causing the attention distribution to flatten as the context size grows. This reduces the model's ability to prioritize key ...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	529,149
cs/0111012	Intelligent Anticipated Exploration of Web Sites	In this paper we describe a web search agent, called Global Search Agent (hereafter GSA for short). GSA integrates and enhances several search techniques in order to achieve significant improvements in the user-perceived quality of delivered information as compared to usual web search engines. GSA features intelligent ...	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	false	false	537,454
2112.13058	Tri-Transformer Hawkes Process: Three Heads are better than one	Abstract. Most of the real world data we encounter are asynchronous event sequence, so the last decades have been characterized by the implementation of various point process into the field of social networks,electronic medical records and financial transactions. At the beginning, Hawkes process and its variants which ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	273,129
2304.05144	Phase Calibration of Distributed Antenna Arrays	Antenna arrays can be either reciprocity calibrated (R-calibrated), which facilitates reciprocity-based beamforming, or fully calibrated (F-calibrated), which additionally facilitates transmission and reception in specific physical directions. We first expose, to provide context, the fundamental principles of over-the-...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	357,509
1706.09865	Generalising Random Forest Parameter Optimisation to Include Stability and Cost	Random forests are among the most popular classification and regression methods used in industrial applications. To be effective, the parameters of random forests must be carefully tuned. This is usually done by choosing values that minimize the prediction error on a held out dataset. We argue that error reduction is o...	false	false	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	76,215
2309.03773	Extending Transductive Knowledge Graph Embedding Models for Inductive Logical Relational Inference	Many downstream inference tasks for knowledge graphs, such as relation prediction, have been handled successfully by knowledge graph embedding techniques in the transductive setting. To address the inductive setting wherein new entities are introduced into the knowledge graph at inference time, more recent work opts fo...	false	false	false	true	true	true	false	false	false	false	false	false	false	false	false	false	false	false	390,503
2408.17175	Codec Does Matter: Exploring the Semantic Shortcoming of Codec for Audio Language Model	Recent advancements in audio generation have been significantly propelled by the capabilities of Large Language Models (LLMs). The existing research on audio LLM has primarily focused on enhancing the architecture and scale of audio language models, as well as leveraging larger datasets, and generally, acoustic codecs,...	false	false	true	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	484,609
2305.18259	GlyphControl: Glyph Conditional Control for Visual Text Generation	Recently, there has been an increasing interest in developing diffusion-based text-to-image generative models capable of generating coherent and well-formed visual text. In this paper, we propose a novel and efficient approach called GlyphControl to address this task. Unlike existing methods that rely on character-awar...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	368,921
2005.06835	RegQCNET: Deep Quality Control for Image-to-template Brain MRI Affine Registration	Affine registration of one or several brain image(s) onto a common reference space is a necessary prerequisite for many image processing tasks, such as brain segmentation or functional analysis. Manual assessment of registration quality is a tedious and time-consuming task, especially in studies comprising a large amou...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	177,125
1705.09847	Lifelong Generative Modeling	Lifelong learning is the problem of learning multiple consecutive tasks in a sequential manner, where knowledge gained from previous tasks is retained and used to aid future learning over the lifetime of the learner. It is essential towards the development of intelligent machines that can adapt to their surroundings. I...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	74,280
1505.04098	Asymptotically Optimal Planning by Feasible Kinodynamic Planning in State-Cost Space	This paper presents an equivalence between feasible kinodynamic planning and optimal kinodynamic planning, in that any optimal planning problem can be transformed into a series of feasible planning problems in a state-cost space whose solutions approach the optimum. This transformation gives rise to a meta-algorithm th...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	43,147
2109.04939	Modeling Human Sentence Processing with Left-Corner Recurrent Neural Network Grammars	In computational linguistics, it has been shown that hierarchical structures make language models (LMs) more human-like. However, the previous literature has been agnostic about a parsing strategy of the hierarchical models. In this paper, we investigated whether hierarchical structures make LMs more human-like, and if...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	254,599
2411.11892	Green My LLM: Studying the key factors affecting the energy consumption of code assistants	In recent years,Large Language Models (LLMs) have significantly improved in generating high-quality code, enabling their integration into developers' Integrated Development Environments (IDEs) as code assistants. These assistants, such as GitHub Copilot, deliver real-time code suggestions and can greatly enhance develo...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	509,212
2201.00384	On the effectiveness of Randomized Signatures as Reservoir for Learning Rough Dynamics	Many finance, physics, and engineering phenomena are modeled by continuous-time dynamical systems driven by highly irregular (stochastic) inputs. A powerful tool to perform time series analysis in this context is rooted in rough path theory and leverages the so-called Signature Transform. This algorithm enjoys strong t...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	273,949
2004.05167	Individual Fairness in Pipelines	It is well understood that a system built from individually fair components may not itself be individually fair. In this work, we investigate individual fairness under pipeline composition. Pipelines differ from ordinary sequential or repeated composition in that individuals may drop out at any stage, and classificatio...	false	false	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	172,109
2210.01794	Implicit Warping for Animation with Image Sets	We present a new implicit warping framework for image animation using sets of source images through the transfer of the motion of a driving video. A single cross- modal attention layer is used to find correspondences between the source images and the driving image, choose the most appropriate features from different so...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	321,402
1905.00084	A Probabilistic Approach for Demand-Aware Ride-Sharing Optimization	Ride-sharing is a modern urban-mobility paradigm with tremendous potential in reducing congestion and pollution. Demand-aware design is a promising avenue for addressing a critical challenge in ride-sharing systems, namely joint optimization of request-vehicle assignment and routing for a fleet of vehicles. In this pap...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	129,394
1908.01289	Dueling Posterior Sampling for Preference-Based Reinforcement Learning	In preference-based reinforcement learning (RL), an agent interacts with the environment while receiving preferences instead of absolute feedback. While there is increasing research activity in preference-based RL, the design of formal frameworks that admit tractable theoretical analysis remains an open challenge. Buil...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	140,721
2203.03833	Quasi-Balanced Self-Training on Noise-Aware Synthesis of Object Point Clouds for Closing Domain Gap	Semantic analyses of object point clouds are largely driven by releasing of benchmarking datasets, including synthetic ones whose instances are sampled from object CAD models. However, learning from synthetic data may not generalize to practical scenarios, where point clouds are typically incomplete, non-uniformly dist...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	284,243
1205.6917	Robust self-triggered coordination with ternary controllers	This paper regards coordination of networked systems, which is studied in the framework of hybrid dynamical systems. We design a coordination scheme which combines the use of ternary controllers with a self-triggered communication policy. The communication policy requires the agents to collect, at each sampling time, r...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	16,259
1208.4042	Measuring quality, reputation and trust in online communities	In the Internet era the information overload and the challenge to detect quality content has raised the issue of how to rank both resources and users in online communities. In this paper we develop a general ranking method that can simultaneously evaluate users' reputation and objects' quality in an iterative procedure...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	18,170
2310.13213	MultiCoNER v2: a Large Multilingual dataset for Fine-grained and Noisy Named Entity Recognition	We present MULTICONER V2, a dataset for fine-grained Named Entity Recognition covering 33 entity classes across 12 languages, in both monolingual and multilingual settings. This dataset aims to tackle the following practical challenges in NER: (i) effective handling of fine-grained classes that include complex entities...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	401,336
2405.16164	Acquiring Better Load Estimates by Combining Anomaly and Change Point Detection in Power Grid Time-series Measurements	In this paper we present novel methodology for automatic anomaly and switch event filtering to improve load estimation in power grid systems. By leveraging unsupervised methods with supervised optimization, our approach prioritizes interpretability while ensuring robust and generalizable performance on unseen data. Thr...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	457,291
2311.09646	Reconstructing Continuous Light Field From Single Coded Image	We propose a method for reconstructing a continuous light field of a target scene from a single observed image. Our method takes the best of two worlds: joint aperture-exposure coding for compressive light-field acquisition, and a neural radiance field (NeRF) for view synthesis. Joint aperture-exposure coding implement...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	408,224
2109.02593	General-Purpose Question-Answering with Macaw	Despite the successes of pretrained language models, there are still few high-quality, general-purpose QA systems that are freely available. In response, we present Macaw, a versatile, generative question-answering (QA) system that we are making available to the community. Macaw is built on UnifiedQA, itself built on T...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	253,800
2402.01306	KTO: Model Alignment as Prospect Theoretic Optimization	Kahneman & Tversky's $\textit{prospect theory}$ tells us that humans perceive random variables in a biased but well-defined manner (1992); for example, humans are famously loss-averse. We show that objectives for aligning LLMs with human feedback implicitly incorporate many of these biases -- the success of these objec...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	425,963
1101.2378	Extracting Features from Ratings: The Role of Factor Models	Performing effective preference-based data retrieval requires detailed and preferentially meaningful structurized information about the current user as well as the items under consideration. A common problem is that representations of items often only consist of mere technical attributes, which do not resemble human pe...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	8,798
2308.04608	Offline coupling of segregated multi-physical simulations with consistent boundary conditions and source terms based on scattered data	This article presents the openCFS submodule scattered data reader for coupling multi-physical simulations performed in different simulation programs. For instance, by considering a forward-coupling of a surface vibration simulation (mechanical system) to an acoustic propagation simulation using time-dependent acoustic ...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	384,474
1309.0872	Producing a Set of Models for the Iron Homeostasis Network	This paper presents a method for modeling biological systems which combines formal techniques on intervals, numerical simulations and satisfaction of Signal Temporal Logic (STL) formulas. The main modeling challenge addressed by this approach is the large uncertainty in the values of the parameters due to the experimen...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	26,819
2105.03852	Towards Dynamic Feature Selection with Attention to Assist Banking Customers in Establishing a New Business	Establishing a new business may involve Knowledge acquisition in various areas, from personal to business and marketing sources. This task is challenging as it requires examining various data islands to uncover hidden patterns and unknown correlations such as purchasing behavior, consumer buying signals, and demographi...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	234,295
2411.15355	UniGaussian: Driving Scene Reconstruction from Multiple Camera Models via Unified Gaussian Representations	Urban scene reconstruction is crucial for real-world autonomous driving simulators. Although existing methods have achieved photorealistic reconstruction, they mostly focus on pinhole cameras and neglect fisheye cameras. In fact, how to effectively simulate fisheye cameras in driving scene remains an unsolved problem. ...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	510,571
2412.18808	Provable Uncertainty Decomposition via Higher-Order Calibration	We give a principled method for decomposing the predictive uncertainty of a model into aleatoric and epistemic components with explicit semantics relating them to the real-world data distribution. While many works in the literature have proposed such decompositions, they lack the type of formal guarantees we provide. O...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	520,581
1705.10768	Reflection Invariant and Symmetry Detection	Symmetry detection and discrimination are of fundamental meaning in science, technology, and engineering. This paper introduces reflection invariants and defines the directional moment to detect symmetry for shape analysis and object recognition. And it demonstrates that detection of reflection symmetry can be done in ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	74,469
1209.0378	Provenance for SPARQL queries	Determining trust of data available in the Semantic Web is fundamental for applications and users, in particular for linked open data obtained from SPARQL endpoints. There exist several proposals in the literature to annotate SPARQL query results with values from abstract models, adapting the seminal works on provenanc...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	18,361
1505.07690	Invertible Orientation Scores of 3D Images	The enhancement and detection of elongated structures in noisy image data is relevant for many biomedical applications. To handle complex crossing structures in 2D images, 2D orientation scores were introduced, which already showed their use in a variety of applications. Here we extend this work to 3D orientation score...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	43,563
1909.12969	Counterfactual States for Atari Agents via Generative Deep Learning	Although deep reinforcement learning agents have produced impressive results in many domains, their decision making is difficult to explain to humans. To address this problem, past work has mainly focused on explaining why an action was chosen in a given state. A different type of explanation that is useful is a counte...	true	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	147,278
2207.10237	SPIN: An Empirical Evaluation on Sharing Parameters of Isotropic Networks	Recent isotropic networks, such as ConvMixer and vision transformers, have found significant success across visual recognition tasks, matching or outperforming non-isotropic convolutional neural networks (CNNs). Isotropic architectures are particularly well-suited to cross-layer weight sharing, an effective neural netw...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	309,181
2310.18458	Do Not Harm Protected Groups in Debiasing Language Representation Models	Language Representation Models (LRMs) trained with real-world data may capture and exacerbate undesired bias and cause unfair treatment of people in various demographic groups. Several techniques have been investigated for applying interventions to LRMs to remove bias in benchmark evaluations on, for example, word embe...	false	false	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	403,552
2006.15987	Robustifying Sequential Neural Processes	When tasks change over time, meta-transfer learning seeks to improve the efficiency of learning a new task via both meta-learning and transfer-learning. While the standard attention has been effective in a variety of settings, we question its effectiveness in improving meta-transfer learning since the tasks being learn...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	184,681
2412.02698	Scaling BERT Models for Turkish Automatic Punctuation and Capitalization Correction	This paper investigates the effectiveness of BERT based models for automated punctuation and capitalization corrections in Turkish texts across five distinct model sizes. The models are designated as Tiny, Mini, Small, Medium, and Base. The design and capabilities of each model are tailored to address the specific chal...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	513,644
cs/0702085	Social Behaviours Applied to P2P Systems: An efficient Algorithm for Resource Organisation	P2P systems are a great solution to the problem of distributing resources. The main issue of P2P networks is that searching and retrieving resources shared by peers is usually expensive and does not take into account similarities among peers. In this paper we present preliminary simulations of PROSA, a novel algorithm ...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	true	540,163
1906.01504	Embedded hyper-parameter tuning by Simulated Annealing	We propose a new metaheuristic training scheme that combines Stochastic Gradient Descent (SGD) and Discrete Optimization in an unconventional way. Our idea is to define a discrete neighborhood of the current SGD point containing a number of "potentially good moves" that exploit gradient information, and to search this ...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	true	133,726
1705.10638	A Receding Horizon Push Recovery Strategy for Balancing the iCub Humanoid Robot	Balancing and reacting to strong and unexpected pushes is a critical requirement for humanoid robots. We recently designed a capture point based approach which interfaces with a momentum-based torque controller and we implemented and validated it on the iCub humanoid robot. In this work we implement a Receding Horizon ...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	74,442
2409.05137	READoc: A Unified Benchmark for Realistic Document Structured Extraction	Document Structured Extraction (DSE) aims to extract structured content from raw documents. Despite the emergence of numerous DSE systems, their unified evaluation remains inadequate, significantly hindering the field's advancement. This problem is largely attributed to existing benchmark paradigms, which exhibit fragm...	false	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	486,649
2308.16278	Autonomous damage assessment of structural columns using low-cost micro aerial vehicles and multi-view computer vision	Structural columns are the crucial load-carrying components of buildings and bridges. Early detection of column damage is important for the assessment of the residual performance and the prevention of system-level collapse. This research proposes an innovative end-to-end micro aerial vehicles (MAVs)-based approach to a...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	388,938
2108.08739	Neural Predictive Control for the Optimization of Smart Grid Flexibility Schedules	Model predictive control (MPC) is a method to formulate the optimal scheduling problem for grid flexibilities in a mathematical manner. The resulting time-constrained optimization problem can be re-solved in each optimization time step using classical optimization methods such as Second Order Cone Programming (SOCP) or...	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	false	false	251,366
2009.13437	A Human-in-the-Loop Approach based on Explainability to Improve NTL Detection	Implementing systems based on Machine Learning to detect fraud and other Non-Technical Losses (NTL) is challenging: the data available is biased, and the algorithms currently used are black-boxes that cannot be either easily trusted or understood by stakeholders. This work explains our human-in-the-loop approach to mit...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	197,745
2009.11239	Deep multi-stations weather forecasting: explainable recurrent convolutional neural networks	Deep learning applied to weather forecasting has started gaining popularity because of the progress achieved by data-driven models. The present paper compares two different deep learning architectures to perform weather prediction on daily data gathered from 18 cities across Europe and spanned over a period of 15 years...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	197,118
2405.14815	Designing A Sustainable Marine Debris Clean-up Framework without Human Labels	Marine debris poses a significant ecological threat to birds, fish, and other animal life. Traditional methods for assessing debris accumulation involve labor-intensive and costly manual surveys. This study introduces a framework that utilizes aerial imagery captured by drones to conduct remote trash surveys. Leveragin...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	456,610
1905.05987	EasiCS: the objective and fine-grained classification method of cervical spondylosis dysfunction	The precise diagnosis is of great significance in developing precise treatment plans to restore neck function and reduce the burden posed by the cervical spondylosis (CS). However, the current available neck function assessment method are subjective and coarse-grained. In this paper, based on the relationship among CS,...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	130,878
2110.12076	Applications of Generative Adversarial Networks in Anomaly Detection: A Systematic Literature Review	Anomaly detection has become an indispensable tool for modern society, applied in a wide range of applications, from detecting fraudulent transactions to malignant brain tumours. Over time, many anomaly detection techniques have been introduced. However, in general, they all suffer from the same problem: a lack of data...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	262,701
2410.06961	Self-Boosting Large Language Models with Synthetic Preference Data	Through alignment with human preferences, Large Language Models (LLMs) have advanced significantly in generating honest, harmless, and helpful responses. However, collecting high-quality preference data is a resource-intensive and creativity-demanding process, especially for the continual improvement of LLMs. We introd...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	496,409
1811.02307	Toward Driving Scene Understanding: A Dataset for Learning Driver Behavior and Causal Reasoning	Driving Scene understanding is a key ingredient for intelligent transportation systems. To achieve systems that can operate in a complex physical and social environment, they need to understand and learn how humans drive and interact with traffic scenes. We present the Honda Research Institute Driving Dataset (HDD), a ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	112,557
2210.01376	Improved High-Probability Regret for Adversarial Bandits with Time-Varying Feedback Graphs	We study high-probability regret bounds for adversarial $K$-armed bandits with time-varying feedback graphs over $T$ rounds. For general strongly observable graphs, we develop an algorithm that achieves the optimal regret $\widetilde{\mathcal{O}}((\sum_{t=1}^T\alpha_t)^{1/2}+\max_{t\in[T]}\alpha_t)$ with high probabili...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	321,243
2011.00446	Efficient Learning of Control Policies for Robust Quadruped Bounding using Pretrained Neural Networks	Bounding is one of the important gaits in quadrupedal locomotion for negotiating obstacles. The authors proposed an effective approach that can learn robust bounding gaits more efficiently despite its large variation in dynamic body movements. The authors first pretrained the neural network (NN) based on data from a ro...	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	false	204,222
2001.08472	Joint Inference on Truth/Rumor and Their Sources in Social Networks	In the contemporary era of information explosion, we are often faced with the mixture of massive \emph{truth} (true information) and \emph{rumor} (false information) flooded over social networks. Under such circumstances, it is very essential to infer whether each claim (e.g., news, messages) is a truth or a rumor, and...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	161,298
2211.17116	Global Convergence of Localized Policy Iteration in Networked Multi-Agent Reinforcement Learning	We study a multi-agent reinforcement learning (MARL) problem where the agents interact over a given network. The goal of the agents is to cooperatively maximize the average of their entropy-regularized long-term rewards. To overcome the curse of dimensionality and to reduce communication, we propose a Localized Policy ...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	true	false	false	false	333,869
2309.09531	Decompose Semantic Shifts for Composed Image Retrieval	Composed image retrieval is a type of image retrieval task where the user provides a reference image as a starting point and specifies a text on how to shift from the starting point to the desired target image. However, most existing methods focus on the composition learning of text and reference images and oversimplif...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	392,653
2405.15182	RFLPA: A Robust Federated Learning Framework against Poisoning Attacks with Secure Aggregation	Federated learning (FL) allows multiple devices to train a model collaboratively without sharing their data. Despite its benefits, FL is vulnerable to privacy leakage and poisoning attacks. To address the privacy concern, secure aggregation (SecAgg) is often used to obtain the aggregation of gradients on sever without ...	false	false	false	false	true	false	false	false	false	false	false	false	true	false	false	false	false	false	456,791
2502.05677	Surprise Potential as a Measure of Interactivity in Driving Scenarios	Validating the safety and performance of an autonomous vehicle (AV) requires benchmarking on real-world driving logs. However, typical driving logs contain mostly uneventful scenarios with minimal interactions between road users. Identifying interactive scenarios in real-world driving logs enables the curation of datas...	false	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	531,720
2112.07089	Building on Huang et al. GlossBERT for Word Sense Disambiguation	We propose to take on the problem ofWord Sense Disambiguation (WSD). In language, words of the same form can take different meanings depending on context. While humans easily infer the meaning or gloss of such words by their context, machines stumble on this task.As such, we intend to replicated and expand upon the res...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	271,372
2303.02890	An Analysis of Physics-Informed Neural Networks	Whilst the partial differential equations that govern the dynamics of our world have been studied in great depth for centuries, solving them for complex, high-dimensional conditions and domains still presents an incredibly large mathematical and computational challenge. Analytical methods can be cumbersome to utilise, ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	349,525
1711.00457	Almost instant brain atlas segmentation for large-scale studies	Large scale studies of group differences in healthy controls and patients and screenings for early stage disease prevention programs require processing and analysis of extensive multisubject datasets. Complexity of the task increases even further when segmenting structural MRI of the brain into an atlas with more than ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	83,721
1810.02494	A note on spanoid rank	We construct a spanoid $\mathcal{S}$ on $n$ elements with $\textsf{rank}(\mathcal{S}) \ge n^c \textsf{f-rank}(\mathcal{S})$ where $c = \log_5 3 - \log_5 2.5 \approx 0.113283$. This answers a question of Dvir-Gopi-Wigderson [DGW18].	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	109,599
2201.09205	Deeply Explain CNN via Hierarchical Decomposition	In computer vision, some attribution methods for explaining CNNs attempt to study how the intermediate features affect the network prediction. However, they usually ignore the feature hierarchies among the intermediate features. This paper introduces a hierarchical decomposition framework to explain CNN's decision-maki...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	276,600
2105.02095	Two-layer neural networks with values in a Banach space	We study two-layer neural networks whose domain and range are Banach spaces with separable preduals. In addition, we assume that the image space is equipped with a partial order, i.e. it is a Riesz space. As the nonlinearity we choose the lattice operation of taking the positive part; in case of $\mathbb R^d$-valued ne...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	233,725
2105.13153	Cardiac Segmentation on CT Images through Shape-Aware Contour Attentions	Cardiac segmentation of atriums, ventricles, and myocardium in computed tomography (CT) images is an important first-line task for presymptomatic cardiovascular disease diagnosis. In several recent studies, deep learning models have shown significant breakthroughs in medical image segmentation tasks. Unlike other organ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	237,225
1405.5732	Self-tuned Visual Subclass Learning with Shared Samples An Incremental Approach	Computer vision tasks are traditionally defined and evaluated using semantic categories. However, it is known to the field that semantic classes do not necessarily correspond to a unique visual class (e.g. inside and outside of a car). Furthermore, many of the feasible learning techniques at hand cannot model a visual ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	33,298
2406.19608	Multi-service collaboration and composition of cloud manufacturing customized production based on problem decomposition	Cloud manufacturing system is a service-oriented and knowledge-based one, which can provide solutions for the large-scale customized production. The service resource allocation is the primary factor that restricts the production time and cost in the cloud manufacturing customized production (CMCP). In order to improve ...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	468,484
1908.01161	Distributed Adaptive Coverage Control of Differential Drive Robotic Sensors	This paper is concerned with the deployment of multiple mobile robots in order to autonomously cover a region Q. The region to be covered is described using a density function which may not be apriori known. In this paper, we pose the coverage problem as an optimization problem over some space of functions on Q. In par...	false	false	false	false	false	false	false	true	false	false	true	false	false	false	true	false	false	false	140,689
0806.2216	An Intelligent Multi-Agent Recommender System for Human Capacity Building	This paper presents a Multi-Agent approach to the problem of recommending training courses to engineering professionals. The recommendation system is built as a proof of concept and limited to the electrical and mechanical engineering disciplines. Through user modelling and data collection from a survey, collaborative ...	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	1,919
2207.05993	A new database of Houma Alliance Book ancient handwritten characters and classifier fusion approach	The Houma Alliance Book is one of the national treasures of the Museum in Shanxi Museum Town in China. It has great historical significance in researching ancient history. To date, the research on the Houma Alliance Book has been staying in the identification of paper documents, which is inefficient to identify and dif...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	307,736
2202.01949	A Reinforcement Learning Framework for PQoS in a Teleoperated Driving Scenario	In recent years, autonomous networks have been designed with Predictive Quality of Service (PQoS) in mind, as a means for applications operating in the industrial and/or automotive sectors to predict unanticipated Quality of Service (QoS) changes and react accordingly. In this context, Reinforcement Learning (RL) has c...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	true	278,652
1506.06155	CO2 Forest: Improved Random Forest by Continuous Optimization of Oblique Splits	We propose a novel algorithm for optimizing multivariate linear threshold functions as split functions of decision trees to create improved Random Forest classifiers. Standard tree induction methods resort to sampling and exhaustive search to find good univariate split functions. In contrast, our method computes a line...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	44,387
2409.15398	Attack Atlas: A Practitioner's Perspective on Challenges and Pitfalls in Red Teaming GenAI	As generative AI, particularly large language models (LLMs), become increasingly integrated into production applications, new attack surfaces and vulnerabilities emerge and put a focus on adversarial threats in natural language and multi-modal systems. Red-teaming has gained importance in proactively identifying weakne...	false	false	false	false	true	false	true	false	false	false	false	false	true	false	false	false	false	false	490,913
2412.02790	An Evolutionary Large Language Model for Hallucination Mitigation	The emergence of LLMs, like ChatGPT and Gemini, has marked the modern era of artificial intelligence applications characterized by high-impact applications generating text, images, and videos. However, these models usually ensue with one critical challenge called hallucination: confident presentation of inaccurate or f...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	513,676
1606.02378	SE3-Nets: Learning Rigid Body Motion using Deep Neural Networks	We introduce SE3-Nets, which are deep neural networks designed to model and learn rigid body motion from raw point cloud data. Based only on sequences of depth images along with action vectors and point wise data associations, SE3-Nets learn to segment effected object parts and predict their motion resulting from the a...	false	false	false	false	true	false	true	true	false	false	false	true	false	false	false	false	false	false	56,947
1403.7657	The Call of the Crowd: Event Participation in Location-based Social Services	Understanding the social and behavioral forces behind event participation is not only interesting from the viewpoint of social science, but also has important applications in the design of personalized event recommender systems. This paper takes advantage of data from a widely used location-based social network, Foursq...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	31,912
2306.01485	Robust low-rank training via approximate orthonormal constraints	With the growth of model and data sizes, a broad effort has been made to design pruning techniques that reduce the resource demand of deep learning pipelines, while retaining model performance. In order to reduce both inference and training costs, a prominent line of work uses low-rank matrix factorizations to represen...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	true	370,480
1905.13178	Better Future through AI: Avoiding Pitfalls and Guiding AI Towards its Full Potential	Artificial Intelligence (AI) technology is rapidly changing many areas of society. While there is tremendous potential in this transition, there are several pitfalls as well. Using the history of computing and the world-wide web as a guide, in this article we identify those pitfalls and actions that lead AI development...	false	false	false	false	true	false	false	false	false	false	false	false	false	true	false	false	false	false	133,021
2111.01275	Recurrent neural network models for working memory of continuous variables: activity manifolds, connectivity patterns, and dynamic codes	Many daily activities and psychophysical experiments involve keeping multiple items in working memory. When items take continuous values (e.g., orientation, contrast, length, loudness) they must be stored in a continuous structure of appropriate dimensions. We investigate how this structure is represented in neural cir...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	264,509
2106.05027	Scientometric engineering: Exploring citation dynamics via arXiv eprints	Scholarly communications have been rapidly integrated into digitised and networked open ecosystems, where preprint servers have played a pivotal role in accelerating the knowledge transfer processes. However, quantitative evidence is scarce regarding how this paradigm shift beyond the traditional journal publication sy...	false	false	false	true	false	false	false	false	false	false	false	false	false	true	false	false	false	true	239,947
1908.09156	A framework for anomaly detection using language modeling, and its applications to finance	In the finance sector, studies focused on anomaly detection are often associated with time-series and transactional data analytics. In this paper, we lay out the opportunities for applying anomaly and deviation detection methods to text corpora and challenges associated with them. We argue that language models that use...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	142,770
1811.06477	Multi-cell LSTM Based Neural Language Model	Language models, being at the heart of many NLP problems, are always of great interest to researchers. Neural language models come with the advantage of distributed representations and long range contexts. With its particular dynamics that allow the cycling of information within the network, `Recurrent neural network' ...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	true	false	false	113,535
2312.09944	Power Minimizing MEC Offloading with QoS Constraints over RIS-Empowered Communications	This work lies at the intersection of two cutting edge technologies envisioned to proliferate in future 6G wireless systems: Multi-access Edge Computing (MEC) and Reconfigurable Intelligent Surfaces (RISs). While the former will bring a powerful information technology environment at the wireless edge, the latter will e...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	415,941
2204.01411	Computer-Aided Extraction of Select MRI Markers of Cerebral Small Vessel Disease: A Systematic Review	Cerebral small vessel disease (CSVD) is a major vascular contributor to cognitive impairment in ageing, including dementias. Imaging remains the most promising method for in vivo studies of CSVD. To replace the subjective and laborious visual rating approaches, emerging studies have applied state-of-the-art artificial ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	289,596
2211.06883	Generalizing distribution of partial rewards for multi-armed bandits with temporally-partitioned rewards	We investigate the Multi-Armed Bandit problem with Temporally-Partitioned Rewards (TP-MAB) setting in this paper. In the TP-MAB setting, an agent will receive subsets of the reward over multiple rounds rather than the entire reward for the arm all at once. In this paper, we introduce a general formulation of how an arm...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	330,057
1203.6027	Causal State Communication	The problem of state communication over a discrete memoryless channel with discrete memoryless state is studied when the state information is available strictly causally at the encoder. It is shown that block Markov encoding, in which the encoder communicates a description of the state sequence in the previous block by...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	15,141
2408.16647	DriveGenVLM: Real-world Video Generation for Vision Language Model based Autonomous Driving	The advancement of autonomous driving technologies necessitates increasingly sophisticated methods for understanding and predicting real-world scenarios. Vision language models (VLMs) are emerging as revolutionary tools with significant potential to influence autonomous driving. In this paper, we propose the DriveGenVL...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	484,405

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