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
1909.01459	Interpretable Word Embeddings via Informative Priors	Word embeddings have demonstrated strong performance on NLP tasks. However, lack of interpretability and the unsupervised nature of word embeddings have limited their use within computational social science and digital humanities. We propose the use of informative priors to create interpretable and domain-informed dime...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	143,901
2201.10873	TransPPG: Two-stream Transformer for Remote Heart Rate Estimate	Non-contact facial video-based heart rate estimation using remote photoplethysmography (rPPG) has shown great potential in many applications (e.g., remote health care) and achieved creditable results in constrained scenarios. However, practical applications require results to be accurate even under complex environment ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	277,126
2007.01113	Entanglement-Assisted Quantum Error Correcting Codes From RS Codes and BCH Codes with Extension Degree 2	Entanglement-assisted quantum error correcting codes (EAQECCs) constructed from Reed-Solomon codes and BCH codes are considered in this work. It is provided a complete and explicit formula for the parameters of EAQECCs coming from any Reed-Solomon code, for the Hermitian metric, and from any BCH code with extension deg...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	185,330
2211.00880	DeepTrace: Learning to Optimize Contact Tracing in Epidemic Networks with Graph Neural Networks	Digital contact tracing aims to curb epidemics by identifying and mitigating public health emergencies through technology. Backward contact tracing, which tracks the sources of infection, proved crucial in places like Japan for identifying COVID-19 infections from superspreading events. This paper presents a novel pers...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	328,034
2112.10138	Anisotropic mesh adaptation for region-based segmentation accounting for image spatial information	A finite element-based image segmentation strategy enhanced by an anisotropic mesh adaptation procedure is presented. The methodology relies on a split Bregman algorithm for the minimisation of a region-based energy functional and on an anisotropic recovery-based error estimate to drive mesh adaptation. More precisely,...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	272,354
1302.5056	Pooling-Invariant Image Feature Learning	Unsupervised dictionary learning has been a key component in state-of-the-art computer vision recognition architectures. While highly effective methods exist for patch-based dictionary learning, these methods may learn redundant features after the pooling stage in a given early vision architecture. In this paper, we of...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	22,272
1903.11337	Chance-Constrained AC Optimal Power Flow -- A Polynomial Chaos Approach	As the share of renewables in the grid increases, the operation of power systems becomes more challenging. The present paper proposes a method to formulate and solve chance-constrained optimal power flow while explicitly considering the full nonlinear AC power flow equations and stochastic uncertainties. We use polynom...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	125,494
2102.11544	Identifying Physical Law of Hamiltonian Systems via Meta-Learning	Hamiltonian mechanics is an effective tool to represent many physical processes with concise yet well-generalized mathematical expressions. A well-modeled Hamiltonian makes it easy for researchers to analyze and forecast many related phenomena that are governed by the same physical law. However, in general, identifying...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	221,465
2007.15911	The role of explainability in creating trustworthy artificial intelligence for health care: a comprehensive survey of the terminology, design choices, and evaluation strategies	Artificial intelligence (AI) has huge potential to improve the health and well-being of people, but adoption in clinical practice is still limited. Lack of transparency is identified as one of the main barriers to implementation, as clinicians should be confident the AI system can be trusted. Explainable AI has the pot...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	189,802
2112.02209	Generalized Likelihood Ratio Test for Adversarially Robust Hypothesis Testing	Machine learning models are known to be susceptible to adversarial attacks which can cause misclassification by introducing small but well designed perturbations. In this paper, we consider a classical hypothesis testing problem in order to develop fundamental insight into defending against such adversarial perturbatio...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	269,759
2408.13823	Improving GNSS Positioning in Challenging Urban Areas by Digital Twin Database Correction	Accurate positioning technology is the foundation for industry and business applications. Although indoor and outdoor positioning techniques have been well studied separately, positioning performance in the intermediate period of changing the positioning environment is still challenging. This paper proposed a digital t...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	483,307
1912.03790	Hardening Random Forest Cyber Detectors Against Adversarial Attacks	Machine learning algorithms are effective in several applications, but they are not as much successful when applied to intrusion detection in cyber security. Due to the high sensitivity to their training data, cyber detectors based on machine learning are vulnerable to targeted adversarial attacks that involve the pert...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	156,690
1511.09139	Discontinuous integral control for mechanical systems	For mechanical systems we present a controller able to track an unknown smooth signal, converging in finite time and by means of a continuous control signal. The control scheme is insensitive against unknown perturbations with bounded derivative. The controller consists of a non locally Lipschitz state feedback control...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	49,636
2209.13425	Resource Allocation for Mobile Metaverse with the Internet of Vehicles over 6G Wireless Communications: A Deep Reinforcement Learning Approach	Improving the interactivity and interconnectivity between people is one of the highlights of the Metaverse. The Metaverse relies on a core approach, digital twinning, which is a means to replicate physical world objects, people, actions and scenes onto the virtual world. Being able to access scenes and information asso...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	319,887
2208.05641	Towards Automated Key-Point Detection in Images with Partial Pool View	Sports analytics has been an up-and-coming field of research among professional sporting organizations and academic institutions alike. With the insurgence and collection of athlete data, the primary goal of such analysis is to improve athletes' performance in a measurable and quantifiable manner. This work is aimed at...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	312,455
2402.00637	Fisheye Camera and Ultrasonic Sensor Fusion For Near-Field Obstacle Perception in Bird's-Eye-View	Accurate obstacle identification represents a fundamental challenge within the scope of near-field perception for autonomous driving. Conventionally, fisheye cameras are frequently employed for comprehensive surround-view perception, including rear-view obstacle localization. However, the performance of such cameras ca...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	425,664
2008.04005	Deterministic error bounds for kernel-based learning techniques under bounded noise	We consider the problem of reconstructing a function from a finite set of noise-corrupted samples. Two kernel algorithms are analyzed, namely kernel ridge regression and $\varepsilon$-support vector regression. By assuming the ground-truth function belongs to the reproducing kernel Hilbert space of the chosen kernel, a...	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	191,099
2501.07324	Foundation Models at Work: Fine-Tuning for Fairness in Algorithmic Hiring	Foundation models require fine-tuning to ensure their generative outputs align with intended results for specific tasks. Automating this fine-tuning process is challenging, as it typically needs human feedback that can be expensive to acquire. We present AutoRefine, a method that leverages reinforcement learning for ta...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	524,358
1910.00138	Custom Extended Sobel Filters	Edge detection is widely and fundamental feature used in various algorithms in computer vision to determine the edges in an image. The edge detection algorithm is used to determine the edges in an image which are further used by various algorithms from line detection to machine learning that can determine objects based...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	147,587
2110.01655	VTAMIQ: Transformers for Attention Modulated Image Quality Assessment	Following the major successes of self-attention and Transformers for image analysis, we investigate the use of such attention mechanisms in the context of Image Quality Assessment (IQA) and propose a novel full-reference IQA method, Vision Transformer for Attention Modulated Image Quality (VTAMIQ). Our method achieves ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	258,840
2404.03769	On Extending the Automatic Test Markup Language (ATML) for Machine Learning	This paper addresses the urgent need for messaging standards in the operational test and evaluation (T&E) of machine learning (ML) applications, particularly in edge ML applications embedded in systems like robots, satellites, and unmanned vehicles. It examines the suitability of the IEEE Standard 1671 (IEEE Std 1671),...	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	true	444,398
2412.15236	CareBot: A Pioneering Full-Process Open-Source Medical Language Model	Recently, both closed-source LLMs and open-source communities have made significant strides, outperforming humans in various general domains. However, their performance in specific professional domains such as medicine, especially within the open-source community, remains suboptimal due to the complexity of medical kno...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	518,999
2009.01947	Practical and Parallelizable Algorithms for Non-Monotone Submodular Maximization with Size Constraint	We present combinatorial and parallelizable algorithms for maximization of a submodular function, not necessarily monotone, with respect to a size constraint. We improve the best approximation factor achieved by an algorithm that has optimal adaptivity and nearly optimal query complexity to $0.193 - \varepsilon$. The c...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	194,422
2101.08204	secureTF: A Secure TensorFlow Framework	Data-driven intelligent applications in modern online services have become ubiquitous. These applications are usually hosted in the untrusted cloud computing infrastructure. This poses significant security risks since these applications rely on applying machine learning algorithms on large datasets which may contain pr...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	true	216,257
2310.08182	XIMAGENET-12: An Explainable AI Benchmark Dataset for Model Robustness Evaluation	Despite the promising performance of existing visual models on public benchmarks, the critical assessment of their robustness for real-world applications remains an ongoing challenge. To bridge this gap, we propose an explainable visual dataset, XIMAGENET-12, to evaluate the robustness of visual models. XIMAGENET-12 co...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	399,299
2307.04018	Revisiting Cross-Lingual Summarization: A Corpus-based Study and A New Benchmark with Improved Annotation	Most existing cross-lingual summarization (CLS) work constructs CLS corpora by simply and directly translating pre-annotated summaries from one language to another, which can contain errors from both summarization and translation processes. To address this issue, we propose ConvSumX, a cross-lingual conversation summar...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	378,247
2009.11201	Harnessing Multilinguality in Unsupervised Machine Translation for Rare Languages	Unsupervised translation has reached impressive performance on resource-rich language pairs such as English-French and English-German. However, early studies have shown that in more realistic settings involving low-resource, rare languages, unsupervised translation performs poorly, achieving less than 3.0 BLEU. In this...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	197,104
2110.10403	AFTer-UNet: Axial Fusion Transformer UNet for Medical Image Segmentation	Recent advances in transformer-based models have drawn attention to exploring these techniques in medical image segmentation, especially in conjunction with the U-Net model (or its variants), which has shown great success in medical image segmentation, under both 2D and 3D settings. Current 2D based methods either dire...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	262,144
2401.15496	Baichuan2-Sum: Instruction Finetune Baichuan2-7B Model for Dialogue Summarization	Large language models (LLMs) like Llama, Baichuan and Bloom models show remarkable ability with instruction fine-tuning in many natural language tasks. Nevertheless, for the dialogue summarization task, which aims to generate summaries for different roles in dialogue, most of the state-of-the-art methods conduct on sma...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	424,471
2502.02100	Topic Modeling in Marathi	While topic modeling in English has become a prevalent and well-explored area, venturing into topic modeling for Indic languages remains relatively rare. The limited availability of resources, diverse linguistic structures, and unique challenges posed by Indic languages contribute to the scarcity of research and applic...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	530,183
1902.02804	SiamVGG: Visual Tracking using Deeper Siamese Networks	Recently, we have seen a rapid development of Deep Neural Network (DNN) based visual tracking solutions. Some trackers combine the DNN-based solutions with Discriminative Correlation Filters (DCF) to extract semantic features and successfully deliver the state-of-the-art tracking accuracy. However, these solutions are ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	120,949
2411.19824	SAT-HMR: Real-Time Multi-Person 3D Mesh Estimation via Scale-Adaptive Tokens	We propose a one-stage framework for real-time multi-person 3D human mesh estimation from a single RGB image. While current one-stage methods, which follow a DETR-style pipeline, achieve state-of-the-art (SOTA) performance with high-resolution inputs, we observe that this particularly benefits the estimation of individ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	512,399
2401.10017	Text Region Multiple Information Perception Network for Scene Text Detection	Segmentation-based scene text detection algorithms can handle arbitrary shape scene texts and have strong robustness and adaptability, so it has attracted wide attention. Existing segmentation-based scene text detection algorithms usually only segment the pixels in the center region of the text, while ignoring other in...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	422,463
2412.10898	Exploring Grokking: Experimental and Mechanistic Investigations	The phenomenon of grokking in over-parameterized neural networks has garnered significant interest. It involves the neural network initially memorizing the training set with zero training error and near-random test error. Subsequent prolonged training leads to a sharp transition from no generalization to perfect genera...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	517,156
2107.12207	Image-Based Parking Space Occupancy Classification: Dataset and Baseline	We introduce a new dataset for image-based parking space occupancy classification: ACPDS. Unlike in prior datasets, each image is taken from a unique view, systematically annotated, and the parking lots in the train, validation, and test sets are unique. We use this dataset to propose a simple baseline model for parkin...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	247,832
2309.04644	Towards Understanding Neural Collapse: The Effects of Batch Normalization and Weight Decay	Neural Collapse (NC) is a geometric structure recently observed at the terminal phase of training deep neural networks, which states that last-layer feature vectors for the same class would "collapse" to a single point, while features of different classes become equally separated. We demonstrate that batch normalizatio...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	390,791
2402.07772	End-to-End Learning for Fair Multiobjective Optimization Under Uncertainty	Many decision processes in artificial intelligence and operations research are modeled by parametric optimization problems whose defining parameters are unknown and must be inferred from observable data. The Predict-Then-Optimize (PtO) paradigm in machine learning aims to maximize downstream decision quality by trainin...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	428,841
2406.04337	Coherent Zero-Shot Visual Instruction Generation	Despite the advances in text-to-image synthesis, particularly with diffusion models, generating visual instructions that require consistent representation and smooth state transitions of objects across sequential steps remains a formidable challenge. This paper introduces a simple, training-free framework to tackle the...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	461,639
2303.10371	UNREAL:Unlabeled Nodes Retrieval and Labeling for Heavily-imbalanced Node Classification	Extremely skewed label distributions are common in real-world node classification tasks. If not dealt with appropriately, it significantly hurts the performance of GNNs in minority classes. Due to its practical importance, there have been a series of recent research devoted to this challenge. Existing over-sampling tec...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	352,416
2310.02097	Leveraging Classic Deconvolution and Feature Extraction in Zero-Shot Image Restoration	Non-blind deconvolution aims to restore a sharp image from its blurred counterpart given an obtained kernel. Existing deep neural architectures are often built based on large datasets of sharp ground truth images and trained with supervision. Sharp, high quality ground truth images, however, are not always available, e...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	396,709
2406.07006	MIPI 2024 Challenge on Few-shot RAW Image Denoising: Methods and Results	The increasing demand for computational photography and imaging on mobile platforms has led to the widespread development and integration of advanced image sensors with novel algorithms in camera systems. However, the scarcity of high-quality data for research and the rare opportunity for in-depth exchange of views fro...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	462,856
1611.01722	Learning to Draw Samples: With Application to Amortized MLE for Generative Adversarial Learning	We propose a simple algorithm to train stochastic neural networks to draw samples from given target distributions for probabilistic inference. Our method is based on iteratively adjusting the neural network parameters so that the output changes along a Stein variational gradient that maximumly decreases the KL divergen...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	63,426
2403.12532	UniBind: LLM-Augmented Unified and Balanced Representation Space to Bind Them All	We present UniBind, a flexible and efficient approach that learns a unified representation space for seven diverse modalities -- images, text, audio, point cloud, thermal, video, and event data. Existing works, eg., ImageBind, treat the image as the central modality and build an image-centered representation space; how...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	439,229
2406.11481	Constrained Reinforcement Learning with Average Reward Objective: Model-Based and Model-Free Algorithms	Reinforcement Learning (RL) serves as a versatile framework for sequential decision-making, finding applications across diverse domains such as robotics, autonomous driving, recommendation systems, supply chain optimization, biology, mechanics, and finance. The primary objective in these applications is to maximize the...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	464,910
2411.16511	Use-Inspired Mobile Robot to Improve Safety of Building Retrofit Workforce in Constrained Spaces	The inspection of confined critical infrastructure such as attics or crawlspaces is challenging for human operators due to insufficient task space, limited visibility, and the presence of hazardous materials. This paper introduces a prototype of PARIS (Precision Application Robot for Inaccessible Spaces): a use-inspire...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	511,054
2307.12854	Multiscale Video Pretraining for Long-Term Activity Forecasting	Long-term activity forecasting is an especially challenging research problem because it requires understanding the temporal relationships between observed actions, as well as the variability and complexity of human activities. Despite relying on strong supervision via expensive human annotations, state-of-the-art forec...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	381,405
1811.08043	Recurrent Iterative Gating Networks for Semantic Segmentation	In this paper, we present an approach for Recurrent Iterative Gating called RIGNet. The core elements of RIGNet involve recurrent connections that control the flow of information in neural networks in a top-down manner, and different variants on the core structure are considered. The iterative nature of this mechanism ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	113,934
2305.00331	Synthetic Cross-language Information Retrieval Training Data	A key stumbling block for neural cross-language information retrieval (CLIR) systems has been the paucity of training data. The appearance of the MS MARCO monolingual training set led to significant advances in the state of the art in neural monolingual retrieval. By translating the MS MARCO documents into other langua...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	361,302
2408.04193	Uncertainty-Aware Crime Prediction With Spatial Temporal Multivariate Graph Neural Networks	Crime forecasting is a critical component of urban analysis and essential for stabilizing society today. Unlike other time series forecasting problems, crime incidents are sparse, particularly in small regions and within specific time periods. Traditional spatial-temporal deep learning models often struggle with this s...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	479,282
2101.07280	Visualizing Missing Surfaces In Colonoscopy Videos using Shared Latent Space Representations	Optical colonoscopy (OC), the most prevalent colon cancer screening tool, has a high miss rate due to a number of factors, including the geometry of the colon (haustral fold and sharp bends occlusions), endoscopist inexperience or fatigue, endoscope field of view, etc. We present a framework to visualize the missed reg...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	215,982
2105.13907	Towards a Very Large Scale Traffic Simulator for Multi-Agent Reinforcement Learning Testbeds	Smart traffic control and management become an emerging application for Deep Reinforcement Learning (DRL) to solve traffic congestion problems in urban networks. Different traffic control and management policies can be tested on the traffic simulation. Current DRL-based studies are mainly supported by the microscopic s...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	237,451
1708.04927	TheoSea: Marching Theory to Light	There is sufficient information in the far-field of a radiating dipole antenna to rediscover the Maxwell Equations and the wave equations of light, including the speed of light $c.$ TheoSea is a Julia program that does this in about a second, and the key insight is that the compactness of theories drives the search. Th...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	79,044
2008.10149	Efficient Online Learning for Cognitive Radar-Cellular Coexistence via Contextual Thompson Sampling	This paper describes a sequential, or online, learning scheme for adaptive radar transmissions that facilitate spectrum sharing with a non-cooperative cellular network. First, the interference channel between the radar and a spatially distant cellular network is modeled. Then, a linear Contextual Bandit (CB) learning f...	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	192,925
2102.06196	Approximation Methods for Geometric Regulation	In these notes we collect some results from several of the authors' works in order to make available a single source and show how the approximate geometric methods for regulation have been developed, and how the control design strategy has evolved from the theoretical methods, involving the regulator equations, to what...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	219,669
1310.7028	Multiplicativity of completely bounded $p$-norms implies a strong converse for entanglement-assisted capacity	The fully quantum reverse Shannon theorem establishes the optimal rate of noiseless classical communication required for simulating the action of many instances of a noisy quantum channel on an arbitrary input state, while also allowing for an arbitrary amount of shared entanglement of an arbitrary form. Turning this t...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	28,003
1912.11343	Robust Visual Tracking via Implicit Low-Rank Constraints and Structural Color Histograms	With the guaranteed discrimination and efficiency of spatial appearance model, Discriminative Correlation Filters (DCF-) based tracking methods have achieved outstanding performance recently. However, the construction of effective temporal appearance model is still challenging on account of filter degeneration becomes ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	158,546
2203.06382	Differentiated Relevances Embedding for Group-based Referring Expression Comprehension	The key of referring expression comprehension lies in capturing the cross-modal visual-linguistic relevance. Existing works typically model the cross-modal relevance in each image, where the anchor object/expression and their positive expression/object have the same attribute as the negative expression/object, but with...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	285,089
2306.12760	Blended-NeRF: Zero-Shot Object Generation and Blending in Existing Neural Radiance Fields	Editing a local region or a specific object in a 3D scene represented by a NeRF or consistently blending a new realistic object into the scene is challenging, mainly due to the implicit nature of the scene representation. We present Blended-NeRF, a robust and flexible framework for editing a specific region of interest...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	true	375,057
2111.12295	Animal behavior classification via deep learning on embedded systems	We develop an end-to-end deep-neural-network-based algorithm for classifying animal behavior using accelerometry data on the embedded system of an artificial intelligence of things (AIoT) device installed in a wearable collar tag. The proposed algorithm jointly performs feature extraction and classification utilizing a...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	267,924
2006.02569	Automated segmentation of retinal fluid volumes from structural and angiographic optical coherence tomography using deep learning	Purpose: We proposed a deep convolutional neural network (CNN), named Retinal Fluid Segmentation Network (ReF-Net) to segment volumetric retinal fluid on optical coherence tomography (OCT) volume. Methods: 3 x 3-mm OCT scans were acquired on one eye by a 70-kHz OCT commercial AngioVue system (RTVue-XR; Optovue, Inc.) f...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	180,072
1407.1255	Dynamic message-passing equations for models with unidirectional dynamics	Understanding and quantifying the dynamics of disordered out-of-equilibrium models is an important problem in many branches of science. Using the dynamic cavity method on time trajectories, we construct a general procedure for deriving the dynamic message-passing equations for a large class of models with unidirectiona...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	34,413
2202.11527	A new LDA formulation with covariates	The Latent Dirichlet Allocation (LDA) model is a popular method for creating mixed-membership clusters. Despite having been originally developed for text analysis, LDA has been used for a wide range of other applications. We propose a new formulation for the LDA model which incorporates covariates. In this model, a neg...	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	false	281,919
2205.03146	CLIP-CLOP: CLIP-Guided Collage and Photomontage	The unabated mystique of large-scale neural networks, such as the CLIP dual image-and-text encoder, popularized automatically generated art. Increasingly more sophisticated generators enhanced the artworks' realism and visual appearance, and creative prompt engineering enabled stylistic expression. Guided by an artist-...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	295,183
cs/0512024	A bound on Grassmannian codes	We give a new asymptotic upper bound on the size of a code in the Grassmannian space. The bound is better than the upper bounds known previously in the entire range of distances except very large values.	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	539,131
2402.07871	Scaling Laws for Fine-Grained Mixture of Experts	Mixture of Experts (MoE) models have emerged as a primary solution for reducing the computational cost of Large Language Models. In this work, we analyze their scaling properties, incorporating an expanded range of variables. Specifically, we introduce a new hyperparameter, granularity, whose adjustment enables precise...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	428,876
2412.08619	Synthetic Vision: Training Vision-Language Models to Understand Physics	Physical reasoning, which involves the interpretation, understanding, and prediction of object behavior in dynamic environments, remains a significant challenge for current Vision-Language Models (VLMs). In this work, we propose two methods to enhance VLMs' physical reasoning capabilities using simulated data. First, w...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	516,173
2209.12708	Faith: An Efficient Framework for Transformer Verification on GPUs	Transformer verification draws increasing attention in machine learning research and industry. It formally verifies the robustness of transformers against adversarial attacks such as exchanging words in a sentence with synonyms. However, the performance of transformer verification is still not satisfactory due to bound...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	319,626
2402.07954	On Leaky-Integrate-and Fire as Spike-Train-Quantization Operator on Dirac-Superimposed Continuous-Time Signals	Leaky-integrate-and-fire (LIF) is studied as a non-linear operator that maps an integrable signal $f$ to a sequence $\eta_f$ of discrete events, the spikes. In the case without any Dirac pulses in the input, it makes no difference whether to set the neuron's potential to zero or to subtract the threshold $\vartheta$ im...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	428,914
2301.09860	A predictive physics-aware hybrid reduced order model for reacting flows	In this work, a new hybrid predictive Reduced Order Model (ROM) is proposed to solve reacting flow problems. This algorithm is based on a dimensionality reduction using Proper Orthogonal Decomposition (POD) combined with deep learning architectures. The number of degrees of freedom is reduced from thousands of temporal...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	341,628
2312.02186	Identifying Spurious Correlations using Counterfactual Alignment	Models driven by spurious correlations often yield poor generalization performance. We propose the counterfactual (CF) alignment method to detect and quantify spurious correlations of black box classifiers. Our methodology is based on counterfactual images generated with respect to one classifier being input into other...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	412,742
2302.05498	Data-Driven Inverse Optimization for Marginal Offer Price Recovery in Electricity Markets	This paper presents a data-driven inverse optimization (IO) approach to recover the marginal offer prices of generators in a wholesale energy market. By leveraging underlying market-clearing processes, we establish a closed-form relationship between the unknown parameters and the publicly available market-clearing resu...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	345,063
2312.12874	Deep-Unfolded Joint Activity and Data Detection for Grant-Free Transmission in Cell-Free Systems	Massive grant-free transmission and cell-free wireless communication systems have emerged as pivotal enablers for massive machine-type communication. This paper proposes a deep-unfolding-based joint activity and data detection (DU-JAD) algorithm for massive grant-free transmission in cell-free systems. We first formula...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	417,142
2006.03732	WOAD: Weakly Supervised Online Action Detection in Untrimmed Videos	Online action detection in untrimmed videos aims to identify an action as it happens, which makes it very important for real-time applications. Previous methods rely on tedious annotations of temporal action boundaries for training, which hinders the scalability of online action detection systems. We propose WOAD, a we...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	180,415
2402.17569	Backpropagation-Based Analytical Derivatives of EKF Covariance for Active Sensing	To enhance accuracy of robot state estimation, active sensing (or perception-aware) methods seek trajectories that maximize the information gathered by the sensors. To this aim, one possibility is to seek trajectories that minimize the (estimation error) covariance matrix output by an extended Kalman filter (EKF), w.r....	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	433,053
2403.06494	An Efficient Solution to the 2D Visibility Problem in Cartesian Grid Maps and its Application in Heuristic Path Planning	This paper introduces a novel, lightweight method to solve the visibility problem for 2D grids. The proposed method evaluates the existence of lines-of-sight from a source point to all other grid cells in a single pass with no preprocessing and independently of the number and shape of obstacles. It has a compute and me...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	true	436,480
2405.16036	Certifying Adapters: Enabling and Enhancing the Certification of Classifier Adversarial Robustness	Randomized smoothing has become a leading method for achieving certified robustness in deep classifiers against l_{p}-norm adversarial perturbations. Current approaches for achieving certified robustness, such as data augmentation with Gaussian noise and adversarial training, require expensive training procedures that ...	false	false	false	false	false	false	true	false	false	false	false	true	true	false	false	false	false	false	457,220
2405.14388	Evaluation of the Programming Skills of Large Language Models	The advent of Large Language Models (LLM) has revolutionized the efficiency and speed with which tasks are completed, marking a significant leap in productivity through technological innovation. As these chatbots tackle increasingly complex tasks, the challenge of assessing the quality of their outputs has become param...	false	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	true	456,400
2007.15404	Regional Rainfall Prediction Using Support Vector Machine Classification of Large-Scale Precipitation Maps	Rainfall prediction helps planners anticipate potential social and economic impacts produced by too much or too little rain. This research investigates a class-based approach to rainfall prediction from 1-30 days in advance. The study made regional predictions based on sequences of daily rainfall maps of the continenta...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	189,661
2406.04300	Text-to-Drive: Diverse Driving Behavior Synthesis via Large Language Models	Generating varied scenarios through simulation is crucial for training and evaluating safety-critical systems, such as autonomous vehicles. Yet, the task of modeling the trajectories of other vehicles to simulate diverse and meaningful close interactions remains prohibitively costly. Adopting language descriptions to g...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	461,611
1606.02387	Angle-of-Attack Modulation in Trajectory Tracking for a Reusable Launch Vehicle	This paper deals with the problem of angle-of-attack modulation with the aim of enhancing transient performance of entry guidance during bank reversals, while compensating adverse effects of fast time-varying transient disturbances. An extended single-input/single-output system is developed in the velocity domain by me...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	56,950
2002.11326	Fail-safe Flight of a Fully-Actuated Quadcopter in a Single Motor Failure	In this paper, we introduce a new quadcopter fail-safe flight solution that can perform the same four controllable degrees-of-freedom flight as a regular multirotor even when a single thruster fails. The new solution employs a novel multirotor platform known as the T3-Multirotor and utilizes a distinctive strategy of a...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	165,676
2310.03767	Deep Reinforcement Learning Algorithms for Hybrid V2X Communication: A Benchmarking Study	In today's era, autonomous vehicles demand a safety level on par with aircraft. Taking a cue from the aerospace industry, which relies on redundancy to achieve high reliability, the automotive sector can also leverage this concept by building redundancy in V2X (Vehicle-to-Everything) technologies. Given the current lac...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	397,416
2405.01798	The Economy and Public Diplomacy: An Analysis of RT's Economic Content and Context on Facebook	With globalization's rise, economic interdependence's impacts have become a prominent factor affecting personal lives, as well as national and international dynamics. This study examines RT's public diplomacy efforts on its non-Russian Facebook accounts over the past five years to identify the prominence of economic to...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	451,516
2310.18833	Model-based Control of the Scanning Tunneling Microscope: Enabling New Modes of Imaging, Spectroscopy, and Lithography	The invention of scanning tunneling microscope (STM) dates back to the work of Binnig and Rohrer in the early 1980s, whose seminal contribution was rewarded by the 1986 Nobel Prize in Physics for the design of the scanning tunneling microscope. Forty years later, the STM remains the best existing tool for studying elec...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	403,724
1905.13179	Toward Runtime-Throttleable Neural Networks	As deep neural network (NN) methods have matured, there has been increasing interest in deploying NN solutions to "edge computing" platforms such as mobile phones or embedded controllers. These platforms are often resource-constrained, especially in energy storage and power, but state-of-the-art NN architectures are de...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	133,022
1903.09266	Reduction of Markov Chains using a Value-of-Information-Based Approach	In this paper, we propose an approach to obtain reduced-order models of Markov chains. Our approach is composed of two information-theoretic processes. The first is a means of comparing pairs of stationary chains on different state spaces, which is done via the negative Kullback-Leibler divergence defined on a model jo...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	125,016
2112.00459	Information Theoretic Representation Distillation	Despite the empirical success of knowledge distillation, current state-of-the-art methods are computationally expensive to train, which makes them difficult to adopt in practice. To address this problem, we introduce two distinct complementary losses inspired by a cheap entropy-like estimator. These losses aim to maxim...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	269,143
2407.17709	PGD-VIO: An Accurate Plane-Aided Visual-Inertial Odometry with Graph-Based Drift Suppression	Generally, high-level features provide more geometrical information compared to point features, which can be exploited to further constrain motions. Planes are commonplace in man-made environments, offering an active means to reduce drift, due to their extensive spatial and temporal observability. To make full use of p...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	476,082
1911.00627	Quadratic video interpolation	Video interpolation is an important problem in computer vision, which helps overcome the temporal limitation of camera sensors. Existing video interpolation methods usually assume uniform motion between consecutive frames and use linear models for interpolation, which cannot well approximate the complex motion in the r...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	151,867
2106.15984	Context-Aware Attention-Based Data Augmentation for POI Recommendation	With the rapid growth of location-based social networks (LBSNs), Point-Of-Interest (POI) recommendation has been broadly studied in this decade. Recently, the next POI recommendation, a natural extension of POI recommendation, has attracted much attention. It aims at suggesting the next POI to a user in spatial and tem...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	243,932
1512.04412	Instance-aware Semantic Segmentation via Multi-task Network Cascades	Semantic segmentation research has recently witnessed rapid progress, but many leading methods are unable to identify object instances. In this paper, we present Multi-task Network Cascades for instance-aware semantic segmentation. Our model consists of three networks, respectively differentiating instances, estimating...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	50,131
1412.4963	Robust Adaptive Quantum Phase Estimation	Quantum parameter estimation is central to many fields such as quantum computation, communications and metrology. Optimal estimation theory has been instrumental in achieving the best accuracy in quantum parameter estimation, which is possible when we have very precise knowledge of and control over the model. However, ...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	38,443
2409.07930	A convolutional neural network approach to deblending seismic data	For economic and efficiency reasons, blended acquisition of seismic data is becoming more and more commonplace. Seismic deblending methods are always computationally demanding and normally consist of multiple processing steps. Besides, the parameter setting is not always trivial. Machine learning-based processing has t...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	487,711
2107.03250	Understanding Intrinsic Robustness Using Label Uncertainty	A fundamental question in adversarial machine learning is whether a robust classifier exists for a given task. A line of research has made some progress towards this goal by studying the concentration of measure, but we argue standard concentration fails to fully characterize the intrinsic robustness of a classificatio...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	245,102
2008.13227	A Compact Deep Architecture for Real-time Saliency Prediction	Saliency computation models aim to imitate the attention mechanism in the human visual system. The application of deep neural networks for saliency prediction has led to a drastic improvement over the last few years. However, deep models have a high number of parameters which makes them less suitable for real-time appl...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	193,796
2309.04339	Online Submodular Maximization via Online Convex Optimization	We study monotone submodular maximization under general matroid constraints in the online setting. We prove that online optimization of a large class of submodular functions, namely, weighted threshold potential functions, reduces to online convex optimization (OCO). This is precisely because functions in this class ad...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	390,688
2409.10749	A Fairness-Oriented Control Framework for Safety-Critical Multi-Robot Systems: Alternative Authority Control	This paper proposes a fair control framework for multi-robot systems, which integrates the newly introduced Alternative Authority Control (AAC) and Flexible Control Barrier Function (F-CBF). Control authority refers to a single robot which can plan its trajectory while considering others as moving obstacles, meaning th...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	488,869
2102.11319	Stratified Experience Replay: Correcting Multiplicity Bias in Off-Policy Reinforcement Learning	Deep Reinforcement Learning (RL) methods rely on experience replay to approximate the minibatched supervised learning setting; however, unlike supervised learning where access to lots of training data is crucial to generalization, replay-based deep RL appears to struggle in the presence of extraneous data. Recent works...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	221,381
2312.15204	DexDLO: Learning Goal-Conditioned Dexterous Policy for Dynamic Manipulation of Deformable Linear Objects	Deformable linear object (DLO) manipulation is needed in many fields. Previous research on deformable linear object (DLO) manipulation has primarily involved parallel jaw gripper manipulation with fixed grasping positions. However, the potential for dexterous manipulation of DLOs using an anthropomorphic hand is under-...	false	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	417,920
2403.06534	SARDet-100K: Towards Open-Source Benchmark and ToolKit for Large-Scale SAR Object Detection	Synthetic Aperture Radar (SAR) object detection has gained significant attention recently due to its irreplaceable all-weather imaging capabilities. However, this research field suffers from both limited public datasets (mostly comprising <2K images with only mono-category objects) and inaccessible source code. To tack...	false	true	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	436,499

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