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
2410.01792	When a language model is optimized for reasoning, does it still show embers of autoregression? An analysis of OpenAI o1	In "Embers of Autoregression" (McCoy et al., 2023), we showed that several large language models (LLMs) have some important limitations that are attributable to their origins in next-word prediction. Here we investigate whether these issues persist with o1, a new system from OpenAI that differs from previous LLMs in th...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	493,948
2410.13352	LAR-ECHR: A New Legal Argument Reasoning Task and Dataset for Cases of the European Court of Human Rights	We present Legal Argument Reasoning (LAR), a novel task designed to evaluate the legal reasoning capabilities of Large Language Models (LLMs). The task requires selecting the correct next statement (from multiple choice options) in a chain of legal arguments from court proceedings, given the facts of the case. We const...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	499,499
2002.10992	Migration Networks: Applications of Network Analysis to Macroscale Migration Patterns	An emerging area of research is the study of macroscale migration patterns as a network of nodes that represent places (e.g., countries, cities, and rural areas) and edges that encode migration ties that connect those places. In this chapter, we first review advances in the study of migration networks and recent work t...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	165,575
1906.06531	Media Environment, Dual Process and Polarization: A Computational Approach	News articles of varying degrees of truthfulness and political alignment, and their influences on the political opinions of the media consumers are modeled as a Bayesian network incorporating a mixture of ideas from dual-reasoning models of Motivated Reasoning and Analytic/Intuitive Reasoning. The result shows that as ...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	135,327
2205.12412	Differentially Private AUC Computation in Vertical Federated Learning	Federated learning has gained great attention recently as a privacy-enhancing tool to jointly train a machine learning model by multiple parties. As a sub-category, vertical federated learning (vFL) focuses on the scenario where features and labels are split into different parties. The prior work on vFL has mostly stud...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	298,522
2406.03409	Robust Knowledge Distillation Based on Feature Variance Against Backdoored Teacher Model	Benefiting from well-trained deep neural networks (DNNs), model compression have captured special attention for computing resource limited equipment, especially edge devices. Knowledge distillation (KD) is one of the widely used compression techniques for edge deployment, by obtaining a lightweight student model from a...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	461,227
2112.12194	Surrogate Likelihoods for Variational Annealed Importance Sampling	Variational inference is a powerful paradigm for approximate Bayesian inference with a number of appealing properties, including support for model learning and data subsampling. By contrast MCMC methods like Hamiltonian Monte Carlo do not share these properties but remain attractive since, contrary to parametric method...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	272,900
2210.14208	Don't Let Me Down! Offloading Robot VFs Up to the Cloud	Recent trends in robotic services propose offloading robot functionalities to the Edge to meet the strict latency requirements of networked robotics. However, the Edge is typically an expensive resource and sometimes the Cloud is also an option, thus, decreasing the cost. Following this idea, we propose Don't Let Me Do...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	true	326,459
1704.06864	On the Trade-Off between Computational Load and Reliability for Network Function Virtualization	Network Function Virtualization (NFV) enables the "softwarization" of network functions, which are implemented on virtual machines hosted on Commercial off-the-shelf (COTS) servers. Both the composition of the virtual network functions (VNFs) into a forwarding graph (FG) at the logical layer and the embedding of the FG...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	72,240
1812.05836	Rethinking Layer-wise Feature Amounts in Convolutional Neural Network Architectures	We characterize convolutional neural networks with respect to the relative amount of features per layer. Using a skew normal distribution as a parametrized framework, we investigate the common assumption of monotonously increasing feature-counts with higher layers of architecture designs. Our evaluation on models with ...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	116,492
2307.01985	Task-Specific Alignment and Multiple Level Transformer for Few-Shot Action Recognition	In the research field of few-shot learning, the main difference between image-based and video-based is the additional temporal dimension. In recent years, some works have used the Transformer to deal with frames, then get the attention feature and the enhanced prototype, and the results are competitive. However, some v...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	377,539
1702.02642	On minimum distance of locally repairable codes	Distributed and cloud storage systems are used to reliably store large-scale data. Erasure codes have been recently proposed and used in real-world distributed and cloud storage systems such as Google File System, Microsoft Azure Storage, and Facebook HDFS-RAID, to enhance the reliability. In order to decrease the repa...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	68,008
2105.04642	SUPR-GAN: SUrgical PRediction GAN for Event Anticipation in Laparoscopic and Robotic Surgery	Comprehension of surgical workflow is the foundation upon which artificial intelligence (AI) and machine learning (ML) holds the potential to assist intraoperative decision-making and risk mitigation. In this work, we move beyond mere identification of past surgical phases, into the prediction of future surgical steps ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	234,565
2310.17875	Siamese-DETR for Generic Multi-Object Tracking	The ability to detect and track the dynamic objects in different scenes is fundamental to real-world applications, e.g., autonomous driving and robot navigation. However, traditional Multi-Object Tracking (MOT) is limited to tracking objects belonging to the pre-defined closed-set categories. Recently, Open-Vocabulary ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	403,322
1604.04327	Invariant feature extraction from event based stimuli	We propose a novel architecture, the event-based GASSOM for learning and extracting invariant representations from event streams originating from neuromorphic vision sensors. The framework is inspired by feed-forward cortical models for visual processing. The model, which is based on the concepts of sparsity and tempor...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	54,624
2202.01841	Transport Score Climbing: Variational Inference Using Forward KL and Adaptive Neural Transport	Variational inference often minimizes the "reverse" Kullbeck-Leibler (KL) KL(q\|\|p) from the approximate distribution q to the posterior p. Recent work studies the "forward" KL KL(p\|\|q), which unlike reverse KL does not lead to variational approximations that underestimate uncertainty. This paper introduces Transport Sc...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	278,600
2011.01014	Chess2vec: Learning Vector Representations for Chess	We conduct the first study of its kind to generate and evaluate vector representations for chess pieces. In particular, we uncover the latent structure of chess pieces and moves, as well as predict chess moves from chess positions. We share preliminary results which anticipate our ongoing work on a neural network archi...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	204,451
1811.01395	A Deep One-Shot Network for Query-based Logo Retrieval	Logo detection in real-world scene images is an important problem with applications in advertisement and marketing. Existing general-purpose object detection methods require large training data with annotations for every logo class. These methods do not satisfy the incremental demand of logo classes necessary for pract...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	112,356
1109.5460	The scaling of human mobility by taxis is exponential	As a significant factor in urban planning, traffic forecasting and prediction of epidemics, modeling patterns of human mobility draws intensive attention from researchers for decades. Power-law distribution and its variations are observed from quite a few real-world human mobility datasets such as the movements of bank...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	12,323
2209.03089	Decoding Demographic un-fairness from Indian Names	Demographic classification is essential in fairness assessment in recommender systems or in measuring unintended bias in online networks and voting systems. Important fields like education and politics, which often lay a foundation for the future of equality in society, need scrutiny to design policies that can better ...	false	false	false	true	false	false	true	false	true	false	false	false	false	true	false	false	false	true	316,397
1402.4893	Anisotropic Mesh Adaptation for Image Representation	Triangular meshes have gained much interest in image representation and have been widely used in image processing. This paper introduces a framework of anisotropic mesh adaptation (AMA) methods to image representation and proposes a GPRAMA method that is based on AMA and greedy-point removal (GPR) scheme. Different tha...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	31,007
1707.00315	Proportionate Adaptive Filtering under Correntropy Criterion in Impulsive Noise Environments	An improved proportionate adaptive filter based on the Maximum Correntropy Criterion (IP-MCC) is proposed for identifying the system with variable sparsity in an impulsive noise environment. Utilization of MCC mitigates the effect of impulse noise while the improved proportionate concepts exploit the underlying system ...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	76,329
2205.05793	Robustness Guarantees for Credal Bayesian Networks via Constraint Relaxation over Probabilistic Circuits	In many domains, worst-case guarantees on the performance (e.g., prediction accuracy) of a decision function subject to distributional shifts and uncertainty about the environment are crucial. In this work we develop a method to quantify the robustness of decision functions with respect to credal Bayesian networks, for...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	296,037
2405.08626	Literature Review on Maneuver-Based Scenario Description for Automated Driving Simulations	The increasing complexity of automated driving functions and their growing operational design domains imply more demanding requirements on their validation. Classical methods such as field tests or formal analyses are not sufficient anymore and need to be complemented by simulations. For simulations, the standard appro...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	454,162
2404.04608	Panoptic Perception: A Novel Task and Fine-grained Dataset for Universal Remote Sensing Image Interpretation	Current remote-sensing interpretation models often focus on a single task such as detection, segmentation, or caption. However, the task-specific designed models are unattainable to achieve the comprehensive multi-level interpretation of images. The field also lacks support for multi-task joint interpretation datasets....	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	444,717
1810.03608	A Unified Dynamic Approach to Sparse Model Selection	Sparse model selection is ubiquitous from linear regression to graphical models where regularization paths, as a family of estimators upon the regularization parameter varying, are computed when the regularization parameter is unknown or decided data-adaptively. Traditional computational methods rely on solving a set o...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	109,847
2008.12205	Random Style Transfer based Domain Generalization Networks Integrating Shape and Spatial Information	Deep learning (DL)-based models have demonstrated good performance in medical image segmentation. However, the models trained on a known dataset often fail when performed on an unseen dataset collected from different centers, vendors and disease populations. In this work, we present a random style transfer network to t...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	193,510
2104.03543	Extended Parallel Corpus for Amharic-English Machine Translation	This paper describes the acquisition, preprocessing, segmentation, and alignment of an Amharic-English parallel corpus. It will be helpful for machine translation of a low-resource language, Amharic. We freely released the corpus for research purposes. Furthermore, we developed baseline statistical and neural machine t...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	229,104
2204.14100	Adversarial Distortion Learning for Medical Image Denoising	We present a novel adversarial distortion learning (ADL) for denoising two- and three-dimensional (2D/3D) biomedical image data. The proposed ADL consists of two auto-encoders: a denoiser and a discriminator. The denoiser removes noise from input data and the discriminator compares the denoised result to its noise-free...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	294,054
2308.10158	HODN: Disentangling Human-Object Feature for HOI Detection	The task of Human-Object Interaction (HOI) detection is to detect humans and their interactions with surrounding objects, where transformer-based methods show dominant advances currently. However, these methods ignore the relationship among humans, objects, and interactions: 1) human features are more contributive than...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	386,601
1804.00397	Analyzing and characterizing political discussions in WhatsApp public groups	We present a thorough characterization of what we believe to be the first significant analysis of the behavior of groups in WhatsApp in the scientific literature. Our characterization of over 270,000 messages and about 7,000 users spanning a 28-day period is done at three different layers. The message layer focuses on ...	false	false	false	true	false	false	false	false	false	false	false	false	false	true	false	false	false	false	94,018
2109.13588	Making Curiosity Explicit in Vision-based RL	Vision-based reinforcement learning (RL) is a promising technique to solve control tasks involving images as the main observation. State-of-the-art RL algorithms still struggle in terms of sample efficiency, especially when using image observations. This has led to an increased attention on integrating state representa...	false	false	false	false	true	false	true	true	false	false	false	true	false	false	false	false	false	false	257,681
2210.16190	Transferable E(3) equivariant parameterization for Hamiltonian of molecules and solids	Using the message-passing mechanism in machine learning (ML) instead of self-consistent iterations to directly build the mapping from structures to electronic Hamiltonian matrices will greatly improve the efficiency of density functional theory (DFT) calculations. In this work, we proposed a general analytic Hamiltonia...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	327,252
2501.18292	Citation Recommendation based on Argumentative Zoning of User Queries	Citation recommendation aims to locate the important papers for scholars to cite. When writing the citing sentences, the authors usually hold different citing intents, which are referred to citation function in citation analysis. Since argumentative zoning is to identify the argumentative and rhetorical structure in sc...	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	true	528,646
2405.00644	ConstrainedZero: Chance-Constrained POMDP Planning using Learned Probabilistic Failure Surrogates and Adaptive Safety Constraints	To plan safely in uncertain environments, agents must balance utility with safety constraints. Safe planning problems can be modeled as a chance-constrained partially observable Markov decision process (CC-POMDP) and solutions often use expensive rollouts or heuristics to estimate the optimal value and action-selection...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	451,000
1912.10292	Deep Audio Prior	Deep convolutional neural networks are known to specialize in distilling compact and robust prior from a large amount of data. We are interested in applying deep networks in the absence of training dataset. In this paper, we introduce deep audio prior (DAP) which leverages the structure of a network and the temporal in...	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	158,285
2011.05003	Joint Super-Resolution and Rectification for Solar Cell Inspection	Visual inspection of solar modules is an important monitoring facility in photovoltaic power plants. Since a single measurement of fast CMOS sensors is limited in spatial resolution and often not sufficient to reliably detect small defects, we apply multi-frame super-resolution (MFSR) to a sequence of low resolution me...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	205,768
1606.09449	Clique-Width and Directed Width Measures for Answer-Set Programming	Disjunctive Answer Set Programming (ASP) is a powerful declarative programming paradigm whose main decision problems are located on the second level of the polynomial hierarchy. Identifying tractable fragments and developing efficient algorithms for such fragments are thus important objectives in order to complement th...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	57,993
1803.00370	Exploiting the Potential of Standard Convolutional Autoencoders for Image Restoration by Evolutionary Search	Researchers have applied deep neural networks to image restoration tasks, in which they proposed various network architectures, loss functions, and training methods. In particular, adversarial training, which is employed in recent studies, seems to be a key ingredient to success. In this paper, we show that simple conv...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	91,658
2203.08606	A Reachability Index for Recursive Label-Concatenated Graph Queries	Reachability queries checking the existence of a path from a source node to a target node are fundamental operators for querying and processing graph data. Current approaches for index-based evaluation of reachability queries either focus on plain reachability or constraint-based reachability with alternation only. In ...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	285,855
1803.05117	MT-Spike: A Multilayer Time-based Spiking Neuromorphic Architecture with Temporal Error Backpropagation	Modern deep learning enabled artificial neural networks, such as Deep Neural Network (DNN) and Convolutional Neural Network (CNN), have achieved a series of breaking records on a broad spectrum of recognition applications. However, the enormous computation and storage requirements associated with such deep and complex ...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	92,578
1702.05376	Towards a Unified Taxonomy of Biclustering Methods	Being an unsupervised machine learning and data mining technique, biclustering and its multimodal extensions are becoming popular tools for analysing object-attribute data in different domains. Apart from conventional clustering techniques, biclustering is searching for homogeneous groups of objects while keeping their...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	68,388
1511.05806	Ranking library materials	Purpose: This paper discusses ranking factors suitable for library materials and shows that ranking in general is a complex process and that ranking for library materials requires a variety of techniques. Design/methodology/approach: The relevant literature is reviewed to provide a systematic overview of suitable ranki...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	true	49,104
1207.4474	On Model Based Synthesis of Embedded Control Software	Many Embedded Systems are indeed Software Based Control Systems (SBCSs), that is control systems whose controller consists of control software running on a microcontroller device. This motivates investigation on Formal Model Based Design approaches for control software. Given the formal model of a plant as a Discrete T...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	true	17,633
2411.16598	DiffBreak: Breaking Diffusion-Based Purification with Adaptive Attacks	Diffusion-based purification (DBP) has emerged as a cornerstone defense against adversarial examples (AEs), widely regarded as robust due to its use of diffusion models (DMs) that project AEs onto the natural data distribution. However, contrary to prior assumptions, we theoretically prove that adaptive gradient-based ...	false	false	false	false	false	false	true	false	false	false	false	true	true	false	false	false	false	false	511,083
2107.06936	Performance of Bayesian linear regression in a model with mismatch	In this paper we analyze, for a model of linear regression with gaussian covariates, the performance of a Bayesian estimator given by the mean of a log-concave posterior distribution with gaussian prior, in the high-dimensional limit where the number of samples and the covariates' dimension are large and proportional. ...	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	246,242
2009.05182	Sequential Convex Programming For Non-Linear Stochastic Optimal Control	This work introduces a sequential convex programming framework for non-linear, finite-dimensional stochastic optimal control, where uncertainties are modeled by a multidimensional Wiener process. We prove that any accumulation point of the sequence of iterates generated by sequential convex programming is a candidate l...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	195,246
2410.16746	SpikMamba: When SNN meets Mamba in Event-based Human Action Recognition	Human action recognition (HAR) plays a key role in various applications such as video analysis, surveillance, autonomous driving, robotics, and healthcare. Most HAR algorithms are developed from RGB images, which capture detailed visual information. However, these algorithms raise concerns in privacy-sensitive environm...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	501,174
2406.04679	XctDiff: Reconstruction of CT Images with Consistent Anatomical Structures from a Single Radiographic Projection Image	In this paper, we present XctDiff, an algorithm framework for reconstructing CT from a single radiograph, which decomposes the reconstruction process into two easily controllable tasks: feature extraction and CT reconstruction. Specifically, we first design a progressive feature extraction strategy that is able to extr...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	461,792
2009.09358	Out-Of-Bag Anomaly Detection	Data anomalies are ubiquitous in real world datasets, and can have an adverse impact on machine learning (ML) systems, such as automated home valuation. Detecting anomalies could make ML applications more responsible and trustworthy. However, the lack of labels for anomalies and the complex nature of real-world dataset...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	196,553
2302.01713	Towards Avoiding the Data Mess: Industry Insights from Data Mesh Implementations	With the increasing importance of data and artificial intelligence, organizations strive to become more data-driven. However, current data architectures are not necessarily designed to keep up with the scale and scope of data and analytics use cases. In fact, existing architectures often fail to deliver the promised va...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	343,715
2105.04100	Z-GCNETs: Time Zigzags at Graph Convolutional Networks for Time Series Forecasting	There recently has been a surge of interest in developing a new class of deep learning (DL) architectures that integrate an explicit time dimension as a fundamental building block of learning and representation mechanisms. In turn, many recent results show that topological descriptors of the observed data, encoding inf...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	234,386
2211.00295	CONDAQA: A Contrastive Reading Comprehension Dataset for Reasoning about Negation	The full power of human language-based communication cannot be realized without negation. All human languages have some form of negation. Despite this, negation remains a challenging phenomenon for current natural language understanding systems. To facilitate the future development of models that can process negation e...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	327,825
2205.11535	Identifying magnetic antiskyrmions while they form with convolutional neural networks	Chiral magnets have attracted a large amount of research interest in recent years because they support a variety of topological defects, such as skyrmions and bimerons, and allow for their observation and manipulation through several techniques. They also have a wide range of applications in the field of spintronics, p...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	298,185
2002.02631	Translating Web Search Queries into Natural Language Questions	Users often query a search engine with a specific question in mind and often these queries are keywords or sub-sentential fragments. For example, if the users want to know the answer for "What's the capital of USA", they will most probably query "capital of USA" or "USA capital" or some keyword-based variation of this....	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	162,982
2402.09752	Vector spectrometer with Hertz-level resolution and super-recognition capability	High-resolution optical spectrometers are crucial in revealing intricate characteristics of signals, determining laser frequencies, measuring physical constants, identifying substances, and advancing biosensing applications. Conventional spectrometers, however, often grapple with inherent trade-offs among spectral reso...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	429,667
2404.15318	VASARI-auto: equitable, efficient, and economical featurisation of glioma MRI	The VASARI MRI feature set is a quantitative system designed to standardise glioma imaging descriptions. Though effective, deriving VASARI is time-consuming and seldom used in clinical practice. This is a problem that machine learning could plausibly automate. Using glioma data from 1172 patients, we developed VASARI-a...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	449,042
1808.04308	Explaining the Unique Nature of Individual Gait Patterns with Deep Learning	Machine learning (ML) techniques such as (deep) artificial neural networks (DNN) are solving very successfully a plethora of tasks and provide new predictive models for complex physical, chemical, biological and social systems. However, in most cases this comes with the disadvantage of acting as a black box, rarely pro...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	105,111
2108.08999	Deep Sequence Modeling: Development and Applications in Asset Pricing	We predict asset returns and measure risk premia using a prominent technique from artificial intelligence -- deep sequence modeling. Because asset returns often exhibit sequential dependence that may not be effectively captured by conventional time series models, sequence modeling offers a promising path with its data-...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	251,459
1406.6046	Hybrid Epidemics - A Case Study on Computer Worm Conficker	Conficker is a computer worm that erupted on the Internet in 2008. It is unique in combining three different spreading strategies: local probing, neighbourhood probing, and global probing. We propose a mathematical model that combines three modes of spreading, local, neighbourhood and global to capture the worm's sprea...	false	false	false	false	true	false	false	false	false	false	false	false	true	false	false	false	false	true	34,084
1904.07577	ASD-DiagNet: A hybrid learning approach for detection of Autism Spectrum Disorder using fMRI data	Mental disorders such as Autism Spectrum Disorders (ASD) are heterogeneous disorders that are notoriously difficult to diagnose, especially in children. The current psychiatric diagnostic process is based purely on the behavioural observation of symptomology (DSM-5/ICD-10) and may be prone to over-prescribing of drugs ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	127,834
2104.13748	QuTI! Quantifying Text-Image Consistency in Multimodal Documents	The World Wide Web and social media platforms have become popular sources for news and information. Typically, multimodal information, e.g., image and text is used to convey information more effectively and to attract attention. While in most cases image content is decorative or depicts additional information, it has a...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	true	232,601
2412.14491	Mediation Analysis for Probabilities of Causation	Probabilities of causation (PoC) offer valuable insights for informed decision-making. This paper introduces novel variants of PoC-controlled direct, natural direct, and natural indirect probability of necessity and sufficiency (PNS). These metrics quantify the necessity and sufficiency of a treatment for producing an ...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	518,727
1706.04717	Recent Progress of Face Image Synthesis	Face synthesis has been a fascinating yet challenging problem in computer vision and machine learning. Its main research effort is to design algorithms to generate photo-realistic face images via given semantic domain. It has been a crucial prepossessing step of main-stream face recognition approaches and an excellent ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	75,388
2501.01264	ProgCo: Program Helps Self-Correction of Large Language Models	Self-Correction aims to enable large language models (LLMs) to self-verify and self-refine their initial responses without external feedback. However, LLMs often fail to effectively self-verify and generate correct feedback, further misleading refinement and leading to the failure of self-correction, especially in comp...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	521,998
2006.11656	For the Thrill of it All: A bridge among Linux, Robot Operating System, Android and Unmanned Aerial Vehicles	Civilian Unmanned Aerial Vehicles (UAVs) are becoming more accessible for domestic use. Currently, UAV manufacturer DJI dominates the market, and their drones have been used for a wide range of applications. Model lines such as the Phantom can be applied for autonomous navigation where Global Positioning System (GPS) s...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	183,321
2103.09424	Escaping Saddle Points in Distributed Newton's Method with Communication Efficiency and Byzantine Resilience	The problem of saddle-point avoidance for non-convex optimization is quite challenging in large scale distributed learning frameworks, such as Federated Learning, especially in the presence of Byzantine workers. The celebrated cubic-regularized Newton method of \cite{nest} is one of the most elegant ways to avoid saddl...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	225,160
1908.00407	InSituNet: Deep Image Synthesis for Parameter Space Exploration of Ensemble Simulations	We propose InSituNet, a deep learning based surrogate model to support parameter space exploration for ensemble simulations that are visualized in situ. In situ visualization, generating visualizations at simulation time, is becoming prevalent in handling large-scale simulations because of the I/O and storage constrain...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	140,507
2002.09666	String stable integral control design for vehicle platoons with disturbances	This paper presents a control design with integral action for vehicle platoons with disturbance that ensures string stability of the closed loop and disturbance rejection. The addition of integral action and a coordinate change allows to develop sufficient smoothness conditions on the closed loop system to ensure that ...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	165,140
2312.12236	Generalization Analysis of Machine Learning Algorithms via the Worst-Case Data-Generating Probability Measure	In this paper, the worst-case probability measure over the data is introduced as a tool for characterizing the generalization capabilities of machine learning algorithms. More specifically, the worst-case probability measure is a Gibbs probability measure and the unique solution to the maximization of the expected loss...	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	416,885
2009.09226	Knowledge Transfer via Pre-training for Recommendation: A Review and Prospect	Recommender systems aim to provide item recommendations for users, and are usually faced with data sparsity problem (e.g., cold start) in real-world scenarios. Recently pre-trained models have shown their effectiveness in knowledge transfer between domains and tasks, which can potentially alleviate the data sparsity pr...	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	196,501
2012.08678	Improved Digital Therapy for Developmental Pediatrics Using Domain-Specific Artificial Intelligence: Machine Learning Study	Background: Automated emotion classification could aid those who struggle to recognize emotions, including children with developmental behavioral conditions such as autism. However, most computer vision emotion recognition models are trained on adult emotion and therefore underperform when applied to child faces. Objec...	true	false	false	false	false	false	false	false	false	false	false	true	false	true	false	false	false	false	211,830
2311.06302	Knowledge-Based Support for Adhesive Selection: Will it Stick?	As the popularity of adhesive joints in industry increases, so does the need for tools to support the process of selecting a suitable adhesive. While some such tools already exist, they are either too limited in scope, or offer too little flexibility in use. This work presents a more advanced tool, that was developed t...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	406,889
2210.01632	Backdoor Attacks in the Supply Chain of Masked Image Modeling	Masked image modeling (MIM) revolutionizes self-supervised learning (SSL) for image pre-training. In contrast to previous dominating self-supervised methods, i.e., contrastive learning, MIM attains state-of-the-art performance by masking and reconstructing random patches of the input image. However, the associated secu...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	321,333
2305.03030	Decentralized and Compositional Interconnection Topology Synthesis for Linear Networked Systems	In this paper, we consider networked systems comprised of interconnected sets of linear subsystems and propose a decentralized and compositional approach to stabilize or dissipativate such linear networked systems via optimally modifying some existing interconnections and/or creating entirely new interconnections. We a...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	362,246
cmp-lg/9507007	An Efficient Algorithm for Surface Generation	A method is given that "inverts" a logic grammar and displays it from the point of view of the logical form, rather than from that of the word string. LR-compiling techniques are used to allow a recursive-descent generation algorithm to perform "functor merging" much in the same way as an LR parser performs prefix merg...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	536,437
1912.00157	Correction Filter for Single Image Super-Resolution: Robustifying Off-the-Shelf Deep Super-Resolvers	The single image super-resolution task is one of the most examined inverse problems in the past decade. In the recent years, Deep Neural Networks (DNNs) have shown superior performance over alternative methods when the acquisition process uses a fixed known downsampling kernel-typically a bicubic kernel. However, sever...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	155,683
1901.09401	SGD: General Analysis and Improved Rates	We propose a general yet simple theorem describing the convergence of SGD under the arbitrary sampling paradigm. Our theorem describes the convergence of an infinite array of variants of SGD, each of which is associated with a specific probability law governing the data selection rule used to form mini-batches. This is...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	119,735
2408.11323	Optimizing Transmit Field Inhomogeneity of Parallel RF Transmit Design in 7T MRI using Deep Learning	Ultrahigh field (UHF) Magnetic Resonance Imaging (MRI) provides a higher signal-to-noise ratio and, thereby, higher spatial resolution. However, UHF MRI introduces challenges such as transmit radiofrequency (RF) field (B1+) inhomogeneities, leading to uneven flip angles and image intensity anomalies. These issues can s...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	482,238
2111.05458	Which priors matter? Benchmarking models for learning latent dynamics	Learning dynamics is at the heart of many important applications of machine learning (ML), such as robotics and autonomous driving. In these settings, ML algorithms typically need to reason about a physical system using high dimensional observations, such as images, without access to the underlying state. Recently, sev...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	265,798
1611.03059	Optimal Surface Segmentation with Convex Priors in Irregularly Sampled Space	Optimal surface segmentation is a state-of-the-art method used for segmentation of multiple globally optimal surfaces in volumetric datasets. The method is widely used in numerous medical image segmentation applications. However, nodes in the graph based optimal surface segmentation method typically encode uniformly di...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	63,646
1704.08861	Multi-antenna Wireless Legitimate Surveillance Systems: Design and Performance Analysis	To improve national security, government agencies have long been committed to enforcing powerful surveillance measures on suspicious individuals or communications. In this paper, we consider a wireless legitimate surveillance system, where a full-duplex multi-antenna legitimate monitor aims to eavesdrop on a dubious co...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	72,588
2006.01451	Careful analysis of XRD patterns with Attention	The important peaks related to the physical properties of a lithium ion rechargeable battery were extracted from the measured X ray diffraction spectrum by a convolutional neural network based on the Attention mechanism. Among the deep features, the lattice constant of the cathodic active material was selected as a cel...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	179,785
2005.00357	Beneath the Tip of the Iceberg: Current Challenges and New Directions in Sentiment Analysis Research	Sentiment analysis as a field has come a long way since it was first introduced as a task nearly 20 years ago. It has widespread commercial applications in various domains like marketing, risk management, market research, and politics, to name a few. Given its saturation in specific subtasks -- such as sentiment polari...	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	175,211
1712.02912	Exploiting Modern Hardware for High-Dimensional Nearest Neighbor Search	Many multimedia information retrieval or machine learning problems require efficient high-dimensional nearest neighbor search techniques. For instance, multimedia objects (images, music or videos) can be represented by high-dimensional feature vectors. Finding two similar multimedia objects then comes down to finding t...	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	true	true	86,365
2109.12651	Why Do We Click: Visual Impression-aware News Recommendation	There is a soaring interest in the news recommendation research scenario due to the information overload. To accurately capture users' interests, we propose to model multi-modal features, in addition to the news titles that are widely used in existing works, for news recommendation. Besides, existing research pays litt...	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	false	true	257,368
1109.5078	Application of distances between terms for flat and hierarchical data	In machine learning, distance-based algorithms, and other approaches, use information that is represented by propositional data. However, this kind of representation can be quite restrictive and, in many cases, it requires more complex structures in order to represent data in a more natural way. Terms are the basis for...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	12,289
2306.01162	Integrated Sensing-Communication-Computation for Edge Artificial Intelligence	Edge artificial intelligence (AI) has been a promising solution towards 6G to empower a series of advanced techniques such as digital twins, holographic projection, semantic communications, and auto-driving, for achieving intelligence of everything. The performance of edge AI tasks, including edge learning and edge AI ...	false	false	false	false	true	false	true	false	false	true	false	false	false	false	false	false	false	false	370,321
2405.16351	A Differential Equation Approach for Wasserstein GANs and Beyond	This paper proposes a new theoretical lens to view Wasserstein generative adversarial networks (WGANs). To minimize the Wasserstein-1 distance between the true data distribution and our estimate of it, we derive a distribution-dependent ordinary differential equation (ODE) which represents the gradient flow of the Wass...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	457,373
1811.11488	Topological Bounds on the Dimension of Orthogonal Representations of Graphs	An orthogonal representation of a graph is an assignment of nonzero real vectors to its vertices such that distinct non-adjacent vertices are assigned to orthogonal vectors. We prove general lower bounds on the dimension of orthogonal representations of graphs using the Borsuk-Ulam theorem from algebraic topology. Our ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	114,793
1902.07511	Dense 3D Visual Mapping via Semantic Simplification	Dense 3D visual mapping estimates as many as possible pixel depths, for each image. This results in very dense point clouds that often contain redundant and noisy information, especially for surfaces that are roughly planar, for instance, the ground or the walls in the scene. In this paper we leverage on semantic image...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	121,999
2501.00942	Efficient Unsupervised Shortcut Learning Detection and Mitigation in Transformers	Shortcut learning, i.e., a model's reliance on undesired features not directly relevant to the task, is a major challenge that severely limits the applications of machine learning algorithms, particularly when deploying them to assist in making sensitive decisions, such as in medical diagnostics. In this work, we lever...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	521,863
2401.14816	Unleashing Data Journalism's Potential: COVID-19 as Catalyst for Newsroom Transformation	In the context of journalism, the COVID-19 pandemic brought unprecedented challenges, necessitating rapid adaptations in newsrooms. Data journalism emerged as a pivotal approach for effectively conveying complex information to the public. Here, we show the profound impact of COVID-19 on data journalism, revealing a sur...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	424,226
1909.03586	Curve Fitting from Probabilistic Emissions and Applications to Dynamic Item Response Theory	Item response theory (IRT) models are widely used in psychometrics and educational measurement, being deployed in many high stakes tests such as the GRE aptitude test. IRT has largely focused on estimation of a single latent trait (e.g. ability) that remains static through the collection of item responses. However, in ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	144,539
2010.14606	Cascaded encoders for unifying streaming and non-streaming ASR	End-to-end (E2E) automatic speech recognition (ASR) models, by now, have shown competitive performance on several benchmarks. These models are structured to either operate in streaming or non-streaming mode. This work presents cascaded encoders for building a single E2E ASR model that can operate in both these modes si...	false	false	true	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	203,502
1907.00544	Unsupervised Adversarial Graph Alignment with Graph Embedding	Graph alignment, also known as network alignment, is a fundamental task in social network analysis. Many recent works have relied on partially labeled cross-graph node correspondences, i.e., anchor links. However, due to the privacy and security issue, the manual labeling of anchor links for diverse scenarios may be pr...	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	false	false	137,084
2207.08350	Towards Understanding The Semidefinite Relaxations of Truncated Least-Squares in Robust Rotation Search	The rotation search problem aims to find a 3D rotation that best aligns a given number of point pairs. To induce robustness against outliers for rotation search, prior work considers truncated least-squares (TLS), which is a non-convex optimization problem, and its semidefinite relaxation (SDR) as a tractable alternati...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	308,557
2403.14472	Detoxifying Large Language Models via Knowledge Editing	This paper investigates using knowledge editing techniques to detoxify Large Language Models (LLMs). We construct a benchmark, SafeEdit, which covers nine unsafe categories with various powerful attack prompts and equips comprehensive metrics for systematic evaluation. We conduct experiments with several knowledge edit...	true	false	false	false	true	false	true	false	true	false	false	true	false	false	false	false	false	false	440,097
2110.00747	Maximum-Likelihood Quantum State Tomography by Cover's Method with Non-Asymptotic Analysis	We propose an iterative algorithm that computes the maximum-likelihood estimate in quantum state tomography. The optimization error of the algorithm converges to zero at an $O ( ( 1 / k ) \log D )$ rate, where $k$ denotes the number of iterations and $D$ denotes the dimension of the quantum state. The per-iteration com...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	258,517
2407.07604	H-FCBFormer Hierarchical Fully Convolutional Branch Transformer for Occlusal Contact Segmentation with Articulating Paper	Occlusal contacts are the locations at which the occluding surfaces of the maxilla and the mandible posterior teeth meet. Occlusal contact detection is a vital tool for restoring the loss of masticatory function and is a mandatory assessment in the field of dentistry, with particular importance in prosthodontics and re...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	471,828

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