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
2501.14322	Relative Layer-Wise Relevance Propagation: a more Robust Neural Networks eXplaination	Machine learning methods are solving very successfully a plethora of tasks, but they have the disadvantage of not providing any information about their decision. Consequently, estimating the reasoning of the system provides additional information. For this, Layer-Wise Relevance Propagation (LRP) is one of the methods i...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	527,082
1109.4104	VOGCLUSTERS: an example of DAME web application	We present the alpha release of the VOGCLUSTERS web application, specialized for data and text mining on globular clusters. It is one of the web2.0 technology based services of Data Mining & Exploration (DAME) Program, devoted to mine and explore heterogeneous information related to globular clusters data.	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	12,235
2001.03869	Finite-Sample Analysis of Image Registration	We study the problem of image registration in the finite-resolution regime and characterize the error probability of algorithms as a function of properties of the transformation and the image capture noise. Specifically, we define a channel-aware Feinstein decoder to obtain upper bounds on the minimum achievable error ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	160,085
1601.00909	The high-conductance state enables neural sampling in networks of LIF neurons	The apparent stochasticity of in-vivo neural circuits has long been hypothesized to represent a signature of ongoing stochastic inference in the brain. More recently, a theoretical framework for neural sampling has been proposed, which explains how sample-based inference can be performed by networks of spiking neurons....	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	50,692
2208.10753	Neural PCA for Flow-Based Representation Learning	Of particular interest is to discover useful representations solely from observations in an unsupervised generative manner. However, the question of whether existing normalizing flows provide effective representations for downstream tasks remains mostly unanswered despite their strong ability for sample generation and ...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	314,187
2202.08019	Model-Based and Data-Driven Control of Event- and Self-Triggered Discrete-Time LTI Systems	The present paper considers the model-based and data-driven control of unknown linear time-invariant discrete-time systems under event-triggering and self-triggering transmission schemes. To this end, we begin by presenting a dynamic event-triggering scheme (ETS) based on periodic sampling, and a discrete-time looped-f...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	280,737
1004.3966	A Message-Passing Algorithm for Counting Short Cycles in a Graph	A message-passing algorithm for counting short cycles in a graph is presented. For bipartite graphs, which are of particular interest in coding, the algorithm is capable of counting cycles of length g, g +2,..., 2g - 2, where g is the girth of the graph. For a general (non-bipartite) graph, cycles of length g; g + 1, ....	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	6,247
2404.14117	Hierarchical localization with panoramic views and triplet loss functions	The main objective of this paper is to tackle visual localization, which is essential for the safe navigation of mobile robots. The solution we propose employs panoramic images and triplet convolutional neural networks. We seek to exploit the properties of such architectures to address both hierarchical and global loca...	false	false	false	false	true	false	false	true	false	false	false	true	false	false	false	false	false	false	448,579
1503.06982	Output Feedback Control of Inhomogeneous Parabolic PDEs with Point Actuation and Point Measurement using SOS and Semi-Separable Kernels	In this paper we use SOS and SDP to design output feedback controllers for a class of one-dimensional parabolic partial differential equations with point measurements and point actuation. Our approach is based on the use of SOS to search for positive quadratic Lyapunov functions, controllers and observers. These Lyapun...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	41,427
2412.14728	LTLf Synthesis Under Unreliable Input	We study the problem of realizing strategies for an LTLf goal specification while ensuring that at least an LTLf backup specification is satisfied in case of unreliability of certain input variables. We formally define the problem and characterize its worst-case complexity as 2EXPTIME-complete, like standard LTLf synth...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	518,839
2001.04238	Nmbr9 as a Constraint Programming Challenge	Modern board games are a rich source of interesting and new challenges for combinatorial problems. The game Nmbr9 is a solitaire style puzzle game using polyominoes. The rules of the game are simple to explain, but modelling the game effectively using constraint programming is hard. This abstract presents the game, con...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	160,190
2006.13044	Scheduling Policy and Power Allocation for Federated Learning in NOMA Based MEC	Federated learning (FL) is a highly pursued machine learning technique that can train a model centrally while keeping data distributed. Distributed computation makes FL attractive for bandwidth limited applications especially in wireless communications. There can be a large number of distributed edge devices connected ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	183,782
2404.02456	PhonologyBench: Evaluating Phonological Skills of Large Language Models	Phonology, the study of speech's structure and pronunciation rules, is a critical yet often overlooked component in Large Language Model (LLM) research. LLMs are widely used in various downstream applications that leverage phonology such as educational tools and poetry generation. Moreover, LLMs can potentially learn i...	false	false	true	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	443,863
1711.03406	Machine Learning Based Fast Power Integrity Classifier	In this paper, we proposed a new machine learning based fast power integrity classifier that quickly flags the EM/IR hotspots. We discussed the features to extract to describe the power grid, cell power density, routing impact and controlled collapse chip connection (C4) bumps, etc. The continuous and discontinuous cas...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	84,205
1611.08024	EEGNet: A Compact Convolutional Network for EEG-based Brain-Computer Interfaces	Brain computer interfaces (BCI) enable direct communication with a computer, using neural activity as the control signal. This neural signal is generally chosen from a variety of well-studied electroencephalogram (EEG) signals. For a given BCI paradigm, feature extractors and classifiers are tailored to the distinct ch...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	64,433
2501.12390	GPS as a Control Signal for Image Generation	We show that the GPS tags contained in photo metadata provide a useful control signal for image generation. We train GPS-to-image models and use them for tasks that require a fine-grained understanding of how images vary within a city. In particular, we train a diffusion model to generate images conditioned on both GPS...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	526,294
2108.13298	E-Commerce Promotions Personalization via Online Multiple-Choice Knapsack with Uplift Modeling	Promotions and discounts are essential components of modern e-commerce platforms, where they are often used to incentivize customers towards purchase completion. Promotions also affect revenue and may incur a monetary loss that is often limited by a dedicated promotional budget. We study the Online Constrained Multiple...	false	false	false	false	true	true	true	false	false	false	false	false	false	false	false	false	false	false	252,765
2007.15109	Outlier-Robust Estimation: Hardness, Minimally Tuned Algorithms, and Applications	Nonlinear estimation in robotics and vision is typically plagued with outliers due to wrong data association, or to incorrect detections from signal processing and machine learning methods. This paper introduces two unifying formulations for outlier-robust estimation, Generalized Maximum Consensus (G-MC) and Generalize...	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	189,569
2406.13385	Explainable by-design Audio Segmentation through Non-Negative Matrix Factorization and Probing	Audio segmentation is a key task for many speech technologies, most of which are based on neural networks, usually considered as black boxes, with high-level performances. However, in many domains, among which health or forensics, there is not only a need for good performance but also for explanations about the output ...	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	465,832
1511.08299	Hierarchical classification of e-commerce related social media	In this paper, we attempt to classify tweets into root categories of the Amazon browse node hierarchy using a set of tweets with browse node ID labels, a much larger set of tweets without labels, and a set of Amazon reviews. Examining twitter data presents unique challenges in that the samples are short (under 140 char...	false	false	false	true	false	true	true	false	true	false	false	false	false	false	false	false	false	false	49,520
2303.15100	An Information Extraction Study: Take In Mind the Tokenization!	Current research on the advantages and trade-offs of using characters, instead of tokenized text, as input for deep learning models, has evolved substantially. New token-free models remove the traditional tokenization step; however, their efficiency remains unclear. Moreover, the effect of tokenization is relatively un...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	354,360
2109.06817	Automatic hippocampal surface generation via 3D U-net and active shape modeling with hybrid particle swarm optimization	In this paper, we proposed and validated a fully automatic pipeline for hippocampal surface generation via 3D U-net coupled with active shape modeling (ASM). Principally, the proposed pipeline consisted of three steps. In the beginning, for each magnetic resonance image, a 3D U-net was employed to obtain the automatic ...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	true	false	false	255,288
2110.09710	Inter-Sense: An Investigation of Sensory Blending in Fiction	This study reports on the semantic organization of English sensory descriptors of the five basic senses of sight, hearing, touch, taste, and smell in a large corpus of over 8,000 fiction books. We introduce a large-scale text data-driven approach based on distributional-semantic word embeddings to identify and extract ...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	261,897
2405.11198	Adaptive Stabilization Based on Machine Learning for Column Generation	Column generation (CG) is a well-established method for solving large-scale linear programs. It involves iteratively optimizing a subproblem containing a subset of columns and using its dual solution to generate new columns with negative reduced costs. This process continues until the dual values converge to the optima...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	455,041
1304.4028	A Fuzzy Logic Based Certain Trust Model for E-Commerce	Trustworthiness especially for service oriented system is very important topic now a day in IT field of the whole world. There are many successful E-commerce organizations presently run in the whole world, but E-commerce has not reached its full potential. The main reason behind this is lack of Trust of people in e-com...	false	false	false	false	true	false	false	false	false	false	false	false	true	false	false	false	false	false	23,972
2310.09755	Beyond Segmentation: Road Network Generation with Multi-Modal LLMs	This paper introduces an innovative approach to road network generation through the utilization of a multi-modal Large Language Model (LLM). Our model is specifically designed to process aerial images of road layouts and produce detailed, navigable road networks within the input images. The core innovation of our syste...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	399,929
2007.08714	Transfer Learning without Knowing: Reprogramming Black-box Machine Learning Models with Scarce Data and Limited Resources	Current transfer learning methods are mainly based on finetuning a pretrained model with target-domain data. Motivated by the techniques from adversarial machine learning (ML) that are capable of manipulating the model prediction via data perturbations, in this paper we propose a novel approach, black-box adversarial r...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	187,717
2008.02198	Domain-Specific Mappings for Generative Adversarial Style Transfer	Style transfer generates an image whose content comes from one image and style from the other. Image-to-image translation approaches with disentangled representations have been shown effective for style transfer between two image categories. However, previous methods often assume a shared domain-invariant content space...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	190,552
2307.01312	Self-Tuning PID Control via a Hybrid Actor-Critic-Based Neural Structure for Quadcopter Control	Proportional-Integrator-Derivative (PID) controller is used in a wide range of industrial and experimental processes. There are a couple of offline methods for tuning PID gains. However, due to the uncertainty of model parameters and external disturbances, real systems such as Quadrotors need more robust and reliable P...	false	false	false	false	true	false	false	true	false	false	true	false	false	false	false	false	false	false	377,307
2401.05318	Analytical Model and Experimental Testing of the SoftFoot: an Adaptive Robot Foot for Walking over Obstacles and Irregular Terrains	Robot feet are crucial for maintaining dynamic stability and propelling the body during walking, especially on uneven terrains. Traditionally, robot feet were mostly designed as flat and stiff pieces of metal, which meets its limitations when the robot is required to step on irregular grounds, e.g. stones. While one co...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	420,714
2208.08200	AHEAD: A Triple Attention Based Heterogeneous Graph Anomaly Detection Approach	Graph anomaly detection on attributed networks has become a prevalent research topic due to its broad applications in many influential domains. In real-world scenarios, nodes and edges in attributed networks usually display distinct heterogeneity, i.e. attributes of different types of nodes show great variety, differen...	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	false	false	313,296
2407.07492	Fine-Grained Classification for Poisonous Fungi Identification with Transfer Learning	FungiCLEF 2024 addresses the fine-grained visual categorization (FGVC) of fungi species, with a focus on identifying poisonous species. This task is challenging due to the size and class imbalance of the dataset, subtle inter-class variations, and significant intra-class variability amongst samples. In this paper, we d...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	471,783
1701.08254	Entropic Causality and Greedy Minimum Entropy Coupling	We study the problem of identifying the causal relationship between two discrete random variables from observational data. We recently proposed a novel framework called entropic causality that works in a very general functional model but makes the assumption that the unobserved exogenous variable has small entropy in t...	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	false	false	67,428
2111.12172	Multi-label Iterated Learning for Image Classification with Label Ambiguity	Transfer learning from large-scale pre-trained models has become essential for many computer vision tasks. Recent studies have shown that datasets like ImageNet are weakly labeled since images with multiple object classes present are assigned a single label. This ambiguity biases models towards a single prediction, whi...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	267,889
2406.06101	On the Consistency of Kernel Methods with Dependent Observations	The consistency of a learning method is usually established under the assumption that the observations are a realization of an independent and identically distributed (i.i.d.) or mixing process. Yet, kernel methods such as support vector machines (SVMs), Gaussian processes, or conditional kernel mean embeddings (CKMEs)...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	462,439
1506.08814	A differential analysis of the power flow equations	The AC power flow equations are fundamental in all aspects of power systems planning and operations. They are routinely solved using Newton-Raphson like methods. However, there is little theoretical understanding of when these algorithms are guaranteed to find a solution of the power flow equations or how long they may...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	44,660
1806.00615	Multiplex Communities and the Emergence of International Conflict	Advances in community detection reveal new insights into multiplex and multilayer networks. Less work, however, investigates the relationship between these communities and outcomes in social systems. We leverage these advances to shed light on the relationship between the cooperative mesostructure of the international ...	false	false	false	true	false	false	false	false	true	false	false	false	false	true	false	false	false	false	99,348
2409.07581	Violence detection in videos using deep recurrent and convolutional neural networks	Violence and abnormal behavior detection research have known an increase of interest in recent years, due mainly to a rise in crimes in large cities worldwide. In this work, we propose a deep learning architecture for violence detection which combines both recurrent neural networks (RNNs) and 2-dimensional convolutiona...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	487,572
1705.05590	Edge-Caching Wireless Networks: Performance Analysis and Optimization	Edge-caching has received much attention as an efficient technique to reduce delivery latency and network congestion during peak-traffic times by bringing data closer to end users. Existing works usually design caching algorithms separately from physical layer design. In this paper, we analyse edge-caching wireless net...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	73,518
2410.22594	Gaussian Derivative Change-point Detection for Early Warnings of Industrial System Failures	An early warning of future system failure is essential for conducting predictive maintenance and enhancing system availability. This paper introduces a three-step framework for assessing system health to predict imminent system breakdowns. First, the Gaussian Derivative Change-Point Detection (GDCPD) algorithm is propo...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	503,687
2104.11276	Constructing a personalized learning path using genetic algorithms approach	A substantial disadvantage of traditional learning is that all students follow the same learning sequence, but not all of them have the same background of knowledge, the same preferences, the same learning goals, and the same needs. Traditional teaching resources, such as textbooks, in most cases pursue students to fol...	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	false	false	231,868
2205.14136	PSL is Dead. Long Live PSL	Property Specification Language (PSL) is a form of temporal logic that has been mainly used in discrete domains (e.g. formal hardware verification). In this paper, we show that by merging machine learning techniques with PSL monitors, we can extend PSL to work on continuous domains. We apply this technique in machine l...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	299,234
2303.09458	Simulation and design of shaped pulses beyond the piecewise-constant approximation	Response functions of resonant circuits create ringing artefacts if their input changes rapidly. When physical limits of electromagnetic spectroscopies are explored, this creates two types of problems. Firstly, simulation: the system must be propagated accurately through every response transient, this may be computatio...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	352,052
2307.06088	Non-Ideal Program-Time Conservation in Charge Trap Flash for Deep Learning	Training deep neural networks (DNNs) is computationally intensive but arrays of non-volatile memories like Charge Trap Flash (CTF) can accelerate DNN operations using in-memory computing. Specifically, the Resistive Processing Unit (RPU) architecture uses the voltage-threshold program by stochastic encoded pulse trains...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	true	378,961
2211.08812	The Levenshtein's Sequence Reconstruction Problem and the Length of the List	In the paper, the Levenshtein's sequence reconstruction problem is considered in the case where at most $t$ substitution errors occur in each of the $N$ channels and the decoder outputs a list of length $\mathcal{L}$. Moreover, it is assumed that the transmitted words are chosen from an $e$-error-correcting code $C \ (...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	330,782
2403.16524	Harnessing the power of LLMs for normative reasoning in MASs	Software agents, both human and computational, do not exist in isolation and often need to collaborate or coordinate with others to achieve their goals. In human society, social mechanisms such as norms ensure efficient functioning, and these techniques have been adopted by researchers in multi-agent systems (MAS) to c...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	441,087
1906.00282	Biomedical Named Entity Recognition via Reference-Set Augmented Bootstrapping	We present a weakly-supervised data augmentation approach to improve Named Entity Recognition (NER) in a challenging domain: extracting biomedical entities (e.g., proteins) from the scientific literature. First, we train a neural NER (NNER) model over a small seed of fully-labeled examples. Second, we use a reference s...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	133,336
2305.04208	Segmentation and Vascular Vectorization for Coronary Artery by Geometry-based Cascaded Neural Network	Segmentation of the coronary artery is an important task for the quantitative analysis of coronary computed tomography angiography (CCTA) images and is being stimulated by the field of deep learning. However, the complex structures with tiny and narrow branches of the coronary artery bring it a great challenge. Coupled...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	362,680
2111.03495	Automated Supervised Feature Selection for Differentiated Patterns of Care	An automated feature selection pipeline was developed using several state-of-the-art feature selection techniques to select optimal features for Differentiating Patterns of Care (DPOC). The pipeline included three types of feature selection techniques; Filters, Wrappers and Embedded methods to select the top K features...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	265,191
1512.08178	Electricity Demand Forecasting by Multi-Task Learning	We explore the application of kernel-based multi-task learning techniques to forecast the demand of electricity in multiple nodes of a distribution network. We show that recently developed output kernel learning techniques are particularly well suited to solve this problem, as they allow to flexibly model the complex s...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	50,496
2107.02281	DeepCEL0 for 2D Single Molecule Localization in Fluorescence Microscopy	In fluorescence microscopy, Single Molecule Localization Microscopy (SMLM) techniques aim at localizing with high precision high density fluorescent molecules by stochastically activating and imaging small subsets of blinking emitters. Super Resolution (SR) plays an important role in this field since it allows to go be...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	244,750
2311.14641	Neuromorphic Intermediate Representation: A Unified Instruction Set for Interoperable Brain-Inspired Computing	Spiking neural networks and neuromorphic hardware platforms that simulate neuronal dynamics are getting wide attention and are being applied to many relevant problems using Machine Learning. Despite a well-established mathematical foundation for neural dynamics, there exists numerous software and hardware solutions and...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	410,177
2406.12202	Fast Global Localization on Neural Radiance Field	Neural Radiance Fields (NeRF) presented a novel way to represent scenes, allowing for high-quality 3D reconstruction from 2D images. Following its remarkable achievements, global localization within NeRF maps is an essential task for enabling a wide range of applications. Recently, Loc-NeRF demonstrated a localization ...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	465,267
2405.17227	Learning Generic and Dynamic Locomotion of Humanoids Across Discrete Terrains	This paper addresses the challenge of terrain-adaptive dynamic locomotion in humanoid robots, a problem traditionally tackled by optimization-based methods or reinforcement learning (RL). Optimization-based methods, such as model-predictive control, excel in finding optimal reaction forces and achieving agile locomotio...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	457,817
2010.13357	Where to Look and How to Describe: Fashion Image Retrieval with an Attentional Heterogeneous Bilinear Network	Fashion products typically feature in compositions of a variety of styles at different clothing parts. In order to distinguish images of different fashion products, we need to extract both appearance (i.e., "how to describe") and localization (i.e.,"where to look") information, and their interactions. To this end, we p...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	203,097
0804.1033	A Semi-Automatic Framework to Discover Epistemic Modalities in Scientific Articles	Documents in scientific newspapers are often marked by attitudes and opinions of the author and/or other persons, who contribute with objective and subjective statements and arguments as well. In this respect, the attitude is often accomplished by a linguistic modality. As in languages like english, french and german, ...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	true	1,542
cs/0511004	Evolutionary Computing	Evolutionary computing (EC) is an exciting development in Computer Science. It amounts to building, applying and studying algorithms based on the Darwinian principles of natural selection. In this paper we briefly introduce the main concepts behind evolutionary computing. We present the main components all evolutionary...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	539,052
1706.08336	Semantically Informed Multiview Surface Refinement	We present a method to jointly refine the geometry and semantic segmentation of 3D surface meshes. Our method alternates between updating the shape and the semantic labels. In the geometry refinement step, the mesh is deformed with variational energy minimization, such that it simultaneously maximizes photo-consistency...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	75,982
2211.13331	Using Focal Loss to Fight Shallow Heuristics: An Empirical Analysis of Modulated Cross-Entropy in Natural Language Inference	There is no such thing as a perfect dataset. In some datasets, deep neural networks discover underlying heuristics that allow them to take shortcuts in the learning process, resulting in poor generalization capability. Instead of using standard cross-entropy, we explore whether a modulated version of cross-entropy call...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	332,438
2303.00111	PixCUE: Joint Uncertainty Estimation and Image Reconstruction in MRI using Deep Pixel Classification	Deep learning (DL) models are capable of successfully exploiting latent representations in MR data and have become state-of-the-art for accelerated MRI reconstruction. However, undersampling the measurements in k-space as well as the over- or under-parameterized and non-transparent nature of DL make these models expose...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	348,487
2202.11180	Selecting cells in a raster database for maximal impact intervention in the presence of spatial interaction: Computational complexity of a Multiple vs. a Single Flow Direction Method	To minimize the sediment flowing to the outlet of a river catchment with minimal effort or cost, it is important to select the best areas to perform a certain intervention, e.g., afforestation. CAMF (Cellular Automata based heuristic for Minimizing Flow) is a method that performs this selection process iteratively in a...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	281,790
2105.14682	Zero-shot Fact Verification by Claim Generation	Neural models for automated fact verification have achieved promising results thanks to the availability of large, human-annotated datasets. However, for each new domain that requires fact verification, creating a dataset by manually writing claims and linking them to their supporting evidence is expensive. We develop ...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	237,766
1410.6976	Distance-Based Influence in Networks: Computation and Maximization	A premise at a heart of network analysis is that entities in a network derive utilities from their connections. The {\em influence} of a seed set $S$ of nodes is defined as the sum over nodes $u$ of the {\em utility} of $S$ to $u$. {\em Distance-based} utility, which is a decreasing function of the distance from $S$ to...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	37,027
2402.00197	Determination of Trace Organic Contaminant Concentration via Machine Classification of Surface-Enhanced Raman Spectra	Accurate detection and analysis of traces of persistent organic pollutants in water is important in many areas, including environmental monitoring and food quality control, due to their long environmental stability and potential bioaccumulation. While conventional analysis of organic pollutants requires expensive equip...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	425,514
1012.5754	Software Effort Estimation with Ridge Regression and Evolutionary Attribute Selection	Software cost estimation is one of the prerequisite managerial activities carried out at the software development initiation stages and also repeated throughout the whole software life-cycle so that amendments to the total cost are made. In software cost estimation typically, a selection of project attributes is employ...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	true	8,662
2201.05570	Precise Stock Price Prediction for Robust Portfolio Design from Selected Sectors of the Indian Stock Market	Stock price prediction is a challenging task and a lot of propositions exist in the literature in this area. Portfolio construction is a process of choosing a group of stocks and investing in them optimally to maximize the return while minimizing the risk. Since the time when Markowitz proposed the Modern Portfolio The...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	275,426
2406.15675	Combining Neural Networks and Symbolic Regression for Analytical Lyapunov Function Discovery	We propose CoNSAL (Combining Neural networks and Symbolic regression for Analytical Lyapunov function) to construct analytical Lyapunov functions for nonlinear dynamic systems. This framework contains a neural Lyapunov function and a symbolic regression component, where symbolic regression is applied to distill the neu...	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	false	true	466,818
1707.01161	Shakespearizing Modern Language Using Copy-Enriched Sequence-to-Sequence Models	Variations in writing styles are commonly used to adapt the content to a specific context, audience, or purpose. However, applying stylistic variations is still by and large a manual process, and there have been little efforts towards automating it. In this paper we explore automated methods to transform text from mode...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	76,480
2103.17020	Semantic-guided Automatic Natural Image Matting with Trimap Generation Network and Light-weight Non-local Attention	Natural image matting aims to precisely separate foreground objects from background using alpha matte. Fully automatic natural image matting without external annotation is challenging. Well-performed matting methods usually require accurate labor-intensive handcrafted trimap as extra input, while the performance of aut...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	227,766
2106.12226	Spatio-Temporal SAR-Optical Data Fusion for Cloud Removal via a Deep Hierarchical Model	Cloud removal is a relevant topic in Remote Sensing as it fosters the usability of high-resolution optical images for Earth monitoring and study. Related techniques have been analyzed for years with a progressively clearer view of the appropriate methods to adopt, from multi-spectral to inpainting methods. Recent appli...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	242,666
1802.08924	Time Series Learning using Monotonic Logical Properties	Cyber-physical systems of today are generating large volumes of time-series data. As manual inspection of such data is not tractable, the need for learning methods to help discover logical structure in the data has increased. We propose a logic-based framework that allows domain-specific knowledge to be embedded into f...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	91,219
1810.05426	On the Existence and Uniqueness of Poincar\'e Maps for Systems with Impulse Effects	The Poincar\'e map is widely used to study the qualitative behavior of dynamical systems. For instance, it can be used to describe the existence of periodic solutions. The Poincar\'e map for dynamical systems with impulse effects was introduced in the last decade and mainly employed to study the existence of limit cycl...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	110,227
2305.00799	How to address monotonicity for model risk management?	In this paper, we study the problem of establishing the accountability and fairness of transparent machine learning models through monotonicity. Although there have been numerous studies on individual monotonicity, pairwise monotonicity is often overlooked in the existing literature. This paper studies transparent neur...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	361,457
2203.06456	Energy networks for state estimation with random sensors using sparse labels	State estimation is required whenever we deal with high-dimensional dynamical systems, as the complete measurement is often unavailable. It is key to gaining insight, performing control or optimizing design tasks. Most deep learning-based approaches require high-resolution labels and work with fixed sensor locations, t...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	285,121
2007.11986	Dog Identification using Soft Biometrics and Neural Networks	This paper addresses the problem of biometric identification of animals, specifically dogs. We apply advanced machine learning models such as deep neural network on the photographs of pets in order to determine the pet identity. In this paper, we explore the possibility of using different types of "soft" biometrics, su...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	188,697
2311.05309	Liquid phase fast electron tomography unravels the true 3D structure of colloidal assemblies	Electron tomography has become a commonly used tool to investigate the three-dimensional (3D) structure of nanomaterials, including colloidal nanoparticle assemblies. However, electron microscopy is typically carried out under high vacuum conditions. Therefore, pre-treatment sample preparation is needed for assemblies ...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	406,541
2204.12144	Motion Planning and Robust Tracking for the Heat Equation using Boundary Control	Robust output tracking is addressed in this paper for a heat equation with Neumann boundary conditions and anti-collocated boundary input and output. The desired reference tracking is solved using the well-known flatness and Lyapunov approaches. The reference profile is obtained by solving the motion planning problem f...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	293,388
2406.02272	Computation-Aware Learning for Stable Control with Gaussian Process	In Gaussian Process (GP) dynamical model learning for robot control, particularly for systems constrained by computational resources like small quadrotors equipped with low-end processors, analyzing stability and designing a stable controller present significant challenges. This paper distinguishes between two types of...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	460,695
2408.09727	Quantitative 3D Map Accuracy Evaluation Hardware and Algorithm for LiDAR(-Inertial) SLAM	Accuracy evaluation of a 3D pointcloud map is crucial for the development of autonomous driving systems. In this work, we propose a user-independent software/hardware system that can quantitatively evaluate the accuracy of a 3D pointcloud map acquired from LiDAR(-Inertial) SLAM. We introduce a LiDAR target that functio...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	481,561
2105.07666	Cortado---An Interactive Tool for Data-Driven Process Discovery and Modeling	Process mining aims to diagnose and improve operational processes. Process mining techniques allow analyzing the event data generated and recorded during the execution of (business) processes to gain valuable insights. Process discovery is a key discipline in process mining that comprises the discovery of process model...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	235,517
2404.13040	Analysis of Classifier-Free Guidance Weight Schedulers	Classifier-Free Guidance (CFG) enhances the quality and condition adherence of text-to-image diffusion models. It operates by combining the conditional and unconditional predictions using a fixed weight. However, recent works vary the weights throughout the diffusion process, reporting superior results but without prov...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	448,128
2203.03598	Audio-visual Generalised Zero-shot Learning with Cross-modal Attention and Language	Learning to classify video data from classes not included in the training data, i.e. video-based zero-shot learning, is challenging. We conjecture that the natural alignment between the audio and visual modalities in video data provides a rich training signal for learning discriminative multi-modal representations. Foc...	false	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	284,149
2106.09174	Can I Be of Further Assistance? Using Unstructured Knowledge Access to Improve Task-oriented Conversational Modeling	Most prior work on task-oriented dialogue systems are restricted to limited coverage of domain APIs. However, users oftentimes have requests that are out of the scope of these APIs. This work focuses on responding to these beyond-API-coverage user turns by incorporating external, unstructured knowledge sources. Our app...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	241,564
1903.08398	Modelling Graph Errors: Towards Robust Graph Signal Processing	The first step for any graph signal processing (GSP) procedure is to learn the graph signal representation, i.e., to capture the dependence structure of the data into an adjacency matrix. Indeed, the adjacency matrix is typically not known a priori and has to be learned. However, it is learned with errors. A little att...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	124,823
2107.10602	CNN-based Realized Covariance Matrix Forecasting	It is well known that modeling and forecasting realized covariance matrices of asset returns play a crucial role in the field of finance. The availability of high frequency intraday data enables the modeling of the realized covariance matrices directly. However, most of the models available in the literature depend on ...	false	true	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	247,342
1404.0101	Quantization for Uplink Transmissions in Two-tier Networks with Femtocells	We propose two novel schemes to level up the sum--rate for a two-tier network with femtocell where the backhaul uplink and downlink connecting the Base Stations have limited capacity. The backhaul links are exploited to transport the information in order to improve the decoding of the macrocell and femtocell messages. ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	31,985
2203.08992	AdaLoGN: Adaptive Logic Graph Network for Reasoning-Based Machine Reading Comprehension	Recent machine reading comprehension datasets such as ReClor and LogiQA require performing logical reasoning over text. Conventional neural models are insufficient for logical reasoning, while symbolic reasoners cannot directly apply to text. To meet the challenge, we present a neural-symbolic approach which, to predic...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	true	false	true	285,984
2002.10025	Triple Wins: Boosting Accuracy, Robustness and Efficiency Together by Enabling Input-Adaptive Inference	Deep networks were recently suggested to face the odds between accuracy (on clean natural images) and robustness (on adversarially perturbed images) (Tsipras et al., 2019). Such a dilemma is shown to be rooted in the inherently higher sample complexity (Schmidt et al., 2018) and/or model capacity (Nakkiran, 2019), for ...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	165,258
cmp-lg/9410012	Does Baum-Welch Re-estimation Help Taggers?	In part of speech tagging by Hidden Markov Model, a statistical model is used to assign grammatical categories to words in a text. Early work in the field relied on a corpus which had been tagged by a human annotator to train the model. More recently, Cutting {\it et al.} (1992) suggest that training can be achieved wi...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	536,194
2205.08910	Strong Converses using Change of Measure and Asymptotic Markov Chains	The main contribution of this paper is a strong converse result for $K$-hop distributed hypothesis testing against independence with multiple (intermediate) decision centers under a Markov condition. Our result shows that the set of type-II error exponents that can simultaneously be achieved at all the terminals does n...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	297,094
1904.13154	Facial Expressions Analysis Under Occlusions Based on Specificities of Facial Motion Propagation	Although much progress has been made in the facial expression analysis field, facial occlusions are still challenging. The main innovation brought by this contribution consists in exploiting the specificities of facial movement propagation for recognizing expressions in presence of important occlusions. The movement in...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	129,310
2403.17801	Towards 3D Vision with Low-Cost Single-Photon Cameras	We present a method for reconstructing 3D shape of arbitrary Lambertian objects based on measurements by miniature, energy-efficient, low-cost single-photon cameras. These cameras, operating as time resolved image sensors, illuminate the scene with a very fast pulse of diffuse light and record the shape of that pulse a...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	441,634
2408.06345	Deep Learning based Key Information Extraction from Business Documents: Systematic Literature Review	Extracting key information from documents represents a large portion of business workloads and therefore offers a high potential for efficiency improvements and process automation. With recent advances in deep learning, a plethora of deep learning-based approaches for Key Information Extraction have been proposed under...	false	false	false	false	false	true	true	false	true	false	false	false	false	false	false	false	false	false	480,169
2104.10974	Abstraction-Based Output-Feedback Control with State-Based Specifications	We consider abstraction-based design of output-feedback controllers for non-linear dynamical systems against specifications over state-based predicates in linear-time temporal logic (LTL). In this context, our contribution is two-fold: (I) we generalize feedback-refinement relations for abstraction-based output-feedbac...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	231,775
1607.03483	Block Models and Personalized PageRank	Methods for ranking the importance of nodes in a network have a rich history in machine learning and across domains that analyze structured data. Recent work has evaluated these methods though the seed set expansion problem: given a subset $S$ of nodes from a community of interest in an underlying graph, can we reliabl...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	58,524
2212.02746	UniGeo: Unifying Geometry Logical Reasoning via Reformulating Mathematical Expression	Geometry problem solving is a well-recognized testbed for evaluating the high-level multi-modal reasoning capability of deep models. In most existing works, two main geometry problems: calculation and proving, are usually treated as two specific tasks, hindering a deep model to unify its reasoning capability on multipl...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	334,867
2308.03811	Non-Convex Bilevel Optimization with Time-Varying Objective Functions	Bilevel optimization has become a powerful tool in a wide variety of machine learning problems. However, the current nonconvex bilevel optimization considers an offline dataset and static functions, which may not work well in emerging online applications with streaming data and time-varying functions. In this work, we ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	384,177
2006.11141	Control of a Rigid Wing Pumping Airborne Wind Energy System in all Operational Phases	The control design of an airborne wind energy system with rigid aircraft, vertical take-off and landing, and pumping operation is described. A hierarchical control structure is implemented, in order to address all operational phases: take-off, transition to power generation, pumping energy generation cycles, transition...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	183,117
1707.08005	Towards Evolutional Compression	Compressing convolutional neural networks (CNNs) is essential for transferring the success of CNNs to a wide variety of applications to mobile devices. In contrast to directly recognizing subtle weights or filters as redundant in a given CNN, this paper presents an evolutionary method to automatically eliminate redunda...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	77,730
2501.06974	Downlink OFDM-FAMA in 5G-NR Systems	Fluid antenna multiple access (FAMA), enabled by the fluid antenna system (FAS), offers a new and straightforward solution to massive connectivity. Previous results on FAMA were primarily based on narrowband channels. This paper studies the adoption of FAMA within the fifth-generation (5G) orthogonal frequency division...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	524,209

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