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
2209.09141	"Guess what I'm doing": Extending legibility to sequential decision tasks	In this paper we investigate the notion of legibility in sequential decision tasks under uncertainty. Previous works that extend legibility to scenarios beyond robot motion either focus on deterministic settings or are computationally too expensive. Our proposed approach, dubbed PoL-MDP, is able to handle uncertainty w...	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	false	318,401
2107.00289	Efficient Analysis of Chemical Reaction Networks Dynamics based on Input-Output Monotonicity	Motivation: A Chemical Reaction Network (CRN) is a set of chemical reactions, which can be very complex and difficult to analyze. Indeed, dynamical properties of CRNs can be described by a set of non-linear differential equations that rarely can be solved in closed-form, but that can instead be used to reason on the sy...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	244,108
2112.07922	Ten years of image analysis and machine learning competitions in dementia	Machine learning methods exploiting multi-parametric biomarkers, especially based on neuroimaging, have huge potential to improve early diagnosis of dementia and to predict which individuals are at-risk of developing dementia. To benchmark algorithms in the field of machine learning and neuroimaging in dementia and ass...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	271,642
2304.13811	A Data-Driven Hybrid Automaton Framework to Modeling Complex Dynamical Systems	In this paper, a computationally efficient data-driven hybrid automaton model is proposed to capture unknown complex dynamical system behaviors using multiple neural networks. The sampled data of the system is divided by valid partitions into groups corresponding to their topologies and based on which, transition guard...	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	360,713
2202.01694	Variational Nearest Neighbor Gaussian Process	Variational approximations to Gaussian processes (GPs) typically use a small set of inducing points to form a low-rank approximation to the covariance matrix. In this work, we instead exploit a sparse approximation of the precision matrix. We propose variational nearest neighbor Gaussian process (VNNGP), which introduc...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	278,555
2311.08206	Human-Centric Autonomous Systems With LLMs for User Command Reasoning	The evolution of autonomous driving has made remarkable advancements in recent years, evolving into a tangible reality. However, a human-centric large-scale adoption hinges on meeting a variety of multifaceted requirements. To ensure that the autonomous system meets the user's intent, it is essential to accurately disc...	false	false	false	false	true	false	false	true	true	false	false	false	false	false	false	false	false	false	407,634
1711.05597	Advances in Variational Inference	Many modern unsupervised or semi-supervised machine learning algorithms rely on Bayesian probabilistic models. These models are usually intractable and thus require approximate inference. Variational inference (VI) lets us approximate a high-dimensional Bayesian posterior with a simpler variational distribution by solv...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	84,605
2403.02080	Hybrid Quantum Neural Network Advantage for Radar-Based Drone Detection and Classification in Low Signal-to-Noise Ratio	In this paper, we investigate the performance of a Hybrid Quantum Neural Network (HQNN) and a comparable classical Convolution Neural Network (CNN) for detection and classification problem using a radar. Specifically, we take a fairly complex radar time-series model derived from electromagnetic theory, namely the Marti...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	434,694
1906.04023	Project Thyia: A Forever Gameplayer	The space of Artificial Intelligence entities is dominated by conversational bots. Some of them fit in our pockets and we take them everywhere we go, or allow them to be a part of human homes. Siri, Alexa, they are recognised as present in our world. But a lot of games research is restricted to existing in the separate...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	134,577
2306.08702	Does mBERT understand Romansh? Evaluating word embeddings using word alignment	We test similarity-based word alignment models (SimAlign and awesome-align) in combination with word embeddings from mBERT and XLM-R on parallel sentences in German and Romansh. Since Romansh is an unseen language, we are dealing with a zero-shot setting. Using embeddings from mBERT, both models reach an alignment erro...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	373,502
1810.10839	Exploiting NOMA for Multi-Beam UAV Communication in Cellular Uplink	Unmanned aerial vehicles (UAVs) are expected to be an important new class of users in the fifth generation (5G) and beyond 5G cellular networks. In particular, there are emerging UAV applications such as aerial photograph and data relaying that require high-speed communications between the UAVs and the ground base stat...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	111,376
2212.14727	Countering Malicious Content Moderation Evasion in Online Social Networks: Simulation and Detection of Word Camouflage	Content moderation is the process of screening and monitoring user-generated content online. It plays a crucial role in stopping content resulting from unacceptable behaviors such as hate speech, harassment, violence against specific groups, terrorism, racism, xenophobia, homophobia, or misogyny, to mention some few, i...	false	false	false	true	true	false	false	false	true	false	false	false	false	false	false	false	false	false	338,718
1707.04046	Stable Distribution Alignment Using the Dual of the Adversarial Distance	Methods that align distributions by minimizing an adversarial distance between them have recently achieved impressive results. However, these approaches are difficult to optimize with gradient descent and they often do not converge well without careful hyperparameter tuning and proper initialization. We investigate whe...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	76,981
1807.02653	When Work Matters: Transforming Classical Network Structures to Graph CNN	Numerous pattern recognition applications can be formed as learning from graph-structured data, including social network, protein-interaction network, the world wide web data, knowledge graph, etc. While convolutional neural network (CNN) facilitates great advances in gridded image/video understanding tasks, very limit...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	102,318
2501.11276	ITCFN: Incomplete Triple-Modal Co-Attention Fusion Network for Mild Cognitive Impairment Conversion Prediction	Alzheimer's disease (AD) is a common neurodegenerative disease among the elderly. Early prediction and timely intervention of its prodromal stage, mild cognitive impairment (MCI), can decrease the risk of advancing to AD. Combining information from various modalities can significantly improve predictive accuracy. Howev...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	525,866
2112.02913	Curriculum Meta-Learning for Few-shot Classification	We propose an adaptation of the curriculum training framework, applicable to state-of-the-art meta learning techniques for few-shot classification. Curriculum-based training popularly attempts to mimic human learning by progressively increasing the training complexity to enable incremental concept learning. As the meta...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	270,015
2006.07549	Hindsight Expectation Maximization for Goal-conditioned Reinforcement Learning	We propose a graphical model framework for goal-conditioned RL, with an EM algorithm that operates on the lower bound of the RL objective. The E-step provides a natural interpretation of how 'learning in hindsight' techniques, such as HER, to handle extremely sparse goal-conditioned rewards. The M-step reduces policy o...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	181,845
2010.12710	Improving Classification through Weak Supervision in Context-specific Conversational Agent Development for Teacher Education	Machine learning techniques applied to the Natural Language Processing (NLP) component of conversational agent development show promising results for improved accuracy and quality of feedback that a conversational agent can provide. The effort required to develop an educational scenario specific conversational agent is...	false	false	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	202,811
2410.13063	Large data limits and scaling laws for tSNE	This work considers large-data asymptotics for t-distributed stochastic neighbor embedding (tSNE), a widely-used non-linear dimension reduction algorithm. We identify an appropriate continuum limit of the tSNE objective function, which can be viewed as a combination of a kernel-based repulsion and an asymptotically-van...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	499,348
2401.04680	CoordGate: Efficiently Computing Spatially-Varying Convolutions in Convolutional Neural Networks	Optical imaging systems are inherently limited in their resolution due to the point spread function (PSF), which applies a static, yet spatially-varying, convolution to the image. This degradation can be addressed via Convolutional Neural Networks (CNNs), particularly through deblurring techniques. However, current sol...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	420,506
2501.06835	X-LeBench: A Benchmark for Extremely Long Egocentric Video Understanding	Long-form egocentric video understanding provides rich contextual information and unique insights into long-term human behaviors, holding significant potential for applications in embodied intelligence, long-term activity analysis, and personalized assistive technologies. However, existing benchmark datasets primarily ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	524,150
2105.10854	Projection-Based Reduced Order Model and Machine Learning Closure for Transient Simulations of High-Re Flows	The paper presents a Projection-Based Reduced-Order Model for simulations of high Reynolds turbulent flows. The PBROM are enhanced by incorporating various models of turbulent viscosity and residual closures to model the effects of interactions among the modes and energy dissipations. Remarkable improvements in predict...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	236,515
1009.3951	Quantifying Information Leakage in Finite Order Deterministic Programs	Information flow analysis is a powerful technique for reasoning about the sensitive information exposed by a program during its execution. While past work has proposed information theoretic metrics (e.g., Shannon entropy, min-entropy, guessing entropy, etc.) to quantify such information leakage, we argue that some of t...	false	false	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	7,604
1711.06195	Neurology-as-a-Service for the Developing World	Electroencephalography (EEG) is an extensively-used and well-studied technique in the field of medical diagnostics and treatment for brain disorders, including epilepsy, migraines, and tumors. The analysis and interpretation of EEGs require physicians to have specialized training, which is not common even among most do...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	84,724
2008.07918	End-to-End Rate Enhancement in C-RAN Using Multi-Pair Two-Way Computation	Cloud radio-access networks (C-RAN) have been proposed as an enabling technology for keeping up with the requirements of next-generation wireless networks. Most existing works on C-RAN consider the uplink or the downlink separately. However, designing the uplink and the downlink jointly may bring additional advantage, ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	192,259
2403.08061	Gaze-based Human-Robot Interaction System for Infrastructure Inspections	Routine inspections for critical infrastructures such as bridges are required in most jurisdictions worldwide. Such routine inspections are largely visual in nature, which are qualitative, subjective, and not repeatable. Although robotic infrastructure inspections address such limitations, they cannot replace the super...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	437,160
2004.07072	Time Series Classification for Locating Forced Oscillation Sources	Forced oscillations are caused by sustained cyclic disturbances. This paper presents a machine learning (ML) based time-series classification method that uses the synchrophasor measurements to locate the sources of forced oscillations for fast disturbance removal. Sequential feature selection is used to identify the mo...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	172,686
2006.04001	Real-Time Model Calibration with Deep Reinforcement Learning	The dynamic, real-time, and accurate inference of model parameters from empirical data is of great importance in many scientific and engineering disciplines that use computational models (such as a digital twin) for the analysis and prediction of complex physical processes. However, fast and accurate inference for proc...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	180,516
1907.08424	Enhancing magic sets with an application to ontological reasoning	Magic sets are a Datalog to Datalog rewriting technique to optimize query answering. The rewritten program focuses on a portion of the stable model(s) of the input program which is sufficient to answer the given query. However, the rewriting may introduce new recursive definitions, which can involve even negation and a...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	139,097
2106.02793	Solving hybrid machine learning tasks by traversing weight space geodesics	Machine learning problems have an intrinsic geometric structure as central objects including a neural network's weight space and the loss function associated with a particular task can be viewed as encoding the intrinsic geometry of a given machine learning problem. Therefore, geometric concepts can be applied to analy...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	239,027
2106.03943	Near-Optimal Dispersion on Arbitrary Anonymous Graphs	Given an undirected, anonymous, port-labeled graph of $n$ memory-less nodes, $m$ edges, and degree $\Delta$, we consider the problem of dispersing $k\leq n$ robots (or tokens) positioned initially arbitrarily on one or more nodes of the graph to exactly $k$ different nodes of the graph, one on each node. The objective ...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	true	239,519
2305.06575	Chain-of-Dictionary Prompting Elicits Translation in Large Language Models	Large language models (LLMs) have shown surprisingly good performance in multilingual neural machine translation (MNMT) even when trained without parallel data. Yet, despite the fact that the amount of training data is gigantic, they still struggle with translating rare words, particularly for low-resource languages. E...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	363,592
2209.14022	Leveraging machine learning for less developed languages: Progress on Urdu text detection	Text detection in natural scene images has applications for autonomous driving, navigation help for elderly and blind people. However, the research on Urdu text detection is usually hindered by lack of data resources. We have developed a dataset of scene images with Urdu text. We present the use of machine learning met...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	320,109
2311.06054	Refining the ONCE Benchmark with Hyperparameter Tuning	In response to the growing demand for 3D object detection in applications such as autonomous driving, robotics, and augmented reality, this work focuses on the evaluation of semi-supervised learning approaches for point cloud data. The point cloud representation provides reliable and consistent observations regardless ...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	406,798
2405.07553	Space Domain based Ecological Cooperative and Adaptive Cruise Control on Rolling Terrain	Cooperative and Adaptive Cruise Control (CACC) is widely focused to enhance driving fuel-efficiency by maintaining a close following gap. The ecology of CACC could be further enhanced by adapting to the rolling terrain. However, current studies cannot ensure both planning optimality and computational efficiency. Firstl...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	453,764
2304.03545	AI Model Disgorgement: Methods and Choices	Responsible use of data is an indispensable part of any machine learning (ML) implementation. ML developers must carefully collect and curate their datasets, and document their provenance. They must also make sure to respect intellectual property rights, preserve individual privacy, and use data in an ethical way. Over...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	356,858
2407.19078	Practical Marketplace Optimization at Uber Using Causally-Informed Machine Learning	Budget allocation of marketplace levers, such as incentives for drivers and promotions for riders, has long been a technical and business challenge at Uber; understanding lever budget changes' impact and estimating cost efficiency to achieve predefined budgets is crucial, with the goal of optimal allocations that maxim...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	476,633
2305.14726	In-Context Demonstration Selection with Cross Entropy Difference	Large language models (LLMs) can use in-context demonstrations to improve performance on zero-shot tasks. However, selecting the best in-context examples is challenging because model performance can vary widely depending on the selected examples. We present a cross-entropy difference (CED) method for selecting in-conte...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	367,215
2201.04233	Finding Your Way Through the Jungle of Big Data Architectures	This paper presents a systematic review of common analytical data architectures based on DAMA-DMBOK and ArchiMate. The paper is work in progress and provides a first view on Gartner's Logical Data Warehouse paradigm, Data Fabric and Dehghani's Data Mesh proposal as well as their interdependencies. It furthermore sketch...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	275,048
1906.10197	Mutual exclusivity as a challenge for deep neural networks	Strong inductive biases allow children to learn in fast and adaptable ways. Children use the mutual exclusivity (ME) bias to help disambiguate how words map to referents, assuming that if an object has one label then it does not need another. In this paper, we investigate whether or not standard neural architectures ha...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	136,379
2109.00741	End-to-End Demand Response Model Identification and Baseline Estimation with Deep Learning	This paper proposes a novel end-to-end deep learning framework that simultaneously identifies demand baselines and the incentive-based agent demand response model, from the net demand measurements and incentive signals. This learning framework is modularized as two modules: 1) the decision making process of a demand re...	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	253,211
2210.01099	Model error and its estimation, with particular application to loss reserving	This paper is concerned with forecast error, particularly in relation to loss reserving. This is generally regarded as consisting of three components, namely parameter, process and model errors. The first two of these components, and their estimation, are well understood, but less so model error. Model error itself is ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	321,130
2312.00586	Explainable Fraud Detection with Deep Symbolic Classification	There is a growing demand for explainable, transparent, and data-driven models within the domain of fraud detection. Decisions made by fraud detection models need to be explainable in the event of a customer dispute. Additionally, the decision-making process in the model must be transparent to win the trust of regulato...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	412,100
2305.08169	Can Learning Deteriorate Control? Analyzing Computational Delays in Gaussian Process-Based Event-Triggered Online Learning	When the dynamics of systems are unknown, supervised machine learning techniques are commonly employed to infer models from data. Gaussian process (GP) regression is a particularly popular learning method for this purpose due to the existence of prediction error bounds. Moreover, GP models can be efficiently updated on...	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	364,188
2303.05155	Aux-Drop: Handling Haphazard Inputs in Online Learning Using Auxiliary Dropouts	Many real-world applications based on online learning produce streaming data that is haphazard in nature, i.e., contains missing features, features becoming obsolete in time, the appearance of new features at later points in time and a lack of clarity on the total number of input features. These challenges make it hard...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	350,363
1304.1828	Network Compression: Worst-Case Analysis	We study the problem of communicating a distributed correlated memoryless source over a memoryless network, from source nodes to destination nodes, under quadratic distortion constraints. We establish the following two complementary results: (a) for an arbitrary memoryless network, among all distributed memoryless sour...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	23,593
1806.05285	A Flexible Convolutional Solver with Application to Photorealistic Style Transfer	We propose a new flexible deep convolutional neural network (convnet) to perform fast visual style transfer. In contrast to existing convnets that address the same task, our architecture derives directly from the structure of the gradient descent originally used to solve the style transfer problem [Gatys et al., 2016]....	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	100,434
2312.11826	Decoupled Textual Embeddings for Customized Image Generation	Customized text-to-image generation, which aims to learn user-specified concepts with a few images, has drawn significant attention recently. However, existing methods usually suffer from overfitting issues and entangle the subject-unrelated information (e.g., background and pose) with the learned concept, limiting the...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	416,729
2310.17281	BEVContrast: Self-Supervision in BEV Space for Automotive Lidar Point Clouds	We present a surprisingly simple and efficient method for self-supervision of 3D backbone on automotive Lidar point clouds. We design a contrastive loss between features of Lidar scans captured in the same scene. Several such approaches have been proposed in the literature from PointConstrast, which uses a contrast at ...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	403,073
1808.05079	SentiALG: Automated Corpus Annotation for Algerian Sentiment Analysis	Data annotation is an important but time-consuming and costly procedure. To sort a text into two classes, the very first thing we need is a good annotation guideline, establishing what is required to qualify for each class. In the literature, the difficulties associated with an appropriate data annotation has been unde...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	105,287
2107.11975	A Transductive Maximum Margin Classifier for Few-Shot Learning	Few-shot learning aims to train a classifier that can generalize well when just a small number of labeled examples per class are given. We introduce a transductive maximum margin classifier for few-shot learning (FS-TMMC). The basic idea of the classical maximum margin classifier is to solve an optimal prediction funct...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	247,757
1404.6304	Non-Reconstructability in the Stochastic Block Model	We consider the problem of clustering (or reconstruction) in the stochastic block model, in the regime where the average degree is constant. For the case of two clusters with equal sizes, recent results by Mossel, Neeman and Sly, and by Massoulie, show that reconstructability undergoes a phase transition at the Kesten-...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	32,574
1511.05819	The relationship between internet user type and user performance when carrying out simple vs. complex search tasks	It is widely known that people become better at an activity if they perform this activity long and often. Yet, the question is whether being active in related areas like communicating online, writing blog articles or commenting on community forums have an impact on a persons ability to perform Web searches, is still un...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	49,108
2409.10048	Audio-Driven Reinforcement Learning for Head-Orientation in Naturalistic Environments	Although deep reinforcement learning (DRL) approaches in audio signal processing have seen substantial progress in recent years, audio-driven DRL for tasks such as navigation, gaze control and head-orientation control in the context of human-robot interaction have received little attention. Here, we propose an audio-dr...	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	488,595
2306.01744	Disproving XAI Myths with Formal Methods -- Initial Results	The advances in Machine Learning (ML) in recent years have been both impressive and far-reaching. However, the deployment of ML models is still impaired by a lack of trust in how the best-performing ML models make predictions. The issue of lack of trust is even more acute in the uses of ML models in high-risk or safety...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	370,577
2106.00507	Towards Quantifiable Dialogue Coherence Evaluation	Automatic dialogue coherence evaluation has attracted increasing attention and is crucial for developing promising dialogue systems. However, existing metrics have two major limitations: (a) they are mostly trained in a simplified two-level setting (coherent vs. incoherent), while humans give Likert-type multi-level co...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	238,147
2210.08336	Decoupling Deep Learning for Interpretable Image Recognition	The interpretability of neural networks has recently received extensive attention. Previous prototype-based explainable networks involved prototype activation in both reasoning and interpretation processes, requiring specific explainable structures for the prototype, thus making the network less accurate as it gains in...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	324,097
2208.04880	Loop Shaping with Scaled Relative Graphs	The Scaled Relative Graph (SRG) is a generalization of the Nyquist diagram that may be plotted for nonlinear operators, and allows nonlinear robustness margins to be defined graphically. This abstract explores techniques for shaping the SRG of an operator in order to maximize these robustness margins.	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	312,251
2402.07024	Finding safe 3D robot grasps through efficient haptic exploration with unscented Bayesian optimization and collision penalty	Robust grasping is a major, and still unsolved, problem in robotics. Information about the 3D shape of an object can be obtained either from prior knowledge (e.g., accurate models of known objects or approximate models of familiar objects) or real-time sensing (e.g., partial point clouds of unknown objects) and can be ...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	428,534
1906.08878	Bayesian Optimisation over Multiple Continuous and Categorical Inputs	Efficient optimisation of black-box problems that comprise both continuous and categorical inputs is important, yet poses significant challenges. We propose a new approach, Continuous and Categorical Bayesian Optimisation (CoCaBO), which combines the strengths of multi-armed bandits and Bayesian optimisation to select ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	136,002
2206.02791	Instance-Dependent Label-Noise Learning with Manifold-Regularized Transition Matrix Estimation	In label-noise learning, estimating the transition matrix has attracted more and more attention as the matrix plays an important role in building statistically consistent classifiers. However, it is very challenging to estimate the transition matrix T(x), where x denotes the instance, because it is unidentifiable under...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	301,035
1910.09021	Musical Instrument Playing Technique Detection Based on FCN: Using Chinese Bowed-Stringed Instrument as an Example	Unlike melody extraction and other aspects of music transcription, research on playing technique detection is still in its early stages. Compared to existing work mostly focused on playing technique detection for individual single notes, we propose a general end-to-end method based on Sound Event Detection by FCN for m...	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	false	false	true	150,042
2009.08150	Extensible Data Skipping	Data skipping reduces I/O for SQL queries by skipping over irrelevant data objects (files) based on their metadata. We extend this notion by allowing developers to define their own data skipping metadata types and indexes using a flexible API. Our framework is the first to natively support data skipping for arbitrary d...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	196,150
2405.12666	SYMPLEX: Controllable Symbolic Music Generation using Simplex Diffusion with Vocabulary Priors	We present a new approach for fast and controllable generation of symbolic music based on the simplex diffusion, which is essentially a diffusion process operating on probabilities rather than the signal space. This objective has been applied in domains such as natural language processing but here we apply it to genera...	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	455,610
2304.02819	GPT detectors are biased against non-native English writers	The rapid adoption of generative language models has brought about substantial advancements in digital communication, while simultaneously raising concerns regarding the potential misuse of AI-generated content. Although numerous detection methods have been proposed to differentiate between AI and human-generated conte...	true	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	356,560
2209.08896	NeuralMarker: A Framework for Learning General Marker Correspondence	We tackle the problem of estimating correspondences from a general marker, such as a movie poster, to an image that captures such a marker. Conventionally, this problem is addressed by fitting a homography model based on sparse feature matching. However, they are only able to handle plane-like markers and the sparse fe...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	318,306
2205.06313	Detailed Balanced Chemical Reaction Networks as Generalized Boltzmann Machines	Can a micron sized sack of interacting molecules understand, and adapt to a constantly-fluctuating environment? Cellular life provides an existence proof in the affirmative, but the principles that allow for life's existence are far from being proven. One challenge in engineering and understanding biochemical computati...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	296,202
2401.09068	DTMM: Deploying TinyML Models on Extremely Weak IoT Devices with Pruning	DTMM is a library designed for efficient deployment and execution of machine learning models on weak IoT devices such as microcontroller units (MCUs). The motivation for designing DTMM comes from the emerging field of tiny machine learning (TinyML), which explores extending the reach of machine learning to many low-end...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	422,139
1608.04581	A novel transfer learning method based on common space mapping and weighted domain matching	In this paper, we propose a novel learning framework for the problem of domain transfer learning. We map the data of two domains to one single common space, and learn a classifier in this common space. Then we adapt the common classifier to the two domains by adding two adaptive functions to it respectively. In the com...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	59,853
cs/0106027	Event Driven Objects	A formal consideration in this paper is given for the essential notations to characterize the object that is distinguished in a problem domain. The distinct object is represented by another idealized object, which is a schematic element. When the existence of an element is significant, then a class of these partial ele...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	true	537,362
2410.16981	Proleptic Temporal Ensemble for Improving the Speed of Robot Tasks Generated by Imitation Learning	Imitation learning, which enables robots to learn behaviors from demonstrations by human, has emerged as a promising solution for generating robot motions in such environments. The imitation learning-based robot motion generation method, however, has the drawback of depending on the demonstrator's task execution speed....	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	501,271
1807.10893	Back-Translation-Style Data Augmentation for End-to-End ASR	In this paper we propose a novel data augmentation method for attention-based end-to-end automatic speech recognition (E2E-ASR), utilizing a large amount of text which is not paired with speech signals. Inspired by the back-translation technique proposed in the field of machine translation, we build a neural text-to-en...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	104,049
1706.02889	MirBot: A collaborative object recognition system for smartphones using convolutional neural networks	MirBot is a collaborative application for smartphones that allows users to perform object recognition. This app can be used to take a photograph of an object, select the region of interest and obtain the most likely class (dog, chair, etc.) by means of similarity search using features extracted from a convolutional neu...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	75,057
2305.14722	Continuous Cross-resolution Remote Sensing Image Change Detection	Most contemporary supervised Remote Sensing (RS) image Change Detection (CD) approaches are customized for equal-resolution bitemporal images. Real-world applications raise the need for cross-resolution change detection, aka, CD based on bitemporal images with different spatial resolutions. Given training samples of a ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	367,212
2110.15133	Deep Calibration of Interest Rates Model	For any financial institution, it is essential to understand the behavior of interest rates. Despite the growing use of Deep Learning, for many reasons (expertise, ease of use, etc.), classic rate models such as CIR and the Gaussian family are still widely used. In this paper, we propose to calibrate the five parameter...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	263,772
2410.17506	Mitigating Graph Covariate Shift via Score-based Out-of-distribution Augmentation	Distribution shifts between training and testing datasets significantly impair the model performance on graph learning. A commonly-taken causal view in graph invariant learning suggests that stable predictive features of graphs are causally associated with labels, whereas varying environmental features lead to distribu...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	501,492
2207.05056	Learning to segment prostate cancer by aggressiveness from scribbles in bi-parametric MRI	In this work, we propose a deep U-Net based model to tackle the challenging task of prostate cancer segmentation by aggressiveness in MRI based on weak scribble annotations. This model extends the size constraint loss proposed by Kervadec et al. 1 in the context of multiclass detection and segmentation task. This model...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	307,406
2408.13224	Quadratic estimation for stochastic systems in the presence of random parameter matrices, time-correlated additive noise and deception attacks	Networked systems usually face different random uncertainties that make the performance of the least-squares (LS) linear filter decline significantly. For this reason, great attention has been paid to the search for other kinds of suboptimal estimators. Among them, the LS quadratic estimation approach has attracted con...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	483,055
2204.06031	A Review on Language Models as Knowledge Bases	Recently, there has been a surge of interest in the NLP community on the use of pretrained Language Models (LMs) as Knowledge Bases (KBs). Researchers have shown that LMs trained on a sufficiently large (web) corpus will encode a significant amount of knowledge implicitly in its parameters. The resulting LM can be prob...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	291,211
1901.00140	Adaptive Quantile Low-Rank Matrix Factorization	Low-rank matrix factorization (LRMF) has received much popularity owing to its successful applications in both computer vision and data mining. By assuming noise to come from a Gaussian, Laplace or mixture of Gaussian distributions, significant efforts have been made on optimizing the (weighted) $L_1$ or $L_2$-norm los...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	117,702
2501.18478	SimpleDepthPose: Fast and Reliable Human Pose Estimation with RGBD-Images	In the rapidly advancing domain of computer vision, accurately estimating the poses of multiple individuals from various viewpoints remains a significant challenge, especially when reliability is a key requirement. This paper introduces a novel algorithm that excels in multi-view, multi-person pose estimation by incorp...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	528,719
2001.05838	Self-Learning AI Framework for Skin Lesion Image Segmentation and Classification	Image segmentation and classification are the two main fundamental steps in pattern recognition. To perform medical image segmentation or classification with deep learning models, it requires training on large image dataset with annotation. The dermoscopy images (ISIC archive) considered for this work does not have gro...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	160,647
1601.06895	Echo State Networks for Self-Organizing Resource Allocation in LTE-U with Uplink-Downlink Decoupling	Uplink-downlink decoupling in which users can be associated to different base stations in the uplink and downlink of heterogeneous small cell networks (SCNs) has attracted significant attention recently. However, most existing works focus on simple association mechanisms in LTE SCNs that operate only in the licensed ba...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	51,356
1305.3446	Nyquist Filter Design using POCS Methods: Including Constraints in Design	The problem of constrained finite impulse response (FIR) filter design is central to signal processing and arises in a variety of disciplines. This paper surveys the design of such filters using Projection onto convex sets (POCS) and discusses certain commonly encountered time and frequency domain constraints. We study...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	24,610
2309.06828	UniBrain: Universal Brain MRI Diagnosis with Hierarchical Knowledge-enhanced Pre-training	Magnetic resonance imaging~(MRI) have played a crucial role in brain disease diagnosis, with which a range of computer-aided artificial intelligence methods have been proposed. However, the early explorations usually focus on the limited types of brain diseases in one study and train the model on the data in a small sc...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	391,562
2106.14143	Sparse Control Synthesis for Uncertain Responsive Loads with Stochastic Stability Guarantees	Recent studies have demonstrated the potential of flexible loads in providing frequency response services. However, uncertainty and variability in various weather-related and end-use behavioral factors often affect the demand-side control performance. This work addresses this problem with the design of a demand-side co...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	243,305
1802.03475	Communication-Computation Efficient Gradient Coding	This paper develops coding techniques to reduce the running time of distributed learning tasks. It characterizes the fundamental tradeoff to compute gradients (and more generally vector summations) in terms of three parameters: computation load, straggler tolerance and communication cost. It further gives an explicit c...	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	true	89,976
0903.3995	Gradient-based adaptive interpolation in super-resolution image restoration	This paper presents a super-resolution method based on gradient-based adaptive interpolation. In this method, in addition to considering the distance between the interpolated pixel and the neighboring valid pixel, the interpolation coefficients take the local gradient of the original image into account. The smaller the...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	3,400
1311.3837	SBML for optimizing decision support's tools	Many theoretical works and tools on epidemiological field reflect the emphasis on decision-making Tools by both public health and the scientific community, which continues to increase. Indeed, in the epidemiological field, modeling tools are proving a very important way in helping to make decision. However, the variety...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	28,439
1706.05350	L2 Regularization versus Batch and Weight Normalization	Batch Normalization is a commonly used trick to improve the training of deep neural networks. These neural networks use L2 regularization, also called weight decay, ostensibly to prevent overfitting. However, we show that L2 regularization has no regularizing effect when combined with normalization. Instead, regulariza...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	75,498
1812.03651	Serverless Computing: One Step Forward, Two Steps Back	Serverless computing offers the potential to program the cloud in an autoscaling, pay-as-you go manner. In this paper we address critical gaps in first-generation serverless computing, which place its autoscaling potential at odds with dominant trends in modern computing: notably data-centric and distributed computing,...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	true	116,070
1809.04662	Compact QC-LDPC Block and SC-LDPC Convolutional Codes for Low-Latency Communications	Low decoding latency and complexity are two important requirements of channel codes used in many applications, like machine-to-machine communications. In this paper, we show how these requirements can be fulfilled by using some special quasi-cyclic low-density parity-check block codes and spatially coupled low-density ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	107,620
2109.00181	CTAL: Pre-training Cross-modal Transformer for Audio-and-Language Representations	Existing audio-language task-specific predictive approaches focus on building complicated late-fusion mechanisms. However, these models are facing challenges of overfitting with limited labels and low model generalization abilities. In this paper, we present a Cross-modal Transformer for Audio-and-Language, i.e., CTAL,...	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	253,033
1805.08443	Scene Coordinate and Correspondence Learning for Image-Based Localization	Scene coordinate regression has become an essential part of current camera re-localization methods. Different versions, such as regression forests and deep learning methods, have been successfully applied to estimate the corresponding camera pose given a single input image. In this work, we propose to regress the scene...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	98,138
2107.04381	Specialists Outperform Generalists in Ensemble Classification	Consider an ensemble of $k$ individual classifiers whose accuracies are known. Upon receiving a test point, each of the classifiers outputs a predicted label and a confidence in its prediction for this particular test point. In this paper, we address the question of whether we can determine the accuracy of the ensemble...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	245,445
2309.06824	Beyond Adapting SAM: Towards End-to-End Ultrasound Image Segmentation via Auto Prompting	End-to-end medical image segmentation is of great value for computer-aided diagnosis dominated by task-specific models, usually suffering from poor generalization. With recent breakthroughs brought by the segment anything model (SAM) for universal image segmentation, extensive efforts have been made to adapt SAM for me...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	391,560
1005.2267	A Fast Compressive Channel Estimation with Modified Smoothed L0 Algorithm	Broadband wireless channel is a time dispersive and becomes strongly frequency selective. In most cases, the channel is composed of a few dominant coefficients and a large part of coefficients is approximately zero or zero. To exploit the sparsity of multi-path channel (MPC), there are various methods have been propose...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	6,476
2207.09453	e3nn: Euclidean Neural Networks	We present e3nn, a generalized framework for creating E(3) equivariant trainable functions, also known as Euclidean neural networks. e3nn naturally operates on geometry and geometric tensors that describe systems in 3D and transform predictably under a change of coordinate system. The core of e3nn are equivariant opera...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	true	false	false	308,904
2303.04132	Exploiting Asymmetry for Synthetic Training Data Generation: SynthIE and the Case of Information Extraction	Large language models (LLMs) have great potential for synthetic data generation. This work shows that useful data can be synthetically generated even for tasks that cannot be solved directly by LLMs: for problems with structured outputs, it is possible to prompt an LLM to perform the task in the reverse direction, by g...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	349,968
2205.07869	Near out-of-distribution detection for low-resolution radar micro-Doppler signatures	Near out-of-distribution detection (OODD) aims at discriminating semantically similar data points without the supervision required for classification. This paper puts forward an OODD use case for radar targets detection extensible to other kinds of sensors and detection scenarios. We emphasize the relevance of OODD and...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	296,748

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