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
1310.1259	A Novel Progressive Image Scanning and Reconstruction Scheme based on Compressed Sensing and Linear Prediction	Compressed sensing (CS) is an innovative technique allowing to represent signals through a small number of their linear projections. In this paper we address the application of CS to the scenario of progressive acquisition of 2D visual signals in a line-by-line fashion. This is an important setting which encompasses di...	false	false	false	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	27,551
1704.04463	On Generalized Bellman Equations and Temporal-Difference Learning	We consider off-policy temporal-difference (TD) learning in discounted Markov decision processes, where the goal is to evaluate a policy in a model-free way by using observations of a state process generated without executing the policy. To curb the high variance issue in off-policy TD learning, we propose a new scheme...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	71,812
1711.00049	Medical Image Segmentation Based on Multi-Modal Convolutional Neural Network: Study on Image Fusion Schemes	Image analysis using more than one modality (i.e. multi-modal) has been increasingly applied in the field of biomedical imaging. One of the challenges in performing the multimodal analysis is that there exist multiple schemes for fusing the information from different modalities, where such schemes are application-depen...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	83,636
1205.5923	Integration of ontology with machine learning to predict the presence of covid-19 based on symptoms	Coronavirus (covid 19) is one of the most dangerous viruses that have spread all over the world. With the increasing number of cases infected with the coronavirus, it has become necessary to address this epidemic by all available means. Detection of the covid-19 is currently one of the world's most difficult challenges...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	16,192
2202.12267	Inflation of test accuracy due to data leakage in deep learning-based classification of OCT images	In the application of deep learning on optical coherence tomography (OCT) data, it is common to train classification networks using 2D images originating from volumetric data. Given the micrometer resolution of OCT systems, consecutive images are often very similar in both visible structures and noise. Thus, an inappro...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	282,172
2207.14465	Fine-grained Retrieval Prompt Tuning	Fine-grained object retrieval aims to learn discriminative representation to retrieve visually similar objects. However, existing top-performing works usually impose pairwise similarities on the semantic embedding spaces or design a localization sub-network to continually fine-tune the entire model in limited data scen...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	310,594
2304.13830	Adaptation to Misspecified Kernel Regularity in Kernelised Bandits	In continuum-armed bandit problems where the underlying function resides in a reproducing kernel Hilbert space (RKHS), namely, the kernelised bandit problems, an important open problem remains of how well learning algorithms can adapt if the regularity of the associated kernel function is unknown. In this work, we stud...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	360,720
2203.16648	Predicting Winners of the Reality TV Dating Show $\textit{The Bachelor}$ Using Machine Learning Algorithms	$\textit{The Bachelor}$ is a reality TV dating show in which a single bachelor selects his wife from a pool of approximately 30 female contestants over eight weeks of filming (American Broadcasting Company 2002). We collected the following data on all 422 contestants that participated in seasons 11 through 25: their Ag...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	288,863
1707.07657	Engineering fast multilevel support vector machines	The computational complexity of solving nonlinear support vector machine (SVM) is prohibitive on large-scale data. In particular, this issue becomes very sensitive when the data represents additional difficulties such as highly imbalanced class sizes. Typically, nonlinear kernels produce significantly higher classifica...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	77,674
2402.02953	Unraveling the Key of Machine Learning Solutions for Android Malware Detection	Android malware detection serves as the front line against malicious apps. With the rapid advancement of machine learning (ML), ML-based Android malware detection has attracted increasing attention due to its capability of automatically capturing malicious patterns from Android APKs. These learning-driven methods have ...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	426,797
2312.14115	LingoQA: Visual Question Answering for Autonomous Driving	We introduce LingoQA, a novel dataset and benchmark for visual question answering in autonomous driving. The dataset contains 28K unique short video scenarios, and 419K annotations. Evaluating state-of-the-art vision-language models on our benchmark shows that their performance is below human capabilities, with GPT-4V ...	false	false	false	false	true	false	false	true	false	false	false	true	false	false	false	false	false	false	417,502
2009.13048	Delay Optimal Cross-Layer Scheduling Over Markov Channels with Power Constraint	We consider a scenario where a power constrained transmitter delivers randomly arriving packets to the destination over Markov time-varying channel and adapts different transmission power to each channel state in order to guarantee successful transmission. To minimize the expected average transmission delay of each pac...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	197,619
1004.3085	Universal Coding of Ergodic Sources for Multiple Decoders with Side Information	A multiterminal lossy coding problem, which includes various problems such as the Wyner-Ziv problem and the complementary delivery problem as special cases, is considered. It is shown that any point in the achievable rate-distortion region can be attained even if the source statistics are not known.	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	6,195
2305.16405	Automatic Extraction of Time-windowed ROS Computation Graphs from ROS Bag Files	Robotic systems react to different environmental stimuli, potentially resulting in the dynamic reconfiguration of the software controlling such systems. One effect of such dynamism is the reconfiguration of the software architecture reconfiguration of the system at runtime. Such reconfigurations might severely impact t...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	true	368,076
2207.04334	Polyhedral Estimation of L-1 and L-infinity Incremental Gains of Nonlinear Systems	We provide novel dissipativity conditions for bounding the incremental L-1 gain of systems. Moreover, we adapt existing results on the L-infinity gain to the incremental setting and relate the incremental L-1 and L-infinity gain bounds through system adjoints. Building on work on optimization based approaches to constr...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	307,165
2205.13586	Comparing the Digital Annealer with Classical Evolutionary Algorithm	In more recent years, there has been increasing research interest in exploiting the use of application specific hardware for solving optimisation problems. Examples of solvers that use specialised hardware are IBM's Quantum System One and D-wave's Quantum Annealer (QA) and Fujitsu's Digital Annealer (DA). These solvers...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	false	false	298,996
2408.00996	IncidentNet: Traffic Incident Detection, Localization and Severity Estimation with Sparse Sensing	Prior art in traffic incident detection relies on high sensor coverage and is primarily based on decision-tree and random forest models that have limited representation capacity and, as a result, cannot detect incidents with high accuracy. This paper presents IncidentNet - a novel approach for classifying, localizing, ...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	478,061
2210.09107	ISEE.U: Distributed online active target localization with unpredictable targets	This paper addresses target localization with an online active learning algorithm defined by distributed, simple and fast computations at each node, with no parameters to tune and where the estimate of the target position at each agent is asymptotically equal in expectation to the centralized maximum-likelihood estimat...	false	false	false	false	false	false	true	true	false	false	true	false	false	false	false	false	false	false	324,424
2403.12982	Knowledge-Reuse Transfer Learning Methods in Molecular and Material Science	Molecules and materials are the foundation for the development of modern advanced industries such as energy storage systems and semiconductor devices. However, traditional trial-and-error methods or theoretical calculations are highly resource-intensive, and extremely long R&D (Research and Development) periods cannot ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	439,421
2305.01034	Model-agnostic Measure of Generalization Difficulty	The measure of a machine learning algorithm is the difficulty of the tasks it can perform, and sufficiently difficult tasks are critical drivers of strong machine learning models. However, quantifying the generalization difficulty of machine learning benchmarks has remained challenging. We propose what is to our knowle...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	361,529
1910.05998	Optimization and Manipulation of Contextual Mutual Spaces for Multi-User Virtual and Augmented Reality Interaction	Spatial computing experiences are physically constrained by the geometry and semantics of the local user environment. This limitation is elevated in remote multi-user interaction scenarios, where finding a common virtual ground physically accessible for all participants becomes challenging. Locating a common accessible...	true	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	149,232
2303.04134	A Hybrid Architecture for Out of Domain Intent Detection and Intent Discovery	Intent Detection is one of the tasks of the Natural Language Understanding (NLU) unit in task-oriented dialogue systems. Out of Scope (OOS) and Out of Domain (OOD) inputs may run these systems into a problem. On the other side, a labeled dataset is needed to train a model for Intent Detection in task-oriented dialogue ...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	349,969
2210.09367	Task and Motion Informed Trees (TMIT*): Almost-Surely Asymptotically Optimal Integrated Task and Motion Planning	High-level autonomy requires discrete and continuous reasoning to decide both what actions to take and how to execute them. Integrated Task and Motion Planning (TMP) algorithms solve these hybrid problems jointly to consider constraints between the discrete symbolic actions (i.e., the task plan) and their continuous ge...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	324,502
2411.12175	AsynEIO: Asynchronous Monocular Event-Inertial Odometry Using Gaussian Process Regression	Event cameras, when combined with inertial sensors, show significant potential for motion estimation in challenging scenarios, such as high-speed maneuvers and low-light environments. There are many methods for producing such estimations, but most boil down to a synchronous discrete-time fusion problem. However, the as...	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	509,321
2011.14266	Distilled Thompson Sampling: Practical and Efficient Thompson Sampling via Imitation Learning	Thompson sampling (TS) has emerged as a robust technique for contextual bandit problems. However, TS requires posterior inference and optimization for action generation, prohibiting its use in many online platforms where latency and ease of deployment are of concern. We operationalize TS by proposing a novel imitation-...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	208,721
2001.05719	Semantic Security for Quantum Wiretap Channels	We consider the problem of semantic security via classical-quantum and quantum wiretap channels and use explicit constructions to transform a non-secure code into a semantically secure code, achieving capacity by means of biregular irreducible functions. Explicit parameters in finite regimes can be extracted from theor...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	160,620
2410.08421	Generalizable autoregressive modeling of time series through functional narratives	Time series data are inherently functions of time, yet current transformers often learn time series by modeling them as mere concatenations of time periods, overlooking their functional properties. In this work, we propose a novel objective for transformers that learn time series by re-interpreting them as temporal fun...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	497,103
cs/0703143	How much feedback is required in MIMO Broadcast Channels?	In this paper, a downlink communication system, in which a Base Station (BS) equipped with M antennas communicates with N users each equipped with K receive antennas ($K \leq M$), is considered. It is assumed that the receivers have perfect Channel State Information (CSI), while the BS only knows the partial CSI, provi...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	540,274
1812.00090	Mixed Precision Quantization of ConvNets via Differentiable Neural Architecture Search	Recent work in network quantization has substantially reduced the time and space complexity of neural network inference, enabling their deployment on embedded and mobile devices with limited computational and memory resources. However, existing quantization methods often represent all weights and activations with the s...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	115,155
2404.02444	The Promises and Pitfalls of Using Language Models to Measure Instruction Quality in Education	Assessing instruction quality is a fundamental component of any improvement efforts in the education system. However, traditional manual assessments are expensive, subjective, and heavily dependent on observers' expertise and idiosyncratic factors, preventing teachers from getting timely and frequent feedback. Differen...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	443,856
2012.06977	MVFNet: Multi-View Fusion Network for Efficient Video Recognition	Conventionally, spatiotemporal modeling network and its complexity are the two most concentrated research topics in video action recognition. Existing state-of-the-art methods have achieved excellent accuracy regardless of the complexity meanwhile efficient spatiotemporal modeling solutions are slightly inferior in per...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	211,296
1306.5920	Sandwiched R\'enyi Divergence Satisfies Data Processing Inequality	Sandwiched (quantum) $\alpha$-R\'enyi divergence has been recently defined in the independent works of Wilde et al. (arXiv:1306.1586) and M\"uller-Lennert et al (arXiv:1306.3142v1). This new quantum divergence has already found applications in quantum information theory. Here we further investigate properties of this n...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	25,443
1910.06023	Deep Semantic Parsing of Freehand Sketches with Homogeneous Transformation, Soft-Weighted Loss, and Staged Learning	In this paper, we propose a novel deep framework for part-level semantic parsing of freehand sketches, which makes three main contributions that are experimentally shown to have substantial practical merit. First, we propose a homogeneous transformation method to address the problem of domain adaptation. For the task o...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	149,239
2412.02153	Revisiting the Initial Steps in Adaptive Gradient Descent Optimization	Adaptive gradient optimization methods, such as Adam, are prevalent in training deep neural networks across diverse machine learning tasks due to their ability to achieve faster convergence. However, these methods often suffer from suboptimal generalization compared to stochastic gradient descent (SGD) and exhibit inst...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	513,407
2008.05416	DXSLAM: A Robust and Efficient Visual SLAM System with Deep Features	A robust and efficient Simultaneous Localization and Mapping (SLAM) system is essential for robot autonomy. For visual SLAM algorithms, though the theoretical framework has been well established for most aspects, feature extraction and association is still empirically designed in most cases, and can be vulnerable in co...	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	191,508
1704.00445	On Kernelized Multi-armed Bandits	We consider the stochastic bandit problem with a continuous set of arms, with the expected reward function over the arms assumed to be fixed but unknown. We provide two new Gaussian process-based algorithms for continuous bandit optimization-Improved GP-UCB (IGP-UCB) and GP-Thomson sampling (GP-TS), and derive correspo...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	71,086
1709.09304	Effective Image Retrieval via Multilinear Multi-index Fusion	Multi-index fusion has demonstrated impressive performances in retrieval task by integrating different visual representations in a unified framework. However, previous works mainly consider propagating similarities via neighbor structure, ignoring the high order information among different visual representations. In th...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	81,604
2205.11729	From Easy to Hard: Two-stage Selector and Reader for Multi-hop Question Answering	Multi-hop question answering (QA) is a challenging task requiring QA systems to perform complex reasoning over multiple documents and provide supporting facts together with the exact answer. Existing works tend to utilize graph-based reasoning and question decomposition to obtain the reasoning chain, which inevitably i...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	298,253
2301.03288	Reconfigurable Intelligent Surfaces 2.0: Beyond Diagonal Phase Shift Matrices	Reconfigurable intelligent surface (RIS) has been envisioned as a promising technique to enable and enhance future wireless communications due to its potential to engineer the wireless channels in a cost-effective manner. Extensive research attention has been drawn to the use of conventional RIS 1.0 with diagonal phase...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	339,750
2502.12669	Perovskite-LLM: Knowledge-Enhanced Large Language Models for Perovskite Solar Cell Research	The rapid advancement of perovskite solar cells (PSCs) has led to an exponential growth in research publications, creating an urgent need for efficient knowledge management and reasoning systems in this domain. We present a comprehensive knowledge-enhanced system for PSCs that integrates three key components. First, we...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	534,996
2209.13517	Formal Conceptual Views in Neural Networks	Explaining neural network models is a challenging task that remains unsolved in its entirety to this day. This is especially true for high dimensional and complex data. With the present work, we introduce two notions for conceptual views of a neural network, specifically a many-valued and a symbolic view. Both provide ...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	319,926
2303.16464	Lipschitzness Effect of a Loss Function on Generalization Performance of Deep Neural Networks Trained by Adam and AdamW Optimizers	The generalization performance of deep neural networks with regard to the optimization algorithm is one of the major concerns in machine learning. This performance can be affected by various factors. In this paper, we theoretically prove that the Lipschitz constant of a loss function is an important factor to diminish ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	354,866
1101.4486	High-rate Space-Time-Frequency Codes Achieving Full-Diversity with Partial Interference Cancellation Group Decoding	The partial interference cancellation (PIC) group decoding has recently been proposed to deal with the decoding complexity and code rate trade-off on the basis of space-time block code (STBC) design criterion when full diversity is achieved. It provides a framework to arrange the rate-complexity-performance tradeoff by...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	8,903
1912.10170	Na\"iveRole: Author-Contribution Extraction and Parsing from Biomedical Manuscripts	Information about the contributions of individual authors to scientific publications is important for assessing authors' achievements. Some biomedical publications have a short section that describes authors' roles and contributions. It is usually written in natural language and hence author contributions cannot be tri...	false	false	false	false	false	true	true	false	true	false	false	false	false	false	false	false	false	true	158,254
2408.07680	A Spitting Image: Modular Superpixel Tokenization in Vision Transformers	Vision Transformer (ViT) architectures traditionally employ a grid-based approach to tokenization independent of the semantic content of an image. We propose a modular superpixel tokenization strategy which decouples tokenization and feature extraction; a shift from contemporary approaches where these are treated as an...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	480,686
2311.03411	ViDa: Visualizing DNA hybridization trajectories with biophysics-informed deep graph embeddings	Visualization tools can help synthetic biologists and molecular programmers understand the complex reactive pathways of nucleic acid reactions, which can be designed for many potential applications and can be modelled using a continuous-time Markov chain (CTMC). Here we present ViDa, a new visualization approach for DN...	true	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	405,847
1903.02775	Hair Segmentation on Time-of-Flight RGBD Images	Robust segmentation of hair from portrait images remains challenging: hair does not conform to a uniform shape, style or even color; dark hair in particular lacks features. We present a novel computational imaging solution that tackles the problem from both input and processing fronts. We explore using Time-of-Flight (...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	123,569
2412.04456	HeatFormer: A Neural Optimizer for Multiview Human Mesh Recovery	We introduce a novel method for human shape and pose recovery that can fully leverage multiple static views. We target fixed-multiview people monitoring, including elderly care and safety monitoring, in which calibrated cameras can be installed at the corners of a room or an open space but whose configuration may vary ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	514,412
2502.11611	Identifying Gender Stereotypes and Biases in Automated Translation from English to Italian using Similarity Networks	This paper is a collaborative effort between Linguistics, Law, and Computer Science to evaluate stereotypes and biases in automated translation systems. We advocate gender-neutral translation as a means to promote gender inclusion and improve the objectivity of machine translation. Our approach focuses on identifying g...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	534,465
2210.03093	Edge-Varying Fourier Graph Networks for Multivariate Time Series Forecasting	The key problem in multivariate time series (MTS) analysis and forecasting aims to disclose the underlying couplings between variables that drive the co-movements. Considerable recent successful MTS methods are built with graph neural networks (GNNs) due to their essential capacity for relational modeling. However, pre...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	321,893
2206.07807	How Adults Understand What Young Children Say	Children's early speech often bears little resemblance to that of adults, and yet parents and other caregivers are able to interpret that speech and react accordingly. Here we investigate how these adult inferences as listeners reflect sophisticated beliefs about what children are trying to communicate, as well as how ...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	302,891
1805.11534	airpred: A Flexible R Package Implementing Methods for Predicting Air Pollution	Fine particulate matter (PM$_{2.5}$) is one of the criteria air pollutants regulated by the Environmental Protection Agency in the United States. There is strong evidence that ambient exposure to (PM$_{2.5}$) increases risk of mortality and hospitalization. Large scale epidemiological studies on the health effects of P...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	98,941
1803.04562	Bias in OLAP Queries: Detection, Explanation, and Removal	On line analytical processing (OLAP) is an essential element of decision-support systems. OLAP tools provide insights and understanding needed for improved decision making. However, the answers to OLAP queries can be biased and lead to perplexing and incorrect insights. In this paper, we propose HypDB, a system to dete...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	92,476
2407.15247	TimeInf: Time Series Data Contribution via Influence Functions	Evaluating the contribution of individual data points to a model's prediction is critical for interpreting model predictions and improving model performance. Existing data contribution methods have been applied to various data types, including tabular data, images, and texts; however, their primary focus has been on i....	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	475,098
1909.11084	It's Not Whom You Know, It's What You (or Your Friends) Can Do: Succint Coalitional Frameworks for Network Centralities	We investigate the representation of measures of network centrality using a framework that blends a social network representation with the succint formalism of cooperative skill games. We discuss the expressiveness of the new framework and highlight some of its advantages, including a fixed-parameter tractability resul...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	true	false	false	true	146,709
1706.09817	Cooperative Slotted ALOHA for Massive M2M Random Access Using Directional Antennas	Slotted ALOHA (SA) algorithms with Successive Interference Cancellation (SIC) decoding have received significant attention lately due to their ability to dramatically increase the throughput of traditional SA. Motivated by increased density of cellular radio access networks due to the introduction of small cells, and d...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	76,206
1904.04333	Polynomial Invariant Theory and Shape Enumerator of Self-Dual Codes in the NRT-Metric	In this paper we consider self-dual NRT-codes, that is, self-dual codes in the metric space endowed with the Niederreiter-Rosenbloom-Tsfasman (NRT-metric). We use polynomial invariant theory to describe the shape enumerator of a binary self-dual, doubly even self-dual, and doubly-doubly even self dual NRT-code $C\subse...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	127,000
1712.03689	The Effectiveness of Data Augmentation for Detection of Gastrointestinal Diseases from Endoscopical Images	The lack, due to privacy concerns, of large public databases of medical pathologies is a well-known and major problem, substantially hindering the application of deep learning techniques in this field. In this article, we investigate the possibility to supply to the deficiency in the number of data by means of data aug...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	86,492
2110.08454	Good Examples Make A Faster Learner: Simple Demonstration-based Learning for Low-resource NER	Recent advances in prompt-based learning have shown strong results on few-shot text classification by using cloze-style templates. Similar attempts have been made on named entity recognition (NER) which manually design templates to predict entity types for every text span in a sentence. However, such methods may suffer...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	261,407
1703.05614	ParaGraphE: A Library for Parallel Knowledge Graph Embedding	Knowledge graph embedding aims at translating the knowledge graph into numerical representations by transforming the entities and relations into continuous low-dimensional vectors. Recently, many methods [1, 5, 3, 2, 6] have been proposed to deal with this problem, but existing single-thread implementations of them are...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	70,111
2409.08544	Causal GNNs: A GNN-Driven Instrumental Variable Approach for Causal Inference in Networks	As network data applications continue to expand, causal inference within networks has garnered increasing attention. However, hidden confounders complicate the estimation of causal effects. Most methods rely on the strong ignorability assumption, which presumes the absence of hidden confounders-an assumption that is bo...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	487,952
2203.08490	Learning Audio Representations with MLPs	In this paper, we propose an efficient MLP-based approach for learning audio representations, namely timestamp and scene-level audio embeddings. We use an encoder consisting of sequentially stacked gated MLP blocks, which accept 2D MFCCs as inputs. In addition, we also provide a simple temporal interpolation-based algo...	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	285,811
2405.20680	Unraveling and Mitigating Retriever Inconsistencies in Retrieval-Augmented Large Language Models	Although Retrieval-Augmented Large Language Models (RALMs) demonstrate their superiority in terms of factuality, they do not consistently outperform the original retrieval-free Language Models (LMs). Our experiments reveal that this example-level performance inconsistency exists not only between retrieval-augmented and...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	459,468
2412.13791	Physics Reasoner: Knowledge-Augmented Reasoning for Solving Physics Problems with Large Language Models	Physics problems constitute a significant aspect of reasoning, necessitating complicated reasoning ability and abundant physics knowledge. However, existing large language models (LLMs) frequently fail due to a lack of knowledge or incorrect knowledge application. To mitigate these issues, we propose Physics Reasoner, ...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	518,452
2307.04804	S2vNTM: Semi-supervised vMF Neural Topic Modeling	Language model based methods are powerful techniques for text classification. However, the models have several shortcomings. (1) It is difficult to integrate human knowledge such as keywords. (2) It needs a lot of resources to train the models. (3) It relied on large text data to pretrain. In this paper, we propose Sem...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	378,521
2105.00020	Continuous Face Aging via Self-estimated Residual Age Embedding	Face synthesis, including face aging, in particular, has been one of the major topics that witnessed a substantial improvement in image fidelity by using generative adversarial networks (GANs). Most existing face aging approaches divide the dataset into several age groups and leverage group-based training strategies, w...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	233,066
2112.04933	Measuring Wind Turbine Health Using Drifting Concepts	Time series processing is an essential aspect of wind turbine health monitoring. Despite the progress in this field, there is still room for new methods to improve modeling quality. In this paper, we propose two new approaches for the analysis of wind turbine health. Both approaches are based on abstract concepts, impl...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	270,689
2308.07977	Dynamic Attention-Guided Diffusion for Image Super-Resolution	Diffusion models in image Super-Resolution (SR) treat all image regions uniformly, which risks compromising the overall image quality by potentially introducing artifacts during denoising of less-complex regions. To address this, we propose ``You Only Diffuse Areas'' (YODA), a dynamic attention-guided diffusion process...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	385,718
2004.12190	Towards Discourse Parsing-inspired Semantic Storytelling	Previous work of ours on Semantic Storytelling uses text analytics procedures including Named Entity Recognition and Event Detection. In this paper, we outline our longer-term vision on Semantic Storytelling and describe the current conceptual and technical approach. In the project that drives our research we develop A...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	174,159
2406.16966	Mitigating Noisy Supervision Using Synthetic Samples with Soft Labels	Noisy labels are ubiquitous in real-world datasets, especially in the large-scale ones derived from crowdsourcing and web searching. It is challenging to train deep neural networks with noisy datasets since the networks are prone to overfitting the noisy labels during training, resulting in poor generalization performa...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	467,368
1809.07695	Multitask Learning on Graph Neural Networks: Learning Multiple Graph Centrality Measures with a Unified Network	The application of deep learning to symbolic domains remains an active research endeavour. Graph neural networks (GNN), consisting of trained neural modules which can be arranged in different topologies at run time, are sound alternatives to tackle relational problems which lend themselves to graph representations. In ...	false	false	false	true	true	false	true	false	false	false	false	false	false	false	false	true	false	false	108,336
2310.13139	The logic of rational graph neural networks	The expressivity of Graph Neural Networks (GNNs) can be described via appropriate fragments of the first order logic. Any query of the two variable fragment of graded modal logic (GC2) interpreted over labeled graphs can be expressed using a Rectified Linear Unit (ReLU) GNN whose size does not grow with graph input siz...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	401,312
2109.02866	Readying Medical Students for Medical AI: The Need to Embed AI Ethics Education	Medical students will almost inevitably encounter powerful medical AI systems early in their careers. Yet, contemporary medical education does not adequately equip students with the basic clinical proficiency in medical AI needed to use these tools safely and effectively. Education reform is urgently needed, but not ea...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	253,880
2408.10053	Privacy Checklist: Privacy Violation Detection Grounding on Contextual Integrity Theory	Privacy research has attracted wide attention as individuals worry that their private data can be easily leaked during interactions with smart devices, social platforms, and AI applications. Computer science researchers, on the other hand, commonly study privacy issues through privacy attacks and defenses on segmented ...	false	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	481,699
2312.01364	Tradeoff of age-of-information and power under reliability constraint for short-packet communication with block-length adaptation	In applications such as remote estimation and monitoring, update packets are transmitted by power-constrained devices using short-packet codes over wireless networks. Therefore, networks need to be end-to-end optimized using information freshness metrics such as age of information under transmit power and reliability c...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	412,421
2309.15270	Consistent Query Answering for Primary Keys on Path Queries	We study the data complexity of consistent query answering (CQA) on databases that may violate the primary key constraints. A repair is a maximal consistent subset of the database. For a Boolean query $q$, the problem $\mathsf{CERTAINTY}(q)$ takes a database as input, and asks whether or not each repair satisfies $q$. ...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	394,902
2203.11565	Multi-layer Clustering-based Residual Sparsifying Transform for Low-dose CT Image Reconstruction	The recently proposed sparsifying transform models incur low computational cost and have been applied to medical imaging. Meanwhile, deep models with nested network structure reveal great potential for learning features in different layers. In this study, we propose a network-structured sparsifying transform learning a...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	286,962
1905.07000	IMHO Fine-Tuning Improves Claim Detection	Claims are the central component of an argument. Detecting claims across different domains or data sets can often be challenging due to their varying conceptualization. We propose to alleviate this problem by fine tuning a language model using a Reddit corpus of 5.5 million opinionated claims. These claims are self-lab...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	131,123
2404.14402	A mean curvature flow arising in adversarial training	We connect adversarial training for binary classification to a geometric evolution equation for the decision boundary. Relying on a perspective that recasts adversarial training as a regularization problem, we introduce a modified training scheme that constitutes a minimizing movements scheme for a nonlocal perimeter f...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	448,668
1504.03024	Almost Lossless Analog Compression without Phase Information	We propose an information-theoretic framework for phase retrieval. Specifically, we consider the problem of recovering an unknown n-dimensional vector x up to an overall sign factor from m=Rn phaseless measurements with compression rate R and derive a general achievability bound for R. Surprisingly, it turns out that t...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	41,988
2109.04546	Math Word Problem Generation with Mathematical Consistency and Problem Context Constraints	We study the problem of generating arithmetic math word problems (MWPs) given a math equation that specifies the mathematical computation and a context that specifies the problem scenario. Existing approaches are prone to generating MWPs that are either mathematically invalid or have unsatisfactory language quality. Th...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	254,435
2009.07185	Critical Thinking for Language Models	This paper takes a first step towards a critical thinking curriculum for neural auto-regressive language models. We introduce a synthetic corpus of deductively valid arguments, and generate artificial argumentative texts to train and evaluate GPT-2. Significant transfer learning effects can be observed: Training a mode...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	195,855
2008.10164	Model-Free Adaptive Control based on Modified Full-Form-Dynamic-Linearization	Current model-free adaptive control (MFAC) method has no been analysed in linear system and is not straightforward for the practical engineers to understand accurately. This correspondence presents a family of MFAC based on a modified equivalent-dynamic-linearization model (EDLM), which facilitates to show the working ...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	192,932
1804.09253	DeepTriangle: A Deep Learning Approach to Loss Reserving	We propose a novel approach for loss reserving based on deep neural networks. The approach allows for joint modeling of paid losses and claims outstanding, and incorporation of heterogeneous inputs. We validate the models on loss reserving data across lines of business, and show that they improve on the predictive accu...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	95,934
2402.18018	Communication Efficient ConFederated Learning: An Event-Triggered SAGA Approach	Federated learning (FL) is a machine learning paradigm that targets model training without gathering the local data dispersed over various data sources. Standard FL, which employs a single server, can only support a limited number of users, leading to degraded learning capability. In this work, we consider a multi-serv...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	433,245
2401.06019	Automatic UAV-based Airport Pavement Inspection Using Mixed Real and Virtual Scenarios	Runway and taxiway pavements are exposed to high stress during their projected lifetime, which inevitably leads to a decrease in their condition over time. To make sure airport pavement condition ensure uninterrupted and resilient operations, it is of utmost importance to monitor their condition and conduct regular ins...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	420,991
2001.02319	Perception and Navigation in Autonomous Systems in the Era of Learning: A Survey	Autonomous systems possess the features of inferring their own state, understanding their surroundings, and performing autonomous navigation. With the applications of learning systems, like deep learning and reinforcement learning, the visual-based self-state estimation, environment perception and navigation capabiliti...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	159,701
2005.02578	Differentiable Greedy Submodular Maximization: Guarantees, Gradient Estimators, and Applications	Motivated by, e.g., sensitivity analysis and end-to-end learning, the demand for differentiable optimization algorithms has been significantly increasing. In this paper, we establish a theoretically guaranteed versatile framework that makes the greedy algorithm for monotone submodular function maximization differentiab...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	175,916
1801.08586	Reconstructing a cascade from temporal observations	Given a subset of active nodes in a network can we re- construct the cascade that has generated these observa- tions? This is a problem that has been studied in the literature, but here we focus in the case that tempo- ral information is available about the active nodes. In particular, we assume that in addition to the...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	88,966
2002.07088	GRAPHITE: Generating Automatic Physical Examples for Machine-Learning Attacks on Computer Vision Systems	This paper investigates an adversary's ease of attack in generating adversarial examples for real-world scenarios. We address three key requirements for practical attacks for the real-world: 1) automatically constraining the size and shape of the attack so it can be applied with stickers, 2) transform-robustness, i.e.,...	false	false	false	false	false	false	true	false	false	false	false	true	true	false	false	false	false	false	164,379
2211.16285	Evaluating Unsupervised Text Classification: Zero-shot and Similarity-based Approaches	Text classification of unseen classes is a challenging Natural Language Processing task and is mainly attempted using two different types of approaches. Similarity-based approaches attempt to classify instances based on similarities between text document representations and class description representations. Zero-shot ...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	333,586
0806.3978	Information In The Non-Stationary Case	Information estimates such as the ``direct method'' of Strong et al. (1998) sidestep the difficult problem of estimating the joint distribution of response and stimulus by instead estimating the difference between the marginal and conditional entropies of the response. While this is an effective estimation strategy, it...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	1,980
2205.10183	Prototypical Calibration for Few-shot Learning of Language Models	In-context learning of GPT-like models has been recognized as fragile across different hand-crafted templates, and demonstration permutations. In this work, we propose prototypical calibration to adaptively learn a more robust decision boundary for zero- and few-shot classification, instead of greedy decoding. Concrete...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	297,597
1707.01521	Context Aware Document Embedding	Recently, doc2vec has achieved excellent results in different tasks. In this paper, we present a context aware variant of doc2vec. We introduce a novel weight estimating mechanism that generates weights for each word occurrence according to its contribution in the context, using deep neural networks. Our context aware ...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	76,552
1811.09364	Learning pronunciation from a foreign language in speech synthesis networks	Although there are more than 6,500 languages in the world, the pronunciations of many phonemes sound similar across the languages. When people learn a foreign language, their pronunciation often reflects their native language's characteristics. This motivates us to investigate how the speech synthesis network learns th...	false	false	true	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	114,233
0708.1150	A Practical Ontology for the Large-Scale Modeling of Scholarly Artifacts and their Usage	The large-scale analysis of scholarly artifact usage is constrained primarily by current practices in usage data archiving, privacy issues concerned with the dissemination of usage data, and the lack of a practical ontology for modeling the usage domain. As a remedy to the third constraint, this article presents a scho...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	537
1901.03852	One-view occlusion detection for stereo matching with a fully connected CRF model	In this paper, we extend the standard belief propagation (BP) sequential technique proposed in the tree-reweighted sequential method to the fully connected CRF models with the geodesic distance affinity. The proposed method has been applied to the stereo matching problem. Also a new approach to the BP marginal solution...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	118,501
2106.09513	The promise of energy-efficient battery-powered urban aircraft	Improvements in rechargeable batteries are enabling several electric urban air mobility (UAM) aircraft designs with up to 300 miles of range with payload equivalents of up to 7 passengers. We find that novel UAM aircraft consume between 130 Wh/passenger-mile up to ~1,200 Wh/passenger-mile depending on the design and ut...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	241,687
2408.11348	Learning Flock: Enhancing Sets of Particles for Multi~Sub-State Particle Filtering with Neural Augmentation	A leading family of algorithms for state estimation in dynamic systems with multiple sub-states is based on particle filters (PFs). PFs often struggle when operating under complex or approximated modelling (necessitating many particles) with low latency requirements (limiting the number of particles), as is typically t...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	482,252
2010.15251	Fusion Models for Improved Visual Captioning	Visual captioning aims to generate textual descriptions given images or videos. Traditionally, image captioning models are trained on human annotated datasets such as Flickr30k and MS-COCO, which are limited in size and diversity. This limitation hinders the generalization capabilities of these models while also render...	false	false	false	false	true	false	true	false	true	false	false	true	false	false	false	false	false	false	203,707

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