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
2102.04594	Rationally Inattentive Utility Maximization for Interpretable Deep Image Classification	Are deep convolutional neural networks (CNNs) for image classification explainable by utility maximization with information acquisition costs? We demonstrate that deep CNNs behave equivalently (in terms of necessary and sufficient conditions) to rationally inattentive utility maximizers, a generative model used extensi...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	219,162
2210.11279	DialogUSR: Complex Dialogue Utterance Splitting and Reformulation for Multiple Intent Detection	While interacting with chatbots, users may elicit multiple intents in a single dialogue utterance. Instead of training a dedicated multi-intent detection model, we propose DialogUSR, a dialogue utterance splitting and reformulation task that first splits multi-intent user query into several single-intent sub-queries an...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	325,259
2008.05850	Revealing the Hidden Patterns: A Comparative Study on Profiling Subpopulations of MOOC Students	Massive Open Online Courses (MOOCs) exhibit a remarkable heterogeneity of students. The advent of complex "big data" from MOOC platforms is a challenging yet rewarding opportunity to deeply understand how students are engaged in MOOCs. Past research, looking mainly into overall behavior, may have missed patterns relate...	true	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	191,634
2404.03197	A Rolling Horizon Restoration Framework for Post-disaster Restoration of Electrical Distribution Networks	Severe weather events such as floods, hurricanes, earthquakes, and large wind or ice storms can cause extensive damage to electrical distribution networks, requiring a multi-day restoration effort. Complicating the recovery process is the lack of complete and accurate information regarding the extent and locations of d...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	444,161
1609.04779	Characterizing the Language of Online Communities and its Relation to Community Reception	This work investigates style and topic aspects of language in online communities: looking at both utility as an identifier of the community and correlation with community reception of content. Style is characterized using a hybrid word and part-of-speech tag n-gram language model, while topic is represented using Laten...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	61,032
2011.04100	Network Optimization via Smooth Exact Penalty Functions Enabled by Distributed Gradient Computation	This paper proposes a distributed algorithm for a network of agents to solve an optimization problem with separable objective function and locally coupled constraints. Our strategy is based on reformulating the original constrained problem as the unconstrained optimization of a smooth (continuously differentiable) exac...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	205,459
1905.08093	The configuration model for Barabasi-Albert networks	We develop and test a rewiring method (originally proposed by Newman) which allows to build random networks having pre-assigned degree distribution and two-point correlations. For the case of scale-free degree distributions, we discretize the tail of the distribution according to the general prescription by Dorogovtsev...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	131,401
2409.14488	Enhancing LLM-based Autonomous Driving Agents to Mitigate Perception Attacks	There is a growing interest in integrating Large Language Models (LLMs) with autonomous driving (AD) systems. However, AD systems are vulnerable to attacks against their object detection and tracking (ODT) functions. Unfortunately, our evaluation of four recent LLM agents against ODT attacks shows that the attacks are ...	false	false	false	false	true	false	false	false	false	false	false	false	true	false	false	false	false	false	490,488
0910.4686	Moderate Deviations of the Random Riccati Equation	We characterize the invariant filtering measures resulting from Kalman filtering with intermittent observations (\cite{Bruno}), where the observation arrival is modeled as a Bernoulli process. In \cite{Riccati-weakconv}, it was shown that there exists a $\overline{\gamma}^{\{\scriptsize{sb}}}>0$ such that for every obs...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	4,791
cs/0310050	Feedforward Neural Networks with Diffused Nonlinear Weight Functions	In this paper, feedforward neural networks are presented that have nonlinear weight functions based on look--up tables, that are specially smoothed in a regularization called the diffusion. The idea of such a type of networks is based on the hypothesis that the greater number of adaptive parameters per a weight functio...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	538,016
1802.03567	Crit\`eres de qualit\'e d'un classifieur g\'en\'eraliste	This paper considers the problem of choosing a good classifier. For each problem there exist an optimal classifier, but none are optimal, regarding the error rate, in all cases. Because there exists a large number of classifiers, a user would rather prefer an all-purpose classifier that is easy to adjust, in the hope t...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	90,005
2110.11395	SOSP: Efficiently Capturing Global Correlations by Second-Order Structured Pruning	Pruning neural networks reduces inference time and memory costs. On standard hardware, these benefits will be especially prominent if coarse-grained structures, like feature maps, are pruned. We devise two novel saliency-based methods for second-order structured pruning (SOSP) which include correlations among all struc...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	262,463
1105.5849	Diffusion in Networks With Overlapping Community Structure	In this work we study diffusion in networks with community structure. We first replicate and extend work on networks with non-overlapping community structure. We then study diffusion on network models that have overlapping community structure. We study contagions in the standard SIR model, and complex contagions though...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	10,567
1811.07170	Optical Flow Dataset and Benchmark for Visual Crowd Analysis	The performance of optical flow algorithms greatly depends on the specifics of the content and the application for which it is used. Existing and well established optical flow datasets are limited to rather particular contents from which none is close to crowd behavior analysis; whereas such applications heavily utiliz...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	113,693
2502.02170	Graph Neural Networks for O-RAN Mobility Management: A Link Prediction Approach	Mobility performance has been a key focus in cellular networks up to 5G. To enhance handover (HO) performance, 3GPP introduced Conditional Handover (CHO) and Layer 1/Layer 2 Triggered Mobility (LTM) mechanisms in 5G. While these reactive HO strategies address the trade-off between HO failures (HOF) and ping-pong effect...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	530,201
2006.00917	Evaluation of the general applicability of Dragoon for the k-center problem	The k-center problem is a fundamental problem we often face when considering complex service systems. Typical challenges include the placement of warehouses in logistics or positioning of servers for content delivery networks. We previously have proposed Dragoon as an effective algorithm to approach the k-center proble...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	true	true	false	true	179,614
1709.09220	Dataset Construction via Attention for Aspect Term Extraction with Distant Supervision	Aspect Term Extraction (ATE) detects opinionated aspect terms in sentences or text spans, with the end goal of performing aspect-based sentiment analysis. The small amount of available datasets for supervised ATE and the fact that they cover only a few domains raise the need for exploiting other data sources in new and...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	81,588
2407.06525	UnmixingSR: Material-aware Network with Unsupervised Unmixing as Auxiliary Task for Hyperspectral Image Super-resolution	Deep learning-based (DL-based) hyperspectral image (HIS) super-resolution (SR) methods have achieved remarkable performance and attracted attention in industry and academia. Nonetheless, most current methods explored and learned the mapping relationship between low-resolution (LR) and high-resolution (HR) HSIs, leading...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	471,426
2502.05240	Survey on AI-Generated Media Detection: From Non-MLLM to MLLM	The proliferation of AI-generated media poses significant challenges to information authenticity and social trust, making reliable detection methods highly demanded. Methods for detecting AI-generated media have evolved rapidly, paralleling the advancement of Multimodal Large Language Models (MLLMs). Current detection ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	531,520
1604.04421	Stabilizing Transmission Intervals for Nonlinear Delayed Networked Control Systems [Extended Version]	In this article, we consider a nonlinear process with delayed dynamics to be controlled over a communication network in the presence of disturbances and study robustness of the resulting closed-loop system with respect to network-induced phenomena such as sampled, distorted, delayed and lossy data as well as scheduling...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	54,645
2405.17813	The Impacts of Data, Ordering, and Intrinsic Dimensionality on Recall in Hierarchical Navigable Small Worlds	Vector search systems, pivotal in AI applications, often rely on the Hierarchical Navigable Small Worlds (HNSW) algorithm. However, the behaviour of HNSW under real-world scenarios using vectors generated with deep learning models remains under-explored. Existing Approximate Nearest Neighbours (ANN) benchmarks and rese...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	458,128
2409.10995	SynthSOD: Developing an Heterogeneous Dataset for Orchestra Music Source Separation	Recent advancements in music source separation have significantly progressed, particularly in isolating vocals, drums, and bass elements from mixed tracks. These developments owe much to the creation and use of large-scale, multitrack datasets dedicated to these specific components. However, the challenge of extracting...	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	488,968
2404.04281	Similar Data Points Identification with LLM: A Human-in-the-loop Strategy Using Summarization and Hidden State Insights	This study introduces a simple yet effective method for identifying similar data points across non-free text domains, such as tabular and image data, using Large Language Models (LLMs). Our two-step approach involves data point summarization and hidden state extraction. Initially, data is condensed via summarization us...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	444,580
1811.12465	Uncertainty propagation in neural networks for sparse coding	A novel method to propagate uncertainty through the soft-thresholding nonlinearity is proposed in this paper. At every layer the current distribution of the target vector is represented as a spike and slab distribution, which represents the probabilities of each variable being zero, or Gaussian-distributed. Using the p...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	115,018
2410.17514	SRA: A Novel Method to Improve Feature Embedding in Self-supervised Learning for Histopathological Images	Self-supervised learning has become a cornerstone in various areas, particularly histopathological image analysis. Image augmentation plays a crucial role in self-supervised learning, as it generates variations in image samples. However, traditional image augmentation techniques often overlook the unique characteristic...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	501,498
2212.11681	Variational Quantum Soft Actor-Critic for Robotic Arm Control	Deep Reinforcement Learning is emerging as a promising approach for the continuous control task of robotic arm movement. However, the challenges of learning robust and versatile control capabilities are still far from being resolved for real-world applications, mainly because of two common issues of this learning parad...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	337,850
1704.01792	Neural Question Generation from Text: A Preliminary Study	Automatic question generation aims to generate questions from a text passage where the generated questions can be answered by certain sub-spans of the given passage. Traditional methods mainly use rigid heuristic rules to transform a sentence into related questions. In this work, we propose to apply the neural encoder-...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	71,326
2205.01871	UCL-Dehaze: Towards Real-world Image Dehazing via Unsupervised Contrastive Learning	While the wisdom of training an image dehazing model on synthetic hazy data can alleviate the difficulty of collecting real-world hazy/clean image pairs, it brings the well-known domain shift problem. From a different yet new perspective, this paper explores contrastive learning with an adversarial training effort to l...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	294,743
2108.01643	Progressive Transmission using Recurrent Neural Networks	In this paper, we investigate a new machine learning-based transmission strategy called progressive transmission or ProgTr. In ProgTr, there are b variables that should be transmitted using at most T channel uses. The transmitter aims to send the data to the receiver as fast as possible and with as few channel uses as ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	249,091
1706.02633	Real-valued (Medical) Time Series Generation with Recurrent Conditional GANs	Generative Adversarial Networks (GANs) have shown remarkable success as a framework for training models to produce realistic-looking data. In this work, we propose a Recurrent GAN (RGAN) and Recurrent Conditional GAN (RCGAN) to produce realistic real-valued multi-dimensional time series, with an emphasis on their appli...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	75,010
1606.00950	Graph Clustering with Density-Cut	How can we find a good graph clustering of a real-world network, that allows insight into its underlying structure and also potential functions? In this paper, we introduce a new graph clustering algorithm Dcut from a density point of view. The basic idea is to envision the graph clustering as a density-cut problem, su...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	56,731
2408.04682	ToolSandbox: A Stateful, Conversational, Interactive Evaluation Benchmark for LLM Tool Use Capabilities	Recent large language models (LLMs) advancements sparked a growing research interest in tool assisted LLMs solving real-world challenges, which calls for comprehensive evaluation of tool-use capabilities. While previous works focused on either evaluating over stateless web services (RESTful API), based on a single turn...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	479,490
2310.07572	Impact of Label Types on Training SWIN Models with Overhead Imagery	Understanding the impact of data set design on model training and performance can help alleviate the costs associated with generating remote sensing and overhead labeled data. This work examined the impact of training shifted window transformers using bounding boxes and segmentation labels, where the latter are more ex...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	399,022
2211.17042	Spatio-Temporal Crop Aggregation for Video Representation Learning	We propose Spatio-temporal Crop Aggregation for video representation LEarning (SCALE), a novel method that enjoys high scalability at both training and inference time. Our model builds long-range video features by learning from sets of video clip-level features extracted with a pre-trained backbone. To train the model,...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	333,838
2404.06665	Deep Generative Data Assimilation in Multimodal Setting	Robust integration of physical knowledge and data is key to improve computational simulations, such as Earth system models. Data assimilation is crucial for achieving this goal because it provides a systematic framework to calibrate model outputs with observations, which can include remote sensing imagery and ground st...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	445,544
2401.05083	Discrete-Time Stress Matrix-Based Formation Control of General Linear Multi-Agent Systems	This paper considers the distributed leader-follower stress-matrix-based affine formation control problem of discrete-time linear multi-agent systems with static and dynamic leaders. In leader-follower multi-agent formation control, the aim is to drive a set of agents comprising leaders and followers to form any desire...	false	false	false	false	false	false	false	true	false	false	true	false	false	false	true	false	false	false	420,641
2004.06201	Reverse Engineering Configurations of Neural Text Generation Models	This paper seeks to develop a deeper understanding of the fundamental properties of neural text generations models. The study of artifacts that emerge in machine generated text as a result of modeling choices is a nascent research area. Previously, the extent and degree to which these artifacts surface in generated tex...	false	false	false	false	false	true	true	false	true	false	false	false	false	false	false	false	false	false	172,438
2407.00911	Deep Image-to-Recipe Translation	The modern saying, "You Are What You Eat" resonates on a profound level, reflecting the intricate connection between our identities and the food we consume. Our project, Deep Image-to-Recipe Translation, is an intersection of computer vision and natural language generation that aims to bridge the gap between cherished ...	false	false	false	false	false	false	true	false	true	false	false	true	false	false	false	false	false	false	469,054
2309.14736	The Tight Upper Bound for the Size of Single Deletion Error Correcting Codes in Dimension 11	A single deletion error correcting code (SDECC) is a set of fixed-length sequences consisting of two types of symbols, 0 and 1, such that the original sequence can be recovered for at most one deletion error. The upper bound for the size of SDECC is expected to be equal to the size of Varshamov-Tenengolts (VT) code, an...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	394,718
2309.11622	Offline and Online Use of Interval and Set-Based Approaches for Control and State Estimation: A Selection of Methodological Approaches and Their Application	Control and state estimation procedures need to be robust against imprecisely known parameters, uncertainty in initial conditions, and external disturbances. Interval methods and other set-based techniques form the basis for the implementation of powerful approaches that can be used to identify parameters of dynamic sy...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	393,471
1304.3441	Machine Generalization and Human Categorization: An Information-Theoretic View	In designing an intelligent system that must be able to explain its reasoning to a human user, or to provide generalizations that the human user finds reasonable, it may be useful to take into consideration psychological data on what types of concepts and categories people naturally use. The psychological literature on...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	23,880
2407.14237	Hyper-Heuristics Can Profit From Global Variation Operators	In recent work, Lissovoi, Oliveto, and Warwicker (Artificial Intelligence (2023)) proved that the Move Acceptance Hyper-Heuristic (MAHH) leaves the local optimum of the multimodal CLIFF benchmark with remarkable efficiency. The $O(n^3)$ runtime of the MAHH, for almost all cliff widths $d\ge 2,$ is significantly better ...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	false	true	474,706
1711.03240	Cellular Offloading via Downlink Cache Placement	In this paper, the downlink file transmission within a finite lifetime is optimized with the assistance of wireless cache nodes. Specifically, the number of requests within the lifetime of one file is modeled as a Poisson point process. The base station multicasts files to downlink users and the selected the cache node...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	84,179
1702.08524	Local Synchronization of Sampled-Data Systems on Lie Groups	We present a smooth distributed nonlinear control law for local synchronization of identical driftless kinematic agents on a Cartesian product of matrix Lie groups with a connected communication graph. If the agents are initialized sufficiently close to one another, then synchronization is achieved exponentially fast. ...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	69,000
1806.06973	On the Bias of Reed-Muller Codes over Odd Prime Fields	We study the bias of random bounded-degree polynomials over odd prime fields and show that, with probability exponentially close to 1, such polynomials have exponentially small bias. This also yields an exponential tail bound on the weight distribution of Reed-Muller codes over odd prime fields. These results generaliz...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	100,804
2412.16326	When Worse is Better: Navigating the compression-generation tradeoff in visual tokenization	Current image generation methods, such as latent diffusion and discrete token-based generation, depend on a two-stage training approach. In stage 1, an auto-encoder is trained to compress an image into a latent space; in stage 2, a generative model is trained to learn a distribution over that latent space. Most work fo...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	519,466
2001.10944	Exact Blind Community Detection from Signals on Multiple Graphs	Networks and data supported on graphs have become ubiquitous in the sciences and engineering. This paper studies the 'blind' community detection problem, where we seek to infer the community structure of a graph model given the observation of independent graph signals on a set of nodes whose connections are unknown. We...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	161,940
2012.00437	A Unified Structure for Efficient RGB and RGB-D Salient Object Detection	Salient object detection (SOD) has been well studied in recent years, especially using deep neural networks. However, SOD with RGB and RGB-D images is usually treated as two different tasks with different network structures that need to be designed specifically. In this paper, we proposed a unified and efficient struct...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	209,137
1809.02266	BubGAN: Bubble Generative Adversarial Networks for Synthesizing Realistic Bubbly Flow Images	Bubble segmentation and size detection algorithms have been developed in recent years for their high efficiency and accuracy in measuring bubbly two-phase flows. In this work, we proposed an architecture called bubble generative adversarial networks (BubGAN) for the generation of realistic synthetic images which could ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	107,009
1007.1938	Affine equivalence of cubic homogeneous rotation symmetric Boolean functions	Homogeneous rotation symmetric Boolean functions have been extensively studied in recent years because of their applications in cryptography. Little is known about the basic question of when two such functions are affine equivalent. The simplest case of quadratic rotation symmetric functions which are generated by cycl...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	7,043
2205.06995	Comparative evaluation of community-aware centrality measures	Influential nodes play a critical role in boosting or curbing spreading phenomena in complex networks. Numerous centrality measures have been proposed for identifying and ranking the nodes according to their importance. Classical centrality measures rely on various local or global properties of the nodes. They do not t...	false	false	false	true	false	false	false	false	false	false	false	false	false	true	false	false	false	false	296,437
2404.09703	AI Competitions and Benchmarks: Dataset Development	Machine learning is now used in many applications thanks to its ability to predict, generate, or discover patterns from large quantities of data. However, the process of collecting and transforming data for practical use is intricate. Even in today's digital era, where substantial data is generated daily, it is uncommo...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	446,799
1303.6249	A Derivation of the Source-Channel Error Exponent using Non-identical Product Distributions	This paper studies the random-coding exponent of joint source-channel coding for a scheme where source messages are assigned to disjoint subsets (referred to as classes), and codewords are independently generated according to a distribution that depends on the class index of the source message. For discrete memoryless ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	23,259
1402.3926	Sparse Coding Approach for Multi-Frame Image Super Resolution	An image super-resolution method from multiple observation of low-resolution images is proposed. The method is based on sub-pixel accuracy block matching for estimating relative displacements of observed images, and sparse signal representation for estimating the corresponding high-resolution image. Relative displaceme...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	30,916
1904.09048	Automated Focal Loss for Image based Object Detection	Current state-of-the-art object detection algorithms still suffer the problem of imbalanced distribution of training data over object classes and background. Recent work introduced a new loss function called focal loss to mitigate this problem, but at the cost of an additional hyperparameter. Manually tuning this hyper...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	128,259
2310.08114	Multi-Modal Sensor Fusion and Object Tracking for Autonomous Racing	Reliable detection and tracking of surrounding objects are indispensable for comprehensive motion prediction and planning of autonomous vehicles. Due to the limitations of individual sensors, the fusion of multiple sensor modalities is required to improve the overall detection capabilities. Additionally, robust motion ...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	399,273
2305.08778	Copula Variational LSTM for High-dimensional Cross-market Multivariate Dependence Modeling	We address an important yet challenging problem - modeling high-dimensional dependencies across multivariates such as financial indicators in heterogeneous markets. In reality, a market couples and influences others over time, and the financial variables of a market are also coupled. We make the first attempt to integr...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	364,404
2005.05579	Data-driven Algorithm for Scheduling with Total Tardiness	In this paper, we investigate the use of deep learning for solving a classical NP-Hard single machine scheduling problem where the criterion is to minimize the total tardiness. Instead of designing an end-to-end machine learning model, we utilize well known decomposition of the problem and we enhance it with a data-dri...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	176,774
1705.00770	Galois LCD Codes over Finite Fields	In this paper, we study the complementary dual codes in more general setting (which are called Galois LCD codes) by a uniform method. A necessary and sufficient condition for linear codes to be Galois LCD codes is determined, and constacyclic codes to be Galois LCD codes are characterized. Some illustrative examples wh...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	72,746
2103.09085	Exact Sparse Orthogonal Dictionary Learning	Over the past decade, learning a dictionary from input images for sparse modeling has been one of the topics which receive most research attention in image processing and compressed sensing. Most existing dictionary learning methods consider an over-complete dictionary, such as the K-SVD method, which may result in hig...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	225,075
1709.06047	Bayesian Optimization Using Domain Knowledge on the ATRIAS Biped	Controllers in robotics often consist of expert-designed heuristics, which can be hard to tune in higher dimensions. It is typical to use simulation to learn these parameters, but controllers learned in simulation often don't transfer to hardware. This necessitates optimization directly on hardware. However, collecting...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	81,015
1302.3606	On Separation Criterion and Recovery Algorithm for Chain Graphs	Chain graphs give a natural unifying point of view on Markov and Bayesian networks and enlarge the potential of graphical models for description of conditional independence structures. In the paper a direct graphical separation criterion for chain graphs, called c-separation, which generalizes the d-separation criterio...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	22,072
2212.11727	A topological analysis of cointegrated data: a Z24 Bridge case study	The paper studies the topological changes from before and after cointegration, for the natural frequencies of the Z24 Bridge. The second natural frequency is known to be nonlinear in temperature, and this will serve as the main focal point of this work. Cointegration is a method of normalising time series data with res...	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	337,864
2412.09229	UADet: A Remarkably Simple Yet Effective Uncertainty-Aware Open-Set Object Detection Framework	We tackle the challenging problem of Open-Set Object Detection (OSOD), which aims to detect both known and unknown objects in unlabelled images. The main difficulty arises from the absence of supervision for these unknown classes, making it challenging to distinguish them from the background. Existing OSOD detectors ei...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	516,404
2308.12842	Text Similarity from Image Contents using Statistical and Semantic Analysis Techniques	Plagiarism detection is one of the most researched areas among the Natural Language Processing(NLP) community. A good plagiarism detection covers all the NLP methods including semantics, named entities, paraphrases etc. and produces detailed plagiarism reports. Detection of Cross Lingual Plagiarism requires deep knowle...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	387,688
2307.00228	InferTurbo: A Scalable System for Boosting Full-graph Inference of Graph Neural Network over Huge Graphs	GNN inference is a non-trivial task, especially in industrial scenarios with giant graphs, given three main challenges, i.e., scalability tailored for full-graph inference on huge graphs, inconsistency caused by stochastic acceleration strategies (e.g., sampling), and the serious redundant computation issue. To address...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	376,924
2109.03168	Locally Recoverable Streaming Codes for Packet-Erasure Recovery	Streaming codes are a class of packet-level erasure codes that are designed with the goal of ensuring recovery in low-latency fashion, of erased packets over a communication network. It is well-known in the streaming code literature, that diagonally embedding codewords of a $[\tau+1,\tau+1-a]$ Maximum Distance Separabl...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	253,978
2410.17402	Invisible Manipulation Deep Reinforcement Learning Enhanced Stealthy Attacks on Battery Energy Management Systems	This paper introduces "invisible manipulation," an innovative cyber-attack mechanism achieved through strategically timed stealthy false data injection attacks (SFDIAs). By stealthily manipulating measurements of a critical asset prior to the target time period, the attacker can subtly guide the engineering system towa...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	501,443
1808.08378	Fusion++: Volumetric Object-Level SLAM	We propose an online object-level SLAM system which builds a persistent and accurate 3D graph map of arbitrary reconstructed objects. As an RGB-D camera browses a cluttered indoor scene, Mask-RCNN instance segmentations are used to initialise compact per-object Truncated Signed Distance Function (TSDF) reconstructions ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	105,935
2406.13560	Lexically Grounded Subword Segmentation	We present three innovations in tokenization and subword segmentation. First, we propose to use unsupervised morphological analysis with Morfessor as pre-tokenization. Second, we present an algebraic method for obtaining subword embeddings grounded in a word embedding space. Based on that, we design a novel subword seg...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	465,899
2403.08262	BiTT: Bi-directional Texture Reconstruction of Interacting Two Hands from a Single Image	Creating personalized hand avatars is important to offer a realistic experience to users on AR / VR platforms. While most prior studies focused on reconstructing 3D hand shapes, some recent work has tackled the reconstruction of hand textures on top of shapes. However, these methods are often limited to capturing pixel...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	437,261
2112.11243	Projected Sliced Wasserstein Autoencoder-based Hyperspectral Images Anomaly Detection	Anomaly detection (AD) has been an active research area in various domains. Yet, the increasing data scale, complexity, and dimension turn the traditional methods into challenging. Recently, the deep generative model, such as the variational autoencoder (VAE), has sparked a renewed interest in the AD problem. However, ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	272,654
2406.14185	Failure-Resilient Distributed Inference with Model Compression over Heterogeneous Edge Devices	The distributed inference paradigm enables the computation workload to be distributed across multiple devices, facilitating the implementations of deep learning based intelligent services on extremely resource-constrained Internet of Things (IoT) scenarios. Yet it raises great challenges to perform complicated inferenc...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	466,196
1702.02367	Iterative Multi-document Neural Attention for Multiple Answer Prediction	People have information needs of varying complexity, which can be solved by an intelligent agent able to answer questions formulated in a proper way, eventually considering user context and preferences. In a scenario in which the user profile can be considered as a question, intelligent agents able to answer questions ...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	67,965
2011.03176	On the Ergodicity, Bias and Asymptotic Normality of Randomized Midpoint Sampling Method	The randomized midpoint method, proposed by [SL19], has emerged as an optimal discretization procedure for simulating the continuous time Langevin diffusions. Focusing on the case of strong-convex and smooth potentials, in this paper, we analyze several probabilistic properties of the randomized midpoint discretization...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	205,162
1909.03560	Evolving Order and Chaos: Comparing Particle Swarm Optimization and Genetic Algorithms for Global Coordination of Cellular Automata	We apply two evolutionary search algorithms: Particle Swarm Optimization (PSO) and Genetic Algorithms (GAs) to the design of Cellular Automata (CA) that can perform computational tasks requiring global coordination. In particular, we compare search efficiency for PSO and GAs applied to both the density classification p...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	false	false	144,526
2501.16786	Exploring the Role of Explicit Temporal Modeling in Multimodal Large Language Models for Video Understanding	Applying Multimodal Large Language Models (MLLMs) to video understanding presents significant challenges due to the need to model temporal relations across frames. Existing approaches adopt either implicit temporal modeling, relying solely on the LLM decoder, or explicit temporal modeling, employing auxiliary temporal ...	false	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	528,111
2003.09904	On the snappability and singularity-distance of frameworks with bars and triangular plates	In a recent article the author presented a method to measure the snapping capability -- shortly called snappability -- of bar-joint frameworks based on the total elastic strain energy by computing the deformation of all bars using Hooke's law and the definition of Cauchy/Engineering strain. Within the paper at hand, we...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	true	169,180
1304.1093	A New Algorithm for Finding MAP Assignments to Belief Networks	We present a new algorithm for finding maximum a-posterior) (MAP) assignments of values to belief networks. The belief network is compiled into a network consisting only of nodes with boolean (i.e. only 0 or 1) conditional probabilities. The MAP assignment is then found using a best-first search on the resulting networ...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	23,446
cs/0604087	Probabilistic Automata for Computing with Words	Usually, probabilistic automata and probabilistic grammars have crisp symbols as inputs, which can be viewed as the formal models of computing with values. In this paper, we first introduce probabilistic automata and probabilistic grammars for computing with (some special) words in a probabilistic framework, where the ...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	539,404
2209.11228	NamedMask: Distilling Segmenters from Complementary Foundation Models	The goal of this work is to segment and name regions of images without access to pixel-level labels during training. To tackle this task, we construct segmenters by distilling the complementary strengths of two foundation models. The first, CLIP (Radford et al. 2021), exhibits the ability to assign names to image conte...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	319,118
2203.13906	Biolink Model: A Universal Schema for Knowledge Graphs in Clinical, Biomedical, and Translational Science	Within clinical, biomedical, and translational science, an increasing number of projects are adopting graphs for knowledge representation. Graph-based data models elucidate the interconnectedness between core biomedical concepts, enable data structures to be easily updated, and support intuitive queries, visualizations...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	287,795
2502.06813	Policy Guided Tree Search for Enhanced LLM Reasoning	Despite their remarkable capabilities, large language models often struggle with tasks requiring complex reasoning and planning. While existing approaches like Chain-of-Thought prompting and tree search techniques show promise, they are limited by their reliance on predefined heuristics and computationally expensive ex...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	532,259
2405.07257	SPEAK: Speech-Driven Pose and Emotion-Adjustable Talking Head Generation	Most earlier researches on talking face generation have focused on the synchronization of lip motion and speech content. However, head pose and facial emotions are equally important characteristics of natural faces. While audio-driven talking face generation has seen notable advancements, existing methods either overlo...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	453,638
2405.13366	Anticipating Optical Availability in Hybrid RF/FSO Links Using RF Beacons and Deep Learning	Radio frequency (RF) communications offer reliable but low data rates and energy-inefficient satellite links, while free-space optical (FSO) promises high bandwidth but struggles with disturbances imposed by atmospheric effects. A hybrid RF/FSO architecture aims to achieve optimal reliability along with high data rates...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	455,911
2111.06211	Model-Based Reinforcement Learning via Stochastic Hybrid Models	Optimal control of general nonlinear systems is a central challenge in automation. Enabled by powerful function approximators, data-driven approaches to control have recently successfully tackled challenging applications. However, such methods often obscure the structure of dynamics and control behind black-box over-pa...	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	266,013
2412.05265	Reinforcement Learning: An Overview	This manuscript gives a big-picture, up-to-date overview of the field of (deep) reinforcement learning and sequential decision making, covering value-based RL, policy-gradient methods, model-based methods, and various other topics (including a very brief discussion of RL+LLMs).	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	514,756
2112.06643	On the Dynamics of Hopfield Neural Networks on Unit Quaternions	In this paper, we first address the dynamics of the elegant multi-valued quaternionic Hopfield neural network (MV-QHNN) proposed by Minemoto and collaborators. Contrary to what was expected, we show that the MV-QHNN, as well as one of its variation, does not always come to rest at an equilibrium state under the usual c...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	271,251
2207.12773	Quiver neural networks	We develop a uniform theoretical approach towards the analysis of various neural network connectivity architectures by introducing the notion of a quiver neural network. Inspired by quiver representation theory in mathematics, this approach gives a compact way to capture elaborate data flows in complex network architec...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	310,112
2109.04813	TACS: Taxonomy Adaptive Cross-Domain Semantic Segmentation	Traditional domain adaptive semantic segmentation addresses the task of adapting a model to a novel target domain under limited or no additional supervision. While tackling the input domain gap, the standard domain adaptation settings assume no domain change in the output space. In semantic prediction tasks, different ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	254,547
2109.07472	Single-camera Two-Wavelength Imaging Pyrometry for Melt Pool Temperature Measurement and Monitoring in Laser Powder Bed Fusion based Additive Manufacturing	Melt pool (MP) temperature is one of the determining factors and key signatures for the properties of printed components during metal additive manufacturing (AM). The state-of-the art measurement systems are hindered by both the equipment cost and the large-scale data acquisition and processing demands. In this work, w...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	255,540
2408.07430	UAHOI: Uncertainty-aware Robust Interaction Learning for HOI Detection	This paper focuses on Human-Object Interaction (HOI) detection, addressing the challenge of identifying and understanding the interactions between humans and objects within a given image or video frame. Spearheaded by Detection Transformer (DETR), recent developments lead to significant improvements by replacing tradit...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	480,579
1712.02754	On the Duality Between Retinex and Image Dehazing	Image dehazing deals with the removal of undesired loss of visibility in outdoor images due to the presence of fog. Retinex is a color vision model mimicking the ability of the Human Visual System to robustly discount varying illuminations when observing a scene under different spectral lighting conditions. Retinex has...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	86,332
2210.03566	Automated segmentation and morphological characterization of placental histology images based on a single labeled image	In this study, a novel method of data augmentation has been presented for the segmentation of placental histological images when the labeled data are scarce. This method generates new realizations of the placenta intervillous morphology while maintaining the general textures and orientations. As a result, a diversified...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	322,089
2210.10123	Interpolated SelectionConv for Spherical Images and Surfaces	We present a new and general framework for convolutional neural network operations on spherical (or omnidirectional) images. Our approach represents the surface as a graph of connected points that doesn't rely on a particular sampling strategy. Additionally, by using an interpolated version of SelectionConv, we can ope...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	324,792
2112.04737	Asynchronous Semi-Decentralized Federated Edge Learning for Heterogeneous Clients	Federated edge learning (FEEL) has drawn much attention as a privacy-preserving distributed learning framework for mobile edge networks. In this work, we investigate a novel semi-decentralized FEEL (SD-FEEL) architecture where multiple edge servers collaborate to incorporate more data from edge devices in training. Des...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	270,628
2408.05438	Convergence Guarantee of Dynamic Programming for LTL Surrogate Reward	Linear Temporal Logic (LTL) is a formal way of specifying complex objectives for planning problems modeled as Markov Decision Processes (MDPs). The planning problem aims to find the optimal policy that maximizes the satisfaction probability of the LTL objective. One way to solve the planning problem is to use the surro...	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	479,786
2502.02481	Multilingual Machine Translation with Open Large Language Models at Practical Scale: An Empirical Study	Large language models (LLMs) have shown continuously improving multilingual capabilities, and even small-scale open-source models have demonstrated rapid performance enhancement. In this paper, we systematically explore the abilities of open LLMs with less than ten billion parameters to handle multilingual machine tran...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	530,338
2307.06304	Patch n' Pack: NaViT, a Vision Transformer for any Aspect Ratio and Resolution	The ubiquitous and demonstrably suboptimal choice of resizing images to a fixed resolution before processing them with computer vision models has not yet been successfully challenged. However, models such as the Vision Transformer (ViT) offer flexible sequence-based modeling, and hence varying input sequence lengths. W...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	379,028
2409.16620	Optimized Monte Carlo Tree Search for Enhanced Decision Making in the FrozenLake Environment	Monte Carlo Tree Search (MCTS) is a powerful algorithm for solving complex decision-making problems. This paper presents an optimized MCTS implementation applied to the FrozenLake environment, a classic reinforcement learning task characterized by stochastic transitions. The optimization leverages cumulative reward and...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	491,422

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