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
1502.05786	Randomized Assignment of Jobs to Servers in Heterogeneous Clusters of Shared Servers for Low Delay	We consider the job assignment problem in a multi-server system consisting of $N$ parallel processor sharing servers, categorized into $M$ ($\ll N$) different types according to their processing capacity or speed. Jobs of random sizes arrive at the system according to a Poisson process with rate $N \lambda$. Upon each ...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	true	40,409
1601.04859	The Structure of Z_2[u]Z_2[u, v]-additive Codes	In this paper, we study the algebraic structure of Z_2[u]Z_2[u, v]-additive codes which are Z_2[u, v]-submodules where u^2 = v^2 = 0 and uv = vu. In particular, we determine a Gray map from Z_2[u]Z_2 [u, v] to Z_2^{2{\alpha}+8\b{eta}} and study generator and parity check matrices for these codes. Further we study the s...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	51,069
2103.10153	A Probabilistic State Space Model for Joint Inference from Differential Equations and Data	Mechanistic models with differential equations are a key component of scientific applications of machine learning. Inference in such models is usually computationally demanding, because it involves repeatedly solving the differential equation. The main problem here is that the numerical solver is hard to combine with s...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	225,366
2104.08252	ALF -- A Fitness-Based Artificial Life Form for Evolving Large-Scale Neural Networks	Machine Learning (ML) is becoming increasingly important in daily life. In this context, Artificial Neural Networks (ANNs) are a popular approach within ML methods to realize an artificial intelligence. Usually, the topology of ANNs is predetermined. However, there are problems where it is difficult to find a suitable ...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	230,721
1909.04121	AC-Teach: A Bayesian Actor-Critic Method for Policy Learning with an Ensemble of Suboptimal Teachers	The exploration mechanism used by a Deep Reinforcement Learning (RL) agent plays a key role in determining its sample efficiency. Thus, improving over random exploration is crucial to solve long-horizon tasks with sparse rewards. We propose to leverage an ensemble of partial solutions as teachers that guide the agent's...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	144,699
2501.17172	Towards spiking analog hardware implementation of a trajectory interpolation mechanism for smooth closed-loop control of a spiking robot arm	Neuromorphic engineering aims to incorporate the computational principles found in animal brains, into modern technological systems. Following this approach, in this work we propose a closed-loop neuromorphic control system for an event-based robotic arm. The proposed system consists of a shifted Winner-Take-All spikin...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	false	false	528,237
2006.16910	A data science approach to drug safety: Semantic and visual mining of adverse drug events from clinical trials of pain treatments	Clinical trials are the basis of Evidence-Based Medicine. Trial results are reviewed by experts and consensus panels for producing meta-analyses and clinical practice guidelines. However, reviewing these results is a long and tedious task, hence the meta-analyses and guidelines are not updated each time a new trial is ...	false	false	false	false	true	false	false	false	false	false	false	false	false	true	false	false	false	false	184,943
2408.03648	HiQuE: Hierarchical Question Embedding Network for Multimodal Depression Detection	The utilization of automated depression detection significantly enhances early intervention for individuals experiencing depression. Despite numerous proposals on automated depression detection using recorded clinical interview videos, limited attention has been paid to considering the hierarchical structure of the int...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	479,106
cs/0204027	Integrating selectional preferences in WordNet	Selectional preference learning methods have usually focused on word-to-class relations, e.g., a verb selects as its subject a given nominal class. This paper extends previous statistical models to class-to-class preferences, and presents a model that learns selectional preferences for classes of verbs, together with a...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	537,547
2107.10372	Speed Advisory System Using Real-Time Actuated Traffic Light Phase Length Prediction	Speed advisory systems for connected vehicles rely on the estimation of green (or red) light duration at signalized intersections. A particular challenge is to predict the signal phases of semi- and fully-actuated traffic lights. In this paper, we introduce an algorithm that processes traffic measurement data collected...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	247,274
2003.01936	Automatic Signboard Detection and Localization in Densely Populated Developing Cities	Most city establishments of developing cities are digitally unlabeled because of the lack of automatic annotation systems. Hence location and trajectory services such as Google Maps, Uber etc remain underutilized in such cities. Accurate signboard detection in natural scene images is the foremost task for error-free in...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	166,812
1310.6753	Romantic Partnerships and the Dispersion of Social Ties: A Network Analysis of Relationship Status on Facebook	A crucial task in the analysis of on-line social-networking systems is to identify important people --- those linked by strong social ties --- within an individual's network neighborhood. Here we investigate this question for a particular category of strong ties, those involving spouses or romantic partners. We organiz...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	27,987
1109.3701	Active Ranking using Pairwise Comparisons	This paper examines the problem of ranking a collection of objects using pairwise comparisons (rankings of two objects). In general, the ranking of $n$ objects can be identified by standard sorting methods using $n log_2 n$ pairwise comparisons. We are interested in natural situations in which relationships among the o...	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	12,202
2212.03332	Edge Impulse: An MLOps Platform for Tiny Machine Learning	Edge Impulse is a cloud-based machine learning operations (MLOps) platform for developing embedded and edge ML (TinyML) systems that can be deployed to a wide range of hardware targets. Current TinyML workflows are plagued by fragmented software stacks and heterogeneous deployment hardware, making ML model optimization...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	335,078
2008.10875	ETC-NLG: End-to-end Topic-Conditioned Natural Language Generation	Plug-and-play language models (PPLMs) enable topic-conditioned natural language generation by pairing large pre-trained generators with attribute models used to steer the predicted token distribution towards the selected topic. Despite their computational efficiency, PPLMs require large amounts of labeled texts to effe...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	193,126
2203.11283	NeRFusion: Fusing Radiance Fields for Large-Scale Scene Reconstruction	While NeRF has shown great success for neural reconstruction and rendering, its limited MLP capacity and long per-scene optimization times make it challenging to model large-scale indoor scenes. In contrast, classical 3D reconstruction methods can handle large-scale scenes but do not produce realistic renderings. We pr...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	286,859
2102.11275	Large Scale Global Optimization Algorithms for IoT Networks: A Comparative Study	The advent of Internet of Things (IoT) has bring a new era in communication technology by expanding the current inter-networking services and enabling the machine-to-machine communication. IoT massive deployments will create the problem of optimal power allocation. The objective of the optimization problem is to obtain...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	221,374
2209.15292	The Minority Matters: A Diversity-Promoting Collaborative Metric Learning Algorithm	Collaborative Metric Learning (CML) has recently emerged as a popular method in recommendation systems (RS), closing the gap between metric learning and Collaborative Filtering. Following the convention of RS, existing methods exploit unique user representation in their model design. This paper focuses on a challenging...	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	false	320,558
1902.07296	Augmentation for small object detection	In recent years, object detection has experienced impressive progress. Despite these improvements, there is still a significant gap in the performance between the detection of small and large objects. We analyze the current state-of-the-art model, Mask-RCNN, on a challenging dataset, MS COCO. We show that the overlap b...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	121,952
1404.1695	Proceedings of Third Workshop on Robots and Sensors integration in future rescue INformation system (ROSIN 2013)	This is the proceedings of the third workshop on Robots and Sensors integration in future rescue INformation system (ROSIN 2013)	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	32,143
2006.09074	Quantitative Group Testing and the rank of random matrices	Given a random Bernoulli matrix $ A\in \{0,1\}^{m\times n} $, an integer $ 0< k < n $ and the vector $ y:=Ax $, where $ x \in \{0,1\}^n $ is of Hamming weight $ k $, the objective in the {\em Quantitative Group Testing} (QGT) problem is to recover $ x $. This problem is more difficult the smaller $m$ is. For parameter ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	182,414
2412.04893	Automatic Tongue Delineation from MRI Images with a Convolutional Neural Network Approach	Tongue contour extraction from real-time magnetic resonance images is a nontrivial task due to the presence of artifacts manifesting in form of blurring or ghostly contours. In this work, we present results of automatic tongue delineation achieved by means of U-Net auto-encoder convolutional neural network. We present ...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	514,617
2408.10681	HMoE: Heterogeneous Mixture of Experts for Language Modeling	Mixture of Experts (MoE) offers remarkable performance and computational efficiency by selectively activating subsets of model parameters. Traditionally, MoE models use homogeneous experts, each with identical capacity. However, varying complexity in input data necessitates experts with diverse capabilities, while homo...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	481,976
2112.02754	Voltage Stability Constrained Unit Commitment in High IBG-Penetrated Power Systems	With the increasing penetration of renewable energy sources, power system operation has to be adapted to ensure the system stability and security while considering the distinguished feature of the Power Electronics (PE) interfaced generators. The static voltage stability which is mainly compromised by heavy loading con...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	269,951
1904.07842	Kerdock Codes Determine Unitary 2-Designs	The non-linear binary Kerdock codes are known to be Gray images of certain extended cyclic codes of length $N = 2^m$ over $\mathbb{Z}_4$. We show that exponentiating these $\mathbb{Z}_4$-valued codewords by $\imath \triangleq \sqrt{-1}$ produces stabilizer states, that are quantum states obtained using only Clifford un...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	127,899
2306.01603	Decentralized Federated Learning: A Survey and Perspective	Federated learning (FL) has been gaining attention for its ability to share knowledge while maintaining user data, protecting privacy, increasing learning efficiency, and reducing communication overhead. Decentralized FL (DFL) is a decentralized network architecture that eliminates the need for a central server in cont...	false	false	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	true	370,515
1909.13294	Differentially Private Controller Synthesis With Metric Temporal Logic Specifications	Privacy is an important concern in various multiagent systems in which data collected from the agents are sensitive. We propose a differentially private controller synthesis approach for multi-agent systems subject to high-level specifications expressed in metric temporal logic (MTL). We consider a setting where each a...	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	147,378
1307.7226	Diffusion Least Mean P-Power Algorithms for Distributed Estimation in Alpha-Stable Noise Environments	We propose a diffusion least mean p-power (LMP) algorithm for distributed estimation in alpha stable noise environments, which is one of the widely used models that appears in various environments. Compared with the diffusion least mean squares (LMS) algorithm, better performance is obtained for the diffusion LMP metho...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	26,082
2501.16273	Return of the Encoder: Maximizing Parameter Efficiency for SLMs	The dominance of large decoder-only language models has overshadowed encoder-decoder architectures, despite their fundamental efficiency advantages in sequence processing. For small language models (SLMs) - those with 1 billion parameters or fewer - our systematic analysis across GPU, CPU, and NPU platforms reveals tha...	false	false	false	false	true	false	false	false	true	false	false	true	false	false	false	false	false	false	527,884
1606.04143	Algebraic Geometric codes from Kummer Extensions	For Kummer extensions defined by $y^m = f (x)$, where $f (x)$ is a separable polynomial over the finite field $\mathbb{F}_q$, we compute the number of Weierstrass gaps at two totally ramified places. For many totally ramified places we give a criterion to find pure gaps at these points and present families of pure gaps...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	57,194
1910.07794	Stochastic Geometry-Based Analysis of Airborne Base Stations with Laser-powered UAVs	One of the most promising solutions to the problem of limited flight time of unmanned aerial vehicles (UAVs), is providing the UAVs with power through laser beams emitted from Laser Beam Directors (LBDs) deployed on the ground. In this letter, we study the performance of a laser-powered UAV-enabled communication system...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	149,710
2211.02920	GmGM: a Fast Multi-Axis Gaussian Graphical Model	This paper introduces the Gaussian multi-Graphical Model, a model to construct sparse graph representations of matrix- and tensor-variate data. We generalize prior work in this area by simultaneously learning this representation across several tensors that share axes, which is necessary to allow the analysis of multimo...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	328,750
2006.01604	Two-Timescale Optimization for Intelligent Reflecting Surface Aided D2D Underlay Communication	The performance of a device-to-device (D2D) underlay communication system is limited by the co-channel interference between cellular users (CUs) and D2D devices. To address this challenge, an intelligent reflecting surface (IRS) aided D2D underlay system is studied in this paper. A two-timescale optimization scheme is ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	179,819
1408.1534	Followers or Phantoms? An Anatomy of Purchased Twitter Followers	Online Social Media (OSM) is extensively used by contemporary Internet users to communicate, socialize and disseminate information. This has led to the creation of a distinct online social identity which in turn has created the need of online social reputation management techniques. A significant percentage of OSM user...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	35,183
2112.08184	Interactive Visualization and Representation Analysis Applied to Glacier Segmentation	Interpretability has attracted increasing attention in earth observation problems. We apply interactive visualization and representation analysis to guide interpretation of glacier segmentation models. We visualize the activations from a U-Net to understand and evaluate the model performance. We build an online interfa...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	271,718
1907.01591	Combating the Filter Bubble: Designing for Serendipity in a University Course Recommendation System	Collaborative filtering based algorithms, including Recurrent Neural Networks (RNN), tend towards predicting a perpetuation of past observed behavior. In a recommendation context, this can lead to an overly narrow set of suggestions lacking in serendipity and inadvertently placing the user in what is known as a "filter...	false	false	false	false	false	true	false	false	false	false	false	false	false	true	false	false	false	false	137,366
2310.14525	Graph Ranking Contrastive Learning: A Extremely Simple yet Efficient Method	Graph contrastive learning (GCL) has emerged as a representative graph self-supervised method, achieving significant success. The currently prevalent optimization objective for GCL is InfoNCE. Typically, it employs augmentation techniques to obtain two views, where a node in one view acts as the anchor, the correspondi...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	401,894
1809.06408	Crowd-Assisted Polyp Annotation of Virtual Colonoscopy Videos	Virtual colonoscopy (VC) allows a radiologist to navigate through a 3D colon model reconstructed from a computed tomography scan of the abdomen, looking for polyps, the precursors of colon cancer. Polyps are seen as protrusions on the colon wall and haustral folds, visible in the VC fly-through videos. A complete revie...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	108,050
2110.09916	Identification of high order closure terms from fully kinetic simulations using machine learning	Simulations of large-scale plasma systems are typically based on a fluid approximation approach. These models construct a moment-based system of equations that approximate the particle-based physics as a fluid, but as a result lack the small-scale physical processes available to fully kinetic models. Traditionally, emp...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	261,972
2401.06936	Accelerated Sampling of Rare Events using a Neural Network Bias Potential	In the field of computational physics and material science, the efficient sampling of rare events occurring at atomic scale is crucial. It aids in understanding mechanisms behind a wide range of important phenomena, including protein folding, conformal changes, chemical reactions and materials diffusion and deformation...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	421,350
2304.06344	Streamlined Framework for Agile Forecasting Model Development towards Efficient Inventory Management	This paper proposes a framework for developing forecasting models by streamlining the connections between core components of the developmental process. The proposed framework enables swift and robust integration of new datasets, experimentation on different algorithms, and selection of the best models. We start with th...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	357,942
2309.11335	2D-3D Pose Tracking with Multi-View Constraints	Camera localization in 3D LiDAR maps has gained increasing attention due to its promising ability to handle complex scenarios, surpassing the limitations of visual-only localization methods. However, existing methods mostly focus on addressing the cross-modal gaps, estimating camera poses frame by frame without conside...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	393,370
2312.02167	Uncertainty Quantification in Machine Learning Based Segmentation: A Post-Hoc Approach for Left Ventricle Volume Estimation in MRI	Recent studies have confirmed cardiovascular diseases remain responsible for highest death toll amongst non-communicable diseases. Accurate left ventricular (LV) volume estimation is critical for valid diagnosis and management of various cardiovascular conditions, but poses significant challenge due to inherent uncerta...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	412,729
2005.00965	Double-Hard Debias: Tailoring Word Embeddings for Gender Bias Mitigation	Word embeddings derived from human-generated corpora inherit strong gender bias which can be further amplified by downstream models. Some commonly adopted debiasing approaches, including the seminal Hard Debias algorithm, apply post-processing procedures that project pre-trained word embeddings into a subspace orthogon...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	175,450
1509.01719	Unsupervised Cross-Domain Recognition by Identifying Compact Joint Subspaces	This paper introduces a new method to solve the cross-domain recognition problem. Different from the traditional domain adaption methods which rely on a global domain shift for all classes between source and target domain, the proposed method is more flexible to capture individual class variations across domains. By ad...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	46,647
2002.00444	Deep Reinforcement Learning for Autonomous Driving: A Survey	With the development of deep representation learning, the domain of reinforcement learning (RL) has become a powerful learning framework now capable of learning complex policies in high dimensional environments. This review summarises deep reinforcement learning (DRL) algorithms and provides a taxonomy of automated dri...	false	false	false	false	true	false	true	true	false	false	false	false	false	false	false	false	false	false	162,352
2201.01401	Robotic Laser Orientation Planning with a 3D Data-driven Method	This paper focuses on a research problem of robotic controlled laser orientation to minimize errant overcutting of healthy tissue during the course of pathological tissue resection. Laser scalpels have been widely used in surgery to remove pathological tissue targets such as tumors or other lesions. However, different ...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	274,238
2308.02594	SMARLA: A Safety Monitoring Approach for Deep Reinforcement Learning Agents	Deep Reinforcement Learning (DRL) has made significant advancements in various fields, such as autonomous driving, healthcare, and robotics, by enabling agents to learn optimal policies through interactions with their environments. However, the application of DRL in safety-critical domains presents challenges, particul...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	true	383,703
2402.05892	Mamba-ND: Selective State Space Modeling for Multi-Dimensional Data	In recent years, Transformers have become the de-facto architecture for sequence modeling on text and a variety of multi-dimensional data, such as images and video. However, the use of self-attention layers in a Transformer incurs prohibitive compute and memory complexity that scales quadratically w.r.t. the sequence l...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	428,049
1501.06206	Dynamics of Belief: Abduction, Horn Knowledge Base And Database Updates	The dynamics of belief and knowledge is one of the major components of any autonomous system that should be able to incorporate new pieces of information. In order to apply the rationality result of belief dynamics theory to various practical problems, it should be generalized in two respects: first it should allow a c...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	true	true	39,585
2202.04897	InterHT: Knowledge Graph Embeddings by Interaction between Head and Tail Entities	Knowledge graph embedding (KGE) models learn the representation of entities and relations in knowledge graphs. Distance-based methods show promising performance on link prediction task, which predicts the result by the distance between two entity representations. However, most of these methods represent the head entity...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	279,709
2501.00758	Less is More: Token Context-aware Learning for Object Tracking	Recently, several studies have shown that utilizing contextual information to perceive target states is crucial for object tracking. They typically capture context by incorporating multiple video frames. However, these naive frame-context methods fail to consider the importance of each patch within a reference frame, m...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	521,791
2307.11242	On-Sensor Data Filtering using Neuromorphic Computing for High Energy Physics Experiments	This work describes the investigation of neuromorphic computing-based spiking neural network (SNN) models used to filter data from sensor electronics in high energy physics experiments conducted at the High Luminosity Large Hadron Collider. We present our approach for developing a compact neuromorphic model that filter...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	true	false	false	380,836
2302.03657	Toward Face Biometric De-identification using Adversarial Examples	The remarkable success of face recognition (FR) has endangered the privacy of internet users particularly in social media. Recently, researchers turned to use adversarial examples as a countermeasure. In this paper, we assess the effectiveness of using two widely known adversarial methods (BIM and ILLC) for de-identify...	false	false	false	false	false	false	true	false	false	false	false	true	true	false	false	false	false	false	344,421
2204.07054	BrainGB: A Benchmark for Brain Network Analysis with Graph Neural Networks	Mapping the connectome of the human brain using structural or functional connectivity has become one of the most pervasive paradigms for neuroimaging analysis. Recently, Graph Neural Networks (GNNs) motivated from geometric deep learning have attracted broad interest due to their established power for modeling complex ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	true	false	false	291,544
1603.00961	Interactive and Scale Invariant Segmentation of the Rectum/Sigmoid via User-Defined Templates	Among all types of cancer, gynecological malignancies belong to the 4th most frequent type of cancer among women. Besides chemotherapy and external beam radiation, brachytherapy is the standard procedure for the treatment of these malignancies. In the progress of treatment planning, localization of the tumor as the tar...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	52,838
1705.05720	Subjective Knowledge Acquisition and Enrichment Powered By Crowdsourcing	Knowledge bases (KBs) have attracted increasing attention due to its great success in various areas, such as Web and mobile search.Existing KBs are restricted to objective factual knowledge, such as city population or fruit shape, whereas,subjective knowledge, such as big city, which is commonly mentioned in Web and mo...	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	true	false	73,541
2304.14132	Human Semantic Segmentation using Millimeter-Wave Radar Sparse Point Clouds	This paper presents a framework for semantic segmentation on sparse sequential point clouds of millimeter-wave radar. Compared with cameras and lidars, millimeter-wave radars have the advantage of not revealing privacy, having a strong anti-interference ability, and having long detection distance. The sparsity and capt...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	360,841
2105.02668	VideoLT: Large-scale Long-tailed Video Recognition	Label distributions in real-world are oftentimes long-tailed and imbalanced, resulting in biased models towards dominant labels. While long-tailed recognition has been extensively studied for image classification tasks, limited effort has been made for video domain. In this paper, we introduce VideoLT, a large-scale lo...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	233,886
2405.06910	Generative flow induced neural architecture search: Towards discovering optimal architecture in wavelet neural operator	We propose a generative flow-induced neural architecture search algorithm. The proposed approach devices simple feed-forward neural networks to learn stochastic policies to generate sequences of architecture hyperparameters such that the generated states are in proportion with the reward from the terminal state. We dem...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	453,498
2502.02371	Accurate Pocket Identification for Binding-Site-Agnostic Docking	Accurate identification of druggable pockets is essential for structure-based drug design. However, most pocket-identification algorithms prioritize their geometric properties over downstream docking performance. To address this limitation, we developed RAPID-Net, a pocket-finding algorithm for seamless integration wit...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	530,285
2110.13324	Sampling Multiple Nodes in Large Networks: Beyond Random Walks	Sampling random nodes is a fundamental algorithmic primitive in the analysis of massive networks, with many modern graph mining algorithms critically relying on it. We consider the task of generating a large collection of random nodes in the network assuming limited query access (where querying a node reveals its set o...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	true	263,136
2203.17008	It's All In the Teacher: Zero-Shot Quantization Brought Closer to the Teacher	Model quantization is considered as a promising method to greatly reduce the resource requirements of deep neural networks. To deal with the performance drop induced by quantization errors, a popular method is to use training data to fine-tune quantized networks. In real-world environments, however, such a method is fr...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	289,010
2008.10032	Seesaw Loss for Long-Tailed Instance Segmentation	Instance segmentation has witnessed a remarkable progress on class-balanced benchmarks. However, they fail to perform as accurately in real-world scenarios, where the category distribution of objects naturally comes with a long tail. Instances of head classes dominate a long-tailed dataset and they serve as negative sa...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	192,888
2401.12151	Uncoded Storage Coded Transmission Elastic Computing with Straggler Tolerance in Heterogeneous Systems	In 2018, Yang et al. introduced a novel and effective approach, using maximum distance separable (MDS) codes, to mitigate the impact of elasticity in cloud computing systems. This approach is referred to as coded elastic computing. Some limitations of this approach include that it assumes all virtual machines have the ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	423,270
1701.02595	Around power law for PageRank components in Buckley-Osthus model of web graph	In the paper we investigate power law for PageRank components for the Buckley-Osthus model for web graph. We compare different numerical methods for PageRank calculation. With the best method we do a lot of numerical experiments. These experiments confirm the hypothesis about power law. At the end we discuss real model...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	66,573
2310.02859	Tight Sampling in Unbounded Networks	The default approach to deal with the enormous size and limited accessibility of many Web and social media networks is to sample one or more subnetworks from a conceptually unbounded unknown network. Clearly, the extracted subnetworks will crucially depend on the sampling scheme. Motivated by studies of homophily and o...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	397,016
2102.09521	Evolving Fuzzy System Applied to Battery Charge Capacity Prediction for Fault Prognostics	This paper addresses the use of data-driven evolving techniques applied to fault prognostics. In such problems, accurate predictions of multiple steps ahead are essential for the Remaining Useful Life (RUL) estimation of a given asset. The fault prognostics' solutions must be able to model the typical nonlinear behavio...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	220,801
1207.5871	Optimal Sampling Points in Reproducing Kernel Hilbert Spaces	The recent developments of basis pursuit and compressed sensing seek to extract information from as few samples as possible. In such applications, since the number of samples is restricted, one should deploy the sampling points wisely. We are motivated to study the optimal distribution of finite sampling points. Formul...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	17,752
2202.11479	Listen to Interpret: Post-hoc Interpretability for Audio Networks with NMF	This paper tackles post-hoc interpretability for audio processing networks. Our goal is to interpret decisions of a network in terms of high-level audio objects that are also listenable for the end-user. To this end, we propose a novel interpreter design that incorporates non-negative matrix factorization (NMF). In par...	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	281,904
1908.06845	Deep Task-Based Quantization	Quantizers play a critical role in digital signal processing systems. Recent works have shown that the performance of quantization systems acquiring multiple analog signals using scalar analog-to-digital converters (ADCs) can be significantly improved by properly processing the analog signals prior to quantization. How...	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	142,127
2308.11164	Decoupled Contrastive Multi-View Clustering with High-Order Random Walks	In recent, some robust contrastive multi-view clustering (MvC) methods have been proposed, which construct data pairs from neighborhoods to alleviate the false negative issue, i.e., some intra-cluster samples are wrongly treated as negative pairs. Although promising performance has been achieved by these methods, the f...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	387,031
2310.03668	GoLLIE: Annotation Guidelines improve Zero-Shot Information-Extraction	Large Language Models (LLMs) combined with instruction tuning have made significant progress when generalizing to unseen tasks. However, they have been less successful in Information Extraction (IE), lagging behind task-specific models. Typically, IE tasks are characterized by complex annotation guidelines that describ...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	397,369
1907.05928	A machine learning framework for computationally expensive transient models	The promise of machine learning has been explored in a variety of scientific disciplines in the last few years, however, its application on first-principles based computationally expensive tools is still in nascent stage. Even with the advances in computational resources and power, transient simulations of large-scale ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	138,480
2211.16807	Camelira: An Arabic Multi-Dialect Morphological Disambiguator	We present Camelira, a web-based Arabic multi-dialect morphological disambiguation tool that covers four major variants of Arabic: Modern Standard Arabic, Egyptian, Gulf, and Levantine. Camelira offers a user-friendly web interface that allows researchers and language learners to explore various linguistic information,...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	333,760
2305.08363	User-Centric Clustering Under Fairness Scheduling in Cell-Free Massive MIMO	We consider fairness scheduling in a user-centric cell-free massive MIMO network, where $L$ remote radio units, each with $M$ antennas, serve $K_{\rm tot} \approx LM$ user equipments (UEs). Recent results show that the maximum network sum throughput is achieved where $K_{\rm act} \approx \frac{LM}{2}$ UEs are simultane...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	364,258
2406.14313	Robust Few-shot Transfer Learning for Knowledge Base Question Answering with Unanswerable Questions	Real-world KBQA applications require models that are (1) robust -- e.g., can differentiate between answerable and unanswerable questions, and (2) low-resource -- do not require large training data. Towards this goal, we propose the novel task of few-shot transfer for KBQA with unanswerable questions. We present FUn-FuS...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	466,256
2405.17391	Dataset-learning duality and emergent criticality	In artificial neural networks, the activation dynamics of non-trainable variables is strongly coupled to the learning dynamics of trainable variables. During the activation pass, the boundary neurons (e.g., input neurons) are mapped to the bulk neurons (e.g., hidden neurons), and during the learning pass, both bulk and...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	true	false	false	457,885
2206.08829	FedNew: A Communication-Efficient and Privacy-Preserving Newton-Type Method for Federated Learning	Newton-type methods are popular in federated learning due to their fast convergence. Still, they suffer from two main issues, namely: low communication efficiency and low privacy due to the requirement of sending Hessian information from clients to parameter server (PS). In this work, we introduced a novel framework ca...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	true	303,312
2308.12551	A Co-training Approach for Noisy Time Series Learning	In this work, we focus on robust time series representation learning. Our assumption is that real-world time series is noisy and complementary information from different views of the same time series plays an important role while analyzing noisy input. Based on this, we create two views for the input time series throug...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	387,581
2409.05668	Unlearning or Concealment? A Critical Analysis and Evaluation Metrics for Unlearning in Diffusion Models	Recent research has seen significant interest in methods for concept removal and targeted forgetting in text-to-image diffusion models. In this paper, we conduct a comprehensive white-box analysis showing the vulnerabilities in existing diffusion model unlearning methods. We show that existing unlearning methods lead t...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	486,855
1805.08689	Object Detection and Classification in Occupancy Grid Maps using Deep Convolutional Networks	A detailed environment perception is a crucial component of automated vehicles. However, to deal with the amount of perceived information, we also require segmentation strategies. Based on a grid map environment representation, well-suited for sensor fusion, free-space estimation and machine learning, we detect and cla...	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	98,215
2301.10965	Design of Mobile Manipulator for Fire Extinguisher Testing. Part I Key Specifications and Conceptual Design	All flames are extinguished as early as possible, or fire services have to deal with major conflagrations. This leads to the fact that the quality of fire extinguishers has become a very sensitive and important issue in firefighting. Inspired by the development of automatic fire fighting systems, this paper proposes ke...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	341,981
2412.15574	J-EDI QA: Benchmark for deep-sea organism-specific multimodal LLM	Japan Agency for Marine-Earth Science and Technology (JAMSTEC) has made available the JAMSTEC Earth Deep-sea Image (J-EDI), a deep-sea video and image archive (https://www.godac.jamstec.go.jp/jedi/e/index.html). This archive serves as a valuable resource for researchers and scholars interested in deep-sea imagery. The ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	519,183
2010.09616	Cooperative Multi-Sensor Detection under Variable-Length Coding	We investigate the testing-against-independence problem \mw{over a cooperative MAC} with two sensors and a single detector under an average rate constraint on the sensors-detector links. For this setup, we design a variable-length coding scheme that maximizes the achievable type-II error exponent when the type-I error ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	201,612
2002.05123	Over-the-Air Adversarial Flickering Attacks against Video Recognition Networks	Deep neural networks for video classification, just like image classification networks, may be subjected to adversarial manipulation. The main difference between image classifiers and video classifiers is that the latter usually use temporal information contained within the video. In this work we present a manipulation...	false	false	false	false	false	false	true	false	false	false	false	true	true	false	false	false	false	false	163,798
1910.09066	From Importance Sampling to Doubly Robust Policy Gradient	We show that on-policy policy gradient (PG) and its variance reduction variants can be derived by taking finite difference of function evaluations supplied by estimators from the importance sampling (IS) family for off-policy evaluation (OPE). Starting from the doubly robust (DR) estimator (Jiang & Li, 2016), we provid...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	150,061
1501.04925	Controller Synthesis for Linear Time-varying Systems with Adversaries	We present a controller synthesis algorithm for a discrete time reach-avoid problem in the presence of adversaries. Our model of the adversary captures typical malicious attacks envisioned on cyber-physical systems such as sensor spoofing, controller corruption, and actuator intrusion. After formulating the problem in ...	false	false	false	false	false	false	false	false	false	false	true	false	true	false	false	false	false	false	39,437
1809.10284	When is there a Representer Theorem? Reflexive Banach spaces	We consider a general regularised interpolation problem for learning a parameter vector from data. The well known representer theorem says that under certain conditions on the regulariser there exists a solution in the linear span of the data points. This is at the core of kernel methods in machine learning as it makes...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	108,878
2310.00060	Patient-specific computational forecasting of prostate cancer growth during active surveillance using an imaging-informed biomechanistic model	Active surveillance (AS) is a suitable management option for newly-diagnosed prostate cancer (PCa), which usually presents low to intermediate clinical risk. Patients enrolled in AS have their tumor closely monitored via longitudinal multiparametric magnetic resonance imaging (mpMRI), serum prostate-specific antigen te...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	395,803
1108.3711	Doing Better Than UCT: Rational Monte Carlo Sampling in Trees	UCT, a state-of-the art algorithm for Monte Carlo tree sampling (MCTS), is based on UCB, a sampling policy for the Multi-armed Bandit Problem (MAB) that minimizes the accumulated regret. However, MCTS differs from MAB in that only the final choice, rather than all arm pulls, brings a reward, that is, the simple regret,...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	11,721
2010.04452	EpidemiOptim: A Toolbox for the Optimization of Control Policies in Epidemiological Models	Epidemiologists model the dynamics of epidemics in order to propose control strategies based on pharmaceutical and non-pharmaceutical interventions (contact limitation, lock down, vaccination, etc). Hand-designing such strategies is not trivial because of the number of possible interventions and the difficulty to predi...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	199,748
2005.04777	Photometric Multi-View Mesh Refinement for High-Resolution Satellite Images	Modern high-resolution satellite sensors collect optical imagery with ground sampling distances (GSDs) of 30-50cm, which has sparked a renewed interest in photogrammetric 3D surface reconstruction from satellite data. State-of-the-art reconstruction methods typically generate 2.5D elevation data. Here, we present an ap...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	176,559
2407.13698	International Trade Flow Prediction with Bilateral Trade Provisions	This paper presents a novel methodology for predicting international bilateral trade flows, emphasizing the growing importance of Preferential Trade Agreements (PTAs) in the global trade landscape. Acknowledging the limitations of traditional models like the Gravity Model of Trade, this study introduces a two-stage app...	false	true	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	474,475
2106.08376	A Framework for Evaluating Post Hoc Feature-Additive Explainers	Many applications of data-driven models demand transparency of decisions, especially in health care, criminal justice, and other high-stakes environments. Modern trends in machine learning research have led to algorithms that are increasingly intricate to the degree that they are considered to be black boxes. In an eff...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	241,268
1706.01077	Actor-Critic for Linearly-Solvable Continuous MDP with Partially Known Dynamics	In many robotic applications, some aspects of the system dynamics can be modeled accurately while others are difficult to obtain or model. We present a novel reinforcement learning (RL) method for continuous state and action spaces that learns with partial knowledge of the system and without active exploration. It solv...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	74,745
2308.09314	Retro-FPN: Retrospective Feature Pyramid Network for Point Cloud Semantic Segmentation	Learning per-point semantic features from the hierarchical feature pyramid is essential for point cloud semantic segmentation. However, most previous methods suffered from ambiguous region features or failed to refine per-point features effectively, which leads to information loss and ambiguous semantic identification....	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	386,242
0908.0764	Learning about Potential Users of Collaborative Information Retrieval Systems	One of the key components of designing usable and useful collaborative information retrieval systems is to understand the needs of the users of these systems. Our research team has been exploring collaborative information behavior in a variety of organizational settings. Our research goals have been two-fold: First, to...	true	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	4,225
2307.00335	Single Sequence Prediction over Reasoning Graphs for Multi-hop QA	Recent generative approaches for multi-hop question answering (QA) utilize the fusion-in-decoder method~\cite{izacard-grave-2021-leveraging} to generate a single sequence output which includes both a final answer and a reasoning path taken to arrive at that answer, such as passage titles and key facts from those passag...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	376,968
2501.04547	Medical artificial intelligence toolbox (MAIT): an explainable machine learning framework for binary classification, survival modelling, and regression analyses	While machine learning offers diverse techniques suitable for exploring various medical research questions, a cohesive synergistic framework can facilitate the integration and understanding of new approaches within unified model development and interpretation. We therefore introduce the Medical Artificial Intelligence ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	523,259

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