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
2402.01366	MagicTac: A Novel High-Resolution 3D Multi-layer Grid-Based Tactile Sensor	Accurate robotic control over interactions with the environment is fundamentally grounded in understanding tactile contacts. In this paper, we introduce MagicTac, a novel high-resolution grid-based tactile sensor. This sensor employs a 3D multi-layer grid-based design, inspired by the Magic Cube structure. This structu...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	425,987
2102.07455	Video Analytics on IoT devices	Deep Learning (DL) combined with advanced model optimization methods such as RC-NN and Edge2Train has enabled offline execution of large networks on the IoT devices. In this paper, we compare the modern Deep Learning (DL) based video analytics approaches with the standard Computer Vision (CV) based approaches and final...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	220,113
2202.07296	Roomsemble: Progressive web application for intuitive property search	A successful real estate search process involves locating a property that meets a user's search criteria subject to an allocated budget and time constraints. Many studies have investigated modeling housing prices over time. However, little is known about how a user's tastes influence their real estate search and purcha...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	280,512
2407.19401	Towards Secure and Private AI: A Framework for Decentralized Inference	The rapid advancement of ML models in critical sectors such as healthcare, finance, and security has intensified the need for robust data security, model integrity, and reliable outputs. Large multimodal foundational models, while crucial for complex tasks, present challenges in scalability, reliability, and potential ...	false	false	false	false	true	false	false	false	false	false	false	false	true	false	false	false	false	false	476,768
2103.00394	Convergence of Gaussian-smoothed optimal transport distance with sub-gamma distributions and dependent samples	The Gaussian-smoothed optimal transport (GOT) framework, recently proposed by Goldfeld et al., scales to high dimensions in estimation and provides an alternative to entropy regularization. This paper provides convergence guarantees for estimating the GOT distance under more general settings. For the Gaussian-smoothed ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	222,261
1306.5215	Epistemology of Modeling and Simulation: How can we gain Knowledge from Simulations?	Epistemology is the branch of philosophy that deals with gaining knowledge. It is closely related to ontology. The branch that deals with questions like "What is real?" and "What do we know?" as it provides these components. When using modeling and simulation, we usually imply that we are doing so to either apply knowl...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	25,379
2411.13848	Exact and approximate error bounds for physics-informed neural networks	The use of neural networks to solve differential equations, as an alternative to traditional numerical solvers, has increased recently. However, error bounds for the obtained solutions have only been developed for certain equations. In this work, we report important progress in calculating error bounds of physics-infor...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	509,939
2407.05425	ClutterGen: A Cluttered Scene Generator for Robot Learning	We introduce ClutterGen, a physically compliant simulation scene generator capable of producing highly diverse, cluttered, and stable scenes for robot learning. Generating such scenes is challenging as each object must adhere to physical laws like gravity and collision. As the number of objects increases, finding valid...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	470,974
1909.09349	Deep 3D-Zoom Net: Unsupervised Learning of Photo-Realistic 3D-Zoom	The 3D-zoom operation is the positive translation of the camera in the Z-axis, perpendicular to the image plane. In contrast, the optical zoom changes the focal length and the digital zoom is used to enlarge a certain region of an image to the original image size. In this paper, we are the first to formulate an unsuper...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	146,236
1902.07881	All-neural online source separation, counting, and diarization for meeting analysis	Automatic meeting analysis comprises the tasks of speaker counting, speaker diarization, and the separation of overlapped speech, followed by automatic speech recognition. This all has to be carried out on arbitrarily long sessions and, ideally, in an online or block-online manner. While significant progress has been m...	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	122,082
2412.06699	You See it, You Got it: Learning 3D Creation on Pose-Free Videos at Scale	Recent 3D generation models typically rely on limited-scale 3D `gold-labels' or 2D diffusion priors for 3D content creation. However, their performance is upper-bounded by constrained 3D priors due to the lack of scalable learning paradigms. In this work, we present See3D, a visual-conditional multi-view diffusion mode...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	515,337
2008.00189	Performance Analysis of Intelligent Reflecting Surface Aided Communication Systems	This letter presents a detailed performance analysis of the intelligent reflecting surface (IRS) aided single-input single-output communication systems, taking into account of the direct link between the transmitter and receiver. A closed-form upper bound is derived for the ergodic capacity, and an accurate approximati...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	189,934
2106.03022	Fisher-Pitman permutation tests based on nonparametric Poisson mixtures with application to single cell genomics	This paper investigates the theoretical and empirical performance of Fisher-Pitman-type permutation tests for assessing the equality of unknown Poisson mixture distributions. Building on nonparametric maximum likelihood estimators (NPMLEs) of the mixing distribution, these tests are theoretically shown to be able to ad...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	239,133
2009.02296	Variational Deep Learning for the Identification and Reconstruction of Chaotic and Stochastic Dynamical Systems from Noisy and Partial Observations	The data-driven recovery of the unknown governing equations of dynamical systems has recently received an increasing interest. However, the identification of governing equations remains challenging when dealing with noisy and partial observations. Here, we address this challenge and investigate variational deep learnin...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	194,512
2003.03481	A Comparison of Amputee and Able-Bodied Inter-Subject Variability in Myoelectric Control	Despite decades of research and development of pattern recognition approaches, the clinical usability of myoelectriccontrolled prostheses is still limited. One of the main issues is the high inter-subject variability that necessitates long and frequent user-specific training. Cross-user models present an opportunity to...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	167,233
1906.09721	A Game-Theoretic Approach to Adversarial Linear Support Vector Classification	In this paper, we employ a game-theoretic model to analyze the interaction between an adversary and a classifier. There are two classes (i.e., positive and negative classes) to which data points can belong. The adversary is interested in maximizing the probability of miss-detection for the positive class (i.e., false n...	false	false	false	false	false	false	true	false	false	false	true	false	true	false	false	false	false	false	136,248
1511.01861	Modeling trend progression through an extension of the Polya Urn Process	Knowing how and when trends are formed is a frequently visited research goal. In our work, we focus on the progression of trends through (social) networks. We use a random graph (RG) model to mimic the progression of a trend through the network. The context of the trend is not included in our model. We show that every ...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	48,555
2409.17805	Cascade Prompt Learning for Vision-Language Model Adaptation	Prompt learning has surfaced as an effective approach to enhance the performance of Vision-Language Models (VLMs) like CLIP when applied to downstream tasks. However, current learnable prompt tokens are primarily used for the single phase of adapting to tasks (i.e., adapting prompt), easily leading to overfitting risks...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	491,986
2012.14106	Diagnosis/Prognosis of COVID-19 Images: Challenges, Opportunities, and Applications	The novel Coronavirus disease, COVID-19, has rapidly and abruptly changed the world as we knew in 2020. It becomes the most unprecedent challenge to analytic epidemiology in general and signal processing theories in specific. Given its high contingency nature and adverse effects across the world, it is important to dev...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	213,411
1305.1230	Rate Distortion Function for a Class of Relative Entropy Sources	This paper deals with rate distortion or source coding with fidelity criterion, in measure spaces, for a class of source distributions. The class of source distributions is described by a relative entropy constraint set between the true and a nominal distribution. The rate distortion problem for the class is thus formu...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	24,419
1905.12096	Planning with State Abstractions for Non-Markovian Task Specifications	Often times, we specify tasks for a robot using temporal language that can also span different levels of abstraction. The example command ``go to the kitchen before going to the second floor'' contains spatial abstraction, given that ``floor'' consists of individual rooms that can also be referred to in isolation ("kit...	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	false	132,645
1011.4098	Understanding Cascading Failures in Power Grids	In the past, we have observed several large blackouts, i.e. loss of power to large areas. It has been noted by several researchers that these large blackouts are a result of a cascade of failures of various components. As a power grid is made up of several thousands or even millions of components (relays, breakers, tra...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	8,272
1702.02302	Autonomous Braking System via Deep Reinforcement Learning	In this paper, we propose a new autonomous braking system based on deep reinforcement learning. The proposed autonomous braking system automatically decides whether to apply the brake at each time step when confronting the risk of collision using the information on the obstacle obtained by the sensors. The problem of d...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	67,959
2311.06595	From Classification to Generation: Insights into Crosslingual Retrieval Augmented ICL	The remarkable ability of Large Language Models (LLMs) to understand and follow instructions has sometimes been limited by their in-context learning (ICL) performance in low-resource languages. To address this, we introduce a novel approach that leverages cross-lingual retrieval-augmented in-context learning (CREA-ICL)...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	407,000
1909.09287	Spherical Kernel for Efficient Graph Convolution on 3D Point Clouds	We propose a spherical kernel for efficient graph convolution of 3D point clouds. Our metric-based kernels systematically quantize the local 3D space to identify distinctive geometric relationships in the data. Similar to the regular grid CNN kernels, the spherical kernel maintains translation-invariance and asymmetry ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	146,216
2310.06306	Ensemble Active Learning by Contextual Bandits for AI Incubation in Manufacturing	It is challenging but important to save annotation efforts in streaming data acquisition to maintain data quality for supervised learning base learners. We propose an ensemble active learning method to actively acquire samples for annotation by contextual bandits, which is will enforce the exploration-exploitation bala...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	398,517
2307.01181	Fitting an ellipsoid to a quadratic number of random points	We consider the problem $(\mathrm{P})$ of fitting $n$ standard Gaussian random vectors in $\mathbb{R}^d$ to the boundary of a centered ellipsoid, as $n, d \to \infty$. This problem is conjectured to have a sharp feasibility transition: for any $\varepsilon > 0$, if $n \leq (1 - \varepsilon) d^2 / 4$ then $(\mathrm{P})$...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	377,261
2006.01371	Energy-Efficient Cyclical Trajectory Design for UAV-Aided Maritime Data Collection in Wind	Unmanned aerial vehicles (UAVs), especially fixed-wing ones that withstand strong winds, have great potential for oceanic exploration and research. This paper studies a UAV-aided maritime data collection system with a fixed-wing UAV dispatched to collect data from marine buoys. We aim to minimize the UAV's energy consu...	false	false	false	false	false	false	false	false	false	true	true	false	false	false	false	false	false	true	179,751
2211.12159	Modified Dynamic Programming Algorithms for GLOSA Systems with Stochastic Signal Switching Times	A discrete-time stochastic optimal control problem was recently proposed to address the GLOSA (Green Light Optimal Speed Advisory) problem in cases where the next signal switching time is decided in real time and is therefore uncertain in advance. The corresponding numerical solution via SDP (Stochastic Dynamic Program...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	332,018
2410.14483	Spectral Representations for Accurate Causal Uncertainty Quantification with Gaussian Processes	Accurate uncertainty quantification for causal effects is essential for robust decision making in complex systems, but remains challenging in non-parametric settings. One promising framework represents conditional distributions in a reproducing kernel Hilbert space and places Gaussian process priors on them to infer po...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	500,043
2006.15854	A Framework for Pre-processing of Social Media Feeds based on Integrated Local Knowledge Base	Most of the previous studies on the semantic analysis of social media feeds have not considered the issue of ambiguity that is associated with slangs, abbreviations, and acronyms that are embedded in social media posts. These noisy terms have implicit meanings and form part of the rich semantic context that must be ana...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	184,642
1805.01282	Multi-label Learning Based Deep Transfer Neural Network for Facial Attribute Classification	Deep Neural Network (DNN) has recently achieved outstanding performance in a variety of computer vision tasks, including facial attribute classification. The great success of classifying facial attributes with DNN often relies on a massive amount of labelled data. However, in real-world applications, labelled data are ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	96,626
2311.01282	FlashDecoding++: Faster Large Language Model Inference on GPUs	As the Large Language Model (LLM) becomes increasingly important in various domains. However, the following challenges still remain unsolved in accelerating LLM inference: (1) Synchronized partial softmax update. The softmax operation requires a synchronized update operation among each partial softmax result, leading t...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	404,982
2207.03104	Quantum Advantage in Variational Bayes Inference	Variational Bayes (VB) inference algorithm is used widely to estimate both the parameters and the unobserved hidden variables in generative statistical models. The algorithm -- inspired by variational methods used in computational physics -- is iterative and can get easily stuck in local minima, even when classical tec...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	306,732
2406.01575	Contextual Bilevel Reinforcement Learning for Incentive Alignment	The optimal policy in various real-world strategic decision-making problems depends both on the environmental configuration and exogenous events. For these settings, we introduce Contextual Bilevel Reinforcement Learning (CB-RL), a stochastic bilevel decision-making model, where the lower level consists of solving a co...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	460,372
2310.10865	Will the Prince Get True Love's Kiss? On the Model Sensitivity to Gender Perturbation over Fairytale Texts	Recent studies show that traditional fairytales are rife with harmful gender biases. To help mitigate these gender biases in fairytales, this work aims to assess learned biases of language models by evaluating their robustness against gender perturbations. Specifically, we focus on Question Answering (QA) tasks in fair...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	400,414
cs/0203004	Stereotypical Reasoning: Logical Properties	Stereotypical reasoning assumes that the situation at hand is one of a kind and that it enjoys the properties generally associated with that kind of situation. It is one of the most basic forms of nonmonotonic reasoning. A formal model for stereotypical reasoning is proposed and the logical properties of this form of r...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	537,520
1109.0660	Mismatch and resolution in compressive imaging	Highly coherent sensing matrices arise in discretization of continuum problems such as radar and medical imaging when the grid spacing is below the Rayleigh threshold as well as in using highly coherent, redundant dictionaries as sparsifying operators. Algorithms (BOMP, BLOOMP) based on techniques of band exclusion and...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	11,955
2409.18839	MinerU: An Open-Source Solution for Precise Document Content Extraction	Document content analysis has been a crucial research area in computer vision. Despite significant advancements in methods such as OCR, layout detection, and formula recognition, existing open-source solutions struggle to consistently deliver high-quality content extraction due to the diversity in document types and co...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	492,426
2305.17910	AI Audit: A Card Game to Reflect on Everyday AI Systems	An essential element of K-12 AI literacy is educating learners about the ethical and societal implications of AI systems. Previous work in AI ethics literacy have developed curriculum and classroom activities that engage learners in reflecting on the ethical implications of AI systems and developing responsible AI. The...	true	false	false	false	true	false	false	false	false	false	false	false	false	true	false	false	false	false	368,793
2501.05842	Orthogonal projection-based regularization for efficient model augmentation	Deep-learning-based nonlinear system identification has shown the ability to produce reliable and highly accurate models in practice. However, these black-box models lack physical interpretability, and often a considerable part of the learning effort is spent on capturing already expected/known behavior due to first-pr...	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	523,743
2304.02860	Towards an Effective and Efficient Transformer for Rain-by-snow Weather Removal	Rain-by-snow weather removal is a specialized task in weather-degraded image restoration aiming to eliminate coexisting rain streaks and snow particles. In this paper, we propose RSFormer, an efficient and effective Transformer that addresses this challenge. Initially, we explore the proximity of convolution networks (...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	356,584
1907.12930	Attention Guided Network for Retinal Image Segmentation	Learning structural information is critical for producing an ideal result in retinal image segmentation. Recently, convolutional neural networks have shown a powerful ability to extract effective representations. However, convolutional and pooling operations filter out some useful structural information. In this paper,...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	140,251
2106.13953	In-N-Out: Towards Good Initialization for Inpainting and Outpainting	In computer vision, recovering spatial information by filling in masked regions, e.g., inpainting, has been widely investigated for its usability and wide applicability to other various applications: image inpainting, image extrapolation, and environment map estimation. Most of them are studied separately depending on ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	243,244
2308.11473	IT3D: Improved Text-to-3D Generation with Explicit View Synthesis	Recent strides in Text-to-3D techniques have been propelled by distilling knowledge from powerful large text-to-image diffusion models (LDMs). Nonetheless, existing Text-to-3D approaches often grapple with challenges such as over-saturation, inadequate detailing, and unrealistic outputs. This study presents a novel str...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	387,151
2003.02763	A Quantitative History of A.I. Research in the United States and China	Motivated by recent interest in the status and consequences of competition between the U.S. and China in A.I. research, we analyze 60 years of abstract data scraped from Scopus to explore and quantify trends in publications on A.I. topics from institutions affiliated with each country. We find the total volume of publi...	false	false	false	false	true	false	false	false	false	false	false	false	false	true	false	false	false	true	167,035
2006.16047	Stay with Your Community: Bridges between Clusters Trigger Expansion of COVID-19	The spreading of virus infection is here simulated over artificial human networks. The real-space urban life of people is modeled as a modified scale-free network with constraints. A scale-free network has been adopted in several studies for modeling on-line communities so far but is modified here for the aim to repres...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	184,697
2106.03097	Preservation of the Global Knowledge by Not-True Distillation in Federated Learning	In federated learning, a strong global model is collaboratively learned by aggregating clients' locally trained models. Although this precludes the need to access clients' data directly, the global model's convergence often suffers from data heterogeneity. This study starts from an analogy to continual learning and sug...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	239,173
1511.08136	Unifying Decision Trees Split Criteria Using Tsallis Entropy	The construction of efficient and effective decision trees remains a key topic in machine learning because of their simplicity and flexibility. A lot of heuristic algorithms have been proposed to construct near-optimal decision trees. ID3, C4.5 and CART are classical decision tree algorithms and the split criteria they...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	49,506
1301.4793	LMMSE Estimation and Interpolation of Continuous-Time Signals from Discrete-Time Samples Using Factor Graphs	The factor graph approach to discrete-time linear Gaussian state space models is well developed. The paper extends this approach to continuous-time linear systems/filters that are driven by white Gaussian noise. By Gaussian message passing, we then obtain MAP/MMSE/LMMSE estimates of the input signal, or of the state, o...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	21,283
2309.15641	Efficient Exact Subgraph Matching via GNN-based Path Dominance Embedding (Technical Report)	The classic problem of exact subgraph matching returns those subgraphs in a large-scale data graph that are isomorphic to a given query graph, which has gained increasing importance in many real-world applications such as social network analysis, knowledge graph discovery in the Semantic Web, bibliographical network mi...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	395,051
2208.08130	Knowledge Graph Curation: A Practical Framework	Knowledge Graphs (KGs) have shown to be very important for applications such as personal assistants, question-answering systems, and search engines. Therefore, it is crucial to ensure their high quality. However, KGs inevitably contain errors, duplicates, and missing values, which may hinder their adoption and utility ...	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	true	false	313,272
1905.04598	Robustness of Object Recognition under Extreme Occlusion in Humans and Computational Models	Most objects in the visual world are partially occluded, but humans can recognize them without difficulty. However, it remains unknown whether object recognition models like convolutional neural networks (CNNs) can handle real-world occlusion. It is also a question whether efforts to make these models robust to constan...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	130,512
1608.08678	Sparse Recovery With Integrality Constraints	We investigate conditions for the unique recoverability of sparse integer-valued signals from a small number of linear measurements. Both the objective of minimizing the number of nonzero components, the so-called $\ell_0$-norm, as well as its popular substitute, the $\ell_1$-norm, are covered. Furthermore, integrality...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	60,384
2007.09550	Predicting risk of late age-related macular degeneration using deep learning	By 2040, age-related macular degeneration (AMD) will affect approximately 288 million people worldwide. Identifying individuals at high risk of progression to late AMD, the sight-threatening stage, is critical for clinical actions, including medical interventions and timely monitoring. Although deep learning has shown ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	187,991
2410.10454	Improve Meta-learning for Few-Shot Text Classification with All You Can Acquire from the Tasks	Meta-learning has emerged as a prominent technology for few-shot text classification and has achieved promising performance. However, existing methods often encounter difficulties in drawing accurate class prototypes from support set samples, primarily due to probable large intra-class differences and small inter-class...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	498,085
2411.14565	Privacy-Preserving Video Anomaly Detection: A Survey	Video Anomaly Detection (VAD) aims to automatically analyze spatiotemporal patterns in surveillance videos collected from open spaces to detect anomalous events that may cause harm without physical contact. However, vision-based surveillance systems such as closed-circuit television often capture personally identifiabl...	false	false	false	false	false	false	true	false	false	false	false	true	true	false	false	false	false	false	510,231
1808.08250	LMI-Based Reset Unknown Input Observer for State Estimation of Linear Uncertain Systems	This paper proposes a novel kind of Unknown Input Observer (UIO) called Reset Unknown Input Observer (R-UIO) for state estimation of linear systems in the presence of disturbance using Linear Matrix Inequality (LMI) techniques. In R-UIO, the states of the observer are reset to the after-reset value based on an appropri...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	105,894
2009.06902	Collaborative Distillation in the Parameter and Spectrum Domains for Video Action Recognition	Recent years have witnessed the significant progress of action recognition task with deep networks. However, most of current video networks require large memory and computational resources, which hinders their applications in practice. Existing knowledge distillation methods are limited to the image-level spatial domai...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	195,785
1602.08728	Cache Size Allocation in Backhaul Limited Wireless Networks	Caching popular content at base stations is a powerful supplement to existing limited backhaul links for accommodating the exponentially increasing mobile data traffic. Given the limited cache budget, we investigate the cache size allocation problem in cellular networks to maximize the user success probability (USP), t...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	52,682
1506.06081	A Convergent Gradient Descent Algorithm for Rank Minimization and Semidefinite Programming from Random Linear Measurements	We propose a simple, scalable, and fast gradient descent algorithm to optimize a nonconvex objective for the rank minimization problem and a closely related family of semidefinite programs. With $O(r^3 \kappa^2 n \log n)$ random measurements of a positive semidefinite $n \times n$ matrix of rank $r$ and condition numbe...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	44,379
2408.16031	EMP: Enhance Memory in Data Pruning	Recently, large language and vision models have shown strong performance, but due to high pre-training and fine-tuning costs, research has shifted towards faster training via dataset pruning. Previous methods used sample loss as an evaluation criterion, aiming to select the most "difficult" samples for training. Howeve...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	484,172
2208.09942	SeNMFk-SPLIT: Large Corpora Topic Modeling by Semantic Non-negative Matrix Factorization with Automatic Model Selection	As the amount of text data continues to grow, topic modeling is serving an important role in understanding the content hidden by the overwhelming quantity of documents. One popular topic modeling approach is non-negative matrix factorization (NMF), an unsupervised machine learning (ML) method. Recently, Semantic NMF wi...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	313,894
2301.13852	ChatGPT or Human? Detect and Explain. Explaining Decisions of Machine Learning Model for Detecting Short ChatGPT-generated Text	ChatGPT has the ability to generate grammatically flawless and seemingly-human replies to different types of questions from various domains. The number of its users and of its applications is growing at an unprecedented rate. Unfortunately, use and abuse come hand in hand. In this paper, we study whether a machine lear...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	343,056
2102.04986	Hyperedge Prediction using Tensor Eigenvalue Decomposition	Link prediction in graphs is studied by modeling the dyadic interactions among two nodes. The relationships can be more complex than simple dyadic interactions and could require the user to model super-dyadic associations among nodes. Such interactions can be modeled using a hypergraph, which is a generalization of a g...	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	false	false	219,288
2007.10252	XMixup: Efficient Transfer Learning with Auxiliary Samples by Cross-domain Mixup	Transferring knowledge from large source datasets is an effective way to fine-tune the deep neural networks of the target task with a small sample size. A great number of algorithms have been proposed to facilitate deep transfer learning, and these techniques could be generally categorized into two groups - Regularized...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	188,218
1103.4198	Continuous-time performance limitations for overshoot and resulted tracking measures	A dual formulation for the problem of determining absolute performance limitations on overshoot, undershoot, maximum amplitude and fluctuation minimization for continuous-time feedback systems is constructed. Determining, for example, the minimum possible overshoot attainable by all possible stabilizing controllers is ...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	9,705
2006.07533	FakePolisher: Making DeepFakes More Detection-Evasive by Shallow Reconstruction	At this moment, GAN-based image generation methods are still imperfect, whose upsampling design has limitations in leaving some certain artifact patterns in the synthesized image. Such artifact patterns can be easily exploited (by recent methods) for difference detection of real and GAN-synthesized images. However, the...	false	false	false	false	false	false	true	false	false	false	false	true	true	false	false	false	false	false	181,839
1310.1975	ARKref: a rule-based coreference resolution system	ARKref is a tool for noun phrase coreference. It is a deterministic, rule-based system that uses syntactic information from a constituent parser, and semantic information from an entity recognition component. Its architecture is based on the work of Haghighi and Klein (2009). ARKref was originally written in 2009. At t...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	27,618
1610.02732	Investigating the effects Diversity Mechanisms have on Evolutionary Algorithms in Dynamic Environments	Evolutionary algorithms have been successfully applied to a variety of optimisation problems in stationary environments. However, many real world optimisation problems are set in dynamic environments where the success criteria shifts regularly. Population diversity affects algorithmic performance, particularly on multi...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	62,146
1110.0105	Multi-Agent Programming Contest 2011 - The Python-DTU Team	We provide a brief description of the Python-DTU system, including the overall design, the tools and the algorithms that we plan to use in the agent contest.	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	12,437
1801.00173	Theory of Deep Learning III: explaining the non-overfitting puzzle	A main puzzle of deep networks revolves around the absence of overfitting despite large overparametrization and despite the large capacity demonstrated by zero training error on randomly labeled data. In this note, we show that the dynamics associated to gradient descent minimization of nonlinear networks is topologica...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	87,519
2301.01583	Why Capsule Neural Networks Do Not Scale: Challenging the Dynamic Parse-Tree Assumption	Capsule neural networks replace simple, scalar-valued neurons with vector-valued capsules. They are motivated by the pattern recognition system in the human brain, where complex objects are decomposed into a hierarchy of simpler object parts. Such a hierarchy is referred to as a parse-tree. Conceptually, capsule neural...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	339,269
1711.10299	Expurgated Bounds for the Asymmetric Broadcast Channel	This work contains two main contributions concerning the expurgation of hierarchical ensembles for the asymmetric broadcast channel. The first is an analysis of the optimal maximum likelihood (ML) decoders for the weak and strong user. Two different methods of code expurgation will be used, that will provide two compet...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	85,566
2007.15987	Genetic Improvement @ ICSE 2020	Following Prof. Mark Harman of Facebook's keynote and formal presentations (which are recorded in the proceedings) there was a wide ranging discussion at the eighth international Genetic Improvement workshop, GI-2020 @ ICSE (held as part of the 42nd ACM/IEEE International Conference on Software Engineering on Friday 3r...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	false	true	189,814
2009.08052	GeneraLight: Improving Environment Generalization of Traffic Signal Control via Meta Reinforcement Learning	The heavy traffic congestion problem has always been a concern for modern cities. To alleviate traffic congestion, researchers use reinforcement learning (RL) to develop better traffic signal control (TSC) algorithms in recent years. However, most RL models are trained and tested in the same traffic flow environment, w...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	196,118
1812.04056	Accelerating Convolutional Neural Networks via Activation Map Compression	The deep learning revolution brought us an extensive array of neural network architectures that achieve state-of-the-art performance in a wide variety of Computer Vision tasks including among others, classification, detection and segmentation. In parallel, we have also been observing an unprecedented demand in computat...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	116,128
2306.07045	Data-Driven Bilateral Generalized Two-Dimensional Quaternion Principal Component Analysis with Application to Color Face Recognition	A new data-driven bilateral generalized two-dimensional quaternion principal component analysis (BiG2DQPCA) is presented to extract the features of matrix samples from both row and column directions. This general framework directly works on the 2D color images without vectorizing and well preserves the spatial and colo...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	372,857
2409.04329	Enhancing Sequential Music Recommendation with Personalized Popularity Awareness	In the realm of music recommendation, sequential recommender systems have shown promise in capturing the dynamic nature of music consumption. Nevertheless, traditional Transformer-based models, such as SASRec and BERT4Rec, while effective, encounter challenges due to the unique characteristics of music listening habits...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	486,356
1502.05516	Outage Capacity of Rayleigh Product Channels: a Free Probability Approach	The Rayleigh product channel model is useful in capturing the performance degradation due to rank deficiency of MIMO channels. In this paper, such a performance degradation is investigated via the channel outage probability assuming slowly varying channel with delay-constrained decoding. Using techniques of free probab...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	40,376
2202.06012	Cloud-based computational model predictive control using a parallel multi-block ADMM approach	Heavy computational load for solving nonconvex problems for large-scale systems or systems with real-time demands at each sample step has been recognized as one of the reasons for preventing a wider application of nonlinear model predictive control (NMPC). To improve the real-time feasibility of NMPC with input nonline...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	280,060
2410.17880	A utility-based spatial analysis of residential street-level conditions; A case study of Rotterdam	Residential location choices are traditionally modelled using factors related to accessibility and socioeconomic environments, neglecting the importance of local street-level conditions. Arguably, this neglect is due to data practices. Today, however, street-level images -- which are highly effective at encoding street...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	501,650
1904.08992	Quantum-Assisted Clustering Algorithms for NISQ-Era Devices	In the NISQ-era of quantum computing, we should not expect to see quantum devices that provide an exponential improvement in runtime for practical problems, due to the lack of error correction and small number of qubits available. Nevertheless, these devices should be able to provide other performance improvements, par...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	128,236
2311.17280	Does VLN Pretraining Work with Nonsensical or Irrelevant Instructions?	Data augmentation via back-translation is common when pretraining Vision-and-Language Navigation (VLN) models, even though the generated instructions are noisy. But: does that noise matter? We find that nonsensical or irrelevant language instructions during pretraining can have little effect on downstream performance f...	false	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	411,234
2412.01641	Linearly Homomorphic Signature with Tight Security on Lattice	At present, in lattice-based linearly homomorphic signature schemes, especially under the standard model, there are very few schemes with tight security. This paper constructs the first lattice-based linearly homomorphic signature scheme that achieves tight security against existential unforgeability under chosen-messa...	false	false	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	513,207
2012.04092	Conditional independence structures over four discrete random variables revisited: conditional Ingleton inequalities	The paper deals with conditional linear information inequalities valid for entropy functions induced by discrete random variables. Specifically, the so-called conditional Ingleton inequalities are in the center of interest: these are valid under conditional independence assumptions on the inducing random variables. We ...	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	false	false	210,339
1504.07379	Fast Quasi-Threshold Editing	We introduce Quasi-Threshold Mover (QTM), an algorithm to solve the quasi-threshold (also called trivially perfect) graph editing problem with edge insertion and deletion. Given a graph it computes a quasi-threshold graph which is close in terms of edit count. This edit problem is NP-hard. We present an extensive exper...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	true	42,530
1609.09395	Micropolis Interdependency Modeling using Open Hybrid Automata	Micropolis is a virtual city that is used for various studies, such as modeling and analyzing water networks. In this paper we model the various interdependencies between three major infrastructures in Micropolis, the power system, the communication network and the water network, in order to study cascades effects betw...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	61,711
1903.03229	Deductive Optimization of Relational Data Storage	Optimizing the physical data storage and retrieval of data are two key database management problems. In this paper, we propose a language that can express a wide range of physical database layouts, going well beyond the row- and column-based methods that are widely used in database management systems. We use deductive ...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	true	123,675
2211.09267	Reflect, Not Reflex: Inference-Based Common Ground Improves Dialogue Response Quality	Human communication relies on common ground (CG), the mutual knowledge and beliefs shared by participants, to produce coherent and interesting conversations. In this paper, we demonstrate that current response generation (RG) models produce generic and dull responses in dialogues because they act reflexively, failing t...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	330,920
2209.12755	New Spectrally Constrained Sequence Sets with Optimal {Periodic} Cross-Correlation	Spectrally constrained sequences (SCSs) play an important role in modern communication and radar systems operating over non-contiguous spectrum. Despite numerous research attempts over the past years, very few works are known on the constructions of optimal SCSs with low cross-correlations. In this paper, we address su...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	319,642
2105.03173	Use of High Dimensional Modeling for automatic variables selection: the best path algorithm	This paper presents a new algorithm for automatic variables selection. In particular, using the Graphical Models properties it is possible to develop a method that can be used in the contest of large dataset. The advantage of this algorithm is that can be combined with different forecasting models. In this research we ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	234,068
2110.05021	Cross Domain Emotion Recognition using Few Shot Knowledge Transfer	Emotion recognition from text is a challenging task due to diverse emotion taxonomies, lack of reliable labeled data in different domains, and highly subjective annotation standards. Few-shot and zero-shot techniques can generalize across unseen emotions by projecting the documents and emotion labels onto a shared embe...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	260,131
2311.07171	calamanCy: A Tagalog Natural Language Processing Toolkit	We introduce calamanCy, an open-source toolkit for constructing natural language processing (NLP) pipelines for Tagalog. It is built on top of spaCy, enabling easy experimentation and integration with other frameworks. calamanCy addresses the development gap by providing a consistent API for building NLP applications a...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	407,228
1809.10801	Effective Cloud Detection and Segmentation using a Gradient-Based Algorithm for Satellite Imagery; Application to improve PERSIANN-CCS	Being able to effectively identify clouds and monitor their evolution is one important step toward more accurate quantitative precipitation estimation and forecast. In this study, a new gradient-based cloud-image segmentation technique is developed using tools from image processing techniques. This method integrates mo...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	108,987
1811.11489	Fixed-length Bit-string Representation of Fingerprint by Normalized Local Structures	In this paper, we propose a method to represent a fingerprint image by an ordered, fixed-length bit-string providing improved accuracy performance, faster matching time and compressibility. First, we devise a novel minutia-based local structure modeled by a mixture of 2D elliptical Gaussian functions in the pixel space...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	114,794
2104.07190	An Alignment-Agnostic Model for Chinese Text Error Correction	This paper investigates how to correct Chinese text errors with types of mistaken, missing and redundant characters, which is common for Chinese native speakers. Most existing models based on detect-correct framework can correct mistaken characters errors, but they cannot deal with missing or redundant characters. The ...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	230,324
2010.09470	Dos and Don'ts of Machine Learning in Computer Security	With the growing processing power of computing systems and the increasing availability of massive datasets, machine learning algorithms have led to major breakthroughs in many different areas. This development has influenced computer security, spawning a series of work on learning-based security systems, such as for ma...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	201,552
1905.13344	Deterministic PAC-Bayesian generalization bounds for deep networks via generalizing noise-resilience	The ability of overparameterized deep networks to generalize well has been linked to the fact that stochastic gradient descent (SGD) finds solutions that lie in flat, wide minima in the training loss -- minima where the output of the network is resilient to small random noise added to its parameters. So far this observ...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	133,086
1508.01191	A different perspective on a scale for pairwise comparisons	One of the major challenges for collective intelligence is inconsistency, which is unavoidable whenever subjective assessments are involved. Pairwise comparisons allow one to represent such subjective assessments and to process them by analyzing, quantifying and identifying the inconsistencies. We propose using small...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	45,765

Subsets and Splits

No community queries yet

The top public SQL queries from the community will appear here once available.