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
2309.10526	NSOAMT -- New Search Only Approach to Machine Translation	Translation automation mechanisms and tools have been developed for several years to bring people who speak different languages together. A "new search only approach to machine translation" was adopted to tackle some of the slowness and inaccuracy of the other technologies. The idea is to develop a solution that, by in...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	393,048
1906.02286	A Generic Synchronous Dataflow Architecture to Rapidly Prototype and Deploy Robot Controllers	The paper presents a software architecture to optimize the process of prototyping and deploying robot controllers that are synthesized using model-based design methodologies. The architecture is composed of a framework and a pipeline. Therefore, the contribution of the paper is twofold. First, we introduce an open-sour...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	133,993
2002.03063	Storyboard: Optimizing Precomputed Summaries for Aggregation	An emerging class of data systems partition their data and precompute approximate summaries (i.e., sketches and samples) for each segment to reduce query costs. They can then aggregate and combine the segment summaries to estimate results without scanning the raw data. However, given limited storage space each summary ...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	163,122
2106.02694	Efficient Classification of Very Large Images with Tiny Objects	An increasing number of applications in computer vision, specially, in medical imaging and remote sensing, become challenging when the goal is to classify very large images with tiny informative objects. Specifically, these classification tasks face two key challenges: $i$) the size of the input image is usually in the...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	238,976
2403.05873	LEGION: Harnessing Pre-trained Language Models for GitHub Topic Recommendations with Distribution-Balance Loss	Open-source development has revolutionized the software industry by promoting collaboration, transparency, and community-driven innovation. Today, a vast amount of various kinds of open-source software, which form networks of repositories, is often hosted on GitHub - a popular software development platform. To enhance ...	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	true	436,196
2501.16986	Generative quantum combinatorial optimization by means of a novel conditional generative quantum eigensolver	Quantum computing is entering a transformative phase with the emergence of logical quantum processors, which hold the potential to tackle complex problems beyond classical capabilities. While significant progress has been made, applying quantum algorithms to real-world problems remains challenging. Hybrid quantum-class...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	528,175
2110.06483	False Negative Distillation and Contrastive Learning for Personalized Outfit Recommendation	Personalized outfit recommendation has recently been in the spotlight with the rapid growth of the online fashion industry. However, recommending outfits has two significant challenges that should be addressed. The first challenge is that outfit recommendation often requires a complex and large model that utilizes visu...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	260,637
2305.11512	Enriching Disentanglement: From Logical Definitions to Quantitative Metrics	Disentangling the explanatory factors in complex data is a promising approach for generalizable and data-efficient representation learning. While a variety of quantitative metrics for learning and evaluating disentangled representations have been proposed, it remains unclear what properties these metrics truly quantify...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	365,573
1903.08097	Natural Language Generation at Scale: A Case Study for Open Domain Question Answering	Current approaches to Natural Language Generation (NLG) for dialog mainly focus on domain-specific, task-oriented applications (e.g. restaurant booking) using limited ontologies (up to 20 slot types), usually without considering the previous conversation context. Furthermore, these approaches require large amounts of d...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	124,766
2303.11508	AI-in-the-Loop -- The impact of HMI in AI-based Application	Artificial intelligence (AI) and human-machine interaction (HMI) are two keywords that usually do not fit embedded applications. Within the steps needed before applying AI to solve a specific task, HMI is usually missing during the AI architecture design and the training of an AI model. The human-in-the-loop concept is...	true	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	352,875
1702.00288	Low-Dose CT with a Residual Encoder-Decoder Convolutional Neural Network (RED-CNN)	Given the potential X-ray radiation risk to the patient, low-dose CT has attracted a considerable interest in the medical imaging field. The current main stream low-dose CT methods include vendor-specific sinogram domain filtration and iterative reconstruction, but they need to access original raw data whose formats ar...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	67,639
2404.15625	Optimizing OOD Detection in Molecular Graphs: A Novel Approach with Diffusion Models	The open-world test dataset is often mixed with out-of-distribution (OOD) samples, where the deployed models will struggle to make accurate predictions. Traditional detection methods need to trade off OOD detection and in-distribution (ID) classification performance since they share the same representation learning mod...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	449,167
1910.10035	Scanner Invariant Multiple Sclerosis Lesion Segmentation from MRI	This paper presents a simple and effective generalization method for magnetic resonance imaging (MRI) segmentation when data is collected from multiple MRI scanning sites and as a consequence is affected by (site-)domain shifts. We propose to integrate a traditional encoder-decoder network with a regularization network...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	150,372
2209.12721	MIMO Integrated Sensing and Communication: CRB-Rate Tradeoff	This paper studies a multiple-input multiple-output (MIMO) integrated sensing and communication (ISAC) system, in which a multi-antenna base station (BS) sends unified wireless signals to estimate one sensing target and communicate with a multi-antenna communication user (CU) simultaneously. We consider both the point ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	319,631
1909.08278	SINR Analysis of Different Multicarrier Waveforms over Doubly Dispersive Channels	Wireless channels generally exhibit dispersion in both time and frequency domain, known as doubly selective or doubly dispersive channels. To combat the delay spread effect, multicarrier modulation (MCM) such as orthogonal frequency division multiplexing (OFDM) and its universal filtered variant (UF-OFDM) is employed, ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	145,942
1903.06524	Extended framework of Hamilton's principle applied to Duffing oscillation	The paper begins with a novel variational formulation of Duffing equation using the extended framework of Hamilton's principle (EHP). This formulation properly accounts for initial conditions, and it recovers all the governing differential equations as its Euler-Lagrange equation. Thus, it provides elegant structure fo...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	124,403
2008.05730	Iterative Surrogate Model Optimization (ISMO): An active learning algorithm for PDE constrained optimization with deep neural networks	We present a novel active learning algorithm, termed as iterative surrogate model optimization (ISMO), for robust and efficient numerical approximation of PDE constrained optimization problems. This algorithm is based on deep neural networks and its key feature is the iterative selection of training data through a feed...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	191,595
2501.02173	The Efficiency vs. Accuracy Trade-off: Optimizing RAG-Enhanced LLM Recommender Systems Using Multi-Head Early Exit	The deployment of Large Language Models (LLMs) in recommender systems for predicting Click-Through Rates (CTR) necessitates a delicate balance between computational efficiency and predictive accuracy. This paper presents an optimization framework that combines Retrieval-Augmented Generation (RAG) with an innovative mul...	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	false	522,377
2302.05811	Hierarchical control and learning of a foraging CyberOctopus	Inspired by the unique neurophysiology of the octopus, we propose a hierarchical framework that simplifies the coordination of multiple soft arms by decomposing control into high-level decision making, low-level motor activation, and local reflexive behaviors via sensory feedback. When evaluated in the illustrative pro...	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	345,175
2112.14414	Efficient Algorithms for Maximal k-Biplex Enumeration	Mining maximal subgraphs with cohesive structures from a bipartite graph has been widely studied. One important cohesive structure on bipartite graphs is k-biplex, where each vertex on one side disconnects at most k vertices on the other side. In this paper, we study the maximal k-biplex enumeration problem which enume...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	true	273,521
2406.11323	Transparency, Privacy, and Fairness in Recommender Systems	Recommender systems have become a pervasive part of our daily online experience, and are one of the most widely used applications of artificial intelligence and machine learning. Therefore, regulations and requirements for trustworthy artificial intelligence, for example, the European AI Act, which includes notions suc...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	464,842
1911.04239	Hybrid Precoding for Multi-User Millimeter Wave Massive MIMO Systems: A Deep Learning Approach	In multi-user millimeter wave (mmWave) multiple-input-multiple-output (MIMO) systems, hybrid precoding is a crucial task to lower the complexity and cost while achieving a sufficient sum-rate. Previous works on hybrid precoding were usually based on optimization or greedy approaches. These methods either provide higher...	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	152,935
2303.01913	Bespoke: A Block-Level Neural Network Optimization Framework for Low-Cost Deployment	As deep learning models become popular, there is a lot of need for deploying them to diverse device environments. Because it is costly to develop and optimize a neural network for every single environment, there is a line of research to search neural networks for multiple target environments efficiently. However, exist...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	349,152
2404.19360	Large Language Model Informed Patent Image Retrieval	In patent prosecution, image-based retrieval systems for identifying similarities between current patent images and prior art are pivotal to ensure the novelty and non-obviousness of patent applications. Despite their growing popularity in recent years, existing attempts, while effective at recognizing images within th...	false	false	false	false	false	true	false	false	true	false	false	true	false	false	false	false	false	false	450,615
2011.09586	SAFARI: Safe and Active Robot Imitation Learning with Imagination	One of the main issues in Imitation Learning is the erroneous behavior of an agent when facing out-of-distribution situations, not covered by the set of demonstrations given by the expert. In this work, we tackle this problem by introducing a novel active learning and control algorithm, SAFARI. During training, it allo...	false	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	207,232
2410.14062	Data-driven rainfall prediction at a regional scale: a case study with Ghana	With a warming planet, tropical regions are expected to experience the brunt of climate change, with more intense and more volatile rainfall events. Currently, state-of-the-art numerical weather prediction (NWP) models are known to struggle to produce skillful rainfall forecasts in tropical regions of Africa. There is ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	499,845
1910.04527	The Quest for Interpretable and Responsible Artificial Intelligence	Artificial Intelligence (AI) provides many opportunities to improve private and public life. Discovering patterns and structures in large troves of data in an automated manner is a core component of data science, and currently drives applications in computational biology, finance, law and robotics. However, such a high...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	148,790
1910.03244	Self-Paced Deep Regression Forests for Facial Age Estimation	Facial age estimation is an important and challenging problem in computer vision. Existing approaches usually employ deep neural networks (DNNs) to fit the mapping from facial features to age, even though there exist some noisy and confusing samples. We argue that it is more desirable to distinguish noisy and confusing...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	148,455
2412.18956	Musings About the Future of Search: A Return to the Past?	When you have a question, the most effective way to have the question answered is to directly connect with experts on the topic and have a conversation with them. Prior to the invention of writing, this was the only way. Although effective, this solution exhibits scalability challenges. Writing allowed knowledge to be ...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	520,653
2412.08480	InvDiff: Invariant Guidance for Bias Mitigation in Diffusion Models	As one of the most successful generative models, diffusion models have demonstrated remarkable efficacy in synthesizing high-quality images. These models learn the underlying high-dimensional data distribution in an unsupervised manner. Despite their success, diffusion models are highly data-driven and prone to inherit...	false	false	false	false	false	true	true	false	false	false	false	true	false	false	false	false	false	false	516,103
1810.10110	Resource-Constrained Simultaneous Detection and Labeling of Objects in High-Resolution Satellite Images	We describe a strategy for detection and classification of man-made objects in large high-resolution satellite photos under computational resource constraints. We detect and classify candidate objects by using five pipelines of convolutional neural network processing (CNN), run in parallel. Each pipeline has its own un...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	111,201
2406.16995	tcrLM: a lightweight protein language model for predicting T cell receptor and epitope binding specificity	The anti-cancer immune response relies on the bindings between T-cell receptors (TCRs) and antigens, which elicits adaptive immunity to eliminate tumor cells. This ability of the immune system to respond to novel various neoantigens arises from the immense diversity of TCR repository. However, TCR diversity poses a sig...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	467,390
2310.01291	3DHR-Co: A Collaborative Test-time Refinement Framework for In-the-Wild 3D Human-Body Reconstruction Task	The field of 3D human-body reconstruction (abbreviated as 3DHR) that utilizes parametric pose and shape representations has witnessed significant advancements in recent years. However, the application of 3DHR techniques to handle real-world, diverse scenes, known as in-the-wild data, still faces limitations. The primar...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	396,356
cs/0406031	A Public Reference Implementation of the RAP Anaphora Resolution Algorithm	This paper describes a standalone, publicly-available implementation of the Resolution of Anaphora Procedure (RAP) given by Lappin and Leass (1994). The RAP algorithm resolves third person pronouns, lexical anaphors, and identifies pleonastic pronouns. Our implementation, JavaRAP, fills a current need in anaphora resol...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	538,238
1807.11172	Leveraging Medical Sentiment to Understand Patients Health on Social Media	The unprecedented growth of Internet users in recent years has resulted in an abundance of unstructured information in the form of social media text. A large percentage of this population is actively engaged in health social networks to share health-related information. In this paper, we address an important and timely...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	104,122
1412.5143	Detecting Redundant CSS Rules in HTML5 Applications: A Tree-Rewriting Approach	HTML5 applications normally have a large set of CSS (Cascading Style Sheets) rules for data display. Each CSS rule consists of a node selector (given in an XPath-like query language) and a declaration block (assigning values to selected nodes' display attributes). As web applications evolve, maintaining CSS files can e...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	true	38,458
2308.15803	Funnel-based Control for Reach-Avoid-Stay Specifications	The paper addresses the problem of controller synthesis for control-affine nonlinear systems to meet reach-avoid-stay specifications. Specifically, the goal of the research is to obtain a closed-form control law ensuring that the trajectories of the nonlinear system, reach a target set while avoiding all unsafe regions...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	388,796
2410.04335	ReTok: Replacing Tokenizer to Enhance Representation Efficiency in Large Language Model	Tokenizer is an essential component for large language models (LLMs), and a tokenizer with a high compression rate can improve the model's representation and processing efficiency. However, the tokenizer cannot ensure high compression rate in all scenarios, and an increase in the average input and output lengths will i...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	495,241
1807.01066	Behaviour Policy Estimation in Off-Policy Policy Evaluation: Calibration Matters	In this work, we consider the problem of estimating a behaviour policy for use in Off-Policy Policy Evaluation (OPE) when the true behaviour policy is unknown. Via a series of empirical studies, we demonstrate how accurate OPE is strongly dependent on the calibration of estimated behaviour policy models: how precisely ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	101,980
2304.11858	Fitness-for-Duty Classification using Temporal Sequences of Iris Periocular images	Fitness for Duty (FFD) techniques detects whether a subject is Fit to perform their work safely, which means no reduced alertness condition and security, or if they are Unfit, which means alertness condition reduced by sleepiness or consumption of alcohol and drugs. Human iris behaviour provides valuable information to...	true	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	360,002
1806.08547	Analysis of Evolutionary Algorithms in Dynamic and Stochastic Environments	Many real-world optimization problems occur in environments that change dynamically or involve stochastic components. Evolutionary algorithms and other bio-inspired algorithms have been widely applied to dynamic and stochastic problems. This survey gives an overview of major theoretical developments in the area of runt...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	101,173
2205.06655	Unified Modeling of Multi-Domain Multi-Device ASR Systems	Modern Automatic Speech Recognition (ASR) systems often use a portfolio of domain-specific models in order to get high accuracy for distinct user utterance types across different devices. In this paper, we propose an innovative approach that integrates the different per-domain per-device models into a unified model, us...	false	false	true	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	296,306
2309.10015	SYNDICOM: Improving Conversational Commonsense with Error-Injection and Natural Language Feedback	Commonsense reasoning is a critical aspect of human communication. Despite recent advances in conversational AI driven by large language models, commonsense reasoning remains a challenging task. In this work, we introduce SYNDICOM - a method for improving commonsense in dialogue response generation. SYNDICOM consists o...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	392,838
2310.05002	Self-Knowledge Guided Retrieval Augmentation for Large Language Models	Large language models (LLMs) have shown superior performance without task-specific fine-tuning. Despite the success, the knowledge stored in the parameters of LLMs could still be incomplete and difficult to update due to the computational costs. As complementary, retrieval-based methods can offer non-parametric world k...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	397,931
2404.01402	ContactHandover: Contact-Guided Robot-to-Human Object Handover	Robot-to-human object handover is an important step in many human robot collaboration tasks. A successful handover requires the robot to maintain a stable grasp on the object while making sure the human receives the object in a natural and easy-to-use manner. We propose ContactHandover, a robot to human handover system...	false	false	false	false	true	false	false	true	false	false	false	true	false	false	false	false	false	false	443,409
2308.02323	Dataflow Dialogue Generation	We demonstrate task-oriented dialogue generation within the dataflow dialogue paradigm. We show an example of agenda driven dialogue generation for the MultiWOZ domain, and an example of generation without an agenda for the SMCalFlow domain, where we show an improvement in the accuracy of the translation of user reques...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	383,576
2407.11393	CIC-BART-SSA: Controllable Image Captioning with Structured Semantic Augmentation	Controllable Image Captioning (CIC) aims at generating natural language descriptions for an image, conditioned on information provided by end users, e.g., regions, entities or events of interest. However, available image-language datasets mainly contain captions that describe the entirety of an image, making them ineff...	false	false	false	false	true	false	true	false	true	false	false	true	false	false	false	false	false	false	473,430
2309.02902	ViCGCN: Graph Convolutional Network with Contextualized Language Models for Social Media Mining in Vietnamese	Social media processing is a fundamental task in natural language processing with numerous applications. As Vietnamese social media and information science have grown rapidly, the necessity of information-based mining on Vietnamese social media has become crucial. However, state-of-the-art research faces several signif...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	390,205
1703.08855	Apache Lucene as Content-Based-Filtering Recommender System: 3 Lessons Learned	For the past few years, we used Apache Lucene as recommendation frame-work in our scholarly-literature recommender system of the reference-management software Docear. In this paper, we share three lessons learned from our work with Lucene. First, recommendations with relevance scores below 0.025 tend to have significan...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	70,656
1105.5667	Complexity of and Algorithms for Borda Manipulation	We prove that it is NP-hard for a coalition of two manipulators to compute how to manipulate the Borda voting rule. This resolves one of the last open problems in the computational complexity of manipulating common voting rules. Because of this NP-hardness, we treat computing a manipulation as an approximation problem ...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	10,555
1912.08664	Hierarchical Deep Q-Network from Imperfect Demonstrations in Minecraft	We present Hierarchical Deep Q-Network (HDQfD) that took first place in the MineRL competition. HDQfD works on imperfect demonstrations and utilizes the hierarchical structure of expert trajectories. We introduce the procedure of extracting an effective sequence of meta-actions and subgoals from demonstration data. We ...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	157,889
2203.12344	How Do You Do It? Fine-Grained Action Understanding with Pseudo-Adverbs	We aim to understand how actions are performed and identify subtle differences, such as 'fold firmly' vs. 'fold gently'. To this end, we propose a method which recognizes adverbs across different actions. However, such fine-grained annotations are difficult to obtain and their long-tailed nature makes it challenging to...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	287,238
1904.01913	A Polymatroid Approach to Generalized Weights of Rank Metric Codes	We consider the notion of a $(q,m)$-polymatroid, due to Shiromoto, and the more general notion of $(q,m)$-demi-polymatroid, and show how generalized weights can be defined for them. Further, we establish a duality for these weights analogous to Wei duality for generalized Hamming weights of linear codes. The correspond...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	126,283
2104.02929	Minimax Kernel Machine Learning for a Class of Doubly Robust Functionals with Application to Proximal Causal Inference	Robins et al. (2008) introduced a class of influence functions (IFs) which could be used to obtain doubly robust moment functions for the corresponding parameters. However, that class does not include the IF of parameters for which the nuisance functions are solutions to integral equations. Such parameters are particul...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	228,907
2408.04026	Multimodal Gender Fairness in Depression Prediction: Insights on Data from the USA & China	Social agents and robots are increasingly being used in wellbeing settings. However, a key challenge is that these agents and robots typically rely on machine learning (ML) algorithms to detect and analyse an individual's mental wellbeing. The problem of bias and fairness in ML algorithms is becoming an increasingly gr...	false	false	false	false	true	false	true	true	false	false	false	false	false	false	false	false	false	false	479,221
2409.01560	Blocks as Probes: Dissecting Categorization Ability of Large Multimodal Models	Categorization, a core cognitive ability in humans that organizes objects based on common features, is essential to cognitive science as well as computer vision. To evaluate the categorization ability of visual AI models, various proxy tasks on recognition from datasets to open world scenarios have been proposed. Recen...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	485,382
1402.5564	Structure Tensor Based Image Interpolation Method	Feature preserving image interpolation is an active area in image processing field. In this paper a new direct edge directed image super-resolution algorithm based on structure tensors is proposed. Using an isotropic Gaussian filter, the structure tensor at each pixel of the input image is computed and the pixels are c...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	31,076
2105.00261	DeepMultiCap: Performance Capture of Multiple Characters Using Sparse Multiview Cameras	We propose DeepMultiCap, a novel method for multi-person performance capture using sparse multi-view cameras. Our method can capture time varying surface details without the need of using pre-scanned template models. To tackle with the serious occlusion challenge for close interacting scenes, we combine a recently prop...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	233,151
2001.03311	Guess First to Enable Better Compression and Adversarial Robustness	Machine learning models are generally vulnerable to adversarial examples, which is in contrast to the robustness of humans. In this paper, we try to leverage one of the mechanisms in human recognition and propose a bio-inspired classification framework in which model inference is conditioned on label hypothesis. We pro...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	159,941
2106.03155	SoftDICE for Imitation Learning: Rethinking Off-policy Distribution Matching	We present SoftDICE, which achieves state-of-the-art performance for imitation learning. SoftDICE fixes several key problems in ValueDICE, an off-policy distribution matching approach for sample-efficient imitation learning. Specifically, the objective of ValueDICE contains logarithms and exponentials of expectations, ...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	239,200
2210.17514	Cost-aware Generalized $\alpha$-investing for Multiple Hypothesis Testing	We consider the problem of sequential multiple hypothesis testing with nontrivial data collection costs. This problem appears, for example, when conducting biological experiments to identify differentially expressed genes of a disease process. This work builds on the generalized $\alpha$-investing framework which enabl...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	327,717
2010.13665	An Approach to Evaluating Learning Algorithms for Decision Trees	Learning algorithms produce software models for realising critical classification tasks. Decision trees models are simpler than other models such as neural network and they are used in various critical domains such as the medical and the aeronautics. Low or unknown learning ability algorithms does not permit us to trus...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	true	203,210
2407.10559	LIP-CAR: contrast agent reduction by a deep learned inverse problem	The adoption of contrast agents in medical imaging protocols is crucial for accurate and timely diagnosis. While highly effective and characterized by an excellent safety profile, the use of contrast agents has its limitation, including rare risk of allergic reactions, potential environmental impact and economic burden...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	473,033
2106.09794	A Distance-based Separability Measure for Internal Cluster Validation	To evaluate clustering results is a significant part of cluster analysis. Since there are no true class labels for clustering in typical unsupervised learning, many internal cluster validity indices (CVIs), which use predicted labels and data, have been created. Without true labels, to design an effective CVI is as dif...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	true	241,791
1705.02399	Temporal Analysis of Influence to Predict Users' Adoption in Online Social Networks	Different measures have been proposed to predict whether individuals will adopt a new behavior in online social networks, given the influence produced by their neighbors. In this paper, we show one can achieve significant improvement over these standard measures, extending them to consider a pair of time constraints. T...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	72,981
2306.15961	Disentangled Variational Auto-encoder Enhanced by Counterfactual Data for Debiasing Recommendation	Recommender system always suffers from various recommendation biases, seriously hindering its development. In this light, a series of debias methods have been proposed in the recommender system, especially for two most common biases, i.e., popularity bias and amplified subjective bias. However, exsisting debias methods...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	376,226
2203.16681	Face Relighting with Geometrically Consistent Shadows	Most face relighting methods are able to handle diffuse shadows, but struggle to handle hard shadows, such as those cast by the nose. Methods that propose techniques for handling hard shadows often do not produce geometrically consistent shadows since they do not directly leverage the estimated face geometry while synt...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	288,874
2403.03666	Provable Filter for Real-world Graph Clustering	Graph clustering, an important unsupervised problem, has been shown to be more resistant to advances in Graph Neural Networks (GNNs). In addition, almost all clustering methods focus on homophilic graphs and ignore heterophily. This significantly limits their applicability in practice, since real-world graphs exhibit a...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	435,293
2304.03693	Model-Agnostic Gender Debiased Image Captioning	Image captioning models are known to perpetuate and amplify harmful societal bias in the training set. In this work, we aim to mitigate such gender bias in image captioning models. While prior work has addressed this problem by forcing models to focus on people to reduce gender misclassification, it conversely generate...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	356,908
2301.11909	Quantized Deep Path-following Control on a Microcontroller	Model predictive Path-Following Control (MPFC) is a viable option for motion systems in many application domains. However, despite considerable progress on tailored numerical methods for predictive control, the real-time implementation of predictive control and MPFC on small-scale autonomous platforms with low-cost emb...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	342,313
2312.10560	Optimizing Dense Feed-Forward Neural Networks	Deep learning models have been widely used during the last decade due to their outstanding learning and abstraction capacities. However, one of the main challenges any scientist has to face using deep learning models is to establish the network's architecture. Due to this difficulty, data scientists usually build over ...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	416,219
1609.03184	Optimal User Loading in Massive MIMO Systems with Regularized Zero Forcing Precoding	We consider a downlink multiuser multiple-input multiple output (MIMO) system employing regularized zero-forcing (RZF) precoding. We derive the asymptotic signal-to-leakage-plus-noise ratio (SLNR) as both the number of antennas and the number of users go to infinity at a fixed ratio. Focusing on the symmetric uncorrela...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	60,843
2502.12379	OCT Data is All You Need: How Vision Transformers with and without Pre-training Benefit Imaging	Optical Coherence Tomography (OCT) provides high-resolution cross-sectional images useful for diagnosing various diseases, but their distinct characteristics from natural images raise questions about whether large-scale pre-training on datasets like ImageNet is always beneficial. In this paper, we investigate the impac...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	534,832
2108.07378	PnP-3D: A Plug-and-Play for 3D Point Clouds	With the help of the deep learning paradigm, many point cloud networks have been invented for visual analysis. However, there is great potential for development of these networks since the given information of point cloud data has not been fully exploited. To improve the effectiveness of existing networks in analyzing ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	250,894
2405.20524	Practical implementation of geometric quasi-cyclic LDPC codes	We detail for the first time a complete explicit description of the quasi-cyclic structure of all classical finite generalized quadrangles. Using these descriptions we construct families of quasi-cyclic LDPC codes derived from the point-line incidence matrix of the quadrangles by explicitly calculating quasi-cyclic gen...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	459,387
1610.02809	Energy Efficient Design for Tactile Internet	Ensuring the ultra-low end-to-end latency and ultrahigh reliability required by tactile internet is challenging. This is especially true when the stringent Quality-of-Service (QoS) requirement is expected to be satisfied not at the cost of significantly reducing spectral efficiency and energy efficiency (EE). In this p...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	62,160
1212.3844	Three-Receiver Broadcast Channel with Side Information	Three-Receiver broadcast channels (BC) are of interest due to their information-theoretic differences with two-receiver one. In this paper, we derive achievable rate regions for two classes of 3-receiver BC with side information (SI), i.e. Multilevel BC (MBC) and 3-receiver less noisy BC, using a combination of superpo...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	20,428
2309.15329	BASED: Bundle-Adjusting Surgical Endoscopic Dynamic Video Reconstruction using Neural Radiance Fields	Reconstruction of deformable scenes from endoscopic videos is important for many applications such as intraoperative navigation, surgical visual perception, and robotic surgery. It is a foundational requirement for realizing autonomous robotic interventions for minimally invasive surgery. However, previous approaches i...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	394,929
2411.15367	Exploiting Watermark-Based Defense Mechanisms in Text-to-Image Diffusion Models for Unauthorized Data Usage	Text-to-image diffusion models, such as Stable Diffusion, have shown exceptional potential in generating high-quality images. However, recent studies highlight concerns over the use of unauthorized data in training these models, which may lead to intellectual property infringement or privacy violations. A promising app...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	510,576
1608.02741	Dwell-time stability and stabilization conditions for linear positive impulsive and switched systems	Several results regarding the stability and the stabilization of linear impulsive positive systems under arbitrary, constant, minimum, maximum and range dwell-time are obtained. The proposed stability conditions characterize the pointwise decrease of a linear copositive Lyapunov function and are formulated in terms of ...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	59,596
2303.01804	Are All Point Clouds Suitable for Completion? Weakly Supervised Quality Evaluation Network for Point Cloud Completion	In the practical application of point cloud completion tasks, real data quality is usually much worse than the CAD datasets used for training. A small amount of noisy data will usually significantly impact the overall system's accuracy. In this paper, we propose a quality evaluation network to score the point clouds an...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	349,117
2501.02353	Reweighting Improves Conditional Risk Bounds	In this work, we study the weighted empirical risk minimization (weighted ERM) schema, in which an additional data-dependent weight function is incorporated when the empirical risk function is being minimized. We show that under a general ``balanceable" Bernstein condition, one can design a weighted ERM estimator to ac...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	522,448
2305.15022	Hierarchical clustering with dot products recovers hidden tree structure	In this paper we offer a new perspective on the well established agglomerative clustering algorithm, focusing on recovery of hierarchical structure. We recommend a simple variant of the standard algorithm, in which clusters are merged by maximum average dot product and not, for example, by minimum distance or within-cl...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	367,401
2212.00744	Improving astroBERT using Semantic Textual Similarity	The NASA Astrophysics Data System (ADS) is an essential tool for researchers that allows them to explore the astronomy and astrophysics scientific literature, but it has yet to exploit recent advances in natural language processing. At ADASS 2021, we introduced astroBERT, a machine learning language model tailored to t...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	334,175
2009.09229	Detailed Dynamic Model of Antagonistic PAM System and its Experimental Validation: Sensor-less Angle and Torque Control with UKF	This study proposes a detailed nonlinear mathematical model of an antagonistic pneumatic artificial muscle (PAM) actuator system for estimating the joint angle and torque using an unscented Kalman filter (UKF). The proposed model is described in a hybrid state-space representation. It includes the contraction force of ...	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	196,502
2109.00899	CE-Dedup: Cost-Effective Convolutional Neural Nets Training based on Image Deduplication	Attributed to the ever-increasing large image datasets, Convolutional Neural Networks (CNNs) have become popular for vision-based tasks. It is generally admirable to have larger-sized datasets for higher network training accuracies. However, the impact of dataset quality has not to be involved. It is reasonable to assu...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	253,267
2411.18699	An indicator for effectiveness of text-to-image guardrails utilizing the Single-Turn Crescendo Attack (STCA)	The Single-Turn Crescendo Attack (STCA), first introduced in Aqrawi and Abbasi [2024], is an innovative method designed to bypass the ethical safeguards of text-to-text AI models, compelling them to generate harmful content. This technique leverages a strategic escalation of context within a single prompt, combined wit...	false	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	511,972
2007.13299	Enhanced Beam Alignment for Millimeter Wave MIMO Systems: A Kolmogorov Model	We present an enhancement to the problem of beam alignment in millimeter wave (mmWave) multiple-input multiple-output (MIMO) systems, based on a modification of the machine learning-based criterion, called Kolmogorov model (KM), previously applied to the beam alignment problem. Unlike the previous KM, whose computation...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	189,086
2306.06559	Straggler-Resilient Decentralized Learning via Adaptive Asynchronous Updates	With the increasing demand for large-scale training of machine learning models, fully decentralized optimization methods have recently been advocated as alternatives to the popular parameter server framework. In this paradigm, each worker maintains a local estimate of the optimal parameter vector, and iteratively updat...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	372,659
1607.02137	Fundamental Parameters of Main-Sequence Stars in an Instant with Machine Learning	Owing to the remarkable photometric precision of space observatories like Kepler, stellar and planetary systems beyond our own are now being characterized en masse for the first time. These characterizations are pivotal for endeavors such as searching for Earth-like planets and solar twins, understanding the mechanisms...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	58,308
2401.06469	Batch-ICL: Effective, Efficient, and Order-Agnostic In-Context Learning	In this paper, by treating in-context learning (ICL) as a meta-optimization process, we explain why LLMs are sensitive to the order of ICL examples. This understanding leads us to the development of Batch-ICL, an effective, efficient, and order-agnostic inference algorithm for ICL. Differing from the standard N-shot le...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	421,171
2412.00140	Differentiable Topology Estimating from Curvatures for 3D Shapes	In the field of data-driven 3D shape analysis and generation, the estimation of global topological features from localized representations such as point clouds, voxels, and neural implicit fields is a longstanding challenge. This paper introduces a novel, differentiable algorithm tailored to accurately estimate the glo...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	512,527
2104.05940	Dynamic Texture Synthesis by Incorporating Long-range Spatial and Temporal Correlations	The main challenge of dynamic texture synthesis lies in how to maintain spatial and temporal consistency in synthesized videos. The major drawback of existing dynamic texture synthesis models comes from poor treatment of the long-range texture correlation and motion information. To address this problem, we incorporate ...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	229,905
2306.09631	AUGUST: an Automatic Generation Understudy for Synthesizing Conversational Recommendation Datasets	High-quality data is essential for conversational recommendation systems and serves as the cornerstone of the network architecture development and training strategy design. Existing works contribute heavy human efforts to manually labeling or designing and extending recommender dialogue templates. However, they suffer ...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	373,905
1305.7181	Lensless Imaging by Compressive Sensing	In this paper, we propose a lensless compressive imaging architecture. The architecture consists of two components, an aperture assembly and a sensor. No lens is used. The aperture assembly consists of a two dimensional array of aperture elements. The transmittance of each aperture element is independently controllable...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	24,881
2309.11966	NeuralLabeling: A versatile toolset for labeling vision datasets using Neural Radiance Fields	We present NeuralLabeling, a labeling approach and toolset for annotating 3D scenes using either bounding boxes or meshes and generating segmentation masks, affordance maps, 2D bounding boxes, 3D bounding boxes, 6DOF object poses, depth maps, and object meshes. NeuralLabeling uses Neural Radiance Fields (NeRF) as a ren...	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	393,609
1910.06592	FacTweet: Profiling Fake News Twitter Accounts	We present an approach to detect fake news in Twitter at the account level using a neural recurrent model and a variety of different semantic and stylistic features. Our method extracts a set of features from the timelines of news Twitter accounts by reading their posts as chunks, rather than dealing with each tweet in...	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	false	false	false	149,389
1206.1579	An Efficient Hybrid Ant Colony System for the Generalized Traveling Salesman Problem	The Generalized Traveling Salesman Problem (GTSP) is an extension of the well-known Traveling Salesman Problem (TSP), where the node set is partitioned into clusters, and the objective is to find the shortest cycle visiting each cluster exactly once. In this paper, we present a new hybrid Ant Colony System (ACS) algori...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	16,385
1607.08822	SPICE: Semantic Propositional Image Caption Evaluation	There is considerable interest in the task of automatically generating image captions. However, evaluation is challenging. Existing automatic evaluation metrics are primarily sensitive to n-gram overlap, which is neither necessary nor sufficient for the task of simulating human judgment. We hypothesize that semantic pr...	false	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	59,207
2409.15004	ViBERTgrid BiLSTM-CRF: Multimodal Key Information Extraction from Unstructured Financial Documents	Multimodal key information extraction (KIE) models have been studied extensively on semi-structured documents. However, their investigation on unstructured documents is an emerging research topic. The paper presents an approach to adapt a multimodal transformer (i.e., ViBERTgrid previously explored on semi-structured d...	false	false	false	false	true	true	false	false	true	false	false	true	false	false	false	false	false	false	490,717

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