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
1705.04863	Adaptive Modularity Maximization via Edge Weighting Scheme	Modularity maximization is one of the state-of-the-art methods for community detection that has gained popularity in the last decade. Yet it suffers from the resolution limit problem by preferring under certain conditions large communities over small ones. To solve this problem, we propose to expand the meaning of the ...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	73,392
1901.06767	LayoutGAN: Generating Graphic Layouts with Wireframe Discriminators	Layout is important for graphic design and scene generation. We propose a novel Generative Adversarial Network, called LayoutGAN, that synthesizes layouts by modeling geometric relations of different types of 2D elements. The generator of LayoutGAN takes as input a set of randomly-placed 2D graphic elements and uses se...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	119,076
2007.13404	YOLOpeds: Efficient Real-Time Single-Shot Pedestrian Detection for Smart Camera Applications	Deep Learning-based object detectors can enhance the capabilities of smart camera systems in a wide spectrum of machine vision applications including video surveillance, autonomous driving, robots and drones, smart factory, and health monitoring. Pedestrian detection plays a key role in all these applications and deep ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	189,117
2406.12142	Slicing Through Bias: Explaining Performance Gaps in Medical Image Analysis using Slice Discovery Methods	Machine learning models have achieved high overall accuracy in medical image analysis. However, performance disparities on specific patient groups pose challenges to their clinical utility, safety, and fairness. This can affect known patient groups - such as those based on sex, age, or disease subtype - as well as prev...	false	false	false	false	true	false	true	false	false	false	false	true	false	true	false	false	false	false	465,241
2107.10387	Design of a Graphical User Interface for Few-Shot Machine Learning Classification of Electron Microscopy Data	The recent growth in data volumes produced by modern electron microscopes requires rapid, scalable, and flexible approaches to image segmentation and analysis. Few-shot machine learning, which can richly classify images from a handful of user-provided examples, is a promising route to high-throughput analysis. However,...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	247,279
2409.04619	Low-latency Secure Integrated Sensing and Communication with Transmitter Actions	This paper considers an information theoretic model of secure integrated sensing and communication, represented as a wiretap channel with action dependent states. This model allows securing part of a transmitted message against a sensed target that eavesdrops the communication, while enabling transmitter actions to cha...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	486,448
2403.09054	Keyformer: KV Cache Reduction through Key Tokens Selection for Efficient Generative Inference	Transformers have emerged as the underpinning architecture for Large Language Models (LLMs). In generative language models, the inference process involves two primary phases: prompt processing and token generation. Token generation, which constitutes the majority of the computational workload, primarily entails vector-...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	true	437,604
1601.06008	A Robust Frame-based Nonlinear Prediction System for Automatic Speech Coding	In this paper, we propose a neural-based coding scheme in which an artificial neural network is exploited to automatically compress and decompress speech signals by a trainable approach. Having a two-stage training phase, the system can be fully specified to each speech frame and have robust performance across differen...	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	51,194
2412.04925	$S^3$: Synonymous Semantic Space for Improving Zero-Shot Generalization of Vision-Language Models	Recently, many studies have been conducted to enhance the zero-shot generalization ability of vision-language models (e.g., CLIP) by addressing the semantic misalignment between image and text embeddings in downstream tasks. Although many efforts have been made, existing methods barely consider the fact that a class of...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	514,631
0803.3117	On the Diversity-Multiplexing Tradeoff in Multiple-Relay Network	This paper studies the setup of a multiple-relay network in which $K$ half-duplex multiple-antenna relays assist in the transmission between a/several multiple-antenna transmitter(s) and a multiple-antenna receiver. Each two nodes are assumed to be either connected through a quasi-static Rayleigh fading channel, or dis...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	1,471
2104.09856	Permutation-Invariant Variational Autoencoder for Graph-Level Representation Learning	Recently, there has been great success in applying deep neural networks on graph structured data. Most work, however, focuses on either node- or graph-level supervised learning, such as node, link or graph classification or node-level unsupervised learning (e.g. node clustering). Despite its wide range of possible appl...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	231,379
1605.05826	Declarative Machine Learning - A Classification of Basic Properties and Types	Declarative machine learning (ML) aims at the high-level specification of ML tasks or algorithms, and automatic generation of optimized execution plans from these specifications. The fundamental goal is to simplify the usage and/or development of ML algorithms, which is especially important in the context of large-scal...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	true	true	56,052
2311.09806	EvaSurf: Efficient View-Aware Implicit Textured Surface Reconstruction	Reconstructing real-world 3D objects has numerous applications in computer vision, such as virtual reality, video games, and animations. Ideally, 3D reconstruction methods should generate high-fidelity results with 3D consistency in real-time. Traditional methods match pixels between images using photo-consistency cons...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	408,308
1905.06464	Streetscape augmentation using generative adversarial networks: insights related to health and wellbeing	Deep learning using neural networks has provided advances in image style transfer, merging the content of one image (e.g., a photo) with the style of another (e.g., a painting). Our research shows this concept can be extended to analyse the design of streetscapes in relation to health and wellbeing outcomes. An Austral...	false	false	false	false	false	false	true	false	false	false	false	true	false	true	false	false	false	false	130,996
2408.16089	Is Personality Prediction Possible Based on Reddit Comments?	In this assignment, we examine whether there is a correlation between the personality type of a person and the texts they wrote. In order to do this, we aggregated datasets of Reddit comments labeled with the Myers-Briggs Type Indicator (MBTI) of the author and built different supervised classifiers based on BERT to tr...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	484,188
2110.11439	(Optimal) Online Bipartite Matching with Degree Information	We propose a model for online graph problems where algorithms are given access to an oracle that predicts (e.g., based on modeling assumptions or on past data) the degrees of nodes in the graph. Within this model, we study the classic problem of online bipartite matching, and a natural greedy matching algorithm called ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	262,481
2402.11955	Analysis of Multidomain Abstractive Summarization Using Salience Allocation	This paper explores the realm of abstractive text summarization through the lens of the SEASON (Salience Allocation as Guidance for Abstractive SummarizatiON) technique, a model designed to enhance summarization by leveraging salience allocation techniques. The study evaluates SEASON's efficacy by comparing it with pro...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	430,657
2401.13950	AM-SORT: Adaptable Motion Predictor with Historical Trajectory Embedding for Multi-Object Tracking	Many multi-object tracking (MOT) approaches, which employ the Kalman Filter as a motion predictor, assume constant velocity and Gaussian-distributed filtering noises. These assumptions render the Kalman Filter-based trackers effective in linear motion scenarios. However, these linear assumptions serve as a key limitati...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	423,911
2005.03300	Reducing Communication in Graph Neural Network Training	Graph Neural Networks (GNNs) are powerful and flexible neural networks that use the naturally sparse connectivity information of the data. GNNs represent this connectivity as sparse matrices, which have lower arithmetic intensity and thus higher communication costs compared to dense matrices, making GNNs harder to scal...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	176,120
2309.12215	Regionally Additive Models: Explainable-by-design models minimizing feature interactions	Generalized Additive Models (GAMs) are widely used explainable-by-design models in various applications. GAMs assume that the output can be represented as a sum of univariate functions, referred to as components. However, this assumption fails in ML problems where the output depends on multiple features simultaneously....	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	393,701
1907.07380	Scheduling to Minimize Age of Synchronization in Wireless Broadcast Networks with Random Updates	In this work, a wireless broadcast network with a base station (BS) sending random time-sensitive information updates to multiple users with interference constraints is considered. The Age of Synchronization (AoS), namely the amount of time elapsed since the information stored at the network user becomes desynchronized...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	138,863
2210.15285	SAN: a robust end-to-end ASR model architecture	In this paper, we propose a novel Siamese Adversarial Network (SAN) architecture for automatic speech recognition, which aims at solving the difficulty of fuzzy audio recognition. Specifically, SAN constructs two sub-networks to differentiate the audio feature input and then introduces a loss to unify the output distri...	false	false	true	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	326,889
2303.05470	Spawrious: A Benchmark for Fine Control of Spurious Correlation Biases	The problem of spurious correlations (SCs) arises when a classifier relies on non-predictive features that happen to be correlated with the labels in the training data. For example, a classifier may misclassify dog breeds based on the background of dog images. This happens when the backgrounds are correlated with other...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	350,477
1110.5000	On Noisy Network Coding for a Gaussian Relay Chain Network with Correlated Noises	Noisy network coding, which elegantly combines the conventional compress-and-forward relaying strategy and ideas from network coding, has recently drawn much attention for its simplicity and optimality in achieving to within constant gap of the capacity of the multisource multicast Gaussian network. The constant-gap re...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	12,740
2002.00226	Domain segmentation and adjustment for generalized zero-shot learning	In the generalized zero-shot learning, synthesizing unseen data with generative models has been the most popular method to address the imbalance of training data between seen and unseen classes. However, this method requires that the unseen semantic information is available during the training stage, and training gener...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	162,299
1901.06268	Comparing two deep learning sequence-based models for protein-protein interaction prediction	Biological data are extremely diverse, complex but also quite sparse. The recent developments in deep learning methods are offering new possibilities for the analysis of complex data. However, it is easy to be get a deep learning model that seems to have good results but is in fact either overfitting the training data ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	118,962
1810.04428	Improving Neural Text Simplification Model with Simplified Corpora	Text simplification (TS) can be viewed as monolingual translation task, translating between text variations within a single language. Recent neural TS models draw on insights from neural machine translation to learn lexical simplification and content reduction using encoder-decoder model. But different from neural mach...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	110,040
2111.13175	Homogeneous Low-Resolution Face Recognition Method based Correlation Features	Face recognition technology has been widely adopted in many mission-critical scenarios like means of human identification, controlled admission, and mobile device access, etc. Security surveillance is a typical scenario of face recognition technology. Because the low-resolution feature of surveillance video and images ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	268,212
1703.02196	Cooperative Epistemic Multi-Agent Planning for Implicit Coordination	Epistemic planning can be used for decision making in multi-agent situations with distributed knowledge and capabilities. Recently, Dynamic Epistemic Logic (DEL) has been shown to provide a very natural and expressive framework for epistemic planning. We extend the DEL-based epistemic planning framework to include pers...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	true	false	false	true	69,513
2209.00470	Negation detection in Dutch clinical texts: an evaluation of rule-based and machine learning methods	As structured data are often insufficient, labels need to be extracted from free text in electronic health records when developing models for clinical information retrieval and decision support systems. One of the most important contextual properties in clinical text is negation, which indicates the absence of findings...	false	false	false	false	false	true	true	false	true	false	false	false	false	false	false	false	false	false	315,592
1805.10850	Inducing Grammars with and for Neural Machine Translation	Machine translation systems require semantic knowledge and grammatical understanding. Neural machine translation (NMT) systems often assume this information is captured by an attention mechanism and a decoder that ensures fluency. Recent work has shown that incorporating explicit syntax alleviates the burden of modelin...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	98,782
2009.13881	Lipschitz neural networks are dense in the set of all Lipschitz functions	This note shows that, for a fixed Lipschitz constant $L > 0$, one layer neural networks that are $L$-Lipschitz are dense in the set of all $L$-Lipschitz functions with respect to the uniform norm on bounded sets.	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	197,874
2502.13144	RAD: Training an End-to-End Driving Policy via Large-Scale 3DGS-based Reinforcement Learning	Existing end-to-end autonomous driving (AD) algorithms typically follow the Imitation Learning (IL) paradigm, which faces challenges such as causal confusion and the open-loop gap. In this work, we establish a 3DGS-based closed-loop Reinforcement Learning (RL) training paradigm. By leveraging 3DGS techniques, we constr...	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	535,229
1810.11388	Deep Intrinsically Motivated Continuous Actor-Critic for Efficient Robotic Visuomotor Skill Learning	In this paper, we present a new intrinsically motivated actor-critic algorithm for learning continuous motor skills directly from raw visual input. Our neural architecture is composed of a critic and an actor network. Both networks receive the hidden representation of a deep convolutional autoencoder which is trained t...	false	false	false	false	true	false	true	true	false	false	false	false	false	false	false	false	false	false	111,489
2307.08576	A Study on the Performance of Generative Pre-trained Transformer (GPT) in Simulating Depressed Individuals on the Standardized Depressive Symptom Scale	Background: Depression is a common mental disorder with societal and economic burden. Current diagnosis relies on self-reports and assessment scales, which have reliability issues. Objective approaches are needed for diagnosing depression. Objective: Evaluate the potential of GPT technology in diagnosing depression. As...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	379,854
2406.15217	Rate-Splitting Multiple Access for Overloaded Multi-group Multicast: A First Experimental Study	Multi-group multicast (MGM) is an increasingly important form of multi-user wireless communications with several potential applications, such as video streaming, federated learning, safety-critical vehicular communications, etc. Rate-Splitting Multiple Access (RSMA) is a powerful interference management technique that ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	466,658
1203.1833	Crowdsourcing Predictors of Behavioral Outcomes	Generating models from large data sets -- and determining which subsets of data to mine -- is becoming increasingly automated. However choosing what data to collect in the first place requires human intuition or experience, usually supplied by a domain expert. This paper describes a new approach to machine science whic...	false	false	false	true	false	false	false	false	false	false	false	false	false	true	false	false	false	false	14,787
1808.01338	Detailed Human Avatars from Monocular Video	We present a novel method for high detail-preserving human avatar creation from monocular video. A parameterized body model is refined and optimized to maximally resemble subjects from a video showing them from all sides. Our avatars feature a natural face, hairstyle, clothes with garment wrinkles, and high-resolution ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	104,545
2206.07762	Physics-Infused Fuzzy Generative Adversarial Network for Robust Failure Prognosis	Prognostics aid in the longevity of fielded systems or products. Quantifying the system's current health enable prognosis to enhance the operator's decision-making to preserve the system's health. Creating a prognosis for a system can be difficult due to (a) unknown physical relationships and/or (b) irregularities in d...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	302,871
2407.15359	UF-HOBI at "Discharge Me!": A Hybrid Solution for Discharge Summary Generation Through Prompt-based Tuning of GatorTronGPT Models	Automatic generation of discharge summaries presents significant challenges due to the length of clinical documentation, the dispersed nature of patient information, and the diverse terminology used in healthcare. This paper presents a hybrid solution for generating discharge summary sections as part of our participati...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	475,152
1802.00157	Optimal LRC codes for all lenghts n <= q	A family of distance-optimal LRC codes from certain subcodes of $q$-ary Reed-Solomon codes, proposed by I.~Tamo and A.~Barg in 2014, assumes that the code length $n$ is a multiple of $r+1.$ By shortening codes from this family, we show that it is possible to lift this assumption, still obtaining distance-optimal codes.	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	89,359
2412.16564	Predictive Monitoring of Black-Box Dynamical Systems	We study the problem of predictive runtime monitoring of black-box dynamical systems with quantitative safety properties. The black-box setting stipulates that the exact semantics of the dynamical system and the controller are unknown, and that we are only able to observe the state of the controlled (aka, closed-loop) ...	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	false	false	519,589
1907.08338	Batch Uniformization for Minimizing Maximum Anomaly Score of DNN-based Anomaly Detection in Sounds	Use of an autoencoder (AE) as a normal model is a state-of-the-art technique for unsupervised-anomaly detection in sounds (ADS). The AE is trained to minimize the sample mean of the anomaly score of normal sounds in a mini-batch. One problem with this approach is that the anomaly score of rare-normal sounds becomes hig...	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	139,082
2011.09695	Deep LF-Net: Semantic Lung Segmentation from Indian Chest Radiographs Including Severely Unhealthy Images	A chest radiograph, commonly called chest x-ray (CxR), plays a vital role in the diagnosis of various lung diseases, such as lung cancer, tuberculosis, pneumonia, and many more. Automated segmentation of the lungs is an important step to design a computer-aided diagnostic tool for examination of a CxR. Precise lung seg...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	207,267
2406.06621	LinkQ: An LLM-Assisted Visual Interface for Knowledge Graph Question-Answering	We present LinkQ, a system that leverages a large language model (LLM) to facilitate knowledge graph (KG) query construction through natural language question-answering. Traditional approaches often require detailed knowledge of a graph querying language, limiting the ability for users -- even experts -- to acquire val...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	462,701
1403.0448	Hybrid evolving clique-networks and their communicability	Aiming to understand real-world hierarchical networks whose degree distributions are neither power law nor exponential, we construct a hybrid clique network that includes both homogeneous and inhomogeneous parts, and introduce an inhomogeneity parameter to tune the ratio between the homogeneous part and the inhomogeneo...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	31,293
2204.05021	Landmarks and Regions: A Robust Approach to Data Extraction	We propose a new approach to extracting data items or field values from semi-structured documents. Examples of such problems include extracting passenger name, departure time and departure airport from a travel itinerary, or extracting price of an item from a purchase receipt. Traditional approaches to data extraction ...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	true	290,879
2103.08017	Transient growth of accelerated optimization algorithms	Optimization algorithms are increasingly being used in applications with limited time budgets. In many real-time and embedded scenarios, only a few iterations can be performed and traditional convergence metrics cannot be used to evaluate performance in these non-asymptotic regimes. In this paper, we examine the transi...	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	224,768
1805.02677	Gradient Descent for One-Hidden-Layer Neural Networks: Polynomial Convergence and SQ Lower Bounds	We study the complexity of training neural network models with one hidden nonlinear activation layer and an output weighted sum layer. We analyze Gradient Descent applied to learning a bounded target function on $n$ real-valued inputs. We give an agnostic learning guarantee for GD: starting from a randomly initialized ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	96,896
2006.16152	Leveraging Subword Embeddings for Multinational Address Parsing	Address parsing consists of identifying the segments that make up an address such as a street name or a postal code. Because of its importance for tasks like record linkage, address parsing has been approached with many techniques. Neural network methods defined a new state-of-the-art for address parsing. While this ap...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	184,727
2102.06243	Deep Reinforcement Agent for Scheduling in HPC	Cluster scheduler is crucial in high-performance computing (HPC). It determines when and which user jobs should be allocated to available system resources. Existing cluster scheduling heuristics are developed by human experts based on their experience with specific HPC systems and workloads. However, the increasing com...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	true	219,683
1907.07034	Uncertainty-aware Self-ensembling Model for Semi-supervised 3D Left Atrium Segmentation	Training deep convolutional neural networks usually requires a large amount of labeled data. However, it is expensive and time-consuming to annotate data for medical image segmentation tasks. In this paper, we present a novel uncertainty-aware semi-supervised framework for left atrium segmentation from 3D MR images. Ou...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	138,769
2404.03453	Conditioning of Banach Space Valued Gaussian Random Variables: An Approximation Approach Based on Martingales	In this paper we investigate the conditional distributions of two Banach space valued, jointly Gaussian random variables. We show that these conditional distributions are again Gaussian and that their means and covariances are determined by a general finite dimensional approximation scheme based upon a martingale appro...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	444,266
2104.13458	Robust Classification via Support Vector Machines	Classification models are very sensitive to data uncertainty, and finding robust classifiers that are less sensitive to data uncertainty has raised great interest in the machine learning literature. This paper aims to construct robust \emph{Support Vector Machine} classifiers under feature data uncertainty via two prob...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	232,505
2305.04763	Large-scale and Efficient Texture Mapping Algorithm via Loopy Belief Propagation	Texture mapping as a fundamental task in 3D modeling has been well established for well-acquired aerial assets under consistent illumination, yet it remains a challenge when it is scaled to large datasets with images under varying views and illuminations. A well-performed texture mapping algorithm must be able to effic...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	362,895
1301.5160	See the Tree Through the Lines: The Shazoo Algorithm -- Full Version --	Predicting the nodes of a given graph is a fascinating theoretical problem with applications in several domains. Since graph sparsification via spanning trees retains enough information while making the task much easier, trees are an important special case of this problem. Although it is known how to predict the nodes ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	21,319
1912.13149	Revisiting Paraphrase Question Generator using Pairwise Discriminator	In this paper, we propose a method for obtaining sentence-level embeddings. While the problem of securing word-level embeddings is very well studied, we propose a novel method for obtaining sentence-level embeddings. This is obtained by a simple method in the context of solving the paraphrase generation task. If we use...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	159,015
2203.13097	IA-FaceS: A Bidirectional Method for Semantic Face Editing	Semantic face editing has achieved substantial progress in recent years. Known as a growingly popular method, latent space manipulation performs face editing by changing the latent code of an input face to liberate users from painting skills. However, previous latent space manipulation methods usually encode an entire ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	287,506
2210.04061	A General Security Approach for Soft-information Decoding against Smart Bursty Jammers	Malicious attacks such as jamming can cause significant disruption or complete denial of service (DoS) to wireless communication protocols. Moreover, jamming devices are getting smarter, making them difficult to detect. Forward error correction, which adds redundancy to data, is commonly deployed to protect communicati...	false	false	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	322,284
2306.03786	Residual-based error bound for physics-informed neural networks	Neural networks are universal approximators and are studied for their use in solving differential equations. However, a major criticism is the lack of error bounds for obtained solutions. This paper proposes a technique to rigorously evaluate the error bound of Physics-Informed Neural Networks (PINNs) on most linear or...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	371,478
2012.04468	Active Learning Methods for Efficient Hybrid Biophysical Variable Retrieval	Kernel-based machine learning regression algorithms (MLRAs) are potentially powerful methods for being implemented into operational biophysical variable retrieval schemes. However, they face difficulties in coping with large training datasets. With the increasing amount of optical remote sensing data made available for...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	210,467
1903.10556	The Random Conditional Distribution for Higher-Order Probabilistic Inference	The need to condition distributional properties such as expectation, variance, and entropy arises in algorithmic fairness, model simplification, robustness and many other areas. At face value however, distributional properties are not random variables, and hence conditioning them is a semantic error and type error in p...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	125,298
2108.08278	Two parameter Leak Estimation in Non invasive Ventilation	In this paper we present a method for the estimation of leaks in non-invasive ventilation. Accurate estimation of leaks is a key component of a ventilator, since it determines the ventilator performance in terms of patient-ventilator synchrony and air volume delivery. In particular, in non-invasive ventilation, the pat...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	251,201
2311.18799	X-InstructBLIP: A Framework for aligning X-Modal instruction-aware representations to LLMs and Emergent Cross-modal Reasoning	Recent research has achieved significant advancements in visual reasoning tasks through learning image-to-language projections and leveraging the impressive reasoning abilities of Large Language Models (LLMs). This paper introduces an efficient and effective framework that integrates multiple modalities (images, 3D, au...	false	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	411,820
2009.13853	Efficient SVDD Sampling with Approximation Guarantees for the Decision Boundary	Support Vector Data Description (SVDD) is a popular one-class classifiers for anomaly and novelty detection. But despite its effectiveness, SVDD does not scale well with data size. To avoid prohibitive training times, sampling methods select small subsets of the training data on which SVDD trains a decision boundary ho...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	197,867
2202.10642	Local Sliced-Wasserstein Feature Sets for Illumination-invariant Face Recognition	We present a new method for face recognition from digital images acquired under varying illumination conditions. The method is based on mathematical modeling of local gradient distributions using the Radon Cumulative Distribution Transform (R-CDT). We demonstrate that lighting variations cause certain types of deformat...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	281,603
1810.04937	Location Dependency in Video Prediction	Deep convolutional neural networks are used to address many computer vision problems, including video prediction. The task of video prediction requires analyzing the video frames, temporally and spatially, and constructing a model of how the environment evolves. Convolutional neural networks are spatially invariant, th...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	110,137
2108.07124	Using Cyber Terrain in Reinforcement Learning for Penetration Testing	Reinforcement learning (RL) has been applied to attack graphs for penetration testing, however, trained agents do not reflect reality because the attack graphs lack operational nuances typically captured within the intelligence preparation of the battlefield (IPB) that include notions of (cyber) terrain. In particular,...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	true	250,835
1210.7631	The fortresses of Ejin: an example of outlining a site from satellite images	From 1960's to 1970's, the Chinese Army built some fortified artificial hills. Some of them are located in the Inner Mongolia, Western China. These large fortresses are surrounded by moats. For some of them it is still possible to see earthworks, trenches and ditches, the planning of which could have a symbolic meaning...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	19,449
2312.10486	Time-Constrained Continuous Subgraph Matching Using Temporal Information for Filtering and Backtracking	Real-time analysis of graphs containing temporal information, such as social media streams, Q&A networks, and cyber data sources, plays an important role in various applications. Among them, detecting patterns is one of the fundamental graph analysis problems. In this paper, we study time-constrained continuous subgrap...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	416,191
2410.05694	DiffusionGuard: A Robust Defense Against Malicious Diffusion-based Image Editing	Recent advances in diffusion models have introduced a new era of text-guided image manipulation, enabling users to create realistic edited images with simple textual prompts. However, there is significant concern about the potential misuse of these methods, especially in creating misleading or harmful content. Although...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	495,864
2411.12853	Integrating Secondary Structures Information into Triangular Spatial Relationships (TSR) for Advanced Protein Classification	Protein structures represent the key to deciphering biological functions. The more detailed form of similarity among these proteins is sometimes overlooked by the conventional structural comparison methods. In contrast, further advanced methods, such as Triangular Spatial Relationship (TSR), have been demonstrated to m...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	509,570
2101.03295	Estimation of Missing Data in Intelligent Transportation System	Missing data is a challenge in many applications, including intelligent transportation systems (ITS). In this paper, we study traffic speed and travel time estimations in ITS, where portions of the collected data are missing due to sensor instability and communication errors at collection points. These practical issues...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	214,885
2101.05661	A Pipeline for Vision-Based On-Orbit Proximity Operations Using Deep Learning and Synthetic Imagery	Deep learning has become the gold standard for image processing over the past decade. Simultaneously, we have seen growing interest in orbital activities such as satellite servicing and debris removal that depend on proximity operations between spacecraft. However, two key challenges currently pose a major barrier to t...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	true	215,495
2106.01329	Introducing "Neuromorphic Computing and Engineering"	The standard nature of computing is currently being challenged by a range of problems that start to hinder technological progress. One of the strategies being proposed to address some of these problems is to develop novel brain-inspired processing methods and technologies, and apply them to a wide range of application ...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	true	238,457
1407.2883	Understanding Co-evolution in Large Multi-relational Social Networks	Understanding dynamics of evolution in large social networks is an important problem. In this paper, we characterize evolution in large multi-relational social networks. The proliferation of online media such as Twitter, Facebook, Orkut and MMORPGs\footnote{Massively Multi-player Online Role Playing Games} have created...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	34,572
2110.15739	Scalable Inference in SDEs by Direct Matching of the Fokker-Planck-Kolmogorov Equation	Simulation-based techniques such as variants of stochastic Runge-Kutta are the de facto approach for inference with stochastic differential equations (SDEs) in machine learning. These methods are general-purpose and used with parametric and non-parametric models, and neural SDEs. Stochastic Runge-Kutta relies on the us...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	263,988
2011.10690	Self-adapting Robustness in Demand Learning	We study dynamic pricing over a finite number of periods in the presence of demand model ambiguity. Departing from the typical no-regret learning environment, where price changes are allowed at any time, pricing decisions are made at pre-specified points in time and each price can be applied to a large number of arriva...	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	false	207,588
cs/9809021	Producing NLP-based On-line Contentware	For its internal needs as well as for commercial purposes, CDC Group has produced several NLP-based on-line contentware applications for years. The development process of such applications is subject to numerous constraints such as quality of service, integration of new advances in NLP, direct reactions from users, con...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	true	540,393
2105.13792	Early Exiting with Ensemble Internal Classifiers	As a simple technique to accelerate inference of large-scale pre-trained models, early exiting has gained much attention in the NLP community. It allows samples to exit early at internal classifiers without passing through the entire model. Most existing work usually trains the internal classifiers independently and em...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	237,406
2312.02087	VideoSwap: Customized Video Subject Swapping with Interactive Semantic Point Correspondence	Current diffusion-based video editing primarily focuses on structure-preserved editing by utilizing various dense correspondences to ensure temporal consistency and motion alignment. However, these approaches are often ineffective when the target edit involves a shape change. To embark on video editing with shape chang...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	412,683
2305.15807	Small Total-Cost Constraints in Contextual Bandits with Knapsacks, with Application to Fairness	We consider contextual bandit problems with knapsacks [CBwK], a problem where at each round, a scalar reward is obtained and vector-valued costs are suffered. The learner aims to maximize the cumulative rewards while ensuring that the cumulative costs are lower than some predetermined cost constraints. We assume that c...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	367,793
2412.15277	PLPP: Prompt Learning with Perplexity Is Self-Distillation for Vision-Language Models	Pre-trained Vision-Language (VL) models such as CLIP have demonstrated their excellent performance across numerous downstream tasks. A recent method, Context Optimization (CoOp), further improves the performance of VL models on downstream tasks by introducing prompt learning. CoOp optimizes a set of learnable vectors, ...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	519,036
1702.02287	Name Disambiguation in Anonymized Graphs using Network Embedding	In real-world, our DNA is unique but many people share names. This phenomenon often causes erroneous aggregation of documents of multiple persons who are namesake of one another. Such mistakes deteriorate the performance of document retrieval, web search, and more seriously, cause improper attribution of credit or blam...	false	false	false	true	false	true	false	false	true	false	false	false	false	false	false	false	false	false	67,957
1811.05788	Learning to Compensate Photovoltaic Power Fluctuations from Images of the Sky by Imitating an Optimal Policy	The energy output of photovoltaic (PV) power plants depends on the environment and thus fluctuates over time. As a result, PV power can cause instability in the power grid, in particular when increasingly used. Limiting the rate of change of the power output is a common way to mitigate these fluctuations, often with th...	false	false	false	false	true	false	true	false	false	false	true	false	false	false	false	false	false	false	113,388
2405.10244	Towards Task-Compatible Compressible Representations	We identify an issue in multi-task learnable compression, in which a representation learned for one task does not positively contribute to the rate-distortion performance of a different task as much as expected, given the estimated amount of information available in it. We interpret this issue using the predictive $\ma...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	454,688
2009.14363	Co-design of Control and Planning for Multi-rotor UAVs with Signal Temporal Logic Specifications	Urban Air Mobility (UAM), or the scenario where multiple manned and Unmanned Aerial Vehicles (UAVs) carry out various tasks over urban airspaces, is a transportation concept of the future that is gaining prominence. UAM missions with complex spatial, temporal and reactive requirements can be succinctly represented usin...	false	false	false	false	false	false	false	true	false	false	true	false	false	false	true	false	false	false	198,002
2104.14335	ELF-VC: Efficient Learned Flexible-Rate Video Coding	While learned video codecs have demonstrated great promise, they have yet to achieve sufficient efficiency for practical deployment. In this work, we propose several novel ideas for learned video compression which allow for improved performance for the low-latency mode (I- and P-frames only) along with a considerable i...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	232,791
1609.05009	Optimal Channel Shortener Design for Reduced-State Soft-Output Viterbi Equalizer in Single-Carrier Systems	We consider optimal channel shortener design for reduced-state soft-output Viterbi equalizer (RS-SOVE) in single-carrier (SC) systems. To use RS-SOVE, three receiver filters need to be designed: a prefilter, a target response and a feedback filter. The collection of these three filters are commonly referred to as the \...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	61,061
2412.18600	ZeroHSI: Zero-Shot 4D Human-Scene Interaction by Video Generation	Human-scene interaction (HSI) generation is crucial for applications in embodied AI, virtual reality, and robotics. While existing methods can synthesize realistic human motions in 3D scenes and generate plausible human-object interactions, they heavily rely on datasets containing paired 3D scene and motion capture dat...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	520,487
1308.6604	A smart local moving algorithm for large-scale modularity-based community detection	We introduce a new algorithm for modularity-based community detection in large networks. The algorithm, which we refer to as a smart local moving algorithm, takes advantage of a well-known local moving heuristic that is also used by other algorithms. Compared with these other algorithms, our proposed algorithm uses the...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	26,727
2407.00141	Towards Secure and Efficient Data Scheduling for Vehicular Social Networks	Efficient data transmission scheduling within vehicular environments poses a significant challenge due to the high mobility of such networks. Contemporary research predominantly centers on crafting cooperative scheduling algorithms tailored for vehicular networks. Notwithstanding, the intricacies of orchestrating sched...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	468,747
2112.03020	Temporal-Spatial Causal Interpretations for Vision-Based Reinforcement Learning	Deep reinforcement learning (RL) agents are becoming increasingly proficient in a range of complex control tasks. However, the agent's behavior is usually difficult to interpret due to the introduction of black-box function, making it difficult to acquire the trust of users. Although there have been some interesting in...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	270,057
2501.04971	Self-Adaptive Ising Machines for Constrained Optimization	Ising machines (IM) are physics-inspired alternatives to von Neumann architectures for solving hard optimization tasks. By mapping binary variables to coupled Ising spins, IMs can naturally solve unconstrained combinatorial optimization problems such as finding maximum cuts in graphs. However, despite their importance ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	true	false	true	523,417
1808.01725	Liquid Pouring Monitoring via Rich Sensory Inputs	Humans have the amazing ability to perform very subtle manipulation task using a closed-loop control system with imprecise mechanics (i.e., our body parts) but rich sensory information (e.g., vision, tactile, etc.). In the closed-loop system, the ability to monitor the state of the task via rich sensory information is ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	104,636
1909.08542	Extremely Weak Supervised Image-to-Image Translation for Semantic Segmentation	Recent advances in generative models and adversarial training have led to a flourishing image-to-image (I2I) translation literature. The current I2I translation approaches require training images from the two domains that are either all paired (supervised) or all unpaired (unsupervised). In practice, obtaining paired t...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	146,001
2103.12809	Multipath-based SLAM using Belief Propagation with Interacting Multiple Dynamic Models	In this paper, we present a Bayesian multipath-based simultaneous localization and mapping (SLAM) algorithm that continuously adapts interacting multiple models (IMM) parameters to describe the mobile agent state dynamics. The time-evolution of the IMM parameters is described by a Markov chain and the parameters are in...	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	226,286
2403.01781	Integrating Efficient Optimal Transport and Functional Maps For Unsupervised Shape Correspondence Learning	In the realm of computer vision and graphics, accurately establishing correspondences between geometric 3D shapes is pivotal for applications like object tracking, registration, texture transfer, and statistical shape analysis. Moving beyond traditional hand-crafted and data-driven feature learning methods, we incorpor...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	434,573
2407.20556	Survey of Design Paradigms for Social Robots	The demand for social robots in fields like healthcare, education, and entertainment increases due to their emotional adaptation features. These robots leverage multimodal communication, incorporating speech, facial expressions, and gestures to enhance user engagement and emotional support. The understanding of design ...	false	false	false	false	false	false	false	true	true	false	false	false	false	true	false	false	false	false	477,201
1808.07302	Hybrid ASP-based Approach to Pattern Mining	Detecting small sets of relevant patterns from a given dataset is a central challenge in data mining. The relevance of a pattern is based on user-provided criteria; typically, all patterns that satisfy certain criteria are considered relevant. Rule-based languages like Answer Set Programming (ASP) seem well-suited for ...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	105,716

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