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
1712.01935	How to Learn a Model Checker	We show how machine-learning techniques, particularly neural networks, offer a very effective and highly efficient solution to the approximate model-checking problem for continuous and hybrid systems, a solution where the general-purpose model checker is replaced by a model-specific classifier trained by sampling model...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	86,205
2007.03196	ASGN: An Active Semi-supervised Graph Neural Network for Molecular Property Prediction	Molecular property prediction (e.g., energy) is an essential problem in chemistry and biology. Unfortunately, many supervised learning methods usually suffer from the problem of scarce labeled molecules in the chemical space, where such property labels are generally obtained by Density Functional Theory (DFT) calculati...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	185,980
1707.07270	MatchZoo: A Toolkit for Deep Text Matching	In recent years, deep neural models have been widely adopted for text matching tasks, such as question answering and information retrieval, showing improved performance as compared with previous methods. In this paper, we introduce the MatchZoo toolkit that aims to facilitate the designing, comparing and sharing of dee...	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	77,588
1909.00704	Learning Real Estate Automated Valuation Models from Heterogeneous Data Sources	Real estate appraisal is a complex and important task, that can be made more precise and faster with the help of automated valuation tools. Usually the value of some property is determined by taking into account both structural and geographical characteristics. However, while geographical information is easily found, o...	false	false	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	143,700
2410.16956	Towards an Assisted Simulation Planning for Co-Simulation of Cyber-Physical Energy Systems	Increasing complexity in the power system and the transformation towards a smart grid lead to the necessity of new tools and methods for the development and testing of new technologies. One testing method is co-simulation, which allows coupling simulation components from different domains to test their interaction. Bec...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	501,262
1302.2168	Optimal Throughput-Outage Trade-off in Wireless One-Hop Caching Networks	We consider a wireless device-to-device (D2D) network where the nodes have cached information from a library of possible files. Inspired by the current trend in the standardization of the D2D mode for 4th generation wireless networks, we restrict to one-hop communication: each node place a request to a file in the libr...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	21,920
2309.15477	A Tutorial on Uniform B-Spline	This document facilitates understanding of core concepts about uniform B-spline and its matrix representation.	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	true	394,978
1903.08693	Using Local Experiences for Global Motion Planning	Sampling-based planners are effective in many real-world applications such as robotics manipulation, navigation, and even protein modeling. However, it is often challenging to generate a collision-free path in environments where key areas are hard to sample. In the absence of any prior information, sampling-based plann...	false	false	false	false	true	false	true	true	false	false	false	false	false	false	false	false	false	false	124,884
1412.8147	Improving Persian Document Classification Using Semantic Relations between Words	With the increase of information, document classification as one of the methods of text mining, plays vital role in many management and organizing information. Document classification is the process of assigning a document to one or more predefined category labels. Document classification includes different parts such ...	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	false	38,896
2409.13000	Introducing the Large Medical Model: State of the art healthcare cost and risk prediction with transformers trained on patient event sequences	With U.S. healthcare spending approaching $5T (NHE Fact Sheet 2024), and 25% of it estimated to be wasteful (Waste in the US the health care system: estimated costs and potential for savings, n.d.), the need to better predict risk and optimal patient care is evermore important. This paper introduces the Large Medical M...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	489,812
2012.00268	Secrecy Performance of Body-Centric Communications over Alternate Rician Shadowed Fading Channels	In this paper, we investigate the physical layer security over the Alternate Rician Shadowed fading channel, which is a novel channel model for body-centric wireless links and land mobile satellite. We derive exact closed-form expressions for the average secrecy capacity (ASC), secrecy outage probability (SOP), and pro...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	209,077
1605.05578	Spectrum Sharing in mmWave Cellular Networks via Cell Association, Coordination, and Beamforming	This paper investigates the extent to which spectrum sharing in mmWave networks with multiple cellular operators is a viable alternative to traditional dedicated spectrum allocation. Specifically, we develop a general mathematical framework by which to characterize the performance gain that can be obtained when spectru...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	56,023
1912.00147	Integrating Graph Contextualized Knowledge into Pre-trained Language Models	Complex node interactions are common in knowledge graphs, and these interactions also contain rich knowledge information. However, traditional methods usually treat a triple as a training unit during the knowledge representation learning (KRL) procedure, neglecting contextualized information of the nodes in knowledge g...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	155,681
2110.13577	Open Rule Induction	Rules have a number of desirable properties. It is easy to understand, infer new knowledge, and communicate with other inference systems. One weakness of the previous rule induction systems is that they only find rules within a knowledge base (KB) and therefore cannot generalize to more open and complex real-world rule...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	263,234
1809.06357	Apple Flower Detection using Deep Convolutional Networks	To optimize fruit production, a portion of the flowers and fruitlets of apple trees must be removed early in the growing season. The proportion to be removed is determined by the bloom intensity, i.e., the number of flowers present in the orchard. Several automated computer vision systems have been proposed to estimate...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	108,038
1412.6726	Decentralized Formation Control with A Quadratic Lyapunov Function	In this paper, we investigate a decentralized formation control algorithm for an undirected formation control model. Unlike other formation control problems where only the shape of a configuration counts, we emphasize here also its Euclidean embedding. By following this decentralized formation control law, the agents w...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	38,704
2408.04981	Early Exit Strategies for Approximate k-NN Search in Dense Retrieval	Learned dense representations are a popular family of techniques for encoding queries and documents using high-dimensional embeddings, which enable retrieval by performing approximate k nearest-neighbors search (A-kNN). A popular technique for making A-kNN search efficient is based on a two-level index, where the embed...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	479,609
2306.15765	A Novel Two Stream Decision Level Fusion of Vision and Inertial Sensors Data for Automatic Multimodal Human Activity Recognition System	This paper presents a novel multimodal human activity recognition system. It uses a two-stream decision level fusion of vision and inertial sensors. In the first stream, raw RGB frames are passed to a part affinity field-based pose estimation network to detect the keypoints of the user. These keypoints are then pre-pro...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	376,143
2106.11858	MEAL: Manifold Embedding-based Active Learning	Image segmentation is a common and challenging task in autonomous driving. Availability of sufficient pixel-level annotations for the training data is a hurdle. Active learning helps learning from small amounts of data by suggesting the most promising samples for labeling. In this work, we propose a new pool-based meth...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	242,533
2402.11163	KG-Agent: An Efficient Autonomous Agent Framework for Complex Reasoning over Knowledge Graph	In this paper, we aim to improve the reasoning ability of large language models (LLMs) over knowledge graphs (KGs) to answer complex questions. Inspired by existing methods that design the interaction strategy between LLMs and KG, we propose an autonomous LLM-based agent framework, called KG-Agent, which enables a smal...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	430,262
2410.03877	A Federated Distributionally Robust Support Vector Machine with Mixture of Wasserstein Balls Ambiguity Set for Distributed Fault Diagnosis	The training of classification models for fault diagnosis tasks using geographically dispersed data is a crucial task for original equipment manufacturers (OEMs) seeking to provide long-term service contracts (LTSCs) to their customers. Due to privacy and bandwidth constraints, such models must be trained in a federate...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	495,020
1401.3538	Full-Duplex Transceiver System Calculations: Analysis of ADC and Linearity Challenges	Despite the intensive recent research on wireless single-channel full-duplex communications, relatively little is known about the transceiver chain nonidealities of full-duplex devices. In this paper, the effect of nonlinear distortion occurring in the transmitter power amplifier (PA) and the receiver chain is analyzed...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	29,907
2405.13972	Infinite-Dimensional Feature Interaction	The past neural network design has largely focused on feature representation space dimension and its capacity scaling (e.g., width, depth), but overlooked the feature interaction space scaling. Recent advancements have shown shifted focus towards element-wise multiplication to facilitate higher-dimensional feature in...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	456,171
2201.09206	A Transformer-Based Feature Segmentation and Region Alignment Method For UAV-View Geo-Localization	Cross-view geo-localization is a task of matching the same geographic image from different views, e.g., unmanned aerial vehicle (UAV) and satellite. The most difficult challenges are the position shift and the uncertainty of distance and scale. Existing methods are mainly aimed at digging for more comprehensive fine-gr...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	276,601
2101.09614	A Methodology for the Development of RL-Based Adaptive Traffic Signal Controllers	This article proposes a methodology for the development of adaptive traffic signal controllers using reinforcement learning. Our methodology addresses the lack of standardization in the literature that renders the comparison of approaches in different works meaningless, due to differences in metrics, environments, and ...	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	216,655
2406.13074	PIPPIN: Generating variable length full events from partons	This paper presents a novel approach for directly generating full events at detector-level from parton-level information, leveraging cutting-edge machine learning techniques. To address the challenge of multiplicity variations between parton and reconstructed object spaces, we employ transformers, score-based models an...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	465,680
2105.01688	Height Estimation of Children under Five Years using Depth Images	Malnutrition is a global health crisis and is the leading cause of death among children under five. Detecting malnutrition requires anthropometric measurements of weight, height, and middle-upper arm circumference. However, measuring them accurately is a challenge, especially in the global south, due to limited resourc...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	233,587
1811.07011	Robust Control of the Sit-to-Stand Movement for a Powered Lower Limb Orthosis	The sit-to-stand movement is a key feature for wide adoption of powered lower limb orthoses for patients with complete paraplegia. In this paper we study the control of the ascending phase of the sit-to-stand movement for a minimally actuated powered lower limb orthosis at the hips. First, we generate a pool of finite ...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	113,637
2203.15725	Physics-/Model-Based and Data-Driven Methods for Low-Dose Computed Tomography: A survey	Since 2016, deep learning (DL) has advanced tomographic imaging with remarkable successes, especially in low-dose computed tomography (LDCT) imaging. Despite being driven by big data, the LDCT denoising and pure end-to-end reconstruction networks often suffer from the black box nature and major issues such as instabili...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	288,513
1404.6150	Compressed Sensing Based Direct Conversion Receiver With Interference Reducing Sampling	This paper describes a direct conversion receiver applying compressed sensing with the objective to relax the analog filtering requirements seen in the traditional architecture. The analog filter is cumbersome in an \gls{IC} design and relaxing its requirements is an advantage in terms of die area, performance and robu...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	32,565
2305.17949	A Learning-based Nonlinear Model Predictive Controller for a Real Go-Kart based on Black-box Dynamics Modeling through Gaussian Processes	Lately, Nonlinear Model Predictive Control (NMPC)has been successfully applied to (semi-) autonomous driving problems and has proven to be a very promising technique. However, accurate control models for real vehicles could require costly and time-demanding specific measurements. To address this problem, the exploitati...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	368,810
2201.12596	MVPTR: Multi-Level Semantic Alignment for Vision-Language Pre-Training via Multi-Stage Learning	Previous vision-language pre-training models mainly construct multi-modal inputs with tokens and objects (pixels) followed by performing cross-modality interaction between them. We argue that the input of only tokens and object features limits high-level semantic alignment like phrase-to-region grounding. Meanwhile, mu...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	true	277,706
2405.09455	Efficient pooling designs and screening performance in group testing for two type defectives	Group testing is utilized in the case when we want to find a few defectives among large amount of items. Testing n items one by one requires n tests, but if the ratio of defectives is small, group testing is an efficient way to reduce the number of tests. Many research have been developed for group testing for a single...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	454,407
2403.13799	Reverse Training to Nurse the Reversal Curse	Large language models (LLMs) have a surprising failure: when trained on "A has a feature B", they do not generalize to "B is a feature of A", which is termed the Reversal Curse. Even when training with trillions of tokens this issue still appears due to Zipf's law - hence even if we train on the entire internet. This w...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	439,782
1701.00561	Robust and Real-time Deep Tracking Via Multi-Scale Domain Adaptation	Visual tracking is a fundamental problem in computer vision. Recently, some deep-learning-based tracking algorithms have been achieving record-breaking performances. However, due to the high complexity of deep learning, most deep trackers suffer from low tracking speed, and thus are impractical in many real-world appli...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	66,285
2010.05625	Post-Training BatchNorm Recalibration	We revisit non-blocking simultaneous multithreading (NB-SMT) introduced previously by Shomron and Weiser (2020). NB-SMT trades accuracy for performance by occasionally "squeezing" more than one thread into a shared multiply-and-accumulate (MAC) unit. However, the method of accommodating more than one thread in a shared...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	200,204
2003.12052	Corella: A Private Multi Server Learning Approach based on Correlated Queries	The emerging applications of machine learning algorithms on mobile devices motivate us to offload the computation tasks of training a model or deploying a trained one to the cloud or at the edge of the network. One of the major challenges in this setup is to guarantee the privacy of the client data. Various methods hav...	false	false	false	false	false	false	true	false	false	true	false	false	true	false	false	false	false	false	169,799
cs/0205028	NLTK: The Natural Language Toolkit	NLTK, the Natural Language Toolkit, is a suite of open source program modules, tutorials and problem sets, providing ready-to-use computational linguistics courseware. NLTK covers symbolic and statistical natural language processing, and is interfaced to annotated corpora. Students augment and replace existing componen...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	537,579
1803.11097	Learning Deep Models for Face Anti-Spoofing: Binary or Auxiliary Supervision	Face anti-spoofing is the crucial step to prevent face recognition systems from a security breach. Previous deep learning approaches formulate face anti-spoofing as a binary classification problem. Many of them struggle to grasp adequate spoofing cues and generalize poorly. In this paper, we argue the importance of aux...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	93,818
1704.02249	Learned Watershed: End-to-End Learning of Seeded Segmentation	Learned boundary maps are known to outperform hand- crafted ones as a basis for the watershed algorithm. We show, for the first time, how to train watershed computation jointly with boundary map prediction. The estimator for the merging priorities is cast as a neural network that is con- volutional (over space) and rec...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	71,414
2207.01613	Doubly-Asynchronous Value Iteration: Making Value Iteration Asynchronous in Actions	Value iteration (VI) is a foundational dynamic programming method, important for learning and planning in optimal control and reinforcement learning. VI proceeds in batches, where the update to the value of each state must be completed before the next batch of updates can begin. Completing a single batch is prohibitive...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	306,242
1607.02537	Multi-level Contextual RNNs with Attention Model for Scene Labeling	Context in image is crucial for scene labeling while existing methods only exploit local context generated from a small surrounding area of an image patch or a pixel, by contrast long-range and global contextual information is ignored. To handle this issue, we in this work propose a novel approach for scene labeling by...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	58,365
1905.10118	On the Performance Analysis of Binary Hypothesis Testing with Byzantine Sensors	We investigate the impact of Byzantine attacks in distributed detection under binary hypothesis testing. It is assumed that a fraction of the transmitted sensor measurements are compromised by the injected data from a Byzantine attacker, whose purpose is to confuse the decision maker at the fusion center. From the pers...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	131,967
2005.02298	Reducing Uncertainty by Fusing Dynamic Occupancy Grid Maps in a Cloud-based Collective Environment Model	Accurate environment perception is essential for automated vehicles. Since occlusions and inaccuracies regularly occur, the exchange and combination of perception data of multiple vehicles seems promising. This paper describes a method to combine perception data of automated and connected vehicles in the form of eviden...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	175,835
2005.10893	Evaluating Neural Morphological Taggers for Sanskrit	Neural sequence labelling approaches have achieved state of the art results in morphological tagging. We evaluate the efficacy of four standard sequence labelling models on Sanskrit, a morphologically rich, fusional Indian language. As its label space can theoretically contain more than 40,000 labels, systems that expl...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	178,311
2402.14762	MT-Bench-101: A Fine-Grained Benchmark for Evaluating Large Language Models in Multi-Turn Dialogues	The advent of Large Language Models (LLMs) has drastically enhanced dialogue systems. However, comprehensively evaluating the dialogue abilities of LLMs remains a challenge. Previous benchmarks have primarily focused on single-turn dialogues or provided coarse-grained and incomplete assessments of multi-turn dialogues,...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	431,825
2410.15597	A Comprehensive Comparative Study of Individual ML Models and Ensemble Strategies for Network Intrusion Detection Systems	The escalating frequency of intrusions in networked systems has spurred the exploration of new research avenues in devising artificial intelligence (AI) techniques for intrusion detection systems (IDS). Various AI techniques have been used to automate network intrusion detection tasks, yet each model possesses distinct...	false	false	false	false	true	false	true	false	false	false	false	false	true	false	false	false	false	false	500,625
2402.08983	SafeDecoding: Defending against Jailbreak Attacks via Safety-Aware Decoding	As large language models (LLMs) become increasingly integrated into real-world applications such as code generation and chatbot assistance, extensive efforts have been made to align LLM behavior with human values, including safety. Jailbreak attacks, aiming to provoke unintended and unsafe behaviors from LLMs, remain a...	false	false	false	false	true	false	false	false	true	false	false	false	true	false	false	false	false	false	429,321
2212.02762	Automated Identification of Eviction Status from Electronic Health Record Notes	Objective: Evictions are important social and behavioral determinants of health. Evictions are associated with a cascade of negative events that can lead to unemployment, housing insecurity/homelessness, long-term poverty, and mental health problems. In this study, we developed a natural language processing system to a...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	334,878
2203.04502	Learning Invariant Stabilizing Controllers for Frequency Regulation under Variable Inertia	Declines in cost and concerns about the environmental impact of traditional generation have boosted the penetration of renewables and non-conventional distributed energy resources into the power grid. The intermittent availability of these resources causes the inertia of the power system to vary over time. As a result,...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	284,493
2410.05700	Log-concave Sampling from a Convex Body with a Barrier: a Robust and Unified Dikin Walk	We consider the problem of sampling from a $d$-dimensional log-concave distribution $\pi(\theta) \propto \exp(-f(\theta))$ for $L$-Lipschitz $f$, constrained to a convex body with an efficiently computable self-concordant barrier function, contained in a ball of radius $R$ with a $w$-warm start. We propose a \emph{ro...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	495,868
2403.04871	ACORN: Performant and Predicate-Agnostic Search Over Vector Embeddings and Structured Data	Applications increasingly leverage mixed-modality data, and must jointly search over vector data, such as embedded images, text and video, as well as structured data, such as attributes and keywords. Proposed methods for this hybrid search setting either suffer from poor performance or support a severely restricted set...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	false	435,768
2305.14332	Evaluating and Modeling Attribution for Cross-Lingual Question Answering	Trustworthy answer content is abundant in many high-resource languages and is instantly accessible through question answering systems, yet this content can be hard to access for those that do not speak these languages. The leap forward in cross-lingual modeling quality offered by generative language models offers much ...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	366,977
1405.7471	Effect of Different Distance Measures on the Performance of K-Means Algorithm: An Experimental Study in Matlab	K-means algorithm is a very popular clustering algorithm which is famous for its simplicity. Distance measure plays a very important rule on the performance of this algorithm. We have different distance measure techniques available. But choosing a proper technique for distance calculation is totally dependent on the ty...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	33,469
1905.03175	A Hardware-Oriented and Memory-Efficient Method for CTC Decoding	The Connectionist Temporal Classification (CTC) has achieved great success in sequence to sequence analysis tasks such as automatic speech recognition (ASR) and scene text recognition (STR). These applications can use the CTC objective function to train the recurrent neural networks (RNNs), and decode the outputs of RN...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	130,142
1509.08967	Very Deep Multilingual Convolutional Neural Networks for LVCSR	Convolutional neural networks (CNNs) are a standard component of many current state-of-the-art Large Vocabulary Continuous Speech Recognition (LVCSR) systems. However, CNNs in LVCSR have not kept pace with recent advances in other domains where deeper neural networks provide superior performance. In this paper we propo...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	true	false	false	47,428
1811.05625	Model-guided Multi-path Knowledge Aggregation for Aerial Saliency Prediction	As an emerging vision platform, a drone can look from many abnormal viewpoints which brings many new challenges into the classic vision task of video saliency prediction. To investigate these challenges, this paper proposes a large-scale video dataset for aerial saliency prediction, which consists of ground-truth salie...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	113,355
2007.14479	Toward Agile Maneuvers in Highly Constrained Spaces: Learning from Hallucination	While classical approaches to autonomous robot navigation currently enable operation in certain environments, they break down in tightly constrained spaces, e.g., where the robot needs to engage in agile maneuvers to squeeze between obstacles. Recent machine learning techniques have the potential to address this shortc...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	189,409
1906.07008	Hallucinated Adversarial Learning for Robust Visual Tracking	Humans can easily learn new concepts from just a single exemplar, mainly due to their remarkable ability to imagine or hallucinate what the unseen exemplar may look like in different settings. Incorporating such an ability to hallucinate diverse new samples of the tracked instance can help the trackers alleviate the ov...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	135,488
2304.13530	Key-value information extraction from full handwritten pages	We propose a Transformer-based approach for information extraction from digitized handwritten documents. Our approach combines, in a single model, the different steps that were so far performed by separate models: feature extraction, handwriting recognition and named entity recognition. We compare this integrated appro...	false	false	false	false	true	true	false	false	false	false	false	true	false	false	false	false	false	false	360,612
1809.06417	Radiative Transport Based Flame Volume Reconstruction from Videos	We introduce a novel approach for flame volume reconstruction from videos using inexpensive charge-coupled device (CCD) consumer cameras. The approach includes an economical data capture technique using inexpensive CCD cameras. Leveraging the smear feature of the CCD chip, we present a technique for synchronizing CCD c...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	108,053
1512.04476	Social Media Image Analysis for Public Health	Several projects have shown the feasibility to use textual social media data to track public health concerns, such as temporal influenza patterns or geographical obesity patterns. In this paper, we look at whether geo-tagged images from Instagram also provide a viable data source. Especially for "lifestyle" diseases, s...	false	false	false	true	false	false	false	false	false	false	false	false	false	true	false	false	false	false	50,139
2008.07511	Cybersecurity of Electric Vehicle Smart Charging Management Systems	In concept, a smart charging management system (SCMS) optimizes the charging of plug-in vehicles (PEVs) and provides various grid services including voltage control, frequency regulation, peak shaving, renewable energy integration support, spinning reserve, and emergency demand response. These functionalities largely d...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	192,131
2402.16285	A Comparison of Deep Learning Models for Proton Background Rejection with the AMS Electromagnetic Calorimeter	The Alpha Magnetic Spectrometer (AMS) is a high-precision particle detector onboard the International Space Station containing six different subdetectors. The Transition Radiation Detector and Electromagnetic Calorimeter (ECAL) are used to separate electrons/positrons from the abundant cosmic-ray proton background. T...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	432,495
2009.13112	Learning to Stop: A Simple yet Effective Approach to Urban Vision-Language Navigation	Vision-and-Language Navigation (VLN) is a natural language grounding task where an agent learns to follow language instructions and navigate to specified destinations in real-world environments. A key challenge is to recognize and stop at the correct location, especially for complicated outdoor environments. Existing m...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	197,643
2206.07161	GraphFM: Improving Large-Scale GNN Training via Feature Momentum	Training of graph neural networks (GNNs) for large-scale node classification is challenging. A key difficulty lies in obtaining accurate hidden node representations while avoiding the neighborhood explosion problem. Here, we propose a new technique, named feature momentum (FM), that uses a momentum step to incorporate ...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	302,620
2010.00330	Workflow Provenance in the Lifecycle of Scientific Machine Learning	Machine Learning (ML) has already fundamentally changed several businesses. More recently, it has also been profoundly impacting the computational science and engineering domains, like geoscience, climate science, and health science. In these domains, users need to perform comprehensive data analyses combining scientif...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	true	true	198,252
2409.09756	MesonGS: Post-training Compression of 3D Gaussians via Efficient Attribute Transformation	3D Gaussian Splatting demonstrates excellent quality and speed in novel view synthesis. Nevertheless, the huge file size of the 3D Gaussians presents challenges for transmission and storage. Current works design compact models to replace the substantial volume and attributes of 3D Gaussians, along with intensive traini...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	488,459
1512.08084	Time delay estimator for predetermined repeated signal robust to narrowband interference	In this paper, time delay estimation techniques robust to narrowband interference (NBI) are proposed. Owing to the deluge of wireless signal interference these days, narrowband interference is a common problem for communication and positioning systems. To mitigate the effect of this narrow band interference, we propose...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	50,488
2002.08601	A Hierarchical Framework for Ambient Signals based Load Modeling with Exploring the Hidden Quasi-convexity	Load modeling is an important issue in modeling a power system. The approach of ambient signals-based load modeling (ASLM) was recently proposed to better track the time-varying changes of load models. To improve computation efficiency and model structure complexity, a hierarchical framework for ASLM is proposed in thi...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	164,811
2307.09701	Efficiency Pentathlon: A Standardized Arena for Efficiency Evaluation	Rising computational demands of modern natural language processing (NLP) systems have increased the barrier to entry for cutting-edge research while posing serious environmental concerns. Yet, progress on model efficiency has been impeded by practical challenges in model evaluation and comparison. For example, hardware...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	380,255
1202.1081	Some Comments on the Strong Simplex Conjecture	In the disproof of the Strong Simplex Conjecture presented in [Steiner, 1994], a counterexample signal set was found that has higher average probability of correct optimal decoding than the corresponding regular simplex signal set, when compared at small values of the signal-to-noise ratio. The latter was defined as th...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	14,161
1911.12178	The Nonstochastic Control Problem	We consider the problem of controlling an unknown linear dynamical system in the presence of (nonstochastic) adversarial perturbations and adversarial convex loss functions. In contrast to classical control, the a priori determination of an optimal controller here is hindered by the latter's dependence on the yet unkno...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	155,332
1810.10132	Smoothed Online Optimization for Regression and Control	We consider Online Convex Optimization (OCO) in the setting where the costs are $m$-strongly convex and the online learner pays a switching cost for changing decisions between rounds. We show that the recently proposed Online Balanced Descent (OBD) algorithm is constant competitive in this setting, with competitive rat...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	111,207
1412.7119	Pragmatic Neural Language Modelling in Machine Translation	This paper presents an in-depth investigation on integrating neural language models in translation systems. Scaling neural language models is a difficult task, but crucial for real-world applications. This paper evaluates the impact on end-to-end MT quality of both new and existing scaling techniques. We show when expl...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	38,767
2205.03185	On boundary conditions parametrized by analytic functions	Computer algebra can answer various questions about partial differential equations using symbolic algorithms. However, the inclusion of data into equations is rare in computer algebra. Therefore, recently, computer algebra models have been combined with Gaussian processes, a regression model in machine learning, to des...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	295,195
2411.19056	Stochastic models for online optimization	In this paper, we propose control-theoretic methods as tools for the design of online optimization algorithms that are able to address dynamic, noisy, and partially uncertain time-varying quadratic objective functions. Our approach introduces two algorithms specifically tailored for scenarios where the cost function fo...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	512,103
2112.04369	Adaptive R-Peak Detection on Wearable ECG Sensors for High-Intensity Exercise	Objective: Continuous monitoring of biosignals via wearable sensors has quickly expanded in the medical and wellness fields. At rest, automatic detection of vital parameters is generally accurate. However, in conditions such as high-intensity exercise, sudden physiological changes occur to the signals, compromising the...	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	270,505
2210.15147	A Curriculum Learning Approach for Multi-domain Text Classification Using Keyword weight Ranking	Text classification is a very classic NLP task, but it has two prominent shortcomings: On the one hand, text classification is deeply domain-dependent. That is, a classifier trained on the corpus of one domain may not perform so well in another domain. On the other hand, text classification models require a lot of anno...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	326,828
2102.09094	Quiz-Style Question Generation for News Stories	A large majority of American adults get at least some of their news from the Internet. Even though many online news products have the goal of informing their users about the news, they lack scalable and reliable tools for measuring how well they are achieving this goal, and therefore have to resort to noisy proxy metri...	false	false	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	220,662
2203.09739	Do Deep Networks Transfer Invariances Across Classes?	To generalize well, classifiers must learn to be invariant to nuisance transformations that do not alter an input's class. Many problems have "class-agnostic" nuisance transformations that apply similarly to all classes, such as lighting and background changes for image classification. Neural networks can learn these i...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	286,270
2204.00488	GrADyS-GS -- A ground station for managing field experiments with Autonomous Vehicles and Wireless Sensor Networks	In many kinds of research, collecting data is tailored to individual research. It is usual to use dedicated and not reusable software to collect data. GrADyS Ground Station framework (GrADyS-GS) aims to collect data in a reusable manner with dynamic background tools. This technical report describes GrADyS-GS, a ground ...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	true	289,275
2202.06226	Feature Construction and Selection for PV Solar Power Modeling	Using solar power in the process industry can reduce greenhouse gas emissions and make the production process more sustainable. However, the intermittent nature of solar power renders its usage challenging. Building a model to predict photovoltaic (PV) power generation allows decision-makers to hedge energy shortages a...	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	280,140
2301.06698	Tactile Tool Manipulation	Humans can effortlessly perform very complex, dexterous manipulation tasks by reacting to sensor observations. In contrast, robots can not perform reactive manipulation and they mostly operate in open-loop while interacting with their environment. Consequently, the current manipulation algorithms either are inefficient...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	340,717
1604.04493	Enforcing uniqueness in one-dimensional phase retrieval by additional signal information in time domain	Considering the ambiguousness of the discrete-time phase retrieval problem to recover a signal from its Fourier intensities, one can ask the question: what additional information about the unknown signal do we need to select the correct solution within the large solution set? Based on a characterization of the occurrin...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	54,655
2103.00378	Towards Efficient Local Causal Structure Learning	Local causal structure learning aims to discover and distinguish direct causes (parents) and direct effects (children) of a variable of interest from data. While emerging successes have been made, existing methods need to search a large space to distinguish direct causes from direct effects of a target variable T. To t...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	222,253
2409.00993	Evolution of Social Norms in LLM Agents using Natural Language	Recent advancements in Large Language Models (LLMs) have spurred a surge of interest in leveraging these models for game-theoretical simulations, where LLMs act as individual agents engaging in social interactions. This study explores the potential for LLM agents to spontaneously generate and adhere to normative strate...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	485,166
2009.12547	Causal Intervention for Weakly-Supervised Semantic Segmentation	We present a causal inference framework to improve Weakly-Supervised Semantic Segmentation (WSSS). Specifically, we aim to generate better pixel-level pseudo-masks by using only image-level labels -- the most crucial step in WSSS. We attribute the cause of the ambiguous boundaries of pseudo-masks to the confounding con...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	197,459
2104.11861	Class-Incremental Experience Replay for Continual Learning under Concept Drift	Modern machine learning systems need to be able to cope with constantly arriving and changing data. Two main areas of research dealing with such scenarios are continual learning and data stream mining. Continual learning focuses on accumulating knowledge and avoiding forgetting, assuming information once learned should...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	232,037
2406.14508	Evidence of a log scaling law for political persuasion with large language models	Large language models can now generate political messages as persuasive as those written by humans, raising concerns about how far this persuasiveness may continue to increase with model size. Here, we generate 720 persuasive messages on 10 U.S. political issues from 24 language models spanning several orders of magnit...	true	false	false	false	true	false	false	false	true	false	false	false	false	true	false	false	false	false	466,344
2109.15207	Language-Aligned Waypoint (LAW) Supervision for Vision-and-Language Navigation in Continuous Environments	In the Vision-and-Language Navigation (VLN) task an embodied agent navigates a 3D environment, following natural language instructions. A challenge in this task is how to handle 'off the path' scenarios where an agent veers from a reference path. Prior work supervises the agent with actions based on the shortest path f...	false	false	false	false	false	false	false	true	true	false	false	true	false	false	false	false	false	false	258,218
2212.00325	HashVFL: Defending Against Data Reconstruction Attacks in Vertical Federated Learning	Vertical Federated Learning (VFL) is a trending collaborative machine learning model training solution. Existing industrial frameworks employ secure multi-party computation techniques such as homomorphic encryption to ensure data security and privacy. Despite these efforts, studies have revealed that data leakage remai...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	334,019
2407.09781	Dense Multimodal Alignment for Open-Vocabulary 3D Scene Understanding	Recent vision-language pre-training models have exhibited remarkable generalization ability in zero-shot recognition tasks. Previous open-vocabulary 3D scene understanding methods mostly focus on training 3D models using either image or text supervision while neglecting the collective strength of all modalities. In thi...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	472,714
2107.00090	Mesh-based graph convolutional neural networks for modeling materials with microstructure	Predicting the evolution of a representative sample of a material with microstructure is a fundamental problem in homogenization. In this work we propose a graph convolutional neural network that utilizes the discretized representation of the initial microstructure directly, without segmentation or clustering. Compared...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	244,033
2305.04750	Sense, Imagine, Act: Multimodal Perception Improves Model-Based Reinforcement Learning for Head-to-Head Autonomous Racing	Model-based reinforcement learning (MBRL) techniques have recently yielded promising results for real-world autonomous racing using high-dimensional observations. MBRL agents, such as Dreamer, solve long-horizon tasks by building a world model and planning actions by latent imagination. This approach involves explicitl...	false	false	false	false	true	false	true	true	false	false	false	false	false	false	false	false	false	false	362,891
2310.17799	Probabilistic Multi-product Trading in Sequential Intraday and Frequency-Regulation Markets	With the increasing integration of power plants into the frequency-regulation markets, the importance of optimal trading has grown substantially. This paper conducts an in-depth analysis of their optimal trading behavior in sequential day-ahead, intraday, and frequency-regulation markets. We introduce a probabilistic m...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	403,294
2311.14271	Segmentation-Based Parametric Painting	We introduce a novel image-to-painting method that facilitates the creation of large-scale, high-fidelity paintings with human-like quality and stylistic variation. To process large images and gain control over the painting process, we introduce a segmentation-based painting process and a dynamic attention map approach...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	410,048
2001.05061	Non-Intrusive Parametric Model Order Reduction With Error Correction Modeling for Changing Well Locations Using a Machine Learning Framework	The objective of this paper is to develop a global non-intrusive Parametric Model Order Reduction (PMOR) methodology for the problem of changing well locations in an oil field, that can eventually be used for well placement optimization to gain significant computational savings. In this work, we propose a proper orthog...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	160,429
1902.01286	Real-Time Steganalysis for Stream Media Based on Multi-channel Convolutional Sliding Windows	Previous VoIP steganalysis methods face great challenges in detecting speech signals at low embedding rates, and they are also generally difficult to perform real-time detection, making them hard to truly maintain cyberspace security. To solve these two challenges, in this paper, combined with the sliding window detect...	false	false	false	false	true	false	false	false	false	false	false	false	true	false	false	false	false	true	120,621
2402.07926	From Data Creator to Data Reuser: Distance Matters	Sharing research data is necessary, but not sufficient, for data reuse. Open science policies focus more heavily on data sharing than on reuse, yet both are complex, labor-intensive, expensive, and require infrastructure investments by multiple stakeholders. The value of data reuse lies in relationships between creator...	true	false	false	false	false	true	false	false	false	false	false	false	false	true	false	false	false	true	428,900

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