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
2303.10951	Tracker Meets Night: A Transformer Enhancer for UAV Tracking	Most previous progress in object tracking is realized in daytime scenes with favorable illumination. State-of-the-arts can hardly carry on their superiority at night so far, thereby considerably blocking the broadening of visual tracking-related unmanned aerial vehicle (UAV) applications. To realize reliable UAV tracki...	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	352,646
1909.03723	Machine learning for automatic construction of pseudo-realistic pediatric abdominal phantoms	Machine Learning (ML) is proving extremely beneficial in many healthcare applications. In pediatric oncology, retrospective studies that investigate the relationship between treatment and late adverse effects still rely on simple heuristics. To assess the effects of radiation therapy, treatment plans are typically simu...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	144,582
1601.04621	Probabilistic Inference of Twitter Users' Age based on What They Follow	Twitter provides an open and rich source of data for studying human behaviour at scale and is widely used in social and network sciences. However, a major criticism of Twitter data is that demographic information is largely absent. Enhancing Twitter data with user ages would advance our ability to study social network ...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	51,044
1710.10727	Exact Topology and Parameter Estimation in Distribution Grids with Minimal Observability	Limited presence of nodal and line meters in distribution grids hinders their optimal operation and participation in real-time markets. In particular lack of real-time information on the grid topology and infrequently calibrated line parameters (impedances) adversely affect the accuracy of any operational power flow co...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	83,452
1410.3080	Tree-Structure Bayesian Compressive Sensing for Video	A Bayesian compressive sensing framework is developed for video reconstruction based on the color coded aperture compressive temporal imaging (CACTI) system. By exploiting the three dimension (3D) tree structure of the wavelet and Discrete Cosine Transformation (DCT) coefficients, a Bayesian compressive sensing inversi...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	36,680
1810.11518	An Acceleration Scheme to The Local Directional Pattern	This study seeks to improve the running time of the Local Directional Pattern (LDP) during feature extraction using a newly proposed acceleration scheme to LDP. LDP is considered to be computationally expensive. To confirm this, the running time of the LDP to gray level co-occurrence matrix (GLCM) were it was establish...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	111,513
2009.02126	Evaluating the Impact of COVID-19 on Cyberbullying through Bayesian Trend Analysis	COVID-19's impact has surpassed from personal and global health to our social life. In terms of digital presence, it is speculated that during pandemic, there has been a significant rise in cyberbullying. In this paper, we have examined the hypothesis of whether cyberbullying and reporting of such incidents have increa...	false	false	false	true	false	false	false	false	false	false	false	false	false	true	false	false	false	false	194,473
1903.05174	Richness of Deep Echo State Network Dynamics	Reservoir Computing (RC) is a popular methodology for the efficient design of Recurrent Neural Networks (RNNs). Recently, the advantages of the RC approach have been extended to the context of multi-layered RNNs, with the introduction of the Deep Echo State Network (DeepESN) model. In this paper, we study the quality o...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	true	false	false	124,109
1707.09030	A Locally Adapting Technique for Boundary Detection using Image Segmentation	Rapid growth in the field of quantitative digital image analysis is paving the way for researchers to make precise measurements about objects in an image. To compute quantities from the image such as the density of compressed materials or the velocity of a shockwave, we must determine object boundaries. Images containi...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	77,932
2206.04742	Accelerating Asynchronous Federated Learning Convergence via Opportunistic Mobile Relaying	This paper presents a study on asynchronous Federated Learning (FL) in a mobile network setting. The majority of FL algorithms assume that communication between clients and the server is always available, however, this is not the case in many real-world systems. To address this issue, the paper explores the impact of m...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	301,749
2402.03368	Empirical and Experimental Perspectives on Big Data in Recommendation Systems: A Comprehensive Survey	This survey paper provides a comprehensive analysis of big data algorithms in recommendation systems, addressing the lack of depth and precision in existing literature. It proposes a two-pronged approach: a thorough analysis of current algorithms and a novel, hierarchical taxonomy for precise categorization. The taxono...	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	false	false	426,986
2102.09003	Domain Impression: A Source Data Free Domain Adaptation Method	Unsupervised Domain adaptation methods solve the adaptation problem for an unlabeled target set, assuming that the source dataset is available with all labels. However, the availability of actual source samples is not always possible in practical cases. It could be due to memory constraints, privacy concerns, and chall...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	220,638
1408.0549	Downlink Cellular Network Analysis with Multi-slope Path Loss Models	Existing cellular network analyses, and even simulations, typically use the standard path loss model where received power decays like $\\|x\\|^{-\alpha}$ over a distance $\\|x\\|$. This standard path loss model is quite idealized, and in most scenarios the path loss exponent $\alpha$ is itself a function of $\\|x\\|$, typica...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	35,094
2312.06445	Towards a Unified Naming Scheme for Thermo-Active Soft Actuators: A Review of Materials, Working Principles, and Applications	Soft robotics is a rapidly growing field that spans the fields of chemistry, materials science, and engineering. Due to the diverse background of the field, there have been contrasting naming schemes such as 'intelligent', 'smart' and 'adaptive' materials which add vagueness to the broad innovation among literature. Th...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	414,521
2308.12968	Scenimefy: Learning to Craft Anime Scene via Semi-Supervised Image-to-Image Translation	Automatic high-quality rendering of anime scenes from complex real-world images is of significant practical value. The challenges of this task lie in the complexity of the scenes, the unique features of anime style, and the lack of high-quality datasets to bridge the domain gap. Despite promising attempts, previous eff...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	387,740
2112.13939	SPIDER: Searching Personalized Neural Architecture for Federated Learning	Federated learning (FL) is an efficient learning framework that assists distributed machine learning when data cannot be shared with a centralized server due to privacy and regulatory restrictions. Recent advancements in FL use predefined architecture-based learning for all the clients. However, given that clients' dat...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	273,399
2405.12750	Generative AI in Cybersecurity: A Comprehensive Review of LLM Applications and Vulnerabilities	This paper provides a comprehensive review of the future of cybersecurity through Generative AI and Large Language Models (LLMs). We explore LLM applications across various domains, including hardware design security, intrusion detection, software engineering, design verification, cyber threat intelligence, malware det...	false	false	false	false	true	false	false	false	false	false	false	false	true	false	false	false	false	false	455,639
2301.02499	Evaluating counterfactual explanations using Pearl's counterfactual method	Counterfactual explanations (CEs) are methods for generating an alternative scenario that produces a different desirable outcome. For example, if a student is predicted to fail a course, then counterfactual explanations can provide the student with alternate ways so that they would be predicted to pass. The application...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	339,528
2201.06164	Synthesis and Reconstruction of Fingerprints using Generative Adversarial Networks	Deep learning-based models have been shown to improve the accuracy of fingerprint recognition. While these algorithms show exceptional performance, they require large-scale fingerprint datasets for training and evaluation. In this work, we propose a novel fingerprint synthesis and reconstruction framework based on the ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	275,631
2412.09816	Distributed Inverse Dynamics Control for Quadruped Robots using Geometric Optimization	This paper presents a distributed inverse dynamics controller (DIDC) for quadruped robots that addresses the limitations of existing reactive controllers: simplified dynamical models, the inability to handle exact friction cone constraints, and the high computational requirements of whole-body controllers. Current meth...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	516,655
1710.00969	Event Identification as a Decision Process with Non-linear Representation of Text	We propose scale-free Identifier Network(sfIN), a novel model for event identification in documents. In general, sfIN first encodes a document into multi-scale memory stacks, then extracts special events via conducting multi-scale actions, which can be considered as a special type of sequence labelling. The design of l...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	81,947
2412.09817	Enhancing Multimodal Large Language Models Complex Reason via Similarity Computation	Multimodal large language models have experienced rapid growth, and numerous different models have emerged. The interpretability of LVLMs remains an under-explored area. Especially when faced with more complex tasks such as chain-of-thought reasoning, its internal mechanisms still resemble a black box that is difficult...	false	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	516,656
2309.12102	SemEval-2022 Task 7: Identifying Plausible Clarifications of Implicit and Underspecified Phrases in Instructional Texts	We describe SemEval-2022 Task 7, a shared task on rating the plausibility of clarifications in instructional texts. The dataset for this task consists of manually clarified how-to guides for which we generated alternative clarifications and collected human plausibility judgements. The task of participating systems was ...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	393,661
2111.04356	A comprehensive assessment of accuracy of adaptive integration of cut cells for laminar fluid-structure interaction problems	Finite element methods based on cut-cells are becoming increasingly popular because of their advantages over formulations based on body-fitted meshes for problems with moving interfaces. In such methods, the cells (or elements) which are cut by the interface between two different domains need to be integrated using spe...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	265,467
2403.05290	Foundational propositions of hesitant fuzzy soft $\beta$-covering approximation spaces	Soft set theory serves as a mathematical framework for handling uncertain information, and hesitant fuzzy sets find extensive application in scenarios involving uncertainty and hesitation. Hesitant fuzzy sets exhibit diverse membership degrees, giving rise to various forms of inclusion relationships among them. This ar...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	435,946
2305.06436	Multi-Robot Coordination and Layout Design for Automated Warehousing	With the rapid progress in Multi-Agent Path Finding (MAPF), researchers have studied how MAPF algorithms can be deployed to coordinate hundreds of robots in large automated warehouses. While most works try to improve the throughput of such warehouses by developing better MAPF algorithms, we focus on improving the throu...	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	true	false	false	363,534
2212.01992	Fast and accurate factorized neural transducer for text adaption of end-to-end speech recognition models	Neural transducer is now the most popular end-to-end model for speech recognition, due to its naturally streaming ability. However, it is challenging to adapt it with text-only data. Factorized neural transducer (FNT) model was proposed to mitigate this problem. The improved adaptation ability of FNT on text-only adapt...	false	false	true	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	334,639
2202.03875	MixCycle: Unsupervised Speech Separation via Cyclic Mixture Permutation Invariant Training	We introduce two unsupervised source separation methods, which involve self-supervised training from single-channel two-source speech mixtures. Our first method, mixture permutation invariant training (MixPIT), enables learning a neural network model which separates the underlying sources via a challenging proxy task w...	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	279,374
2005.05672	Learning and Evaluating Emotion Lexicons for 91 Languages	Emotion lexicons describe the affective meaning of words and thus constitute a centerpiece for advanced sentiment and emotion analysis. Yet, manually curated lexicons are only available for a handful of languages, leaving most languages of the world without such a precious resource for downstream applications. Even wor...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	176,796
2103.06716	Learning Word-Level Confidence For Subword End-to-End ASR	We study the problem of word-level confidence estimation in subword-based end-to-end (E2E) models for automatic speech recognition (ASR). Although prior works have proposed training auxiliary confidence models for ASR systems, they do not extend naturally to systems that operate on word-pieces (WP) as their vocabulary....	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	224,388
2111.12521	Probabilistic Behavioral Distance and Tuning - Reducing and aggregating complex systems	Given a complex system with a given interface to the rest of the world, what does it mean for a the system to behave close to a simpler specification describing the behavior at the interface? We give several definitions for useful notions of distances between a complex system and a specification by combining a behavior...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	267,993
2010.03790	Text-based RL Agents with Commonsense Knowledge: New Challenges, Environments and Baselines	Text-based games have emerged as an important test-bed for Reinforcement Learning (RL) research, requiring RL agents to combine grounded language understanding with sequential decision making. In this paper, we examine the problem of infusing RL agents with commonsense knowledge. Such knowledge would allow agents to ef...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	199,530
2410.23933	Language Models can Self-Lengthen to Generate Long Texts	Recent advancements in Large Language Models (LLMs) have significantly enhanced their ability to process long contexts, yet a notable gap remains in generating long, aligned outputs. This limitation stems from a training gap where pre-training lacks effective instructions for long-text generation, and post-training dat...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	504,254
1807.01622	Neural Processes	A neural network (NN) is a parameterised function that can be tuned via gradient descent to approximate a labelled collection of data with high precision. A Gaussian process (GP), on the other hand, is a probabilistic model that defines a distribution over possible functions, and is updated in light of data via the rul...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	102,099
2202.05049	Fair When Trained, Unfair When Deployed: Observable Fairness Measures are Unstable in Performative Prediction Settings	Many popular algorithmic fairness measures depend on the joint distribution of predictions, outcomes, and a sensitive feature like race or gender. These measures are sensitive to distribution shift: a predictor which is trained to satisfy one of these fairness definitions may become unfair if the distribution changes. ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	279,747
2201.01895	Event-based EV Charging Scheduling in A Microgrid of Buildings	With the popularization of the electric vehicles (EVs), EV charging demand is becoming an important load in the building. Considering the mobility of EVs from building to building and their uncertain charging demand, it is of great practical interest to control the EV charging process in a microgrid of buildings to opt...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	274,391
2002.08267	Multilogue-Net: A Context Aware RNN for Multi-modal Emotion Detection and Sentiment Analysis in Conversation	Sentiment Analysis and Emotion Detection in conversation is key in several real-world applications, with an increase in modalities available aiding a better understanding of the underlying emotions. Multi-modal Emotion Detection and Sentiment Analysis can be particularly useful, as applications will be able to use spec...	false	false	true	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	164,702
1805.07431	Can machine learning identify interesting mathematics? An exploration using empirically observed laws	We explore the possibility of using machine learning to identify interesting mathematical structures by using certain quantities that serve as fingerprints. In particular, we extract features from integer sequences using two empirical laws: Benford's law and Taylor's law and experiment with various classifiers to ident...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	97,812
1803.06916	Simulating the future urban growth in Xiongan New Area: a upcoming big city in China	China made the announement to create the Xiongan New Area in Hebei in April 1,2017. Thus a new magacity about 110km south west of Beijing will emerge. Xiongan New Area is of great practial significant and historical significant for transferring Beijing's non-capital function. Simulating the urban dynamics in Xiongan Ne...	false	false	false	false	true	false	false	false	false	false	false	false	false	true	false	false	false	false	92,930
2309.00399	Fine-grained Recognition with Learnable Semantic Data Augmentation	Fine-grained image recognition is a longstanding computer vision challenge that focuses on differentiating objects belonging to multiple subordinate categories within the same meta-category. Since images belonging to the same meta-category usually share similar visual appearances, mining discriminative visual cues is t...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	389,301
2411.00151	NIMBA: Towards Robust and Principled Processing of Point Clouds With SSMs	Transformers have become dominant in large-scale deep learning tasks across various domains, including text, 2D and 3D vision. However, the quadratic complexity of their attention mechanism limits their efficiency as the sequence length increases, particularly in high-resolution 3D data such as point clouds. Recently, ...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	504,455
1602.04485	Benefits of depth in neural networks	For any positive integer $k$, there exist neural networks with $\Theta(k^3)$ layers, $\Theta(1)$ nodes per layer, and $\Theta(1)$ distinct parameters which can not be approximated by networks with $\mathcal{O}(k)$ layers unless they are exponentially large --- they must possess $\Omega(2^k)$ nodes. This result is prove...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	true	false	false	52,142
2210.09100	Estimating the Cost of Executing Link Traversal based SPARQL Queries	An increasing number of organisations in almost all fields have started adopting semantic web technologies for publishing their data as open, linked and interoperable (RDF) datasets, queryable through the SPARQL language and protocol. Link traversal has emerged as a SPARQL query processing method that exploits the Link...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	324,422
2404.04397	Generating Synthetic Ground Truth Distributions for Multi-step Trajectory Prediction using Probabilistic Composite B\'ezier Curves	An appropriate data basis grants one of the most important aspects for training and evaluating probabilistic trajectory prediction models based on neural networks. In this regard, a common shortcoming of current benchmark datasets is their limitation to sets of sample trajectories and a lack of actual ground truth dist...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	444,629
cs/0603078	Consensus Propagation	We propose consensus propagation, an asynchronous distributed protocol for averaging numbers across a network. We establish convergence, characterize the convergence rate for regular graphs, and demonstrate that the protocol exhibits better scaling properties than pairwise averaging, an alternative that has received mu...	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	false	true	539,340
2004.14607	Can Your Context-Aware MT System Pass the DiP Benchmark Tests? : Evaluation Benchmarks for Discourse Phenomena in Machine Translation	Despite increasing instances of machine translation (MT) systems including contextual information, the evidence for translation quality improvement is sparse, especially for discourse phenomena. Popular metrics like BLEU are not expressive or sensitive enough to capture quality improvements or drops that are minor in s...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	174,953
2403.06202	Pursuit Winning Strategies for Reach-Avoid Games with Polygonal Obstacles	This paper studies a multiplayer reach-avoid differential game in the presence of general polygonal obstacles that block the players' motions. The pursuers cooperate to protect a convex region from the evaders who try to reach the region. We propose a multiplayer onsite and close-to-goal (MOCG) pursuit strategy that ca...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	true	436,346
1905.06455	On Norm-Agnostic Robustness of Adversarial Training	Adversarial examples are carefully perturbed in-puts for fooling machine learning models. A well-acknowledged defense method against such examples is adversarial training, where adversarial examples are injected into training data to increase robustness. In this paper, we propose a new attack to unveil an undesired pro...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	130,992
2007.01941	Multigrid for Bundle Adjustment	Bundle adjustment is an important global optimization step in many structure from motion pipelines. Performance is dependent on the speed of the linear solver used to compute steps towards the optimum. For large problems, the current state of the art scales superlinearly with the number of cameras in the problem. We in...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	185,576
2408.07703	Knowledge Distillation with Refined Logits	Recent research on knowledge distillation has increasingly focused on logit distillation because of its simplicity, effectiveness, and versatility in model compression. In this paper, we introduce Refined Logit Distillation (RLD) to address the limitations of current logit distillation methods. Our approach is motivate...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	480,693
1701.02344	Database Engines: Evolution of Greenness	Context: Information Technology consumes up to 10\% of the world's electricity generation, contributing to CO2 emissions and high energy costs. Data centers, particularly databases, use up to 23% of this energy. Therefore, building an energy-efficient (green) database engine could reduce energy consumption and CO2 emis...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	true	66,535
1907.09997	An Improved Convolutional Neural Network System for Automatically Detecting Rebar in GPR Data	As a mature technology, Ground Penetration Radar (GPR) is now widely employed in detecting rebar and other embedded elements in concrete structures. Manually recognizing rebar from GPR data is a time-consuming and error-prone procedure. Although there are several approaches to automatically detect rebar, it is still ch...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	139,516
1610.01052	A novel and effective scoring scheme for structure classification and pairwise similarity measurement	Protein tertiary structure defines its functions, classification and binding sites. Similar structural characteristics between two proteins often lead to the similar characteristics thereof. Determining structural similarity accurately in real time is a crucial research issue. In this paper, we present a novel and effe...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	61,915
2110.08419	Robustness Challenges in Model Distillation and Pruning for Natural Language Understanding	Recent work has focused on compressing pre-trained language models (PLMs) like BERT where the major focus has been to improve the in-distribution performance for downstream tasks. However, very few of these studies have analyzed the impact of compression on the generalizability and robustness of compressed models for o...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	261,387
1606.04695	Strategic Attentive Writer for Learning Macro-Actions	We present a novel deep recurrent neural network architecture that learns to build implicit plans in an end-to-end manner by purely interacting with an environment in reinforcement learning setting. The network builds an internal plan, which is continuously updated upon observation of the next input from the environmen...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	57,300
2312.08851	Achelous++: Power-Oriented Water-Surface Panoptic Perception Framework on Edge Devices based on Vision-Radar Fusion and Pruning of Heterogeneous Modalities	Urban water-surface robust perception serves as the foundation for intelligent monitoring of aquatic environments and the autonomous navigation and operation of unmanned vessels, especially in the context of waterway safety. It is worth noting that current multi-sensor fusion and multi-task learning models consume subs...	false	true	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	415,474
1303.3965	Bit Level Soft Decision Decoding of Triple Parity Reed Solomon Codes through Automorphism Groups	This paper discusses bit-level soft decoding of triple-parity Reed-Solomon (RS) codes through automorphism permutation. A new method for identifying the automorphism groups of RS binary images is first developed. The new algorithm runs effectively, and can handle more RS codes and capture more automorphism groups than ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	22,970
cs/0511042	Dimensions of Neural-symbolic Integration - A Structured Survey	Research on integrated neural-symbolic systems has made significant progress in the recent past. In particular the understanding of ways to deal with symbolic knowledge within connectionist systems (also called artificial neural networks) has reached a critical mass which enables the community to strive for applicable ...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	false	true	539,074
2203.02846	Region Proposal Rectification Towards Robust Instance Segmentation of Biological Images	Top-down instance segmentation framework has shown its superiority in object detection compared to the bottom-up framework. While it is efficient in addressing over-segmentation, top-down instance segmentation suffers from over-crop problem. However, a complete segmentation mask is crucial for biological image analysis...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	283,884
1304.2388	Joint Iterative Power Adjustment and Interference Suppression Algorithms for Cooperative DS-CDMA Networks	This work presents joint iterative power allocation and interference suppression algorithms for DS-CDMA networks which employ multiple relays and the amplify and forward cooperation strategy. We propose a joint constrained optimization framework that considers the allocation of power levels across the relays subject to...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	23,694
2402.18807	On the Decision-Making Abilities in Role-Playing using Large Language Models	Large language models (LLMs) are now increasingly utilized for role-playing tasks, especially in impersonating domain-specific experts, primarily through role-playing prompts. When interacting in real-world scenarios, the decision-making abilities of a role significantly shape its behavioral patterns. In this paper, we...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	433,568
2011.09113	Effectiveness of Arbitrary Transfer Sets for Data-free Knowledge Distillation	Knowledge Distillation is an effective method to transfer the learning across deep neural networks. Typically, the dataset originally used for training the Teacher model is chosen as the "Transfer Set" to conduct the knowledge transfer to the Student. However, this original training data may not always be freely availa...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	207,096
2407.11484	The Oscars of AI Theater: A Survey on Role-Playing with Language Models	This survey explores the burgeoning field of role-playing with language models, focusing on their development from early persona-based models to advanced character-driven simulations facilitated by Large Language Models (LLMs). Initially confined to simple persona consistency due to limited model capabilities, role-pla...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	473,479
2103.05131	Few-Shot Learning of an Interleaved Text Summarization Model by Pretraining with Synthetic Data	Interleaved texts, where posts belonging to different threads occur in a sequence, commonly occur in online chat posts, so that it can be time-consuming to quickly obtain an overview of the discussions. Existing systems first disentangle the posts by threads and then extract summaries from those threads. A major issue ...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	223,872
1607.01274	Temporal Topic Analysis with Endogenous and Exogenous Processes	We consider the problem of modeling temporal textual data taking endogenous and exogenous processes into account. Such text documents arise in real world applications, including job advertisements and economic news articles, which are influenced by the fluctuations of the general economy. We propose a hierarchical Baye...	false	false	false	false	false	true	true	false	true	false	false	false	false	false	false	false	false	false	58,204
1612.01392	An Extended Treatment of Uncertainty Constrained robotic Exploration: An Integrated Exploration Planner	Efficient robotic exploration of unknown, sensor limited, global-information-deficient environments poses unique challenges to path planning algorithms. In these difficult environments, no deterministic guarantees on path completion and mission success can be made in general. Integrated Exploration (IE), which strives ...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	65,074
2101.09401	Adaptively Sparse Regularization for Blind Image Restoration	Image quality is the basis of image communication and understanding tasks. Due to the blur and noise effects caused by imaging, transmission and other processes, the image quality is degraded. Blind image restoration is widely used to improve image quality, where the main goal is to faithfully estimate the blur kernel ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	216,585
2009.09929	CVPR 2020 Continual Learning in Computer Vision Competition: Approaches, Results, Current Challenges and Future Directions	In the last few years, we have witnessed a renewed and fast-growing interest in continual learning with deep neural networks with the shared objective of making current AI systems more adaptive, efficient and autonomous. However, despite the significant and undoubted progress of the field in addressing the issue of cat...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	196,739
2103.17182	Positive-Negative Momentum: Manipulating Stochastic Gradient Noise to Improve Generalization	It is well-known that stochastic gradient noise (SGN) acts as implicit regularization for deep learning and is essentially important for both optimization and generalization of deep networks. Some works attempted to artificially simulate SGN by injecting random noise to improve deep learning. However, it turned out tha...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	227,810
2211.13844	Ladder Siamese Network: a Method and Insights for Multi-level Self-Supervised Learning	Siamese-network-based self-supervised learning (SSL) suffers from slow convergence and instability in training. To alleviate this, we propose a framework to exploit intermediate self-supervisions in each stage of deep nets, called the Ladder Siamese Network. Our self-supervised losses encourage the intermediate layers ...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	332,618
2203.08182	DSOL: A Fast Direct Sparse Odometry Scheme	In this paper, we describe Direct Sparse Odometry Lite (DSOL), an improved version of Direct Sparse Odometry (DSO). We propose several algorithmic and implementation enhancements which speed up computation by a significant factor (on average 5x) even on resource constrained platforms. The increase in speed allows us to...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	285,701
1510.07932	Downlink Power Control in Two-Tier Cellular Networks with Energy-Harvesting Small Cells as Stochastic Games	Energy harvesting in cellular networks is an emerging technique to enhance the sustainability of power-constrained wireless devices. This paper considers the co-channel deployment of a macrocell overlaid with small cells. The small cell base stations (SBSs) harvest energy from environmental sources whereas the macrocel...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	48,245
2307.02623	FLuID: Mitigating Stragglers in Federated Learning using Invariant Dropout	Federated Learning (FL) allows machine learning models to train locally on individual mobile devices, synchronizing model updates via a shared server. This approach safeguards user privacy; however, it also generates a heterogeneous training environment due to the varying performance capabilities across devices. As a r...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	377,747
2406.03999	Unveiling the Dynamics of Information Interplay in Supervised Learning	In this paper, we use matrix information theory as an analytical tool to analyze the dynamics of the information interplay between data representations and classification head vectors in the supervised learning process. Specifically, inspired by the theory of Neural Collapse, we introduce matrix mutual information rati...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	461,480
1910.02673	Interpretable Disentanglement of Neural Networks by Extracting Class-Specific Subnetwork	We propose a novel perspective to understand deep neural networks in an interpretable disentanglement form. For each semantic class, we extract a class-specific functional subnetwork from the original full model, with compressed structure while maintaining comparable prediction performance. The structure representation...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	148,310
2108.06701	Reference Service Model for Federated Identity Management	With the pandemic of COVID-19, people around the world increasingly work from home. Each natural person typically has several digital identities with different associated information. During the last years, various identity and access management approaches have gained attraction, helping for example to access other org...	false	false	false	false	false	false	false	false	false	false	true	true	true	false	false	false	false	true	250,690
2206.14092	Learning the Solution Operator of Boundary Value Problems using Graph Neural Networks	As an alternative to classical numerical solvers for partial differential equations (PDEs) subject to boundary value constraints, there has been a surge of interest in investigating neural networks that can solve such problems efficiently. In this work, we design a general solution operator for two different time-indep...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	305,174
1905.04638	Kyrix: Interactive Visual Data Exploration at Scale	Scalable interactive visual data exploration is crucial in many domains due to increasingly large datasets generated at rapid rates. Details-on-demand provides a useful interaction paradigm for exploring large datasets, where users start at an overview, find regions of interest, zoom in to see detailed views, zoom out ...	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	130,522
1912.07618	Deep Learning for Cardiologist-level Myocardial Infarction Detection in Electrocardiograms	Myocardial infarction is the leading cause of death worldwide. In this paper, we design domain-inspired neural network models to detect myocardial infarction. First, we study the contribution of various leads. This systematic analysis, first of its kind in the literature, indicates that out of 15 ECG leads, data from t...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	157,642
1911.05426	Mean-Field Transmission Power Control in Dense Networks, Part II -- Social Welfare Evaluation	We consider uplink power control in wireless communication when massive users compete over the channel resources. In Part I, we have formulated massive transmission power control contest in a mean-field game framework. In this part, our goal is to investigate whether the power-domain non-orthogonal multiple access (NOM...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	153,256
2108.08211	MBRS : Enhancing Robustness of DNN-based Watermarking by Mini-Batch of Real and Simulated JPEG Compression	Based on the powerful feature extraction ability of deep learning architecture, recently, deep-learning based watermarking algorithms have been widely studied. The basic framework of such algorithm is the auto-encoder like end-to-end architecture with an encoder, a noise layer and a decoder. The key to guarantee robust...	false	false	false	false	false	false	false	false	false	false	false	true	true	false	false	false	false	false	251,176
2303.15715	Foundation Models and Fair Use	Existing foundation models are trained on copyrighted material. Deploying these models can pose both legal and ethical risks when data creators fail to receive appropriate attribution or compensation. In the United States and several other countries, copyrighted content may be used to build foundation models without in...	false	false	false	false	true	false	true	false	false	false	false	false	false	true	false	false	false	false	354,593
2011.06777	ROLL: Visual Self-Supervised Reinforcement Learning with Object Reasoning	Current image-based reinforcement learning (RL) algorithms typically operate on the whole image without performing object-level reasoning. This leads to inefficient goal sampling and ineffective reward functions. In this paper, we improve upon previous visual self-supervised RL by incorporating object-level reasoning a...	false	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	206,337
2407.10274	Enhancing Weakly-Supervised Histopathology Image Segmentation with Knowledge Distillation on MIL-Based Pseudo-Labels	Segmenting tumors in histological images is vital for cancer diagnosis. While fully supervised models excel with pixel-level annotations, creating such annotations is labor-intensive and costly. Accurate histopathology image segmentation under weakly-supervised conditions with coarse-grained image labels is still a cha...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	472,913
1711.00950	Beyond normality: Learning sparse probabilistic graphical models in the non-Gaussian setting	We present an algorithm to identify sparse dependence structure in continuous and non-Gaussian probability distributions, given a corresponding set of data. The conditional independence structure of an arbitrary distribution can be represented as an undirected graph (or Markov random field), but most algorithms for lea...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	83,804
2204.00976	FedGBF: An efficient vertical federated learning framework via gradient boosting and bagging	Federated learning, conducive to solving data privacy and security problems, has attracted increasing attention recently. However, the existing federated boosting model sequentially builds a decision tree model with the weak base learner, resulting in redundant boosting steps and high interactive communication costs. I...	false	false	false	false	true	false	true	false	false	false	false	false	true	false	false	false	false	true	289,459
1904.03961	Filter Pruning by Switching to Neighboring CNNs with Good Attributes	Filter pruning is effective to reduce the computational costs of neural networks. Existing methods show that updating the previous pruned filter would enable large model capacity and achieve better performance. However, during the iterative pruning process, even if the network weights are updated to new values, the pru...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	126,895
1810.08944	MS-BACO: A new Model Selection algorithm using Binary Ant Colony Optimization for neural complexity and error reduction	Stabilizing the complexity of Feedforward Neural Networks (FNNs) for the given approximation task can be managed by defining an appropriate model magnitude which is also greatly correlated with the generalization quality and computational efficiency. However, deciding on the right level of model complexity can be highl...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	110,949
2105.04807	ORCEA: Object Recognition by Continuous Evidence Assimilation	ORCEA is a novel object recognition method applicable for objects describable by a generative model. The primary goal of ORCEA is to maintain a probability density distribution of possible matches over the object parameter space, while continuously updating it with incoming evidence; detection and regression are by-pro...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	234,625
2102.10063	Probabilistically Guaranteed Satisfaction of Temporal Logic Constraints During Reinforcement Learning	We propose a novel constrained reinforcement learning method for finding optimal policies in Markov Decision Processes while satisfying temporal logic constraints with a desired probability throughout the learning process. An automata-theoretic approach is proposed to ensure the probabilistic satisfaction of the constr...	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	220,967
2011.10577	Deep learning insights into cosmological structure formation	The evolution of linear initial conditions present in the early universe into extended halos of dark matter at late times can be computed using cosmological simulations. However, a theoretical understanding of this complex process remains elusive; in particular, the role of anisotropic information in the initial condit...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	207,547
2306.04949	Robust Learning with Progressive Data Expansion Against Spurious Correlation	While deep learning models have shown remarkable performance in various tasks, they are susceptible to learning non-generalizable spurious features rather than the core features that are genuinely correlated to the true label. In this paper, beyond existing analyses of linear models, we theoretically examine the learni...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	371,983
2407.20708	Integer-Valued Training and Spike-Driven Inference Spiking Neural Network for High-performance and Energy-efficient Object Detection	Brain-inspired Spiking Neural Networks (SNNs) have bio-plausibility and low-power advantages over Artificial Neural Networks (ANNs). Applications of SNNs are currently limited to simple classification tasks because of their poor performance. In this work, we focus on bridging the performance gap between ANNs and SNNs o...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	477,258
2412.15998	CNN-LSTM Hybrid Deep Learning Model for Remaining Useful Life Estimation	Remaining Useful Life (RUL) of a component or a system is defined as the length from the current time to the end of the useful life. Accurate RUL estimation plays a crucial role in Predictive Maintenance applications. Traditional regression methods, both linear and non-linear, have struggled to achieve high accuracy in...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	519,333
2408.13723	EMG-Based Hand Gesture Recognition through Diverse Domain Feature Enhancement and Machine Learning-Based Approach	Surface electromyography (EMG) serves as a pivotal tool in hand gesture recognition and human-computer interaction, offering a non-invasive means of signal acquisition. This study presents a novel methodology for classifying hand gestures using EMG signals. To address the challenges associated with feature extraction w...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	483,263
2207.10892	Bi-directional Contrastive Learning for Domain Adaptive Semantic Segmentation	We present a novel unsupervised domain adaptation method for semantic segmentation that generalizes a model trained with source images and corresponding ground-truth labels to a target domain. A key to domain adaptive semantic segmentation is to learn domain-invariant and discriminative features without target ground-t...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	309,431
2308.10641	Statistical Analysis of Geometric Algorithms in Vehicular Visible Light Positioning	Vehicular visible light positioning (VLP) methods find relative locations of vehicles by estimating the positions of intensity-modulated head/tail lights of one vehicle (target) with respect to another (ego). Estimation is done in two steps: 1) relative bearing or range of the transmitter-receiver link is measured over...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	386,822
2001.09842	Electric Field Propagation Through Singular Value Decomposition	We demonstrate that the singular value decomposition algorithm in conjunction with the fast Fourier transform or finite difference procedures provides a straightforward and accurate method for rapidly propagating electric fields in the one-way Helmholtz formalism.	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	161,680
2310.07630	Differentiable Euler Characteristic Transforms for Shape Classification	The Euler Characteristic Transform (ECT) has proven to be a powerful representation, combining geometrical and topological characteristics of shapes and graphs. However, the ECT was hitherto unable to learn task-specific representations. We overcome this issue and develop a novel computational layer that enables learni...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	399,046
2308.10320	Hyper Association Graph Matching with Uncertainty Quantification for Coronary Artery Semantic Labeling	Coronary artery disease (CAD) is one of the primary causes leading to death worldwide. Accurate extraction of individual arterial branches on invasive coronary angiograms (ICA) is important for stenosis detection and CAD diagnosis. However, deep learning-based models face challenges in generating semantic segmentation ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	386,678

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