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
1905.09870	Gradient Descent can Learn Less Over-parameterized Two-layer Neural Networks on Classification Problems	Recently, several studies have proven the global convergence and generalization abilities of the gradient descent method for two-layer ReLU networks. Most studies especially focused on the regression problems with the squared loss function, except for a few, and the importance of the positivity of the neural tangent ke...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	131,854
2302.10146	Multi-generational labour markets: data-driven discovery of multi-perspective system parameters using machine learning	Economic issues, such as inflation, energy costs, taxes, and interest rates, are a constant presence in our daily lives and have been exacerbated by global events such as pandemics, environmental disasters, and wars. A sustained history of financial crises reveals significant weaknesses and vulnerabilities in the found...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	346,686
2311.04551	Earth Observation based multi-scale analysis of crop diversity in the European Union: first insights for agro-environmental policies	To understand the resilience of farms and the agricultural sector, as well as the provision of ecosystem services, we need to characterize and quantify crop diversity. Using a 10m resolution satellite-derived product, we created datasets of crop diversity across spatial and administrative scales for 27 EU countries and...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	406,271
2409.00140	Statistical Analysis of the Impact of Quaternion Components in Convolutional Neural Networks	In recent years, several models using Quaternion-Valued Convolutional Neural Networks (QCNNs) for different problems have been proposed. Although the definition of the quaternion convolution layer is the same, there are different adaptations of other atomic components to the quaternion domain, e.g., pooling layers, act...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	true	false	false	484,805
2008.01552	A Reinforcement Learning Method For Power Suppliers' Strategic Bidding with Insufficient Information	Power suppliers can exercise market power to gain higher profit. However, this becomes difficult when external information is extremely rare. To get a promising performance in an extremely incomplete information market environment, a novel model-free reinforcement learning algorithm based on the Learning Automata (LA) ...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	190,368
1611.06612	RefineNet: Multi-Path Refinement Networks for High-Resolution Semantic Segmentation	Recently, very deep convolutional neural networks (CNNs) have shown outstanding performance in object recognition and have also been the first choice for dense classification problems such as semantic segmentation. However, repeated subsampling operations like pooling or convolution striding in deep CNNs lead to a sign...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	64,220
2204.05823	Adaptive Cross-Attention-Driven Spatial-Spectral Graph Convolutional Network for Hyperspectral Image Classification	Recently, graph convolutional networks (GCNs) have been developed to explore spatial relationship between pixels, achieving better classification performance of hyperspectral images (HSIs). However, these methods fail to sufficiently leverage the relationship between spectral bands in HSI data. As such, we propose an a...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	291,155
2405.02769	Linear Convergence of Independent Natural Policy Gradient in Games with Entropy Regularization	This work focuses on the entropy-regularized independent natural policy gradient (NPG) algorithm in multi-agent reinforcement learning. In this work, agents are assumed to have access to an oracle with exact policy evaluation and seek to maximize their respective independent rewards. Each individual's reward is assumed...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	false	false	false	451,906
1803.05795	RUSSE'2018: A Shared Task on Word Sense Induction for the Russian Language	The paper describes the results of the first shared task on word sense induction (WSI) for the Russian language. While similar shared tasks were conducted in the past for some Romance and Germanic languages, we explore the performance of sense induction and disambiguation methods for a Slavic language that shares many ...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	92,705
1907.04380	Don't Take the Premise for Granted: Mitigating Artifacts in Natural Language Inference	Natural Language Inference (NLI) datasets often contain hypothesis-only biases---artifacts that allow models to achieve non-trivial performance without learning whether a premise entails a hypothesis. We propose two probabilistic methods to build models that are more robust to such biases and better transfer across dat...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	138,093
1904.04232	A Closer Look at Few-shot Classification	Few-shot classification aims to learn a classifier to recognize unseen classes during training with limited labeled examples. While significant progress has been made, the growing complexity of network designs, meta-learning algorithms, and differences in implementation details make a fair comparison difficult. In this...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	126,977
2312.01260	Rethinking PGD Attack: Is Sign Function Necessary?	Neural networks have demonstrated success in various domains, yet their performance can be significantly degraded by even a small input perturbation. Consequently, the construction of such perturbations, known as adversarial attacks, has gained significant attention, many of which fall within "white-box" scenarios wher...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	412,377
2202.13996	Risk-Neutral Market Simulation	We develop a risk-neutral spot and equity option market simulator for a single underlying, under which the joint market process is a martingale. We leverage an efficient low-dimensional representation of the market which preserves no static arbitrage, and employ neural spline flows to simulate samples which are free fr...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	282,814
2305.17593	Data Minimization at Inference Time	In domains with high stakes such as law, recruitment, and healthcare, learning models frequently rely on sensitive user data for inference, necessitating the complete set of features. This not only poses significant privacy risks for individuals but also demands substantial human effort from organizations to verify inf...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	368,667
2408.17168	EMHI: A Multimodal Egocentric Human Motion Dataset with HMD and Body-Worn IMUs	Egocentric human pose estimation (HPE) using wearable sensors is essential for VR/AR applications. Most methods rely solely on either egocentric-view images or sparse Inertial Measurement Unit (IMU) signals, leading to inaccuracies due to self-occlusion in images or the sparseness and drift of inertial sensors. Most im...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	484,606
1409.6022	Exact Analysis of k-Connectivity in Secure Sensor Networks with Unreliable Links	The Eschenauer--Gligor (EG) random key predistribution scheme has been widely recognized as a typical approach to secure communications in wireless sensor networks (WSNs). However, there is a lack of precise probability analysis on the reliable connectivity of WSNs under the EG scheme. To address this, we rigorously de...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	true	36,212
2404.00068	A Data-Driven Predictive Analysis on Cyber Security Threats with Key Risk Factors	Cyber risk refers to the risk of defacing reputation, monetary losses, or disruption of an organization or individuals, and this situation usually occurs by the unconscious use of cyber systems. The cyber risk is unhurriedly increasing day by day and it is right now a global threat. Developing countries like Bangladesh...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	442,751
2209.10179	Reconstructing Robot Operations via Radio-Frequency Side-Channel	Connected teleoperated robotic systems play a key role in ensuring operational workflows are carried out with high levels of accuracy and low margins of error. In recent years, a variety of attacks have been proposed that actively target the robot itself from the cyber domain. However, little attention has been paid to...	false	false	false	false	false	false	true	true	false	false	false	false	true	false	false	false	false	false	318,781
2107.13362	Graph Constrained Data Representation Learning for Human Motion Segmentation	Recently, transfer subspace learning based approaches have shown to be a valid alternative to unsupervised subspace clustering and temporal data clustering for human motion segmentation (HMS). These approaches leverage prior knowledge from a source domain to improve clustering performance on a target domain, and curren...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	248,177
2401.03753	Color-$S^{4}L$: Self-supervised Semi-supervised Learning with Image Colorization	This work addresses the problem of semi-supervised image classification tasks with the integration of several effective self-supervised pretext tasks. Different from widely-used consistency regularization within semi-supervised learning, we explored a novel self-supervised semi-supervised learning framework (Color-$S^{...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	420,217
2206.05225	ClamNet: Using contrastive learning with variable depth Unets for medical image segmentation	Unets have become the standard method for semantic segmentation of medical images, along with fully convolutional networks (FCN). Unet++ was introduced as a variant of Unet, in order to solve some of the problems facing Unet and FCNs. Unet++ provided networks with an ensemble of variable depth Unets, hence eliminating ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	301,928
2212.04325	Lattice-Free Sequence Discriminative Training for Phoneme-Based Neural Transducers	Recently, RNN-Transducers have achieved remarkable results on various automatic speech recognition tasks. However, lattice-free sequence discriminative training methods, which obtain superior performance in hybrid models, are rarely investigated in RNN-Transducers. In this work, we propose three lattice-free training o...	false	false	true	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	335,407
2307.06824	CLAIMED -- the open source framework for building coarse-grained operators for accelerated discovery in science	In modern data-driven science, reproducibility and reusability are key challenges. Scientists are well skilled in the process from data to publication. Although some publication channels require source code and data to be made accessible, rerunning and verifying experiments is usually hard due to a lack of standards. T...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	true	false	379,184
2105.12006	The incel lexicon: Deciphering the emergent cryptolect of a global misogynistic community	Evolving out of a gender-neutral framing of an involuntary celibate identity, the concept of `incels' has come to refer to an online community of men who bear antipathy towards themselves, women, and society-at-large for their perceived inability to find and maintain sexual relationships. By exploring incel language us...	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	false	false	false	236,886
1902.03361	Image Decomposition and Classification through a Generative Model	We demonstrate in this paper that a generative model can be designed to perform classification tasks under challenging settings, including adversarial attacks and input distribution shifts. Specifically, we propose a conditional variational autoencoder that learns both the decomposition of inputs and the distributions ...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	121,080
1905.04511	Unified Generator-Classifier for Efficient Zero-Shot Learning	Generative models have achieved state-of-the-art performance for the zero-shot learning problem, but they require re-training the classifier every time a new object category is encountered. The traditional semantic embedding approaches, though very elegant, usually do not perform at par with their generative counterpar...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	130,489
2108.06017	AGKD-BML: Defense Against Adversarial Attack by Attention Guided Knowledge Distillation and Bi-directional Metric Learning	While deep neural networks have shown impressive performance in many tasks, they are fragile to carefully designed adversarial attacks. We propose a novel adversarial training-based model by Attention Guided Knowledge Distillation and Bi-directional Metric Learning (AGKD-BML). The attention knowledge is obtained from a...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	250,480
2502.10723	A Mathematics Framework of Artificial Shifted Population Risk and Its Further Understanding Related to Consistency Regularization	Data augmentation is an important technique in training deep neural networks as it enhances their ability to generalize and remain robust. While data augmentation is commonly used to expand the sample size and act as a consistency regularization term, there is a lack of research on the relationship between them. To add...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	534,022
1904.08159	3D Object Recognition with Ensemble Learning --- A Study of Point Cloud-Based Deep Learning Models	In this study, we present an analysis of model-based ensemble learning for 3D point-cloud object classification and detection. An ensemble of multiple model instances is known to outperform a single model instance, but there is little study of the topic of ensemble learning for 3D point clouds. First, an ensemble of mu...	false	false	false	false	true	false	true	true	false	false	false	true	false	false	false	false	false	false	127,994
2306.17193	Uncovering the Limits of Machine Learning for Automatic Vulnerability Detection	Recent results of machine learning for automatic vulnerability detection (ML4VD) have been very promising. Given only the source code of a function $f$, ML4VD techniques can decide if $f$ contains a security flaw with up to 70% accuracy. However, as evident in our own experiments, the same top-performing models are una...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	376,630
1109.5560	Temporal effects in the growth of networks	We show that to explain the growth of the citation network by preferential attachment (PA), one has to accept that individual nodes exhibit heterogeneous fitness values that decay with time. While previous PA-based models assumed either heterogeneity or decay in isolation, we propose a simple analytically treatable mod...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	true	12,330
2502.07809	Analyzing the Resource Utilization of Lambda Functions on Mobile Devices: Case Studies on Kotlin and Swift	With billions of smartphones in use globally, the daily time spent on these devices contributes significantly to overall electricity consumption. Given this scale, even minor reductions in smartphone power use could result in substantial energy savings. This study explores the impact of Lambda functions on resource con...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	true	532,766
2403.11304	Pioneering SE(2)-Equivariant Trajectory Planning for Automated Driving	Planning the trajectory of the controlled ego vehicle is a key challenge in automated driving. As for human drivers, predicting the motions of surrounding vehicles is important to plan the own actions. Recent motion prediction methods utilize equivariant neural networks to exploit geometric symmetries in the scene. How...	false	false	false	false	true	false	false	true	false	false	false	false	false	false	true	false	false	false	438,632
1011.1212	CplexA: a Mathematica package to study macromolecular-assembly control of gene expression	Summary: Macromolecular assembly vertebrates essential cellular processes, such as gene regulation and signal transduction. A major challenge for conventional computational methods to study these processes is tackling the exponential increase of the number of configurational states with the number of components. CplexA...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	8,139
2404.06484	Public-private funding models in open source software development: A case study on scikit-learn	Governments are increasingly funding open source software (OSS) development to support software security, digital sovereignty, and national competitiveness in science and innovation, amongst others. However, little is known about how OSS developers evaluate the relative benefits and drawbacks of governmental funding fo...	false	false	false	false	true	false	true	false	false	false	false	false	false	true	false	false	false	true	445,486
1811.08790	Learning Quadratic Games on Networks	Individuals, or organizations, cooperate with or compete against one another in a wide range of practical situations. Such strategic interactions are often modeled as games played on networks, where an individual's payoff depends not only on her action but also on that of her neighbors. The current literature has large...	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	false	true	114,126
2009.06680	SML: Semantic Meta-learning for Few-shot Semantic Segmentation	The significant amount of training data required for training Convolutional Neural Networks has become a bottleneck for applications like semantic segmentation. Few-shot semantic segmentation algorithms address this problem, with an aim to achieve good performance in the low-data regime, with few annotated training ima...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	195,711
1805.12238	High-Quality Disjoint and Overlapping Community Structure in Large-Scale Complex Networks	In this paper, we propose an improved version of an agglomerative hierarchical clustering algorithm that performs disjoint community detection in large-scale complex networks. The improved algorithm is achieved after replacing the local structural similarity used in the original algorithm, with the recently proposed Dy...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	true	99,121
2010.05350	Google Landmark Recognition 2020 Competition Third Place Solution	We present our third place solution to the Google Landmark Recognition 2020 competition. It is an ensemble of global features only Sub-center ArcFace models. We introduce dynamic margins for ArcFace loss, a family of tune-able margin functions of class size, designed to deal with the extreme imbalance in GLDv2 dataset....	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	200,089
2502.12476	CoCo-CoLa: Evaluating Language Adherence in Multilingual LLMs	Multilingual Large Language Models (LLMs) develop cross-lingual abilities despite being trained on limited parallel data. However, they often struggle to generate responses in the intended language, favoring high-resource languages such as English. In this work, we introduce CoCo-CoLa (Correct Concept - Correct Languag...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	534,884
2111.10824	A Blockchain-Based Approach for Collaborative Formalization of Mathematics and Programs	Formalization of mathematics is the process of digitizing mathematical knowledge, which allows for formal proof verification as well as efficient semantic searches. Given the large and ever-increasing gap between the set of formalized and unformalized mathematical knowledge, there is a clear need to encourage more comp...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	true	267,459
1906.10489	Keep soft robots soft -- a data-driven based trade-off between feed-forward and feedback control	Tracking control for soft robots is challenging due to uncertainties in the system model and environment. Using high feedback gains to overcome this issue results in an increasing stiffness that clearly destroys the inherent safety property of soft robots. However, accurate models for feed-forward control are often dif...	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	136,445
2409.09715	Generative Semantic Communication via Textual Prompts: Latency Performance Tradeoffs	This paper develops an edge-device collaborative Generative Semantic Communications (Gen SemCom) framework leveraging pre-trained Multi-modal/Vision Language Models (M/VLMs) for ultra-low-rate semantic communication via textual prompts. The proposed framework optimizes the use of M/VLMs on the wireless edge/device to g...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	488,439
1805.06158	Investigating the Agility Bias in DNS Graph Mining	The concept of agile domain name system (DNS) refers to dynamic and rapidly changing mappings between domain names and their Internet protocol (IP) addresses. This empirical paper evaluates the bias from this kind of agility for DNS-based graph theoretical data mining applications. By building on two conventional metri...	false	false	false	true	false	false	false	false	false	false	false	false	true	false	false	false	false	true	97,549
2105.01289	Representation Learning for Clustering via Building Consensus	In this paper, we focus on unsupervised representation learning for clustering of images. Recent advances in deep clustering and unsupervised representation learning are based on the idea that different views of an input image (generated through data augmentation techniques) must be close in the representation space (e...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	233,484
2305.00221	Sensor Equivariance by LiDAR Projection Images	In this work, we propose an extension of conventional image data by an additional channel in which the associated projection properties are encoded. This addresses the issue of sensor-dependent object representation in projection-based sensors, such as LiDAR, which can lead to distorted physical and geometric propertie...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	361,258
2408.12598	ND-SDF: Learning Normal Deflection Fields for High-Fidelity Indoor Reconstruction	Neural implicit reconstruction via volume rendering has demonstrated its effectiveness in recovering dense 3D surfaces. However, it is non-trivial to simultaneously recover meticulous geometry and preserve smoothness across regions with differing characteristics. To address this issue, previous methods typically employ...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	482,797
2106.13109	Machine learning to tame divergent density functional approximations: a new path to consensus materials design principles	Computational virtual high-throughput screening (VHTS) with density functional theory (DFT) and machine-learning (ML)-acceleration is essential in rapid materials discovery. By necessity, efficient DFT-based workflows are carried out with a single density functional approximation (DFA). Nevertheless, properties evaluat...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	242,970
1803.01500	Memorization Precedes Generation: Learning Unsupervised GANs with Memory Networks	We propose an approach to address two issues that commonly occur during training of unsupervised GANs. First, since GANs use only a continuous latent distribution to embed multiple classes or clusters of data, they often do not correctly handle the structural discontinuity between disparate classes in a latent space. S...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	91,894
cs/0207093	Preference Queries	The handling of user preferences is becoming an increasingly important issue in present-day information systems. Among others, preferences are used for information filtering and extraction to reduce the volume of data presented to the user. They are also used to keep track of user profiles and formulate policies to imp...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	537,670
2209.05227	DUET: A Tuning-Free Device-Cloud Collaborative Parameters Generation Framework for Efficient Device Model Generalization	Device Model Generalization (DMG) is a practical yet under-investigated research topic for on-device machine learning applications. It aims to improve the generalization ability of pre-trained models when deployed on resource-constrained devices, such as improving the performance of pre-trained cloud models on smart mo...	false	false	false	false	true	true	false	false	false	false	false	true	false	false	false	false	false	true	317,030
2211.14864	A Faster, Lighter and Stronger Deep Learning-Based Approach for Place Recognition	Visual Place Recognition is an essential component of systems for camera localization and loop closure detection, and it has attracted widespread interest in multiple domains such as computer vision, robotics and AR/VR. In this work, we propose a faster, lighter and stronger approach that can generate models with fewer...	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	333,026
2210.01820	MOAT: Alternating Mobile Convolution and Attention Brings Strong Vision Models	This paper presents MOAT, a family of neural networks that build on top of MObile convolution (i.e., inverted residual blocks) and ATtention. Unlike the current works that stack separate mobile convolution and transformer blocks, we effectively merge them into a MOAT block. Starting with a standard Transformer block, w...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	321,416
2309.02401	Prototype-based Dataset Comparison	Dataset summarisation is a fruitful approach to dataset inspection. However, when applied to a single dataset the discovery of visual concepts is restricted to those most prominent. We argue that a comparative approach can expand upon this paradigm to enable richer forms of dataset inspection that go beyond the most pr...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	390,026
2307.13332	The Optimal Approximation Factors in Misspecified Off-Policy Value Function Estimation	Theoretical guarantees in reinforcement learning (RL) are known to suffer multiplicative blow-up factors with respect to the misspecification error of function approximation. Yet, the nature of such \emph{approximation factors} -- especially their optimal form in a given learning problem -- is poorly understood. In thi...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	381,550
2309.05689	Large Language Model for Science: A Study on P vs. NP	In this work, we use large language models (LLMs) to augment and accelerate research on the P versus NP problem, one of the most important open problems in theoretical computer science and mathematics. Specifically, we propose Socratic reasoning, a general framework that promotes in-depth thinking with LLMs for complex...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	391,174
2311.00424	Tracking capelin spawning migration -- Integrating environmental data and Individual-based modeling	This paper presents a modeling framework for tracking the spawning migration of the capelin, which is a fish species in the Barents Sea. The framework combines an individual-based model (IBM) with artificial neural networks (ANNs). The ANNs determine the direction of the fish's movement based on local environmental inf...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	404,645
2211.15644	Efficient Mirror Detection via Multi-level Heterogeneous Learning	We present HetNet (Multi-level \textbf{Het}erogeneous \textbf{Net}work), a highly efficient mirror detection network. Current mirror detection methods focus more on performance than efficiency, limiting the real-time applications (such as drones). Their lack of efficiency is aroused by the common design of adopting hom...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	333,343
1702.07634	Thermal Transients in District Heating Systems	Heat fluxes in a district heating pipeline systems need to be controlled on the scale from minutes to an hour to adjust to evolving demand. There are two principal ways to control the heat flux - keep temperature fixed but adjust velocity of the carrier (typically water) or keep the velocity flow steady but then adjust...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	68,814
1809.00773	Sequence-to-Action: End-to-End Semantic Graph Generation for Semantic Parsing	This paper proposes a neural semantic parsing approach -- Sequence-to-Action, which models semantic parsing as an end-to-end semantic graph generation process. Our method simultaneously leverages the advantages from two recent promising directions of semantic parsing. Firstly, our model uses a semantic graph to represe...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	106,648
1709.01602	Dynamic Multiscale Tree Learning Using Ensemble Strong Classifiers for Multi-label Segmentation of Medical Images with Lesions	We introduce a dynamic multiscale tree (DMT) architecture that learns how to leverage the strengths of different existing classifiers for supervised multi-label image segmentation. Unlike previous works that simply aggregate or cascade classifiers for addressing image segmentation and labeling tasks, we propose to embe...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	80,111
2412.03268	RFSR: Improving ISR Diffusion Models via Reward Feedback Learning	Generative diffusion models (DM) have been extensively utilized in image super-resolution (ISR). Most of the existing methods adopt the denoising loss from DDPMs for model optimization. We posit that introducing reward feedback learning to finetune the existing models can further improve the quality of the generated im...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	513,893
2211.08864	PrivacyProber: Assessment and Detection of Soft-Biometric Privacy-Enhancing Techniques	Soft-biometric privacy-enhancing techniques represent machine learning methods that aim to: (i) mitigate privacy concerns associated with face recognition technology by suppressing selected soft-biometric attributes in facial images (e.g., gender, age, ethnicity) and (ii) make unsolicited extraction of sensitive person...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	330,801
2402.04599	Meet JEANIE: a Similarity Measure for 3D Skeleton Sequences via Temporal-Viewpoint Alignment	Video sequences exhibit significant nuisance variations (undesired effects) of speed of actions, temporal locations, and subjects' poses, leading to temporal-viewpoint misalignment when comparing two sets of frames or evaluating the similarity of two sequences. Thus, we propose Joint tEmporal and cAmera viewpoiNt alIgn...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	427,520
2310.07355	IMITATE: Clinical Prior Guided Hierarchical Vision-Language Pre-training	In the field of medical Vision-Language Pre-training (VLP), significant efforts have been devoted to deriving text and image features from both clinical reports and associated medical images. However, most existing methods may have overlooked the opportunity in leveraging the inherent hierarchical structure of clinical...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	398,942
2307.04973	SAM-U: Multi-box prompts triggered uncertainty estimation for reliable SAM in medical image	Recently, Segmenting Anything has taken an important step towards general artificial intelligence. At the same time, its reliability and fairness have also attracted great attention, especially in the field of health care. In this study, we propose multi-box prompts triggered uncertainty estimation for SAM cues to demo...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	378,569
1907.12133	An Empirical Study on Leveraging Scene Graphs for Visual Question Answering	Visual question answering (Visual QA) has attracted significant attention these years. While a variety of algorithms have been proposed, most of them are built upon different combinations of image and language features as well as multi-modal attention and fusion. In this paper, we investigate an alternative approach in...	false	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	140,041
2107.11856	Graph Representation Learning on Tissue-Specific Multi-Omics	Combining different modalities of data from human tissues has been critical in advancing biomedical research and personalised medical care. In this study, we leverage a graph embedding model (i.e VGAE) to perform link prediction on tissue-specific Gene-Gene Interaction (GGI) networks. Through ablation experiments, we p...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	247,722
1812.10869	Hypergraph Clustering: A Modularity Maximization Approach	Clustering on hypergraphs has been garnering increased attention with potential applications in network analysis, VLSI design and computer vision, among others. In this work, we generalize the framework of modularity maximization for clustering on hypergraphs. To this end, we introduce a hypergraph null model, analogou...	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	false	false	117,454
1901.07132	Universal Rules for Fooling Deep Neural Networks based Text Classification	Recently, deep learning based natural language processing techniques are being extensively used to deal with spam mail, censorship evaluation in social networks, among others. However, there is only a couple of works evaluating the vulnerabilities of such deep neural networks. Here, we go beyond attacks to investigate,...	false	false	false	false	false	false	true	false	true	false	false	false	true	false	false	false	false	false	119,154
2206.12848	Analysis of Stochastic Processes through Replay Buffers	Replay buffers are a key component in many reinforcement learning schemes. Yet, their theoretical properties are not fully understood. In this paper we analyze a system where a stochastic process X is pushed into a replay buffer and then randomly sampled to generate a stochastic process Y from the replay buffer. We pro...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	304,754
2501.13950	DEFEND: A Large-scale 1M Dataset and Foundation Model for Tobacco Addiction Prevention	While tobacco advertising innovates at unprecedented speed, traditional surveillance methods remain frozen in time, especially in the context of social media. The lack of large-scale, comprehensive datasets and sophisticated monitoring systems has created a widening gap between industry advancement and public health ov...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	526,896
1209.4022	Game Theoretic Formation of a Centrality Based Network	We model the formation of networks as a game where players aspire to maximize their own centrality by increasing the number of other players to which they are path-wise connected, while simultaneously incurring a cost for each added adjacent edge. We simulate the interactions between players using an algorithm that fac...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	true	18,615
1903.01024	Mixed-Triggered Reliable Control for Singular Networked Cascade Control Systems with Randomly Occurring Cyber Attack	In this paper, the issue of mixed-triggered reliable dissipative control is investigated for singular networked cascade control systems (NCCSs) with actuator saturation and randomly occurring cyber attacks. In order to utilize the limited communication resources effectively, a more general mixed-triggered scheme is est...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	123,170
1803.03916	Deep reinforcement learning for time series: playing idealized trading games	Deep Q-learning is investigated as an end-to-end solution to estimate the optimal strategies for acting on time series input. Experiments are conducted on two idealized trading games. 1) Univariate: the only input is a wave-like price time series, and 2) Bivariate: the input includes a random stepwise price time series...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	92,355
2007.05353	List Viterbi Decoding of PAC Codes	Polarization-adjusted convolutional (PAC) codes are special concatenated codes in which we employ a one-to-one convolutional transform as a pre-coding step before the polar transform. In this scheme, the polar transform (as a mapper) and the successive cancellation process (as a demapper) present a synthetic vector cha...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	186,648
2307.03748	Incentive-Theoretic Bayesian Inference for Collaborative Science	Contemporary scientific research is a distributed, collaborative endeavor, carried out by teams of researchers, regulatory institutions, funding agencies, commercial partners, and scientific bodies, all interacting with each other and facing different incentives. To maintain scientific rigor, statistical methods should...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	378,136
1912.12795	Quantifying the Performance of Federated Transfer Learning	The scarcity of data and isolated data islands encourage different organizations to share data with each other to train machine learning models. However, there are increasing concerns on the problems of data privacy and security, which urges people to seek a solution like Federated Transfer Learning (FTL) to share trai...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	158,930
1906.05828	Overcoming Mean-Field Approximations in Recurrent Gaussian Process Models	We identify a new variational inference scheme for dynamical systems whose transition function is modelled by a Gaussian process. Inference in this setting has either employed computationally intensive MCMC methods, or relied on factorisations of the variational posterior. As we demonstrate in our experiments, the fact...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	135,133
2201.09786	Aerial Energy Provisioning for Massive Energy-Constrained IoT by UAVs	Autonomy of devices is a major challenge in many Internet of Things (IoT) applications, in particular when the nodes are deployed remotely or difficult to assess places. In this paper we present an approach to provide energy to these devices by Unmanned Aerial Vehicles (UAVs). Therefore, the two major challenges, findi...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	true	276,782
2403.02496	Choose Your Own Adventure: Interactive E-Books to Improve Word Knowledge and Comprehension Skills	The purpose of this feasibility study was to examine the potential impact of reading digital interactive e-books on essential skills that support reading comprehension with third-fifth grade students. Students read two e-Books that taught word learning and comprehension monitoring strategies in the service of learning ...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	434,820
2103.09414	Toward Neural-Network-Guided Program Synthesis and Verification	We propose a novel framework of program and invariant synthesis called neural network-guided synthesis. We first show that, by suitably designing and training neural networks, we can extract logical formulas over integers from the weights and biases of the trained neural networks. Based on the idea, we have implemented...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	225,157
2106.12561	Fine-Grained Data Selection for Improved Energy Efficiency of Federated Edge Learning	In Federated edge learning (FEEL), energy-constrained devices at the network edge consume significant energy when training and uploading their local machine learning models, leading to a decrease in their lifetime. This work proposes novel solutions for energy-efficient FEEL by jointly considering local training data, ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	242,764
2007.11709	Adversarial Attacks against Face Recognition: A Comprehensive Study	Face recognition (FR) systems have demonstrated outstanding verification performance, suggesting suitability for real-world applications ranging from photo tagging in social media to automated border control (ABC). In an advanced FR system with deep learning-based architecture, however, promoting the recognition effici...	false	false	false	false	false	false	true	false	false	false	false	true	true	false	false	false	false	false	188,609
2412.06299	4D Gaussian Splatting with Scale-aware Residual Field and Adaptive Optimization for Real-time Rendering of Temporally Complex Dynamic Scenes	Reconstructing dynamic scenes from video sequences is a highly promising task in the multimedia domain. While previous methods have made progress, they often struggle with slow rendering and managing temporal complexities such as significant motion and object appearance/disappearance. In this paper, we propose SaRO-GS ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	515,196
2301.05797	FedSSC: Shared Supervised-Contrastive Federated Learning	Federated learning is widely used to perform decentralized training of a global model on multiple devices while preserving the data privacy of each device. However, it suffers from heterogeneous local data on each training device which increases the difficulty to reach the same level of accuracy as the centralized trai...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	340,447
2404.01618	Multi-Robot Collaborative Navigation with Formation Adaptation	Multi-robot collaborative navigation is an essential ability where teamwork and synchronization are keys. In complex and uncertain environments, adaptive formation is vital, as rigid formations prove to be inadequate. The ability of robots to dynamically adjust their formation enables navigation through unpredictable s...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	443,498
2305.02780	Interpretable Regional Descriptors: Hyperbox-Based Local Explanations	This work introduces interpretable regional descriptors, or IRDs, for local, model-agnostic interpretations. IRDs are hyperboxes that describe how an observation's feature values can be changed without affecting its prediction. They justify a prediction by providing a set of "even if" arguments (semi-factual explanatio...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	362,171
2403.07949	Algorithmic Bayesian Epistemology	One aspect of the algorithmic lens in theoretical computer science is a view on other scientific disciplines that focuses on satisfactory solutions that adhere to real-world constraints, as opposed to solutions that would be optimal ignoring such constraints. The algorithmic lens has provided a unique and important per...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	437,112
1902.06130	Atlas-based automated detection of swim bladder in Medaka embryo	Fish embryo models are increasingly being used both for the assessment of chemicals efficacy and potential toxicity. This article proposes a methodology to automatically detect the swim bladder on 2D images of Medaka fish embryos seen either in dorsal view or in lateral view. After embryo segmentation and for each stud...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	121,688
2301.03236	Optimistic Meta-Gradients	We study the connection between gradient-based meta-learning and convex op-timisation. We observe that gradient descent with momentum is a special case of meta-gradients, and building on recent results in optimisation, we prove convergence rates for meta-learning in the single task setting. While a meta-learned update ...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	339,741
2212.07860	Multi-Level Association Rule Mining for Wireless Network Time Series Data	Key performance indicators(KPIs) are of great significance in the monitoring of wireless network service quality. The network service quality can be improved by adjusting relevant configuration parameters(CPs) of the base station. However, there are numerous CPs and different cells may affect each other, which bring gr...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	336,550
1912.13192	PV-RCNN: Point-Voxel Feature Set Abstraction for 3D Object Detection	We present a novel and high-performance 3D object detection framework, named PointVoxel-RCNN (PV-RCNN), for accurate 3D object detection from point clouds. Our proposed method deeply integrates both 3D voxel Convolutional Neural Network (CNN) and PointNet-based set abstraction to learn more discriminative point cloud f...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	159,029
2105.07593	Differentiable SLAM-net: Learning Particle SLAM for Visual Navigation	Simultaneous localization and mapping (SLAM) remains challenging for a number of downstream applications, such as visual robot navigation, because of rapid turns, featureless walls, and poor camera quality. We introduce the Differentiable SLAM Network (SLAM-net) along with a navigation architecture to enable planar rob...	false	false	false	false	true	false	true	true	false	false	false	true	false	false	false	false	false	false	235,483
2302.03202	Exploring the Benefits of Training Expert Language Models over Instruction Tuning	Recently, Language Models (LMs) instruction-tuned on multiple tasks, also known as multitask-prompted fine-tuning (MT), have shown the capability to generalize to unseen tasks. Previous work has shown that scaling the number of training tasks is the key component in making stronger MT LMs. In this work, we report an un...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	344,254
2202.00602	Meta-Learning Hypothesis Spaces for Sequential Decision-making	Obtaining reliable, adaptive confidence sets for prediction functions (hypotheses) is a central challenge in sequential decision-making tasks, such as bandits and model-based reinforcement learning. These confidence sets typically rely on prior assumptions on the hypothesis space, e.g., the known kernel of a Reproducin...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	278,201
2407.04733	Accurate Passive Radar via an Uncertainty-Aware Fusion of Wi-Fi Sensing Data	Wi-Fi devices can effectively be used as passive radar systems that sense what happens in the surroundings and can even discern human activity. We propose, for the first time, a principled architecture which employs Variational Auto-Encoders for estimating a latent distribution responsible for generating the data, and ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	470,681
2207.06333	6D Camera Relocalization in Visually Ambiguous Extreme Environments	We propose a novel method to reliably estimate the pose of a camera given a sequence of images acquired in extreme environments such as deep seas or extraterrestrial terrains. Data acquired under these challenging conditions are corrupted by textureless surfaces, image degradation, and presence of repetitive and highly...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	307,846
2210.10114	Transferable Unlearnable Examples	With more people publishing their personal data online, unauthorized data usage has become a serious concern. The unlearnable strategies have been introduced to prevent third parties from training on the data without permission. They add perturbations to the users' data before publishing, which aims to make the models ...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	324,790
1312.3913	Blowfish Privacy: Tuning Privacy-Utility Trade-offs using Policies	Privacy definitions provide ways for trading-off the privacy of individuals in a statistical database for the utility of downstream analysis of the data. In this paper, we present Blowfish, a class of privacy definitions inspired by the Pufferfish framework, that provides a rich interface for this trade-off. In particu...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	29,080

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