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
2408.07724	"Normalized Stress" is Not Normalized: How to Interpret Stress Correctly	Stress is among the most commonly employed quality metrics and optimization criteria for dimension reduction projections of high dimensional data. Complex, high dimensional data is ubiquitous across many scientific disciplines, including machine learning, biology, and the social sciences. One of the primary methods of ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	480,702
2208.03624	Graph R-CNN: Towards Accurate 3D Object Detection with Semantic-Decorated Local Graph	Two-stage detectors have gained much popularity in 3D object detection. Most two-stage 3D detectors utilize grid points, voxel grids, or sampled keypoints for RoI feature extraction in the second stage. Such methods, however, are inefficient in handling unevenly distributed and sparse outdoor points. This paper solves ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	311,847
1608.04171	Power Data Classification: A Hybrid of a Novel Local Time Warping and LSTM	In this paper, for the purpose of data centre energy consumption monitoring and analysis, we propose to detect the running programs in a server by classifying the observed power consumption series. Time series classification problem has been extensively studied with various distance measurements developed; also recentl...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	true	false	false	59,789
2201.12417	Why Should I Trust You, Bellman? The Bellman Error is a Poor Replacement for Value Error	In this work, we study the use of the Bellman equation as a surrogate objective for value prediction accuracy. While the Bellman equation is uniquely solved by the true value function over all state-action pairs, we find that the Bellman error (the difference between both sides of the equation) is a poor proxy for the ...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	277,633
2006.15406	Listen carefully and tell: an audio captioning system based on residual learning and gammatone audio representation	Automated audio captioning is machine listening task whose goal is to describe an audio using free text. An automated audio captioning system has to be implemented as it accepts an audio as input and outputs as textual description, that is, the caption of the signal. This task can be useful in many applications such as...	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	184,492
2005.00356	Understanding the Perceived Quality of Video Predictions	The study of video prediction models is believed to be a fundamental approach to representation learning for videos. While a plethora of generative models for predicting the future frame pixel values given the past few frames exist, the quantitative evaluation of the predicted frames has been found to be extremely chal...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	175,210
2304.11473	(Vector) Space is Not the Final Frontier: Product Search as Program Synthesis	As ecommerce continues growing, huge investments in ML and NLP for Information Retrieval are following. While the vector space model dominated retrieval modelling in product search - even as vectorization itself greatly changed with the advent of deep learning -, our position paper argues in a contrarian fashion that p...	false	false	false	false	false	true	true	false	true	false	false	false	false	false	false	false	false	false	359,830
1706.06282	Universal Components of Real-world Diffusion Dynamics based on Point Processes	Bursts in human and natural activities are highly clustered in time, suggesting that these activities are influenced by previous events within the social or natural system. Bursty behavior in the real world conveys information of underlying diffusion processes, which have been the focus of diverse scientific communitie...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	75,656
1805.08026	A Correlation Measure Based on Vector-Valued $L_p$-Norms	In this paper, we introduce a new measure of correlation for bipartite quantum states. This measure depends on a parameter $\alpha$, and is defined in terms of vector-valued $L_p$-norms. The measure is within a constant of the exponential of $\alpha$-R\'enyi mutual information, and reduces to the trace norm (total vari...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	98,026
1611.08484	Vertex-centred Method to Detect Communities in Evolving Networks	Finding communities in evolving networks is a difficult task and raises issues different from the classic static detection case. We introduce an approach based on the recent vertex-centred paradigm. The proposed algorithm, named DynLOCNeSs, detects communities by scanning and evaluating each vertex neighbourhood, which...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	true	64,512
2312.13533	Automated Clinical Coding for Outpatient Departments	Computerised clinical coding approaches aim to automate the process of assigning a set of codes to medical records. While there is active research pushing the state of the art on clinical coding for hospitalized patients, the outpatient setting -- where doctors tend to non-hospitalised patients -- is overlooked. Althou...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	417,341
1910.12004	Model-agnostic Approaches to Handling Noisy Labels When Training Sound Event Classifiers	Label noise is emerging as a pressing issue in sound event classification. This arises as we move towards larger datasets that are difficult to annotate manually, but it is even more severe if datasets are collected automatically from online repositories, where labels are inferred through automated heuristics applied t...	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	150,933
2411.01292	Causal reasoning in difference graphs	Understanding causal mechanisms across different populations is essential for designing effective public health interventions. Recently, difference graphs have been introduced as a tool to visually represent causal variations between two distinct populations. While there has been progress in inferring these graphs from...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	505,002
2107.00862	User Role Discovery and Optimization Method based on K-means + Reinforcement learning in Mobile Applications	With the widespread use of mobile phones, users can share their location and activity anytime, anywhere, as a form of check in data. These data reflect user features. Long term stable, and a set of user shared features can be abstracted as user roles. The role is closely related to the user's social background, occupat...	true	false	false	false	true	false	false	false	false	false	false	false	false	true	false	false	false	false	244,300
0712.1345	Sequential operators in computability logic	Computability logic (CL) (see http://www.cis.upenn.edu/~giorgi/cl.html) is a semantical platform and research program for redeveloping logic as a formal theory of computability, as opposed to the formal theory of truth which it has more traditionally been. Formulas in CL stand for (interactive) computational problems, ...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	1,010
2207.00431	Stain Isolation-based Guidance for Improved Stain Translation	Unsupervised and unpaired domain translation using generative adversarial neural networks, and more precisely CycleGAN, is state of the art for the stain translation of histopathology images. It often, however, suffers from the presence of cycle-consistent but non structure-preserving errors. We propose an alternative ...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	305,758
2302.00906	New Constructions of Optimal Binary LCD Codes	Linear complementary dual (LCD) codes can provide an optimum linear coding solution for the two-user binary adder channel. LCD codes also can be used to against side-channel attacks and fault non-invasive attacks. Let $d_{LCD}(n, k)$ denote the maximum value of $d$ for which a binary $[n,k, d]$ LCD code exists. In \cit...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	343,401
2004.11075	Fast Convex Relaxations using Graph Discretizations	Matching and partitioning problems are fundamentals of computer vision applications with examples in multilabel segmentation, stereo estimation and optical-flow computation. These tasks can be posed as non-convex energy minimization problems and solved near-globally optimal by recent convex lifting approaches. Yet, app...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	173,806
2203.05564	HDL: Hybrid Deep Learning for the Synthesis of Myocardial Velocity Maps in Digital Twins for Cardiac Analysis	Synthetic digital twins based on medical data accelerate the acquisition, labelling and decision making procedure in digital healthcare. A core part of digital healthcare twins is model-based data synthesis, which permits the generation of realistic medical signals without requiring to cope with the modelling complexit...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	284,839
2412.20946	Generalizing in Net-Zero Microgrids: A Study with Federated PPO and TRPO	This work addresses the challenge of optimal energy management in microgrids through a collaborative and privacy-preserving framework. We propose the FedTRPO methodology, which integrates Federated Learning (FL) and Trust Region Policy Optimization (TRPO) to manage distributed energy resources (DERs) efficiently. Using...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	521,410
1412.2773	Cooperative Change Detection for Online Power Quality Monitoring	This paper considers the real-time power quality monitoring in power grid systems. The goal is to detect the occurrence of disturbances in the nominal sinusoidal voltage/current signal as quickly as possible such that protection measures can be taken in time. Based on an autoregressive (AR) model for the disturbance, w...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	38,233
1708.07903	Nationality Classification Using Name Embeddings	Nationality identification unlocks important demographic information, with many applications in biomedical and sociological research. Existing name-based nationality classifiers use name substrings as features and are trained on small, unrepresentative sets of labeled names, typically extracted from Wikipedia. As a res...	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	false	false	false	79,552
2104.09804	SE-SSD: Self-Ensembling Single-Stage Object Detector From Point Cloud	We present Self-Ensembling Single-Stage object Detector (SE-SSD) for accurate and efficient 3D object detection in outdoor point clouds. Our key focus is on exploiting both soft and hard targets with our formulated constraints to jointly optimize the model, without introducing extra computation in the inference. Specif...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	231,362
1210.5502	OpenCFU, a New Free and Open-Source Software to Count Cell Colonies and Other Circular Objects	Counting circular objects such as cell colonies is an important source of information for biologists. Although this task is often time-consuming and subjective, it is still predominantly performed manually. The aim of the present work is to provide a new tool to enumerate circular objects from digital pictures and vide...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	19,283
1502.07310	Pantheon 1.0, a manually verified dataset of globally famous biographies	We present the Pantheon 1.0 dataset: a manually verified dataset of individuals that have transcended linguistic, temporal, and geographic boundaries. The Pantheon 1.0 dataset includes the 11,341 biographies present in more than 25 languages in Wikipedia and is enriched with: (i) manually verified demographic informati...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	40,565
2102.01161	Adjoint Rigid Transform Network: Task-conditioned Alignment of 3D Shapes	Most learning methods for 3D data (point clouds, meshes) suffer significant performance drops when the data is not carefully aligned to a canonical orientation. Aligning real world 3D data collected from different sources is non-trivial and requires manual intervention. In this paper, we propose the Adjoint Rigid Trans...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	218,008
2409.15721	Applying Incremental Learning in Binary-Addition-Tree Algorithm for Dynamic Binary-State Network Reliability	This paper presents a novel approach to enhance the Binary-Addition-Tree algorithm (BAT) by integrating incremental learning techniques. BAT, known for its simplicity in development, implementation, and application, is a powerful implicit enumeration method for solving network reliability and optimization problems. How...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	491,029
2408.11535	SAM-REF: Rethinking Image-Prompt Synergy for Refinement in Segment Anything	The advent of the Segment Anything Model (SAM) marks a significant milestone for interactive segmentation using generalist models. As a late fusion model, SAM extracts image embeddings once and merges them with prompts in later interactions. This strategy limits the models ability to extract detailed information from t...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	482,333
2011.08071	JNLP Team: Deep Learning for Legal Processing in COLIEE 2020	We propose deep learning based methods for automatic systems of legal retrieval and legal question-answering in COLIEE 2020. These systems are all characterized by being pre-trained on large amounts of data before being finetuned for the specified tasks. This approach helps to overcome the data scarcity and achieve goo...	false	false	false	false	false	true	true	false	true	false	false	false	false	false	false	false	false	false	206,769
1406.6322	Stylized facts in Brazilian vote distributions	Elections, specially in countries such as Brazil with an electorate of the order of 100 million people, yield large-scale data-sets embodying valuable information on the dynamics through which individuals influence each other and make choices. In this work we perform an extensive analysis of data sets available for Bra...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	34,111
2410.14833	A novel approach towards the classification of Bone Fracture from Musculoskeletal Radiography images using Attention Based Transfer Learning	Computer-aided diagnosis (CAD) is today considered a vital tool in the field of biological image categorization, segmentation, and other related tasks. The current breakthrough in computer vision algorithms and deep learning approaches has substantially enhanced the effectiveness and precision of apps built to recogniz...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	500,229
1306.0158	Virality Prediction and Community Structure in Social Networks	How does network structure affect diffusion? Recent studies suggest that the answer depends on the type of contagion. Complex contagions, unlike infectious diseases (simple contagions), are affected by social reinforcement and homophily. Hence, the spread within highly clustered communities is enhanced, while diffusion...	false	false	false	true	false	false	false	false	false	false	false	false	false	true	false	false	false	false	24,935
2310.11398	Neural Attention: Enhancing QKV Calculation in Self-Attention Mechanism with Neural Networks	In the realm of deep learning, the self-attention mechanism has substantiated its pivotal role across a myriad of tasks, encompassing natural language processing and computer vision. Despite achieving success across diverse applications, the traditional self-attention mechanism primarily leverages linear transformation...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	400,623
2103.00092	The Age of Correlated Features in Supervised Learning based Forecasting	In this paper, we analyze the impact of information freshness on supervised learning based forecasting. In these applications, a neural network is trained to predict a time-varying target (e.g., solar power), based on multiple correlated features (e.g., temperature, humidity, and cloud coverage). The features are colle...	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	222,143
2310.01156	Neural Fiber Activation in Unipolar vs Bipolar Deep Brain Stimulation	Deep Brain Stimulation (DBS) is an established and powerful treatment method in various neurological disorders. It involves chronically delivering electrical pulses to a certain stimulation target in the brain in order to alleviate the symptoms of a disease. Traditionally, the effect of DBS on neural tissue has been mo...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	396,302
2208.05621	ARMANI: Part-level Garment-Text Alignment for Unified Cross-Modal Fashion Design	Cross-modal fashion image synthesis has emerged as one of the most promising directions in the generation domain due to the vast untapped potential of incorporating multiple modalities and the wide range of fashion image applications. To facilitate accurate generation, cross-modal synthesis methods typically rely on Co...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	312,444
2401.12970	Raidar: geneRative AI Detection viA Rewriting	We find that large language models (LLMs) are more likely to modify human-written text than AI-generated text when tasked with rewriting. This tendency arises because LLMs often perceive AI-generated text as high-quality, leading to fewer modifications. We introduce a method to detect AI-generated content by prompting ...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	423,556
1608.01212	A Novel Approach for Data-Driven Automatic Site Recommendation and Selection	This paper presents a novel, generic, and automatic method for data-driven site selection. Site selection is one of the most crucial and important decisions made by any company. Such a decision depends on various factors of sites, including socio-economic, geographical, ecological, as well as specific requirements of c...	false	false	false	false	true	false	false	false	false	false	false	false	false	true	false	false	false	false	59,395
2106.09958	Novelty Detection via Contrastive Learning with Negative Data Augmentation	Novelty detection is the process of determining whether a query example differs from the learned training distribution. Previous methods attempt to learn the representation of the normal samples via generative adversarial networks (GANs). However, they will suffer from instability training, mode dropping, and low discr...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	241,851
2112.10273	Design of a synthetic integral feedback circuit: dynamic analysis and DNA implementation	The design and implementation of regulation motifs ensuring robust perfect adaptation are challenging problems in synthetic biology. Indeed, the design of high-yield robust metabolic pathways producing, for instance, drug precursors and biofuels, could be easily imagined to rely on such a control strategy in order to o...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	272,390
2009.08816	Improved Coding over Sets for DNA-Based Data Storage	Error-correcting codes over sets, with applications to DNA storage, are studied. The DNA-storage channel receives a set of sequences, and produces a corrupted version of the set, including sequence loss, symbol substitution, symbol insertion/deletion, and limited-magnitude errors in symbols. Various parameter regimes a...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	196,354
2102.05954	Demarcating Endogenous and Exogenous Opinion Dynamics: An Experimental Design Approach	The networked opinion diffusion in online social networks (OSN) is often governed by the two genres of opinions - endogenous opinions that are driven by the influence of social contacts among users, and exogenous opinions which are formed by external effects like news, feeds etc. Accurate demarcation of endogenous and ...	false	false	false	true	true	false	true	false	false	false	false	false	false	false	false	false	false	false	219,589
1812.00099	Understanding Unequal Gender Classification Accuracy from Face Images	Recent work shows unequal performance of commercial face classification services in the gender classification task across intersectional groups defined by skin type and gender. Accuracy on dark-skinned females is significantly worse than on any other group. In this paper, we conduct several analyses to try to uncover t...	false	false	false	false	false	false	false	false	false	false	false	true	false	true	false	false	false	false	115,158
2404.18612	Enhancing Prosthetic Safety and Environmental Adaptability: A Visual-Inertial Prosthesis Motion Estimation Approach on Uneven Terrains	Environment awareness is crucial for enhancing walking safety and stability of amputee wearing powered prosthesis when crossing uneven terrains such as stairs and obstacles. However, existing environmental perception systems for prosthesis only provide terrain types and corresponding parameters, which fails to prevent ...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	450,330
1804.02555	Drive Video Analysis for the Detection of Traffic Near-Miss Incidents	Because of their recent introduction, self-driving cars and advanced driver assistance system (ADAS) equipped vehicles have had little opportunity to learn, the dangerous traffic (including near-miss incident) scenarios that provide normal drivers with strong motivation to drive safely. Accordingly, as a means of provi...	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	94,430
2304.02396	AutoRL Hyperparameter Landscapes	Although Reinforcement Learning (RL) has shown to be capable of producing impressive results, its use is limited by the impact of its hyperparameters on performance. This often makes it difficult to achieve good results in practice. Automated RL (AutoRL) addresses this difficulty, yet little is known about the dynamics...	false	false	false	false	true	false	true	true	false	false	true	false	false	false	false	false	false	false	356,423
2312.06088	SECNN: Squeeze-and-Excitation Convolutional Neural Network for Sentence Classification	Sentence classification is one of the basic tasks of natural language processing. Convolution neural network (CNN) has the ability to extract n-grams features through convolutional filters and capture local correlations between consecutive words in parallel, so CNN is a popular neural network architecture to dealing wi...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	414,364
1904.03604	BriskStream: Scaling Data Stream Processing on Shared-Memory Multicore Architectures	We introduce BriskStream, an in-memory data stream processing system (DSPSs) specifically designed for modern shared-memory multicore architectures. BriskStream's key contribution is an execution plan optimization paradigm, namely RLAS, which takes relative-location (i.e., NUMA distance) of each pair of producer-consum...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	126,788
1207.2000	Hycon2 Benchmark: Power Network System	As a benchmark exercise for testing software and methods developed in Hycon2 for decentralized and distributed control, we address the problem of designing the Automatic Generation Control (AGC) layer in power network systems. In particular, we present three different scenarios and discuss performance levels that can b...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	17,352
1907.09732	Variational Registration of Multiple Images with the SVD based SqN Distance Measure	Image registration, especially the quantification of image similarity, is an important task in image processing. Various approaches for the comparison of two images are discussed in the literature. However, although most of these approaches perform very well in a two image scenario, an extension to a multiple images sc...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	139,461
1411.5417	Private Empirical Risk Minimization Beyond the Worst Case: The Effect of the Constraint Set Geometry	Empirical Risk Minimization (ERM) is a standard technique in machine learning, where a model is selected by minimizing a loss function over constraint set. When the training dataset consists of private information, it is natural to use a differentially private ERM algorithm, and this problem has been the subject of a l...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	37,739
1910.12707	Outlining where humans live -- The World Settlement Footprint 2015	Human settlements are the cause and consequence of most environmental and societal changes on Earth; however, their location and extent is still under debate. We provide here a new 10m resolution (0.32 arc sec) global map of human settlements on Earth for the year 2015, namely the World Settlement Footprint 2015 (WSF20...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	151,171
2204.03507	Reliable Transiently-Powered Communication	Frequent power failures can introduce significant packet losses during communication among energy harvesting batteryless wireless sensors. Nodes should be aware of the energy level of their neighbors to guarantee the success of communication and avoid wasting energy. This paper presents TRAP (TRAnsiently-powered Protoc...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	true	290,328
1702.05743	DR2-Net: Deep Residual Reconstruction Network for Image Compressive Sensing	Most traditional algorithms for compressive sensing image reconstruction suffer from the intensive computation. Recently, deep learning-based reconstruction algorithms have been reported, which dramatically reduce the time complexity than iterative reconstruction algorithms. In this paper, we propose a novel \textbf{D}...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	68,464
1303.5400	Objection-Based Causal Networks	This paper introduces the notion of objection-based causal networks which resemble probabilistic causal networks except that they are quantified using objections. An objection is a logical sentence and denotes a condition under which a, causal dependency does not exist. Objection-based causal networks enjoy almost all ...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	23,088
2308.09656	Safe Collision and Clamping Reaction for Parallel Robots During Human-Robot Collaboration	Parallel robots (PRs) offer the potential for safe human-robot collaboration because of their low moving masses. Due to the in-parallel kinematic chains, the risk of contact in the form of collisions and clamping at a chain increases. Ensuring safety is investigated in this work through various contact reactions on a r...	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	386,377
1808.01878	From traffic conflict simulation to traffic crash simulation: introducing traffic safety indicators based on the explicit simulation of potential driver errors	This paper introduces a general simulation framework that can allow the simulation of crashes and the evaluation of consequences on existing microsimulation packages. A specific family of simple and reproducible conflict indicators is proposed and applied to many case studies. In this approach driver failures are simul...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	104,663
1911.10109	Implementation of Optical Deep Neural Networks using the Fabry-Perot Interferometer	Future developments in deep learning applications requiring large datasets will be limited by power and speed limitations of silicon based Von-Neumann computing architectures. Optical architectures provide a low power and high speed hardware alternative. Recent publications have suggested promising implementations of o...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	154,731
2208.06303	Triple-View Feature Learning for Medical Image Segmentation	Deep learning models, e.g. supervised Encoder-Decoder style networks, exhibit promising performance in medical image segmentation, but come with a high labelling cost. We propose TriSegNet, a semi-supervised semantic segmentation framework. It uses triple-view feature learning on a limited amount of labelled data and a...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	312,670
2404.01830	Doubly-Robust Off-Policy Evaluation with Estimated Logging Policy	We introduce a novel doubly-robust (DR) off-policy evaluation (OPE) estimator for Markov decision processes, DRUnknown, designed for situations where both the logging policy and the value function are unknown. The proposed estimator initially estimates the logging policy and then estimates the value function model by m...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	443,604
1808.01741	Logical Semantics and Commonsense Knowledge: Where Did we Go Wrong, and How to Go Forward, Again	We argue that logical semantics might have faltered due to its failure in distinguishing between two fundamentally very different types of concepts: ontological concepts, that should be types in a strongly-typed ontology, and logical concepts, that are predicates corresponding to properties of and relations between obj...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	104,639
1301.6743	An Update Semantics for Defeasible Obligations	The deontic logic DUS is a Deontic Update Semantics for prescriptive obligations based on the update semantics of Veltman. In DUS the definition of logical validity of obligations is not based on static truth values but on dynamic action transitions. In this paper prescriptive defeasible obligations are formalized in u...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	21,536
1405.7102	Detection Bank: An Object Detection Based Video Representation for Multimedia Event Recognition	While low-level image features have proven to be effective representations for visual recognition tasks such as object recognition and scene classification, they are inadequate to capture complex semantic meaning required to solve high-level visual tasks such as multimedia event detection and recognition. Recognition o...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	33,434
2408.09822	SurgicaL-CD: Generating Surgical Images via Unpaired Image Translation with Latent Consistency Diffusion Models	Computer-assisted surgery (CAS) systems are designed to assist surgeons during procedures, thereby reducing complications and enhancing patient care. Training machine learning models for these systems requires a large corpus of annotated datasets, which is challenging to obtain in the surgical domain due to patient pri...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	481,604
2003.00229	User-Level Privacy-Preserving Federated Learning: Analysis and Performance Optimization	Federated learning (FL), as a type of collaborative machine learning framework, is capable of preserving private data from mobile terminals (MTs) while training the data into useful models. Nevertheless, from a viewpoint of information theory, it is still possible for a curious server to infer private information from ...	false	false	false	false	true	false	true	false	false	false	false	false	true	false	false	false	false	false	166,234
2105.02796	Practical and Rigorous Uncertainty Bounds for Gaussian Process Regression	Gaussian Process Regression is a popular nonparametric regression method based on Bayesian principles that provides uncertainty estimates for its predictions. However, these estimates are of a Bayesian nature, whereas for some important applications, like learning-based control with safety guarantees, frequentist uncer...	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	233,931
1807.11661	Caging Loops in Shape Embedding Space: Theory and Computation	We propose to synthesize feasible caging grasps for a target object through computing Caging Loops, a closed curve defined in the shape embedding space of the object. Different from the traditional methods, our approach decouples caging loops from the surface geometry of target objects through working in the embedding ...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	104,222
1708.05812	Discovery of Visual Semantics by Unsupervised and Self-Supervised Representation Learning	The success of deep learning in computer vision is rooted in the ability of deep networks to scale up model complexity as demanded by challenging visual tasks. As complexity is increased, so is the need for large amounts of labeled data to train the model. This is associated with a costly human annotation effort. To ad...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	79,199
2111.12265	Distribution Estimation to Automate Transformation Policies for Self-Supervision	In recent visual self-supervision works, an imitated classification objective, called pretext task, is established by assigning labels to transformed or augmented input images. The goal of pretext can be predicting what transformations are applied to the image. However, it is observed that image transformations already...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	267,913
1711.10765	Learning nonlinear state-space models using smooth particle-filter-based likelihood approximations	When classical particle filtering algorithms are used for maximum likelihood parameter estimation in nonlinear state-space models, a key challenge is that estimates of the likelihood function and its derivatives are inherently noisy. The key idea in this paper is to run a particle filter based on a current parameter es...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	85,666
2501.02104	Equivalence of Informations Characterizes Bregman Divergences	Bregman divergences are a class of distance-like comparison functions which play fundamental roles in optimization, statistics, and information theory. One important property of Bregman divergences is that they cause two useful formulations of information content (in the sense of variability or non-uniformity) in a wei...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	522,349
2009.14096	Weakly Supervised-Based Oversampling for High Imbalance and High Dimensionality Data Classification	With the abundance of industrial datasets, imbalanced classification has become a common problem in several application domains. Oversampling is an effective method to solve imbalanced classification. One of the main challenges of the existing oversampling methods is to accurately label the new synthetic samples. Inacc...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	197,935
2304.07357	Efficient Incremental Penetration Depth Estimation between Convex Geometries	Penetration depth (PD) is essential for robotics due to its extensive applications in dynamic simulation, motion planning, haptic rendering, etc. The Expanding Polytope Algorithm (EPA) is the de facto standard for this problem, which estimates PD by expanding an inner polyhedral approximation of an implicit set. In thi...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	true	358,315
2310.06916	Distributed Transfer Learning with 4th Gen Intel Xeon Processors	In this paper, we explore how transfer learning, coupled with Intel Xeon, specifically 4th Gen Intel Xeon scalable processor, defies the conventional belief that training is primarily GPU-dependent. We present a case study where we achieved near state-of-the-art accuracy for image classification on a publicly available...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	true	398,759
2012.10369	Upper and Lower Bounds on the Performance of Kernel PCA	Principal Component Analysis (PCA) is a popular method for dimension reduction and has attracted an unfailing interest for decades. More recently, kernel PCA (KPCA) has emerged as an extension of PCA but, despite its use in practice, a sound theoretical understanding of KPCA is missing. We contribute several lower and ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	212,328
2305.17768	AIMS: All-Inclusive Multi-Level Segmentation	Despite the progress of image segmentation for accurate visual entity segmentation, completing the diverse requirements of image editing applications for different-level region-of-interest selections remains unsolved. In this paper, we propose a new task, All-Inclusive Multi-Level Segmentation (AIMS), which segments vi...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	368,742
2103.03447	User-Centric Cooperative MEC Service Offloading	Mobile edge computing provides users with a cloud environment close to the edge of the wireless network, supporting the computing intensive applications that have low latency requirements. The combination of offloading with the wireless communication brings new challenges. This paper investigates the service caching pr...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	223,283
2107.01002	WiCluster: Passive Indoor 2D/3D Positioning using WiFi without Precise Labels	We introduce WiCluster, a new machine learning (ML) approach for passive indoor positioning using radio frequency (RF) channel state information (CSI). WiCluster can predict both a zone-level position and a precise 2D or 3D position, without using any precise position labels during training. Prior CSI-based indoor posi...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	true	244,348
2406.08042	Efficient Network Traffic Feature Sets for IoT Intrusion Detection	The use of Machine Learning (ML) models in cybersecurity solutions requires high-quality data that is stripped of redundant, missing, and noisy information. By selecting the most relevant features, data integrity and model efficiency can be significantly improved. This work evaluates the feature sets provided by a comb...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	true	463,328
1906.00460	On The Radon-Nikodym Spectral Approach With Optimal Clustering	Problems of interpolation, classification, and clustering are considered. In the tenets of Radon--Nikodym approach $\langle f(\mathbf{x})\psi^2 \rangle / \langle\psi^2\rangle$, where the $\psi(\mathbf{x})$ is a linear function on input attributes, all the answers are obtained from a generalized eigenproblem $\|f\|\psi^{[...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	true	133,407
1707.02786	Learning to Compose Task-Specific Tree Structures	For years, recursive neural networks (RvNNs) have been shown to be suitable for representing text into fixed-length vectors and achieved good performance on several natural language processing tasks. However, the main drawback of RvNNs is that they require structured input, which makes data preparation and model implem...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	76,757
2411.18199	Semantic Edge Computing and Semantic Communications in 6G Networks: A Unifying Survey and Research Challenges	Semantic Edge Computing (SEC) and Semantic Communications (SemComs) have been proposed as viable approaches to achieve real-time edge-enabled intelligence in sixth-generation (6G) wireless networks. On one hand, SemCom leverages the strength of Deep Neural Networks (DNNs) to encode and communicate the semantic informat...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	511,769
1707.04202	Multi-Antenna Assisted Virtual Full-Duplex Relaying with Reliability-Aware Iterative Decoding	In this paper, a multi-antenna assisted virtual full-duplex (FD) relaying with reliability-aware iterative decoding at destination node is proposed to improve system spectral efficiency and reliability. This scheme enables two half-duplex relay nodes, mimicked as FD relaying, to alternatively serve as transmitter and r...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	77,001
2004.09702	Heterogeneous Causal Learning for Effectiveness Optimization in User Marketing	User marketing is a key focus of consumer-based internet companies. Learning algorithms are effective to optimize marketing campaigns which increase user engagement, and facilitates cross-marketing to related products. By attracting users with rewards, marketing methods are effective to boost user activity in the desir...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	173,420
2105.04534	Improving Fairness of AI Systems with Lossless De-biasing	In today's society, AI systems are increasingly used to make critical decisions such as credit scoring and patient triage. However, great convenience brought by AI systems comes with troubling prevalence of bias against underrepresented groups. Mitigating bias in AI systems to increase overall fairness has emerged as a...	false	false	false	false	true	false	true	false	false	false	false	false	false	true	false	false	false	false	234,537
2305.09442	Towards Automatic Identification of Globally Valid Geometric Flat Outputs via Numerical Optimization	Differential flatness enables efficient planning and control for underactuated robotic systems, but we lack a systematic and practical means of identifying a flat output (or determining whether one exists) for an arbitrary robotic system. In this work, we leverage recent results elucidating the role of symmetry in cons...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	364,633
2107.04952	Learn from Anywhere: Rethinking Generalized Zero-Shot Learning with Limited Supervision	A common problem with most zero and few-shot learning approaches is they suffer from bias towards seen classes resulting in sub-optimal performance. Existing efforts aim to utilize unlabeled images from unseen classes (i.e transductive zero-shot) during training to enable generalization. However, this limits their use ...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	245,615
2407.07227	Uncovering the Interaction Equation: Quantifying the Effect of User Interactions on Social Media Homepage Recommendations	Social media platforms depend on algorithms to select, curate, and deliver content personalized for their users. These algorithms leverage users' past interactions and extensive content libraries to retrieve and rank content that personalizes experiences and boosts engagement. Among various modalities through which thi...	false	false	false	true	false	false	false	false	false	false	false	false	false	true	false	false	false	false	471,677
2202.02423	Improved Information Theoretic Generalization Bounds for Distributed and Federated Learning	We consider information-theoretic bounds on expected generalization error for statistical learning problems in a networked setting. In this setting, there are $K$ nodes, each with its own independent dataset, and the models from each node have to be aggregated into a final centralized model. We consider both simple ave...	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	278,796
1905.08869	Source Localization and Tracking for Dynamic Radio Cartography using Directional Antennas	Utilization of directional antennas is a promising solution for efficient spectrum sensing and accurate source localization and tracking. Spectrum sensors equipped with directional antennas should constantly scan the space in order to track emitting sources and discover new activities in the area of interest. In this p...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	131,590
2002.03308	Face Hallucination with Finishing Touches	Obtaining a high-quality frontal face image from a low-resolution (LR) non-frontal face image is primarily important for many facial analysis applications. However, mainstreams either focus on super-resolving near-frontal LR faces or frontalizing non-frontal high-resolution (HR) faces. It is desirable to perform both t...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	163,223
2310.10762	Exploring hyperelastic material model discovery for human brain cortex: multivariate analysis vs. artificial neural network approaches	Traditional computational methods, such as the finite element analysis, have provided valuable insights into uncovering the underlying mechanisms of brain physical behaviors. However, precise predictions of brain physics require effective constitutive models to represent the intricate mechanical properties of brain tis...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	400,375
2406.03144	A Combination Model for Time Series Prediction using LSTM via Extracting Dynamic Features Based on Spatial Smoothing and Sequential General Variational Mode Decomposition	In order to solve the problems such as difficult to extract effective features and low accuracy of sales volume prediction caused by complex relationships such as market sales volume in time series prediction, we proposed a time series prediction method of market sales volume based on Sequential General VMD and spatial...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	461,110
2102.04172	Directed particle swarm optimization with Gaussian-process-based function forecasting	Particle swarm optimization (PSO) is an iterative search method that moves a set of candidate solution around a search-space towards the best known global and local solutions with randomized step lengths. PSO frequently accelerates optimization in practical applications, where gradients are not available and function e...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	true	false	false	219,015
1903.01693	Less is More: Semi-Supervised Causal Inference for Detecting Pathogenic Users in Social Media	Recent years have witnessed a surge of manipulation of public opinion and political events by malicious social media actors. These users are referred to as "Pathogenic Social Media (PSM)" accounts. PSMs are key users in spreading misinformation in social media to viral proportions. These accounts can be either controll...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	123,319
2007.07061	Polarization in Networks: Identification-alienation Framework	We introduce a model of polarization in networks as a unifying framework for the measurement of polarization that covers a wide range of applications. We consider a sufficiently general setup for this purpose: node- and edge-weighted, undirected, and connected networks. We generalize the axiomatic characterization of E...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	187,213
1006.0475	Prediction with Advice of Unknown Number of Experts	In the framework of prediction with expert advice, we consider a recently introduced kind of regret bounds: the bounds that depend on the effective instead of nominal number of experts. In contrast to the NormalHedge bound, which mainly depends on the effective number of experts and also weakly depends on the nominal o...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	6,654
2006.05675	IMUTube: Automatic Extraction of Virtual on-body Accelerometry from Video for Human Activity Recognition	The lack of large-scale, labeled data sets impedes progress in developing robust and generalized predictive models for on-body sensor-based human activity recognition (HAR). Labeled data in human activity recognition is scarce and hard to come by, as sensor data collection is expensive, and the annotation is time-consu...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	181,161
1507.07147	True Online Emphatic TD($\lambda$): Quick Reference and Implementation Guide	This document is a guide to the implementation of true online emphatic TD($\lambda$), a model-free temporal-difference algorithm for learning to make long-term predictions which combines the emphasis idea (Sutton, Mahmood & White 2015) and the true-online idea (van Seijen & Sutton 2014). The setting used here includes ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	45,452
2405.05190	Is Transductive Learning Equivalent to PAC Learning?	Much of learning theory is concerned with the design and analysis of probably approximately correct (PAC) learners. The closely related transductive model of learning has recently seen more scrutiny, with its learners often used as precursors to PAC learners. Our goal in this work is to understand and quantify the exac...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	452,829

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