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
2210.10619	Restricted Bernoulli Matrix Factorization: Balancing the trade-off between prediction accuracy and coverage in classification based collaborative filtering	Reliability measures associated with the prediction of the machine learning models are critical to strengthening user confidence in artificial intelligence. Therefore, those models that are able to provide not only predictions, but also reliability, enjoy greater popularity. In the field of recommender systems, reliabi...	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	false	false	324,989
2304.09752	Attributing Image Generative Models using Latent Fingerprints	Generative models have enabled the creation of contents that are indistinguishable from those taken from nature. Open-source development of such models raised concerns about the risks of their misuse for malicious purposes. One potential risk mitigation strategy is to attribute generative models via fingerprinting. Cur...	false	false	false	false	false	false	true	false	false	false	false	true	true	false	false	false	false	false	359,157
2309.15127	Grad DFT: a software library for machine learning enhanced density functional theory	Density functional theory (DFT) stands as a cornerstone method in computational quantum chemistry and materials science due to its remarkable versatility and scalability. Yet, it suffers from limitations in accuracy, particularly when dealing with strongly correlated systems. To address these shortcomings, recent work ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	394,858
2106.10258	Bridging the Gap Between Object Detection and User Intent via Query-Modulation	When interacting with objects through cameras, or pictures, users often have a specific intent. For example, they may want to perform a visual search. With most object detection models relying on image pixels as their sole input, undesired results are not uncommon. Most typically: lack of a high-confidence detection on...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	241,955
1703.00760	Sampling Variations of Lead Sheets	Machine-learning techniques have been recently used with spectacular results to generate artefacts such as music or text. However, these techniques are still unable to capture and generate artefacts that are convincingly structured. In this paper we present an approach to generate structured musical sequences. We intro...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	69,222
2211.00729	A Bayesian Framework on Asymmetric Mixture of Factor Analyser	Mixture of factor analyzer (MFA) model is an efficient model for the analysis of high dimensional data through which the factor-analyzer technique based on the covariance matrices reducing the number of free parameters. The model also provides an important methodology to determine latent groups in data. There are sever...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	327,971
1304.5666	The Structure and Quantum Capacity of a Partially Degradable Quantum Channel	The quantum capacity of degradable quantum channels has been proven to be additive. On the other hand, there is no general rule for the behavior of quantum capacity for non-degradable quantum channels. We introduce the set of partially degradable (PD) quantum channels to answer the question of additivity of quantum cap...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	24,104
2104.02120	Nonlinear model reduction for slow-fast stochastic systems near unknown invariant manifolds	We introduce a nonlinear stochastic model reduction technique for high-dimensional stochastic dynamical systems that have a low-dimensional invariant effective manifold with slow dynamics, and high-dimensional, large fast modes. Given only access to a black box simulator from which short bursts of simulation can be obt...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	228,594
2012.12754	Estimation of Driver's Gaze Region from Head Position and Orientation using Probabilistic Confidence Regions	A smart vehicle should be able to understand human behavior and predict their actions to avoid hazardous situations. Specific traits in human behavior can be automatically predicted, which can help the vehicle make decisions, increasing safety. One of the most important aspects pertaining to the driving task is the dri...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	213,023
1803.01711	Resampling Forgery Detection Using Deep Learning and A-Contrario Analysis	The amount of digital imagery recorded has recently grown exponentially, and with the advancement of software, such as Photoshop or Gimp, it has become easier to manipulate images. However, most images on the internet have not been manipulated and any automated manipulation detection algorithm must carefully control th...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	91,931
2410.01092	Semantic Segmentation of Unmanned Aerial Vehicle Remote Sensing Images using SegFormer	The escalating use of Unmanned Aerial Vehicles (UAVs) as remote sensing platforms has garnered considerable attention, proving invaluable for ground object recognition. While satellite remote sensing images face limitations in resolution and weather susceptibility, UAV remote sensing, employing low-speed unmanned aircr...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	493,605
1406.5917	BSTree: an Incremental Indexing Structure for Similarity Search and Real Time Monitoring of Data Streams	In this work, a new indexing technique of data streams called BSTree is proposed. This technique uses the method of data discretization, SAX [4], to reduce online the dimensionality of data streams. It draws on Btree to build the index and finally uses an LRV (least Recently visited) pruning technique to rid the index ...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	34,075
2006.07484	dagger: A Python Framework for Reproducible Machine Learning Experiment Orchestration	Many research directions in machine learning, particularly in deep learning, involve complex, multi-stage experiments, commonly involving state-mutating operations acting on models along multiple paths of execution. Although machine learning frameworks provide clean interfaces for defining model architectures and unbra...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	181,808
2303.05584	SOCIALGYM 2.0: Simulator for Multi-Agent Social Robot Navigation in Shared Human Spaces	We present SocialGym 2, a multi-agent navigation simulator for social robot research. Our simulator models multiple autonomous agents, replicating real-world dynamics in complex environments, including doorways, hallways, intersections, and roundabouts. Unlike traditional simulators that concentrate on single robots wi...	false	false	false	false	true	false	false	true	false	false	false	false	false	false	true	false	false	false	350,517
2306.05426	SequenceMatch: Imitation Learning for Autoregressive Sequence Modelling with Backtracking	In many domains, autoregressive models can attain high likelihood on the task of predicting the next observation. However, this maximum-likelihood (MLE) objective does not necessarily match a downstream use-case of autoregressively generating high-quality sequences. The MLE objective weights sequences proportionally to...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	372,195
2206.15134	InsMix: Towards Realistic Generative Data Augmentation for Nuclei Instance Segmentation	Nuclei Segmentation from histology images is a fundamental task in digital pathology analysis. However, deep-learning-based nuclei segmentation methods often suffer from limited annotations. This paper proposes a realistic data augmentation method for nuclei segmentation, named InsMix, that follows a Copy-Paste-Smooth ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	305,496
1609.01958	Object Tracking via Dynamic Feature Selection Processes	DFST proposes an optimized visual tracking algorithm based on the real-time selection of locally and temporally discriminative features. A feature selection mechanism is embedded in the Adaptive colour Names (CN) tracking system that adaptively selects the top-ranked discriminative features for tracking. DFST provides ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	60,661
2212.06415	Collision probability reduction method for tracking control in automatic docking / berthing using reinforcement learning	Automation of berthing maneuvers in shipping is a pressing issue as the berthing maneuver is one of the most stressful tasks seafarers undertake. Berthing control problems are often tackled via tracking a predefined trajectory or path. Maintaining a tracking error of zero under an uncertain environment is impossible; t...	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	336,109
1906.08675	Performance Evaluation Methodology for Long-Term Visual Object Tracking	A long-term visual object tracking performance evaluation methodology and a benchmark are proposed. Performance measures are designed by following a long-term tracking definition to maximize the analysis probing strength. The new measures outperform existing ones in interpretation potential and in better distinguishing...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	135,938
2409.01374	H-ARC: A Robust Estimate of Human Performance on the Abstraction and Reasoning Corpus Benchmark	The Abstraction and Reasoning Corpus (ARC) is a visual program synthesis benchmark designed to test challenging out-of-distribution generalization in humans and machines. Since 2019, limited progress has been observed on the challenge using existing artificial intelligence methods. Comparing human and machine performan...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	485,313
2308.04796	Bayes Risk Consistency of Nonparametric Classification Rules for Spike Trains Data	Spike trains data find a growing list of applications in computational neuroscience, imaging, streaming data and finance. Machine learning strategies for spike trains are based on various neural network and probabilistic models. The probabilistic approach is relying on parametric or nonparametric specifications of the ...	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	384,562
1408.0101	Memetic Search in Differential Evolution Algorithm	Differential Evolution (DE) is a renowned optimization stratagem that can easily solve nonlinear and comprehensive problems. DE is a well known and uncomplicated population based probabilistic approach for comprehensive optimization. It has apparently outperformed a number of Evolutionary Algorithms and further search ...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	35,059
2310.07491	Model-based Clustering of Individuals' Ecological Momentary Assessment Time-series Data for Improving Forecasting Performance	Through Ecological Momentary Assessment (EMA) studies, a number of time-series data is collected across multiple individuals, continuously monitoring various items of emotional behavior. Such complex data is commonly analyzed in an individual level, using personalized models. However, it is believed that additional inf...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	398,994
1903.06646	Adversarial Networks for Camera Pose Regression and Refinement	Despite recent advances on the topic of direct camera pose regression using neural networks, accurately estimating the camera pose of a single RGB image still remains a challenging task. To address this problem, we introduce a novel framework based, in its core, on the idea of implicitly learning the joint distribution...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	124,433
2301.13441	CMLCompiler: A Unified Compiler for Classical Machine Learning	Classical machine learning (CML) occupies nearly half of machine learning pipelines in production applications. Unfortunately, it fails to utilize the state-of-the-practice devices fully and performs poorly. Without a unified framework, the hybrid deployments of deep learning (DL) and CML also suffer from severe perfor...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	342,917
1407.2515	RankMerging: A supervised learning-to-rank framework to predict links in large social network	Uncovering unknown or missing links in social networks is a difficult task because of their sparsity and because links may represent different types of relationships, characterized by different structural patterns. In this paper, we define a simple yet efficient supervised learning-to-rank framework, called RankMerging...	false	false	false	true	false	true	true	false	false	false	false	false	false	false	false	false	false	false	34,533
2305.17435	On the Noise Sensitivity of the Randomized SVD	The randomized singular value decomposition (R-SVD) is a popular sketching-based algorithm for efficiently computing the partial SVD of a large matrix. When the matrix is low-rank, the R-SVD produces its partial SVD exactly; but when the rank is large, it only yields an approximation. Motivated by applications in dat...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	368,590
2407.01784	Analyzing Persuasive Strategies in Meme Texts: A Fusion of Language Models with Paraphrase Enrichment	This paper describes our approach to hierarchical multi-label detection of persuasion techniques in meme texts. Our model, developed as a part of the recent SemEval task, is based on fine-tuning individual language models (BERT, XLM-RoBERTa, and mBERT) and leveraging a mean-based ensemble model in addition to dataset a...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	469,446
1705.02176	Discrete Modeling of Multi-Transmitter Neural Networks with Neuron Competition	We propose a novel discrete model of central pattern generators (CPG), neuronal ensembles generating rhythmic activity. The model emphasizes the role of nonsynaptic interactions and the diversity of electrical properties in nervous systems. Neurons in the model release different neurotransmitters into the shared extrac...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	72,944
2411.11066	TS-LLaVA: Constructing Visual Tokens through Thumbnail-and-Sampling for Training-Free Video Large Language Models	Recent advances in multimodal Large Language Models (LLMs) have shown great success in understanding multi-modal contents. For video understanding tasks, training-based video LLMs are difficult to build due to the scarcity of high-quality, curated video-text paired data. In contrast, paired image-text data are much eas...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	508,906
2109.11028	On physics-informed data-driven isotropic and anisotropic constitutive models through probabilistic machine learning and space-filling sampling	Data-driven constitutive modeling is an emerging field in computational solid mechanics with the prospect of significantly relieving the computational costs of hierarchical computational methods. Traditionally, these surrogates have been trained using datasets which map strain inputs to stress outputs directly. Data-dr...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	256,817
2408.17287	Optimizing Interaction Space: Enlarging the Capture Volume for Multiple Portable Motion Capture Devices	Markerless motion capture devices such as the Leap Motion Controller (LMC) have been extensively used for tracking hand, wrist, and forearm positions as an alternative to Marker-based Motion Capture (MMC). However, previous studies have highlighted the subpar performance of LMC in reliably recording hand kinematics. In...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	484,651
2005.12366	Robust exact differentiators with predefined convergence time	The problem of exactly differentiating a signal with bounded second derivative is considered. A class of differentiators is proposed, which converge to the derivative of such a signal within a fixed, i.e., a finite and uniformly bounded convergence time. A tuning procedure is derived that allows to assign an arbitrary,...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	178,703
2101.11482	Deriving the Traveler Behavior Information from Social Media: A Case Study in Manhattan with Twitter	Social media platforms, such as Twitter, provide a totally new perspective in dealing with the traffic problems and is anticipated to complement the traditional methods. The geo-tagged tweets can provide the Twitter users' location information and is being applied in traveler behavior analysis. This paper explores the ...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	true	217,295
1112.0404	A Cyclic Representation of Discrete Coordination Procedures	We show that any discrete opinion pooling procedure with positive weights can be asymptotically approximated by DeGroot's procedure whose communication digraph is a Hamiltonian cycle with loops. In this cycle, the weight of each arc (which is not a loop) is inversely proportional to the influence of the agent the arc l...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	true	false	false	true	13,292
2303.05228	A classification of S-boxes generated by Orthogonal Cellular Automata	Most of the approaches published in the literature to construct S-boxes via Cellular Automata (CA) work by either iterating a finite CA for several time steps, or by a one-shot application of the global rule. The main characteristic that brings together these works is that they employ a single CA rule to define the vec...	false	false	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	350,391
1108.4380	Determinantal Representations and the Hermite Matrix	We consider the problem of writing real polynomials as determinants of symmetric linear matrix polynomials. This problem of algebraic geometry, whose roots go back to the nineteenth century, has recently received new attention from the viewpoint of convex optimization. We relate the question to sums of squares decompos...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	11,766
2109.05013	PWPAE: An Ensemble Framework for Concept Drift Adaptation in IoT Data Streams	As the number of Internet of Things (IoT) devices and systems have surged, IoT data analytics techniques have been developed to detect malicious cyber-attacks and secure IoT systems; however, concept drift issues often occur in IoT data analytics, as IoT data is often dynamic data streams that change over time, causing...	false	false	false	false	true	false	true	false	false	false	false	false	true	false	false	false	false	false	254,628
2311.01722	Heterogeneous federated collaborative filtering using FAIR: Federated Averaging in Random Subspaces	Recommendation systems (RS) for items (e.g., movies, books) and ads are widely used to tailor content to users on various internet platforms. Traditionally, recommendation models are trained on a central server. However, due to rising concerns for data privacy and regulations like the GDPR, federated learning is an inc...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	405,148
1902.10374	Domain-Constrained Advertising Keyword Generation	Advertising (ad for short) keyword suggestion is important for sponsored search to improve online advertising and increase search revenue. There are two common challenges in this task. First, the keyword bidding problem: hot ad keywords are very expensive for most of the advertisers because more advertisers are bidding...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	122,664
1602.05723	The effects of marine protected areas over time and species dispersal potential: A quantitative conservation conflict attempt	Protected areas are an important conservation measure. However, there are controversial findings regarding whether closed areas are beneficial for species and habitat conservation as well as landings. Species dispersal is acknowledged as a key factor for the design and impacts of closed areas. A series of agent based m...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	52,290
2303.04035	Data Assimilation for Combined Parameter and State Estimation in Stochastic Continuous-Discrete Nonlinear Systems	Data assimilation (DA) provides a general framework for estimation in dynamical systems based on the concepts of Bayesian inference. This constitutes a common basis for the different linear and nonlinear filtering and smoothing techniques which gives a better understanding of the characteristics and limitations of each...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	349,942
cs/0111060	Gradient-based Reinforcement Planning in Policy-Search Methods	We introduce a learning method called ``gradient-based reinforcement planning'' (GREP). Unlike traditional DP methods that improve their policy backwards in time, GREP is a gradient-based method that plans ahead and improves its policy before it actually acts in the environment. We derive formulas for the exact policy ...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	537,461
2311.12398	RFTrans: Leveraging Refractive Flow of Transparent Objects for Surface Normal Estimation and Manipulation	Transparent objects are widely used in our daily lives, making it important to teach robots to interact with them. However, it's not easy because the reflective and refractive effects can make depth cameras fail to give accurate geometry measurements. To solve this problem, this paper introduces RFTrans, an RGB-D-based...	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	409,318
1705.02101	TALL: Temporal Activity Localization via Language Query	This paper focuses on temporal localization of actions in untrimmed videos. Existing methods typically train classifiers for a pre-defined list of actions and apply them in a sliding window fashion. However, activities in the wild consist of a wide combination of actors, actions and objects; it is difficult to design a...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	72,928
2404.06243	ActNetFormer: Transformer-ResNet Hybrid Method for Semi-Supervised Action Recognition in Videos	Human action or activity recognition in videos is a fundamental task in computer vision with applications in surveillance and monitoring, self-driving cars, sports analytics, human-robot interaction and many more. Traditional supervised methods require large annotated datasets for training, which are expensive and time...	true	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	true	445,385
2303.07100	A Feature-based Approach for the Recognition of Image Quality Degradation in Automotive Applications	Cameras play a crucial role in modern driver assistance systems and are an essential part of the sensor technology for automated driving. The quality of images captured by in-vehicle cameras highly influences the performance of visual perception systems. This paper presents a feature-based algorithm to detect certain e...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	351,114
2402.14857	Is the System Message Really Important to Jailbreaks in Large Language Models?	The rapid evolution of Large Language Models (LLMs) has rendered them indispensable in modern society. While security measures are typically to align LLMs with human values prior to release, recent studies have unveiled a concerning phenomenon named "Jailbreak". This term refers to the unexpected and potentially harmfu...	false	false	false	false	true	false	false	false	true	false	false	false	true	false	false	false	false	false	431,878
1805.09001	One-to-one Mapping between Stimulus and Neural State: Memory and Classification	Synaptic strength can be seen as probability to propagate impulse, and according to synaptic plasticity, function could exist from propagation activity to synaptic strength. If the function satisfies constraints such as continuity and monotonicity, neural network under external stimulus will always go to fixed point, a...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	98,316
1606.04160	The Crossover Process: Learnability and Data Protection from Inference Attacks	It is usual to consider data protection and learnability as conflicting objectives. This is not always the case: we show how to jointly control inference --- seen as the attack --- and learnability by a noise-free process that mixes training examples, the Crossover Process (cp). One key point is that the cp~is typicall...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	57,198
1908.10535	Push for Center Learning via Orthogonalization and Subspace Masking for Person Re-Identification	Person re-identification aims to identify whether pairs of images belong to the same person or not. This problem is challenging due to large differences in camera views, lighting and background. One of the mainstream in learning CNN features is to design loss functions which reinforce both the class separation and intr...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	143,141
1507.07516	Media-Based Modulation for Next-Generation Wireless: A Survey and Some New Developments	The idea of media-based modulation (MBM) is to embed information in the channel states via intentional perturbations of the transmission media. This article covers a broad range of topics regarding MBM, expanding on its benefits and reviewing relevant challenges, alluding to potential future research directions. The ar...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	45,485
1901.05459	Permutation Decoding of Polar Codes	A new permutation decoding approach for polar codes is presented. The complexity of the algorithm is similar to that of a successive cancellation list (SCL) decoder, while it can be implemented with the latency of a successive cancellation decoder. As opposed to the SCL algorithm, the sorting operation is not used in t...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	118,792
2304.02836	Longitudinal Multimodal Transformer Integrating Imaging and Latent Clinical Signatures From Routine EHRs for Pulmonary Nodule Classification	The accuracy of predictive models for solitary pulmonary nodule (SPN) diagnosis can be greatly increased by incorporating repeat imaging and medical context, such as electronic health records (EHRs). However, clinically routine modalities such as imaging and diagnostic codes can be asynchronous and irregularly sampled ...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	356,570
2206.07680	Learning Large-scale Subsurface Simulations with a Hybrid Graph Network Simulator	Subsurface simulations use computational models to predict the flow of fluids (e.g., oil, water, gas) through porous media. These simulations are pivotal in industrial applications such as petroleum production, where fast and accurate models are needed for high-stake decision making, for example, for well placement opt...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	302,838
1402.0555	Taming the Monster: A Fast and Simple Algorithm for Contextual Bandits	We present a new algorithm for the contextual bandit learning problem, where the learner repeatedly takes one of $K$ actions in response to the observed context, and observes the reward only for that chosen action. Our method assumes access to an oracle for solving fully supervised cost-sensitive classification problem...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	30,571
2007.07092	A Normative approach to Attest Digital Discrimination	Digital discrimination is a form of discrimination whereby users are automatically treated unfairly, unethically or just differently based on their personal data by a machine learning (ML) system. Examples of digital discrimination include low-income neighbourhood's targeted with high-interest loans or low credit score...	false	false	false	false	true	false	false	false	false	false	false	false	false	true	false	false	false	false	187,225
2308.02465	Label Inference Attacks against Node-level Vertical Federated GNNs	Federated learning enables collaborative training of machine learning models by keeping the raw data of the involved workers private. Three of its main objectives are to improve the models' privacy, security, and scalability. Vertical Federated Learning (VFL) offers an efficient cross-silo setting where a few parties c...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	383,638
1012.1272	A statistical mechanics approach to Granovetter theory	In this paper we try to bridge breakthroughs in quantitative sociology/econometrics pioneered during the last decades by Mac Fadden, Brock-Durlauf, Granovetter and Watts-Strogats through introducing a minimal model able to reproduce essentially all the features of social behavior highlighted by these authors. Our model...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	8,434
2110.12770	DP-XGBoost: Private Machine Learning at Scale	The big-data revolution announced ten years ago does not seem to have fully happened at the expected scale. One of the main obstacle to this, has been the lack of data circulation. And one of the many reasons people and organizations did not share as much as expected is the privacy risk associated with data sharing ope...	false	false	false	false	true	false	true	false	false	false	false	false	true	false	false	false	false	false	262,962
1806.08130	Behavior-based evaluation of session satisfaction	Nowadays, web search becomes more and more popular all over the world. Many researchers and developers have done lots of studies on behaviors of search users. In practice, the full understanding of these behaviors can not only help to evaluate the usefulness of newly-developed ranking algorithms and other changes of se...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	101,101
2502.06072	ID policy (with reassignment) is asymptotically optimal for heterogeneous weakly-coupled MDPs	Heterogeneity poses a fundamental challenge for many real-world large-scale decision-making problems but remains largely understudied. In this paper, we study the fully heterogeneous setting of a prominent class of such problems, known as weakly-coupled Markov decision processes (WCMDPs). Each WCMDP consists of $N$ arm...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	531,907
1911.10614	Deep Mixture Density Network for Probabilistic Object Detection	Mistakes/uncertainties in object detection could lead to catastrophes when deploying robots in the real world. In this paper, we measure the uncertainties of object localization to minimize this kind of risk. Uncertainties emerge upon challenging cases like occlusion. The bounding box borders of an occluded object can ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	154,886
2306.06620	ARIST: An Effective API Argument Recommendation Approach	Learning and remembering to use APIs are difficult. Several techniques have been proposed to assist developers in using APIs. Most existing techniques focus on recommending the right API methods to call, but very few techniques focus on recommending API arguments. In this paper, we propose ARIST, a novel automated argu...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	372,684
2109.00060	Data-Driven Reduced-Order Modeling of Spatiotemporal Chaos with Neural Ordinary Differential Equations	Dissipative partial differential equations that exhibit chaotic dynamics tend to evolve to attractors that exist on finite-dimensional manifolds. We present a data-driven reduced order modeling method that capitalizes on this fact by finding the coordinates of this manifold and finding an ordinary differential equation...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	252,985
2412.01167	HumekaFL: Automated Detection of Neonatal Asphyxia Using Federated Learning	Birth Apshyxia (BA) is a severe condition characterized by insufficient supply of oxygen to a newborn during the delivery. BA is one of the primary causes of neonatal death in the world. Although there has been a decline in neonatal deaths over the past two decades, the developing world, particularly sub-Saharan Africa...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	512,986
2310.17407	Meaning and understanding in large language models	Can a machine understand the meanings of natural language? Recent developments in the generative large language models (LLMs) of artificial intelligence have led to the belief that traditional philosophical assumptions about machine understanding of language need to be revised. This article critically evaluates the pre...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	403,122
2409.13906	A Change Language for Ontologies and Knowledge Graphs	Ontologies and knowledge graphs (KGs) are general-purpose computable representations of some domain, such as human anatomy, and are frequently a crucial part of modern information systems. Most of these structures change over time, incorporating new knowledge or information that was previously missing. Managing these c...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	490,226
2102.05015	Optimal SIC Ordering and Power Allocation in Downlink Multi-Cell NOMA Systems	In this work, we propose a globally optimal joint successive interference cancellation (SIC) ordering and power allocation (JSPA) algorithm for the sum-rate maximization problem in downlink multi-cell non-orthogonal multiple access (NOMA) systems. The proposed algorithm is based on the exploration of base stations (BSs...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	219,300
2310.15072	RD-VIO: Robust Visual-Inertial Odometry for Mobile Augmented Reality in Dynamic Environments	It is typically challenging for visual or visual-inertial odometry systems to handle the problems of dynamic scenes and pure rotation. In this work, we design a novel visual-inertial odometry (VIO) system called RD-VIO to handle both of these two problems. Firstly, we propose an IMU-PARSAC algorithm which can robustly ...	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	402,149
1605.05795	Robust Reserve Capacity Provision and Peak Load Reduction from Buildings in Smart Grids	This paper proposes a robust demand-side control algorithm in a smart grid environment for heating, ventilation and air conditioning (HVAC) systems. A robust model predictive control (RMPC) scheme in a receding horizon fashion is deployed, which optimizes electricity cost and capacity market participation of the HVAC s...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	56,045
2201.01661	Evaluation of Thermal Imaging on Embedded GPU Platforms for Application in Vehicular Assistance Systems	This study is focused on evaluating the real-time performance of thermal object detection for smart and safe vehicular systems by deploying the trained networks on GPU & single-board EDGE-GPU computing platforms for onboard automotive sensor suite testing. A novel large-scale thermal dataset comprising of > 35,000 dist...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	274,314
2410.06950	Faithful Interpretation for Graph Neural Networks	Currently, attention mechanisms have garnered increasing attention in Graph Neural Networks (GNNs), such as Graph Attention Networks (GATs) and Graph Transformers (GTs). It is not only due to the commendable boost in performance they offer but also its capacity to provide a more lucid rationale for model behaviors, whi...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	496,405
2104.07905	Ego-Exo: Transferring Visual Representations from Third-person to First-person Videos	We introduce an approach for pre-training egocentric video models using large-scale third-person video datasets. Learning from purely egocentric data is limited by low dataset scale and diversity, while using purely exocentric (third-person) data introduces a large domain mismatch. Our idea is to discover latent signal...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	230,592
2404.05051	Skill Transfer and Discovery for Sim-to-Real Learning: A Representation-Based Viewpoint	We study sim-to-real skill transfer and discovery in the context of robotics control using representation learning. We draw inspiration from spectral decomposition of Markov decision processes. The spectral decomposition brings about representation that can linearly represent the state-action value function induced by ...	false	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	444,917
2005.01571	Frugal Optimization for Cost-related Hyperparameters	The increasing demand for democratizing machine learning algorithms calls for hyperparameter optimization (HPO) solutions at low cost. Many machine learning algorithms have hyperparameters which can cause a large variation in the training cost. But this effect is largely ignored in existing HPO methods, which are incap...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	175,613
0903.1716	Improved Lower Bounds on Capacities of Symmetric 2-Dimensional Constraints using Rayleigh Quotients	A method for computing lower bounds on capacities of 2-dimensional constraints having a symmetric presentation in either the horizontal or the vertical direction is presented. The method is a generalization of the method of Calkin and Wilf (SIAM J. Discrete Math., 1998). Previous best lower bounds on capacities of cert...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	3,321
2410.11008	V2I-Calib++: A Multi-terminal Spatial Calibration Approach in Urban Intersections for Collaborative Perception	Urban intersections, dense with pedestrian and vehicular traffic and compounded by GPS signal obstructions from high-rise buildings, are among the most challenging areas in urban traffic systems. Traditional single-vehicle intelligence systems often perform poorly in such environments due to a lack of global traffic fl...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	498,342
1605.08881	Sparse Coding and Counting for Robust Visual Tracking	In this paper, we propose a novel sparse coding and counting method under Bayesian framwork for visual tracking. In contrast to existing methods, the proposed method employs the combination of L0 and L1 norm to regularize the linear coefficients of incrementally updated linear basis. The sparsity constraint enables the...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	56,492
1002.0709	Aggregating Algorithm competing with Banach lattices	The paper deals with on-line regression settings with signals belonging to a Banach lattice. Our algorithms work in a semi-online setting where all the inputs are known in advance and outcomes are unknown and given step by step. We apply the Aggregating Algorithm to construct a prediction method whose cumulative loss o...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	5,605
2305.03860	Towards the Neuromorphic Computing for Offroad Robot Environment Perception and Navigation	My research objective is to explicitly bridge the gap between high computational performance and low power dissipation of robot on-board hardware by designing a bio-inspired tapered whisker neuromorphic computing (also called reservoir computing) system for offroad robot environment perception and navigation, that cent...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	362,537
2412.17803	Examining Imbalance Effects on Performance and Demographic Fairness of Clinical Language Models	Data imbalance is a fundamental challenge in applying language models to biomedical applications, particularly in ICD code prediction tasks where label and demographic distributions are uneven. While state-of-the-art language models have been increasingly adopted in biomedical tasks, few studies have systematically exa...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	520,117
1906.08231	Holistic evaluation of XML queries with structural preferences on an annotated strong dataguide	With the emergence of XML as de facto format for storing and exchanging information over the Internet, the search for ever more innovative and effective techniques for their querying is a major and current concern of the XML database community. Several studies carried out to help solve this problem are mostly oriented ...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	false	135,811
2111.07613	Generate plane quad mesh with neural networks and tree search	The quality of mesh generation has long been considered a vital aspect in providing engineers with reliable simulation results throughout the history of the Finite Element Method (FEM). The element extraction method, which is currently the most robust method, is used in business software. However, in order to speed up ...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	true	266,430
2309.10817	Assessing the capacity of a denoising diffusion probabilistic model to reproduce spatial context	Diffusion models have emerged as a popular family of deep generative models (DGMs). In the literature, it has been claimed that one class of diffusion models -- denoising diffusion probabilistic models (DDPMs) -- demonstrate superior image synthesis performance as compared to generative adversarial networks (GANs). To ...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	393,166
2312.09588	NeuroFlow: Development of lightweight and efficient model integration scheduling strategy for autonomous driving system	This paper proposes a specialized autonomous driving system that takes into account the unique constraints and characteristics of automotive systems, aiming for innovative advancements in autonomous driving technology. The proposed system systematically analyzes the intricate data flow in autonomous driving and provide...	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	false	415,800
2410.11001	Graph of Records: Boosting Retrieval Augmented Generation for Long-context Summarization with Graphs	Retrieval-augmented generation (RAG) has revitalized Large Language Models (LLMs) by injecting non-parametric factual knowledge. Compared with long-context LLMs, RAG is considered an effective summarization tool in a more concise and lightweight manner, which can interact with LLMs multiple times using diverse queries ...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	498,338
1402.4862	Learning the Parameters of Determinantal Point Process Kernels	Determinantal point processes (DPPs) are well-suited for modeling repulsion and have proven useful in many applications where diversity is desired. While DPPs have many appealing properties, such as efficient sampling, learning the parameters of a DPP is still considered a difficult problem due to the non-convex nature...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	31,001
2202.00710	Improving Sample Efficiency of Value Based Models Using Attention and Vision Transformers	Much of recent Deep Reinforcement Learning success is owed to the neural architecture's potential to learn and use effective internal representations of the world. While many current algorithms access a simulator to train with a large amount of data, in realistic settings, including while playing games that may be play...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	278,232
2109.09923	AutoPhoto: Aesthetic Photo Capture using Reinforcement Learning	The process of capturing a well-composed photo is difficult and it takes years of experience to master. We propose a novel pipeline for an autonomous agent to automatically capture an aesthetic photograph by navigating within a local region in a scene. Instead of classical optimization over heuristics such as the rule-...	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	256,448
2111.15207	NeeDrop: Self-supervised Shape Representation from Sparse Point Clouds using Needle Dropping	There has been recently a growing interest for implicit shape representations. Contrary to explicit representations, they have no resolution limitations and they easily deal with a wide variety of surface topologies. To learn these implicit representations, current approaches rely on a certain level of shape supervisio...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	true	268,860
1805.03963	Monotone Learning with Rectified Wire Networks	We introduce a new neural network model, together with a tractable and monotone online learning algorithm. Our model describes feed-forward networks for classification, with one output node for each class. The only nonlinear operation is rectification using a ReLU function with a bias. However, there is a rectifier on ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	true	false	false	97,148
2409.07402	What to align in multimodal contrastive learning?	Humans perceive the world through multisensory integration, blending the information of different modalities to adapt their behavior. Contrastive learning offers an appealing solution for multimodal self-supervised learning. Indeed, by considering each modality as a different view of the same entity, it learns to align...	false	false	false	false	true	false	true	false	true	false	false	true	false	false	false	false	false	false	487,502
2006.12971	Gaining Insight into SARS-CoV-2 Infection and COVID-19 Severity Using Self-supervised Edge Features and Graph Neural Networks	A molecular and cellular understanding of how SARS-CoV-2 variably infects and causes severe COVID-19 remains a bottleneck in developing interventions to end the pandemic. We sought to use deep learning to study the biology of SARS-CoV-2 infection and COVID-19 severity by identifying transcriptomic patterns and cell typ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	183,761
2207.06294	Reinforcement Learning Assisted Recursive QAOA	Variational quantum algorithms such as the Quantum Approximation Optimization Algorithm (QAOA) in recent years have gained popularity as they provide the hope of using NISQ devices to tackle hard combinatorial optimization problems. It is, however, known that at low depth, certain locality constraints of QAOA limit its...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	307,836
1107.1750	Structural and Dynamical Patterns on Online Social Networks: the Spanish May 15th Movement as a case study	The number of people using online social networks in their everyday life is continuously growing at a pace never saw before. This new kind of communication has an enormous impact on opinions, cultural trends, information spreading and even in the commercial success of new products. More importantly, social online netwo...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	11,218
1810.06498	SynSeg-Net: Synthetic Segmentation Without Target Modality Ground Truth	A key limitation of deep convolutional neural networks (DCNN) based image segmentation methods is the lack of generalizability. Manually traced training images are typically required when segmenting organs in a new imaging modality or from distinct disease cohort. The manual efforts can be alleviated if the manually tr...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	110,443
2312.03025	Training on Synthetic Data Beats Real Data in Multimodal Relation Extraction	The task of multimodal relation extraction has attracted significant research attention, but progress is constrained by the scarcity of available training data. One natural thought is to extend existing datasets with cross-modal generative models. In this paper, we consider a novel problem setting, where only unimodal ...	false	false	false	false	true	false	true	false	true	false	false	true	false	false	false	false	false	false	413,101
2003.06961	Online detection of local abrupt changes in high-dimensional Gaussian graphical models	The problem of identifying change points in high-dimensional Gaussian graphical models (GGMs) in an online fashion is of interest, due to new applications in biology, economics and social sciences. The offline version of the problem, where all the data are a priori available, has led to a number of methods and associat...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	168,278
2409.02418	MOSMOS: Multi-organ segmentation facilitated by medical report supervision	Owing to a large amount of multi-modal data in modern medical systems, such as medical images and reports, Medical Vision-Language Pre-training (Med-VLP) has demonstrated incredible achievements in coarse-grained downstream tasks (i.e., medical classification, retrieval, and visual question answering). However, the pro...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	485,693

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