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
2105.05204	Development of a Multi-Task Learning V-Net for Pulmonary Lobar Segmentation on Computed Tomography and Application to Diseased Lungs	Automated lobar segmentation allows regional evaluation of lung disease and is important for diagnosis and therapy planning. Advanced statistical workflows permitting such evaluation is a needed area within respiratory medicine; their adoption remains slow, with poor workflow accuracy. Diseased lung regions often produ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	234,745
1703.00767	Attentive Recurrent Comparators	Rapid learning requires flexible representations to quickly adopt to new evidence. We develop a novel class of models called Attentive Recurrent Comparators (ARCs) that form representations of objects by cycling through them and making observations. Using the representations extracted by ARCs, we develop a way of appro...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	69,224
1604.04618	Make Up Your Mind: The Price of Online Queries in Differential Privacy	We consider the problem of answering queries about a sensitive dataset subject to differential privacy. The queries may be chosen adversarially from a larger set Q of allowable queries in one of three ways, which we list in order from easiest to hardest to answer: Offline: The queries are chosen all at once and the d...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	true	54,672
nlin/0703036	Statistical User Model for the Internet Access	A new statistical based model approach to characterize a user's behavior in an Internet access link is presented. The real patterns of Internet traffic in a heterogeneous Campus Network are studied. We find three clearly different patterns of individual user's behavior, study their common features and group particular ...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	true	540,798
2311.02583	SSL-DG: Rethinking and Fusing Semi-supervised Learning and Domain Generalization in Medical Image Segmentation	Deep learning-based medical image segmentation is an essential yet challenging task in clinical practice, which arises from restricted access to annotated data coupled with the occurrence of domain shifts. Previous attempts have focused on isolated solutions, while disregarding their inter-connectedness. In this paper,...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	405,504
1605.08883	User-based solutions for increasing level of service in bike-sharing transportation systems	Bike-sharing transportation systems have been well studied from a top-down viewpoint, either for an optimal conception of the system, or for a better statistical understanding of their working mechanisms in the aim of the optimization of the management strategy. Yet bottom-up approaches that could include behavior of u...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	56,494
2304.04051	Generating a Graph Colouring Heuristic with Deep Q-Learning and Graph Neural Networks	The graph colouring problem consists of assigning labels, or colours, to the vertices of a graph such that no two adjacent vertices share the same colour. In this work we investigate whether deep reinforcement learning can be used to discover a competitive construction heuristic for graph colouring. Our proposed approa...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	357,054
2311.16141	Brain-Inspired Efficient Pruning: Exploiting Criticality in Spiking Neural Networks	Spiking Neural Networks (SNNs) have gained significant attention due to the energy-efficient and multiplication-free characteristics. Despite these advantages, deploying large-scale SNNs on edge hardware is challenging due to limited resource availability. Network pruning offers a viable approach to compress the networ...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	true	false	false	410,789
2501.03045	Single-Channel Distance-Based Source Separation for Mobile GPU in Outdoor and Indoor Environments	This study emphasizes the significance of exploring distance-based source separation (DSS) in outdoor environments. Unlike existing studies that primarily focus on indoor settings, the proposed model is designed to capture the unique characteristics of outdoor audio sources. It incorporates advanced techniques, includi...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	522,741
1911.08453	Planning with Goal-Conditioned Policies	Planning methods can solve temporally extended sequential decision making problems by composing simple behaviors. However, planning requires suitable abstractions for the states and transitions, which typically need to be designed by hand. In contrast, model-free reinforcement learning (RL) can acquire behaviors from l...	false	false	false	false	true	false	true	true	false	false	false	false	false	false	false	false	false	false	154,195
1904.04817	Learning from Videos with Deep Convolutional LSTM Networks	This paper explores the use of convolution LSTMs to simultaneously learn spatial- and temporal-information in videos. A deep network of convolutional LSTMs allows the model to access the entire range of temporal information at all spatial scales of the data. We describe our experiments involving convolution LSTMs for l...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	127,135
2305.17589	Graph Inductive Biases in Transformers without Message Passing	Transformers for graph data are increasingly widely studied and successful in numerous learning tasks. Graph inductive biases are crucial for Graph Transformers, and previous works incorporate them using message-passing modules and/or positional encodings. However, Graph Transformers that use message-passing inherit kn...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	368,664
2011.07743	Beyond I.I.D.: Three Levels of Generalization for Question Answering on Knowledge Bases	Existing studies on question answering on knowledge bases (KBQA) mainly operate with the standard i.i.d assumption, i.e., training distribution over questions is the same as the test distribution. However, i.i.d may be neither reasonably achievable nor desirable on large-scale KBs because 1) true user distribution is h...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	206,659
2310.03022	Decision ConvFormer: Local Filtering in MetaFormer is Sufficient for Decision Making	The recent success of Transformer in natural language processing has sparked its use in various domains. In offline reinforcement learning (RL), Decision Transformer (DT) is emerging as a promising model based on Transformer. However, we discovered that the attention module of DT is not appropriate to capture the inher...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	397,099
1410.8664	Algorithmic Design for Competitive Influence Maximization Problems	Given the popularity of the viral marketing campaign in online social networks, finding an effective method to identify a set of most influential nodes so to compete well with other viral marketing competitors is of upmost importance. We propose a "General Competitive Independent Cascade (GCIC)" model to describe the g...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	true	37,180
1110.6864	Asymptotics for numbers of line segments and lines in a square grid	We present an asymptotic formula for the number of line segments connecting q+1 points of an nxn square grid, and a sharper formula, assuming the Riemann hypothesis. We also present asymptotic formulas for the number of lines through at least q points and, respectively, through exactly q points of the grid. The well-kn...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	12,834
2004.11472	Multiple Segmentations of Thai Sentences for Neural Machine Translation	Thai is a low-resource language, so it is often the case that data is not available in sufficient quantities to train an Neural Machine Translation (NMT) model which perform to a high level of quality. In addition, the Thai script does not use white spaces to delimit the boundaries between words, which adds more comple...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	173,917
1209.0913	Structuring Relevant Feature Sets with Multiple Model Learning	Feature selection is one of the most prominent learning tasks, especially in high-dimensional datasets in which the goal is to understand the mechanisms that underly the learning dataset. However most of them typically deliver just a flat set of relevant features and provide no further information on what kind of struc...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	18,402
2309.12378	Unsupervised Semantic Segmentation Through Depth-Guided Feature Correlation and Sampling	Traditionally, training neural networks to perform semantic segmentation required expensive human-made annotations. But more recently, advances in the field of unsupervised learning have made significant progress on this issue and towards closing the gap to supervised algorithms. To achieve this, semantic knowledge is ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	393,781
2004.07928	MARLeME: A Multi-Agent Reinforcement Learning Model Extraction Library	Multi-Agent Reinforcement Learning (MARL) encompasses a powerful class of methodologies that have been applied in a wide range of fields. An effective way to further empower these methodologies is to develop libraries and tools that could expand their interpretability and explainability. In this work, we introduce MARL...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	172,905
2007.04093	Machine learning and data analytics for the IoT	The Internet of Things (IoT) applications have grown in exorbitant numbers, generating a large amount of data required for intelligent data processing. However, the varying IoT infrastructures (i.e., cloud, edge, fog) and the limitations of the IoT application layer protocols in transmitting/receiving messages become t...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	186,256
2408.10332	Spectral Guarantees for Adversarial Streaming PCA	In streaming PCA, we see a stream of vectors $x_1, \dotsc, x_n \in \mathbb{R}^d$ and want to estimate the top eigenvector of their covariance matrix. This is easier if the spectral ratio $R = \lambda_1 / \lambda_2$ is large. We ask: how large does $R$ need to be to solve streaming PCA in $\widetilde{O}(d)$ space? Exist...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	481,809
2106.02318	AdaTag: Multi-Attribute Value Extraction from Product Profiles with Adaptive Decoding	Automatic extraction of product attribute values is an important enabling technology in e-Commerce platforms. This task is usually modeled using sequence labeling architectures, with several extensions to handle multi-attribute extraction. One line of previous work constructs attribute-specific models, through separate...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	238,826
2406.05535	Perturbation Towards Easy Samples Improves Targeted Adversarial Transferability	The transferability of adversarial perturbations provides an effective shortcut for black-box attacks. Targeted perturbations have greater practicality but are more difficult to transfer between models. In this paper, we experimentally and theoretically demonstrated that neural networks trained on the same dataset have...	false	false	false	false	true	false	true	false	false	false	false	false	true	false	false	false	false	false	462,176
2405.01682	Leveraging Prompt-Learning for Structured Information Extraction from Crohn's Disease Radiology Reports in a Low-Resource Language	Automatic conversion of free-text radiology reports into structured data using Natural Language Processing (NLP) techniques is crucial for analyzing diseases on a large scale. While effective for tasks in widely spoken languages like English, generative large language models (LLMs) typically underperform with less comm...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	451,457
2210.11905	Exploration of the Usage of Color Terms by Color-blind Participants in Online Discussion Platforms	Prominent questions about the role of sensory vs. linguistic input in the way we acquire and use language have been extensively studied in the psycholinguistic literature. However, the relative effect of various factors in a person's overall experience on their linguistic system remains unclear. We study this question ...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	325,499
2211.05497	Effect of Device Mismatches in Differential Oscillatory Neural Networks	Analog implementation of Oscillatory Neural Networks (ONNs) has the potential to implement fast and ultra-low-power computing capabilities. One of the drawbacks of analog implementation is component mismatches which cause desynchronization and instability in ONNs. Emerging devices like memristors and VO2 are particular...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	329,572
1904.05033	Better Word Embeddings by Disentangling Contextual n-Gram Information	Pre-trained word vectors are ubiquitous in Natural Language Processing applications. In this paper, we show how training word embeddings jointly with bigram and even trigram embeddings, results in improved unigram embeddings. We claim that training word embeddings along with higher n-gram embeddings helps in the remova...	false	false	false	false	true	true	true	false	true	false	false	false	false	false	false	false	false	false	127,192
2408.04638	Affective Computing in the Era of Large Language Models: A Survey from the NLP Perspective	Affective Computing (AC), integrating computer science, psychology, and cognitive science knowledge, aims to enable machines to recognize, interpret, and simulate human emotions.To create more value, AC can be applied to diverse scenarios, including social media, finance, healthcare, education, etc. Affective Computing...	false	false	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	479,456
2502.02238	Using ChatGPT to refine draft conceptual schemata in supply-driven design of multidimensional cubes	Refinement is a critical step in supply-driven conceptual design of multidimensional cubes because it can hardly be automated. In fact, it includes steps such as the labeling of attributes as descriptive and the removal of uninteresting attributes, thus relying on the end-users' requirements on the one hand, and on the...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	true	530,235
2403.00584	Generalized User Representations for Transfer Learning	We present a novel framework for user representation in large-scale recommender systems, aiming at effectively representing diverse user taste in a generalized manner. Our approach employs a two-stage methodology combining representation learning and transfer learning. The representation learning model uses an autoenco...	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	false	434,034
2411.18729	Multi-Task Model Merging via Adaptive Weight Disentanglement	Model merging has recently gained attention as an economical and scalable approach to incorporate task-specific weights from various tasks into a unified multi-task model. For example, in Task Arithmetic (TA), adding the fine-tuned weights of different tasks can enhance the model's performance on those tasks, while sub...	false	false	false	false	false	false	true	false	true	false	false	true	false	false	false	false	false	false	511,984
2410.13883	Transformers Utilization in Chart Understanding: A Review of Recent Advances & Future Trends	In recent years, interest in vision-language tasks has grown, especially those involving chart interactions. These tasks are inherently multimodal, requiring models to process chart images, accompanying text, underlying data tables, and often user queries. Traditionally, Chart Understanding (CU) relied on heuristics an...	true	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	499,755
2406.16012	Wound Tissue Segmentation in Diabetic Foot Ulcer Images Using Deep Learning: A Pilot Study	Identifying individual tissues, so-called tissue segmentation, in diabetic foot ulcer (DFU) images is a challenging task and little work has been published, largely due to the limited availability of a clinical image dataset. To address this gap, we have created a DFUTissue dataset for the research community to evaluat...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	466,961
2211.02133	Streaming Audio-Visual Speech Recognition with Alignment Regularization	In this work, we propose a streaming AV-ASR system based on a hybrid connectionist temporal classification (CTC)/attention neural network architecture. The audio and the visual encoder neural networks are both based on the conformer architecture, which is made streamable using chunk-wise self-attention (CSA) and causal...	false	false	true	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	328,477
2108.08368	Computing Steiner Trees using Graph Neural Networks	Graph neural networks have been successful in many learning problems and real-world applications. A recent line of research explores the power of graph neural networks to solve combinatorial and graph algorithmic problems such as subgraph isomorphism, detecting cliques, and the traveling salesman problem. However, many...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	251,227
2107.11662	Inference of collective Gaussian hidden Markov models	We consider inference problems for a class of continuous state collective hidden Markov models, where the data is recorded in aggregate (collective) form generated by a large population of individuals following the same dynamics. We propose an aggregate inference algorithm called collective Gaussian forward-backward al...	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	247,652
2203.10444	VGSE: Visually-Grounded Semantic Embeddings for Zero-Shot Learning	Human-annotated attributes serve as powerful semantic embeddings in zero-shot learning. However, their annotation process is labor-intensive and needs expert supervision. Current unsupervised semantic embeddings, i.e., word embeddings, enable knowledge transfer between classes. However, word embeddings do not always re...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	286,539
1906.02171	Estimating Feature-Label Dependence Using Gini Distance Statistics	Identifying statistical dependence between the features and the label is a fundamental problem in supervised learning. This paper presents a framework for estimating dependence between numerical features and a categorical label using generalized Gini distance, an energy distance in reproducing kernel Hilbert spaces (RK...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	133,964
1803.09909	A Divide-and-Conquer Approach to Compressed Sensing MRI	Compressed sensing (CS) theory assures us that we can accurately reconstruct magnetic resonance images using fewer k-space measurements than the Nyquist sampling rate requires. In traditional CS-MRI inversion methods, the fact that the energy within the Fourier measurement domain is distributed non-uniformly is often n...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	93,608
1510.05956	Optimal Cluster Recovery in the Labeled Stochastic Block Model	We consider the problem of community detection or clustering in the labeled Stochastic Block Model (LSBM) with a finite number $K$ of clusters of sizes linearly growing with the global population of items $n$. Every pair of items is labeled independently at random, and label $\ell$ appears with probability $p(i,j,\ell)...	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	false	false	48,068
1303.0339	Learning Hash Functions Using Column Generation	Fast nearest neighbor searching is becoming an increasingly important tool in solving many large-scale problems. Recently a number of approaches to learning data-dependent hash functions have been developed. In this work, we propose a column generation based method for learning data-dependent hash functions on the basi...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	22,551
2208.03408	A novel deep learning-based approach for sleep apnea detection using single-lead ECG signals	Sleep apnea (SA) is a type of sleep disorder characterized by snoring and chronic sleeplessness, which can lead to serious conditions such as high blood pressure, heart failure, and cardiomyopathy (enlargement of the muscle tissue of the heart). The electrocardiogram (ECG) plays a critical role in identifying SA since ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	311,776
2009.08801	SciBERT-based Semantification of Bioassays in the Open Research Knowledge Graph	As a novel contribution to the problem of semantifying biological assays, in this paper, we propose a neural-network-based approach to automatically semantify, thereby structure, unstructured bioassay text descriptions. Experimental evaluations, to this end, show promise as the neural-based semantification significantl...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	196,349
2105.10816	Novel Deep Learning Architecture for Heart Disease Prediction using Convolutional Neural Network	Healthcare is one of the most important aspects of human life. Heart disease is known to be one of the deadliest diseases which is hampering the lives of many people around the world. Heart disease must be detected early so the loss of lives can be prevented. The availability of large-scale data for medical diagnosis h...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	236,502
1412.6753	Temporal effects in trend prediction: identifying the most popular nodes in the future	Prediction is an important problem in different science domains. In this paper, we focus on trend prediction in complex networks, i.e. to identify the most popular nodes in the future. Due to the preferential attachment mechanism in real systems, nodes' recent degree and cumulative degree have been successfully applied...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	38,712
2407.09281	Predicting and Understanding Human Action Decisions: Insights from Large Language Models and Cognitive Instance-Based Learning	Large Language Models (LLMs) have demonstrated their capabilities across various tasks, from language translation to complex reasoning. Understanding and predicting human behavior and biases are crucial for artificial intelligence (AI) assisted systems to provide useful assistance, yet it remains an open question wheth...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	472,517
2305.17696	SQuARe: A Large-Scale Dataset of Sensitive Questions and Acceptable Responses Created Through Human-Machine Collaboration	The potential social harms that large language models pose, such as generating offensive content and reinforcing biases, are steeply rising. Existing works focus on coping with this concern while interacting with ill-intentioned users, such as those who explicitly make hate speech or elicit harmful responses. However, ...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	368,709
2007.10835	Soft Expert Reward Learning for Vision-and-Language Navigation	Vision-and-Language Navigation (VLN) requires an agent to find a specified spot in an unseen environment by following natural language instructions. Dominant methods based on supervised learning clone expert's behaviours and thus perform better on seen environments, while showing restricted performance on unseen ones. ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	188,392
2302.06415	AISYN: AI-driven Reinforcement Learning-Based Logic Synthesis Framework	Logic synthesis is one of the most important steps in design and implementation of digital chips with a big impact on final Quality of Results (QoR). For a most general input circuit modeled by a Directed Acyclic Graph (DAG), many logic synthesis problems such as delay or area minimization are NP-Complete, hence, no op...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	345,395
2409.06740	Data-efficient and Interpretable Inverse Materials Design using a Disentangled Variational Autoencoder	Inverse materials design has proven successful in accelerating novel material discovery. Many inverse materials design methods use unsupervised learning where a latent space is learned to offer a compact description of materials representations. A latent space learned this way is likely to be entangled, in terms of the...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	487,258
2202.11280	Learning Multi-step Robotic Manipulation Policies from Visual Observation of Scene and Q-value Predictions of Previous Action	In this work, we focus on multi-step manipulation tasks that involve long-horizon planning and considers progress reversal. Such tasks interlace high-level reasoning that consists of the expected states that can be attained to achieve an overall task and low-level reasoning that decides what actions will yield these st...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	281,833
2101.07629	A family of codes with locality containing optimal codes	Locally recoverable codes were introduced by Gopalan et al. in 2012, and in the same year Prakash et al. introduced the concept of codes with locality, which are a type of locally recoverable codes. In this work we introduce a new family of codes with locality, which are subcodes of a certain family of evaluation codes...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	216,096
2303.11086	Pluralistic Aging Diffusion Autoencoder	Face aging is an ill-posed problem because multiple plausible aging patterns may correspond to a given input. Most existing methods often produce one deterministic estimation. This paper proposes a novel CLIP-driven Pluralistic Aging Diffusion Autoencoder (PADA) to enhance the diversity of aging patterns. First, we emp...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	352,699
2404.13690	Detecting Compromised IoT Devices Using Autoencoders with Sequential Hypothesis Testing	IoT devices fundamentally lack built-in security mechanisms to protect themselves from security attacks. Existing works on improving IoT security mostly focus on detecting anomalous behaviors of IoT devices. However, these existing anomaly detection schemes may trigger an overwhelmingly large number of false alerts, re...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	448,402
2501.06841	Faithful Counterfactual Visual Explanations (FCVE)	Deep learning models in computer vision have made remarkable progress, but their lack of transparency and interpretability remains a challenge. The development of explainable AI can enhance the understanding and performance of these models. However, existing techniques often struggle to provide convincing explanations ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	524,154
2104.08438	Bayesian graph convolutional neural networks via tempered MCMC	Deep learning models, such as convolutional neural networks, have long been applied to image and multi-media tasks, particularly those with structured data. More recently, there has been more attention to unstructured data that can be represented via graphs. These types of data are often found in health and medicine, s...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	230,796
2206.10520	SFace: Privacy-friendly and Accurate Face Recognition using Synthetic Data	Recent deep face recognition models proposed in the literature utilized large-scale public datasets such as MS-Celeb-1M and VGGFace2 for training very deep neural networks, achieving state-of-the-art performance on mainstream benchmarks. Recently, many of these datasets, e.g., MS-Celeb-1M and VGGFace2, are retracted du...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	303,931
1812.10666	Neural Architecture Search Over a Graph Search Space	Neural Architecture Search (NAS) enabled the discovery of state-of-the-art architectures in many domains. However, the success of NAS depends on the definition of the search space. Current search spaces are defined as a static sequence of decisions and a set of available actions for each decision. Each possible sequenc...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	117,419
2104.10777	Viking: Variational Bayesian Variance Tracking	We consider the problem of time series forecasting in an adaptive setting. We focus on the inference of state-space models under unknown and potentially time-varying noise variances. We introduce an augmented model in which the variances are represented as auxiliary gaussian latent variables in a tracking mode. As vari...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	231,704
2112.03277	Automatic quality control framework for more reliable integration of machine learning-based image segmentation into medical workflows	Machine learning algorithms underpin modern diagnostic-aiding software, which has proved valuable in clinical practice, particularly in radiology. However, inaccuracies, mainly due to the limited availability of clinical samples for training these algorithms, hamper their wider applicability, acceptance, and recognitio...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	270,151
1807.03491	Deep-speare: A Joint Neural Model of Poetic Language, Meter and Rhyme	In this paper, we propose a joint architecture that captures language, rhyme and meter for sonnet modelling. We assess the quality of generated poems using crowd and expert judgements. The stress and rhyme models perform very well, as generated poems are largely indistinguishable from human-written poems. Expert evalua...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	102,537
2309.12694	Recurrent Temporal Revision Graph Networks	Temporal graphs offer more accurate modeling of many real-world scenarios than static graphs. However, neighbor aggregation, a critical building block of graph networks, for temporal graphs, is currently straightforwardly extended from that of static graphs. It can be computationally expensive when involving all histor...	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	false	false	393,900
2303.06532	Automated Design of Metaheuristic Algorithms: A Survey	Metaheuristics have gained great success in academia and practice because their search logic can be applied to any problem with available solution representation, solution quality evaluation, and certain notions of locality. Manually designing metaheuristic algorithms for solving a target problem is criticized for bein...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	true	false	false	350,882
2306.17578	Role of single particle motility statistics on efficiency of targeted delivery of micro-robot swarms	The study of dynamics of single active particles plays an important role in the development of artificial or hybrid micro-systems for bio-medical and other applications at micro-scale. Here, we utilize the results of these studies to better understand their implications for the specific application of drug delivery. We...	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	376,749
2303.08900	Contextual Trust	Trust is an important aspect of human life. It provides instrumental value in allowing us to collaborate on and defer actions to others, and intrinsic value in our intimate relationships with romantic partners, family, and friends. In this paper I examine the nature of trust from a philosophical perspective. Specifical...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	true	false	false	false	351,814
1212.1362	Stochastic model for the vocabulary growth in natural languages	We propose a stochastic model for the number of different words in a given database which incorporates the dependence on the database size and historical changes. The main feature of our model is the existence of two different classes of words: (i) a finite number of core-words which have higher frequency and do not af...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	20,169
2502.09100	Logical Reasoning in Large Language Models: A Survey	With the emergence of advanced reasoning models like OpenAI o3 and DeepSeek-R1, large language models (LLMs) have demonstrated remarkable reasoning capabilities. However, their ability to perform rigorous logical reasoning remains an open question. This survey synthesizes recent advancements in logical reasoning within...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	533,309
2206.02013	Causal Discovery in Heterogeneous Environments Under the Sparse Mechanism Shift Hypothesis	Machine learning approaches commonly rely on the assumption of independent and identically distributed (i.i.d.) data. In reality, however, this assumption is almost always violated due to distribution shifts between environments. Although valuable learning signals can be provided by heterogeneous data from changing dis...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	300,706
1401.2119	Maximum Throughput for a Cognitive Radio Multi-Antenna User with Multiple Primary Users	We investigate a cognitive radio scenario involving a single cognitive transmitter equipped with $\mathcal{K}$ antennas sharing the spectrum with $\mathcal{M}$ primary users (PUs) transmitting over orthogonal bands. Each terminal has a queue to store its incoming traffic. We propose a novel protocol where the cognitive...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	29,717
1810.00090	Cell Grid Architecture for Maritime Route Prediction on AIS Data Streams	The 2018 Grand Challenge targets the problem of accurate predictions on data streams produced by automatic identification system (AIS) equipment, describing naval traffic. This paper reports the technical details of a custom solution, which exposes multiple tuning parameters, making its configurability one of the main ...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	109,084
1405.4681	Containment control of multi-agent systems with measurement noises	In this paper, containment control of multi-agent systems with measurement noises is studied under directed networks. When the leaders are stationary, a stochastic approximation type protocol is employed to solve the containment control of multi-agent systems. By using stochastic analysis tools and algebraic graph theo...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	33,205
1903.01063	NoRML: No-Reward Meta Learning	Efficiently adapting to new environments and changes in dynamics is critical for agents to successfully operate in the real world. Reinforcement learning (RL) based approaches typically rely on external reward feedback for adaptation. However, in many scenarios this reward signal might not be readily available for the ...	false	false	false	false	true	false	true	true	false	false	false	false	false	false	false	false	false	false	123,182
2303.14376	ViPFormer: Efficient Vision-and-Pointcloud Transformer for Unsupervised Pointcloud Understanding	Recently, a growing number of work design unsupervised paradigms for point cloud processing to alleviate the limitation of expensive manual annotation and poor transferability of supervised methods. Among them, CrossPoint follows the contrastive learning framework and exploits image and point cloud data for unsupervise...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	354,060
2204.12073	One-pass additive-error subset selection for $\ell_{p}$ subspace approximation	We consider the problem of subset selection for $\ell_{p}$ subspace approximation, that is, to efficiently find a \emph{small} subset of data points such that solving the problem optimally for this subset gives a good approximation to solving the problem optimally for the original input. Previously known subset selecti...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	293,362
2311.11959	Correlated Attention in Transformers for Multivariate Time Series	Multivariate time series (MTS) analysis prevails in real-world applications such as finance, climate science and healthcare. The various self-attention mechanisms, the backbone of the state-of-the-art Transformer-based models, efficiently discover the temporal dependencies, yet cannot well capture the intricate cross-c...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	409,142
2308.00246	EEG-based Cognitive Load Classification using Feature Masked Autoencoding and Emotion Transfer Learning	Cognitive load, the amount of mental effort required for task completion, plays an important role in performance and decision-making outcomes, making its classification and analysis essential in various sensitive domains. In this paper, we present a new solution for the classification of cognitive load using electroenc...	true	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	382,869
2206.03611	FedPop: A Bayesian Approach for Personalised Federated Learning	Personalised federated learning (FL) aims at collaboratively learning a machine learning model taylored for each client. Albeit promising advances have been made in this direction, most of existing approaches works do not allow for uncertainty quantification which is crucial in many applications. In addition, personali...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	301,343
1012.4905	Convolutional Goppa codes defined on fibrations	We define a new class of Convolutional Codes in terms of fibrations of algebraic varieties generalizaing our previous constructions of Convolutional Goppa Codes. Using this general construction we can give several examples of Maximum Distance Separable (MDS) Convolutional Codes.	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	8,625
1806.01623	Adaptive twisting sliding mode control for quadrotor unmanned aerial vehicles	This work addresses the problem of robust attitude control of quadcopters. First, the mathematical model of the quadcopter is derived considering factors such as nonlinearity, external disturbances, uncertain dynamics and strong coupling. An adaptive twisting sliding mode control algorithm is then developed with the ob...	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	99,596
2207.00003	A Multi-stage Framework with Mean Subspace Computation and Recursive Feedback for Online Unsupervised Domain Adaptation	In this paper, we address the Online Unsupervised Domain Adaptation (OUDA) problem and propose a novel multi-stage framework to solve real-world situations when the target data are unlabeled and arriving online sequentially in batches. To project the data from the source and the target domains to a common subspace and ...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	305,607
2211.01365	QuACK: Accelerating Gradient-Based Quantum Optimization with Koopman Operator Learning	Quantum optimization, a key application of quantum computing, has traditionally been stymied by the linearly increasing complexity of gradient calculations with an increasing number of parameters. This work bridges the gap between Koopman operator theory, which has found utility in applications because it allows for a ...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	328,206
2207.11641	Clustered Cell-Free Networking: A Graph Partitioning Approach	By moving to millimeter wave (mmWave) frequencies, base stations (BSs) will be densely deployed to provide seamless coverage in sixth generation (6G) mobile communication systems, which, unfortunately, leads to severe cell-edge problem. In addition, with massive multiple-input-multiple-output (MIMO) antenna arrays empl...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	309,717
1309.0634	Skew Handling in Aggregate Streaming Queries on GPUs	Nowadays, the data to be processed by database systems has grown so large that any conventional, centralized technique is inadequate. At the same time, general purpose computation on GPU (GPGPU) recently has successfully drawn attention from the data management community due to its ability to achieve significant speed-...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	true	26,797
2305.01758	Adversarial Generative NMF for Single Channel Source Separation	The idea of adversarial learning of regularization functionals has recently been introduced in the wider context of inverse problems. The intuition behind this method is the realization that it is not only necessary to learn the basic features that make up a class of signals one wants to represent, but also, or even mo...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	361,788
2309.06176	Dual-Path Temporal Map Optimization for Make-up Temporal Video Grounding	Make-up temporal video grounding (MTVG) aims to localize the target video segment which is semantically related to a sentence describing a make-up activity, given a long video. Compared with the general video grounding task, MTVG focuses on meticulous actions and changes on the face. The make-up instruction step, usual...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	391,337
2407.14812	GaitMA: Pose-guided Multi-modal Feature Fusion for Gait Recognition	Gait recognition is a biometric technology that recognizes the identity of humans through their walking patterns. Existing appearance-based methods utilize CNN or Transformer to extract spatial and temporal features from silhouettes, while model-based methods employ GCN to focus on the special topological structure of ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	474,917
2112.12033	Encoding protein dynamic information in graph representation for functional residue identification	Recent advances in protein function prediction exploit graph-based deep learning approaches to correlate the structural and topological features of proteins with their molecular functions. However, proteins in vivo are not static but dynamic molecules that alter conformation for functional purposes. Here we apply norma...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	272,859
1609.05539	On Randomized Distributed Coordinate Descent with Quantized Updates	In this paper, we study the randomized distributed coordinate descent algorithm with quantized updates. In the literature, the iteration complexity of the randomized distributed coordinate descent algorithm has been characterized under the assumption that machines can exchange updates with an infinite precision. We con...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	61,160
2302.00633	Deep Dependency Networks for Multi-Label Classification	We propose a simple approach which combines the strengths of probabilistic graphical models and deep learning architectures for solving the multi-label classification task, focusing specifically on image and video data. First, we show that the performance of previous approaches that combine Markov Random Fields with ne...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	343,288
1708.03498	Neural Expectation Maximization	Many real world tasks such as reasoning and physical interaction require identification and manipulation of conceptual entities. A first step towards solving these tasks is the automated discovery of distributed symbol-like representations. In this paper, we explicitly formalize this problem as inference in a spatial m...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	true	false	false	78,784
1907.12268	Discovering Association with Copula Entropy	Discovering associations is of central importance in scientific practices. Currently, most researches consider only linear association measured by correlation coefficient, which has its theoretical limitations. In this paper, we propose a new method for discovering association with copula entropy -- a universal applica...	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	140,073
2110.13957	Unbiased Graph Embedding with Biased Graph Observations	Graph embedding techniques are pivotal in real-world machine learning tasks that operate on graph-structured data, such as social recommendation and protein structure modeling. Embeddings are mostly performed on the node level for learning representations of each node. Since the formation of a graph is inevitably affec...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	263,356
2010.09555	Learning a Low-dimensional Representation of a Safe Region for Safe Reinforcement Learning on Dynamical Systems	For safely applying reinforcement learning algorithms on high-dimensional nonlinear dynamical systems, a simplified system model is used to formulate a safe reinforcement learning framework. Based on the simplified system model, a low-dimensional representation of the safe region is identified and is used to provide sa...	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	201,587
2202.07253	Exploiting Data Sparsity in Secure Cross-Platform Social Recommendation	Social recommendation has shown promising improvements over traditional systems since it leverages social correlation data as an additional input. Most existing work assumes that all data are available to the recommendation platform. However, in practice, user-item interaction data (e.g.,rating) and user-user social da...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	280,488
1602.03458	Super-Resolved Retinal Image Mosaicing	The acquisition of high-resolution retinal fundus images with a large field of view (FOV) is challenging due to technological, physiological and economic reasons. This paper proposes a fully automatic framework to reconstruct retinal images of high spatial resolution and increased FOV from multiple low-resolution image...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	51,999
2409.09646	A Simple HMM with Self-Supervised Representations for Phone Segmentation	Despite the recent advance in self-supervised representations, unsupervised phonetic segmentation remains challenging. Most approaches focus on improving phonetic representations with self-supervised learning, with the hope that the improvement can transfer to phonetic segmentation. In this paper, contrary to recent ap...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	488,409
2409.05751	Design of a Variable Stiffness Quasi-Direct Drive Cable-Actuated Tensegrity Robot	Tensegrity robots excel in tasks requiring extreme levels of deformability and robustness. However, there are challenges in state estimation and payload versatility due to their high number of degrees of freedom and unconventional shape. This paper introduces a modular three-bar tensegrity robot featuring a customizabl...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	486,884
2305.13102	Observations on LLMs for Telecom Domain: Capabilities and Limitations	The landscape for building conversational interfaces (chatbots) has witnessed a paradigm shift with recent developments in generative Artificial Intelligence (AI) based Large Language Models (LLMs), such as ChatGPT by OpenAI (GPT3.5 and GPT4), Google's Bard, Large Language Model Meta AI (LLaMA), among others. In this p...	true	false	false	false	true	true	true	false	true	false	false	false	false	false	false	false	false	false	366,349
0712.4321	Subsystem Code Constructions	Subsystem codes are the most versatile class of quantum error-correcting codes known to date that combine the best features of all known passive and active error-control schemes. The subsystem code is a subspace of the quantum state space that is decomposed into a tensor product of two vector spaces: the subsystem and ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	1,095

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