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
2110.15453	Using Text Analytics for Health to Get Meaningful Insights from a Corpus of COVID Scientific Papers	Since the beginning of COVID pandemic, there have been around 700000 scientific papers published on the subject. A human researcher cannot possibly get acquainted with such a huge text corpus -- and therefore developing AI-based tools to help navigating this corpus and deriving some useful insights from it is highly ne...	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	true	263,884
1302.5592	A tournament of order 24 with two disjoint TEQ-retentive sets	Brandt et al. (2013) have recently disproved a conjecture by Schwartz (1990) by non-constructively showing the existence of a counterexample with about 10^136 alternatives. We provide a concrete counterexample for Schwartz's conjecture with only 24 alternatives.	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	22,309
2408.04652	Leveraging Large Language Models with Chain-of-Thought and Prompt Engineering for Traffic Crash Severity Analysis and Inference	Harnessing the power of Large Language Models (LLMs), this study explores the use of three state-of-the-art LLMs, specifically GPT-3.5-turbo, LLaMA3-8B, and LLaMA3-70B, for crash severity inference, framing it as a classification task. We generate textual narratives from original traffic crash tabular data using a pre-...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	479,468
2106.13579	Graph model selection by edge probability sequential inference	Graphs are widely used for describing systems made up of many interacting components and for understanding the structure of their interactions. Various statistical models exist, which describe this structure as the result of a combination of constraints and randomness. %Model selection techniques need to automatically ...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	243,122
2211.16808	Efficient Adversarial Input Generation via Neural Net Patching	The generation of adversarial inputs has become a crucial issue in establishing the robustness and trustworthiness of deep neural nets, especially when they are used in safety-critical application domains such as autonomous vehicles and precision medicine. However, the problem poses multiple practical challenges, inclu...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	333,761
2402.13934	Do Efficient Transformers Really Save Computation?	As transformer-based language models are trained on increasingly large datasets and with vast numbers of parameters, finding more efficient alternatives to the standard Transformer has become very valuable. While many efficient Transformers and Transformer alternatives have been proposed, none provide theoretical guara...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	431,469
2410.03825	MonST3R: A Simple Approach for Estimating Geometry in the Presence of Motion	Estimating geometry from dynamic scenes, where objects move and deform over time, remains a core challenge in computer vision. Current approaches often rely on multi-stage pipelines or global optimizations that decompose the problem into subtasks, like depth and flow, leading to complex systems prone to errors. In this...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	494,993
2405.20589	Selective Knowledge Sharing for Personalized Federated Learning Under Capacity Heterogeneity	Federated Learning (FL) stands to gain significant advantages from collaboratively training capacity-heterogeneous models, enabling the utilization of private data and computing power from low-capacity devices. However, the focus on personalizing capacity-heterogeneous models based on client-specific data has been limi...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	true	459,418
2309.00879	Towards Certified Probabilistic Robustness with High Accuracy	Adversarial examples pose a security threat to many critical systems built on neural networks (such as face recognition systems, and self-driving cars). While many methods have been proposed to build robust models, how to build certifiably robust yet accurate neural network models remains an open problem. For example, ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	389,456
1810.04440	New Vistas to study Bhartrhari: Cognitive NLP	The Sanskrit grammatical tradition which has commenced with Panini's Astadhyayi mostly as a Padasastra has culminated as a Vakyasastra, at the hands of Bhartrhari. The grammarian-philosopher Bhartrhari and his authoritative work 'Vakyapadiya' have been a matter of study for modern scholars, at least for more than 50 ye...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	110,044
1907.08243	Joint Learning of Named Entity Recognition and Entity Linking	Named entity recognition (NER) and entity linking (EL) are two fundamentally related tasks, since in order to perform EL, first the mentions to entities have to be detected. However, most entity linking approaches disregard the mention detection part, assuming that the correct mentions have been previously detected. In...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	139,056
1306.3134	Opinion dynamics and wisdom under out-group discrimination	We study a DeGroot-like opinion dynamics model in which agents may oppose other agents. As an underlying motivation, in our setup, agents want to adjust their opinions to match those of the agents of their 'in-group' and, in addition, they want to adjust their opinions to match the 'inverse' of those of the agents of t...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	false	false	false	25,183
1901.11512	Minimizing Negative Transfer of Knowledge in Multivariate Gaussian Processes: A Scalable and Regularized Approach	Recently there has been an increasing interest in the multivariate Gaussian process (MGP) which extends the Gaussian process (GP) to deal with multiple outputs. One approach to construct the MGP and account for non-trivial commonalities amongst outputs employs a convolution process (CP). The CP is based on the idea of ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	120,280
2306.04001	One-Dimensional Deep Image Prior for Curve Fitting of S-Parameters from Electromagnetic Solvers	A key problem when modeling signal integrity for passive filters and interconnects in IC packages is the need for multiple S-parameter measurements within a desired frequency band to obtain adequate resolution. These samples are often computationally expensive to obtain using electromagnetic (EM) field solvers. Therefo...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	371,561
2408.11145	Total Uncertainty Quantification in Inverse PDE Solutions Obtained with Reduced-Order Deep Learning Surrogate Models	We propose an approximate Bayesian method for quantifying the total uncertainty in inverse PDE solutions obtained with machine learning surrogate models, including operator learning models. The proposed method accounts for uncertainty in the observations and PDE and surrogate models. First, we use the surrogate model t...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	482,155
2409.16636	Training Language Models to Win Debates with Self-Play Improves Judge Accuracy	We test the robustness of debate as a method of scalable oversight by training models to debate with data generated via self-play. In a long-context reading comprehension task, we find that language model based evaluators answer questions more accurately when judging models optimized to win debates. By contrast, we fin...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	491,432
2502.09810	$\Lambda$CDM and early dark energy in latent space: a data-driven parametrization of the CMB temperature power spectrum	Finding the best parametrization for cosmological models in the absence of first-principle theories is an open question. We propose a data-driven parametrization of cosmological models given by the disentangled 'latent' representation of a variational autoencoder (VAE) trained to compress cosmic microwave background (C...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	533,609
1906.09084	Joint Detection of Malicious Domains and Infected Clients	Detection of malware-infected computers and detection of malicious web domains based on their encrypted HTTPS traffic are challenging problems, because only addresses, timestamps, and data volumes are observable. The detection problems are coupled, because infected clients tend to interact with malicious domains. Traff...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	136,059
2007.02265	AM-GCN: Adaptive Multi-channel Graph Convolutional Networks	Graph Convolutional Networks (GCNs) have gained great popularity in tackling various analytics tasks on graph and network data. However, some recent studies raise concerns about whether GCNs can optimally integrate node features and topological structures in a complex graph with rich information. In this paper, we firs...	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	false	false	185,688
2501.06978	Towards a visually interpretable analysis of Two-Phase Locking membership	Two-phase locking (2PL) is a consolidated policy commonly adopted by Database Management Systems to enforce serializability of a schedule. While the policy is well understood, both in its standard and in the strict version, automatically deriving a suitable tabular/graphical analysis of schedules with respect to 2PL is...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	524,211
2410.18551	IMAN: An Adaptive Network for Robust NPC Mortality Prediction with Missing Modalities	Accurate prediction of mortality in nasopharyngeal carcinoma (NPC), a complex malignancy particularly challenging in advanced stages, is crucial for optimizing treatment strategies and improving patient outcomes. However, this predictive process is often compromised by the high-dimensional and heterogeneous nature of N...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	501,932
2408.10175	Fairness Under Cover: Evaluating the Impact of Occlusions on Demographic Bias in Facial Recognition	This study investigates the effects of occlusions on the fairness of face recognition systems, particularly focusing on demographic biases. Using the Racial Faces in the Wild (RFW) dataset and synthetically added realistic occlusions, we evaluate their effect on the performance of face recognition models trained on the...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	481,747
2312.05405	Guaranteed Trust Region Optimization via Two-Phase KL Penalization	On-policy reinforcement learning (RL) has become a popular framework for solving sequential decision problems due to its computational efficiency and theoretical simplicity. Some on-policy methods guarantee every policy update is constrained to a trust region relative to the prior policy to ensure training stability. T...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	414,068
2005.07820	KEIS@JUST at SemEval-2020 Task 12: Identifying Multilingual Offensive Tweets Using Weighted Ensemble and Fine-Tuned BERT	This research presents our team KEIS@JUST participation at SemEval-2020 Task 12 which represents shared task on multilingual offensive language. We participated in all the provided languages for all subtasks except sub-task-A for the English language. Two main approaches have been developed the first is performed to ta...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	177,401
1402.5886	Near Optimal Bayesian Active Learning for Decision Making	How should we gather information to make effective decisions? We address Bayesian active learning and experimental design problems, where we sequentially select tests to reduce uncertainty about a set of hypotheses. Instead of minimizing uncertainty per se, we consider a set of overlapping decision regions of these hyp...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	31,127
1912.11209	Variable feature weighted fuzzy k-means algorithm for high dimensional data	This paper presents a new fuzzy k-means algorithm for the clustering of high-dimensional data in various subspaces. Since high-dimensional data, some features might be irrelevant and relevant but may have different significance in the clustering process. For better clustering, it is crucial to incorporate the contribut...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	158,510
2407.15641	Generating Sample-Based Musical Instruments Using Neural Audio Codec Language Models	In this paper, we propose and investigate the use of neural audio codec language models for the automatic generation of sample-based musical instruments based on text or reference audio prompts. Our approach extends a generative audio framework to condition on pitch across an 88-key spectrum, velocity, and a combined t...	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	475,264
2309.15687	Breaking On-Chip Communication Anonymity using Flow Correlation Attacks	Network-on-Chip (NoC) is widely used to facilitate communication between components in sophisticated System-on-Chip (SoC) designs. Security of the on-chip communication is crucial because exploiting any vulnerability in shared NoC would be a goldmine for an attacker that puts the entire computing infrastructure at risk...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	true	395,073
2011.05088	MP-ResNet: Multi-path Residual Network for the Semantic segmentation of High-Resolution PolSAR Images	There are limited studies on the semantic segmentation of high-resolution Polarimetric Synthetic Aperture Radar (PolSAR) images due to the scarcity of training data and the inference of speckle noises. The Gaofen contest has provided open access of a high-quality PolSAR semantic segmentation dataset. Taking this chance...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	205,800
2311.00226	Transformers are Provably Optimal In-context Estimators for Wireless Communications	Pre-trained transformers exhibit the capability of adapting to new tasks through in-context learning (ICL), where they efficiently utilize a limited set of prompts without explicit model optimization. The canonical communication problem of estimating transmitted symbols from received observations can be modelled as a...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	404,554
1705.09207	Learning Structured Text Representations	In this paper, we focus on learning structure-aware document representations from data without recourse to a discourse parser or additional annotations. Drawing inspiration from recent efforts to empower neural networks with a structural bias, we propose a model that can encode a document while automatically inducing r...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	74,156
2202.05271	A Field of Experts Prior for Adapting Neural Networks at Test Time	Performance of convolutional neural networks (CNNs) in image analysis tasks is often marred in the presence of acquisition-related distribution shifts between training and test images. Recently, it has been proposed to tackle this problem by fine-tuning trained CNNs for each test image. Such test-time-adaptation (TTA) ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	279,830
cs/0212004	Minimal-Change Integrity Maintenance Using Tuple Deletions	We address the problem of minimal-change integrity maintenance in the context of integrity constraints in relational databases. We assume that integrity-restoration actions are limited to tuple deletions. We identify two basic computational issues: repair checking (is a database instance a repair of a given database?) ...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	537,738
2203.11547	Explainability in reinforcement learning: perspective and position	Artificial intelligence (AI) has been embedded into many aspects of people's daily lives and it has become normal for people to have AI make decisions for them. Reinforcement learning (RL) models increase the space of solvable problems with respect to other machine learning paradigms. Some of the most interesting appli...	false	false	false	false	true	false	false	false	false	false	false	false	false	true	false	false	false	false	286,953
2112.02242	Recommender systems: when memory matters	In this paper, we study the effect of long memory in the learnability of a sequential recommender system including users' implicit feedback. We propose an online algorithm, where model parameters are updated user per user over blocks of items constituted by a sequence of unclicked items followed by a clicked one. We il...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	269,775
2501.17282	From Natural Language to Extensive-Form Game Representations	We introduce a framework for translating game descriptions in natural language into extensive-form representations in game theory, leveraging Large Language Models (LLMs) and in-context learning. Given the varying levels of strategic complexity in games, such as perfect versus imperfect information, directly applying i...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	true	false	false	true	528,272
1902.09469	Embedded Agency	Traditional models of rational action treat the agent as though it is cleanly separated from its environment, and can act on that environment from the outside. Such agents have a known functional relationship with their environment, can model their environment in every detail, and do not need to reason about themselves...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	122,417
2104.12077	Hybrid Satellite-UAV-Terrestrial Networks for 6G Ubiquitous Coverage: A Maritime Communications Perspective	In the coming smart ocean era, reliable and efficient communications are crucial for promoting a variety of maritime activities. Current maritime communication networks (MCNs) mainly rely on marine satellites and on-shore base stations (BSs). The former generally provides limited transmission rate, while the latter lac...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	232,107
2502.06494	GuideLLM: Exploring LLM-Guided Conversation with Applications in Autobiography Interviewing	Although Large Language Models (LLMs) succeed in human-guided conversations such as instruction following and question answering, the potential of LLM-guided conversations-where LLMs direct the discourse and steer the conversation's objectives-remains under-explored. In this study, we first characterize LLM-guided conv...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	532,105
1012.1666	SPARQL Assist Language-Neutral Query Composer	SPARQL query composition is difficult for the lay-person or even the experienced bioinformatician in cases where the data model is unfamiliar. Established best-practices and internationalization concerns dictate that semantic web ontologies should use terms with opaque identifiers, further complicating the task. We pre...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	8,468
2408.03500	e-Health CSIRO at RRG24: Entropy-Augmented Self-Critical Sequence Training for Radiology Report Generation	The Shared Task on Large-Scale Radiology Report Generation (RRG24) aims to expedite the development of assistive systems for interpreting and reporting on chest X-ray (CXR) images. This task challenges participants to develop models that generate the findings and impression sections of radiology reports from CXRs from ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	479,036
1805.07852	Accelerated Bayesian Optimization throughWeight-Prior Tuning	Bayesian optimization (BO) is a widely-used method for optimizing expensive (to evaluate) problems. At the core of most BO methods is the modeling of the objective function using a Gaussian Process (GP) whose covariance is selected from a set of standard covariance functions. From a weight-space view, this models the o...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	97,962
2104.04572	Smart and Secure CAV Networks Empowered by AI-Enabled Blockchain: The Next Frontier for Intelligent Safe Driving Assessment	Securing safe driving for connected and autonomous vehicles (CAVs) continues to be a widespread concern, despite various sophisticated functions delivered by artificial intelligence for in-vehicle devices. Diverse malicious network attacks are ubiquitous, along with the worldwide implementation of the Internet of Vehic...	false	false	false	false	true	false	false	false	false	false	false	false	true	false	false	false	false	true	229,426
2205.01464	Inducing and Using Alignments for Transition-based AMR Parsing	Transition-based parsers for Abstract Meaning Representation (AMR) rely on node-to-word alignments. These alignments are learned separately from parser training and require a complex pipeline of rule-based components, pre-processing, and post-processing to satisfy domain-specific constraints. Parsers also train on a po...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	294,593
2110.07532	Drone technology: interdisciplinary systematic assessment of knowledge gaps and potential solutions	Despite being a hot research topic for a decade, drones are still not part of our everyday life. In this article, we analyze the reasons for this state of affairs and look for ways of improving the situation. We rely on the achievements of the so-called Technology Assessment (TA), an interdisciplinary research field ai...	false	false	false	false	false	false	false	true	false	false	true	false	false	true	false	false	false	false	261,026
2010.07958	Video Object Segmentation with Adaptive Feature Bank and Uncertain-Region Refinement	We propose a new matching-based framework for semi-supervised video object segmentation (VOS). Recently, state-of-the-art VOS performance has been achieved by matching-based algorithms, in which feature banks are created to store features for region matching and classification. However, how to effectively organize info...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	201,001
2309.09167	From Knowing to Doing: Learning Diverse Motor Skills through Instruction Learning	Recent years have witnessed many successful trials in the robot learning field. For contact-rich robotic tasks, it is challenging to learn coordinated motor skills by reinforcement learning. Imitation learning solves this problem by using a mimic reward to encourage the robot to track a given reference trajectory. Howe...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	392,493
2210.12430	Speech Emotion Recognition via an Attentive Time-Frequency Neural Network	Spectrogram is commonly used as the input feature of deep neural networks to learn the high(er)-level time-frequency pattern of speech signal for speech emotion recognition (SER). \textcolor{black}{Generally, different emotions correspond to specific energy activations both within frequency bands and time frames on spe...	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	325,739
1909.03373	Artificial intelligence empowered multi-AGVs in manufacturing systems	AGVs are driverless robotic vehicles that picks up and delivers materials. How to improve the efficiency while preventing deadlocks is the core issue in designing AGV systems. In this paper, we propose an approach to tackle this problem.The proposed approach includes a traditional AGV scheduling algorithm, which aims a...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	144,455
2304.13525	Thermal Vision for Soil Assessment in a Multipurpose Environmental Chamber under Martian Conditions towards Robot Navigation	Soil assessment is important for mobile robot planning and navigation on natural and planetary environments. Terramechanic characteristics can be inferred from the thermal behaviour of soils under the influence of sunlight using remote sensors such as Long-Wave Infrared cameras. However, this behaviour is greatly affec...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	360,611
1909.09318	Inverse Kinematics for Serial Kinematic Chains via Sum of Squares Optimization	Inverse kinematics is a fundamental problem for articulated robots: fast and accurate algorithms are needed for translating task-related workspace constraints and goals into feasible joint configurations. In general, inverse kinematics for serial kinematic chains is a difficult nonlinear problem, for which closed form ...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	146,229
2111.14911	Optimizing High-Dimensional Physics Simulations via Composite Bayesian Optimization	Physical simulation-based optimization is a common task in science and engineering. Many such simulations produce image- or tensor-based outputs where the desired objective is a function of those outputs, and optimization is performed over a high-dimensional parameter space. We develop a Bayesian optimization method le...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	268,741
2205.01071	Sharing and Caring: Creating a Culture of Constructive Criticism in Computational Legal Studies	We introduce seven foundational principles for creating a culture of constructive criticism in computational legal studies. Beginning by challenging the current perception of papers as the primary scholarly output, we call for a more comprehensive interpretation of publications. We then suggest to make these publicatio...	false	false	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	294,470
1809.03868	Dual-label Deep LSTM Dereverberation For Speaker Verification	In this paper, we present a reverberation removal approach for speaker verification, utilizing dual-label deep neural networks (DNNs). The networks perform feature mapping between the spectral features of reverberant and clean speech. Long short term memory recurrent neural networks (LSTMs) are trained to map corrupted...	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	107,423
2306.02532	R-Mixup: Riemannian Mixup for Biological Networks	Biological networks are commonly used in biomedical and healthcare domains to effectively model the structure of complex biological systems with interactions linking biological entities. However, due to their characteristics of high dimensionality and low sample size, directly applying deep learning models on biologica...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	370,935
1806.01259	Learning a Code: Machine Learning for Approximate Non-Linear Coded Computation	Machine learning algorithms are typically run on large scale, distributed compute infrastructure that routinely face a number of unavailabilities such as failures and temporary slowdowns. Adding redundant computations using coding-theoretic tools called "codes" is an emerging technique to alleviate the adverse effects ...	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	99,514
2405.02383	A Fresh Look at Sanity Checks for Saliency Maps	The Model Parameter Randomisation Test (MPRT) is highly recognised in the eXplainable Artificial Intelligence (XAI) community due to its fundamental evaluative criterion: explanations should be sensitive to the parameters of the model they seek to explain. However, recent studies have raised several methodological conc...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	451,753
1003.1494	Formal Concept Analysis for Information Retrieval	In this paper we describe a mechanism to improve Information Retrieval (IR) on the web. The method is based on Formal Concepts Analysis (FCA) that it is makes semantical relations during the queries, and allows a reorganizing, in the shape of a lattice of concepts, the answers provided by a search engine. We proposed f...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	5,867
1805.01416	Dimensional emotion recognition using visual and textual cues	This paper addresses the problem of automatic emotion recognition in the scope of the One-Minute Gradual-Emotional Behavior challenge (OMG-Emotion challenge). The underlying objective of the challenge is the automatic estimation of emotion expressions in the two-dimensional emotion representation space (i.e., arousal a...	false	false	false	false	true	false	false	false	true	false	false	true	false	false	false	false	false	false	96,655
2004.02331	Steering Self-Supervised Feature Learning Beyond Local Pixel Statistics	We introduce a novel principle for self-supervised feature learning based on the discrimination of specific transformations of an image. We argue that the generalization capability of learned features depends on what image neighborhood size is sufficient to discriminate different image transformations: The larger the r...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	171,194
1804.01071	Average performance analysis of the stochastic gradient method for online PCA	This paper studies the complexity of the stochastic gradient algorithm for PCA when the data are observed in a streaming setting. We also propose an online approach for selecting the learning rate. Simulation experiments confirm the practical relevance of the plain stochastic gradient approach and that drastic improvem...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	94,169
2408.06740	DiffLoRA: Generating Personalized Low-Rank Adaptation Weights with Diffusion	Personalized text-to-image generation has gained significant attention for its capability to generate high-fidelity portraits of specific identities conditioned on user-defined prompts. Existing methods typically involve test-time fine-tuning or incorporating an additional pre-trained branch. However, these approaches ...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	480,326
2106.15327	Patch-Based Image Restoration using Expectation Propagation	This paper presents a new Expectation Propagation (EP) framework for image restoration using patch-based prior distributions. While Monte Carlo techniques are classically used to sample from intractable posterior distributions, they can suffer from scalability issues in high-dimensional inference problems such as image...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	243,715
1712.06496	Consensus in Self-similar Hierarchical Graphs and Sierpi\'nski Graphs: Convergence Speed, Delay Robustness, and Coherence	The hierarchical graphs and Sierpi\'nski graphs are constructed iteratively, which have the same number of vertices and edges at any iteration, but exhibit quite different structural properties: the hierarchical graphs are non-fractal and small-world, while the Sierpi\'nski graphs are fractal and "large-world". Both gr...	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	false	86,897
2206.05671	Reinforcement Learning for Vision-based Object Manipulation with Non-parametric Policy and Action Primitives	The object manipulation is a crucial ability for a service robot, but it is hard to solve with reinforcement learning due to some reasons such as sample efficiency. In this paper, to tackle this object manipulation, we propose a novel framework, AP-NPQL (Non-Parametric Q Learning with Action Primitives), that can effic...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	302,091
2410.09213	iFANnpp: Nuclear Power Plant Digital Twin for Robots and Autonomous Intelligence	Robotics has gained significant attention due to its autonomy and ability to automate in the nuclear industry. However, the increasing complexity of robots has led to a growing demand for advanced simulation and control methods to predict robot behavior and optimize plant performance. Most existing digital twins only a...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	497,486
2403.01187	A Compositional Typed Semantics for Universal Dependencies	Languages may encode similar meanings using different sentence structures. This makes it a challenge to provide a single set of formal rules that can derive meanings from sentences in many languages at once. To overcome the challenge, we can take advantage of language-general connections between meaning and syntax, and...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	434,293
2404.06637	GeoSynth: Contextually-Aware High-Resolution Satellite Image Synthesis	We present GeoSynth, a model for synthesizing satellite images with global style and image-driven layout control. The global style control is via textual prompts or geographic location. These enable the specification of scene semantics or regional appearance respectively, and can be used together. We train our model on...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	445,531
2401.06143	Redefining Recon: Bridging Gaps with UAVs, 360 degree Cameras, and Neural Radiance Fields	In the realm of digital situational awareness during disaster situations, accurate digital representations, like 3D models, play an indispensable role. To ensure the safety of rescue teams, robotic platforms are often deployed to generate these models. In this paper, we introduce an innovative approach that synergizes ...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	421,030
0903.4132	Switcher-random-walks: a cognitive-inspired mechanism for network exploration	Semantic memory is the subsystem of human memory that stores knowledge of concepts or meanings, as opposed to life specific experiences. The organization of concepts within semantic memory can be understood as a semantic network, where the concepts (nodes) are associated (linked) to others depending on perceptions, sim...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	3,406
2209.00137	Partial Counterfactual Identification for Infinite Horizon Partially Observable Markov Decision Process	This paper investigates the problem of bounding possible output from a counterfactual query given a set of observational data. While various works of literature have described methodologies to generate efficient algorithms that provide an optimal bound for the counterfactual query, all of them assume a finite-horizon c...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	315,495
1909.08079	Relaxed Softmax for learning from Positive and Unlabeled data	In recent years, the softmax model and its fast approximations have become the de-facto loss functions for deep neural networks when dealing with multi-class prediction. This loss has been extended to language modeling and recommendation, two fields that fall into the framework of learning from Positive and Unlabeled d...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	145,859
2404.18255	PatentGPT: A Large Language Model for Intellectual Property	In recent years, large language models(LLMs) have attracted significant attention due to their exceptional performance across a multitude of natural language process tasks, and have been widely applied in various fields. However, the application of large language models in the Intellectual Property (IP) domain is chall...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	450,192
1907.11780	Understanding Adversarial Robustness: The Trade-off between Minimum and Average Margin	Deep models, while being extremely versatile and accurate, are vulnerable to adversarial attacks: slight perturbations that are imperceptible to humans can completely flip the prediction of deep models. Many attack and defense mechanisms have been proposed, although a satisfying solution still largely remains elusive. ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	139,937
2007.09880	Mixture Representation Learning with Coupled Autoencoders	Jointly identifying a mixture of discrete and continuous factors of variability without supervision is a key problem in unraveling complex phenomena. Variational inference has emerged as a promising method to learn interpretable mixture representations. However, posterior approximation in high-dimensional latent spaces...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	188,097
1002.1531	A Large-System Analysis of the Imperfect-CSIT Gaussian Broadcast Channel with a DPC-based Transmission Strategy	The Gaussian broadcast channel (GBC) with $K$ transmit antennas and $K$ single-antenna users is considered for the case in which the channel state information is obtained at the transmitter via a finite-rate feedback link of capacity $r$ bits per user. The throughput (i.e., the sum-rate normalized by $K$) of the GBC is...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	5,651
2211.08682	Parameter-Efficient Tuning on Layer Normalization for Pre-trained Language Models	Conventional fine-tuning encounters increasing difficulties given the size of current Pre-trained Language Models, which makes parameter-efficient tuning become the focal point of frontier research. Previous methods in this field add tunable adapters into MHA or/and FFN of Transformer blocks to enable PLMs achieve tran...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	330,727
1809.01254	Collaborative Artificial Intelligence (AI) for User-Cell association in Ultra-Dense Cellular Systems	In this paper, the problem of cell association between small base stations (SBSs) and users in dense wireless networks is studied using artificial intelligence (AI) techniques. The problem is formulated as a mean-field game in which the users' goal is to maximize their data rate by exploiting local data and the data av...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	106,758
2412.01215	EsurvFusion: An evidential multimodal survival fusion model based on Gaussian random fuzzy numbers	Multimodal survival analysis aims to combine heterogeneous data sources (e.g., clinical, imaging, text, genomics) to improve the prediction quality of survival outcomes. However, this task is particularly challenging due to high heterogeneity and noise across data sources, which vary in structure, distribution, and con...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	513,009
2212.00998	Credit Assignment for Trained Neural Networks Based on Koopman Operator Theory	Credit assignment problem of neural networks refers to evaluating the credit of each network component to the final outputs. For an untrained neural network, approaches to tackling it have made great contributions to parameter update and model revolution during the training phase. This problem on trained neural network...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	334,273
2110.11283	The Effect of Wearing a Face Mask on Face Image Quality	Due to the COVID-19 situation, face masks have become a main part of our daily life. Wearing mouth-and-nose protection has been made a mandate in many public places, to prevent the spread of the COVID-19 virus. However, face masks affect the performance of face recognition, since a large area of the face is covered. Th...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	262,426
2005.08460	Bayesian convolutional neural network based MRI brain extraction on nonhuman primates	Brain extraction or skull stripping of magnetic resonance images (MRI) is an essential step in neuroimaging studies, the accuracy of which can severely affect subsequent image processing procedures. Current automatic brain extraction methods demonstrate good results on human brains, but are often far from satisfactory ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	177,626
2209.00841	Geometric and Learning-based Mesh Denoising: A Comprehensive Survey	Mesh denoising is a fundamental problem in digital geometry processing. It seeks to remove surface noise, while preserving surface intrinsic signals as accurately as possible. While the traditional wisdom has been built upon specialized priors to smooth surfaces, learning-based approaches are making their debut with gr...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	315,698
2207.04820	Assessing Ranking and Effectiveness of Evolutionary Algorithm Hyperparameters Using Global Sensitivity Analysis Methodologies	We present a comprehensive global sensitivity analysis of two single-objective and two multi-objective state-of-the-art global optimization evolutionary algorithms as an algorithm configuration problem. That is, we investigate the quality of influence hyperparameters have on the performance of algorithms in terms of th...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	false	false	307,324
1712.02779	Exploring the Landscape of Spatial Robustness	The study of adversarial robustness has so far largely focused on perturbations bound in p-norms. However, state-of-the-art models turn out to be also vulnerable to other, more natural classes of perturbations such as translations and rotations. In this work, we thoroughly investigate the vulnerability of neural networ...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	true	false	false	86,337
1201.1997	An Enhanced DMT-optimality Criterion for STBC-schemes for Asymmetric MIMO Systems	For any $n_t$ transmit, $n_r$ receive antenna ($n_t\times n_r$) MIMO system in a quasi-static Rayleigh fading environment, it was shown by Elia et al. that linear space-time block code-schemes (LSTBC-schemes) which have the non-vanishing determinant (NVD) property are diversity-multiplexing gain tradeoff (DMT)-optimal ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	13,748
2206.03032	Intelligent Circuit Design and Implementation with Machine Learning	The stagnation of EDA technologies roots from insufficient knowledge reuse. In practice, very similar simulation or optimization results may need to be repeatedly constructed from scratch. This motivates my research on introducing more 'intelligence' to EDA with machine learning (ML), which explores complex correlation...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	301,126
1401.5767	A refined analysis of the Poisson channel in the high-photon-efficiency regime	We study the discrete-time Poisson channel under the constraint that its average input power (in photons per channel use) must not exceed some constant E. We consider the wideband, high-photon-efficiency extreme where E approaches zero, and where the channel's "dark current" approaches zero proportionally with E. Impro...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	30,240
1609.09822	Stepping Stabilization Using a Combination of DCM Tracking and Step Adjustment	In this paper, a method for stabilizing biped robots stepping by a combination of Divergent Component of Motion (DCM) tracking and step adjustment is proposed. In this method, the DCM trajectory is generated, consistent with the predefined footprints. Furthermore, a swing foot trajectory modification strategy is propos...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	61,770
1305.7053	A Local Active Contour Model for Image Segmentation with Intensity Inhomogeneity	A novel locally statistical active contour model (ACM) for image segmentation in the presence of intensity inhomogeneity is presented in this paper. The inhomogeneous objects are modeled as Gaussian distributions of different means and variances, and a moving window is used to map the original image into another domain...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	24,867
2207.05221	Language Models (Mostly) Know What They Know	We study whether language models can evaluate the validity of their own claims and predict which questions they will be able to answer correctly. We first show that larger models are well-calibrated on diverse multiple choice and true/false questions when they are provided in the right format. Thus we can approach self...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	307,451
1805.12547	Long-time predictive modeling of nonlinear dynamical systems using neural networks	We study the use of feedforward neural networks (FNN) to develop models of nonlinear dynamical systems from data. Emphasis is placed on predictions at long times, with limited data availability. Inspired by global stability analysis, and the observation of the strong correlation between the local error and the maximum ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	99,202
2409.15692	Walking with Terrain Reconstruction: Learning to Traverse Risky Sparse Footholds	Traversing risky terrains with sparse footholds presents significant challenges for legged robots, requiring precise foot placement in safe areas. Current learning-based methods often rely on implicit feature representations without supervising physically significant estimation targets. This limits the policy's ability...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	491,015
2401.00148	TPatch: A Triggered Physical Adversarial Patch	Autonomous vehicles increasingly utilize the vision-based perception module to acquire information about driving environments and detect obstacles. Correct detection and classification are important to ensure safe driving decisions. Existing works have demonstrated the feasibility of fooling the perception models such ...	false	false	false	false	false	false	false	false	false	false	false	true	true	false	false	false	false	false	418,895
2501.11968	Bridging Visualization and Optimization: Multimodal Large Language Models on Graph-Structured Combinatorial Optimization	Graph-structured combinatorial challenges are inherently difficult due to their nonlinear and intricate nature, often rendering traditional computational methods ineffective or expensive. However, these challenges can be more naturally tackled by humans through visual representations that harness our innate ability for...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	526,117
2109.09829	Towards Energy-Efficient and Secure Edge AI: A Cross-Layer Framework	The security and privacy concerns along with the amount of data that is required to be processed on regular basis has pushed processing to the edge of the computing systems. Deploying advanced Neural Networks (NN), such as deep neural networks (DNNs) and spiking neural networks (SNNs), that offer state-of-the-art resul...	false	false	false	false	true	false	true	false	false	false	false	false	true	false	false	true	false	true	256,411
2412.16946	Video Domain Incremental Learning for Human Action Recognition in Home Environments	It is significantly challenging to recognize daily human actions in homes due to the diversity and dynamic changes in unconstrained home environments. It spurs the need to continually adapt to various users and scenes. Fine-tuning current video understanding models on newly encountered domains often leads to catastroph...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	519,765
2406.13942	Synthesizing Multimodal Electronic Health Records via Predictive Diffusion Models	Synthesizing electronic health records (EHR) data has become a preferred strategy to address data scarcity, improve data quality, and model fairness in healthcare. However, existing approaches for EHR data generation predominantly rely on state-of-the-art generative techniques like generative adversarial networks, vari...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	466,072
2412.02316	Optimizing Plastic Waste Collection in Water Bodies Using Heterogeneous Autonomous Surface Vehicles with Deep Reinforcement Learning	This paper presents a model-free deep reinforcement learning framework for informative path planning with heterogeneous fleets of autonomous surface vehicles to locate and collect plastic waste. The system employs two teams of vehicles: scouts and cleaners. Coordination between these teams is achieved through a deep re...	false	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	513,493
2501.03226	BoostStep: Boosting mathematical capability of Large Language Models via improved single-step reasoning	Large language models (LLMs) have demonstrated impressive ability in solving complex mathematical problems with multi-step reasoning and can be further enhanced with well-designed in-context learning (ICL) examples. However, this potential is often constrained by two major challenges in ICL: granularity mismatch and ir...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	522,801

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