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
1012.0196	Coarse Graining for Synchronization in Directed Networks	Coarse graining model is a promising way to analyze and visualize large-scale networks. The coarse-grained networks are required to preserve the same statistical properties as well as the dynamic behaviors as the initial networks. Some methods have been proposed and found effective in undirected networks, while the stu...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	8,382
0909.3382	Statistical mechanical analysis of the Kronecker channel model for MIMO wireless communication	The Kronecker channel model of wireless communication is analyzed using statistical mechanics methods. In the model, spatial proximities among transmission/reception antennas are taken into account as certain correlation matrices, which generally yield non-trivial dependence among symbols to be estimated. This prevents...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	4,521
1005.0624	The Gaussian Many-to-1 Interference Channel with Confidential Messages	The many-to-one interference channel has received interest by virtue of embodying the essence of an interference network while being more tractable than the general K-user interference channel. In this paper, we introduce information theoretic secrecy to this model and consider the many-to-one interference channel with...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	6,402
2112.12385	DILF-EN framework for Class-Incremental Learning	Deep learning models suffer from catastrophic forgetting of the classes in the older phases as they get trained on the classes introduced in the new phase in the class-incremental learning setting. In this work, we show that the effect of catastrophic forgetting on the model prediction varies with the change in orienta...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	272,951
1711.09154	Observer-Side Parameter Estimation For Adaptive Control	In adaptive control, a controller is precisely designed for a certain model of the system, but that model's parameters are updated online by another mechanism called the adaptive update. This allows the controller to aim for the benefits of exact model knowledge while simultaneously remaining robust to model uncertaint...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	85,333
2410.12880	Navigating the Cultural Kaleidoscope: A Hitchhiker's Guide to Sensitivity in Large Language Models	As LLMs are increasingly deployed in global applications, the importance of cultural sensitivity becomes paramount, ensuring that users from diverse backgrounds feel respected and understood. Cultural harm can arise when these models fail to align with specific cultural norms, resulting in misrepresentations or violati...	false	false	false	false	true	false	false	false	true	false	false	false	false	true	false	false	false	false	499,264
2010.03028	A deep learning pipeline for identification of motor units in musculoskeletal ultrasound	Ultrasound imaging provides information from a large part of the muscle. It has recently been shown that ultrafast ultrasound imaging can be used to record and analyze the mechanical response of individual MUs using blind source separation. In this work, we present an alternative method - a deep learning pipeline - to ...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	199,245
2211.02987	Differentiable Neural Computers with Memory Demon	A Differentiable Neural Computer (DNC) is a neural network with an external memory which allows for iterative content modification via read, write and delete operations. We show that information theoretic properties of the memory contents play an important role in the performance of such architectures. We introduce a...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	true	false	false	328,779
2306.01729	Improving Generalization in Task-oriented Dialogues with Workflows and Action Plans	Task-oriented dialogue is difficult in part because it involves understanding user intent, collecting information from the user, executing API calls, and generating helpful and fluent responses. However, for complex tasks one must also correctly do all of these things over multiple steps, and in a specific order. While...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	370,565
2012.07044	Monitoring multimode processes: a modified PCA algorithm with continual learning ability	For multimode processes, one generally establishes local monitoring models corresponding to local modes. However, the significant features of previous modes may be catastrophically forgotten when a monitoring model for the current mode is built. It would result in an abrupt performance decrease. It could be an effectiv...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	211,325
2309.11009	Controllable Dynamic Appearance for Neural 3D Portraits	Recent advances in Neural Radiance Fields (NeRFs) have made it possible to reconstruct and reanimate dynamic portrait scenes with control over head-pose, facial expressions and viewing direction. However, training such models assumes photometric consistency over the deformed region e.g. the face must be evenly lit as i...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	393,236
2303.11060	Quantile and moment neural networks for learning functionals of distributions	We study news neural networks to approximate function of distributions in a probability space. Two classes of neural networks based on quantile and moment approximation are proposed to learn these functions and are theoretically supported by universal approximation theorems. By mixing the quantile and moment features i...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	352,690
1603.00223	Segmental Recurrent Neural Networks for End-to-end Speech Recognition	We study the segmental recurrent neural network for end-to-end acoustic modelling. This model connects the segmental conditional random field (CRF) with a recurrent neural network (RNN) used for feature extraction. Compared to most previous CRF-based acoustic models, it does not rely on an external system to provide fe...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	true	false	false	52,756
2405.14382	Multi-purpose robot for rehabilitation of small diameter water pipes	Rehabilitating cast iron pipes through lining offers several advantages, including increased durability, reduced water leaks, and minimal disruption.This approach presents a cost effective and environmentally friendly solution by sealing cracks and joints, extending the pipeline's lifespan, and reducing water wastage, ...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	456,395
1509.07315	On Turnpike and Dissipativity Properties of Continuous-Time Optimal Control Problems	This paper investigates the relations between three different properties, which are of importance in optimal control problems: dissipativity of the underlying dynamics with respect to a specific supply rate, optimal operation at steady state, and the turnpike property. We show in a continuous-time setting that if along...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	47,252
2304.01815	Consolidated Control Barrier Functions: Synthesis and Online Verification via Adaptation under Input Constraints	In this paper, we develop a novel adaptation-based approach to constrained control design under multiple state and input constraints. Specifically, we introduce a method for synthesizing any number of time-varying candidate control barrier functions (CBF) into one consolidated CBF (C-CBF) candidate, and propose a predi...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	356,215
2311.01235	Advancing the Search Frontier with AI Agents	As many of us in the information retrieval (IR) research community know and appreciate, search is far from being a solved problem. Millions of people struggle with tasks on search engines every day. Often, their struggles relate to the intrinsic complexity of their task and the failure of search systems to fully unders...	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	false	false	404,964
1911.01861	Biconditional Generative Adversarial Networks for Multiview Learning with Missing Views	In this paper, we present a conditional GAN with two generators and a common discriminator for multiview learning problems where observations have two views, but one of them may be missing for some of the training samples. This is for example the case for multilingual collections where documents are not available in al...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	152,215
2212.00608	Exploiting Kernel Compression on BNNs	Binary Neural Networks (BNNs) are showing tremendous success on realistic image classification tasks. Notably, their accuracy is similar to the state-of-the-art accuracy obtained by full-precision models tailored to edge devices. In this regard, BNNs are very amenable to edge devices since they employ 1-bit to store th...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	true	334,123
2305.13869	Trend-Based SAC Beam Control Method with Zero-Shot in Superconducting Linear Accelerator	The superconducting linear accelerator is a highly flexiable facility for modern scientific discoveries, necessitating weekly reconfiguration and tuning. Accordingly, minimizing setup time proves essential in affording users with ample experimental time. We propose a trend-based soft actor-critic(TBSAC) beam control me...	false	false	false	false	true	false	true	false	false	false	true	false	false	false	false	false	false	false	366,731
1903.03878	Scene Memory Transformer for Embodied Agents in Long-Horizon Tasks	Many robotic applications require the agent to perform long-horizon tasks in partially observable environments. In such applications, decision making at any step can depend on observations received far in the past. Hence, being able to properly memorize and utilize the long-term history is crucial. In this work, we pro...	false	false	false	false	false	false	true	true	false	false	false	true	false	false	false	false	false	false	123,844
1607.01690	A New Hierarchical Redundancy Eliminated Tree Augmented Naive Bayes Classifier for Coping with Gene Ontology-based Features	The Tree Augmented Naive Bayes classifier is a type of probabilistic graphical model that can represent some feature dependencies. In this work, we propose a Hierarchical Redundancy Eliminated Tree Augmented Naive Bayes (HRE-TAN) algorithm, which considers removing the hierarchical redundancy during the classifier lear...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	58,249
1608.07888	Online Monotone Optimization	This paper presents a new framework for analyzing and designing no-regret algorithms for dynamic (possibly adversarial) systems. The proposed framework generalizes the popular online convex optimization framework and extends it to its natural limit allowing it to capture a notion of regret that is intuitive for more ge...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	60,282
1708.05711	Computer-aided position planning of miniplates to treat facial bone defects	In this contribution, a software system for computer-aided position planning of miniplates to treat facial bone defects is proposed. The intra-operatively used bone plates have to be passively adapted on the underlying bone contours for adequate bone fragment stabilization. However, this procedure can lead to frequent ...	false	true	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	79,183
cs/0510050	Integration of the DOLCE top-level ontology into the OntoSpec methodology	This report describes a new version of the OntoSpec methodology for ontology building. Defined by the LaRIA Knowledge Engineering Team (University of Picardie Jules Verne, Amiens, France), OntoSpec aims at helping builders to model ontological knowledge (upstream of formal representation). The methodology relies on a s...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	539,023
2108.08447	MvSR-NAT: Multi-view Subset Regularization for Non-Autoregressive Machine Translation	Conditional masked language models (CMLM) have shown impressive progress in non-autoregressive machine translation (NAT). They learn the conditional translation model by predicting the random masked subset in the target sentence. Based on the CMLM framework, we introduce Multi-view Subset Regularization (MvSR), a novel...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	251,249
2104.09441	One More Check: Making "Fake Background" Be Tracked Again	The one-shot multi-object tracking, which integrates object detection and ID embedding extraction into a unified network, has achieved groundbreaking results in recent years. However, current one-shot trackers solely rely on single-frame detections to predict candidate bounding boxes, which may be unreliable when facin...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	231,241
2404.09833	Video2Game: Real-time, Interactive, Realistic and Browser-Compatible Environment from a Single Video	Creating high-quality and interactive virtual environments, such as games and simulators, often involves complex and costly manual modeling processes. In this paper, we present Video2Game, a novel approach that automatically converts videos of real-world scenes into realistic and interactive game environments. At the h...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	446,850
2007.10740	Balanced Meta-Softmax for Long-Tailed Visual Recognition	Deep classifiers have achieved great success in visual recognition. However, real-world data is long-tailed by nature, leading to the mismatch between training and testing distributions. In this paper, we show that the Softmax function, though used in most classification tasks, gives a biased gradient estimation under ...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	188,362
1812.00469	Anchor Box Optimization for Object Detection	In this paper, we propose a general approach to optimize anchor boxes for object detection. Nowadays, anchor boxes are widely adopted in state-of-the-art detection frameworks. However, these frameworks usually pre-define anchor box shapes in heuristic ways and fix the sizes during training. To improve the accuracy and ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	115,266
2207.10950	Scale dependant layer for self-supervised nuclei encoding	Recent developments in self-supervised learning give us the possibility to further reduce human intervention in multi-step pipelines where the focus evolves around particular objects of interest. In the present paper, the focus lays in the nuclei in histopathology images. In particular we aim at extracting cellular inf...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	309,454
2207.05842	RZCR: Zero-shot Character Recognition via Radical-based Reasoning	The long-tail effect is a common issue that limits the performance of deep learning models on real-world datasets. Character image datasets are also affected by such unbalanced data distribution due to differences in character usage frequency. Thus, current character recognition methods are limited when applied in the ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	307,682
2301.00641	Federated Multi-Agent Deep Reinforcement Learning Approach via Physics-Informed Reward for Multi-Microgrid Energy Management	The utilization of large-scale distributed renewable energy promotes the development of the multi-microgrid (MMG), which raises the need of developing an effective energy management method to minimize economic costs and keep self energy-sufficiency. The multi-agent deep reinforcement learning (MADRL) has been widely us...	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	338,974
2411.07860	Integrating Chaotic Evolutionary and Local Search Techniques in Decision Space for Enhanced Evolutionary Multi-Objective Optimization	This paper presents innovative approaches to optimization problems, focusing on both Single-Objective Multi-Modal Optimization (SOMMOP) and Multi-Objective Optimization (MOO). In SOMMOP, we integrate chaotic evolution with niching techniques, as well as Persistence-Based Clustering combined with Gaussian mutation. The ...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	507,704
1802.06274	Automatic Classification of Roof Shapes for Multicopter Emergency Landing Site Selection	Geographic information systems (GIS) now provide accurate maps of terrain, roads, waterways, and building footprints and heights. Aircraft, particularly small unmanned aircraft systems, can exploit additional information such as building roof structure to improve navigation accuracy and safety particularly in urban reg...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	90,629
2306.06071	Adversarial Attack On Yolov5 For Traffic And Road Sign Detection	This paper implements and investigates popular adversarial attacks on the YOLOv5 Object Detection algorithm. The paper explores the vulnerability of the YOLOv5 to adversarial attacks in the context of traffic and road sign detection. The paper investigates the impact of different types of attacks, including the Limited...	false	false	false	false	false	false	true	false	false	false	false	true	true	false	false	false	false	false	372,429
2210.08463	Two classes of narrow-sense BCH codes and their duals	BCH codes and their dual codes are two special subclasses of cyclic codes and are the best linear codes in many cases. A lot of progress on the study of BCH cyclic codes has been made, but little is known about the minimum distances of the duals of BCH codes. Recently, a new concept called dually-BCH code was introduce...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	324,158
2110.07932	A framework for the analysis of fully coupled normal and tangential contact problems with complex interfaces	An extension to the interface finite element with eMbedded Profile for Joint Roughness (MPJR interface finite element) is herein proposed for solving the frictional contact problem between a rigid indenter of any complex shape and an elastic body under generic oblique load histories. The actual shape of the indenter is...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	261,188
2312.14936	PerCNet: Periodic Complete Representation for Crystal Graphs	Crystal material representation is the foundation of crystal material research. Existing works consider crystal molecules as graph data with different representation methods and leverage the advantages of techniques in graph learning. A reasonable crystal representation method should capture the local and global inform...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	417,799
2201.09798	IMO$^3$: Interactive Multi-Objective Off-Policy Optimization	Most real-world optimization problems have multiple objectives. A system designer needs to find a policy that trades off these objectives to reach a desired operating point. This problem has been studied extensively in the setting of known objective functions. We consider a more practical but challenging setting of unk...	false	true	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	276,786
1406.0304	Transductive Learning for Multi-Task Copula Processes	We tackle the problem of multi-task learning with copula process. Multivariable prediction in spatial and spatial-temporal processes such as natural resource estimation and pollution monitoring have been typically addressed using techniques based on Gaussian processes and co-Kriging. While the Gaussian prior assumption...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	33,550
2305.16378	Sim-Suction: Learning a Suction Grasp Policy for Cluttered Environments Using a Synthetic Benchmark	This paper presents Sim-Suction, a robust object-aware suction grasp policy for mobile manipulation platforms with dynamic camera viewpoints, designed to pick up unknown objects from cluttered environments. Suction grasp policies typically employ data-driven approaches, necessitating large-scale, accurately-annotated s...	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	false	368,066
1910.02532	Probabilistic Successor Representations with Kalman Temporal Differences	The effectiveness of Reinforcement Learning (RL) depends on an animal's ability to assign credit for rewards to the appropriate preceding stimuli. One aspect of understanding the neural underpinnings of this process involves understanding what sorts of stimulus representations support generalisation. The Successor Repr...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	148,269
2310.12646	TRUSTED: The Paired 3D Transabdominal Ultrasound and CT Human Data for Kidney Segmentation and Registration Research	Inter-modal image registration (IMIR) and image segmentation with abdominal Ultrasound (US) data has many important clinical applications, including image-guided surgery, automatic organ measurement and robotic navigation. However, research is severely limited by the lack of public datasets. We propose TRUSTED (the Tri...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	401,099
2411.13496	Advancing Heatwave Forecasting via Distribution Informed-Graph Neural Networks (DI-GNNs): Integrating Extreme Value Theory with GNNs	Heatwaves, prolonged periods of extreme heat, have intensified in frequency and severity due to climate change, posing substantial risks to public health, ecosystems, and infrastructure. Despite advancements in Machine Learning (ML) modeling, accurate heatwave forecasting at weather scales (1--15 days) remains challeng...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	509,800
1705.09765	Deep Matching and Validation Network -- An End-to-End Solution to Constrained Image Splicing Localization and Detection	Image splicing is a very common image manipulation technique that is sometimes used for malicious purposes. A splicing detec- tion and localization algorithm usually takes an input image and produces a binary decision indicating whether the input image has been manipulated, and also a segmentation mask that corre- spon...	false	false	false	false	false	false	false	false	false	false	false	true	true	false	false	false	false	false	74,258
1712.03052	Corotational Cut Finite Element Method for real-time surgical simulation: application to needle insertion simulation	This paper describes the use of the corotational cut Finite Element Method (FEM) for real-time surgical simulation. Users only need to provide a background mesh which is not necessarily conforming to the boundaries/interfaces of the simulated object. The details of the surface, which can be directly obtained from binar...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	86,388
1907.07904	On the relation between Loss Functions and T-Norms	Deep learning has been shown to achieve impressive results in several domains like computer vision and natural language processing. A key element of this success has been the development of new loss functions, like the popular cross-entropy loss, which has been shown to provide faster convergence and to reduce the vani...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	138,997
1810.07117	Universal Uhrig dynamical decoupling for bosonic systems	We construct efficient deterministic dynamical decoupling schemes protecting continuous variable degrees of freedom. Our schemes target decoherence induced by quadratic system-bath interactions with analytic time-dependence. We show how to suppress such interactions to $N$-th order using only $N$ pulses. Furthermore, w...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	110,572
2411.01487	DSDE: Using Proportion Estimation to Improve Model Selection for Out-of-Distribution Detection	Model library is an effective tool for improving the performance of single-model Out-of-Distribution (OoD) detector, mainly through model selection and detector fusion. However, existing methods in the literature do not provide uncertainty quantification for model selection results. Additionally, the model ensemble pro...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	505,096
1807.01473	Supervised Reinforcement Learning with Recurrent Neural Network for Dynamic Treatment Recommendation	Dynamic treatment recommendation systems based on large-scale electronic health records (EHRs) become a key to successfully improve practical clinical outcomes. Prior relevant studies recommend treatments either use supervised learning (e.g. matching the indicator signal which denotes doctor prescriptions), or reinforc...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	102,071
1903.11971	The Global Convergence Analysis of the Bat Algorithm Using a Markovian Framework and Dynamical System Theory	The bat algorithm (BA) has been shown to be effective to solve a wider range of optimization problems. However, there is not much theoretical analysis concerning its convergence and stability. In order to prove the convergence of the bat algorithm, we have built a Markov model for the algorithm and proved that the stat...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	125,613
2102.10639	Privacy-Preserving Wireless Federated Learning Exploiting Inherent Hardware Impairments	We consider a wireless federated learning system where multiple data holder edge devices collaborate to train a global model via sharing their parameter updates with an honest-but-curious parameter server. We demonstrate that the inherent hardware-induced distortion perturbing the model updates of the edge devices can ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	221,163
cs/0605041	Asymptotically Optimal Multiple-access Communication via Distributed Rate Splitting	We consider the multiple-access communication problem in a distributed setting for both the additive white Gaussian noise channel and the discrete memoryless channel. We propose a scheme called Distributed Rate Splitting to achieve the optimal rates allowed by information theory in a distributed manner. In this scheme,...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	539,441
1709.02549	A Novel Low-Complexity Framework in Ultra-Wideband Imaging for Breast Cancer Detection	In this research work, a novel framework is pro- posed as an efficient successor to traditional imaging methods for breast cancer detection in order to decrease the computational complexity. In this framework, the breast is devided into seg- ments in an iterative process and in each iteration, the one having the most p...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	80,291
2305.09931	Mitigating Group Bias in Federated Learning: Beyond Local Fairness	The issue of group fairness in machine learning models, where certain sub-populations or groups are favored over others, has been recognized for some time. While many mitigation strategies have been proposed in centralized learning, many of these methods are not directly applicable in federated learning, where data is ...	false	false	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	364,833
2404.04852	EnQuery: Ensemble Policies for Diverse Query-Generation in Preference Alignment of Robot Navigation	To align mobile robot navigation policies with user preferences through reinforcement learning from human feedback (RLHF), reliable and behavior-diverse user queries are required. However, deterministic policies fail to generate a variety of navigation trajectory suggestions for a given navigation task. In this paper, ...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	444,819
2502.11096	Mixture of Tunable Experts -- Behavior Modification of DeepSeek-R1 at Inference Time	We present the Mixture-of-Tunable-Experts (MoTE), a method that extends the Mixture-of-Experts architecture of Large Language Models (LLMs). Without additional training, MoTE enables meaningful and focused behavior changes in LLMs on-the-fly during inference time. By analyzing the digital LLM brain of DeepSeek-R1 using...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	534,189
2401.14236	Exploring the Unexplored: Understanding the Impact of Layer Adjustments on Image Classification	This paper investigates how adjustments to deep learning architectures impact model performance in image classification. Small-scale experiments generate initial insights although the trends observed are not consistent with the entire dataset. Filtering operations in the image processing pipeline are crucial, with imag...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	424,018
2307.10824	Parse and Recall: Towards Accurate Lung Nodule Malignancy Prediction like Radiologists	Lung cancer is a leading cause of death worldwide and early screening is critical for improving survival outcomes. In clinical practice, the contextual structure of nodules and the accumulated experience of radiologists are the two core elements related to the accuracy of identification of benign and malignant nodules....	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	380,703
1905.05276	Transtemporal edges and crosslayer edges in incompressible high-order networks	This work presents some outcomes of a theoretical investigation of incompressible high-order networks defined by a generalized graph representation. We study some of their network topological properties and how these may be related to real-world complex networks. We show that these networks have very short diameter, hi...	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	false	false	true	130,672
2412.18407	A Statistical Framework for Ranking LLM-Based Chatbots	Large language models (LLMs) have transformed natural language processing, with frameworks like Chatbot Arena providing pioneering platforms for evaluating these models. By facilitating millions of pairwise comparisons based on human judgments, Chatbot Arena has become a cornerstone in LLM evaluation, offering rich dat...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	520,410
2406.05746	Methodology and Real-World Applications of Dynamic Uncertain Causality Graph for Clinical Diagnosis with Explainability and Invariance	AI-aided clinical diagnosis is desired in medical care. Existing deep learning models lack explainability and mainly focus on image analysis. The recently developed Dynamic Uncertain Causality Graph (DUCG) approach is causality-driven, explainable, and invariant across different application scenarios, without problems ...	true	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	462,273
1512.03324	Mapping the Region of Entropic Vectors with Support Enumeration & Information Geometry	The region of entropic vectors is a convex cone that has been shown to be at the core of many fundamental limits for problems in multiterminal data compression, network coding, and multimedia transmission. This cone has been shown to be non-polyhedral for four or more random variables, however its boundary remains unkn...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	50,021
1809.02235	A Bandit Approach to Multiple Testing with False Discovery Control	We propose an adaptive sampling approach for multiple testing which aims to maximize statistical power while ensuring anytime false discovery control. We consider $n$ distributions whose means are partitioned by whether they are below or equal to a baseline (nulls), versus above the baseline (actual positives). In addi...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	106,998
1811.11705	An Adversarial Approach for Explainable AI in Intrusion Detection Systems	Despite the growing popularity of modern machine learning techniques (e.g. Deep Neural Networks) in cyber-security applications, most of these models are perceived as a black-box for the user. Adversarial machine learning offers an approach to increase our understanding of these models. In this paper we present an appr...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	114,836
2209.12444	Self-supervised similarity models based on well-logging data	Adopting data-based approaches leads to model improvement in numerous Oil&Gas logging data processing problems. These improvements become even more sound due to new capabilities provided by deep learning. However, usage of deep learning is limited to areas where researchers possess large amounts of high-quality data. W...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	319,540
2105.04876	Benchmarking down-scaled (not so large) pre-trained language models	Large Transformer-based language models are pre-trained on corpora of varying sizes, for a different number of steps and with different batch sizes. At the same time, more fundamental components, such as the pre-training objective or architectural hyperparameters, are modified. In total, it is therefore difficult to as...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	234,648
1711.11191	Neural Response Generation with Dynamic Vocabularies	We study response generation for open domain conversation in chatbots. Existing methods assume that words in responses are generated from an identical vocabulary regardless of their inputs, which not only makes them vulnerable to generic patterns and irrelevant noise, but also causes a high cost in decoding. We propose...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	85,745
2205.09948	GDSRec: Graph-Based Decentralized Collaborative Filtering for Social Recommendation	Generating recommendations based on user-item interactions and user-user social relations is a common use case in web-based systems. These connections can be naturally represented as graph-structured data and thus utilizing graph neural networks (GNNs) for social recommendation has become a promising research direction...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	297,486
2410.17565	Double Banking on Knowledge: Customized Modulation and Prototypes for Multi-Modality Semi-supervised Medical Image Segmentation	Multi-modality (MM) semi-supervised learning (SSL) based medical image segmentation has recently gained increasing attention for its ability to utilize MM data and reduce reliance on labeled images. However, current methods face several challenges: (1) Complex network designs hinder scalability to scenarios with more t...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	501,525
2406.12356	A Gradient Accumulation Method for Dense Retriever under Memory Constraint	InfoNCE loss is commonly used to train dense retriever in information retrieval tasks. It is well known that a large batch is essential to stable and effective training with InfoNCE loss, which requires significant hardware resources. Due to the dependency of large batch, dense retriever has bottleneck of application a...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	465,360
2312.00312	Segment Anything Model-guided Collaborative Learning Network for Scribble-supervised Polyp Segmentation	Polyp segmentation plays a vital role in accurately locating polyps at an early stage, which holds significant clinical importance for the prevention of colorectal cancer. Various polyp segmentation methods have been developed using fully-supervised deep learning techniques. However, pixel-wise annotation for polyp ima...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	411,998
2410.13313	Mitigating Biases to Embrace Diversity: A Comprehensive Annotation Benchmark for Toxic Language	This study introduces a prescriptive annotation benchmark grounded in humanities research to ensure consistent, unbiased labeling of offensive language, particularly for casual and non-mainstream language uses. We contribute two newly annotated datasets that achieve higher inter-annotator agreement between human and la...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	499,479
2012.12619	ConvMath: A Convolutional Sequence Network for Mathematical Expression Recognition	Despite the recent advances in optical character recognition (OCR), mathematical expressions still face a great challenge to recognize due to their two-dimensional graphical layout. In this paper, we propose a convolutional sequence modeling network, ConvMath, which converts the mathematical expression description in a...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	212,996
2412.03434	BIMCaP: BIM-based AI-supported LiDAR-Camera Pose Refinement	This paper introduces BIMCaP, a novel method to integrate mobile 3D sparse LiDAR data and camera measurements with pre-existing building information models (BIMs), enhancing fast and accurate indoor mapping with affordable sensors. BIMCaP refines sensor poses by leveraging a 3D BIM and employing a bundle adjustment tec...	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	false	513,959
1608.06057	MIMO Gaussian Broadcast Channels with Common, Private and Confidential Messages	The two-user multiple-input multiple-output (MIMO) Gaussian broadcast channel (BC) with common, private and confidential messages is considered. The transmitter sends a common message to both users, a confidential message to User 1 and a private (non-confidential) message to User 2. The secrecy-capacity region is chara...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	60,067
1712.00926	Deep Sampling Networks	Deep convolutional neural networks achieve excellent image up-sampling performance. However, CNN-based methods tend to restore high-resolution results highly depending on traditional interpolations (e.g. bicubic). In this paper, we present a deep sampling network (DSN) for down-sampling and up-sampling without any chea...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	86,009
2112.08604	Use Image Clustering to Facilitate Technology Assisted Review	During the past decade breakthroughs in GPU hardware and deep neural networks technologies have revolutionized the field of computer vision, making image analytical potentials accessible to a range of real-world applications. Technology Assisted Review (TAR) in electronic discovery though traditionally has dominantly d...	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	false	271,855
2006.12191	Potential customer mining application of smart home products based on LightGBM PU learning and Spark ML algorithm practice	This paper studies the case of big data-based intelligent product potential customer mining internal competition in China Telecom Shanghai Company. Huge amounts of data based on big data table, the use of machine Learning and data analysis technology, using the algorithm of LightGBM, PySpark machine Learning algorithms...	false	false	false	false	false	true	false	false	false	false	false	false	false	true	false	false	false	false	183,505
2412.03056	Point-GN: A Non-Parametric Network Using Gaussian Positional Encoding for Point Cloud Classification	This paper introduces Point-GN, a novel non-parametric network for efficient and accurate 3D point cloud classification. Unlike conventional deep learning models that rely on a large number of trainable parameters, Point-GN leverages non-learnable components-specifically, Farthest Point Sampling (FPS), k-Nearest Neighb...	false	false	false	false	true	false	true	true	false	false	false	true	false	false	false	false	false	false	513,796
2302.07135	Fast-MC-PET: A Novel Deep Learning-aided Motion Correction and Reconstruction Framework for Accelerated PET	Patient motion during PET is inevitable. Its long acquisition time not only increases the motion and the associated artifacts but also the patient's discomfort, thus PET acceleration is desirable. However, accelerating PET acquisition will result in reconstructed images with low SNR, and the image quality will still be...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	345,632
2005.11651	Successive Refinement of Privacy	This work examines a novel question: how much randomness is needed to achieve local differential privacy (LDP)? A motivating scenario is providing {\em multiple levels of privacy} to multiple analysts, either for distribution or for heavy-hitter estimation, using the \emph{same} (randomized) output. We call this settin...	false	false	false	false	false	false	true	false	false	true	false	false	true	false	false	false	false	false	178,527
2302.06883	Text-Guided Scene Sketch-to-Photo Synthesis	We propose a method for scene-level sketch-to-photo synthesis with text guidance. Although object-level sketch-to-photo synthesis has been widely studied, whole-scene synthesis is still challenging without reference photos that adequately reflect the target style. To this end, we leverage knowledge from recent large-sc...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	345,564
2308.11924	Diverse Policies Converge in Reward-free Markov Decision Processe	Reinforcement learning has achieved great success in many decision-making tasks, and traditional reinforcement learning algorithms are mainly designed for obtaining a single optimal solution. However, recent works show the importance of developing diverse policies, which makes it an emerging research topic. Despite the...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	387,335
2208.09367	Dialogue Policies for Confusion Mitigation in Situated HRI	Confusion is a mental state triggered by cognitive disequilibrium that can occur in many types of task-oriented interaction, including Human-Robot Interaction (HRI). People may become confused while interacting with robots due to communicative or even task-centred challenges. To build a smooth and engaging HRI, it is i...	true	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	313,678
2003.05171	Vector symbolic architectures for context-free grammars	Background / introduction. Vector symbolic architectures (VSA) are a viable approach for the hyperdimensional representation of symbolic data, such as documents, syntactic structures, or semantic frames. Methods. We present a rigorous mathematical framework for the representation of phrase structure trees and parse tre...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	167,797
1712.01550	G-CORE: A Core for Future Graph Query Languages	We report on a community effort between industry and academia to shape the future of graph query languages. We argue that existing graph database management systems should consider supporting a query language with two key characteristics. First, it should be composable, meaning, that graphs are the input and the output...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	86,120
1807.08855	Weak in the NEES?: Auto-tuning Kalman Filters with Bayesian Optimization	Kalman filters are routinely used for many data fusion applications including navigation, tracking, and simultaneous localization and mapping problems. However, significant time and effort is frequently required to tune various Kalman filter model parameters, e.g. process noise covariance, pre-whitening filter models f...	false	false	false	false	false	false	true	true	false	false	true	false	false	false	false	false	false	false	103,612
2304.09167	Optimal PAC Bounds Without Uniform Convergence	In statistical learning theory, determining the sample complexity of realizable binary classification for VC classes was a long-standing open problem. The results of Simon and Hanneke established sharp upper bounds in this setting. However, the reliance of their argument on the uniform convergence principle limits its ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	358,963
2210.10836	Scene Text Recognition with Semantics	Scene Text Recognition (STR) models have achieved high performance in recent years on benchmark datasets where text images are presented with minimal noise. Traditional STR recognition pipelines take a cropped image as sole input and attempt to identify the characters present. This infrastructure can fail in instances ...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	325,075
2306.11334	Depth and DOF Cues Make A Better Defocus Blur Detector	Defocus blur detection (DBD) separates in-focus and out-of-focus regions in an image. Previous approaches mistakenly mistook homogeneous areas in focus for defocus blur regions, likely due to not considering the internal factors that cause defocus blur. Inspired by the law of depth, depth of field (DOF), and defocus, w...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	374,557
2405.13640	Knowledge Graph Reasoning with Self-supervised Reinforcement Learning	Reinforcement learning (RL) is an effective method of finding reasoning pathways in incomplete knowledge graphs (KGs). To overcome the challenges of a large action space, a self-supervised pre-training method is proposed to warm up the policy network before the RL training stage. To alleviate the distributional mismatc...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	456,021
2412.21033	Plancraft: an evaluation dataset for planning with LLM agents	We present Plancraft, a multi-modal evaluation dataset for LLM agents. Plancraft has both a text-only and multi-modal interface, based on the Minecraft crafting GUI. We include the Minecraft Wiki to evaluate tool use and Retrieval Augmented Generation (RAG), as well as an oracle planner and oracle RAG information extra...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	521,435
2007.13332	Few-shot Knowledge Transfer for Fine-grained Cartoon Face Generation	In this paper, we are interested in generating fine-grained cartoon faces for various groups. We assume that one of these groups consists of sufficient training data while the others only contain few samples. Although the cartoon faces of these groups share similar style, the appearances in various groups could still h...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	189,099
1904.03508	C2S2: Cost-aware Channel Sparse Selection for Progressive Network Pruning	This paper describes a channel-selection approach for simplifying deep neural networks. Specifically, we propose a new type of generic network layer, called pruning layer, to seamlessly augment a given pre-trained model for compression. Each pruning layer, comprising $1 \times 1$ depth-wise kernels, is represented with...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	126,745
2010.14535	Neural Architecture Search of SPD Manifold Networks	In this paper, we propose a new neural architecture search (NAS) problem of Symmetric Positive Definite (SPD) manifold networks, aiming to automate the design of SPD neural architectures. To address this problem, we first introduce a geometrically rich and diverse SPD neural architecture search space for an efficient S...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	true	false	false	203,474
1605.09776	Waddling Random Walk: Fast and Accurate Mining of Motif Statistics in Large Graphs	Algorithms for mining very large graphs, such as those representing online social networks, to discover the relative frequency of small subgraphs within them are of high interest to sociologists, computer scientists and marketeers alike. However, the computation of these network motif statistics via naive enumeration i...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	56,614
1905.12156	Towards Real Scene Super-Resolution with Raw Images	Most existing super-resolution methods do not perform well in real scenarios due to lack of realistic training data and information loss of the model input. To solve the first problem, we propose a new pipeline to generate realistic training data by simulating the imaging process of digital cameras. And to remedy the i...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	132,672
1907.07617	The iWildCam 2019 Challenge Dataset	Camera Traps (or Wild Cams) enable the automatic collection of large quantities of image data. Biologists all over the world use camera traps to monitor biodiversity and population density of animal species. The computer vision community has been making strides towards automating the species classification challenge in...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	138,923

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