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
2303.17163	Matrix diagonalization and singular value decomposition: Static SageMath and dynamic ChatGPT juxtaposed	We investigated some difficulties that students often face when studying linear algebra at the undergraduate level, and identified some common mistakes and difficulties they often encountered when dealing with topics that require algorithmic thinking skills such as matrix factorization. In particular, we focused on (or...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	355,127
2309.13944	Provable Training for Graph Contrastive Learning	Graph Contrastive Learning (GCL) has emerged as a popular training approach for learning node embeddings from augmented graphs without labels. Despite the key principle that maximizing the similarity between positive node pairs while minimizing it between negative node pairs is well established, some fundamental proble...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	394,414
1409.6336	Evidence for a creative dilemma posed by repeated collaborations	We focused on how repeat collaborations in projects for inventions affect performance. Repeat collaborations have two contradictory aspects. A positive aspect is team development or experience, and a negative aspect is team degeneration or decline. Since both contradicting phenomena are observed, inventors have a dilem...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	36,241
2105.12691	On the Advantages of Multiple Stereo Vision Camera Designs for Autonomous Drone Navigation	In this work we showcase the design and assessment of the performance of a multi-camera UAV, when coupled with state-of-the-art planning and mapping algorithms for autonomous navigation. The system leverages state-of-the-art receding horizon exploration techniques for Next-Best-View (NBV) planning with 3D and semantic ...	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	237,071
1706.00712	Convolutional Neural Networks for Medical Image Analysis: Full Training or Fine Tuning?	Training a deep convolutional neural network (CNN) from scratch is difficult because it requires a large amount of labeled training data and a great deal of expertise to ensure proper convergence. A promising alternative is to fine-tune a CNN that has been pre-trained using, for instance, a large set of labeled natural...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	74,674
2308.02134	Using POMDP-based Approach to Address Uncertainty-Aware Adaptation for Self-Protecting Software	The threats posed by evolving cyberattacks have led to increased research related to software systems that can self-protect. One topic in this domain is Moving Target Defense (MTD), which changes software characteristics in the protected system to make it harder for attackers to exploit vulnerabilities. However, MTD im...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	true	383,503
2412.04814	LiFT: Leveraging Human Feedback for Text-to-Video Model Alignment	Recent advancements in text-to-video (T2V) generative models have shown impressive capabilities. However, these models are still inadequate in aligning synthesized videos with human preferences (e.g., accurately reflecting text descriptions), which is particularly difficult to address, as human preferences are inherent...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	514,580
2404.00353	CBF-Based STL Motion Planning for Social Navigation in Crowded Environment	A motion planning methodology based on the combination of Control Barrier Functions (CBF) and Signal Temporal Logic (STL) is employed in this paper. This methodology allows task completion at any point within a specified time interval, considering a dynamic system subject to velocity constraints. In this work, we apply...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	442,878
1405.6171	Performance Estimation of 2*3 MIMO-MC-CDMA using Convolution Code	In this paper we estimate the performance of 2by3 MIMOMCCDMA system using convolution code in MATLAB which highly reduces BER by increasing the efficiency of system. MIMO and MCCDMA system combination is used to reduce bit error rate and also for forming a new system called MCCDMA which is multi user and multiple acces...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	33,351
2401.06654	Decoupling Pixel Flipping and Occlusion Strategy for Consistent XAI Benchmarks	Feature removal is a central building block for eXplainable AI (XAI), both for occlusion-based explanations (Shapley values) as well as their evaluation (pixel flipping, PF). However, occlusion strategies can vary significantly from simple mean replacement up to inpainting with state-of-the-art diffusion models. This a...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	421,231
2004.03450	Learning to Accelerate Decomposition for Multi-Directional 3D Printing	Multi-directional 3D printing has the capability of decreasing or eliminating the need for support structures. Recent work proposed a beam-guided search algorithm to find an optimized sequence of plane-clipping, which gives volume decomposition of a given 3D model. Different printing directions are employed in differen...	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	true	171,571
1804.00921	DeSIGN: Design Inspiration from Generative Networks	Can an algorithm create original and compelling fashion designs to serve as an inspirational assistant? To help answer this question, we design and investigate different image generation models associated with different loss functions to boost creativity in fashion generation. The dimensions of our explorations include...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	94,148
2310.05178	Optimizing Large Language Models to Expedite the Development of Smart Contracts	Programming has always been at the heart of technological innovation in the 21st century. With the advent of blockchain technologies and the proliferation of web3 paradigms of decentralised applications, smart contracts have been very instrumental in enabling developers to build applications that reside on decentralise...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	true	398,024
2405.00423	$\alpha$-leakage by R\'{e}nyi Divergence and Sibson Mutual Information	For $\tilde{f}(t) = \exp(\frac{\alpha-1}{\alpha}t)$, this paper proposes a $\tilde{f}$-mean information gain measure. R\'{e}nyi divergence is shown to be the maximum $\tilde{f}$-mean information gain incurred at each elementary event $y$ of channel output $Y$ and Sibson mutual information is the $\tilde{f}$-mean of thi...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	450,914
2302.04149	Domain Adaptation of Synthetic Driving Datasets for Real-World Autonomous Driving	While developing perception based deep learning models, the benefit of synthetic data is enormous. However, performance of networks trained with synthetic data for certain computer vision tasks degrade significantly when tested on real world data due to the domain gap between them. One of the popular solutions in bridg...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	344,606
2212.00898	Hierarchical Model Selection for Graph Neural Netoworks	Node classification on graph data is a major problem, and various graph neural networks (GNNs) have been proposed. Variants of GNNs such as H2GCN and CPF outperform graph convolutional networks (GCNs) by improving on the weaknesses of the traditional GNN. However, there are some graph data which these GNN variants fail...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	334,233
1207.4707	Correction to "A Note on Gallager's Capacity Theorem for Waveform Channels"	We correct an alleged contradiction to Gallager's capacity theorem for waveform channels as presented in a poster at the 2012 IEEE International Symposium on Information Theory.	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	17,660
1904.07105	MRI Tissue Magnetism Quantification through Total Field Inversion with Deep Neural Networks	Quantitative susceptibility mapping (QSM) utilizes MRI signal phase to infer estimates of local tissue magnetism (magnetic susceptibility), which has been shown useful to provide novel image contrast and as biomarkers of abnormal tissue. QSM requires addressing a challenging post-processing problem: filtering of image ...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	127,710
2111.05498	Attention Approximates Sparse Distributed Memory	While Attention has come to be an important mechanism in deep learning, there remains limited intuition for why it works so well. Here, we show that Transformer Attention can be closely related under certain data conditions to Kanerva's Sparse Distributed Memory (SDM), a biologically plausible associative memory model....	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	265,811
2309.10217	An Empirical Study of Attention Networks for Semantic Segmentation	Semantic segmentation is a vital problem in computer vision. Recently, a common solution to semantic segmentation is the end-to-end convolution neural network, which is much more accurate than traditional methods.Recently, the decoders based on attention achieve state-of-the-art (SOTA) performance on various datasets. ...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	392,912
1909.09814	Graph Convolutions over Constituent Trees for Syntax-Aware Semantic Role Labeling	Semantic role labeling (SRL) is the task of identifying predicates and labeling argument spans with semantic roles. Even though most semantic-role formalisms are built upon constituent syntax and only syntactic constituents can be labeled as arguments (e.g., FrameNet and PropBank), all the recent work on syntax-aware S...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	146,362
1411.0076	User Capacity of Pilot-Contaminated TDD Massive MIMO Systems	Pilot contamination has been regarded as a main limiting factor of time division duplexing (TDD) massive multiple-input-multiple-output (Massive MIMO) systems, as it will make the signal-to-interference-plus-noise ratio (SINR) saturated. However, how pilot contamination will limit the user capacity of downlink Massive ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	37,210
2305.14955	DC-Net: Divide-and-Conquer for Salient Object Detection	In this paper, we introduce Divide-and-Conquer into the salient object detection (SOD) task to enable the model to learn prior knowledge that is for predicting the saliency map. We design a novel network, Divide-and-Conquer Network (DC-Net) which uses two encoders to solve different subtasks that are conducive to predi...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	367,353
2007.06257	Rewiring the Transformer with Depth-Wise LSTMs	Stacking non-linear layers allows deep neural networks to model complicated functions, and including residual connections in Transformer layers is beneficial for convergence and performance. However, residual connections may make the model "forget" distant layers and fail to fuse information from previous layers effect...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	186,955
2107.02565	Prioritized training on points that are learnable, worth learning, and not yet learned (workshop version)	We introduce Goldilocks Selection, a technique for faster model training which selects a sequence of training points that are "just right". We propose an information-theoretic acquisition function -- the reducible validation loss -- and compute it with a small proxy model -- GoldiProx -- to efficiently choose training ...	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	244,867
2212.10784	Can NLI Provide Proper Indirect Supervision for Low-resource Biomedical Relation Extraction?	Two key obstacles in biomedical relation extraction (RE) are the scarcity of annotations and the prevalence of instances without explicitly pre-defined labels due to low annotation coverage. Existing approaches, which treat biomedical RE as a multi-class classification task, often result in poor generalization in low-r...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	337,606
2308.04883	Deep Generative Networks for Heterogeneous Augmentation of Cranial Defects	The design of personalized cranial implants is a challenging and tremendous task that has become a hot topic in terms of process automation with the use of deep learning techniques. The main challenge is associated with the high diversity of possible cranial defects. The lack of appropriate data sources negatively infl...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	384,596
1207.2743	The evolutionary origins of modularity	A central biological question is how natural organisms are so evolvable (capable of quickly adapting to new environments). A key driver of evolvability is the widespread modularity of biological networks--their organization as functional, sparsely connected subunits--but there is no consensus regarding why modularity i...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	17,414
2206.12727	A coupled phase field formulation for modelling fatigue cracking in lithium-ion battery electrode particles	Electrode particle cracking is one of the main phenomena driving battery capacity degradation. Recent phase field fracture studies have investigated particle cracking behaviour. However, only the beginning of life has been considered and effects such as damage accumulation have been neglected. Here, a multi-physics pha...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	304,707
quant-ph/0702072	Markovian Entanglement Networks	Graphical models of probabilistic dependencies have been extensively investigated in the context of classical uncertainty. However, in some domains (most notably, in computational physics and quantum computing) the nature of the relevant uncertainty is non-classical, and the laws of classical probability theory are sup...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	540,913
2206.08736	Generalised Policy Improvement with Geometric Policy Composition	We introduce a method for policy improvement that interpolates between the greedy approach of value-based reinforcement learning (RL) and the full planning approach typical of model-based RL. The new method builds on the concept of a geometric horizon model (GHM, also known as a gamma-model), which models the discounte...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	303,272
2009.12610	Deep Learning-based Four-region Lung Segmentation in Chest Radiography for COVID-19 Diagnosis	Purpose. Imaging plays an important role in assessing severity of COVID 19 pneumonia. However, semantic interpretation of chest radiography (CXR) findings does not include quantitative description of radiographic opacities. Most current AI assisted CXR image analysis framework do not quantify for regional variations of...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	197,480
1910.05340	EDEN: Enabling Energy-Efficient, High-Performance Deep Neural Network Inference Using Approximate DRAM	The effectiveness of deep neural networks (DNN) in vision, speech, and language processing has prompted a tremendous demand for energy-efficient high-performance DNN inference systems. Due to the increasing memory intensity of most DNN workloads, main memory can dominate the system's energy consumption and stall time. ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	149,030
2307.09499	Variable Independence in Linear Real Arithmetic	Variable independence and decomposability are algorithmic techniques for simplifying logical formulas by tearing apart connections between free variables. These techniques were originally proposed to speed up query evaluation in constraint databases, in particular by representing the query as a Boolean combination of f...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	true	380,199
2401.17546	Effective Multi-Stage Training Model For Edge Computing Devices In Intrusion Detection	Intrusion detection poses a significant challenge within expansive and persistently interconnected environments. As malicious code continues to advance and sophisticated attack methodologies proliferate, various advanced deep learning-based detection approaches have been proposed. Nevertheless, the complexity and accur...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	425,237
2409.19911	Replace Anyone in Videos	Recent advancements in controllable human-centric video generation, particularly with the rise of diffusion models, have demonstrated considerable progress. However, achieving precise and localized control over human motion, e.g., replacing or inserting individuals into videos while exhibiting desired motion patterns, ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	492,904
2009.10526	Adversarial Training with Stochastic Weight Average	Adversarial training deep neural networks often experience serious overfitting problem. Recently, it is explained that the overfitting happens because the sample complexity of training data is insufficient to generalize robustness. In traditional machine learning, one way to relieve overfitting from the lack of data is...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	196,913
2308.16659	Autoencoder-based Online Data Quality Monitoring for the CMS Electromagnetic Calorimeter	The online Data Quality Monitoring system (DQM) of the CMS electromagnetic calorimeter (ECAL) is a crucial operational tool that allows ECAL experts to quickly identify, localize, and diagnose a broad range of detector issues that would otherwise hinder physics-quality data taking. Although the existing ECAL DQM system...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	389,070
1605.08831	Weighted Residuals for Very Deep Networks	Deep residual networks have recently shown appealing performance on many challenging computer vision tasks. However, the original residual structure still has some defects making it difficult to converge on very deep networks. In this paper, we introduce a weighted residual network to address the incompatibility betwee...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	56,480
2112.13966	Online Adversarial Knowledge Distillation for Graph Neural Networks	Knowledge distillation, a technique recently gaining popularity for enhancing model generalization in Convolutional Neural Networks (CNNs), operates under the assumption that both teacher and student models are trained on identical data distributions. However, its effect on Graph Neural Networks (GNNs) is less than sat...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	273,407
2306.10336	Fair Causal Feature Selection	Fair feature selection for classification decision tasks has recently garnered significant attention from researchers. However, existing fair feature selection algorithms fall short of providing a full explanation of the causal relationship between features and sensitive attributes, potentially impacting the accuracy o...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	374,189
2011.11052	Efficient embedding network for 3D brain tumor segmentation	3D medical image processing with deep learning greatly suffers from a lack of data. Thus, studies carried out in this field are limited compared to works related to 2D natural image analysis, where very large datasets exist. As a result, powerful and efficient 2D convolutional neural networks have been developed and tr...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	207,701
1812.10793	Automated Adaptation Strategies for Stream Learning	Automation of machine learning model development is increasingly becoming an established research area. While automated model selection and automated data pre-processing have been studied in depth, there is, however, a gap concerning automated model adaptation strategies when multiple strategies are available. Manually...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	117,441
2310.09147	Exploring Sparse Spatial Relation in Graph Inference for Text-Based VQA	Text-based visual question answering (TextVQA) faces the significant challenge of avoiding redundant relational inference. To be specific, a large number of detected objects and optical character recognition (OCR) tokens result in rich visual relationships. Existing works take all visual relationships into account for ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	399,669
2310.16753	PROMINET: Prototype-based Multi-View Network for Interpretable Email Response Prediction	Email is a widely used tool for business communication, and email marketing has emerged as a cost-effective strategy for enterprises. While previous studies have examined factors affecting email marketing performance, limited research has focused on understanding email response behavior by considering email content and...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	402,860
2211.17180	Nonlinear Advantage: Trained Networks Might Not Be As Complex as You Think	We perform an empirical study of the behaviour of deep networks when fully linearizing some of its feature channels through a sparsity prior on the overall number of nonlinear units in the network. In experiments on image classification and machine translation tasks, we investigate how much we can simplify the network ...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	333,888
2001.05540	Insertion-Deletion Transformer	We propose the Insertion-Deletion Transformer, a novel transformer-based neural architecture and training method for sequence generation. The model consists of two phases that are executed iteratively, 1) an insertion phase and 2) a deletion phase. The insertion phase parameterizes a distribution of insertions on the c...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	160,567
1809.09219	Fast Signal Recovery from Saturated Measurements by Linear Loss and Nonconvex Penalties	Sign information is the key to overcoming the inevitable saturation error in compressive sensing systems, which causes information loss and results in bias. For sparse signal recovery from saturation, we propose to use a linear loss to improve the effectiveness from existing methods that utilize hard constraints/hinge ...	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	108,659
2310.01436	Graph Neural Architecture Search with GPT-4	Graph Neural Architecture Search (GNAS) has shown promising results in automatically designing graph neural networks. However, GNAS still requires intensive human labor with rich domain knowledge to design the search space and search strategy. In this paper, we integrate GPT-4 into GNAS and propose a new GPT-4 based Gr...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	396,429
1312.0914	Characterizing the Rate Region of the (4,3,3) Exact-Repair Regenerating Codes	Exact-repair regenerating codes are considered for the case (n,k,d)=(4,3,3), for which a complete characterization of the rate region is provided. This characterization answers in the affirmative the open question whether there exists a non-vanishing gap between the optimal bandwidth-storage tradeoff of the functional-...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	28,821
1901.09094	Derandomized Load Balancing using Random Walks on Expander Graphs	In a computing center with a huge amount of machines, when a job arrives, a dispatcher need to decide which machine to route this job to based on limited information. A classical method, called the power-of-$d$ choices algorithm is to pick $d$ servers independently at random and dispatch the job to the least loaded ser...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	true	119,645
2204.12155	Bias-Variance Decompositions for Margin Losses	We introduce a novel bias-variance decomposition for a range of strictly convex margin losses, including the logistic loss (minimized by the classic LogitBoost algorithm), as well as the squared margin loss and canonical boosting loss. Furthermore, we show that, for all strictly convex margin losses, the expected risk ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	293,391
2007.09208	Asynchronous Federated Learning with Reduced Number of Rounds and with Differential Privacy from Less Aggregated Gaussian Noise	The feasibility of federated learning is highly constrained by the server-clients infrastructure in terms of network communication. Most newly launched smartphones and IoT devices are equipped with GPUs or sufficient computing hardware to run powerful AI models. However, in case of the original synchronous federated le...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	187,858
2207.10574	Co-Located Human-Human Interaction Analysis using Nonverbal Cues: A Survey	Automated co-located human-human interaction analysis has been addressed by the use of nonverbal communication as measurable evidence of social and psychological phenomena. We survey the computing studies (since 2010) detecting phenomena related to social traits (e.g., leadership, dominance, personality traits), social...	true	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	true	309,309
2004.14582	Bilateral Attention Network for RGB-D Salient Object Detection	Most existing RGB-D salient object detection (SOD) methods focus on the foreground region when utilizing the depth images. However, the background also provides important information in traditional SOD methods for promising performance. To better explore salient information in both foreground and background regions, th...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	174,943
2406.09052	Data-Free Generative Replay for Class-Incremental Learning on Imbalanced Data	Continual learning is a challenging problem in machine learning, especially for image classification tasks with imbalanced datasets. It becomes even more challenging when it involves learning new classes incrementally. One method for incremental class learning, addressing dataset imbalance, is rehearsal using previousl...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	463,745
2310.07381	Extremal Mechanisms for Pointwise Maximal Leakage	Data publishing under privacy constraints can be achieved with mechanisms that add randomness to data points when released to an untrusted party, thereby decreasing the data's utility. In this paper, we analyze this privacy-utility tradeoff for the pointwise maximal leakage privacy measure and a general class of convex...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	398,952
1609.02117	DoF Analysis in a Two-Layered Heterogeneous Wireless Interference Network	Degrees of freedom (DoF) is studied in the downlink of a heterogenous wireless network modeled as a two-layered interference network. The first layer of the interference network is the backhaul layer between macro base stations (MB) and small cell base stations (SB), which is modeled as a Wyner type linear network. The...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	60,690
2302.08575	Foundation Models for Natural Language Processing -- Pre-trained Language Models Integrating Media	This open access book provides a comprehensive overview of the state of the art in research and applications of Foundation Models and is intended for readers familiar with basic Natural Language Processing (NLP) concepts. Over the recent years, a revolutionary new paradigm has been developed for training models for NLP...	false	false	false	false	false	false	true	false	true	false	false	true	false	false	false	false	false	true	346,088
2002.11318	Can we have it all? On the Trade-off between Spatial and Adversarial Robustness of Neural Networks	(Non-)robustness of neural networks to small, adversarial pixel-wise perturbations, and as more recently shown, to even random spatial transformations (e.g., translations, rotations) entreats both theoretical and empirical understanding. Spatial robustness to random translations and rotations is commonly attained via e...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	true	false	false	165,672
2206.15242	Secure Heterogeneous Multi-Robot Collaboration and Docking with Hyperledger Fabric Blockchain	In recent years, multi-robot systems have received increasing attention from both industry and academia. Besides the need of accurate and robust estimation of relative localization, security and trust in the system are essential to enable wider adoption. In this paper, we propose a framework using Hyperledger Fabric fo...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	305,532
2406.01078	Unseen Visual Anomaly Generation	Visual anomaly detection (AD) presents significant challenges due to the scarcity of anomalous data samples. While numerous works have been proposed to synthesize anomalous samples, these synthetic anomalies often lack authenticity or require extensive training data, limiting their applicability in real-world scenarios...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	460,161
2102.09242	DSRN: an Efficient Deep Network for Image Relighting	Custom and natural lighting conditions can be emulated in images of the scene during post-editing. Extraordinary capabilities of the deep learning framework can be utilized for such purpose. Deep image relighting allows automatic photo enhancement by illumination-specific retouching. Most of the state-of-the-art method...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	220,709
2207.00594	Time-aware Dynamic Graph Embedding for Asynchronous Structural Evolution	Dynamic graphs refer to graphs whose structure dynamically changes over time. Despite the benefits of learning vertex representations (i.e., embeddings) for dynamic graphs, existing works merely view a dynamic graph as a sequence of changes within the vertex connections, neglecting the crucial asynchronous nature of su...	false	false	false	true	true	false	true	false	false	false	false	false	false	false	false	false	false	false	305,815
2408.06960	Measuring User Understanding in Dialogue-based XAI Systems	The field of eXplainable Artificial Intelligence (XAI) is increasingly recognizing the need to personalize and/or interactively adapt the explanation to better reflect users' explanation needs. While dialogue-based approaches to XAI have been proposed recently, the state-of-the-art in XAI is still characterized by what...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	480,405
2008.06164	Unsupervised Image Restoration Using Partially Linear Denoisers	Deep neural network based methods are the state of the art in various image restoration problems. Standard supervised learning frameworks require a set of noisy measurement and clean image pairs for which a distance between the output of the restoration model and the ground truth, clean images is minimized. The ground ...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	191,718
2411.19233	Gaussians-to-Life: Text-Driven Animation of 3D Gaussian Splatting Scenes	State-of-the-art novel view synthesis methods achieve impressive results for multi-view captures of static 3D scenes. However, the reconstructed scenes still lack "liveliness," a key component for creating engaging 3D experiences. Recently, novel video diffusion models generate realistic videos with complex motion and ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	512,167
2012.11792	Are We On The Same Page? Hierarchical Explanation Generation for Planning Tasks in Human-Robot Teaming using Reinforcement Learning	Providing explanations is considered an imperative ability for an AI agent in a human-robot teaming framework. The right explanation provides the rationale behind an AI agent's decision-making. However, to maintain the human teammate's cognitive demand to comprehend the provided explanations, prior works have focused o...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	212,730
1909.11646	High Fidelity Speech Synthesis with Adversarial Networks	Generative adversarial networks have seen rapid development in recent years and have led to remarkable improvements in generative modelling of images. However, their application in the audio domain has received limited attention, and autoregressive models, such as WaveNet, remain the state of the art in generative mode...	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	146,874
2305.06862	A General Framework for Visualizing Embedding Spaces of Neural Survival Analysis Models Based on Angular Information	We propose a general framework for visualizing any intermediate embedding representation used by any neural survival analysis model. Our framework is based on so-called anchor directions in an embedding space. We show how to estimate these anchor directions using clustering or, alternatively, using user-supplied "conce...	true	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	363,688
1609.06838	Deep-Learned Collision Avoidance Policy for Distributed Multi-Agent Navigation	High-speed, low-latency obstacle avoidance that is insensitive to sensor noise is essential for enabling multiple decentralized robots to function reliably in cluttered and dynamic environments. While other distributed multi-agent collision avoidance systems exist, these systems require online geometric optimization wh...	false	false	false	false	true	false	false	true	false	false	false	true	false	false	false	false	false	false	61,353
2008.03285	Physics-Based Dexterous Manipulations with Estimated Hand Poses and Residual Reinforcement Learning	Dexterous manipulation of objects in virtual environments with our bare hands, by using only a depth sensor and a state-of-the-art 3D hand pose estimator (HPE), is challenging. While virtual environments are ruled by physics, e.g. object weights and surface frictions, the absence of force feedback makes the task challe...	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	190,855
2302.06186	Multiscale Graph Neural Network Autoencoders for Interpretable Scientific Machine Learning	The goal of this work is to address two limitations in autoencoder-based models: latent space interpretability and compatibility with unstructured meshes. This is accomplished here with the development of a novel graph neural network (GNN) autoencoding architecture with demonstrations on complex fluid flow applications...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	345,320
2006.02528	Learning across label confidence distributions using Filtered Transfer Learning	Performance of neural network models relies on the availability of large datasets with minimal levels of uncertainty. Transfer Learning (TL) models have been proposed to resolve the issue of small dataset size by letting the model train on a bigger, task-related reference dataset and then fine-tune on a smaller, task-s...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	180,059
1908.11056	Targeted Source Detection for Environmental Data	In the face of growing needs for water and energy, a fundamental understanding of the environmental impacts of human activities becomes critical for managing water and energy resources, remedying water pollution, and making regulatory policy wisely. Among activities that impact the environment, oil and gas production, ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	143,285
2011.08954	Multi-agent Reinforcement Learning Accelerated MCMC on Multiscale Inversion Problem	In this work, we propose a multi-agent actor-critic reinforcement learning (RL) algorithm to accelerate the multi-level Monte Carlo Markov Chain (MCMC) sampling algorithms. The policies (actors) of the agents are used to generate the proposal in the MCMC steps; and the critic, which is centralized, is in charge of esti...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	207,037
1602.04886	Fast, Robust, Continuous Monocular Egomotion Computation	We propose robust methods for estimating camera egomotion in noisy, real-world monocular image sequences in the general case of unknown observer rotation and translation with two views and a small baseline. This is a difficult problem because of the nonconvex cost function of the perspective camera motion equation and ...	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	52,191
2010.01027	Beyond Chemical 1D knowledge using Transformers	In the present paper we evaluated efficiency of the recent Transformer-CNN models to predict target properties based on the augmented stereochemical SMILES. We selected a well-known Cliff activity dataset as well as a Dipole moment dataset and compared the effect of three representations for R/S stereochemistry in SMIL...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	198,482
1603.07704	Probabilistic Reasoning via Deep Learning: Neural Association Models	In this paper, we propose a new deep learning approach, called neural association model (NAM), for probabilistic reasoning in artificial intelligence. We propose to use neural networks to model association between any two events in a domain. Neural networks take one event as input and compute a conditional probability ...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	true	false	false	53,662
2408.14738	Learning Differentially Private Diffusion Models via Stochastic Adversarial Distillation	While the success of deep learning relies on large amounts of training datasets, data is often limited in privacy-sensitive domains. To address this challenge, generative model learning with differential privacy has emerged as a solution to train private generative models for desensitized data generation. However, the ...	false	false	false	false	false	false	true	false	false	false	false	true	true	false	false	false	false	false	483,649
2301.11323	Joint Training of Deep Ensembles Fails Due to Learner Collusion	Ensembles of machine learning models have been well established as a powerful method of improving performance over a single model. Traditionally, ensembling algorithms train their base learners independently or sequentially with the goal of optimizing their joint performance. In the case of deep ensembles of neural net...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	342,096
2212.07194	Traffic Flow Prediction via Variational Bayesian Inference-based Encoder-Decoder Framework	Accurate traffic flow prediction, a hotspot for intelligent transportation research, is the prerequisite for mastering traffic and making travel plans. The speed of traffic flow can be affected by roads condition, weather, holidays, etc. Furthermore, the sensors to catch the information about traffic flow will be inter...	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	false	336,333
1512.05844	Domain Adaptation and Transfer Learning in StochasticNets	Transfer learning is a recent field of machine learning research that aims to resolve the challenge of dealing with insufficient training data in the domain of interest. This is a particular issue with traditional deep neural networks where a large amount of training data is needed. Recently, StochasticNets was propose...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	50,262
1705.03415	3D Placement of an Unmanned Aerial Vehicle Base Station (UAV-BS) for Energy-Efficient Maximal Coverage	Unmanned Aerial Vehicle mounted base stations (UAV-BSs) can provide wireless services in a variety of scenarios. In this letter, we propose an optimal placement algorithm for UAV-BSs that maximizes the number of covered users using the minimum transmit power. We decouple the UAV-BS deployment problem in the vertical an...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	73,180
1708.02757	Isointense infant brain MRI segmentation with a dilated convolutional neural network	Quantitative analysis of brain MRI at the age of 6 months is difficult because of the limited contrast between white matter and gray matter. In this study, we use a dilated triplanar convolutional neural network in combination with a non-dilated 3D convolutional neural network for the segmentation of white matter, gray...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	78,655
2202.10553	Guidelines and Evaluation of Clinical Explainable AI in Medical Image Analysis	Explainable artificial intelligence (XAI) is essential for enabling clinical users to get informed decision support from AI and comply with evidence-based medical practice. Applying XAI in clinical settings requires proper evaluation criteria to ensure the explanation technique is both technically sound and clinically ...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	281,560
1610.03863	High Dimensional Uncertainty Quantification for an Electrothermal Field Problem using Stochastic Collocation on Sparse Grids and Tensor Train Decompositions	The temperature developed in bondwires of integrated circuits (ICs) is a possible source of malfunction, and has to be taken into account during the design phase of an IC. Due to manufacturing tolerances, a bondwire's geometrical characteristics are uncertain parameters, and as such their impact has to be examined with...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	62,306
2409.03525	FrozenSeg: Harmonizing Frozen Foundation Models for Open-Vocabulary Segmentation	Open-vocabulary segmentation poses significant challenges, as it requires segmenting and recognizing objects across an open set of categories in unconstrained environments. Building on the success of powerful vision-language (ViL) foundation models, such as CLIP, recent efforts sought to harness their zero-short capabi...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	486,080
2208.01935	A Multi-Dimensional Matrix Pencil-Based Channel Prediction Method for Massive MIMO with Mobility	This paper addresses the mobility problem in massive multiple-input multiple-output systems, which leads to significant performance losses in the practical deployment of the fifth generation mobile communication networks. We propose a novel channel prediction method based on multi-dimensional matrix pencil (MDMP), whic...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	311,329
2104.03169	Empowering Prosumer Communities in Smart Grid with Wireless Communications and Federated Edge Learning	The exponential growth of distributed energy resources is enabling the transformation of traditional consumers in the smart grid into prosumers. Such transition presents a promising opportunity for sustainable energy trading. Yet, the integration of prosumers in the energy market imposes new considerations in designing...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	228,997
1611.07954	Emergent Predication Structure in Hidden State Vectors of Neural Readers	A significant number of neural architectures for reading comprehension have recently been developed and evaluated on large cloze-style datasets. We present experiments supporting the emergence of "predication structure" in the hidden state vectors of these readers. More specifically, we provide evidence that the hidden...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	64,430
2405.20550	Uncertainty Quantification for Deep Learning	A complete and statistically consistent uncertainty quantification for deep learning is provided, including the sources of uncertainty arising from (1) the new input data, (2) the training and testing data (3) the weight vectors of the neural network, and (4) the neural network because it is not a perfect predictor. Us...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	459,402
2409.09371	WeatherReal: A Benchmark Based on In-Situ Observations for Evaluating Weather Models	In recent years, AI-based weather forecasting models have matched or even outperformed numerical weather prediction systems. However, most of these models have been trained and evaluated on reanalysis datasets like ERA5. These datasets, being products of numerical models, often diverge substantially from actual observa...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	488,295
2106.14444	A Knowledge-Grounded Dialog System Based on Pre-Trained Language Models	We present a knowledge-grounded dialog system developed for the ninth Dialog System Technology Challenge (DSTC9) Track 1 - Beyond Domain APIs: Task-oriented Conversational Modeling with Unstructured Knowledge Access. We leverage transfer learning with existing language models to accomplish the tasks in this challenge t...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	243,414
2012.05458	Beyond Class-Conditional Assumption: A Primary Attempt to Combat Instance-Dependent Label Noise	Supervised learning under label noise has seen numerous advances recently, while existing theoretical findings and empirical results broadly build up on the class-conditional noise (CCN) assumption that the noise is independent of input features given the true label. In this work, we present a theoretical hypothesis te...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	210,792
2108.07511	LIF-Seg: LiDAR and Camera Image Fusion for 3D LiDAR Semantic Segmentation	Camera and 3D LiDAR sensors have become indispensable devices in modern autonomous driving vehicles, where the camera provides the fine-grained texture, color information in 2D space and LiDAR captures more precise and farther-away distance measurements of the surrounding environments. The complementary information fro...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	250,942
2203.12149	On generalized quasi-cyclic codes over $\mathbb{Z}_4$	Based on good algebraic structures and practicabilities, generalized quasi-cyclic (GQC) codes play important role in coding theory. In this paper, we study some results on GQC codes over $\mathbb{Z}_4$ including the normalized generating set, the minimum generating set and the normalized generating set of their dual co...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	287,159
2410.12876	In-context KV-Cache Eviction for LLMs via Attention-Gate	The KV-Cache technique has become the standard for the inference of large language models (LLMs). It caches states of self-attention to avoid recomputation. Yet, it is widely criticized that KV-Cache can become a bottleneck of the LLM inference system, especially when confronted with ultra-large models and long-context...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	499,260
2501.10156	Tethered Variable Inertial Attitude Control Mechanisms through a Modular Jumping Limbed Robot	This paper presents the concept of a tethered variable inertial attitude control mechanism for a modular jumping-limbed robot designed for planetary exploration in low-gravity environments. The system, named SPLITTER, comprises two sub-10 kg quadrupedal robots connected by a tether, capable of executing successive jump...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	525,416
2102.08524	Applications of optimal nonlinear control to a whole-brain network of FitzHugh-Nagumo oscillators	We apply the framework of optimal nonlinear control to steer the dynamics of a whole-brain network of FitzHugh-Nagumo oscillators. Its nodes correspond to the cortical areas of an atlas-based segmentation of the human cerebral cortex, and the inter-node coupling strengths are derived from Diffusion Tensor Imaging data ...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	220,482

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