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
2405.18180	Safe Reinforcement Learning in Black-Box Environments via Adaptive Shielding	Empowering safe exploration of reinforcement learning (RL) agents during training is a critical challenge towards their deployment in many real-world scenarios. When prior knowledge of the domain or task is unavailable, training RL agents in unknown, \textit{black-box} environments presents an even greater safety risk....	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	458,307
1904.12735	DeepHMap++: Combined Projection Grouping and Correspondence Learning for Full DoF Pose Estimation	In recent years, estimating the 6D pose of object instances with convolutional neural network (CNN) has received considerable attention. Depending on whether intermediate cues are used, the relevant literature can be roughly divided into two broad categories: direct methods and two stage pipelines. For the latter, inte...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	129,210
1703.00868	Using Synthetic Data to Train Neural Networks is Model-Based Reasoning	We draw a formal connection between using synthetic training data to optimize neural network parameters and approximate, Bayesian, model-based reasoning. In particular, training a neural network using synthetic data can be viewed as learning a proposal distribution generator for approximate inference in the synthetic-d...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	69,248
1610.05516	Active Network Alignment: A Matching-Based Approach	Network alignment is the problem of matching the nodes of two graphs, maximizing the similarity of the matched nodes and the edges between them. This problem is encountered in a wide array of applications-from biological networks to social networks to ontologies-where multiple networked data sources need to be integrat...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	62,525
2207.14537	An Industrial Applicable Approach towards Design Optimization of a Mechanism: a Coronaventilator Case Study	Design optimization of mechanisms is a promising research area as it results in more energy-efficient machines without compromising performance. However, machine builders do not actually use the potential described in the literature as these methods require too much theoretical analysis. This paper introduces a conve...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	310,622
2101.02584	Oscillatory Residual Stresses in Steady Angular Channel Extrusion	Angular channel extrusion has evolved as processes that can induce significant strengthening of the formed product through grain refinement. However, significant residual stresses are developed in the extruded product whose quantification is necessary for accurate process design and subsequent heat treatment. Experimen...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	214,677
2310.11699	MISAR: A Multimodal Instructional System with Augmented Reality	Augmented reality (AR) requires the seamless integration of visual, auditory, and linguistic channels for optimized human-computer interaction. While auditory and visual inputs facilitate real-time and contextual user guidance, the potential of large language models (LLMs) in this landscape remains largely untapped. Ou...	false	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	400,745
1707.07067	Multipath Multiplexing for Capacity Enhancement in SIMO Wireless Systems	This paper proposes a novel and simple orthogonal faster than Nyquist (OFTN) data transmission and detection approach for a single input multiple output (SIMO) system. It is assumed that the signal having a bandwidth $B$ is transmitted through a wireless channel with $L$ multipath components. Under this assumption, the...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	77,538
2412.21069	Privacy-Aware Multi-Device Cooperative Edge Inference with Distributed Resource Bidding	Mobile edge computing (MEC) has empowered mobile devices (MDs) in supporting artificial intelligence (AI) applications through collaborative efforts with proximal MEC servers. Unfortunately, despite the great promise of device-edge cooperative AI inference, data privacy becomes an increasing concern. In this paper, we ...	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	true	521,449
2102.11730	RGB-D Railway Platform Monitoring and Scene Understanding for Enhanced Passenger Safety	Automated monitoring and analysis of passenger movement in safety-critical parts of transport infrastructures represent a relevant visual surveillance task. Recent breakthroughs in visual representation learning and spatial sensing opened up new possibilities for detecting and tracking humans and objects within a 3D sp...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	221,507
2109.00151	Asynchronous Federated Learning for Sensor Data with Concept Drift	Federated learning (FL) involves multiple distributed devices jointly training a shared model without any of the participants having to reveal their local data to a centralized server. Most of previous FL approaches assume that data on devices are fixed and stationary during the training process. However, this assumpti...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	253,021
2407.08822	FedMedICL: Towards Holistic Evaluation of Distribution Shifts in Federated Medical Imaging	For medical imaging AI models to be clinically impactful, they must generalize. However, this goal is hindered by (i) diverse types of distribution shifts, such as temporal, demographic, and label shifts, and (ii) limited diversity in datasets that are siloed within single medical institutions. While these limitations ...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	472,320
1611.07759	Multi-View 3D Object Detection Network for Autonomous Driving	This paper aims at high-accuracy 3D object detection in autonomous driving scenario. We propose Multi-View 3D networks (MV3D), a sensory-fusion framework that takes both LIDAR point cloud and RGB images as input and predicts oriented 3D bounding boxes. We encode the sparse 3D point cloud with a compact multi-view repre...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	64,403
2001.03640	Unsupervised multi-modal Styled Content Generation	The emergence of deep generative models has recently enabled the automatic generation of massive amounts of graphical content, both in 2D and in 3D. Generative Adversarial Networks (GANs) and style control mechanisms, such as Adaptive Instance Normalization (AdaIN), have proved particularly effective in this context, c...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	160,024
2006.07573	GIPFA: Generating IPA Pronunciation from Audio	Transcribing spoken audio samples into the International Phonetic Alphabet (IPA) has long been reserved for experts. In this study, we examine the use of an Artificial Neural Network (ANN) model to automatically extract the IPA phonemic pronunciation of a word based on its audio pronunciation, hence its name Generating...	false	false	true	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	181,859
2101.00822	Outline to Story: Fine-grained Controllable Story Generation from Cascaded Events	Large-scale pretrained language models have shown thrilling generation capabilities, especially when they generate consistent long text in thousands of words with ease. However, users of these models can only control the prefix of sentences or certain global aspects of generated text. It is challenging to simultaneousl...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	214,218
2404.01331	LLaVA-Gemma: Accelerating Multimodal Foundation Models with a Compact Language Model	We train a suite of multimodal foundation models (MMFM) using the popular LLaVA framework with the recently released Gemma family of large language models (LLMs). Of particular interest is the 2B parameter Gemma model, which provides opportunities to construct capable small-scale MMFMs. In line with findings from other...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	443,376
2106.00765	Connectivity constrains quantum codes	Quantum low-density parity-check (LDPC) codes are an important class of quantum error correcting codes. In such codes, each qubit only affects a constant number of syndrome bits, and each syndrome bit only relies on some constant number of qubits. Constructing quantum LDPC codes is challenging. It is an open problem to...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	238,242
2003.10378	Weighting NTBEA for Game AI Optimisation	The N-Tuple Bandit Evolutionary Algorithm (NTBEA) has proven very effective in optimising algorithm parameters in Game AI. A potential weakness is the use of a simple average of all component Tuples in the model. This study investigates a refinement to the N-Tuple model used in NTBEA by weighting these component Tuples...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	169,317
2310.20347	Automatic Generators for a Family of Matrix Multiplication Routines with Apache TVM	We explore the utilization of the Apache TVM open source framework to automatically generate a family of algorithms that follow the approach taken by popular linear algebra libraries, such as GotoBLAS2, BLIS and OpenBLAS, in order to obtain high-performance blocked formulations of the general matrix multiplication (GEM...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	404,355
2205.10548	Three-Dimensional Segmentation of the Left Ventricle in Late Gadolinium Enhanced MR Images of Chronic Infarction Combining Long- and Short-Axis Information	Automatic segmentation of the left ventricle (LV) in late gadolinium enhanced (LGE) cardiac MR (CMR) images is difficult due to the intensity heterogeneity arising from accumulation of contrast agent in infarcted myocardium. In this paper, we present a comprehensive framework for automatic 3D segmentation of the LV in ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	297,753
1612.01655	Fine-grained Recurrent Neural Networks for Automatic Prostate Segmentation in Ultrasound Images	Boundary incompleteness raises great challenges to automatic prostate segmentation in ultrasound images. Shape prior can provide strong guidance in estimating the missing boundary, but traditional shape models often suffer from hand-crafted descriptors and local information loss in the fitting procedure. In this paper,...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	65,124
2101.10420	Spectrum Attention Mechanism for Time Series Classification	Time series classification(TSC) has always been an important and challenging research task. With the wide application of deep learning, more and more researchers use deep learning models to solve TSC problems. Since time series always contains a lot of noise, which has a negative impact on network training, people usua...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	216,919
2308.04424	A Bi-directional Multi-hop Inference Model for Joint Dialog Sentiment Classification and Act Recognition	The joint task of Dialog Sentiment Classification (DSC) and Act Recognition (DAR) aims to predict the sentiment label and act label for each utterance in a dialog simultaneously. However, current methods encode the dialog context in only one direction, which limits their ability to thoroughly comprehend the context. Mo...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	384,406
2403.17782	GenesisTex: Adapting Image Denoising Diffusion to Texture Space	We present GenesisTex, a novel method for synthesizing textures for 3D geometries from text descriptions. GenesisTex adapts the pretrained image diffusion model to texture space by texture space sampling. Specifically, we maintain a latent texture map for each viewpoint, which is updated with predicted noise on the ren...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	441,624
2408.06324	Online Vehicle Routing with Pickups and Deliveries under Time-Dependent Travel-Time Constraints	The Vehicle Routing Problem with pickups, deliveries and spatiotemporal service constraints ($VRPPDSTC$) is a quite challenging algorithmic problem that can be dealt with in either an offline or an online fashion. In this work, we focus on a generalization, called $VRPPDSTCtd$, in which the travel-time metric is \emph{...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	480,160
2502.10070	Topological Neural Networks over the Air	Topological neural networks (TNNs) are information processing architectures that model representations from data lying over topological spaces (e.g., simplicial or cell complexes) and allow for decentralized implementation through localized communications over different neighborhoods. Existing TNN architectures have no...	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	533,724
2410.15311	Who is Undercover? Guiding LLMs to Explore Multi-Perspective Team Tactic in the Game	Large Language Models (LLMs) are pivotal AI agents in complex tasks but still face challenges in open decision-making problems within complex scenarios. To address this, we use the language logic game ``Who is Undercover?'' (WIU) as an experimental platform to propose the Multi-Perspective Team Tactic (MPTT) framework....	false	false	false	false	true	false	false	false	true	false	false	false	false	true	false	false	false	false	500,475
2408.05207	Design and Fabrication of Soft Locomotion Robots based on Spatial Compliant Mechanisms	Soft robotics has emerged as a promising technology that holds great potential for various application areas. This is due to soft materials unique properties, including flexibility, safety, and shock absorption, among others. Despite many advancement in the field, the development of effective design methodologies and p...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	479,697
2302.05846	OAMatcher: An Overlapping Areas-based Network for Accurate Local Feature Matching	Local feature matching is an essential component in many visual applications. In this work, we propose OAMatcher, a Tranformer-based detector-free method that imitates humans behavior to generate dense and accurate matches. Firstly, OAMatcher predicts overlapping areas to promote effective and clean global context aggr...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	345,192
2005.10402	Studying Product Competition Using Representation Learning	Studying competition and market structure at the product level instead of brand level can provide firms with insights on cannibalization and product line optimization. However, it is computationally challenging to analyze product-level competition for the millions of products available on e-commerce platforms. We intro...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	178,164
2411.00161	Residual Deep Gaussian Processes on Manifolds	We propose practical deep Gaussian process models on Riemannian manifolds, similar in spirit to residual neural networks. With manifold-to-manifold hidden layers and an arbitrary last layer, they can model manifold- and scalar-valued functions, as well as vector fields. We target data inherently supported on manifolds,...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	504,460
2401.01839	Frequency Domain Modality-invariant Feature Learning for Visible-infrared Person Re-Identification	Visible-infrared person re-identification (VI-ReID) is challenging due to the significant cross-modality discrepancies between visible and infrared images. While existing methods have focused on designing complex network architectures or using metric learning constraints to learn modality-invariant features, they often...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	419,516
1709.06743	pandapower - an Open Source Python Tool for Convenient Modeling, Analysis and Optimization of Electric Power Systems	pandapower is a Python based, BSD-licensed power system analysis tool aimed at automation of static and quasi-static analysis and optimization of balanced power systems. It provides power flow, optimal power flow, state estimation, topological graph searches and short circuit calculations according to IEC 60909. pandap...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	81,164
1905.03420	A deep learning approach for analyzing the composition of chemometric data	We propose novel deep learning based chemometric data analysis technique. We trained L2 regularized sparse autoencoder end-to-end for reducing the size of the feature vector to handle the classic problem of the curse of dimensionality in chemometric data analysis. We introduce a novel technique of automatic selection o...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	130,196
2103.14282	Rethinking Graph Neural Architecture Search from Message-passing	Graph neural networks (GNNs) emerged recently as a standard toolkit for learning from data on graphs. Current GNN designing works depend on immense human expertise to explore different message-passing mechanisms, and require manual enumeration to determine the proper message-passing depth. Inspired by the strong search...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	226,805
2301.00391	PiPAD: Pipelined and Parallel Dynamic GNN Training on GPUs	Dynamic Graph Neural Networks (DGNNs) have been broadly applied in various real-life applications, such as link prediction and pandemic forecast, to capture both static structural information and temporal characteristics from dynamic graphs. Combining both time-dependent and -independent components, DGNNs manifest subs...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	338,891
2004.06997	Prolog Technology Reinforcement Learning Prover	We present a reinforcement learning toolkit for experiments with guiding automated theorem proving in the connection calculus. The core of the toolkit is a compact and easy to extend Prolog-based automated theorem prover called plCoP. plCoP builds on the leanCoP Prolog implementation and adds learning-guided Monte-Carl...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	172,660
2411.13553	AI-generated Image Detection: Passive or Watermark?	While text-to-image models offer numerous benefits, they also pose significant societal risks. Detecting AI-generated images is crucial for mitigating these risks. Detection methods can be broadly categorized into passive and watermark-based approaches: passive detectors rely on artifacts present in AI-generated images...	false	false	false	false	false	false	true	false	false	false	false	true	true	false	false	false	false	false	509,826
2404.15279	Jointly Modeling Spatio-Temporal Features of Tactile Signals for Action Classification	Tactile signals collected by wearable electronics are essential in modeling and understanding human behavior. One of the main applications of tactile signals is action classification, especially in healthcare and robotics. However, existing tactile classification methods fail to capture the spatial and temporal feature...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	449,024
2402.19407	MENTOR: Multi-level Self-supervised Learning for Multimodal Recommendation	With the increasing multimedia information, multimodal recommendation has received extensive attention. It utilizes multimodal information to alleviate the data sparsity problem in recommendation systems, thus improving recommendation accuracy. However, the reliance on labeled data severely limits the performance of mu...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	433,789
2106.02096	Shape-Preserving Dimensionality Reduction : An Algorithm and Measures of Topological Equivalence	We introduce a linear dimensionality reduction technique preserving topological features via persistent homology. The method is designed to find linear projection $L$ which preserves the persistent diagram of a point cloud $\mathbb{X}$ via simulated annealing. The projection $L$ induces a set of canonical simplicial ma...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	238,720
2006.01772	Fast and automated biomarker detection in breath samples with machine learning	Volatile organic compounds (VOCs) in human breath can reveal a large spectrum of health conditions and can be used for fast, accurate and non-invasive diagnostics. Gas chromatography-mass spectrometry (GC-MS) is used to measure VOCs, but its application is limited by expert-driven data analysis that is time-consuming, ...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	179,861
2006.13921	Determining Secondary Attributes for Credit Evaluation in P2P Lending	There has been an increased need for secondary means of credit evaluation by both traditional banking organizations as well as peer-to-peer lending entities. This is especially important in the present technological era where sticking with strict primary credit histories doesn't help distinguish between a 'good' and a ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	184,075
2010.15344	Sea-Net: Squeeze-And-Excitation Attention Net For Diabetic Retinopathy Grading	Diabetes is one of the most common disease in individuals. \textit{Diabetic retinopathy} (DR) is a complication of diabetes, which could lead to blindness. Automatic DR grading based on retinal images provides a great diagnostic and prognostic value for treatment planning. However, the subtle differences among severity...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	203,737
2007.03834	Language Modeling with Reduced Densities	This work originates from the observation that today's state-of-the-art statistical language models are impressive not only for their performance, but also - and quite crucially - because they are built entirely from correlations in unstructured text data. The latter observation prompts a fundamental question that lies...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	186,179
2502.03333	RadVLM: A Multitask Conversational Vision-Language Model for Radiology	The widespread use of chest X-rays (CXRs), coupled with a shortage of radiologists, has driven growing interest in automated CXR analysis and AI-assisted reporting. While existing vision-language models (VLMs) show promise in specific tasks such as report generation or abnormality detection, they often lack support for...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	530,675
2202.03957	Bingham Policy Parameterization for 3D Rotations in Reinforcement Learning	We propose a new policy parameterization for representing 3D rotations during reinforcement learning. Today in the continuous control reinforcement learning literature, many stochastic policy parameterizations are Gaussian. We argue that universally applying a Gaussian policy parameterization is not always desirable fo...	false	false	false	false	true	false	true	true	false	false	false	true	false	false	false	false	false	false	279,403
2106.15764	The Threat of Offensive AI to Organizations	AI has provided us with the ability to automate tasks, extract information from vast amounts of data, and synthesize media that is nearly indistinguishable from the real thing. However, positive tools can also be used for negative purposes. In particular, cyber adversaries can use AI (such as machine learning) to enhan...	false	false	false	false	true	false	true	false	false	false	false	false	true	true	false	false	false	false	243,859
1904.12138	Exploring Information Centrality for Intrusion Detection in Large Networks	Modern networked systems are constantly under threat from systemic attacks. There has been a massive upsurge in the number of devices connected to a network as well as the associated traffic volume. This has intensified the need to better understand all possible attack vectors during system design and implementation. F...	false	false	false	true	false	false	true	false	false	false	false	false	true	false	false	false	false	false	129,024
2203.01479	Weightless Neural Networks for Efficient Edge Inference	Weightless Neural Networks (WNNs) are a class of machine learning model which use table lookups to perform inference. This is in contrast with Deep Neural Networks (DNNs), which use multiply-accumulate operations. State-of-the-art WNN architectures have a fraction of the implementation cost of DNNs, but still lag behin...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	283,389
2307.15029	Adaptive Segmentation Network for Scene Text Detection	Inspired by deep convolution segmentation algorithms, scene text detectors break the performance ceiling of datasets steadily. However, these methods often encounter threshold selection bottlenecks and have poor performance on text instances with extreme aspect ratios. In this paper, we propose to automatically learn t...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	382,129
1404.0695	Multi-objective Flower Algorithm for Optimization	Flower pollination algorithm is a new nature-inspired algorithm, based on the characteristics of flowering plants. In this paper, we extend this flower algorithm to solve multi-objective optimization problems in engineering. By using the weighted sum method with random weights, we show that the proposed multi-objective...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	32,039
1908.07899	Evaluating Defensive Distillation For Defending Text Processing Neural Networks Against Adversarial Examples	Adversarial examples are artificially modified input samples which lead to misclassifications, while not being detectable by humans. These adversarial examples are a challenge for many tasks such as image and text classification, especially as research shows that many adversarial examples are transferable between diffe...	false	false	false	false	false	false	true	false	true	false	false	false	true	false	false	true	false	false	142,411
2111.07876	Winning Solution of the AIcrowd SBB Flatland Challenge 2019-2020	This report describes the main ideas of the solution which won the AIcrowd SBB Flatland Challenge 2019-2020, with a score of 99% (meaning that, on average, 99% of the agents were routed to their destinations within the allotted time steps). The details of the task can be found on the competition's website. The solution...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	266,502
1912.04197	A parallel-GPU code for asteroid aggregation problems with angular particles	The paper presents a numerical implementation of the gravitational N-body problem with contact interactions between non-spherically shaped bodies. The work builds up on a previous implementation of the code and extends its capabilities. The number of bodies handled is significantly increased through the use of a CUDA/G...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	156,783
1708.01372	Hashtag Healthcare: From Tweets to Mental Health Journals Using Deep Transfer Learning	As the popularity of social media platforms continues to rise, an ever-increasing amount of human communication and self- expression takes place online. Most recent research has focused on mining social media for public user opinion about external entities such as product reviews or sentiment towards political news. Ho...	false	false	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	78,372
2303.17915	Multiple Instance Ensembling For Paranasal Anomaly Classification In The Maxillary Sinus	Paranasal anomalies are commonly discovered during routine radiological screenings and can present with a wide range of morphological features. This diversity can make it difficult for convolutional neural networks (CNNs) to accurately classify these anomalies, especially when working with limited datasets. Additionall...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	355,392
2305.15817	Sharpness-Aware Minimization Revisited: Weighted Sharpness as a Regularization Term	Deep Neural Networks (DNNs) generalization is known to be closely related to the flatness of minima, leading to the development of Sharpness-Aware Minimization (SAM) for seeking flatter minima and better generalization. In this paper, we revisit the loss of SAM and propose a more general method, called WSAM, by incorpo...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	367,800
2211.06566	Innovative Drug-like Molecule Generation from Flow-based Generative Model	To design a drug given a biological molecule by using deep learning methods, there are many successful models published recently. People commonly used generative models to design new molecules given certain protein. LiGAN was regarded as the baseline of deep learning model which was developed on convolutional neural ne...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	329,936
2109.04941	Best-Arm Identification in Correlated Multi-Armed Bandits	In this paper we consider the problem of best-arm identification in multi-armed bandits in the fixed confidence setting, where the goal is to identify, with probability $1-\delta$ for some $\delta>0$, the arm with the highest mean reward in minimum possible samples from the set of arms $\mathcal{K}$. Most existing best...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	254,601
1910.00998	SummAE: Zero-Shot Abstractive Text Summarization using Length-Agnostic Auto-Encoders	We propose an end-to-end neural model for zero-shot abstractive text summarization of paragraphs, and introduce a benchmark task, ROCSumm, based on ROCStories, a subset for which we collected human summaries. In this task, five-sentence stories (paragraphs) are summarized with one sentence, using human summaries only f...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	true	false	false	147,818
2306.03022	Interpretable Alzheimer's Disease Classification Via a Contrastive Diffusion Autoencoder	In visual object classification, humans often justify their choices by comparing objects to prototypical examples within that class. We may therefore increase the interpretability of deep learning models by imbuing them with a similar style of reasoning. In this work, we apply this principle by classifying Alzheimer's ...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	371,158
2210.14859	Recursive Secondary Controller for Voltage Profile Improvement Based on Primary Virtual Admittance Control	This paper proposes a recursive, virtual admittance based, secondary controller for DG units that improves the voltage profile in distribution networks. First, the adaptation of the virtual admittance concept for the goal of voltage regulation is explained. Then, a recursive secondary controller is developed to periodi...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	326,710
2412.12358	BioRAGent: A Retrieval-Augmented Generation System for Showcasing Generative Query Expansion and Domain-Specific Search for Scientific Q&A	We present BioRAGent, an interactive web-based retrieval-augmented generation (RAG) system for biomedical question answering. The system uses large language models (LLMs) for query expansion, snippet extraction, and answer generation while maintaining transparency through citation links to the source documents and disp...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	517,833
2201.07906	The Role of Facial Expressions and Emotion in ASL	There is little prior work on quantifying the relationships between facial expressions and emotionality in American Sign Language. In this final report, we provide two methods for studying these relationships through probability and prediction. Using a large corpus of natural signing manually annotated with facial feat...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	276,164
2106.09837	Future Ultra-Dense LEO Satellite Networks: A Cell-Free Massive MIMO Approach	Low Earth orbit (LEO) satellite networks (SatNets) are envisioned to play a crucial role in providing global and ubiquitous connectivity efficiently. Accordingly, in the coming years, thousands of LEO satellites will be launched to create ultradense LEO mega-constellations, and the Third Generation Partnership Project ...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	true	241,806
2008.04270	Sketching semidefinite programs for faster clustering	Many clustering problems enjoy solutions by semidefinite programming. Theoretical results in this vein frequently consider data with a planted clustering and a notion of signal strength such that the semidefinite program exactly recovers the planted clustering when the signal strength is sufficiently large. In practice...	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	true	191,184
2501.13818	Ensuring Medical AI Safety: Explainable AI-Driven Detection and Mitigation of Spurious Model Behavior and Associated Data	Deep neural networks are increasingly employed in high-stakes medical applications, despite their tendency for shortcut learning in the presence of spurious correlations, which can have potentially fatal consequences in practice. Detecting and mitigating shortcut behavior is a challenging task that often requires signi...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	526,836
1610.08865	Hit-and-Run for Sampling and Planning in Non-Convex Spaces	We propose the Hit-and-Run algorithm for planning and sampling problems in non-convex spaces. For sampling, we show the first analysis of the Hit-and-Run algorithm in non-convex spaces and show that it mixes fast as long as certain smoothness conditions are satisfied. In particular, our analysis reveals an intriguing c...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	62,976
2309.00834	Approximating Fair $k$-Min-Sum-Radii in Euclidean Space	The $k$-center problem is a classical clustering problem in which one is asked to find a partitioning of a point set $P$ into $k$ clusters such that the maximum radius of any cluster is minimized. It is well-studied. But what if we add up the radii of the clusters instead of only considering the cluster with maximum ra...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	389,438
2409.02455	An Effective Tag Assignment Approach for Billboard Advertisement	Billboard Advertisement has gained popularity due to its significant outrage in return on investment. To make this advertisement approach more effective, the relevant information about the product needs to be reached to the relevant set of people. This can be achieved if the relevant set of tags can be mapped to the co...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	true	485,713
2010.15821	Cream of the Crop: Distilling Prioritized Paths For One-Shot Neural Architecture Search	One-shot weight sharing methods have recently drawn great attention in neural architecture search due to high efficiency and competitive performance. However, weight sharing across models has an inherent deficiency, i.e., insufficient training of subnetworks in hypernetworks. To alleviate this problem, we present a sim...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	203,877
1906.01105	Training Neural Machine Translation To Apply Terminology Constraints	This paper proposes a novel method to inject custom terminology into neural machine translation at run time. Previous works have mainly proposed modifications to the decoding algorithm in order to constrain the output to include run-time-provided target terms. While being effective, these constrained decoding methods a...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	133,592
0901.1898	Efficient and Guaranteed Rank Minimization by Atomic Decomposition	Recht, Fazel, and Parrilo provided an analogy between rank minimization and $\ell_0$-norm minimization. Subject to the rank-restricted isometry property, nuclear norm minimization is a guaranteed algorithm for rank minimization. The resulting semidefinite formulation is a convex problem but in practice the algorithms f...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	2,948
2307.04674	Optimal Robot Path Planning In a Collaborative Human-Robot Team with Intermittent Human Availability	This paper presents a solution for the problem of optimal planning for a robot in a collaborative human-robot team, where the human supervisor is intermittently available to assist the robot in completing tasks more quickly. Specifically, we address the challenge of computing the fastest path between two configurations...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	378,482
2012.05299	Optimal oracle inequalities for solving projected fixed-point equations	Linear fixed point equations in Hilbert spaces arise in a variety of settings, including reinforcement learning, and computational methods for solving differential and integral equations. We study methods that use a collection of random observations to compute approximate solutions by searching over a known low-dimensi...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	210,728
2110.03143	Meta-UDA: Unsupervised Domain Adaptive Thermal Object Detection using Meta-Learning	Object detectors trained on large-scale RGB datasets are being extensively employed in real-world applications. However, these RGB-trained models suffer a performance drop under adverse illumination and lighting conditions. Infrared (IR) cameras are robust under such conditions and can be helpful in real-world applicat...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	259,381
1911.02898	The LIG system for the English-Czech Text Translation Task of IWSLT 2019	In this paper, we present our submission for the English to Czech Text Translation Task of IWSLT 2019. Our system aims to study how pre-trained language models, used as input embeddings, can improve a specialized machine translation system trained on few data. Therefore, we implemented a Transformer-based encoder-decod...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	152,488
2409.16082	GS-Net: Global Self-Attention Guided CNN for Multi-Stage Glaucoma Classification	Glaucoma is a common eye disease that leads to irreversible blindness unless timely detected. Hence, glaucoma detection at an early stage is of utmost importance for a better treatment plan and ultimately saving the vision. The recent literature has shown the prominence of CNN-based methods to detect glaucoma from reti...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	491,199
2403.05306	A Collaborative Robot-Assisted Manufacturing Assembly Process	An effective human-robot collaborative process results in the reduction of the operator's workload, promoting a more efficient, productive, safer and less error-prone working environment. However, the implementation of collaborative robots in industry is still challenging. In this work, we compare manual and robot-assi...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	435,952
1310.0311	Multiclass Road Sign Detection using Multiplicative Kernel	We consider the problem of multiclass road sign detection using a classification function with multiplicative kernel comprised from two kernels. We show that problems of detection and within-foreground classification can be jointly solved by using one kernel to measure object-background differences and another one to a...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	27,475
2104.13268	Semi-supervised Superpixel-based Multi-Feature Graph Learning for Hyperspectral Image Data	Graphs naturally lend themselves to model the complexities of Hyperspectral Image (HSI) data as well as to serve as semi-supervised classifiers by propagating given labels among nearest neighbours. In this work, we present a novel framework for the classification of HSI data in light of a very limited amount of labelle...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	232,454
1212.0435	Network Growth with Arbitrary Initial Conditions: Analytical Results for Uniform and Preferential Attachment	This paper provides time-dependent expressions for the expected degree distribution of a given network that is subject to growth, as a function of time. We consider both uniform attachment, where incoming nodes form links to existing nodes selected uniformly at random, and preferential attachment, when probabilities ar...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	20,098
2405.06658	ProteinEngine: Empower LLM with Domain Knowledge for Protein Engineering	Large language models (LLMs) have garnered considerable attention for their proficiency in tackling intricate tasks, particularly leveraging their capacities for zero-shot and in-context learning. However, their utility has been predominantly restricted to general tasks due to an absence of domain-specific knowledge. T...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	453,377
2107.08011	Adaptive first-order methods revisited: Convex optimization without Lipschitz requirements	We propose a new family of adaptive first-order methods for a class of convex minimization problems that may fail to be Lipschitz continuous or smooth in the standard sense. Specifically, motivated by a recent flurry of activity on non-Lipschitz (NoLips) optimization, we consider problems that are continuous or smooth ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	246,597
2211.06344	Simultaneous Active and Passive Information Transfer for RIS-Aided MIMO Systems: Iterative Decoding and Evolution Analysis	This paper investigates the potential of reconfigurable intelligent surface (RIS) for passive information transfer in a RIS-aided multiple-input multiple-output (MIMO) system. We propose a novel simultaneous active and passive information transfer (SAPIT) scheme. In SAPIT, the transmitter (Tx) and the RIS deliver infor...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	329,857
2411.18539	AdaVLN: Towards Visual Language Navigation in Continuous Indoor Environments with Moving Humans	Visual Language Navigation is a task that challenges robots to navigate in realistic environments based on natural language instructions. While previous research has largely focused on static settings, real-world navigation must often contend with dynamic human obstacles. Hence, we propose an extension to the task, ter...	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	511,907
1211.5938	Social Network Games	One of the natural objectives of the field of the social networks is to predict agents' behaviour. To better understand the spread of various products through a social network arXiv:1105.2434 introduced a threshold model, in which the nodes influenced by their neighbours can adopt one out of several alternatives. To an...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	true	19,944
2102.01497	Clickbait Headline Detection in Indonesian News Sites using Multilingual Bidirectional Encoder Representations from Transformers (M-BERT)	Click counts are related to the amount of money that online advertisers paid to news sites. Such business models forced some news sites to employ a dirty trick of click-baiting, i.e., using a hyperbolic and interesting words, sometimes unfinished sentence in a headline to purposefully tease the readers. Some Indonesian...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	218,131
2111.01930	Deep learning for identification and face, gender, expression recognition under constraints	Biometric recognition based on the full face is an extensive research area. However, using only partially visible faces, such as in the case of veiled-persons, is a challenging task. Deep convolutional neural network (CNN) is used in this work to extract the features from veiled-person face images. We found that the si...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	264,704
1809.07499	MASON: A Model AgnoStic ObjectNess Framework	This paper proposes a simple, yet very effective method to localize dominant foreground objects in an image, to pixel-level precision. The proposed method 'MASON' (Model-AgnoStic ObjectNess) uses a deep convolutional network to generate category-independent and model-agnostic heat maps for any image. The network is not...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	108,293
2011.02886	Short-Term Memory Optimization in Recurrent Neural Networks by Autoencoder-based Initialization	Training RNNs to learn long-term dependencies is difficult due to vanishing gradients. We explore an alternative solution based on explicit memorization using linear autoencoders for sequences, which allows to maximize the short-term memory and that can be solved with a closed-form solution without backpropagation. We ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	205,070
2210.00756	CERBERUS: Simple and Effective All-In-One Automotive Perception Model with Multi Task Learning	Perceiving the surrounding environment is essential for enabling autonomous or assisted driving functionalities. Common tasks in this domain include detecting road users, as well as determining lane boundaries and classifying driving conditions. Over the last few years, a large variety of powerful Deep Learning models ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	320,996
1904.04516	Uncertainty Measures and Prediction Quality Rating for the Semantic Segmentation of Nested Multi Resolution Street Scene Images	In the semantic segmentation of street scenes the reliability of the prediction and therefore uncertainty measures are of highest interest. We present a method that generates for each input image a hierarchy of nested crops around the image center and presents these, all re-scaled to the same size, to a neural network ...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	127,061
1709.06316	Predicting Video Saliency with Object-to-Motion CNN and Two-layer Convolutional LSTM	Over the past few years, deep neural networks (DNNs) have exhibited great success in predicting the saliency of images. However, there are few works that apply DNNs to predict the saliency of generic videos. In this paper, we propose a novel DNN-based video saliency prediction method. Specifically, we establish a large...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	81,085
2406.15265	Perception of Phonological Assimilation by Neural Speech Recognition Models	Human listeners effortlessly compensate for phonological changes during speech perception, often unconsciously inferring the intended sounds. For example, listeners infer the underlying /n/ when hearing an utterance such as "clea[m] pan", where [m] arises from place assimilation to the following labial [p]. This articl...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	466,676
1803.08456	Deep Reinforcement Learning with Model Learning and Monte Carlo Tree Search in Minecraft	Deep reinforcement learning has been successfully applied to several visual-input tasks using model-free methods. In this paper, we propose a model-based approach that combines learning a DNN-based transition model with Monte Carlo tree search to solve a block-placing task in Minecraft. Our learned transition model pre...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	93,262
cmp-lg/9704007	Combining Unsupervised Lexical Knowledge Methods for Word Sense Disambiguation	This paper presents a method to combine a set of unsupervised algorithms that can accurately disambiguate word senses in a large, completely untagged corpus. Although most of the techniques for word sense resolution have been presented as stand-alone, it is our belief that full-fledged lexical ambiguity resolution shou...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	536,715
1701.00241	Access Strategy in Super WiFi Network Powered by Solar Energy Harvesting: A POMDP Method	The recently announced Super Wi-Fi Network proposal in United States is aiming to enable Internet access in a nation-wide area. As traditional cable-connected power supply system becomes impractical or costly for a wide range wireless network, new infrastructure deployment for Super Wi-Fi is required. The fast developi...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	66,247

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