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
2309.01812	Into the Single Cell Multiverse: an End-to-End Dataset for Procedural Knowledge Extraction in Biomedical Texts	Many of the most commonly explored natural language processing (NLP) information extraction tasks can be thought of as evaluations of declarative knowledge, or fact-based information extraction. Procedural knowledge extraction, i.e., breaking down a described process into a series of steps, has received much less atten...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	389,814
1812.05288	Dynamic Transfer Learning for Named Entity Recognition	State-of-the-art named entity recognition (NER) systems have been improving continuously using neural architectures over the past several years. However, many tasks including NER require large sets of annotated data to achieve such performance. In particular, we focus on NER from clinical notes, which is one of the mos...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	116,387
2103.14795	Ensemble-in-One: Learning Ensemble within Random Gated Networks for Enhanced Adversarial Robustness	Adversarial attacks have rendered high security risks on modern deep learning systems. Adversarial training can significantly enhance the robustness of neural network models by suppressing the non-robust features. However, the models often suffer from significant accuracy loss on clean data. Ensemble training methods h...	false	false	false	false	false	false	true	false	false	false	false	true	true	false	false	false	false	false	226,971
2410.20153	The inexact power augmented Lagrangian method for constrained nonconvex optimization	This work introduces an unconventional inexact augmented Lagrangian method, where the augmenting term is a Euclidean norm raised to a power between one and two. The proposed algorithm is applicable to a broad class of constrained nonconvex minimization problems, that involve nonlinear equality constraints over a convex...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	502,672
1609.09253	Heuristic with elements of tabu search for Truck and Trailer Routing Problem	Vehicle Routing Problem is a well-known problem in logistics and transportation, and the variety of such problems is explained by the fact that it occurs in many real-life situations. It is an NP-hard combinatorial optimization problem and finding an exact optimal solution is practically impossible. In this work, Site-...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	61,698
2108.09529	Term Interrelations and Trends in Software Engineering	The Software Engineering (SE) community is prolific, making it challenging for experts to keep up with the flood of new papers and for neophytes to enter the field. Therefore, we posit that the community may benefit from a tool extracting terms and their interrelations from the SE community's text corpus and showing te...	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	true	251,634
2012.12469	Augmenting Policy Learning with Routines Discovered from a Single Demonstration	Humans can abstract prior knowledge from very little data and use it to boost skill learning. In this paper, we propose routine-augmented policy learning (RAPL), which discovers routines composed of primitive actions from a single demonstration and uses discovered routines to augment policy learning. To discover routin...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	true	212,944
2405.11547	Certified Robust Accuracy of Neural Networks Are Bounded due to Bayes Errors	Adversarial examples pose a security threat to many critical systems built on neural networks. While certified training improves robustness, it also decreases accuracy noticeably. Despite various proposals for addressing this issue, the significant accuracy drop remains. More importantly, it is not clear whether there ...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	455,189
0803.1985	An Investigation of the Sequential Sampling Method for Crossdocking Simulation Output Variance Reduction	This paper investigates the reduction of variance associated with a simulation output performance measure, using the Sequential Sampling method while applying minimum simulation replications, for a class of JIT (Just in Time) warehousing system called crossdocking. We initially used the Sequential Sampling method to at...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	1,434
2102.00400	Multi-access Coded Caching from a New Class of Cross Resolvable Designs	Multi-access coded caching schemes from cross resolvable designs (CRD) have been reported recently \cite{KNRarXiv}. To be able to compare coded caching schemes with different number of users and possibly with different number of caches a new metric called rate-per-user was introduced and it was shown that under this ne...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	217,769
1204.1080	Memory Resilient Gain-scheduled State-Feedback Control of Uncertain LTI/LPV Systems with Time-Varying Delays	The stabilization of uncertain LTI/LPV time delay systems with time varying delays by state-feedback controllers is addressed. At the difference of other works in the literature, the proposed approach allows for the synthesis of resilient controllers with respect to uncertainties on the implemented delay. It is emphasi...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	15,286
2301.09190	Apples and Oranges? Assessing Image Quality over Content Recognition	Image recognition and quality assessment are two important viewing tasks, while potentially following different visual mechanisms. This paper investigates if the two tasks can be performed in a multitask learning manner. A sequential spatial-channel attention module is proposed to simulate the visual attention and cont...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	341,428
2302.11550	Scaling Robot Learning with Semantically Imagined Experience	Recent advances in robot learning have shown promise in enabling robots to perform a variety of manipulation tasks and generalize to novel scenarios. One of the key contributing factors to this progress is the scale of robot data used to train the models. To obtain large-scale datasets, prior approaches have relied on ...	false	false	false	false	true	false	true	true	true	false	false	true	false	false	false	false	false	false	347,244
1904.03775	ANTNets: Mobile Convolutional Neural Networks for Resource Efficient Image Classification	Deep convolutional neural networks have achieved remarkable success in computer vision. However, deep neural networks require large computing resources to achieve high performance. Although depthwise separable convolution can be an efficient module to approximate a standard convolution, it often leads to reduced repres...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	126,832
1509.06004	A Parallel Framework for Parametric Maximum Flow Problems in Image Segmentation	This paper presents a framework that supports the implementation of parallel solutions for the widespread parametric maximum flow computational routines used in image segmentation algorithms. The framework is based on supergraphs, a special construction combining several image graphs into a larger one, and works on var...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	47,109
2409.15612	Revolutionizing Biomarker Discovery: Leveraging Generative AI for Bio-Knowledge-Embedded Continuous Space Exploration	Biomarker discovery is vital in advancing personalized medicine, offering insights into disease diagnosis, prognosis, and therapeutic efficacy. Traditionally, the identification and validation of biomarkers heavily depend on extensive experiments and statistical analyses. These approaches are time-consuming, demand ext...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	490,982
2309.05450	A Comparison between Frame-based and Event-based Cameras for Flapping-Wing Robot Perception	Perception systems for ornithopters face severe challenges. The harsh vibrations and abrupt movements caused during flapping are prone to produce motion blur and strong lighting condition changes. Their strict restrictions in weight, size, and energy consumption also limit the type and number of sensors to mount onboar...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	391,088
2010.00130	Computing Graph Neural Networks: A Survey from Algorithms to Accelerators	Graph Neural Networks (GNNs) have exploded onto the machine learning scene in recent years owing to their capability to model and learn from graph-structured data. Such an ability has strong implications in a wide variety of fields whose data is inherently relational, for which conventional neural networks do not perfo...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	198,190
1905.06010	Automatic Model Selection for Neural Networks	Neural networks and deep learning are changing the way that artificial intelligence is being done. Efficiently choosing a suitable network architecture and fine-tune its hyper-parameters for a specific dataset is a time-consuming task given the staggering number of possible alternatives. In this paper, we address the p...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	130,883
1909.07907	Pointer-based Fusion of Bilingual Lexicons into Neural Machine Translation	Neural machine translation (NMT) systems require large amounts of high quality in-domain parallel corpora for training. State-of-the-art NMT systems still face challenges related to out-of-vocabulary words and dealing with low-resource language pairs. In this paper, we propose and compare several models for fusion of b...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	145,811
2201.05225	Learning-Based MIMO Channel Estimation under Spectrum Efficient Pilot Allocation and Feedback	Wireless links using massive MIMO transceivers are vital for next generation wireless communications networks networks. Precoding in Massive MIMO transmission requires accurate downlink channel state information (CSI). Many recent works have effectively applied deep learning (DL) to jointly train UE-side compression ne...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	275,319
1501.00092	Image Super-Resolution Using Deep Convolutional Networks	We propose a deep learning method for single image super-resolution (SR). Our method directly learns an end-to-end mapping between the low/high-resolution images. The mapping is represented as a deep convolutional neural network (CNN) that takes the low-resolution image as the input and outputs the high-resolution one....	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	true	false	false	38,958
0912.4571	Fast Alternating Linearization Methods for Minimizing the Sum of Two Convex Functions	We present in this paper first-order alternating linearization algorithms based on an alternating direction augmented Lagrangian approach for minimizing the sum of two convex functions. Our basic methods require at most $O(1/\epsilon)$ iterations to obtain an $\epsilon$-optimal solution, while our accelerated (i.e., fa...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	5,205
2108.10710	PocketNet: Extreme Lightweight Face Recognition Network using Neural Architecture Search and Multi-Step Knowledge Distillation	Deep neural networks have rapidly become the mainstream method for face recognition (FR). However, this limits the deployment of such models that contain an extremely large number of parameters to embedded and low-end devices. In this work, we present an extremely lightweight and accurate FR solution, namely PocketNet....	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	251,983
2412.14538	Overview of AI and Communication for 6G Network: Fundamentals, Challenges, and Future Research Opportunities	With the growing demand for seamless connectivity and intelligent communication, the integration of artificial intelligence (AI) and sixth-generation (6G) communication networks has emerged as a transformative paradigm. By embedding AI capabilities across various network layers, this integration enables optimized resou...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	518,754
1908.03984	Online Maneuver Design for UAV-Enabled NOMA Systems via Reinforcement Learning	This paper considers an unmanned aerial vehicle enabled-up link non-orthogonal multiple-access system, where multiple mobile users on the ground send independent messages to a unmanned aerial vehicle in the sky via non-orthogonal multiple-access transmission. Our objective is to design the unmanned aerial vehicle dynam...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	141,371
1209.0715	The Synthesis and Analysis of Stochastic Switching Circuits	Stochastic switching circuits are relay circuits that consist of stochastic switches called pswitches. The study of stochastic switching circuits has widespread applications in many fields of computer science, neuroscience, and biochemistry. In this paper, we discuss several properties of stochastic switching circuits,...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	18,378
2001.06487	Algorithms in Multi-Agent Systems: A Holistic Perspective from Reinforcement Learning and Game Theory	Deep reinforcement learning (RL) has achieved outstanding results in recent years, which has led a dramatic increase in the number of methods and applications. Recent works are exploring learning beyond single-agent scenarios and considering multi-agent scenarios. However, they are faced with lots of challenges and are...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	true	false	false	true	160,810
1607.01895	Random Walk Graph Laplacian based Smoothness Prior for Soft Decoding of JPEG Images	Given the prevalence of JPEG compressed images, optimizing image reconstruction from the compressed format remains an important problem. Instead of simply reconstructing a pixel block from the centers of indexed DCT coefficient quantization bins (hard decoding), soft decoding reconstructs a block by selecting appropria...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	58,274
2406.13674	Rethinking Abdominal Organ Segmentation (RAOS) in the clinical scenario: A robustness evaluation benchmark with challenging cases	Deep learning has enabled great strides in abdominal multi-organ segmentation, even surpassing junior oncologists on common cases or organs. However, robustness on corner cases and complex organs remains a challenging open problem for clinical adoption. To investigate model robustness, we collected and annotated the RA...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	465,949
2305.11385	Robust MPC with Zone Tracking	We propose a robust nonlinear model predictive control design with generalized zone tracking (ZMPC) in this work. The proposed ZMPC has guaranteed convergence into the target zone in the presence of bounded disturbance. The proposed approach achieves this by modifying the actual target zone such that the effect of dist...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	365,502
2307.16288	Towards Learned Predictability of Storage Systems	With the rapid development of cloud computing and big data technologies, storage systems have become a fundamental building block of datacenters, incorporating hardware innovations such as flash solid state drives and non-volatile memories, as well as software infrastructures such as RAID and distributed file systems. ...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	true	true	382,549
2004.07098	DeeSCo: Deep heterogeneous ensemble with Stochastic Combinatory loss for gaze estimation	From medical research to gaming applications, gaze estimation is becoming a valuable tool. While there exists a number of hardware-based solutions, recent deep learning-based approaches, coupled with the availability of large-scale databases, have allowed to provide a precise gaze estimate using only consumer sensors. ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	172,690
1912.03851	Intelligent Coordination among Multiple Traffic Intersections Using Multi-Agent Reinforcement Learning	We use Asynchronous Advantage Actor Critic (A3C) for implementing an AI agent in the controllers that optimize flow of traffic across a single intersection and then extend it to multiple intersections by considering a multi-agent setting. We explore three different methodologies to address the multi-agent problem - (1)...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	true	false	false	false	156,708
2002.05665	FRSign: A Large-Scale Traffic Light Dataset for Autonomous Trains	In the realm of autonomous transportation, there have been many initiatives for open-sourcing self-driving cars datasets, but much less for alternative methods of transportation such as trains. In this paper, we aim to bridge the gap by introducing FRSign, a large-scale and accurate dataset for vision-based railway tra...	false	false	false	false	false	false	true	false	false	false	false	true	false	true	false	false	false	false	163,970
2410.14589	Dialetto, ma Quanto Dialetto? Transcribing and Evaluating Dialects on a Continuum	There is increasing interest in looking at dialects in NLP. However, most work to date still treats dialects as discrete categories. For instance, evaluative work in variation-oriented NLP for English often works with Indian English or African-American Venacular English as homogeneous categories (Faisal et al., 2024; Z...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	500,089
2402.03230	Architecture Analysis and Benchmarking of 3D U-shaped Deep Learning Models for Thoracic Anatomical Segmentation	Recent rising interests in patient-specific thoracic surgical planning and simulation require efficient and robust creation of digital anatomical models from automatic medical image segmentation algorithms. Deep learning (DL) is now state-of-the-art in various radiological tasks, and U-shaped DL models have particularl...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	426,913
2106.02100	Double Descent Optimization Pattern and Aliasing: Caveats of Noisy Labels	Optimization plays a key role in the training of deep neural networks. Deciding when to stop training can have a substantial impact on the performance of the network during inference. Under certain conditions, the generalization error can display a double descent pattern during training: the learning curve is non-monot...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	238,722
2210.08643	A General Framework for Auditing Differentially Private Machine Learning	We present a framework to statistically audit the privacy guarantee conferred by a differentially private machine learner in practice. While previous works have taken steps toward evaluating privacy loss through poisoning attacks or membership inference, they have been tailored to specific models or have demonstrated l...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	324,229
1703.04106	Weight Spectrum of Quasi-Perfect Binary Codes with Distance 4	We consider the weight spectrum of a class of quasi-perfect binary linear codes with code distance 4. For example, extended Hamming code and Panchenko code are the known members of this class. Also, it is known that in many cases Panchenko code has the minimal number of weight 4 codewords. We give exact recursive formu...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	69,839
2402.04264	Analysis of Hopfield Model as Associative Memory	This article delves into the Hopfield neural network model, drawing inspiration from biological neural systems. The exploration begins with an overview of the model's foundations, incorporating insights from mechanical statistics to deepen our understanding. Focusing on audio retrieval, the study demonstrates the Hopfi...	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	false	false	427,382
1507.07882	Occlusion-Aware Object Localization, Segmentation and Pose Estimation	We present a learning approach for localization and segmentation of objects in an image in a manner that is robust to partial occlusion. Our algorithm produces a bounding box around the full extent of the object and labels pixels in the interior that belong to the object. Like existing segmentation aware detection appr...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	45,514
2405.01284	Behavior Imitation for Manipulator Control and Grasping with Deep Reinforcement Learning	The existing Motion Imitation models typically require expert data obtained through MoCap devices, but the vast amount of training data needed is difficult to acquire, necessitating substantial investments of financial resources, manpower, and time. This project combines 3D human pose estimation with reinforcement lear...	false	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	451,299
2103.13452	A Portable, Self-Contained Neuroprosthetic Hand with Deep Learning-Based Finger Control	Objective: Deep learning-based neural decoders have emerged as the prominent approach to enable dexterous and intuitive control of neuroprosthetic hands. Yet few studies have materialized the use of deep learning in clinical settings due to its high computational requirements. Methods: Recent advancements of edge compu...	true	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	false	226,487
2412.11974	Emma-X: An Embodied Multimodal Action Model with Grounded Chain of Thought and Look-ahead Spatial Reasoning	Traditional reinforcement learning-based robotic control methods are often task-specific and fail to generalize across diverse environments or unseen objects and instructions. Visual Language Models (VLMs) demonstrate strong scene understanding and planning capabilities but lack the ability to generate actionable polic...	false	false	false	false	true	false	false	true	true	false	false	true	false	false	false	false	false	false	517,665
2004.13856	Less is More: Sample Selection and Label Conditioning Improve Skin Lesion Segmentation	Segmenting skin lesions images is relevant both for itself and for assisting in lesion classification, but suffers from the challenge in obtaining annotated data. In this work, we show that segmentation may improve with less data, by selecting the training samples with best inter-annotator agreement, and conditioning t...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	174,692
2211.14654	Unsupervised Wildfire Change Detection based on Contrastive Learning	The accurate characterization of the severity of the wildfire event strongly contributes to the characterization of the fuel conditions in fire-prone areas, and provides valuable information for disaster response. The aim of this study is to develop an autonomous system built on top of high-resolution multispectral sat...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	332,932
2210.06120	Efficient Gaussian Process Model on Class-Imbalanced Datasets for Generalized Zero-Shot Learning	Zero-Shot Learning (ZSL) models aim to classify object classes that are not seen during the training process. However, the problem of class imbalance is rarely discussed, despite its presence in several ZSL datasets. In this paper, we propose a Neural Network model that learns a latent feature embedding and a Gaussian ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	323,147
1903.05926	Reinforcement Learning with Dynamic Boltzmann Softmax Updates	Value function estimation is an important task in reinforcement learning, i.e., prediction. The Boltzmann softmax operator is a natural value estimator and can provide several benefits. However, it does not satisfy the non-expansion property, and its direct use may fail to converge even in value iteration. In this pape...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	124,265
2405.05513	Automatic question generation for propositional logical equivalences	The increase in academic dishonesty cases among college students has raised concern, particularly due to the shift towards online learning caused by the pandemic. We aim to develop and implement a method capable of generating tailored questions for each student. The use of Automatic Question Generation (AQG) is a possi...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	true	452,946
1602.01608	Appearance Based Robot and Human Activity Recognition System	In this work, we present an appearance based human activity recognition system. It uses background modeling to segment the foreground object and extracts useful discriminative features for representing activities performed by humans and robots. Subspace based method like principal component analysis is used to extract ...	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	51,728
2202.03433	A Coarse-to-fine Morphological Approach With Knowledge-based Rules and Self-adapting Correction for Lung Nodules Segmentation	The segmentation module which precisely outlines the nodules is a crucial step in a computer-aided diagnosis(CAD) system. The most challenging part of such a module is how to achieve high accuracy of the segmentation, especially for the juxtapleural, non-solid and small nodules. In this research, we present a coarse-to...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	279,205
2208.11523	Diverse Title Generation for Stack Overflow Posts with Multiple Sampling Enhanced Transformer	Stack Overflow is one of the most popular programming communities where developers can seek help for their encountered problems. Nevertheless, if inexperienced developers fail to describe their problems clearly, it is hard for them to attract sufficient attention and get the anticipated answers. We propose M$_3$NSCT5, ...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	true	314,459
1807.11345	A Double Jaw Hand Designed for Multi-object Assembly	This paper presents a double jaw hand for industrial assembly. The hand comprises two orthogonal parallel grippers with different mechanisms. The inner gripper is made of a crank-slider mechanism which is compact and able to firmly hold objects like shafts. The outer gripper is made of a parallelogram that has large st...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	104,160
2109.05892	WeakSTIL: Weak whole-slide image level stromal tumor infiltrating lymphocyte scores are all you need	We present WeakSTIL, an interpretable two-stage weak label deep learning pipeline for scoring the percentage of stromal tumor infiltrating lymphocytes (sTIL%) in H&E-stained whole-slide images (WSIs) of breast cancer tissue. The sTIL% score is a prognostic and predictive biomarker for many solid tumor types. However, d...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	254,985
1907.12372	Production Ranking Systems: A Review	The problem of ranking is a multi-billion dollar problem. In this paper we present an overview of several production quality ranking systems. We show that due to conflicting goals of employing the most effective machine learning models and responding to users in real time, ranking systems have evolved into a system of ...	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	false	140,101
2010.13002	Pre-trained Summarization Distillation	Recent state-of-the-art approaches to summarization utilize large pre-trained Transformer models. Distilling these models to smaller student models has become critically important for practical use; however there are many different distillation methods proposed by the NLP literature. Recent work on distilling BERT for ...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	202,959
2204.11091	On-Device Next-Item Recommendation with Self-Supervised Knowledge Distillation	Modern recommender systems operate in a fully server-based fashion. To cater to millions of users, the frequent model maintaining and the high-speed processing for concurrent user requests are required, which comes at the cost of a huge carbon footprint. Meanwhile, users need to upload their behavior data even includin...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	293,030
2501.13347	One Fits All: General Mobility Trajectory Modeling via Masked Conditional Diffusion	Trajectory data play a crucial role in many applications, ranging from network optimization to urban planning. Existing studies on trajectory data are task-specific, and their applicability is limited to the specific tasks on which they have been trained, such as generation, recovery, or prediction. However, the potent...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	526,642
2108.00895	Efficient Sparse Spherical k-Means for Document Clustering	Spherical k-Means is frequently used to cluster document collections because it performs reasonably well in many settings and is computationally efficient. However, the time complexity increases linearly with the number of clusters k, which limits the suitability of the algorithm for larger values of k depending on the...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	true	248,865
2012.07054	Adaptive and Oblivious Randomized Subspace Methods for High-Dimensional Optimization: Sharp Analysis and Lower Bounds	We propose novel randomized optimization methods for high-dimensional convex problems based on restrictions of variables to random subspaces. We consider oblivious and data-adaptive subspaces and study their approximation properties via convex duality and Fenchel conjugates. A suitable adaptive subspace can be generate...	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	211,329
2010.00307	Understanding the hardness of approximate query processing with joins	We study the hardness of Approximate Query Processing (AQP) of various types of queries involving joins over multiple tables of possibly different sizes. In the case where the query result is a single value (e.g., COUNT, SUM, and COUNT(DISTINCT)), we prove worst-case information-theoretic lower bounds for AQP problems ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	true	false	198,247
2304.05741	Learning to search for and detect objects in foveal images using deep learning	The human visual system processes images with varied degrees of resolution, with the fovea, a small portion of the retina, capturing the highest acuity region, which gradually declines toward the field of view's periphery. However, the majority of existing object localization methods rely on images acquired by image se...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	357,731
2110.00915	Control Barrier Function Meets Interval Analysis: Safety-Critical Control with Measurement and Actuation Uncertainties	This paper presents a framework for designing provably safe feedback controllers for sampled-data control affine systems with measurement and actuation uncertainties. Based on the interval Taylor model of nonlinear functions, a sampled-data control barrier function (CBF) condition is proposed which ensures the forward ...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	258,579
2005.06514	3D Face Anti-spoofing with Factorized Bilinear Coding	We have witnessed rapid advances in both face presentation attack models and presentation attack detection (PAD) in recent years. When compared with widely studied 2D face presentation attacks, 3D face spoofing attacks are more challenging because face recognition systems are more easily confused by the 3D characterist...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	177,016
2107.02896	Efficient Detection of Botnet Traffic by features selection and Decision Trees	Botnets are one of the online threats with the biggest presence, causing billionaire losses to global economies. Nowadays, the increasing number of devices connected to the Internet makes it necessary to analyze large amounts of network traffic data. In this work, we focus on increasing the performance on botnet traffi...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	244,975
2206.06448	Assessing Privacy Leakage in Synthetic 3-D PET Imaging using Transversal GAN	Training computer-vision related algorithms on medical images for disease diagnosis or image segmentation is difficult in large part due to privacy concerns. For this reason, generative image models are highly sought after to facilitate data sharing. However, 3-D generative models are understudied, and investigation of...	false	false	false	false	false	false	true	false	false	false	false	true	true	false	false	false	false	false	302,376
2006.01446	Identification of hydrodynamic instability by convolutional neural networks	The onset of hydrodynamic instabilities is of great importance in both industry and daily life, due to the dramatic mechanical and thermodynamic changes for different types of flow motions. In this paper, modern machine learning techniques, especially the convolutional neural networks (CNN), are applied to identify the...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	179,782
2110.05655	Defocus Map Estimation and Deblurring from a Single Dual-Pixel Image	We present a method that takes as input a single dual-pixel image, and simultaneously estimates the image's defocus map -- the amount of defocus blur at each pixel -- and recovers an all-in-focus image. Our method is inspired from recent works that leverage the dual-pixel sensors available in many consumer cameras to a...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	260,340
2410.16041	GFlowNets for Hamiltonian decomposition in groups of compatible operators	Quantum computing presents a promising alternative for the direct simulation of quantum systems with the potential to explore chemical problems beyond the capabilities of classical methods. However, current quantum algorithms are constrained by hardware limitations and the increased number of measurements required to a...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	500,853
2308.01284	Fighting Fire with Fire: Can ChatGPT Detect AI-generated Text?	Large language models (LLMs) such as ChatGPT are increasingly being used for various use cases, including text content generation at scale. Although detection methods for such AI-generated text exist already, we investigate ChatGPT's performance as a detector on such AI-generated text, inspired by works that use ChatGP...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	383,208
2103.15452	Boosting the Speed of Entity Alignment 10*: Dual Attention Matching Network with Normalized Hard Sample Mining	Seeking the equivalent entities among multi-source Knowledge Graphs (KGs) is the pivotal step to KGs integration, also known as \emph{entity alignment} (EA). However, most existing EA methods are inefficient and poor in scalability. A recent summary points out that some of them even require several days to deal with a ...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	227,213
1411.1323	Fast cooling for a system of stochastic oscillators	We study feedback control of coupled nonlinear stochastic oscillators in a force field. We first consider the problem of asymptotically driving the system to a desired {\em steady state} corresponding to reduced thermal noise. Among the feedback controls achieving the desired asymptotic transfer, we find that the most ...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	37,330
1910.13707	Jointly optimal dereverberation and beamforming	We previously proposed an optimal (in the maximum likelihood sense) convolutional beamformer that can perform simultaneous denoising and dereverberation, and showed its superiority over the widely used cascade of a WPE dereverberation filter and a conventional MPDR beamformer. However, it has not been fully investigate...	false	false	true	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	151,459
2205.12729	Deep interpretable ensembles	Ensembles improve prediction performance and allow uncertainty quantification by aggregating predictions from multiple models. In deep ensembling, the individual models are usually black box neural networks, or recently, partially interpretable semi-structured deep transformation models. However, interpretability of th...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	298,690
1903.02793	SR-LSTM: State Refinement for LSTM towards Pedestrian Trajectory Prediction	In crowd scenarios, reliable trajectory prediction of pedestrians requires insightful understanding of their social behaviors. These behaviors have been well investigated by plenty of studies, while it is hard to be fully expressed by hand-craft rules. Recent studies based on LSTM networks have shown great ability to l...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	123,575
2404.17532	Mitigating Collisions in Sidelink NR V2X: A Study on Cooperative Resource Allocation	New Radio (NR) Vehicle-to-Everything (V2X) Sidelink (SL), an integral part of the 5G NR standard, is expected to revolutionize the automotive and rail industries by enabling direct and low-latency exchange of critical information between traffic participants independently of cellular networks. However, this advancement...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	true	449,891
0805.3091	A simple randomized algorithm for sequential prediction of ergodic time series	We present a simple randomized procedure for the prediction of a binary sequence. The algorithm uses ideas from recent developments of the theory of the prediction of individual sequences. We show that if the sequence is a realization of a stationary and ergodic random process then the average number of mistakes conver...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	1,794
2006.11440	Local Convolutions Cause an Implicit Bias towards High Frequency Adversarial Examples	Adversarial Attacks are still a significant challenge for neural networks. Recent work has shown that adversarial perturbations typically contain high-frequency features, but the root cause of this phenomenon remains unknown. Inspired by theoretical work on linear full-width convolutional models, we hypothesize that th...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	183,233
2401.00633	On Discprecncies between Perturbation Evaluations of Graph Neural Network Attributions	Neural networks are increasingly finding their way into the realm of graphs and modeling relationships between features. Concurrently graph neural network explanation approaches are being invented to uncover relationships between the nodes of the graphs. However, there is a disparity between the existing attribution me...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	419,050
2301.04746	Switchable Lightweight Anti-symmetric Processing (SLAP) with CNN Outspeeds Data Augmentation by Smaller Sample -- Application in Gomoku Reinforcement Learning	To replace data augmentation, this paper proposed a method called SLAP to intensify experience to speed up machine learning and reduce the sample size. SLAP is a model-independent protocol/function to produce the same output given different transformation variants. SLAP improved the convergence speed of convolutional n...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	340,154
2403.04775	Superposition with Delayed Unification	Classically, in saturation-based proof systems, unification has been considered atomic. However, it is also possible to move unification to the calculus level, turning the steps of the unification algorithm into inferences. For calculi that rely on unification procedures returning large or even infinite sets of unifier...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	435,723
2212.13079	Semi-Supervised Domain Adaptation for Semantic Segmentation of Roads from Satellite Images	This paper presents the preliminary findings of a semi-supervised segmentation method for extracting roads from sattelite images. Artificial Neural Networks and image segmentation methods are among the most successful methods for extracting road data from satellite images. However, these models require large amounts of...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	338,217
2205.01239	A Performance-Consistent and Computation-Efficient CNN System for High-Quality Automated Brain Tumor Segmentation	The research on developing CNN-based fully-automated Brain-Tumor-Segmentation systems has been progressed rapidly. For the systems to be applicable in practice, a good The research on developing CNN-based fully-automated Brain-Tumor-Segmentation systems has been progressed rapidly. For the systems to be applicable in p...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	294,516
2502.14442	Stochastic Resonance Improves the Detection of Low Contrast Images in Deep Learning Models	Stochastic resonance describes the utility of noise in improving the detectability of weak signals in certain types of systems. It has been observed widely in natural and engineered settings, but its utility in image classification with rate-based neural networks has not been studied extensively. In this analysis a sim...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	535,834
1008.1150	Modeling the growth of fingerprints improves matching for adolescents	We study the effect of growth on the fingerprints of adolescents, based on which we suggest a simple method to adjust for growth when trying to recover a juvenile's fingerprint in a database years later. Based on longitudinal data sets in juveniles' criminal records, we show that growth essentially leads to an isotropi...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	7,209
2308.05295	Multimodal Pretrained Models for Verifiable Sequential Decision-Making: Planning, Grounding, and Perception	Recently developed pretrained models can encode rich world knowledge expressed in multiple modalities, such as text and images. However, the outputs of these models cannot be integrated into algorithms to solve sequential decision-making tasks. We develop an algorithm that utilizes the knowledge from pretrained models ...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	384,735
2302.10172	Identity-Based Attribute Prototypes Distinguish Communities on Twitter	This paper examines the link between conversational communities on Twitter and their members' expressions of social identity. It specifically tests the presence of community prototypes, or collections of attributes which define a group through meta-contrast: high in-group cohesiveness and high out-group distinctiveness...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	346,696
1907.10761	Bilingual Lexicon Induction through Unsupervised Machine Translation	A recent research line has obtained strong results on bilingual lexicon induction by aligning independently trained word embeddings in two languages and using the resulting cross-lingual embeddings to induce word translation pairs through nearest neighbor or related retrieval methods. In this paper, we propose an alter...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	139,696
2412.11829	Robust Contact-rich Manipulation through Implicit Motor Adaptation	Contact-rich manipulation plays a vital role in daily human activities, yet uncertain physical parameters pose significant challenges for both model-based and model-free planning and control. A promising approach to address this challenge is to develop policies robust to a wide range of parameters. Domain adaptation an...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	517,603
1707.02610	Few-Shot Learning Through an Information Retrieval Lens	Few-shot learning refers to understanding new concepts from only a few examples. We propose an information retrieval-inspired approach for this problem that is motivated by the increased importance of maximally leveraging all the available information in this low-data regime. We define a training objective that aims to...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	76,732
2411.09688	Squeezed Attention: Accelerating Long Context Length LLM Inference	Emerging Large Language Model (LLM) applications require long input prompts to perform complex downstream tasks like document analysis and code generation. For these long context length applications, the length of the input prompt poses a significant challenge in terms of inference efficiency since the inference costs ...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	508,330
1801.00516	Exploiting symmetry for discrete-time reachability computations	We present a method of computing backward reachable sets for nonlinear discrete-time control systems possessing continuous symmetries. The starting point is a dynamic game formulation of reachability analysis where control inputs aim to maintain the state variables within a target tube despite disturbances. Our method ...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	87,579
cmp-lg/9706018	A Model of Lexical Attraction and Repulsion	This paper introduces new methods based on exponential families for modeling the correlations between words in text and speech. While previous work assumed the effects of word co-occurrence statistics to be constant over a window of several hundred words, we show that their influence is nonstationary on a much smaller ...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	536,756
2302.08146	CluCDD:Contrastive Dialogue Disentanglement via Clustering	A huge number of multi-participant dialogues happen online every day, which leads to difficulty in understanding the nature of dialogue dynamics for both humans and machines. Dialogue disentanglement aims at separating an entangled dialogue into detached sessions, thus increasing the readability of long disordered dial...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	345,959
2009.14757	Attention-Aware Noisy Label Learning for Image Classification	Deep convolutional neural networks (CNNs) learned on large-scale labeled samples have achieved remarkable progress in computer vision, such as image/video classification. The cheapest way to obtain a large body of labeled visual data is to crawl from websites with user-supplied labels, such as Flickr. However, these sa...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	198,131
2204.11852	Completing Networks by Learning Local Connection Patterns	Network completion is a harder problem than link prediction because it does not only try to infer missing links but also nodes. Different methods have been proposed to solve this problem, but few of them employed structural information - the similarity of local connection patterns. In this paper, we propose a model nam...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	293,294
2406.01797	The Empirical Impact of Forgetting and Transfer in Continual Visual Odometry	As robotics continues to advance, the need for adaptive and continuously-learning embodied agents increases, particularly in the realm of assistance robotics. Quick adaptability and long-term information retention are essential to operate in dynamic environments typical of humans' everyday lives. A lifelong learning pa...	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	460,464
2403.10555	KARINA: An Efficient Deep Learning Model for Global Weather Forecast	Deep learning-based, data-driven models are gaining prevalence in climate research, particularly for global weather prediction. However, training the global weather data at high resolution requires massive computational resources. Therefore, we present a new model named KARINA to overcome the substantial computational ...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	438,240
2007.00730	From Spectrum Wavelet to Vertex Propagation: Graph Convolutional Networks Based on Taylor Approximation	Graph convolutional networks (GCN) have been recently utilized to extract the underlying structures of datasets with some labeled data and high-dimensional features. Existing GCNs mostly rely on a first-order Chebyshev approximation of graph wavelet-kernels. Such a generic propagation model does not always suit the var...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	185,191

Subsets and Splits

No community queries yet

The top public SQL queries from the community will appear here once available.