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
2502.01377	Data-Efficient Model for Psychological Resilience Prediction based on Neurological Data	Psychological resilience, defined as the ability to rebound from adversity, is crucial for mental health. Compared with traditional resilience assessments through self-reported questionnaires, resilience assessments based on neurological data offer more objective results with biological markers, hence significantly enh...	false	true	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	529,829
2411.07538	Unraveling the Gradient Descent Dynamics of Transformers	While the Transformer architecture has achieved remarkable success across various domains, a thorough theoretical foundation explaining its optimization dynamics is yet to be fully developed. In this study, we aim to bridge this understanding gap by answering the following two core questions: (1) Which types of Transfo...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	507,572
1807.09469	Supervised and Semi-Supervised Deep Neural Networks for CSI-Based Authentication	From the viewpoint of physical-layer authentication, spoofing attacks can be foiled by checking channel state information (CSI). Existing CSI-based authentication algorithms mostly require a deep knowledge of the channel to deliver decent performance. In this paper, we investigate CSI-based authenticators that can spar...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	103,732
2101.01461	PointCutMix: Regularization Strategy for Point Cloud Classification	As 3D point cloud analysis has received increasing attention, the insufficient scale of point cloud datasets and the weak generalization ability of networks become prominent. In this paper, we propose a simple and effective augmentation method for the point cloud data, named PointCutMix, to alleviate those problems. It...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	214,377
2309.12140	Unsupervised Domain Adaptation for Self-Driving from Past Traversal Features	The rapid development of 3D object detection systems for self-driving cars has significantly improved accuracy. However, these systems struggle to generalize across diverse driving environments, which can lead to safety-critical failures in detecting traffic participants. To address this, we propose a method that utili...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	393,676
2311.13485	Deep-learning-based acceleration of MRI for radiotherapy planning of pediatric patients with brain tumors	Magnetic Resonance Imaging (MRI) is a non-invasive diagnostic and radiotherapy (RT) planning tool, offering detailed insights into the anatomy of the human body. The extensive scan time is stressful for patients, who must remain motionless in a prolonged imaging procedure that prioritizes reduction of imaging artifacts...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	409,751
2104.01778	AST: Audio Spectrogram Transformer	In the past decade, convolutional neural networks (CNNs) have been widely adopted as the main building block for end-to-end audio classification models, which aim to learn a direct mapping from audio spectrograms to corresponding labels. To better capture long-range global context, a recent trend is to add a self-atten...	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	228,475
1410.7404	Maximally Informative Hierarchical Representations of High-Dimensional Data	We consider a set of probabilistic functions of some input variables as a representation of the inputs. We present bounds on how informative a representation is about input data. We extend these bounds to hierarchical representations so that we can quantify the contribution of each layer towards capturing the informati...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	37,071
2408.10645	CoRA: Collaborative Information Perception by Large Language Model's Weights for Recommendation	Involving collaborative information in Large Language Models (LLMs) is a promising technique for adapting LLMs for recommendation. Existing methods achieve this by concatenating collaborative features with text tokens into a unified sequence input and then fine-tuning to align these features with LLM's input space. Alt...	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	false	481,956
1902.02422	Principal Model Analysis Based on Partial Least Squares	Motivated by the Bagging Partial Least Squares (PLS) and Principal Component Analysis (PCA) algorithms, we propose a Principal Model Analysis (PMA) method in this paper. In the proposed PMA algorithm, the PCA and the PLS are combined. In the method, multiple PLS models are trained on sub-training sets, derived from the...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	120,874
2406.13249	R^2AG: Incorporating Retrieval Information into Retrieval Augmented Generation	Retrieval augmented generation (RAG) has been applied in many scenarios to augment large language models (LLMs) with external documents provided by retrievers. However, a semantic gap exists between LLMs and retrievers due to differences in their training objectives and architectures. This misalignment forces LLMs to p...	false	false	false	false	true	true	false	false	true	false	false	false	false	false	false	false	false	false	465,770
2101.12339	Are Top School Students More Critical of Their Professors? Mining Comments on RateMyProfessor.com	Student reviews and comments on RateMyProfessor.com reflect realistic learning experiences of students. Such information provides a large-scale data source to examine the teaching quality of the lecturers. In this paper, we propose an in-depth analysis of these comments. First, we partition our data into different comp...	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	false	217,537
1609.02020	Random matrices meet machine learning: a large dimensional analysis of LS-SVM	This article proposes a performance analysis of kernel least squares support vector machines (LS-SVMs) based on a random matrix approach, in the regime where both the dimension of data $p$ and their number $n$ grow large at the same rate. Under a two-class Gaussian mixture model for the input data, we prove that the LS...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	60,674
1502.00946	Classification of Hyperspectral Imagery on Embedded Grassmannians	We propose an approach for capturing the signal variability in hyperspectral imagery using the framework of the Grassmann manifold. Labeled points from each class are sampled and used to form abstract points on the Grassmannian. The resulting points on the Grassmannian have representations as orthonormal matrices and a...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	39,889
2202.01483	Variable Slope Trapezoidal Circulating Current Injection to Attenuate Capacitor Voltage Ripple in Modular Multilevel Converter Based Variable Speed Motor Drives Application	The main challenge in using the Modular Multilevel Converter-based constant-torque variable-speed motor drives is increased sub-module capacitor voltage ripples (SM-CVR) at lowfundamental frequency operation, due to the inverse relationship between SM-CVR and operating frequency. To address this issue, a variable slope...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	278,500
2302.00618	Synthetic Prompting: Generating Chain-of-Thought Demonstrations for Large Language Models	Large language models can perform various reasoning tasks by using chain-of-thought prompting, which guides them to find answers through step-by-step demonstrations. However, the quality of the prompts depends on the demonstrations given to the models, and creating many of them by hand is costly. We introduce Synthetic...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	343,281
2408.12077	Through-the-Wall Radar Human Activity Micro-Doppler Signature Representation Method Based on Joint Boulic-Sinusoidal Pendulum Model	With the help of micro-Doppler signature, ultra-wideband (UWB) through-the-wall radar (TWR) enables the reconstruction of range and velocity information of limb nodes to accurately identify indoor human activities. However, existing methods are usually trained and validated directly using range-time maps (RTM) and Dopp...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	482,571
1906.10971	NeuroTrajectory: A Neuroevolutionary Approach to Local State Trajectory Learning for Autonomous Vehicles	Autonomous vehicles are controlled today either based on sequences of decoupled perception-planning-action operations, either based on End2End or Deep Reinforcement Learning (DRL) systems. Current deep learning solutions for autonomous driving are subject to several limitations (e.g. they estimate driving actions throu...	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	136,558
2210.05013	Supervisory Coordination of Robotic Fiber Positioners in Multi-Object Spectrographs	In this paper, we solve the complete coordination problem of robotic fiber positioners using supervisory control theory. In particular, we model positioners and their behavioral specifications as discrete-event systems by the discretization of their motion spaces. We synthesize a coordination supervisor associated with...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	322,660
2302.00178	Program Generation from Diverse Video Demonstrations	The ability to use inductive reasoning to extract general rules from multiple observations is a vital indicator of intelligence. As humans, we use this ability to not only interpret the world around us, but also to predict the outcomes of the various interactions we experience. Generalising over multiple observations i...	false	false	false	false	false	false	true	false	true	false	false	true	false	false	false	false	false	false	343,126
2302.10575	Managing multi-facet bias in collaborative filtering recommender systems	Due to the extensive growth of information available online, recommender systems play a more significant role in serving people's interests. Traditional recommender systems mostly use an accuracy-focused approach to produce recommendations. Today's research suggests that this single-dimension approach can lead the syst...	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	false	346,856
2305.18374	Pure Spectral Graph Embeddings: Reinterpreting Graph Convolution for Top-N Recommendation	The use of graph convolution in the development of recommender system algorithms has recently achieved state-of-the-art results in the collaborative filtering task (CF). While it has been demonstrated that the graph convolution operation is connected to a filtering operation on the graph spectral domain, the theoretica...	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	false	368,993
2407.09709	GOFA: A Generative One-For-All Model for Joint Graph Language Modeling	Foundation models, such as Large Language Models (LLMs) or Large Vision Models (LVMs), have emerged as one of the most powerful tools in the respective fields. However, unlike text and image data, graph data do not have a definitive structure, posing great challenges to developing a Graph Foundation Model (GFM). For ex...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	472,690
2110.08920	Semantics of Conjectures	This paper aims to expand and detail the notion of formal semantics of Conjectures by applying a theoretic-model approach. After a short introduction to the concepts and basics of Conjectures, we will start from the notion of Simple Interpretation of RDF, applying and extending the semantic rules and conditions to full...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	261,605
2311.05128	Exploring and Analyzing Wildland Fire Data Via Machine Learning Techniques	This research project investigated the correlation between a 10 Hz time series of thermocouple temperatures and turbulent kinetic energy (TKE) computed from wind speeds collected from a small experimental prescribed burn at the Silas Little Experimental Forest in New Jersey, USA. The primary objective of this project w...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	406,483
2206.10128	Bridging the Gap Between Indexing and Retrieval for Differentiable Search Index with Query Generation	The Differentiable Search Index (DSI) is an emerging paradigm for information retrieval. Unlike traditional retrieval architectures where index and retrieval are two different and separate components, DSI uses a single transformer model to perform both indexing and retrieval. In this paper, we identify and tackle an ...	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	303,805
2412.11634	Predicting the Original Appearance of Damaged Historical Documents	Historical documents encompass a wealth of cultural treasures but suffer from severe damages including character missing, paper damage, and ink erosion over time. However, existing document processing methods primarily focus on binarization, enhancement, etc., neglecting the repair of these damages. To this end, we pre...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	517,503
2106.08117	Semantic Representation and Inference for NLP	Semantic representation and inference is essential for Natural Language Processing (NLP). The state of the art for semantic representation and inference is deep learning, and particularly Recurrent Neural Networks (RNNs), Convolutional Neural Networks (CNNs), and transformer Self-Attention models. This thesis investiga...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	241,190
2311.14062	Hardware Resilience Properties of Text-Guided Image Classifiers	This paper presents a novel method to enhance the reliability of image classification models during deployment in the face of transient hardware errors. By utilizing enriched text embeddings derived from GPT-3 with question prompts per class and CLIP pretrained text encoder, we investigate their impact as an initializa...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	409,976
2406.09326	PianoMotion10M: Dataset and Benchmark for Hand Motion Generation in Piano Performance	Recently, artificial intelligence techniques for education have been received increasing attentions, while it still remains an open problem to design the effective music instrument instructing systems. Although key presses can be directly derived from sheet music, the transitional movements among key presses require mo...	false	false	true	false	true	false	false	false	false	false	false	true	false	false	false	false	false	true	463,874
2301.12264	Controlling Steering with Energy-Based Models	So-called implicit behavioral cloning with energy-based models has shown promising results in robotic manipulation tasks. We tested if the method's advantages carry on to controlling the steering of a real self-driving car with an end-to-end driving model. We performed an extensive comparison of the implicit behavioral...	false	false	false	false	true	false	true	true	false	false	false	false	false	false	false	false	false	false	342,465
2402.09552	STEER: Assessing the Economic Rationality of Large Language Models	There is increasing interest in using LLMs as decision-making "agents." Doing so includes many degrees of freedom: which model should be used; how should it be prompted; should it be asked to introspect, conduct chain-of-thought reasoning, etc? Settling these questions -- and more broadly, determining whether an LLM ag...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	429,569
1702.00535	Composing Differential Privacy and Secure Computation: A case study on scaling private record linkage	Private record linkage (PRL) is the problem of identifying pairs of records that are similar as per an input matching rule from databases held by two parties that do not trust one another. We identify three key desiderata that a PRL solution must ensure: 1) perfect precision and high recall of matching pairs, 2) a proo...	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	true	false	67,672
1601.00211	A fractal dimension based optimal wavelet packet analysis technique for classification of meningioma brain tumours	With the heterogeneous nature of tissue texture, using a single resolution approach for optimum classification might not suffice. In contrast, a multiresolution wavelet packet analysis can decompose the input signal into a set of frequency subbands giving the opportunity to characterise the texture at the appropriate f...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	50,617
2102.07973	A Sub-band Approach to Deep Denoising Wavelet Networks and a Frequency-adaptive Loss for Perceptual Quality	In this paper, we propose two contributions to neural network based denoising. First, we propose applying separate convolutional layers to each sub-band of discrete wavelet transform (DWT) as opposed to the common usage of DWT which concatenates all sub-bands and applies a single convolution layer. We show that our app...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	220,295
2405.18314	Deriving Causal Order from Single-Variable Interventions: Guarantees & Algorithm	Targeted and uniform interventions to a system are crucial for unveiling causal relationships. While several methods have been developed to leverage interventional data for causal structure learning, their practical application in real-world scenarios often remains challenging. Recent benchmark studies have highlighted...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	458,369
2008.06884	DeVLBert: Learning Deconfounded Visio-Linguistic Representations	In this paper, we propose to investigate the problem of out-of-domain visio-linguistic pretraining, where the pretraining data distribution differs from that of downstream data on which the pretrained model will be fine-tuned. Existing methods for this problem are purely likelihood-based, leading to the spurious correl...	false	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	191,924
2501.14510	Deep-BrownConrady: Prediction of Camera Calibration and Distortion Parameters Using Deep Learning and Synthetic Data	This research addresses the challenge of camera calibration and distortion parameter prediction from a single image using deep learning models. The main contributions of this work are: (1) demonstrating that a deep learning model, trained on a mix of real and synthetic images, can accurately predict camera and lens par...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	527,149
2006.10137	MoFlow: An Invertible Flow Model for Generating Molecular Graphs	Generating molecular graphs with desired chemical properties driven by deep graph generative models provides a very promising way to accelerate drug discovery process. Such graph generative models usually consist of two steps: learning latent representations and generation of molecular graphs. However, to generate nove...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	182,775
1908.06263	A Sensitivity Analysis of Attention-Gated Convolutional Neural Networks for Sentence Classification	In this paper, we investigate the effect of different hyperparameters as well as different combinations of hyperparameters settings on the performance of the Attention-Gated Convolutional Neural Networks (AGCNNs), e.g., the kernel window size, the number of feature maps, the keep rate of the dropout layer, and the acti...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	141,953
2402.02449	Surfing the modeling of PoS taggers in low-resource scenarios	The recent trend towards the application of deep structured techniques has revealed the limits of huge models in natural language processing. This has reawakened the interest in traditional machine learning algorithms, which have proved still to be competitive in certain contexts, in particular low-resource settings. I...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	426,568
2306.00488	Reconstructing Graph Diffusion History from a Single Snapshot	Diffusion on graphs is ubiquitous with numerous high-impact applications. In these applications, complete diffusion histories play an essential role in terms of identifying dynamical patterns, reflecting on precaution actions, and forecasting intervention effects. Despite their importance, complete diffusion histories ...	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	false	false	370,034
2008.01160	A Spectral Energy Distance for Parallel Speech Synthesis	Speech synthesis is an important practical generative modeling problem that has seen great progress over the last few years, with likelihood-based autoregressive neural models now outperforming traditional concatenative systems. A downside of such autoregressive models is that they require executing tens of thousands o...	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	190,211
2003.03633	AL2: Progressive Activation Loss for Learning General Representations in Classification Neural Networks	The large capacity of neural networks enables them to learn complex functions. To avoid overfitting, networks however require a lot of training data that can be expensive and time-consuming to collect. A common practical approach to attenuate overfitting is the use of network regularization techniques. We propose a nov...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	167,302
2310.05951	Reducing the False Positive Rate Using Bayesian Inference in Autonomous Driving Perception	Object recognition is a crucial step in perception systems for autonomous and intelligent vehicles, as evidenced by the numerous research works in the topic. In this paper, object recognition is explored by using multisensory and multimodality approaches, with the intention of reducing the false positive rate (FPR). Th...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	398,368
2307.07046	A metric learning approach for endoscopic kidney stone identification	Several Deep Learning (DL) methods have recently been proposed for an automated identification of kidney stones during an ureteroscopy to enable rapid therapeutic decisions. Even if these DL approaches led to promising results, they are mainly appropriate for kidney stone types for which numerous labelled data are avai...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	379,259
2409.14456	Scoring rule nets: beyond mean target prediction in multivariate regression	Probabilistic regression models trained with maximum likelihood estimation (MLE), can sometimes overestimate variance to an unacceptable degree. This is mostly problematic in the multivariate domain. While univariate models often optimize the popular Continuous Ranked Probability Score (CRPS), in the multivariate domai...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	490,472
2109.09903	AirDOS: Dynamic SLAM benefits from Articulated Objects	Dynamic Object-aware SLAM (DOS) exploits object-level information to enable robust motion estimation in dynamic environments. Existing methods mainly focus on identifying and excluding dynamic objects from the optimization. In this paper, we show that feature-based visual SLAM systems can also benefit from the presence...	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	256,439
1810.02977	Fast Object Learning and Dual-arm Coordination for Cluttered Stowing, Picking, and Packing	Robotic picking from cluttered bins is a demanding task, for which Amazon Robotics holds challenges. The 2017 Amazon Robotics Challenge (ARC) required stowing items into a storage system, picking specific items, and packing them into boxes. In this paper, we describe the entry of team NimbRo Picking. Our deep object pe...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	109,704
2407.10910	DataDream: Few-shot Guided Dataset Generation	While text-to-image diffusion models have been shown to achieve state-of-the-art results in image synthesis, they have yet to prove their effectiveness in downstream applications. Previous work has proposed to generate data for image classifier training given limited real data access. However, these methods struggle to...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	473,181
1803.11111	Bag of Recurrence Patterns Representation for Time-Series Classification	Time-Series Classification (TSC) has attracted a lot of attention in pattern recognition, because wide range of applications from different domains such as finance and health informatics deal with time-series signals. Bag of Features (BoF) model has achieved a great success in TSC task by summarizing signals according ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	93,821
1909.00958	Graph Representation Learning: A Survey	Research on graph representation learning has received a lot of attention in recent years since many data in real-world applications come in form of graphs. High-dimensional graph data are often in irregular form, which makes them more difficult to analyze than image/video/audio data defined on regular lattices. Variou...	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	false	false	143,768
2403.05658	Feature CAM: Interpretable AI in Image Classification	Deep Neural Networks have often been called the black box because of the complex, deep architecture and non-transparency presented by the inner layers. There is a lack of trust to use Artificial Intelligence in critical and high-precision fields such as security, finance, health, and manufacturing industries. A lot of ...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	true	436,095
2204.14170	Tractable Uncertainty for Structure Learning	Bayesian structure learning allows one to capture uncertainty over the causal directed acyclic graph (DAG) responsible for generating given data. In this work, we present Tractable Uncertainty for STructure learning (TRUST), a framework for approximate posterior inference that relies on probabilistic circuits as the re...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	294,075
1901.10143	Learning to Validate the Quality of Detected Landmarks	We present a new loss function for the validation of image landmarks detected via Convolutional Neural Networks (CNN). The network learns to estimate how accurate its landmark estimation is. This loss function is applicable to all regression-based location estimations and allows the exclusion of unreliable landmarks fr...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	119,942
2305.19043	A Heat Diffusion Perspective on Geodesic Preserving Dimensionality Reduction	Diffusion-based manifold learning methods have proven useful in representation learning and dimensionality reduction of modern high dimensional, high throughput, noisy datasets. Such datasets are especially present in fields like biology and physics. While it is thought that these methods preserve underlying manifold s...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	369,351
2112.12530	Long-Term Optimal Delivery Planning for Replacing the Liquefied Petroleum Gas Cylinder	In the daily operation of liquefied petroleum gas service, gas providers visit customers and replace cylinders if the gas is about to run out. For a long time, frequent visits to customers were required because they could not determine the amount of remaining gas without a staff visit and observation. To solve this pro...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	true	272,987
2007.11437	Learning generalized Nash equilibria in multi-agent dynamical systems via extremum seeking control	In this paper, we consider the problem of learning a generalized Nash equilibrium (GNE) in strongly monotone games. First, we propose a novel continuous-time solution algorithm that uses regular projections and first-order information. As second main contribution, we design a data-driven variant of the former algorithm...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	188,553
2210.14406	RedPen: Region- and Reason-Annotated Dataset of Unnatural Speech	Even with recent advances in speech synthesis models, the evaluation of such models is based purely on human judgement as a single naturalness score, such as the Mean Opinion Score (MOS). The score-based metric does not give any further information about which parts of speech are unnatural or why human judges believe t...	false	false	true	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	326,532
1107.4264	Accelerating Radio Astronomy Cross-Correlation with Graphics Processing Units	We present a highly parallel implementation of the cross-correlation of time-series data using graphics processing units (GPUs), which is scalable to hundreds of independent inputs and suitable for the processing of signals from "Large-N" arrays of many radio antennas. The computational part of the algorithm, the X-eng...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	11,393
2410.20352	An approach to hummed-tune and song sequences matching	Melody stuck in your head, also known as "earworm", is tough to get rid of, unless you listen to it again or sing it out loud. But what if you can not find the name of that song? It must be an intolerable feeling. Recognizing a song name base on humming sound is not an easy task for a human being and should be done by ...	false	false	true	false	true	true	false	false	false	false	false	false	false	false	false	false	false	false	502,783
2404.06418	Studying the Impact of Latent Representations in Implicit Neural Networks for Scientific Continuous Field Reconstruction	Learning a continuous and reliable representation of physical fields from sparse sampling is challenging and it affects diverse scientific disciplines. In a recent work, we present a novel model called MMGN (Multiplicative and Modulated Gabor Network) with implicit neural networks. In this work, we design additional st...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	445,461
2007.14284	Discrepancy Minimization in Domain Generalization with Generative Nearest Neighbors	Domain generalization (DG) deals with the problem of domain shift where a machine learning model trained on multiple-source domains fail to generalize well on a target domain with different statistics. Multiple approaches have been proposed to solve the problem of domain generalization by learning domain invariant repr...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	189,358
2204.12805	A Scalable Combinatorial Solver for Elastic Geometrically Consistent 3D Shape Matching	We present a scalable combinatorial algorithm for globally optimizing over the space of geometrically consistent mappings between 3D shapes. We use the mathematically elegant formalism proposed by Windheuser et al. (ICCV 2011) where 3D shape matching was formulated as an integer linear program over the space of orienta...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	293,609
1412.3596	EgoSampling: Fast-Forward and Stereo for Egocentric Videos	While egocentric cameras like GoPro are gaining popularity, the videos they capture are long, boring, and difficult to watch from start to end. Fast forwarding (i.e. frame sampling) is a natural choice for faster video browsing. However, this accentuates the shake caused by natural head motion, making the fast forwarde...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	38,305
2210.15510	Fusion-based Few-Shot Morphing Attack Detection and Fingerprinting	The vulnerability of face recognition systems to morphing attacks has posed a serious security threat due to the wide adoption of face biometrics in the real world. Most existing morphing attack detection (MAD) methods require a large amount of training data and have only been tested on a few predefined attack models. ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	326,987
2004.04157	MetaSleepLearner: A Pilot Study on Fast Adaptation of Bio-signals-Based Sleep Stage Classifier to New Individual Subject Using Meta-Learning	Identifying bio-signals based-sleep stages requires time-consuming and tedious labor of skilled clinicians. Deep learning approaches have been introduced in order to challenge the automatic sleep stage classification conundrum. However, the difficulties can be posed in replacing the clinicians with the automatic system...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	171,801
2501.00715	eRevise+RF: A Writing Evaluation System for Assessing Student Essay Revisions and Providing Formative Feedback	The ability to revise essays in response to feedback is important for students' writing success. An automated writing evaluation (AWE) system that supports students in revising their essays is thus essential. We present eRevise+RF, an enhanced AWE system for assessing student essay revisions (e.g., changes made to an e...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	521,770
2410.08710	Preferential Normalizing Flows	Eliciting a high-dimensional probability distribution from an expert via noisy judgments is notoriously challenging, yet useful for many applications, such as prior elicitation and reward modeling. We introduce a method for eliciting the expert's belief density as a normalizing flow based solely on preferential questio...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	497,242
2006.13570	Hyperparameter Ensembles for Robustness and Uncertainty Quantification	Ensembles over neural network weights trained from different random initialization, known as deep ensembles, achieve state-of-the-art accuracy and calibration. The recently introduced batch ensembles provide a drop-in replacement that is more parameter efficient. In this paper, we design ensembles not only over weights...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	183,961
1911.07316	Channel Estimation in Massive MIMO under Hardware Non-Linearities: Bayesian Methods versus Deep Learning	This paper considers the joint impact of non-linear hardware impairments at the base station (BS) and user equipments (UEs) on the uplink performance of single-cell massive MIMO (multiple-input multiple-output) in practical Rician fading environments. First, Bussgang decomposition-based effective channels and distortio...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	153,803
2311.17393	Comparison of metaheuristics for the firebreak placement problem: a simulation-based optimization approach	The problem of firebreak placement is crucial for fire prevention, and its effectiveness at landscape scale will depend on their ability to impede the progress of future wildfires. To provide an adequate response, it is therefore necessary to consider the stochastic nature of fires, which are highly unpredictable from ...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	411,283
1807.00751	Understanding the Effectiveness of Lipschitz-Continuity in Generative Adversarial Nets	In this paper, we investigate the underlying factor that leads to failure and success in the training of GANs. We study the property of the optimal discriminative function and show that in many GANs, the gradient from the optimal discriminative function is not reliable, which turns out to be the fundamental cause of fa...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	101,910
1807.06666	Payoff Control in the Iterated Prisoner's Dilemma	Repeated game has long been the touchstone model for agents' long-run relationships. Previous results suggest that it is particularly difficult for a repeated game player to exert an autocratic control on the payoffs since they are jointly determined by all participants. This work discovers that the scale of a player's...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	true	false	false	true	103,169
2203.09772	Completing Partial Point Clouds with Outliers by Collaborative Completion and Segmentation	Most existing point cloud completion methods are only applicable to partial point clouds without any noises and outliers, which does not always hold in practice. We propose in this paper an end-to-end network, named CS-Net, to complete the point clouds contaminated by noises or containing outliers. In our CS-Net, the c...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	286,283
2402.18216	LLM Task Interference: An Initial Study on the Impact of Task-Switch in Conversational History	With the recent emergence of powerful instruction-tuned large language models (LLMs), various helpful conversational Artificial Intelligence (AI) systems have been deployed across many applications. When prompted by users, these AI systems successfully perform a wide range of tasks as part of a conversation. To provide...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	433,343
2404.16364	ReZero: Boosting MCTS-based Algorithms by Backward-view and Entire-buffer Reanalyze	Monte Carlo Tree Search (MCTS)-based algorithms, such as MuZero and its derivatives, have achieved widespread success in various decision-making domains. These algorithms employ the reanalyze process to enhance sample efficiency from stale data, albeit at the expense of significant wall-clock time consumption. To addre...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	449,460
1804.06817	Automated detection of vulnerable plaque in intravascular ultrasound images	Acute Coronary Syndrome (ACS) is a syndrome caused by a decrease in blood flow in the coronary arteries. The ACS is usually related to coronary thrombosis and is primarily caused by plaque rupture followed by plaque erosion and calcified nodule. Thin-cap fibroatheroma (TCFA) is known to be the most similar lesion morph...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	95,390
2408.03943	Building Machines that Learn and Think with People	What do we want from machine intelligence? We envision machines that are not just tools for thought, but partners in thought: reasonable, insightful, knowledgeable, reliable, and trustworthy systems that think with us. Current artificial intelligence (AI) systems satisfy some of these criteria, some of the time. In thi...	true	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	479,202
2403.05002	LHMap-loc: Cross-Modal Monocular Localization Using LiDAR Point Cloud Heat Map	Localization using a monocular camera in the pre-built LiDAR point cloud map has drawn increasing attention in the field of autonomous driving and mobile robotics. However, there are still many challenges (e.g. difficulties of map storage, poor localization robustness in large scenes) in accurately and efficiently impl...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	435,819
2111.06345	Poisoning Knowledge Graph Embeddings via Relation Inference Patterns	We study the problem of generating data poisoning attacks against Knowledge Graph Embedding (KGE) models for the task of link prediction in knowledge graphs. To poison KGE models, we propose to exploit their inductive abilities which are captured through the relationship patterns like symmetry, inversion and compositio...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	true	false	false	266,046
2106.02293	Cross-language Sentence Selection via Data Augmentation and Rationale Training	This paper proposes an approach to cross-language sentence selection in a low-resource setting. It uses data augmentation and negative sampling techniques on noisy parallel sentence data to directly learn a cross-lingual embedding-based query relevance model. Results show that this approach performs as well as or bette...	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	238,813
1909.13857	Black-box Adversarial Attacks with Bayesian Optimization	We focus on the problem of black-box adversarial attacks, where the aim is to generate adversarial examples using information limited to loss function evaluations of input-output pairs. We use Bayesian optimization~(BO) to specifically cater to scenarios involving low query budgets to develop query efficient adversaria...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	147,536
2410.22303	$\mathsf{OPA}$: One-shot Private Aggregation with Single Client Interaction and its Applications to Federated Learning	Our work aims to minimize interaction in secure computation due to the high cost and challenges associated with communication rounds, particularly in scenarios with many clients. In this work, we revisit the problem of secure aggregation in the single-server setting where a single evaluation server can securely aggrega...	false	false	false	false	true	false	true	false	false	false	false	false	true	false	false	false	false	false	503,572
1612.00343	Global Minimum for a Finsler Elastica Minimal Path Approach	In this paper, we propose a novel curvature-penalized minimal path model via an orientation-lifted Finsler metric and the Euler elastica curve. The original minimal path model computes the globally minimal geodesic by solving an Eikonal partial differential equation (PDE). Essentially, this first-order model is unable ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	64,863
2202.06170	An AI-based Domain-Decomposition Non-Intrusive Reduced-Order Model for Extended Domains applied to Multiphase Flow in Pipes	The modelling of multiphase flow in a pipe presents a significant challenge for high-resolution computational fluid dynamics (CFD) models due to the high aspect ratio (length over diameter) of the domain. In subsea applications, the pipe length can be several hundreds of kilometres versus a pipe diameter of just a few ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	280,122
2010.06073	A catalog of broad morphology of Pan-STARRS galaxies based on deep learning	Autonomous digital sky surveys such as Pan-STARRS have the ability to image a very large number of galactic and extra-galactic objects, and the large and complex nature of the image data reinforces the use of automation. Here we describe the design and implementation of a data analysis process for automatic broad morph...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	200,353
2208.01864	Pyramidal Denoising Diffusion Probabilistic Models	Recently, diffusion model have demonstrated impressive image generation performances, and have been extensively studied in various computer vision tasks. Unfortunately, training and evaluating diffusion models consume a lot of time and computational resources. To address this problem, here we present a novel pyramidal ...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	311,300
2112.11749	Class-aware Sounding Objects Localization via Audiovisual Correspondence	Audiovisual scenes are pervasive in our daily life. It is commonplace for humans to discriminatively localize different sounding objects but quite challenging for machines to achieve class-aware sounding objects localization without category annotations, i.e., localizing the sounding object and recognizing its category...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	true	272,793
2005.09015	Patch based Colour Transfer using SIFT Flow	We propose a new colour transfer method with Optimal Transport (OT) to transfer the colour of a sourceimage to match the colour of a target image of the same scene that may exhibit large motion changes betweenimages. By definition OT does not take into account any available information about correspondences whencomputi...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	177,781
2305.12162	A Scalable Neural Network for DSIC Affine Maximizer Auction Design	Automated auction design aims to find empirically high-revenue mechanisms through machine learning. Existing works on multi item auction scenarios can be roughly divided into RegretNet-like and affine maximizer auctions (AMAs) approaches. However, the former cannot strictly ensure dominant strategy incentive compatibil...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	true	false	false	true	365,869
2307.11573	Control- & Task-Aware Optimal Design of Actuation System for Legged Robots using Binary Integer Linear Programming	Athletic robots demand a whole-body actuation system design that utilizes motors up to the boundaries of their performance. However, creating such robots poses challenges of integrating design principles and reasoning of practical design choices. This paper presents a design framework that guides designers to find opti...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	380,960
2108.10715	From One to Many: A Deep Learning Coincident Gravitational-Wave Search	Gravitational waves from the coalescence of compact-binary sources are now routinely observed by Earth bound detectors. The most sensitive search algorithms convolve many different pre-calculated gravitational waveforms with the detector data and look for coincident matches between different detectors. Machine learning...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	251,986
2405.12939	Aggregation of Reasoning: A Hierarchical Framework for Enhancing Answer Selection in Large Language Models	Recent advancements in Chain-of-Thought prompting have facilitated significant breakthroughs for Large Language Models (LLMs) in complex reasoning tasks. Current research enhances the reasoning performance of LLMs by sampling multiple reasoning chains and ensembling based on the answer frequency. However, this approach...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	455,706
2012.14228	Causal World Models by Unsupervised Deconfounding of Physical Dynamics	The capability of imagining internally with a mental model of the world is vitally important for human cognition. If a machine intelligent agent can learn a world model to create a "dream" environment, it can then internally ask what-if questions -- simulate the alternative futures that haven't been experienced in the ...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	213,445
1808.01766	On Optimizing Deep Convolutional Neural Networks by Evolutionary Computing	Optimization for deep networks is currently a very active area of research. As neural networks become deeper, the ability in manually optimizing the network becomes harder. Mini-batch normalization, identification of effective respective fields, momentum updates, introduction of residual blocks, learning rate adoption,...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	104,647
2410.17603	Scaling Analysis in a Multi-Energy System	This paper presents a scaling study on the planning phase of a multi-energy system (MES), which is becoming increasingly prominent in the energy sector. The research aims to investigate the interactions and challenges associated with integrating heat and electrical systems and scaling their components. In this context,...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	501,543
2404.02852	Toward Inference-optimal Mixture-of-Expert Large Language Models	Mixture-of-Expert (MoE) based large language models (LLMs), such as the recent Mixtral and DeepSeek-MoE, have shown great promise in scaling model size without suffering from the quadratic growth of training cost of dense transformers. Like dense models, training MoEs requires answering the same question: given a train...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	444,023
2209.04425	The Role Of Biology In Deep Learning	Artificial neural networks took a lot of inspiration from their biological counterparts in becoming our best machine perceptual systems. This work summarizes some of that history and incorporates modern theoretical neuroscience into experiments with artificial neural networks from the field of deep learning. Specifical...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	true	false	false	316,781
1312.6415	Measurement Analysis and Channel Modeling for TOA-Based Ranging in Tunnels	A robust and accurate positioning solution is required to increase the safety in GPS-denied environments. Although there is a lot of available research in this area, little has been done for confined environments such as tunnels. Therefore, we organized a measurement campaign in a basement tunnel of Link\"{o}ping unive...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	29,363

Subsets and Splits

No community queries yet

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