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
1312.0485	Precise Semidefinite Programming Formulation of Atomic Norm Minimization for Recovering d-Dimensional ($d\geq 2$) Off-the-Grid Frequencies	Recent research in off-the-grid compressed sensing (CS) has demonstrated that, under certain conditions, one can successfully recover a spectrally sparse signal from a few time-domain samples even though the dictionary is continuous. In particular, atomic norm minimization was proposed in \cite{tang2012csotg} to recove...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	28,787
1911.01629	RNN-T For Latency Controlled ASR With Improved Beam Search	Neural transducer-based systems such as RNN Transducers (RNN-T) for automatic speech recognition (ASR) blend the individual components of a traditional hybrid ASR systems (acoustic model, language model, punctuation model, inverse text normalization) into one single model. This greatly simplifies training and inference...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	152,157
2302.10017	Stable Motion Primitives via Imitation and Contrastive Learning	Learning from humans allows non-experts to program robots with ease, lowering the resources required to build complex robotic solutions. Nevertheless, such data-driven approaches often lack the ability to provide guarantees regarding their learned behaviors, which is critical for avoiding failures and/or accidents. In ...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	346,653
2309.06323	SAMPLING: Scene-adaptive Hierarchical Multiplane Images Representation for Novel View Synthesis from a Single Image	Recent novel view synthesis methods obtain promising results for relatively small scenes, e.g., indoor environments and scenes with a few objects, but tend to fail for unbounded outdoor scenes with a single image as input. In this paper, we introduce SAMPLING, a Scene-adaptive Hierarchical Multiplane Images Representat...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	391,383
1110.3382	Sampling Techniques in Bayesian Finite Element Model Updating	Recent papers in the field of Finite Element Model (FEM) updating have highlighted the benefits of Bayesian techniques. The Bayesian approaches are designed to deal with the uncertainties associated with complex systems, which is the main problem in the development and updating of FEMs. This paper highlights the comple...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	12,669
2011.00618	Triage of Potential COVID-19 Patients from Chest X-ray Images using Hierarchical Convolutional Networks	The current COVID-19 pandemic has motivated the researchers to use artificial intelligence techniques for a potential alternative to reverse transcription-polymerase chain reaction (RT-PCR) due to the limited scale of testing. The chest X-ray (CXR) is one of the alternatives to achieve fast diagnosis but the unavailabi...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	204,298
1603.07541	Position-aided Large-scale MIMO Channel Estimation for High-Speed Railway Communication Systems	We consider channel estimation for high-speed railway communication systems, where both the transmitter and the receiver are equipped with large-scale antenna arrays. It is known that the throughput of conventional training schemes monotonically decreases with the mobility. Assuming that the moving terminal employs a l...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	53,639
2108.02776	Sinsy: A Deep Neural Network-Based Singing Voice Synthesis System	This paper presents Sinsy, a deep neural network (DNN)-based singing voice synthesis (SVS) system. In recent years, DNNs have been utilized in statistical parametric SVS systems, and DNN-based SVS systems have demonstrated better performance than conventional hidden Markov model-based ones. SVS systems are required to ...	false	false	true	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	249,444
1510.02786	Recovering a Hidden Community Beyond the Kesten-Stigum Threshold in $O(\|E\| \log^*\|V\|)$ Time	Community detection is considered for a stochastic block model graph of n vertices, with K vertices in the planted community, edge probability p for pairs of vertices both in the community, and edge probability q for other pairs of vertices. The main focus of the paper is on weak recovery of the community based on th...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	true	47,754
2311.06082	A high throughput Intrusion Detection System (IDS) to enhance the security of data transmission among research centers	Data breaches and cyberattacks represent a severe problem in higher education institutions and universities that can result in illegal access to sensitive information and data loss. To enhance the security of data transmission, Intrusion Prevention Systems (IPS, i.e., firewalls) and Intrusion Detection Systems (IDS, i....	false	false	false	false	false	false	false	false	false	false	true	false	true	false	false	false	false	false	406,810
2405.16796	DualContrast: Unsupervised Disentangling of Content and Transformations with Implicit Parameterization	Unsupervised disentanglement of content and transformation is significantly important for analyzing shape-focused scientific image datasets, given their efficacy in solving downstream image-based shape-analyses tasks. The existing relevant works address the problem by explicitly parameterizing the transformation latent...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	457,606
2004.01608	Learning 2-opt Heuristics for the Traveling Salesman Problem via Deep Reinforcement Learning	Recent works using deep learning to solve the Traveling Salesman Problem (TSP) have focused on learning construction heuristics. Such approaches find TSP solutions of good quality but require additional procedures such as beam search and sampling to improve solutions and achieve state-of-the-art performance. However, f...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	170,961
1905.09894	PHom-GeM: Persistent Homology for Generative Models	Generative neural network models, including Generative Adversarial Network (GAN) and Auto-Encoders (AE), are among the most popular neural network models to generate adversarial data. The GAN model is composed of a generator that produces synthetic data and of a discriminator that discriminates between the generator's ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	131,869
2307.06720	Weakly supervised marine animal detection from remote sensing images using vector-quantized variational autoencoder	This paper studies a reconstruction-based approach for weakly-supervised animal detection from aerial images in marine environments. Such an approach leverages an anomaly detection framework that computes metrics directly on the input space, enhancing interpretability and anomaly localization compared to feature embedd...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	379,160
2110.03588	A transformer-based deep learning approach for classifying brain metastases into primary organ sites using clinical whole brain MRI	Treatment decisions for brain metastatic disease rely on knowledge of the primary organ site, and currently made with biopsy and histology. Here we develop a novel deep learning approach for accurate non-invasive digital histology with whole-brain MRI data. Our IRB-approved single-site retrospective study was comprised...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	259,555
2110.00852	Efficient and passive learning of networked dynamical systems driven by non-white exogenous inputs	We consider a networked linear dynamical system with $p$ agents/nodes. We study the problem of learning the underlying graph of interactions/dependencies from observations of the nodal trajectories over a time-interval $T$. We present a regularized non-casual consistent estimator for this problem and analyze its sample...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	258,558
1806.11306	Excavate Condition-invariant Space by Intrinsic Encoder	As the human, we can recognize the places across a wide range of changing environmental conditions such as those caused by weathers, seasons, and day-night cycles. We excavate and memorize the stable semantic structure of different places and scenes. For example, we can recognize tree whether the bare tree in winter or...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	101,688
1806.05464	Event-triggered controllers based on the supremum norm of sampling-induced error	The paper proposes a novel event-triggered control scheme for nonlinear systems based on the input-delay method. Specifically, the closed-loop system is associated with a pair of auxiliary input and output. The auxiliary output is defined as the derivative of the continuous-time input function, while the auxiliary inpu...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	100,477
2108.02563	GuavaNet: A deep neural network architecture for automatic sensory evaluation to predict degree of acceptability for Guava by a consumer	This thesis is divided into two parts:Part I: Analysis of Fruits, Vegetables, Cheese and Fish based on Image Processing using Computer Vision and Deep Learning: A Review. It consists of a comprehensive review of image processing, computer vision and deep learning techniques applied to carry out analysis of fruits, vege...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	249,369
1901.00204	Augmentation Scheme for Dealing with Imbalanced Network Traffic Classification Using Deep Learning	One of the most important tasks in network management is identifying different types of traffic flows. As a result, a type of management service, called Network Traffic Classifier (NTC), has been introduced. One type of NTCs that has gained huge attention in recent years applies deep learning on packets in order to cla...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	117,715
1804.09120	On Optimal Index Codes for Interlinked Cycle Structures with Outer Cycles	For index coding problems with special structure on the side-information graphs called Interlinked Cycle (IC) structures index codes have been proposed in the literature (C. Thapa, L. Ong, and S. Johnson, "Interlinked Cycles for Index Coding: Generalizing Cycles and Cliques", in \textit{IEEE Trans. Inf. Theory, vol. 63...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	95,909
2311.13693	Scalable CP Decomposition for Tensor Learning using GPU Tensor Cores	CP decomposition is a powerful tool for data science, especially gene analysis, deep learning, and quantum computation. However, the application of tensor decomposition is largely hindered by the exponential increment of the computational complexity and storage consumption with the size of tensors. While the data in ou...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	true	409,832
2109.05320	Deformation-Aware Data-Driven Grasp Synthesis	Grasp synthesis for 3D deformable objects remains a little-explored topic, most works aiming to minimize deformations. However, deformations are not necessarily harmful -- humans are, for example, able to exploit deformations to generate new potential grasps. How to achieve that on a robot is though an open question. T...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	254,745
1406.2721	Learning Latent Variable Gaussian Graphical Models	Gaussian graphical models (GGM) have been widely used in many high-dimensional applications ranging from biological and financial data to recommender systems. Sparsity in GGM plays a central role both statistically and computationally. Unfortunately, real-world data often does not fit well to sparse graphical models. I...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	33,779
2003.13074	A Novel Method of Extracting Topological Features from Word Embeddings	In recent years, topological data analysis has been utilized for a wide range of problems to deal with high dimensional noisy data. While text representations are often high dimensional and noisy, there are only a few work on the application of topological data analysis in natural language processing. In this paper, we...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	170,097
1908.03015	Augmenting Variational Autoencoders with Sparse Labels: A Unified Framework for Unsupervised, Semi-(un)supervised, and Supervised Learning	We present a new flavor of Variational Autoencoder (VAE) that interpolates seamlessly between unsupervised, semi-supervised and fully supervised learning domains. We show that unlabeled datapoints not only boost unsupervised tasks, but also the classification performance. Vice versa, every label not only improves class...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	true	false	false	141,142
2407.16982	Diffree: Text-Guided Shape Free Object Inpainting with Diffusion Model	This paper addresses an important problem of object addition for images with only text guidance. It is challenging because the new object must be integrated seamlessly into the image with consistent visual context, such as lighting, texture, and spatial location. While existing text-guided image inpainting methods can ...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	475,810
1812.08318	Generating lyrics with variational autoencoder and multi-modal artist embeddings	We present a system for generating song lyrics lines conditioned on the style of a specified artist. The system uses a variational autoencoder with artist embeddings. We propose the pre-training of artist embeddings with the representations learned by a CNN classifier, which is trained to predict artists based on MEL s...	false	false	true	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	116,981
1506.07902	Minimax Structured Normal Means Inference	We provide a unified treatment of a broad class of noisy structure recovery problems, known as structured normal means problems. In this setting, the goal is to identify, from a finite collection of Gaussian distributions with different means, the distribution that produced some observed data. Recent work has studied s...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	44,561
2408.08805	CIKMar: A Dual-Encoder Approach to Prompt-Based Reranking in Educational Dialogue Systems	In this study, we introduce CIKMar, an efficient approach to educational dialogue systems powered by the Gemma Language model. By leveraging a Dual-Encoder ranking system that incorporates both BERT and SBERT model, we have designed CIKMar to deliver highly relevant and accurate responses, even with the constraints of ...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	481,159
1805.04715	Unsupervised Semantic Frame Induction using Triclustering	We use dependency triples automatically extracted from a Web-scale corpus to perform unsupervised semantic frame induction. We cast the frame induction problem as a triclustering problem that is a generalization of clustering for triadic data. Our replicable benchmarks demonstrate that the proposed graph-based approach...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	97,292
2502.11067	A Survey on Active Feature Acquisition Strategies	Active feature acquisition studies the challenge of making accurate predictions while limiting the cost of collecting complete data. By selectively acquiring only the most informative features for each instance, these strategies enable efficient decision-making in scenarios where data collection is expensive or time-co...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	534,173
2404.12845	TartuNLP @ SIGTYP 2024 Shared Task: Adapting XLM-RoBERTa for Ancient and Historical Languages	We present our submission to the unconstrained subtask of the SIGTYP 2024 Shared Task on Word Embedding Evaluation for Ancient and Historical Languages for morphological annotation, POS-tagging, lemmatization, character- and word-level gap-filling. We developed a simple, uniform, and computationally lightweight approac...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	448,056
2409.10047	Bearing-Distance Based Flocking with Zone-Based Interactions	This paper presents a novel zone-based flocking control approach suitable for dynamic multi-agent systems (MAS). Inspired by Reynolds behavioral rules for $boids$, flocking behavioral rules with the zones of repulsion, conflict, attraction, and surveillance are introduced. For each agent, using only bearing and distanc...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	488,594
2008.07301	Computational timeline reconstruction of the stories surrounding Trump: Story turbulence, narrative control, and collective chronopathy	Measuring the specific kind, temporal ordering, diversity, and turnover rate of stories surrounding any given subject is essential to developing a complete reckoning of that subject's historical impact. Here, we use Twitter as a distributed news and opinion aggregation source to identify and track the dynamics of the d...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	192,064
1812.09544	Smoothing Traffic Flow via Control of Autonomous Vehicles	The emergence of autonomous vehicles is expected to revolutionize road transportation in the near future. Although large-scale numerical simulations and small-scale experiments have shown promising results, a comprehensive theoretical understanding to smooth traffic flow via autonomous vehicles is lacking. In this pape...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	117,177
2103.16207	Modelling and Control of a Knuckle Boom Crane	Cranes come in various sizes and designs to perform different tasks. Depending on their dynamic properties, they can be classified as gantry cranes and rotary cranes. In this paper we will focus on the so called 'knuckle boom' cranes which are among the most common types of rotary cranes. Compared with the other kinds ...	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	227,502
1504.03306	Virus Propagation in Multiple Profile Networks	Suppose we have a virus or one competing idea/product that propagates over a multiple profile (e.g., social) network. Can we predict what proportion of the network will actually get "infected" (e.g., spread the idea or buy the competing product), when the nodes of the network appear to have different sensitivity based ...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	42,018
2005.02392	Deep Constraint-based Propagation in Graph Neural Networks	The popularity of deep learning techniques renewed the interest in neural architectures able to process complex structures that can be represented using graphs, inspired by Graph Neural Networks (GNNs). We focus our attention on the originally proposed GNN model of Scarselli et al. 2009, which encodes the state of the ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	175,862
2410.13588	Cross-Domain Sequential Recommendation via Neural Process	Cross-Domain Sequential Recommendation (CDSR) is a hot topic in sequence-based user interest modeling, which aims at utilizing a single model to predict the next items for different domains. To tackle the CDSR, many methods are focused on domain overlapped users' behaviors fitting, which heavily relies on the same user...	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	false	499,597
2401.05669	ConcEPT: Concept-Enhanced Pre-Training for Language Models	Pre-trained language models (PLMs) have been prevailing in state-of-the-art methods for natural language processing, and knowledge-enhanced PLMs are further proposed to promote model performance in knowledge-intensive tasks. However, conceptual knowledge, one essential kind of knowledge for human cognition, still remai...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	420,869
1803.07314	Dual Polarized Modulation and Reception for Next Generation Mobile Satellite Communications	This paper presents the novel application of Polarized Modulation (PMod) for increasing the throughput in mobile satellite transmissions. One of the major drawbacks in mobile satellite communications is the fact that the power budget is often restrictive, making unaffordable to improve the spectral efficiency without a...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	93,019
1604.07806	Using Indirect Encoding of Multiple Brains to Produce Multimodal Behavior	An important challenge in neuroevolution is to evolve complex neural networks with multiple modes of behavior. Indirect encodings can potentially answer this challenge. Yet in practice, indirect encodings do not yield effective multimodal controllers. Thus, this paper introduces novel multimodal extensions to HyperNEAT...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	false	false	55,131
1904.10283	Modified Covariance Intersection for Data Fusion in Distributed Non-homogeneous Monitoring Systems Network	Monitoring networks contain monitoring nodes which observe an area of interest to detect any possible existing object and estimate its states. Each node has characteristics such as probability of detection and clutter density which may have different values for distinct nodes in non-homogeneous monitoring networks. Thi...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	128,595
1408.5845	Analysis of a Reduced-Communication Diffusion LMS Algorithm	In diffusion-based algorithms for adaptive distributed estimation, each node of an adaptive network estimates a target parameter vector by creating an intermediate estimate and then combining the intermediate estimates available within its closed neighborhood. We analyze the performance of a reduced-communication diffu...	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	true	35,587
2401.07591	Multimodal Crowd Counting with Pix2Pix GANs	Most state-of-the-art crowd counting methods use color (RGB) images to learn the density map of the crowd. However, these methods often struggle to achieve higher accuracy in densely crowded scenes with poor illumination. Recently, some studies have reported improvement in the accuracy of crowd counting models using a ...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	421,601
2312.10656	VidToMe: Video Token Merging for Zero-Shot Video Editing	Diffusion models have made significant advances in generating high-quality images, but their application to video generation has remained challenging due to the complexity of temporal motion. Zero-shot video editing offers a solution by utilizing pre-trained image diffusion models to translate source videos into new on...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	416,267
2409.10331	Research and Design of a Financial Intelligent Risk Control Platform Based on Big Data Analysis and Deep Machine Learning	In the financial field of the United States, the application of big data technology has become one of the important means for financial institutions to enhance competitiveness and reduce risks. The core objective of this article is to explore how to fully utilize big data technology to achieve complete integration of i...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	488,707
1911.11430	Independence Promoted Graph Disentangled Networks	We address the problem of disentangled representation learning with independent latent factors in graph convolutional networks (GCNs). The current methods usually learn node representation by describing its neighborhood as a perceptual whole in a holistic manner while ignoring the entanglement of the latent factors. Ho...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	155,126
2101.09512	Unsupervised clustering of series using dynamic programming	We are interested in clustering parts of a given single multi-variate series in an unsupervised manner. We would like to segment and cluster the series such that the resulting blocks present in each cluster are coherent with respect to a known model (e.g. physics model). Data points are said to be coherent if they can ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	216,622
1807.03027	Image Restoration Using Conditional Random Fields and Scale Mixtures of Gaussians	This paper proposes a general framework for internal patch-based image restoration based on Conditional Random Fields (CRF). Unlike related models based on Markov Random Fields (MRF), our approach explicitly formulates the posterior distribution for the entire image. The potential functions are taken as proportional to...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	102,409
1208.5281	Expected Supremum of a Random Linear Combination of Shifted Kernels	We address the expected supremum of a linear combination of shifts of the sinc kernel with random coefficients. When the coefficients are Gaussian, the expected supremum is of order \sqrt{\log n}, where n is the number of shifts. When the coefficients are uniformly bounded, the expected supremum is of order \log\log n....	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	18,262
2006.10032	Self-training Avoids Using Spurious Features Under Domain Shift	In unsupervised domain adaptation, existing theory focuses on situations where the source and target domains are close. In practice, conditional entropy minimization and pseudo-labeling work even when the domain shifts are much larger than those analyzed by existing theory. We identify and analyze one particular settin...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	182,748
2012.04809	Semi-Supervised Off Policy Reinforcement Learning	Reinforcement learning (RL) has shown great success in estimating sequential treatment strategies which take into account patient heterogeneity. However, health-outcome information, which is used as the reward for reinforcement learning methods, is often not well coded but rather embedded in clinical notes. Extracting ...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	210,575
2311.09329	A Comparative Analysis of Machine Learning Models for Early Detection of Hospital-Acquired Infections	As more and more infection-specific machine learning models are developed and planned for clinical deployment, simultaneously running predictions from different models may provide overlapping or even conflicting information. It is important to understand the concordance and behavior of parallel models in deployment. In...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	408,092
1903.02054	Size of Interventional Markov Equivalence Classes in Random DAG Models	Directed acyclic graph (DAG) models are popular for capturing causal relationships. From observational and interventional data, a DAG model can only be determined up to its \emph{interventional Markov equivalence class} (I-MEC). We investigate the size of MECs for random DAG models generated by uniformly sampling and o...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	123,405
2408.12259	How Safe is Your Safety Metric? Automatic Concatenation Tests for Metric Reliability	Consider a scenario where a harmfulness evaluation metric intended to filter unsafe responses from a Large Language Model. When applied to individual harmful prompt-response pairs, it correctly flags them as unsafe by assigning a high-risk score. Yet, if those same pairs are concatenated, the metrics decision unexpecte...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	482,657
2407.09189	Segmenting Medical Images with Limited Data	While computer vision has proven valuable for medical image segmentation, its application faces challenges such as limited dataset sizes and the complexity of effectively leveraging unlabeled images. To address these challenges, we present a novel semi-supervised, consistency-based approach termed the data-efficient me...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	472,487
2407.08377	Long-range Turbulence Mitigation: A Large-scale Dataset and A Coarse-to-fine Framework	Long-range imaging inevitably suffers from atmospheric turbulence with severe geometric distortions due to random refraction of light. The further the distance, the more severe the disturbance. Despite existing research has achieved great progress in tackling short-range turbulence, there is less attention paid to long...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	472,142
2405.20984	Bayesian Design Principles for Offline-to-Online Reinforcement Learning	Offline reinforcement learning (RL) is crucial for real-world applications where exploration can be costly or unsafe. However, offline learned policies are often suboptimal, and further online fine-tuning is required. In this paper, we tackle the fundamental dilemma of offline-to-online fine-tuning: if the agent remain...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	459,598
2501.12281	MoGERNN: An Inductive Traffic Predictor for Unobserved Locations in Dynamic Sensing Networks	Given a partially observed road network, how can we predict the traffic state of unobserved locations? While deep learning approaches show exceptional performance in traffic prediction, most assume sensors at all locations of interest, which is impractical due to financial constraints. Furthermore, these methods typica...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	526,242
1406.5824	VideoSET: Video Summary Evaluation through Text	In this paper we present VideoSET, a method for Video Summary Evaluation through Text that can evaluate how well a video summary is able to retain the semantic information contained in its original video. We observe that semantics is most easily expressed in words, and develop a text-based approach for the evaluation. ...	false	false	false	false	false	true	false	false	true	false	false	true	false	false	false	false	false	false	34,070
1904.12774	Routing Networks and the Challenges of Modular and Compositional Computation	Compositionality is a key strategy for addressing combinatorial complexity and the curse of dimensionality. Recent work has shown that compositional solutions can be learned and offer substantial gains across a variety of domains, including multi-task learning, language modeling, visual question answering, machine comp...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	true	false	false	129,222
1602.06215	Big Data Meets Telcos: A Proactive Caching Perspective	Mobile cellular networks are becoming increasingly complex to manage while classical deployment/optimization techniques and current solutions (i.e., cell densification, acquiring more spectrum, etc.) are cost-ineffective and thus seen as stopgaps. This calls for development of novel approaches that leverage recent adva...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	52,340
1703.08000	Weakly Supervised Object Localization Using Things and Stuff Transfer	We propose to help weakly supervised object localization for classes where location annotations are not available, by transferring things and stuff knowledge from a source set with available annotations. The source and target classes might share similar appearance (e.g. bear fur is similar to cat fur) or appear against...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	70,500
1511.08130	A Roadmap towards Machine Intelligence	The development of intelligent machines is one of the biggest unsolved challenges in computer science. In this paper, we propose some fundamental properties these machines should have, focusing in particular on communication and learning. We discuss a simple environment that could be used to incrementally teach a machi...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	49,504
2406.12406	Fast Rates for Bandit PAC Multiclass Classification	We study multiclass PAC learning with bandit feedback, where inputs are classified into one of $K$ possible labels and feedback is limited to whether or not the predicted labels are correct. Our main contribution is in designing a novel learning algorithm for the agnostic $(\varepsilon,\delta)$-PAC version of the probl...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	465,389
2412.03390	Enhancing Supply Chain Visibility with Generative AI: An Exploratory Case Study on Relationship Prediction in Knowledge Graphs	A key stumbling block in effective supply chain risk management for companies and policymakers is a lack of visibility on interdependent supply network relationships. Relationship prediction, also called link prediction is an emergent area of supply chain surveillance research that aims to increase the visibility of su...	false	true	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	513,941
2207.10448	An Efficient Spatio-Temporal Pyramid Transformer for Action Detection	The task of action detection aims at deducing both the action category and localization of the start and end moment for each action instance in a long, untrimmed video. While vision Transformers have driven the recent advances in video understanding, it is non-trivial to design an efficient architecture for action dete...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	309,272
1706.02932	Unsupervised learning of object frames by dense equivariant image labelling	One of the key challenges of visual perception is to extract abstract models of 3D objects and object categories from visual measurements, which are affected by complex nuisance factors such as viewpoint, occlusion, motion, and deformations. Starting from the recent idea of viewpoint factorization, we propose a new app...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	75,066
1611.05092	Partitioning Strategies and Task Allocation for Target-tracking with Multiple Guards in Polygonal Environments	This paper presents an algorithm to deploy a team of {\it free} guards equipped with omni-directional cameras for tracking a bounded speed intruder inside a simply-connected polygonal environment. The proposed algorithm partitions the environment into smaller polygons, and assigns a guard to each partition so that the ...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	63,948
2003.04956	SQUIRL: Robust and Efficient Learning from Video Demonstration of Long-Horizon Robotic Manipulation Tasks	Recent advances in deep reinforcement learning (RL) have demonstrated its potential to learn complex robotic manipulation tasks. However, RL still requires the robot to collect a large amount of real-world experience. To address this problem, recent works have proposed learning from expert demonstrations (LfD), particu...	false	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	167,708
2006.08949	Utility-Based Graph Summarization: New and Improved	A fundamental challenge in graph mining is the ever-increasing size of datasets. Graph summarization aims to find a compact representation resulting in faster algorithms and reduced storage needs. The flip side of graph summarization is the loss of utility which diminishes its usability. The key questions we address in...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	true	182,371
1611.04233	A New Recurrent Neural CRF for Learning Non-linear Edge Features	Conditional Random Field (CRF) and recurrent neural models have achieved success in structured prediction. More recently, there is a marriage of CRF and recurrent neural models, so that we can gain from both non-linear dense features and globally normalized CRF objective. These recurrent neural CRF models mainly focus ...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	63,815
2111.10549	An End-to-End Framework for Dynamic Crime Profiling of Places	Much effort is being made to ensure the safety of people. One of the main requirements of travellers and city administrators is to have knowledge of places that are more prone to criminal activities. To rate a place as a potential crime location, it needs the past crime history at that location. Such data is not easily...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	true	267,357
2211.05827	Development and Evaluation of the Institutionally Farmed Research On-line Repository and Management System (InFORMs) towards Knowledge-Sharing and Utilization	This paper presents the usability, acceptability and extent of compliance to ISO 25010:2011 of the developed project InFORMS. Key features that aid in the ease of use, access to, and management of the research resource emerged. From the responses, the developed application evidently suggests its usability and complianc...	false	false	false	false	false	true	false	false	false	false	false	false	false	true	false	false	true	false	329,686
2501.13132	A Hierarchical Reinforcement Learning Framework for Multi-UAV Combat Using Leader-Follower Strategy	Multi-UAV air combat is a complex task involving multiple autonomous UAVs, an evolving field in both aerospace and artificial intelligence. This paper aims to enhance adversarial performance through collaborative strategies. Previous approaches predominantly discretize the action space into predefined actions, limiting...	false	false	false	false	true	false	false	true	false	false	true	false	false	false	true	false	false	false	526,573
2004.06220	Embedded model discrepancy: A case study of Zika modeling	Mathematical models of epidemiological systems enable investigation of and predictions about potential disease outbreaks. However, commonly used models are often highly simplified representations of incredibly complex systems. Because of these simplifications, the model output, of say new cases of a disease over time, ...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	172,443
2302.03745	Structural Robustness of Complex Networks: A Survey of A Posteriori Measures	Network robustness is critical for various industrial and social networks against malicious attacks, which has various meanings in different research contexts and here it refers to the ability of a network to sustain its functionality when a fraction of the network fail to work due to attacks. The rapid development of ...	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	false	344,450
2308.04025	MSAC: Multiple Speech Attribute Control Method for Reliable Speech Emotion Recognition	Despite notable progress, speech emotion recognition (SER) remains challenging due to the intricate and ambiguous nature of speech emotion, particularly in wild world. While current studies primarily focus on recognition and generalization abilities, our research pioneers an investigation into the reliability of SER me...	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	384,260
2403.07842	Quantifying and Mitigating Privacy Risks for Tabular Generative Models	Synthetic data from generative models emerges as the privacy-preserving data-sharing solution. Such a synthetic data set shall resemble the original data without revealing identifiable private information. The backbone technology of tabular synthesizers is rooted in image generative models, ranging from Generative Adve...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	437,047
1705.09587	Enhancement of SSD by concatenating feature maps for object detection	We propose an object detection method that improves the accuracy of the conventional SSD (Single Shot Multibox Detector), which is one of the top object detection algorithms in both aspects of accuracy and speed. The performance of a deep network is known to be improved as the number of feature maps increases. However,...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	74,229
2203.06760	CMKD: CNN/Transformer-Based Cross-Model Knowledge Distillation for Audio Classification	Audio classification is an active research area with a wide range of applications. Over the past decade, convolutional neural networks (CNNs) have been the de-facto standard building block for end-to-end audio classification models. Recently, neural networks based solely on self-attention mechanisms such as the Audio S...	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	285,215
2011.00289	Smoothly Adaptively Centered Ridge Estimator	With a focus on linear models with smooth functional covariates, we propose a penalization framework (SACR) based on the nonzero centered ridge, where the center of the penalty is optimally reweighted in a supervised way, starting from the ordinary ridge solution as the initial centerfunction. In particular, we introdu...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	204,157
2005.13438	InteractionNet: Modeling and Explaining of Noncovalent Protein-Ligand Interactions with Noncovalent Graph Neural Network and Layer-Wise Relevance Propagation	Expanding the scope of graph-based, deep-learning models to noncovalent protein-ligand interactions has earned increasing attention in structure-based drug design. Modeling the protein-ligand interactions with graph neural networks (GNNs) has experienced difficulties in the conversion of protein-ligand complex structur...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	179,009
1604.06743	Latent Contextual Bandits and their Application to Personalized Recommendations for New Users	Personalized recommendations for new users, also known as the cold-start problem, can be formulated as a contextual bandit problem. Existing contextual bandit algorithms generally rely on features alone to capture user variability. Such methods are inefficient in learning new users' interests. In this paper we propose ...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	54,986
2201.02485	Automated Dissipation Control for Turbulence Simulation with Shell Models	The application of machine learning (ML) techniques, especially neural networks, has seen tremendous success at processing images and language. This is because we often lack formal models to understand visual and audio input, so here neural networks can unfold their abilities as they can model solely from data. In the ...	false	true	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	274,557
2202.05254	Deep Learning in Random Neural Fields: Numerical Experiments via Neural Tangent Kernel	A biological neural network in the cortex forms a neural field. Neurons in the field have their own receptive fields, and connection weights between two neurons are random but highly correlated when they are in close proximity in receptive fields. In this paper, we investigate such neural fields in a multilayer archite...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	279,818
2410.11064	Parsing altered brain connectivity in neurodevelopmental disorders by integrating graph-based normative modeling and deep generative networks	Divergent brain connectivity is thought to underlie the behavioral and cognitive symptoms observed in many neurodevelopmental disorders. Quantifying divergence from neurotypical connectivity patterns offers a promising pathway to inform diagnosis and therapeutic interventions. While advanced neuroimaging techniques, su...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	498,364
2308.04814	Neuro-Symbolic RDF and Description Logic Reasoners: The State-Of-The-Art and Challenges	Ontologies are used in various domains, with RDF and OWL being prominent standards for ontology development. RDF is favored for its simplicity and flexibility, while OWL enables detailed domain knowledge representation. However, as ontologies grow larger and more expressive, reasoning complexity increases, and traditio...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	384,571
2502.02768	Planning with affordances: Integrating learned affordance models and symbolic planning	Intelligent agents working in real-world environments must be able to learn about the environment and its capabilities which enable them to take actions to change to the state of the world to complete a complex multi-step task in a photorealistic environment. Learning about the environment is especially important to pe...	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	false	530,461
1904.01538	Spatial Attentive Single-Image Deraining with a High Quality Real Rain Dataset	Removing rain streaks from a single image has been drawing considerable attention as rain streaks can severely degrade the image quality and affect the performance of existing outdoor vision tasks. While recent CNN-based derainers have reported promising performances, deraining remains an open problem for two reasons. ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	126,164
1702.02471	Identifiability and parameter estimation of the single particle lithium-ion battery model	This paper investigates the identifiability and estimation of the parameters of the single particle model (SPM) for lithium-ion battery simulation. Identifiability is addressed both in principle and in practice. The approach begins by grouping parameters and partially non-dimensionalising the SPM to determine the maxim...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	67,983
2302.11939	One Fits All:Power General Time Series Analysis by Pretrained LM	Although we have witnessed great success of pre-trained models in natural language processing (NLP) and computer vision (CV), limited progress has been made for general time series analysis. Unlike NLP and CV where a unified model can be used to perform different tasks, specially designed approach still dominates in ea...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	347,367
2210.13464	Graph Reinforcement Learning-based CNN Inference Offloading in Dynamic Edge Computing	This paper studies the computational offloading of CNN inference in dynamic multi-access edge computing (MEC) networks. To address the uncertainties in communication time and Edge servers' available capacity, we use early-exit mechanism to terminate the computation earlier to meet the deadline of inference tasks. We de...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	true	326,180
1412.6493	A la Carte - Learning Fast Kernels	Kernel methods have great promise for learning rich statistical representations of large modern datasets. However, compared to neural networks, kernel methods have been perceived as lacking in scalability and flexibility. We introduce a family of fast, flexible, lightly parametrized and general purpose kernel learning ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	38,639
2305.10272	Demonstrating Large-Scale Package Manipulation via Learned Metrics of Pick Success	Automating warehouse operations can reduce logistics overhead costs, ultimately driving down the final price for consumers, increasing the speed of delivery, and enhancing the resiliency to workforce fluctuations. The past few years have seen increased interest in automating such repeated tasks but mostly in controlled...	false	false	false	false	true	false	true	true	false	false	false	false	false	false	false	false	false	false	364,983
2409.07401	Convergence of continuous-time stochastic gradient descent with applications to linear deep neural networks	We study a continuous-time approximation of the stochastic gradient descent process for minimizing the expected loss in learning problems. The main results establish general sufficient conditions for the convergence, extending the results of Chatterjee (2022) established for (nonstochastic) gradient descent. We show ho...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	487,501
2311.02191	SparsePoser: Real-time Full-body Motion Reconstruction from Sparse Data	Accurate and reliable human motion reconstruction is crucial for creating natural interactions of full-body avatars in Virtual Reality (VR) and entertainment applications. As the Metaverse and social applications gain popularity, users are seeking cost-effective solutions to create full-body animations that are compara...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	405,329
2305.08877	M$^2$DAR: Multi-View Multi-Scale Driver Action Recognition with Vision Transformer	Ensuring traffic safety and preventing accidents is a critical goal in daily driving, where the advancement of computer vision technologies can be leveraged to achieve this goal. In this paper, we present a multi-view, multi-scale framework for naturalistic driving action recognition and localization in untrimmed video...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	364,443

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