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
1102.2615	Guaranteeing Convergence of Iterative Skewed Voting Algorithms for Image Segmentation	In this paper we provide rigorous proof for the convergence of an iterative voting-based image segmentation algorithm called Active Masks. Active Masks (AM) was proposed to solve the challenging task of delineating punctate patterns of cells from fluorescence microscope images. Each iteration of AM consists of a linear...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	9,145
2411.13886	CLFace: A Scalable and Resource-Efficient Continual Learning Framework for Lifelong Face Recognition	An important aspect of deploying face recognition (FR) algorithms in real-world applications is their ability to learn new face identities from a continuous data stream. However, the online training of existing deep neural network-based FR algorithms, which are pre-trained offline on large-scale stationary datasets, en...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	509,958
1510.02533	New Optimisation Methods for Machine Learning	A thesis submitted for the degree of Doctor of Philosophy of The Australian National University. In this work we introduce several new optimisation methods for problems in machine learning. Our algorithms broadly fall into two categories: optimisation of finite sums and of graph structured objectives. The finite sum ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	47,728
1712.07199	Cognitive Database: A Step towards Endowing Relational Databases with Artificial Intelligence Capabilities	We propose Cognitive Databases, an approach for transparently enabling Artificial Intelligence (AI) capabilities in relational databases. A novel aspect of our design is to first view the structured data source as meaningful unstructured text, and then use the text to build an unsupervised neural network model using a ...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	true	true	false	87,007
2004.09745	Automatically Identifying Political Ads on Facebook: Towards Understanding of Manipulation via User Targeting	The reports of Russian interference in the 2016 United States elections brought into the center of public attention concerns related to the ability of foreign actors to increase social discord and take advantage of personal user data for political purposes. It has raised questions regarding the ways and the extent to w...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	173,441
1905.00586	Estimating Kullback-Leibler Divergence Using Kernel Machines	Recently, a method called the Mutual Information Neural Estimator (MINE) that uses neural networks has been proposed to estimate mutual information and more generally the Kullback-Leibler (KL) divergence between two distributions. The method uses the Donsker-Varadhan representation to arrive at the estimate of the KL d...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	129,516
2211.03004	Bringing Online Egocentric Action Recognition into the wild	To enable a safe and effective human-robot cooperation, it is crucial to develop models for the identification of human activities. Egocentric vision seems to be a viable solution to solve this problem, and therefore many works provide deep learning solutions to infer human actions from first person videos. However, al...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	328,784
2312.06282	Rank-Metric Codes and Their Parameters	We present the theory of linear rank-metric codes from the point of view of their fundamental parameters. These are: the minimum rank distance, the rank distribution, the maximum rank, the covering radius, and the field size. The focus of this chapter is on the interplay among these parameters and on their significance...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	414,452
2303.13917	Convolutional Neural Networks for the classification of glitches in gravitational-wave data streams	We investigate the use of Convolutional Neural Networks (including the modern ConvNeXt network family) to classify transient noise signals (i.e.~glitches) and gravitational waves in data from the Advanced LIGO detectors. First, we use models with a supervised learning approach, both trained from scratch using the Gravi...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	353,884
2306.16993	Weight Compander: A Simple Weight Reparameterization for Regularization	Regularization is a set of techniques that are used to improve the generalization ability of deep neural networks. In this paper, we introduce weight compander (WC), a novel effective method to improve generalization by reparameterizing each weight in deep neural networks using a nonlinear function. It is a general, in...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	376,556
1802.08417	Geometric Lower Bounds for Distributed Parameter Estimation under Communication Constraints	We consider parameter estimation in distributed networks, where each sensor in the network observes an independent sample from an underlying distribution and has $k$ bits to communicate its sample to a centralized processor which computes an estimate of a desired parameter. We develop lower bounds for the minimax risk ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	91,104
2109.07717	R-PCC: A Baseline for Range Image-based Point Cloud Compression	In autonomous vehicles or robots, point clouds from LiDAR can provide accurate depth information of objects compared with 2D images, but they also suffer a large volume of data, which is inconvenient for data storage or transmission. In this paper, we propose a Range image-based Point Cloud Compression method, R-PCC, w...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	255,623
2201.12648	Private Boosted Decision Trees via Smooth Re-Weighting	Protecting the privacy of people whose data is used by machine learning algorithms is important. Differential Privacy is the appropriate mathematical framework for formal guarantees of privacy, and boosted decision trees are a popular machine learning technique. So we propose and test a practical algorithm for boosting...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	true	277,721
1503.07795	Multi-Labeled Classification of Demographic Attributes of Patients: a case study of diabetics patients	Automated learning of patients demographics can be seen as multi-label problem where a patient model is based on different race and gender groups. The resulting model can be further integrated into Privacy-Preserving Data Mining, where it can be used to assess risk of identification of different patient groups. Our pro...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	41,516
2407.19468	MVPbev: Multi-view Perspective Image Generation from BEV with Test-time Controllability and Generalizability	This work aims to address the multi-view perspective RGB generation from text prompts given Bird-Eye-View(BEV) semantics. Unlike prior methods that neglect layout consistency, lack the ability to handle detailed text prompts, or are incapable of generalizing to unseen view points, MVPbev simultaneously generates cross-...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	476,799
2002.08500	Processing topical queries on images of historical newspaper pages	Historical newspapers are a source of research for the human and social sciences. However, these image collections are difficult to read by machine due to the low quality of the print, the lack of standardization of the pages in addition to the low quality photograph of some files. This paper presents the processing mo...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	164,772
2011.08837	Dominant Z-Eigenpairs of Tensor Kronecker Products are Decoupled and Applications to Higher-Order Graph Matching	Tensor Kronecker products, the natural generalization of the matrix Kronecker product, are independently emerging in multiple research communities. Like their matrix counterpart, the tensor generalization gives structure for implicit multiplication and factorization theorems. We present a theorem that decouples the dom...	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	false	false	207,012
1801.09005	A Two-point Method for PTZ Camera Calibration in Sports	Calibrating narrow field of view soccer cameras is challenging because there are very few field markings in the image. Unlike previous solutions, we propose a two-point method, which requires only two point correspondences given the prior knowledge of base location and orientation of a pan-tilt-zoom (PTZ) camera. We de...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	89,023
2401.13511	Tissue Cross-Section and Pen Marking Segmentation in Whole Slide Images	Tissue segmentation is a routine preprocessing step to reduce the computational cost of whole slide image (WSI) analysis by excluding background regions. Traditional image processing techniques are commonly used for tissue segmentation, but often require manual adjustments to parameter values for atypical cases, fail t...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	423,751
2204.09410	Generating 3D Molecules for Target Protein Binding	A fundamental problem in drug discovery is to design molecules that bind to specific proteins. To tackle this problem using machine learning methods, here we propose a novel and effective framework, known as GraphBP, to generate 3D molecules that bind to given proteins by placing atoms of specific types and locations t...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	292,428
2406.03680	Meta-learning for Positive-unlabeled Classification	We propose a meta-learning method for positive and unlabeled (PU) classification, which improves the performance of binary classifiers obtained from only PU data in unseen target tasks. PU learning is an important problem since PU data naturally arise in real-world applications such as outlier detection and information...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	461,333
2312.11057	DataElixir: Purifying Poisoned Dataset to Mitigate Backdoor Attacks via Diffusion Models	Dataset sanitization is a widely adopted proactive defense against poisoning-based backdoor attacks, aimed at filtering out and removing poisoned samples from training datasets. However, existing methods have shown limited efficacy in countering the ever-evolving trigger functions, and often leading to considerable deg...	false	false	false	false	true	false	false	false	false	false	false	true	true	false	false	false	false	false	416,432
1502.04381	Random Walks, Markov Processes and the Multiscale Modular Organization of Complex Networks	Most methods proposed to uncover communities in complex networks rely on combinatorial graph properties. Usually an edge-counting quality function, such as modularity, is optimized over all partitions of the graph compared against a null random graph model. Here we introduce a systematic dynamical framework to design a...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	40,262
2405.10516	Language Models can Evaluate Themselves via Probability Discrepancy	In this paper, we initiate our discussion by demonstrating how Large Language Models (LLMs), when tasked with responding to queries, display a more even probability distribution in their answers if they are more adept, as opposed to their less skilled counterparts. Expanding on this foundational insight, we propose a n...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	454,788
1411.3935	Overlapping Community Discovery Methods: A Survey	The detection of overlapping communities is a challenging problem which is gaining increasing interest in recent years because of the natural attitude of individuals, observed in real-world networks, to participate in multiple groups at the same time. This review gives a description of the main proposals in the field. ...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	37,555
1904.10079	The MineRL 2019 Competition on Sample Efficient Reinforcement Learning using Human Priors	Though deep reinforcement learning has led to breakthroughs in many difficult domains, these successes have required an ever-increasing number of samples. As state-of-the-art reinforcement learning (RL) systems require an exponentially increasing number of samples, their development is restricted to a continually shrin...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	128,544
1506.01367	A Nearly Optimal and Agnostic Algorithm for Properly Learning a Mixture of k Gaussians, for any Constant k	Learning a Gaussian mixture model (GMM) is a fundamental problem in machine learning, learning theory, and statistics. One notion of learning a GMM is proper learning: here, the goal is to find a mixture of $k$ Gaussians $\mathcal{M}$ that is close to the density $f$ of the unknown distribution from which we draw sampl...	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	true	43,794
2401.00110	Diffusion Model with Perceptual Loss	Diffusion models without guidance tend to generate unrealistic samples, yet the cause of this problem is not fully studied. Our analysis suggests that the loss objective plays an important role in shaping the learned distribution and the common mean squared error loss is not optimal. We hypothesize that a better loss o...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	418,882
cs/0306066	The COMPASS Event Store in 2002	COMPASS, the fixed-target experiment at CERN studying the structure of the nucleon and spectroscopy, collected over 260 TB during summer 2002 run. All these data, together with reconstructed events information, were put from the beginning in a database infrastructure based on Objectivity/DB and on the hierarchical stor...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	537,880
2306.16009	Accelerating Transducers through Adjacent Token Merging	Recent end-to-end automatic speech recognition (ASR) systems often utilize a Transformer-based acoustic encoder that generates embedding at a high frame rate. However, this design is inefficient, particularly for long speech signals due to the quadratic computation of self-attention. To address this, we propose a new m...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	376,244
2308.09812	Reliability and Delay Analysis of 3-Dimensional Networks with Multi-Connectivity: Satellite, HAPs, and Cellular Communications	Aerial vehicles (AVs) such as electric vertical take-off and landing (eVTOL) aircraft make aerial passenger transportation a reality in urban environments. However, their communication connectivity is still under research to realize their safe and full-scale operation. This paper envisages a multi-connectivity (MC) ena...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	386,435
2203.01844	Stochastic Model Predictive Control using Initial State Optimization	We propose a stochastic MPC scheme using an optimization over the initial state for the predicted trajectory. Considering linear discrete-time systems under unbounded additive stochastic disturbances subject to chance constraints, we use constraint tightening based on probabilistic reachable sets to design the MPC. The...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	283,527
1705.01359	FOIL it! Find One mismatch between Image and Language caption	In this paper, we aim to understand whether current language and vision (LaVi) models truly grasp the interaction between the two modalities. To this end, we propose an extension of the MSCOCO dataset, FOIL-COCO, which associates images with both correct and "foil" captions, that is, descriptions of the image that are ...	false	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	true	72,832
2110.04622	Does Preprocessing Help Training Over-parameterized Neural Networks?	Deep neural networks have achieved impressive performance in many areas. Designing a fast and provable method for training neural networks is a fundamental question in machine learning. The classical training method requires paying $\Omega(mnd)$ cost for both forward computation and backward computation, where $m$ is...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	259,966
2407.13307	Conformal Performance Range Prediction for Segmentation Output Quality Control	Recent works have introduced methods to estimate segmentation performance without ground truth, relying solely on neural network softmax outputs. These techniques hold potential for intuitive output quality control. However, such performance estimates rely on calibrated softmax outputs, which is often not the case in m...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	474,329
2501.18793	OT-Transformer: A Continuous-time Transformer Architecture with Optimal Transport Regularization	Transformers have achieved state-of-the-art performance in numerous tasks. In this paper, we propose a continuous-time formulation of transformers. Specifically, we consider a dynamical system whose governing equation is parametrized by transformer blocks. We leverage optimal transport theory to regularize the training...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	528,857
2205.03719	Odor Descriptor Understanding through Prompting	Embeddings from contemporary natural language processing (NLP) models are commonly used as numerical representations for words or sentences. However, odor descriptor words, like "leather" or "fruity", vary significantly between their commonplace usage and their olfactory usage, as a result traditional methods for gener...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	295,385
2410.19580	Hybrid Memetic Search for Electric Vehicle Routing with Time Windows, Simultaneous Pickup-Delivery, and Partial Recharges	With growing environmental concerns, electric vehicles for logistics have gained significant attention within the computational intelligence community in recent years. This work addresses an emerging and significant extension of the electric vehicle routing problem (EVRP), namely EVRP with time windows, simultaneous pi...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	502,376
1511.01440	A low-complexity 2D signal space diversity solution for future broadcasting systems	-DVB-T2 was the first industrial standard deploying rotated and cyclic Q delayed (RCQD)modulation to improve performance over fading channels. This enablesimportantgains compared toconventional quadrature amplitude modulations(QAM) under severe channel conditions.However, the corresponding demodulation complexitystill ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	48,503
1805.07502	On Deep Ensemble Learning from a Function Approximation Perspective	In this paper, we propose to provide a general ensemble learning framework based on deep learning models. Given a group of unit models, the proposed deep ensemble learning framework will effectively combine their learning results via a multilayered ensemble model. In the case when the unit model mathematical mappings a...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	true	false	false	97,847
2110.10522	CIM-PPO:Proximal Policy Optimization with Liu-Correntropy Induced Metric	As a popular Deep Reinforcement Learning (DRL) algorithm, Proximal Policy Optimization (PPO) has demonstrated remarkable efficacy in numerous complex tasks. According to the penalty mechanism in a surrogate, PPO can be classified into PPO with KL divergence (PPO-KL) and PPO with Clip (PPO-Clip). In this paper, we analy...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	262,181
2401.12351	Performance Analysis of 6G Multiuser Massive MIMO-OFDM THz Wireless Systems with Hybrid Beamforming under Intercarrier Interference	6G networks are expected to provide more diverse capabilities than their predecessors and are likely to support applications beyond current mobile applications, such as virtual and augmented reality (VR/AR), AI, and the Internet of Things (IoT). In contrast to typical multiple-input multiple-output (MIMO) systems, THz ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	423,345
cs/0701062	Network Coding over a Noisy Relay : a Belief Propagation Approach	In recent years, network coding has been investigated as a method to obtain improvements in wireless networks. A typical assumption of previous work is that relay nodes performing network coding can decode the messages from sources perfectly. On a simple relay network, we design a scheme to obtain network coding gain e...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	540,040
2203.02867	Diffusion Maps : Using the Semigroup Property for Parameter Tuning	Diffusion maps (DM) constitute a classic dimension reduction technique, for data lying on or close to a (relatively) low-dimensional manifold embedded in a much larger dimensional space. The DM procedure consists in constructing a spectral parametrization for the manifold from simulated random walks or diffusion paths ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	283,891
2003.13135	Learning Latent Causal Structures with a Redundant Input Neural Network	Most causal discovery algorithms find causal structure among a set of observed variables. Learning the causal structure among latent variables remains an important open problem, particularly when using high-dimensional data. In this paper, we address a problem for which it is known that inputs cause outputs, and these ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	true	false	false	170,116
1612.02485	Comparative Evaluation of Big-Data Systems on Scientific Image Analytics Workloads	Scientific discoveries are increasingly driven by analyzing large volumes of image data. Many new libraries and specialized database management systems (DBMSs) have emerged to support such tasks. It is unclear, however, how well these systems support real-world image analysis use cases, and how performant are the image...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	65,231
1706.04082	Distributed Submodular Maximization with Limited Information	We consider a class of distributed submodular maximization problems in which each agent must choose a single strategy from its strategy set. The global objective is to maximize a submodular function of the strategies chosen by each agent. When choosing a strategy, each agent has access to only a limited number of other...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	true	75,276
2208.02025	OLLIE: Derivation-based Tensor Program Optimizer	Boosting the runtime performance of deep neural networks (DNNs) is critical due to their wide adoption in real-world tasks. Existing approaches to optimizing the tensor algebra expression of a DNN only consider expressions representable by a fixed set of predefined operators, missing possible optimization opportunities...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	311,356
2410.11275	Shallow diffusion networks provably learn hidden low-dimensional structure	Diffusion-based generative models provide a powerful framework for learning to sample from a complex target distribution. The remarkable empirical success of these models applied to high-dimensional signals, including images and video, stands in stark contrast to classical results highlighting the curse of dimensionali...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	498,477
2411.08861	Interaction Testing in Variation Analysis	Relationships of cause and effect are of prime importance for explaining scientific phenomena. Often, rather than just understanding the effects of causes, researchers also wish to understand how a cause $X$ affects an outcome $Y$ mechanistically -- i.e., what are the causal pathways that are activated between $X$ and ...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	508,034
1411.4695	Feedback Solution to Optimal Switching Problems with Switching Cost	The problem of optimal switching between nonlinear autonomous subsystems is investigated in this study where the objective is not only bringing the states to close to the desired point, but also adjusting the switching pattern, in the sense of penalizing switching occurrences and assigning different preferences to util...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	37,664
2407.15312	FMDNN: A Fuzzy-guided Multi-granular Deep Neural Network for Histopathological Image Classification	Histopathological image classification constitutes a pivotal task in computer-aided diagnostics. The precise identification and categorization of histopathological images are of paramount significance for early disease detection and treatment. In the diagnostic process of pathologists, a multi-tiered approach is typica...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	475,125
2310.10667	Enhancing Network Resilience through Machine Learning-powered Graph Combinatorial Optimization: Applications in Cyber Defense and Information Diffusion	With the burgeoning advancements of computing and network communication technologies, network infrastructures and their application environments have become increasingly complex. Due to the increased complexity, networks are more prone to hardware faults and highly susceptible to cyber-attacks. Therefore, for rapidly g...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	400,337
1510.05963	Semantic, Cognitive, and Perceptual Computing: Advances toward Computing for Human Experience	The World Wide Web continues to evolve and serve as the infrastructure for carrying massive amounts of multimodal and multisensory observations. These observations capture various situations pertinent to people's needs and interests along with all their idiosyncrasies. To support human-centered computing that empower p...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	48,070
2104.03693	Learning specialized activation functions with the Piecewise Linear Unit	The choice of activation functions is crucial for modern deep neural networks. Popular hand-designed activation functions like Rectified Linear Unit(ReLU) and its variants show promising performance in various tasks and models. Swish, the automatically discovered activation function, has been proposed and outperforms R...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	229,147
1905.06514	ReshapeGAN: Object Reshaping by Providing A Single Reference Image	The aim of this work is learning to reshape the object in an input image to an arbitrary new shape, by just simply providing a single reference image with an object instance in the desired shape. We propose a new Generative Adversarial Network (GAN) architecture for such an object reshaping problem, named ReshapeGAN. T...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	131,016
1912.02651	Effect of Imbalanced Datasets on Security of Industrial IoT Using Machine Learning	Machine learning algorithms have been shown to be suitable for securing platforms for IT systems. However, due to the fundamental differences between the industrial internet of things (IIoT) and regular IT networks, a special performance review needs to be considered. The vulnerabilities and security requirements of II...	false	false	false	false	false	false	true	false	false	false	true	false	true	false	false	false	true	false	156,405
2103.00191	FisheyeSuperPoint: Keypoint Detection and Description Network for Fisheye Images	Keypoint detection and description is a commonly used building block in computer vision systems particularly for robotics and autonomous driving. However, the majority of techniques to date have focused on standard cameras with little consideration given to fisheye cameras which are commonly used in urban driving and a...	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	222,182
2307.05126	Enhancing Continuous Time Series Modelling with a Latent ODE-LSTM Approach	Due to their dynamic properties such as irregular sampling rate and high-frequency sampling, Continuous Time Series (CTS) are found in many applications. Since CTS with irregular sampling rate are difficult to model with standard Recurrent Neural Networks (RNNs), RNNs have been generalised to have continuous-time hidde...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	378,630
2405.08973	An adaptive approach to Bayesian Optimization with switching costs	We investigate modifications to Bayesian Optimization for a resource-constrained setting of sequential experimental design where changes to certain design variables of the search space incur a switching cost. This models the scenario where there is a trade-off between evaluating more while maintaining the same setup, o...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	454,256
1802.06502	EA-CG: An Approximate Second-Order Method for Training Fully-Connected Neural Networks	For training fully-connected neural networks (FCNNs), we propose a practical approximate second-order method including: 1) an approximation of the Hessian matrix and 2) a conjugate gradient (CG) based method. Our proposed approximate Hessian matrix is memory-efficient and can be applied to any FCNNs where the activatio...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	90,693
2502.00675	ReFoRCE: A Text-to-SQL Agent with Self-Refinement, Format Restriction, and Column Exploration	Text-to-SQL systems have unlocked easier access to critical data insights by enabling natural language queries over structured databases. However, deploying such systems in enterprise environments remains challenging due to factors such as large, complex schemas (> 3000 columns), diverse SQL dialects (e.g., BigQuery, S...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	529,491
1706.08162	Automated text summarisation and evidence-based medicine: A survey of two domains	The practice of evidence-based medicine (EBM) urges medical practitioners to utilise the latest research evidence when making clinical decisions. Because of the massive and growing volume of published research on various medical topics, practitioners often find themselves overloaded with information. As such, natural l...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	75,952
1405.3396	Reducing Dueling Bandits to Cardinal Bandits	We present algorithms for reducing the Dueling Bandits problem to the conventional (stochastic) Multi-Armed Bandits problem. The Dueling Bandits problem is an online model of learning with ordinal feedback of the form "A is preferred to B" (as opposed to cardinal feedback like "A has value 2.5"), giving it wide applica...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	33,087
2109.14922	D{\'e}composition et analyse de trac{\'e}s EMG pour aider au diagnostic des maladies neuromusculaires	The electromyogram (EMG) in needle detection represents one of the steps of the electroneuromyogram (ENMG), an examination commonly performed in neurology. By inserting a needle into a muscle and studying the contraction during effort, the EMG provides extremely useful information on the functioning of the neuromuscula...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	258,116
1906.09972	Classical Music Prediction and Composition by means of Variational Autoencoders	This paper proposes a new model for music prediction based on Variational Autoencoders (VAEs). In this work, VAEs are used in a novel way in order to address two different problems: music representation into the latent space, and using this representation to make predictions of the future values of the musical piece. T...	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	true	false	false	136,322
2402.14034	AgentScope: A Flexible yet Robust Multi-Agent Platform	With the rapid advancement of Large Language Models (LLMs), significant progress has been made in multi-agent applications. However, the complexities in coordinating agents' cooperation and LLMs' erratic performance pose notable challenges in developing robust and efficient multi-agent applications. To tackle these cha...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	true	false	false	false	431,508
1710.11190	Manipulation Planning under Changing External Forces	In this work, we present a manipulation planning algorithm for a robot to keep an object stable under changing external forces. We particularly focus on the case where a human may be applying forceful operations, e.g. cutting or drilling, on an object that the robot is holding. The planner produces an efficient plan by...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	83,537
1909.12289	Attention Forcing for Sequence-to-sequence Model Training	Auto-regressive sequence-to-sequence models with attention mechanism have achieved state-of-the-art performance in many tasks such as machine translation and speech synthesis. These models can be difficult to train. The standard approach, teacher forcing, guides a model with reference output history during training. Th...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	147,083
2012.07828	Robustness Threats of Differential Privacy	Differential privacy (DP) is a gold-standard concept of measuring and guaranteeing privacy in data analysis. It is well-known that the cost of adding DP to deep learning model is its accuracy. However, it remains unclear how it affects robustness of the model. Standard neural networks are not robust to different input ...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	211,588
2103.02913	Quantifying identifiability to choose and audit $\epsilon$ in differentially private deep learning	Differential privacy allows bounding the influence that training data records have on a machine learning model. To use differential privacy in machine learning, data scientists must choose privacy parameters $(\epsilon,\delta)$. Choosing meaningful privacy parameters is key, since models trained with weak privacy param...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	223,112
1904.12012	RevealNet: Seeing Behind Objects in RGB-D Scans	During 3D reconstruction, it is often the case that people cannot scan each individual object from all views, resulting in missing geometry in the captured scan. This missing geometry can be fundamentally limiting for many applications, e.g., a robot needs to know the unseen geometry to perform a precise grasp on an ob...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	128,993
2009.05689	Feedback Control Methods for a Single Machine Infinite Bus System	In this manuscript, we present a high-fidelity physics-based truth model of a Single Machine Infinite Bus (SMIB) system. We also present reduced-order control-oriented nonlinear and linear models of a synchronous generator-turbine system connected to a power grid. The reduced-order control-oriented models are next used...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	195,392
2308.01525	VisAlign: Dataset for Measuring the Degree of Alignment between AI and Humans in Visual Perception	AI alignment refers to models acting towards human-intended goals, preferences, or ethical principles. Given that most large-scale deep learning models act as black boxes and cannot be manually controlled, analyzing the similarity between models and humans can be a proxy measure for ensuring AI safety. In this paper, w...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	383,276
2110.10064	Idiomatic Expression Identification using Semantic Compatibility	Idiomatic expressions are an integral part of natural language and constantly being added to a language. Owing to their non-compositionality and their ability to take on a figurative or literal meaning depending on the sentential context, they have been a classical challenge for NLP systems. To address this challenge, ...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	262,025
1808.01454	T2Net: Synthetic-to-Realistic Translation for Solving Single-Image Depth Estimation Tasks	Current methods for single-image depth estimation use training datasets with real image-depth pairs or stereo pairs, which are not easy to acquire. We propose a framework, trained on synthetic image-depth pairs and unpaired real images, that comprises an image translation network for enhancing realism of input images, ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	104,570
1701.02357	Son of Zorn's Lemma: Targeted Style Transfer Using Instance-aware Semantic Segmentation	Style transfer is an important task in which the style of a source image is mapped onto that of a target image. The method is useful for synthesizing derivative works of a particular artist or specific painting. This work considers targeted style transfer, in which the style of a template image is used to alter only pa...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	66,538
2012.03023	Volumetric Occupancy Mapping With Probabilistic Depth Completion for Robotic Navigation	In robotic applications, a key requirement for safe and efficient motion planning is the ability to map obstacle-free space in unknown, cluttered 3D environments. However, commodity-grade RGB-D cameras commonly used for sensing fail to register valid depth values on shiny, glossy, bright, or distant surfaces, leading t...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	209,957
1912.06269	Learning and Optimization with Bayesian Hybrid Models	Bayesian hybrid models fuse physics-based insights with machine learning constructs to correct for systematic bias. In this paper, we compare Bayesian hybrid models against physics-based glass-box and Gaussian process black-box surrogate models. We consider ballistic firing as an illustrative case study for a Bayesian ...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	157,306
2307.03565	MALIBO: Meta-learning for Likelihood-free Bayesian Optimization	Bayesian optimization (BO) is a popular method to optimize costly black-box functions. While traditional BO optimizes each new target task from scratch, meta-learning has emerged as a way to leverage knowledge from related tasks to optimize new tasks faster. However, existing meta-learning BO methods rely on surrogate ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	378,080
1903.10040	Safe Learning-Based Control of Stochastic Jump Linear Systems: a Distributionally Robust Approach	We consider the problem of designing control laws for stochastic jump linear systems where the disturbances are drawn randomly from a finite sample space according to an unknown distribution, which is estimated from a finite sample of i.i.d. observations. We adopt a distributionally robust approach to compute a mean-sq...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	125,191
1401.5934	Accelerated Assistant to SubOptimum Receiver for Multi Carrier Code Division Multiple Access System	The Multiple Input Multiple output system are considered to be the strongest candidate for the maximum utilization of available bandwidth. In this paper, the MIMO system with the combination of Multi-Carrier Code Division Multiple Access and Space Time Coding using the Alamoutis scheme is considered. A Genetic Algorith...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	30,273
1206.6262	Scaling Life-long Off-policy Learning	We pursue a life-long learning approach to artificial intelligence that makes extensive use of reinforcement learning algorithms. We build on our prior work with general value functions (GVFs) and the Horde architecture. GVFs have been shown able to represent a wide variety of facts about the world's dynamics that may ...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	16,905
2206.04456	Choosing Answers in $\varepsilon$-Best-Answer Identification for Linear Bandits	In pure-exploration problems, information is gathered sequentially to answer a question on the stochastic environment. While best-arm identification for linear bandits has been extensively studied in recent years, few works have been dedicated to identifying one arm that is $\varepsilon$-close to the best one (and not ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	301,635
2103.12926	Beyond Visual Attractiveness: Physically Plausible Single Image HDR Reconstruction for Spherical Panoramas	HDR reconstruction is an important task in computer vision with many industrial needs. The traditional approaches merge multiple exposure shots to generate HDRs that correspond to the physical quantity of illuminance of the scene. However, the tedious capturing process makes such multi-shot approaches inconvenient in p...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	226,325
2407.00518	When Robots Get Chatty: Grounding Multimodal Human-Robot Conversation and Collaboration	We investigate the use of Large Language Models (LLMs) to equip neural robotic agents with human-like social and cognitive competencies, for the purpose of open-ended human-robot conversation and collaboration. We introduce a modular and extensible methodology for grounding an LLM with the sensory perceptions and capab...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	468,897
2410.08847	Unintentional Unalignment: Likelihood Displacement in Direct Preference Optimization	Direct Preference Optimization (DPO) and its variants are increasingly used for aligning language models with human preferences. Although these methods are designed to teach a model to generate preferred responses more frequently relative to dispreferred responses, prior work has observed that the likelihood of preferr...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	497,303
2401.01822	HawkRover: An Autonomous mmWave Vehicular Communication Testbed with Multi-sensor Fusion and Deep Learning	Connected and automated vehicles (CAVs) have become a transformative technology that can change our daily life. Currently, millimeter-wave (mmWave) bands are identified as the promising CAV connectivity solution. While it can provide high data rate, their realization faces many challenges such as high attenuation durin...	false	false	false	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	419,508
2303.07606	PSNet: a deep learning model based digital phase shifting algorithm from a single fringe image	As the gold standard for phase retrieval, phase-shifting algorithm (PS) has been widely used in optical interferometry, fringe projection profilometry, etc. However, capturing multiple fringe patterns in PS limits the algorithm to only a narrow range of application. To this end, a deep learning (DL) model based digital...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	351,314
2110.07716	Adversarial Scene Reconstruction and Object Detection System for Assisting Autonomous Vehicle	In the current computer vision era classifying scenes through video surveillance systems is a crucial task. Artificial Intelligence (AI) Video Surveillance technologies have been advanced remarkably while artificial intelligence and deep learning ascended into the system. Adopting the superior compounds of deep learnin...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	261,092
2205.15704	Mitigating Dataset Bias by Using Per-sample Gradient	The performance of deep neural networks is strongly influenced by the training dataset setup. In particular, when attributes having a strong correlation with the target attribute are present, the trained model can provide unintended prejudgments and show significant inference errors (i.e., the dataset bias problem). Va...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	299,837
2011.08655	Lung Segmentation in Chest X-rays with Res-CR-Net	Deep Neural Networks (DNN) are widely used to carry out segmentation tasks in biomedical images. Most DNNs developed for this purpose are based on some variation of the encoder-decoder U-Net architecture. Here we show that Res-CR-Net, a new type of fully convolutional neural network, which was originally developed for ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	206,946
2112.13588	Survey on Stabilization of Nonlinear Systems via state/output feedback control	This survey paper deals with the stabilization of nonlinear systems by analyzing the controlling method in terms of state feedback and output feedback. A brief overview of some literature on how the feedback controller of some dynamic systems in real applications like robots can be applicable is introduced. The aim is ...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	273,298
2411.05661	Multi-armed Bandits with Missing Outcome	While significant progress has been made in designing algorithms that minimize regret in online decision-making, real-world scenarios often introduce additional complexities, perhaps the most challenging of which is missing outcomes. Overlooking this aspect or simply assuming random missingness invariably leads to bias...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	506,741
0901.1936	A Lower Bound on the Capacity of Wireless Erasure Networks with Random Node Locations	In this paper, a lower bound on the capacity of wireless ad hoc erasure networks is derived in closed form in the canonical case where $n$ nodes are uniformly and independently distributed in the unit area square. The bound holds almost surely and is asymptotically tight. We assume all nodes have fixed transmit power a...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	2,953
2110.05572	EchoVPR: Echo State Networks for Visual Place Recognition	Recognising previously visited locations is an important, but unsolved, task in autonomous navigation. Current visual place recognition (VPR) benchmarks typically challenge models to recover the position of a query image (or images) from sequential datasets that include both spatial and temporal components. Recently, E...	false	false	false	false	false	false	true	true	false	false	false	true	false	false	false	false	false	false	260,314
0804.3259	On Multiuser Power Region of Fading Multiple-Access Channel with Multiple Antennas	This paper is concerned with the fading MIMO-MAC with multiple receive antennas at the base station (BS) and multiple transmit antennas at each mobile terminal (MT). Two multiple-access techniques are considered for scheduling transmissions from each MT to the BS at the same frequency, which are space-division multiple...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	1,607
1201.5154	Finding short vectors in a lattice of Voronoi's first kind	We show that for those lattices of Voronoi's first kind, a vector of shortest nonzero Euclidean length can computed in polynomial time by computing a minimum cut in a graph.	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	13,944
2210.07487	A Scalable Finite Difference Method for Deep Reinforcement Learning	Several low-bandwidth distributable black-box optimization algorithms in the family of finite differences such as Evolution Strategies have recently been shown to perform nearly as well as tailored Reinforcement Learning methods in some Reinforcement Learning domains. One shortcoming of these black-box methods is that ...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	323,733
2202.01290	Cyclical Pruning for Sparse Neural Networks	Current methods for pruning neural network weights iteratively apply magnitude-based pruning on the model weights and re-train the resulting model to recover lost accuracy. In this work, we show that such strategies do not allow for the recovery of erroneously pruned weights. To enable weight recovery, we propose a sim...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	278,429

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