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
1207.4404	Better Mixing via Deep Representations	It has previously been hypothesized, and supported with some experimental evidence, that deeper representations, when well trained, tend to do a better job at disentangling the underlying factors of variation. We study the following related conjecture: better representations, in the sense of better disentangling, can b...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	17,617
2012.13801	Achieving Real-Time LiDAR 3D Object Detection on a Mobile Device	3D object detection is an important task, especially in the autonomous driving application domain. However, it is challenging to support the real-time performance with the limited computation and memory resources on edge-computing devices in self-driving cars. To achieve this, we propose a compiler-aware unified framew...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	213,333
2107.07831	Modeling User Behaviour in Research Paper Recommendation System	User intention which often changes dynamically is considered to be an important factor for modeling users in the design of recommendation systems. Recent studies are starting to focus on predicting user intention (what users want) beyond user preference (what users like). In this work, a user intention model is propose...	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	false	246,542
1901.10254	Learning for Multi-Model and Multi-Type Fitting	Multi-model fitting has been extensively studied from the random sampling and clustering perspectives. Most assume that only a single type/class of model is present and their generalizations to fitting multiple types of models/structures simultaneously are non-trivial. The inherent challenges include choice of types an...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	119,973
2311.13213	Artificial Intelligence in the Service of Entrepreneurial Finance: Knowledge Structure and the Foundational Algorithmic Paradigm	While the application of Artificial Intelligence in Finance has a long tradition, its potential in Entrepreneurship has been intensively explored only recently. In this context, Entrepreneurial Finance is a particularly fertile ground for future Artificial Intelligence proliferation. To support the latter, the study pr...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	409,668
2310.13291	Assessing Privacy Risks in Language Models: A Case Study on Summarization Tasks	Large language models have revolutionized the field of NLP by achieving state-of-the-art performance on various tasks. However, there is a concern that these models may disclose information in the training data. In this study, we focus on the summarization task and investigate the membership inference (MI) attack: give...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	401,386
2102.12258	Classification with abstention but without disparities	Classification with abstention has gained a lot of attention in recent years as it allows to incorporate human decision-makers in the process. Yet, abstention can potentially amplify disparities and lead to discriminatory predictions. The goal of this work is to build a general purpose classification algorithm, which i...	false	false	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	221,674
2206.13628	Multi-scale Network with Attentional Multi-resolution Fusion for Point Cloud Semantic Segmentation	In this paper, we present a comprehensive point cloud semantic segmentation network that aggregates both local and global multi-scale information. First, we propose an Angle Correlation Point Convolution (ACPConv) module to effectively learn the local shapes of points. Second, based upon ACPConv, we introduce a local m...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	305,030
2411.03766	Number Cookbook: Number Understanding of Language Models and How to Improve It	Large language models (LLMs) can solve an increasing number of complex reasoning tasks while making surprising mistakes in basic numerical understanding and processing (such as 9.11 > 9.9). The latter ability is essential for tackling complex arithmetic and mathematical problems and serves as a foundation for most reas...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	506,031
2502.12403	Sensing-based Robustness Challenges in Agricultural Robotic Harvesting	This paper presents the challenges agricultural robotic harvesters face in detecting and localising fruits under various environmental disturbances. In controlled laboratory settings, both the traditional HSV (Hue Saturation Value) transformation and the YOLOv8 (You Only Look Once) deep learning model were employed. Ho...	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	534,846
2407.05192	Optimizing Bipolar Constellations for High-Rate Transmission in Short-Reach Fiber Links with Direct Detection	Bipolar modulation increases the achievable information rate of communication links with direct-detection receivers. This paper optimizes bipolar transmission with a modulator bias offset for short-reach fiber links. A neural network equalizer with successive interference cancellation is shown to gain over 100 Gbit/s c...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	470,861
1905.01248	Asymmetric Dual-Arm Task Execution using an Extended Relative Jacobian	Coordinated dual-arm manipulation tasks can be broadly characterized as possessing absolute and relative motion components. Relative motion tasks, in particular, are inherently redundant in the way they can be distributed between end-effectors. In this work, we analyse cooperative manipulation in terms of the asymmetri...	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	129,669
1705.09045	Cross-Domain Perceptual Reward Functions	In reinforcement learning, we often define goals by specifying rewards within desirable states. One problem with this approach is that we typically need to redefine the rewards each time the goal changes, which often requires some understanding of the solution in the agents environment. When humans are learning to comp...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	74,135
2411.18594	Biomolecular Analysis of Soil Samples and Rock Imagery for Tracing Evidence of Life Using a Mobile Robot	The search for evidence of past life on Mars presents a tremendous challenge that requires the usage of very advanced robotic technologies to overcome it. Current digital microscopic imagers and spectrometers used for astrobiological examination suffer from limitations such as insufficient resolution, narrow detection ...	false	false	false	false	false	false	true	true	false	false	false	true	false	false	false	false	false	false	511,922
2412.11034	SAM-IF: Leveraging SAM for Incremental Few-Shot Instance Segmentation	We propose SAM-IF, a novel method for incremental few-shot instance segmentation leveraging the Segment Anything Model (SAM). SAM-IF addresses the challenges of class-agnostic instance segmentation by introducing a multi-class classifier and fine-tuning SAM to focus on specific target objects. To enhance few-shot learn...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	517,221
1904.08613	Disentangled Representation Learning with Information Maximizing Autoencoder	Learning disentangled representation from any unlabelled data is a non-trivial problem. In this paper we propose Information Maximising Autoencoder (InfoAE) where the encoder learns powerful disentangled representation through maximizing the mutual information between the representation and given information in an unsu...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	128,128
2112.13254	On Dynamic Pricing with Covariates	We consider dynamic pricing with covariates under a generalized linear demand model: a seller can dynamically adjust the price of a product over a horizon of $T$ time periods, and at each time period $t$, the demand of the product is jointly determined by the price and an observable covariate vector $x_t\in\mathbb{R}^d...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	273,188
1912.10824	Differentiable Reasoning on Large Knowledge Bases and Natural Language	Reasoning with knowledge expressed in natural language and Knowledge Bases (KBs) is a major challenge for Artificial Intelligence, with applications in machine reading, dialogue, and question answering. General neural architectures that jointly learn representations and transformations of text are very data-inefficient...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	true	158,422
1804.07344	Effects of sampling skewness of the importance-weighted risk estimator on model selection	Importance-weighting is a popular and well-researched technique for dealing with sample selection bias and covariate shift. It has desirable characteristics such as unbiasedness, consistency and low computational complexity. However, weighting can have a detrimental effect on an estimator as well. In this work, we empi...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	95,505
1501.04686	Deep Convolutional Neural Networks for Action Recognition Using Depth Map Sequences	Recently, deep learning approach has achieved promising results in various fields of computer vision. In this paper, a new framework called Hierarchical Depth Motion Maps (HDMM) + 3 Channel Deep Convolutional Neural Networks (3ConvNets) is proposed for human action recognition using depth map sequences. Firstly, we rot...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	39,396
1907.10982	Overfitting of neural nets under class imbalance: Analysis and improvements for segmentation	Overfitting in deep learning has been the focus of a number of recent works, yet its exact impact on the behavior of neural networks is not well understood. This study analyzes overfitting by examining how the distribution of logits alters in relation to how much the model overfits. Specifically, we find that when trai...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	139,753
2402.06886	Principled Penalty-based Methods for Bilevel Reinforcement Learning and RLHF	Bilevel optimization has been recently applied to many machine learning tasks. However, their applications have been restricted to the supervised learning setting, where static objective functions with benign structures are considered. But bilevel problems such as incentive design, inverse reinforcement learning (RL), ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	428,474
2003.02357	Plasticity-Enhanced Domain-Wall MTJ Neural Networks for Energy-Efficient Online Learning	Machine learning implements backpropagation via abundant training samples. We demonstrate a multi-stage learning system realized by a promising non-volatile memory device, the domain-wall magnetic tunnel junction (DW-MTJ). The system consists of unsupervised (clustering) as well as supervised sub-systems, and generaliz...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	true	false	false	166,915
2302.01073	Learning in Multi-Memory Games Triggers Complex Dynamics Diverging from Nash Equilibrium	Repeated games consider a situation where multiple agents are motivated by their independent rewards throughout learning. In general, the dynamics of their learning become complex. Especially when their rewards compete with each other like zero-sum games, the dynamics often do not converge to their optimum, i.e., the N...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	true	343,472
2205.14716	Vision-Assisted User Clustering for Robust mmWave-NOMA Systems	When operated in the mmWave band, user channels get highly correlated which can be exploited in mmWave-NOMA systems to cluster a set of "correlated" users together. Identifying the set of users to cluster greatly affects the viability of NOMA systems. Typically, only channel state information (CSI) is used to make thes...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	299,466
2305.04603	Privacy-Preserving Representations are not Enough -- Recovering Scene Content from Camera Poses	Visual localization is the task of estimating the camera pose from which a given image was taken and is central to several 3D computer vision applications. With the rapid growth in the popularity of AR/VR/MR devices and cloud-based applications, privacy issues are becoming a very important aspect of the localization pr...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	362,835
2009.07436	Weakly-Supervised Online Hashing	With the rapid development of social websites, recent years have witnessed an explosive growth of social images with user-provided tags which continuously arrive in a streaming fashion. Due to the fast query speed and low storage cost, hashing-based methods for image search have attracted increasing attention. However,...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	195,924
1506.04799	Power Systems Without Fuel	The finiteness of fossil fuels implies that future electric power systems may predominantly source energy from fuel-free renewable resources like wind and solar. Evidently, these power systems without fuel will be environmentally benign, sustainable, and subject to milder failure scenarios. Many of these advantages wer...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	44,210
2305.17355	Rethinking PRL: A Multiscale Progressively Residual Learning Network for Inverse Halftoning	Image inverse halftoning is a classic image restoration task, aiming to recover continuous-tone images from halftone images with only bilevel pixels. Because the halftone images lose much of the original image content, inverse halftoning is a classic ill-problem. Although existing inverse halftoning algorithms achieve ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	368,545
2406.16685	A locking-free isogeometric thin shell formulation based on higher order accurate local strain projection via approximate dual splines	We present a novel isogeometric discretization approach for the Kirchhoff-Love shell formulation based on the Hellinger-Reissner variational principle. For mitigating membrane locking, we discretize the independent strains with spline basis functions that are one degree lower than those used for the displacements. To e...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	467,227
1409.3126	Performance Analysis of Cognitive Radio Systems with Imperfect Channel Sensing and Estimation	In cognitive radio systems, employing sensing-based spectrum access strategies, secondary users are required to perform channel sensing in order to detect the activities of primary users. In realistic scenarios, channel sensing occurs with possible errors due to miss-detections and false alarms. As another challenge, t...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	35,963
2002.11096	Causal Inference With Selectively Deconfounded Data	Given only data generated by a standard confounding graph with unobserved confounder, the Average Treatment Effect (ATE) is not identifiable. To estimate the ATE, a practitioner must then either (a) collect deconfounded data;(b) run a clinical trial; or (c) elucidate further properties of the causal graph that might re...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	165,607
2312.03179	CaloQVAE : Simulating high-energy particle-calorimeter interactions using hybrid quantum-classical generative models	The Large Hadron Collider's high luminosity era presents major computational challenges in the analysis of collision events. Large amounts of Monte Carlo (MC) simulation will be required to constrain the statistical uncertainties of the simulated datasets below these of the experimental data. Modelling of high-energy p...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	413,160
1302.1554	Object-Oriented Bayesian Networks	Bayesian networks provide a modeling language and associated inference algorithm for stochastic domains. They have been successfully applied in a variety of medium-scale applications. However, when faced with a large complex domain, the task of modeling using Bayesian networks begins to resemble the task of programming...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	21,855
2405.14883	Spectral Image Data Fusion for Multisource Data Augmentation	Multispectral and hyperspectral images are increasingly popular in different research fields, such as remote sensing, astronomical imaging, or precision agriculture. However, the amount of free data available to perform machine learning tasks is relatively small. Moreover, artificial intelligence models developed in th...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	456,652
2003.07339	Reinforcement Learning for Electricity Network Operation	This paper presents the background material required for the Learning to Run Power Networks Challenge. The challenge is focused on using Reinforcement Learning to train an agent to manage the real-time operations of a power grid, balancing power flows and making interventions to maintain stability. We present an introd...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	168,398
1711.03711	Synchronization of Kuramoto Oscillators via Cutset Projections	Synchronization in coupled oscillators networks is a remarkable phenomenon of relevance in numerous fields. For Kuramoto oscillators the loss of synchronization is determined by a trade-off between coupling strength and oscillator heterogeneity. Despite extensive prior work, the existing sufficient conditions for synch...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	84,267
1211.5723	The Survey of Data Mining Applications And Feature Scope	In this paper we have focused a variety of techniques, approaches and different areas of the research which are helpful and marked as the important field of data mining Technologies. As we are aware that many Multinational companies and large organizations are operated in different places of the different countries.Eac...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	false	19,914
2101.07292	Data Protection Impact Assessment for the Corona App	Since SARS-CoV-2 started spreading in Europe in early 2020, there has been a strong call for technical solutions to combat or contain the pandemic, with contact tracing apps at the heart of the debates. The EU's General Daten Protection Regulation (GDPR) requires controllers to carry out a data protection impact assess...	false	false	false	true	false	false	false	false	false	false	false	false	true	true	false	false	false	false	215,985
1007.4149	A rate-distortion scenario for the emergence and evolution of noisy molecular codes	We discuss, in terms of rate-distortion theory, the fitness of molecular codes as the problem of designing an optimal information channel. The fitness is governed by an interplay between the cost and quality of the channel, which induces smoothness in the code. By incorporating this code fitness into population dynamic...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	7,110
2402.03327	Uni3D-LLM: Unifying Point Cloud Perception, Generation and Editing with Large Language Models	In this paper, we introduce Uni3D-LLM, a unified framework that leverages a Large Language Model (LLM) to integrate tasks of 3D perception, generation, and editing within point cloud scenes. This framework empowers users to effortlessly generate and modify objects at specified locations within a scene, guided by the ve...	false	false	false	false	true	false	false	false	true	false	false	true	false	false	false	false	false	false	426,963
0909.4995	Geometrical Interpretation of Shannon's Entropy Based on the Born Rule	In this paper we will analyze discrete probability distributions in which probabilities of particular outcomes of some experiment (microstates) can be represented by the ratio of natural numbers (in other words, probabilities are represented by digital numbers of finite representation length). We will introduce several...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	4,581
2206.07920	PInKS: Preconditioned Commonsense Inference with Minimal Supervision	Reasoning with preconditions such as "glass can be used for drinking water unless the glass is shattered" remains an open problem for language models. The main challenge lies in the scarcity of preconditions data and the model's lack of support for such reasoning. We present PInKS, Preconditioned Commonsense Inference ...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	302,937
2106.03974	A Nonlinear Observability Analysis of Ambient Wind Estimation with Uncalibrated Sensors, Inspired by Insect Neural Encoding	Estimating the direction of ambient fluid flow is key for many flying or swimming animals and robots, but can only be accomplished through indirect measurements and active control. Recent work with tethered flying insects indicates that their sensory representation of orientation, apparent flow, direction of movement, ...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	239,534
1806.10306	Unsupervised and Efficient Vocabulary Expansion for Recurrent Neural Network Language Models in ASR	In automatic speech recognition (ASR) systems, recurrent neural network language models (RNNLM) are used to rescore a word lattice or N-best hypotheses list. Due to the expensive training, the RNNLM's vocabulary set accommodates only small shortlist of most frequent words. This leads to suboptimal performance if an inp...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	101,523
1805.00224	Multi-objective path planning of an autonomous mobile robot using hybrid PSO-MFB optimization algorithm	The main aim of this paper is to solve a path planning problem for an autonomous mobile robot in static and dynamic environments. The problem is solved by determining the collision-free path that satisfies the chosen criteria for shortest distance and path smoothness. The proposed path planning algorithm mimics the rea...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	96,386
2010.12687	Robust Correction of Sampling Bias Using Cumulative Distribution Functions	Varying domains and biased datasets can lead to differences between the training and the target distributions, known as covariate shift. Current approaches for alleviating this often rely on estimating the ratio of training and target probability density functions. These techniques require parameter tuning and can be u...	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	202,800
2308.16725	Terrain Diffusion Network: Climatic-Aware Terrain Generation with Geological Sketch Guidance	Sketch-based terrain generation seeks to create realistic landscapes for virtual environments in various applications such as computer games, animation and virtual reality. Recently, deep learning based terrain generation has emerged, notably the ones based on generative adversarial networks (GAN). However, these metho...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	true	389,089
2105.07532	Towards Synthetic Multivariate Time Series Generation for Flare Forecasting	One of the limiting factors in training data-driven, rare-event prediction algorithms is the scarcity of the events of interest resulting in an extreme imbalance in the data. There have been many methods introduced in the literature for overcoming this issue; simple data manipulation through undersampling and oversampl...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	235,462
2004.05422	The Role of Stem Noise in Visual Perception and Image Quality Measurement	This paper considers reference free quality assessment of distorted and noisy images. Specifically, it considers the first and second order statistics of stem noise that can be evaluated given any image. In the research field of Image quality Assessment (IQA), the stem noise is defined as the input of an Auto Regressiv...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	172,181
2003.02089	Gradient Statistics Aware Power Control for Over-the-Air Federated Learning	Federated learning (FL) is a promising technique that enables many edge devices to train a machine learning model collaboratively in wireless networks. By exploiting the superposition nature of wireless waveforms, over-the-air computation (AirComp) can accelerate model aggregation and hence facilitate communication-eff...	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	166,850
cs/0604040	Optimal Distortion-Power Tradeoffs in Sensor Networks: Gauss-Markov Random Processes	We investigate the optimal performance of dense sensor networks by studying the joint source-channel coding problem. The overall goal of the sensor network is to take measurements from an underlying random process, code and transmit those measurement samples to a collector node in a cooperative multiple access channel ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	539,382
2112.10297	DXML: Distributed Extreme Multilabel Classification	As a big data application, extreme multilabel classification has emerged as an important research topic with applications in ranking and recommendation of products and items. A scalable hybrid distributed and shared memory implementation of extreme classification for large scale ranking and recommendation is proposed. ...	false	false	false	false	true	true	true	false	false	false	false	false	false	false	false	false	false	true	272,396
1709.10433	On the Capacity of Face Representation	In this paper we address the following question, given a face representation, how many identities can it resolve? In other words, what is the capacity of the face representation? A scientific basis for estimating the capacity of a given face representation will not only benefit the evaluation and comparison of differen...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	81,780
1904.00956	Guided Meta-Policy Search	Reinforcement learning (RL) algorithms have demonstrated promising results on complex tasks, yet often require impractical numbers of samples since they learn from scratch. Meta-RL aims to address this challenge by leveraging experience from previous tasks so as to more quickly solve new tasks. However, in practice, th...	false	false	false	false	true	false	true	true	false	false	false	false	false	false	false	false	false	false	126,025
2211.01943	Quantized Precoding and RIS-Assisted Modulation for Integrated Sensing and Communications Systems	In this paper, we present a novel reconfigurable intelligent surface (RIS)-assisted integrated sensing and communication (ISAC) system with 1-bit quantization at the ISAC base station. An RIS is introduced in the ISAC system to mitigate the effects of coarse quantization and to enable the co-existence between sensing a...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	328,413
2306.06276	Contrastive Learning for Predicting Cancer Prognosis Using Gene Expression Values	Recent advancements in image classification have demonstrated that contrastive learning (CL) can aid in further learning tasks by acquiring good feature representation from a limited number of data samples. In this paper, we applied CL to tumor transcriptomes and clinical data to learn feature representations in a low-...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	372,541
1901.01660	Deeper and Wider Siamese Networks for Real-Time Visual Tracking	Siamese networks have drawn great attention in visual tracking because of their balanced accuracy and speed. However, the backbone networks used in Siamese trackers are relatively shallow, such as AlexNet [18], which does not fully take advantage of the capability of modern deep neural networks. In this paper, we inves...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	118,030
2107.00778	On Bridging Generic and Personalized Federated Learning for Image Classification	Federated learning is promising for its capability to collaboratively train models with multiple clients without accessing their data, but vulnerable when clients' data distributions diverge from each other. This divergence further leads to a dilemma: "Should we prioritize the learned model's generic performance (for f...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	244,266
1812.03201	Residual Reinforcement Learning for Robot Control	Conventional feedback control methods can solve various types of robot control problems very efficiently by capturing the structure with explicit models, such as rigid body equations of motion. However, many control problems in modern manufacturing deal with contacts and friction, which are difficult to capture with fi...	false	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	115,947
1702.01894	CAAD: Computer Architecture for Autonomous Driving	We describe the computing tasks involved in autonomous driving, examine existing autonomous driving computing platform implementations. To enable autonomous driving, the computing stack needs to simultaneously provide high performance, low power consumption, and low thermal dissipation, at low cost. We discuss possible...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	true	67,892
1701.02273	Visual Multiple-Object Tracking for Unknown Clutter Rate	In multi-object tracking applications, model parameter tuning is a prerequisite for reliable performance. In particular, it is difficult to know statistics of false measurements due to various sensing conditions and changes in the field of views. In this paper we are interested in designing a multi-object tracking algo...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	66,528
2106.09343	Lost in Interpreting: Speech Translation from Source or Interpreter?	Interpreters facilitate multi-lingual meetings but the affordable set of languages is often smaller than what is needed. Automatic simultaneous speech translation can extend the set of provided languages. We investigate if such an automatic system should rather follow the original speaker, or an interpreter to achieve ...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	241,633
2412.05321	Collaborative and parametric insurance on the Ethereum blockchain	This paper introduces a blockchain-based insurance scheme that integrates parametric and collaborative elements. A pool of investors, referred to as surplus providers, locks funds in a smart contract, enabling blockchain users to underwrite parametric insurance contracts. These contracts automatically trigger compensat...	false	true	false	false	false	false	false	false	false	false	false	false	true	true	false	false	false	true	514,785
2212.01995	Approximate Order-Preserving Pattern Mining for Time Series	The order-preserving pattern mining can be regarded as discovering frequent trends in time series, since the same order-preserving pattern has the same relative order which can represent a trend. However, in the case where data noise is present, the relative orders of many meaningful patterns are usually similar rather...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	334,640
1209.1318	Finding and Recommending Scholarly Articles	The rate at which scholarly literature is being produced has been increasing at approximately 3.5 percent per year for decades. This means that during a typical 40 year career the amount of new literature produced each year increases by a factor of four. The methods scholars use to discover relevant literature must cha...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	true	18,433
1401.6380	Properties of spatial coupling in compressed sensing	In this paper we address a series of open questions about the construction of spatially coupled measurement matrices in compressed sensing. For hardware implementations one is forced to depart from the limiting regime of parameters in which the proofs of the so-called threshold saturation work. We investigate quantitat...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	30,341
2004.12232	Reconstruct, Rasterize and Backprop: Dense shape and pose estimation from a single image	This paper presents a new system to obtain dense object reconstructions along with 6-DoF poses from a single image. Geared towards high fidelity reconstruction, several recent approaches leverage implicit surface representations and deep neural networks to estimate a 3D mesh of an object, given a single image. However,...	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	174,175
2101.02559	Robust Machine Learning Systems: Challenges, Current Trends, Perspectives, and the Road Ahead	Machine Learning (ML) techniques have been rapidly adopted by smart Cyber-Physical Systems (CPS) and Internet-of-Things (IoT) due to their powerful decision-making capabilities. However, they are vulnerable to various security and reliability threats, at both hardware and software levels, that compromise their accuracy...	false	false	false	false	true	false	true	false	false	false	true	false	true	false	false	false	false	true	214,671
1106.0987	Nearest Prime Simplicial Complex for Object Recognition	The structure representation of data distribution plays an important role in understanding the underlying mechanism of generating data. In this paper, we propose nearest prime simplicial complex approaches (NSC) by utilizing persistent homology to capture such structures. Assuming that each class is represented with a ...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	true	10,735
2307.06240	DSSE: a drone swarm search environment	The Drone Swarm Search project is an environment, based on PettingZoo, that is to be used in conjunction with multi-agent (or single-agent) reinforcement learning algorithms. It is an environment in which the agents (drones), have to find the targets (shipwrecked people). The agents do not know the position of the targ...	false	false	false	false	true	false	true	true	false	false	true	false	false	false	false	false	false	false	379,004
2211.04370	NESTER: An Adaptive Neurosymbolic Method for Causal Effect Estimation	Causal effect estimation from observational data is a central problem in causal inference. Methods based on potential outcomes framework solve this problem by exploiting inductive biases and heuristics from causal inference. Each of these methods addresses a specific aspect of causal effect estimation, such as controll...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	329,228
1707.05776	Optimizing the Latent Space of Generative Networks	Generative Adversarial Networks (GANs) have achieved remarkable results in the task of generating realistic natural images. In most successful applications, GAN models share two common aspects: solving a challenging saddle point optimization problem, interpreted as an adversarial game between a generator and a discrimi...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	77,293
1410.6641	Partial Optimality by Pruning for MAP-Inference with General Graphical Models	We consider the energy minimization problem for undirected graphical models, also known as MAP-inference problem for Markov random fields which is NP-hard in general. We propose a novel polynomial time algorithm to obtain a part of its optimal non-relaxed integral solution. Our algorithm is initialized with variables t...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	37,000
2103.07220	Real-time Timbre Transfer and Sound Synthesis using DDSP	Neural audio synthesis is an actively researched topic, having yielded a wide range of techniques that leverages machine learning architectures. Google Magenta elaborated a novel approach called Differential Digital Signal Processing (DDSP) that incorporates deep neural networks with preconditioned digital signal proce...	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	224,531
2011.10822	Control and implementation of fluid-driven soft gripper with dynamic uncertainty of object	Soft grippers, for stable grasping of objects, with high compliance could be considered a suitable candidate for replacement of conventional rigid grippers, and in recent years, they have been emerging exponentially in industries. Not only are these highly adaptable grippers capable of static grasping of an object, but...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	207,632
2303.03932	FFT-based Dynamic Token Mixer for Vision	Multi-head-self-attention (MHSA)-equipped models have achieved notable performance in computer vision. Their computational complexity is proportional to quadratic numbers of pixels in input feature maps, resulting in slow processing, especially when dealing with high-resolution images. New types of token-mixer are prop...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	349,899
2404.14099	DynaMMo: Dynamic Model Merging for Efficient Class Incremental Learning for Medical Images	Continual learning, the ability to acquire knowledge from new data while retaining previously learned information, is a fundamental challenge in machine learning. Various approaches, including memory replay, knowledge distillation, model regularization, and dynamic network expansion, have been proposed to address this ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	448,572
quant-ph/0607111	`Plausibilities of plausibilities': an approach through circumstances	Probability-like parameters appearing in some statistical models, and their prior distributions, are reinterpreted through the notion of `circumstance', a term which stands for any piece of knowledge that is useful in assigning a probability and that satisfies some additional logical properties. The idea, which can be ...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	540,901
2411.19626	GREAT: Geometry-Intention Collaborative Inference for Open-Vocabulary 3D Object Affordance Grounding	Open-Vocabulary 3D object affordance grounding aims to anticipate ``action possibilities'' regions on 3D objects with arbitrary instructions, which is crucial for robots to generically perceive real scenarios and respond to operational changes. Existing methods focus on combining images or languages that depict interac...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	512,326
2108.06682	ST3D++: Denoised Self-training for Unsupervised Domain Adaptation on 3D Object Detection	In this paper, we present a self-training method, named ST3D++, with a holistic pseudo label denoising pipeline for unsupervised domain adaptation on 3D object detection. ST3D++ aims at reducing noise in pseudo label generation as well as alleviating the negative impacts of noisy pseudo labels on model training. First,...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	250,681
2002.05274	Solving Missing-Annotation Object Detection with Background Recalibration Loss	This paper focuses on a novel and challenging detection scenario: A majority of true objects/instances is unlabeled in the datasets, so these missing-labeled areas will be regarded as the background during training. Previous art on this problem has proposed to use soft sampling to re-weight the gradients of RoIs based ...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	163,844
2209.12241	Exploring Example Influence in Continual Learning	Continual Learning (CL) sequentially learns new tasks like human beings, with the goal to achieve better Stability (S, remembering past tasks) and Plasticity (P, adapting to new tasks). Due to the fact that past training data is not available, it is valuable to explore the influence difference on S and P among training...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	319,463
2406.09038	CGP++ : A Modern C++ Implementation of Cartesian Genetic Programming	The reference implementation of Cartesian Genetic Programming (CGP) was written in the C programming language. C inherently follows a procedural programming paradigm, which entails challenges in providing a reusable and scalable implementation model for complex structures and methods. Moreover, due to the limiting fact...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	true	false	false	463,734
1109.2215	Finding missing edges and communities in incomplete networks	Many algorithms have been proposed for predicting missing edges in networks, but they do not usually take account of which edges are missing. We focus on networks which have missing edges of the form that is likely to occur in real networks, and compare algorithms that find these missing edges. We also investigate the ...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	12,101
1507.02973	Overcoming data scarcity of Twitter: using tweets as bootstrap with application to autism-related topic content analysis	Notwithstanding recent work which has demonstrated the potential of using Twitter messages for content-specific data mining and analysis, the depth of such analysis is inherently limited by the scarcity of data imposed by the 140 character tweet limit. In this paper we describe a novel approach for targeted knowledge e...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	45,034
2411.02428	NMformer: A Transformer for Noisy Modulation Classification in Wireless Communication	Modulation classification is a very challenging task since the signals intertwine with various ambient noises. Methods are required that can classify them without adding extra steps like denoising, which introduces computational complexity. In this study, we propose a vision transformer (ViT) based model named NMformer...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	505,489
1806.00811	Causal Inference with Noisy and Missing Covariates via Matrix Factorization	Valid causal inference in observational studies often requires controlling for confounders. However, in practice measurements of confounders may be noisy, and can lead to biased estimates of causal effects. We show that we can reduce the bias caused by measurement noise using a large number of noisy measurements of the...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	99,405
2312.10195	SoloPose: One-Shot Kinematic 3D Human Pose Estimation with Video Data Augmentation	While recent two-stage many-to-one deep learning models have demonstrated great success in 3D human pose estimation, such models are inefficient ways to detect 3D key points in a sequential video relative to one-shot and many-to-many models. Another key drawback of two-stage and many-to-one models is that errors in the...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	416,055
1907.07525	Low Barrier Nanomagnet Design for Binary Stochastic Neurons: Design Challenges for Real Nanomagnets with Fabrication Defects	Much attention has been focused on the design of low barrier nanomagnets (LBM), whose magnetizations vary randomly in time owing to thermal noise, for use in binary stochastic neurons (BSN) which are hardware accelerators for machine learning. The performance of BSNs depend on two important parameters: the correlation ...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	138,899
2112.07297	Parameterized codes over graphs	In this article we review known results on parameterized linear codes over graphs, introduced by Renter\'ia, Simis and Villarreal in 2011. Very little is known about their basic parameters and invariants. We review in detail the parameters dimension, regularity and minimum distance. As regards the parameter dimension, ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	271,437
2108.07007	Flying Guide Dog: Walkable Path Discovery for the Visually Impaired Utilizing Drones and Transformer-based Semantic Segmentation	Lacking the ability to sense ambient environments effectively, blind and visually impaired people (BVIP) face difficulty in walking outdoors, especially in urban areas. Therefore, tools for assisting BVIP are of great importance. In this paper, we propose a novel "flying guide dog" prototype for BVIP assistance using d...	true	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	250,806
2412.08948	Mojito: Motion Trajectory and Intensity Control for Video Generation	Recent advancements in diffusion models have shown great promise in producing high-quality video content. However, efficiently training video diffusion models capable of integrating directional guidance and controllable motion intensity remains a challenging and under-explored area. To tackle these challenges, this pap...	false	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	516,292
2207.03348	Human-Robot Commensality: Bite Timing Prediction for Robot-Assisted Feeding in Groups	We develop data-driven models to predict when a robot should feed during social dining scenarios. Being able to eat independently with friends and family is considered one of the most memorable and important activities for people with mobility limitations. While existing robotic systems for feeding people with mobility...	true	false	false	false	true	false	true	true	false	false	false	false	false	false	false	false	false	false	306,811
1707.09112	Almost everywhere injectivity conditions for the matrix recovery problem	Matrix recovery is raised in many areas. In this paper, we build up a framework for almost everywhere matrix recovery which means to recover almost all the $P\in {\mathcal M}\subset {\mathbb H}^{p\times q}$ from $Tr(A_jP), j=1,\ldots,N$ where $A_j\in V_j\subset {\mathbb H}^{p\times q}$. We mainly focus on the following...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	77,954
2312.03730	FakeWatch ElectionShield: A Benchmarking Framework to Detect Fake News for Credible US Elections	In today's technologically driven world, the spread of fake news, particularly during crucial events such as elections, presents an increasing challenge to the integrity of information. To address this challenge, we introduce FakeWatch ElectionShield, an innovative framework carefully designed to detect fake news. We h...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	413,384
2405.19854	RTGen: Generating Region-Text Pairs for Open-Vocabulary Object Detection	Open-vocabulary object detection (OVD) requires solid modeling of the region-semantic relationship, which could be learned from massive region-text pairs. However, such data is limited in practice due to significant annotation costs. In this work, we propose RTGen to generate scalable open-vocabulary region-text pairs ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	459,096
2108.04289	ACE: A Novel Approach for the Statistical Analysis of Pairwise Connectivity	Analysing correlations between streams of events is an important problem. It arises for example in Neurosciences, when the connectivity of neurons should be inferred from spike trains that record neurons' individual spiking activity. While recently some approaches for inferring delayed synaptic connections have been pr...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	249,954
2309.15676	Joint Sampling and Optimisation for Inverse Rendering	When dealing with difficult inverse problems such as inverse rendering, using Monte Carlo estimated gradients to optimise parameters can slow down convergence due to variance. Averaging many gradient samples in each iteration reduces this variance trivially. However, for problems that require thousands of optimisation ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	395,068
1608.00426	The $\epsilon$-capacity of a gain matrix and tolerable disturbances: Discrete-time perturbed linear systems	Discrete-time linear systems with perturbed initial state are considered. A disturbance that infects the initial state is said to be $\epsilon$-tolerable if the corresponding output signal is relatively insensitive to their effects. In this paper, we will define a new set that characterize each gain matrix K and the as...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	59,282

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