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
2312.15241	Measuring Value Alignment	As artificial intelligence (AI) systems become increasingly integrated into various domains, ensuring that they align with human values becomes critical. This paper introduces a novel formalism to quantify the alignment between AI systems and human values, using Markov Decision Processes (MDPs) as the foundational mode...	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	false	false	417,935
2406.19623	FRA-DiagSys: A Transformer Winding Fault Diagnosis System for Identifying Fault Types and degrees Using Frequency Response Analysis	The electric power transformer is a critical component in electrical distribution networks, and the diagnosis of faults in transformers is an important research area. Frequency Response Analysis (FRA) methods are widely used for analyzing winding faults in transformers, particularly in Chinese power stations. However, ...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	468,492
0710.4051	On the capacity achieving covariance matrix for Rician MIMO channels: an asymptotic approach	The capacity-achieving input covariance matrices for coherent block-fading correlated MIMO Rician channels are determined. In this case, no closed-form expressions for the eigenvectors of the optimum input covariance matrix are available. An approximation of the average mutual information is evaluated in this paper in ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	814
2403.03273	DINOv2 based Self Supervised Learning For Few Shot Medical Image Segmentation	Deep learning models have emerged as the cornerstone of medical image segmentation, but their efficacy hinges on the availability of extensive manually labeled datasets and their adaptability to unforeseen categories remains a challenge. Few-shot segmentation (FSS) offers a promising solution by endowing models with th...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	435,128
2402.17866	Towards spatiotemporal integration of bus transit with data-driven approaches	This study aims to propose an approach for spatiotemporal integration of bus transit, which enables users to change bus lines by paying a single fare. This could increase bus transit efficiency and, consequently, help to make this mode of transportation more attractive. Usually, this strategy is allowed for a few hours...	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	433,174
2304.05080	Investigating Imbalances Between SAR and Optical Utilization for Multi-Modal Urban Mapping	Accurate urban maps provide essential information to support sustainable urban development. Recent urban mapping methods use multi-modal deep neural networks to fuse Synthetic Aperture Radar (SAR) and optical data. However, multi-modal networks may rely on just one modality due to the greedy nature of learning. In turn...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	357,479
2310.14985	LLM-Based Agent Society Investigation: Collaboration and Confrontation in Avalon Gameplay	This paper explores the open research problem of understanding the social behaviors of LLM-based agents. Using Avalon as a testbed, we employ system prompts to guide LLM agents in gameplay. While previous studies have touched on gameplay with LLM agents, research on their social behaviors is lacking. We propose a novel...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	402,112
2005.13818	Travel Time Prediction using Tree-Based Ensembles	In this paper, we consider the task of predicting travel times between two arbitrary points in an urban scenario. We view this problem from two temporal perspectives: long-term forecasting with a horizon of several days and short-term forecasting with a horizon of one hour. Both of these perspectives are relevant for p...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	179,111
2410.12809	Cerebral microbleeds: Association with cognitive decline and pathology build-up	Cerebral microbleeds, markers of brain damage from vascular and amyloid pathologies, are linked to cognitive decline in aging, but their role in Alzheimer's disease (AD) onset and progression remains unclear. This study aimed to explore whether the presence and location of lobar microbleeds are associated with amyloid-...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	499,203
2407.16691	Automatic Equalization for Individual Instrument Tracks Using Convolutional Neural Networks	We propose a novel approach for the automatic equalization of individual musical instrument tracks. Our method begins by identifying the instrument present within a source recording in order to choose its corresponding ideal spectrum as a target. Next, the spectral difference between the recording and the target is cal...	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	475,689
2311.09768	Utilizing dataset affinity prediction in object detection to assess training data	Data pooling offers various advantages, such as increasing the sample size, improving generalization, reducing sampling bias, and addressing data sparsity and quality, but it is not straightforward and may even be counterproductive. Assessing the effectiveness of pooling datasets in a principled manner is challenging d...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	408,286
2212.06803	Fair Infinitesimal Jackknife: Mitigating the Influence of Biased Training Data Points Without Refitting	In consequential decision-making applications, mitigating unwanted biases in machine learning models that yield systematic disadvantage to members of groups delineated by sensitive attributes such as race and gender is one key intervention to strive for equity. Focusing on demographic parity and equality of opportunity...	false	false	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	336,216
2112.06295	Towards More Efficient Insertion Transformer with Fractional Positional Encoding	Auto-regressive neural sequence models have been shown to be effective across text generation tasks. However, their left-to-right decoding order prevents generation from being parallelized. Insertion Transformer (Stern et al., 2019) is an attractive alternative that allows outputting multiple tokens in a single generat...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	271,121
1702.06362	Negative-Unlabeled Tensor Factorization for Location Category Inference from Highly Inaccurate Mobility Data	Identifying significant location categories visited by mobile users is the key to a variety of applications. This is an extremely challenging task due to the possible deviation between the estimated location coordinate and the actual location, which could be on the order of kilometers. To estimate the actual location c...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	68,590
2011.12853	Error analysis of a demodulation procedure for multicarrier signals with slowly-varying carriers	We propose a procedure to demodulate analog signals encoded by a multicarrier modulator, with slowly-varying carrier shapes. We prove that the asymptotic demodulation error can be made arbitrarily small. The intended application is the "sensorless" control of AC electric motors at or near standstill, through the decodi...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	208,294
2206.04928	GAMR: A Guided Attention Model for (visual) Reasoning	Humans continue to outperform modern AI systems in their ability to flexibly parse and understand complex visual scenes. Here, we present a novel module for visual reasoning, the Guided Attention Model for (visual) Reasoning (GAMR), which instantiates an active vision theory -- positing that the brain solves complex vi...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	true	false	true	301,829
2408.09494	Source-Free Test-Time Adaptation For Online Surface-Defect Detection	Surface defect detection is significant in industrial production. However, detecting defects with varying textures and anomaly classes during the test time is challenging. This arises due to the differences in data distributions between source and target domains. Collecting and annotating new data from the target domai...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	481,465
2411.02477	Building a Synthetic Vascular Model: Evaluation in an Intracranial Aneurysms Detection Scenario	We hereby present a full synthetic model, able to mimic the various constituents of the cerebral vascular tree, including the cerebral arteries, bifurcations and intracranial aneurysms. This model intends to provide a substantial dataset of brain arteries which could be used by a 3D convolutional neural network to effi...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	505,527
2006.12038	Bandit algorithms: Letting go of logarithmic regret for statistical robustness	We study regret minimization in a stochastic multi-armed bandit setting and establish a fundamental trade-off between the regret suffered under an algorithm, and its statistical robustness. Considering broad classes of underlying arms' distributions, we show that bandit learning algorithms with logarithmic regret are a...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	183,455
1807.08604	A Frequency-Domain Characterization of Optimal Error Covariance for the Kalman-Bucy Filter	In this paper, we discover that the trace of the division of the optimal output estimation error covariance over the noise covariance attained by the Kalman-Bucy filter can be explicitly expressed in terms of the plant dynamics and noise statistics in a frequency-domain integral characterization. Towards this end, we e...	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	103,578
1811.03407	A Factor Graph Approach to Automated Design of Bayesian Signal Processing Algorithms	The benefits of automating design cycles for Bayesian inference-based algorithms are becoming increasingly recognized by the machine learning community. As a result, interest in probabilistic programming frameworks has much increased over the past few years. This paper explores a specific probabilistic programming para...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	112,833
2411.01073	AttackQA: Development and Adoption of a Dataset for Assisting Cybersecurity Operations using Fine-tuned and Open-Source LLMs	Retrieval-augmented generation (RAG) on specialized domain datasets has shown improved performance when large language models (LLMs) are fine-tuned for generating responses to user queries. In this study, we develop a cybersecurity question-answering (Q\&A) dataset, called AttackQA, and employ it to build a RAG-based Q...	false	false	false	false	true	false	true	false	false	false	false	false	true	false	false	false	false	false	504,898
2402.15247	A Bargaining-based Approach for Feature Trading in Vertical Federated Learning	Vertical Federated Learning (VFL) has emerged as a popular machine learning paradigm, enabling model training across the data and the task parties with different features about the same user set while preserving data privacy. In production environment, VFL usually involves one task party and one data party. Fair and ec...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	true	false	false	false	432,058
2005.01047	Fusion of visible and infrared images via complex function	We propose an algorithm for the fusion of partial images collected from the visual and infrared cameras such that the visual and infrared images are the real and imaginary parts of a complex function. The proposed image fusion algorithm of the complex function is a generalization for the algorithm of conventional image...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	175,477
2210.16976	Representation Learning for General-sum Low-rank Markov Games	We study multi-agent general-sum Markov games with nonlinear function approximation. We focus on low-rank Markov games whose transition matrix admits a hidden low-rank structure on top of an unknown non-linear representation. The goal is to design an algorithm that (1) finds an $\varepsilon$-equilibrium policy sample e...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	327,521
2206.01918	Automated Audio Captioning with Epochal Difficult Captions for Curriculum Learning	In this paper, we propose an algorithm, Epochal Difficult Captions, to supplement the training of any model for the Automated Audio Captioning task. Epochal Difficult Captions is an elegant evolution to the keyword estimation task that previous work have used to train the encoder of the AAC model. Epochal Difficult Cap...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	300,670
1810.09912	Efficient Bayesian Experimental Design for Implicit Models	Bayesian experimental design involves the optimal allocation of resources in an experiment, with the aim of optimising cost and performance. For implicit models, where the likelihood is intractable but sampling from the model is possible, this task is particularly difficult and therefore largely unexplored. This is mai...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	111,151
2403.11958	Language Evolution with Deep Learning	Computational modeling plays an essential role in the study of language emergence. It aims to simulate the conditions and learning processes that could trigger the emergence of a structured language within a simulated controlled environment. Several methods have been used to investigate the origin of our language, incl...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	438,949
2408.01323	FANNO: Augmenting High-Quality Instruction Data with Open-Sourced LLMs Only	Instruction fine-tuning stands as a crucial advancement in leveraging large language models (LLMs) for enhanced task performance. However, the annotation of instruction datasets has traditionally been expensive and laborious, often relying on manual annotations or costly API calls of proprietary LLMs. To address these ...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	478,187
2405.20748	OpenTensor: Reproducing Faster Matrix Multiplication Discovering Algorithms	OpenTensor is a reproduction of AlphaTensor, which discovered a new algorithm that outperforms the state-of-the-art methods for matrix multiplication by Deep Reinforcement Learning (DRL). While AlphaTensor provides a promising framework for solving scientific problems, it is really hard to reproduce due to the massive ...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	true	459,501
1805.00367	A Multi-State Diagnosis and Prognosis Framework with Feature Learning for Tool Condition Monitoring	In this paper, a multi-state diagnosis and prognosis (MDP) framework is proposed for tool condition monitoring via a deep belief network based multi-state approach (DBNMS). For fault diagnosis, a cost-sensitive deep belief network (namely ECS-DBN) is applied to deal with the imbalanced data problem for tool state estim...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	96,429
0809.2754	Algorithmic information theory	We introduce algorithmic information theory, also known as the theory of Kolmogorov complexity. We explain the main concepts of this quantitative approach to defining `information'. We discuss the extent to which Kolmogorov's and Shannon's information theory have a common purpose, and where they are fundamentally diffe...	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	2,355
2410.03772	Precision Knowledge Editing: Enhancing Safety in Large Language Models	Large language models (LLMs) have demonstrated remarkable capabilities, but they also pose risks related to the generation of toxic or harmful content. This work introduces Precision Knowledge Editing (PKE), an advanced technique that builds upon existing knowledge editing methods to more effectively identify and modif...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	494,957
2109.00124	Learning Coated Adversarial Camouflages for Object Detectors	An adversary can fool deep neural network object detectors by generating adversarial noises. Most of the existing works focus on learning local visible noises in an adversarial "patch" fashion. However, the 2D patch attached to a 3D object tends to suffer from an inevitable reduction in attack performance as the viewpo...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	253,007
2104.06403	Lite-HRNet: A Lightweight High-Resolution Network	We present an efficient high-resolution network, Lite-HRNet, for human pose estimation. We start by simply applying the efficient shuffle block in ShuffleNet to HRNet (high-resolution network), yielding stronger performance over popular lightweight networks, such as MobileNet, ShuffleNet, and Small HRNet. We find tha...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	230,066
2306.06203	FLSL: Feature-level Self-supervised Learning	Current self-supervised learning (SSL) methods (e.g., SimCLR, DINO, VICReg,MOCOv3) target primarily on representations at instance level and do not generalize well to dense prediction tasks, such as object detection and segmentation.Towards aligning SSL with dense predictions, this paper demonstrates for the first time...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	372,507
2208.06031	Handling big tabular data of ICT supply chains: a multi-task, machine-interpretable approach	Due to the characteristics of Information and Communications Technology (ICT) products, the critical information of ICT devices is often summarized in big tabular data shared across supply chains. Therefore, it is critical to automatically interpret tabular structures with the surging amount of electronic assets. To tr...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	312,570
2112.06108	3D LiDAR Aided GNSS NLOS Mitigation in Urban Canyons	In this paper, we propose a 3D LiDAR aided global navigation satellite system (GNSS) non-line-of-sight (NLOS) mitigation method caused by both static buildings and dynamic objects. A sliding window map describing the surrounding of the ego-vehicle is first generated, based on real-time 3D point clouds from a 3D LiDAR s...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	271,056
1402.0501	Large-deviation properties of resilience of transportation networks	Distributions of the resilience of transport networks are studied numerically, in particular the large-deviation tails. Thus, not only typical quantities like average or variance but the distributions over the (almost) full support can be studied. For a proof of principle, a simple transport model based on the edge-bet...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	30,568
2303.05490	On the Expressiveness and Generalization of Hypergraph Neural Networks	This extended abstract describes a framework for analyzing the expressiveness, learning, and (structural) generalization of hypergraph neural networks (HyperGNNs). Specifically, we focus on how HyperGNNs can learn from finite datasets and generalize structurally to graph reasoning problems of arbitrary input sizes. Our...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	350,484
2402.10433	Fusing Neural and Physical: Augment Protein Conformation Sampling with Tractable Simulations	The protein dynamics are common and important for their biological functions and properties, the study of which usually involves time-consuming molecular dynamics (MD) simulations in silico. Recently, generative models has been leveraged as a surrogate sampler to obtain conformation ensembles with orders of magnitude f...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	429,960
2301.05264	Security-Aware Approximate Spiking Neural Networks	Deep Neural Networks (DNNs) and Spiking Neural Networks (SNNs) are both known for their susceptibility to adversarial attacks. Therefore, researchers in the recent past have extensively studied the robustness and defense of DNNs and SNNs under adversarial attacks. Compared to accurate SNNs (AccSNN), approximate SNNs (A...	false	false	false	false	true	false	false	false	false	false	false	false	true	false	false	false	false	true	340,300
2007.07486	Content-based Recommendations for Radio Stations with Deep Learned Audio Fingerprints	The world of linear radio broadcasting is characterized by a wide variety of stations and played content. That is why finding stations playing the preferred content is a tough task for a potential listener, especially due to the overwhelming number of offered choices. Here, recommender systems usually step in but exist...	true	false	true	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	187,350
2406.11608	Learning Hierarchical Semantic Classification by Grounding on Consistent Image Segmentations	Hierarchical semantic classification requires the prediction of a taxonomy tree instead of a single flat level of the tree, where both accuracies at individual levels and consistency across levels matter. We can train classifiers for individual levels, which has accuracy but not consistency, or we can train only the fi...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	464,962
2110.09171	Projected Model Counting: Beyond Independent Support	The past decade has witnessed a surge of interest in practical techniques for projected model counting. Despite significant advancements, however, performance scaling remains the Achilles' heel of this field. A key idea used in modern counters is to count models projected on an \emph{independent support} that is often ...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	261,712
2010.14772	Around the variational principle for metric mean dimension	We study variational principles for metric mean dimension. First we prove that in the variational principle of Lindenstrauss and Tsukamoto it suffices to take supremum over ergodic measures. Second we derive a variational principle for metric mean dimension involving growth rates of measure-theoretic entropy of partiti...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	203,567
2305.10869	Free Lunch for Privacy Preserving Distributed Graph Learning	Learning on graphs is becoming prevalent in a wide range of applications including social networks, robotics, communication, medicine, etc. These datasets belonging to entities often contain critical private information. The utilization of data for graph learning applications is hampered by the growing privacy concerns...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	365,271
2405.13022	LLMs can learn self-restraint through iterative self-reflection	In order to be deployed safely, Large Language Models (LLMs) must be capable of dynamically adapting their behavior based on their level of knowledge and uncertainty associated with specific topics. This adaptive behavior, which we refer to as self-restraint, is non-trivial to teach since it depends on the internal kno...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	455,755
1601.07381	Investigating echo state networks dynamics by means of recurrence analysis	In this paper, we elaborate over the well-known interpretability issue in echo state networks. The idea is to investigate the dynamics of reservoir neurons with time-series analysis techniques taken from research on complex systems. Notably, we analyze time-series of neuron activations with Recurrence Plots (RPs) and R...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	51,425
1412.7288	Observations Concerning the probability of the existence of annihilators for balanced boolean functions	LFSR-based stream ciphers with nonlinear filters or combiners are susceptible to algebraic attacks using linearization methods to solve an overdefined system of nonlinear equations. And this process is greatly enhanced if the filtering or combining function has a low degree annihilator. To prevent such an attack, one w...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	38,795
1912.02889	Learning Super-resolved Depth from Active Gated Imaging	Environment perception for autonomous driving is doomed by the trade-off between range-accuracy and resolution: current sensors that deliver very precise depth information are usually restricted to low resolution because of technology or cost limitations. In this work, we exploit depth information from an active gated ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	156,454
2502.00792	RTBAgent: A LLM-based Agent System for Real-Time Bidding	Real-Time Bidding (RTB) enables advertisers to place competitive bids on impression opportunities instantaneously, striving for cost-effectiveness in a highly competitive landscape. Although RTB has widely benefited from the utilization of technologies such as deep learning and reinforcement learning, the reliability o...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	529,549
2009.14108	Align-RUDDER: Learning From Few Demonstrations by Reward Redistribution	Reinforcement learning algorithms require many samples when solving complex hierarchical tasks with sparse and delayed rewards. For such complex tasks, the recently proposed RUDDER uses reward redistribution to leverage steps in the Q-function that are associated with accomplishing sub-tasks. However, often only few ep...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	197,937
2309.06105	Towards Visual Taxonomy Expansion	Taxonomy expansion task is essential in organizing the ever-increasing volume of new concepts into existing taxonomies. Most existing methods focus exclusively on using textual semantics, leading to an inability to generalize to unseen terms and the "Prototypical Hypernym Problem." In this paper, we propose Visual Taxo...	false	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	391,313
1711.02545	Online Learning for Changing Environments using Coin Betting	A key challenge in online learning is that classical algorithms can be slow to adapt to changing environments. Recent studies have proposed "meta" algorithms that convert any online learning algorithm to one that is adaptive to changing environments, where the adaptivity is analyzed in a quantity called the strongly-ad...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	84,079
2105.04419	VDB-EDT: An Efficient Euclidean Distance Transform Algorithm Based on VDB Data Structure	This paper presents a fundamental algorithm, called VDB-EDT, for Euclidean distance transform (EDT) based on the VDB data structure. The algorithm executes on grid maps and generates the corresponding distance field for recording distance information against obstacles, which forms the basis of numerous motion planning ...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	234,501
2112.06635	Bounds in the Lee Metric and Optimal Codes	In this paper we investigate known Singleton-like bounds in the Lee metric and characterize optimal codes, which turn out to be very few. We then focus on Plotkin-like bounds in the Lee metric and present a new bound that extends and refines a previously known, and out-performs it in the case of non-free codes. We then...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	271,246
2410.01124	Synthetic imagery for fuzzy object detection: A comparative study	The fuzzy object detection is a challenging field of research in computer vision (CV). Distinguishing between fuzzy and non-fuzzy object detection in CV is important. Fuzzy objects such as fire, smoke, mist, and steam present significantly greater complexities in terms of visual features, blurred edges, varying shapes,...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	493,622
1709.07166	Semi-Automated Nasal PAP Mask Sizing using Facial Photographs	We present a semi-automated system for sizing nasal Positive Airway Pressure (PAP) masks based upon a neural network model that was trained with facial photographs of both PAP mask users and non-users. It demonstrated an accuracy of 72% in correctly sizing a mask and 96% accuracy sizing to within 1 mask size group. The...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	81,234
2401.05676	Exploring Self- and Cross-Triplet Correlations for Human-Object Interaction Detection	Human-Object Interaction (HOI) detection plays a vital role in scene understanding, which aims to predict the HOI triplet in the form of <human, object, action>. Existing methods mainly extract multi-modal features (e.g., appearance, object semantics, human pose) and then fuse them together to directly predict HOI trip...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	420,871
2402.01738	C4Q: A Chatbot for Quantum	Quantum computing is a growing field that promises many real-world applications such as quantum cryptography or quantum finance. The number of people able to use quantum computing is however still very small. This limitation comes from the difficulty to understand the concepts and to know how to start coding. Therefore...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	426,185
2308.04635	Where's the Liability in Harmful AI Speech?	Generative AI, in particular text-based "foundation models" (large models trained on a huge variety of information including the internet), can generate speech that could be problematic under a wide range of liability regimes. Machine learning practitioners regularly "red team" models to identify and mitigate such prob...	false	false	false	false	true	false	false	false	false	false	false	false	false	true	false	false	false	false	384,486
1905.03809	Wearable Sensor Data Based Human Activity Recognition using Machine Learning: A new approach	Recent years have witnessed the rapid development of human activity recognition (HAR) based on wearable sensor data. One can find many practical applications in this area, especially in the field of health care. Many machine learning algorithms such as Decision Trees, Support Vector Machine, Naive Bayes, K-Nearest Neig...	true	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	130,293
2401.07451	Zone-Specific CSI Feedback for Massive MIMO: A Situation-Aware Deep Learning Approach	Massive MIMO basestations, operating with frequency-division duplexing (FDD), require the users to feedback their channel state information (CSI) in order to design the precoding matrices. Given the powerful capabilities of deep neural networks in learning quantization codebooks, utilizing these networks in compressing...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	421,541
1305.4801	Mining top-k granular association rules for recommendation	Recommender systems are important for e-commerce companies as well as researchers. Recently, granular association rules have been proposed for cold-start recommendation. However, existing approaches reserve only globally strong rules; therefore some users may receive no recommendation at all. In this paper, we propose ...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	24,718
2304.02704	Real-Time Dense 3D Mapping of Underwater Environments	This paper addresses real-time dense 3D reconstruction for a resource-constrained Autonomous Underwater Vehicle (AUV). Underwater vision-guided operations are among the most challenging as they combine 3D motion in the presence of external forces, limited visibility, and absence of global positioning. Obstacle avoidanc...	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	356,512
2201.00548	Hybrid intelligence for dynamic job-shop scheduling with deep reinforcement learning and attention mechanism	The dynamic job-shop scheduling problem (DJSP) is a class of scheduling tasks that specifically consider the inherent uncertainties such as changing order requirements and possible machine breakdown in realistic smart manufacturing settings. Since traditional methods cannot dynamically generate effective scheduling str...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	273,994
2106.16198	In-distribution adversarial attacks on object recognition models using gradient-free search	Neural networks are susceptible to small perturbations in the form of 2D rotations and shifts, image crops, and even changes in object colors. Past works attribute these errors to dataset bias, claiming that models fail on these perturbed samples as they do not belong to the training data distribution. Here, we challen...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	244,000
1811.07698	Towards Global Explanations for Credit Risk Scoring	In this paper we propose a method to obtain global explanations for trained black-box classifiers by sampling their decision function to learn alternative interpretable models. The envisaged approach provides a unified solution to approximate non-linear decision boundaries with simpler classifiers while retaining the o...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	113,846
2110.04632	DenseNet approach to segmentation and classification of dermatoscopic skin lesions images	At present, cancer is one of the most important health issues in the world. Because early detection and appropriate treatment in cancer are very effective in the recovery and survival of patients, image processing as a diagnostic tool can help doctors to diagnose in the first recognition of cancer. One of the most impo...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	259,970
2309.05256	Examining the Effect of Pre-training on Time Series Classification	Although the pre-training followed by fine-tuning paradigm is used extensively in many fields, there is still some controversy surrounding the impact of pre-training on the fine-tuning process. Currently, experimental findings based on text and image data lack consensus. To delve deeper into the unsupervised pre-traini...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	391,016
1907.09356	Decentralized Deep Learning with Arbitrary Communication Compression	Decentralized training of deep learning models is a key element for enabling data privacy and on-device learning over networks, as well as for efficient scaling to large compute clusters. As current approaches suffer from limited bandwidth of the network, we propose the use of communication compression in the decentral...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	139,337
2110.03478	Complex-valued Federated Learning with Differential Privacy and MRI Applications	Federated learning enhanced with Differential Privacy (DP) is a powerful privacy-preserving strategy to protect individuals sharing their sensitive data for processing in fields such as medicine and healthcare. Many medical applications, for example magnetic resonance imaging (MRI), rely on complex-valued signal proces...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	259,517
2308.13641	ML-Powered Index Tuning: An Overview of Recent Progress and Open Challenges	The scale and complexity of workloads in modern cloud services have brought into sharper focus a critical challenge in automated index tuning -- the need to recommend high-quality indexes while maintaining index tuning scalability. This challenge is further compounded by the requirement for automated index implementati...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	true	false	387,994
1204.5028	Regenerating Codes: A System Perspective	The explosion of the amount of data stored in cloud systems calls for more efficient paradigms for redundancy. While replication is widely used to ensure data availability, erasure correcting codes provide a much better trade-off between storage and availability. Regenerating codes are good candidates for they also off...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	15,628
1903.05350	The Fourier Spectral Characterization for the Correlation-Immune Functions over Fp	The correlation-immune functions serve as an important metric for measuring resistance of a cryptosystem against correlation attacks. Existing literature emphasize on matrices, orthogonal arrays and Walsh-Hadamard spectra to characterize the correlation-immune functions over $\mathbb{F}_p$ ($p \geq 2$ is a prime). %wit...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	124,147
2405.02412	Deep Learning and Transfer Learning Architectures for English Premier League Player Performance Forecasting	This paper presents a groundbreaking model for forecasting English Premier League (EPL) player performance using convolutional neural networks (CNNs). We evaluate Ridge regression, LightGBM and CNNs on the task of predicting upcoming player FPL score based on historical FPL data over the previous weeks. Our baseline mo...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	451,758
1707.07255	Detecting and Grouping Identical Objects for Region Proposal and Classification	Often multiple instances of an object occur in the same scene, for example in a warehouse. Unsupervised multi-instance object discovery algorithms are able to detect and identify such objects. We use such an algorithm to provide object proposals to a convolutional neural network (CNN) based classifier. This results in ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	77,585
2006.16701	Hierarchical Qualitative Clustering: clustering mixed datasets with critical qualitative information	Clustering can be used to extract insights from data or to verify some of the assumptions held by the domain experts, namely data segmentation. In the literature, few methods can be applied in clustering qualitative values using the context associated with other variables present in the data, without losing interpretab...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	184,884
2009.11990	A fast and accurate physics-informed neural network reduced order model with shallow masked autoencoder	Traditional linear subspace reduced order models (LS-ROMs) are able to accelerate physical simulations, in which the intrinsic solution space falls into a subspace with a small dimension, i.e., the solution space has a small Kolmogorov n-width. However, for physical phenomena not of this type, e.g., any advection-domin...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	true	false	true	197,303
1909.04596	Prediction of Overall Survival of Brain Tumor Patients	Automated brain tumor segmentation plays an important role in the diagnosis and prognosis of the patient. In addition, features from the tumorous brain help in predicting patients overall survival. The main focus of this paper is to segment tumor from BRATS 2018 benchmark dataset and use age, shape and volumetric featu...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	144,848
2104.08448	Data Distillation for Text Classification	Deep learning techniques have achieved great success in many fields, while at the same time deep learning models are getting more complex and expensive to compute. It severely hinders the wide applications of these models. In order to alleviate this problem, model distillation emerges as an effective means to compress ...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	230,804
2004.06002	Dynamic R-CNN: Towards High Quality Object Detection via Dynamic Training	Although two-stage object detectors have continuously advanced the state-of-the-art performance in recent years, the training process itself is far from crystal. In this work, we first point out the inconsistency problem between the fixed network settings and the dynamic training procedure, which greatly affects the pe...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	172,391
2406.06220	Label-Looping: Highly Efficient Decoding for Transducers	This paper introduces a highly efficient greedy decoding algorithm for Transducer-based speech recognition models. We redesign the standard nested-loop design for RNN-T decoding, swapping loops over frames and labels: the outer loop iterates over labels, while the inner loop iterates over frames searching for the next ...	false	false	true	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	462,485
1911.02562	Gextext: Disease Network Extraction from Biomedical Literature	PURPOSE: We propose a fully unsupervised method to learn latent disease networks directly from unstructured biomedical text corpora. This method addresses current challenges in unsupervised knowledge extraction, such as the detection of long-range dependencies and requirements for large training corpora. METHODS: Let C...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	true	152,395
1905.05947	Joint haze image synthesis and dehazing with mmd-vae losses	Fog and haze are weathers with low visibility which are adversarial to the driving safety of intelligent vehicles equipped with optical sensors like cameras and LiDARs. Therefore image dehazing for perception enhancement and haze image synthesis for testing perception abilities are equivalently important in the develop...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	130,864
2004.13154	Voxgraph: Globally Consistent, Volumetric Mapping using Signed Distance Function Submaps	Globally consistent dense maps are a key requirement for long-term robot navigation in complex environments. While previous works have addressed the challenges of dense mapping and global consistency, most require more computational resources than may be available on-board small robots. We propose a framework that crea...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	174,458
1904.03436	Unsupervised Embedding Learning via Invariant and Spreading Instance Feature	This paper studies the unsupervised embedding learning problem, which requires an effective similarity measurement between samples in low-dimensional embedding space. Motivated by the positive concentrated and negative separated properties observed from category-wise supervised learning, we propose to utilize the insta...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	126,718
2306.03042	SERT: A Transfomer Based Model for Spatio-Temporal Sensor Data with Missing Values for Environmental Monitoring	Environmental monitoring is crucial to our understanding of climate change, biodiversity loss and pollution. The availability of large-scale spatio-temporal data from sources such as sensors and satellites allows us to develop sophisticated models for forecasting and understanding key drivers. However, the data collect...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	371,167
1709.05172	Optimal Base Station Design with Limited Fronthaul: Massive Bandwidth or Massive MIMO?	To reach a cost-efficient 5G architecture, the use of remote radio heads connected through a fronthaul to baseband controllers is a promising solution. However, the fronthaul links must support high bit rates as 5G networks are projected to use wide bandwidths and many antennas. Upgrading all of the existing fronthaul ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	80,800
1201.4116	Analysis of Cell Load Coupling for LTE Network Planning and Optimization	System-centric modeling and analysis are of key significance in planning and optimizing cellular networks. In this paper, we provide a mathematical analysis of performance modeling for LTE networks. The system model characterizes the coupling relation between the cell load factors, taking into account non-uniform traff...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	13,898
cs/0510032	Polar Polytopes and Recovery of Sparse Representations	Suppose we have a signal y which we wish to represent using a linear combination of a number of basis atoms a_i, y=sum_i x_i a_i = Ax. The problem of finding the minimum L0 norm representation for y is a hard problem. The Basis Pursuit (BP) approach proposes to find the minimum L1 norm representation instead, which cor...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	539,010
1706.05011	Measuring Personalization of Web Search	Web search is an integral part of our daily lives. Recently, there has been a trend of personalization in Web search, where different users receive different results for the same search query. The increasing level of personalization is leading to concerns about Filter Bubble effects, where certain users are simply unab...	false	false	false	false	false	true	false	false	false	false	false	false	false	true	false	false	false	false	75,427
2407.03018	An Organism Starts with a Single Pix-Cell: A Neural Cellular Diffusion for High-Resolution Image Synthesis	Generative modeling seeks to approximate the statistical properties of real data, enabling synthesis of new data that closely resembles the original distribution. Generative Adversarial Networks (GANs) and Denoising Diffusion Probabilistic Models (DDPMs) represent significant advancements in generative modeling, drawin...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	469,988
2211.07104	MetaKRec: Collaborative Meta-Knowledge Enhanced Recommender System	Knowledge graph (KG) enhanced recommendation has demonstrated improved performance in the recommendation system (RecSys) and attracted considerable research interest. Recently the literature has adopted neural graph networks (GNNs) on the collaborative knowledge graph and built an end-to-end KG-enhanced RecSys. However...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	330,130
2110.14810	Telling Creative Stories Using Generative Visual Aids	Can visual artworks created using generative visual algorithms inspire human creativity in storytelling? We asked writers to write creative stories from a starting prompt, and provided them with visuals created by generative AI models from the same prompt. Compared to a control group, writers who used the visuals as st...	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	263,648
2406.06671	Controlling Counterfactual Harm in Decision Support Systems Based on Prediction Sets	Decision support systems based on prediction sets help humans solve multiclass classification tasks by narrowing down the set of potential label values to a subset of them, namely a prediction set, and asking them to always predict label values from the prediction sets. While this type of systems have been proven to be...	true	false	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	462,735
2304.07125	Keeping the Questions Conversational: Using Structured Representations to Resolve Dependency in Conversational Question Answering	Having an intelligent dialogue agent that can engage in conversational question answering (ConvQA) is now no longer limited to Sci-Fi movies only and has, in fact, turned into a reality. These intelligent agents are required to understand and correctly interpret the sequential turns provided as the context of the given...	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	358,237
2212.14591	Mixture of von Mises-Fisher distribution with sparse prototypes	Mixtures of von Mises-Fisher distributions can be used to cluster data on the unit hypersphere. This is particularly adapted for high-dimensional directional data such as texts. We propose in this article to estimate a von Mises mixture using a l 1 penalized likelihood. This leads to sparse prototypes that improve clus...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	338,663
1908.09528	Thinking Globally, Acting Locally: Distantly Supervised Global-to-Local Knowledge Selection for Background Based Conversation	Background Based Conversations (BBCs) have been introduced to help conversational systems avoid generating overly generic responses. In a BBC, the conversation is grounded in a knowledge source. A key challenge in BBCs is Knowledge Selection (KS): given a conversational context, try to find the appropriate background k...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	142,876

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