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
1905.07790	Correlation Coefficients and Semantic Textual Similarity	A large body of research into semantic textual similarity has focused on constructing state-of-the-art embeddings using sophisticated modelling, careful choice of learning signals and many clever tricks. By contrast, little attention has been devoted to similarity measures between these embeddings, with cosine similari...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	131,325
2408.02795	Entity Retrieval for Answering Entity-Centric Questions	The similarity between the question and indexed documents is a crucial factor in document retrieval for retrieval-augmented question answering. Although this is typically the only method for obtaining the relevant documents, it is not the sole approach when dealing with entity-centric questions. In this study, we propo...	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	478,765
2102.03355	Thermal Control of Laser Powder Bed Fusion Using Deep Reinforcement Learning	Powder-based additive manufacturing techniques provide tools to construct intricate structures that are difficult to manufacture using conventional methods. In Laser Powder Bed Fusion, components are built by selectively melting specific areas of the powder bed, to form the two-dimensional cross-section of the specific...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	218,719
2306.11518	One model to rule them all: ranking Slovene summarizers	Text summarization is an essential task in natural language processing, and researchers have developed various approaches over the years, ranging from rule-based systems to neural networks. However, there is no single model or approach that performs well on every type of text. We propose a system that recommends the mo...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	374,623
2307.00895	Synthesis of Contrast-Enhanced Breast MRI Using Multi-b-Value DWI-based Hierarchical Fusion Network with Attention Mechanism	Magnetic resonance imaging (MRI) is the most sensitive technique for breast cancer detection among current clinical imaging modalities. Contrast-enhanced MRI (CE-MRI) provides superior differentiation between tumors and invaded healthy tissue, and has become an indispensable technique in the detection and evaluation of...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	377,169
1805.12152	Robustness May Be at Odds with Accuracy	We show that there may exist an inherent tension between the goal of adversarial robustness and that of standard generalization. Specifically, training robust models may not only be more resource-consuming, but also lead to a reduction of standard accuracy. We demonstrate that this trade-off between the standard accura...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	true	false	false	99,101
2004.12247	Hierarchical Multi Task Learning with Subword Contextual Embeddings for Languages with Rich Morphology	Morphological information is important for many sequence labeling tasks in Natural Language Processing (NLP). Yet, existing approaches rely heavily on manual annotations or external software to capture this information. In this study, we propose using subword contextual embeddings to capture the morphological informati...	false	false	false	false	false	true	true	false	true	false	false	false	false	false	false	false	false	false	174,180
2008.05708	Robust Image Matching By Dynamic Feature Selection	Estimating dense correspondences between images is a long-standing image under-standing task. Recent works introduce convolutional neural networks (CNNs) to extract high-level feature maps and find correspondences through feature matching. However,high-level feature maps are in low spatial resolution and therefore insu...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	191,587
1612.00402	Reduced Order Models for Pricing European and American Options under Stochastic Volatility and Jump-Diffusion Models	European options can be priced by solving parabolic partial(-integro) differential equations under stochastic volatility and jump-diffusion models like Heston, Merton, and Bates models. American option prices can be obtained by solving linear complementary problems (LCPs) with the same operators. A finite difference di...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	64,878
1904.01537	Speech denoising by parametric resynthesis	This work proposes the use of clean speech vocoder parameters as the target for a neural network performing speech enhancement. These parameters have been designed for text-to-speech synthesis so that they both produce high-quality resyntheses and also are straightforward to model with neural networks, but have not bee...	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	126,163
2404.13515	FedTrans: Efficient Federated Learning via Multi-Model Transformation	Federated learning (FL) aims to train machine learning (ML) models across potentially millions of edge client devices. Yet, training and customizing models for FL clients is notoriously challenging due to the heterogeneity of client data, device capabilities, and the massive scale of clients, making individualized mode...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	true	448,327
1804.07354	The Aftermath of Disbanding an Online Hateful Community	Harassing and hateful speech in online spaces has become a common problem for platform maintainers and their users. The toxicity created by such content can discourage user participation and engagement. Therefore, it is crucial for and a common goal of platform managers to diminish hateful and harmful content. Over the...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	95,510
2007.06709	Deep Image Orientation Angle Detection	Estimating and rectifying the orientation angle of any image is a pretty challenging task. Initial work used the hand engineering features for this purpose, where after the invention of deep learning using convolution-based neural network showed significant improvement in this problem. However, this paper shows that th...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	187,097
2309.08915	On non-expandable cross-bifix-free codes	A cross-bifix-free code of length $n$ over $\mathbb{Z}_q$ is defined as a non-empty subset of $\mathbb{Z}_q^n$ satisfying that the prefix set of each codeword is disjoint from the suffix set of every codeword. Cross-bifix-free codes have found important applications in digital communication systems. One of the main res...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	392,382
2409.01930	Efficient LLM Context Distillation	This paper specifically investigates context distillation a method that extends the utility of task-specific examples by internalizing them, thus augmenting the example set accessible for model inference.	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	485,511
2012.11094	Complexity of zigzag sampling algorithm for strongly log-concave distributions	We study the computational complexity of zigzag sampling algorithm for strongly log-concave distributions. The zigzag process has the advantage of not requiring time discretization for implementation, and that each proposed bouncing event requires only one evaluation of partial derivative of the potential, while its co...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	212,525
2410.01336	VectorGraphNET: Graph Attention Networks for Accurate Segmentation of Complex Technical Drawings	This paper introduces a new approach to extract and analyze vector data from technical drawings in PDF format. Our method involves converting PDF files into SVG format and creating a feature-rich graph representation, which captures the relationships between vector entities using geometrical information. We then apply ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	493,724
2310.01885	Synthetic CT Generation via Variant Invertible Network for All-digital Brain PET Attenuation Correction	Attenuation correction (AC) is essential for the generation of artifact-free and quantitatively accurate positron emission tomography (PET) images. However, AC of PET faces challenges including inter-scan motion and erroneous transformation of structural voxel-intensities to PET attenuation-correction factors. Nowadays...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	396,622
1805.08587	Deep Feature Aggregation and Image Re-ranking with Heat Diffusion for Image Retrieval	Image retrieval based on deep convolutional features has demonstrated state-of-the-art performance in popular benchmarks. In this paper, we present a unified solution to address deep convolutional feature aggregation and image re-ranking by simulating the dynamics of heat diffusion. A distinctive problem in image retri...	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	false	98,180
2211.07855	Relationship of the language distance to English ability of a country	Language difference is one of the factors that hinder the acquisition of second language skills. In this article, we introduce a novel solution that leverages the strength of deep neural networks to measure the semantic dissimilarity between languages based on their word distributions in the embedding space of the mult...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	330,393
2009.06625	Revealing Secrets in SPARQL Session Level	Based on Semantic Web technologies, knowledge graphs help users to discover information of interest by using live SPARQL services. Answer-seekers often examine intermediate results iteratively and modify SPARQL queries repeatedly in a search session. In this context, understanding user behaviors is critical for effecti...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	true	false	195,702
1704.03844	Determining Song Similarity via Machine Learning Techniques and Tagging Information	The task of determining item similarity is a crucial one in a recommender system. This constitutes the base upon which the recommender system will work to determine which items are more likely to be enjoyed by a user, resulting in more user engagement. In this paper we tackle the problem of determining song similarity ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	71,699
2303.03327	Tight Bounds for $\gamma$-Regret via the Decision-Estimation Coefficient	In this work, we give a statistical characterization of the $\gamma$-regret for arbitrary structured bandit problems, the regret which arises when comparing against a benchmark that is $\gamma$ times the optimal solution. The $\gamma$-regret emerges in structured bandit problems over a function class $\mathcal{F}$ wher...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	349,685
2005.06667	Exploiting Multi-Layer Grid Maps for Surround-View Semantic Segmentation of Sparse LiDAR Data	In this paper, we consider the transformation of laser range measurements into a top-view grid map representation to approach the task of LiDAR-only semantic segmentation. Since the recent publication of the SemanticKITTI data set, researchers are now able to study semantic segmentation of urban LiDAR sequences based o...	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	177,076
2405.11995	Safe by Design Autonomous Driving Systems	Developing safe autonomous driving systems is a major scientific and technical challenge. Existing AI-based end-to-end solutions do not offer the necessary safety guarantees, while traditional systems engineering approaches are defeated by the complexity of the problem. Currently, there is an increasing interest in hyb...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	455,375
2302.03916	QS-ADN: Quasi-Supervised Artifact Disentanglement Network for Low-Dose CT Image Denoising by Local Similarity Among Unpaired Data	Deep learning has been successfully applied to low-dose CT (LDCT) image denoising for reducing potential radiation risk. However, the widely reported supervised LDCT denoising networks require a training set of paired images, which is expensive to obtain and cannot be perfectly simulated. Unsupervised learning utilizes...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	344,518
2305.06967	Data quality dimensions for fair AI	Artificial Intelligence (AI) systems are not intrinsically neutral and biases trickle in any type of technological tool. In particular when dealing with people, the impact of AI algorithms' technical errors originating with mislabeled data is undeniable. As they feed wrong and discriminatory classifications, these syst...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	363,722
2405.16954	Convergence of SGD with momentum in the nonconvex case: A time window-based analysis	The stochastic gradient descent method with momentum (SGDM) is a common approach for solving large-scale and stochastic optimization problems. Despite its popularity, the convergence behavior of SGDM remains less understood in nonconvex scenarios. This is primarily due to the absence of a sufficient descent property an...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	457,695
2011.00459	A multiscale model of terrain dynamics for real-time earthmoving simulation	A multiscale model for real-time simulation of terrain dynamics is explored. To represent the dynamics on different scales the model combines the description of soil as a continuous solid, as distinct particles and as rigid multibodies. The models are dynamically coupled to each other and to the earthmoving equipment. ...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	204,229
2211.06721	Using Features at Multiple Temporal and Spatial Resolutions to Predict Human Behavior in Real Time	When performing complex tasks, humans naturally reason at multiple temporal and spatial resolutions simultaneously. We contend that for an artificially intelligent agent to effectively model human teammates, i.e., demonstrate computational theory of mind (ToM), it should do the same. In this paper, we present an approa...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	329,998
1903.06023	Deep Distribution Regression	Due to their flexibility and predictive performance, machine-learning based regression methods have become an important tool for predictive modeling and forecasting. However, most methods focus on estimating the conditional mean or specific quantiles of the target quantity and do not provide the full conditional distri...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	124,294
2010.10217	Quantum circuit architecture search for variational quantum algorithms	Variational quantum algorithms (VQAs) are expected to be a path to quantum advantages on noisy intermediate-scale quantum devices. However, both empirical and theoretical results exhibit that the deployed ansatz heavily affects the performance of VQAs such that an ansatz with a larger number of quantum gates enables a ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	201,822
2411.19941	Perception Test 2024: Challenge Summary and a Novel Hour-Long VideoQA Benchmark	Following the successful 2023 edition, we organised the Second Perception Test challenge as a half-day workshop alongside the IEEE/CVF European Conference on Computer Vision (ECCV) 2024, with the goal of benchmarking state-of-the-art video models and measuring the progress since last year using the Perception Test benc...	false	false	false	false	false	false	true	false	true	false	false	true	false	false	false	false	false	false	512,436
2407.19439	Business and Regulatory Responses to Artificial Intelligence: Dynamic Regulation, Innovation Ecosystems and the Strategic Management of Disruptive Technology	Identifying and then implementing an effective response to disruptive new AI technologies is enormously challenging for any business looking to integrate AI into their operations, as well as regulators looking to leverage AI-related innovation as a mechanism for achieving regional economic growth. These business and re...	false	false	false	false	true	false	false	false	false	false	false	false	false	true	false	false	false	false	476,788
1905.00111	Universal Mutual Information Privacy Guarantees for Smart Meters	Smart meters enable improvements in electricity distribution system efficiency at some cost in customer privacy. Users with home batteries can mitigate this privacy loss by applying charging policies that mask their underlying energy use. A battery charging policy is proposed and shown to provide universal privacy guar...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	129,397
2109.06363	Sensor Adversarial Traits: Analyzing Robustness of 3D Object Detection Sensor Fusion Models	A critical aspect of autonomous vehicles (AVs) is the object detection stage, which is increasingly being performed with sensor fusion models: multimodal 3D object detection models which utilize both 2D RGB image data and 3D data from a LIDAR sensor as inputs. In this work, we perform the first study to analyze the rob...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	255,121
2305.19005	Hybrid Driven Learning for Channel Estimation in Intelligent Reflecting Surface Aided Millimeter Wave Communications	Intelligent reflecting surfaces (IRS) have been proposed in millimeter wave (mmWave) and terahertz (THz) systems to achieve both coverage and capacity enhancement, where the design of hybrid precoders, combiners, and the IRS typically relies on channel state information. In this paper, we address the problem of uplink ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	369,338
1802.08701	Machine learning based hyperspectral image analysis: A survey	Hyperspectral sensors enable the study of the chemical properties of scene materials remotely for the purpose of identification, detection, and chemical composition analysis of objects in the environment. Hence, hyperspectral images captured from earth observing satellites and aircraft have been increasingly important ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	91,158
2008.11227	A Computationally Efficient Multiclass Time-Frequency Common Spatial Pattern Analysis on EEG Motor Imagery	Common spatial pattern (CSP) is a popular feature extraction method for electroencephalogram (EEG) motor imagery (MI). This study modifies the conventional CSP algorithm to improve the multi-class MI classification accuracy and ensure the computation process is efficient. The EEG MI data is gathered from the Brain-Comp...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	193,206
2310.10497	LocSelect: Target Speaker Localization with an Auditory Selective Hearing Mechanism	The prevailing noise-resistant and reverberation-resistant localization algorithms primarily emphasize separating and providing directional output for each speaker in multi-speaker scenarios, without association with the identity of speakers. In this paper, we present a target speaker localization algorithm with a sele...	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	400,249
1002.4048	A Hough Transform based Technique for Text Segmentation	Text segmentation is an inherent part of an OCR system irrespective of the domain of application of it. The OCR system contains a segmentation module where the text lines, words and ultimately the characters must be segmented properly for its successful recognition. The present work implements a Hough transform based t...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	5,753
2410.03432	EB-NeRD: A Large-Scale Dataset for News Recommendation	Personalized content recommendations have been pivotal to the content experience in digital media from video streaming to social networks. However, several domain specific challenges have held back adoption of recommender systems in news publishing. To address these challenges, we introduce the Ekstra Bladet News Recom...	false	false	false	false	true	true	true	false	false	false	false	false	false	false	false	false	false	false	494,778
1506.00406	Monolingually Derived Phrase Scores for Phrase Based SMT Using Neural Networks Vector Representations	In this paper, we propose two new features for estimating phrase-based machine translation parameters from mainly monolingual data. Our method is based on two recently introduced neural network vector representation models for words and sentences. It is the first time that these models have been used in an end to end p...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	43,665
2502.04400	Adaptive Prototype Knowledge Transfer for Federated Learning with Mixed Modalities and Heterogeneous Tasks	Multimodal Federated Learning (MFL) enables multiple clients to collaboratively train models on multimodal data while ensuring clients' privacy. However, modality and task heterogeneity hinder clients from learning a unified representation, weakening local model generalization, especially in MFL with mixed modalities w...	false	false	false	false	true	false	true	false	false	false	false	false	true	false	false	false	false	true	531,139
1907.07061	How much real data do we actually need: Analyzing object detection performance using synthetic and real data	In recent years, deep learning models have resulted in a huge amount of progress in various areas, including computer vision. By nature, the supervised training of deep models requires a large amount of data to be available. This ideal case is usually not tractable as the data annotation is a tremendously exhausting an...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	138,774
2111.05534	Verifying Controllers with Convolutional Neural Network-based Perception: A Case for Intelligible, Safe, and Precise Abstractions	Convolutional Neural Networks (CNN) for object detection, lane detection, and segmentation now sit at the head of most autonomy pipelines, and yet, their safety analysis remains an important challenge. Formal analysis of perception models is fundamentally difficult because their correctness is hard if not impossible to...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	true	265,825
2401.02873	Optimal Chaining of Vehicle Plans with Time Windows	For solving problems from the domain of Mobility-on-Demand (MoD), we often need to connect vehicle plans into plans spanning longer time, a process we call plan chaining. As we show in this work, chaining of the plans can be used to reduce the size of MoD providers' fleet (fleet-sizing problem) but also to reduce the t...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	419,873
2012.03642	Generalised Perceptron Learning	We present a generalisation of Rosenblatt's traditional perceptron learning algorithm to the class of proximal activation functions and demonstrate how this generalisation can be interpreted as an incremental gradient method applied to a novel energy function. This novel energy function is based on a generalised Bregma...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	210,191
2411.15183	Balancing property optimization and constraint satisfaction for constrained multi-property molecular optimization	Molecular optimization, which aims to discover improved molecules from a vast chemical search space, is a critical step in chemical development. Various artificial intelligence technologies have demonstrated high effectiveness and efficiency on molecular optimization tasks. However, few of these technologies focus on b...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	510,474
1512.00567	Rethinking the Inception Architecture for Computer Vision	Convolutional networks are at the core of most state-of-the-art computer vision solutions for a wide variety of tasks. Since 2014 very deep convolutional networks started to become mainstream, yielding substantial gains in various benchmarks. Although increased model size and computational cost tend to translate to imm...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	49,723
2403.04866	A Modular End-to-End Multimodal Learning Method for Structured and Unstructured Data	Multimodal learning is a rapidly growing research field that has revolutionized multitasking and generative modeling in AI. While much of the research has focused on dealing with unstructured data (e.g., language, images, audio, or video), structured data (e.g., tabular data, time series, or signals) has received less ...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	435,766
2405.04952	Evolving R2 to R2+: Optimal, Delayed Line-of-sight Vector-based Path Planning	A vector-based any-angle path planner, R2, is evolved in to R2+ in this paper. By delaying line-of-sight, R2 and R2+ search times are largely unaffected by the distance between the start and goal points, but are exponential in the worst case with respect to the number of collisions during searches. To improve search ti...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	452,741
2410.14135	Inverse Reinforcement Learning from Non-Stationary Learning Agents	In this paper, we study an inverse reinforcement learning problem that involves learning the reward function of a learning agent using trajectory data collected while this agent is learning its optimal policy. To address this problem, we propose an inverse reinforcement learning method that allows us to estimate the po...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	499,882
2412.02899	Adaptive LiDAR Odometry and Mapping for Autonomous Agricultural Mobile Robots in Unmanned Farms	Unmanned and intelligent agricultural systems are crucial for enhancing agricultural efficiency and for helping mitigate the effect of labor shortage. However, unlike urban environments, agricultural fields impose distinct and unique challenges on autonomous robotic systems, such as the unstructured and dynamic nature ...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	513,730
1502.05082	What makes for effective detection proposals?	Current top performing object detectors employ detection proposals to guide the search for objects, thereby avoiding exhaustive sliding window search across images. Despite the popularity and widespread use of detection proposals, it is unclear which trade-offs are made when using them during object detection. We provi...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	40,333
2302.09780	Compressing Tabular Data via Latent Variable Estimation	Data used for analytics and machine learning often take the form of tables with categorical entries. We introduce a family of lossless compression algorithms for such data that proceed in four steps: $(i)$ Estimate latent variables associated to rows and columns; $(ii)$ Partition the table in blocks according to the ro...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	346,563
2305.06036	FusionDepth: Complement Self-Supervised Monocular Depth Estimation with Cost Volume	Multi-view stereo depth estimation based on cost volume usually works better than self-supervised monocular depth estimation except for moving objects and low-textured surfaces. So in this paper, we propose a multi-frame depth estimation framework which monocular depth can be refined continuously by multi-frame sequent...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	363,380
1802.03887	A Systems Theory Approach to the Synthesis of Minimum Noise Phase-Insensitive Quantum Amplifiers	We present a systems theory approach to the proof of a result bounding the required level of added quantum noise in a phase-insensitive quantum amplifier. We also present a synthesis procedure for constructing a quantum optical phase-insensitive quantum amplifier which adds the minimum level of quantum noise and achiev...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	90,091
2501.07818	A Multi-Encoder Frozen-Decoder Approach for Fine-Tuning Large Language Models	Among parameter-efficient fine-tuning methods, freezing has emerged as a popular strategy for speeding up training, reducing catastrophic forgetting, and improving downstream performance. We investigate the impact of freezing the decoder in a multi-task setup comprising diverse natural language tasks, aiming to reduce ...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	524,523
1909.03785	Belief Regulated Dual Propagation Nets for Learning Action Effects on Groups of Articulated Objects	In recent years, graph neural networks have been successfully applied for learning the dynamics of complex and partially observable physical systems. However, their use in the robotics domain is, to date, still limited. In this paper, we introduce Belief Regulated Dual Propagation Networks (BRDPN), a general-purpose le...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	144,601
2103.13427	Multi-Label Classification Neural Networks with Hard Logical Constraints	Multi-label classification (MC) is a standard machine learning problem in which a data point can be associated with a set of classes. A more challenging scenario is given by hierarchical multi-label classification (HMC) problems, in which every prediction must satisfy a given set of hard constraints expressing subclass...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	226,478
1902.04335	Hyperbolic Disk Embeddings for Directed Acyclic Graphs	Obtaining continuous representations of structural data such as directed acyclic graphs (DAGs) has gained attention in machine learning and artificial intelligence. However, embedding complex DAGs in which both ancestors and descendants of nodes are exponentially increasing is difficult. Tackling in this problem, we de...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	121,318
2209.08097	Noise transfer for unsupervised domain adaptation of retinal OCT images	Optical coherence tomography (OCT) imaging from different camera devices causes challenging domain shifts and can cause a severe drop in accuracy for machine learning models. In this work, we introduce a minimal noise adaptation method based on a singular value decomposition (SVDNA) to overcome the domain gap between t...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	317,992
2010.16409	Inherent Trade-offs in the Fair Allocation of Treatments	Explicit and implicit bias clouds human judgement, leading to discriminatory treatment of minority groups. A fundamental goal of algorithmic fairness is to avoid the pitfalls in human judgement by learning policies that improve the overall outcomes while providing fair treatment to protected classes. In this paper, we ...	false	false	false	false	true	false	true	false	false	false	false	false	false	true	false	false	false	false	204,057
2402.10009	Zero-Shot Unsupervised and Text-Based Audio Editing Using DDPM Inversion	Editing signals using large pre-trained models, in a zero-shot manner, has recently seen rapid advancements in the image domain. However, this wave has yet to reach the audio domain. In this paper, we explore two zero-shot editing techniques for audio signals, which use DDPM inversion with pre-trained diffusion models....	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	429,766
2208.04820	Rapid Development of a Mobile Robot Simulation Environment	Robotics simulation provides many advantages during the development of an intelligent ground vehicle (IGV) such as testing the software components in varying scenarios without requiring a complete physical robot. This paper discusses a 3D simulation environment created using rapid application development and the Unity ...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	312,233
2501.18277	Sebra: Debiasing Through Self-Guided Bias Ranking	Ranking samples by fine-grained estimates of spuriosity (the degree to which spurious cues are present) has recently been shown to significantly benefit bias mitigation, over the traditional binary biased-\textit{vs}-unbiased partitioning of train sets. However, this spuriosity ranking comes with the requirement of hum...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	528,638
1904.05236	Curriculum semi-supervised segmentation	This study investigates a curriculum-style strategy for semi-supervised CNN segmentation, which devises a regression network to learn image-level information such as the size of a target region. These regressions are used to effectively regularize the segmentation network, constraining softmax predictions of the unlabe...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	127,244
1907.01453	A Note On The Popularity of Stochastic Optimization Algorithms in Different Fields: A Quantitative Analysis from 2007 to 2017	Stochastic optimization algorithms are often used to solve complex large-scale optimization problems in various fields. To date, there have been a number of stochastic optimization algorithms such as Genetic Algorithm, Cuckoo Search, Tabu Search, Simulated Annealing, Particle Swarm Optimization, Ant Colony Optimization...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	137,334
2302.03768	Catch Me If You Can: Improving Adversaries in Cyber-Security With Q-Learning Algorithms	The ongoing rise in cyberattacks and the lack of skilled professionals in the cybersecurity domain to combat these attacks show the need for automated tools capable of detecting an attack with good performance. Attackers disguise their actions and launch attacks that consist of multiple actions, which are difficult to ...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	true	false	false	false	344,456
2408.16119	Data Formulator 2: Iteratively Creating Rich Visualizations with AI	To create rich visualizations, data analysts often need to iterate back and forth among data processing and chart specification to achieve their goals. To achieve this, analysts need not only proficiency in data transformation and visualization tools but also efforts to manage the branching history consisting of many d...	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	484,196
2310.07197	MatChat: A Large Language Model and Application Service Platform for Materials Science	The prediction of chemical synthesis pathways plays a pivotal role in materials science research. Challenges, such as the complexity of synthesis pathways and the lack of comprehensive datasets, currently hinder our ability to predict these chemical processes accurately. However, recent advancements in generative artif...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	398,868
2405.02935	Enabling Patient-side Disease Prediction via the Integration of Patient Narratives	Disease prediction holds considerable significance in modern healthcare, because of its crucial role in facilitating early intervention and implementing effective prevention measures. However, most recent disease prediction approaches heavily rely on laboratory test outcomes (e.g., blood tests and medical imaging from ...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	451,981
2306.09835	Subset Selection Based On Multiple Rankings in the Presence of Bias: Effectiveness of Fairness Constraints for Multiwinner Voting Score Functions	We consider the problem of subset selection where one is given multiple rankings of items and the goal is to select the highest ``quality'' subset. Score functions from the multiwinner voting literature have been used to aggregate rankings into quality scores for subsets. We study this setting of subset selection probl...	false	false	false	false	true	false	true	false	false	false	false	false	false	true	false	false	false	true	373,984
1911.11876	Dataset-On-Demand: Automatic View Search and Presentation for Data Discovery	Many data problems are solved when the right view of a combination of datasets is identified. Finding such a view is challenging because of the many tables spread across many databases, data lakes, and cloud storage in modern organizations. Finding relevant tables, and identifying how to combine them is a difficult and...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	155,243
2210.15543	Beyond the Return: Off-policy Function Estimation under User-specified Error-measuring Distributions	Off-policy evaluation often refers to two related tasks: estimating the expected return of a policy and estimating its value function (or other functions of interest, such as density ratios). While recent works on marginalized importance sampling (MIS) show that the former can enjoy provable guarantees under realizable...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	327,001
2405.13997	Sigmoid Gating is More Sample Efficient than Softmax Gating in Mixture of Experts	The softmax gating function is arguably the most popular choice in mixture of experts modeling. Despite its widespread use in practice, the softmax gating may lead to unnecessary competition among experts, potentially causing the undesirable phenomenon of representation collapse due to its inherent structure. In respon...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	456,188
2110.15278	Self-supervised EEG Representation Learning for Automatic Sleep Staging	Background: Deep learning models have shown great success in automating tasks in sleep medicine by learning from carefully annotated Electroencephalogram (EEG) data. However, effectively utilizing a large amount of raw EEG remains a challenge. Objective: In this paper, we aim to learn robust vector representations fr...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	263,831
2012.00202	Field-wise Learning for Multi-field Categorical Data	We propose a new method for learning with multi-field categorical data. Multi-field categorical data are usually collected over many heterogeneous groups. These groups can reflect in the categories under a field. The existing methods try to learn a universal model that fits all data, which is challenging and inevitably...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	209,054
2502.07645	Beyond Behavior Cloning: Robustness through Interactive Imitation and Contrastive Learning	Behavior cloning (BC) traditionally relies on demonstration data, assuming the demonstrated actions are optimal. This can lead to overfitting under noisy data, particularly when expressive models are used (e.g., the energy-based model in Implicit BC). To address this, we extend behavior cloning into an iterative proces...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	532,700
2009.11027	KoBE: Knowledge-Based Machine Translation Evaluation	We propose a simple and effective method for machine translation evaluation which does not require reference translations. Our approach is based on (1) grounding the entity mentions found in each source sentence and candidate translation against a large-scale multilingual knowledge base, and (2) measuring the recall of...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	197,050
1803.03186	RAN Enablers for 5G Radio Resource Management	This paper presents the description of several key RAN enablers for the radio resource management (RRM) framework of the fifth generation (5G) radio access network (RAN), referred to as building blocks of the 5G RRM. In particular, the following key RAN enablers are discussed: i) interference management techniques for ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	92,203
2012.10711	Quantum reinforcement learning in continuous action space	Quantum reinforcement learning (QRL) is one promising algorithm proposed for near-term quantum devices. Early QRL proposals are effective at solving problems in discrete action space, but often suffer from the curse of dimensionality in the continuous domain due to discretization. To address this problem, we propose a ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	212,420
1706.06987	A Generative Model of Group Conversation	Conversations with non-player characters (NPCs) in games are typically confined to dialogue between a human player and a virtual agent, where the conversation is initiated and controlled by the player. To create richer, more believable environments for players, we need conversational behavior to reflect initiative on t...	true	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	75,779
2207.00740	PhilaeX: Explaining the Failure and Success of AI Models in Malware Detection	The explanation to an AI model's prediction used to support decision making in cyber security, is of critical importance. It is especially so when the model's incorrect prediction can lead to severe damages or even losses to lives and critical assets. However, most existing AI models lack the ability to provide explana...	false	false	false	false	true	false	true	false	false	false	false	false	true	false	false	false	false	false	305,872
1807.00686	YH Technologies at ActivityNet Challenge 2018	This notebook paper presents an overview and comparative analysis of our systems designed for the following five tasks in ActivityNet Challenge 2018: temporal action proposals, temporal action localization, dense-captioning events in videos, trimmed action recognition, and spatio-temporal action localization.	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	101,896
2009.07165	Beyond Individualized Recourse: Interpretable and Interactive Summaries of Actionable Recourses	As predictive models are increasingly being deployed in high-stakes decision-making, there has been a lot of interest in developing algorithms which can provide recourses to affected individuals. While developing such tools is important, it is even more critical to analyse and interpret a predictive model, and vet it t...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	195,851
2007.05428	Joint Blind Deconvolution and Robust Principal Component Analysis for Blood Flow Estimation in Medical Ultrasound Imaging	This paper addresses the problem of high-resolution Doppler blood flow estimation from an ultrafast sequence of ultrasound images. Formulating the separation of clutter and blood components as an inverse problem has been shown in the literature to be a good alternative to spatio-temporal singular value decomposition (S...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	186,671
1712.10136	Learning Deep and Compact Models for Gesture Recognition	We look at the problem of developing a compact and accurate model for gesture recognition from videos in a deep-learning framework. Towards this we propose a joint 3DCNN-LSTM model that is end-to-end trainable and is shown to be better suited to capture the dynamic information in actions. The solution achieves close to...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	87,456
2209.02681	How important are activation functions in regression and classification? A survey, performance comparison, and future directions	Inspired by biological neurons, the activation functions play an essential part in the learning process of any artificial neural network commonly used in many real-world problems. Various activation functions have been proposed in the literature for classification as well as regression tasks. In this work, we survey th...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	316,276
2411.10004	EyeDiff: text-to-image diffusion model improves rare eye disease diagnosis	The rising prevalence of vision-threatening retinal diseases poses a significant burden on the global healthcare systems. Deep learning (DL) offers a promising solution for automatic disease screening but demands substantial data. Collecting and labeling large volumes of ophthalmic images across various modalities enco...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	508,453
2406.12387	Performant ASR Models for Medical Entities in Accented Speech	Recent strides in automatic speech recognition (ASR) have accelerated their application in the medical domain where their performance on accented medical named entities (NE) such as drug names, diagnoses, and lab results, is largely unknown. We rigorously evaluate multiple ASR models on a clinical English dataset of 93...	false	false	true	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	465,379
1506.06068	A general framework for the IT-based clustering methods	Previously, we proposed a physically inspired rule to organize the data points in a sparse yet effective structure, called the in-tree (IT) graph, which is able to capture a wide class of underlying cluster structures in the datasets, especially for the density-based datasets. Although there are some redundant edges or...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	44,375
1101.3929	Characteristic Generators and Dualization for Tail-Biting Trellises	This paper focuses on dualizing tail-biting trellises, particularly KV-trellises. These trellises are based on characteristic generators, as introduced by Koetter/Vardy (2003), and may be regarded as a natural generalization of minimal conventional trellises, even though they are not necessarily minimal. Two dualizatio...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	8,867
2404.14092	Multi-agent Reinforcement Learning-based Joint Precoding and Phase Shift Optimization for RIS-aided Cell-Free Massive MIMO Systems	Cell-free (CF) massive multiple-input multiple-output (mMIMO) is a promising technique for achieving high spectral efficiency (SE) using multiple distributed access points (APs). However, harsh propagation environments often lead to significant communication performance degradation due to high penetration loss. To over...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	448,570
2002.04169	Edge Cache-assisted Secure Low-Latency Millimeter Wave Transmission	In this paper, we consider an edge cache-assisted millimeter wave cloud radio access network (C-RAN). Each remote radio head (RRH) in the C-RAN has a local cache, which can pre-fetch and store the files requested by the actuators. Multiple RRHs form a cluster to cooperatively serve the actuators, which acquire their re...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	163,527
2305.10472	Nine tips for ecologists using machine learning	Due to their high predictive performance and flexibility, machine learning models are an appropriate and efficient tool for ecologists. However, implementing a machine learning model is not yet a trivial task and may seem intimidating to ecologists with no previous experience in this area. Here we provide a series of t...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	365,092
2309.00625	Identifying Secure Operating Ranges for DER Control using Bilevel Optimization	Active distribution grids are accommodating an increasing number of controllable electric loads and distributed energy resources (DERs). A majority of these DERs are managed by entities other than the distribution utility, such as individual customers or third-party aggregators, who control the loads and DERs without c...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	389,362
2406.05510	Representation Learning with Conditional Information Flow Maximization	This paper proposes an information-theoretic representation learning framework, named conditional information flow maximization, to extract noise-invariant sufficient representations for the input data and target task. It promotes the learned representations have good feature uniformity and sufficient predictive abilit...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	462,165
2303.01313	Weakly-supervised HOI Detection via Prior-guided Bi-level Representation Learning	Human object interaction (HOI) detection plays a crucial role in human-centric scene understanding and serves as a fundamental building-block for many vision tasks. One generalizable and scalable strategy for HOI detection is to use weak supervision, learning from image-level annotations only. This is inherently challe...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	348,923

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