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
1005.0340	Statistical Learning in Automated Troubleshooting: Application to LTE Interference Mitigation	This paper presents a method for automated healing as part of off-line automated troubleshooting. The method combines statistical learning with constraint optimization. The automated healing aims at locally optimizing radio resource management (RRM) or system parameters of cells with poor performance in an iterative ma...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	6,387
2105.08837	Fusion-DHL: WiFi, IMU, and Floorplan Fusion for Dense History of Locations in Indoor Environments	The paper proposes a multi-modal sensor fusion algorithm that fuses WiFi, IMU, and floorplan information to infer an accurate and dense location history in indoor environments. The algorithm uses 1) an inertial navigation algorithm to estimate a relative motion trajectory from IMU sensor data; 2) a WiFi-based localizat...	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	235,881
2206.08737	N$^2$M$^2$: Learning Navigation for Arbitrary Mobile Manipulation Motions in Unseen and Dynamic Environments	Despite its importance in both industrial and service robotics, mobile manipulation remains a significant challenge as it requires a seamless integration of end-effector trajectory generation with navigation skills as well as reasoning over long-horizons. Existing methods struggle to control the large configuration spa...	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	false	303,273
2006.00148	Topic Detection and Summarization of User Reviews	A massive amount of reviews are generated daily from various platforms. It is impossible for people to read through tons of reviews and to obtain useful information. Automatic summarizing customer reviews thus is important for identifying and extracting the essential information to help users to obtain the gist of the ...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	179,383
2204.03737	Mixing Signals: Data Augmentation Approach for Deep Learning Based Modulation Recognition	With the rapid development of deep learning, automatic modulation recognition (AMR), as an important task in cognitive radio, has gradually transformed from traditional feature extraction and classification to automatic classification by deep learning technology. However, deep learning models are data-driven methods, w...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	290,414
2412.04914	Achieving Group Fairness through Independence in Predictive Process Monitoring	Predictive process monitoring focuses on forecasting future states of ongoing process executions, such as predicting the outcome of a particular case. In recent years, the application of machine learning models in this domain has garnered significant scientific attention. When using historical execution data, which may...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	514,625
1502.06096	Reinforcement Learning in a Neurally Controlled Robot Using Dopamine Modulated STDP	Recent work has shown that dopamine-modulated STDP can solve many of the issues associated with reinforcement learning, such as the distal reward problem. Spiking neural networks provide a useful technique in implementing reinforcement learning in an embodied context as they can deal with continuous parameter spaces an...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	false	false	40,452
2411.06214	Early Prediction of Natural Gas Pipeline Leaks Using the MKTCN Model	Natural gas pipeline leaks pose severe risks, leading to substantial economic losses and potential hazards to human safety. In this study, we develop an accurate model for the early prediction of pipeline leaks. To the best of our knowledge, unlike previous anomaly detection, this is the first application to use intern...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	507,018
1810.08125	Procedurally Provisioned Access Control for Robotic Systems	Security of robotics systems, as well as of the related middleware infrastructures, is a critical issue for industrial and domestic IoT, and it needs to be continuously assessed throughout the whole development lifecycle. The next generation open source robotic software stack, ROS2, is now targeting support for Secure ...	false	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	110,760
1704.02737	Secure Mode Distinguishability for Switching Systems Subject to Sparse Attacks	Switching systems are an important mathematical formalism when dealing with Cyber-Physical Systems (CPSs). In this paper we provide conditions for the exact reconstruction of the initial discrete state of a switching system, when only the continuous output is measurable, and the discrete output signal is not available....	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	71,504
2306.01120	Frequency-dependent Switching Control for Disturbance Attenuation of Linear Systems	The generalized Kalman-Yakubovich-Popov lemma as established by Iwasaki and Hara in 2005 marks a milestone in the analysis and synthesis of linear systems from a finite-frequency perspective. Given a pre-specified frequency band, it allows us to produce passive controllers with excellent in-band disturbance attenuation...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	370,301
1206.4671	Dependent Hierarchical Normalized Random Measures for Dynamic Topic Modeling	We develop dependent hierarchical normalized random measures and apply them to dynamic topic modeling. The dependency arises via superposition, subsampling and point transition on the underlying Poisson processes of these measures. The measures used include normalised generalised Gamma processes that demonstrate power ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	16,722
1906.06301	Video-Driven Speech Reconstruction using Generative Adversarial Networks	Speech is a means of communication which relies on both audio and visual information. The absence of one modality can often lead to confusion or misinterpretation of information. In this paper we present an end-to-end temporal model capable of directly synthesising audio from silent video, without needing to transform ...	false	false	true	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	135,258
2212.02224	Bi-Level Optimization Augmented with Conditional Variational Autoencoder for Autonomous Driving in Dense Traffic	Autonomous driving has a natural bi-level structure. The goal of the upper behavioural layer is to provide appropriate lane change, speeding up, and braking decisions to optimize a given driving task. However, this layer can only indirectly influence the driving efficiency through the lower-level trajectory planner, wh...	false	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	334,726
1910.09362	Improving Word Representations: A Sub-sampled Unigram Distribution for Negative Sampling	Word2Vec is the most popular model for word representation and has been widely investigated in literature. However, its noise distribution for negative sampling is decided by empirical trials and the optimality has always been ignored. We suggest that the distribution is a sub-optimal choice, and propose to use a sub-s...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	150,170
1904.02773	Adaptive Sequential Machine Learning	A framework previously introduced in [3] for solving a sequence of stochastic optimization problems with bounded changes in the minimizers is extended and applied to machine learning problems such as regression and classification. The stochastic optimization problems arising in these machine learning problems is solved...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	126,518
1508.01761	Reducible Cyclic Codes Constructed as the Direct Sum of Two Semiprimitive Cyclic Codes	We present a family of reducible cyclic codes constructed as the direct sum of two different semiprimitive two-weight irreducible cyclic codes. This family generalizes the class of reducible cyclic codes that was reported in the main result of B. Wang, {\em et al.} \cite{once}. Moreover, despite of what was stated ther...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	45,820
2112.03723	Shrub Ensembles for Online Classification	Online learning algorithms have become a ubiquitous tool in the machine learning toolbox and are frequently used in small, resource-constraint environments. Among the most successful online learning methods are Decision Tree (DT) ensembles. DT ensembles provide excellent performance while adapting to changes in the dat...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	true	270,318
2305.09842	A Note on Dimensionality Reduction in Deep Neural Networks using Empirical Interpolation Method	Empirical interpolation method (EIM) is a well-known technique to efficiently approximate parameterized functions. This paper proposes to use EIM algorithm to efficiently reduce the dimension of the training data within supervised machine learning. This is termed as DNN-EIM. Applications in data science (e.g., MNIST) a...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	364,793
1804.02343	Telepresence System based on Simulated Holographic Display	We present a telepresence system based on a custom-made simulated holographic display that produces a full 3D model of the remote participants using commodity depth sensors. Our display is composed of a video projector and a quadrangular pyramid made of acrylic, that allows the user to experience an omnidirectional vis...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	94,385
1909.10416	Biomedical Mention Disambiguation using a Deep Learning Approach	Automatically locating named entities in natural language text - named entity recognition - is an important task in the biomedical domain. Many named entity mentions are ambiguous between several bioconcept types, however, causing text spans to be annotated as more than one type when simultaneously recognizing multiple...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	146,540
2307.05650	Evidence-based Hand Hygiene. Can You Trust the Fluorescent-based Assessment Methods?	Healthcare-Associated Infections present a major threat to patient safety globally. According to studies, more than 50% of HAI could be prevented by proper hand hygiene. Effectiveness of hand hygiene is regularly evaluated with the fluorescent method: performing hand hygiene with a handrub containing an ultra violet (U...	true	false	false	false	false	false	false	false	false	false	true	true	false	false	false	false	false	false	378,821
2312.11015	T-Code: Simple Temporal Latent Code for Efficient Dynamic View Synthesis	Novel view synthesis for dynamic scenes is one of the spotlights in computer vision. The key to efficient dynamic view synthesis is to find a compact representation to store the information across time. Though existing methods achieve fast dynamic view synthesis by tensor decomposition or hash grid feature concatenatio...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	416,412
2409.12379	Enhancing 3D Robotic Vision Robustness by Minimizing Adversarial Mutual Information through a Curriculum Training Approach	Adversarial attacks exploit vulnerabilities in a model's decision boundaries through small, carefully crafted perturbations that lead to significant mispredictions. In 3D vision, the high dimensionality and sparsity of data greatly expand the attack surface, making 3D vision particularly vulnerable for safety-critical ...	false	false	false	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	489,554
2008.00820	Generating Visually Aligned Sound from Videos	We focus on the task of generating sound from natural videos, and the sound should be both temporally and content-wise aligned with visual signals. This task is extremely challenging because some sounds generated \emph{outside} a camera can not be inferred from video content. The model may be forced to learn an incorre...	false	false	true	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	190,125
2203.05780	Acoustic To Articulatory Speech Inversion Using Multi-Resolution Spectro-Temporal Representations Of Speech Signals	Multi-resolution spectro-temporal features of a speech signal represent how the brain perceives sounds by tuning cortical cells to different spectral and temporal modulations. These features produce a higher dimensional representation of the speech signals. The purpose of this paper is to evaluate how well the auditory...	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	284,909
1803.07690	Adaptive Super-twisting Second-order Sliding Mode for Attitude Control of Quadcopter UAVs	This work addresses the modelling and control aspects for quadcopter or drone unmanned aerial vehicles (UAVs). First, the mathematical model of the drone is derived by identifying significant parameters and the negligible ones are treated as disturbances. The control design begins with the switching surface selection, ...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	93,104
2005.10018	Revisiting Concentration of Missing Mass	We revisit the problem of \emph{missing mass concentration}, developing a new method of estimating concentration of heterogenic sums, in spirit of celebrated Rosenthal's inequality. As a result we slightly improve the state-of-art bounds due to Ben-Hamou at al., and simplify the proofs.	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	178,065
2307.11214	FairMobi-Net: A Fairness-aware Deep Learning Model for Urban Mobility Flow Generation	Generating realistic human flows across regions is essential for our understanding of urban structures and population activity patterns, enabling important applications in the fields of urban planning and management. However, a notable shortcoming of most existing mobility generation methodologies is neglect of predict...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	380,828
2309.03710	A State Representation for Diminishing Rewards	A common setting in multitask reinforcement learning (RL) demands that an agent rapidly adapt to various stationary reward functions randomly sampled from a fixed distribution. In such situations, the successor representation (SR) is a popular framework which supports rapid policy evaluation by decoupling a policy's ex...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	390,478
cs/0510037	Hi\'{e}rarchisation des r\`{e}gles d'association en fouille de textes	Extraction of association rules is widely used as a data mining method. However, one of the limit of this approach comes from the large number of extracted rules and the difficulty for a human expert to deal with the totality of these rules. We propose to solve this problem by structuring the set of rules into hierarch...	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	false	false	539,015
2406.18679	Speakers Unembedded: Embedding-free Approach to Long-form Neural Diarization	End-to-end neural diarization (EEND) models offer significant improvements over traditional embedding-based Speaker Diarization (SD) approaches but falls short on generalizing to long-form audio with large number of speakers. EEND-vector-clustering method mitigates this by combining local EEND with global clustering of...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	468,131
1904.04552	BoLTVOS: Box-Level Tracking for Video Object Segmentation	We approach video object segmentation (VOS) by splitting the task into two sub-tasks: bounding box level tracking, followed by bounding box segmentation. Following this paradigm, we present BoLTVOS (Box-Level Tracking for VOS), which consists of an R-CNN detector conditioned on the first-frame bounding box to detect th...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	127,067
2203.12267	PEAR: Personalized Re-ranking with Contextualized Transformer for Recommendation	The goal of recommender systems is to provide ordered item lists to users that best match their interests. As a critical task in the recommendation pipeline, re-ranking has received increasing attention in recent years. In contrast to conventional ranking models that score each item individually, re-ranking aims to exp...	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	false	287,204
1708.08995	Analyzing Cloud Optical Properties Using Sky Cameras	Clouds play a significant role in the fluctuation of solar radiation received by the earth's surface. It is important to study the various cloud properties, as it impacts the total solar irradiance falling on the earth's surface. One of such important optical properties of the cloud is the Cloud Optical Thickness (COT)...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	79,712
2412.12710	Enhancing Naturalness in LLM-Generated Utterances through Disfluency Insertion	Disfluencies are a natural feature of spontaneous human speech but are typically absent from the outputs of Large Language Models (LLMs). This absence can diminish the perceived naturalness of synthesized speech, which is an important criteria when building conversational agents that aim to mimick human behaviours. We ...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	517,994
2402.16690	Risk-Aware Non-Myopic Motion Planner for Large-Scale Robotic Swarm Using CVaR Constraints	Swarm robotics has garnered significant attention due to its ability to accomplish elaborate and synchronized tasks. Existing methodologies for motion planning of swarm robotic systems mainly encounter difficulties in scalability and safety guarantee. To address these limitations, we propose a Risk-aware swarm mOtion p...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	432,657
2207.08329	Bayesian Quickest Change Detection of an Intruder in Acknowledgments for Private Remote State Estimation	For geographically separated cyber-physical systems, state estimation at a remote monitoring or control site is important to ensure stability and reliability of the system. Often for safety or commercial reasons it is necessary to ensure confidentiality of the process state and control information. A current topic of i...	false	false	false	false	false	false	false	false	false	true	true	false	false	false	false	false	false	false	308,546
1001.2327	Wiretap Channel with Causal State Information	A lower bound on the secrecy capacity of the wiretap channel with state information available causally at both the encoder and decoder is established. The lower bound is shown to be strictly larger than that for the noncausal case by Liu and Chen. Achievability is proved using block Markov coding, Shannon strategy, and...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	5,378
2312.13100	SEER-ZSL: Semantic Encoder-Enhanced Representations for Generalized Zero-Shot Learning	Zero-Shot Learning (ZSL) presents the challenge of identifying categories not seen during training. This task is crucial in domains where it is costly, prohibited, or simply not feasible to collect training data. ZSL depends on a mapping between the visual space and available semantic information. Prior works learn a m...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	417,206
1006.2588	Agnostic Active Learning Without Constraints	We present and analyze an agnostic active learning algorithm that works without keeping a version space. This is unlike all previous approaches where a restricted set of candidate hypotheses is maintained throughout learning, and only hypotheses from this set are ever returned. By avoiding this version space approach, ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	6,775
2001.11369	Learning to Structure Long-term Dependence for Sequential Recommendation	Sequential recommendation recommends items based on sequences of users' historical actions. The key challenge in it is how to effectively model the influence from distant actions to the action to be predicted, i.e., recognizing the long-term dependence structure; and it remains an underexplored problem. To better model...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	162,054
1906.04055	Enabling Robust State Estimation through Measurement Error Covariance Adaptation	Accurate platform localization is an integral component of most robotic systems. As these robotic systems become more ubiquitous, it is necessary to develop robust state estimation algorithms that are able to withstand novel and non-cooperative environments. When dealing with novel and non-cooperative environments, lit...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	134,590
2309.10000	Detecting covariate drift in text data using document embeddings and dimensionality reduction	Detecting covariate drift in text data is essential for maintaining the reliability and performance of text analysis models. In this research, we investigate the effectiveness of different document embeddings, dimensionality reduction techniques, and drift detection methods for identifying covariate drift in text data....	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	392,826
1205.2623	Virtual Vector Machine for Bayesian Online Classification	In a typical online learning scenario, a learner is required to process a large data stream using a small memory buffer. Such a requirement is usually in conflict with a learner's primary pursuit of prediction accuracy. To address this dilemma, we introduce a novel Bayesian online classi cation algorithm, called the Vi...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	15,930
1506.00333	Learning to Answer Questions From Image Using Convolutional Neural Network	In this paper, we propose to employ the convolutional neural network (CNN) for the image question answering (QA). Our proposed CNN provides an end-to-end framework with convolutional architectures for learning not only the image and question representations, but also their inter-modal interactions to produce the answer...	false	false	false	false	false	false	true	false	true	false	false	true	false	false	false	true	false	false	43,654
1803.09156	An Overview of Vulnerabilities of Voice Controlled Systems	Over the last few years, a rapidly increasing number of Internet-of-Things (IoT) systems that adopt voice as the primary user input have emerged. These systems have been shown to be vulnerable to various types of voice spoofing attacks. However, how exactly these techniques differ or relate to each other has not been e...	false	false	false	false	true	false	false	false	false	false	false	false	true	false	false	false	false	false	93,434
1605.07717	Deep Structured Energy Based Models for Anomaly Detection	In this paper, we attack the anomaly detection problem by directly modeling the data distribution with deep architectures. We propose deep structured energy based models (DSEBMs), where the energy function is the output of a deterministic deep neural network with structure. We develop novel model architectures to integ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	56,327
2210.15104	TRScore: A Novel GPT-based Readability Scorer for ASR Segmentation and Punctuation model evaluation and selection	Punctuation and Segmentation are key to readability in Automatic Speech Recognition (ASR), often evaluated using F1 scores that require high-quality human transcripts and do not reflect readability well. Human evaluation is expensive, time-consuming, and suffers from large inter-observer variability, especially in conv...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	326,802
1412.5910	Games for Active XML Revisited	The paper studies the rewriting mechanisms for intensional documents in the Active XML framework, abstracted in the form of active context-free games. The safe rewriting problem studied in this paper is to decide whether the first player, Juliet, has a winning strategy for a given game and (nested) word; this correspon...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	true	38,541
2402.13183	Robust Model Predictive Control for nonlinear discrete-time systems using iterative time-varying constraint tightening	Robust Model Predictive Control (MPC) for nonlinear systems is a problem that poses significant challenges as highlighted by the diversity of approaches proposed in the last decades. Often compromises with respect to computational load, conservatism, generality, or implementation complexity have to be made, and finding...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	431,140
1709.02457	Reservoir of Diverse Adaptive Learners and Stacking Fast Hoeffding Drift Detection Methods for Evolving Data Streams	The last decade has seen a surge of interest in adaptive learning algorithms for data stream classification, with applications ranging from predicting ozone level peaks, learning stock market indicators, to detecting computer security violations. In addition, a number of methods have been developed to detect concept dr...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	true	false	80,266
2304.00180	FCC: Fusing Conversation History and Candidate Provenance for Contextual Response Ranking in Dialogue Systems	Response ranking in dialogues plays a crucial role in retrieval-based conversational systems. In a multi-turn dialogue, to capture the gist of a conversation, contextual information serves as essential knowledge to achieve this goal. In this paper, we present a flexible neural framework that can integrate contextual in...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	355,583
2101.10710	Visual explanation of black-box model: Similarity Difference and Uniqueness (SIDU) method	Explainable Artificial Intelligence (XAI) has in recent years become a well-suited framework to generate human understandable explanations of "black-box" models. In this paper, a novel XAI visual explanation algorithm known as the Similarity Difference and Uniqueness (SIDU) method that can effectively localize entire o...	true	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	217,022
2307.15640	CLIP Brings Better Features to Visual Aesthetics Learners	The success of pre-training approaches on a variety of downstream tasks has revitalized the field of computer vision. Image aesthetics assessment (IAA) is one of the ideal application scenarios for such methods due to subjective and expensive labeling procedure. In this work, an unified and flexible two-phase \textbf{C...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	382,323
2403.19369	RAIL: Robot Affordance Imagination with Large Language Models	This paper introduces an automatic affordance reasoning paradigm tailored to minimal semantic inputs, addressing the critical challenges of classifying and manipulating unseen classes of objects in household settings. Inspired by human cognitive processes, our method integrates generative language models and physics-ba...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	442,317
2410.07740	The Impact of Grid Storage on Balancing Costs and Carbon Emissions in Great Britain	Grid energy storage can help to balance supply and demand, but its financial viability and operational carbon emissions impact is poorly understood because of the complexity of grid constraints and market outcomes. We analyse the impact of several technologies (Li-ion and flow batteries, pumped hydro, hydrogen) on Grea...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	496,776
2401.13499	LDCA: Local Descriptors with Contextual Augmentation for Few-Shot Learning	Few-shot image classification has emerged as a key challenge in the field of computer vision, highlighting the capability to rapidly adapt to new tasks with minimal labeled data. Existing methods predominantly rely on image-level features or local descriptors, often overlooking the holistic context surrounding these de...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	423,746
2402.00035	Robustness Assessment of a Runway Object Classifier for Safe Aircraft Taxiing	As deep neural networks (DNNs) are becoming the prominent solution for many computational problems, the aviation industry seeks to explore their potential in alleviating pilot workload and in improving operational safety. However, the use of DNNs in this type of safety-critical applications requires a thorough certific...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	true	425,445
2411.10512	On the Privacy Risk of In-context Learning	Large language models (LLMs) are excellent few-shot learners. They can perform a wide variety of tasks purely based on natural language prompts provided to them. These prompts contain data of a specific downstream task -- often the private dataset of a party, e.g., a company that wants to leverage the LLM for their pur...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	508,678
2112.05295	3D Scene Understanding at Urban Intersection using Stereo Vision and Digital Map	The driving behavior at urban intersections is very complex. It is thus crucial for autonomous vehicles to comprehensively understand challenging urban traffic scenes in order to navigate intersections and prevent accidents. In this paper, we introduce a stereo vision and 3D digital map based approach to spatially and ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	270,796
2205.14692	Generalization bounds and algorithms for estimating conditional average treatment effect of dosage	We investigate the task of estimating the conditional average causal effect of treatment-dosage pairs from a combination of observational data and assumptions on the causal relationships in the underlying system. This has been a longstanding challenge for fields of study such as epidemiology or economics that require a...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	299,459
2003.04294	Optimizing Streaming Parallelism on Heterogeneous Many-Core Architectures: A Machine Learning Based Approach	This article presents an automatic approach to quickly derive a good solution for hardware resource partition and task granularity for task-based parallel applications on heterogeneous many-core architectures. Our approach employs a performance model to estimate the resulting performance of the target application under...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	167,517
2110.12192	Dual Shape Guided Segmentation Network for Organs-at-Risk in Head and Neck CT Images	The accurate segmentation of organs-at-risk (OARs) in head and neck CT images is a critical step for radiation therapy of head and neck cancer patients. However, manual delineation for numerous OARs is time-consuming and laborious, even for expert oncologists. Moreover, manual delineation results are susceptible to hig...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	262,749
2305.17663	Lexical Retrieval Hypothesis in Multimodal Context	Multimodal corpora have become an essential language resource for language science and grounded natural language processing (NLP) systems due to the growing need to understand and interpret human communication across various channels. In this paper, we first present our efforts in building the first Multimodal Corpus f...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	368,692
2009.12691	A Multi-Agent System for Solving the Dynamic Capacitated Vehicle Routing Problem with Stochastic Customers using Trajectory Data Mining	The worldwide growth of e-commerce has created new challenges for logistics companies, one of which is being able to deliver products quickly and at low cost, which reflects directly in the way of sorting packages, needing to eliminate steps such as storage and batch creation. Our work presents a multi-agent system tha...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	197,505
1205.3252	Two-way Wireless Video Communication using Randomized Cooperation, Network Coding and Packet Level FEC	Two-way real-time video communication in wireless networks requires high bandwidth, low delay and error resiliency. This paper addresses these demands by proposing a system with the integration of Network Coding (NC), user cooperation using Randomized Distributed Space-time Coding (R-DSTC) and packet level Forward Erro...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	16,010
1807.07006	Cruise Missile Target Trajectory Movement Prediction based on Optimal 3D Kalman Filter with Firefly Algorithm	It is hoped that there will never be a war in the world, but one of the defensive requirements of any country during the war is the missiles used for destruction and defense. Todays, missiles movement from origin to destination is an important problem due to abundant application of missiles in wars. This is important b...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	103,245
2206.15017	Consensus Function from an $L_p^q-$norm Regularization Term for its Use as Adaptive Activation Functions in Neural Networks	The design of a neural network is usually carried out by defining the number of layers, the number of neurons per layer, their connections or synapses, and the activation function that they will execute. The training process tries to optimize the weights assigned to those connections, together with the biases of the ne...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	305,459
2304.02282	About optimal loss function for training physics-informed neural networks under respecting causality	A method is presented that allows to reduce a problem described by differential equations with initial and boundary conditions to the problem described only by differential equations. The advantage of using the modified problem for physics-informed neural networks (PINNs) methodology is that it becomes possible to repr...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	356,389
1202.0357	Channel Identification and its Impact on Quantum LDPC Code Performance	In this work we probe the impact of channel estimation on the performance of quantum LDPC codes. Our channel estimation is based on an optimal estimate of the relevant decoherence parameter via its quantum Fisher information. Using state-of-the art quantum LDPC codes designed for the quantum depolarization channel, and...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	14,075
2411.03475	Self Supervised Networks for Learning Latent Space Representations of Human Body Scans and Motions	This paper introduces self-supervised neural network models to tackle several fundamental problems in the field of 3D human body analysis and processing. First, we propose VariShaPE (Varifold Shape Parameter Estimator), a novel architecture for the retrieval of latent space representations of body shapes and poses. Thi...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	505,908
1903.01611	Stabilizing the Lottery Ticket Hypothesis	Pruning is a well-established technique for removing unnecessary structure from neural networks after training to improve the performance of inference. Several recent results have explored the possibility of pruning at initialization time to provide similar benefits during training. In particular, the "lottery ticket h...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	123,296
2304.12592	MMRDN: Consistent Representation for Multi-View Manipulation Relationship Detection in Object-Stacked Scenes	Manipulation relationship detection (MRD) aims to guide the robot to grasp objects in the right order, which is important to ensure the safety and reliability of grasping in object stacked scenes. Previous works infer manipulation relationship by deep neural network trained with data collected from a predefined view, w...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	360,280
2204.00570	Connect, Not Collapse: Explaining Contrastive Learning for Unsupervised Domain Adaptation	We consider unsupervised domain adaptation (UDA), where labeled data from a source domain (e.g., photographs) and unlabeled data from a target domain (e.g., sketches) are used to learn a classifier for the target domain. Conventional UDA methods (e.g., domain adversarial training) learn domain-invariant features to imp...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	289,307
2310.02025	DeepZero: Scaling up Zeroth-Order Optimization for Deep Model Training	Zeroth-order (ZO) optimization has become a popular technique for solving machine learning (ML) problems when first-order (FO) information is difficult or impossible to obtain. However, the scalability of ZO optimization remains an open problem: Its use has primarily been limited to relatively small-scale ML problems, ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	396,679
2311.00201	Federated Natural Policy Gradient and Actor Critic Methods for Multi-task Reinforcement Learning	Federated reinforcement learning (RL) enables collaborative decision making of multiple distributed agents without sharing local data trajectories. In this work, we consider a multi-task setting, in which each agent has its own private reward function corresponding to different tasks, while sharing the same transition ...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	404,542
2103.13628	HufuNet: Embedding the Left Piece as Watermark and Keeping the Right Piece for Ownership Verification in Deep Neural Networks	Due to the wide use of highly-valuable and large-scale deep neural networks (DNNs), it becomes crucial to protect the intellectual property of DNNs so that the ownership of disputed or stolen DNNs can be verified. Most existing solutions embed backdoors in DNN model training such that DNN ownership can be verified by t...	false	false	false	false	true	false	false	false	false	false	false	false	true	false	false	false	false	false	226,560
2410.15374	Explainability of Point Cloud Neural Networks Using SMILE: Statistical Model-Agnostic Interpretability with Local Explanations	In today's world, the significance of explainable AI (XAI) is growing in robotics and point cloud applications, as the lack of transparency in decision-making can pose considerable safety risks, particularly in autonomous systems. As these technologies are integrated into real-world environments, ensuring that model de...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	500,509
2210.06459	Differentially private multivariate medians	Statistical tools which satisfy rigorous privacy guarantees are necessary for modern data analysis. It is well-known that robustness against contamination is linked to differential privacy. Despite this fact, using multivariate medians for differentially private and robust multivariate location estimation has not been ...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	323,300
2006.06624	SLIC-UAV: A Method for monitoring recovery in tropical restoration projects through identification of signature species using UAVs	Logged forests cover four million square kilometres of the tropics and restoring these forests is essential if we are to avoid the worst impacts of climate change, yet monitoring recovery is challenging. Tracking the abundance of visually identifiable, early-successional species enables successional status and thereby ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	181,495
1203.2768	On the Performance Limits of Pilot-Based Estimation of Bandlimited Frequency-Selective Communication Channels	In this paper the problem of assessing bounds on the accuracy of pilot-based estimation of a bandlimited frequency selective communication channel is tackled. Mean square error is taken as a figure of merit in channel estimation and a tapped-delay line model is adopted to represent a continuous time channel via a finit...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	14,853
2201.02912	{\lambda}-Scaled-Attention: A Novel Fast Attention Mechanism for Efficient Modeling of Protein Sequences	Attention-based deep networks have been successfully applied on textual data in the field of NLP. However, their application on protein sequences poses additional challenges due to the weak semantics of the protein words, unlike the plain text words. These unexplored challenges faced by the standard attention technique...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	274,693
1007.1361	Top-K Color Queries for Document Retrieval	In this paper we describe a new efficient (in fact optimal) data structure for the {\em top-$K$ color problem}. Each element of an array $A$ is assigned a color $c$ with priority $p(c)$. For a query range $[a,b]$ and a value $K$, we have to report $K$ colors with the highest priorities among all colors that occur in $A...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	true	7,025
1909.02635	Effective Use of Transformer Networks for Entity Tracking	Tracking entities in procedural language requires understanding the transformations arising from actions on entities as well as those entities' interactions. While self-attention-based pre-trained language encoders like GPT and BERT have been successfully applied across a range of natural language understanding tasks, ...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	144,245
2310.02476	ML4EJ: Decoding the Role of Urban Features in Shaping Environmental Injustice Using Interpretable Machine Learning	Understanding the key factors shaping environmental hazard exposures and their associated environmental injustice issues is vital for formulating equitable policy measures. Traditional perspectives on environmental injustice have primarily focused on the socioeconomic dimensions, often overlooking the influence of hete...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	396,862
2405.01753	A Feedback Linearized Model Predictive Control Strategy for Input-Constrained Self-Driving Cars	This paper proposes a novel real-time affordable solution to the trajectory tracking control problem for self-driving cars subject to longitudinal and steering angular velocity constraints. To this end, we develop a dual-mode Model Predictive Control (MPC) solution starting from an input-output feedback linearized desc...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	451,490
2006.13984	Consistency of Anchor-based Spectral Clustering	Anchor-based techniques reduce the computational complexity of spectral clustering algorithms. Although empirical tests have shown promising results, there is currently a lack of theoretical support for the anchoring approach. We define a specific anchor-based algorithm and show that it is amenable to rigorous analysis...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	184,086
2412.16253	Interactive Scene Authoring with Specialized Generative Primitives	Generating high-quality 3D digital assets often requires expert knowledge of complex design tools. We introduce Specialized Generative Primitives, a generative framework that allows non-expert users to author high-quality 3D scenes in a seamless, lightweight, and controllable manner. Each primitive is an efficient gene...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	519,443
2203.09581	SepTr: Separable Transformer for Audio Spectrogram Processing	Following the successful application of vision transformers in multiple computer vision tasks, these models have drawn the attention of the signal processing community. This is because signals are often represented as spectrograms (e.g. through Discrete Fourier Transform) which can be directly provided as input to visi...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	286,199
2105.14038	Learning to Extend Program Graphs to Work-in-Progress Code	Source code spends most of its time in a broken or incomplete state during software development. This presents a challenge to machine learning for code, since high-performing models typically rely on graph structured representations of programs derived from traditional program analyses. Such analyses may be undefined f...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	237,497
2101.06433	New Low Rank Optimization Model and Convex Approach for Robust Spectral Compressed Sensing	This paper investigates recovery of an undamped spectrally sparse signal and its spectral components from a set of regularly spaced samples within the framework of spectral compressed sensing and super-resolution. We show that the existing Hankel-based optimization methods suffer from the fundamental limitation that th...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	215,716
1812.08237	A Novel Large-scale Ordinal Regression Model	Ordinal regression (OR) is a special multiclass classification problem where an order relation exists among the labels. Recent years, people share their opinions and sentimental judgments conveniently with social networks and E-Commerce so that plentiful large-scale OR problems arise. However, few studies have focused ...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	116,958
2204.01955	Autoregressive 3D Shape Generation via Canonical Mapping	With the capacity of modeling long-range dependencies in sequential data, transformers have shown remarkable performances in a variety of generative tasks such as image, audio, and text generation. Yet, taming them in generating less structured and voluminous data formats such as high-resolution point clouds have seldo...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	289,777
1911.00234	Active$^2$ Learning: Actively reducing redundancies in Active Learning methods for Sequence Tagging and Machine Translation	While deep learning is a powerful tool for natural language processing (NLP) problems, successful solutions to these problems rely heavily on large amounts of annotated samples. However, manually annotating data is expensive and time-consuming. Active Learning (AL) strategies reduce the need for huge volumes of labeled...	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	false	151,772
1801.07814	Blockchain moderated by empty blocks to reduce the energetic impact of crypto-moneys	While cryptocurrencies and blockchain applications continue to gain popularity, their energy cost is evidently becoming unsustainable. In most instances, the main cost comes from the required amount of energy for the Proof-of-Work, and this cost is inherent to the design. In addition, useless costs from discarded work ...	false	false	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	88,855
2301.12820	Transferring Multiple Policies to Hotstart Reinforcement Learning in an Air Compressor Management Problem	Many instances of similar or almost-identical industrial machines or tools are often deployed at once, or in quick succession. For instance, a particular model of air compressor may be installed at hundreds of customers. Because these tools perform distinct but highly similar tasks, it is interesting to be able to quic...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	342,679
2405.15154	Online Prompt Pricing based on Combinatorial Multi-Armed Bandit and Hierarchical Stackelberg Game	Generation models have shown promising performance in various tasks, making trading around machine learning models possible. In this paper, we aim at a novel prompt trading scenario, prompt bundle trading (PBT) system, and propose an online pricing mechanism. Based on the combinatorial multi-armed bandit (CMAB) and thr...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	456,772
2407.08481	SliceMamba with Neural Architecture Search for Medical Image Segmentation	Despite the progress made in Mamba-based medical image segmentation models, existing methods utilizing unidirectional or multi-directional feature scanning mechanisms struggle to effectively capture dependencies between neighboring positions, limiting the discriminant representation learning of local features. These lo...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	472,182
1806.00899	Recent advances and opportunities in scene classification of aerial images with deep models	Scene classification is a fundamental task in interpretation of remote sensing images, and has become an active research topic in remote sensing community due to its important role in a wide range of applications. Over the past years, tremendous efforts have been made for developing powerful approaches for scene classi...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	99,427

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