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
1909.01539	What Happens on the Edge, Stays on the Edge: Toward Compressive Deep Learning	Machine learning at the edge offers great benefits such as increased privacy and security, low latency, and more autonomy. However, a major challenge is that many devices, in particular edge devices, have very limited memory, weak processors, and scarce energy supply. We propose a hybrid hardware-software framework tha...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	143,932
2009.01110	Perceptual Deep Neural Networks: Adversarial Robustness through Input Recreation	Adversarial examples have shown that albeit highly accurate, models learned by machines, differently from humans, have many weaknesses. However, humans' perception is also fundamentally different from machines, because we do not see the signals which arrive at the retina but a rather complex recreation of them. In this...	false	false	false	false	true	false	true	false	false	false	false	true	true	false	false	false	false	false	194,225
2308.00393	A Survey of Time Series Anomaly Detection Methods in the AIOps Domain	Internet-based services have seen remarkable success, generating vast amounts of monitored key performance indicators (KPIs) as univariate or multivariate time series. Monitoring and analyzing these time series are crucial for researchers, service operators, and on-call engineers to detect outliers or anomalies indicat...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	382,923
1708.00625	Deep Recurrent Generative Decoder for Abstractive Text Summarization	We propose a new framework for abstractive text summarization based on a sequence-to-sequence oriented encoder-decoder model equipped with a deep recurrent generative decoder (DRGN). Latent structure information implied in the target summaries is learned based on a recurrent latent random model for improving the summ...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	78,243
2502.00718	"I am bad": Interpreting Stealthy, Universal and Robust Audio Jailbreaks in Audio-Language Models	The rise of multimodal large language models has introduced innovative human-machine interaction paradigms but also significant challenges in machine learning safety. Audio-Language Models (ALMs) are especially relevant due to the intuitive nature of spoken communication, yet little is known about their failure modes. ...	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	529,517
2408.14253	Text3DAug -- Prompted Instance Augmentation for LiDAR Perception	LiDAR data of urban scenarios poses unique challenges, such as heterogeneous characteristics and inherent class imbalance. Therefore, large-scale datasets are necessary to apply deep learning methods. Instance augmentation has emerged as an efficient method to increase dataset diversity. However, current methods requir...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	483,473
2309.00372	On the Localization of Ultrasound Image Slices within Point Distribution Models	Thyroid disorders are most commonly diagnosed using high-resolution Ultrasound (US). Longitudinal nodule tracking is a pivotal diagnostic protocol for monitoring changes in pathological thyroid morphology. This task, however, imposes a substantial cognitive load on clinicians due to the inherent challenge of maintainin...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	389,289
2209.01779	Representation Learning for Non-Melanoma Skin Cancer using a Latent Autoencoder	Generative learning is a powerful tool for representation learning, and shows particular promise for problems in biomedical imaging. However, in this context, sampling from the distribution is secondary to finding representations of real images, which often come with labels and explicitly represent the content and qual...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	316,010
2010.02721	An improved bound on $\ell_q$ norms of noisy functions	Let $T_{\epsilon}$, $0 \le \epsilon \le 1/2$, be the noise operator acting on functions on the boolean cube $\{0,1\}^n$. Let $f$ be a nonnegative function on $\{0,1\}^n$ and let $q \ge 1$. In arXiv:1809.09696 the $\ell_q$ norm of $T_{\epsilon} f$ was upperbounded by the average $\ell_q$ norm of conditional expectations...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	199,143
2307.08811	Co(ve)rtex: ML Models as storage channels and their (mis-)applications	Machine learning (ML) models are overparameterized to support generality and avoid overfitting. The state of these parameters is essentially a "don't-care" with respect to the primary model provided that this state does not interfere with the primary model. In both hardware and software systems, don't-care states and u...	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	379,949
2212.14489	Inference of interaction kernels in mean-field models of opinion dynamics	In models of opinion dynamics, many parameters -- either in the form of constants or in the form of functions -- play a critical role in describing, calibrating, and forecasting how opinions change with time. When examining a model of opinion dynamics, it is beneficial to infer its parameters using empirical data. In t...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	338,632
1808.10013	The implicit fairness criterion of unconstrained learning	We clarify what fairness guarantees we can and cannot expect to follow from unconstrained machine learning. Specifically, we characterize when unconstrained learning on its own implies group calibration, that is, the outcome variable is conditionally independent of group membership given the score. We show that under r...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	106,313
1610.07250	Random Multiple Access for M2M Communications with QoS Guarantees	We propose a novel random multiple access (RMA) scheme with quality of service (QoS) guarantees for machine-to-machine (M2M) communications. We consider a slotted uncoordinated data transmission period during which machine type communication (MTC) devices transmit over the same radio channel. Based on the latency requi...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	62,759
2203.08143	HiSA-SMFM: Historical and Sentiment Analysis based Stock Market Forecasting Model	One of the pillars to build a country's economy is the stock market. Over the years, people are investing in stock markets to earn as much profit as possible from the amount of money that they possess. Hence, it is vital to have a prediction model which can accurately predict future stock prices. With the help of machi...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	285,689
2405.04393	Efficient Online Set-valued Classification with Bandit Feedback	Conformal prediction is a distribution-free method that wraps a given machine learning model and returns a set of plausible labels that contain the true label with a prescribed coverage rate. In practice, the empirical coverage achieved highly relies on fully observed label information from data both in the training ph...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	452,557
1807.09954	Multi-temporal Sentinel-1 and -2 Data Fusion for Optical Image Simulation	In this paper, we present the optical image simulation from a synthetic aperture radar (SAR) data using deep learning based methods. Two models, i.e., optical image simulation directly from the SAR data and from multi-temporal SARoptical data, are proposed to testify the possibilities. The deep learning based methods t...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	103,838
1905.02538	Rethinking Learning-based Demosaicing, Denoising, and Super-Resolution Pipeline	Imaging is usually a mixture problem of incomplete color sampling, noise degradation, and limited resolution. This mixture problem is typically solved by a sequential solution that applies demosaicing (DM), denoising (DN), and super-resolution (SR) sequentially in a fixed and predefined pipeline (execution order of tas...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	129,997
2307.02882	Contrast Is All You Need	In this study, we analyze data-scarce classification scenarios, where available labeled legal data is small and imbalanced, potentially hurting the quality of the results. We focused on two finetuning objectives; SetFit (Sentence Transformer Finetuning), a contrastive learning setup, and a vanilla finetuning setup on a...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	377,852
2103.13110	A computational framework for modeling cell-matrix interactions in soft biological tissues	Living soft tissues appear to promote the development and maintenance of a preferred mechanical state within a defined tolerance around a so-called set-point. This phenomenon is often referred to as mechanical homeostasis. In contradiction to the prominent role of mechanical homeostasis in various (patho)physiological ...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	226,393
1405.0792	On Exact Learning Monotone DNF from Membership Queries	In this paper, we study the problem of learning a monotone DNF with at most $s$ terms of size (number of variables in each term) at most $r$ ($s$ term $r$-MDNF) from membership queries. This problem is equivalent to the problem of learning a general hypergraph using hyperedge-detecting queries, a problem motivated by a...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	32,803
2311.17518	The devil is in the fine-grained details: Evaluating open-vocabulary object detectors for fine-grained understanding	Recent advancements in large vision-language models enabled visual object detection in open-vocabulary scenarios, where object classes are defined in free-text formats during inference. In this paper, we aim to probe the state-of-the-art methods for open-vocabulary object detection to determine to what extent they unde...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	411,330
2407.07744	Belief Information based Deep Channel Estimation for Massive MIMO Systems	In the next generation wireless communication system, transmission rates should continue to rise to support emerging scenarios, e.g., the immersive communications. From the perspective of communication system evolution, multiple-input multiple-output (MIMO) technology remains pivotal for enhancing transmission rates. H...	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	false	false	471,885
1910.10944	Preference-Based Batch and Sequential Teaching: Towards a Unified View of Models	Algorithmic machine teaching studies the interaction between a teacher and a learner where the teacher selects labeled examples aiming at teaching a target hypothesis. In a quest to lower teaching complexity and to achieve more natural teacher-learner interactions, several teaching models and complexity measures have b...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	150,636
cs/0006009	Knowledge and common knowledge in a distributed environment	Reasoning about knowledge seems to play a fundamental role in distributed systems. Indeed, such reasoning is a central part of the informal intuitive arguments used in the design of distributed protocols. Communication in a distributed system can be viewed as the act of transforming the system's state of knowledge. Thi...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	537,122
1602.08350	Large-Scale Detection of Non-Technical Losses in Imbalanced Data Sets	Non-technical losses (NTL) such as electricity theft cause significant harm to our economies, as in some countries they may range up to 40% of the total electricity distributed. Detecting NTLs requires costly on-site inspections. Accurate prediction of NTLs for customers using machine learning is therefore crucial. To ...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	52,633
1710.10468	Speaker Diarization with LSTM	For many years, i-vector based audio embedding techniques were the dominant approach for speaker verification and speaker diarization applications. However, mirroring the rise of deep learning in various domains, neural network based audio embeddings, also known as d-vectors, have consistently demonstrated superior spe...	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	83,391
1606.07736	Issues in evaluating semantic spaces using word analogies	The offset method for solving word analogies has become a standard evaluation tool for vector-space semantic models: it is considered desirable for a space to represent semantic relations as consistent vector offsets. We show that the method's reliance on cosine similarity conflates offset consistency with largely irre...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	57,773
2406.02832	Efficient Minimum Bayes Risk Decoding using Low-Rank Matrix Completion Algorithms	Minimum Bayes Risk (MBR) decoding is a powerful decoding strategy widely used for text generation tasks, but its quadratic computational complexity limits its practical application. This paper presents a novel approach for approximating MBR decoding using matrix completion techniques, focusing on the task of machine tr...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	460,957
2009.06732	Efficient Transformers: A Survey	Transformer model architectures have garnered immense interest lately due to their effectiveness across a range of domains like language, vision and reinforcement learning. In the field of natural language processing for example, Transformers have become an indispensable staple in the modern deep learning stack. Recent...	false	false	false	false	true	true	true	false	true	false	false	true	false	false	false	false	false	false	195,726
2212.01958	Deep reinforcement learning of event-triggered communication and consensus-based control for distributed cooperative transport	In this paper, we present a solution to a design problem of control strategies for multi-agent cooperative transport. Although existing learning-based methods assume that the number of agents is the same as that in the training environment, the number might differ in reality considering that the robots' batteries may c...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	334,629
cs/0505078	On the Parity-Check Density and Achievable Rates of LDPC Codes	The paper introduces new bounds on the asymptotic density of parity-check matrices and the achievable rates under ML decoding of binary linear block codes transmitted over memoryless binary-input output-symmetric channels. The lower bounds on the parity-check density are expressed in terms of the gap between the channe...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	538,739
2401.06003	TRIPS: Trilinear Point Splatting for Real-Time Radiance Field Rendering	Point-based radiance field rendering has demonstrated impressive results for novel view synthesis, offering a compelling blend of rendering quality and computational efficiency. However, also latest approaches in this domain are not without their shortcomings. 3D Gaussian Splatting [Kerbl and Kopanas et al. 2023] strug...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	420,986
1307.6616	Does generalization performance of $l^q$ regularization learning depend on $q$? A negative example	$l^q$-regularization has been demonstrated to be an attractive technique in machine learning and statistical modeling. It attempts to improve the generalization (prediction) capability of a machine (model) through appropriately shrinking its coefficients. The shape of a $l^q$ estimator differs in varying choices of the...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	26,038
2307.00183	Long-Tailed Continual Learning For Visual Food Recognition	Deep learning based food recognition has achieved remarkable progress in predicting food types given an eating occasion image. However, there are two major obstacles that hinder deployment in real world scenario. First, as new foods appear sequentially overtime, a trained model needs to learn the new classes continuous...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	376,905
1809.05955	3D Path Planning from a Single 2D Fluoroscopic Image for Robot Assisted Fenestrated Endovascular Aortic Repair	The current standard of intra-operative navigation during Fenestrated Endovascular Aortic Repair (FEVAR) calls for need of 3D alignments between inserted devices and aortic branches. The navigation commonly via 2D fluoroscopic images, lacks anatomical information, resulting in longer operation hours and radiation expos...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	107,918
2107.13090	Policy Gradient Methods Find the Nash Equilibrium in N-player General-sum Linear-quadratic Games	We consider a general-sum N-player linear-quadratic game with stochastic dynamics over a finite horizon and prove the global convergence of the natural policy gradient method to the Nash equilibrium. In order to prove the convergence of the method, we require a certain amount of noise in the system. We give a condition...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	248,093
2408.16425	A Comparative Study of Hyperparameter Tuning Methods	The study emphasizes the challenge of finding the optimal trade-off between bias and variance, especially as hyperparameter optimization increases in complexity. Through empirical analysis, three hyperparameter tuning algorithms Tree-structured Parzen Estimator (TPE), Genetic Search, and Random Search are evaluated acr...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	484,325
2501.10325	DiffStereo: High-Frequency Aware Diffusion Model for Stereo Image Restoration	Diffusion models (DMs) have achieved promising performance in image restoration but haven't been explored for stereo images. The application of DM in stereo image restoration is confronted with a series of challenges. The need to reconstruct two images exacerbates DM's computational cost. Additionally, existing latent ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	525,471
2310.18299	Enhancing the Performance of a Biomimetic Robotic Elbow-and-Forearm System Through Bionics-Inspired Optimization	This paper delineates the formulation and verification of an innovative robotic forearm and elbow design, mirroring the intricate biomechanics of human skeletal and ligament systems. Conventional robotic models often undervalue the substantial function of soft tissues, leading to a compromise between compactness, safet...	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	403,463
2109.03975	Membership Inference Attacks Against Temporally Correlated Data in Deep Reinforcement Learning	While significant research advances have been made in the field of deep reinforcement learning, there have been no concrete adversarial attack strategies in literature tailored for studying the vulnerability of deep reinforcement learning algorithms to membership inference attacks. In such attacking systems, the advers...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	254,239
1307.5636	A generalized back-door criterion	We generalize Pearl's back-door criterion for directed acyclic graphs (DAGs) to more general types of graphs that describe Markov equivalence classes of DAGs and/or allow for arbitrarily many hidden variables. We also give easily checkable necessary and sufficient graphical criteria for the existence of a set of variab...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	25,960
2406.04904	XTTS: a Massively Multilingual Zero-Shot Text-to-Speech Model	Most Zero-shot Multi-speaker TTS (ZS-TTS) systems support only a single language. Although models like YourTTS, VALL-E X, Mega-TTS 2, and Voicebox explored Multilingual ZS-TTS they are limited to just a few high/medium resource languages, limiting the applications of these models in most of the low/medium resource lang...	false	false	true	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	461,904
1711.05032	Energy-Delay-Distortion Problem	An energy-limited source trying to transmit multiple packets to a destination with possibly different sizes is considered. With limited energy, the source cannot potentially transmit all bits of all packets. In addition, there is a delay cost associated with each packet. Thus, the source has to choose, how many bits to...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	84,482
2407.09573	Have We Reached AGI? Comparing ChatGPT, Claude, and Gemini to Human Literacy and Education Benchmarks	Recent advancements in AI, particularly in large language models (LLMs) like ChatGPT, Claude, and Gemini, have prompted questions about their proximity to Artificial General Intelligence (AGI). This study compares LLM performance on educational benchmarks with Americans' average educational attainment and literacy leve...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	472,651
2502.04262	Efficient Randomized Experiments Using Foundation Models	Randomized experiments are the preferred approach for evaluating the effects of interventions, but they are costly and often yield estimates with substantial uncertainty. On the other hand, in silico experiments leveraging foundation models offer a cost-effective alternative that can potentially attain higher statistic...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	531,043
1405.4758	Lipschitz Bandits: Regret Lower Bounds and Optimal Algorithms	We consider stochastic multi-armed bandit problems where the expected reward is a Lipschitz function of the arm, and where the set of arms is either discrete or continuous. For discrete Lipschitz bandits, we derive asymptotic problem specific lower bounds for the regret satisfied by any algorithm, and propose OSLB and ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	33,208
2501.17493	Certifying Pareto-Optimality in Multi-Objective Maximum Satisfiability	Due to the wide employment of automated reasoning in the analysis and construction of correct systems, the results reported by automated reasoning engines must be trustworthy. For Boolean satisfiability (SAT) solvers - and more recently SAT-based maximum satisfiability (MaxSAT) solvers - trustworthiness is obtained by ...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	528,353
1804.00880	Weakly Supervised Instance Segmentation using Class Peak Response	Weakly supervised instance segmentation with image-level labels, instead of expensive pixel-level masks, remains unexplored. In this paper, we tackle this challenging problem by exploiting class peak responses to enable a classification network for instance mask extraction. With image labels supervision only, CNN class...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	94,138
1405.3772	INAUT, a Controlled Language for the French Coast Pilot Books Instructions nautiques	We describe INAUT, a controlled natural language dedicated to collaborative update of a knowledge base on maritime navigation and to automatic generation of coast pilot books (Instructions nautiques) of the French National Hydrographic and Oceanographic Service SHOM. INAUT is based on French language and abundantly use...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	33,117
2011.07320	Co-optimisation and Settlement of Power-Gas Coupled System in Day-ahead Market under Multiple Uncertainties	The interdependency of power systems and natural gas systems is being reinforced by the emerging power-to-gas facilities (PtGs), and the existing gas-fired generators. To jointly improve the efficiency and security under diverse uncertainties from renewable energy resources and load demands, it is essential to co-optim...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	206,511
1301.6707	Attention-Sensitive Alerting	We introduce utility-directed procedures for mediating the flow of potentially distracting alerts and communications to computer users. We present models and inference procedures that balance the context-sensitive costs of deferring alerts with the cost of interruption. We describe the challenge of reasoning about such...	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	21,500
1906.11941	Learning Policies through Quantile Regression	Policy gradient based reinforcement learning algorithms coupled with neural networks have shown success in learning complex policies in the model free continuous action space control setting. However, explicitly parameterized policies are limited by the scope of the chosen parametric probability distribution. We show t...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	136,803
2205.06217	Exploiting symmetry in variational quantum machine learning	Variational quantum machine learning is an extensively studied application of near-term quantum computers. The success of variational quantum learning models crucially depends on finding a suitable parametrization of the model that encodes an inductive bias relevant to the learning task. However, precious little is kno...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	296,174
1601.05569	Towards a General Software Engineering Methodology for the Internet of Things	As research in the Internet of Thing area progresses, and a multitude of proposals exist to solve a variety of problems, the need for a general principled software engineering approach for the systematic development of IoT systems and applications arises. In this paper, by synthesizing form the state of the art in the ...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	true	51,137
1503.01445	Toxicity Prediction using Deep Learning	Everyday we are exposed to various chemicals via food additives, cleaning and cosmetic products and medicines -- and some of them might be toxic. However testing the toxicity of all existing compounds by biological experiments is neither financially nor logistically feasible. Therefore the government agencies NIH, EPA ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	true	false	false	40,831
2111.11267	Sequential locality of graphs and its hypothesis testing	The adjacency matrix is the most fundamental and intuitive object in graph analysis that is useful not only mathematically but also for visualizing the structures of graphs. Because the appearance of an adjacency matrix is critically affected by the ordering of rows and columns, or vertex ordering, statistical assessme...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	267,605
2006.01601	Optimizing carbon tax for decentralized electricity markets using an agent-based model	Averting the effects of anthropogenic climate change requires a transition from fossil fuels to low-carbon technology. A way to achieve this is to decarbonize the electricity grid. However, further efforts must be made in other fields such as transport and heating for full decarbonization. This would reduce carbon emis...	false	false	false	false	true	false	true	false	false	false	true	false	false	false	false	false	false	false	179,817
2305.03716	3D Small Object Detection with Dynamic Spatial Pruning	In this paper, we propose an efficient feature pruning strategy for 3D small object detection. Conventional 3D object detection methods struggle on small objects due to the weak geometric information from a small number of points. Although increasing the spatial resolution of feature representations can improve the det...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	362,486
1803.04742	VERSE: Versatile Graph Embeddings from Similarity Measures	Embedding a web-scale information network into a low-dimensional vector space facilitates tasks such as link prediction, classification, and visualization. Past research has addressed the problem of extracting such embeddings by adopting methods from words to graphs, without defining a clearly comprehensible graph-rela...	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	false	false	92,506
2211.12124	A Large-Scale Dataset for Biomedical Keyphrase Generation	Keyphrase generation is the task consisting in generating a set of words or phrases that highlight the main topics of a document. There are few datasets for keyphrase generation in the biomedical domain and they do not meet the expectations in terms of size for training generative models. In this paper, we introduce kp...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	332,003
1410.7100	Estimating the intrinsic dimension in fMRI space via dataset fractal analysis - Counting the `cpu cores' of the human brain	Functional Magnetic Resonance Imaging (fMRI) is a powerful non-invasive tool for localizing and analyzing brain activity. This study focuses on one very important aspect of the functional properties of human brain, specifically the estimation of the level of parallelism when performing complex cognitive tasks. Using fM...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	37,039
2109.07680	Jointly Modeling Aspect and Polarity for Aspect-based Sentiment Analysis in Persian Reviews	Identification of user's opinions from natural language text has become an exciting field of research due to its growing applications in the real world. The research field is known as sentiment analysis and classification, where aspect category detection (ACD) and aspect category polarity (ACP) are two important sub-ta...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	255,604
2209.07436	Statistical process monitoring of artificial neural networks	The rapid advancement of models based on artificial intelligence demands innovative monitoring techniques which can operate in real time with low computational costs. In machine learning, especially if we consider artificial neural networks (ANNs), the models are often trained in a supervised manner. Consequently, the ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	317,748
1905.05984	Modern Problems Require Modern Solutions: Hybrid Concepts for Industrial Intrusion Detection	The concept of Industry 4.0 brings a disruption into the processing industry. It is characterised by a high degree of intercommunication, embedded computation, resulting in a decentralised and distributed handling of data. Additionally, cloud-storage and Software-as-a-Service (SaaS) approaches enhance a centralised sto...	false	false	false	false	true	false	false	false	false	false	false	false	true	false	false	false	false	false	130,877
2103.07151	Enabling Smart Reflection in Integrated Air-Ground Wireless Network: IRS Meets UAV	Intelligent reflecting surface (IRS) and unmanned aerial vehicle (UAV) have emerged as two promising technologies to boost the performance of wireless communication networks, by proactively altering the wireless communication channels via smart signal reflection and maneuver control, respectively. However, they face di...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	224,511
1902.08213	Towards Visually Grounded Sub-Word Speech Unit Discovery	In this paper, we investigate the manner in which interpretable sub-word speech units emerge within a convolutional neural network model trained to associate raw speech waveforms with semantically related natural image scenes. We show how diphone boundaries can be superficially extracted from the activation patterns of...	false	false	true	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	122,140
2212.09946	Dialog2API: Task-Oriented Dialogue with API Description and Example Programs	Functionality and dialogue experience are two important factors of task-oriented dialogue systems. Conventional approaches with closed schema (e.g., conversational semantic parsing) often fail as both the functionality and dialogue experience are strongly constrained by the underlying schema. We introduce a new paradig...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	337,265
2208.14153	Identifying Weight-Variant Latent Causal Models	The task of causal representation learning aims to uncover latent higher-level causal representations that affect lower-level observations. Identifying true latent causal representations from observed data, while allowing instantaneous causal relations among latent variables, remains a challenge, however. To this end, ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	315,236
2206.07332	Modelling of AC/DC Interactions of Converter-Interfaced Resources for Harmonic Power-Flow Studies in Microgrids	Power distribution systems experience a large-scale integration of Converter-Interfaced Distributed Energy Resources (CIDERs). As acknowledged by recent literature, the interaction of individual CIDER components and different CIDERs through the grid can lead to undesirable amplification of harmonic frequencies and, ult...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	302,702
2307.01031	On the validity of using the delta method for calculating the uncertainty of the predictions from an overparameterized model	The uncertainty in the prediction calculated using the delta method for an overparameterized (parametric) black-box model is shown to be larger or equal to the uncertainty in the prediction of a canonical (minimal) model. Equality holds if the additional parameters of the overparameterized model do not add flexibility ...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	377,214
2307.08966	Multi-Robot Patrol Algorithm with Distributed Coordination and Consciousness of the Base Station's Situation Awareness	Multi-robot patrolling is the potential application for robotic systems to survey wide areas efficiently without human burdens and mistakes. However, such systems have few examples of real-world applications due to their lack of human predictability. This paper proposes an algorithm: Local Reactive (LR) for multi-robot...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	380,001
1512.05671	Characterizing the Demographics Behind the #BlackLivesMatter Movement	The debates on minority issues are often dominated by or held among the concerned minority: gender equality debates have often failed to engage men, while those about race fail to effectively engage the dominant group. To test this observation, we study the #BlackLivesMatter}movement and hashtag on Twitter--which has e...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	50,250
2210.16271	MiCRO: Multi-interest Candidate Retrieval Online	Providing personalized recommendations in an environment where items exhibit ephemerality and temporal relevancy (e.g. in social media) presents a few unique challenges: (1) inductively understanding ephemeral appeal for items in a setting where new items are created frequently, (2) adapting to trends within engagement...	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	false	327,282
2412.11654	Smoothness Really Matters: A Simple Yet Effective Approach for Unsupervised Graph Domain Adaptation	Unsupervised Graph Domain Adaptation (UGDA) seeks to bridge distribution shifts between domains by transferring knowledge from labeled source graphs to given unlabeled target graphs. Existing UGDA methods primarily focus on aligning features in the latent space learned by graph neural networks (GNNs) across domains, of...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	517,514
2412.17505	More is Less? A Simulation-Based Approach to Dynamic Interactions between Biases in Multimodal Models	Multimodal machine learning models, such as those that combine text and image modalities, are increasingly used in critical domains including public safety, security, and healthcare. However, these systems inherit biases from their single modalities. This study proposes a systemic framework for analyzing dynamic multim...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	520,001
2003.12091	Online and Real-time Object Tracking Algorithm with Extremely Small Matrices	Online and Real-time Object Tracking is an interesting workload that can be used to track objects (e.g., car, human, animal) in a series of video sequences in real-time. For simple object tracking on edge devices, the output of object tracking could be as simple as drawing a bounding box around a detected object and in...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	169,808
2501.01821	SDPO: Segment-Level Direct Preference Optimization for Social Agents	Social agents powered by large language models (LLMs) can simulate human social behaviors but fall short in handling complex goal-oriented social dialogues. Direct Preference Optimization (DPO) has proven effective in aligning LLM behavior with human preferences across a variety of agent tasks. Existing DPO-based appro...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	522,227
2310.05082	Cross-head mutual Mean-Teaching for semi-supervised medical image segmentation	Semi-supervised medical image segmentation (SSMIS) has witnessed substantial advancements by leveraging limited labeled data and abundant unlabeled data. Nevertheless, existing state-of-the-art (SOTA) methods encounter challenges in accurately predicting labels for the unlabeled data, giving rise to disruptive noise du...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	397,974
2309.02572	Experience Capture in Shipbuilding through Computer Applications and Neural Networks	It has always been a severe loss for any establishment when an experienced hand retires or moves to another firm. The specific details of what his job/position entails will always make the work more efficient. To curtail such losses, it is possible to implement a system that takes input from a new employee regarding th...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	390,086
2310.00665	Balancing Efficiency vs. Effectiveness and Providing Missing Label Robustness in Multi-Label Stream Classification	Available works addressing multi-label classification in a data stream environment focus on proposing accurate models; however, these models often exhibit inefficiency and cannot balance effectiveness and efficiency. In this work, we propose a neural network-based approach that tackles this issue and is suitable for hi...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	396,086
1805.02257	Bayesian Regularization for Graphical Models with Unequal Shrinkage	We consider a Bayesian framework for estimating a high-dimensional sparse precision matrix, in which adaptive shrinkage and sparsity are induced by a mixture of Laplace priors. Besides discussing our formulation from the Bayesian standpoint, we investigate the MAP (maximum a posteriori) estimator from a penalized likel...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	96,816
2303.17491	Language Models can Solve Computer Tasks	Agents capable of carrying out general tasks on a computer can improve efficiency and productivity by automating repetitive tasks and assisting in complex problem-solving. Ideally, such agents should be able to solve new computer tasks presented to them through natural language commands. However, previous approaches to...	true	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	355,223
1808.08850	WiSeBE: Window-based Sentence Boundary Evaluation	Sentence Boundary Detection (SBD) has been a major research topic since Automatic Speech Recognition transcripts have been used for further Natural Language Processing tasks like Part of Speech Tagging, Question Answering or Automatic Summarization. But what about evaluation? Do standard evaluation metrics like precisi...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	106,052
2402.12071	EmoBench: Evaluating the Emotional Intelligence of Large Language Models	Recent advances in Large Language Models (LLMs) have highlighted the need for robust, comprehensive, and challenging benchmarks. Yet, research on evaluating their Emotional Intelligence (EI) is considerably limited. Existing benchmarks have two major shortcomings: first, they mainly focus on emotion recognition, neglec...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	430,703
2209.10006	Should we agree to disagree about Twitter's bot problem?	Bots, simply defined as accounts controlled by automation, can be used as a weapon for online manipulation and pose a threat to the health of platforms. Researchers have studied online platforms to detect, estimate, and characterize bot accounts. Concerns about the prevalence of bots were raised following Elon Musk's b...	false	false	false	true	false	false	false	false	false	false	false	false	false	true	false	false	false	false	318,702
1911.06239	Unreliable Multi-Armed Bandits: A Novel Approach to Recommendation Systems	We use a novel modification of Multi-Armed Bandits to create a new model for recommendation systems. We model the recommendation system as a bandit seeking to maximize reward by pulling on arms with unknown rewards. The catch however is that this bandit can only access these arms through an unreliable intermediate that...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	153,488
2410.13280	Hybrid bundle-adjusting 3D Gaussians for view consistent rendering with pose optimization	Novel view synthesis has made significant progress in the field of 3D computer vision. However, the rendering of view-consistent novel views from imperfect camera poses remains challenging. In this paper, we introduce a hybrid bundle-adjusting 3D Gaussians model that enables view-consistent rendering with pose optimiza...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	499,459
2310.19041	On Linear Separation Capacity of Self-Supervised Representation Learning	Recent advances in self-supervised learning have highlighted the efficacy of data augmentation in learning data representation from unlabeled data. Training a linear model atop these enhanced representations can yield an adept classifier. Despite the remarkable empirical performance, the underlying mechanisms that enab...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	403,821
2411.07742	Efficient 3D Perception on Multi-Sweep Point Cloud with Gumbel Spatial Pruning	This paper studies point cloud perception within outdoor environments. Existing methods face limitations in recognizing objects located at a distance or occluded, due to the sparse nature of outdoor point clouds. In this work, we observe a significant mitigation of this problem by accumulating multiple temporally conse...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	507,661
2108.09404	Safe Transformative AI via a Windfall Clause	Society could soon see transformative artificial intelligence (TAI). Models of competition for TAI show firms face strong competitive pressure to deploy TAI systems before they are safe. This paper explores a proposed solution to this problem, a Windfall Clause, where developers commit to donating a significant portion...	false	false	false	false	true	false	false	false	false	false	false	false	false	true	false	false	false	false	251,585
2012.05705	Automatic Micro-sleep Detection under Car-driving Simulation Environment using Night-sleep EEG	A micro-sleep is a short sleep that lasts from 1 to 30 secs. Its detection during driving is crucial to prevent accidents that could claim a lot of people's lives. Electroencephalogram (EEG) is suitable to detect micro-sleep because EEG was associated with consciousness and sleep. Deep learning showed great performance...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	true	false	false	210,870
2311.03739	Leveraging Large Language Models for Automated Proof Synthesis in Rust	Formal verification can provably guarantee the correctness of critical system software, but the high proof burden has long hindered its wide adoption. Recently, Large Language Models (LLMs) have shown success in code analysis and synthesis. In this paper, we present a combination of LLMs and static analysis to synthesi...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	405,972
1612.08171	KS_JU@DPIL-FIRE2016:Detecting Paraphrases in Indian Languages Using Multinomial Logistic Regression Model	In this work, we describe a system that detects paraphrases in Indian Languages as part of our participation in the shared Task on detecting paraphrases in Indian Languages (DPIL) organized by Forum for Information Retrieval Evaluation (FIRE) in 2016. Our paraphrase detection method uses a multinomial logistic regressi...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	66,038
2408.15657	TeFF: Tracking-enhanced Forgetting-free Few-shot 3D LiDAR Semantic Segmentation	In autonomous driving, 3D LiDAR plays a crucial role in understanding the vehicle's surroundings. However, the newly emerged, unannotated objects presents few-shot learning problem for semantic segmentation. This paper addresses the limitations of current few-shot semantic segmentation by exploiting the temporal contin...	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	484,028
2308.04579	RECipe: Does a Multi-Modal Recipe Knowledge Graph Fit a Multi-Purpose Recommendation System?	Over the past two decades, recommendation systems (RSs) have used machine learning (ML) solutions to recommend items, e.g., movies, books, and restaurants, to clients of a business or an online platform. Recipe recommendation, however, has not yet received much attention compared to those applications. We introduce REC...	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	false	384,459
2006.05327	mEBAL: A Multimodal Database for Eye Blink Detection and Attention Level Estimation	This work presents mEBAL, a multimodal database for eye blink detection and attention level estimation. The eye blink frequency is related to the cognitive activity and automatic detectors of eye blinks have been proposed for many tasks including attention level estimation, analysis of neuro-degenerative diseases, dece...	true	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	181,026
2410.03887	Solving Dual Sourcing Problems with Supply Mode Dependent Failure Rates	This paper investigates dual sourcing problems with supply mode dependent failure rates, particularly relevant in managing spare parts for downtime-critical assets. To enhance resilience, businesses increasingly adopt dual sourcing strategies using both conventional and additive manufacturing techniques. This paper exp...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	495,025
2303.06261	Interpretable Outlier Summarization	Outlier detection is critical in real applications to prevent financial fraud, defend network intrusions, or detecting imminent device failures. To reduce the human effort in evaluating outlier detection results and effectively turn the outliers into actionable insights, the users often expect a system to automatically...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	true	false	350,763
2001.09052	Enhancing Virtual Ontology Based Access over Tabular Data with Morph-CSV	Ontology-Based Data Access (OBDA) has traditionally focused on providing a unified view of heterogeneous datasets, either by materializing integrated data into RDF or by performing on-the fly querying via SPARQL query translation. In the specific case of tabular datasets represented as several CSV or Excel files, query...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	161,468
1911.07348	Robotic Sculpting with Collision-free Motion Planning in Voxel Space	In this paper, we explore the task of robot sculpting. We propose a search based planning algorithm to solve the problem of sculpting by material removal with a multi-axis manipulator. We generate collision free trajectories for a manipulator using best-first search in voxel space. We also show significant speedup of o...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	153,816

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