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
2109.14326	DeepAnalyze: Learning to Localize Crashes at Scale	Crash localization, an important step in debugging crashes, is challenging when dealing with an extremely large number of diverse applications and platforms and underlying root causes. Large-scale error reporting systems, e.g., Windows Error Reporting (WER), commonly rely on manually developed rules and heuristics to l...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	true	257,935
1509.01310	The influence of Chunking on Dependency Crossing and Distance	This paper hypothesizes that chunking plays important role in reducing dependency distance and dependency crossings. Computer simulations, when compared with natural languages,show that chunking reduces mean dependency distance (MDD) of a linear sequence of nodes (constrained by continuity or projectivity) to that of n...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	46,589
2003.06000	Human Grasp Classification for Reactive Human-to-Robot Handovers	Transfer of objects between humans and robots is a critical capability for collaborative robots. Although there has been a recent surge of interest in human-robot handovers, most prior research focus on robot-to-human handovers. Further, work on the equally critical human-to-robot handovers often assumes humans can pla...	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	168,001
2209.11146	MLGWSC-1: The first Machine Learning Gravitational-Wave Search Mock Data Challenge	We present the results of the first Machine Learning Gravitational-Wave Search Mock Data Challenge (MLGWSC-1). For this challenge, participating groups had to identify gravitational-wave signals from binary black hole mergers of increasing complexity and duration embedded in progressively more realistic noise. The fina...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	319,093
2312.06734	DiffCast: A Unified Framework via Residual Diffusion for Precipitation Nowcasting	Precipitation nowcasting is an important spatio-temporal prediction task to predict the radar echoes sequences based on current observations, which can serve both meteorological science and smart city applications. Due to the chaotic evolution nature of the precipitation systems, it is a very challenging problem. Previ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	414,666
2107.13826	Adaptive Sampling of Dynamic Systems for Generation of Fast and Accurate Surrogate Models	For economic nonlinear model predictive control and dynamic real-time optimization fast and accurate models are necessary. Consequently, the use of dynamic surrogate models to mimic complex rigorous models is increasingly coming into focus. For dynamic systems, the focus so far had been on identifying a system's behavi...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	248,316
2205.00861	Star-specific Key-homomorphic PRFs from Learning with Linear Regression	We introduce a novel method to derandomize the learning with errors (LWE) problem by generating deterministic yet sufficiently independent LWE instances that are constructed by using linear regression models, which are generated via (wireless) communication errors. We also introduce star-specific key-homomorphic (SSKH)...	false	false	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	294,403
2008.00188	Augmented Skeleton Based Contrastive Action Learning with Momentum LSTM for Unsupervised Action Recognition	Action recognition via 3D skeleton data is an emerging important topic in these years. Most existing methods either extract hand-crafted descriptors or learn action representations by supervised learning paradigms that require massive labeled data. In this paper, we for the first time propose a contrastive action learn...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	189,933
1910.14356	Certifiable Robustness to Graph Perturbations	Despite the exploding interest in graph neural networks there has been little effort to verify and improve their robustness. This is even more alarming given recent findings showing that they are extremely vulnerable to adversarial attacks on both the graph structure and the node attributes. We propose the first method...	false	false	false	true	false	false	true	false	false	false	false	false	true	false	false	false	false	false	151,630
2103.04789	Look, Cast and Mold: Learning 3D Shape Manifold from Single-view Synthetic Data	Inferring the stereo structure of objects in the real world is a challenging yet practical task. To equip deep models with this ability usually requires abundant 3D supervision which is hard to acquire. It is promising that we can simply benefit from synthetic data, where pairwise ground-truth is easy to access. Nevert...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	223,766
2110.12569	Conductance and Social Capital: Modeling and Empirically Measuring Online Social Influence	Social influence pervades our everyday lives and lays the foundation for complex social phenomena. In a crisis like the COVID-19 pandemic, social influence can determine whether life-saving information is adopted. Existing literature studying online social influence suffers from several drawbacks. First, a disconnect a...	false	false	false	true	false	false	false	false	false	false	false	false	false	true	false	false	false	false	262,888
2210.07469	StyLEx: Explaining Style Using Human Lexical Annotations	Large pre-trained language models have achieved impressive results on various style classification tasks, but they often learn spurious domain-specific words to make predictions (Hayati et al., 2021). While human explanation highlights stylistic tokens as important features for this task, we observe that model explanat...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	323,724
2305.15261	Random periodic sampling patterns for shift-invariant spaces	We consider multi-variate signals spanned by the integer shifts of a set of generating functions with distinct frequency profiles and the problem of reconstructing them from samples taken on a random periodic set. We show that such a sampling strategy succeeds with high probability provided that the density of the samp...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	367,533
2412.03176	Automatic detection of diseases in Spanish clinical notes combining medical language models and ontologies	In this paper we present a hybrid method for the automatic detection of dermatological pathologies in medical reports. We use a large language model combined with medical ontologies to predict, given a first appointment or follow-up medical report, the pathology a person may suffer from. The results show that teaching ...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	513,852
1712.01600	Deep learning for semantic segmentation of remote sensing images with rich spectral content	With the rapid development of Remote Sensing acquisition techniques, there is a need to scale and improve processing tools to cope with the observed increase of both data volume and richness. Among popular techniques in remote sensing, Deep Learning gains increasing interest but depends on the quality of the training d...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	86,126
1901.08753	On Output Activation Functions for Adversarial Losses: A Theoretical Analysis via Variational Divergence Minimization and An Empirical Study on MNIST Classification	Recent years have seen adversarial losses been applied to many fields. Their applications extend beyond the originally proposed generative modeling to conditional generative and discriminative settings. While prior work has proposed various output activation functions and regularization approaches, some open questions ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	119,571
2409.02290	Unsupervised Welding Defect Detection Using Audio And Video	In this work we explore the application of AI to robotic welding. Robotic welding is a widely used technology in many industries, but robots currently do not have the capability to detect welding defects which get introduced due to various reasons in the welding process. We describe how deep-learning methods can be app...	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	485,637
2407.08330	HDT: Hierarchical Document Transformer	In this paper, we propose the Hierarchical Document Transformer (HDT), a novel sparse Transformer architecture tailored for structured hierarchical documents. Such documents are extremely important in numerous domains, including science, law or medicine. However, most existing solutions are inefficient and fail to make...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	472,124
2011.13042	RetroGNN: Approximating Retrosynthesis by Graph Neural Networks for De Novo Drug Design	De novo molecule generation often results in chemically unfeasible molecules. A natural idea to mitigate this problem is to bias the search process towards more easily synthesizable molecules using a proxy for synthetic accessibility. However, using currently available proxies still results in highly unrealistic compou...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	208,345
2009.06857	Current Limitations of Language Models: What You Need is Retrieval	We classify and re-examine some of the current approaches to improve the performance-computes trade-off of language models, including (1) non-causal models (such as masked language models), (2) extension of batch length with efficient attention, (3) recurrence, (4) conditional computation and (5) retrieval. We identify...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	195,769
1401.0579	More Algorithms for Provable Dictionary Learning	In dictionary learning, also known as sparse coding, the algorithm is given samples of the form $y = Ax$ where $x\in \mathbb{R}^m$ is an unknown random sparse vector and $A$ is an unknown dictionary matrix in $\mathbb{R}^{n\times m}$ (usually $m > n$, which is the overcomplete case). The goal is to learn $A$ and $x$. T...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	29,570
2309.04094	Gabor frames and higher dimensional boundaries in signal analysis on manifolds	We provide a construction of Gabor frames that encode local linearizations of a signal detected on a curved smooth manifold of arbitrary dimension, with Gabor filters that can detect the presence of higher-dimensional boundaries in the manifold signal. We describe an application in configuration spaces in robotics with...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	390,612
2204.00112	Gallium Oxide Heterojunction Diodes for Improved High-Temperature Performance	${\beta}$-Ga${_2}$O${_3}$ based semiconductor devices are expected to have significantly improved high-power and high-temperature performance due to its ultra-wide bandgap of close to 5 eV. However, the high-temperature operation of these ultra-wide-bandgap devices is usually limited by the relatively low 1-2 eV built-...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	289,136
2409.14264	The Differential and Boomerang Properties of a Class of Binomials	Let $q$ be an odd prime power with $q\equiv 3\ ({\rm{mod}}\ 4)$. In this paper, we study the differential and boomerang properties of the function $F_{2,u}(x)=x^2\big(1+u\eta(x)\big)$ over $\mathbb{F}_{q}$, where $u\in\mathbb{F}_{q}^*$ and $\eta$ is the quadratic character of $\mathbb{F}_{q}$. We determine the differen...	false	false	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	490,396
2309.12941	Trusta: Reasoning about Assurance Cases with Formal Methods and Large Language Models	Assurance cases can be used to argue for the safety of products in safety engineering. In safety-critical areas, the construction of assurance cases is indispensable. Trustworthiness Derivation Trees (TDTs) enhance assurance cases by incorporating formal methods, rendering it possible for automatic reasoning about assu...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	393,983
2010.02256	An Ensemble Approach for Automatic Structuring of Radiology Reports	Automatic structuring of electronic medical records is of high demand for clinical workflow solutions to facilitate extraction, storage, and querying of patient care information. However, developing a scalable solution is extremely challenging, specifically for radiology reports, as most healthcare institutes use eithe...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	198,942
1903.05216	Learning Gaussian Policies from Corrective Human Feedback	Learning from human feedback is a viable alternative to control design that does not require modelling or control expertise. Particularly, learning from corrective advice garners advantages over evaluative feedback as it is a more intuitive and scalable format. The current state-of-the-art in this field, COACH, has pro...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	124,118
2208.14175	RAGUEL: Recourse-Aware Group Unfairness Elimination	While machine learning and ranking-based systems are in widespread use for sensitive decision-making processes (e.g., determining job candidates, assigning credit scores), they are rife with concerns over unintended biases in their outcomes, which makes algorithmic fairness (e.g., demographic parity, equal opportunity)...	false	false	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	315,244
2306.01799	Pairwise Ranking Losses of Click-Through Rates Prediction for Welfare Maximization in Ad Auctions	We study the design of loss functions for click-through rates (CTR) to optimize (social) welfare in advertising auctions. Existing works either only focus on CTR predictions without consideration of business objectives (e.g., welfare) in auctions or assume that the distribution over the participants' expected cost-per-...	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	true	370,613
2402.12416	Aligning Individual and Collective Objectives in Multi-Agent Cooperation	Among the research topics in multi-agent learning, mixed-motive cooperation is one of the most prominent challenges, primarily due to the mismatch between individual and collective goals. The cutting-edge research is focused on incorporating domain knowledge into rewards and introducing additional mechanisms to incenti...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	true	false	false	false	430,844
1209.6140	DAARIA: Driver Assistance by Augmented Reality for Intelligent Automobile	Taking into account the drivers' state is a major challenge for designing new advanced driver assistance systems. In this paper we present a driver assistance system strongly coupled to the user. DAARIA 1 stands for Driver Assistance by Augmented Reality for Intelligent Automobile. It is an augmented reality interface ...	true	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	18,792
1602.08715	Identification of Parallel Passages Across a Large Hebrew/Aramaic Corpus	We propose a method for efficiently finding all parallel passages in a large corpus, even if the passages are not quite identical due to rephrasing and orthographic variation. The key ideas are the representation of each word in the corpus by its two most infrequent letters, finding matched pairs of strings of four or ...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	52,680
1803.08225	PersonLab: Person Pose Estimation and Instance Segmentation with a Bottom-Up, Part-Based, Geometric Embedding Model	We present a box-free bottom-up approach for the tasks of pose estimation and instance segmentation of people in multi-person images using an efficient single-shot model. The proposed PersonLab model tackles both semantic-level reasoning and object-part associations using part-based modeling. Our model employs a convol...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	93,219
2407.14110	MC-PanDA: Mask Confidence for Panoptic Domain Adaptation	Domain adaptive panoptic segmentation promises to resolve the long tail of corner cases in natural scene understanding. Previous state of the art addresses this problem with cross-task consistency, careful system-level optimization and heuristic improvement of teacher predictions. In contrast, we propose to build upon ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	474,654
1806.07370	Constructing Fast Network through Deconstruction of Convolution	Convolutional neural networks have achieved great success in various vision tasks; however, they incur heavy resource costs. By using deeper and wider networks, network accuracy can be improved rapidly. However, in an environment with limited resources (e.g., mobile applications), heavy networks may not be usable. This...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	100,904
1801.09322	Benchmarking Clinical Decision Support Search	Finding relevant literature underpins the practice of evidence-based medicine. From 2014 to 2016, TREC conducted a clinical decision support track, wherein participants were tasked with finding articles relevant to clinical questions posed by physicians. In total, 87 teams have participated over the past three years, g...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	89,084
2401.03116	Advancing DDoS Attack Detection: A Synergistic Approach Using Deep Residual Neural Networks and Synthetic Oversampling	Distributed Denial of Service (DDoS) attacks pose a significant threat to the stability and reliability of online systems. Effective and early detection of such attacks is pivotal for safeguarding the integrity of networks. In this work, we introduce an enhanced approach for DDoS attack detection by leveraging the capa...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	419,968
2201.11388	Contrastive Embedding Distribution Refinement and Entropy-Aware Attention for 3D Point Cloud Classification	Learning a powerful representation from point clouds is a fundamental and challenging problem in the field of computer vision. Different from images where RGB pixels are stored in the regular grid, for point clouds, the underlying semantic and structural information of point clouds is the spatial layout of the points. ...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	277,285
2003.08885	Unique Geometry and Texture from Corresponding Image Patches	We present a sufficient condition for recovering unique texture and viewpoints from unknown orthographic projections of a flat texture process. We show that four observations are sufficient in general, and we characterize the ambiguous cases. The results are applicable to shape from texture and texture-based structure ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	168,899
1602.01416	On the Relay-Fallback Tradeoff in Millimeter Wave Wireless System	Millimeter wave (mmWave) communications systems are promising candidate to support extremely high data rate services in future wireless networks. MmWave communications exhibit high penetration loss (blockage) and require directional transmissions to compensate for severe channel attenuations and for high noise powers. ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	51,696
2307.12607	ExWarp: Extrapolation and Warping-based Temporal Supersampling for High-frequency Displays	High-frequency displays are gaining immense popularity because of their increasing use in video games and virtual reality applications. However, the issue is that the underlying GPUs cannot continuously generate frames at this high rate -- this results in a less smooth and responsive experience. Furthermore, if the fra...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	true	381,317
2202.03961	Predicting Voting Outcomes in the Presence of Communities, Echo Chambers and Multiple Parties	A recently proposed graph-theoretic metric, the influence gap, has shown to be a reliable predictor of the effect of social influence in two-party elections, albeit only tested on regular and scale-free graphs. Here, we investigate whether the influence gap is able to predict the outcome of multi-party elections on net...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	false	false	false	279,405
1410.7263	Pricing in Social Networks with Negative Externalities	We study the problems of pricing an indivisible product to consumers who are embedded in a given social network. The goal is to maximize the revenue of the seller. We assume impatient consumers who buy the product as soon as the seller posts a price not greater than their values of the product. The product's value for ...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	true	37,056
2212.01131	Activating the Discriminability of Novel Classes for Few-shot Segmentation	Despite the remarkable success of existing methods for few-shot segmentation, there remain two crucial challenges. First, the feature learning for novel classes is suppressed during the training on base classes in that the novel classes are always treated as background. Thus, the semantics of novel classes are not well...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	334,320
2006.14822	A survey of loss functions for semantic segmentation	Image Segmentation has been an active field of research as it has a wide range of applications, ranging from automated disease detection to self-driving cars. In the past five years, various papers came up with different objective loss functions used in different cases such as biased data, sparse segmentation, etc. In ...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	184,343
1802.08286	Reliability and Market Price of Energy in the Presence of Intermittent and Non-Dispatchable Renewable Energies	The intermittent nature of the renewable energies increases the operation costs of conventional generators. As the share of energy supplied by renewable sources increases, these costs also increase. In this paper, we quantify these costs by developing a market clearing price of energy in the presence of renewable energ...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	91,066
0908.1453	Training Process Reduction Based On Potential Weights Linear Analysis To Accelarate Back Propagation Network	Learning is the important property of Back Propagation Network (BPN) and finding the suitable weights and thresholds during training in order to improve training time as well as achieve high accuracy. Currently, data pre-processing such as dimension reduction input values and pre-training are the contributing factors i...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	4,258
2412.19938	Towards Strong AI: Transformational Beliefs and Scientific Creativity	Strong artificial intelligence (AI) is envisioned to possess general cognitive abilities and scientific creativity comparable to human intelligence, encompassing both knowledge acquisition and problem-solving. While remarkable progress has been made in weak AI, the realization of strong AI remains a topic of intense de...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	521,028
2102.13419	Iterative SE(3)-Transformers	When manipulating three-dimensional data, it is possible to ensure that rotational and translational symmetries are respected by applying so-called SE(3)-equivariant models. Protein structure prediction is a prominent example of a task which displays these symmetries. Recent work in this area has successfully made use ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	222,060
1906.11452	Traffic Management Strategies for Multi-Robotic Rigid Payload Transport Systems	In this work, we address traffic management of multiple payload transport systems comprising of non-holonomic robots. We consider loosely coupled rigid robot formations carrying a payload from one place to another. Each payload transport system (PTS) moves in various kinds of environments with obstacles. We ensure each...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	136,671
2112.03536	Learning Pixel-Adaptive Weights for Portrait Photo Retouching	Portrait photo retouching is a photo retouching task that emphasizes human-region priority and group-level consistency. The lookup table-based method achieves promising retouching performance by learning image-adaptive weights to combine 3-dimensional lookup tables (3D LUTs) and conducting pixel-to-pixel color transfor...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	270,241
2111.10968	Functorial aggregation	We study polynomial comonads and polynomial bicomodules. Polynomial comonads amount to categories. Polynomial bicomodules between categories amount to parametric right adjoint functors between corresponding copresheaf categories. These may themselves be understood as generalized polynomial functors. They are also calle...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	267,506
2407.20870	Mean of Means: A 10-dollar Solution for Human Localization with Calibration-free and Unconstrained Camera Settings	Accurate human localization is crucial for various applications, especially in the Metaverse era. Existing high precision solutions rely on expensive, tag-dependent hardware, while vision-based methods offer a cheaper, tag-free alternative. However, current vision solutions based on stereo vision face limitations due t...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	477,312
2305.12997	Evaluating Privacy Leakage in Split Learning	Privacy-Preserving machine learning (PPML) can help us train and deploy models that utilize private information. In particular, on-device machine learning allows us to avoid sharing raw data with a third-party server during inference. On-device models are typically less accurate when compared to their server counterpar...	false	false	false	false	true	false	true	false	false	false	false	false	true	false	false	false	false	false	366,292
1704.04576	NEXT: A Neural Network Framework for Next POI Recommendation	The task of next POI recommendation has been studied extensively in recent years. However, developing an unified recommendation framework to incorporate multiple factors associated with both POIs and users remains challenging, because of the heterogeneity nature of these information. Further, effective mechanisms to ha...	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	false	false	71,836
1309.1226	Graded Causation and Defaults	Recent work in psychology and experimental philosophy has shown that judgments of actual causation are often influenced by consideration of defaults, typicality, and normality. A number of philosophers and computer scientists have also suggested that an appeal to such factors can help deal with problems facing existing...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	26,844
2309.09092	The Impact of Debiasing on the Performance of Language Models in Downstream Tasks is Underestimated	Pre-trained language models trained on large-scale data have learned serious levels of social biases. Consequently, various methods have been proposed to debias pre-trained models. Debiasing methods need to mitigate only discriminatory bias information from the pre-trained models, while retaining information that is us...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	392,467
1510.01970	Towards a general framework for an observation and knowledge based model of occupant behaviour in office buildings	This paper proposes a new general approach based on Bayesian networks to model the human behaviour. This approach represents human behaviour withprobabilistic cause-effect relations based not only on previous works, but also with conditional probabilities coming either from expert knowledge or deduced from observations...	false	false	false	false	true	false	false	false	false	false	false	false	false	true	false	false	false	false	47,674
2305.17353	Complementary and Integrative Health Lexicon (CIHLex) and Entity Recognition in the Literature	Objective: Our study aimed to construct an exhaustive Complementary and Integrative Health (CIH) Lexicon (CIHLex) to better represent the often underrepresented physical and psychological CIH approaches in standard terminologies. We also intended to apply advanced Natural Language Processing (NLP) models such as Bidire...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	368,544
1907.01707	On Adaptivity Gaps of Influence Maximization under the Independent Cascade Model with Full Adoption Feedback	In this paper, we study the adaptivity gap of the influence maximization problem under independent cascade model when full-adoption feedback is available. Our main results are to derive upper bounds on several families of well-studied influence graphs, including in-arborescences, out-arborescences and bipartite graphs....	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	137,417
2502.03128	Metis: A Foundation Speech Generation Model with Masked Generative Pre-training	We introduce Metis, a foundation model for unified speech generation. Unlike previous task-specific or multi-task models, Metis follows a pre-training and fine-tuning paradigm. It is pre-trained on large-scale unlabeled speech data using masked generative modeling and then fine-tuned to adapt to diverse speech generati...	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	530,606
2004.13249	Conversational Word Embedding for Retrieval-Based Dialog System	Human conversations contain many types of information, e.g., knowledge, common sense, and language habits. In this paper, we propose a conversational word embedding method named PR-Embedding, which utilizes the conversation pairs $ \left\langle{post, reply} \right\rangle$ to learn word embedding. Different from previou...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	174,492
1810.08388	Online Balanced Motion Generation for Humanoid Robots	Reducing the complexity of higher order problems can enable solving them in analytical ways. In this paper, we propose an analytic whole body motion generator for humanoid robots. Our approach targets inexpensive platforms that possess position controlled joints and have limited feedback capabilities. By analysing the ...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	110,813
2004.11803	Scan-based Semantic Segmentation of LiDAR Point Clouds: An Experimental Study	Autonomous vehicles need to have a semantic understanding of the three-dimensional world around them in order to reason about their environment. State of the art methods use deep neural networks to predict semantic classes for each point in a LiDAR scan. A powerful and efficient way to process LiDAR measurements is to ...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	174,022
2210.01461	In the realm of hybrid Brain: Human Brain and AI	With the recent developments in neuroscience and engineering, it is now possible to record brain signals and decode them. Also, a growing number of stimulation methods have emerged to modulate and influence brain activity. Current brain-computer interface (BCI) technology is mainly on therapeutic outcomes, it already d...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	true	false	false	321,275
2203.05248	Look Backward and Forward: Self-Knowledge Distillation with Bidirectional Decoder for Neural Machine Translation	Neural Machine Translation(NMT) models are usually trained via unidirectional decoder which corresponds to optimizing one-step-ahead prediction. However, this kind of unidirectional decoding framework may incline to focus on local structure rather than global coherence. To alleviate this problem, we propose a novel met...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	284,758
2111.09985	DeMFI: Deep Joint Deblurring and Multi-Frame Interpolation with Flow-Guided Attentive Correlation and Recursive Boosting	In this paper, we propose a novel joint deblurring and multi-frame interpolation (DeMFI) framework, called DeMFI-Net, which accurately converts blurry videos of lower-frame-rate to sharp videos at higher-frame-rate based on flow-guided attentive-correlation-based feature bolstering (FAC-FB) module and recursive boostin...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	267,165
2205.14817	Mitigating Out-of-Distribution Data Density Overestimation in Energy-Based Models	Deep energy-based models (EBMs), which use deep neural networks (DNNs) as energy functions, are receiving increasing attention due to their ability to learn complex distributions. To train deep EBMs, the maximum likelihood estimation (MLE) with short-run Langevin Monte Carlo (LMC) is often used. While the MLE with shor...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	299,496
2305.11665	A Generic Performance Model for Deep Learning in a Distributed Environment	Performance modelling of a deep learning application is essential to improve and quantify the efficiency of the model framework. However, existing performance models are mostly case-specific, with limited capability for the new deep learning frameworks/applications. In this paper, we propose a generic performance model...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	365,644
2302.01973	Measuring The Impact Of Programming Language Distribution	Current benchmarks for evaluating neural code models focus on only a small subset of programming languages, excluding many popular languages such as Go or Rust. To ameliorate this issue, we present the BabelCode framework for execution-based evaluation of any benchmark in any language. BabelCode enables new investigati...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	true	343,801
1911.01537	Verification and Parameter Synthesis for Stochastic Systems using Optimistic Optimization	We present an algorithm for formal verification and parameter synthesis of continuous state-space Markov chains. This class of problems captures the design and analysis of a wide variety of autonomous and cyber-physical systems defined by nonlinear and black-box modules. In order to solve these problems, one has to max...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	152,130
1812.11142	The Diagrammatic AI Language (DIAL): Version 0.1	Currently, there is no consistent model for visually or formally representing the architecture of AI systems. This lack of representation brings interpretability, correctness and completeness challenges in the description of existing models and systems. DIAL (The Diagrammatic AI Language) has been created with the aspi...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	117,503
2403.16610	Distributed collaborative anomalous sound detection by embedding sharing	To develop a machine sound monitoring system, a method for detecting anomalous sound is proposed. In this paper, we explore a method for multiple clients to collaboratively learn an anomalous sound detection model while keeping their raw data private from each other. In the context of industrial machine anomalous sound...	false	false	true	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	441,123
1505.01554	Webly Supervised Learning of Convolutional Networks	We present an approach to utilize large amounts of web data for learning CNNs. Specifically inspired by curriculum learning, we present a two-step approach for CNN training. First, we use easy images to train an initial visual representation. We then use this initial CNN and adapt it to harder, more realistic images by...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	42,852
2403.13241	Tackling Noisy Labels with Network Parameter Additive Decomposition	Given data with noisy labels, over-parameterized deep networks suffer overfitting mislabeled data, resulting in poor generalization. The memorization effect of deep networks shows that although the networks have the ability to memorize all noisy data, they would first memorize clean training data, and then gradually me...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	439,528
2501.18128	Unraveling the Capabilities of Language Models in News Summarization	Given the recent introduction of multiple language models and the ongoing demand for improved Natural Language Processing tasks, particularly summarization, this work provides a comprehensive benchmarking of 20 recent language models, focusing on smaller ones for the news summarization task. In this work, we systematic...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	528,586
2301.09689	Graph Neural Networks for Decentralized Multi-Agent Perimeter Defense	In this work, we study the problem of decentralized multi-agent perimeter defense that asks for computing actions for defenders with local perceptions and communications to maximize the capture of intruders. One major challenge for practical implementations is to make perimeter defense strategies scalable for large-sca...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	false	false	false	341,570
1911.02320	Nonverbal Robot Feedback for Human Teachers	Robots can learn preferences from human demonstrations, but their success depends on how informative these demonstrations are. Being informative is unfortunately very challenging, because during teaching, people typically get no transparency into what the robot already knows or has learned so far. In contrast, human st...	true	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	152,336
1512.06479	Information Flows? A Critique of Transfer Entropies	A central task in analyzing complex dynamics is to determine the loci of information storage and the communication topology of information flows within a system. Over the last decade and a half, diagnostics for the latter have come to be dominated by the transfer entropy. Via straightforward examples, we show that it a...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	50,324
2409.19638	BadHMP: Backdoor Attack against Human Motion Prediction	Precise future human motion prediction over subsecond horizons from past observations is crucial for various safety-critical applications. To date, only one study has examined the vulnerability of human motion prediction to evasion attacks. In this paper, we propose BadHMP, the first backdoor attack that targets specif...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	492,781
2401.13693	Challenge design roadmap	Challenges can be seen as a type of game that motivates participants to solve serious tasks. As a result, competition organizers must develop effective game rules. However, these rules have multiple objectives beyond making the game enjoyable for participants. These objectives may include solving real-world problems, a...	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	423,817
2408.08447	SpectralEarth: Training Hyperspectral Foundation Models at Scale	Foundation models have triggered a paradigm shift in computer vision and are increasingly being adopted in remote sensing, particularly for multispectral imagery. Yet, their potential in hyperspectral imaging (HSI) remains untapped due to the absence of comprehensive and globally representative hyperspectral datasets. ...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	480,996
2308.14456	Speech Self-Supervised Representations Benchmarking: a Case for Larger Probing Heads	Self-supervised learning (SSL) leverages large datasets of unlabeled speech to reach impressive performance with reduced amounts of annotated data. The high number of proposed approaches fostered the emergence of comprehensive benchmarks that evaluate their performance on a set of downstream tasks exploring various asp...	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	388,337
2210.15527	Exploiting Features and Logits in Heterogeneous Federated Learning	Due to the rapid growth of IoT and artificial intelligence, deploying neural networks on IoT devices is becoming increasingly crucial for edge intelligence. Federated learning (FL) facilitates the management of edge devices to collaboratively train a shared model while maintaining training data local and private. Howev...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	326,994
2408.13926	FedGlu: A personalized federated learning-based glucose forecasting algorithm for improved performance in glycemic excursion regions	Continuous glucose monitoring (CGM) devices provide real-time glucose monitoring and timely alerts for glycemic excursions, improving glycemic control among patients with diabetes. However, identifying rare events like hypoglycemia and hyperglycemia remain challenging due to their infrequency. Moreover, limited access ...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	483,349
2002.00423	An Experimental Study of Formula Embeddings for Automated Theorem Proving in First-Order Logic	Automated theorem proving in first-order logic is an active research area which is successfully supported by machine learning. While there have been various proposals for encoding logical formulas into numerical vectors -- from simple strings to more involved graph-based embeddings -- little is known about how these di...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	true	162,348
1602.02950	Spoofing detection under noisy conditions: a preliminary investigation and an initial database	Spoofing detection for automatic speaker verification (ASV), which is to discriminate between live speech and attacks, has received increasing attentions recently. However, all the previous studies have been done on the clean data without significant additive noise. To simulate the real-life scenarios, we perform a pre...	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	51,938
2401.07927	Are self-explanations from Large Language Models faithful?	Instruction-tuned Large Language Models (LLMs) excel at many tasks and will even explain their reasoning, so-called self-explanations. However, convincing and wrong self-explanations can lead to unsupported confidence in LLMs, thus increasing risk. Therefore, it's important to measure if self-explanations truly reflect...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	421,697
2306.06693	Open Brain AI. Automatic Language Assessment	Language assessment plays a crucial role in diagnosing and treating individuals with speech, language, and communication disorders caused by neurogenic conditions, whether developmental or acquired. However, current assessment methods are manual, laborious, and time-consuming to administer and score, causing additional...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	372,716
2307.11955	Implicit Interpretation of Importance Weight Aware Updates	Due to its speed and simplicity, subgradient descent is one of the most used optimization algorithms in convex machine learning algorithms. However, tuning its learning rate is probably its most severe bottleneck to achieve consistent good performance. A common way to reduce the dependency on the learning rate is to us...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	381,086
2011.04798	Learning identifiable and interpretable latent models of high-dimensional neural activity using pi-VAE	The ability to record activities from hundreds of neurons simultaneously in the brain has placed an increasing demand for developing appropriate statistical techniques to analyze such data. Recently, deep generative models have been proposed to fit neural population responses. While these methods are flexible and expre...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	205,693
2301.07557	Targeted Image Reconstruction by Sampling Pre-trained Diffusion Model	A trained neural network model contains information on the training data. Given such a model, malicious parties can leverage the "knowledge" in this model and design ways to print out any usable information (known as model inversion attack). Therefore, it is valuable to explore the ways to conduct a such attack and dem...	false	false	false	false	true	false	true	false	false	false	false	false	true	false	false	false	false	false	340,956
2008.11092	Looking Deeper into Tabular LIME	In this paper, we present a thorough theoretical analysis of the default implementation of LIME in the case of tabular data. We prove that in the large sample limit, the interpretable coefficients provided by Tabular LIME can be computed in an explicit way as a function of the algorithm parameters and some expectation ...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	193,180
cmp-lg/9506020	GLR-Parsing of Word Lattices Using a Beam Search Method	This paper presents an approach that allows the efficient integration of speech recognition and language understanding using Tomita's generalized LR-parsing algorithm. For this purpose the GLRP-algorithm is revised so that an agenda mechanism can be used to control the flow of computation of the parsing process. This n...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	536,424
2104.08936	Knowledge Graph Anchored Information-Extraction for Domain-Specific Insights	The growing quantity and complexity of data pose challenges for humans to consume information and respond in a timely manner. For businesses in domains with rapidly changing rules and regulations, failure to identify changes can be costly. In contrast to expert analysis or the development of domain-specific ontology an...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	231,042
2405.19600	Rethinking Spectral Augmentation for Contrast-based Graph Self-Supervised Learning	The recent surge in contrast-based graph self-supervised learning has prominently featured an intensified exploration of spectral cues. Spectral augmentation, which involves modifying a graph's spectral properties such as eigenvalues or eigenvectors, is widely believed to enhance model performance. However, an intrigui...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	458,965
2102.13269	Many-to-One Distribution Learning and K-Nearest Neighbor Smoothing for Thoracic Disease Identification	Chest X-rays are an important and accessible clinical imaging tool for the detection of many thoracic diseases. Over the past decade, deep learning, with a focus on the convolutional neural network (CNN), has become the most powerful computer-aided diagnosis technology for improving disease identification performance. ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	222,003
1809.02403	Deep Recurrent Survival Analysis	Survival analysis is a hotspot in statistical research for modeling time-to-event information with data censorship handling, which has been widely used in many applications such as clinical research, information system and other fields with survivorship bias. Many works have been proposed for survival analysis ranging ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	107,046
2311.14848	Robotic Detection and Estimation of Single Scuba Diver Respiration Rate from Underwater Video	Human respiration rate (HRR) is an important physiological metric for diagnosing a variety of health conditions from stress levels to heart conditions. Estimation of HRR is well-studied in controlled terrestrial environments, yet robotic estimation of HRR as an indicator of diver stress in underwater for underwater hum...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	410,277
1906.06253	A Simple and Effective Approach to Automatic Post-Editing with Transfer Learning	Automatic post-editing (APE) seeks to automatically refine the output of a black-box machine translation (MT) system through human post-edits. APE systems are usually trained by complementing human post-edited data with large, artificial data generated through back-translations, a time-consuming process often no easier...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	135,246

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