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
2408.12446	EX-DRL: Hedging Against Heavy Losses with EXtreme Distributional Reinforcement Learning	Recent advancements in Distributional Reinforcement Learning (DRL) for modeling loss distributions have shown promise in developing hedging strategies in derivatives markets. A common approach in DRL involves learning the quantiles of loss distributions at specified levels using Quantile Regression (QR). This method is...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	482,737
1904.12386	Application of Autoencoder-Assisted Recurrent Neural Networks to Prevent Cases of Sudden Infant Death Syndrome	This project develops and trains a Recurrent Neural Network (RNN) that monitors sleeping infants from an auxiliary microphone for cases of Sudden Infant Death Syndrome (SIDS), manifested in sudden or gradual respiratory arrest. To minimize invasiveness and maximize economic viability, an electret microphone, and parabo...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	129,101
1011.0051	Proceedings Fourth Workshop on Membrane Computing and Biologically Inspired Process Calculi 2010	The 4th Workshop on Membrane Computing and Biologically Inspired Process Calculi (MeCBIC 2010) is organized in Jena as a satellite event of the Eleventh International Conference on Membrane Computing (CMC11). Biological membranes play a fundamental role in the complex reactions which take place in cells of living organ...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	8,078
2103.13747	Statistical Modeling of the Human Body as an Extended Antenna	In this paper we investigate the possibility of modeling a single antenna alone and in close proximity to a physical object by means of discrete point source scatterers. The scatter point model allows joint modeling of a physical antenna and the human body as a single extended object with direction dependent scattering...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	226,597
2411.05636	Video RWKV:Video Action Recognition Based RWKV	To address the challenges of high computational costs and long-distance dependencies in exist ing video understanding methods, such as CNNs and Transformers, this work introduces RWKV to the video domain in a novel way. We propose a LSTM CrossRWKV (LCR) framework, designed for spatiotemporal representation learning to ...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	506,733
2002.12499	On Catastrophic Interference in Atari 2600 Games	Model-free deep reinforcement learning is sample inefficient. One hypothesis -- speculated, but not confirmed -- is that catastrophic interference within an environment inhibits learning. We test this hypothesis through a large-scale empirical study in the Arcade Learning Environment (ALE) and, indeed, find supporting ...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	166,052
2010.04855	Kernel Methods for Causal Functions: Dose, Heterogeneous, and Incremental Response Curves	We propose estimators based on kernel ridge regression for nonparametric causal functions such as dose, heterogeneous, and incremental response curves. Treatment and covariates may be discrete or continuous in general spaces. Due to a decomposition property specific to the RKHS, our estimators have simple closed form s...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	199,888
2202.13506	Keyword Optimization in Sponsored Search Advertising: A Multi-Level Computational Framework	In sponsored search advertising, keywords serve as an essential bridge linking advertisers, search users and search engines. Advertisers have to deal with a series of keyword decisions throughout the entire lifecycle of search advertising campaigns. This paper proposes a multi-level and closed-form computational framew...	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	false	false	282,635
2311.09389	Neural machine translation for automated feedback on children's early-stage writing	In this work, we address the problem of assessing and constructing feedback for early-stage writing automatically using machine learning. Early-stage writing is typically vastly different from conventional writing due to phonetic spelling and lack of proper grammar, punctuation, spacing etc. Consequently, early-stage w...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	408,117
2210.11594	Photo-realistic 360 Head Avatars in the Wild	Delivering immersive, 3D experiences for human communication requires a method to obtain 360 degree photo-realistic avatars of humans. To make these experiences accessible to all, only commodity hardware, like mobile phone cameras, should be necessary to capture the data needed for avatar creation. For avatars to be re...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	325,370
2112.00891	Event Neural Networks	Video data is often repetitive; for example, the contents of adjacent frames are usually strongly correlated. Such redundancy occurs at multiple levels of complexity, from low-level pixel values to textures and high-level semantics. We propose Event Neural Networks (EvNets), which leverage this redundancy to achieve co...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	269,284
2108.09923	Convolutional Filtering and Neural Networks with Non Commutative Algebras	In this paper we introduce and study the algebraic generalization of non commutative convolutional neural networks. We leverage the theory of algebraic signal processing to model convolutional non commutative architectures, and we derive concrete stability bounds that extend those obtained in the literature for commuta...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	251,749
2206.13269	Wasserstein Distributionally Robust Estimation in High Dimensions: Performance Analysis and Optimal Hyperparameter Tuning	Wasserstein distributionally robust optimization has recently emerged as a powerful framework for robust estimation, enjoying good out-of-sample performance guarantees, well-understood regularization effects, and computationally tractable reformulations. In such framework, the estimator is obtained by minimizing the wo...	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	304,893
2312.17617	Large Language Models for Generative Information Extraction: A Survey	Information extraction (IE) aims to extract structural knowledge from plain natural language texts. Recently, generative Large Language Models (LLMs) have demonstrated remarkable capabilities in text understanding and generation. As a result, numerous works have been proposed to integrate LLMs for IE tasks based on a g...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	418,810
2308.02261	Adaptive Proximal Gradient Method for Convex Optimization	In this paper, we explore two fundamental first-order algorithms in convex optimization, namely, gradient descent (GD) and proximal gradient method (ProxGD). Our focus is on making these algorithms entirely adaptive by leveraging local curvature information of smooth functions. We propose adaptive versions of GD and Pr...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	383,556
cs/0103013	CRL at Ntcir2	We have developed systems of two types for NTCIR2. One is an enhenced version of the system we developed for NTCIR1 and IREX. It submitted retrieval results for JJ and CC tasks. A variety of parameters were tried with the system. It used such characteristics of newspapers as locational information in the CC tasks. The ...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	537,304
2308.10761	CoNe: Contrast Your Neighbours for Supervised Image Classification	Image classification is a longstanding problem in computer vision and machine learning research. Most recent works (e.g. SupCon , Triplet, and max-margin) mainly focus on grouping the intra-class samples aggressively and compactly, with the assumption that all intra-class samples should be pulled tightly towards their ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	386,868
2410.18147	MEC-IP: Efficient Discovery of Markov Equivalent Classes via Integer Programming	This paper presents a novel Integer Programming (IP) approach for discovering the Markov Equivalent Class (MEC) of Bayesian Networks (BNs) through observational data. The MEC-IP algorithm utilizes a unique clique-focusing strategy and Extended Maximal Spanning Graphs (EMSG) to streamline the search for MEC, thus overco...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	501,775
2001.07098	Audio Summarization with Audio Features and Probability Distribution Divergence	The automatic summarization of multimedia sources is an important task that facilitates the understanding of an individual by condensing the source while maintaining relevant information. In this paper we focus on audio summarization based on audio features and the probability of distribution divergence. Our method, ba...	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	160,966
1701.07531	Design of Improved Quasi-Cyclic Protograph-Based Raptor-Like LDPC Codes for Short Block-Lengths	Protograph-based Raptor-like low-density parity-check codes (PBRL codes) are a recently proposed family of easily encodable and decodable rate-compatible LDPC (RC-LDPC) codes. These codes have an excellent iterative decoding threshold and performance across all design rates. PBRL codes designed thus far, for both long ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	67,309
1702.08813	Fixed-point Based Hierarchical MPC Control Design For a Cryogenic Refrigerator	In this paper, a simple and general hierarchical control framework is proposed and validated through the interconnection of the Joule-Thompson and the Brayton cycle stages of a cryogenic refrigerator. The proposed framework enables to handle the case of destabilizing interconnections through state and/or control signal...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	69,072
2209.04427	Zydeco-Style Spike Sorting Low Power VLSI Architecture for IoT BCI Implants	Brain Computer Interface (BCI) has great potential for solving many brain signal analysis limitations, mental disorder resolutions, and restoring missing limb functionality via neural-controlled implants. However, there is no single available, and safe implant for daily life usage exists yet. Most of the proposed impla...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	316,782
2302.06001	On Second-Order Derivatives of Rigid-Body Dynamics: Theory & Implementation	Model-based control for robots has increasingly been dependent on optimization-based methods like Differential Dynamic Programming and iterative LQR (iLQR). These methods can form the basis of Model-Predictive Control (MPC), which is commonly used for controlling legged robots. Computing the partial derivatives of the ...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	345,250
2103.02650	Successor Feature Sets: Generalizing Successor Representations Across Policies	Successor-style representations have many advantages for reinforcement learning: for example, they can help an agent generalize from past experience to new goals, and they have been proposed as explanations of behavioral and neural data from human and animal learners. They also form a natural bridge between model-based...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	223,023
2412.17836	Look Ahead Text Understanding and LLM Stitching	This paper proposes a look ahead text understanding problem with look ahead section identification (LASI) as an example. This problem may appear in generative AI as well as human interactions, where we want to understand the direction of a developing text or conversation. We tackle the problem using transformer-based L...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	520,140
2004.06445	Stochastic modeling of non-linear adsorption with Gaussian kernel density estimators	Adsorption is a relevant process in many fields, such as product manufacturing or pollution remediation in porous materials. Adsorption takes place at the molecular scale, amenable to be modeled by Lagrangian numerical methods. We have proposed a chemical diffusion-reaction model for the simulation of adsorption, based...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	172,507
2312.11774	Text-Image Conditioned Diffusion for Consistent Text-to-3D Generation	By lifting the pre-trained 2D diffusion models into Neural Radiance Fields (NeRFs), text-to-3D generation methods have made great progress. Many state-of-the-art approaches usually apply score distillation sampling (SDS) to optimize the NeRF representations, which supervises the NeRF optimization with pre-trained text-...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	416,706
2010.08167	Piecewise-Linear Motion Planning amidst Static, Moving, or Morphing Obstacles	We propose a novel method for planning shortest length piecewise-linear motions through complex environments punctured with static, moving, or even morphing obstacles. Using a moment optimization approach, we formulate a hierarchy of semidefinite programs that yield increasingly refined lower bounds converging monotoni...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	201,081
2402.04157	Controller synthesis for input-state data with measurement errors	We consider the problem of designing a state-feedback controller for a linear system, based only on noisy input-state data. We focus on input-state data corrupted by measurement errors, which, albeit less investigated, are as relevant as process disturbances in applications. For energy and instantaneous bounds on these...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	427,345
2410.10130	DecKG: Decentralized Collaborative Learning with Knowledge Graph Enhancement for POI Recommendation	Decentralized collaborative learning for Point-of-Interest (POI) recommendation has gained research interest due to its advantages in privacy preservation and efficiency, as it keeps data locally and leverages collaborative learning among clients to train models in a decentralized manner. However, since local data is o...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	497,926
2312.03737	A Generic NLI approach for Classification of Sentiment Associated with Therapies	This paper describes our system for addressing SMM4H 2023 Shared Task 2 on "Classification of sentiment associated with therapies (aspect-oriented)". In our work, we adopt an approach based on Natural language inference (NLI) to formulate this task as a sentence pair classification problem, and train transformer models...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	413,391
1205.0110	Modelling spatial patterns of economic activity in the Netherlands	Understanding how spatial configurations of economic activity emerge is important when formulating spatial planning and economic policy. Not only micro-simulation and agent-based model such as UrbanSim, ILUMAS and SIMFIRMS, but also Simon's model of hierarchical concentration have widely applied, for this purpose. Thes...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	15,748
2501.04353	DeFusion: An Effective Decoupling Fusion Network for Multi-Modal Pregnancy Prediction	Temporal embryo images and parental fertility table indicators are both valuable for pregnancy prediction in \textbf{in vitro fertilization embryo transfer} (IVF-ET). However, current machine learning models cannot make full use of the complementary information between the two modalities to improve pregnancy prediction...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	523,195
0806.4958	Deterministic Designs with Deterministic Guarantees: Toeplitz Compressed Sensing Matrices, Sequence Designs and System Identification	In this paper we present a new family of discrete sequences having "random like" uniformly decaying auto-correlation properties. The new class of infinite length sequences are higher order chirps constructed using irrational numbers. Exploiting results from the theory of continued fractions and diophantine approximatio...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	2,016
1910.05298	Neural Generation for Czech: Data and Baselines	We present the first dataset targeted at end-to-end NLG in Czech in the restaurant domain, along with several strong baseline models using the sequence-to-sequence approach. While non-English NLG is under-explored in general, Czech, as a morphologically rich language, makes the task even harder: Since Czech requires in...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	149,014
2403.04063	Assigning Entities to Teams as a Hypergraph Discovery Problem	We propose a team assignment algorithm based on a hypergraph approach focusing on resilience and diffusion optimization. Specifically, our method is based on optimizing the algebraic connectivity of the Laplacian matrix of an edge-dependent vertex-weighted hypergraph. We used constrained simulated annealing, where we c...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	435,436
2305.13637	IdEALS: Idiomatic Expressions for Advancement of Language Skills	Although significant progress has been made in developing methods for Grammatical Error Correction (GEC), addressing word choice improvements has been notably lacking and enhancing sentence expressivity by replacing phrases with advanced expressions is an understudied aspect. In this paper, we focus on this area and pr...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	366,595
1403.0965	Design Challenges of Millimeter Wave Communications: A MAC Layer Perspective	As the spectrum is becoming more scarce due to exponential demand of formidable data quantities, the new millimiterwave (mmW) band is considered as an enabling player of 5G communications to provide multi-gigabits wireless acccess. MmW communications exhibit high attenuation and blockage, directionality due to massive ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	31,346
2011.03696	Data--driven Image Restoration with Option--driven Learning for Big and Small Astronomical Image Datasets	Image restoration methods are commonly used to improve the quality of astronomical images. In recent years, developments of deep neural networks and increments of the number of astronomical images have evoked a lot of data--driven image restoration methods. However, most of these methods belong to supervised learning a...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	205,320
2205.02737	Koopman pose predictions for temporally consistent human walking estimations	We tackle the problem of tracking the human lower body as an initial step toward an automatic motion assessment system for clinical mobility evaluation, using a multimodal system that combines Inertial Measurement Unit (IMU) data, RGB images, and point cloud depth measurements. This system applies the factor graph repr...	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	295,043
physics/0511201	Strategies for fast convergence in semiotic dynamics	Semiotic dynamics is a novel field that studies how semiotic conventions spread and stabilize in a population of agents. This is a central issue both for theoretical and technological reasons since large system made up of communicating agents, like web communities or artificial embodied agents teams, are getting widesp...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	true	540,819
2405.09355	Vision-Based Neurosurgical Guidance: Unsupervised Localization and Camera-Pose Prediction	Localizing oneself during endoscopic procedures can be problematic due to the lack of distinguishable textures and landmarks, as well as difficulties due to the endoscopic device such as a limited field of view and challenging lighting conditions. Expert knowledge shaped by years of experience is required for localizat...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	454,377
2111.05485	A Structure Feature Algorithm for Multi-modal Forearm Registration	Augmented reality technology based on image registration is becoming increasingly popular for the convenience of pre-surgery preparation and medical education. This paper focuses on the registration of forearm images and digital anatomical models. Due to the difference in texture features of forearm multi-modal images,...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	265,808
2204.08896	Model Checking Strategic Abilities in Information-sharing Systems	We introduce a subclass of concurrent game structures (CGS) with imperfect information in which agents are endowed with private data-sharing capabilities. Importantly, our CGSs are such that it is still decidable to model-check these CGSs against a relevant fragment of ATL. These systems can be thought as a generalisat...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	292,248
2406.14162	DIRAS: Efficient LLM Annotation of Document Relevance in Retrieval Augmented Generation	Retrieval Augmented Generation (RAG) is widely employed to ground responses to queries on domain-specific documents. But do RAG implementations leave out important information when answering queries that need an integrated analysis of information (e.g., Tell me good news in the stock market today.)? To address these co...	false	false	false	false	true	true	false	false	true	false	false	false	false	false	false	false	false	false	466,185
2001.03509	Deformable Groupwise Image Registration using Low-Rank and Sparse Decomposition	Low-rank and sparse decompositions and robust PCA (RPCA) are highly successful techniques in image processing and have recently found use in groupwise image registration. In this paper, we investigate the drawbacks of the most common RPCA-dissimi\-larity metric in image registration and derive an improved version. In p...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	159,996
2109.08544	Conversational Multi-Hop Reasoning with Neural Commonsense Knowledge and Symbolic Logic Rules	One of the challenges faced by conversational agents is their inability to identify unstated presumptions of their users' commands, a task trivial for humans due to their common sense. In this paper, we propose a zero-shot commonsense reasoning system for conversational agents in an attempt to achieve this. Our reasone...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	true	255,931
1610.05932	An algorithmic approach using multivariate polynomials for the nonlinearity of Boolean functions	The nonlinearity of a Boolean function is a key property in deciding its suitability for cryptographic purposes, e.g. as a combining function in stream ciphers, and so the nonlinearity computation is an important problem for applications. Traditional methods to compute the nonlinearity are based on transforms, such as ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	62,583
2108.01495	Cross-Modal Analysis of Human Detection for Robotics: An Industrial Case Study	Advances in sensing and learning algorithms have led to increasingly mature solutions for human detection by robots, particularly in selected use-cases such as pedestrian detection for self-driving cars or close-range person detection in consumer settings. Despite this progress, the simple question "which sensor-algori...	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	249,045
1807.08666	ASR-free CNN-DTW keyword spotting using multilingual bottleneck features for almost zero-resource languages	We consider multilingual bottleneck features (BNFs) for nearly zero-resource keyword spotting. This forms part of a United Nations effort using keyword spotting to support humanitarian relief programmes in parts of Africa where languages are severely under-resourced. We use 1920 isolated keywords (40 types, 34 minutes)...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	103,591
2408.00754	Coarse Correspondences Boost Spatial-Temporal Reasoning in Multimodal Language Model	Multimodal language models (MLLMs) are increasingly being applied in real-world environments, necessitating their ability to interpret 3D spaces and comprehend temporal dynamics. Current methods often rely on specialized architectural designs or task-specific fine-tuning to achieve this. We introduce Coarse Corresponde...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	477,959
2106.08849	How memory architecture affects learning in a simple POMDP: the two-hypothesis testing problem	Reinforcement learning is generally difficult for partially observable Markov decision processes (POMDPs), which occurs when the agent's observation is partial or noisy. To seek good performance in POMDPs, one strategy is to endow the agent with a finite memory, whose update is governed by the policy. However, policy o...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	241,443
2310.16176	Correction with Backtracking Reduces Hallucination in Summarization	Abstractive summarization aims at generating natural language summaries of a source document that are succinct while preserving the important elements. Despite recent advances, neural text summarization models are known to be susceptible to hallucinating (or more correctly confabulating), that is to produce summaries w...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	402,611
2001.00560	Vehicle Platooning Impact on Drag Coefficients and Energy/Fuel Saving Implications	In this paper, empirical data from the literature are used to develop general power models that capture the impact of a vehicle position, in a platoon of homogeneous vehicles, and the distance gap to its lead (and following) vehicle on its drag coefficient. These models are developed for light duty vehicles, buses, and...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	159,245
2409.15888	Investigating Gender Bias in Lymph-node Segmentation with Anatomical Priors	Radiotherapy requires precise segmentation of organs at risk (OARs) and of the Clinical Target Volume (CTV) to maximize treatment efficacy and minimize toxicity. While deep learning (DL) has significantly advanced automatic contouring, complex targets like CTVs remain challenging. This study explores the use of simpler...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	491,112
2410.18142	Analyzing Nobel Prize Literature with Large Language Models	This study examines the capabilities of advanced Large Language Models (LLMs), particularly the o1 model, in the context of literary analysis. The outputs of these models are compared directly to those produced by graduate-level human participants. By focusing on two Nobel Prize-winning short stories, 'Nine Chapters' b...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	501,772
2210.16938	A view on model misspecification in uncertainty quantification	Estimating uncertainty of machine learning models is essential to assess the quality of the predictions that these models provide. However, there are several factors that influence the quality of uncertainty estimates, one of which is the amount of model misspecification. Model misspecification always exists as models ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	327,507
1706.08799	NOMA based Random Access with Multichannel ALOHA	In nonorthogonal multiple access (NOMA), the power difference of multiple signals is exploited for multiple access and successive interference cancellation (SIC) is employed at a receiver to mitigate co-channel interference. Thus, NOMA is usually employed for coordinated transmissions and mostly applied to downlink tra...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	76,054
2404.10597	Hardware-aware training of models with synaptic delays for digital event-driven neuromorphic processors	Configurable synaptic delays are a basic feature in many neuromorphic neural network hardware accelerators. However, they have been rarely used in model implementations, despite their promising impact on performance and efficiency in tasks that exhibit complex (temporal) dynamics, as it has been unclear how to optimize...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	false	true	447,166
2206.05657	LUEM : Local User Engagement Maximization in Networks	Understanding a social network is a fundamental problem in social network analysis because of its numerous applications. Recently, user engagement in networks has received extensive attention from many research groups. However, most user engagement models focus on global user engagement to maximize (or minimize) the nu...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	302,085
2006.05544	Resolution-Enhanced MRI-Guided Navigation of Spinal Cellular Injection Robot	This paper presents a method of navigating a surgical robot beyond the resolution of magnetic resonance imaging (MRI) by using a resolution enhancement technique enabled by high-precision piezoelectric actuation. The surgical robot was specifically designed for injecting stem cells into the spinal cord. This particular...	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	181,101
1501.00752	A Deep-structured Conditional Random Field Model for Object Silhouette Tracking	In this work, we introduce a deep-structured conditional random field (DS-CRF) model for the purpose of state-based object silhouette tracking. The proposed DS-CRF model consists of a series of state layers, where each state layer spatially characterizes the object silhouette at a particular point in time. The interact...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	39,023
2211.02003	Distributed DP-Helmet: Scalable Differentially Private Non-interactive Averaging of Single Layers	In this work, we propose two differentially private, non-interactive, distributed learning algorithms in a framework called Distributed DP-Helmet. Our framework is based on what we coin blind averaging: each user locally learns and noises a model and all users then jointly compute the mean of their models via a secure ...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	328,443
2308.06929	Predicting Listing Prices In Dynamic Short Term Rental Markets Using Machine Learning Models	Our research group wanted to take on the difficult task of predicting prices in a dynamic market. And short term rentals such as Airbnb listings seemed to be the perfect proving ground to do such a thing. Airbnb has revolutionized the travel industry by providing a platform for homeowners to rent out their properties t...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	385,327
2410.07352	Generating Origin-Destination Matrices in Neural Spatial Interaction Models	Agent-based models (ABMs) are proliferating as decision-making tools across policy areas in transportation, economics, and epidemiology. In these models, a central object of interest is the discrete origin-destination matrix which captures spatial interactions and agent trip counts between locations. Existing approache...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	496,586
1907.03702	Identifying Missing Component in the Bechdel Test Using Principal Component Analysis Method	A lot has been said and discussed regarding the rationale and significance of the Bechdel Score. It became a digital sensation in 2013 when Swedish cinemas began to showcase the Bechdel test score of a film alongside its rating. The test has drawn criticism from experts and the film fraternity regarding its use to rate...	false	false	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	137,912
2403.11472	Accelerating String-Key Learned Index Structures via Memoization-based Incremental Training	Learned indexes use machine learning models to learn the mappings between keys and their corresponding positions in key-value indexes. These indexes use the mapping information as training data. Learned indexes require frequent retrainings of their models to incorporate the changes introduced by update queries. To effi...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	true	true	438,715
2104.09719	Effects of Interregional Travels and Vaccination in Infection Spreads Simulated by Lattice of SEIRS Circuits	The SEIRS model, an extension of the SEIR model for analyzing and predicting the spread of virus infection, was further extended to consider the movement of people across regions. In contrast to previous models that con-sider the risk of travelers from/to other regions, we consider two factors. First, we consider the m...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	231,329
1806.04074	Semantically Selective Augmentation for Deep Compact Person Re-Identification	We present a deep person re-identification approach that combines semantically selective, deep data augmentation with clustering-based network compression to generate high performance, light and fast inference networks. In particular, we propose to augment limited training data via sampling from a deep convolutional ge...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	100,153
2409.04411	Approximating Metric Magnitude of Point Sets	Metric magnitude is a measure of the "size" of point clouds with many desirable geometric properties. It has been adapted to various mathematical contexts and recent work suggests that it can enhance machine learning and optimization algorithms. But its usability is limited due to the computational cost when the datase...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	486,383
2310.05195	GMMFormer: Gaussian-Mixture-Model Based Transformer for Efficient Partially Relevant Video Retrieval	Given a text query, partially relevant video retrieval (PRVR) seeks to find untrimmed videos containing pertinent moments in a database. For PRVR, clip modeling is essential to capture the partial relationship between texts and videos. Current PRVR methods adopt scanning-based clip construction to achieve explicit clip...	false	false	false	false	true	true	false	false	false	false	false	true	false	false	false	false	false	true	398,036
1301.2866	Generalized Multiscale Finite Element Methods (GMsFEM)	In this paper, we propose a general approach called Generalized Multiscale Finite Element Method (GMsFEM) for performing multiscale simulations for problems without scale separation over a complex input space. As in multiscale finite element methods (MsFEMs), the main idea of the proposed approach is to construct a sma...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	21,050
2112.04807	Effective dimension of machine learning models	Making statements about the performance of trained models on tasks involving new data is one of the primary goals of machine learning, i.e., to understand the generalization power of a model. Various capacity measures try to capture this ability, but usually fall short in explaining important characteristics of models ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	270,651
2306.01864	Discovering COVID-19 Coughing and Breathing Patterns from Unlabeled Data Using Contrastive Learning with Varying Pre-Training Domains	Rapid discovery of new diseases, such as COVID-19 can enable a timely epidemic response, preventing the large-scale spread and protecting public health. However, limited research efforts have been taken on this problem. In this paper, we propose a contrastive learning-based modeling approach for COVID-19 coughing and b...	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	370,644
2202.13526	Sparse Graph Learning with Spectrum Prior for Deep Graph Convolutional Networks	A graph convolutional network (GCN) employs a graph filtering kernel tailored for data with irregular structures. However, simply stacking more GCN layers does not improve performance; instead, the output converges to an uninformative low-dimensional subspace, where the convergence rate is characterized by the graph sp...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	282,648
2405.00574	EALD-MLLM: Emotion Analysis in Long-sequential and De-identity videos with Multi-modal Large Language Model	Emotion AI is the ability of computers to understand human emotional states. Existing works have achieved promising progress, but two limitations remain to be solved: 1) Previous studies have been more focused on short sequential video emotion analysis while overlooking long sequential video. However, the emotions in s...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	450,973
2111.14302	Self-supervised Feature-Gate Coupling for Dynamic Network Pruning	Gating modules have been widely explored in dynamic network pruning to reduce the run-time computational cost of deep neural networks while preserving the representation of features. Despite the substantial progress, existing methods remain ignoring the consistency between feature and gate distributions, which may lead...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	268,556
2006.00556	Modeling Personalized Item Frequency Information for Next-basket Recommendation	Next-basket recommendation (NBR) is prevalent in e-commerce and retail industry. In this scenario, a user purchases a set of items (a basket) at a time. NBR performs sequential modeling and recommendation based on a sequence of baskets. NBR is in general more complex than the widely studied sequential (session-based) r...	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	false	179,504
1103.5043	An Empirical Study of Real-World SPARQL Queries	Understanding how users tailor their SPARQL queries is crucial when designing query evaluation engines or fine-tuning RDF stores with performance in mind. In this paper we analyze 3 million real-world SPARQL queries extracted from logs of the DBPedia and SWDF public endpoints. We aim at finding which are the most used ...	true	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	false	false	9,759
1509.05472	Learning to Hash for Indexing Big Data - A Survey	The explosive growth in big data has attracted much attention in designing efficient indexing and search methods recently. In many critical applications such as large-scale search and pattern matching, finding the nearest neighbors to a query is a fundamental research problem. However, the straightforward solution usin...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	47,046
2002.02530	Machine learning on DNA-encoded libraries: A new paradigm for hit-finding	DNA-encoded small molecule libraries (DELs) have enabled discovery of novel inhibitors for many distinct protein targets of therapeutic value through screening of libraries with up to billions of unique small molecules. We demonstrate a new approach applying machine learning to DEL selection data by identifying active ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	162,948
2308.05576	Do Language Models' Words Refer?	What do language models (LMs) do with language? Everyone agrees that they can produce sequences of (mostly) coherent strings of English. But do those sentences mean something, or are LMs simply babbling in a convincing simulacrum of language use? Here we will address one aspect of this broad question: whether LMs' word...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	384,838
1912.05137	Is Feature Diversity Necessary in Neural Network Initialization?	Standard practice in training neural networks involves initializing the weights in an independent fashion. The results of recent work suggest that feature "diversity" at initialization plays an important role in training the network. However, other initialization schemes with reduced feature diversity have also been sh...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	157,029
0704.0805	Opportunistic Relay Selection with Limited Feedback	It has been shown that a decentralized relay selection protocol based on opportunistic feedback from the relays yields good throughput performance in dense wireless networks. This selection strategy supports a hybrid-ARQ transmission approach where relays forward parity information to the destination in the event of a ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	15
2408.05920	Urban Region Pre-training and Prompting: A Graph-based Approach	Urban region representation is crucial for various urban downstream tasks. However, despite the proliferation of methods and their success, acquiring general urban region knowledge and adapting to different tasks remains challenging. Previous work often neglects the spatial structures and functional layouts between ent...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	480,001
2002.02073	Truncated Hilbert Transform: Uniqueness and a Chebyshev series Expansion Approach	We derive a stronger uniqueness result if a function with compact support and its truncated Hilbert transform are known on the same interval by using the Sokhotski-Plemelj formulas. To find a function from its truncated Hilbert transform, we express them in the Chebyshev polynomial series and then suggest two methods t...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	162,818
1911.07485	Locally recoverable $J$-affine variety codes	A locally recoverable (LRC) code is a code over a finite field $\mathbb{F}_q$ such that any erased coordinate of a codeword can be recovered from a small number of other coordinates in that codeword. We construct LRC codes correcting more than one erasure, which are subfield-subcodes of some $J$-affine variety codes. F...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	153,868
2206.12452	Vibration fault detection in wind turbines based on normal behaviour models without feature engineering	Most wind turbines are remotely monitored 24/7 to allow for an early detection of operation problems and developing damage. We present a new fault detection method for vibration-monitored drivetrains that does not require any feature engineering. Our method relies on a simple model architecture to enable a straightforw...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	304,604
2306.10683	Spatial-Temporal Graph Learning with Adversarial Contrastive Adaptation	Spatial-temporal graph learning has emerged as a promising solution for modeling structured spatial-temporal data and learning region representations for various urban sensing tasks such as crime forecasting and traffic flow prediction. However, most existing models are vulnerable to the quality of the generated region...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	374,319
1903.06695	Development details and computational benchmarking of DEPAM	In the big data era of observational oceanography, passive acoustics datasets are becoming too high volume to be processed on local computers due to their processor and memory limitations. As a result there is a current need for our community to turn to cloud-based distributed computing. We present a scalable computing...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	124,444
2006.11524	Neuro-Symbolic Visual Reasoning: Disentangling "Visual" from "Reasoning"	Visual reasoning tasks such as visual question answering (VQA) require an interplay of visual perception with reasoning about the question semantics grounded in perception. However, recent advances in this area are still primarily driven by perception improvements (e.g. scene graph generation) rather than reasoning. Ne...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	true	false	true	183,273
1906.05437	Conditioning of Reinforcement Learning Agents and its Policy Regularization Application	The outcome of Jacobian singular values regularization was studied for supervised learning problems. It also was shown that Jacobian conditioning regularization can help to avoid the ``mode-collapse'' problem in Generative Adversarial Networks. In this paper, we try to answer the following question: Can information abo...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	135,024
2210.17218	Artificial intelligence in government: Concepts, standards, and a unified framework	Recent advances in artificial intelligence (AI), especially in generative language modelling, hold the promise of transforming government. Given the advanced capabilities of new AI systems, it is critical that these are embedded using standard operational procedures, clear epistemic criteria, and behave in alignment wi...	true	false	false	false	true	false	true	false	false	false	true	false	false	true	false	false	false	false	327,608
2104.05327	MinkLoc++: Lidar and Monocular Image Fusion for Place Recognition	We introduce a discriminative multimodal descriptor based on a pair of sensor readings: a point cloud from a LiDAR and an image from an RGB camera. Our descriptor, named MinkLoc++, can be used for place recognition, re-localization and loop closure purposes in robotics or autonomous vehicles applications. We use late f...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	229,685
2405.02571	ViTALS: Vision Transformer for Action Localization in Surgical Nephrectomy	Surgical action localization is a challenging computer vision problem. While it has promising applications including automated training of surgery procedures, surgical workflow optimization, etc., appropriate model design is pivotal to accomplishing this task. Moreover, the lack of suitable medical datasets adds an add...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	451,813
2401.06470	UNEX-RL: Reinforcing Long-Term Rewards in Multi-Stage Recommender Systems with UNidirectional EXecution	In recent years, there has been a growing interest in utilizing reinforcement learning (RL) to optimize long-term rewards in recommender systems. Since industrial recommender systems are typically designed as multi-stage systems, RL methods with a single agent face challenges when optimizing multiple stages simultaneou...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	421,172
2207.09964	On a Generalized Framework for Time-Aware Knowledge Graphs	Knowledge graphs have emerged as an effective tool for managing and standardizing semistructured domain knowledge in a human- and machine-interpretable way. In terms of graph-based domain applications, such as embeddings and graph neural networks, current research is increasingly taking into account the time-related ev...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	309,091
2311.16091	Interactive Autonomous Navigation with Internal State Inference and Interactivity Estimation	Deep reinforcement learning (DRL) provides a promising way for intelligent agents (e.g., autonomous vehicles) to learn to navigate complex scenarios. However, DRL with neural networks as function approximators is typically considered a black box with little explainability and often suffers from suboptimal performance, ...	false	false	false	false	true	false	true	true	false	false	false	true	false	false	true	false	false	false	410,749
2109.09655	Impact of Surface and Pore Characteristics on Fatigue Life of Laser Powder Bed Fusion Ti-6Al-4V Alloy Described by Neural Network Models	In this study, the effects of surface roughness and pore characteristics on fatigue lives of laser powder bed fusion (LPBF) Ti-6Al-4V parts were investigated. The 197 fatigue bars were printed using the same laser power but with varied scanning speeds. These actions led to variations in the geometries of microscale por...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	256,349
2410.15616	Weighted Diversified Sampling for Efficient Data-Driven Single-Cell Gene-Gene Interaction Discovery	Gene-gene interactions play a crucial role in the manifestation of complex human diseases. Uncovering significant gene-gene interactions is a challenging task. Here, we present an innovative approach utilizing data-driven computational tools, leveraging an advanced Transformer model, to unearth noteworthy gene-gene int...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	500,638

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