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
2010.11689	Cross-Spectral Iris Matching Using Conditional Coupled GAN	Cross-spectral iris recognition is emerging as a promising biometric approach to authenticating the identity of individuals. However, matching iris images acquired at different spectral bands shows significant performance degradation when compared to single-band near-infrared (NIR) matching due to the spectral gap betw...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	202,372
1310.7961	Evaluation the efficiency of artificial bee colony and the firefly algorithm in solving the continuous optimization problem	Now the Meta-Heuristic algorithms have been used vastly in solving the problem of continuous optimization. In this paper the Artificial Bee Colony (ABC) algorithm and the Firefly Algorithm (FA) are valuated. And for presenting the efficiency of the algorithms and also for more analysis of them, the continuous optimizat...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	false	false	28,071
2309.14065	AsymFormer: Asymmetrical Cross-Modal Representation Learning for Mobile Platform Real-Time RGB-D Semantic Segmentation	Understanding indoor scenes is crucial for urban studies. Considering the dynamic nature of indoor environments, effective semantic segmentation requires both real-time operation and high accuracy.To address this, we propose AsymFormer, a novel network that improves real-time semantic segmentation accuracy using RGB-D ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	394,455
1304.5678	Analytic Feature Selection for Support Vector Machines	Support vector machines (SVMs) rely on the inherent geometry of a data set to classify training data. Because of this, we believe SVMs are an excellent candidate to guide the development of an analytic feature selection algorithm, as opposed to the more commonly used heuristic methods. We propose a filter-based feature...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	24,106
2308.01732	Towards Self-organizing Personal Knowledge Assistants in Evolving Corporate Memories	This paper presents a retrospective overview of a decade of research in our department towards self-organizing personal knowledge assistants in evolving corporate memories. Our research is typically inspired by real-world problems and often conducted in interdisciplinary collaborations with research and industry partne...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	383,343
2303.01719	Groups of linear isometries on weighted poset block spaces	In this paper, we introduce a new family of metrics, weighted poset block metric, that combine the weighted coordinates poset metric introduced by Panek et al. [(\ref{panek})] and the metric for linear error-block codes introduced by Feng et al. [(\ref{FENG})]. This type of metrics include many classical metrics such a...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	349,078
2306.02589	DAGrid: Directed Accumulator Grid	Recent research highlights that the Directed Accumulator (DA), through its parametrization of geometric priors into neural networks, has notably improved the performance of medical image recognition, particularly with small and imbalanced datasets. However, DA's potential in pixel-wise dense predictions is unexplored. ...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	370,968
2401.02225	Trajectory-Oriented Policy Optimization with Sparse Rewards	Mastering deep reinforcement learning (DRL) proves challenging in tasks featuring scant rewards. These limited rewards merely signify whether the task is partially or entirely accomplished, necessitating various exploration actions before the agent garners meaningful feedback. Consequently, the majority of existing DRL...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	419,645
2102.08099	EPE-NAS: Efficient Performance Estimation Without Training for Neural Architecture Search	Neural Architecture Search (NAS) has shown excellent results in designing architectures for computer vision problems. NAS alleviates the need for human-defined settings by automating architecture design and engineering. However, NAS methods tend to be slow, as they require large amounts of GPU computation. This bottlen...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	220,345
2006.12919	Distance Correlation Sure Independence Screening for Accelerated Feature Selection in Parkinson's Disease Vocal Data	With the abundance of machine learning methods available and the temptation of using them all in an ensemble method, having a model-agnostic method of feature selection is incredibly alluring. Principal component analysis was developed in 1901 and has been a strong contender in this role since, but in the end is an uns...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	183,752
2207.13450	Skimming, Locating, then Perusing: A Human-Like Framework for Natural Language Video Localization	This paper addresses the problem of natural language video localization (NLVL). Almost all existing works follow the "only look once" framework that exploits a single model to directly capture the complex cross- and self-modal relations among video-query pairs and retrieve the relevant segment. However, we argue that t...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	310,314
1512.05294	Feature Representation for ICU Mortality	Good predictors of ICU Mortality have the potential to identify high-risk patients earlier, improve ICU resource allocation, or create more accurate population-level risk models. Machine learning practitioners typically make choices about how to represent features in a particular model, but these choices are seldom eva...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	50,215
2401.12241	Power System Resource Expansion Planning	Power System Resource Planning is the recurrent process of studying and determining what facilities and procedures should be provided to satisfy and promote appropriate future demands for electricity. The electric power system as planned should meet or balance societal goals. These include availability of electricity t...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	423,314
2302.10418	MAC-PO: Multi-Agent Experience Replay via Collective Priority Optimization	Experience replay is crucial for off-policy reinforcement learning (RL) methods. By remembering and reusing the experiences from past different policies, experience replay significantly improves the training efficiency and stability of RL algorithms. Many decision-making problems in practice naturally involve multiple ...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	true	false	false	false	346,797
2305.06101	Access-Redundancy Tradeoffs in Quantized Linear Computations	Linear real-valued computations over distributed datasets are common in many applications, most notably as part of machine learning inference. In particular, linear computations that are quantized, i.e., where the coefficients are restricted to a predetermined set of values (such as $\pm 1$), have gained increasing int...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	363,401
cs/0006019	A Compact Architecture for Dialogue Management Based on Scripts and Meta-Outputs	We describe an architecture for spoken dialogue interfaces to semi-autonomous systems that transforms speech signals through successive representations of linguistic, dialogue, and domain knowledge. Each step produces an output, and a meta-output describing the transformation, with an executable program in a simple scr...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	537,128
2306.04237	Randomized 3D Scene Generation for Generalizable Self-Supervised Pre-Training	Capturing and labeling real-world 3D data is laborious and time-consuming, which makes it costly to train strong 3D models. To address this issue, recent works present a simple method by generating randomized 3D scenes without simulation and rendering. Although models pre-trained on the generated synthetic data gain im...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	371,664
2403.00877	Disaggregated Multi-Tower: Topology-aware Modeling Technique for Efficient Large-Scale Recommendation	We study a mismatch between the deep learning recommendation models' flat architecture, common distributed training paradigm and hierarchical data center topology. To address the associated inefficiencies, we propose Disaggregated Multi-Tower (DMT), a modeling technique that consists of (1) Semantic-preserving Tower Tr...	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	true	434,169
2310.14772	Predictor-Rejector Multi-Class Abstention: Theoretical Analysis and Algorithms	We study the key framework of learning with abstention in the multi-class classification setting. In this setting, the learner can choose to abstain from making a prediction with some pre-defined cost. We present a series of new theoretical and algorithmic results for this learning problem in the predictor-rejector fra...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	402,014
2311.12944	SkyCharge: Deploying Unmanned Aerial Vehicles for Dynamic Load Optimization in Solar Small Cell 5G Networks	The power requirements posed by the fifth-generation and beyond cellular networks are an important constraint in network deployment and require energy-efficient solutions. In this work, we propose a novel user load transfer approach using airborne base stations (BS) mounted on drones for reliable and secure power redis...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	true	false	true	409,563
1401.2663	Dictionary-Based Concept Mining: An Application for Turkish	In this study, a dictionary-based method is used to extract expressive concepts from documents. So far, there have been many studies concerning concept mining in English, but this area of study for Turkish, an agglutinative language, is still immature. We used dictionary instead of WordNet, a lexical database grouping ...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	29,770
2501.04864	A hybrid pressure formulation of the face-centred finite volume method for viscous laminar incompressible flows	This work presents a hybrid pressure face-centred finite volume (FCFV) solver to simulate steady-state incompressible Navier-Stokes flows. The method leverages the robustness, in the incompressible limit, of the hybridisable discontinuous Galerkin paradigm for compressible and weakly compressible flows to derive the fo...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	523,373
2207.06759	Work In Progress: Safety and Robustness Verification of Autoencoder-Based Regression Models using the NNV Tool	This work in progress paper introduces robustness verification for autoencoder-based regression neural network (NN) models, following state-of-the-art approaches for robustness verification of image classification NNs. Despite the ongoing progress in developing verification methods for safety and robustness in various ...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	307,984
2404.03502	AI and the Problem of Knowledge Collapse	While artificial intelligence has the potential to process vast amounts of data, generate new insights, and unlock greater productivity, its widespread adoption may entail unforeseen consequences. We identify conditions under which AI, by reducing the cost of access to certain modes of knowledge, can paradoxically harm...	false	false	false	false	true	false	false	false	false	false	false	false	false	true	false	false	false	false	444,282
1302.4546	Random-walk domination in large graphs: problem definitions and fast solutions	We introduce and formulate two types of random-walk domination problems in graphs motivated by a number of applications in practice (e.g., item-placement problem in online social network, Ads-placement problem in advertisement networks, and resource-placement problem in P2P networks). Specifically, given a graph $G$, t...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	true	22,162
2202.00360	Accelerating Deep Reinforcement Learning for Digital Twin Network Optimization with Evolutionary Strategies	The recent growth of emergent network applications (e.g., satellite networks, vehicular networks) is increasing the complexity of managing modern communication networks. As a result, the community proposed the Digital Twin Networks (DTN) as a key enabler of efficient network management. Network operators can leverage t...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	278,111
2211.07165	Model Evaluation in Medical Datasets Over Time	Machine learning models deployed in healthcare systems face data drawn from continually evolving environments. However, researchers proposing such models typically evaluate them in a time-agnostic manner, with train and test splits sampling patients throughout the entire study period. We introduce the Evaluation on Med...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	330,152
1803.05044	Learning to Explore with Meta-Policy Gradient	The performance of off-policy learning, including deep Q-learning and deep deterministic policy gradient (DDPG), critically depends on the choice of the exploration policy. Existing exploration methods are mostly based on adding noise to the on-going actor policy and can only explore \emph{local} regions close to what ...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	92,559
1807.08919	The Variational Homoencoder: Learning to learn high capacity generative models from few examples	Hierarchical Bayesian methods can unify many related tasks (e.g. k-shot classification, conditional and unconditional generation) as inference within a single generative model. However, when this generative model is expressed as a powerful neural network such as a PixelCNN, we show that existing learning techniques typ...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	103,627
1402.6067	Regular path queries on graphs with data: A rigid approach	Regular path queries (RPQ) is a classical navigational query formalism for graph databases to specify constraints on labeled paths. Recently, RPQs have been extended by Libkin and Vrgo$\rm \check{c}$ to incorporate data value comparisons among different nodes on paths, called regular path queries with data (RDPQ). It h...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	true	31,145
2110.02483	Detecting and Quantifying Malicious Activity with Simulation-based Inference	We propose the use of probabilistic programming techniques to tackle the malicious user identification problem in a recommendation algorithm. Probabilistic programming provides numerous advantages over other techniques, including but not limited to providing a disentangled representation of how malicious users acted un...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	259,142
2108.02817	THALIS: Human-Machine Analysis of Longitudinal Symptoms in Cancer Therapy	Although cancer patients survive years after oncologic therapy, they are plagued with long-lasting or permanent residual symptoms, whose severity, rate of development, and resolution after treatment vary largely between survivors. The analysis and interpretation of symptoms is complicated by their partial co-occurrence...	true	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	249,455
2305.04477	Behavior Contrastive Learning for Unsupervised Skill Discovery	In reinforcement learning, unsupervised skill discovery aims to learn diverse skills without extrinsic rewards. Previous methods discover skills by maximizing the mutual information (MI) between states and skills. However, such an MI objective tends to learn simple and static skills and may hinder exploration. In this ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	362,786
1803.05347	Illumination-aware Faster R-CNN for Robust Multispectral Pedestrian Detection	Multispectral images of color-thermal pairs have shown more effective than a single color channel for pedestrian detection, especially under challenging illumination conditions. However, there is still a lack of studies on how to fuse the two modalities effectively. In this paper, we deeply compare six different convol...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	92,623
1304.5583	Distributed Low-rank Subspace Segmentation	Vision problems ranging from image clustering to motion segmentation to semi-supervised learning can naturally be framed as subspace segmentation problems, in which one aims to recover multiple low-dimensional subspaces from noisy and corrupted input data. Low-Rank Representation (LRR), a convex formulation of the subs...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	true	24,095
1906.02975	Audio tagging with noisy labels and minimal supervision	This paper introduces Task 2 of the DCASE2019 Challenge, titled "Audio tagging with noisy labels and minimal supervision". This task was hosted on the Kaggle platform as "Freesound Audio Tagging 2019". The task evaluates systems for multi-label audio tagging using a large set of noisy-labeled data, and a much smaller s...	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	134,240
2410.16846	Safe Load Balancing in Software-Defined-Networking	High performance, reliability and safety are crucial properties of any Software-Defined-Networking (SDN) system. Although the use of Deep Reinforcement Learning (DRL) algorithms has been widely studied to improve performance, their practical applications are still limited as they fail to ensure safe operations in explo...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	501,213
2307.02457	DeSRA: Detect and Delete the Artifacts of GAN-based Real-World Super-Resolution Models	Image super-resolution (SR) with generative adversarial networks (GAN) has achieved great success in restoring realistic details. However, it is notorious that GAN-based SR models will inevitably produce unpleasant and undesirable artifacts, especially in practical scenarios. Previous works typically suppress artifacts...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	true	377,696
1009.3657	On Bounded Weight Codes	The maximum size of a binary code is studied as a function of its length N, minimum distance D, and minimum codeword weight W. This function B(N,D,W) is first characterized in terms of its exponential growth rate in the limit as N tends to infinity for fixed d=D/N and w=W/N. The exponential growth rate of B(N,D,W) is s...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	7,588
0911.5548	A Decision-Optimization Approach to Quantum Mechanics and Game Theory	The fundamental laws of quantum world upsets the logical foundation of classic physics. They are completely counter-intuitive with many bizarre behaviors. However, this paper shows that they may make sense from the perspective of a general decision-optimization principle for cooperation. This principle also offers a ge...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	5,049
1809.06196	Intermediate Deep Feature Compression: the Next Battlefield of Intelligent Sensing	The recent advances of hardware technology have made the intelligent analysis equipped at the front-end with deep learning more prevailing and practical. To better enable the intelligent sensing at the front-end, instead of compressing and transmitting visual signals or the ultimately utilized top-layer deep learning f...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	107,994
2412.04746	Diff4Steer: Steerable Diffusion Prior for Generative Music Retrieval with Semantic Guidance	Modern music retrieval systems often rely on fixed representations of user preferences, limiting their ability to capture users' diverse and uncertain retrieval needs. To address this limitation, we introduce Diff4Steer, a novel generative retrieval framework that employs lightweight diffusion models to synthesize dive...	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	false	false	true	514,544
2102.00615	Modeling Method for the Coupling Relations of Microgrid Cyber-Physical Systems Driven by Hybrid Spatiotemporal Events	The essence of the microgrid cyber-physical system (CPS) lies in the cyclical conversion of information flow and energy flow. Most of the existing coupling models are modeled with static networks and interface structures, in which the closed-loop data flow characteristic is not fully considered. It is difficult for the...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	217,845
2112.11721	Towards Malicious address identification in Bitcoin	The temporal aspect of blockchain transactions enables us to study the address's behavior and detect if it is involved in any illicit activity. However, due to the concept of change addresses (used to thwart replay attacks), temporal aspects are not directly applicable in the Bitcoin blockchain. Several pre-processing ...	false	false	false	false	false	false	true	false	false	false	false	false	true	true	false	false	false	true	272,784
2108.05319	Machine Learning Model Drift Detection Via Weak Data Slices	Detecting drift in performance of Machine Learning (ML) models is an acknowledged challenge. For ML models to become an integral part of business applications it is essential to detect when an ML model drifts away from acceptable operation. However, it is often the case that actual labels are difficult and expensive to...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	250,273
2012.09164	Point Transformer	Self-attention networks have revolutionized natural language processing and are making impressive strides in image analysis tasks such as image classification and object detection. Inspired by this success, we investigate the application of self-attention networks to 3D point cloud processing. We design self-attention ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	211,982
2108.00002	Bayesian Optimization in Materials Science: A Survey	Bayesian optimization is used in many areas of AI for the optimization of black-box processes and has achieved impressive improvements of the state of the art for a lot of applications. It intelligently explores large and complex design spaces while minimizing the number of evaluations of the expensive underlying proce...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	248,570
1902.05247	3D Graph Embedding Learning with a Structure-aware Loss Function for Point Cloud Semantic Instance Segmentation	This paper introduces a novel approach for 3D semantic instance segmentation on point clouds. A 3D convolutional neural network called submanifold sparse convolutional network is used to generate semantic predictions and instance embeddings simultaneously. To obtain discriminative embeddings for each 3D instance, a str...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	121,505
1410.2479	Spatial Diffuseness Features for DNN-Based Speech Recognition in Noisy and Reverberant Environments	We propose a spatial diffuseness feature for deep neural network (DNN)-based automatic speech recognition to improve recognition accuracy in reverberant and noisy environments. The feature is computed in real-time from multiple microphone signals without requiring knowledge or estimation of the direction of arrival, an...	false	false	true	false	false	false	false	false	true	false	false	false	false	false	false	true	false	false	36,620
2402.00957	Credal Learning Theory	Statistical learning theory is the foundation of machine learning, providing theoretical bounds for the risk of models learned from a (single) training set, assumed to issue from an unknown probability distribution. In actual deployment, however, the data distribution may (and often does) vary, causing domain adaptatio...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	425,794
2404.19745	Analyzing Transport Policies in Developing Countries with ABM	Deciphering travel behavior and mode choices is a critical aspect of effective urban transportation system management, particularly in developing countries where unique socio-economic and cultural conditions complicate decision-making. Agent-based simulations offer a valuable tool for modeling transportation systems, e...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	450,755
2312.15873	Investigating Inter-Satellite Link Spanning Patterns on Networking Performance in Mega-constellations	Low Earth orbit (LEO) mega-constellations rely on inter-satellite links (ISLs) to provide global connectivity. We note that in addition to the general constellation parameters, the ISL spanning patterns are also greatly influence the final network structure and thus the network performance. In this work, we formulate...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	true	418,171
2205.14094	Failure Detection in Medical Image Classification: A Reality Check and Benchmarking Testbed	Failure detection in automated image classification is a critical safeguard for clinical deployment. Detected failure cases can be referred to human assessment, ensuring patient safety in computer-aided clinical decision making. Despite its paramount importance, there is insufficient evidence about the ability of state...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	299,215
2209.14265	360FusionNeRF: Panoramic Neural Radiance Fields with Joint Guidance	We present a method to synthesize novel views from a single $360^\circ$ panorama image based on the neural radiance field (NeRF). Prior studies in a similar setting rely on the neighborhood interpolation capability of multi-layer perceptions to complete missing regions caused by occlusion, which leads to artifacts in t...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	320,194
1706.09705	Composition of Gray Isometries	In classical coding theory, Gray isometries are usually defined as mappings between finite Frobenius rings, which include the ring $Z_m$ of integers modulo $m$, and the finite fields. In this paper, we derive an isometric mapping from $Z_8$ to $Z_4^2$ from the composition of the Gray isometries on $Z_8$ and on $Z_4^2$....	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	76,181
2103.10166	Discriminative Singular Spectrum Classifier with Applications on Bioacoustic Signal Recognition	Automatic analysis of bioacoustic signals is a fundamental tool to evaluate the vitality of our planet. Frogs and bees, for instance, may act like biological sensors providing information about environmental changes. This task is fundamental for ecological monitoring still includes many challenges such as nonuniform si...	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	225,369
2401.11439	General Flow as Foundation Affordance for Scalable Robot Learning	We address the challenge of acquiring real-world manipulation skills with a scalable framework. We hold the belief that identifying an appropriate prediction target capable of leveraging large-scale datasets is crucial for achieving efficient and universal learning. Therefore, we propose to utilize 3D flow, which repre...	false	false	false	false	true	false	false	true	false	false	false	true	false	false	false	false	false	false	423,004
2007.01867	TLIO: Tight Learned Inertial Odometry	In this work we propose a tightly-coupled Extended Kalman Filter framework for IMU-only state estimation. Strap-down IMU measurements provide relative state estimates based on IMU kinematic motion model. However the integration of measurements is sensitive to sensor bias and noise, causing significant drift within seco...	false	false	false	false	false	false	true	true	false	false	false	true	false	false	false	false	false	false	185,556
2003.07424	Parallel sequence tagging for concept recognition	Background: Named Entity Recognition (NER) and Normalisation (NEN) are core components of any text-mining system for biomedical texts. In a traditional concept-recognition pipeline, these tasks are combined in a serial way, which is inherently prone to error propagation from NER to NEN. We propose a parallel architectu...	false	false	false	false	false	true	true	false	true	false	false	false	false	false	false	false	false	false	168,415
2305.04075	PointCMP: Contrastive Mask Prediction for Self-supervised Learning on Point Cloud Videos	Self-supervised learning can extract representations of good quality from solely unlabeled data, which is appealing for point cloud videos due to their high labelling cost. In this paper, we propose a contrastive mask prediction (PointCMP) framework for self-supervised learning on point cloud videos. Specifically, our ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	362,623
2407.05562	Focus on the Whole Character: Discriminative Character Modeling for Scene Text Recognition	Recently, scene text recognition (STR) models have shown significant performance improvements. However, existing models still encounter difficulties in recognizing challenging texts that involve factors such as severely distorted and perspective characters. These challenging texts mainly cause two problems: (1) Large I...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	471,030
1905.03700	Unsupervised automatic classification of Scanning Electron Microscopy (SEM) images of CD4+ cells with varying extent of HIV virion infection	Archiving large sets of medical or cell images in digital libraries may require ordering randomly scattered sets of image data according to specific criteria, such as the spatial extent of a specific local color or contrast content that reveals different meaningful states of a physiological structure, tissue, or cell i...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	130,267
2407.17624	Forecasting Credit Ratings: A Case Study where Traditional Methods Outperform Generative LLMs	Large Language Models (LLMs) have been shown to perform well for many downstream tasks. Transfer learning can enable LLMs to acquire skills that were not targeted during pre-training. In financial contexts, LLMs can sometimes beat well-established benchmarks. This paper investigates how well LLMs perform in the task of...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	476,048
1006.1346	C-HiLasso: A Collaborative Hierarchical Sparse Modeling Framework	Sparse modeling is a powerful framework for data analysis and processing. Traditionally, encoding in this framework is performed by solving an L1-regularized linear regression problem, commonly referred to as Lasso or Basis Pursuit. In this work we combine the sparsity-inducing property of the Lasso model at the indivi...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	6,695
0807.1513	A First-Order Non-Homogeneous Markov Model for the Response of Spiking Neurons Stimulated by Small Phase-Continuous Signals	We present a first-order non-homogeneous Markov model for the interspike-interval density of a continuously stimulated spiking neuron. The model allows the conditional interspike-interval density and the stationary interspike-interval density to be expressed as products of two separate functions, one of which describes...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	2,047
2310.06143	HydraViT: Adaptive Multi-Branch Transformer for Multi-Label Disease Classification from Chest X-ray Images	Chest X-ray is an essential diagnostic tool in the identification of chest diseases given its high sensitivity to pathological abnormalities in the lungs. However, image-driven diagnosis is still challenging due to heterogeneity in size and location of pathology, as well as visual similarities and co-occurrence of sepa...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	398,438
2407.12787	GameVibe: A Multimodal Affective Game Corpus	As online video and streaming platforms continue to grow, affective computing research has undergone a shift towards more complex studies involving multiple modalities. However, there is still a lack of readily available datasets with high-quality audiovisual stimuli. In this paper, we present GameVibe, a novel affect ...	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	474,072
2405.08848	Automated Repair of AI Code with Large Language Models and Formal Verification	The next generation of AI systems requires strong safety guarantees. This report looks at the software implementation of neural networks and related memory safety properties, including NULL pointer deference, out-of-bound access, double-free, and memory leaks. Our goal is to detect these vulnerabilities, and automatica...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	454,233
1811.08223	A Semi-supervised Spatial Spectral Regularized Manifold Local Scaling Cut With HGF for Dimensionality Reduction of Hyperspectral Images	Hyperspectral images (HSI) contain a wealth of information over hundreds of contiguous spectral bands, making it possible to classify materials through subtle spectral discrepancies. However, the classification of this rich spectral information is accompanied by the challenges like high dimensionality, singularity, lim...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	113,988
1810.11043	One-Shot Hierarchical Imitation Learning of Compound Visuomotor Tasks	We consider the problem of learning multi-stage vision-based tasks on a real robot from a single video of a human performing the task, while leveraging demonstration data of subtasks with other objects. This problem presents a number of major challenges. Video demonstrations without teleoperation are easy for humans to...	false	false	false	false	true	false	true	true	false	false	false	true	false	false	false	false	false	false	111,418
2207.01112	Augment to Detect Anomalies with Continuous Labelling	Anomaly detection is to recognize samples that differ in some respect from the training observations. These samples which do not conform to the distribution of normal data are called outliers or anomalies. In real-world anomaly detection problems, the outliers are absent, not well defined, or have a very limited number...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	306,039
2007.06637	Storing Encoded Episodes as Concepts for Continual Learning	The two main challenges faced by continual learning approaches are catastrophic forgetting and memory limitations on the storage of data. To cope with these challenges, we propose a novel, cognitively-inspired approach which trains autoencoders with Neural Style Transfer to encode and store images. Reconstructed images...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	187,067
1809.00238	A Machine Learning Driven IoT Solution for Noise Classification in Smart Cities	We present a machine learning based method for noise classification using a low-power and inexpensive IoT unit. We use Mel-frequency cepstral coefficients for audio feature extraction and supervised classification algorithms (that is, support vector machine and k-nearest neighbors) for noise classification. We evaluate...	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	106,531
2310.11305	MiniZero: Comparative Analysis of AlphaZero and MuZero on Go, Othello, and Atari Games	This paper presents MiniZero, a zero-knowledge learning framework that supports four state-of-the-art algorithms, including AlphaZero, MuZero, Gumbel AlphaZero, and Gumbel MuZero. While these algorithms have demonstrated super-human performance in many games, it remains unclear which among them is most suitable or effi...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	400,591
2107.08918	Self-Promoted Prototype Refinement for Few-Shot Class-Incremental Learning	Few-shot class-incremental learning is to recognize the new classes given few samples and not forget the old classes. It is a challenging task since representation optimization and prototype reorganization can only be achieved under little supervision. To address this problem, we propose a novel incremental prototype l...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	246,876
2110.07933	Relation Preserving Triplet Mining for Stabilising the Triplet Loss in Re-identification Systems	Object appearances change dramatically with pose variations. This creates a challenge for embedding schemes that seek to map instances with the same object ID to locations that are as close as possible. This issue becomes significantly heightened in complex computer vision tasks such as re-identification(reID). In this...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	261,189
2011.12012	A More Biologically Plausible Local Learning Rule for ANNs	The backpropagation algorithm is often debated for its biological plausibility. However, various learning methods for neural architecture have been proposed in search of more biologically plausible learning. Most of them have tried to solve the "weight transport problem" and try to propagate errors backward in the arch...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	208,026
2106.04561	Safe Deep Q-Network for Autonomous Vehicles at Unsignalized Intersection	We propose a safe DRL approach for autonomous vehicle (AV) navigation through crowds of pedestrians while making a left turn at an unsignalized intersection. Our method uses two long-short term memory (LSTM) models that are trained to generate the perceived state of the environment and the future trajectories of pedest...	false	false	false	false	true	false	true	true	false	false	false	false	false	false	false	false	false	false	239,776
1909.05624	Detecting Parking Spaces in a Parcel using Satellite Images	Remote Sensing Images from satellites have been used in various domains for detecting and understanding structures on the ground surface. In this work, satellite images were used for localizing parking spaces and vehicles in parking lots for a given parcel using an RCNN based Neural Network Architectures. Parcel shapef...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	145,142
2302.04040	Sample-efficient Multi-objective Molecular Optimization with GFlowNets	Many crucial scientific problems involve designing novel molecules with desired properties, which can be formulated as a black-box optimization problem over the discrete chemical space. In practice, multiple conflicting objectives and costly evaluations (e.g., wet-lab experiments) make the diversity of candidates param...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	344,566
1609.07088	Learning Modular Neural Network Policies for Multi-Task and Multi-Robot Transfer	Reinforcement learning (RL) can automate a wide variety of robotic skills, but learning each new skill requires considerable real-world data collection and manual representation engineering to design policy classes or features. Using deep reinforcement learning to train general purpose neural network policies alleviate...	false	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	61,391
2405.14473	Poisson Variational Autoencoder	Variational autoencoders (VAEs) employ Bayesian inference to interpret sensory inputs, mirroring processes that occur in primate vision across both ventral (Higgins et al., 2021) and dorsal (Vafaii et al., 2023) pathways. Despite their success, traditional VAEs rely on continuous latent variables, which deviates sharpl...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	456,445
1305.7014	Tweets Miner for Stock Market Analysis	In this paper, we present a software package for the data mining of Twitter microblogs for the purpose of using them for the stock market analysis. The package is written in R langauge using apropriate R packages. The model of tweets has been considered. We have also compared stock market charts with frequent sets of k...	false	false	false	true	false	true	false	false	true	false	false	false	false	false	false	false	false	false	24,865
2003.12828	Learning medical triage from clinicians using Deep Q-Learning	Medical Triage is of paramount importance to healthcare systems, allowing for the correct orientation of patients and allocation of the necessary resources to treat them adequately. While reliable decision-tree methods exist to triage patients based on their presentation, those trees implicitly require human inference ...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	170,025
2007.08723	End-to-end Deep Prototype and Exemplar Models for Predicting Human Behavior	Traditional models of category learning in psychology focus on representation at the category level as opposed to the stimulus level, even though the two are likely to interact. The stimulus representations employed in such models are either hand-designed by the experimenter, inferred circuitously from human judgments,...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	187,720
2206.12296	Intelligent Request Strategy Design in Recommender System	Waterfall Recommender System (RS), a popular form of RS in mobile applications, is a stream of recommended items consisting of successive pages that can be browsed by scrolling. In waterfall RS, when a user finishes browsing a page, the edge (e.g., mobile phones) would send a request to the cloud server to get a new pa...	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	false	false	304,547
1904.09348	Compact Scene Graphs for Layout Composition and Patch Retrieval	Structured representations such as scene graphs serve as an efficient and compact representation that can be used for downstream rendering or retrieval tasks. However, existing efforts to generate realistic images from scene graphs perform poorly on scene composition for cluttered or complex scenes. We propose two cont...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	128,346
2401.12672	ChatGraph: Chat with Your Graphs	Graph analysis is fundamental in real-world applications. Traditional approaches rely on SPARQL-like languages or clicking-and-dragging interfaces to interact with graph data. However, these methods either require users to possess high programming skills or support only a limited range of graph analysis functionalities...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	423,463
1204.5226	An Optimal and Distributed Method for Voltage Regulation in Power Distribution Systems	This paper addresses the problem of voltage regulation in power distribution networks with deep-penetration of distributed energy resources, e.g., renewable-based generation, and storage-capable loads such as plug-in hybrid electric vehicles. We cast the problem as an optimization program, where the objective is to min...	false	false	false	false	false	false	false	false	false	true	true	false	false	false	false	false	false	false	15,635
2311.09726	MS-Former: Memory-Supported Transformer for Weakly Supervised Change Detection with Patch-Level Annotations	Fully supervised change detection methods have achieved significant advancements in performance, yet they depend severely on acquiring costly pixel-level labels. Considering that the patch-level annotations also contain abundant information corresponding to both changed and unchanged objects in bi-temporal images, an i...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	408,261
1912.12814	RC-DARTS: Resource Constrained Differentiable Architecture Search	Recent advances show that Neural Architectural Search (NAS) method is able to find state-of-the-art image classification deep architectures. In this paper, we consider the one-shot NAS problem for resource constrained applications. This problem is of great interest because it is critical to choose different architectur...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	true	false	false	158,936
2409.15985	DataGpt-SQL-7B: An Open-Source Language Model for Text-to-SQL	In addressing the pivotal role of translating natural language queries into SQL commands, we propose a suite of compact, fine-tuned models and self-refine mechanisms to democratize data access and analysis for non-expert users, mitigating risks associated with closed-source Large Language Models. Specifically, we const...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	491,156
2501.12012	TabularARGN: A Flexible and Efficient Auto-Regressive Framework for Generating High-Fidelity Synthetic Data	Synthetic data generation for tabular datasets must balance fidelity, efficiency, and versatility to meet the demands of real-world applications. We introduce the Tabular Auto-Regressive Generative Network (TabularARGN), a flexible framework designed to handle mixed-type, multivariate, and sequential datasets. By train...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	526,129
1206.6424	Anytime Marginal MAP Inference	This paper presents a new anytime algorithm for the marginal MAP problem in graphical models. The algorithm is described in detail, its complexity and convergence rate are studied, and relations to previous theoretical results for the problem are discussed. It is shown that the algorithm runs in polynomial-time if the ...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	16,959
2407.11487	PRET: Planning with Directed Fidelity Trajectory for Vision and Language Navigation	Vision and language navigation is a task that requires an agent to navigate according to a natural language instruction. Recent methods predict sub-goals on constructed topology map at each step to enable long-term action planning. However, they suffer from high computational cost when attempting to support such high-l...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	473,482
1911.02825	Improving Grammatical Error Correction with Machine Translation Pairs	We propose a novel data synthesis method to generate diverse error-corrected sentence pairs for improving grammatical error correction, which is based on a pair of machine translation models of different qualities (i.e., poor and good). The poor translation model resembles the ESL (English as a second language) learner...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	152,466
2111.11441	pmSensing: A Participatory Sensing Network for Predictive Monitoring of Particulate Matter	This work presents a proposal for a wireless sensor network for participatory sensing, with IoT sensing devices developed especially for monitoring and predicting air quality, as alternatives of high cost meteorological stations. The system, called pmSensing, aims to measure particulate material. A validation is done b...	false	false	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	true	267,672
2208.01812	Distributed Event-Triggered Nonlinear Fusion Estimation under Resource Constraints	This paper studies the event-triggered distributed fusion estimation problems for a class of nonlinear networked multisensor fusion systems without noise statistical characteristics. When considering the limited resource problems of two kinds of communication channels (i.e., sensor-to-remote estimator channel and smart...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	true	311,270
2109.13767	Identifying and Mitigating Gender Bias in Hyperbolic Word Embeddings	Euclidean word embedding models such as GloVe and Word2Vec have been shown to reflect human-like gender biases. In this paper, we extend the study of gender bias to the recently popularized hyperbolic word embeddings. We propose gyrocosine bias, a novel measure for quantifying gender bias in hyperbolic word representat...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	257,739
2310.06113	When is Agnostic Reinforcement Learning Statistically Tractable?	We study the problem of agnostic PAC reinforcement learning (RL): given a policy class $\Pi$, how many rounds of interaction with an unknown MDP (with a potentially large state and action space) are required to learn an $\epsilon$-suboptimal policy with respect to $\Pi$? Towards that end, we introduce a new complexity ...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	398,426

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