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
2209.07098	Multi-Modal Masked Autoencoders for Medical Vision-and-Language Pre-Training	Medical vision-and-language pre-training provides a feasible solution to extract effective vision-and-language representations from medical images and texts. However, few studies have been dedicated to this field to facilitate medical vision-and-language understanding. In this paper, we propose a self-supervised learni...	false	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	317,629
2111.13321	Learning source-aware representations of music in a discrete latent space	In recent years, neural network based methods have been proposed as a method that cangenerate representations from music, but they are not human readable and hardly analyzable oreditable by a human. To address this issue, we propose a novel method to learn source-awarelatent representations of music through Vector-Quan...	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	268,260
2204.05972	S-DABT: Schedule and Dependency-Aware Bug Triage in Open-Source Bug Tracking Systems	Fixing bugs in a timely manner lowers various potential costs in software maintenance. However, manual bug fixing scheduling can be time-consuming, cumbersome, and error-prone. In this paper, we propose the Schedule and Dependency-aware Bug Triage (S-DABT), a bug triaging method that utilizes integer programming and ma...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	291,199
2008.05409	Enhancing Fiber Orientation Distributions using convolutional Neural Networks	Accurate local fiber orientation distribution (FOD) modeling based on diffusion magnetic resonance imaging (dMRI) capable of resolving complex fiber configurations benefits from specific acquisition protocols that sample a high number of gradient directions (b-vecs), a high maximum b-value(b-vals), and multiple b-value...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	191,506
2209.09502	GAMA: Generative Adversarial Multi-Object Scene Attacks	The majority of methods for crafting adversarial attacks have focused on scenes with a single dominant object (e.g., images from ImageNet). On the other hand, natural scenes include multiple dominant objects that are semantically related. Thus, it is crucial to explore designing attack strategies that look beyond learn...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	318,539
2409.08885	Interactive Masked Image Modeling for Multimodal Object Detection in Remote Sensing	Object detection in remote sensing imagery plays a vital role in various Earth observation applications. However, unlike object detection in natural scene images, this task is particularly challenging due to the abundance of small, often barely visible objects across diverse terrains. To address these challenges, multi...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	488,093
2106.08174	Automatic linear measurements of the fetal brain on MRI with deep neural networks	Timely, accurate and reliable assessment of fetal brain development is essential to reduce short and long-term risks to fetus and mother. Fetal MRI is increasingly used for fetal brain assessment. Three key biometric linear measurements important for fetal brain evaluation are Cerebral Biparietal Diameter (CBD), Bone B...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	241,209
1906.11426	Hierarchical Data Reduction and Learning	This paper describes a hierarchical learning strategy for generating sparse representations of multivariate datasets. The hierarchy arises from approximation spaces considered at successively finer scales. A detailed analysis of stability, convergence and behavior of error functionals associated with the approximations...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	136,663
2411.06048	An Empirical Analysis on Spatial Reasoning Capabilities of Large Multimodal Models	Large Multimodal Models (LMMs) have achieved strong performance across a range of vision and language tasks. However, their spatial reasoning capabilities are under-investigated. In this paper, we construct a novel VQA dataset, Spatial-MM, to comprehensively study LMMs' spatial understanding and reasoning capabilities....	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	506,944
0904.2311	Source Coding with a Side Information "Vending Machine"	We study source coding in the presence of side information, when the system can take actions that affect the availability, quality, or nature of the side information. We begin by extending the Wyner-Ziv problem of source coding with decoder side information to the case where the decoder is allowed to choose actions aff...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	3,543
1703.00522	Understanding Synthetic Gradients and Decoupled Neural Interfaces	When training neural networks, the use of Synthetic Gradients (SG) allows layers or modules to be trained without update locking - without waiting for a true error gradient to be backpropagated - resulting in Decoupled Neural Interfaces (DNIs). This unlocked ability of being able to update parts of a neural network asy...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	true	false	false	69,177
2211.11825	Multi-Directional Subspace Editing in Style-Space	This paper describes a new technique for finding disentangled semantic directions in the latent space of StyleGAN. Our method identifies meaningful orthogonal subspaces that allow editing of one human face attribute, while minimizing undesired changes in other attributes. Our model is capable of editing a single attrib...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	331,885
2306.14051	Decision-Dependent Distributionally Robust Markov Decision Process Method in Dynamic Epidemic Control	In this paper, we present a Distributionally Robust Markov Decision Process (DRMDP) approach for addressing the dynamic epidemic control problem. The Susceptible-Exposed-Infectious-Recovered (SEIR) model is widely used to represent the stochastic spread of infectious diseases, such as COVID-19. While Markov Decision Pr...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	375,516
2108.03803	Mis-spoke or mis-lead: Achieving Robustness in Multi-Agent Communicative Reinforcement Learning	Recent studies in multi-agent communicative reinforcement learning (MACRL) have demonstrated that multi-agent coordination can be greatly improved by allowing communication between agents. Meanwhile, adversarial machine learning (ML) has shown that ML models are vulnerable to attacks. Despite the increasing concern abo...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	false	false	false	249,783
1706.00504	Dynamic Stripes: Exploiting the Dynamic Precision Requirements of Activation Values in Neural Networks	Stripes is a Deep Neural Network (DNN) accelerator that uses bit-serial computation to offer performance that is proportional to the fixed-point precision of the activation values. The fixed-point precisions are determined a priori using profiling and are selected at a per layer granularity. This paper presents Dynamic...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	true	false	false	74,627
2406.07228	Haptic Repurposing with GenAI	Mixed Reality aims to merge the digital and physical worlds to create immersive human-computer interactions. Despite notable advancements, the absence of realistic haptic feedback often breaks the immersive experience by creating a disconnect between visual and tactile perceptions. This paper introduces Haptic Repurpos...	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	462,950
1904.01184	Towards Efficient and Unbiased Implementation of Lipschitz Continuity in GANs	Lipschitz continuity recently becomes popular in generative adversarial networks (GANs). It was observed that the Lipschitz regularized discriminator leads to improved training stability and sample quality. The mainstream implementations of Lipschitz continuity include gradient penalty and spectral normalization. In th...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	126,077
2010.08178	Generating Diverse Translation from Model Distribution with Dropout	Despite the improvement of translation quality, neural machine translation (NMT) often suffers from the lack of diversity in its generation. In this paper, we propose to generate diverse translations by deriving a large number of possible models with Bayesian modelling and sampling models from them for inference. The p...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	201,087
1402.2020	Binary Stereo Matching	In this paper, we propose a novel binary-based cost computation and aggregation approach for stereo matching problem. The cost volume is constructed through bitwise operations on a series of binary strings. Then this approach is combined with traditional winner-take-all strategy, resulting in a new local stereo matchin...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	30,741
2004.01395	Neural Architecture Generator Optimization	Neural Architecture Search (NAS) was first proposed to achieve state-of-the-art performance through the discovery of new architecture patterns, without human intervention. An over-reliance on expert knowledge in the search space design has however led to increased performance (local optima) without significant architec...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	true	false	false	170,899
1412.5202	Multi-criteria neutrosophic decision making method based on score and accuracy functions under neutrosophic environment	A neutrosophic set is a more general platform, which can be used to present uncertainty, imprecise, incomplete and inconsistent. In this paper a score function and an accuracy function for single valued neutrosophic sets is firstly proposed to make the distinction between them. Then the idea is extended to interval neu...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	38,459
2501.03611	Is social media hindering or helping Academic Performance? A case study of Walter Sisulu University Buffalo City Campus	Social media platforms are popular among higher education students and have seen increased usage for academic purposes, especially during the COVID-19 pandemic. However, excessive use of social media can negatively impact students' academic performance. This preliminary study examines social media's impact on students'...	false	false	false	true	false	false	false	false	false	false	false	false	false	true	false	false	false	false	522,937
1907.12868	2D and 3D Segmentation of uncertain local collagen fiber orientations in SHG microscopy	Collagen fiber orientations in bones, visible with Second Harmonic Generation (SHG) microscopy, represent the inner structure and its alteration due to influences like cancer. While analyses of these orientations are valuable for medical research, it is not feasible to analyze the needed large amounts of local orientat...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	140,216
2007.14128	BUT-FIT at SemEval-2020 Task 5: Automatic detection of counterfactual statements with deep pre-trained language representation models	This paper describes BUT-FIT's submission at SemEval-2020 Task 5: Modelling Causal Reasoning in Language: Detecting Counterfactuals. The challenge focused on detecting whether a given statement contains a counterfactual (Subtask 1) and extracting both antecedent and consequent parts of the counterfactual from the text ...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	189,313
2204.02802	Dimensionality Expansion of Load Monitoring Time Series and Transfer Learning for EMS	Energy management systems (EMS) rely on (non)-intrusive load monitoring (N)ILM to monitor and manage appliances and help residents be more energy efficient and thus more frugal. The robustness as well as the transfer potential of the most promising machine learning solutions for (N)ILM is not yet fully understood as th...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	290,088
1112.4811	Phase-Quantized Block Noncoherent Communication	Analog-to-digital conversion (ADC) is a key bottleneck in scaling DSP-centric receiver architectures to multiGigabit/s speeds. Recent information-theoretic results, obtained under ideal channel conditions (perfect synchronization, no dispersion), indicate that low-precision ADC (1-4 bits) could be a suitable choice for...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	13,543
1512.02181	The Teaching Dimension of Linear Learners	Teaching dimension is a learning theoretic quantity that specifies the minimum training set size to teach a target model to a learner. Previous studies on teaching dimension focused on version-space learners which maintain all hypotheses consistent with the training data, and cannot be applied to modern machine learner...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	49,915
1201.6046	Extended Extremes of Information Combining	Extremes of information combining inequalities play an important role in the analysis of sparse-graph codes under message-passing decoding. We introduce new tools for the derivation of such inequalities, and show by means of a concrete examples how they can be applied to solve some optimization problems in the analysis...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	13,987
2306.07535	Learning with Delayed Payoffs in Population Games using Kullback-Leibler Divergence Regularization	We study a multi-agent decision problem in large population games. Agents from multiple populations select strategies for repeated interactions with one another. At each stage of these interactions, agents use their decision-making model to revise their strategy selections based on payoffs determined by an underlying g...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	373,051
2303.02366	Resilient Strong Structural Controllability in Networks using Leaky Forcing in Graphs	This paper studies the problem of selecting input nodes (leaders) to make networks strong structurally controllable despite misbehaving nodes and edges. We utilize a graph-based characterization of network strong structural controllability (SSC) in terms of zero forcing in graphs, which is a dynamic coloring of nodes. ...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	349,325
2110.00841	Transfer Learning Approaches for Knowledge Discovery in Grid-based Geo-Spatiotemporal Data	Extracting and meticulously analyzing geo-spatiotemporal features is crucial to recognize intricate underlying causes of natural events, such as floods. Limited evidence about hidden factors leading to climate change makes it challenging to predict regional water discharge accurately. In addition, the explosive growth ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	258,554
2311.08662	Evaluating Concurrent Robustness of Language Models Across Diverse Challenge Sets	Language models, characterized by their black-box nature, often hallucinate and display sensitivity to input perturbations, causing concerns about trust. To enhance trust, it is imperative to gain a comprehensive understanding of the model's failure modes and develop effective strategies to improve their performance. I...	false	false	false	false	true	true	false	false	true	false	false	false	false	false	false	false	false	false	407,818
1810.09378	biggy: An Implementation of Unified Framework for Big Data Management System	Various tools, softwares and systems are proposed and implemented to tackle the challenges in big data on different emphases, e.g., data analysis, data transaction, data query, data storage, data visualization, data privacy. In this paper, we propose datar, a new prospective and unified framework for Big Data Managemen...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	111,045
2406.02411	Decoupling of neural network calibration measures	A lot of effort is currently invested in safeguarding autonomous driving systems, which heavily rely on deep neural networks for computer vision. We investigate the coupling of different neural network calibration measures with a special focus on the Area Under the Sparsification Error curve (AUSE) metric. We elaborate...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	460,757
1909.12996	Distributed Iterative Gating Networks for Semantic Segmentation	In this paper, we present a canonical structure for controlling information flow in neural networks with an efficient feedback routing mechanism based on a strategy of Distributed Iterative Gating (DIGNet). The structure of this mechanism derives from a strong conceptual foundation and presents a light-weight mechanism...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	147,289
1909.09901	Learning a Fixed-Length Fingerprint Representation	We present DeepPrint, a deep network, which learns to extract fixed-length fingerprint representations of only 200 bytes. DeepPrint incorporates fingerprint domain knowledge, including alignment and minutiae detection, into the deep network architecture to maximize the discriminative power of its representation. The co...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	146,388
1802.05891	Training Deep Face Recognition Systems with Synthetic Data	Recent advances in deep learning have significantly increased the performance of face recognition systems. The performance and reliability of these models depend heavily on the amount and quality of the training data. However, the collection of annotated large datasets does not scale well and the control over the quali...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	90,534
2004.09656	Tightening Exploration in Upper Confidence Reinforcement Learning	The upper confidence reinforcement learning (UCRL2) algorithm introduced in (Jaksch et al., 2010) is a popular method to perform regret minimization in unknown discrete Markov Decision Processes under the average-reward criterion. Despite its nice and generic theoretical regret guarantees, this algorithm and its varian...	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	173,403
2310.09681	Safe Region Multi-Agent Formation Control With Velocity Tracking	This paper provides a solution to the problem of safe region formation control with reference velocity tracking for a second-order multi-agent system without velocity measurements. Safe region formation control is a control problem where the agents are expected to attain the desired formation while reaching the target ...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	399,890
2312.13511	Symmetry-enforcing neural networks with applications to constitutive modeling	The use of machine learning techniques to homogenize the effective behavior of arbitrary microstructures has been shown to be not only efficient but also accurate. In a recent work, we demonstrated how to combine state-of-the-art micromechanical modeling and advanced machine learning techniques to homogenize complex mi...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	417,334
2106.08443	Reproducing Kernel Hilbert Space, Mercer's Theorem, Eigenfunctions, Nystr\"om Method, and Use of Kernels in Machine Learning: Tutorial and Survey	This is a tutorial and survey paper on kernels, kernel methods, and related fields. We start with reviewing the history of kernels in functional analysis and machine learning. Then, Mercer kernel, Hilbert and Banach spaces, Reproducing Kernel Hilbert Space (RKHS), Mercer's theorem and its proof, frequently used kernels...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	241,290
2402.01296	Bi-CryptoNets: Leveraging Different-Level Privacy for Encrypted Inference	Privacy-preserving neural networks have attracted increasing attention in recent years, and various algorithms have been developed to keep the balance between accuracy, computational complexity and information security from the cryptographic view. This work takes a different view from the input data and structure of ne...	false	false	false	false	false	false	true	false	false	false	false	true	true	false	false	false	false	false	425,956
2210.06733	Codes from incidence matrices of hypergraphs	Binary codes are constructed from incidence matrices of hypergraphs. A combinatroial description is given for the minimum distances of such codes via a combinatorial tool called ``eonv". This combinatorial approach provides a faster alternative method of finding the minimum distance, which is known to be a hard problem...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	323,420
1001.1446	Using Financial Ratios to Identify Romanian Distressed Companies	In the context of the current financial crisis, when more companies are facing bankruptcy or insolvency, the paper aims to find methods to identify distressed firms by using financial ratios. The study will focus on identifying a group of Romanian listed companies, for which financial data for the year 2008 were availa...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	5,305
1710.08005	Smart "Predict, then Optimize"	Many real-world analytics problems involve two significant challenges: prediction and optimization. Due to the typically complex nature of each challenge, the standard paradigm is predict-then-optimize. By and large, machine learning tools are intended to minimize prediction error and do not account for how the predict...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	83,026
2108.12659	DKM: Differentiable K-Means Clustering Layer for Neural Network Compression	Deep neural network (DNN) model compression for efficient on-device inference is becoming increasingly important to reduce memory requirements and keep user data on-device. To this end, we propose a novel differentiable k-means clustering layer (DKM) and its application to train-time weight clustering-based DNN model c...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	252,570
2502.12359	LanP: Rethinking the Impact of Language Priors in Large Vision-Language Models	Large Vision-Language Models (LVLMs) have shown impressive performance in various tasks. However, LVLMs suffer from hallucination, which hinders their adoption in the real world. Existing studies emphasized that the strong language priors of LVLMs can overpower visual information, causing hallucinations. However, the p...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	534,819
2005.03213	Efficient Characterization of Dynamic Response Variation Using Multi-Fidelity Data Fusion through Composite Neural Network	Uncertainties in a structure is inevitable, which generally lead to variation in dynamic response predictions. For a complex structure, brute force Monte Carlo simulation for response variation analysis is infeasible since one single run may already be computationally costly. Data driven meta-modeling approaches have t...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	176,087
1807.02701	DeepSource: Point Source Detection using Deep Learning	Point source detection at low signal-to-noise is challenging for astronomical surveys, particularly in radio interferometry images where the noise is correlated. Machine learning is a promising solution, allowing the development of algorithms tailored to specific telescope arrays and science cases. We present DeepSourc...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	102,330
2012.00564	Facetwise Mesh Refinement for Multi-View Stereo	Mesh refinement is a fundamental step for accurate Multi-View Stereo. It modifies the geometry of an initial manifold mesh to minimize the photometric error induced in a set of camera pairs. This initial mesh is usually the output of volumetric 3D reconstruction based on min-cut over Delaunay Triangulations. Such metho...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	209,176
2003.09044	VisuoSpatial Foresight for Multi-Step, Multi-Task Fabric Manipulation	Robotic fabric manipulation has applications in home robotics, textiles, senior care and surgery. Existing fabric manipulation techniques, however, are designed for specific tasks, making it difficult to generalize across different but related tasks. We extend the Visual Foresight framework to learn fabric dynamics tha...	false	false	false	false	true	false	false	true	false	false	false	true	false	false	false	false	false	false	168,939
2311.03236	Out-of-distribution Detection Learning with Unreliable Out-of-distribution Sources	Out-of-distribution (OOD) detection discerns OOD data where the predictor cannot make valid predictions as in-distribution (ID) data, thereby increasing the reliability of open-world classification. However, it is typically hard to collect real out-of-distribution (OOD) data for training a predictor capable of discerni...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	405,770
2410.09949	MisinfoEval: Generative AI in the Era of "Alternative Facts"	The spread of misinformation on social media platforms threatens democratic processes, contributes to massive economic losses, and endangers public health. Many efforts to address misinformation focus on a knowledge deficit model and propose interventions for improving users' critical thinking through access to facts. ...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	497,845
1911.03043	Estimating Normalizing Constants for Log-Concave Distributions: Algorithms and Lower Bounds	Estimating the normalizing constant of an unnormalized probability distribution has important applications in computer science, statistical physics, machine learning, and statistics. In this work, we consider the problem of estimating the normalizing constant $Z=\int_{\mathbb{R}^d} e^{-f(x)}\,\mathrm{d}x$ to within a m...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	152,531
1405.7720	Full-Duplex Systems Using Multi-Reconfigurable Antennas	Full-duplex systems are expected to achieve 100% rate improvement over half-duplex systems if the self-interference signal can be significantly mitigated. In this paper, we propose the first full-duplex system utilizing Multi-Reconfigurable Antenna (MRA) with ?90% rate improvement compared to half-duplex systems. MRA i...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	33,488
1901.11211	Accuracy vs. Efficiency: Achieving Both through FPGA-Implementation Aware Neural Architecture Search	A fundamental question lies in almost every application of deep neural networks: what is the optimal neural architecture given a specific dataset? Recently, several Neural Architecture Search (NAS) frameworks have been developed that use reinforcement learning and evolutionary algorithm to search for the solution. Howe...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	120,198
1907.01881	Enumerative Sphere Shaping for Rate Adaptation and Reach Increase in WDM Transmission Systems	The performance of enumerative sphere shaping (ESS), constant composition distribution matching (CCDM), and uniform signalling are compared at the same forward error correction rate. ESS is shown to offer a reach increase of approximately 10% and 22% compared to CCDM and uniform signalling, respectively.	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	137,465
1901.06486	Towards Universal End-to-End Affect Recognition from Multilingual Speech by ConvNets	We propose an end-to-end affect recognition approach using a Convolutional Neural Network (CNN) that handles multiple languages, with applications to emotion and personality recognition from speech. We lay the foundation of a universal model that is trained on multiple languages at once. As affect is shared across all ...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	119,012
2501.10219	Robust Egoistic Rigid Body Localization	We consider a robust and self-reliant (or "egoistic") variation of the rigid body localization (RBL) problem, in which a primary rigid body seeks to estimate the pose (i.e., location and orientation) of another rigid body (or "target"), relative to its own, without the assistance of external infrastructure, without pri...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	525,443
2302.02924	Dropout Injection at Test Time for Post Hoc Uncertainty Quantification in Neural Networks	Among Bayesian methods, Monte-Carlo dropout provides principled tools for evaluating the epistemic uncertainty of neural networks. Its popularity recently led to seminal works that proposed activating the dropout layers only during inference for evaluating uncertainty. This approach, which we call dropout injection, pr...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	344,157
2003.02014	Redesigning SLAM for Arbitrary Multi-Camera Systems	Adding more cameras to SLAM systems improves robustness and accuracy but complicates the design of the visual front-end significantly. Thus, most systems in the literature are tailored for specific camera configurations. In this work, we aim at an adaptive SLAM system that works for arbitrary multi-camera setups. To th...	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	166,835
1710.04036	Porcellio scaber algorithm (PSA) for solving constrained optimization problems	In this paper, we extend a bio-inspired algorithm called the porcellio scaber algorithm (PSA) to solve constrained optimization problems, including a constrained mixed discrete-continuous nonlinear optimization problem. Our extensive experiment results based on benchmark optimization problems show that the PSA has a be...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	82,415
1607.02801	Bounds on the Number of Measurements for Reliable Compressive Classification	This paper studies the classification of high-dimensional Gaussian signals from low-dimensional noisy, linear measurements. In particular, it provides upper bounds (sufficient conditions) on the number of measurements required to drive the probability of misclassification to zero in the low-noise regime, both for rando...	false	false	false	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	58,420
2401.00170	L3Cube-MahaSocialNER: A Social Media based Marathi NER Dataset and BERT models	This work introduces the L3Cube-MahaSocialNER dataset, the first and largest social media dataset specifically designed for Named Entity Recognition (NER) in the Marathi language. The dataset comprises 18,000 manually labeled sentences covering eight entity classes, addressing challenges posed by social media data, inc...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	418,908
2308.02249	Finding Tori: Self-supervised Learning for Analyzing Korean Folk Song	In this paper, we introduce a computational analysis of the field recording dataset of approximately 700 hours of Korean folk songs, which were recorded around 1980-90s. Because most of the songs were sung by non-expert musicians without accompaniment, the dataset provides several challenges. To address this challenge,...	false	false	true	false	false	true	true	false	false	false	false	false	false	false	false	false	false	false	383,554
2410.24075	Identifying Spatio-Temporal Drivers of Extreme Events	The spatio-temporal relations of impacts of extreme events and their drivers in climate data are not fully understood and there is a need of machine learning approaches to identify such spatio-temporal relations from data. The task, however, is very challenging since there are time delays between extremes and their dri...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	504,317
2003.11568	Massive Access in Multi-cell Wireless Networks Using Reed-Muller Codes	Providing connectivity to a massive number of devices is a key challenge in 5G wireless systems. In particular, it is crucial to develop efficient methods for active device identification and message decoding in a multi-cell network with fading and path loss uncertainties. In this paper, we design such a scheme using s...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	169,645
2206.00388	Transfer without Forgetting	This work investigates the entanglement between Continual Learning (CL) and Transfer Learning (TL). In particular, we shed light on the widespread application of network pretraining, highlighting that it is itself subject to catastrophic forgetting. Unfortunately, this issue leads to the under-exploitation of knowledge...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	300,106
2412.16100	Logical Consistency of Large Language Models in Fact-checking	In recent years, large language models (LLMs) have demonstrated significant success in performing varied natural language tasks such as language translation, question-answering, summarizing, fact-checking, etc. Despite LLMs' impressive ability to generate human-like texts, LLMs are infamous for their inconsistent respo...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	519,357
2404.14568	UVMap-ID: A Controllable and Personalized UV Map Generative Model	Recently, diffusion models have made significant strides in synthesizing realistic 2D human images based on provided text prompts. Building upon this, researchers have extended 2D text-to-image diffusion models into the 3D domain for generating human textures (UV Maps). However, some important problems about UV Map Gen...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	448,727
1312.2222	A Stability Result for Sparse Convolutions	We will establish in this note a stability result for sparse convolutions on torsion-free additive (discrete) abelian groups. Sparse convolutions on torsion-free groups are free of cancellations and hence admit stability, i.e. injectivity with a universal lower bound $\alpha=\alpha(s,f)$, only depending on the cardinal...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	28,941
2307.03089	Volumetric Occupancy Detection: A Comparative Analysis of Mapping Algorithms	Despite the growing interest in innovative functionalities for collaborative robotics, volumetric detection remains indispensable for ensuring basic security. However, there is a lack of widely used volumetric detection frameworks specifically tailored to this domain, and existing evaluation metrics primarily focus on ...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	377,918
1801.05112	Exact Error and Erasure Exponents for the Asymmetric Broadcast Channel	Consider the asymmetric broadcast channel with a random superposition codebook, which may be comprised of constant composition or \iid codewords. By applying Forney's optimal decoder for individual messages and the message pair for the receiver that decodes both messages, exact (ensemble-tight) error and erasure expone...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	88,393
2304.13892	Discovering Object-Centric Generalized Value Functions From Pixels	Deep Reinforcement Learning has shown significant progress in extracting useful representations from high-dimensional inputs albeit using hand-crafted auxiliary tasks and pseudo rewards. Automatically learning such representations in an object-centric manner geared towards control and fast adaptation remains an open re...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	360,744
2210.07100	Dissipative residual layers for unsupervised implicit parameterization of data manifolds	We propose an unsupervised technique for implicit parameterization of data manifolds. In our approach, the data is assumed to belong to a lower dimensional manifold in a higher dimensional space, and the data points are viewed as the endpoints of the trajectories originating outside the manifold. Under this assumption,...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	323,573
2203.16241	Biclustering Algorithms Based on Metaheuristics: A Review	Biclustering is an unsupervised machine learning technique that simultaneously clusters rows and columns in a data matrix. Biclustering has emerged as an important approach and plays an essential role in various applications such as bioinformatics, text mining, and pattern recognition. However, finding significant bicl...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	288,708
1911.06963	Optimal Storage Control for Dynamic Pricing	Renewable energy brings huge uncertainties to the power system, which challenges the traditional power system operation with limited flexible resources. One promising solution is to introduce dynamic pricing to more consumers, which, if designed properly, could enable an active demand side. To further exploit flexibili...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	153,676
2206.00649	Differentiable programming for functional connectomics	Mapping the functional connectome has the potential to uncover key insights into brain organisation. However, existing workflows for functional connectomics are limited in their adaptability to new data, and principled workflow design is a challenging combinatorial problem. We introduce a new analytic paradigm and soft...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	300,201
1012.4072	Stochastic Control of Event-Driven Feedback in Multi-Antenna Interference Channels	Spatial interference avoidance is a simple and effective way of mitigating interference in multi-antenna wireless networks. The deployment of this technique requires channel-state information (CSI) feedback from each receiver to all interferers, resulting in substantial network overhead. To address this issue, this pap...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	8,583
2405.00853	Efficient Algorithms for Learning Monophonic Halfspaces in Graphs	We study the problem of learning a binary classifier on the vertices of a graph. In particular, we consider classifiers given by monophonic halfspaces, partitions of the vertices that are convex in a certain abstract sense. Monophonic halfspaces, and related notions such as geodesic halfspaces,have recently attracted i...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	451,104
2305.13403	GATology for Linguistics: What Syntactic Dependencies It Knows	Graph Attention Network (GAT) is a graph neural network which is one of the strategies for modeling and representing explicit syntactic knowledge and can work with pre-trained models, such as BERT, in downstream tasks. Currently, there is still a lack of investigation into how GAT learns syntactic knowledge from the pe...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	366,486
2108.12582	Distilling the Knowledge of Large-scale Generative Models into Retrieval Models for Efficient Open-domain Conversation	Despite the remarkable performance of large-scale generative models in open-domain conversation, they are known to be less practical for building real-time conversation systems due to high latency. On the other hand, retrieval models could return responses with much lower latency but show inferior performance to the la...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	252,539
2101.02496	The joint role of geometry and illumination on material recognition	Observing and recognizing materials is a fundamental part of our daily life. Under typical viewing conditions, we are capable of effortlessly identifying the objects that surround us and recognizing the materials they are made of. Nevertheless, understanding the underlying perceptual processes that take place to accura...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	true	214,648
2111.13164	Neural network stochastic differential equation models with applications to financial data forecasting	In this article, we employ a collection of stochastic differential equations with drift and diffusion coefficients approximated by neural networks to predict the trend of chaotic time series which has big jump properties. Our contributions are, first, we propose a model called L\'evy induced stochastic differential equ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	268,210
2406.03085	Exploring User Retrieval Integration towards Large Language Models for Cross-Domain Sequential Recommendation	Cross-Domain Sequential Recommendation (CDSR) aims to mine and transfer users' sequential preferences across different domains to alleviate the long-standing cold-start issue. Traditional CDSR models capture collaborative information through user and item modeling while overlooking valuable semantic information. Recent...	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	false	461,084
2302.10303	Interpretable Out-Of-Distribution Detection Using Pattern Identification	Out-of-distribution (OoD) detection for data-based programs is a goal of paramount importance. Common approaches in the literature tend to train detectors requiring inside-of-distribution (in-distribution, or IoD) and OoD validation samples, and/or implement confidence metrics that are often abstract and therefore diff...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	346,750
2210.00888	Smart-Badge: A wearable badge with multi-modal sensors for kitchen activity recognition	Human health is closely associated with their daily behavior and environment. However, keeping a healthy lifestyle is still challenging for most people as it is difficult to recognize their living behaviors and identify their surrounding situations to take appropriate action. Human activity recognition is a promising a...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	321,044
2307.10033	Non-Parametric Self-Identification and Model Predictive Control of Dexterous In-Hand Manipulation	Building hand-object models for dexterous in-hand manipulation remains a crucial and open problem. Major challenges include the difficulty of obtaining the geometric and dynamical models of the hand, object, and time-varying contacts, as well as the inevitable physical and perception uncertainties. Instead of building ...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	380,391
2110.07681	Large Scale Substitution-based Word Sense Induction	We present a word-sense induction method based on pre-trained masked language models (MLMs), which can cheaply scale to large vocabularies and large corpora. The result is a corpus which is sense-tagged according to a corpus-derived sense inventory and where each sense is associated with indicative words. Evaluation on...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	261,079
1902.06881	Proper-Composite Loss Functions in Arbitrary Dimensions	The study of a machine learning problem is in many ways is difficult to separate from the study of the loss function being used. One avenue of inquiry has been to look at these loss functions in terms of their properties as scoring rules via the proper-composite representation, in which predictions are mapped to probab...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	121,867
1311.5636	Learning Non-Linear Feature Maps	Feature selection plays a pivotal role in learning, particularly in areas were parsimonious features can provide insight into the underlying process, such as biology. Recent approaches for non-linear feature selection employing greedy optimisation of Centred Kernel Target Alignment(KTA), while exhibiting strong results...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	28,577
2310.01932	Automatic Data Processing for Space Robotics Machine Learning	Autonomous terrain classification is an important problem in planetary navigation, whether the goal is to identify scientific sites of interest or to traverse treacherous areas safely. Past Martian rovers have relied on human operators to manually identify a navigable path from transmitted imagery. Our goals on Mars in...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	396,639
1609.00836	Towards Segmenting Consumer Stereo Videos: Benchmark, Baselines and Ensembles	Are we ready to segment consumer stereo videos? The amount of this data type is rapidly increasing and encompasses rich information of appearance, motion and depth cues. However, the segmentation of such data is still largely unexplored. First, we propose therefore a new benchmark: videos, annotations and metrics to me...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	60,526
1202.2528	Using Covariance Matrices as Feature Descriptors for Vehicle Detection from a Fixed Camera	A method is developed to distinguish between cars and trucks present in a video feed of a highway. The method builds upon previously done work using covariance matrices as an accurate descriptor for regions. Background subtraction and other similar proven image processing techniques are used to identify the regions whe...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	14,284
2501.18028	KNN and K-means in Gini Prametric Spaces	This paper introduces innovative enhancements to the K-means and K-nearest neighbors (KNN) algorithms based on the concept of Gini prametric spaces. Unlike traditional distance metrics, Gini-based measures incorporate both value-based and rank-based information, improving robustness to noise and outliers. The main cont...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	528,540
2408.04910	Unleashing Artificial Cognition: Integrating Multiple AI Systems	In this study, we present an innovative fusion of language models and query analysis techniques to unlock cognition in artificial intelligence. The introduced open-source AI system seamlessly integrates a Chess engine with a language model, enabling it to predict moves and provide strategic explanations. Leveraging a v...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	479,580
1909.11117	Semi-supervised classification on graphs using explicit diffusion dynamics	Classification tasks based on feature vectors can be significantly improved by including within deep learning a graph that summarises pairwise relationships between the samples. Intuitively, the graph acts as a conduit to channel and bias the inference of class labels. Here, we study classification methods that conside...	false	false	false	true	false	true	true	false	false	false	false	false	false	false	false	false	false	false	146,712
2204.08988	CPU- and GPU-based Distributed Sampling in Dirichlet Process Mixtures for Large-scale Analysis	In the realm of unsupervised learning, Bayesian nonparametric mixture models, exemplified by the Dirichlet Process Mixture Model (DPMM), provide a principled approach for adapting the complexity of the model to the data. Such models are particularly useful in clustering tasks where the number of clusters is unknown. De...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	292,278
1904.09745	Tetra-Tagging: Word-Synchronous Parsing with Linear-Time Inference	We present a constituency parsing algorithm that, like a supertagger, works by assigning labels to each word in a sentence. In order to maximally leverage current neural architectures, the model scores each word's tags in parallel, with minimal task-specific structure. After scoring, a left-to-right reconciliation phas...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	128,470
2208.11083	Learn Basic Skills and Reuse: Modularized Adaptive Neural Architecture Search (MANAS)	Human intelligence is able to first learn some basic skills for solving basic problems and then assemble such basic skills into complex skills for solving complex or new problems. For example, the basic skills "dig hole," "put tree," "backfill" and "watering" compose a complex skill "plant a tree". Besides, some basic ...	false	false	false	false	true	true	true	false	false	false	false	false	false	false	false	false	false	true	314,308

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