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
2401.07301	Small Language Model Can Self-correct	Generative Language Models (LMs) such as ChatGPT have exhibited remarkable performance across various downstream tasks. Nevertheless, one of their most prominent drawbacks is generating inaccurate or false information with a confident tone. Previous studies have devised sophisticated pipelines and prompts to induce lar...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	421,486
2407.01920	To Forget or Not? Towards Practical Knowledge Unlearning for Large Language Models	Large Language Models (LLMs) trained on extensive corpora inevitably retain sensitive data, such as personal privacy information and copyrighted material. Recent advancements in knowledge unlearning involve updating LLM parameters to erase specific knowledge. However, current unlearning paradigms are mired in vague for...	false	false	false	false	true	false	true	false	true	false	false	true	false	false	false	false	false	true	469,511
2106.00992	NVC-Net: End-to-End Adversarial Voice Conversion	Voice conversion has gained increasing popularity in many applications of speech synthesis. The idea is to change the voice identity from one speaker into another while keeping the linguistic content unchanged. Many voice conversion approaches rely on the use of a vocoder to reconstruct the speech from acoustic feature...	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	238,340
1401.3840	Grounding FO and FO(ID) with Bounds	Grounding is the task of reducing a first-order theory and finite domain to an equivalent propositional theory. It is used as preprocessing phase in many logic-based reasoning systems. Such systems provide a rich first-order input language to a user and can rely on efficient propositional solvers to perform the actual ...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	29,956
1803.05209	Building Sparse Deep Feedforward Networks using Tree Receptive Fields	Sparse connectivity is an important factor behind the success of convolutional neural networks and recurrent neural networks. In this paper, we consider the problem of learning sparse connectivity for feedforward neural networks (FNNs). The key idea is that a unit should be connected to a small number of units at the n...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	92,601
2001.00426	Graph Signal Processing -- Part III: Machine Learning on Graphs, from Graph Topology to Applications	Many modern data analytics applications on graphs operate on domains where graph topology is not known a priori, and hence its determination becomes part of the problem definition, rather than serving as prior knowledge which aids the problem solution. Part III of this monograph starts by addressing ways to learn graph...	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	159,210
2405.00903	A Named Entity Recognition and Topic Modeling-based Solution for Locating and Better Assessment of Natural Disasters in Social Media	Over the last decade, similar to other application domains, social media content has been proven very effective in disaster informatics. However, due to the unstructured nature of the data, several challenges are associated with disaster analysis in social media content. To fully explore the potential of social media c...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	451,122
2010.08285	Protograph-Based Low-Density Parity-Check Hadamard Codes	In this paper, we propose a new method to design low-density parity-check Hadamard (LDPC-Hadamard) codes, a type of ultimate-Shannon-limit approaching channel codes. The technique is based on applying Hadamard constraints to the check nodes in a generalized protograph-based LDPC code, followed by lifting the generalize...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	201,139
2310.17316	Defect Spectrum: A Granular Look of Large-Scale Defect Datasets with Rich Semantics	Defect inspection is paramount within the closed-loop manufacturing system. However, existing datasets for defect inspection often lack precision and semantic granularity required for practical applications. In this paper, we introduce the Defect Spectrum, a comprehensive benchmark that offers precise, semantic-abundan...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	403,082
1908.03298	On the Fundamental Limits of MIMO Massive Access Communication	The multiple access channel (MAC) with many-user is a general model for massive machine type communications. In this paradigm, the number of users may be comparable or even larger than the coding blocklength $n$. In contrast, classical MAC often assumes fixed and small number of the users. In this paper, we consider th...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	141,214
1910.00193	Parallel Algorithm for Approximating Nash Equilibrium in Multiplayer Stochastic Games with Application to Naval Strategic Planning	Many real-world domains contain multiple agents behaving strategically with probabilistic transitions and uncertain (potentially infinite) duration. Such settings can be modeled as stochastic games. While algorithms have been developed for solving (i.e., computing a game-theoretic solution concept such as Nash equilibr...	false	false	false	false	true	false	false	false	false	false	false	false	true	false	true	false	false	true	147,603
2112.02713	Joint Symmetry Detection and Shape Matching for Non-Rigid Point Cloud	Despite the success of deep functional maps in non-rigid 3D shape matching, there exists no learning framework that models both self-symmetry and shape matching simultaneously. This is despite the fact that errors due to symmetry mismatch are a major challenge in non-rigid shape matching. In this paper, we propose a no...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	true	269,932
2402.09263	Uncertainty-Aware Transient Stability-Constrained Preventive Redispatch: A Distributional Reinforcement Learning Approach	Transient stability-constrained preventive redispatch plays a crucial role in ensuring power system security and stability. Since redispatch strategies need to simultaneously satisfy complex transient constraints and the economic need, model-based formulation and optimization become extremely challenging. In addition, ...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	429,440
1611.02256	A Big-Data Approach to Handle Many Process Variations: Tensor Recovery and Applications	Fabrication process variations are a major source of yield degradation in the nano-scale design of integrated circuits (IC), microelectromechanical systems (MEMS) and photonic circuits. Stochastic spectral methods are a promising technique to quantify the uncertainties caused by process variations. Despite their superi...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	63,530
2409.07422	Controllable retinal image synthesis using conditional StyleGAN and latent space manipulation for improved diagnosis and grading of diabetic retinopathy	Diabetic retinopathy (DR) is a consequence of diabetes mellitus characterized by vascular damage within the retinal tissue. Timely detection is paramount to mitigate the risk of vision loss. However, training robust grading models is hindered by a shortage of annotated data, particularly for severe cases. This paper pr...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	487,510
2210.05929	Few-shot Backdoor Attacks via Neural Tangent Kernels	In a backdoor attack, an attacker injects corrupted examples into the training set. The goal of the attacker is to cause the final trained model to predict the attacker's desired target label when a predefined trigger is added to test inputs. Central to these attacks is the trade-off between the success rate of the att...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	323,064
2103.03571	Cycle Self-Training for Domain Adaptation	Mainstream approaches for unsupervised domain adaptation (UDA) learn domain-invariant representations to narrow the domain shift. Recently, self-training has been gaining momentum in UDA, which exploits unlabeled target data by training with target pseudo-labels. However, as corroborated in this work, under distributio...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	223,332
1611.05003	Light Field Stitching for Extended Synthetic Aperture	Through capturing spatial and angular radiance distribution, light field cameras introduce new capabilities that are not possible with conventional cameras. So far in the light field imaging literature, the focus has been on the theory and applications of single light field capture. By combining multiple light fields, ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	63,936
1805.01891	Power Law in Sparsified Deep Neural Networks	The power law has been observed in the degree distributions of many biological neural networks. Sparse deep neural networks, which learn an economical representation from the data, resemble biological neural networks in many ways. In this paper, we study if these artificial networks also exhibit properties of the power...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	96,722
2312.17199	Tractable Function-Space Variational Inference in Bayesian Neural Networks	Reliable predictive uncertainty estimation plays an important role in enabling the deployment of neural networks to safety-critical settings. A popular approach for estimating the predictive uncertainty of neural networks is to define a prior distribution over the network parameters, infer an approximate posterior dist...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	418,647
1007.3884	New Results for the MAP Problem in Bayesian Networks	This paper presents new results for the (partial) maximum a posteriori (MAP) problem in Bayesian networks, which is the problem of querying the most probable state configuration of some of the network variables given evidence. First, it is demonstrated that the problem remains hard even in networks with very simple top...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	7,099
cs/0608093	Connection between continuous and digital n-manifolds and the Poincare conjecture	We introduce LCL covers of closed n-dimensional manifolds by n-dimensional disks and study their properties. We show that any LCL cover of an n-dimensional sphere can be converted to the minimal LCL cover, which consists of 2n+2 disks. We prove that an LCL collection of n-disks is a cover of a continuous n-sphere if an...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	539,655
2411.10513	Any2Any: Incomplete Multimodal Retrieval with Conformal Prediction	Autonomous agents perceive and interpret their surroundings by integrating multimodal inputs, such as vision, audio, and LiDAR. These perceptual modalities support retrieval tasks, such as place recognition in robotics. However, current multimodal retrieval systems encounter difficulties when parts of the data are miss...	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	true	508,679
2309.01983	Quaternary Conjucyclic Codes with an Application to EAQEC Codes	Conjucyclic codes are part of a family of codes that includes cyclic, constacyclic, and quasi-cyclic codes, among others. Despite their importance in quantum error correction, they have not received much attention in the literature. This paper focuses on additive conjucyclic (ACC) codes over $\mathbb{F}_4$ and investig...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	389,878
2102.13185	Off-Policy Imitation Learning from Observations	Learning from Observations (LfO) is a practical reinforcement learning scenario from which many applications can benefit through the reuse of incomplete resources. Compared to conventional imitation learning (IL), LfO is more challenging because of the lack of expert action guidance. In both conventional IL and LfO, di...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	221,972
2312.03987	Cost-Effective In-Context Learning for Entity Resolution: A Design Space Exploration	Entity resolution (ER) is an important data integration task with a wide spectrum of applications. The state-of-the-art solutions on ER rely on pre-trained language models (PLMs), which require fine-tuning on a lot of labeled matching/non-matching entity pairs. Recently, large languages models (LLMs), such as GPT-4, ha...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	413,497
2102.07638	AI Uncertainty Based on Rademacher Complexity and Shannon Entropy	In this paper from communication channel coding perspective we are able to present both a theoretical and practical discussion of AI's uncertainty, capacity and evolution for pattern classification based on the classical Rademacher complexity and Shannon entropy. First AI capacity is defined as in communication channel...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	220,169
2206.11309	GODEL: Large-Scale Pre-Training for Goal-Directed Dialog	We introduce GODEL (Grounded Open Dialogue Language Model), a large pre-trained language model for dialog. In contrast with earlier models such as DialoGPT, GODEL leverages a new phase of grounded pre-training designed to better support adapting GODEL to a wide range of downstream dialog tasks that require information ...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	304,224
1907.01154	Adaptive Music Composition for Games	The generation of music that adapts dynamically to content and actions has an important role in building more immersive, memorable and emotive game experiences. To date, the development of adaptive music systems for video games is limited by both the nature of algorithms used for real-time music generation and the limi...	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	137,244
0905.0374	Interference Alignment with Limited Feedback	We consider single-antenna interference networks where M sources, each with an average transmit power of P/M, communicate with M destinations over frequency-selective channels (with L taps each) and each destination has perfect knowledge of its channels from each of the sources. Assuming that there exist error-free non...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	3,631
2404.00466	Computation and Communication Efficient Lightweighting Vertical Federated Learning	The exploration of computational and communication efficiency within Federated Learning (FL) has emerged as a prominent and crucial field of study. While most existing efforts to enhance these efficiencies have focused on Horizontal FL, the distinct processes and model structures of Vertical FL preclude the direct appl...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	442,939
2009.06687	Methods of the Vehicle Re-identification	Most of researchers use the vehicle re-identification based on classification. This always requires an update with the new vehicle models in the market. In this paper, two types of vehicle re-identification will be presented. First, the standard method, which needs an image from the search vehicle. VRIC and VehicleID d...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	195,713
0902.3196	Symbolic Computing with Incremental Mindmaps to Manage and Mine Data Streams - Some Applications	In our understanding, a mind-map is an adaptive engine that basically works incrementally on the fundament of existing transactional streams. Generally, mind-maps consist of symbolic cells that are connected with each other and that become either stronger or weaker depending on the transactional stream. Based on the un...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	false	false	3,191
2002.05308	Efficient Adaptive Experimental Design for Average Treatment Effect Estimation	We study how to efficiently estimate average treatment effects (ATEs) using adaptive experiments. In adaptive experiments, experimenters sequentially assign treatments to experimental units while updating treatment assignment probabilities based on past data. We start by defining the efficient treatment-assignment prob...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	163,856
2401.14534	Meta-Learning Linear Quadratic Regulators: A Policy Gradient MAML Approach for Model-free LQR	We investigate the problem of learning linear quadratic regulators (LQR) in a multi-task, heterogeneous, and model-free setting. We characterize the stability and personalization guarantees of a policy gradient-based (PG) model-agnostic meta-learning (MAML) (Finn et al., 2017) approach for the LQR problem under differe...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	424,127
1603.05355	GeoReach: An Efficient Approach for Evaluating Graph Reachability Queries with Spatial Range Predicates	Graphs are widely used to model data in many application domains. Thanks to the wide spread use of GPS-enabled devices, many applications assign a spatial attribute to graph vertices (e.g., geo-tagged social media). Users may issue a Reachability Query with Spatial Range Predicate (abbr. RangeReach). RangeReach finds w...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	false	53,352
2307.07691	A Survey on Change Detection Techniques in Document Images	The problem of change detection in images finds application in different domains like diagnosis of diseases in the medical field, detecting growth patterns of cities through remote sensing, and finding changes in legal documents and contracts. However, this paper presents a survey on core techniques and rules to detect...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	379,504
2404.14007	Infusion: Preventing Customized Text-to-Image Diffusion from Overfitting	Text-to-image (T2I) customization aims to create images that embody specific visual concepts delineated in textual descriptions. However, existing works still face a main challenge, concept overfitting. To tackle this challenge, we first analyze overfitting, categorizing it into concept-agnostic overfitting, which unde...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	448,532
2109.10243	Beam Refinement and User State Acquisition via Integrated Sensing and Communication with OFDM	The performance of millimeter wave (mmWave) communications strongly relies on accurate beamforming both at base station and user terminal sides, referred to as beam alignment (BA). Existing BA algorithms provide initial yet coarse angle estimates as they typically use a codebook of a finite number of discreteized beams...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	256,553
2204.04059	Deep Learning-Based Intra Mode Derivation for Versatile Video Coding	In intra coding, Rate Distortion Optimization (RDO) is performed to achieve the optimal intra mode from a pre-defined candidate list. The optimal intra mode is also required to be encoded and transmitted to the decoder side besides the residual signal, where lots of coding bits are consumed. To further improve the perf...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	290,524
2402.02464	A Graph is Worth $K$ Words: Euclideanizing Graph using Pure Transformer	Can we model Non-Euclidean graphs as pure language or even Euclidean vectors while retaining their inherent information? The Non-Euclidean property have posed a long term challenge in graph modeling. Despite recent graph neural networks and graph transformers efforts encoding graphs as Euclidean vectors, recovering the...	false	false	false	true	true	false	true	false	false	false	false	false	false	false	false	false	false	false	426,577
2103.08890	LabelGit: A Dataset for Software Repositories Classification using Attributed Dependency Graphs	Software repository hosting services contain large amounts of open-source software, with GitHub hosting more than 100 million repositories, from new to established ones. Given this vast amount of projects, there is a pressing need for a search based on the software's content and features. However, even though GitHub of...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	225,019
2205.15173	Self-Supervised Pre-training of Vision Transformers for Dense Prediction Tasks	We present a new self-supervised pre-training of Vision Transformers for dense prediction tasks. It is based on a contrastive loss across views that compares pixel-level representations to global image representations. This strategy produces better local features suitable for dense prediction tasks as opposed to contra...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	299,631
1106.1325	Shearlets and Optimally Sparse Approximations	Multivariate functions are typically governed by anisotropic features such as edges in images or shock fronts in solutions of transport-dominated equations. One major goal both for the purpose of compression as well as for an efficient analysis is the provision of optimally sparse approximations of such functions. Rece...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	10,749
2204.06769	Learning topological defects formation with neural networks in a quantum phase transition	Neural networks possess formidable representational power, rendering them invaluable in solving complex quantum many-body systems. While they excel at analyzing static solutions, nonequilibrium processes, including critical dynamics during a quantum phase transition, pose a greater challenge for neural networks. To add...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	291,444
2411.14647	Benchmarking Multimodal Models for Ukrainian Language Understanding Across Academic and Cultural Domains	While the evaluation of multimodal English-centric models is an active area of research with numerous benchmarks, there is a profound lack of benchmarks or evaluation suites for low- and mid-resource languages. We introduce ZNO-Vision, a comprehensive multimodal Ukrainian-centric benchmark derived from standardized uni...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	510,259
2203.04699	Gym-saturation: an OpenAI Gym environment for saturation provers	`gym-saturation` is an OpenAI Gym environment for reinforcement learning (RL) agents capable of proving theorems. Currently, only theorems written in a formal language of the Thousands of Problems for Theorem Provers (TPTP) library in clausal normal form (CNF) are supported. `gym-saturation` implements the 'given claus...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	284,564
1906.08464	A Hierarchical Architecture for Sequential Decision-Making in Autonomous Driving using Deep Reinforcement Learning	Tactical decision making is a critical feature for advanced driving systems, that incorporates several challenges such as complexity of the uncertain environment and reliability of the autonomous system. In this work, we develop a multi-modal architecture that includes the environmental modeling of ego surrounding and ...	false	false	false	false	true	false	true	true	false	false	true	false	false	false	false	false	false	false	135,870
2310.19093	Extending the Cooperative Dual-Task Space in Conformal Geometric Algebra	In this work, we are presenting an extension of the cooperative dual-task space (CDTS) in conformal geometric algebra. The CDTS was first defined using dual quaternion algebra and is a well established framework for the simplified definition of tasks using two manipulators. By integrating conformal geometric algebra, w...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	403,850
2406.08604	GRU-Net: Gaussian Attention Aided Dense Skip Connection Based MultiResUNet for Breast Histopathology Image Segmentation	Breast cancer is a major global health concern. Pathologists face challenges in analyzing complex features from pathological images, which is a time-consuming and labor-intensive task. Therefore, efficient computer-based diagnostic tools are needed for early detection and treatment planning. This paper presents a modif...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	463,547
2410.20305	Accelerating Direct Preference Optimization with Prefix Sharing	Offline paired preference optimization algorithms have become a popular approach for fine-tuning on preference data, outperforming traditional supervised fine-tuning in various tasks. However, traditional implementations often involve redundant computations, especially for tasks with long shared prompts. We introduce p...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	502,754
2206.09426	ADBench: Anomaly Detection Benchmark	Given a long list of anomaly detection algorithms developed in the last few decades, how do they perform with regard to (i) varying levels of supervision, (ii) different types of anomalies, and (iii) noisy and corrupted data? In this work, we answer these key questions by conducting (to our best knowledge) the most com...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	303,568
2110.10932	Subspace Detours Meet Gromov-Wasserstein	In the context of optimal transport methods, the subspace detour approach was recently presented by Muzellec and Cuturi (2019). It consists in building a nearly optimal transport plan in the measures space from an optimal transport plan in a wisely chosen subspace, onto which the original measures are projected. The co...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	262,307
2404.09352	Counteracting Concept Drift by Learning with Future Malware Predictions	The accuracy of deployed malware-detection classifiers degrades over time due to changes in data distributions and increasing discrepancies between training and testing data. This phenomenon is known as the concept drift. While the concept drift can be caused by various reasons in general, new malicious files are creat...	false	false	false	false	true	false	false	false	false	false	false	false	true	false	false	false	false	false	446,636
1806.06387	Mind the gap: quantification of incomplete ablation patterns after pulmonary vein isolation using minimum path search	Pulmonary vein isolation (PVI) is a common procedure for the treatment of atrial fibrillation (AF). A successful isolation produces a continuous lesion (scar) completely encircling the veins that stops activation waves from propagating to the atrial body. Unfortunately, the encircling lesion is often incomplete, becomi...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	100,689
2007.08364	A high fidelity synthetic face framework for computer vision	Analysis of faces is one of the core applications of computer vision, with tasks ranging from landmark alignment, head pose estimation, expression recognition, and face recognition among others. However, building reliable methods requires time-consuming data collection and often even more time-consuming manual annotati...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	187,604
2210.02033	Learning Video-independent Eye Contact Segmentation from In-the-Wild Videos	Human eye contact is a form of non-verbal communication and can have a great influence on social behavior. Since the location and size of the eye contact targets vary across different videos, learning a generic video-independent eye contact detector is still a challenging task. In this work, we address the task of one-...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	321,497
1609.00053	Analysis of the Self Projected Matching Pursuit Algorithm	The convergence and numerical analysis of a low memory implementation of the Orthogonal Matching Pursuit greedy strategy, which is termed Self Projected Matching Pursuit, is presented. This approach renders an iterative way of solving the least squares problem with much less storage requirement than direct linear algeb...	false	false	false	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	60,428
2409.12333	Scale-specific auxiliary multi-task contrastive learning for deep liver vessel segmentation	Extracting hepatic vessels from abdominal images is of high interest for clinicians since it allows to divide the liver into functionally-independent Couinaud segments. In this respect, an automated liver blood vessel extraction is widely summoned. Despite the significant growth in performance of semantic segmentation ...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	489,536
2102.13517	Detection of Alzheimer's Disease Using Graph-Regularized Convolutional Neural Network Based on Structural Similarity Learning of Brain Magnetic Resonance Images	Objective: This paper presents an Alzheimer's disease (AD) detection method based on learning structural similarity between Magnetic Resonance Images (MRIs) and representing this similarity as a graph. Methods: We construct the similarity graph using embedded features of the input image (i.e., Non-Demented (ND), Very M...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	222,084
2203.09476	Uncertainty with UAV Search of Multiple Goal-oriented Targets	This paper considers the complex problem of a team of UAVs searching targets under uncertainty. The goal of the UAV team is to find all of the moving targets as quickly as possible before they arrive at their selected goal. The uncertainty considered is threefold: First, the UAVs do not know the targets' locations and ...	false	false	false	false	true	false	true	true	false	false	false	false	false	false	false	false	false	false	286,166
1911.08635	Robust Deep Neural Networks Inspired by Fuzzy Logic	Deep neural networks have achieved impressive performance and become the de-facto standard in many tasks. However, troubling phenomena such as adversarial and fooling examples suggest that the generalization they make is flawed. I argue that among the roots of the phenomena are two geometric properties of common deep l...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	154,249
2410.19696	Age of Coded Updates In Gossip Networks Under Memory and Memoryless Schemes	We consider an information update system on a gossip network, where a source node encodes information into $n$ total keys such that any subset of at least $k+1$ keys can fully reconstruct the original information. This encoding process follows the principles of a $k$-out-of-$n$ threshold system. The encoded updates are...	false	false	false	false	false	false	false	false	false	true	true	false	false	false	false	false	false	false	502,420
2011.00776	Incorporating Gas Pipeline Leakage Failure Modes in Risk Evaluation of Electricity-Gas Integrated Energy Systems	In the existing literatures for the risk evaluation of electricity-gas integrated energy system (EGIES), the impacts of gas leakage in pipelines are ignored. This paper presents a method to incorporate the failure modes of gas pipeline leakage in EGIES risk evaluation. A Markov state transition model of gas pipeline wi...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	204,366
2105.03647	Informative and Representative Triplet Selection for Multilabel Remote Sensing Image Retrieval	Learning the similarity between remote sensing (RS) images forms the foundation for content-based RS image retrieval (CBIR). Recently, deep metric learning approaches that map the semantic similarity of images into an embedding (metric) space have been found very popular in RS. A common approach for learning the metric...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	234,213
2502.03729	Action-Free Reasoning for Policy Generalization	End-to-end imitation learning offers a promising approach for training robot policies. However, generalizing to new settings remains a significant challenge. Although large-scale robot demonstration datasets have shown potential for inducing generalization, they are resource-intensive to scale. In contrast, human video...	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	false	530,836
1610.09300	Globally Optimal Training of Generalized Polynomial Neural Networks with Nonlinear Spectral Methods	The optimization problem behind neural networks is highly non-convex. Training with stochastic gradient descent and variants requires careful parameter tuning and provides no guarantee to achieve the global optimum. In contrast we show under quite weak assumptions on the data that a particular class of feedforward neur...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	63,037
2008.12950	Path Planning Followed by Kinodynamic Smoothing for Multirotor Aerial Vehicles (MAVs)	We explore path planning followed by kinodynamic smoothing while ensuring the vehicle dynamics feasibility for MAVs. We have chosen a geometrically based motion planning technique \textquotedblleft RRT\textquotedblright\; for this purpose. In the proposed technique, we modified original RRT introducing an adaptive se...	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	193,718
2008.10516	Exoplanet Validation with Machine Learning: 50 new validated Kepler planets	Over 30% of the ~4000 known exoplanets to date have been discovered using 'validation', where the statistical likelihood of a transit arising from a false positive (FP), non-planetary scenario is calculated. For the large majority of these validated planets calculations were performed using the vespa algorithm (Morton ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	193,021
1707.00569	An In-Depth Analysis of Visual Tracking with Siamese Neural Networks	This survey presents a deep analysis of the learning and inference capabilities in nine popular trackers. It is neither intended to study the whole literature nor is it an attempt to review all kinds of neural networks proposed for visual tracking. We focus instead on Siamese neural networks which are a promising start...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	76,373
2410.09576	The Future of Learning in the Age of Generative AI: Automated Question Generation and Assessment with Large Language Models	In recent years, large language models (LLMs) and generative AI have revolutionized natural language processing (NLP), offering unprecedented capabilities in education. This chapter explores the transformative potential of LLMs in automated question generation and answer assessment. It begins by examining the mechanism...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	497,660
2203.16640	Task-driven Modular Co-design of Vehicle Control Systems	When designing autonomous systems, we need to consider multiple trade-offs at various abstraction levels, and the choices of single (hardware and software) components need to be studied jointly. In this work we consider the problem of designing the control algorithm as well as the platform on which it is executed. In p...	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	288,859
2409.12517	Scaling FP8 training to trillion-token LLMs	We train, for the first time, large language models using FP8 precision on datasets up to 2 trillion tokens -- a 20-fold increase over previous limits. Through these extended training runs, we uncover critical instabilities in FP8 training that were not observable in earlier works with shorter durations. We trace these...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	489,626
2304.09058	Revisiting k-NN for Fine-tuning Pre-trained Language Models	Pre-trained Language Models (PLMs), as parametric-based eager learners, have become the de-facto choice for current paradigms of Natural Language Processing (NLP). In contrast, k-Nearest-Neighbor (kNN) classifiers, as the lazy learning paradigm, tend to mitigate over-fitting and isolated noise. In this paper, we revisi...	false	false	false	false	true	true	true	false	true	false	false	false	false	false	false	false	true	false	358,918
2006.08700	A multi-stage looking-ahead holding strategy to stabilize a high-frequency bus line	If a bus line becomes unstable, passengers waiting time will be lengthened and buses capacities will be mismatched. To stabilize a high-frequency bus line, many holding strategies have been proposed. Among these strategies, some need to take oversimplified assumptions to simplify the formulation of a real bus line; som...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	182,268
1805.12017	Robustifying Models Against Adversarial Attacks by Langevin Dynamics	Adversarial attacks on deep learning models have compromised their performance considerably. As remedies, a lot of defense methods were proposed, which however, have been circumvented by newer attacking strategies. In the midst of this ensuing arms race, the problem of robustness against adversarial attacks still remai...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	99,071
2211.15736	Post-training Quantization on Diffusion Models	Denoising diffusion (score-based) generative models have recently achieved significant accomplishments in generating realistic and diverse data. These approaches define a forward diffusion process for transforming data into noise and a backward denoising process for sampling data from noise. Unfortunately, the generati...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	333,373
1606.02825	Arbitrage-Free Combinatorial Market Making via Integer Programming	We present a new combinatorial market maker that operates arbitrage-free combinatorial prediction markets specified by integer programs. Although the problem of arbitrage-free pricing, while maintaining a bound on the subsidy provided by the market maker, is #P-hard in the worst case, we posit that the typical case mig...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	57,022
2403.04670	End-to-end Conditional Robust Optimization	The field of Contextual Optimization (CO) integrates machine learning and optimization to solve decision making problems under uncertainty. Recently, a risk sensitive variant of CO, known as Conditional Robust Optimization (CRO), combines uncertainty quantification with robust optimization in order to promote safety an...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	435,684
2311.10764	Deep Group Interest Modeling of Full Lifelong User Behaviors for CTR Prediction	Extracting users' interests from their lifelong behavior sequence is crucial for predicting Click-Through Rate (CTR). Most current methods employ a two-stage process for efficiency: they first select historical behaviors related to the candidate item and then deduce the user's interest from this narrowed-down behavior ...	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	false	false	408,638
2108.01548	Inference via Sparse Coding in a Hierarchical Vision Model	Sparse coding has been incorporated in models of the visual cortex for its computational advantages and connection to biology. But how the level of sparsity contributes to performance on visual tasks is not well understood. In this work, sparse coding has been integrated into an existing hierarchical V2 model (Hosoya a...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	249,062
2410.06065	Posets and Bounded Probabilities for Discovering Order-inducing Features in Event Knowledge Graphs	Event knowledge graphs (EKG) extend the classical notion of a trace to capture multiple, interacting views of a process execution. In this paper, we tackle the open problem of automating EKG discovery from uncurated data through a principled, probabilistic framing based on the outcome space resulting from featured-deri...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	496,037
2410.15425	Accelerated Sub-Image Search For Variable-Size Patches Identification Based On Virtual Time Series Transformation And Segmentation	This paper addresses two tasks: (i) fixed-size objects such as hay bales are to be identified in an aerial image for a given reference image of the object, and (ii) variable-size patches such as areas on fields requiring spot spraying or other handling are to be identified in an image for a given small-scale reference ...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	500,531
2403.02922	From Spectra to Biophysical Insights: End-to-End Learning with a Biased Radiative Transfer Model	Advances in machine learning have boosted the use of Earth observation data for climate change research. Yet, the interpretability of machine-learned representations remains a challenge, particularly in understanding forests' biophysical reactions to climate change. Traditional methods in remote sensing that invert rad...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	434,995
2402.04275	Motion Mapping Cognition: A Nondecomposable Primary Process in Human Vision	Human intelligence seems so mysterious that we have not successfully understood its foundation until now. Here, I want to present a basic cognitive process, motion mapping cognition (MMC), which should be a nondecomposable primary function in human vision. Wherein, I point out that, MMC process can be used to explain m...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	427,387
1303.7034	Energy Efficient Cooperative Strategies for Relay-Assisted Downlink Cellular Systems Part II: Practical Design	In a companion paper [1], we present a general approach to evaluate the impact of cognition in a downlink cellular system in which multiple relays assist the transmission of the base station. This approach is based on a novel theoretical tool which produces transmission schemes involving rate-splitting, superposition c...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	23,312
2409.20530	Dual Encoder GAN Inversion for High-Fidelity 3D Head Reconstruction from Single Images	3D GAN inversion aims to project a single image into the latent space of a 3D Generative Adversarial Network (GAN), thereby achieving 3D geometry reconstruction. While there exist encoders that achieve good results in 3D GAN inversion, they are predominantly built on EG3D, which specializes in synthesizing near-frontal...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	true	493,158
1911.08963	Parallel Implementations for Computing the Minimum Distance of a Random Linear Code on Multicomputers	The minimum distance of a linear code is a key concept in information theory. Therefore, the time required by its computation is very important to many problems in this area. In this paper, we introduce a family of implementations of the Brouwer-Zimmermann algorithm for distributed-memory architectures for computing th...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	154,363
1805.05008	Integrating Hypertension Phenotype and Genotype with Hybrid Non-negative Matrix Factorization	Hypertension is a heterogeneous syndrome in need of improved subtyping using phenotypic and genetic measurements so that patients in different subtypes share similar pathophysiologic mechanisms and respond more uniformly to targeted treatments. Existing machine learning approaches often face challenges in integrating p...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	97,359
1907.01930	Interference Avoidance Position Planning in Dual-hop and Multi-hop UAV Relay Networks	We consider unmanned aerial vehicle (UAV)-assisted wireless communication employing UAVs as relay nodes to increase the throughput between a pair of transmitter and receiver. We focus on developing effective methods to position the UAV(s) in the sky in the presence of interference in the environment, the existence of w...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	true	137,478
1311.0320	An Improved Solution for Restricted and Uncertain TRQ	CSPTRQ is an interesting problem and its has attracted much attention. The CSPTRQ is a variant of the traditional PTRQ. As objects moving in a constrained-space are common, clearly, it can also find many applications. At the first sight, our problem can be easily tackled by extending existing methods used to answer the...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	28,141
2103.10493	Image Synthesis for Data Augmentation in Medical CT using Deep Reinforcement Learning	Deep learning has shown great promise for CT image reconstruction, in particular to enable low dose imaging and integrated diagnostics. These merits, however, stand at great odds with the low availability of diverse image data which are needed to train these neural networks. We propose to overcome this bottleneck via a...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	225,469
2010.10915	Contrastive Learning of General-Purpose Audio Representations	We introduce COLA, a self-supervised pre-training approach for learning a general-purpose representation of audio. Our approach is based on contrastive learning: it learns a representation which assigns high similarity to audio segments extracted from the same recording while assigning lower similarity to segments from...	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	202,056
1507.02045	What Your Username Says About You	Usernames are ubiquitous on the Internet, and they are often suggestive of user demographics. This work looks at the degree to which gender and language can be inferred from a username alone by making use of unsupervised morphology induction to decompose usernames into sub-units. Experimental results on the two tasks d...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	44,936
1912.01497	Robust and Secure Wireless Communications via Intelligent Reflecting Surfaces	In this paper, intelligent reflecting surfaces (IRSs) are employed to enhance the physical layer security in a challenging radio environment. In particular, a multi-antenna access point (AP) has to serve multiple single-antenna legitimate users, which do not have line-of-sight communication links, in the presence of mu...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	156,104
2409.01266	Double Machine Learning meets Panel Data -- Promises, Pitfalls, and Potential Solutions	Estimating causal effect using machine learning (ML) algorithms can help to relax functional form assumptions if used within appropriate frameworks. However, most of these frameworks assume settings with cross-sectional data, whereas researchers often have access to panel data, which in traditional methods helps to dea...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	485,278
2404.00674	Knowledge NeRF: Few-shot Novel View Synthesis for Dynamic Articulated Objects	We present Knowledge NeRF to synthesize novel views for dynamic scenes. Reconstructing dynamic 3D scenes from few sparse views and rendering them from arbitrary perspectives is a challenging problem with applications in various domains. Previous dynamic NeRF methods learn the deformation of articulated objects from mon...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	443,057
2108.06084	The Stability-Efficiency Dilemma: Investigating Sequence Length Warmup for Training GPT Models	Recent works have demonstrated great success in pre-training large-scale autoregressive language models on massive GPUs. To reduce the wall-clock training time, a common practice is to increase the batch size and learning rate. However, such practice is often brittle and leads to a so-called stability-efficiency dilemm...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	250,497
2305.14263	LIMIT: Language Identification, Misidentification, and Translation using Hierarchical Models in 350+ Languages	Knowing the language of an input text/audio is a necessary first step for using almost every NLP tool such as taggers, parsers, or translation systems. Language identification is a well-studied problem, sometimes even considered solved; in reality, due to lack of data and computational challenges, current systems canno...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	366,936
2304.09802	Generalization and Estimation Error Bounds for Model-based Neural Networks	Model-based neural networks provide unparalleled performance for various tasks, such as sparse coding and compressed sensing problems. Due to the strong connection with the sensing model, these networks are interpretable and inherit prior structure of the problem. In practice, model-based neural networks exhibit higher...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	359,177

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