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
2101.11835	Reducing ReLU Count for Privacy-Preserving CNN Speedup	Privacy-Preserving Machine Learning algorithms must balance classification accuracy with data privacy. This can be done using a combination of cryptographic and machine learning tools such as Convolutional Neural Networks (CNN). CNNs typically consist of two types of operations: a convolutional or linear layer, followe...	false	false	false	false	false	false	false	false	false	false	false	true	true	false	false	false	false	false	217,405
2107.03339	Optimization of Electrolyte Rebalancing in Vanadium Redox Flow Batteries	This paper presents a novel algorithm to optimize energy capacity restoration of vanadium redox flow batteries (VRFBs). VRFB technologies can have their lives prolonged through a partially restoration of the lost capacity by electrolyte rebalancing. Our algorithm finds the optimal number and time of these rebalancing s...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	245,131
2406.00384	CapeX: Category-Agnostic Pose Estimation from Textual Point Explanation	Conventional 2D pose estimation models are constrained by their design to specific object categories. This limits their applicability to predefined objects. To overcome these limitations, category-agnostic pose estimation (CAPE) emerged as a solution. CAPE aims to facilitate keypoint localization for diverse object cat...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	459,827
1402.5371	On the Equivalence of Two Security Notions for Hierarchical Key Assignment Schemes in the Unconditional Setting	The access control problem in a hierarchy can be solved by using a hierarchical key assignment scheme, where each class is assigned an encryption key and some private information. A formal security analysis for hierarchical key assignment schemes has been traditionally considered in two different settings, i.e., the un...	false	false	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	31,051
1906.05108	Secure Federated Matrix Factorization	To protect user privacy and meet law regulations, federated (machine) learning is obtaining vast interests in recent years. The key principle of federated learning is training a machine learning model without needing to know each user's personal raw private data. In this paper, we propose a secure matrix factorization ...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	134,928
1108.2475	Undithering using linear filtering and non-linear diffusion techniques	Data compression is a method of improving the efficiency of transmission and storage of images. Dithering, as a method of data compression, can be used to convert an 8-bit gray level image into a 1-bit / binary image. Undithering is the process of reconstruction of gray image from binary image obtained from dithering o...	false	false	false	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	11,629
2203.11207	Hybrid training of optical neural networks	Optical neural networks are emerging as a promising type of machine learning hardware capable of energy-efficient, parallel computation. Today's optical neural networks are mainly developed to perform optical inference after in silico training on digital simulators. However, various physical imperfections that cannot b...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	286,840
2311.06697	Trusted Source Alignment in Large Language Models	Large language models (LLMs) are trained on web-scale corpora that inevitably include contradictory factual information from sources of varying reliability. In this paper, we propose measuring an LLM property called trusted source alignment (TSA): the model's propensity to align with content produced by trusted publish...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	407,039
2206.00878	EfficientNeRF: Efficient Neural Radiance Fields	Neural Radiance Fields (NeRF) has been wildly applied to various tasks for its high-quality representation of 3D scenes. It takes long per-scene training time and per-image testing time. In this paper, we present EfficientNeRF as an efficient NeRF-based method to represent 3D scene and synthesize novel-view images. Alt...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	300,292
1806.10019	Adversarial Active Exploration for Inverse Dynamics Model Learning	We present an adversarial active exploration for inverse dynamics model learning, a simple yet effective learning scheme that incentivizes exploration in an environment without any human intervention. Our framework consists of a deep reinforcement learning (DRL) agent and an inverse dynamics model contesting with each ...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	101,471
2301.01946	EPR-Net: Constructing non-equilibrium potential landscape via a variational force projection formulation	We present EPR-Net, a novel and effective deep learning approach that tackles a crucial challenge in biophysics: constructing potential landscapes for high-dimensional non-equilibrium steady-state (NESS) systems. EPR-Net leverages a nice mathematical fact that the desired negative potential gradient is simply the ortho...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	339,373
2407.03990	Autoencoded Image Compression for Secure and Fast Transmission	With exponential growth in the use of digital image data, the need for efficient transmission methods has become imperative. Traditional image compression techniques often sacrifice image fidelity for reduced file sizes, challenging maintaining quality and efficiency. They also compromise security, leaving images vulne...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	470,383
1812.00033	Learning from a tiny dataset of manual annotations: a teacher/student approach for surgical phase recognition	Vision algorithms capable of interpreting scenes from a real-time video stream are necessary for computer-assisted surgery systems to achieve context-aware behavior. In laparoscopic procedures one particular algorithm needed for such systems is the identification of surgical phases, for which the current state of the a...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	115,137
2307.13282	High-Resolution Volumetric Reconstruction for Clothed Humans	We present a novel method for reconstructing clothed humans from a sparse set of, e.g., 1 to 6 RGB images. Despite impressive results from recent works employing deep implicit representation, we revisit the volumetric approach and demonstrate that better performance can be achieved with proper system design. The volume...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	381,536
2105.08919	Comparing Kullback-Leibler Divergence and Mean Squared Error Loss in Knowledge Distillation	Knowledge distillation (KD), transferring knowledge from a cumbersome teacher model to a lightweight student model, has been investigated to design efficient neural architectures. Generally, the objective function of KD is the Kullback-Leibler (KL) divergence loss between the softened probability distributions of the t...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	235,910
2012.06509	Addressing Visual Search in Open and Closed Set Settings	Searching for small objects in large images is a task that is both challenging for current deep learning systems and important in numerous real-world applications, such as remote sensing and medical imaging. Thorough scanning of very large images is computationally expensive, particularly at resolutions sufficient to c...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	211,139
2112.13076	Virtuoso: Video-based Intelligence for real-time tuning on SOCs	Efficient and adaptive computer vision systems have been proposed to make computer vision tasks, such as image classification and object detection, optimized for embedded or mobile devices. These solutions, quite recent in their origin, focus on optimizing the model (a deep neural network, DNN) or the system by designi...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	273,132
2410.16432	Fair Bilevel Neural Network (FairBiNN): On Balancing fairness and accuracy via Stackelberg Equilibrium	The persistent challenge of bias in machine learning models necessitates robust solutions to ensure parity and equal treatment across diverse groups, particularly in classification tasks. Current methods for mitigating bias often result in information loss and an inadequate balance between accuracy and fairness. To add...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	501,024
1703.08068	Sequential Recurrent Neural Networks for Language Modeling	Feedforward Neural Network (FNN)-based language models estimate the probability of the next word based on the history of the last N words, whereas Recurrent Neural Networks (RNN) perform the same task based only on the last word and some context information that cycles in the network. This paper presents a novel approa...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	70,514
2008.09689	Fine-tune BERT for E-commerce Non-Default Search Ranking	The quality of non-default ranking on e-commerce platforms, such as based on ascending item price or descending historical sales volume, often suffers from acute relevance problems, since the irrelevant items are much easier to be exposed at the top of the ranking results. In this work, we propose a two-stage ranking s...	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	192,792
2304.02154	A Multimodal Data Set of Human Handovers with Design Implications for Human-Robot Handovers	Handovers are basic yet sophisticated motor tasks performed seamlessly by humans. They are among the most common activities in our daily lives and social environments. This makes mastering the art of handovers critical for a social and collaborative robot. In this work, we present an experimental study that involved hu...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	356,335
1905.07193	MaMiC: Macro and Micro Curriculum for Robotic Reinforcement Learning	Shaping in humans and animals has been shown to be a powerful tool for learning complex tasks as compared to learning in a randomized fashion. This makes the problem less complex and enables one to solve the easier sub task at hand first. Generating a curriculum for such guided learning involves subjecting the agent to...	false	false	false	false	true	false	true	true	false	false	false	false	false	false	false	false	false	false	131,172
1706.05889	Computing the channel capacity of a communication system affected by uncertain transition probabilities	We study the problem of computing the capacity of a discrete memoryless channel under uncertainty affecting the channel law matrix, and possibly with a constraint on the average cost of the input distribution. The problem has been formulated in the literature as a max-min problem. We use the robust optimization methodo...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	75,593
2312.09651	What to Remember: Self-Adaptive Continual Learning for Audio Deepfake Detection	The rapid evolution of speech synthesis and voice conversion has raised substantial concerns due to the potential misuse of such technology, prompting a pressing need for effective audio deepfake detection mechanisms. Existing detection models have shown remarkable success in discriminating known deepfake audio, but st...	false	false	true	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	415,830
2109.08967	Ensemble Learning using Error Correcting Output Codes: New Classification Error Bounds	New bounds on classification error rates for the error-correcting output code (ECOC) approach in machine learning are presented. These bounds have exponential decay complexity with respect to codeword length and theoretically validate the effectiveness of the ECOC approach. Bounds are derived for two different models: ...	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	256,097
2207.12859	Adaptive occlusion sensitivity analysis for visually explaining video recognition networks	This paper proposes a method for visually explaining the decision-making process of video recognition networks with a temporal extension of occlusion sensitivity analysis, called Adaptive Occlusion Sensitivity Analysis (AOSA). The key idea here is to occlude a specific volume of data by a 3D mask in an input 3D tempora...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	310,139
2301.04366	Multimodal Inverse Cloze Task for Knowledge-based Visual Question Answering	We present a new pre-training method, Multimodal Inverse Cloze Task, for Knowledge-based Visual Question Answering about named Entities (KVQAE). KVQAE is a recently introduced task that consists in answering questions about named entities grounded in a visual context using a Knowledge Base. Therefore, the interaction b...	false	false	false	false	false	true	true	false	true	false	false	false	false	false	false	false	false	true	340,039
2206.02886	Graph Rationalization with Environment-based Augmentations	Rationale is defined as a subset of input features that best explains or supports the prediction by machine learning models. Rationale identification has improved the generalizability and interpretability of neural networks on vision and language data. In graph applications such as molecule and polymer property predict...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	301,063
1304.3144	Logical Probability Preferences	We present a unified logical framework for representing and reasoning about both probability quantitative and qualitative preferences in probability answer set programming, called probability answer set optimization programs. The proposed framework is vital to allow defining probability quantitative preferences over th...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	23,838
2405.16122	Prompt Optimization with EASE? Efficient Ordering-aware Automated Selection of Exemplars	Large language models (LLMs) have shown impressive capabilities in real-world applications. The capability of in-context learning (ICL) allows us to adapt an LLM to downstream tasks by including input-label exemplars in the prompt without model fine-tuning. However, the quality of these exemplars in the prompt greatly ...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	457,268
1906.10642	Validating Coordination Schemes between Transmission and Distribution System Operators using a Laboratory-Based Approach	The secure operation of future power systems will rely on better coordination between transmission system and distribution system operators. Increasing integration of renewables throughout the whole system is challenging the traditional operation. To tackle this problem, the SmartNet project proposes and evaluates five...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	136,475
1912.08519	Real-Time Object Detection and Localization in Compressive Sensed Video on Embedded Hardware	Every day around the world, interminable terabytes of data are being captured for surveillance purposes. A typical 1-2MP CCTV camera generates around 7-12GB of data per day. Frame-by-frame processing of such enormous amount of data requires hefty computational resources. In recent years, compressive sensing approaches ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	157,855
2410.20248	Convergence Guarantees for the DeepWalk Embedding on Block Models	Graph embeddings have emerged as a powerful tool for understanding the structure of graphs. Unlike classical spectral methods, recent methods such as DeepWalk, Node2Vec, etc. are based on solving nonlinear optimization problems on the graph, using local information obtained by performing random walks. These techniques ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	502,719
2004.03438	Beer Organoleptic Optimisation: Utilising Swarm Intelligence and Evolutionary Computation Methods	Customisation in food properties is a challenging task involving optimisation of the production process with the demand to support computational creativity which is geared towards ensuring the presence of alternatives. This paper addresses the personalisation of beer properties in the specific case of craft beers where...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	171,566
1206.1800	Compressive neural representation of sparse, high-dimensional probabilities	This paper shows how sparse, high-dimensional probability distributions could be represented by neurons with exponential compression. The representation is a novel application of compressive sensing to sparse probability distributions rather than to the usual sparse signals. The compressive measurements correspond to e...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	16,396
2307.07889	LLM Comparative Assessment: Zero-shot NLG Evaluation through Pairwise Comparisons using Large Language Models	Current developments in large language models (LLMs) have enabled impressive zero-shot capabilities across various natural language tasks. An interesting application of these systems is in the automated assessment of natural language generation (NLG), a highly challenging area with great practical benefit. In this pape...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	379,580
2104.14406	Dynamical prediction of two meteorological factors using the deep neural network and the long short-term memory $(2)$	This paper presents the predictive accuracy using two-variate meteorological factors, average temperature and average humidity, in neural network algorithms. We analyze result in five learning architectures such as the traditional artificial neural network, deep neural network, and extreme learning machine, long short-...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	232,816
2202.11149	Incorporating social norms into a configurable agent-based model of the decision to perform commuting behaviour	Interventions to increase active commuting have been recommended as a method to increase population physical activity, but evidence is mixed. Social norms related to travel behaviour may influence the uptake of active commuting interventions but are rarely considered in their design and evaluation. In this study we dev...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	281,782
2210.00999	Latent State Marginalization as a Low-cost Approach for Improving Exploration	While the maximum entropy (MaxEnt) reinforcement learning (RL) framework -- often touted for its exploration and robustness capabilities -- is usually motivated from a probabilistic perspective, the use of deep probabilistic models has not gained much traction in practice due to their inherent complexity. In this work,...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	321,096
1810.02567	Online Learning to Rank with Features	We introduce a new model for online ranking in which the click probability factors into an examination and attractiveness function and the attractiveness function is a linear function of a feature vector and an unknown parameter. Only relatively mild assumptions are made on the examination function. A novel algorithm f...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	109,620
1405.4308	Coarse-to-Fine Classification via Parametric and Nonparametric Models for Computer-Aided Diagnosis	Classification is one of the core problems in Computer-Aided Diagnosis (CAD), targeting for early cancer detection using 3D medical imaging interpretation. High detection sensitivity with desirably low false positive (FP) rate is critical for a CAD system to be accepted as a valuable or even indispensable tool in radio...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	33,152
1405.7134	Role Discovery in Networks	Roles represent node-level connectivity patterns such as star-center, star-edge nodes, near-cliques or nodes that act as bridges to different regions of the graph. Intuitively, two nodes belong to the same role if they are structurally similar. Roles have been mainly of interest to sociologists, but more recently, role...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	33,435
1804.00706	Synergy: A HW/SW Framework for High Throughput CNNs on Embedded Heterogeneous SoC	Convolutional Neural Networks (CNN) have been widely deployed in diverse application domains. There has been significant progress in accelerating both their training and inference using high-performance GPUs, FPGAs, and custom ASICs for datacenter-scale environments. The recent proliferation of mobile and IoT devices h...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	94,096
2208.06175	The Weighting Game: Evaluating Quality of Explainability Methods	The objective of this paper is to assess the quality of explanation heatmaps for image classification tasks. To assess the quality of explainability methods, we approach the task through the lens of accuracy and stability. In this work, we make the following contributions. Firstly, we introduce the Weighting Game, wh...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	312,630
2305.00208	Deep Learning Based Channel Estimation in High Mobility Communications Using Bi-RNN Networks	Doubly-selective channel estimation represents a key element in ensuring communication reliability in wireless systems. Due to the impact of multi-path propagation and Doppler interference in dynamic environments, doubly-selective channel estimation becomes challenging. Conventional channel estimation schemes encounter...	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	false	false	361,252
2106.16207	When the Echo Chamber Shatters: Examining the Use of Community-Specific Language Post-Subreddit Ban	Community-level bans are a common tool against groups that enable online harassment and harmful speech. Unfortunately, the efficacy of community bans has only been partially studied and with mixed results. Here, we provide a flexible unsupervised methodology to identify in-group language and track user activity on Redd...	false	false	false	true	false	false	false	false	false	false	false	false	false	true	false	false	false	false	244,002
2206.02171	Near-Term Advances in Quantum Natural Language Processing	This paper describes experiments showing that some tasks in natural language processing (NLP) can already be performed using quantum computers, though so far only with small datasets. We demonstrate various approaches to topic classification. The first uses an explicit word-based approach, in which word-topic scoring...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	300,780
2204.04504	TANet: Thread-Aware Pretraining for Abstractive Conversational Summarization	Although pre-trained language models (PLMs) have achieved great success and become a milestone in NLP, abstractive conversational summarization remains a challenging but less studied task. The difficulty lies in two aspects. One is the lack of large-scale conversational summary data. Another is that applying the existi...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	290,678
1603.03102	Robust Design of Spectrum-Sharing Networks	In spectrum-sharing networks, primary users have the right to preempt secondary users, which significantly degrades the performance of underlying secondary users. In this paper, we use backup channels to provide reliability guarantees for secondary users. In particular, we study the optimal white channel assignment tha...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	53,077
2408.16336	GL-TSVM: A robust and smooth twin support vector machine with guardian loss function	Twin support vector machine (TSVM), a variant of support vector machine (SVM), has garnered significant attention due to its $3/4$ times lower computational complexity compared to SVM. However, due to the utilization of the hinge loss function, TSVM is sensitive to outliers or noise. To remedy it, we introduce the guar...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	484,296
2312.10798	Land use/land cover classification of fused Sentinel-1 and Sentinel-2 imageries using ensembles of Random Forests	The study explores the synergistic combination of Synthetic Aperture Radar (SAR) and Visible-Near Infrared-Short Wave Infrared (VNIR-SWIR) imageries for land use/land cover (LULC) classification. Image fusion, employing Bayesian fusion, merges SAR texture bands with VNIR-SWIR imageries. The research aims to investigate...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	416,319
1901.02885	Swarm coordination of mini-UAVs for target search using imperfect sensors	Unmanned Aerial Vehicles (UAVs) have a great potential to support search tasks in unstructured environments. Small, lightweight, low speed and agile UAVs, such as multi-rotors platforms can incorporate many kinds of sensors that are suitable for detecting object of interests in cluttered outdoor areas. However, due to ...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	118,296
1703.06000	Semi-Supervised Deep Learning for Fully Convolutional Networks	Deep learning usually requires large amounts of labeled training data, but annotating data is costly and tedious. The framework of semi-supervised learning provides the means to use both labeled data and arbitrary amounts of unlabeled data for training. Recently, semi-supervised deep learning has been intensively studi...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	70,160
1407.5495	Uplink Contention Based SCMA for 5G Radio Access	Fifth generation (5G) wireless networks are expected to support very diverse applications and terminals. Massive connectivity with a large number of devices is an important requirement for 5G networks. Current LTE system is not able to efficiently support massive connectivity, especially on the uplink (UL). Among the i...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	34,782
2409.01829	Deep non-parametric logistic model with case-control data and external summary information	The case-control sampling design serves as a pivotal strategy in mitigating the imbalanced structure observed in binary data. We consider the estimation of a non-parametric logistic model with the case-control data supplemented by external summary information. The incorporation of external summary information ensures t...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	485,482
2009.04917	The 2020 Sturgis Motorcycle Rally and COVID-19	The Sturgis Motorcycle Rally that took place from August 7-16 was one of the largest public gatherings since the start of the COVID-19 outbreak. Over 460,000 visitors from across the United States travelled to Sturgis, South Dakota to attend the ten day event. Using anonymous cell phone tracking data we identify the ho...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	195,174
1912.03639	Multiphysics Simulation of Plasmonic Photoconductive Devices using Discontinuous Galerkin Methods	Plasmonic nanostructures significantly improve the performance of photoconductive devices (PCDs) in generating terahertz radiation. However, they are geometrically intricate and result in complicated electromagnetic (EM) field and carrier interactions under a bias voltage and upon excitation by an optical EM wave. Thes...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	156,651
2106.14861	Doing good by fighting fraud: Ethical anti-fraud systems for mobile payments	App builders commonly use security challenges, a form of step-up authentication, to add security to their apps. However, the ethical implications of this type of architecture has not been studied previously. In this paper, we present a large-scale measurement study of running an existing anti-fraud security challenge, ...	false	false	false	false	true	false	true	false	false	false	false	false	true	true	false	false	false	false	243,543
2312.15237	Towards Fine-Grained Explainability for Heterogeneous Graph Neural Network	Heterogeneous graph neural networks (HGNs) are prominent approaches to node classification tasks on heterogeneous graphs. Despite the superior performance, insights about the predictions made from HGNs are obscure to humans. Existing explainability techniques are mainly proposed for GNNs on homogeneous graphs. They foc...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	417,933
2403.16276	Empowering LLMs with Pseudo-Untrimmed Videos for Audio-Visual Temporal Understanding	Large language models (LLMs) have demonstrated remarkable capabilities in natural language and multimodal domains. By fine-tuning multimodal LLMs with temporal annotations from well-annotated datasets, e.g., dense video captioning datasets, their temporal understanding capacity in video-language tasks can be obtained. ...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	440,959
2311.01043	LLM4Drive: A Survey of Large Language Models for Autonomous Driving	Autonomous driving technology, a catalyst for revolutionizing transportation and urban mobility, has the tend to transition from rule-based systems to data-driven strategies. Traditional module-based systems are constrained by cumulative errors among cascaded modules and inflexible pre-set rules. In contrast, end-to-en...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	404,895
1711.03195	A Multi-Robot Cooperation Framework for Sewing Personalized Stent Grafts	This paper presents a multi-robot system for manufacturing personalized medical stent grafts. The proposed system adopts a modular design, which includes: a (personalized) mandrel module, a bimanual sewing module, and a vision module. The mandrel module incorporates the personalized geometry of patients, while the bima...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	84,170
2501.17345	Testing Conditional Mean Independence Using Generative Neural Networks	Conditional mean independence (CMI) testing is crucial for statistical tasks including model determination and variable importance evaluation. In this work, we introduce a novel population CMI measure and a bootstrap-based testing procedure that utilizes deep generative neural networks to estimate the conditional mean ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	528,301
2303.01598	A Meta-Learning Approach to Predicting Performance and Data Requirements	We propose an approach to estimate the number of samples required for a model to reach a target performance. We find that the power law, the de facto principle to estimate model performance, leads to large error when using a small dataset (e.g., 5 samples per class) for extrapolation. This is because the log-performanc...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	349,033
2210.16189	Preferential Subsampling for Stochastic Gradient Langevin Dynamics	Stochastic gradient MCMC (SGMCMC) offers a scalable alternative to traditional MCMC, by constructing an unbiased estimate of the gradient of the log-posterior with a small, uniformly-weighted subsample of the data. While efficient to compute, the resulting gradient estimator may exhibit a high variance and impact sampl...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	327,251
2011.09350	Asymmetric Private Set Intersection with Applications to Contact Tracing and Private Vertical Federated Machine Learning	We present a multi-language, cross-platform, open-source library for asymmetric private set intersection (PSI) and PSI-Cardinality (PSI-C). Our protocol combines traditional DDH-based PSI and PSI-C protocols with compression based on Bloom filters that helps reduce communication in the asymmetric setting. Currently, ou...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	207,156
1407.4863	A Comparative Study of Meta-heuristic Algorithms for Solving Quadratic Assignment Problem	Quadratic Assignment Problem (QAP) is an NP-hard combinatorial optimization problem, therefore, solving the QAP requires applying one or more of the meta-heuristic algorithms. This paper presents a comparative study between Meta-heuristic algorithms: Genetic Algorithm, Tabu Search, and Simulated annealing for solving a...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	false	false	34,734
2003.10873	Monocular Human Pose and Shape Reconstruction using Part Differentiable Rendering	Superior human pose and shape reconstruction from monocular images depends on removing the ambiguities caused by occlusions and shape variance. Recent works succeed in regression-based methods which estimate parametric models directly through a deep neural network supervised by 3D ground truth. However, 3D ground truth...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	169,461
2406.08046	A Robust Pipeline for Classification and Detection of Bleeding Frames in Wireless Capsule Endoscopy using Swin Transformer and RT-DETR	In this paper, we present our approach to the Auto WCEBleedGen Challenge V2 2024. Our solution combines the Swin Transformer for the initial classification of bleeding frames and RT-DETR for further detection of bleeding in Wireless Capsule Endoscopy (WCE), enhanced by a series of image preprocessing steps. These steps...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	463,330
2410.21443	TACO: Adversarial Camouflage Optimization on Trucks to Fool Object Detectors	Adversarial attacks threaten the reliability of machine learning models in critical applications like autonomous vehicles and defense systems. As object detectors become more robust with models like YOLOv8, developing effective adversarial methodologies is increasingly challenging. We present Truck Adversarial Camoufla...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	503,247
2501.05783	UV-Attack: Physical-World Adversarial Attacks for Person Detection via Dynamic-NeRF-based UV Mapping	In recent research, adversarial attacks on person detectors using patches or static 3D model-based texture modifications have struggled with low success rates due to the flexible nature of human movement. Modeling the 3D deformations caused by various actions has been a major challenge. Fortunately, advancements in Neu...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	523,726
2306.01377	A systematic literature review on the code smells datasets and validation mechanisms	The accuracy reported for code smell-detecting tools varies depending on the dataset used to evaluate the tools. Our survey of 45 existing datasets reveals that the adequacy of a dataset for detecting smells highly depends on relevant properties such as the size, severity level, project types, number of each type of sm...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	370,427
1207.2940	Expectation Propagation in Gaussian Process Dynamical Systems: Extended Version	Rich and complex time-series data, such as those generated from engineering systems, financial markets, videos or neural recordings, are now a common feature of modern data analysis. Explaining the phenomena underlying these diverse data sets requires flexible and accurate models. In this paper, we promote Gaussian pro...	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	17,428
1804.00498	Land use mapping in the Three Gorges Reservoir Area based on semantic segmentation deep learning method	The Three Gorges Dam, a massive cross-century project spans the Yangtze River by the town of Sandouping, located in Yichang, Hubei province, China, was built to provide great power, improve the River shipping, control floods in the upper reaches of the Yangtze River, and increase the dry season flow in the middle and l...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	94,047
2409.09403	AI-Driven Virtual Teacher for Enhanced Educational Efficiency: Leveraging Large Pretrain Models for Autonomous Error Analysis and Correction	Students frequently make mistakes while solving mathematical problems, and traditional error correction methods are both time-consuming and labor-intensive. This paper introduces an innovative \textbf{V}irtual \textbf{A}I \textbf{T}eacher system designed to autonomously analyze and correct student \textbf{E}rrors (VATE...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	true	488,305
1702.03196	Universal Semantic Parsing	Universal Dependencies (UD) offer a uniform cross-lingual syntactic representation, with the aim of advancing multilingual applications. Recent work shows that semantic parsing can be accomplished by transforming syntactic dependencies to logical forms. However, this work is limited to English, and cannot process depen...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	68,089
1908.06214	Computing Linear Restrictions of Neural Networks	A linear restriction of a function is the same function with its domain restricted to points on a given line. This paper addresses the problem of computing a succinct representation for a linear restriction of a piecewise-linear neural network. This primitive, which we call ExactLine, allows us to exactly characterize ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	141,940
1603.05905	Three Formulations of the Kuramoto Model as a System of Polynomial Equations	We compare three formulations of stationary equations of the Kuramoto model as systems of polynomial equations. In the comparison, we present bounds on the numbers of real equilibria based on the work of Bernstein, Kushnirenko, and Khovanskii, and performance of methods for the optimisation over the set of equilibria b...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	53,409
2407.20566	Monocular Human-Object Reconstruction in the Wild	Learning the prior knowledge of the 3D human-object spatial relation is crucial for reconstructing human-object interaction from images and understanding how humans interact with objects in 3D space. Previous works learn this prior from datasets collected in controlled environments, but due to the diversity of domains,...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	477,206
2412.09726	The Unreasonable Effectiveness of Gaussian Score Approximation for Diffusion Models and its Applications	By learning the gradient of smoothed data distributions, diffusion models can iteratively generate samples from complex distributions. The learned score function enables their generalization capabilities, but how the learned score relates to the score of the underlying data manifold remains largely unclear. Here, we ai...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	516,614
1602.05110	Generating images with recurrent adversarial networks	Gatys et al. (2015) showed that optimizing pixels to match features in a convolutional network with respect reference image features is a way to render images of high visual quality. We show that unrolling this gradient-based optimization yields a recurrent computation that creates images by incrementally adding onto a...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	52,214
2412.13158	On Model Extrapolation in Marginal Shapley Values	As the use of complex machine learning models continues to grow, so does the need for reliable explainability methods. One of the most popular methods for model explainability is based on Shapley values. There are two most commonly used approaches to calculating Shapley values which produce different results when featu...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	518,183
0901.4192	Fixing Convergence of Gaussian Belief Propagation	Gaussian belief propagation (GaBP) is an iterative message-passing algorithm for inference in Gaussian graphical models. It is known that when GaBP converges it converges to the correct MAP estimate of the Gaussian random vector and simple sufficient conditions for its convergence have been established. In this paper w...	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	3,062
1706.02831	Online Energy Management for a Sustainable Smart Home with an HVAC Load and Random Occupancy	In this paper, we investigate the problem of minimizing the sum of energy cost and thermal discomfort cost in a long-term time horizon for a sustainable smart home with a Heating, Ventilation, and Air Conditioning (HVAC) load. Specifically, we first formulate a stochastic program to minimize the time average expected t...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	75,043
1804.10184	Lessons from the Bible on Modern Topics: Low-Resource Multilingual Topic Model Evaluation	Multilingual topic models enable document analysis across languages through coherent multilingual summaries of the data. However, there is no standard and effective metric to evaluate the quality of multilingual topics. We introduce a new intrinsic evaluation of multilingual topic models that correlates well with human...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	96,108
1910.05595	Facial Expression Recognition Using Human to Animated-Character Expression Translation	Facial expression recognition is a challenging task due to two major problems: the presence of inter-subject variations in facial expression recognition dataset and impure expressions posed by human subjects. In this paper we present a novel Human-to-Animation conditional Generative Adversarial Network (HA-GAN) to over...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	149,115
2103.01578	Convergence Rate of the (1+1)-Evolution Strategy with Success-Based Step-Size Adaptation on Convex Quadratic Functions	The (1+1)-evolution strategy (ES) with success-based step-size adaptation is analyzed on a general convex quadratic function and its monotone transformation, that is, $f(x) = g((x - x^)^\mathrm{T} H (x - x^))$, where $g:\mathbb{R}\to\mathbb{R}$ is a strictly increasing function, $H$ is a positive-definite symmetric m...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	222,675
cmp-lg/9505009	Compilation of HPSG to TAG	We present an implemented compilation algorithm that translates HPSG into lexicalized feature-based TAG, relating concepts of the two theories. While HPSG has a more elaborated principle-based theory of possible phrase structures, TAG provides the means to represent lexicalized structures more explicitly. Our objective...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	536,368
2307.04223	Real-time Human Detection in Fire Scenarios using Infrared and Thermal Imaging Fusion	Fire is considered one of the most serious threats to human lives which results in a high probability of fatalities. Those severe consequences stem from the heavy smoke emitted from a fire that mostly restricts the visibility of escaping victims and rescuing squad. In such hazardous circumstances, the use of a vision-b...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	378,334
2110.05186	A MultiModal Social Robot Toward Personalized Emotion Interaction	Human emotions are expressed through multiple modalities, including verbal and non-verbal information. Moreover, the affective states of human users can be the indicator for the level of engagement and successful interaction, suitable for the robot to use as a rewarding factor to optimize robotic behaviors through inte...	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	false	260,195
2206.04327	Language Identification for Austronesian Languages	This paper provides language identification models for low- and under-resourced languages in the Pacific region with a focus on previously unavailable Austronesian languages. Accurate language identification is an important part of developing language resources. The approach taken in this paper combines 29 Austronesian...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	301,585
2106.12556	Real-time Outdoor Localization Using Radio Maps: A Deep Learning Approach	Global Navigation Satellite Systems typically perform poorly in urban environments, where the likelihood of line-of-sight conditions between devices and satellites is low. Therefore, alternative location methods are required to achieve good accuracy. We present LocUNet: A convolutional, end-to-end trained neural networ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	242,763
2305.01698	DeepAqua: Self-Supervised Semantic Segmentation of Wetland Surface Water Extent with SAR Images using Knowledge Distillation	Deep learning and remote sensing techniques have significantly advanced water monitoring abilities; however, the need for annotated data remains a challenge. This is particularly problematic in wetland detection, where water extent varies over time and space, demanding multiple annotations for the same area. In this pa...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	361,770
2411.12602	SAM Carries the Burden: A Semi-Supervised Approach Refining Pseudo Labels for Medical Segmentation	Semantic segmentation is a crucial task in medical imaging. Although supervised learning techniques have proven to be effective in performing this task, they heavily depend on large amounts of annotated training data. The recently introduced Segment Anything Model (SAM) enables prompt-based segmentation and offers zero...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	509,467
2305.00169	An Evidential Real-Time Multi-Mode Fault Diagnosis Approach Based on Broad Learning System	Fault diagnosis is a crucial area of research in industry. Industrial processes exhibit diverse operating conditions, where data often have non-Gaussian, multi-mode, and center-drift characteristics. Data-driven approaches are currently the main focus in the field, but continuous fault classification and parameter upda...	false	false	false	false	true	false	true	false	false	false	true	false	false	false	false	false	false	false	361,237
2012.03440	Deterministic Scheduling for Low-latency Wireless Transmissions with Continuous Channel States	High energy efficiency and low latency have always been the significant goals pursued by the designer of wireless networks. One efficient way to achieve these goals is cross-layer scheduling based on the system states in different layers, such as queuing state and channel state. However, most existing works in cross-la...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	210,116
2112.00882	Robust and Adaptive Temporal-Difference Learning Using An Ensemble of Gaussian Processes	Value function approximation is a crucial module for policy evaluation in reinforcement learning when the state space is large or continuous. The present paper takes a generative perspective on policy evaluation via temporal-difference (TD) learning, where a Gaussian process (GP) prior is presumed on the sought value f...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	269,279
1903.04778	Semi-Supervised Self-Taught Deep Learning for Finger Bones Segmentation	Segmentation stands at the forefront of many high-level vision tasks. In this study, we focus on segmenting finger bones within a newly introduced semi-supervised self-taught deep learning framework which consists of a student network and a stand-alone teacher module. The whole system is boosted in a life-long learning...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	124,045
2010.04582	Denoising Multi-Source Weak Supervision for Neural Text Classification	We study the problem of learning neural text classifiers without using any labeled data, but only easy-to-provide rules as multiple weak supervision sources. This problem is challenging because rule-induced weak labels are often noisy and incomplete. To address these two challenges, we design a label denoiser, which es...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	199,795
1401.4447	Leaf Classification Using Shape, Color, and Texture Features	Several methods to identify plants have been proposed by several researchers. Commonly, the methods did not capture color information, because color was not recognized as an important aspect to the identification. In this research, shape and vein, color, and texture features were incorporated to classify a leaf. In thi...	false	false	false	false	false	false	false	false	false	false	false	true	false	true	false	false	false	false	30,070

Subsets and Splits

No community queries yet

The top public SQL queries from the community will appear here once available.