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
2211.03489	Resilience of Wireless Ad Hoc Federated Learning against Model Poisoning Attacks	Wireless ad hoc federated learning (WAFL) is a fully decentralized collaborative machine learning framework organized by opportunistically encountered mobile nodes. Compared to conventional federated learning, WAFL performs model training by weakly synchronizing the model parameters with others, and this shows great re...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	true	328,947
2209.10951	An Information Minimization Based Contrastive Learning Model for Unsupervised Sentence Embeddings Learning	Unsupervised sentence embeddings learning has been recently dominated by contrastive learning methods (e.g., SimCSE), which keep positive pairs similar and push negative pairs apart. The contrast operation aims to keep as much information as possible by maximizing the mutual information between positive instances, whic...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	319,030
1711.05857	An Optimal and Progressive Approach to Online Search of Top-k Influential Communities	Community search over large graphs is a fundamental problem in graph analysis. Recent studies propose to compute top-k influential communities, where each reported community not only is a cohesive subgraph but also has a high influence value. The existing approaches to the problem of top-k influential community search ...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	false	84,654
1905.02473	Ensemble of Convolutional Neural Networks Trained with Different Activation Functions	Activation functions play a vital role in the training of Convolutional Neural Networks. For this reason, to develop efficient and performing functions is a crucial problem in the deep learning community. Key to these approaches is to permit a reliable parameter learning, avoiding vanishing gradient problems. The goal ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	129,981
1612.05760	Kleinberg's Grid Reloaded	One of the key features of small-worlds is the ability to route messages with few hops only using local knowledge of the topology. In 2000, Kleinberg proposed a model based on an augmented grid that asymptotically exhibits such property. In this paper, we propose to revisit the original model from a simulation-based pe...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	65,725
2405.03932	CleanGraph: Human-in-the-loop Knowledge Graph Refinement and Completion	This paper presents CleanGraph, an interactive web-based tool designed to facilitate the refinement and completion of knowledge graphs. Maintaining the reliability of knowledge graphs, which are grounded in high-quality and error-free facts, is crucial for real-world applications such as question-answering and informat...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	452,366
2501.14233	A Data-driven Dynamic Temporal Correlation Modeling Framework for Renewable Energy Scenario Generation	Renewable energy power is influenced by the atmospheric system, which exhibits nonlinear and time-varying features. To address this, a dynamic temporal correlation modeling framework is proposed for renewable energy scenario generation. A novel decoupled mapping path is employed for joint probability distribution model...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	527,035
1910.07022	Measuring the Completeness of Theories	We use machine learning to provide a tractable measure of the amount of predictable variation in the data that a theory captures, which we call its "completeness." We apply this measure to three problems: assigning certain equivalents to lotteries, initial play in games, and human generation of random sequences. We dis...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	149,503
2308.05101	DOST -- Domain Obedient Self-supervised Training for Multi Label Classification with Noisy Labels	The enormous demand for annotated data brought forth by deep learning techniques has been accompanied by the problem of annotation noise. Although this issue has been widely discussed in machine learning literature, it has been relatively unexplored in the context of "multi-label classification" (MLC) tasks which featu...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	384,681
1912.03959	Stealing Knowledge from Protected Deep Neural Networks Using Composite Unlabeled Data	As state-of-the-art deep neural networks are deployed at the core of more advanced Al-based products and services, the incentive for copying them (i.e., their intellectual properties) by rival adversaries is expected to increase considerably over time. The best way to extract or steal knowledge from such networks is by...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	true	false	false	156,730
2410.14743	Efficient Deep Learning Board: Training Feedback Is Not All You Need	Current automatic deep learning (i.e., AutoDL) frameworks rely on training feedback from actual runs, which often hinder their ability to provide quick and clear performance predictions for selecting suitable DL systems. To address this issue, we propose EfficientDL, an innovative deep learning board designed for autom...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	500,186
2409.03938	Deep Clustering of Remote Sensing Scenes through Heterogeneous Transfer Learning	This paper proposes a method for unsupervised whole-image clustering of a target dataset of remote sensing scenes with no labels. The method consists of three main steps: (1) finetuning a pretrained deep neural network (DINOv2) on a labelled source remote sensing imagery dataset and using it to extract a feature vector...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	486,232
2110.01517	Skill Induction and Planning with Latent Language	We present a framework for learning hierarchical policies from demonstrations, using sparse natural language annotations to guide the discovery of reusable skills for autonomous decision-making. We formulate a generative model of action sequences in which goals generate sequences of high-level subtask descriptions, and...	false	false	false	false	true	false	true	true	true	false	false	true	false	false	false	false	false	false	258,795
2401.08501	ValUES: A Framework for Systematic Validation of Uncertainty Estimation in Semantic Segmentation	Uncertainty estimation is an essential and heavily-studied component for the reliable application of semantic segmentation methods. While various studies exist claiming methodological advances on the one hand, and successful application on the other hand, the field is currently hampered by a gap between theory and prac...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	421,909
2302.12139	Automated Extraction of Fine-Grained Standardized Product Information from Unstructured Multilingual Web Data	Extracting structured information from unstructured data is one of the key challenges in modern information retrieval applications, including e-commerce. Here, we demonstrate how recent advances in machine learning, combined with a recently published multilingual data set with standardized fine-grained product category...	false	false	false	false	true	true	true	false	false	false	false	false	false	false	false	false	false	false	347,449
2006.16365	Multi-Partition Embedding Interaction with Block Term Format for Knowledge Graph Completion	Knowledge graph completion is an important task that aims to predict the missing relational link between entities. Knowledge graph embedding methods perform this task by representing entities and relations as embedding vectors and modeling their interactions to compute the matching score of each triple. Previous work h...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	184,779
2101.00717	Algorithmic Complexities in Backpropagation and Tropical Neural Networks	In this note, we propose a novel technique to reduce the algorithmic complexity of neural network training by using matrices of tropical real numbers instead of matrices of real numbers. Since the tropical arithmetics replaces multiplication with addition, and addition with max, we theoretically achieve several order o...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	214,176
2105.08568	Fixed $\beta$-VAE Encoding for Curious Exploration in Complex 3D Environments	Curiosity is a general method for augmenting an environment reward with an intrinsic reward, which encourages exploration and is especially useful in sparse reward settings. As curiosity is calculated using next state prediction error, the type of state encoding used has a large impact on performance. Random features a...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	235,796
1807.07663	Automatically Designing CNN Architectures for Medical Image Segmentation	Deep neural network architectures have traditionally been designed and explored with human expertise in a long-lasting trial-and-error process. This process requires huge amount of time, expertise, and resources. To address this tedious problem, we propose a novel algorithm to optimally find hyperparameters of a deep n...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	103,355
2010.02475	Joint COCO and Mapillary Workshop at ICCV 2019: COCO Instance Segmentation Challenge Track	In this report, we present our object detection/instance segmentation system, MegDetV2, which works in a two-pass fashion, first to detect instances then to obtain segmentation. Our baseline detector is mainly built on a new designed RPN, called RPN++. On the COCO-2019 detection/instance-segmentation test-dev dataset, ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	199,041
2210.15760	Towards Improving Workers' Safety and Progress Monitoring of Construction Sites Through Construction Site Understanding	An important component of computer vision research is object detection. In recent years, there has been tremendous progress in the study of construction site images. However, there are obvious problems in construction object detection, including complex backgrounds, varying-sized objects, and poor imaging quality. In t...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	327,069
2210.03057	Language Models are Multilingual Chain-of-Thought Reasoners	We evaluate the reasoning abilities of large language models in multilingual settings. We introduce the Multilingual Grade School Math (MGSM) benchmark, by manually translating 250 grade-school math problems from the GSM8K dataset (Cobbe et al., 2021) into ten typologically diverse languages. We find that the ability t...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	321,879
1612.00108	When to Reset Your Keys: Optimal Timing of Security Updates via Learning	Cybersecurity is increasingly threatened by advanced and persistent attacks. As these attacks are often designed to disable a system (or a critical resource, e.g., a user account) repeatedly, it is crucial for the defender to keep updating its security measures to strike a balance between the risk of being compromised ...	false	false	false	false	true	false	true	false	false	false	false	false	true	false	false	false	false	false	64,820
2211.04847	Hyper-Parameter Auto-Tuning for Sparse Bayesian Learning	Choosing the values of hyper-parameters in sparse Bayesian learning (SBL) can significantly impact performance. However, the hyper-parameters are normally tuned manually, which is often a difficult task. Most recently, effective automatic hyper-parameter tuning was achieved by using an empirical auto-tuner. In this wor...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	329,365
1503.03753	From Group Recommendations to Group Formation	There has been significant recent interest in the area of group recommendations, where, given groups of users of a recommender system, one wants to recommend top-k items to a group that maximize the satisfaction of the group members, according to a chosen semantics of group satisfaction. Examples semantics of satisfact...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	false	41,090
2202.00509	Decentralized Stochastic Variance Reduced Extragradient Method	This paper studies decentralized convex-concave minimax optimization problems of the form $\min_x\max_y f(x,y) \triangleq\frac{1}{m}\sum_{i=1}^m f_i(x,y)$, where $m$ is the number of agents and each local function can be written as $f_i(x,y)=\frac{1}{n}\sum_{j=1}^n f_{i,j}(x,y)$. We propose a novel decentralized optimi...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	278,167
2008.04848	Exposing Deep-faked Videos by Anomalous Co-motion Pattern Detection	Recent deep learning based video synthesis approaches, in particular with applications that can forge identities such as "DeepFake", have raised great security concerns. Therefore, corresponding deep forensic methods are proposed to tackle this problem. However, existing methods are either based on unexplainable deep n...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	191,335
2005.00340	Adversarial Synthesis of Human Pose from Text	This work focuses on synthesizing human poses from human-level text descriptions. We propose a model that is based on a conditional generative adversarial network. It is designed to generate 2D human poses conditioned on human-written text descriptions. The model is trained and evaluated using the COCO dataset, which c...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	175,203
1801.00056	f-Divergence constrained policy improvement	To ensure stability of learning, state-of-the-art generalized policy iteration algorithms augment the policy improvement step with a trust region constraint bounding the information loss. The size of the trust region is commonly determined by the Kullback-Leibler (KL) divergence, which not only captures the notion of d...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	87,498
2412.21009	Towards Identity-Aware Cross-Modal Retrieval: a Dataset and a Baseline	Recent advancements in deep learning have significantly enhanced content-based retrieval methods, notably through models like CLIP that map images and texts into a shared embedding space. However, these methods often struggle with domain-specific entities and long-tail concepts absent from their training data, particul...	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	true	521,430
1907.01468	How we do things with words: Analyzing text as social and cultural data	In this article we describe our experiences with computational text analysis. We hope to achieve three primary goals. First, we aim to shed light on thorny issues not always at the forefront of discussions about computational text analysis methods. Second, we hope to provide a set of best practices for working with thi...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	137,337
1604.05979	Performance Evaluation of User Scheduling for Full-Duplex Small Cells in Ultra-Dense Networks	Full-duplex (FD) communication is an emerging technology that can potentially double the throughput of cellular networks. Preliminary studies in single-cell or small FD network deployments have revealed promising rate gains using self-interference cancellation (SIC) techniques and receive processing. Nevertheless, the ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	54,886
1810.05596	Custom Dual Transportation Mode Detection by Smartphone Devices Exploiting Sensor Diversity	Making applications aware of the mobility experienced by the user can open the door to a wide range of novel services in different use-cases, from smart parking to vehicular traffic monitoring. In the literature, there are many different studies demonstrating the theoretical possibility of performing Transportation Mod...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	110,268
2110.02582	FADNet++: Real-Time and Accurate Disparity Estimation with Configurable Networks	Deep neural networks (DNNs) have achieved great success in the area of computer vision. The disparity estimation problem tends to be addressed by DNNs which achieve much better prediction accuracy than traditional hand-crafted feature-based methods. However, the existing DNNs hardly serve both efficient computation and...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	259,178
2305.12100	How Spurious Features Are Memorized: Precise Analysis for Random and NTK Features	Deep learning models are known to overfit and memorize spurious features in the training dataset. While numerous empirical studies have aimed at understanding this phenomenon, a rigorous theoretical framework to quantify it is still missing. In this paper, we consider spurious features that are uncorrelated with the le...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	365,838
1905.10044	BoolQ: Exploring the Surprising Difficulty of Natural Yes/No Questions	In this paper we study yes/no questions that are naturally occurring --- meaning that they are generated in unprompted and unconstrained settings. We build a reading comprehension dataset, BoolQ, of such questions, and show that they are unexpectedly challenging. They often query for complex, non-factoid information, a...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	131,932
1902.07323	Large-scale mammography CAD with Deformable Conv-Nets	State-of-the-art deep learning methods for image processing are evolving into increasingly complex meta-architectures with a growing number of modules. Among them, region-based fully convolutional networks (R-FCN) and deformable convolutional nets (DCN) can improve CAD for mammography: R-FCN optimizes for speed and low...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	121,958

Subsets and Splits

No community queries yet

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