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
1703.10251	Dialectical Rough Sets, Parthood and Figures of Opposition-1	In one perspective, the main theme of this research revolves around the inverse problem in the context of general rough sets that concerns the existence of rough basis for given approximations in a context. Granular operator spaces and variants were recently introduced by the present author as an optimal framework for ...	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	false	true	70,883
2108.12226	Injecting Text in Self-Supervised Speech Pretraining	Self-supervised pretraining for Automated Speech Recognition (ASR) has shown varied degrees of success. In this paper, we propose to jointly learn representations during pretraining from two different modalities: speech and text. The proposed method, tts4pretrain complements the power of contrastive learning in self-su...	false	false	true	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	252,437
1910.05265	Automating dynamic consent decisions for the processing of social media data in health research	Social media have become a rich source of data, particularly in health research. Yet, the use of such data raises significant ethical questions about the need for the informed consent of those being studied. Consent mechanisms, if even obtained, are typically broad and inflexible, or place a significant burden on the p...	true	false	false	true	false	false	true	false	false	false	false	false	false	true	false	false	false	false	149,004
1804.00506	Towards Explanation of DNN-based Prediction with Guided Feature Inversion	While deep neural networks (DNN) have become an effective computational tool, the prediction results are often criticized by the lack of interpretability, which is essential in many real-world applications such as health informatics. Existing attempts based on local interpretations aim to identify relevant features con...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	94,051
2003.00353	Clinical Text Summarization with Syntax-Based Negation and Semantic Concept Identification	In the era of clinical information explosion, a good strategy for clinical text summarization is helpful to improve the clinical workflow. The ideal summarization strategy can preserve important information in the informative but less organized, ill-structured clinical narrative texts. Instead of using pure statistical...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	166,267
2404.16891	Attacks on Third-Party APIs of Large Language Models	Large language model (LLM) services have recently begun offering a plugin ecosystem to interact with third-party API services. This innovation enhances the capabilities of LLMs, but it also introduces risks, as these plugins developed by various third parties cannot be easily trusted. This paper proposes a new attackin...	false	false	false	false	true	false	false	false	true	false	false	false	true	true	false	false	false	false	449,661
1911.10576	Facial Landmark Correlation Analysis	We present a facial landmark position correlation analysis as well as its applications. Although numerous facial landmark detection methods have been presented in the literature, few of them explicitly take into account the inherent relationship among landmarks. To reveal and interpret this relationship, we propose to ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	154,877
2106.05468	Multi-VFL: A Vertical Federated Learning System for Multiple Data and Label Owners	Vertical Federated Learning (VFL) refers to the collaborative training of a model on a dataset where the features of the dataset are split among multiple data owners, while label information is owned by a single data owner. In this paper, we propose a novel method, Multi Vertical Federated Learning (Multi-VFL), to trai...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	240,102
2206.02897	Distributive Justice as the Foundational Premise of Fair ML: Unification, Extension, and Interpretation of Group Fairness Metrics	Group fairness metrics are an established way of assessing the fairness of prediction-based decision-making systems. However, these metrics are still insufficiently linked to philosophical theories, and their moral meaning is often unclear. In this paper, we propose a comprehensive framework for group fairness metrics,...	false	false	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	301,068
2205.02141	RecipeSnap -- a lightweight image-to-recipe model	In this paper we want to address the problem of automation for recognition of photographed cooking dishes and generating the corresponding food recipes. Current image-to-recipe models are computation expensive and require powerful GPUs for model training and implementation. High computational cost prevents those existi...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	294,858
2207.14685	Replacing the Framingham-based equation for prediction of cardiovascular disease risk and adverse outcome by using artificial intelligence and retinal imaging	Purpose: To create and evaluate the accuracy of an artificial intelligence Deep learning platform (ORAiCLE) capable of using only retinal fundus images to predict both an individuals overall 5 year cardiovascular risk (CVD) and the relative contribution of the component risk factors that comprise this risk. Methods: We...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	310,678
1402.6636	Analysis of Multibeam SONAR Data using Dissimilarity Representations	This paper considers the problem of low-dimensional visualisation of very high dimensional information sources for the purpose of situation awareness in the maritime environment. In response to the requirement for human decision support aids to reduce information overload (and specifically, data amenable to inter-point...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	31,193
1102.5364	On Outage Probability and Diversity-Multiplexing Tradeoff in MIMO Relay Channels	Fading MIMO relay channels are studied analytically, when the source and destination are equipped with multiple antennas and the relays have a single one. Compact closed-form expressions are obtained for the outage probability under i.i.d. and correlated Rayleigh-fading links. Low-outage approximations are derived, whi...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	9,373
2403.18413	HyRRT-Connect: A Bidirectional Rapidly-Exploring Random Trees Motion Planning Algorithm for Hybrid Systems	This paper proposes a bidirectional rapidly-exploring random trees (RRT) algorithm to solve the motion planning problem for hybrid systems. The proposed algorithm, called HyRRT-Connect, propagates in both forward and backward directions in hybrid time until an overlap between the forward and backward propagation result...	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	441,927
2408.04107	ZACK: Zero-Overhead LLM Inference Acceleration via Dimensionality Compression of the Key-Value Cache	In large-language models, memory constraints in the Key-Value Cache (KVC) pose a challenge during inference. In this work, we propose ZACK, the first KV dimensionality compression system that achieves zero-overhead compression and decompression and also reduces attention computation time. It complements and can be comb...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	479,246
2103.10147	A Physics-based and Data-driven Linear Three-Phase Power Flow Model for Distribution Power Systems	Distribution power systems (DPSs) are mostly unbalanced, and their loads may have notable static voltage characteristics (ZIP loads). Hence, despite abundant papers on linear single-phase power flow models, it is still necessary to study linear three-phase distribution power flow models. To this end, this paper propose...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	225,363
1306.1323	Verdict Accuracy of Quick Reduct Algorithm using Clustering and Classification Techniques for Gene Expression Data	In most gene expression data, the number of training samples is very small compared to the large number of genes involved in the experiments. However, among the large amount of genes, only a small fraction is effective for performing a certain task. Furthermore, a small subset of genes is desirable in developing gene e...	false	true	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	25,036
1303.2547	On a family of binary completely transitive codes with growing covering radius	A new family of binary linear completely transitive (and, therefore, completely regular) codes is constructed. The covering radius of these codes is growing with the length of the code. In particular, for any integer r > 1, there exist two codes with d=3, covering radius r and length 2r(4r-1) and (2r+1)(4r+1), respecti...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	22,843
2107.10350	Uncertainty-Aware Task Allocation for Distributed Autonomous Robots	This paper addresses task-allocation problems with uncertainty in situational awareness for distributed autonomous robots (DARs). The uncertainty propagation over a task-allocation process is done by using the Unscented transform that uses the Sigma-Point sampling mechanism. It has great potential to be employed for ge...	false	false	false	false	true	false	false	true	false	false	true	false	false	false	false	false	false	false	247,271
1905.11229	A Novel Demodulation and Estimation Algorithm for Blackout Communication: Extract Principal Components with Deep Learning	For reentry or near space communication, owing to the influence of the time-varying plasma sheath channel environment, the received IQ baseband signals are severely rotated on the constellation. Researches have shown that the frequency of electron density varies from 20kHz to 100 kHz which is on the same order as the s...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	132,365
2205.02673	On Disentangled and Locally Fair Representations	We study the problem of performing classification in a manner that is fair for sensitive groups, such as race and gender. This problem is tackled through the lens of disentangled and locally fair representations. We learn a locally fair representation, such that, under the learned representation, the neighborhood of ea...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	295,018
1906.02667	Application of Machine Learning to accidents detection at directional drilling	We present a data-driven algorithm and mathematical model for anomaly alarming at directional drilling. The algorithm is based on machine learning. It compares the real-time drilling telemetry with one corresponding to past accidents and analyses the level of similarity. The model performs a time-series comparison usin...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	134,140
2304.12406	AutoFocusFormer: Image Segmentation off the Grid	Real world images often have highly imbalanced content density. Some areas are very uniform, e.g., large patches of blue sky, while other areas are scattered with many small objects. Yet, the commonly used successive grid downsampling strategy in convolutional deep networks treats all areas equally. Hence, small object...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	360,192
2405.18679	Vim-F: Visual State Space Model Benefiting from Learning in the Frequency Domain	In recent years, State Space Models (SSMs) with efficient hardware-aware designs, known as the Mamba deep learning models, have made significant progress in modeling long sequences such as language understanding. Therefore, building efficient and general-purpose visual backbones based on SSMs is a promising direction. ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	458,529
2001.11921	Predicting Goal-directed Attention Control Using Inverse-Reinforcement Learning	Understanding how goal states control behavior is a question ripe for interrogation by new methods from machine learning. These methods require large and labeled datasets to train models. To annotate a large-scale image dataset with observed search fixations, we collected 16,184 fixations from people searching for eith...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	162,201
2004.09802	Spatio-Temporal Dual Affine Differential Invariant for Skeleton-based Action Recognition	The dynamics of human skeletons have significant information for the task of action recognition. The similarity between trajectories of corresponding joints is an indicating feature of the same action, while this similarity may subject to some distortions that can be modeled as the combination of spatial and temporal a...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	173,458
2404.19218	Flight Trajectory Prediction Using an Enhanced CNN-LSTM Network	Aiming at the problem of low accuracy of flight trajectory prediction caused by the high speed of fighters, the diversity of tactical maneuvers, and the transient nature of situational change in close range air combat, this paper proposes an enhanced CNN-LSTM network as a fighter flight trajectory prediction method. Fi...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	450,545
2311.16075	BioLORD-2023: Semantic Textual Representations Fusing LLM and Clinical Knowledge Graph Insights	In this study, we investigate the potential of Large Language Models to complement biomedical knowledge graphs in the training of semantic models for the biomedical and clinical domains. Drawing on the wealth of the UMLS knowledge graph and harnessing cutting-edge Large Language Models, we propose a new state-of-the-ar...	false	false	false	false	true	true	false	false	true	false	false	false	false	false	false	false	false	false	410,740
1012.3311	Validating XML Documents in the Streaming Model with External Memory	We study the problem of validating XML documents of size $N$ against general DTDs in the context of streaming algorithms. The starting point of this work is a well-known space lower bound. There are XML documents and DTDs for which $p$-pass streaming algorithms require $\Omega(N/p)$ space. We show that when allowing ...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	true	8,546
2111.12927	Rethinking Generic Camera Models for Deep Single Image Camera Calibration to Recover Rotation and Fisheye Distortion	Although recent learning-based calibration methods can predict extrinsic and intrinsic camera parameters from a single image, the accuracy of these methods is degraded in fisheye images. This degradation is caused by mismatching between the actual projection and expected projection. To address this problem, we propose ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	268,128
2103.06877	Fast and Accurate Model Scaling	In this work we analyze strategies for convolutional neural network scaling; that is, the process of scaling a base convolutional network to endow it with greater computational complexity and consequently representational power. Example scaling strategies may include increasing model width, depth, resolution, etc. Whil...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	224,428
2406.01894	SVASTIN: Sparse Video Adversarial Attack via Spatio-Temporal Invertible Neural Networks	Robust and imperceptible adversarial video attack is challenging due to the spatial and temporal characteristics of videos. The existing video adversarial attack methods mainly take a gradient-based approach and generate adversarial videos with noticeable perturbations. In this paper, we propose a novel Sparse Adversar...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	460,507
2409.19993	Mitigating Backdoor Threats to Large Language Models: Advancement and Challenges	The advancement of Large Language Models (LLMs) has significantly impacted various domains, including Web search, healthcare, and software development. However, as these models scale, they become more vulnerable to cybersecurity risks, particularly backdoor attacks. By exploiting the potent memorization capacity of LLM...	false	false	false	false	true	false	true	false	true	false	true	false	true	false	false	false	false	false	492,953
2207.10273	Don't Forget Me: Accurate Background Recovery for Text Removal via Modeling Local-Global Context	Text removal has attracted increasingly attention due to its various applications on privacy protection, document restoration, and text editing. It has shown significant progress with deep neural network. However, most of the existing methods often generate inconsistent results for complex background. To address this i...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	309,194
2103.01929	SoundCLR: Contrastive Learning of Representations For Improved Environmental Sound Classification	Environmental Sound Classification (ESC) is a challenging field of research in non-speech audio processing. Most of current research in ESC focuses on designing deep models with special architectures tailored for specific audio datasets, which usually cannot exploit the intrinsic patterns in the data. However recent st...	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	222,794
2112.04953	Machine Learning for Utility Prediction in Argument-Based Computational Persuasion	Automated persuasion systems (APS) aim to persuade a user to believe something by entering into a dialogue in which arguments and counterarguments are exchanged. To maximize the probability that an APS is successful in persuading a user, it can identify a global policy that will allow it to select the best arguments it...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	true	270,696
2212.11611	Maximising Influence Spread in Complex Networks by Utilising Community-based Driver Nodes as Seeds	Finding a small subset of influential nodes to maximise influence spread in a complex network is an active area of research. Different methods have been proposed in the past to identify a set of seed nodes that can help achieve a faster spread of influence in the network. This paper combines driver node selection metho...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	337,837
2102.10015	Information-Theoretic Abstractions for Resource-Constrained Agents via Mixed-Integer Linear Programming	In this paper, a mixed-integer linear programming formulation for the problem of obtaining task-relevant, multi-resolution, graph abstractions for resource-constrained agents is presented. The formulation leverages concepts from information-theoretic signal compression, specifically the information bottleneck (IB) meth...	false	false	false	false	true	false	false	true	false	true	false	false	false	false	false	false	false	false	220,951
1612.04668	Multi-resolution community detection in massive networks	Aiming at improving the efficiency and accuracy of community detection in complex networks, we proposed a new algorithm, which is based on the idea that communities could be detected from subnetworks by comparing the internal and external cohesion of each subnetwork. In our method, similar nodes are firstly gathered in...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	65,556
2410.14584	MCSFF: Multi-modal Consistency and Specificity Fusion Framework for Entity Alignment	Multi-modal entity alignment (MMEA) is essential for enhancing knowledge graphs and improving information retrieval and question-answering systems. Existing methods often focus on integrating modalities through their complementarity but overlook the specificity of each modality, which can obscure crucial features and r...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	500,085
2409.00082	Towards Human-Level Understanding of Complex Process Engineering Schematics: A Pedagogical, Introspective Multi-Agent Framework for Open-Domain Question Answering	In the chemical and process industries, Process Flow Diagrams (PFDs) and Piping and Instrumentation Diagrams (P&IDs) are critical for design, construction, and maintenance. Recent advancements in Generative AI, such as Large Multimodal Models (LMMs) like GPT4 (Omni), have shown promise in understanding and interpreting...	false	false	false	false	true	false	true	false	true	false	false	true	false	false	false	false	false	false	484,760
cs/0505016	Visual Character Recognition using Artificial Neural Networks	The recognition of optical characters is known to be one of the earliest applications of Artificial Neural Networks, which partially emulate human thinking in the domain of artificial intelligence. In this paper, a simplified neural approach to recognition of optical or visual characters is portrayed and discussed. The...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	538,705
2312.06316	SemiSAM: Enhancing Semi-Supervised Medical Image Segmentation via SAM-Assisted Consistency Regularization	Semi-supervised learning has attracted much attention due to its less dependence on acquiring abundant annotations from experts compared to fully supervised methods, which is especially important for medical image segmentation which typically requires intensive pixel/voxel-wise labeling by domain experts. Although semi...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	414,470
2012.11634	Exploring and Analyzing Machine Commonsense Benchmarks	Commonsense question-answering (QA) tasks, in the form of benchmarks, are constantly being introduced for challenging and comparing commonsense QA systems. The benchmarks provide question sets that systems' developers can use to train and test new models before submitting their implementations to official leaderboards....	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	212,685
2001.07370	Simultaneous Mode, Input and State Set-Valued Observers with Applications to Resilient Estimation Against Sparse Attacks	A simultaneous mode, input and state set-valued observer is proposed for hidden mode switched linear systems with bounded-norm noise and unknown input signals. The observer consists of two constituents: (i) a bank of mode-matched observers and (ii) a mode estimator. Each mode-matched observer recursively outputs the mo...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	161,021
2003.06471	DNN+NeuroSim V2.0: An End-to-End Benchmarking Framework for Compute-in-Memory Accelerators for On-chip Training	DNN+NeuroSim is an integrated framework to benchmark compute-in-memory (CIM) accelerators for deep neural networks, with hierarchical design options from device-level, to circuit-level and up to algorithm-level. A python wrapper is developed to interface NeuroSim with a popular machine learning platform: Pytorch, to su...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	168,117
2007.03198	Regional Image Perturbation Reduces $L_p$ Norms of Adversarial Examples While Maintaining Model-to-model Transferability	Regional adversarial attacks often rely on complicated methods for generating adversarial perturbations, making it hard to compare their efficacy against well-known attacks. In this study, we show that effective regional perturbations can be generated without resorting to complex methods. We develop a very simple regio...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	185,981
1901.04791	Mixed Variational Inference	The Laplace approximation has been one of the workhorses of Bayesian inference. It often delivers good approximations in practice despite the fact that it does not strictly take into account where the volume of posterior density lies. Variational approaches avoid this issue by explicitly minimising the Kullback-Leibler...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	118,659
2203.13595	Fast Hybrid Image Retargeting	Image retargeting changes the aspect ratio of images while aiming to preserve content and minimise noticeable distortion. Fast and high-quality methods are particularly relevant at present, due to the large variety of image and display aspect ratios. We propose a retargeting method that quantifies and limits warping di...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	287,689
2210.12647	Binary sequences with a low correlation via cyclotomic function fields with odd characteristics	Sequences with a low correlation have very important applications in communications, cryptography, and compressed sensing. In the literature, many efforts have been made to construct good sequences with various lengths where binary sequences attracts great attention. As a result, various constructions of good binary se...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	325,838
1812.05447	Generating Hard Examples for Pixel-wise Classification	Pixel-wise classification in remote sensing identifies entities in large-scale satellite-based images at the pixel level. Few fully annotated large-scale datasets for pixel-wise classification exist due to the challenges of annotating individual pixels. Training data scarcity inevitably ensues from the annotation chall...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	116,411
1912.12740	Practice of Streaming Processing of Dynamic Graphs: Concepts, Models, and Systems	Graph processing has become an important part of various areas of computing, including machine learning, medical applications, social network analysis, computational sciences, and others. A growing amount of the associated graph processing workloads are dynamic, with millions of edges added or removed per second. Graph...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	true	158,920
2312.16218	Hyper-VolTran: Fast and Generalizable One-Shot Image to 3D Object Structure via HyperNetworks	Solving image-to-3D from a single view is an ill-posed problem, and current neural reconstruction methods addressing it through diffusion models still rely on scene-specific optimization, constraining their generalization capability. To overcome the limitations of existing approaches regarding generalization and consis...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	418,311
1708.06694	Structure constrained by metadata in networks of chess players	Chess is an emblematic sport that stands out because of its age, popularity and complexity. It has served to study human behavior from the perspective of a wide number of disciplines, from cognitive skills such as memory and learning, to aspects like innovation and decision making. Given that an extensive documentation...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	79,363
2406.04038	Road Network Representation Learning with the Third Law of Geography	Road network representation learning aims to learn compressed and effective vectorized representations for road segments that are applicable to numerous tasks. In this paper, we identify the limitations of existing methods, particularly their overemphasis on the distance effect as outlined in the First Law of Geography...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	461,494
2412.08614	Benchmarking Large Vision-Language Models via Directed Scene Graph for Comprehensive Image Captioning	Generating detailed captions comprehending text-rich visual content in images has received growing attention for Large Vision-Language Models (LVLMs). However, few studies have developed benchmarks specifically tailored for detailed captions to measure their accuracy and comprehensiveness. In this paper, we introduce a...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	516,170
2104.01070	MOST: A Multi-Oriented Scene Text Detector with Localization Refinement	Over the past few years, the field of scene text detection has progressed rapidly that modern text detectors are able to hunt text in various challenging scenarios. However, they might still fall short when handling text instances of extreme aspect ratios and varying scales. To tackle such difficulties, we propose in t...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	228,220
cmp-lg/9405031	An Attributive Logic of Set Descriptions and Set Operations	This paper provides a model theoretic semantics to feature terms augmented with set descriptions. We provide constraints to specify HPSG style set descriptions, fixed cardinality set descriptions, set-membership constraints, restricted universal role quantifications, set union, intersection, subset and disjointness. A ...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	536,075
2411.06615	Field Insights for Portable Vine Robots in Urban Search and Rescue	Soft, growing vine robots are well-suited for exploring cluttered, unknown environments, and are theorized to be performant during structural collapse incidents caused by earthquakes, fires, explosions, and material flaws. These vine robots grow from the tip, enabling them to navigate rubble-filled passageways easily. ...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	507,189
0801.1306	Capacity Bounds for the Gaussian Interference Channel	The capacity region of the two-user Gaussian Interference Channel (IC) is studied. Three classes of channels are considered: weak, one-sided, and mixed Gaussian IC. For the weak Gaussian IC, a new outer bound on the capacity region is obtained that outperforms previously known outer bounds. The sum capacity for a certa...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	1,145
1606.05177	HARQ and AMC: Friends or Foes?	To ensure reliable communication in randomly varying and error-prone channels, wireless systems use adaptive modulation and coding (AMC) as well as hybrid ARQ (HARQ). In order to elucidate their compatibility and interaction, we compare the throughput provided by AMC, HARQ, and their combination (AMC-HARQ) under two op...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	57,364
2108.13332	Overcoming Data Availability Attacks in Blockchain Systems: Short Code-Length LDPC Code Design for Coded Merkle Tree	Light nodes are clients in blockchain systems that only store a small portion of the blockchain ledger. In certain blockchains, light nodes are vulnerable to a data availability (DA) attack where a malicious node makes the light nodes accept an invalid block by hiding the invalid portion of the block from the nodes in ...	false	false	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	252,776
2405.00208	A Primer on the Inner Workings of Transformer-based Language Models	The rapid progress of research aimed at interpreting the inner workings of advanced language models has highlighted a need for contextualizing the insights gained from years of work in this area. This primer provides a concise technical introduction to the current techniques used to interpret the inner workings of Tran...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	450,829
1705.06000	One Shot Joint Colocalization and Cosegmentation	This paper presents a novel framework in which image cosegmentation and colocalization are cast into a single optimization problem that integrates information from low level appearance cues with that of high level localization cues in a very weakly supervised manner. In contrast to multi-task learning paradigm that lea...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	73,584
2409.17774	Faithfulness and the Notion of Adversarial Sensitivity in NLP Explanations	Faithfulness is arguably the most critical metric to assess the reliability of explainable AI. In NLP, current methods for faithfulness evaluation are fraught with discrepancies and biases, often failing to capture the true reasoning of models. We introduce Adversarial Sensitivity as a novel approach to faithfulness ev...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	491,974
1511.02633	Norm minimized Scattering Data from Intensity Spectra	We apply the $l_1$ minimizing technique of compressive sensing (CS) to non-linear quadratic observations. For the example of coherent X-ray scattering we provide the formulae for a Kalman filter approach to quadratic CS and show how to reconstruct the scattering data from their spatial intensity distribution.	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	48,668
2406.07877	Hierarchical Reinforcement Learning for Swarm Confrontation with High Uncertainty	In swarm robotics, confrontation including the pursuit-evasion game is a key scenario. High uncertainty caused by unknown opponents' strategies, dynamic obstacles, and insufficient training complicates the action space into a hybrid decision process. Although the deep reinforcement learning method is significant for sw...	false	false	false	false	true	false	true	true	false	false	false	false	false	false	false	false	false	false	463,254
2006.11132	Deep Transformation-Invariant Clustering	Recent advances in image clustering typically focus on learning better deep representations. In contrast, we present an orthogonal approach that does not rely on abstract features but instead learns to predict image transformations and performs clustering directly in image space. This learning process naturally fits in...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	183,112
1511.03897	IfcWoD, Semantically Adapting IFC Model Relations into OWL Properties	In the context of Building Information Modelling, ontologies have been identified as interesting in achieving information interoperability. Regarding the construction and facility management domains, several IFC (Industry Foundation Classes) based ontologies have been developed, such as IfcOWL. In the context of ontolo...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	48,813
2401.08202	IsamasRed: A Public Dataset Tracking Reddit Discussions on Israel-Hamas Conflict	The conflict between Israel and Palestinians significantly escalated after the October 7, 2023 Hamas attack, capturing global attention. To understand the public discourse on this conflict, we present a meticulously compiled dataset-IsamasRed-comprising nearly 400,000 conversations and over 8 million comments from Redd...	false	false	false	true	false	false	false	false	false	false	false	false	false	true	false	false	false	true	421,803
1608.03757	Student's t Distribution based Estimation of Distribution Algorithms for Derivative-free Global Optimization	In this paper, we are concerned with a branch of evolutionary algorithms termed estimation of distribution (EDA), which has been successfully used to tackle derivative-free global optimization problems. For existent EDA algorithms, it is a common practice to use a Gaussian distribution or a mixture of Gaussian componen...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	59,721
2005.05812	Estimating the Cheeger constant using machine learning	In this paper, we use machine learning to show that the Cheeger constant of a connected regular graph has a predominant linear dependence on the largest two eigenvalues of the graph spectrum. We also show that a trained deep neural network on graphs of smaller sizes can be used as an effective estimator in estimating t...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	176,835
2305.13235	SPARSEFIT: Few-shot Prompting with Sparse Fine-tuning for Jointly Generating Predictions and Natural Language Explanations	Models that generate natural language explanations (NLEs) for their predictions have recently gained increasing interest. However, this approach usually demands large datasets of human-written NLEs for the ground-truth answers at training time, which can be expensive and potentially infeasible for some applications. Wh...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	366,411
2405.09362	On the Saturation Effect of Kernel Ridge Regression	The saturation effect refers to the phenomenon that the kernel ridge regression (KRR) fails to achieve the information theoretical lower bound when the smoothness of the underground truth function exceeds certain level. The saturation effect has been widely observed in practices and a saturation lower bound of KRR has ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	454,381
1305.5663	Applications of Clifford's Geometric Algebra	We survey the development of Clifford's geometric algebra and some of its engineering applications during the last 15 years. Several recently developed applications and their merits are discussed in some detail. We thus hope to clearly demonstrate the benefit of developing problem solutions in a unified framework for a...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	24,775
2106.10529	Graph Neural Networks for Learning Real-Time Prices in Electricity Market	Solving the optimal power flow (OPF) problem in real-time electricity market improves the efficiency and reliability in the integration of low-carbon energy resources into the power grids. To address the scalability and adaptivity issues of existing end-to-end OPF learning solutions, we propose a new graph neural netwo...	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	242,055
1405.3515	Temporal Analysis of Language through Neural Language Models	We provide a method for automatically detecting change in language across time through a chronologically trained neural language model. We train the model on the Google Books Ngram corpus to obtain word vector representations specific to each year, and identify words that have changed significantly from 1900 to 2009. T...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	33,095
2403.00067	Query-OPT: Optimizing Inference of Large Language Models via Multi-Query Instructions in Meeting Summarization	This work focuses on the task of query-based meeting summarization in which the summary of a context (meeting transcript) is generated in response to a specific query. When using Large Language Models (LLMs) for this task, usually a new call to the LLM inference endpoint/API is triggered for each new query, even if the...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	433,849
2411.02669	Semantic-Aligned Adversarial Evolution Triangle for High-Transferability Vision-Language Attack	Vision-language pre-training (VLP) models excel at interpreting both images and text but remain vulnerable to multimodal adversarial examples (AEs). Advancing the generation of transferable AEs, which succeed across unseen models, is key to developing more robust and practical VLP models. Previous approaches augment im...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	505,606
2311.06643	Privacy Risks Analysis and Mitigation in Federated Learning for Medical Images	Federated learning (FL) is gaining increasing popularity in the medical domain for analyzing medical images, which is considered an effective technique to safeguard sensitive patient data and comply with privacy regulations. However, several recent studies have revealed that the default settings of FL may leak private ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	407,016
1811.08355	Design and Analysis of Distributed State Estimation Algorithms Based on Belief Propagation and Applications in Smart Grids	We present a detailed study on application of factor graphs and the belief propagation (BP) algorithm to the power system state estimation (SE) problem. We start from the BP solution for the linear DC model, for which we provide a detailed convergence analysis. Using BP-based DC model we propose a fast real-time state ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	114,020
2201.10665	Writer Recognition Using Off-line Handwritten Single Block Characters	Block characters are often used when filling paper forms for a variety of purposes. We investigate if there is biometric information contained within individual digits of handwritten text. In particular, we use personal identity numbers consisting of the six digits of the date of birth, DoB. We evaluate two recognition...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	277,064
2402.00069	Using the Abstract Computer Architecture Description Language to Model AI Hardware Accelerators	Artificial Intelligence (AI) has witnessed remarkable growth, particularly through the proliferation of Deep Neural Networks (DNNs). These powerful models drive technological advancements across various domains. However, to harness their potential in real-world applications, specialized hardware accelerators are essent...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	425,470
1112.0383	Bounds on and Constructions of Unit Time-Phase Signal Sets	Digital signals are complex-valued functions on $\Z_n$. Signal sets with certain properties are required in various communication systems. Traditional signal sets consider only the time distortion during transmission. Recently, signal sets against both the time and phase distortion have been studied, and are called {\e...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	13,289
1810.00839	Network Modeling and Pathway Inference from Incomplete Data ("PathInf")	In this work, we developed a network inference method from incomplete data ("PathInf") , as massive and non-uniformly distributed missing values is a common challenge in practical problems. PathInf is a two-stages inference model. In the first stage, it applies a data summarization model based on maximum likelihood to ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	109,270
2004.05086	Secret Key Generation from Vector Gaussian Sources with Public and Private Communications	In this paper, we consider the problem of secret key generation with one-way communication through both a rate-limited public channel and a rate-limited secure channels where the public channel is from Alice to Bob and Eve and the secure channel is from Alice to Bob. In this model, we do not pose any constraints on the...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	172,087
2011.09946	Data Driven Modeling of Interfacial Traction Separation Relations using a Thermodynamically Consistent Neural Network	For multilayer structures, interfacial failure is one of the most important elements related to device reliability. For cohesive zone modelling, traction-separation relations represent the adhesive interactions across interfaces. However, existing theoretical models do not currently capture traction-separation relation...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	207,368
2103.05243	On the Generalization Power of Overfitted Two-Layer Neural Tangent Kernel Models	In this paper, we study the generalization performance of min $\ell_2$-norm overfitting solutions for the neural tangent kernel (NTK) model of a two-layer neural network with ReLU activation that has no bias term. We show that, depending on the ground-truth function, the test error of overfitted NTK models exhibits cha...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	223,916
2312.08413	Privacy Constrained Fairness Estimation for Decision Trees	The protection of sensitive data becomes more vital, as data increases in value and potency. Furthermore, the pressure increases from regulators and society on model developers to make their Artificial Intelligence (AI) models non-discriminatory. To boot, there is a need for interpretable, transparent AI models for hig...	false	false	false	false	false	false	true	false	false	false	false	false	true	true	false	false	false	false	415,302
1802.06450	Reducing Initial Cell-search Latency in mmWave Networks	Millimeter-wave (mmWave) networks rely on directional transmissions, in both control plane and data plane, to overcome severe path-loss. Nevertheless, the use of narrow beams complicates the initial cell-search procedure where we lack sufficient information for beamforming. In this paper, we investigate the feasibility...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	90,675
2405.19590	Weights Augmentation: it has never ever ever ever let her model down	Weight play an essential role in deep learning network models. Unlike network structure design, this article proposes the concept of weight augmentation, focusing on weight exploration. The core of Weight Augmentation Strategy (WAS) is to adopt random transformed weight coefficients training and transformed coefficient...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	458,960
1806.06298	Deformable Generator Networks: Unsupervised Disentanglement of Appearance and Geometry	We present a deformable generator model to disentangle the appearance and geometric information for both image and video data in a purely unsupervised manner. The appearance generator network models the information related to appearance, including color, illumination, identity or category, while the geometric generator...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	100,674
2307.08672	FedDefender: Backdoor Attack Defense in Federated Learning	Federated Learning (FL) is a privacy-preserving distributed machine learning technique that enables individual clients (e.g., user participants, edge devices, or organizations) to train a model on their local data in a secure environment and then share the trained model with an aggregator to build a global model collab...	false	false	false	false	true	false	true	false	false	false	false	true	true	false	false	false	false	false	379,894
1509.07859	Information Limits for Recovering a Hidden Community	We study the problem of recovering a hidden community of cardinality $K$ from an $n \times n$ symmetric data matrix $A$, where for distinct indices $i,j$, $A_{ij} \sim P$ if $i, j$ both belong to the community and $A_{ij} \sim Q$ otherwise, for two known probability distributions $P$ and $Q$ depending on $n$. If $P={\r...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	47,297
2306.15091	Understanding In-Context Learning via Supportive Pretraining Data	In-context learning (ICL) improves language models' performance on a variety of NLP tasks by simply demonstrating a handful of examples at inference time. It is not well understood why ICL ability emerges, as the model has never been specifically trained on such demonstrations. Unlike prior work that explores implicit ...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	375,904
1804.10516	Rate-Splitting Multiple Access for Coordinated Multi-Point Joint Transmission	As a promising downlink multiple access scheme, Rate-Splitting Multiple Access (RSMA) has been shown to achieve superior spectral and energy efficiencies compared with Space-Division Multiple Access (SDMA) and Non-Orthogonal Multiple Access (NOMA) in downlink single-cell systems. By relying on linearly precoded rate-sp...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	96,176
2207.06223	Job Offers Classifier using Neural Networks and Oversampling Methods	Both policy and research benefit from a better understanding of individuals' jobs. However, as large-scale administrative records are increasingly employed to represent labor market activity, new automatic methods to classify jobs will become necessary. We developed an automatic job offers classifier using a dataset co...	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	false	307,807
1705.09422	Text-Independent Speaker Verification Using 3D Convolutional Neural Networks	In this paper, a novel method using 3D Convolutional Neural Network (3D-CNN) architecture has been proposed for speaker verification in the text-independent setting. One of the main challenges is the creation of the speaker models. Most of the previously-reported approaches create speaker models based on averaging the ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	74,194
2304.12650	THUIR at WSDM Cup 2023 Task 1: Unbiased Learning to Rank	This paper introduces the approaches we have used to participate in the WSDM Cup 2023 Task 1: Unbiased Learning to Rank. In brief, we have attempted a combination of both traditional IR models and transformer-based cross-encoder architectures. To further enhance the ranking performance, we also considered a series of f...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	360,305
2102.10289	Recurrent Model Predictive Control: Learning an Explicit Recurrent Controller for Nonlinear Systems	This paper proposes an offline control algorithm, called Recurrent Model Predictive Control (RMPC), to solve large-scale nonlinear finite-horizon optimal control problems. It can be regarded as an explicit solver of traditional Model Predictive Control (MPC) algorithms, which can adaptively select appropriate model pre...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	221,038

Subsets and Splits

No community queries yet

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