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
2407.07670	Stochastic Gradient Descent for Two-layer Neural Networks	This paper presents a comprehensive study on the convergence rates of the stochastic gradient descent (SGD) algorithm when applied to overparameterized two-layer neural networks. Our approach combines the Neural Tangent Kernel (NTK) approximation with convergence analysis in the Reproducing Kernel Hilbert Space (RKHS) ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	471,853
cs/0005009	PSPACE Reasoning for Graded Modal Logics	We present a PSPACE algorithm that decides satisfiability of the graded modal logic Gr(K_R)---a natural extension of propositional modal logic K_R by counting expressions---which plays an important role in the area of knowledge representation. The algorithm employs a tableaux approach and is the first known algorithm w...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	537,096
2412.04714	PCTreeS: 3D Point Cloud Tree Species Classification Using Airborne LiDAR Images	Reliable large-scale data on the state of forests is crucial for monitoring ecosystem health, carbon stock, and the impact of climate change. Current knowledge of tree species distribution relies heavily on manual data collection in the field, which often takes years to complete, resulting in limited datasets that cove...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	514,525
1203.1406	Communication over Individual Channels -- a general framework	We consider the problem of communicating over a channel for which no mathematical model is specified, and the achievable rates are determined as a function of the channel input and output sequences known a-posteriori, without assuming any a-priori relation between them. In a previous paper we have shown that the empiri...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	14,748
2501.02021	Weakly Supervised Learning on Large Graphs	Graph classification plays a pivotal role in various domains, including pathology, where images can be represented as graphs. In this domain, images can be represented as graphs, where nodes might represent individual nuclei, and edges capture the spatial or functional relationships between them. Often, the overall lab...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	522,316
2411.07378	Data-Driven Analysis of AI in Medical Device Software in China: Deep Learning and General AI Trends Based on Regulatory Data	Artificial intelligence (AI) in medical device software (MDSW) represents a transformative clinical technology, attracting increasing attention within both the medical community and the regulators. In this study, we leverage a data-driven approach to automatically extract and analyze AI-enabled medical devices (AIMD) f...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	507,497
2309.11093	K-pop Lyric Translation: Dataset, Analysis, and Neural-Modelling	Lyric translation, a field studied for over a century, is now attracting computational linguistics researchers. We identified two limitations in previous studies. Firstly, lyric translation studies have predominantly focused on Western genres and languages, with no previous study centering on K-pop despite its populari...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	true	393,278
2402.08072	Enhancing Programming Error Messages in Real Time with Generative AI	Generative AI is changing the way that many disciplines are taught, including computer science. Researchers have shown that generative AI tools are capable of solving programming problems, writing extensive blocks of code, and explaining complex code in simple terms. Particular promise has been shown in using generativ...	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	428,944
1605.02615	A Non-Convex Blind Calibration Method for Randomised Sensing Strategies	The implementation of computational sensing strategies often faces calibration problems typically solved by means of multiple, accurately chosen training signals, an approach that can be resource-consuming and cumbersome. Conversely, blind calibration does not require any training, but corresponds to a bilinear inverse...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	55,650
2004.02494	Adaptive Social Learning	This work proposes a novel strategy for social learning by introducing the critical feature of adaptation. In social learning, several distributed agents update continually their belief about a phenomenon of interest through: i) direct observation of streaming data that they gather locally; and ii) diffusion of their b...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	171,246
2208.14244	Expressions Causing Differences in Emotion Recognition in Social Networking Service Documents	It is often difficult to correctly infer a writer's emotion from text exchanged online, and differences in recognition between writers and readers can be problematic. In this paper, we propose a new framework for detecting sentences that create differences in emotion recognition between the writer and the reader and fo...	false	false	false	true	true	false	true	false	true	false	false	false	false	false	false	false	false	false	315,266
2305.19801	Predicting protein stability changes under multiple amino acid substitutions using equivariant graph neural networks	The accurate prediction of changes in protein stability under multiple amino acid substitutions is essential for realising true in-silico protein re-design. To this purpose, we propose improvements to state-of-the-art Deep learning (DL) protein stability prediction models, enabling first-of-a-kind predictions for varia...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	369,692
cmp-lg/9710007	A Corpus-Based Investigation of Definite Description Use	We present the results of a study of definite descriptions use in written texts aimed at assessing the feasibility of annotating corpora with information about definite description interpretation. We ran two experiments, in which subjects were asked to classify the uses of definite descriptions in a corpus of 33 newspa...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	536,822
2306.09337	Generative Proxemics: A Prior for 3D Social Interaction from Images	Social interaction is a fundamental aspect of human behavior and communication. The way individuals position themselves in relation to others, also known as proxemics, conveys social cues and affects the dynamics of social interaction. Reconstructing such interaction from images presents challenges because of mutual oc...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	373,782
2007.09451	Feature Pyramid Transformer	Feature interactions across space and scales underpin modern visual recognition systems because they introduce beneficial visual contexts. Conventionally, spatial contexts are passively hidden in the CNN's increasing receptive fields or actively encoded by non-local convolution. Yet, the non-local spatial interactions ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	187,948
2011.00425	Analyzing the Effect of Multi-task Learning for Biomedical Named Entity Recognition	Developing high-performing systems for detecting biomedical named entities has major implications. State-of-the-art deep-learning based solutions for entity recognition often require large annotated datasets, which is not available in the biomedical domain. Transfer learning and multi-task learning have been shown to i...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	204,211
2112.11490	Do Androids Dream of Electric Fences? Safety-Aware Reinforcement Learning with Latent Shielding	The growing trend of fledgling reinforcement learning systems making their way into real-world applications has been accompanied by growing concerns for their safety and robustness. In recent years, a variety of approaches have been put forward to address the challenges of safety-aware reinforcement learning; however, ...	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	true	false	true	272,721
2501.05000	Load Forecasting for Households and Energy Communities: Are Deep Learning Models Worth the Effort?	Accurate load forecasting is crucial for predictive control in many energy domain applications, with significant economic and ecological implications. To address these implications, this study provides an extensive benchmark of state-of-the-art deep learning models for short-term load forecasting in energy communities....	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	523,427
1911.11380	Using Physics-Informed Super-Resolution Generative Adversarial Networks for Subgrid Modeling in Turbulent Reactive Flows	Turbulence is still one of the main challenges for accurately predicting reactive flows. Therefore, the development of new turbulence closures which can be applied to combustion problems is essential. Data-driven modeling has become very popular in many fields over the last years as large, often extensively labeled, da...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	155,105
2309.03990	Derivation of Coordinate Descent Algorithms from Optimal Control Theory	Recently, it was posited that disparate optimization algorithms may be coalesced in terms of a central source emanating from optimal control theory. Here we further this proposition by showing how coordinate descent algorithms may be derived from this emerging new principle. In particular, we show that basic coordinate...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	390,578
2401.04934	Fully Decentralized Cooperative Multi-Agent Reinforcement Learning: A Survey	Cooperative multi-agent reinforcement learning is a powerful tool to solve many real-world cooperative tasks, but restrictions of real-world applications may require training the agents in a fully decentralized manner. Due to the lack of information about other agents, it is challenging to derive algorithms that can co...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	true	false	false	false	420,593
1104.1605	Efficient Top-K Retrieval in Online Social Tagging Networks	We consider in this paper top-k query answering in social tagging systems, also known as folksonomies. This problem requires a significant departure from existing, socially agnostic techniques. In a network-aware context, one can (and should) exploit the social links, which can indicate how users relate to the seeker a...	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	true	false	9,923
1408.6762	Chatbot for admissions	The communication of potential students with a university department is performed manually and it is a very time consuming procedure. The opportunity to communicate with on a one-to-one basis is highly valued. However with many hundreds of applications each year, one-to-one conversations are not feasible in most cases....	false	false	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	35,657
2310.12103	Quality Diversity through Human Feedback: Towards Open-Ended Diversity-Driven Optimization	Reinforcement Learning from Human Feedback (RLHF) has shown potential in qualitative tasks where easily defined performance measures are lacking. However, there are drawbacks when RLHF is commonly used to optimize for average human preferences, especially in generative tasks that demand diverse model responses. Meanwhi...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	false	false	400,906
1806.06484	A Multi-Observer Approach for Attack Detection and Isolation of Discrete-Time Nonlinear Systems	We address the problem of attack detection and isolation for a class of discrete-time nonlinear systems under (potentially unbounded) sensor attacks and measurement noise. We consider the case when a subset of sensors is subject to additive false data injection attacks. Using a bank of observers, each observer leading ...	false	false	false	false	false	false	false	false	false	false	true	false	true	false	false	false	false	false	100,714
1012.4401	A Note on a Characterization of R\'enyi Measures and its Relation to Composite Hypothesis Testing	The R\'enyi information measures are characterized in terms of their Shannon counterparts, and properties of the former are recovered from first principle via the associated properties of the latter. Motivated by this characterization, a two-sensor composite hypothesis testing problem is presented, and the optimal wors...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	8,599
2212.03294	Cube Interestingness: Novelty, Relevance, Peculiarity and Surprise	In this paper, we discuss methods to assess the interestingness of a query in an environment of data cubes. We assume a hierarchical multidimensional database, storing data cubes and level hierarchies. We start with a comprehensive review of related work in the fields of studies of human behavior and computer science. ...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	335,063
2305.13092	Improved Compositional Generalization by Generating Demonstrations for Meta-Learning	Meta-learning and few-shot prompting are viable methods to induce certain types of compositional behaviour. However, these methods can be very sensitive to the choice of support examples used. Choosing good supports from the training data for a given test query is already a difficult problem, but in some cases solving ...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	366,345
1908.06391	PANet: Few-Shot Image Semantic Segmentation with Prototype Alignment	Despite the great progress made by deep CNNs in image semantic segmentation, they typically require a large number of densely-annotated images for training and are difficult to generalize to unseen object categories. Few-shot segmentation has thus been developed to learn to perform segmentation from only a few annotate...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	142,008
1811.06017	Performance Estimation of Synthesis Flows cross Technologies using LSTMs and Transfer Learning	Due to the increasing complexity of Integrated Circuits (ICs) and System-on-Chip (SoC), developing high-quality synthesis flows within a short market time becomes more challenging. We propose a general approach that precisely estimates the Quality-of-Result (QoR), such as delay and area, of unseen synthesis flows for s...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	113,427
2407.10403	Cooperative Reward Shaping for Multi-Agent Pathfinding	The primary objective of Multi-Agent Pathfinding (MAPF) is to plan efficient and conflict-free paths for all agents. Traditional multi-agent path planning algorithms struggle to achieve efficient distributed path planning for multiple agents. In contrast, Multi-Agent Reinforcement Learning (MARL) has been demonstrated ...	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	false	472,973
2108.05530	Flow-Aware Platoon Formation of Connected Automated Vehicles	Connected Automated Vehicles (CAVs) bring promise of increasing traffic capacity and energy efficiency by forming platoons with short headways on the road. However at low CAV penetration, the capacity gain will be small because the CAVs that randomly enter the road will be sparsely distributed, diminishing the probabil...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	250,325
2110.03921	ViDT: An Efficient and Effective Fully Transformer-based Object Detector	Transformers are transforming the landscape of computer vision, especially for recognition tasks. Detection transformers are the first fully end-to-end learning systems for object detection, while vision transformers are the first fully transformer-based architecture for image classification. In this paper, we integrat...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	259,683
1405.0237	An RIP-based approach to $\Sigma\Delta$ quantization for compressed sensing	In this paper, we provide a new approach to estimating the error of reconstruction from $\Sigma\Delta$ quantized compressed sensing measurements. Our method is based on the restricted isometry property (RIP) of a certain projection of the measurement matrix. Our result yields simple proofs and a slight generalizati...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	32,756
1907.10892	Simultaneous multi-view instance detection with learned geometric soft-constraints	We propose to jointly learn multi-view geometry and warping between views of the same object instances for robust cross-view object detection. What makes multi-view object instance detection difficult are strong changes in viewpoint, lighting conditions, high similarity of neighbouring objects, and strong variability i...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	139,729
2308.11322	CiteTracker: Correlating Image and Text for Visual Tracking	Existing visual tracking methods typically take an image patch as the reference of the target to perform tracking. However, a single image patch cannot provide a complete and precise concept of the target object as images are limited in their ability to abstract and can be ambiguous, which makes it difficult to track t...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	387,095
2303.00465	Uzbek text's correspondence with the educational potential of pupils: a case study of the School corpus	One of the major challenges of an educational system is choosing appropriate content considering pupils' age and intellectual potential. In this article the experiment of primary school grades (from 1st to 4th grades) is considered for automatically determining the correspondence of an educational materials recommended...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	348,608
2412.01223	PainterNet: Adaptive Image Inpainting with Actual-Token Attention and Diverse Mask Control	Recently, diffusion models have exhibited superior performance in the area of image inpainting. Inpainting methods based on diffusion models can usually generate realistic, high-quality image content for masked areas. However, due to the limitations of diffusion models, existing methods typically encounter problems in ...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	513,013
2107.01629	The Role of "Live" in Livestreaming Markets: Evidence Using Orthogonal Random Forest	A common belief about the growing medium of livestreaming is that its value lies in its "live" component. We examine this belief by comparing how the price elasticity of demand for live events varies before, on the day of, and after livestream. We do this using unique and rich data from a large livestreaming platform t...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	244,547
1109.3791	WebCloud: Recruiting web browsers for content distribution	We are at the beginning of a shift in how content is created and exchanged over the web. While content was previously created primarily by a small set of entities, today, individual users -- empowered by devices like digital cameras and services like online social networks -- are creating content that represents a sign...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	12,212
2412.12213	The AI Black-Scholes: Finance-Informed Neural Network	In the realm of option pricing, existing models are typically classified into principle-driven methods, such as solving partial differential equations (PDEs) that pricing function satisfies, and data-driven approaches, such as machine learning (ML) techniques that parameterize the pricing function directly. While princ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	517,793
2306.10313	Adversaries with Limited Information in the Friedkin--Johnsen Model	In recent years, online social networks have been the target of adversaries who seek to introduce discord into societies, to undermine democracies and to destabilize communities. Often the goal is not to favor a certain side of a conflict but to increase disagreement and polarization. To get a mathematical understandin...	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	false	true	374,179
1906.07153	Adversarial attacks on Copyright Detection Systems	It is well-known that many machine learning models are susceptible to adversarial attacks, in which an attacker evades a classifier by making small perturbations to inputs. This paper discusses how industrial copyright detection tools, which serve a central role on the web, are susceptible to adversarial attacks. We di...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	135,521
2303.16067	Lazy learning: a biologically-inspired plasticity rule for fast and energy efficient synaptic plasticity	When training neural networks for classification tasks with backpropagation, parameters are updated on every trial, even if the sample is classified correctly. In contrast, humans concentrate their learning effort on errors. Inspired by human learning, we introduce lazy learning, which only learns on incorrect samples....	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	true	false	false	354,722
2309.07565	Dubins Curve Based Continuous-Curvature Trajectory Planning for Autonomous Mobile Robots	AMR is widely used in factories to replace manual labor to reduce costs and improve efficiency. However, it is often difficult for logistics robots to plan the optimal trajectory and unreasonable trajectory planning can lead to low transport efficiency and high energy consumption. In this paper, we propose a method to ...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	391,828
2403.04758	KnowledgeVIS: Interpreting Language Models by Comparing Fill-in-the-Blank Prompts	Recent growth in the popularity of large language models has led to their increased usage for summarizing, predicting, and generating text, making it vital to help researchers and engineers understand how and why they work. We present KnowledgeVis, a human-in-the-loop visual analytics system for interpreting language m...	true	false	false	false	true	false	true	false	false	false	false	false	false	true	false	false	false	false	435,713
2109.09276	From None to Severe: Predicting Severity in Movie Scripts	In this paper, we introduce the task of predicting severity of age-restricted aspects of movie content based solely on the dialogue script. We first investigate categorizing the ordinal severity of movies on 5 aspects: Sex, Violence, Profanity, Substance consumption, and Frightening scenes. The problem is handled using...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	256,218
2210.01300	Revealing Unobservables by Deep Learning: Generative Element Extraction Networks (GEEN)	Latent variable models are crucial in scientific research, where a key variable, such as effort, ability, and belief, is unobserved in the sample but needs to be identified. This paper proposes a novel method for estimating realizations of a latent variable $X^*$ in a random sample that contains its multiple measuremen...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	321,204
1911.01879	Impedance-Based Whole-System Modeling for a Composite Grid via Frame-Dynamics Embedding	The paper establishes a methodology to overcome the difficulty of dynamic frame alignment and system separation in impedance modeling of ac grids, and thereby enables impedance-based whole-system modeling of generator-converter composite power systems. The methodology is based on a frame-dynamics-embedding transformati...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	152,223
2408.08896	LLMJudge: LLMs for Relevance Judgments	The LLMJudge challenge is organized as part of the LLM4Eval workshop at SIGIR 2024. Test collections are essential for evaluating information retrieval (IR) systems. The evaluation and tuning of a search system is largely based on relevance labels, which indicate whether a document is useful for a specific search and u...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	481,195
2410.12123	The Moral Case for Using Language Model Agents for Recommendation	Our information and communication environment has fallen short of the ideals that networked global communication might have served. Identifying all the causes of its pathologies is difficult, but existing recommender systems very likely play a contributing role. In this paper, which draws on the normative tools of phil...	false	false	false	false	false	true	false	false	false	false	false	false	false	true	false	false	false	false	498,860
2408.14718	Residual-based Adaptive Huber Loss (RAHL) -- Design of an improved Huber loss for CQI prediction in 5G networks	The Channel Quality Indicator (CQI) plays a pivotal role in 5G networks, optimizing infrastructure dynamically to ensure high Quality of Service (QoS). Recent research has focused on improving CQI estimation in 5G networks using machine learning. In this field, the selection of the proper loss function is critical for ...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	483,640
1012.5074	Power-Rate Allocation in DS/CDMA Based on Discretized Verhulst Equilibrium	This paper proposes to extend the discrete Verhulst power equilibrium approach, previously suggested in [1], to the power-rate optimal allocation problem. Multirate users associated to different types of traffic are aggregated to distinct user' classes, with the assurance of minimum rate allocation per user and QoS. He...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	8,631
2402.00965	Multi-Modal Machine Learning Framework for Automated Seizure Detection in Laboratory Rats	A multi-modal machine learning system uses multiple unique data sources and types to improve its performance. This article proposes a system that combines results from several types of models, all of which are trained on different data signals. As an example to illustrate the efficacy of the system, an experiment is de...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	425,795
1506.01436	A Distributed and Privacy-Aware Speed Advisory System for Optimising Conventional and Electric Vehicles Networks	One of the key ideas to make Intelligent Transportation Systems (ITS) work effectively is to deploy advanced communication and cooperative control technologies among the vehicles and road infrastructures. In this spirit, we propose a consensus-based distributed speed advisory system that optimally determines a recommen...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	43,800
2403.15378	Long-CLIP: Unlocking the Long-Text Capability of CLIP	Contrastive Language-Image Pre-training (CLIP) has been the cornerstone for zero-shot classification, text-image retrieval, and text-image generation by aligning image and text modalities. Despite its widespread adoption, a significant limitation of CLIP lies in the inadequate length of text input. The length of the te...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	440,519
2312.06978	CLASS-M: Adaptive stain separation-based contrastive learning with pseudo-labeling for histopathological image classification	Histopathological image classification is an important task in medical image analysis. Recent approaches generally rely on weakly supervised learning due to the ease of acquiring case-level labels from pathology reports. However, patch-level classification is preferable in applications where only a limited number of ca...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	414,749
2311.08840	An MRL-Based Design Solution for RIS-Assisted MU-MIMO Wireless System under Time-Varying Channels	Utilizing Deep Reinforcement Learning (DRL) for Reconfigurable Intelligent Surface (RIS) assisted wireless communication has been extensively researched. However, existing DRL methods either act as a simple optimizer or only solve problems with concurrent Channel State Information (CSI) represented in the training data...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	407,890
2306.07973	PrivaScissors: Enhance the Privacy of Collaborative Inference through the Lens of Mutual Information	Edge-cloud collaborative inference empowers resource-limited IoT devices to support deep learning applications without disclosing their raw data to the cloud server, thus preserving privacy. Nevertheless, prior research has shown that collaborative inference still results in the exposure of data and predictions from ed...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	373,228
1103.0941	Estimating $\beta$-mixing coefficients	The literature on statistical learning for time series assumes the asymptotic independence or ``mixing' of the data-generating process. These mixing assumptions are never tested, nor are there methods for estimating mixing rates from data. We give an estimator for the $\beta$-mixing rate based on a single stationary sa...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	9,480
2104.04987	AutoGL: A Library for Automated Graph Learning	Recent years have witnessed an upsurge in research interests and applications of machine learning on graphs. However, manually designing the optimal machine learning algorithms for different graph datasets and tasks is inflexible, labor-intensive, and requires expert knowledge, limiting its adaptivity and applicability...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	229,560
2403.11373	Reconstruct before Query: Continual Missing Modality Learning with Decomposed Prompt Collaboration	Pre-trained large multi-modal models (LMMs) exploit fine-tuning to adapt diverse user applications. Nevertheless, fine-tuning may face challenges due to deactivated sensors (e.g., cameras turned off for privacy or technical issues), yielding modality-incomplete data and leading to inconsistency in training data and the...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	438,666
1208.5429	Fast Erasure-and-Error Decoding and Systematic Encoding of a Class of Affine Variety Codes	In this paper, a lemma in algebraic coding theory is established, which is frequently appeared in the encoding and decoding for algebraic codes such as Reed-Solomon codes and algebraic geometry codes. This lemma states that two vector spaces, one corresponds to information symbols and the other is indexed by the suppor...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	18,272
2105.14215	Biomimetic Control of Myoelectric Prosthetic Hand Based on a Lambda-type Muscle Model	Myoelectric prosthetic hands are intended to replace the function of the amputee's lost arm. Therefore, developing robotic prosthetics that can mimic not only the appearance and functionality of humans but also characteristics unique to human movements is paramount. Although the impedance model was proposed to realize ...	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	237,584
1311.2342	Anatomy of Graph Matching based on an XQuery and RDF Implementation	Graphs are becoming one of the most popular data modeling paradigms since they are able to model complex relationships that cannot be easily captured using traditional data models. One of the major tasks of graph management is graph matching, which aims to find all of the subgraphs in a data graph that match a query gr...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	28,311
2107.04282	LIFE: A Generalizable Autodidactic Pipeline for 3D OCT-A Vessel Segmentation	Optical coherence tomography (OCT) is a non-invasive imaging technique widely used for ophthalmology. It can be extended to OCT angiography (OCT-A), which reveals the retinal vasculature with improved contrast. Recent deep learning algorithms produced promising vascular segmentation results; however, 3D retinal vessel ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	245,412
2306.07632	NeuS-PIR: Learning Relightable Neural Surface using Pre-Integrated Rendering	This paper presents a method, namely NeuS-PIR, for recovering relightable neural surfaces using pre-integrated rendering from multi-view images or video. Unlike methods based on NeRF and discrete meshes, our method utilizes implicit neural surface representation to reconstruct high-quality geometry, which facilitates t...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	373,090
2106.13511	Evaluation of Deep-Learning-Based Voice Activity Detectors and Room Impulse Response Models in Reverberant Environments	State-of-the-art deep-learning-based voice activity detectors (VADs) are often trained with anechoic data. However, real acoustic environments are generally reverberant, which causes the performance to significantly deteriorate. To mitigate this mismatch between training data and real data, we simulate an augmented tra...	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	243,098
1409.4161	Crowdsourcing Pareto-Optimal Object Finding by Pairwise Comparisons	This is the first study on crowdsourcing Pareto-optimal object finding, which has applications in public opinion collection, group decision making, and information exploration. Departing from prior studies on crowdsourcing skyline and ranking queries, it considers the case where objects do not have explicit attributes ...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	true	false	36,047
1912.03699	Minimum Class Confusion for Versatile Domain Adaptation	There are a variety of Domain Adaptation (DA) scenarios subject to label sets and domain configurations, including closed-set and partial-set DA, as well as multi-source and multi-target DA. It is notable that existing DA methods are generally designed only for a specific scenario, and may underperform for scenarios th...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	156,666
2309.15946	Unified Long-Term Time-Series Forecasting Benchmark	In order to support the advancement of machine learning methods for predicting time-series data, we present a comprehensive dataset designed explicitly for long-term time-series forecasting. We incorporate a collection of datasets obtained from diverse, dynamic systems and real-life records. Each dataset is standardize...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	true	false	false	395,162
2410.15012	Pathologist-like explainable AI for interpretable Gleason grading in prostate cancer	The aggressiveness of prostate cancer, the most common cancer in men worldwide, is primarily assessed based on histopathological data using the Gleason scoring system. While artificial intelligence (AI) has shown promise in accurately predicting Gleason scores, these predictions often lack inherent explainability, pote...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	500,319
1703.04213	MetaPAD: Meta Pattern Discovery from Massive Text Corpora	Mining textual patterns in news, tweets, papers, and many other kinds of text corpora has been an active theme in text mining and NLP research. Previous studies adopt a dependency parsing-based pattern discovery approach. However, the parsing results lose rich context around entities in the patterns, and the process is...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	69,857
1412.4842	Similarity Group-by Operators for Multi-dimensional Relational Data	The SQL group-by operator plays an important role in summarizing and aggregating large datasets in a data analytic stack.While the standard group-by operator, which is based on equality, is useful in several applications, allowing similarity aware grouping provides a more realistic view on real-world data that could le...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	38,430
2405.18687	Advancing Household Robotics: Deep Interactive Reinforcement Learning for Efficient Training and Enhanced Performance	The market for domestic robots made to perform household chores is growing as these robots relieve people of everyday responsibilities. Domestic robots are generally welcomed for their role in easing human labor, in contrast to industrial robots, which are frequently criticized for displacing human workers. But before ...	false	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	458,535
2006.08195	Neural Networks Fail to Learn Periodic Functions and How to Fix It	Previous literature offers limited clues on how to learn a periodic function using modern neural networks. We start with a study of the extrapolation properties of neural networks; we prove and demonstrate experimentally that the standard activations functions, such as ReLU, tanh, sigmoid, along with their variants, al...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	182,103
2402.04523	SumRec: A Framework for Recommendation using Open-Domain Dialogue	Chat dialogues contain considerable useful information about a speaker's interests, preferences, and experiences.Thus, knowledge from open-domain chat dialogue can be used to personalize various systems and offer recommendations for advanced information.This study proposed a novel framework SumRec for recommending info...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	427,487
2006.00327	Probabilistic self-learning framework for Low-dose CT Denoising	Despite the indispensable role of X-ray computed tomography (CT) in diagnostic medicine field, the associated ionizing radiation is still a major concern considering that it may cause genetic and cancerous diseases. Decreasing the exposure can reduce the dose and hence the radiation-related risk, but will also induce h...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	179,436
1812.02378	Auto-Encoding Scene Graphs for Image Captioning	We propose Scene Graph Auto-Encoder (SGAE) that incorporates the language inductive bias into the encoder-decoder image captioning framework for more human-like captions. Intuitively, we humans use the inductive bias to compose collocations and contextual inference in discourse. For example, when we see the relation `p...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	115,745
2310.19580	A Perceptual Shape Loss for Monocular 3D Face Reconstruction	Monocular 3D face reconstruction is a wide-spread topic, and existing approaches tackle the problem either through fast neural network inference or offline iterative reconstruction of face geometry. In either case carefully-designed energy functions are minimized, commonly including loss terms like a photometric loss, ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	404,043
1909.13819	Unsupervised Pose Flow Learning for Pose Guided Synthesis	Pose guided synthesis aims to generate a new image in an arbitrary target pose while preserving the appearance details from the source image. Existing approaches rely on either hard-coded spatial transformations or 3D body modeling. They often overlook complex non-rigid pose deformation or unmatched occluded regions, t...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	147,525
2410.16542	A Theoretical Study of Neural Network Expressive Power via Manifold Topology	A prevalent assumption regarding real-world data is that it lies on or close to a low-dimensional manifold. When deploying a neural network on data manifolds, the required size, i.e., the number of neurons of the network, heavily depends on the intricacy of the underlying latent manifold. While significant advancements...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	501,077
2210.03042	Perception of Personality Traits in Crowds of Virtual Humans	This paper proposes a perceptual visual analysis regarding the personality of virtual humans. Many studies have presented findings regarding the way human beings perceive virtual humans with respect to their faces, body animation, motion in the virtual environment and etc. We are interested in investigating the way peo...	true	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	321,872
1604.03915	Removing Clouds and Recovering Ground Observations in Satellite Image Sequences via Temporally Contiguous Robust Matrix Completion	We consider the problem of removing and replacing clouds in satellite image sequences, which has a wide range of applications in remote sensing. Our approach first detects and removes the cloud-contaminated part of the image sequences. It then recovers the missing scenes from the clean parts using the proposed "TECROMA...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	54,577
2409.15794	Towards Universal Large-Scale Foundational Model for Natural Gas Demand Forecasting	In the context of global energy strategy, accurate natural gas demand forecasting is crucial for ensuring efficient resource allocation and operational planning. Traditional forecasting methods struggle to cope with the growing complexity and variability of gas consumption patterns across diverse industries and commerc...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	491,067
2102.00751	Learning to Combat Noisy Labels via Classification Margins	A deep neural network trained on noisy labels is known to quickly lose its power to discriminate clean instances from noisy ones. After the early learning phase has ended, the network memorizes the noisy instances, which leads to a significant degradation in its generalization performance. To resolve this issue, we pro...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	217,900
2501.03782	Vision Transformer Neural Architecture Search for Out-of-Distribution Generalization: Benchmark and Insights	While ViTs have achieved across machine learning tasks, deploying them in real-world scenarios faces a critical challenge: generalizing under OoD shifts. A crucial research gap exists in understanding how to design ViT architectures, both manually and automatically, for better OoD generalization. To this end, we introd...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	522,986
2405.20867	Automatic Channel Pruning for Multi-Head Attention	Despite the strong performance of Transformers, their quadratic computation complexity presents challenges in applying them to vision tasks. Automatic pruning is one of effective methods for reducing computation complexity without heuristic approaches. However, directly applying it to multi-head attention is not straig...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	true	459,555
1004.4610	Mobility Prediction in Wireless Ad Hoc Networks using Neural Networks	Mobility prediction allows estimating the stability of paths in a mobile wireless Ad Hoc networks. Identifying stable paths helps to improve routing by reducing the overhead and the number of connection interruptions. In this paper, we introduce a neural network based method for mobility prediction in Ad Hoc networks. ...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	6,290
1706.00120	Superhuman Accuracy on the SNEMI3D Connectomics Challenge	For the past decade, convolutional networks have been used for 3D reconstruction of neurons from electron microscopic (EM) brain images. Recent years have seen great improvements in accuracy, as evidenced by submissions to the SNEMI3D benchmark challenge. Here we report the first submission to surpass the estimate of h...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	74,566
2210.05233	Estimation of Doubly-Dispersive Channels in Linearly Precoded Multicarrier Systems Using Smoothness Regularization	In this paper, we propose a novel channel estimation scheme for pulse-shaped multicarrier systems using smoothness regularization for ultra-reliable low-latency communication (URLLC). It can be applied to any multicarrier system with or without linear precoding to estimate challenging doubly-dispersive channels. A rece...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	322,763
2310.02735	Extracting Rules from Event Data for Study Planning	In this study, we examine how event data from campus management systems can be used to analyze the study paths of higher education students. The main goal is to offer valuable guidance for their study planning. We employ process and data mining techniques to explore the impact of sequences of taken courses on academic ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	396,977
2402.13485	ProPD: Dynamic Token Tree Pruning and Generation for LLM Parallel Decoding	Recent advancements in generative large language models (LLMs) have significantly boosted the performance in natural language processing tasks. However, their efficiency is hampered by the inherent limitations in autoregressive token generation. While parallel decoding with token tree verification, e.g., Medusa, has be...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	431,266
1802.06764	Stability of meanings versus rate of replacement of words: an experimental test	The words of a language are randomly replaced in time by new ones, but it has long been known that words corresponding to some items (meanings) are less frequently replaced than others. Usually, the rate of replacement for a given item is not directly observable, but it is inferred by the estimated stability which, on ...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	90,737
1803.07710	Inference in Probabilistic Graphical Models by Graph Neural Networks	A fundamental computation for statistical inference and accurate decision-making is to compute the marginal probabilities or most probable states of task-relevant variables. Probabilistic graphical models can efficiently represent the structure of such complex data, but performing these inferences is generally difficul...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	93,108
1203.2556	A Probabilistic Transmission Expansion Planning Methodology based on Roulette Wheel Selection and Social Welfare	A new probabilistic methodology for transmission expansion planning (TEP) that does not require a priori specification of new/additional transmission capacities and uses the concept of social welfare has been proposed. Two new concepts have been introduced in this paper: (i) roulette wheel methodology has been used to ...	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	false	false	14,839
2002.08948	I-SPEC: An End-to-End Framework for Learning Transportable, Shift-Stable Models	Shifts in environment between development and deployment cause classical supervised learning to produce models that fail to generalize well to new target distributions. Recently, many solutions which find invariant predictive distributions have been developed. Among these, graph-based approaches do not require data fro...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	164,913
1705.02704	Linear Network Coding for Two-Unicast-$Z$ Networks: A Commutative Algebraic Perspective and Fundamental Limits	We consider a two-unicast-$Z$ network over a directed acyclic graph of unit capacitated edges; the two-unicast-$Z$ network is a special case of two-unicast networks where one of the destinations has apriori side information of the unwanted (interfering) message. In this paper, we settle open questions on the limits of ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	73,044
2007.13516	Hardware Implementation of Hyperbolic Tangent Function using Catmull-Rom Spline Interpolation	Deep neural networks yield the state of the art results in many computer vision and human machine interface tasks such as object recognition, speech recognition etc. Since, these networks are computationally expensive, customized accelerators are designed for achieving the required performance at lower cost and power. ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	189,154
2105.08476	Link Prediction on N-ary Relational Facts: A Graph-based Approach	Link prediction on knowledge graphs (KGs) is a key research topic. Previous work mainly focused on binary relations, paying less attention to higher-arity relations although they are ubiquitous in real-world KGs. This paper considers link prediction upon n-ary relational facts and proposes a graph-based approach to thi...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	235,767

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