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
2111.09930	Learning To Estimate Regions Of Attraction Of Autonomous Dynamical Systems Using Physics-Informed Neural Networks	When learning to perform motor tasks in a simulated environment, neural networks must be allowed to explore their action space to discover new potentially viable solutions. However, in an online learning scenario with physical hardware, this exploration must be constrained by relevant safety considerations in order to ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	267,150
2210.11113	PAC-Bayesian Learning of Optimization Algorithms	We apply the PAC-Bayes theory to the setting of learning-to-optimize. To the best of our knowledge, we present the first framework to learn optimization algorithms with provable generalization guarantees (PAC-bounds) and explicit trade-off between a high probability of convergence and a high convergence speed. Even in ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	325,187
2311.00474	Diffusion models for probabilistic programming	We propose Diffusion Model Variational Inference (DMVI), a novel method for automated approximate inference in probabilistic programming languages (PPLs). DMVI utilizes diffusion models as variational approximations to the true posterior distribution by deriving a novel bound to the marginal likelihood objective used i...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	404,668
1906.03741	BIGPATENT: A Large-Scale Dataset for Abstractive and Coherent Summarization	Most existing text summarization datasets are compiled from the news domain, where summaries have a flattened discourse structure. In such datasets, summary-worthy content often appears in the beginning of input articles. Moreover, large segments from input articles are present verbatim in their respective summaries. T...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	134,478
2210.11947	Generalizing over Long Tail Concepts for Medical Term Normalization	Medical term normalization consists in mapping a piece of text to a large number of output classes. Given the small size of the annotated datasets and the extremely long tail distribution of the concepts, it is of utmost importance to develop models that are capable to generalize to scarce or unseen concepts. An import...	false	false	false	false	true	true	false	false	true	false	false	false	false	false	false	false	false	false	325,520
2309.13457	Turbulence in Focus: Benchmarking Scaling Behavior of 3D Volumetric Super-Resolution with BLASTNet 2.0 Data	Analysis of compressible turbulent flows is essential for applications related to propulsion, energy generation, and the environment. Here, we present BLASTNet 2.0, a 2.2 TB network-of-datasets containing 744 full-domain samples from 34 high-fidelity direct numerical simulations, which addresses the current limited ava...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	394,215
2407.21553	CXSimulator: A User Behavior Simulation using LLM Embeddings for Web-Marketing Campaign Assessment	This paper presents the Customer Experience (CX) Simulator, a novel framework designed to assess the effects of untested web-marketing campaigns through user behavior simulations. The proposed framework leverages large language models (LLMs) to represent various events in a user's behavioral history, such as viewing an...	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	477,587
2304.04579	Coherent Concept-based Explanations in Medical Image and Its Application to Skin Lesion Diagnosis	Early detection of melanoma is crucial for preventing severe complications and increasing the chances of successful treatment. Existing deep learning approaches for melanoma skin lesion diagnosis are deemed black-box models, as they omit the rationale behind the model prediction, compromising the trustworthiness and ac...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	357,271
2306.03235	Information Flow Control in Machine Learning through Modular Model Architecture	In today's machine learning (ML) models, any part of the training data can affect the model output. This lack of control for information flow from training data to model output is a major obstacle in training models on sensitive data when access control only allows individual users to access a subset of data. To enable...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	371,239
2101.08490	Estimating Average Treatment Effects via Orthogonal Regularization	Decision-making often requires accurate estimation of treatment effects from observational data. This is challenging as outcomes of alternative decisions are not observed and have to be estimated. Previous methods estimate outcomes based on unconfoundedness but neglect any constraints that unconfoundedness imposes on t...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	216,338
2407.00719	A Whole-Process Certifiably Robust Aggregation Method Against Backdoor Attacks in Federated Learning	Federated Learning (FL) has garnered widespread adoption across various domains such as finance, healthcare, and cybersecurity. Nonetheless, FL remains under significant threat from backdoor attacks, wherein malicious actors insert triggers into trained models, enabling them to perform certain tasks while still meeting...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	true	468,983
1901.08572	Width Provably Matters in Optimization for Deep Linear Neural Networks	We prove that for an $L$-layer fully-connected linear neural network, if the width of every hidden layer is $\tilde\Omega (L \cdot r \cdot d_{\mathrm{out}} \cdot \kappa^3 )$, where $r$ and $\kappa$ are the rank and the condition number of the input data, and $d_{\mathrm{out}}$ is the output dimension, then gradient des...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	119,518
2405.01553	Empirical Studies of Parameter Efficient Methods for Large Language Models of Code and Knowledge Transfer to R	Parameter Efficient Fine-Tuning (PEFT) methods are proposed as an alternative fine-tuning approach for Large Language Models (LLM) to minimize high training costs. While prior research demonstrates the effectiveness of PEFT methods in knowledge transfer using smaller language models, their application to larger LLMs, p...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	451,394
1906.08804	Derivation of the Variational Bayes Equations	The derivation of key equations for the variational Bayes approach is well-known in certain circles. However, translating the fundamental derivations (e.g., as found in Beal's work) to Friston's notation is somewhat delicate. Further, the notion of using variational Bayes in the context of a system with a Markov blanke...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	135,967
1807.01425	Region Growing Curriculum Generation for Reinforcement Learning	Learning a policy capable of moving an agent between any two states in the environment is important for many robotics problems involving navigation and manipulation. Due to the sparsity of rewards in such tasks, applying reinforcement learning in these scenarios can be challenging. Common approaches for tackling this p...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	102,058
2402.05232	Universal Neural Functionals	A challenging problem in many modern machine learning tasks is to process weight-space features, i.e., to transform or extract information from the weights and gradients of a neural network. Recent works have developed promising weight-space models that are equivariant to the permutation symmetries of simple feedforwar...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	427,781
2308.11501	Four years of multi-modal odometry and mapping on the rail vehicles	Precise, seamless, and efficient train localization as well as long-term railway environment monitoring is the essential property towards reliability, availability, maintainability, and safety (RAMS) engineering for railroad systems. Simultaneous localization and mapping (SLAM) is right at the core of solving the two p...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	387,164
2302.07116	Team DETR: Guide Queries as a Professional Team in Detection Transformers	Recent proposed DETR variants have made tremendous progress in various scenarios due to their streamlined processes and remarkable performance. However, the learned queries usually explore the global context to generate the final set prediction, resulting in redundant burdens and unfaithful results. More specifically, ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	345,624
cs/0512006	Capacity-Achieving Ensembles of Accumulate-Repeat-Accumulate Codes for the Erasure Channel with Bounded Complexity	The paper introduces ensembles of accumulate-repeat-accumulate (ARA) codes which asymptotically achieve capacity on the binary erasure channel (BEC) with {\em bounded complexity}, per information bit, of encoding and decoding. It also introduces symmetry properties which play a central role in the construction of capac...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	539,119
2311.08152	Towards Reasoning in Large Language Models via Multi-Agent Peer Review Collaboration	Large Language Models (LLMs) have shown remarkable capabilities in general natural language processing tasks but often fall short in complex reasoning tasks. Recent studies have explored human-like problem-solving strategies, such as self-correct, to push further the boundary of single-model reasoning ability. In this ...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	407,611
0806.0905	Channel Capacity Limits of Cognitive Radio in Asymmetric Fading Environments	Cognitive radio technology is an innovative radio design concept which aims to increase spectrum utilization by exploiting unused spectrum in dynamically changing environments. By extending previous results, we investigate the capacity gains achievable with this dynamic spectrum approach in asymmetric fading channels. ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	1,876
1909.04518	Virtual organelle self-coding for fluorescence imaging via adversarial learning	Fluorescence microscopy plays a vital role in understanding the subcellular structures of living cells. However, it requires considerable effort in sample preparation related to chemical fixation, staining, cost, and time. To reduce those factors, we present a virtual fluorescence staining method based on deep neural n...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	144,827
2305.15793	Feature space reduction method for ultrahigh-dimensional, multiclass data: Random forest-based multiround screening (RFMS)	In recent years, numerous screening methods have been published for ultrahigh-dimensional data that contain hundreds of thousands of features; however, most of these features cannot handle data with thousands of classes. Prediction models built to authenticate users based on multichannel biometric data result in this t...	false	true	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	367,787
2407.09032	DRM Revisited: A Complete Error Analysis	In this work, we address a foundational question in the theoretical analysis of the Deep Ritz Method (DRM) under the over-parameteriztion regime: Given a target precision level, how can one determine the appropriate number of training samples, the key architectural parameters of the neural networks, the step size for t...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	472,423
2107.05176	Zero-Shot Compositional Concept Learning	In this paper, we study the problem of recognizing compositional attribute-object concepts within the zero-shot learning (ZSL) framework. We propose an episode-based cross-attention (EpiCA) network which combines merits of cross-attention mechanism and episode-based training strategy to recognize novel compositional co...	false	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	245,695
1807.06214	Knockoffs for the mass: new feature importance statistics with false discovery guarantees	An important problem in machine learning and statistics is to identify features that causally affect the outcome. This is often impossible to do from purely observational data, and a natural relaxation is to identify features that are correlated with the outcome even conditioned on all other observed features. For exam...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	103,083
2311.05903	Establishing Performance Baselines in Fine-Tuning, Retrieval-Augmented Generation and Soft-Prompting for Non-Specialist LLM Users	Research into methods for improving the performance of large language models (LLMs) through fine-tuning, retrieval-augmented generation (RAG) and soft-prompting has tended to focus on the use of highly technical or high-cost techniques, making many of the newly discovered approaches comparatively inaccessible to non-te...	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	false	false	406,750
2209.04562	Bayan Algorithm: Detecting Communities in Networks Through Exact and Approximate Optimization of Modularity	Community detection is a classic network problem with extensive applications in various fields. Its most common method is using modularity maximization heuristics which rarely return an optimal partition or anything similar. Partitions with globally optimal modularity are difficult to compute, and therefore have been u...	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	false	true	316,813
2203.04961	Sharing Generative Models Instead of Private Data: A Simulation Study on Mammography Patch Classification	Early detection of breast cancer in mammography screening via deep-learning based computer-aided detection systems shows promising potential in improving the curability and mortality rates of breast cancer. However, many clinical centres are restricted in the amount and heterogeneity of available data to train such mod...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	284,666
2209.02062	"Dummy Grandpa, do you know anything?": Identifying and Characterizing Ad hominem Fallacy Usage in the Wild	Today, participating in discussions on online forums is extremely commonplace and these discussions have started rendering a strong influence on the overall opinion of online users. Naturally, twisting the flow of the argument can have a strong impact on the minds of naive users, which in the long run might have socio-...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	316,091
2202.11217	Differentiable and Learnable Robot Models	Building differentiable simulations of physical processes has recently received an increasing amount of attention. Specifically, some efforts develop differentiable robotic physics engines motivated by the computational benefits of merging rigid body simulations with modern differentiable machine learning libraries. He...	false	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	281,807
1810.06305	Hyperparameter Learning via Distributional Transfer	Bayesian optimisation is a popular technique for hyperparameter learning but typically requires initial exploration even in cases where similar prior tasks have been solved. We propose to transfer information across tasks using learnt representations of training datasets used in those tasks. This results in a joint Gau...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	110,413
2110.05523	UnfairGAN: An Enhanced Generative Adversarial Network for Raindrop Removal from A Single Image	Image deraining is a new challenging problem in real-world applications, such as autonomous vehicles. In a bad weather condition of heavy rainfall, raindrops, mainly hitting glasses or windshields, can significantly reduce observation ability. Moreover, raindrops spreading over the glass can yield refraction's physical...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	260,299
2009.07632	Helping Users Tackle Algorithmic Threats on Social Media: A Multimedia Research Agenda	Participation on social media platforms has many benefits but also poses substantial threats. Users often face an unintended loss of privacy, are bombarded with mis-/disinformation, or are trapped in filter bubbles due to over-personalized content. These threats are further exacerbated by the rise of hidden AI-driven a...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	true	195,999
1809.03734	How much should you ask? On the question structure in QA systems	Datasets that boosted state-of-the-art solutions for Question Answering (QA) systems prove that it is possible to ask questions in natural language manner. However, users are still used to query-like systems where they type in keywords to search for answer. In this study we validate which parts of questions are essenti...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	107,397
2402.07632	Overconfident and Unconfident AI Hinder Human-AI Collaboration	AI transparency is a central pillar of responsible AI deployment and effective human-AI collaboration. A critical approach is communicating uncertainty, such as displaying AI's confidence level, or its correctness likelihood (CL), to users. However, these confidence levels are often uncalibrated, either overestimating ...	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	428,790
2309.09658	A Novel Method of Fuzzy Topic Modeling based on Transformer Processing	Topic modeling is admittedly a convenient way to monitor markets trend. Conventionally, Latent Dirichlet Allocation, LDA, is considered a must-do model to gain this type of information. By given the merit of deducing keyword with token conditional probability in LDA, we can know the most possible or essential topic. Ho...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	392,693
2306.00091	A General Framework for Equivariant Neural Networks on Reductive Lie Groups	Reductive Lie Groups, such as the orthogonal groups, the Lorentz group, or the unitary groups, play essential roles across scientific fields as diverse as high energy physics, quantum mechanics, quantum chromodynamics, molecular dynamics, computer vision, and imaging. In this paper, we present a general Equivariant Neu...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	369,863
2212.03282	MobilePTX: Sparse Coding for Pneumothorax Detection Given Limited Training Examples	Point-of-Care Ultrasound (POCUS) refers to clinician-performed and interpreted ultrasonography at the patient's bedside. Interpreting these images requires a high level of expertise, which may not be available during emergencies. In this paper, we support POCUS by developing classifiers that can aid medical professiona...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	335,057
2103.04957	Learning to Represent and Predict Sets with Deep Neural Networks	In this thesis, we develop various techniques for working with sets in machine learning. Each input or output is not an image or a sequence, but a set: an unordered collection of multiple objects, each object described by a feature vector. Their unordered nature makes them suitable for modeling a wide variety of data, ...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	223,816
1601.06023	Gaussian Approximation for the Downlink Interference in Heterogeneous Cellular Networks	This paper derives Gaussian approximation bounds for the standardized aggregate wireless interference (AWI) in the downlink of K-tier heterogeneous cellular networks when base stations in each tier are distributed over the plane according to a (possibly non-homogeneous) Poisson process. The proposed methodology is gene...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	51,199
2404.14591	Predicting the Temporal Dynamics of Prosthetic Vision	Retinal implants are a promising treatment option for degenerative retinal disease. While numerous models have been developed to simulate the appearance of elicited visual percepts ("phosphenes"), these models often either focus solely on spatial characteristics or inadequately capture the complex temporal dynamics obs...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	448,734
1812.07546	Supervised Domain Enablement Attention for Personalized Domain Classification	In large-scale domain classification for natural language understanding, leveraging each user's domain enablement information, which refers to the preferred or authenticated domains by the user, with attention mechanism has been shown to improve the overall domain classification performance. In this paper, we propose a...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	116,834
2102.06336	Dancing along Battery: Enabling Transformer with Run-time Reconfigurability on Mobile Devices	A pruning-based AutoML framework for run-time reconfigurability, namely RT3, is proposed in this work. This enables Transformer-based large Natural Language Processing (NLP) models to be efficiently executed on resource-constrained mobile devices and reconfigured (i.e., switching models for dynamic hardware conditions)...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	219,717
2306.15619	DCID: Deep Canonical Information Decomposition	We consider the problem of identifying the signal shared between two one-dimensional target variables, in the presence of additional multivariate observations. Canonical Correlation Analysis (CCA)-based methods have traditionally been used to identify shared variables, however, they were designed for multivariate targe...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	376,080
2204.10743	An Evaluation of Intra-Transaction Parallelism in Actor-Relational Database Systems	Over the past decade, we have witnessed a dramatic evolution in main-memory capacity and multi-core parallelism of server hardware. To leverage this hardware potential, multi-core in-memory OLTP database systems have been extensively re-designed. The core objective of this re-design has been scaling up sequential execu...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	292,897
2204.04991	TRUE: Re-evaluating Factual Consistency Evaluation	Grounded text generation systems often generate text that contains factual inconsistencies, hindering their real-world applicability. Automatic factual consistency evaluation may help alleviate this limitation by accelerating evaluation cycles, filtering inconsistent outputs and augmenting training data. While attracti...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	290,873
2310.09916	Socially reactive navigation models for mobile robots in dynamic environments	The objective of this work is to expand upon previous works, considering socially acceptable behaviours within robot navigation and interaction, and allow a robot to closely approach static and dynamic individuals or groups. The space models developed in this dissertation are adaptive, that is, capable of changing over...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	400,004
2410.10054	AlphaLoRA: Assigning LoRA Experts Based on Layer Training Quality	Parameter-efficient fine-tuning methods, such as Low-Rank Adaptation (LoRA), are known to enhance training efficiency in Large Language Models (LLMs). Due to the limited parameters of LoRA, recent studies seek to combine LoRA with Mixture-of-Experts (MoE) to boost performance across various tasks. However, inspired by ...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	497,893
1302.7314	Torque Saturation in Bipedal Robotic Walking through Control Lyapunov Function Based Quadratic Programs	This paper presents a novel method for directly incorporating user-defined control input saturations into the calculation of a control Lyapunov function (CLF)-based walking controller for a biped robot. Previous work by the authors has demonstrated the effectiveness of CLF controllers for stabilizing periodic gaits for...	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	22,519
2402.15784	IRConStyle: Image Restoration Framework Using Contrastive Learning and Style Transfer	Recently, the contrastive learning paradigm has achieved remarkable success in high-level tasks such as classification, detection, and segmentation. However, contrastive learning applied in low-level tasks, like image restoration, is limited, and its effectiveness is uncertain. This raises a question: Why does the cont...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	432,288
2411.02594	"It's a conversation, not a quiz": A Risk Taxonomy and Reflection Tool for LLM Adoption in Public Health	Recent breakthroughs in large language models (LLMs) have generated both interest and concern about their potential adoption as accessible information sources or communication tools across different domains. In public health -- where stakes are high and impacts extend across populations -- adopting LLMs poses unique ch...	true	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	505,570
2107.04891	Inference of Shape Expression Schemas Typed RDF Graphs	We consider the problem of constructing a Shape Expression Schema (ShEx) that describes the structure of a given input RDF graph. We employ the framework of grammatical inference, where the objective is to find an inference algorithm that is both sound i.e., always producing a schema that validates the input RDF graph,...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	245,593
1903.05157	Simple Physical Adversarial Examples against End-to-End Autonomous Driving Models	Recent advances in machine learning, especially techniques such as deep neural networks, are promoting a range of high-stakes applications, including autonomous driving, which often relies on deep learning for perception. While deep learning for perception has been shown to be vulnerable to a host of subtle adversarial...	false	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	124,104
2211.07350	Does Debiasing Inevitably Degrade the Model Performance	Gender bias in language models has attracted sufficient attention because it threatens social justice. However, most of the current debiasing methods degraded the model's performance on other tasks while the degradation mechanism is still mysterious. We propose a theoretical framework explaining the three candidate mec...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	330,208
1501.06166	HARQ Buffer Management: An Information-Theoretic View	A key practical constraint on the design of Hybrid automatic repeat request (HARQ) schemes is the size of the on-chip buffer that is available at the receiver to store previously received packets. In fact, in modern wireless standards such as LTE and LTE-A, the HARQ buffer size is one of the main drivers of the modem a...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	39,579
2109.01120	Automatic Diagnosis of Schizophrenia in EEG Signals Using CNN-LSTM Models	Schizophrenia (SZ) is a mental disorder whereby due to the secretion of specific chemicals in the brain, the function of some brain regions is out of balance, leading to the lack of coordination between thoughts, actions, and emotions. This study provides various intelligent deep learning (DL)-based methods for automat...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	253,344
2108.11368	CDCGen: Cross-Domain Conditional Generation via Normalizing Flows and Adversarial Training	How to generate conditional synthetic data for a domain without utilizing information about its labels/attributes? Our work presents a solution to the above question. We propose a transfer learning-based framework utilizing normalizing flows, coupled with both maximum-likelihood and adversarial training. We model a sou...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	252,166
1803.11285	Revisiting Oxford and Paris: Large-Scale Image Retrieval Benchmarking	In this paper we address issues with image retrieval benchmarking on standard and popular Oxford 5k and Paris 6k datasets. In particular, annotation errors, the size of the dataset, and the level of challenge are addressed: new annotation for both datasets is created with an extra attention to the reliability of the gr...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	93,860
2410.06114	UnSeGArmaNet: Unsupervised Image Segmentation using Graph Neural Networks with Convolutional ARMA Filters	The data-hungry approach of supervised classification drives the interest of the researchers toward unsupervised approaches, especially for problems such as medical image segmentation, where labeled data are difficult to get. Motivated by the recent success of Vision transformers (ViT) in various computer vision tasks,...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	496,054
2403.02698	Causal Walk: Debiasing Multi-Hop Fact Verification with Front-Door Adjustment	Conventional multi-hop fact verification models are prone to rely on spurious correlations from the annotation artifacts, leading to an obvious performance decline on unbiased datasets. Among the various debiasing works, the causal inference-based methods become popular by performing theoretically guaranteed debiasing ...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	434,905
2407.05489	How Effective are State Space Models for Machine Translation?	Transformers are the current architecture of choice for NLP, but their attention layers do not scale well to long contexts. Recent works propose to replace attention with linear recurrent layers -- this is the case for state space models, which enjoy efficient training and inference. However, it remains unclear whether...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	471,000
2101.01601	Bilateral Grid Learning for Stereo Matching Networks	Real-time performance of stereo matching networks is important for many applications, such as automatic driving, robot navigation and augmented reality (AR). Although significant progress has been made in stereo matching networks in recent years, it is still challenging to balance real-time performance and accuracy. In...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	214,404
2012.00493	Problems of representation of electrocardiograms in convolutional neural networks	Using electrocardiograms as an example, we demonstrate the characteristic problems that arise when modeling one-dimensional signals containing inaccurate repeating pattern by means of standard convolutional networks. We show that these problems are systemic in nature. They are due to how convolutional networks work wit...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	209,158
1804.03288	Enabling a Pepper Robot to provide Automated and Interactive Tours of a Robotics Laboratory	The Pepper robot has become a widely recognised face for the perceived potential of social robots to enter our homes and businesses. However, to date, commercial and research applications of the Pepper have been largely restricted to roles in which the robot is able to remain stationary. This restriction is the result ...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	94,593
2410.04929	Goal-Conditioned Terminal Value Estimation for Real-time and Multi-task Model Predictive Control	While MPC enables nonlinear feedback control by solving an optimal control problem at each timestep, the computational burden tends to be significantly large, making it difficult to optimize a policy within the control period. To address this issue, one possible approach is to utilize terminal value learning to reduce ...	false	false	false	false	false	false	true	true	false	false	true	false	false	false	false	false	false	false	495,499
2001.11535	SGP-DT: Semantic Genetic Programming Based on Dynamic Targets	Semantic GP is a promising approach that introduces semantic awareness during genetic evolution. This paper presents a new Semantic GP approach based on Dynamic Target (SGP-DT) that divides the search problem into multiple GP runs. The evolution in each run is guided by a new (dynamic) target based on the residual erro...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	162,094
2010.04412	Fair and Representative Subset Selection from Data Streams	We study the problem of extracting a small subset of representative items from a large data stream. In many data mining and machine learning applications such as social network analysis and recommender systems, this problem can be formulated as maximizing a monotone submodular function subject to a cardinality constrai...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	199,732
2502.12442	HopRAG: Multi-Hop Reasoning for Logic-Aware Retrieval-Augmented Generation	Retrieval-Augmented Generation (RAG) systems often struggle with imperfect retrieval, as traditional retrievers focus on lexical or semantic similarity rather than logical relevance. To address this, we propose HopRAG, a novel RAG framework that augments retrieval with logical reasoning through graph-structured knowled...	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	534,864
1705.05424	Attack Detection in Sensor Network Target Localization Systems with Quantized Data	We consider a sensor network focused on target localization, where sensors measure the signal strength emitted from the target. Each measurement is quantized to one bit and sent to the fusion center. A general attack is considered at some sensors that attempts to cause the fusion center to produce an inaccurate estimat...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	73,485
1708.04343	Spectral Methods for Passive Imaging: Non-asymptotic Performance and Robustness	We study the problem of passive imaging through convolutive channels. A scene is illuminated with an unknown, unstructured source, and the measured response is the convolution of this source with multiple channel responses, each of which is time-limited. Spectral methods based on the commutativity of convolution, first...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	78,924
2302.10870	On Provable Copyright Protection for Generative Models	There is a growing concern that learned conditional generative models may output samples that are substantially similar to some copyrighted data $C$ that was in their training set. We give a formal definition of $\textit{near access-freeness (NAF)}$ and prove bounds on the probability that a model satisfying this defin...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	346,984
1902.06992	Stochastic Conditional Gradient++	In this paper, we consider the general non-oblivious stochastic optimization where the underlying stochasticity may change during the optimization procedure and depends on the point at which the function is evaluated. We develop Stochastic Frank-Wolfe++ ($\text{SFW}{++} $), an efficient variant of the conditional gradi...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	121,892
1811.06060	Hybrid Generative-Discriminative Models for Inverse Materials Design	Discovering new physical products and processes often demands enormous experimentation and expensive simulation. To design a new product with certain target characteristics, an extensive search is performed in the design space by trying out a large number of design combinations before reaching to the target characteris...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	113,441
2204.04968	Bimodal Camera Pose Prediction for Endoscopy	Deducing the 3D structure of endoscopic scenes from images is exceedingly challenging. In addition to deformation and view-dependent lighting, tubular structures like the colon present problems stemming from their self-occluding and repetitive anatomical structure. In this paper, we propose SimCol, a synthetic dataset ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	290,863
2311.15994	Adversarial Doodles: Interpretable and Human-drawable Attacks Provide Describable Insights	DNN-based image classifiers are susceptible to adversarial attacks. Most previous adversarial attacks do not have clear patterns, making it difficult to interpret attacks' results and gain insights into classifiers' mechanisms. Therefore, we propose Adversarial Doodles, which have interpretable shapes. We optimize blac...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	410,716
2304.01636	Label-guided Attention Distillation for Lane Segmentation	Contemporary segmentation methods are usually based on deep fully convolutional networks (FCNs). However, the layer-by-layer convolutions with a growing receptive field is not good at capturing long-range contexts such as lane markers in the scene. In this paper, we address this issue by designing a distillation method...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	356,158
2309.12729	Coordinated Information Campaigns on Social Media: A Multifaceted Framework for Detection and Analysis	The prevalence of coordinated information campaigns in social media platforms has significant negative consequences across various domains, including social, political, and economic processes. This paper proposes a multifaceted framework for detecting and analysing coordinated message promotion on social media. By simu...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	393,915
2212.07761	Successive Interference Cancellation for Bandlimited Channels with Direct Detection	The maximum information rates for bandlimited channels with direct detection are achieved with joint detection and decoding (JDD), but JDD is often too complex to implement. Two receiver structures are studied to reduce complexity: separate detection and decoding (SDD) and successive interference cancellation (SIC). Fo...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	336,512
1702.01783	From Formalised State Machines to Implementations of Robotic Controllers	Controllers for autonomous robotic systems can be specified using state machines. However, these are typically developed in an ad hoc manner without formal semantics, which makes it difficult to analyse the controller. Simulations are often used during the development, but a rigorous connection between the designed con...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	true	67,865
2404.00727	A Controlled Reevaluation of Coreference Resolution Models	All state-of-the-art coreference resolution (CR) models involve finetuning a pretrained language model. Whether the superior performance of one CR model over another is due to the choice of language model or other factors, such as the task-specific architecture, is difficult or impossible to determine due to lack of a ...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	443,082
2402.09066	Solid Waste Detection, Monitoring and Mapping in Remote Sensing Images: A Survey	The detection and characterization of illegal solid waste disposal sites are essential for environmental protection, particularly for mitigating pollution and health hazards. Improperly managed landfills contaminate soil and groundwater via rainwater infiltration, posing threats to both animals and humans. Traditional ...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	429,353
cmp-lg/9510003	A Proposal for Word Sense Disambiguation using Conceptual Distance	This paper presents a method for the resolution of lexical ambiguity and its automatic evaluation over the Brown Corpus. The method relies on the use of the wide-coverage noun taxonomy of WordNet and the notion of conceptual distance among concepts, captured by a Conceptual Density formula developed for this purpose. T...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	536,464
1306.0710	On the Optimum Cyclic Subcode Chains of $\mathcal{RM}(2,m)^*$ for Increasing Message Length	The distance profiles of linear block codes can be employed to design variational coding scheme for encoding message with variational length and getting lower decoding error probability by large minimum Hamming distance. %, e.g. the design of TFCI in CDMA and the researches on the second-order Reed-Muller code $\mathca...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	24,982
2207.13744	Lighting (In)consistency of Paint by Text	Whereas generative adversarial networks are capable of synthesizing highly realistic images of faces, cats, landscapes, or almost any other single category, paint-by-text synthesis engines can -- from a single text prompt -- synthesize realistic images of seemingly endless categories with arbitrary configurations and c...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	true	310,388
2202.07857	Graph-Augmented Normalizing Flows for Anomaly Detection of Multiple Time Series	Anomaly detection is a widely studied task for a broad variety of data types; among them, multiple time series appear frequently in applications, including for example, power grids and traffic networks. Detecting anomalies for multiple time series, however, is a challenging subject, owing to the intricate interdependen...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	280,685
2404.07837	Data-Driven System Identification of Quadrotors Subject to Motor Delays	Recently non-linear control methods like Model Predictive Control (MPC) and Reinforcement Learning (RL) have attracted increased interest in the quadrotor control community. In contrast to classic control methods like cascaded PID controllers, MPC and RL heavily rely on an accurate model of the system dynamics. The pro...	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	445,993
2309.01360	Random Projections of Sparse Adjacency Matrices	We analyze a random projection method for adjacency matrices, studying its utility in representing sparse graphs. We show that these random projections retain the functionality of their underlying adjacency matrices while having extra properties that make them attractive as dynamic graph representations. In particular,...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	389,656
2301.08815	DiffusionCT: Latent Diffusion Model for CT Image Standardization	Computed tomography (CT) is one of the modalities for effective lung cancer screening, diagnosis, treatment, and prognosis. The features extracted from CT images are now used to quantify spatial and temporal variations in tumors. However, CT images obtained from various scanners with customized acquisition protocols ma...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	341,298
2502.14522	Investigating the Generalizability of ECG Noise Detection Across Diverse Data Sources and Noise Types	Electrocardiograms (ECGs) are essential for monitoring cardiac health, allowing clinicians to analyze heart rate variability (HRV), detect abnormal rhythms, and diagnose cardiovascular diseases. However, ECG signals, especially those from wearable devices, are often affected by noise artifacts caused by motion, muscle ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	535,867
2305.11489	Incomplete Multi-view Clustering via Diffusion Completion	Incomplete multi-view clustering is a challenging and non-trivial task to provide effective data analysis for large amounts of unlabeled data in the real world. All incomplete multi-view clustering methods need to address the problem of how to reduce the impact of missing views. To address this issue, we propose diffus...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	365,561
2409.01205	Geometric Scaling Laws for Axial Flux Permanent Magnet Motors in In-Wheel Powertrain Topologies	In this paper, we present geometric scaling models for axial flux motors (AFMs) to be used for in-wheel powertrain design optimization purposes. We first present a vehicle and powertrain model, with emphasis on the electric motor model. We construct the latter by formulating the analytical scaling laws for AFMs, based ...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	485,254
2007.06426	Multitask Non-Autoregressive Model for Human Motion Prediction	Human motion prediction, which aims at predicting future human skeletons given the past ones, is a typical sequence-to-sequence problem. Therefore, extensive efforts have been continued on exploring different RNN-based encoder-decoder architectures. However, by generating target poses conditioned on the previously gene...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	187,013
1602.02524	Mean and variance of the LQG cost function	Linear Quadratic Gaussian (LQG) systems are well-understood and methods to minimize the expected cost are readily available. Less is known about the statistical properties of the resulting cost function. The contribution of this paper is a set of analytic expressions for the mean and variance of the LQG cost function. ...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	51,870
2407.03704	Neural Probabilistic Logic Learning for Knowledge Graph Reasoning	Knowledge graph (KG) reasoning is a task that aims to predict unknown facts based on known factual samples. Reasoning methods can be divided into two categories: rule-based methods and KG-embedding based methods. The former possesses precise reasoning capabilities but finds it challenging to reason efficiently over lar...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	470,260
2109.13101	Half a Dozen Real-World Applications of Evolutionary Multitasking, and More	Until recently, the potential to transfer evolved skills across distinct optimization problem instances (or tasks) was seldom explored in evolutionary computation. The concept of evolutionary multitasking (EMT) fills this gap. It unlocks a population's implicit parallelism to jointly solve a set of tasks, hence creatin...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	false	true	257,527
2305.19943	Central limit theorem for the overlaps on the Nishimori line	The overlap distribution of the Sherrington-Kirkpatrick model on the Nishimori line has been proved to be self averaging for large volumes. Here we study the joint distribution of the rescaled overlaps around their common mean and prove that it converges to a Gaussian vector.	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	369,747
2401.09944	WindSeer: Real-time volumetric wind prediction over complex terrain aboard a small UAV	Real-time high-resolution wind predictions are beneficial for various applications including safe manned and unmanned aviation. Current weather models require too much compute and lack the necessary predictive capabilities as they are valid only at the scale of multiple kilometers and hours - much lower spatial and tem...	false	false	false	false	true	false	true	true	false	false	false	false	false	false	false	false	false	false	422,434
2210.03264	Unsupervised Neural Stylistic Text Generation using Transfer learning and Adapters	Research has shown that personality is a key driver to improve engagement and user experience in conversational systems. Conversational agents should also maintain a consistent persona to have an engaging conversation with a user. However, text generation datasets are often crowd sourced and thereby have an averaging e...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	321,958
2307.11289	PI-VEGAN: Physics Informed Variational Embedding Generative Adversarial Networks for Stochastic Differential Equations	We present a new category of physics-informed neural networks called physics informed variational embedding generative adversarial network (PI-VEGAN), that effectively tackles the forward, inverse, and mixed problems of stochastic differential equations. In these scenarios, the governing equations are known, but only a...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	380,850

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