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
1808.04803	Hierarchical binary CNNs for landmark localization with limited resources	Our goal is to design architectures that retain the groundbreaking performance of Convolutional Neural Networks (CNNs) for landmark localization and at the same time are lightweight, compact and suitable for applications with limited computational resources. To this end, we make the following contributions: (a) we are ...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	105,235
2411.16079	Debiasing Classifiers by Amplifying Bias with Latent Diffusion and Large Language Models	Neural networks struggle with image classification when biases are learned and misleads correlations, affecting their generalization and performance. Previous methods require attribute labels (e.g. background, color) or utilizes Generative Adversarial Networks (GANs) to mitigate biases. We introduce DiffuBias, a novel ...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	510,879
2107.03840	Molecular Communication with Passive Receivers in Anomalous Diffusion Channels	We consider anomalous diffusion for molecular communication with a passive receiver. We first consider the probability density function of molecules' location at a given time in a space of arbitrary dimension. The expected number of observed molecules inside a receptor space of the receiver at certain time is derived t...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	245,267
2103.02781	Structure-Preserving Progressive Low-rank Image Completion for Defending Adversarial Attacks	Deep neural networks recognize objects by analyzing local image details and summarizing their information along the inference layers to derive the final decision. Because of this, they are prone to adversarial attacks. Small sophisticated noise in the input images can accumulate along the network inference path and pro...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	223,063
1904.03971	Jointly Measuring Diversity and Quality in Text Generation Models	Text generation is an important Natural Language Processing task with various applications. Although several metrics have already been introduced to evaluate the text generation methods, each of them has its own shortcomings. The most widely used metrics such as BLEU only consider the quality of generated sentences and...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	126,898
2412.02197	Cascaded Multi-Scale Attention for Enhanced Multi-Scale Feature Extraction and Interaction with Low-Resolution Images	In real-world applications of image recognition tasks, such as human pose estimation, cameras often capture objects, like human bodies, at low resolutions. This scenario poses a challenge in extracting and leveraging multi-scale features, which is often essential for precise inference. To address this challenge, we pro...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	513,430
2204.06508	FactGraph: Evaluating Factuality in Summarization with Semantic Graph Representations	Despite recent improvements in abstractive summarization, most current approaches generate summaries that are not factually consistent with the source document, severely restricting their trust and usage in real-world applications. Recent works have shown promising improvements in factuality error identification using ...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	291,361
2002.00864	Optimal Iterative Sketching with the Subsampled Randomized Hadamard Transform	Random projections or sketching are widely used in many algorithmic and learning contexts. Here we study the performance of iterative Hessian sketch for least-squares problems. By leveraging and extending recent results from random matrix theory on the limiting spectrum of matrices randomly projected with the subsample...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	162,505
1502.05599	Spread of Influence in Weighted Networks under Time and Budget Constraints	Given a network represented by a weighted directed graph G, we consider the problem of finding a bounded cost set of nodes S such that the influence spreading from S in G, within a given time bound, is as large as possible. The dynamic that governs the spread of influence is the following: initially only elements in S ...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	true	40,386
1504.02526	Learning Arbitrary Statistical Mixtures of Discrete Distributions	We study the problem of learning from unlabeled samples very general statistical mixture models on large finite sets. Specifically, the model to be learned, $\vartheta$, is a probability distribution over probability distributions $p$, where each such $p$ is a probability distribution over $[n] = \{1,2,\dots,n\}$. When...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	41,935
1912.09522	Event Outlier Detection in Continuous Time	Continuous-time event sequences represent discrete events occurring in continuous time. Such sequences arise frequently in real-life. Usually we expect the sequences to follow some regular pattern over time. However, sometimes these patterns may be interrupted by unexpected absence or occurrences of events. Identificat...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	158,085
2309.08009	Measuring the Quality of Text-to-Video Model Outputs: Metrics and Dataset	Evaluating the quality of videos generated from text-to-video (T2V) models is important if they are to produce plausible outputs that convince a viewer of their authenticity. We examine some of the metrics used in this area and highlight their limitations. The paper presents a dataset of more than 1,000 generated video...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	391,998
2401.04854	Are Language Models More Like Libraries or Like Librarians? Bibliotechnism, the Novel Reference Problem, and the Attitudes of LLMs	Are LLMs cultural technologies like photocopiers or printing presses, which transmit information but cannot create new content? A challenge for this idea, which we call bibliotechnism, is that LLMs generate novel text. We begin with a defense of bibliotechnism, showing how even novel text may inherit its meaning from o...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	420,560
2403.10105	Belief Aided Navigation using Bayesian Reinforcement Learning for Avoiding Humans in Blind Spots	Recent research on mobile robot navigation has focused on socially aware navigation in crowded environments. However, existing methods do not adequately account for human robot interactions and demand accurate location information from omnidirectional sensors, rendering them unsuitable for practical applications. In re...	false	false	false	false	true	false	true	true	false	false	false	false	false	false	false	false	false	false	438,059
2209.01258	Object-based active inference	The world consists of objects: distinct entities possessing independent properties and dynamics. For agents to interact with the world intelligently, they must translate sensory inputs into the bound-together features that describe each object. These object-based representations form a natural basis for planning behavi...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	315,818
2201.00985	Variational Stacked Local Attention Networks for Diverse Video Captioning	While describing Spatio-temporal events in natural language, video captioning models mostly rely on the encoder's latent visual representation. Recent progress on the encoder-decoder model attends encoder features mainly in linear interaction with the decoder. However, growing model complexity for visual data encourage...	false	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	274,116
2212.00564	Leveraging Single-View Images for Unsupervised 3D Point Cloud Completion	Point clouds captured by scanning devices are often incomplete due to occlusion. To overcome this limitation, point cloud completion methods have been developed to predict the complete shape of an object based on its partial input. These methods can be broadly classified as supervised or unsupervised. However, both cat...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	334,102
1408.4692	Seeing through bag-of-visual-word glasses: towards understanding quantization effects in feature extraction methods	Vector-quantized local features frequently used in bag-of-visual-words approaches are the backbone of popular visual recognition systems due to both their simplicity and their performance. Despite their success, bag-of-words-histograms basically contain low-level image statistics (e.g., number of edges of different ori...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	35,480
2312.15843	A New Framework for Bounding Reachability Probabilities of Continuous-time Stochastic Systems	This manuscript presents an innovative framework for constructing barrier functions to bound reachability probabilities for continuous-time stochastic systems described by stochastic differential equations (SDEs). The reachability probabilities considered in this paper encompass two aspects: the probability of reaching...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	418,154
1904.01150	Thickened 2D Networks for Efficient 3D Medical Image Segmentation	There has been a debate in 3D medical image segmentation on whether to use 2D or 3D networks, where both pipelines have advantages and disadvantages. 2D methods enjoy a low inference time and greater transfer-ability while 3D methods are superior in performance for hard targets requiring contextual information. This pa...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	126,066
2204.00734	SkeleVision: Towards Adversarial Resiliency of Person Tracking with Multi-Task Learning	Person tracking using computer vision techniques has wide ranging applications such as autonomous driving, home security and sports analytics. However, the growing threat of adversarial attacks raises serious concerns regarding the security and reliability of such techniques. In this work, we study the impact of multi-...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	289,363
2305.06299	Summarizing, Simplifying, and Synthesizing Medical Evidence Using GPT-3 (with Varying Success)	Large language models, particularly GPT-3, are able to produce high quality summaries of general domain news articles in few- and zero-shot settings. However, it is unclear if such models are similarly capable in more specialized, high-stakes domains such as biomedicine. In this paper, we enlist domain experts (individ...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	363,481
2402.08812	Intelligent Canvas: Enabling Design-Like Exploratory Visual Data Analysis with Generative AI through Rapid Prototyping, Iteration and Curation	Complex data analysis inherently seeks unexpected insights through exploratory visual analysis methods, transcending logical, step-by-step processing. However, existing interfaces such as notebooks and dashboards have limitations in exploration and comparison for visual data analysis. Addressing these limitations, we i...	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	429,248
2306.16819	Graph Denoising Diffusion for Inverse Protein Folding	Inverse protein folding is challenging due to its inherent one-to-many mapping characteristic, where numerous possible amino acid sequences can fold into a single, identical protein backbone. This task involves not only identifying viable sequences but also representing the sheer diversity of potential solutions. Howev...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	376,497
2409.12507	Towards Low-latency Event-based Visual Recognition with Hybrid Step-wise Distillation Spiking Neural Networks	Spiking neural networks (SNNs) have garnered significant attention for their low power consumption and high biological interpretability. Their rich spatio-temporal information processing capability and event-driven nature make them ideally well-suited for neuromorphic datasets. However, current SNNs struggle to balance...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	489,622
2109.03699	Sample and Communication-Efficient Decentralized Actor-Critic Algorithms with Finite-Time Analysis	Actor-critic (AC) algorithms have been widely adopted in decentralized multi-agent systems to learn the optimal joint control policy. However, existing decentralized AC algorithms either do not preserve the privacy of agents or are not sample and communication-efficient. In this work, we develop two decentralized AC an...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	254,143
2502.06280	IceBerg: Debiased Self-Training for Class-Imbalanced Node Classification	Graph Neural Networks (GNNs) have achieved great success in dealing with non-Euclidean graph-structured data and have been widely deployed in many real-world applications. However, their effectiveness is often jeopardized under class-imbalanced training sets. Most existing studies have analyzed class-imbalanced node cl...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	532,015
2105.09866	Deep learning in physics: a study of dielectric quasi-cubic particles in a uniform electric field	Solving physics problems for which we know the equations, boundary conditions and symmetries can be done by deep learning. The constraints can be either imposed as terms in a loss function or used to formulate a neural ansatz. In the present case study, we calculate the induced field inside and outside a dielectric cub...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	236,188
2003.00045	Library Adoption Dynamics in Software Teams	When a group of people strives to understand new information, struggle ensues as various ideas compete for attention. Steep learning curves are surmounted as teams learn together. To understand how these team dynamics play out in software development, we explore Git logs, which provide a complete change history of soft...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	166,178
2401.02203	Robust bilinear factor analysis based on the matrix-variate $t$ distribution	Factor Analysis based on multivariate $t$ distribution ($t$fa) is a useful robust tool for extracting common factors on heavy-tailed or contaminated data. However, $t$fa is only applicable to vector data. When $t$fa is applied to matrix data, it is common to first vectorize the matrix observations. This introduces two ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	419,638
2202.08557	CADRE: A Cascade Deep Reinforcement Learning Framework for Vision-based Autonomous Urban Driving	Vision-based autonomous urban driving in dense traffic is quite challenging due to the complicated urban environment and the dynamics of the driving behaviors. Widely-applied methods either heavily rely on hand-crafted rules or learn from limited human experience, which makes them hard to generalize to rare but critica...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	280,929
0909.4592	Autocorrelation-Run Formula for Binary Sequences	The autocorrelation function and the run structure are two basic notions for binary sequences, and have been used as two independent postulates to test randomness of binary sequences ever since Golomb 1955. In this paper, we prove for binary sequence that the autocorrelation function is in fact completely determined by...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	4,568
2007.06290	Paranoid Transformer: Reading Narrative of Madness as Computational Approach to Creativity	This papers revisits the receptive theory in context of computational creativity. It presents a case study of a Paranoid Transformer - a fully autonomous text generation engine with raw output that could be read as the narrative of a mad digital persona without any additional human post-filtering. We describe technical...	false	false	false	false	true	false	false	false	true	false	false	false	false	true	false	false	false	false	186,972
2010.11437	Task-Adaptive Feature Transformer for Few-Shot Segmentation	Few-shot learning allows machines to classify novel classes using only a few labeled samples. Recently, few-shot segmentation aiming at semantic segmentation on low sample data has also seen great interest. In this paper, we propose a learnable module for few-shot segmentation, the task-adaptive feature transformer (TA...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	202,256
2312.06454	Point Transformer with Federated Learning for Predicting Breast Cancer HER2 Status from Hematoxylin and Eosin-Stained Whole Slide Images	Directly predicting human epidermal growth factor receptor 2 (HER2) status from widely available hematoxylin and eosin (HE)-stained whole slide images (WSIs) can reduce technical costs and expedite treatment selection. Accurately predicting HER2 requires large collections of multi-site WSIs. Federated learning enables ...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	414,525
2112.06194	Improving Performance of Federated Learning based Medical Image Analysis in Non-IID Settings using Image Augmentation	Federated Learning (FL) is a suitable solution for making use of sensitive data belonging to patients, people, companies, or industries that are obligatory to work under rigid privacy constraints. FL mainly or partially supports data privacy and security issues and provides an alternative to model problems facilitating...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	271,090
2304.10977	Evaluating Transformer Language Models on Arithmetic Operations Using Number Decomposition	In recent years, Large Language Models such as GPT-3 showed remarkable capabilities in performing NLP tasks in the zero and few shot settings. On the other hand, the experiments highlighted the difficulty of GPT-3 in carrying out tasks that require a certain degree of reasoning, such as arithmetic operations. In this p...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	359,622
2012.02111	Deep Inverse Sensor Models as Priors for evidential Occupancy Mapping	With the recent boost in autonomous driving, increased attention has been paid on radars as an input for occupancy mapping. Besides their many benefits, the inference of occupied space based on radar detections is notoriously difficult because of the data sparsity and the environment dependent noise (e.g. multipath ref...	false	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	209,651
2012.14251	A Differential-Cascaded Paradigm for Control of Nonlinear Systems	This paper focuses on developing a new paradigm motivated by investigating the consensus problem of networked Lagrangian systems with time-varying delay and switching topologies. We present adaptive controllers with piecewise continuous or arbitrary times differentiable control torques for realizing consensus of Lagran...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	213,452
2203.08949	Latent-Variable Advantage-Weighted Policy Optimization for Offline RL	Offline reinforcement learning methods hold the promise of learning policies from pre-collected datasets without the need to query the environment for new transitions. This setting is particularly well-suited for continuous control robotic applications for which online data collection based on trial-and-error is costly...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	285,966
2409.14791	Multiscale scattered data analysis in samplet coordinates	We study multiscale scattered data interpolation schemes for globally supported radial basis functions, with a focus on the Mat\'ern class. The multiscale approximation is constructed through a sequence of residual corrections, where radial basis functions with different lengthscale parameters are employed to capture v...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	490,631
2405.15320	Organic Data-Driven Approach for Turkish Grammatical Error Correction and LLMs	Grammatical Error Correction has seen significant progress with the recent advancements in deep learning. As those methods require huge amounts of data, synthetic datasets are being built to fill this gap. Unfortunately, synthetic datasets are not organic enough in some cases and even require clean data to start with. ...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	456,873
1902.06626	Mockingbird: Defending Against Deep-Learning-Based Website Fingerprinting Attacks with Adversarial Traces	Website Fingerprinting (WF) is a type of traffic analysis attack that enables a local passive eavesdropper to infer the victim's activity, even when the traffic is protected by a VPN or an anonymity system like Tor. Leveraging a deep-learning classifier, a WF attacker can gain over 98% accuracy on Tor traffic. In this ...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	121,802
2008.06204	Structure-Aware Network for Lane Marker Extraction with Dynamic Vision Sensor	Lane marker extraction is a basic yet necessary task for autonomous driving. Although past years have witnessed major advances in lane marker extraction with deep learning models, they all aim at ordinary RGB images generated by frame-based cameras, which limits their performance in extreme cases, like huge illuminatio...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	191,730
1708.04907	Multi-View Stereo with Single-View Semantic Mesh Refinement	While 3D reconstruction is a well-established and widely explored research topic, semantic 3D reconstruction has only recently witnessed an increasing share of attention from the Computer Vision community. Semantic annotations allow in fact to enforce strong class-dependent priors, as planarity for ground and walls, wh...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	79,041
2411.05804	Reliability-Based Design Optimization Incorporating Extended Optimal Uncertainty Quantification	Reliability-based design optimization (RBDO) approaches aim to identify the best design of an engineering problem, whilst the probability of failure (PoF) remains below an acceptable value. Thus, the incorporation of the sharpest bounds on the PoF under given constraints on the uncertain input quantities strongly stren...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	506,802
1401.5245	Edge detection of binary images using the method of masks	In this work the method of masks, creating and using of inverted image masks, together with binary operation of image data are used in edge detection of binary images, monochrome images, which yields about 300 times faster than ordinary methods. The method is divided into three stages: Mask construction, Fundamental ed...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	30,180
1803.05785	Aggregated Sparse Attention for Steering Angle Prediction	In this paper, we apply the attention mechanism to autonomous driving for steering angle prediction. We propose the first model, applying the recently introduced sparse attention mechanism to visual domain, as well as the aggregated extension for this model. We show the improvement of the proposed method, comparing to ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	92,701
1811.07492	DeepSeeNet: A deep learning model for automated classification of patient-based age-related macular degeneration severity from color fundus photographs	In assessing the severity of age-related macular degeneration (AMD), the Age-Related Eye Disease Study (AREDS) Simplified Severity Scale predicts the risk of progression to late AMD. However, its manual use requires the time-consuming participation of expert practitioners. Although several automated deep learning syste...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	113,789
1802.03359	Minimum weight codewords in dual Algebraic-Geometric codes from the Giulietti-Korchm\'aros curve	In this paper we investigate the number of minimum weight codewords of some dual Algebraic-Geometric codes associated with the Giulietti-Korchm\'aros maximal curve, by computing the maximal number of intersections between the Giulietti-Korchm\'aros curve and lines, plane conics and plane cubics.	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	89,953
2106.10084	Subjective Bias in Abstractive Summarization	Due to the subjectivity of the summarization, it is a good practice to have more than one gold summary for each training document. However, many modern large-scale abstractive summarization datasets have only one-to-one samples written by different human with different styles. The impact of this phenomenon is understud...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	241,893
1906.07749	A Framework for Parallelizing OWL Classification in Description Logic Reasoners	In this paper we report on a black-box approach to parallelize existing description logic (DL) reasoners for the Web Ontology Language (OWL). We focus on OWL ontology classification, which is an important inference service and supported by every major OWL/DL reasoner. We propose a flexible parallel framework which can ...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	135,670
1901.11143	Natural Analysts in Adaptive Data Analysis	Adaptive data analysis is frequently criticized for its pessimistic generalization guarantees. The source of these pessimistic bounds is a model that permits arbitrary, possibly adversarial analysts that optimally use information to bias results. While being a central issue in the field, still lacking are notions of na...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	120,179
2002.05540	SpotNet: Self-Attention Multi-Task Network for Object Detection	Humans are very good at directing their visual attention toward relevant areas when they search for different types of objects. For instance, when we search for cars, we will look at the streets, not at the top of buildings. The motivation of this paper is to train a network to do the same via a multi-task learning app...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	163,932
1605.01020	Implicit large-eddy simulation of compressible flows using the Interior Embedded Discontinuous Galerkin method	We present a high-order implicit large-eddy simulation (ILES) approach for simulating transitional turbulent flows. The approach consists of an Interior Embedded Discontinuous Galerkin (IEDG) method for the discretization of the compressible Navier-Stokes equations and a parallel preconditioned Newton-GMRES solver for ...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	55,419
0711.1383	On Minimal Tree Realizations of Linear Codes	A tree decomposition of the coordinates of a code is a mapping from the coordinate set to the set of vertices of a tree. A tree decomposition can be extended to a tree realization, i.e., a cycle-free realization of the code on the underlying tree, by specifying a state space at each edge of the tree, and a local constr...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	880
1708.03940	Leveraging Sparse and Dense Feature Combinations for Sentiment Classification	Neural networks are one of the most popular approaches for many natural language processing tasks such as sentiment analysis. They often outperform traditional machine learning models and achieve the state-of-art results on most tasks. However, many existing deep learning models are complex, difficult to train and prov...	false	false	false	false	false	true	true	false	true	false	false	false	false	false	false	false	false	false	78,851
1501.01773	Estimates for the growth of inverse determinant sums of quasi-orthogonal and number field lattices	Inverse determinant sums appear naturally as a tool for analyzing performance of space-time codes in Rayleigh fading channels. This work will analyze the growth of inverse determinant sums of a family of quasi-orthogonal codes and will show that the growths are in logarithmic class. This is considerably lower than that...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	39,120
1809.08116	On the Optimal Broadcast Rate of the Two-Sender Unicast Index Coding Problem with Fully-Participated Interactions	The problem of two-sender unicast index coding consists of two senders and a set of receivers. Each receiver demands a unique message and possesses some of the messages demanded by other receivers as its side-information. Every demanded message is present with at least one of the senders. Senders avail the knowledge of...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	108,433
2403.05713	tsGT: Stochastic Time Series Modeling With Transformer	Time series methods are of fundamental importance in virtually any field of science that deals with temporally structured data. Recently, there has been a surge of deterministic transformer models with time series-specific architectural biases. In this paper, we go in a different direction by introducing tsGT, a stocha...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	436,116
2405.10446	Tell me more: Intent Fulfilment Framework for Enhancing User Experiences in Conversational XAI	The evolution of Explainable Artificial Intelligence (XAI) has emphasised the significance of meeting diverse user needs. The approaches to identifying and addressing these needs must also advance, recognising that explanation experiences are subjective, user-centred processes that interact with users towards a better ...	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	454,762
1806.11103	Comment on: Decomposition of structural learning about directed acyclic graphs [1]	We propose an alternative proof concerning necessary and sufficient conditions to split the problem of searching for d-separators and building the skeleton of a DAG into small problems for every node of a separation tree T. The proof is simpler than the original [1]. The same proof structure has been used in [2] for le...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	101,649
1603.07195	A Decentralized Quasi-Newton Method for Dual Formulations of Consensus Optimization	This paper considers consensus optimization problems where each node of a network has access to a different summand of an aggregate cost function. Nodes try to minimize the aggregate cost function, while they exchange information only with their neighbors. We modify the dual decomposition method to incorporate a curvat...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	53,599
1709.03572	Real-Time Multiple Object Tracking - A Study on the Importance of Speed	In this project, we implement a multiple object tracker, following the tracking-by-detection paradigm, as an extension of an existing method. It works by modelling the movement of objects by solving the filtering problem, and associating detections with predicted new locations in new frames using the Hungarian algorith...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	80,484
1804.01466	Gaussian Process Subset Scanning for Anomalous Pattern Detection in Non-iid Data	Identifying anomalous patterns in real-world data is essential for understanding where, when, and how systems deviate from their expected dynamics. Yet methods that separately consider the anomalousness of each individual data point have low detection power for subtle, emerging irregularities. Additionally, recent dete...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	94,227
2209.01514	A Novel Nearest Neighbors Algorithm Based on Power Muirhead Mean	This paper introduces the innovative Power Muirhead Mean K-Nearest Neighbors (PMM-KNN) algorithm, a novel data classification approach that combines the K-Nearest Neighbors method with the adaptive Power Muirhead Mean operator. The proposed methodology aims to address the limitations of traditional KNN by leveraging th...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	315,913
1807.05748	Learning Stochastic Differential Equations With Gaussian Processes Without Gradient Matching	We introduce a novel paradigm for learning non-parametric drift and diffusion functions for stochastic differential equation (SDE). The proposed model learns to simulate path distributions that match observations with non-uniform time increments and arbitrary sparseness, which is in contrast with gradient matching that...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	102,991
1811.04407	An initial attempt of combining visual selective attention with deep reinforcement learning	Visual attention serves as a means of feature selection mechanism in the perceptual system. Motivated by Broadbent's leaky filter model of selective attention, we evaluate how such mechanism could be implemented and affect the learning process of deep reinforcement learning. We visualize and analyze the feature maps of...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	113,084
2402.01878	LiPO: Listwise Preference Optimization through Learning-to-Rank	Aligning language models (LMs) with curated human feedback is critical to control their behaviors in real-world applications. Several recent policy optimization methods, such as DPO and SLiC, serve as promising alternatives to the traditional Reinforcement Learning from Human Feedback (RLHF) approach. In practice, huma...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	426,269
2411.02795	The Evolution of RWKV: Advancements in Efficient Language Modeling	This paper reviews the development of the Receptance Weighted Key Value (RWKV) architecture, emphasizing its advancements in efficient language modeling. RWKV combines the training efficiency of Transformers with the inference efficiency of RNNs through a novel linear attention mechanism. We examine its core innovation...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	505,664
2304.13432	Design and analysis of bent functions using $\mathcal{M}$-subspaces	In this article, we provide the first systematic analysis of bent functions $f$ on $\mathbb{F}_2^{n}$ in the Maiorana-McFarland class $\mathcal{MM}$ regarding the origin and cardinality of their $\mathcal{M}$-subspaces, i.e., vector subspaces on which the second-order derivatives of $f$ vanish. By imposing restrictions...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	360,579
2205.03316	Application of Clustering Algorithms for Dimensionality Reduction in Infrastructure Resilience Prediction Models	Recent studies increasingly adopt simulation-based machine learning (ML) models to analyze critical infrastructure system resilience. For realistic applications, these ML models consider the component-level characteristics that influence the network response during emergencies. However, such an approach could result in...	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	295,245
2305.11092	Universal Domain Adaptation from Foundation Models: A Baseline Study	Foundation models (e.g., CLIP or DINOv2) have shown their impressive learning and transfer capabilities in a wide range of visual tasks, by training on a large corpus of data and adapting to specific downstream tasks. It is, however, interesting that foundation models have not been fully explored for universal domain a...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	365,380
1901.06140	Backbone Can Not be Trained at Once: Rolling Back to Pre-trained Network for Person Re-Identification	In person re-identification (ReID) task, because of its shortage of trainable dataset, it is common to utilize fine-tuning method using a classification network pre-trained on a large dataset. However, it is relatively difficult to sufficiently fine-tune the low-level layers of the network due to the gradient vanishing...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	118,934
2412.06082	Are foundation models for computer vision good conformal predictors?	Recent advances in self-supervision and constrastive learning have brought the performance of foundation models to unprecedented levels in a variety of tasks. Fueled by this progress, these models are becoming the prevailing approach for a wide array of real-world vision problems, including risk-sensitive and high-stak...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	515,081
2401.10942	Machine Unlearning for Recommendation Systems: An Insight	This review explores machine unlearning (MUL) in recommendation systems, addressing adaptability, personalization, privacy, and bias challenges. Unlike traditional models, MUL dynamically adjusts system knowledge based on shifts in user preferences and ethical considerations. The paper critically examines MUL's basics,...	false	false	false	false	true	true	true	false	false	false	false	false	false	false	false	false	false	false	422,826
1610.02323	Almost ISS property for feedback connected systems	Small-gain conditions used in analysis of feedback interconnections are contraction conditions which imply certain stability properties. Such conditions are applied to a finite or infinite interval. In this paper we consider the case, when a small-gain condition is applied to several disjunct intervals and use the dens...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	62,082
1806.09835	Graph-to-Sequence Learning using Gated Graph Neural Networks	Many NLP applications can be framed as a graph-to-sequence learning problem. Previous work proposing neural architectures on this setting obtained promising results compared to grammar-based approaches but still rely on linearisation heuristics and/or standard recurrent networks to achieve the best performance. In this...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	101,438
2303.10472	Practical and Matching Gradient Variance Bounds for Black-Box Variational Bayesian Inference	Understanding the gradient variance of black-box variational inference (BBVI) is a crucial step for establishing its convergence and developing algorithmic improvements. However, existing studies have yet to show that the gradient variance of BBVI satisfies the conditions used to study the convergence of stochastic gra...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	352,469
1904.12323	An approach to image denoising using manifold approximation without clean images	Image restoration has been an extensively researched topic in numerous fields. With the advent of deep learning, a lot of the current algorithms were replaced by algorithms that are more flexible and robust. Deep networks have demonstrated impressive performance in a variety of tasks like blind denoising, image enhance...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	129,077
2407.04221	Autoverse: An Evolvable Game Language for Learning Robust Embodied Agents	We introduce Autoverse, an evolvable, domain-specific language for single-player 2D grid-based games, and demonstrate its use as a scalable training ground for Open-Ended Learning (OEL) algorithms. Autoverse uses cellular-automaton-like rewrite rules to describe game mechanics, allowing it to express various game envir...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	470,475
2111.00137	Efficient Inference Without Trading-off Regret in Bandits: An Allocation Probability Test for Thompson Sampling	Using bandit algorithms to conduct adaptive randomised experiments can minimise regret, but it poses major challenges for statistical inference (e.g., biased estimators, inflated type-I error and reduced power). Recent attempts to address these challenges typically impose restrictions on the exploitative nature of the ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	264,112
2412.11299	How not to Stitch Representations to Measure Similarity: Task Loss Matching versus Direct Matching	Measuring the similarity of the internal representations of deep neural networks is an important and challenging problem. Model stitching has been proposed as a possible approach, where two half-networks are connected by mapping the output of the first half-network to the input of the second one. The representations ar...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	517,346
1506.08643	Diffusion of innovations in Axelrod's model	Axelrod's model for the dissemination of culture contains two key factors required to model the process of diffusion of innovations, namely, social influence (i.e., individuals become more similar when they interact) and homophily (i.e., individuals interact preferentially with similar others). The strength of these so...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	44,640
2403.12352	A New Intelligent Reflecting Surface-Aided Electromagnetic Stealth Strategy	Electromagnetic wave absorbing material (EWAM) plays an essential role in manufacturing stealth aircraft, which can achieve the electromagnetic stealth (ES) by reducing the strength of the signal reflected back to the radar system. However, the stealth performance is limited by the coating thickness, incident wave angl...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	439,127
2208.11636	ImitAL: Learned Active Learning Strategy on Synthetic Data	Active Learning (AL) is a well-known standard method for efficiently obtaining annotated data by first labeling the samples that contain the most information based on a query strategy. In the past, a large variety of such query strategies has been proposed, with each generation of new strategies increasing the runtime ...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	314,497
1811.06609	A Spectral View of Adversarially Robust Features	Given the apparent difficulty of learning models that are robust to adversarial perturbations, we propose tackling the simpler problem of developing adversarially robust features. Specifically, given a dataset and metric of interest, the goal is to return a function (or multiple functions) that 1) is robust to adversar...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	113,562
2008.03903	Online Optimization of Switched LTI Systems Using Continuous-Time and Hybrid Accelerated Gradient Flows	This paper studies the design of feedback controllers to steer a switching linear time-invariant dynamical system towards the solution trajectory of a time-varying convex optimization problem. We propose two types of controllers: (i) a continuous controller inspired by the online gradient descent method, and (ii) a hyb...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	191,065
2004.09502	Multi-Scale Thermal to Visible Face Verification via Attribute Guided Synthesis	Thermal-to-visible face verification is a challenging problem due to the large domain discrepancy between the modalities. Existing approaches either attempt to synthesize visible faces from thermal faces or learn domain-invariant robust features from these modalities for cross-modal matching. In this paper, we use attr...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	173,359
2312.03928	Adaptive Weighted Co-Learning for Cross-Domain Few-Shot Learning	Due to the availability of only a few labeled instances for the novel target prediction task and the significant domain shift between the well annotated source domain and the target domain, cross-domain few-shot learning (CDFSL) induces a very challenging adaptation problem. In this paper, we propose a simple Adaptive ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	413,487
2306.00674	CRS-FL: Conditional Random Sampling for Communication-Efficient and Privacy-Preserving Federated Learning	Federated Learning (FL), a privacy-oriented distributed ML paradigm, is being gaining great interest in Internet of Things because of its capability to protect participants data privacy. Studies have been conducted to address challenges existing in standard FL, including communication efficiency and privacy-preserving....	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	true	370,118
2308.06924	FedEdge AI-TC: A Semi-supervised Traffic Classification Method based on Trusted Federated Deep Learning for Mobile Edge Computing	As a typical entity of MEC (Mobile Edge Computing), 5G CPE (Customer Premise Equipment)/HGU (Home Gateway Unit) has proven to be a promising alternative to traditional Smart Home Gateway. Network TC (Traffic Classification) is a vital service quality assurance and security management method for communication networks, ...	false	false	false	false	true	false	false	false	false	false	false	false	true	false	false	false	false	true	385,322
2005.10058	On embedding Lambek calculus into commutative categorial grammars	We consider tensor grammars, which are an example of \commutative" grammars, based on the classical (rather than intuitionistic) linear logic. They can be seen as a surface representation of abstract categorial grammars ACG in the sense that derivations of ACG translate to derivations of tensor grammars and this transl...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	true	178,081
2303.02753	Frequency-domain Blind Quality Assessment of Blurred and Blocking-artefact Images using Gaussian Process Regression model	Most of the standard image and video codecs are block-based and depending upon the compression ratio the compressed images/videos suffer from different distortions. At low ratios, blurriness is observed and as compression increases blocking artifacts occur. Generally, in order to reduce blockiness, images are low-pass ...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	349,474
1810.02690	Robotics CTF (RCTF), a playground for robot hacking	Robots state of insecurity is onstage. There is an emerging concern about major robot vulnerabilities and their adverse consequences. However, there is still a considerable gap between robotics and cybersecurity domains. For the purpose of filling that gap, the present technical report presents the Robotics CTF (RCTF),...	false	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	109,644
2105.10559	Hyper-Convolution Networks for Biomedical Image Segmentation	The convolution operation is a central building block of neural network architectures widely used in computer vision. The size of the convolution kernels determines both the expressiveness of convolutional neural networks (CNN), as well as the number of learnable parameters. Increasing the network capacity to capture r...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	236,431
2104.04250	Periodic Load Rejection for Floating Offshore Wind Turbines via Constrained Subspace Predictive Repetitive Control	Individual Pitch Control (IPC) is an effective control strategy to mitigate the blade loads on large-scale wind turbines. Since IPC usually requires high pitch actuation, the safety constraints of the pitch actuator should be taken into account when designing the controller. This paper introduces a constrained Subspace...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	229,339
2207.04639	A Dual-Polarization Information Guided Network for SAR Ship Classification	How to fully utilize polarization to enhance synthetic aperture radar (SAR) ship classification remains an unresolved issue. Thus, we propose a dual-polarization information guided network (DPIG-Net) to solve it.	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	307,264
2112.08656	DREAM: Improving Situational QA by First Elaborating the Situation	When people answer questions about a specific situation, e.g., "I cheated on my mid-term exam last week. Was that wrong?", cognitive science suggests that they form a mental picture of that situation before answering. While we do not know how language models (LMs) answer such questions, we conjecture that they may answ...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	271,884
2007.03028	Labeling of Multilingual Breast MRI Reports	Medical reports are an essential medium in recording a patient's condition throughout a clinical trial. They contain valuable information that can be extracted to generate a large labeled dataset needed for the development of clinical tools. However, the majority of medical reports are stored in an unregularized format...	false	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	185,921

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