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
2412.04094	Magnetic Resonance Imaging Feature-Based Subtyping and Model Ensemble for Enhanced Brain Tumor Segmentation	Accurate and automatic segmentation of brain tumors in multi-parametric magnetic resonance imaging (mpMRI) is essential for quantitative measurements, which play an increasingly important role in clinical diagnosis and prognosis. The International Brain Tumor Segmentation (BraTS) Challenge 2024 offers a unique benchmar...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	514,257
2306.17840	Statler: State-Maintaining Language Models for Embodied Reasoning	There has been a significant research interest in employing large language models to empower intelligent robots with complex reasoning. Existing work focuses on harnessing their abilities to reason about the histories of their actions and observations. In this paper, we explore a new dimension in which large language m...	false	false	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	376,826
2203.00736	3D Skeleton-based Human Motion Prediction with Manifold-Aware GAN	In this work we propose a novel solution for 3D skeleton-based human motion prediction. The objective of this task consists in forecasting future human poses based on a prior skeleton pose sequence. This involves solving two main challenges still present in recent literature; (1) discontinuity of the predicted motion w...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	283,097
2109.03158	Idiosyncratic but not Arbitrary: Learning Idiolects in Online Registers Reveals Distinctive yet Consistent Individual Styles	An individual's variation in writing style is often a function of both social and personal attributes. While structured social variation has been extensively studied, e.g., gender based variation, far less is known about how to characterize individual styles due to their idiosyncratic nature. We introduce a new approac...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	253,973
2210.13060	Is the Envelope Beneficial to Non-Orthogonal Multiple Access?	Non-orthogonal multiple access (NOMA) is capable of serving different numbers of users in the same time-frequency resource element, and this feature can be leveraged to carry additional information. In the orthogonal frequency division multiplexing (OFDM) system, we propose a novel enhanced NOMA scheme, called NOMA wit...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	326,032
2411.03814	MRJ-Agent: An Effective Jailbreak Agent for Multi-Round Dialogue	Large Language Models (LLMs) demonstrate outstanding performance in their reservoir of knowledge and understanding capabilities, but they have also been shown to be prone to illegal or unethical reactions when subjected to jailbreak attacks. To ensure their responsible deployment in critical applications, it is crucial...	false	false	false	false	true	false	false	false	true	false	false	false	true	false	false	false	false	false	506,047
1907.06226	Lexical Simplification with Pretrained Encoders	Lexical simplification (LS) aims to replace complex words in a given sentence with their simpler alternatives of equivalent meaning. Recently unsupervised lexical simplification approaches only rely on the complex word itself regardless of the given sentence to generate candidate substitutions, which will inevitably pr...	false	false	false	false	true	true	false	false	true	false	false	false	false	false	false	false	false	false	138,563
2403.11551	New Constructions of Reversible DNA Codes	DNA codes have many applications, such as in data storage, DNA computing, etc. Good DNA codes have large sizes and satisfy some certain constraints. In this paper, we present a new construction method for reversible DNA codes. We show that the DNA codes obtained using our construction method can satisfy some desired co...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	438,755
1912.07966	KonVid-150k: A Dataset for No-Reference Video Quality Assessment of Videos in-the-Wild	Video quality assessment (VQA) methods focus on particular degradation types, usually artificially induced on a small set of reference videos. Hence, most traditional VQA methods under-perform in-the-wild. Deep learning approaches have had limited success due to the small size and diversity of existing VQA datasets, ei...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	157,732
2104.10907	XCrossNet: Feature Structure-Oriented Learning for Click-Through Rate Prediction	Click-Through Rate (CTR) prediction is a core task in nowadays commercial recommender systems. Feature crossing, as the mainline of research on CTR prediction, has shown a promising way to enhance predictive performance. Even though various models are able to learn feature interactions without manual feature engineer...	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	false	false	231,758
2104.06057	LioNets: A Neural-Specific Local Interpretation Technique Exploiting Penultimate Layer Information	Artificial Intelligence (AI) has a tremendous impact on the unexpected growth of technology in almost every aspect. AI-powered systems are monitoring and deciding about sensitive economic and societal issues. The future is towards automation, and it must not be prevented. However, this is a conflicting viewpoint for a ...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	229,947
2110.14590	TMBuD: A dataset for urban scene building detection	Building recognition and 3D reconstruction of human made structures in urban scenarios has become an interesting and actual topic in the image processing domain. For this research topic the Computer Vision and Augmented Reality areas intersect for creating a better understanding of the urban scenario for various topics...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	263,579
2210.06680	Walk a Mile in Their Shoes: a New Fairness Criterion for Machine Learning	The old empathetic adage, ``Walk a mile in their shoes,'' asks that one imagine the difficulties others may face. This suggests a new ML counterfactual fairness criterion, based on a \textit{group} level: How would members of a nonprotected group fare if their group were subject to conditions in some protected group? I...	false	false	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	323,390
1909.03039	Improved Hierarchical Patient Classification with Language Model Pretraining over Clinical Notes	Clinical notes in electronic health records contain highly heterogeneous writing styles, including non-standard terminology or abbreviations. Using these notes in predictive modeling has traditionally required preprocessing (e.g. taking frequent terms or topic modeling) that removes much of the richness of the source d...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	144,353
2403.04024	Enhancing chest X-ray datasets with privacy-preserving large language models and multi-type annotations: a data-driven approach for improved classification	In chest X-ray (CXR) image analysis, rule-based systems are usually employed to extract labels from reports for dataset releases. However, there is still room for improvement in label quality. These labelers typically output only presence labels, sometimes with binary uncertainty indicators, which limits their usefulne...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	435,423
2408.07192	Solving Truly Massive Budgeted Monotonic POMDPs with Oracle-Guided Meta-Reinforcement Learning	Monotonic Partially Observable Markov Decision Processes (POMDPs), where the system state progressively decreases until a restorative action is performed, can be used to model sequential repair problems effectively. This paper considers the problem of solving budget-constrained multi-component monotonic POMDPs, where a...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	480,488
1312.5734	Time-varying Learning and Content Analytics via Sparse Factor Analysis	We propose SPARFA-Trace, a new machine learning-based framework for time-varying learning and content analytics for education applications. We develop a novel message passing-based, blind, approximate Kalman filter for sparse factor analysis (SPARFA), that jointly (i) traces learner concept knowledge over time, (ii) an...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	29,257
2410.21414	CT2C-QA: Multimodal Question Answering over Chinese Text, Table and Chart	Multimodal Question Answering (MMQA) is crucial as it enables comprehensive understanding and accurate responses by integrating insights from diverse data representations such as tables, charts, and text. Most existing researches in MMQA only focus on two modalities such as image-text QA, table-text QA and chart-text Q...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	503,238
2005.11862	Climbing down Charney's ladder: Machine Learning and the post-Dennard era of computational climate science	The advent of digital computing in the 1950s sparked a revolution in the science of weather and climate. Meteorology, long based on extrapolating patterns in space and time, gave way to computational methods in a decade of advances in numerical weather forecasting. Those same methods also gave rise to computational cli...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	178,589
2010.10907	Analyzing the Source and Target Contributions to Predictions in Neural Machine Translation	In Neural Machine Translation (and, more generally, conditional language modeling), the generation of a target token is influenced by two types of context: the source and the prefix of the target sequence. While many attempts to understand the internal workings of NMT models have been made, none of them explicitly eval...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	202,053
1905.10498	Cold Case: The Lost MNIST Digits	Although the popular MNIST dataset [LeCun et al., 1994] is derived from the NIST database [Grother and Hanaoka, 1995], the precise processing steps for this derivation have been lost to time. We propose a reconstruction that is accurate enough to serve as a replacement for the MNIST dataset, with insignificant changes ...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	132,079
2110.01307	Collective eXplainable AI: Explaining Cooperative Strategies and Agent Contribution in Multiagent Reinforcement Learning with Shapley Values	While Explainable Artificial Intelligence (XAI) is increasingly expanding more areas of application, little has been applied to make deep Reinforcement Learning (RL) more comprehensible. As RL becomes ubiquitous and used in critical and general public applications, it is essential to develop methods that make it better...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	true	false	false	true	258,722
2210.12974	Investigating Neuron Disturbing in Fusing Heterogeneous Neural Networks	Fusing deep learning models trained on separately located clients into a global model in a one-shot communication round is a straightforward implementation of Federated Learning. Although current model fusion methods are shown experimentally valid in fusing neural networks with almost identical architectures, they are ...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	true	325,996
2003.07308	A Novel Jamming Attacks Detection Approach Based on Machine Learning for Wireless Communication	Jamming attacks target a wireless network creating an unwanted denial of service. 5G is vulnerable to these attacks despite its resilience prompted by the use of millimeter wave bands. Over the last decade, several types of jamming detection techniques have been proposed, including fuzzy logic, game theory, channel sur...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	168,383
2002.02815	Switchable Precision Neural Networks	Instantaneous and on demand accuracy-efficiency trade-off has been recently explored in the context of neural networks slimming. In this paper, we propose a flexible quantization strategy, termed Switchable Precision neural Networks (SP-Nets), to train a shared network capable of operating at multiple quantization leve...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	163,038
2412.18743	Successes and Limitations of Object-centric Models at Compositional Generalisation	In recent years, it has been shown empirically that standard disentangled latent variable models do not support robust compositional learning in the visual domain. Indeed, in spite of being designed with the goal of factorising datasets into their constituent factors of variations, disentangled models show extremely li...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	520,551
1904.08980	Exploring the Limitations of Behavior Cloning for Autonomous Driving	Driving requires reacting to a wide variety of complex environment conditions and agent behaviors. Explicitly modeling each possible scenario is unrealistic. In contrast, imitation learning can, in theory, leverage data from large fleets of human-driven cars. Behavior cloning in particular has been successfully used to...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	128,232
1706.04964	Learning Deep ResNet Blocks Sequentially using Boosting Theory	Deep neural networks are known to be difficult to train due to the instability of back-propagation. A deep \emph{residual network} (ResNet) with identity loops remedies this by stabilizing gradient computations. We prove a boosting theory for the ResNet architecture. We construct $T$ weak module classifiers, each conta...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	75,418
2409.11003	Single-stage TTS with Masked Audio Token Modeling and Semantic Knowledge Distillation	Audio token modeling has become a powerful framework for speech synthesis, with two-stage approaches employing semantic tokens remaining prevalent. In this paper, we aim to simplify this process by introducing a semantic knowledge distillation method that enables high-quality speech generation in a single stage. Our pr...	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	488,972
1706.06660	Crowdsourcing with Sparsely Interacting Workers	We consider estimation of worker skills from worker-task interaction data (with unknown labels) for the single-coin crowd-sourcing binary classification model in symmetric noise. We define the (worker) interaction graph whose nodes are workers and an edge between two nodes indicates whether or not the two workers parti...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	75,713
2002.06274	Single Unit Status in Deep Convolutional Neural Network Codes for Face Identification: Sparseness Redefined	Deep convolutional neural networks (DCNNs) trained for face identification develop representations that generalize over variable images, while retaining subject (e.g., gender) and image (e.g., viewpoint) information. Identity, gender, and viewpoint codes were studied at the "neural unit" and ensemble levels of a face-i...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	164,134
2007.13049	A Dual Iterative Refinement Method for Non-rigid Shape Matching	In this work, a simple and efficient dual iterative refinement (DIR) method is proposed for dense correspondence between two nearly isometric shapes. The key idea is to use dual information, such as spatial and spectral, or local and global features, in a complementary and effective way, and extract more accurate infor...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	189,004
2403.18739	Usage-Specific Survival Modeling Based on Operational Data and Neural Networks	Accurate predictions of when a component will fail are crucial when planning maintenance, and by modeling the distribution of these failure times, survival models have shown to be particularly useful in this context. The presented methodology is based on conventional neural network-based survival models that are traine...	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	442,061
1904.01689	The Tower of Babel Meets Web 2.0: User-Generated Content and its Applications in a Multilingual Context	This study explores language's fragmenting effect on user-generated content by examining the diversity of knowledge representations across 25 different Wikipedia language editions. This diversity is measured at two levels: the concepts that are included in each edition and the ways in which these concepts are described...	true	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	126,209
2312.00326	Agent-OM: Leveraging LLM Agents for Ontology Matching	Ontology matching (OM) enables semantic interoperability between different ontologies and resolves their conceptual heterogeneity by aligning related entities. OM systems currently have two prevailing design paradigms: conventional knowledge-based expert systems and newer machine learning-based predictive systems. Whil...	false	false	false	false	true	true	false	false	true	false	false	false	false	false	false	false	false	false	412,003
2410.04982	Safe Learning-Based Optimization of Model Predictive Control: Application to Battery Fast-Charging	Model predictive control (MPC) is a powerful tool for controlling complex nonlinear systems under constraints, but often struggles with model uncertainties and the design of suitable cost functions. To address these challenges, we discuss an approach that integrates MPC with safe Bayesian optimization to optimize long-...	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	495,518
1504.07416	Application of neural networks to identify trolls in social networks	In this paper we developed and tested a new algorithm of detecting in social networks users (so-called trolls) who behave in an insulting and provocative way towards other users. In order to detect trolls it is proposed to unite users in groups where all the members have a similar way of communicating. Defining the num...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	42,534
2402.09898	Asymptotic construction of locally repairable codes with multiple recovering sets	Locally repairable codes have been extensively investigated due to practical applications in distributed and cloud storage systems in recent years. However, not much work on asymptotic behavior of locally repairable codes has been done. In particular, there is few result on constructive lower bound of asymptotic behavi...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	429,725
2407.04172	ChartGemma: Visual Instruction-tuning for Chart Reasoning in the Wild	Given the ubiquity of charts as a data analysis, visualization, and decision-making tool across industries and sciences, there has been a growing interest in developing pre-trained foundation models as well as general purpose instruction-tuned models for chart understanding and reasoning. However, existing methods suff...	false	false	false	false	true	false	false	false	true	false	false	true	false	false	false	false	false	false	470,452
2501.14173	Constrained Fuel and Time Optimal 6DOF Powered Descent Guidance Using Indirect Optimization	Powered descent guidance (PDG) problems subject to six-degrees-of-freedom (6DOF) dynamics allow for enforcement of practical attitude constraints. However, numerical solutions to 6DOF PDG problems are challenging due to fast rotational dynamics coupled with translational dynamics, and the presence of highly nonlinear s...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	527,007
1708.00651	A Multi-Objective Learning to re-Rank Approach to Optimize Online Marketplaces for Multiple Stakeholders	Multi-objective recommender systems address the difficult task of recommending items that are relevant to multiple, possibly conflicting, criteria. However these systems are most often designed to address the objective of one single stakeholder, typically, in online commerce, the consumers whose input and purchasing de...	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	false	78,249
1706.00066	Descriptions of Objectives and Processes of Mechanical Learning	In [1], we introduced mechanical learning and proposed 2 approaches to mechanical learning. Here, we follow one such approach to well describe the objects and the processes of learning. We discuss 2 kinds of patterns: objective and subjective pattern. Subjective pattern is crucial for learning machine. We prove that fo...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	74,553
1511.04317	Novel Feature Extraction, Selection and Fusion for Effective Malware Family Classification	Modern malware is designed with mutation characteristics, namely polymorphism and metamorphism, which causes an enormous growth in the number of variants of malware samples. Categorization of malware samples on the basis of their behaviors is essential for the computer security community, because they receive huge numb...	false	false	false	false	true	false	false	false	false	false	false	false	true	false	false	false	false	false	48,874
2009.02902	TransModality: An End2End Fusion Method with Transformer for Multimodal Sentiment Analysis	Multimodal sentiment analysis is an important research area that predicts speaker's sentiment tendency through features extracted from textual, visual and acoustic modalities. The central challenge is the fusion method of the multimodal information. A variety of fusion methods have been proposed, but few of them adopt ...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	194,692
1603.09631	Data Collection for Interactive Learning through the Dialog	This paper presents a dataset collected from natural dialogs which enables to test the ability of dialog systems to learn new facts from user utterances throughout the dialog. This interactive learning will help with one of the most prevailing problems of open domain dialog system, which is the sparsity of facts a dial...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	53,947
1906.00910	Learning Representations by Maximizing Mutual Information Across Views	We propose an approach to self-supervised representation learning based on maximizing mutual information between features extracted from multiple views of a shared context. For example, one could produce multiple views of a local spatio-temporal context by observing it from different locations (e.g., camera positions w...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	133,541
1703.01024	Exponential Moving Average Model in Parallel Speech Recognition Training	As training data rapid growth, large-scale parallel training with multi-GPUs cluster is widely applied in the neural network model learning currently.We present a new approach that applies exponential moving average method in large-scale parallel training of neural network model. It is a non-interference strategy that ...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	69,274
2412.18733	Intra- and Inter-modal Context Interaction Modeling for Conversational Speech Synthesis	Conversational Speech Synthesis (CSS) aims to effectively take the multimodal dialogue history (MDH) to generate speech with appropriate conversational prosody for target utterance. The key challenge of CSS is to model the interaction between the MDH and the target utterance. Note that text and speech modalities in MDH...	false	false	true	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	520,547
2402.02034	Universal Post-Training Reverse-Engineering Defense Against Backdoors in Deep Neural Networks	A variety of defenses have been proposed against backdoors attacks on deep neural network (DNN) classifiers. Universal methods seek to reliably detect and/or mitigate backdoors irrespective of the incorporation mechanism used by the attacker, while reverse-engineering methods often explicitly assume one. In this paper,...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	true	false	false	426,346
1808.04761	Cache Telepathy: Leveraging Shared Resource Attacks to Learn DNN Architectures	Deep Neural Networks (DNNs) are fast becoming ubiquitous for their ability to attain good accuracy in various machine learning tasks. A DNN's architecture (i.e., its hyper-parameters) broadly determines the DNN's accuracy and performance, and is often confidential. Attacking a DNN in the cloud to obtain its architectur...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	true	105,228
2407.19567	Sharp Bounds for Poly-GNNs and the Effect of Graph Noise	We investigate the classification performance of graph neural networks with graph-polynomial features, poly-GNNs, on the problem of semi-supervised node classification. We analyze poly-GNNs under a general contextual stochastic block model (CSBM) by providing a sharp characterization of the rate of separation between c...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	476,840
2405.18156	VividPose: Advancing Stable Video Diffusion for Realistic Human Image Animation	Human image animation involves generating a video from a static image by following a specified pose sequence. Current approaches typically adopt a multi-stage pipeline that separately learns appearance and motion, which often leads to appearance degradation and temporal inconsistencies. To address these issues, we prop...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	458,298
2409.13546	Certified Adversarial Robustness via Partition-based Randomized Smoothing	A reliable application of deep neural network classifiers requires robustness certificates against adversarial perturbations. Gaussian smoothing is a widely analyzed approach to certifying robustness against norm-bounded perturbations, where the certified prediction radius depends on the variance of the Gaussian noise ...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	490,042
1211.1716	Blind Signal Separation in the Presence of Gaussian Noise	A prototypical blind signal separation problem is the so-called cocktail party problem, with n people talking simultaneously and n different microphones within a room. The goal is to recover each speech signal from the microphone inputs. Mathematically this can be modeled by assuming that we are given samples from an n...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	19,623
2112.01363	Breaking the Convergence Barrier: Optimization via Fixed-Time Convergent Flows	Accelerated gradient methods are the cornerstones of large-scale, data-driven optimization problems that arise naturally in machine learning and other fields concerning data analysis. We introduce a gradient-based optimization framework for achieving acceleration, based on the recently introduced notion of fixed-time s...	false	false	false	false	true	false	true	false	false	false	true	false	false	false	false	false	false	false	269,458
1712.02053	On Path Memory in List Successive Cancellation Decoder of Polar Codes	Polar code is a breakthrough in coding theory. Using list successive cancellation decoding with large list size L, polar codes can achieve excellent error correction performance. The L partial decoded vectors are stored in the path memory and updated according to the results of list management. In the state-of-the-art ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	86,231
1806.01729	EasyConvPooling: Random Pooling with Easy Convolution for Accelerating Training and Testing	Convolution operations dominate the overall execution time of Convolutional Neural Networks (CNNs). This paper proposes an easy yet efficient technique for both Convolutional Neural Network training and testing. The conventional convolution and pooling operations are replaced by Easy Convolution and Random Pooling (ECP...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	99,616
2108.03533	Improving Similar Language Translation With Transfer Learning	We investigate transfer learning based on pre-trained neural machine translation models to translate between (low-resource) similar languages. This work is part of our contribution to the WMT 2021 Similar Languages Translation Shared Task where we submitted models for different language pairs, including French-Bambara,...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	249,690
2211.17106	Diffusion Probabilistic Model Made Slim	Despite the recent visually-pleasing results achieved, the massive computational cost has been a long-standing flaw for diffusion probabilistic models (DPMs), which, in turn, greatly limits their applications on resource-limited platforms. Prior methods towards efficient DPM, however, have largely focused on accelerati...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	333,863
2001.09650	The Whole Is Greater Than the Sum of Its Nonrigid Parts	According to Aristotle, a philosopher in Ancient Greece, "the whole is greater than the sum of its parts". This observation was adopted to explain human perception by the Gestalt psychology school of thought in the twentieth century. Here, we claim that observing part of an object which was previously acquired as a who...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	true	161,633
1107.2974	Quantum Filtering for Systems Driven by Fields in Single Photon States and Superposition of Coherent States using Non-Markovian Embeddings	The purpose of this paper is to determine quantum master and filter equations for systems coupled to fields in certain non-classical continuous-mode states. Specifically, we consider two types of field states (i) single photon states, and (ii) superpositions of coherent states. The system and field are described using ...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	11,303
1904.09238	A Model for the Influence of Media on the Ideology of Content in Online Social Networks	Many people rely on online social networks as sources of news and information, and the spread of media content with ideologies across the political spectrum influences online discussions and impacts actions offline. To examine the impact of media in online social networks, we generalize bounded-confidence models of opi...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	128,322
1711.02578	Image Captioning and Classification of Dangerous Situations	Current robot platforms are being employed to collaborate with humans in a wide range of domestic and industrial tasks. These environments require autonomous systems that are able to classify and communicate anomalous situations such as fires, injured persons, car accidents; or generally, any potentially dangerous situ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	84,082
2501.13426	Auto-Prompting SAM for Weakly Supervised Landslide Extraction	Weakly supervised landslide extraction aims to identify landslide regions from remote sensing data using models trained with weak labels, particularly image-level labels. However, it is often challenged by the imprecise boundaries of the extracted objects due to the lack of pixel-wise supervision and the properties of ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	526,684
2411.03891	Calibrating for the Future:Enhancing Calorimeter Longevity with Deep Learning	In the realm of high-energy physics, the longevity of calorimeters is paramount. Our research introduces a deep learning strategy to refine the calibration process of calorimeters used in particle physics experiments. We develop a Wasserstein GAN inspired methodology that adeptly calibrates the misalignment in calorime...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	506,079
2310.16441	Grokking in Linear Estimators -- A Solvable Model that Groks without Understanding	Grokking is the intriguing phenomenon where a model learns to generalize long after it has fit the training data. We show both analytically and numerically that grokking can surprisingly occur in linear networks performing linear tasks in a simple teacher-student setup with Gaussian inputs. In this setting, the full tr...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	402,732
1904.00817	DeepPoint3D: Learning Discriminative Local Descriptors using Deep Metric Learning on 3D Point Clouds	Learning local descriptors is an important problem in computer vision. While there are many techniques for learning local patch descriptors for 2D images, recently efforts have been made for learning local descriptors for 3D points. The recent progress towards solving this problem in 3D leverages the strong feature rep...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	125,991
1712.07106	Exploring High-Dimensional Structure via Axis-Aligned Decomposition of Linear Projections	Two-dimensional embeddings remain the dominant approach to visualize high dimensional data. The choice of embeddings ranges from highly non-linear ones, which can capture complex relationships but are difficult to interpret quantitatively, to axis-aligned projections, which are easy to interpret but are limited to biva...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	86,988
2310.17403	Detection Defenses: An Empty Promise against Adversarial Patch Attacks on Optical Flow	Adversarial patches undermine the reliability of optical flow predictions when placed in arbitrary scene locations. Therefore, they pose a realistic threat to real-world motion detection and its downstream applications. Potential remedies are defense strategies that detect and remove adversarial patches, but their infl...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	403,119
1811.00883	Deep Segment Attentive Embedding for Duration Robust Speaker Verification	LSTM-based speaker verification usually uses a fixed-length local segment randomly truncated from an utterance to learn the utterance-level speaker embedding, while using the average embedding of all segments of a test utterance to verify the speaker, which results in a critical mismatch between testing and training. T...	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	112,217
1906.05491	A Computational Analysis of Natural Languages to Build a Sentence Structure Aware Artificial Neural Network	Natural languages are complexly structured entities. They exhibit characterising regularities that can be exploited to link them one another. In this work, I compare two morphological aspects of languages: Written Patterns and Sentence Structure. I show how languages spontaneously group by similarity in both analyses a...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	135,044
1307.0516	Dynamical Structure of a Traditional Amazonian Social Network	Reciprocity is a vital feature of social networks, but relatively little is known about its temporal structure or the mechanisms underlying its persistence in real world behavior. In pursuit of these two questions, we study the stationary and dynamical signals of reciprocity in a network of manioc beer (Spanish: chicha...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	25,559
2208.04879	On the incremental form of dissipativity	Following the seminal work of Zames, the input-output theory of the 70s acknowledged that incremental properties (e.g. incremental gain) are the relevant quantities to study in nonlinear feedback system analysis. Yet, non-incremental analysis has dominated the use of dissipativity theory in nonlinear control from the 8...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	312,250
2405.02929	Unified Dynamic Scanpath Predictors Outperform Individually Trained Neural Models	Previous research on scanpath prediction has mainly focused on group models, disregarding the fact that the scanpaths and attentional behaviors of individuals are diverse. The disregard of these differences is especially detrimental to social human-robot interaction, whereby robots commonly emulate human gaze based on ...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	451,978
2210.15362	A Novel Approach for Neuromorphic Vision Data Compression based on Deep Belief Network	A neuromorphic camera is an image sensor that emulates the human eyes capturing only changes in local brightness levels. They are widely known as event cameras, silicon retinas or dynamic vision sensors (DVS). DVS records asynchronous per-pixel brightness changes, resulting in a stream of events that encode the brightn...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	326,916
1804.02286	Chart Parsing Multimodal Grammars	The short note describes the chart parser for multimodal type-logical grammars which has been developed in conjunction with the type-logical treebank for French. The chart parser presents an incomplete but fast implementation of proof search for multimodal type-logical grammars using the "deductive parsing" framework. ...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	94,377
2203.14623	Dynamic state and parameter estimation in multi-machine power systems - Experimental demonstration using real-world PMU-measurements	Dynamic state and parameter estimation (DSE) plays a key role for reliably monitoring and operating future, power-electronics-dominated power systems. While DSE is a very active research field, experimental applications of proposed algorithms to real-world systems remain scarce. This motivates the present paper, in whi...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	288,075
2305.11304	pTSE: A Multi-model Ensemble Method for Probabilistic Time Series Forecasting	Various probabilistic time series forecasting models have sprung up and shown remarkably good performance. However, the choice of model highly relies on the characteristics of the input time series and the fixed distribution that the model is based on. Due to the fact that the probability distributions cannot be averag...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	365,464
2210.16365	Elastic Weight Consolidation Improves the Robustness of Self-Supervised Learning Methods under Transfer	Self-supervised representation learning (SSL) methods provide an effective label-free initial condition for fine-tuning downstream tasks. However, in numerous realistic scenarios, the downstream task might be biased with respect to the target label distribution. This in turn moves the learned fine-tuned model posterior...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	327,304
2409.15733	EvoFA: Evolvable Fast Adaptation for EEG Emotion Recognition	Electroencephalography (EEG)-based emotion recognition has gained significant traction due to its accuracy and objectivity. However, the non-stationary nature of EEG signals leads to distribution drift over time, causing severe performance degradation when the model is reused. While numerous domain adaptation (DA) appr...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	491,036
2412.10743	NeuralPLexer3: Accurate Biomolecular Complex Structure Prediction with Flow Models	Structure determination is essential to a mechanistic understanding of diseases and the development of novel therapeutics. Machine-learning-based structure prediction methods have made significant advancements by computationally predicting protein and bioassembly structures from sequences and molecular topology alone. ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	517,083
1506.00251	Kinetics of Social Contagion	Diffusion of information, behavioral patterns or innovations follows diverse pathways depending on a number of conditions, including the structure of the underlying social network, the sensitivity to peer pressure and the influence of media. Here we study analytically and by simulations a general model that incorporate...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	43,642
2106.12091	BFTrainer: Low-Cost Training of Neural Networks on Unfillable Supercomputer Nodes	Supercomputer FCFS-based scheduling policies result in many transient idle nodes, a phenomenon that is only partially alleviated by backfill scheduling methods that promote small jobs to run before large jobs. Here we describe how to realize a novel use for these otherwise wasted resources, namely, deep neural network ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	242,615
1710.11278	Approximating Continuous Functions by ReLU Nets of Minimal Width	This article concerns the expressive power of depth in deep feed-forward neural nets with ReLU activations. Specifically, we answer the following question: for a fixed $d_{in}\geq 1,$ what is the minimal width $w$ so that neural nets with ReLU activations, input dimension $d_{in}$, hidden layer widths at most $w,$ and ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	83,560
1710.10013	Declarative vs Rule-based Control for Flocking Dynamics	The popularity of rule-based flocking models, such as Reynolds' classic flocking model, raises the question of whether more declarative flocking models are possible. This question is motivated by the observation that declarative models are generally simpler and easier to design, understand, and analyze than operational...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	83,300
1407.4422	Subspace Restricted Boltzmann Machine	The subspace Restricted Boltzmann Machine (subspaceRBM) is a third-order Boltzmann machine where multiplicative interactions are between one visible and two hidden units. There are two kinds of hidden units, namely, gate units and subspace units. The subspace units reflect variations of a pattern in data and the gate u...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	34,701
2407.00698	NourishNet: Proactive Severity State Forecasting of Food Commodity Prices for Global Warning Systems	Price volatility in global food commodities is a critical signal indicating potential disruptions in the food market. Understanding forthcoming changes in these prices is essential for bolstering food security, particularly for nations at risk. The Food and Agriculture Organization of the United Nations (FAO) previousl...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	true	468,974
2205.04978	NeRF-Editing: Geometry Editing of Neural Radiance Fields	Implicit neural rendering, especially Neural Radiance Field (NeRF), has shown great potential in novel view synthesis of a scene. However, current NeRF-based methods cannot enable users to perform user-controlled shape deformation in the scene. While existing works have proposed some approaches to modify the radiance f...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	295,804
2210.07054	Scaling Back-Translation with Domain Text Generation for Sign Language Gloss Translation	Sign language gloss translation aims to translate the sign glosses into spoken language texts, which is challenging due to the scarcity of labeled gloss-text parallel data. Back translation (BT), which generates pseudo-parallel data by translating in-domain spoken language texts into sign glosses, has been applied to a...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	323,557
1010.2993	Broadcasting with an Energy Harvesting Rechargeable Transmitter	In this paper, we investigate the transmission completion time minimization problem in a two-user additive white Gaussian noise (AWGN) broadcast channel, where the transmitter is able to harvest energy from the nature, using a rechargeable battery. The harvested energy is modeled to arrive at the transmitter randomly d...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	7,906
2306.12623	SEAL: Simultaneous Exploration and Localization in Multi-Robot Systems	The availability of accurate localization is critical for multi-robot exploration strategies; noisy or inconsistent localization causes failure in meeting exploration objectives. We aim to achieve high localization accuracy with contemporary exploration map belief and vice versa without needing global localization info...	false	false	false	false	true	false	false	true	false	false	false	false	false	false	true	false	false	false	374,999
2407.21592	Does the Source of a Warning Matter? Examining the Effectiveness of Veracity Warning Labels Across Warners	In this study, we conducted an online, between-subjects experiment (N = 2,049) to better understand the impact of warning label sources on information trust and sharing intentions. Across four warners (the social media platform, other social media users, Artificial Intelligence (AI), and fact checkers), we found that a...	true	false	false	true	false	false	false	false	false	false	false	false	false	true	false	false	false	false	477,602
1009.4966	The minimum distance of parameterized codes on projective tori	Let X be a subset of a projective space, over a finite field K, which is parameterized by the monomials arising from the edges of a clutter. Let I(X) be the vanishing ideal of X. It is shown that I(X) is a complete intersection if and only if X is a projective torus. In this case we determine the minimum distance of an...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	7,657
2203.15259	Eigencontours: Novel Contour Descriptors Based on Low-Rank Approximation	Novel contour descriptors, called eigencontours, based on low-rank approximation are proposed in this paper. First, we construct a contour matrix containing all object boundaries in a training set. Second, we decompose the contour matrix into eigencontours via the best rank-M approximation. Third, we represent an objec...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	288,316
1910.06536	Demand Adaptive Multi-Objective Electric Taxi Fleet Dispatching with Carbon Emission Analysis	As a foreseeable future mode of transport with lower emissions and higher efficiencies, electric vehicles have received worldwide attention. For convenient centralized management, taxis are considered as the fleet with electrification priority. In this work, we focus on the study on electric taxis dispatching, with con...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	149,369
2405.12926	Trusting Fair Data: Leveraging Quality in Fairness-Driven Data Removal Techniques	In this paper, we deal with bias mitigation techniques that remove specific data points from the training set to aim for a fair representation of the population in that set. Machine learning models are trained on these pre-processed datasets, and their predictions are expected to be fair. However, such approaches may e...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	455,699
1707.03619	A unified approach to source and message compression	We study the problem of source and message compression in the one-shot setting for the point-to-point and multi-party scenarios (with and without side information). We derive achievability results for these tasks in a unified manner, using the techniques of convex-split, which was introduced in [Anshu,Devabathini and J...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	76,898
1701.00338	Assessing Uncertainties in X-ray Single-particle Three-dimensional reconstructions	Modern technology for producing extremely bright and coherent X-ray laser pulses provides the possibility to acquire a large number of diffraction patterns from individual biological nanoparticles, including proteins, viruses, and DNA. These two-dimensional diffraction patterns can be practically reconstructed and retr...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	66,262
2406.13711	Imagining In-distribution States: How Predictable Robot Behavior Can Enable User Control Over Learned Policies	It is crucial that users are empowered to take advantage of the functionality of a robot and use their understanding of that functionality to perform novel and creative tasks. Given a robot trained with Reinforcement Learning (RL), a user may wish to leverage that autonomy along with their familiarity of how they expec...	true	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	false	465,966
1901.03960	Introducing a Generative Adversarial Network Model for Lagrangian Trajectory Simulation	We introduce a generative adversarial network (GAN) model to simulate the 3-dimensional Lagrangian motion of particles trapped in the recirculation zone of a buoyancy-opposed flame. The GAN model comprises a stochastic recurrent neural network, serving as a generator, and a convoluted neural network, serving as a discr...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	118,533

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