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
2502.02363	FAB-PPI: Frequentist, Assisted by Bayes, Prediction-Powered Inference	Prediction-powered inference (PPI) enables valid statistical inference by combining experimental data with machine learning predictions. When a sufficient number of high-quality predictions is available, PPI results in more accurate estimates and tighter confidence intervals than traditional methods. In this paper, we ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	530,281
2011.12245	Effect of barren plateaus on gradient-free optimization	Barren plateau landscapes correspond to gradients that vanish exponentially in the number of qubits. Such landscapes have been demonstrated for variational quantum algorithms and quantum neural networks with either deep circuits or global cost functions. For obvious reasons, it is expected that gradient-based optimizer...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	208,098
2307.13365	Empower Your Model with Longer and Better Context Comprehension	Recently, with the emergence of numerous Large Language Models (LLMs), the implementation of AI has entered a new era. Irrespective of these models' own capacity and structure, there is a growing demand for LLMs to possess enhanced comprehension of longer and more complex contexts with relatively smaller sizes. Models ...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	381,560
2406.00239	A Review of Pulse-Coupled Neural Network Applications in Computer Vision and Image Processing	Research in neural models inspired by mammal's visual cortex has led to many spiking neural networks such as pulse-coupled neural networks (PCNNs). These models are oscillating, spatio-temporal models stimulated with images to produce several time-based responses. This paper reviews PCNN's state of the art, covering it...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	true	false	false	459,758
2307.09112	NU-MCC: Multiview Compressive Coding with Neighborhood Decoder and Repulsive UDF	Remarkable progress has been made in 3D reconstruction from single-view RGB-D inputs. MCC is the current state-of-the-art method in this field, which achieves unprecedented success by combining vision Transformers with large-scale training. However, we identified two key limitations of MCC: 1) The Transformer decoder i...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	380,061
2404.10034	A Realistic Protocol for Evaluation of Weakly Supervised Object Localization	Weakly Supervised Object Localization (WSOL) allows training deep learning models for classification and localization (LOC) using only global class-level labels. The absence of bounding box (bbox) supervision during training raises challenges in the literature for hyper-parameter tuning, model selection, and evaluation...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	446,936
2104.06509	Automatic Assembly Planning based on Digital Product Descriptions	This paper proposes a new concept in which a digital twin derived from a digital product description will automatically perform assembly planning and orchestrate the production resources in a manufacturing cell. Thus the manufacturing cell has generic services with minimal assumptions about what kind of product will be...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	true	230,096
2301.11912	OccRob: Efficient SMT-Based Occlusion Robustness Verification of Deep Neural Networks	Occlusion is a prevalent and easily realizable semantic perturbation to deep neural networks (DNNs). It can fool a DNN into misclassifying an input image by occluding some segments, possibly resulting in severe errors. Therefore, DNNs planted in safety-critical systems should be verified to be robust against occlusions...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	342,315
1904.01722	An Empirical Study of the Application of Machine Learning and Keyword Terms Methodologies to Privilege-Document Review Projects in Legal Matters	Protecting privileged communications and data from disclosure is paramount for legal teams. Unrestricted legal advice, such as attorney-client communications or litigation strategy. are vital to the legal process and are exempt from disclosure in litigations or regulatory events. To protect this information from being ...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	126,223
2402.11691	Stochastic Nonlinear Dynamical Modelling of SRAM Bitcells in Retention Mode	SRAM bitcells in retention mode behave as autonomous stochastic nonlinear dynamical systems. From observation of variability-aware transient noise simulations, we provide an unidimensional model, fully characterizable by conventional deterministic SPICE simulations, insightfully explaining the mechanism of intrinsic no...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	430,518
2307.12595	Underlaid Sensing Pilot for Integrated Sensing and Communications	This paper investigates a novel underlaid sensing pilot signal design for integrated sensing and communications (ISAC) in an OFDM-based communication system. The proposed two-dimensional (2D) pilot signal is first generated on the delay-Doppler (DD) plane and then converted to the time-frequency (TF) plane for multiple...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	381,314
2110.13708	TNTC: two-stream network with transformer-based complementarity for gait-based emotion recognition	Recognizing the human emotion automatically from visual characteristics plays a vital role in many intelligent applications. Recently, gait-based emotion recognition, especially gait skeletons-based characteristic, has attracted much attention, while many available methods have been proposed gradually. The popular pipe...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	263,288
2502.03795	Distribution learning via neural differential equations: minimal energy regularization and approximation theory	Neural ordinary differential equations (ODEs) provide expressive representations of invertible transport maps that can be used to approximate complex probability distributions, e.g., for generative modeling, density estimation, and Bayesian inference. We show that for a large class of transport maps $T$, there exists a...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	530,863
2401.17173	Zero-Shot Reinforcement Learning via Function Encoders	Although reinforcement learning (RL) can solve many challenging sequential decision making problems, achieving zero-shot transfer across related tasks remains a challenge. The difficulty lies in finding a good representation for the current task so that the agent understands how it relates to previously seen tasks. To ...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	425,128
2302.12770	Signalling for Electricity Demand Response: When is Truth Telling Optimal?	Utilities and transmission system operators (TSO) around the world implement demand response programs for reducing electricity consumption by sending information on the state of balance between supply demand to end-use consumers. We construct a Bayesian persuasion model to analyse such demand response programs. Using a...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	true	347,689
2005.13736	L^2UWE: A Framework for the Efficient Enhancement of Low-Light Underwater Images Using Local Contrast and Multi-Scale Fusion	Images captured underwater often suffer from suboptimal illumination settings that can hide important visual features, reducing their quality. We present a novel single-image low-light underwater image enhancer, L^2UWE, that builds on our observation that an efficient model of atmospheric lighting can be derived from l...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	179,081
1609.00565	Skipping Word: A Character-Sequential Representation based Framework for Question Answering	Recent works using artificial neural networks based on word distributed representation greatly boost the performance of various natural language learning tasks, especially question answering. Though, they also carry along with some attendant problems, such as corpus selection for embedding learning, dictionary transfor...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	60,494
1802.07088	i-RevNet: Deep Invertible Networks	It is widely believed that the success of deep convolutional networks is based on progressively discarding uninformative variability about the input with respect to the problem at hand. This is supported empirically by the difficulty of recovering images from their hidden representations, in most commonly used network ...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	90,826
1802.04023	Fair and Diverse DPP-based Data Summarization	Sampling methods that choose a subset of the data proportional to its diversity in the feature space are popular for data summarization. However, recent studies have noted the occurrence of bias (under- or over-representation of a certain gender or race) in such data summarization methods. In this paper we initiate a s...	false	false	false	false	false	true	true	false	false	false	false	false	false	true	false	false	false	false	90,124
2107.05856	eProduct: A Million-Scale Visual Search Benchmark to Address Product Recognition Challenges	Large-scale product recognition is one of the major applications of computer vision and machine learning in the e-commerce domain. Since the number of products is typically much larger than the number of categories of products, image-based product recognition is often cast as a visual search rather than a classificatio...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	245,911
2112.07057	NEORL: NeuroEvolution Optimization with Reinforcement Learning	We present an open-source Python framework for NeuroEvolution Optimization with Reinforcement Learning (NEORL) developed at the Massachusetts Institute of Technology. NEORL offers a global optimization interface of state-of-the-art algorithms in the field of evolutionary computation, neural networks through reinforceme...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	true	false	false	271,359
1909.01763	Video Affective Effects Prediction with Multi-modal Fusion and Shot-Long Temporal Context	Predicting the emotional impact of videos using machine learning is a challenging task considering the varieties of modalities, the complicated temporal contex of the video as well as the time dependency of the emotional states. Feature extraction, multi-modal fusion and temporal context fusion are crucial stages for p...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	143,996
2306.15117	Continual Learning for Out-of-Distribution Pedestrian Detection	A continual learning solution is proposed to address the out-of-distribution generalization problem for pedestrian detection. While recent pedestrian detection models have achieved impressive performance on various datasets, they remain sensitive to shifts in the distribution of the inference data. Our method adopts an...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	375,915
2411.07184	SAMPart3D: Segment Any Part in 3D Objects	3D part segmentation is a crucial and challenging task in 3D perception, playing a vital role in applications such as robotics, 3D generation, and 3D editing. Recent methods harness the powerful Vision Language Models (VLMs) for 2D-to-3D knowledge distillation, achieving zero-shot 3D part segmentation. However, these m...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	507,432
2109.02846	Datasets: A Community Library for Natural Language Processing	The scale, variety, and quantity of publicly-available NLP datasets has grown rapidly as researchers propose new tasks, larger models, and novel benchmarks. Datasets is a community library for contemporary NLP designed to support this ecosystem. Datasets aims to standardize end-user interfaces, versioning, and document...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	253,873
2310.00339	FedLPA: One-shot Federated Learning with Layer-Wise Posterior Aggregation	Efficiently aggregating trained neural networks from local clients into a global model on a server is a widely researched topic in federated learning. Recently, motivated by diminishing privacy concerns, mitigating potential attacks, and reducing communication overhead, one-shot federated learning (i.e., limiting clien...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	395,935
2303.08140	Digital staining in optical microscopy using deep learning -- a review	Until recently, conventional biochemical staining had the undisputed status as well-established benchmark for most biomedical problems related to clinical diagnostics, fundamental research and biotechnology. Despite this role as gold-standard, staining protocols face several challenges, such as a need for extensive, ma...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	351,529
2103.13677	Explainability Guided Multi-Site COVID-19 CT Classification	Radiologist examination of chest CT is an effective way for screening COVID-19 cases. In this work, we overcome three challenges in the automation of this process: (i) the limited number of supervised positive cases, (ii) the lack of region-based supervision, and (iii) the variability across acquisition sites. These ch...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	226,575
1201.1656	A MacWilliams type identity for m-spotty generalized Lee weight enumerators over $\mathbb{Z}_q$ q	Burst errors are very common in practice. There have been many designs in order to control and correct such errors. Recently, a new class of byte error control codes called spotty byte error control codes has been specifically designed to fit the large capacity memory systems that use high-density random access memory ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	13,727
2410.21595	Deep Trees for (Un)structured Data: Tractability, Performance, and Interpretability	Decision Trees have remained a popular machine learning method for tabular datasets, mainly due to their interpretability. However, they lack the expressiveness needed to handle highly nonlinear or unstructured datasets. Motivated by recent advances in tree-based machine learning (ML) techniques and first-order optimiz...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	503,311
2308.10019	Interpretation on Multi-modal Visual Fusion	In this paper, we present an analytical framework and a novel metric to shed light on the interpretation of the multimodal vision community. Our approach involves measuring the proposed semantic variance and feature similarity across modalities and levels, and conducting semantic and quantitative analyses through compr...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	386,534
2406.10214	Universal randomised signatures for generative time series modelling	Randomised signature has been proposed as a flexible and easily implementable alternative to the well-established path signature. In this article, we employ randomised signature to introduce a generative model for financial time series data in the spirit of reservoir computing. Specifically, we propose a novel Wasserst...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	464,279
2405.12081	Selective Annotation via Data Allocation: These Data Should Be Triaged to Experts for Annotation Rather Than the Model	To obtain high-quality annotations under limited budget, semi-automatic annotation methods are commonly used, where a portion of the data is annotated by experts and a model is then trained to complete the annotations for the remaining data. However, these methods mainly focus on selecting informative data for expert a...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	455,400
1409.1805	How memory generates heterogeneous dynamics in temporal networks	Empirical temporal networks display strong heterogeneities in their dynamics, which profoundly affect processes taking place on these networks, such as rumor and epidemic spreading. Despite the recent wealth of data on temporal networks, little work has been devoted to the understanding of how such heterogeneities can ...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	35,860
2302.05270	Conceptual Views on Tree Ensemble Classifiers	Random Forests and related tree-based methods are popular for supervised learning from table based data. Apart from their ease of parallelization, their classification performance is also superior. However, this performance, especially parallelizability, is offset by the loss of explainability. Statistical methods are ...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	true	344,985
2110.01562	Enhancing Voluntary Motion with Modular, Backdrivable, Powered Hip and Knee Orthoses	Mobility disabilities are prominent in society with wide-ranging detriments to affected individuals. Addressing the specific deficits of individuals within this heterogeneous population requires modular, partial-assist, lower-limb exoskeletons. This paper introduces the Modular Backdrivable Lower-limb Unloading Exoskel...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	258,810
2312.10801	Scope Compliance Uncertainty Estimate	The zeitgeist of the digital era has been dominated by an expanding integration of Artificial Intelligence~(AI) in a plethora of applications across various domains. With this expansion, however, questions of the safety and reliability of these methods come have become more relevant than ever. Consequently, a run-time ...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	416,320
2105.05528	WildGait: Learning Gait Representations from Raw Surveillance Streams	The use of gait for person identification has important advantages such as being non-invasive, unobtrusive, not requiring cooperation and being less likely to be obscured compared to other biometrics. Existing methods for gait recognition require cooperative gait scenarios, in which a single person is walking multiple ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	234,840
1608.01373	Matching Community Structure Across Online Social Networks	The discovery of community structure in networks is a problem of considerable interest in recent years. In online social networks, often times, users are simultaneously involved in multiple social media sites, some of which share common social relationships. It is of great interest to uncover a shared community structu...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	59,413
2301.12471	Maximising Weather Forecasting Accuracy through the Utilisation of Graph Neural Networks and Dynamic GNNs	Weather forecasting is an essential task to tackle global climate change. Weather forecasting requires the analysis of multivariate data generated by heterogeneous meteorological sensors. These sensors comprise of ground-based sensors, radiosonde, and sensors mounted on satellites, etc., To analyze the data generated b...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	342,542
2106.05589	AUGNLG: Few-shot Natural Language Generation using Self-trained Data Augmentation	Natural Language Generation (NLG) is a key component in a task-oriented dialogue system, which converts the structured meaning representation (MR) to the natural language. For large-scale conversational systems, where it is common to have over hundreds of intents and thousands of slots, neither template-based approache...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	240,153
2212.05116	Leveraging Contextual Data Augmentation for Generalizable Melanoma Detection	While skin cancer detection has been a valuable deep learning application for years, its evaluation has often neglected the context in which testing images are assessed. Traditional melanoma classifiers assume that their testing environments are comparable to the structured images they are trained on. This paper challe...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	335,676
1405.2092	Full-Duplex Cloud Radio Access Networks: An Information-Theoretic Viewpoint	The conventional design of cellular systems prescribes the separation of uplink and downlink transmissions via time-division or frequency-division duplex. Recent advances in analog and digital domain self-interference interference cancellation challenge the need for this arrangement and open up the possibility to opera...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	32,947
2410.22582	Analytical Solution for Inverse Kinematics	This paper introduces a closed-form analytical solution for the inverse kinematics (IK) of a 6 Degrees of Freedom (DOF) serial robotic manipulator arm, configured with six revolute joints and utilized within the Lunar Exploration Rover System (LERS). As a critical asset for conducting precise operations in the demandin...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	503,679
2103.01616	Interpretable Multi-Modal Hate Speech Detection	With growing role of social media in shaping public opinions and beliefs across the world, there has been an increased attention to identify and counter the problem of hate speech on social media. Hate speech on online spaces has serious manifestations, including social polarization and hate crimes. While prior works h...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	222,688
2312.06722	EgoPlan-Bench: Benchmarking Multimodal Large Language Models for Human-Level Planning	The pursuit of artificial general intelligence (AGI) has been accelerated by Multimodal Large Language Models (MLLMs), which exhibit superior reasoning, generalization capabilities, and proficiency in processing multimodal inputs. A crucial milestone in the evolution of AGI is the attainment of human-level planning, a ...	false	false	false	false	false	false	false	true	true	false	false	true	false	false	false	false	false	false	414,656
2102.06564	Analysis of Interpolation based Image In-painting Approaches	Interpolation and internal painting are one of the basic approaches in image internal painting, which is used to eliminate undesirable parts that occur in digital images or to enhance faulty parts. This study was designed to compare the interpolation algorithms used in image in-painting in the literature. Errors and no...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	219,798
1412.1441	Scalable, High-Quality Object Detection	Current high-quality object detection approaches use the scheme of salience-based object proposal methods followed by post-classification using deep convolutional features. This spurred recent research in improving object proposal methods. However, domain agnostic proposal generation has the principal drawback that the...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	38,100
cs/0205071	A Scalable Architecture for Harvest-Based Digital Libraries - The ODU/Southampton Experiments	This paper discusses the requirements of current and emerging applications based on the Open Archives Initiative (OAI) and emphasizes the need for a common infrastructure to support them. Inspired by HTTP proxy, cache, gateway and web service concepts, a design for a scalable and reliable infrastructure that aims at sa...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	true	537,592
2005.04871	Spanning Attack: Reinforce Black-box Attacks with Unlabeled Data	Adversarial black-box attacks aim to craft adversarial perturbations by querying input-output pairs of machine learning models. They are widely used to evaluate the robustness of pre-trained models. However, black-box attacks often suffer from the issue of query inefficiency due to the high dimensionality of the input ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	176,585
2006.13713	Coconut: a scalable bottom-up approach for building data series indexes	Many modern applications produce massive amounts of data series that need to be analyzed, requiring efficient similarity search operations. However, the state-of-the-art data series indexes that are used for this purpose do not scale well for massive datasets in terms of performance, or storage costs. We pinpoint the p...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	184,001
2311.09835	ML-Bench: Evaluating Large Language Models and Agents for Machine Learning Tasks on Repository-Level Code	Despite Large Language Models (LLMs) like GPT-4 achieving impressive results in function-level code generation, they struggle with repository-scale code understanding (e.g., coming up with the right arguments for calling routines), requiring a deeper comprehension of complex file interactions. Also, recently, people ha...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	408,331
2406.17414	Consensus Learning with Deep Sets for Essential Matrix Estimation	Robust estimation of the essential matrix, which encodes the relative position and orientation of two cameras, is a fundamental step in structure from motion pipelines. Recent deep-based methods achieved accurate estimation by using complex network architectures that involve graphs, attention layers, and hard pruning s...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	467,561
2502.00618	DesCLIP: Robust Continual Adaptation via General Attribute Descriptions for Pretrained Vision-Language Models	Continual adaptation of vision-language models (VLMs) focuses on leveraging cross-modal pretrained knowledge to incrementally adapt for expanding downstream tasks and datasets, while tackling the challenge of knowledge forgetting. Existing research often focuses on connecting visual features with specific class text in...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	529,463
2310.00294	RIS-aided Near-Field MIMO Communications: Codebook and Beam Training Design	Downlink reconfigurable intelligent surface (RIS)-assisted multi-input-multi-output (MIMO) systems are considered with far-field, near-field, and hybrid-far-near-field channels. According to the angular or distance information contained in the received signals, 1) a distance-based codebook is designed for near-field MI...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	395,916
2201.06297	Transfer Learning for Quantum Classifiers: An Information-Theoretic Generalization Analysis	A key component of a quantum machine learning model operating on classical inputs is the design of an embedding circuit mapping inputs to a quantum state. This paper studies a transfer learning setting in which classical-to-quantum embedding is carried out by an arbitrary parametric quantum circuit that is pre-trained ...	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	275,680
2501.13978	Chain of Grounded Objectives: Bridging Process and Goal-oriented Prompting for Code Generation	The use of Large Language Models (LLMs) for code generation has gained significant attention in recent years. Existing methods often aim to improve the quality of generated code by incorporating additional contextual information or guidance into input prompts. Many of these approaches adopt sequential reasoning strateg...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	true	526,923
2411.09803	Using a Single-Parity-Check to Reduce the Guesswork of Guessing Codeword Decoding	Guessing Codeword Decoding (GCD) is a recently proposed soft-input forward error correction decoder for arbitrary binary linear codes. Inspired by recent proposals that leverage binary linear codebook structure to reduce the number of queries made by Guessing Random Additive Noise Decoding (GRAND), for binary linear co...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	508,370
1611.00260	Surrogate-Assisted Partial Order-based Evolutionary Optimisation	In this paper, we propose a novel approach (SAPEO) to support the survival selection process in multi-objective evolutionary algorithms with surrogate models - it dynamically chooses individuals to evaluate exactly based on the model uncertainty and the distinctness of the population. We introduce variants that differ ...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	63,196
1904.09251	Contact-Aided Invariant Extended Kalman Filtering for Robot State Estimation	Legged robots require knowledge of pose and velocity in order to maintain stability and execute walking paths. Current solutions either rely on vision data, which is susceptible to environmental and lighting conditions, or fusion of kinematic and contact data with measurements from an inertial measurement unit (IMU). I...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	128,325
2502.05588	Optimizing Information Freshness of IEEE 802.11ax Uplink OFDMA-Based Random Access	The latest WiFi standard, IEEE 802.11ax (WiFi 6), introduces a novel uplink random access mechanism called uplink orthogonal frequency division multiple access-based random access (UORA). While existing work has evaluated the performance of UORA using conventional performance metrics, such as throughput and delay, its ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	531,679
2409.05471	Fast Computation of Kemeny's Constant for Directed Graphs	Kemeny's constant for random walks on a graph is defined as the mean hitting time from one node to another selected randomly according to the stationary distribution. It has found numerous applications and attracted considerable research interest. However, exact computation of Kemeny's constant requires matrix inversio...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	486,786
1904.12213	Towards Recognizing Phrase Translation Processes: Experiments on English-French	When translating phrases (words or group of words), human translators, consciously or not, resort to different translation processes apart from the literal translation, such as Idiom Equivalence, Generalization, Particularization, Semantic Modulation, etc. Translators and linguists (such as Vinay and Darbelnet, Newmark...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	129,044
cs/0607110	A Theory of Probabilistic Boosting, Decision Trees and Matryoshki	We present a theory of boosting probabilistic classifiers. We place ourselves in the situation of a user who only provides a stopping parameter and a probabilistic weak learner/classifier and compare three types of boosting algorithms: probabilistic Adaboost, decision tree, and tree of trees of ... of trees, which we c...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	539,610
1805.00638	A Deep Network for Arousal-Valence Emotion Prediction with Acoustic-Visual Cues	In this paper, we comprehensively describe the methodology of our submissions to the One-Minute Gradual-Emotion Behavior Challenge 2018.	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	96,479
1907.02670	Zero-shot Learning for Audio-based Music Classification and Tagging	Audio-based music classification and tagging is typically based on categorical supervised learning with a fixed set of labels. This intrinsically cannot handle unseen labels such as newly added music genres or semantic words that users arbitrarily choose for music retrieval. Zero-shot learning can address this problem ...	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	137,660
2011.04891	Hierarchical Reinforcement Learning for Relay Selection and Power Optimization in Two-Hop Cooperative Relay Network	Cooperative communication is an effective approach to improve spectrum utilization. In order to reduce outage probability of communication system, most studies propose various schemes for relay selection and power allocation, which are based on the assumption of channel state information (CSI). However, it is difficult...	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	205,732
2106.10934	GRAND: Graph Neural Diffusion	We present Graph Neural Diffusion (GRAND) that approaches deep learning on graphs as a continuous diffusion process and treats Graph Neural Networks (GNNs) as discretisations of an underlying PDE. In our model, the layer structure and topology correspond to the discretisation choices of temporal and spatial operators. ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	242,217
2108.10513	Maximum Likelihood Estimation for Multimodal Learning with Missing Modality	Multimodal learning has achieved great successes in many scenarios. Compared with unimodal learning, it can effectively combine the information from different modalities to improve the performance of learning tasks. In reality, the multimodal data may have missing modalities due to various reasons, such as sensor failu...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	251,916
2302.01171	Boosting Low-Data Instance Segmentation by Unsupervised Pre-training with Saliency Prompt	Recently, inspired by DETR variants, query-based end-to-end instance segmentation (QEIS) methods have outperformed CNN-based models on large-scale datasets. Yet they would lose efficacy when only a small amount of training data is available since it's hard for the crucial queries/kernels to learn localization and shape...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	343,506
2412.07958	PAFFA: Premeditated Actions For Fast Agents	Modern AI assistants have made significant progress in natural language understanding and API/tool integration, with emerging efforts to incorporate diverse interfaces (such as Web interfaces) for enhanced scalability and functionality. However, current approaches that heavily rely on repeated LLM-driven HTML parsing a...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	515,872
1905.00197	Enhanced Orthogonal Frequency-Division Multiplexing with Subcarrier Number Modulation	A novel modulation scheme termed orthogonal frequency-division multiplexing with subcarrier number modulation (OFDM-SNM) has been proposed and regarded as one of the promising candidate modulation schemes for next generation networks. Although OFDM-SNM is capable of having a higher spectral efficiency (SE) than OFDM wi...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	129,417
1303.7445	Agent-based modeling of a price information trading business	We describe an agent-based simulation of a fictional (but feasible) information trading business. The Gas Price Information Trader (GPIT) buys information about real-time gas prices in a metropolitan area from drivers and resells the information to drivers who need to refuel their vehicles. Our simulation uses real w...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	23,349
2202.07012	Building Inspection Toolkit: Unified Evaluation and Strong Baselines for Damage Recognition	In recent years, several companies and researchers have started to tackle the problem of damage recognition within the scope of automated inspection of built structures. While companies are neither willing to publish associated data nor models, researchers are facing the problem of data shortage on one hand and inconsi...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	280,395
2203.15366	Face segmentation: A comparison between visible and thermal images	Face segmentation is a first step for face biometric systems. In this paper we present a face segmentation algorithm for thermographic images. This algorithm is compared with the classic Viola and Jones algorithm used for visible images. Experimental results reveal that, when segmenting a multispectral (visible and the...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	288,372
1009.5004	On reverse-engineering the KUKA Robot Language	Most commercial manufacturers of industrial robots require their robots to be programmed in a proprietary language tailored to the domain - a typical domain-specific language (DSL). However, these languages oftentimes suffer from shortcomings such as controller-specific design, limited expressiveness and a lack of exte...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	7,675
2103.14910	MINE: Towards Continuous Depth MPI with NeRF for Novel View Synthesis	In this paper, we propose MINE to perform novel view synthesis and depth estimation via dense 3D reconstruction from a single image. Our approach is a continuous depth generalization of the Multiplane Images (MPI) by introducing the NEural radiance fields (NeRF). Given a single image as input, MINE predicts a 4-channel...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	true	227,016
1511.03328	Semantic Image Segmentation with Task-Specific Edge Detection Using CNNs and a Discriminatively Trained Domain Transform	Deep convolutional neural networks (CNNs) are the backbone of state-of-art semantic image segmentation systems. Recent work has shown that complementing CNNs with fully-connected conditional random fields (CRFs) can significantly enhance their object localization accuracy, yet dense CRF inference is computationally exp...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	48,742
cs/0104013	Shooting Over or Under the Mark: Towards a Reliable and Flexible Anticipation in the Economy	The real monetary economy is grounded upon monetary flow equilibration or the activity of actualizing monetary flow continuity at each economic agent except for the central bank. Every update of monetary flow continuity at each agent constantly causes monetary flow equilibration at the neighborhood agents. Every moneta...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	537,316
1011.1547	Being Rational or Aggressive? A Revisit to Dunbar's Number in Online Social Networks	Recent years have witnessed the explosion of online social networks (OSNs). They provide powerful IT-innovations for online social activities such as organizing contacts, publishing contents, and sharing interests between friends who may never meet before. As more and more people become the active users of online socia...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	8,157
2011.14373	Mutual Coupling and Unit Cell Aware Optimization for Reconfigurable Intelligent Surfaces	Reconfigurable intelligent surfaces (RISs) are an emerging technology for enhancing the performance of wireless networks at a low and affordable cost, complexity, and power consumption. We introduce an algorithm for optimizing a single-input single-output RIS-assisted system in which the RIS is modeled by using an elec...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	208,758
cmp-lg/9605012	A New Statistical Parser Based on Bigram Lexical Dependencies	This paper describes a new statistical parser which is based on probabilities of dependencies between head-words in the parse tree. Standard bigram probability estimation techniques are extended to calculate probabilities of dependencies between pairs of words. Tests using Wall Street Journal data show that the method ...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	536,540
2406.09141	Optimal Control of Agent-Based Dynamics under Deep Galerkin Feedback Laws	Ever since the concepts of dynamic programming were introduced, one of the most difficult challenges has been to adequately address high-dimensional control problems. With growing dimensionality, the utilisation of Deep Neural Networks promises to circumvent the issue of an otherwise exponentially increasing complexity...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	463,785
2312.04802	MimicDiffusion: Purifying Adversarial Perturbation via Mimicking Clean Diffusion Model	Deep neural networks (DNNs) are vulnerable to adversarial perturbation, where an imperceptible perturbation is added to the image that can fool the DNNs. Diffusion-based adversarial purification focuses on using the diffusion model to generate a clean image against such adversarial attacks. Unfortunately, the generativ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	413,830
2108.04330	Creating synthetic night-time visible-light meteorological satellite images using the GAN method	Meteorology satellite visible light images is critical for meteorology support and forecast. However, there is no such kind of data during night time. To overcome this, we propose a method based on deep learning to create synthetic satellite visible light images during night. Specifically, to produce more realistic pro...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	249,964
1811.10839	Maximizing Multivariate Information with Error-Correcting Codes	Multivariate mutual information provides a conceptual framework for characterizing higher-order interactions in complex systems. Two well-known measures of multivariate information---total correlation and dual total correlation---admit a spectrum of measures with varying sensitivity to intermediate orders of dependence...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	114,610
1606.02767	Theoretical Robopsychology: Samu Has Learned Turing Machines	From the point of view of a programmer, the robopsychology is a synonym for the activity is done by developers to implement their machine learning applications. This robopsychological approach raises some fundamental theoretical questions of machine learning. Our discussion of these questions is constrained to Turing m...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	57,012
2106.02436	Stochastic Multi-Armed Bandits with Unrestricted Delay Distributions	We study the stochastic Multi-Armed Bandit (MAB) problem with random delays in the feedback received by the algorithm. We consider two settings: the reward-dependent delay setting, where realized delays may depend on the stochastic rewards, and the reward-independent delay setting. Our main contribution is algorithms t...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	238,875
2108.01771	On Exponential Utility and Conditional Value-at-Risk as Risk-Averse Performance Criteria	The standard approach to risk-averse control is to use the Exponential Utility (EU) functional, which has been studied for several decades. Like other risk-averse utility functionals, EU encodes risk aversion through an increasing convex mapping $\varphi$ of objective costs to subjective costs. An objective cost is a r...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	249,117
2203.11516	NOSNOC: A Software Package for Numerical Optimal Control of Nonsmooth Systems	This letter introduces the NOnSmooth Numerical Optimal Control (NOSNOC) open-source software package. It is a modular MATLAB tool based on CasADi and IPOPT for numerically solving Optimal Control Problems (OCP) with piecewise smooth systems (PSS). The tool supports: 1) automatic reformulation of systems with state jump...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	286,946
1811.10273	A combined network and machine learning approaches for product market forecasting	Sustainable financial markets play an important role in the functioning of human society. Still, the detection and prediction of risk in financial markets remain challenging and draw much attention from the scientific community. Here we develop a new approach based on combined network theory and machine learning to stu...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	114,455
2204.08828	Detect-and-describe: Joint learning framework for detection and description of objects	Traditional object detection answers two questions; "what" (what the object is?) and "where" (where the object is?). "what" part of the object detection can be fine-grained further i.e. "what type", "what shape" and "what material" etc. This results in the shifting of the object detection tasks to the object descriptio...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	292,228
2410.02321	Convergence of Score-Based Discrete Diffusion Models: A Discrete-Time Analysis	Diffusion models have achieved great success in generating high-dimensional samples across various applications. While the theoretical guarantees for continuous-state diffusion models have been extensively studied, the convergence analysis of the discrete-state counterparts remains under-explored. In this paper, we stu...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	494,227
2402.09970	Accelerating Parallel Sampling of Diffusion Models	Diffusion models have emerged as state-of-the-art generative models for image generation. However, sampling from diffusion models is usually time-consuming due to the inherent autoregressive nature of their sampling process. In this work, we propose a novel approach that accelerates the sampling of diffusion models by ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	429,752
1802.04349	Intuitive Hand Teleoperation by Novice Operators Using a Continuous Teleoperation Subspace	Human-in-the-loop manipulation is useful in when autonomous grasping is not able to deal sufficiently well with corner cases or cannot operate fast enough. Using the teleoperator's hand as an input device can provide an intuitive control method but requires mapping between pose spaces which may not be similar. We propo...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	90,196
2303.01316	Interactive robots as inclusive tools to increase diversity in higher education	There is a major lack of diversity in engineering, technology, and computing subjects in higher education. The resulting underrepresentation of some population groups contributes largely to gender and ethnicity pay gaps and social disadvantages. We aim to increase the diversity among students in such subjects by invest...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	348,924
2411.10176	Let people fail! Exploring the influence of explainable virtual and robotic agents in learning-by-doing tasks	Collaborative decision-making with artificial intelligence (AI) agents presents opportunities and challenges. While human-AI performance often surpasses that of individuals, the impact of such technology on human behavior remains insufficiently understood, primarily when AI agents can provide justifiable explanations f...	true	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	false	508,534
2205.04391	High-Cardinality Geometrical Constellation Shaping for the Nonlinear Fibre Channel	This paper presents design methods for highly efficient optimisation of geometrically shaped constellations to maximise data throughput in optical communications. It describes methods to analytically calculate the information-theoretical loss and the gradient of this loss as a function of the input constellation shape....	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	295,628
2204.11590	Unsupervised Domain Adaptation for Monocular 3D Object Detection via Self-Training	Monocular 3D object detection (Mono3D) has achieved unprecedented success with the advent of deep learning techniques and emerging large-scale autonomous driving datasets. However, drastic performance degradation remains an unwell-studied challenge for practical cross-domain deployment as the lack of labels on the targ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	293,207
2212.13680	Statistical-CSI-Based Antenna Selection and Precoding in Uplink MIMO	Classical antenna selection schemes require instantaneous channel state information (CSI). This leads to high signaling overhead in the system. This work proposes a novel joint receive antenna selection and precoding scheme for multiuser multiple-input multiple-output uplink transmission that relies only on the long-te...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	338,371

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