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
2307.14612	GenCo: An Auxiliary Generator from Contrastive Learning for Enhanced Few-Shot Learning in Remote Sensing	Classifying and segmenting patterns from a limited number of examples is a significant challenge in remote sensing and earth observation due to the difficulty in acquiring accurately labeled data in large quantities. Previous studies have shown that meta-learning, which involves episodic training on query and support s...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	381,991
1606.00127	On the Capacity of an Elemental Two-Way Two-Tier Network	A basic setup of a two-tier network, where two mobile users exchange messages with a multi-antenna macrocell basestation, is studied from a rate perspective subject to beamforming and power constraints. The communication is facilitated by two femtocell basestations which act as relays as there is no direct link between...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	56,632
2006.12368	Vibration transfer path analysis and path ranking for NVH optimization of a vehicle interior	By new advancements in vehicle manufacturing; evaluation of vehicle quality assurance has got a more critical issue. Today noise and vibration generated inside and outside the vehicles are more important factors for customers than previous. So far several researchers have focused on interior noise transfer path analysi...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	183,560
2009.00578	Linear-Quadratic Zero-Sum Mean-Field Type Games: Optimality Conditions and Policy Optimization	In this paper, zero-sum mean-field type games (ZSMFTG) with linear dynamics and quadratic cost are studied under infinite-horizon discounted utility function. ZSMFTG are a class of games in which two decision makers whose utilities sum to zero, compete to influence a large population of indistinguishable agents. In par...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	194,080
1711.05865	Pricing Football Players using Neural Networks	We designed a multilayer perceptron neural network to predict the price of a football (soccer) player using data on more than 15,000 players from the football simulation video game FIFA 2017. The network was optimized by experimenting with different activation functions, number of neurons and layers, learning rate and ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	84,661
1607.08371	Towards MRI-Based Autonomous Robotic US Acquisitions: A First Feasibility Study	Robotic ultrasound has the potential to assist and guide physicians during interventions. In this work, we present a set of methods and a workflow to enable autonomous MRI-guided ultrasound acquisitions. Our approach uses a structured-light 3D scanner for patient-to-robot and image-to-patient calibration, which in turn...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	59,151
2312.07894	Optimization of Power Control for Autonomous Hybrid Electric Vehicles with Flexible Power Demand	Technology advancement for on-road vehicles has gained significant momentum in the past decades, particularly in the field of vehicle automation and powertrain electrification. The optimization of powertrain controls for autonomous vehicles typically involves a separated consideration of the vehicle's external dynamics...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	415,107
2006.09365	Byzantine-Robust Learning on Heterogeneous Datasets via Bucketing	In Byzantine robust distributed or federated learning, a central server wants to train a machine learning model over data distributed across multiple workers. However, a fraction of these workers may deviate from the prescribed algorithm and send arbitrary messages. While this problem has received significant attention...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	182,529
1905.03853	Genuinely Distributed Byzantine Machine Learning	Machine Learning (ML) solutions are nowadays distributed, according to the so-called server/worker architecture. One server holds the model parameters while several workers train the model. Clearly, such architecture is prone to various types of component failures, which can be all encompassed within the spectrum of a ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	130,306
2106.03746	Efficient Training of Visual Transformers with Small Datasets	Visual Transformers (VTs) are emerging as an architectural paradigm alternative to Convolutional networks (CNNs). Differently from CNNs, VTs can capture global relations between image elements and they potentially have a larger representation capacity. However, the lack of the typical convolutional inductive bias makes...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	239,432
1910.04420	Learning beyond Predefined Label Space via Bayesian Nonparametric Topic Modelling	In real world machine learning applications, testing data may contain some meaningful new categories that have not been seen in labeled training data. To simultaneously recognize new data categories and assign most appropriate category labels to the data actually from known categories, existing models assume the number...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	148,768
1108.5025	Robust Stackelberg game in communication systems	This paper studies multi-user communication systems with two groups of users: leaders which possess system information, and followers which have no system information using the formulation of Stackelberg games. In such games, the leaders play and choose their actions based on their information about the system and the ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	11,809
2406.02936	Radiomics-guided Multimodal Self-attention Network for Predicting Pathological Complete Response in Breast MRI	Breast cancer is the most prevalent cancer among women and predicting pathologic complete response (pCR) after anti-cancer treatment is crucial for patient prognosis and treatment customization. Deep learning has shown promise in medical imaging diagnosis, particularly when utilizing multiple imaging modalities to enha...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	461,013
2304.01074	FinderNet: A Data Augmentation Free Canonicalization aided Loop Detection and Closure technique for Point clouds in 6-DOF separation	We focus on the problem of LiDAR point cloud based loop detection (or Finding) and closure (LDC) in a multi-agent setting. State-of-the-art (SOTA) techniques directly generate learned embeddings of a given point cloud, require large data transfers, and are not robust to wide variations in 6 Degrees-of-Freedom (DOF) vie...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	355,923
2008.03326	Optimal Combination of Linear and Spectral Estimators for Generalized Linear Models	We study the problem of recovering an unknown signal $\boldsymbol x$ given measurements obtained from a generalized linear model with a Gaussian sensing matrix. Two popular solutions are based on a linear estimator $\hat{\boldsymbol x}^{\rm L}$ and a spectral estimator $\hat{\boldsymbol x}^{\rm s}$. The former is a dat...	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	190,862
1709.07809	Neural Machine Translation	Draft of textbook chapter on neural machine translation. a comprehensive treatment of the topic, ranging from introduction to neural networks, computation graphs, description of the currently dominant attentional sequence-to-sequence model, recent refinements, alternative architectures and challenges. Written as chapte...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	81,344
2303.16320	SynthRAD2023 Grand Challenge dataset: generating synthetic CT for radiotherapy	Purpose: Medical imaging has become increasingly important in diagnosing and treating oncological patients, particularly in radiotherapy. Recent advances in synthetic computed tomography (sCT) generation have increased interest in public challenges to provide data and evaluation metrics for comparing different approach...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	354,818
2308.16140	Intergroup Bias in Attitudes Toward Restrictions on Uncivil Political Expression and Its Underlying Mechanisms	There appears to be a dilemma between the freedom of expression and protection from the adverse effects of uncivil political expression online. While previous studies have revealed various factors that affect attitudes toward freedom of expression and speech restrictions, it is less clear whether people have intergroup...	false	false	false	true	false	false	false	false	false	false	false	false	false	true	false	false	false	false	388,910
2502.09528	SteROI-D: System Design and Mapping for Stereo Depth Inference on Regions of Interest	Machine learning algorithms have enabled high quality stereo depth estimation to run on Augmented and Virtual Reality (AR/VR) devices. However, high energy consumption across the full image processing stack prevents stereo depth algorithms from running effectively on battery-limited devices. This paper introduces SteRO...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	533,478
2001.01122	Timely Status Updating Through Intermittent Sensing and Transmission	We consider a novel intermittent status updating model where an energy harvesting node with an intermittent energy source performs status updating to a receiver through non-preemptive sensing and transmission operations. Each operation costs a single energy recharge of the node and the node cannot harvest energy while ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	159,412
2404.04699	Deep Reinforcement Learning Control for Disturbance Rejection in a Nonlinear Dynamic System with Parametric Uncertainty	This work describes a technique for active rejection of multiple independent and time-correlated stochastic disturbances for a nonlinear flexible inverted pendulum with cart system with uncertain model parameters. The control law is determined through deep reinforcement learning, specifically with a continuous actor-cr...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	444,764
2305.16625	Set-based Neural Network Encoding Without Weight Tying	We propose a neural network weight encoding method for network property prediction that utilizes set-to-set and set-to-vector functions to efficiently encode neural network parameters. Our approach is capable of encoding neural networks in a model zoo of mixed architecture and different parameter sizes as opposed to pr...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	true	false	false	368,179
2212.14510	A Machine Learning Case Study for AI-empowered echocardiography of Intensive Care Unit Patients in low- and middle-income countries	We present a Machine Learning (ML) study case to illustrate the challenges of clinical translation for a real-time AI-empowered echocardiography system with data of ICU patients in LMICs. Such ML case study includes data preparation, curation and labelling from 2D Ultrasound videos of 31 ICU patients in LMICs and model...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	338,637
2305.16361	An Experimental Investigation into the Evaluation of Explainability Methods	EXplainable Artificial Intelligence (XAI) aims to help users to grasp the reasoning behind the predictions of an Artificial Intelligence (AI) system. Many XAI approaches have emerged in recent years. Consequently, a subfield related to the evaluation of XAI methods has gained considerable attention, with the aim to det...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	368,052
2404.03354	A Comprehensive Survey on Self-Supervised Learning for Recommendation	Recommender systems play a crucial role in tackling the challenge of information overload by delivering personalized recommendations based on individual user preferences. Deep learning techniques, such as RNNs, GNNs, and Transformer architectures, have significantly propelled the advancement of recommender systems by e...	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	false	false	444,225
1911.03930	Robust Unsupervised Audio-visual Speech Enhancement Using a Mixture of Variational Autoencoders	Recently, an audio-visual speech generative model based on variational autoencoder (VAE) has been proposed, which is combined with a nonnegative matrix factorization (NMF) model for noise variance to perform unsupervised speech enhancement. When visual data is clean, speech enhancement with audio-visual VAE shows a bet...	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	152,834
2405.16203	Evolutionary Large Language Model for Automated Feature Transformation	Feature transformation aims to reconstruct the feature space of raw features to enhance the performance of downstream models. However, the exponential growth in the combinations of features and operations poses a challenge, making it difficult for existing methods to efficiently explore a wide space. Additionally, thei...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	457,307
0903.3926	Designing a GUI for Proofs - Evaluation of an HCI Experiment	Often user interfaces of theorem proving systems focus on assisting particularly trained and skilled users, i.e., proof experts. As a result, the systems are difficult to use for non-expert users. This paper describes a paper and pencil HCI experiment, in which (non-expert) students were asked to make suggestions for a...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	3,399
1904.11157	Out of the Box: A combined approach for handling occlusion in Human Pose Estimation	Human Pose estimation is a challenging problem, especially in the case of 3D pose estimation from 2D images due to many different factors like occlusion, depth ambiguities, intertwining of people, and in general crowds. 2D multi-person human pose estimation in the wild also suffers from the same problems - occlusion, a...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	128,802
1505.00193	Segmentation and Restoration of Images on Surfaces by Parametric Active Contours with Topology Changes	In this article, a new method for segmentation and restoration of images on two-dimensional surfaces is given. Active contour models for image segmentation are extended to images on surfaces. The evolving curves on the surfaces are mathematically described using a parametric approach. For image restoration, a diffusion...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	42,680
1003.0696	Exponential Family Hybrid Semi-Supervised Learning	We present an approach to semi-supervised learning based on an exponential family characterization. Our approach generalizes previous work on coupled priors for hybrid generative/discriminative models. Our model is more flexible and natural than previous approaches. Experimental results on several data sets show that o...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	5,834
1911.08564	Enhancing Generic Segmentation with Learned Region Representations	Current successful approaches for generic (non-semantic) segmentation rely mostly on edge detection and have leveraged the strengths of deep learning mainly by improving the edge detection stage in the algorithmic pipeline. This is in contrast to semantic and instance segmentation, where DNNs are applied directly to ge...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	154,220
2306.06329	HIPODE: Enhancing Offline Reinforcement Learning with High-Quality Synthetic Data from a Policy-Decoupled Approach	Offline reinforcement learning (ORL) has gained attention as a means of training reinforcement learning models using pre-collected static data. To address the issue of limited data and improve downstream ORL performance, recent work has attempted to expand the dataset's coverage through data augmentation. However, most...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	372,559
2005.05735	Human-Robot Collaboration in Microgravity: the Object Handover Problem	Collaborative space robots are an emerging technology with high impact as robots facilitate servicing functions in collaboration with astronauts with higher precision during lengthy tasks, under tight operational schedules, with less risk and costs, making them more efficient and economically more viable. However, huma...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	176,813
2205.14855	Leave-one-out Singular Subspace Perturbation Analysis for Spectral Clustering	The singular subspaces perturbation theory is of fundamental importance in probability and statistics. It has various applications across different fields. We consider two arbitrary matrices where one is a leave-one-column-out submatrix of the other one and establish a novel perturbation upper bound for the distance be...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	299,519
2305.10161	Collective Large-scale Wind Farm Multivariate Power Output Control Based on Hierarchical Communication Multi-Agent Proximal Policy Optimization	Wind power is becoming an increasingly important source of renewable energy worldwide. However, wind farm power control faces significant challenges due to the high system complexity inherent in these farms. A novel communication-based multi-agent deep reinforcement learning large-scale wind farm multivariate control i...	false	false	false	false	true	false	false	false	false	false	true	false	false	false	true	false	false	false	364,937
1906.02113	Reinforcement Learning for Angle-Only Intercept Guidance of Maneuvering Targets	We present a novel guidance law that uses observations consisting solely of seeker line of sight angle measurements and their rate of change. The policy is optimized using reinforcement meta-learning and demonstrated in a simulated terminal phase of a mid-course exo-atmospheric interception. Importantly, the guidance l...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	133,943
1604.04825	Visual saliency detection: a Kalman filter based approach	In this paper we propose a Kalman filter aided saliency detection model which is based on the conjecture that salient regions are considerably different from our "visual expectation" or they are "visually surprising" in nature. In this work, we have structured our model with an immediate objective to predict saliency i...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	54,716
2402.19262	Masks, Signs, And Learning Rate Rewinding	Learning Rate Rewinding (LRR) has been established as a strong variant of Iterative Magnitude Pruning (IMP) to find lottery tickets in deep overparameterized neural networks. While both iterative pruning schemes couple structure and parameter learning, understanding how LRR excels in both aspects can bring us closer to...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	433,733
2101.08020	NEMR: Network Embedding on Metric of Relation	Network embedding maps the nodes of a given network into a low-dimensional space such that the semantic similarities among the nodes can be effectively inferred. Most existing approaches use inner-product of node embedding to measure the similarity between nodes leading to the fact that they lack the capacity to captur...	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	false	false	false	216,206
1201.2905	NegCut: Automatic Image Segmentation based on MRF-MAP	Solving the Maximum a Posteriori on Markov Random Field, MRF-MAP, is a prevailing method in recent interactive image segmentation tools. Although mathematically explicit in its computational targets, and impressive for the segmentation quality, MRF-MAP is hard to accomplish without the interactive information from user...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	13,805
2009.08859	Principal Components of the Meaning	In this paper we argue that (lexical) meaning in science can be represented in a 13 dimension Meaning Space. This space is constructed using principal component analysis (singular decomposition) on the matrix of word category relative information gains, where the categories are those used by the Web of Science, and the...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	196,370
2306.08656	Augment then Smooth: Reconciling Differential Privacy with Certified Robustness	Machine learning models are susceptible to a variety of attacks that can erode trust, including attacks against the privacy of training data, and adversarial examples that jeopardize model accuracy. Differential privacy and certified robustness are effective frameworks for combating these two threats respectively, as t...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	373,486
2304.06826	Collaboration and topic switches in science	Collaboration is a key driver of science and innovation. Mainly motivated by the need to leverage different capacities and expertise to solve a scientific problem, collaboration is also an excellent source of information about the future behavior of scholars. In particular, it allows us to infer the likelihood that sci...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	358,117
2410.13756	CLIMB: Language-Guided Continual Learning for Task Planning with Iterative Model Building	Intelligent and reliable task planning is a core capability for generalized robotics, requiring a descriptive domain representation that sufficiently models all object and state information for the scene. We present CLIMB, a continual learning framework for robot task planning that leverages foundation models and execu...	false	false	false	false	true	false	true	true	false	false	false	false	false	false	false	false	false	false	499,668
2405.05496	Boosting Large Language Models with Continual Learning for Aspect-based Sentiment Analysis	Aspect-based sentiment analysis (ABSA) is an important subtask of sentiment analysis, which aims to extract the aspects and predict their sentiments. Most existing studies focus on improving the performance of the target domain by fine-tuning domain-specific models (trained on source domains) based on the target domain...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	452,938
2212.01852	Band Relevance Factor (BRF): a novel automatic frequency band selection method based on vibration analysis for rotating machinery	The monitoring of rotating machinery has now become a fundamental activity in the industry, given the high criticality in production processes. Extracting useful information from relevant signals is a key factor for effective monitoring: studies in the areas of Informative Frequency Band selection (IFB) and Feature Ext...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	334,598
2306.04748	Analysis, Identification and Prediction of Parkinson Disease Sub-Types and Progression through Machine Learning	This paper represents a groundbreaking advancement in Parkinson disease (PD) research by employing a novel machine learning framework to categorize PD into distinct subtypes and predict its progression. Utilizing a comprehensive dataset encompassing both clinical and neurological parameters, the research applies advanc...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	371,879
2302.01976	SPARLING: Learning Latent Representations with Extremely Sparse Activations	Real-world processes often contain intermediate state that can be modeled as an extremely sparse tensor. We introduce Sparling, a technique that allows you to learn models with intermediate layers that match this state from only end-to-end labeled examples (i.e., no supervision on the intermediate state). Sparling uses...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	343,802
1101.4795	Numerical Evaluation of Algorithmic Complexity for Short Strings: A Glance into the Innermost Structure of Randomness	We describe an alternative method (to compression) that combines several theoretical and experimental results to numerically approximate the algorithmic (Kolmogorov-Chaitin) complexity of all $\sum_{n=1}^82^n$ bit strings up to 8 bits long, and for some between 9 and 16 bits long. This is done by an exhaustive executio...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	8,915
2205.00618	LoopStack: a Lightweight Tensor Algebra Compiler Stack	We present LoopStack, a domain specific compiler stack for tensor operations, composed of a frontend, LoopTool, and an efficient optimizing code generator, LoopNest. This stack enables us to compile entire neural networks and generate code targeting the AVX2, AVX512, NEON, and NEONfp16 instruction sets while incorporat...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	294,319
2005.04048	Sherpa: Robust Hyperparameter Optimization for Machine Learning	Sherpa is a hyperparameter optimization library for machine learning models. It is specifically designed for problems with computationally expensive, iterative function evaluations, such as the hyperparameter tuning of deep neural networks. With Sherpa, scientists can quickly optimize hyperparameters using a variety of...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	176,339
1812.02538	Energy Efficiency in Reinforcement Learning for Wireless Sensor Networks	As sensor networks for health monitoring become more prevalent, so will the need to control their usage and consumption of energy. This paper presents a method which leverages the algorithm's performance and energy consumption. By utilising Reinforcement Learning (RL) techniques, we provide an adaptive framework, which...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	115,784
2011.14752	A Comprehensive Review on Recent Methods and Challenges of Video Description	Video description involves the generation of the natural language description of actions, events, and objects in the video. There are various applications of video description by filling the gap between languages and vision for visually impaired people, generating automatic title suggestion based on content, browsing o...	false	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	208,879
2301.06767	The Recent Advances in Automatic Term Extraction: A survey	Automatic term extraction (ATE) is a Natural Language Processing (NLP) task that eases the effort of manually identifying terms from domain-specific corpora by providing a list of candidate terms. As units of knowledge in a specific field of expertise, extracted terms are not only beneficial for several terminographica...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	340,732
1501.03711	A Stochastic Approach for Resource Allocation with Backhaul and Energy Harvesting Constraints	We propose a novel stochastic radio resource allocation strategy that achieves long-term fairness considering backhaul and air-interface capacity limitations. The base station is considered to be only powered with a finite battery that is recharged by an energy harvesting source. Such energy harvesting is also taken in...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	39,288
1805.04875	On-the-fly Table Generation	Many information needs revolve around entities, which would be better answered by summarizing results in a tabular format, rather than presenting them as a ranked list. Unlike previous work, which is limited to retrieving existing tables, we aim to answer queries by automatically compiling a table in response to a quer...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	97,330
2408.00876	On the Relationship Between Monotone and Squared Probabilistic Circuits	Probabilistic circuits are a unifying representation of functions as computation graphs of weighted sums and products. Their primary application is in probabilistic modeling, where circuits with non-negative weights (monotone circuits) can be used to represent and learn density/mass functions, with tractable marginal i...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	478,011
2308.09954	Eva-KELLM: A New Benchmark for Evaluating Knowledge Editing of LLMs	Large language models (LLMs) possess a wealth of knowledge encoded in their parameters. However, this knowledge may become outdated or unsuitable over time. As a result, there has been a growing interest in knowledge editing for LLMs and evaluating its effectiveness. Existing studies primarily focus on knowledge editin...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	386,505
2402.08621	A Unified Framework for Analyzing Meta-algorithms in Online Convex Optimization	In this paper, we analyze the problem of online convex optimization in different settings, including different feedback types (full-information/semi-bandit/bandit/etc) in either stochastic or non-stochastic setting and different notions of regret (static adversarial regret/dynamic regret/adaptive regret). This is done ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	429,165
2502.13918	Playing Hex and Counter Wargames using Reinforcement Learning and Recurrent Neural Networks	Hex and Counter Wargames are adversarial two-player simulations of real military conflicts requiring complex strategic decision-making. Unlike classical board games, these games feature intricate terrain/unit interactions, unit stacking, large maps of varying sizes, and simultaneous move and combat decisions involving ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	535,575
2306.13284	Correcting discount-factor mismatch in on-policy policy gradient methods	The policy gradient theorem gives a convenient form of the policy gradient in terms of three factors: an action value, a gradient of the action likelihood, and a state distribution involving discounting called the \emph{discounted stationary distribution}. But commonly used on-policy methods based on the policy gradien...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	375,228
2002.03736	Universal Semantic Segmentation for Fisheye Urban Driving Images	Semantic segmentation is a critical method in the field of autonomous driving. When performing semantic image segmentation, a wider field of view (FoV) helps to obtain more information about the surrounding environment, making automatic driving safer and more reliable, which could be offered by fisheye cameras. However...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	163,380
2410.01221	Induced Covariance for Causal Discovery in Linear Sparse Structures	Causal models seek to unravel the cause-effect relationships among variables from observed data, as opposed to mere mappings among them, as traditional regression models do. This paper introduces a novel causal discovery algorithm designed for settings in which variables exhibit linearly sparse relationships. In such s...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	493,663
2311.18749	TransCORALNet: A Two-Stream Transformer CORAL Networks for Supply Chain Credit Assessment Cold Start	This paper proposes an interpretable two-stream transformer CORAL networks (TransCORALNet) for supply chain credit assessment under the segment industry and cold start problem. The model aims to provide accurate credit assessment prediction for new supply chain borrowers with limited historical data. Here, the two-stre...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	411,800
2401.12079	Collaborative Reinforcement Learning Based Unmanned Aerial Vehicle (UAV) Trajectory Design for 3D UAV Tracking	In this paper, the problem of using one active unmanned aerial vehicle (UAV) and four passive UAVs to localize a 3D target UAV in real time is investigated. In the considered model, each passive UAV receives reflection signals from the target UAV, which are initially transmitted by the active UAV. The received reflecti...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	false	false	false	423,252
2110.04627	Vector-quantized Image Modeling with Improved VQGAN	Pretraining language models with next-token prediction on massive text corpora has delivered phenomenal zero-shot, few-shot, transfer learning and multi-tasking capabilities on both generative and discriminative language tasks. Motivated by this success, we explore a Vector-quantized Image Modeling (VIM) approach that ...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	259,968
2109.14934	Prose2Poem: The Blessing of Transformers in Translating Prose to Persian Poetry	Persian Poetry has consistently expressed its philosophy, wisdom, speech, and rationale on the basis of its couplets, making it an enigmatic language on its own to both native and non-native speakers. Nevertheless, the notice able gap between Persian prose and poem has left the two pieces of literature medium-less. Hav...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	258,121
2102.10886	Anchor-Assisted Channel Estimation for Intelligent Reflecting Surface Aided Multiuser Communication	Channel estimation is a practical challenge for intelligent reflecting surface (IRS) aided wireless communication. As the number of IRS reflecting elements or IRS-aided users increases, the channel training overhead becomes excessively high, which results in long delay and low throughput in data transmission. To tackle...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	221,255
1611.04534	3-D Convolutional Neural Networks for Glioblastoma Segmentation	Convolutional Neural Networks (CNN) have emerged as powerful tools for learning discriminative image features. In this paper, we propose a framework of 3-D fully CNN models for Glioblastoma segmentation from multi-modality MRI data. By generalizing CNN models to true 3-D convolutions in learning 3-D tumor MRI data, the...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	63,862
1703.04010	Data-Driven Estimation of Travel Latency Cost Functions via Inverse Optimization in Multi-Class Transportation Networks	We develop a method to estimate from data travel latency cost functions in multi-class transportation networks, which accommodate different types of vehicles with very different characteristics (e.g., cars and trucks). Leveraging our earlier work on inverse variational inequalities, we develop a data-driven approach to...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	69,817
2410.13098	A Little Human Data Goes A Long Way	Faced with an expensive human annotation process, creators of NLP systems increasingly turn to synthetic data generation. While this method shows promise, the extent to which synthetic data can replace human annotation is poorly understood. We investigate the use of synthetic data in Fact Verification (FV) and Question...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	499,367
1603.04222	Multiple seed structure and disconnected networks in respondent-driven sampling	Respondent-driven sampling (RDS) is a link-tracing sampling method that is especially suitable for sampling hidden populations. RDS combines an efficient snowball-type sampling scheme with inferential procedures that yield unbiased population estimates under some assumptions about the sampling procedure and population ...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	53,214
2004.12623	Localizing Grouped Instances for Efficient Detection in Low-Resource Scenarios	State-of-the-art detection systems are generally evaluated on their ability to exhaustively retrieve objects densely distributed in the image, across a wide variety of appearances and semantic categories. Orthogonal to this, many real-life object detection applications, for example in remote sensing, instead require de...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	174,304
2305.13773	Enhanced Fine-grained Motion Diffusion for Text-driven Human Motion Synthesis	The emergence of text-driven motion synthesis technique provides animators with great potential to create efficiently. However, in most cases, textual expressions only contain general and qualitative motion descriptions, while lack fine depiction and sufficient intensity, leading to the synthesized motions that either ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	366,675
2307.04924	Count-Free Single-Photon 3D Imaging with Race Logic	Single-photon cameras (SPCs) have emerged as a promising technology for high-resolution 3D imaging. A single-photon 3D camera determines the round-trip time of a laser pulse by capturing the arrival of individual photons at each camera pixel. Constructing photon-timestamp histograms is a fundamental operation for a sin...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	378,556
1906.05237	Reinforcement Knowledge Graph Reasoning for Explainable Recommendation	Recent advances in personalized recommendation have sparked great interest in the exploitation of rich structured information provided by knowledge graphs. Unlike most existing approaches that only focus on leveraging knowledge graphs for more accurate recommendation, we perform explicit reasoning with knowledge for de...	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	false	134,966
2204.07923	Accelerated MRI With Deep Linear Convolutional Transform Learning	Recent studies show that deep learning (DL) based MRI reconstruction outperforms conventional methods, such as parallel imaging and compressed sensing (CS), in multiple applications. Unlike CS that is typically implemented with pre-determined linear representations for regularization, DL inherently uses a non-linear re...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	291,900
2301.13868	PADL: Language-Directed Physics-Based Character Control	Developing systems that can synthesize natural and life-like motions for simulated characters has long been a focus for computer animation. But in order for these systems to be useful for downstream applications, they need not only produce high-quality motions, but must also provide an accessible and versatile interfac...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	true	343,063
2211.10388	Patch-Based Denoising Diffusion Probabilistic Model for Sparse-View CT Reconstruction	Sparse-view computed tomography (CT) can be used to reduce radiation dose greatly but is suffers from severe image artifacts. Recently, the deep learning based method for sparse-view CT reconstruction has attracted a major attention. However, neural networks often have a limited ability to remove the artifacts when the...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	331,296
1809.06027	BSE: A Minimal Simulation of a Limit-Order-Book Stock Exchange	This paper describes the design, implementation, and successful use of the Bristol Stock Exchange (BSE), a novel minimal simulation of a centralised financial market, based on a Limit Order Book (LOB) such as is common in major stock exchanges. Construction of BSE was motivated by the fact that most of the world's majo...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	107,942
1602.08802	Exploring the coevolution of predator and prey morphology and behavior	A common idiom in biology education states, "Eyes in the front, the animal hunts. Eyes on the side, the animal hides." In this paper, we explore one possible explanation for why predators tend to have forward-facing, high-acuity visual systems. We do so using an agent-based computational model of evolution, where preda...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	52,693
2308.11013	Personalized Event Prediction for Electronic Health Records	Clinical event sequences consist of hundreds of clinical events that represent records of patient care in time. Developing accurate predictive models of such sequences is of a great importance for supporting a variety of models for interpreting/classifying the current patient condition, or predicting adverse clinical e...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	386,966
1508.03868	Visual Affect Around the World: A Large-scale Multilingual Visual Sentiment Ontology	Every culture and language is unique. Our work expressly focuses on the uniqueness of culture and language in relation to human affect, specifically sentiment and emotion semantics, and how they manifest in social multimedia. We develop sets of sentiment- and emotion-polarized visual concepts by adapting semantic struc...	false	false	false	false	false	true	false	false	true	false	false	true	false	false	false	false	false	true	46,051
2002.11017	A Practical Approach to Social Learning	Models of social learning feature either binary signals or abstract signal structures often deprived of micro-foundations. Both models are limited when analyzing interim results or performing empirical analysis. We present a method of generating signal structures which are richer than the binary model, yet are tractabl...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	165,583
2408.08823	Optimal Symmetries in Binary Classification	We explore the role of group symmetries in binary classification tasks, presenting a novel framework that leverages the principles of Neyman-Pearson optimality. Contrary to the common intuition that larger symmetry groups lead to improved classification performance, our findings show that selecting the appropriate grou...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	481,167
2201.03546	Language-driven Semantic Segmentation	We present LSeg, a novel model for language-driven semantic image segmentation. LSeg uses a text encoder to compute embeddings of descriptive input labels (e.g., "grass" or "building") together with a transformer-based image encoder that computes dense per-pixel embeddings of the input image. The image encoder is train...	false	false	false	false	false	false	true	false	true	false	false	true	false	false	false	false	false	false	274,879
1709.05903	E$^2$BoWs: An End-to-End Bag-of-Words Model via Deep Convolutional Neural Network	Traditional Bag-of-visual Words (BoWs) model is commonly generated with many steps including local feature extraction, codebook generation, and feature quantization, etc. Those steps are relatively independent with each other and are hard to be jointly optimized. Moreover, the dependency on hand-crafted local feature m...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	80,977
2501.19072	SpikingSoft: A Spiking Neuron Controller for Bio-inspired Locomotion with Soft Snake Robots	Inspired by the dynamic coupling of moto-neurons and physical elasticity in animals, this work explores the possibility of generating locomotion gaits by utilizing physical oscillations in a soft snake by means of a low-level spiking neural mechanism. To achieve this goal, we introduce the Double Threshold Spiking neur...	false	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	528,996
2412.10805	Are Language Models Agnostic to Linguistically Grounded Perturbations? A Case Study of Indic Languages	Pre-trained language models (PLMs) are known to be susceptible to perturbations to the input text, but existing works do not explicitly focus on linguistically grounded attacks, which are subtle and more prevalent in nature. In this paper, we study whether PLMs are agnostic to linguistically grounded attacks or not. To...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	517,111
2406.08946	Human-Robot Interface for Teleoperated Robotized Planetary Sample Collection and Assembly	As human space exploration evolves toward longer voyages farther from our home planet, in-situ resource utilization (ISRU) becomes increasingly important. Haptic teleoperations are one of the technologies by which such activities can be carried out remotely by humans, whose expertise is still necessary for complex acti...	true	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	463,702
1802.09971	Real-World Repetition Estimation by Div, Grad and Curl	We consider the problem of estimating repetition in video, such as performing push-ups, cutting a melon or playing violin. Existing work shows good results under the assumption of static and stationary periodicity. As realistic video is rarely perfectly static and stationary, the often preferred Fourier-based measureme...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	91,432
2502.00594	Fast Vision Mamba: Pooling Spatial Dimensions for Accelerated Processing	State Space Models (SSMs) with selective scan (Mamba) have been adapted into efficient vision models. Mamba, unlike Vision Transformers, achieves linear complexity for token interactions through a recurrent hidden state process. This sequential processing is enhanced by a parallel scan algorithm, which reduces the comp...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	529,452
0906.1835	Secret-Key Generation using Correlated Sources and Channels	We study the problem of generating a shared secret key between two terminals in a joint source-channel setup -- the sender communicates to the receiver over a discrete memoryless wiretap channel and additionally the terminals have access to correlated discrete memoryless source sequences. We establish lower and upper b...	false	false	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	3,857
1809.00604	Image computing for fibre-bundle endomicroscopy: A review	Endomicroscopy is an emerging imaging modality, that facilitates the acquisition of in vivo, in situ optical biopsies, assisting diagnostic and potentially therapeutic interventions. While there is a diverse and constantly expanding range of commercial and experimental optical biopsy platforms available, fibre-bundle e...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	106,615
0807.1158	Path Gain Algebraic Formulation for the Scalar Linear Network Coding Problem	In the algebraic view, the solution to a network coding problem is seen as a variety specified by a system of polynomial equations typically derived by using edge-to-edge gains as variables. The output from each sink is equated to its demand to obtain polynomial equations. In this work, we propose a method to derive th...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	2,039
2311.17567	Network characteristics of financial networks	We embrace a fresh perspective to auditing by analyzing a large set of companies as complex financial networks rather than static aggregates of balance sheet data. Preliminary analyses show that network centrality measures within these networks could significantly enhance auditors' insights into financial structures. U...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	411,344
2207.07267	ScaleNet: Searching for the Model to Scale	Recently, community has paid increasing attention on model scaling and contributed to developing a model family with a wide spectrum of scales. Current methods either simply resort to a one-shot NAS manner to construct a non-structural and non-scalable model family or rely on a manual yet fixed scaling strategy to scal...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	308,151
2008.08893	Switching Model Predictive Control for Online Structural Reformations of a Foldable Quadrotor	The aim of this article is the formulation of a switching model predictive control framework for the case of a foldable quadrotor with the ability to retain the overall control quality during online structural reformations. The majority of the related scientific publications consider fixed morphology of the aerial vehi...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	192,535
2410.18809	Learning Global Object-Centric Representations via Disentangled Slot Attention	Humans can discern scene-independent features of objects across various environments, allowing them to swiftly identify objects amidst changing factors such as lighting, perspective, size, and position and imagine the complete images of the same object in diverse settings. Existing object-centric learning methods only ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	502,039

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