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
2404.01230	LLM as a Mastermind: A Survey of Strategic Reasoning with Large Language Models	This paper presents a comprehensive survey of the current status and opportunities for Large Language Models (LLMs) in strategic reasoning, a sophisticated form of reasoning that necessitates understanding and predicting adversary actions in multi-agent settings while adjusting strategies accordingly. Strategic reasoni...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	443,324
2407.18525	Is larger always better? Evaluating and prompting large language models for non-generative medical tasks	The use of Large Language Models (LLMs) in medicine is growing, but their ability to handle both structured Electronic Health Record (EHR) data and unstructured clinical notes is not well-studied. This study benchmarks various models, including GPT-based LLMs, BERT-based models, and traditional clinical predictive mode...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	476,410
1905.09265	Bridging Stereo Matching and Optical Flow via Spatiotemporal Correspondence	Stereo matching and flow estimation are two essential tasks for scene understanding, spatially in 3D and temporally in motion. Existing approaches have been focused on the unsupervised setting due to the limited resource to obtain the large-scale ground truth data. To construct a self-learnable objective, co-related ta...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	131,691
1910.06573	IMMVP: An Efficient Daytime and Nighttime On-Road Object Detector	It is hard to detect on-road objects under various lighting conditions. To improve the quality of the classifier, three techniques are used. We define subclasses to separate daytime and nighttime samples. Then we skip similar samples in the training set to prevent overfitting. With the help of the outside training samp...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	149,381
2406.01863	Towards Effective Time-Aware Language Representation: Exploring Enhanced Temporal Understanding in Language Models	In the evolving field of Natural Language Processing, understanding the temporal context of text is increasingly crucial. This study investigates methods to incorporate temporal information during pre-training, aiming to achieve effective time-aware language representation for improved performance on time-related tasks...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	460,494
2301.10460	HAL3D: Hierarchical Active Learning for Fine-Grained 3D Part Labeling	We present the first active learning tool for fine-grained 3D part labeling, a problem which challenges even the most advanced deep learning (DL) methods due to the significant structural variations among the small and intricate parts. For the same reason, the necessary data annotation effort is tremendous, motivating ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	341,815
1307.7973	Connecting Language and Knowledge Bases with Embedding Models for Relation Extraction	This paper proposes a novel approach for relation extraction from free text which is trained to jointly use information from the text and from existing knowledge. Our model is based on two scoring functions that operate by learning low-dimensional embeddings of words and of entities and relationships from a knowledge b...	false	false	false	false	false	true	true	false	true	false	false	false	false	false	false	false	false	false	26,155
2112.08961	Objective hearing threshold identification from auditory brainstem response measurements using supervised and self-supervised approaches	Hearing loss is a major health problem and psychological burden in humans. Mouse models offer a possibility to elucidate genes involved in the underlying developmental and pathophysiological mechanisms of hearing impairment. To this end, large-scale mouse phenotyping programs include auditory phenotyping of single-gene...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	271,991
2103.04136	Perception Framework through Real-Time Semantic Segmentation and Scene Recognition on a Wearable System for the Visually Impaired	As the scene information, including objectness and scene type, are important for people with visual impairment, in this work we present a multi-task efficient perception system for the scene parsing and recognition tasks. Building on the compact ResNet backbone, our designed network architecture has two paths with shar...	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	223,536
1803.03807	CIoTA: Collaborative IoT Anomaly Detection via Blockchain	Due to their rapid growth and deployment, Internet of things (IoT) devices have become a central aspect of our daily lives. However, they tend to have many vulnerabilities which can be exploited by an attacker. Unsupervised techniques, such as anomaly detection, can help us secure the IoT devices. However, an anomaly d...	false	false	false	false	false	false	true	false	false	false	false	false	true	true	false	false	false	true	92,324
2201.12599	Semantic-assisted image compression	Conventional image compression methods typically aim at pixel-level consistency while ignoring the performance of downstream AI tasks.To solve this problem, this paper proposes a Semantic-Assisted Image Compression method (SAIC), which can maintain semantic-level consistency to enable high performance of downstream AI ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	277,707
2006.10643	Erdos Goes Neural: an Unsupervised Learning Framework for Combinatorial Optimization on Graphs	Combinatorial optimization problems are notoriously challenging for neural networks, especially in the absence of labeled instances. This work proposes an unsupervised learning framework for CO problems on graphs that can provide integral solutions of certified quality. Inspired by Erdos' probabilistic method, we use a...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	182,958
2209.06308	Risk-aware Resource Allocation for Multiple UAVs-UGVs Recharging Rendezvous	We study a resource allocation problem for the cooperative aerial-ground vehicle routing application, in which multiple Unmanned Aerial Vehicles (UAVs) with limited battery capacity and multiple Unmanned Ground Vehicles (UGVs) that can also act as a mobile recharging stations need to jointly accomplish a mission such a...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	317,348
2205.11308	Symptom Identification for Interpretable Detection of Multiple Mental Disorders	Mental disease detection (MDD) from social media has suffered from poor generalizability and interpretability, due to lack of symptom modeling. This paper introduces PsySym, the first annotated symptom identification corpus of multiple psychiatric disorders, to facilitate further research progress. PsySym is annotated ...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	298,090
2101.07957	Near-Optimal Regret Bounds for Contextual Combinatorial Semi-Bandits with Linear Payoff Functions	The contextual combinatorial semi-bandit problem with linear payoff functions is a decision-making problem in which a learner chooses a set of arms with the feature vectors in each round under given constraints so as to maximize the sum of rewards of arms. Several existing algorithms have regret bounds that are optimal...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	216,184
2412.05696	Jointly RS Image Deblurring and Super-Resolution with Adjustable-Kernel and Multi-Domain Attention	Remote Sensing (RS) image deblurring and Super-Resolution (SR) are common tasks in computer vision that aim at restoring RS image detail and spatial scale, respectively. However, real-world RS images often suffer from a complex combination of global low-resolution (LR) degeneration and local blurring degeneration. Alth...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	514,933
2012.14633	Supermodularity and valid inequalities for quadratic optimization with indicators	We study the minimization of a rank-one quadratic with indicators and show that the underlying set function obtained by projecting out the continuous variables is supermodular. Although supermodular minimization is, in general, difficult, the specific set function for the rank-one quadratic can be minimized in linear t...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	213,567
2306.02659	Hybrid Trajectory Optimization for Autonomous Terrain Traversal of Articulated Tracked Robots	Autonomous terrain traversal of articulated tracked robots can reduce operator cognitive load to enhance task efficiency and facilitate extensive deployment. We present a novel hybrid trajectory optimization method aimed at generating efficient, stable, and smooth traversal motions. To achieve this, we develop a planar...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	371,000
2312.11795	MELO: Enhancing Model Editing with Neuron-Indexed Dynamic LoRA	Large language models (LLMs) have shown great success in various Natural Language Processing (NLP) tasks, whist they still need updates after deployment to fix errors or keep pace with the changing knowledge in the world. Researchers formulate such problem as Model Editing and have developed various editors focusing on...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	416,715
cs/0701043	Adaptive Alternating Minimization Algorithms	The classical alternating minimization (or projection) algorithm has been successful in the context of solving optimization problems over two variables. The iterative nature and simplicity of the algorithm has led to its application to many areas such as signal processing, information theory, control, and finance. A ge...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	540,026
1810.11194	Distributed Market Clearing Approach for Local Energy Trading in Transactive Market	This paper proposes a market clearing mechanism for energy trading in a local transactive market, where each player can participate in the market as seller or buyer and tries to maximize its welfare individually. Market players send their demand and supply to a local data center, where clearing price is determined to b...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	111,453
2307.16773	AsdKB: A Chinese Knowledge Base for the Early Screening and Diagnosis of Autism Spectrum Disorder	To easily obtain the knowledge about autism spectrum disorder and help its early screening and diagnosis, we create AsdKB, a Chinese knowledge base on autism spectrum disorder. The knowledge base is built on top of various sources, including 1) the disease knowledge from SNOMED CT and ICD-10 clinical descriptions on me...	false	false	false	false	true	true	false	false	true	false	false	false	false	false	false	false	false	false	382,729
2010.11619	Self-Supervised Shadow Removal	Shadow removal is an important computer vision task aiming at the detection and successful removal of the shadow produced by an occluded light source and a photo-realistic restoration of the image contents. Decades of re-search produced a multitude of hand-crafted restoration techniques and, more recently, learned solu...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	202,332
2212.14124	Joint Action is a Framework for Understanding Partnerships Between Humans and Upper Limb Prostheses	Recent advances in upper limb prostheses have led to significant improvements in the number of movements provided by the robotic limb. However, the method for controlling multiple degrees of freedom via user-generated signals remains challenging. To address this issue, various machine learning controllers have been dev...	true	false	false	false	true	false	false	true	false	false	false	false	false	false	true	false	false	false	338,511
2302.03438	Uncoupled Learning of Differential Stackelberg Equilibria with Commitments	In multi-agent problems requiring a high degree of cooperation, success often depends on the ability of the agents to adapt to each other's behavior. A natural solution concept in such settings is the Stackelberg equilibrium, in which the ``leader'' agent selects the strategy that maximizes its own payoff given that th...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	true	false	false	false	344,333
1507.00248	The Network Picture of Labor Flow	We construct a data-driven model of flows in graphs that captures the essential elements of the movement of workers between jobs in the companies (firms) of entire economic systems such as countries. The model is based on the observation that certain job transitions between firms are often repeated over time, showing p...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	44,738
2105.08590	UncertaintyFuseNet: Robust Uncertainty-aware Hierarchical Feature Fusion Model with Ensemble Monte Carlo Dropout for COVID-19 Detection	The COVID-19 (Coronavirus disease 2019) pandemic has become a major global threat to human health and well-being. Thus, the development of computer-aided detection (CAD) systems that are capable to accurately distinguish COVID-19 from other diseases using chest computed tomography (CT) and X-ray data is of immediate pr...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	235,806
2311.03332	Learning Hard-Constrained Models with One Sample	We consider the problem of estimating the parameters of a Markov Random Field with hard-constraints using a single sample. As our main running examples, we use the $k$-SAT and the proper coloring models, as well as general $H$-coloring models; for all of these we obtain both positive and negative results. In contrast t...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	405,804
1609.06953	The Digital Synaptic Neural Substrate: Size and Quality Matters	We investigate the 'Digital Synaptic Neural Substrate' (DSNS) computational creativity approach further with respect to the size and quality of images that can be used to seed the process. In previous work we demonstrated how combining photographs of people and sequences taken from chess games between weak players can ...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	61,367
2303.00505	Robust consensus control of second-order uncertain multiagent systems with velocity and input constraints (extended version)	In this paper, we investigate the consensus problem of second-order multiagent systems under directed graphs. Simple yet robust consensus algorithms that advance existing achievements in accounting for velocity and input constraints, agent uncertainties, and lack of neighboring velocity measurements are proposed. Furth...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	348,619
1911.04464	MIDAS: Microcluster-Based Detector of Anomalies in Edge Streams	Given a stream of graph edges from a dynamic graph, how can we assign anomaly scores to edges in an online manner, for the purpose of detecting unusual behavior, using constant time and memory? Existing approaches aim to detect individually surprising edges. In this work, we propose MIDAS, which focuses on detecting mi...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	152,999
1909.04538	DeepPrivacy: A Generative Adversarial Network for Face Anonymization	We propose a novel architecture which is able to automatically anonymize faces in images while retaining the original data distribution. We ensure total anonymization of all faces in an image by generating images exclusively on privacy-safe information. Our model is based on a conditional generative adversarial network...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	144,832
2210.15134	Learning Variational Motion Prior for Video-based Motion Capture	Motion capture from a monocular video is fundamental and crucial for us humans to naturally experience and interact with each other in Virtual Reality (VR) and Augmented Reality (AR). However, existing methods still struggle with challenging cases involving self-occlusion and complex poses due to the lack of effective ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	326,818
2211.06137	Emergence of Concepts in DNNs?	The present paper reviews and discusses work from computer science that proposes to identify concepts in internal representations (hidden layers) of DNNs. It is examined, first, how existing methods actually identify concepts that are supposedly represented in DNNs. Second, it is discussed how conceptual spaces -- sets...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	329,793
2412.08520	GR-NLP-TOOLKIT: An Open-Source NLP Toolkit for Modern Greek	We present GR-NLP-TOOLKIT, an open-source natural language processing (NLP) toolkit developed specifically for modern Greek. The toolkit provides state-of-the-art performance in five core NLP tasks, namely part-of-speech tagging, morphological tagging, dependency parsing, named entity recognition, and Greeklishto-Greek...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	true	516,123
1806.10283	Optimal Scheduling of Electrolyzer in Power Market with Dynamic Prices	Optimal scheduling of hydrogen production in dynamic pricing power market can maximize the profit of hydrogen producer; however, it highly depends on the accurate forecast of hydrogen consumption. In this paper, we propose a deep leaning based forecasting approach for predicting hydrogen consumption of fuel cell vehicl...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	101,520
2010.06250	Regret minimization in stochastic non-convex learning via a proximal-gradient approach	Motivated by applications in machine learning and operations research, we study regret minimization with stochastic first-order oracle feedback in online constrained, and possibly non-smooth, non-convex problems. In this setting, the minimization of external regret is beyond reach for first-order methods, so we focus o...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	200,422
1806.09935	On the performance of multi-objective estimation of distribution algorithms for combinatorial problems	Fitness landscape analysis investigates features with a high influence on the performance of optimization algorithms, aiming to take advantage of the addressed problem characteristics. In this work, a fitness landscape analysis using problem features is performed for a Multi-objective Bayesian Optimization Algorithm (m...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	101,459
2403.14849	Output-Constrained Lossy Source Coding With Application to Rate-Distortion-Perception Theory	The distortion-rate function of output-constrained lossy source coding with limited common randomness is analyzed for the special case of squared error distortion measure. An explicit expression is obtained when both source and reconstruction distributions are Gaussian. This further leads to a partial characterization ...	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	440,280
2205.09226	Modeling Multi-hop Question Answering as Single Sequence Prediction	Fusion-in-decoder (Fid) (Izacard and Grave, 2020) is a generative question answering (QA) model that leverages passage retrieval with a pre-trained transformer and pushed the state of the art on single-hop QA. However, the complexity of multi-hop QA hinders the effectiveness of the generative QA approach. In this work,...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	297,190
2308.13837	Class-constrained t-SNE: Combining Data Features and Class Probabilities	Data features and class probabilities are two main perspectives when, e.g., evaluating model results and identifying problematic items. Class probabilities represent the likelihood that each instance belongs to a particular class, which can be produced by probabilistic classifiers or even human labeling with uncertaint...	true	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	388,076
2005.03008	Evaluating text coherence based on the graph of the consistency of phrases to identify symptoms of schizophrenia	Different state-of-the-art methods of the detection of schizophrenia symptoms based on the estimation of text coherence have been analyzed. The analysis of a text at the level of phrases has been suggested. The method based on the graph of the consistency of phrases has been proposed to evaluate the semantic coherence ...	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	176,037
2205.03465	Power Control of Grid-Forming Converters Based on Full-State Feedback	The active and reactive power controllers of grid-forming converters are traditionally designed separately, which relies on the assumption of loop decoupling. This paper proposes a full-state feedback control for the power loops of grid-forming converters. First, the power loops are modeled considering their natural co...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	295,286
2009.11321	Improving Dialog Evaluation with a Multi-reference Adversarial Dataset and Large Scale Pretraining	There is an increasing focus on model-based dialog evaluation metrics such as ADEM, RUBER, and the more recent BERT-based metrics. These models aim to assign a high score to all relevant responses and a low score to all irrelevant responses. Ideally, such models should be trained using multiple relevant and irrelevant ...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	197,133
2012.01273	Regularization and False Alarms Quantification: Two Sides of the Explainability Coin	Regularization is a well-established technique in machine learning (ML) to achieve an optimal bias-variance trade-off which in turn reduces model complexity and enhances explainability. To this end, some hyper-parameters must be tuned, enabling the ML model to accurately fit the unseen data as well as the seen data. In...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	209,373
2312.11260	Leveraging Normalization Layer in Adapters With Progressive Learning and Adaptive Distillation for Cross-Domain Few-Shot Learning	Cross-domain few-shot learning presents a formidable challenge, as models must be trained on base classes and then tested on novel classes from various domains with only a few samples at hand. While prior approaches have primarily focused on parameter-efficient methods of using adapters, they often overlook two critica...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	416,495
1912.08283	Progressive VAE Training on Highly Sparse and Imbalanced Data	In this paper, we present a novel approach for training a Variational Autoencoder (VAE) on a highly imbalanced data set. The proposed training of a high-resolution VAE model begins with the training of a low-resolution core model, which can be successfully trained on imbalanced data set. In subsequent training steps, n...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	157,795
1211.6409	Obesity Heuristic, New Way On Artificial Immune Systems	There is a need for new metaphors from immunology to flourish the application areas of Artificial Immune Systems. A metaheuristic called Obesity Heuristic derived from advances in obesity treatment is proposed. The main forces of the algorithm are the generation omega-6 and omega-3 fatty acids. The algorithm works with...	false	false	false	false	true	false	false	false	false	false	false	false	true	false	false	false	false	false	19,975
2412.19828	Quantum Implicit Neural Compression	Signal compression based on implicit neural representation (INR) is an emerging technique to represent multimedia signals with a small number of bits. While INR-based signal compression achieves high-quality reconstruction for relatively low-resolution signals, the accuracy of high-frequency details is significantly de...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	520,985
2110.04983	Understanding the Safety Requirements for Learning-based Power Systems Operations	Recent advancements in machine learning and reinforcement learning have brought increased attention to their applicability in a range of decision-making tasks in the operations of power systems, such as short-term emergency control, Volt/VAr control, long-term residential demand response and battery energy management. ...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	260,116
2108.02859	Evaluating the Tradeoff Between Abstractiveness and Factuality in Abstractive Summarization	Neural models for abstractive summarization tend to generate output that is fluent and well-formed but lacks semantic faithfulness, or factuality, with respect to the input documents. In this paper, we analyze the tradeoff between abstractiveness and factuality of generated summaries across multiple datasets and models...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	249,474
1712.04753	Learning Spontaneity to Improve Emotion Recognition In Speech	We investigate the effect and usefulness of spontaneity (i.e. whether a given speech is spontaneous or not) in speech in the context of emotion recognition. We hypothesize that emotional content in speech is interrelated with its spontaneity, and use spontaneity classification as an auxiliary task to the problem of emo...	true	false	true	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	86,652
1504.05122	Optimal Nudging: Solving Average-Reward Semi-Markov Decision Processes as a Minimal Sequence of Cumulative Tasks	This paper describes a novel method to solve average-reward semi-Markov decision processes, by reducing them to a minimal sequence of cumulative reward problems. The usual solution methods for this type of problems update the gain (optimal average reward) immediately after observing the result of taking an action. The ...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	42,229
2202.05594	The Shapley Value in Machine Learning	Over the last few years, the Shapley value, a solution concept from cooperative game theory, has found numerous applications in machine learning. In this paper, we first discuss fundamental concepts of cooperative game theory and axiomatic properties of the Shapley value. Then we give an overview of the most important ...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	true	279,922
2308.10638	SCULPT: Shape-Conditioned Unpaired Learning of Pose-dependent Clothed and Textured Human Meshes	We present SCULPT, a novel 3D generative model for clothed and textured 3D meshes of humans. Specifically, we devise a deep neural network that learns to represent the geometry and appearance distribution of clothed human bodies. Training such a model is challenging, as datasets of textured 3D meshes for humans are lim...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	true	386,821
2203.14887	HUNIS: High-Performance Unsupervised Nuclei Instance Segmentation	A high-performance unsupervised nuclei instance segmentation (HUNIS) method is proposed in this work. HUNIS consists of two-stage block-wise operations. The first stage includes: 1) adaptive thresholding of pixel intensities, 2) incorporation of nuclei size/shape priors and 3) removal of false positive nuclei instances...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	288,161
2204.00005	Graph-based Active Learning for Semi-supervised Classification of SAR Data	We present a novel method for classification of Synthetic Aperture Radar (SAR) data by combining ideas from graph-based learning and neural network methods within an active learning framework. Graph-based methods in machine learning are based on a similarity graph constructed from the data. When the data consists of ra...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	true	289,102
2312.08220	EventAid: Benchmarking Event-aided Image/Video Enhancement Algorithms with Real-captured Hybrid Dataset	Event cameras are emerging imaging technology that offers advantages over conventional frame-based imaging sensors in dynamic range and sensing speed. Complementing the rich texture and color perception of traditional image frames, the hybrid camera system of event and frame-based cameras enables high-performance imagi...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	415,231
1403.3602	Spontaneous expression classification in the encrypted domain	To date, most facial expression analysis have been based on posed image databases and is carried out without being able to protect the identity of the subjects whose expressions are being recognised. In this paper, we propose and implement a system for classifying facial expressions of images in the encrypted domain ba...	false	false	false	false	false	false	false	false	false	false	false	true	true	false	false	false	false	false	31,585
2401.00676	Digger: Detecting Copyright Content Mis-usage in Large Language Model Training	Pre-training, which utilizes extensive and varied datasets, is a critical factor in the success of Large Language Models (LLMs) across numerous applications. However, the detailed makeup of these datasets is often not disclosed, leading to concerns about data security and potential misuse. This is particularly relevant...	false	false	false	false	false	false	true	false	true	false	false	false	true	false	false	false	false	false	419,065
2111.08900	A GNN-RNN Approach for Harnessing Geospatial and Temporal Information: Application to Crop Yield Prediction	Climate change is posing new challenges to crop-related concerns including food insecurity, supply stability and economic planning. As one of the central challenges, crop yield prediction has become a pressing task in the machine learning field. Despite its importance, the prediction task is exceptionally complicated s...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	266,847
2301.13573	Skill Decision Transformer	Recent work has shown that Large Language Models (LLMs) can be incredibly effective for offline reinforcement learning (RL) by representing the traditional RL problem as a sequence modelling problem (Chen et al., 2021; Janner et al., 2021). However many of these methods only optimize for high returns, and may not extra...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	342,962
2307.04892	Entity Identifier: A Natural Text Parsing-based Framework For Entity Relation Extraction	The field of programming has a diversity of paradigms that are used according to the working framework. While current neural code generation methods are able to learn and generate code directly from text, we believe that this approach is not optimal for certain code tasks, particularly the generation of classes in an o...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	378,544
2306.16482	DenseBAM-GI: Attention Augmented DeneseNet with momentum aided GRU for HMER	The task of recognising Handwritten Mathematical Expressions (HMER) is crucial in the fields of digital education and scholarly research. However, it is difficult to accurately determine the length and complex spatial relationships among symbols in handwritten mathematical expressions. In this study, we present a novel...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	376,369
2206.03261	Optimists at Heart: Why Do We Research Game AI? (Extended Version)	In this paper we survey the motivations behind contemporary game AI research by analysing individual publications, the researchers themselves, and the institutions that influence them. In doing so, we identify some negative effects on our field, caused both by external forces outside of our control as well as instituti...	false	false	false	false	true	false	false	false	false	false	false	false	false	true	false	false	false	false	301,207
2402.16696	Look Before You Leap: Towards Decision-Aware and Generalizable Tool-Usage for Large Language Models	Tool-augmented large language models (LLMs) are attracting widespread attention when accessing up-to-date knowledge and alleviating hallucination issues. Nowadays, advanced closed-source LLMs (e.g., ChatGPT) have demonstrated surprising tool-usage capabilities through prompting and in-context learning techniques. To em...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	432,659
2403.16612	Calibrating Bayesian UNet++ for Sub-Seasonal Forecasting	Seasonal forecasting is a crucial task when it comes to detecting the extreme heat and colds that occur due to climate change. Confidence in the predictions should be reliable since a small increase in the temperatures in a year has a big impact on the world. Calibration of the neural networks provides a way to ensure ...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	441,124
2104.10062	Pseudo-Boolean Functions for Optimal Z-Complementary Code Sets with Flexible Lengths	This paper aims to construct optimal Z-complementary code set (ZCCS) with non-power-of-two (NPT) lengths to enable interference-free multicarrier code-division multiple access (MC-CDMA) systems. The existing ZCCSs with NPT lengths, which are constructed from generalized Boolean functions (GBFs), are sub-optimal only wi...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	231,447
1905.10346	Mask-Guided Portrait Editing with Conditional GANs	Portrait editing is a popular subject in photo manipulation. The Generative Adversarial Network (GAN) advances the generating of realistic faces and allows more face editing. In this paper, we argue about three issues in existing techniques: diversity, quality, and controllability for portrait synthesis and editing. To...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	132,028
2411.08901	SoccerGuard: Investigating Injury Risk Factors for Professional Soccer Players with Machine Learning	We present SoccerGuard, a novel framework for predicting injuries in women's soccer using Machine Learning (ML). This framework can ingest data from multiple sources, including subjective wellness and training load reports from players, objective GPS sensor measurements, third-party player statistics, and injury report...	true	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	508,062
2306.11132	Fairness-aware Message Passing for Graph Neural Networks	Graph Neural Networks (GNNs) have shown great power in various domains. However, their predictions may inherit societal biases on sensitive attributes, limiting their adoption in real-world applications. Although many efforts have been taken for fair GNNs, most existing works just adopt widely used fairness techniques ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	374,484
2406.13225	Communication-Efficient Federated Knowledge Graph Embedding with Entity-Wise Top-K Sparsification	Federated Knowledge Graphs Embedding learning (FKGE) encounters challenges in communication efficiency stemming from the considerable size of parameters and extensive communication rounds. However, existing FKGE methods only focus on reducing communication rounds by conducting multiple rounds of local training in each ...	false	false	false	false	true	true	true	false	false	false	false	false	false	false	false	false	false	false	465,757
2412.17458	Progressive Boundary Guided Anomaly Synthesis for Industrial Anomaly Detection	Unsupervised anomaly detection methods can identify surface defects in industrial images by leveraging only normal samples for training. Due to the risk of overfitting when learning from a single class, anomaly synthesis strategies are introduced to enhance detection capability by generating artificial anomalies. Howev...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	519,984
2412.10570	Adaptive Sampling to Reduce Epistemic Uncertainty Using Prediction Interval-Generation Neural Networks	Obtaining high certainty in predictive models is crucial for making informed and trustworthy decisions in many scientific and engineering domains. However, extensive experimentation required for model accuracy can be both costly and time-consuming. This paper presents an adaptive sampling approach designed to reduce ep...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	516,998
2409.17568	Showing Many Labels in Multi-label Classification Models: An Empirical Study of Adversarial Examples	With the rapid development of Deep Neural Networks (DNNs), they have been applied in numerous fields. However, research indicates that DNNs are susceptible to adversarial examples, and this is equally true in the multi-label domain. To further investigate multi-label adversarial examples, we introduce a novel type of a...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	491,872
2403.11473	Word Order's Impacts: Insights from Reordering and Generation Analysis	Existing works have studied the impacts of the order of words within natural text. They usually analyze it by destroying the original order of words to create a scrambled sequence, and then comparing the models' performance between the original and scrambled sequences. The experimental results demonstrate marginal drop...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	438,716
2103.09991	A Soft-Aided Staircase Decoder Using Three-Level Channel Reliabilities	The soft-aided bit-marking (SABM) algorithm is based on the idea of marking bits as highly reliable bits (HRBs), highly unreliable bits (HUBs), and uncertain bits to improve the performance of hard-decision (HD) decoders. The HRBs and HUBs are used to assist the HD decoders to prevent miscorrections and to decode those...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	225,314
2301.10115	A Robust Hypothesis Test for Tree Ensemble Pruning	Gradient boosted decision trees are some of the most popular algorithms in applied machine learning. They are a flexible and powerful tool that can robustly fit to any tabular dataset in a scalable and computationally efficient way. One of the most critical parameters to tune when fitting these models are the various p...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	341,705
2408.16662	Space3D-Bench: Spatial 3D Question Answering Benchmark	Answering questions about the spatial properties of the environment poses challenges for existing language and vision foundation models due to a lack of understanding of the 3D world notably in terms of relationships between objects. To push the field forward, multiple 3D Q&A datasets were proposed which, overall, prov...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	484,410
2003.07602	Machine Learning the Phenomenology of COVID-19 From Early Infection Dynamics	We present a robust data-driven machine learning analysis of the COVID-19 pandemic from its early infection dynamics, specifically infection counts over time. The goal is to extract actionable public health insights. These insights include the infectious force, the rate of a mild infection becoming serious, estimates f...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	168,484
2407.00609	ESGNN: Towards Equivariant Scene Graph Neural Network for 3D Scene Understanding	Scene graphs have been proven to be useful for various scene understanding tasks due to their compact and explicit nature. However, existing approaches often neglect the importance of maintaining the symmetry-preserving property when generating scene graphs from 3D point clouds. This oversight can diminish the accuracy...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	468,933
2102.10078	Rapid Multi-Physics Simulation for Electro-Thermal Origami Systems	Electro-thermally actuated origami provides a novel method for creating 3-D systems with advanced morphing and functional capabilities. However, it is currently difficult to simulate the multi-physical behavior of such systems because the electro-thermal actuation and large folding deformations are highly interdependen...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	220,971
1703.07048	Whole-Building Fault Detection: A Scalable Approach Using Spectral Methods	In this paper, an extension to rules-based fault detection is demonstrated utilizing properties of the Koopman operator. The Koopman operator is an infinite-dimensional, linear operator that captures nonlinear, finite dimensional dynamics. The definition of the Koopman operator enables algorithms that can evaluate the ...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	70,326
2111.01259	Verifying Contracts for Perturbed Control Systems using Linear Programming	Verifying specifications for large-scale control systems is of utmost importance, but can be hard in practice as most formal verification methods can not handle high-dimensional dynamics. Contract theory has been proposed as a modular alternative to formal verification in which specifications are defined by assumptions...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	264,500
1311.3062	Ants: Mobile Finite State Machines	Consider the Ants Nearby Treasure Search (ANTS) problem introduced by Feinerman, Korman, Lotker, and Sereni (PODC 2012), where $n$ mobile agents, initially placed at the origin of an infinite grid, collaboratively search for an adversarially hidden treasure. In this paper, the model of Feinerman et al. is adapted such ...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	true	28,380
1910.12441	Online News Media Website Ranking Using User Generated Content	News media websites are important online resources that have drawn great attention of text mining researchers. The main aim of this study is to propose a framework for ranking online news websites from different viewpoints. The ranking of news websites is useful information, which can benefit many news-related tasks su...	false	false	false	true	false	true	false	false	true	false	false	false	false	false	false	false	false	false	151,094
1710.09300	Feature learning in feature-sample networks using multi-objective optimization	Data and knowledge representation are fundamental concepts in machine learning. The quality of the representation impacts the performance of the learning model directly. Feature learning transforms or enhances raw data to structures that are effectively exploited by those models. In recent years, several works have bee...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	true	false	false	83,186
1905.12056	Information-Theoretic Registration with Explicit Reorientation of Diffusion-Weighted Images	We present an information-theoretic approach to the registration of images with directional information, and especially for diffusion-Weighted Images (DWI), with explicit optimization over the directional scale. We call it Locally Orderless Registration with Directions (LORD). We focus on normalized mutual information ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	132,632
2412.15301	Parametric $\rho$-Norm Scaling Calibration	Output uncertainty indicates whether the probabilistic properties reflect objective characteristics of the model output. Unlike most loss functions and metrics in machine learning, uncertainty pertains to individual samples, but validating it on individual samples is unfeasible. When validated collectively, it cannot f...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	519,052
1908.04052	Sentence Specified Dynamic Video Thumbnail Generation	With the tremendous growth of videos over the Internet, video thumbnails, providing video content previews, are becoming increasingly crucial to influencing users' online searching experiences. Conventional video thumbnails are generated once purely based on the visual characteristics of videos, and then displayed as r...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	141,398
1705.00601	The Promise of Premise: Harnessing Question Premises in Visual Question Answering	In this paper, we make a simple observation that questions about images often contain premises - objects and relationships implied by the question - and that reasoning about premises can help Visual Question Answering (VQA) models respond more intelligently to irrelevant or previously unseen questions. When presented w...	false	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	72,715
2012.09090	You Are What You Tweet: Profiling Users by Past Tweets to Improve Hate Speech Detection	Hate speech detection research has predominantly focused on purely content-based methods, without exploiting any additional context. We briefly critique pros and cons of this task formulation. We then investigate profiling users by their past utterances as an informative prior to better predict whether new utterances c...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	211,957
2306.09182	Rolling control and dynamics model of two section articulated-wing ornithopter	This paper invented a new rolling control mechanism of two section articulated-wing ornithopter, which is analogues to aileron control in plane, however, similar control mechanism leads to opposite result, indicating the ornithopter supposed to go left now go right instead. This research gives a qualitative dynamics mo...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	373,706
1911.02436	On Data-Processing and Majorization Inequalities for $f$-Divergences with Applications	This paper is focused on derivations of data-processing and majorization inequalities for $f$-divergences, and their applications in information theory and statistics. For the accessibility of the material, the main results are first introduced without proofs, followed by exemplifications of the theorems with further r...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	152,360
1805.10994	Long-term Large-scale Mapping and Localization Using maplab	This paper discusses a large-scale and long-term mapping and localization scenario using the maplab open-source framework. We present a brief overview of the specific algorithms in the system that enable building a consistent map from multiple sessions. We then demonstrate that such a map can be reused even a few month...	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	98,818
2112.15475	Shift-Equivariant Similarity-Preserving Hypervector Representations of Sequences	Hyperdimensional Computing (HDC), also known as Vector-Symbolic Architectures (VSA), is a promising framework for the development of cognitive architectures and artificial intelligence systems, as well as for technical applications and emerging neuromorphic and nanoscale hardware. HDC/VSA operate with hypervectors, i.e...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	true	false	false	273,797
1805.09864	Inverse Rational Control: Inferring What You Think from How You Forage	Complex behaviors are often driven by an internal model, which integrates sensory information over time and facilitates long-term planning. Inferring an agent's internal model is a crucial ingredient in social interactions (theory of mind), for imitation learning, and for interpreting neural activities of behaving agen...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	98,511
2005.07277	SUPER: A Novel Lane Detection System	AI-based lane detection algorithms were actively studied over the last few years. Many have demonstrated superior performance compared with traditional feature-based methods. The accuracy, however, is still generally in the low 80% or high 90%, or even lower when challenging images are used. In this paper, we propose a...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	177,235
1912.07833	Unpaired Image Enhancement Featuring Reinforcement-Learning-Controlled Image Editing Software	This paper tackles unpaired image enhancement, a task of learning a mapping function which transforms input images into enhanced images in the absence of input-output image pairs. Our method is based on generative adversarial networks (GANs), but instead of simply generating images with a neural network, we enhance ima...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	157,702
1806.06595	Uncertainty in multitask learning: joint representations for probabilistic MR-only radiotherapy planning	Multi-task neural network architectures provide a mechanism that jointly integrates information from distinct sources. It is ideal in the context of MR-only radiotherapy planning as it can jointly regress a synthetic CT (synCT) scan and segment organs-at-risk (OAR) from MRI. We propose a probabilistic multi-task networ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	100,734

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