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
2309.06635	Collaborative Dynamic 3D Scene Graphs for Automated Driving	Maps have played an indispensable role in enabling safe and automated driving. Although there have been many advances on different fronts ranging from SLAM to semantics, building an actionable hierarchical semantic representation of urban dynamic scenes and processing information from multiple agents are still challeng...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	391,486
1108.4942	Making Use of Advances in Answer-Set Programming for Abstract Argumentation Systems	Dung's famous abstract argumentation frameworks represent the core formalism for many problems and applications in the field of argumentation which significantly evolved within the last decade. Recent work in the field has thus focused on implementations for these frameworks, whereby one of the main approaches is to us...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	11,801
2111.09337	Temporally Consistent Online Depth Estimation in Dynamic Scenes	Temporally consistent depth estimation is crucial for online applications such as augmented reality. While stereo depth estimation has received substantial attention as a promising way to generate 3D information, there is relatively little work focused on maintaining temporal stability. Indeed, based on our analysis, c...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	266,981
2407.21056	What Matters in Explanations: Towards Explainable Fake Review Detection Focusing on Transformers	Customers' reviews and feedback play crucial role on electronic commerce~(E-commerce) platforms like Amazon, Zalando, and eBay in influencing other customers' purchasing decisions. However, there is a prevailing concern that sellers often post fake or spam reviews to deceive potential customers and manipulate their opi...	false	false	false	true	false	true	false	false	true	false	false	false	false	false	false	false	false	false	477,388
2002.10853	Learning Machines from Simulation to Real World	Learning Machines is developing a flexible, cross-industry, advanced analytics platform, targeted during stealth-stage at a limited number of specific vertical applications. In this paper, we aim to integrate a general machine system to learn a variant of tasks from simulation to real world. In such a machine system, i...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	165,534
2103.15451	Pairing Character Classes in a Deathmatch Shooter Game via a Deep-Learning Surrogate Model	This paper introduces a surrogate model of gameplay that learns the mapping between different game facets, and applies it to a generative system which designs new content in one of these facets. Focusing on the shooter game genre, the paper explores how deep learning can help build a model which combines the game level...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	true	false	false	227,212
2205.03811	Data-Free Adversarial Knowledge Distillation for Graph Neural Networks	Graph neural networks (GNNs) have been widely used in modeling graph structured data, owing to its impressive performance in a wide range of practical applications. Recently, knowledge distillation (KD) for GNNs has enabled remarkable progress in graph model compression and knowledge transfer. However, most of the exis...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	295,434
2410.09303	Exact Byte-Level Probabilities from Tokenized Language Models for FIM-Tasks and Model Ensembles	Tokenization is associated with many poorly understood shortcomings in language models (LMs), yet remains an important component for long sequence scaling purposes. This work studies how tokenization impacts model performance by analyzing and comparing the stochastic behavior of tokenized models with their byte-level, ...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	497,527
2006.12092	High-Resolution Air Quality Prediction Using Low-Cost Sensors	The use of low-cost sensors in air quality monitoring networks is still a much-debated topic among practitioners: they are much cheaper than traditional air quality monitoring stations set up by public authorities (a few hundred dollars compared to a few dozens of thousand dollars) at the cost of a lower accuracy and r...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	183,468
1307.7474	Automatic Mammogram image Breast Region Extraction and Removal of Pectoral Muscle	Currently Mammography is a most effective imaging modality used by radiologists for the screening of breast cancer. Finding an accurate, robust and efficient breast region segmentation technique still remains a challenging problem in digital mammography. Extraction of the breast profile region and the removal of pector...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	26,111
2408.01851	Cost-constrained multi-label group feature selection using shadow features	We consider the problem of feature selection in multi-label classification, considering the costs assigned to groups of features. In this task, the goal is to select a subset of features that will be useful for predicting the label vector, but at the same time, the cost associated with the selected features will not ex...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	478,392
1707.00823	Learning Human Pose Models from Synthesized Data for Robust RGB-D Action Recognition	We propose Human Pose Models that represent RGB and depth images of human poses independent of clothing textures, backgrounds, lighting conditions, body shapes and camera viewpoints. Learning such universal models requires training images where all factors are varied for every human pose. Capturing such data is prohibi...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	76,427
2308.09108	Spectral information criterion for automatic elbow detection	We introduce a generalized information criterion that contains other well-known information criteria, such as Bayesian information Criterion (BIC) and Akaike information criterion (AIC), as special cases. Furthermore, the proposed spectral information criterion (SIC) is also more general than the other information crit...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	386,160
1903.05260	Syntax-aware Neural Semantic Role Labeling with Supertags	We introduce a new syntax-aware model for dependency-based semantic role labeling that outperforms syntax-agnostic models for English and Spanish. We use a BiLSTM to tag the text with supertags extracted from dependency parses, and we feed these supertags, along with words and parts of speech, into a deep highway BiLST...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	124,128
1104.2034	Materials to the Russian-Bulgarian Comparative Dictionary "EAD"	This article presents a fragment of a new comparative dictionary "A comparative dictionary of names of expansive action in Russian and Bulgarian languages". Main features of the new web-based comparative dictionary are placed, the principles of its formation are shown, primary links between the word-matches are classif...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	9,946
2311.09847	Overcoming Data Scarcity in Biomedical Imaging with a Foundational Multi-Task Model	Foundational models, pretrained on a large scale, have demonstrated substantial success across non-medical domains. However, training these models typically requires large, comprehensive datasets, which contrasts with the smaller and more heterogeneous datasets common in biomedical imaging. Here, we propose a multi-tas...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	408,336
2201.02018	Super-Reparametrizations of Weighted CSPs: Properties and Optimization Perspective	The notion of reparametrizations of Weighted CSPs (WCSPs) (also known as equivalence-preserving transformations of WCSPs) is well-known and finds its use in many algorithms to approximate or bound the optimal WCSP value. In contrast, the concept of super-reparametrizations (which are changes of the weights that keep or...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	274,425
2109.00596	Streaming data preprocessing via online tensor recovery for large environmental sensor networks	Measuring the built and natural environment at a fine-grained scale is now possible with low-cost urban environmental sensor networks. However, fine-grained city-scale data analysis is complicated by tedious data cleaning including removing outliers and imputing missing data. While many methods exist to automatically c...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	253,156
2106.00999	Communication-Efficient Split Learning Based on Analog Communication and Over the Air Aggregation	Split-learning (SL) has recently gained popularity due to its inherent privacy-preserving capabilities and ability to enable collaborative inference for devices with limited computational power. Standard SL algorithms assume an ideal underlying digital communication system and ignore the problem of scarce communication...	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	true	238,344
1606.08084	Cyberbullying Identification Using Participant-Vocabulary Consistency	With the rise of social media, people can now form relationships and communities easily regardless of location, race, ethnicity, or gender. However, the power of social media simultaneously enables harmful online behavior such as harassment and bullying. Cyberbullying is a serious social problem, making it an important...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	57,824
2404.01320	Graph-Based Optimisation of Network Expansion in a Dockless Bike Sharing System	Bike-sharing systems (BSSs) are deployed in over a thousand cities worldwide and play an important role in many urban transportation systems. BSSs alleviate congestion, reduce pollution and promote physical exercise. It is essential to explore the spatiotemporal patterns of bike-sharing demand, as well as the factors t...	false	false	false	true	true	false	false	false	false	false	false	false	false	true	false	false	false	false	443,370
2009.09796	Multi-Task Learning with Deep Neural Networks: A Survey	Multi-task learning (MTL) is a subfield of machine learning in which multiple tasks are simultaneously learned by a shared model. Such approaches offer advantages like improved data efficiency, reduced overfitting through shared representations, and fast learning by leveraging auxiliary information. However, the simult...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	196,690
2002.06575	Topological Mapping for Manhattan-like Repetitive Environments	We showcase a topological mapping framework for a challenging indoor warehouse setting. At the most abstract level, the warehouse is represented as a Topological Graph where the nodes of the graph represent a particular warehouse topological construct (e.g. rackspace, corridor) and the edges denote the existence of a p...	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	164,230
2301.07301	PTA-Det: Point Transformer Associating Point cloud and Image for 3D Object Detection	In autonomous driving, 3D object detection based on multi-modal data has become an indispensable approach when facing complex environments around the vehicle. During multi-modal detection, LiDAR and camera are simultaneously applied for capturing and modeling. However, due to the intrinsic discrepancies between the LiD...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	340,879
1809.08860	A Comparative Study: Adaptive Fuzzy Inference Systems for Energy Prediction in Urban Buildings	This investigation aims to study different adaptive fuzzy inference algorithms capable of real-time sequential learning and prediction of time-series data. A brief qualitative description of these algorithms namely meta-cognitive fuzzy inference system (McFIS), sequential adaptive fuzzy inference system (SAFIS) and evo...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	false	false	108,609
0810.3851	Astronomical imaging: The theory of everything	We are developing automated systems to provide homogeneous calibration meta-data for heterogeneous imaging data, using the pixel content of the image alone where necessary. Standardized and complete calibration meta-data permit generative modeling: A good model of the sky through wavelength and time--that is, a model o...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	2,539
1605.02971	Structured Receptive Fields in CNNs	Learning powerful feature representations with CNNs is hard when training data are limited. Pre-training is one way to overcome this, but it requires large datasets sufficiently similar to the target domain. Another option is to design priors into the model, which can range from tuned hyperparameters to fully engineere...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	55,700
1810.01869	Machine Learning Suites for Online Toxicity Detection	To identify and classify toxic online commentary, the modern tools of data science transform raw text into key features from which either thresholding or learning algorithms can make predictions for monitoring offensive conversations. We systematically evaluate 62 classifiers representing 19 major algorithmic families ...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	true	false	false	109,478
2310.17290	RIO: A Benchmark for Reasoning Intention-Oriented Objects in Open Environments	Intention-oriented object detection aims to detect desired objects based on specific intentions or requirements. For instance, when we desire to "lie down and rest", we instinctively seek out a suitable option such as a "bed" or a "sofa" that can fulfill our needs. Previous work in this area is limited either by the nu...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	403,075
2403.10164	CoReEcho: Continuous Representation Learning for 2D+time Echocardiography Analysis	Deep learning (DL) models have been advancing automatic medical image analysis on various modalities, including echocardiography, by offering a comprehensive end-to-end training pipeline. This approach enables DL models to regress ejection fraction (EF) directly from 2D+time echocardiograms, resulting in superior perfo...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	438,083
2409.10584	Manifold-Constrained Nucleus-Level Denoising Diffusion Model for Structure-Based Drug Design	Artificial intelligence models have shown great potential in structure-based drug design, generating ligands with high binding affinities. However, existing models have often overlooked a crucial physical constraint: atoms must maintain a minimum pairwise distance to avoid separation violation, a phenomenon governed by...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	488,812
2406.18139	LOOK-M: Look-Once Optimization in KV Cache for Efficient Multimodal Long-Context Inference	Long-context Multimodal Large Language Models (MLLMs) demand substantial computational resources for inference as the growth of their multimodal Key-Value (KV) cache, in response to increasing input lengths, challenges memory and time efficiency. Unlike single-modality LLMs that manage only textual contexts, the KV cac...	false	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	467,891
2304.05818	Gradient-Free Textual Inversion	Recent works on personalized text-to-image generation usually learn to bind a special token with specific subjects or styles of a few given images by tuning its embedding through gradient descent. It is natural to question whether we can optimize the textual inversions by only accessing the process of model inference. ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	357,752
1902.11113	PixelSteganalysis: Destroying Hidden Information with a Low Degree of Visual Degradation	Steganography is the science of unnoticeably concealing a secret message within a certain image, called a cover image. The cover image with the secret message is called a stego image. Steganography is commonly used for illegal purposes such as terrorist activities and pornography. To thwart covert communications and tr...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	true	122,863
2011.14611	SIR: Self-supervised Image Rectification via Seeing the Same Scene from Multiple Different Lenses	Deep learning has demonstrated its power in image rectification by leveraging the representation capacity of deep neural networks via supervised training based on a large-scale synthetic dataset. However, the model may overfit the synthetic images and generalize not well on real-world fisheye images due to the limited ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	208,834
2012.14142	Perception Consistency Ultrasound Image Super-resolution via Self-supervised CycleGAN	Due to the limitations of sensors, the transmission medium and the intrinsic properties of ultrasound, the quality of ultrasound imaging is always not ideal, especially its low spatial resolution. To remedy this situation, deep learning networks have been recently developed for ultrasound image super-resolution (SR) be...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	213,423
2209.11817	An Efficient Algorithm for Fair Multi-Agent Multi-Armed Bandit with Low Regret	Recently a multi-agent variant of the classical multi-armed bandit was proposed to tackle fairness issues in online learning. Inspired by a long line of work in social choice and economics, the goal is to optimize the Nash social welfare instead of the total utility. Unfortunately previous algorithms either are not eff...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	319,307
2106.14308	Concentration of Contractive Stochastic Approximation and Reinforcement Learning	Using a martingale concentration inequality, concentration bounds `from time $n_0$ on' are derived for stochastic approximation algorithms with contractive maps and both martingale difference and Markov noises. These are applied to reinforcement learning algorithms, in particular to asynchronous Q-learning and TD(0).	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	243,362
2206.10658	Questions Are All You Need to Train a Dense Passage Retriever	We introduce ART, a new corpus-level autoencoding approach for training dense retrieval models that does not require any labeled training data. Dense retrieval is a central challenge for open-domain tasks, such as Open QA, where state-of-the-art methods typically require large supervised datasets with custom hard-negat...	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	303,986
2305.09028	SKI to go Faster: Accelerating Toeplitz Neural Networks via Asymmetric Kernels	Toeplitz Neural Networks (TNNs) (Qin et. al. 2023) are a recent sequence model with impressive results. They require O(n log n) computational complexity and O(n) relative positional encoder (RPE) multi-layer perceptron (MLP) and decay bias calls. We aim to reduce both. We first note that the RPE is a non-SPD (symmetric...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	364,485
2201.08136	Energy Efficiency Maximization in Large-Scale Cell-Free Massive MIMO: A Projected Gradient Approach	This paper considers the fundamental power allocation problem in cell-free massive mutiple-input and multiple-output (MIMO) systems which aims at maximizing the total energy efficiency (EE) under a sum power constraint at each access point (AP) and a quality-of-service (QoS) constraint at each user. Existing solutions ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	276,243
2009.07503	Minimize Exposure Bias of Seq2Seq Models in Joint Entity and Relation Extraction	Joint entity and relation extraction aims to extract relation triplets from plain text directly. Prior work leverages Sequence-to-Sequence (Seq2Seq) models for triplet sequence generation. However, Seq2Seq enforces an unnecessary order on the unordered triplets and involves a large decoding length associated with error...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	195,953
1103.5219	Upper Bounds on the Probability of Error in terms of Mean Divergence Measures	In this paper we shall consider some famous means such as arithmetic, harmonic, geometric, root square mean, etc. Considering the difference of these means, we can establish. some inequalities among them. Interestingly, the difference of mean considered is convex functions. Applying some properties, upper bounds on the...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	9,775
2402.11670	Challenging the Black Box: A Comprehensive Evaluation of Attribution Maps of CNN Applications in Agriculture and Forestry	In this study, we explore the explainability of neural networks in agriculture and forestry, specifically in fertilizer treatment classification and wood identification. The opaque nature of these models, often considered 'black boxes', is addressed through an extensive evaluation of state-of-the-art Attribution Maps (...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	430,505
2006.16863	Makespan minimization of Time-Triggered traffic on a TTEthernet network	The reliability of the increasing number of modern applications and systems strongly depends on interconnecting technology. Complex systems which usually need to exchange, among other things, multimedia data together with safety-related information, as in the automotive or avionic industry, for example, make demands on...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	true	184,933
2108.06649	Semi-supervised 3D Object Detection via Adaptive Pseudo-Labeling	3D object detection is an important task in computer vision. Most existing methods require a large number of high-quality 3D annotations, which are expensive to collect. Especially for outdoor scenes, the problem becomes more severe due to the sparseness of the point cloud and the complexity of urban scenes. Semi-super...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	250,669
2308.04682	Score Priors Guided Deep Variational Inference for Unsupervised Real-World Single Image Denoising	Real-world single image denoising is crucial and practical in computer vision. Bayesian inversions combined with score priors now have proven effective for single image denoising but are limited to white Gaussian noise. Moreover, applying existing score-based methods for real-world denoising requires not only the expli...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	384,509
2310.06670	Domain Generalization by Rejecting Extreme Augmentations	Data augmentation is one of the most effective techniques for regularizing deep learning models and improving their recognition performance in a variety of tasks and domains. However, this holds for standard in-domain settings, in which the training and test data follow the same distribution. For the out-of-domain case...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	398,672
2106.15434	Zoo-Tuning: Adaptive Transfer from a Zoo of Models	With the development of deep networks on various large-scale datasets, a large zoo of pretrained models are available. When transferring from a model zoo, applying classic single-model based transfer learning methods to each source model suffers from high computational burden and cannot fully utilize the rich knowledge...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	243,769
2012.07410	Reasoning in Dialog: Improving Response Generation by Context Reading Comprehension	In multi-turn dialog, utterances do not always take the full form of sentences \cite{Carbonell1983DiscoursePA}, which naturally makes understanding the dialog context more difficult. However, it is essential to fully grasp the dialog context to generate a reasonable response. Hence, in this paper, we propose to improve...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	211,445
2012.15477	Particle Dual Averaging: Optimization of Mean Field Neural Networks with Global Convergence Rate Analysis	We propose the particle dual averaging (PDA) method, which generalizes the dual averaging method in convex optimization to the optimization over probability distributions with quantitative runtime guarantee. The algorithm consists of an inner loop and outer loop: the inner loop utilizes the Langevin algorithm to approx...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	213,803
1606.04199	Deep Recurrent Models with Fast-Forward Connections for Neural Machine Translation	Neural machine translation (NMT) aims at solving machine translation (MT) problems using neural networks and has exhibited promising results in recent years. However, most of the existing NMT models are shallow and there is still a performance gap between a single NMT model and the best conventional MT system. In this ...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	57,210
2405.11512	Going into Orbit: Massively Parallelizing Episodic Reinforcement Learning	The possibilities of robot control have multiplied across various domains through the application of deep reinforcement learning. To overcome safety and sampling efficiency issues, deep reinforcement learning models can be trained in a simulation environment, allowing for faster iteration cycles. This can be enhanced f...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	455,173
2106.14652	Context-aware Heterogeneous Graph Attention Network for User Behavior Prediction in Local Consumer Service Platform	As a new type of e-commerce platform developed in recent years, local consumer service platform provides users with software to consume service to the nearby store or to the home, such as Groupon and Koubei. Different from other common e-commerce platforms, the behavior of users on the local consumer service platform i...	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	false	false	243,481
2403.19441	A Novel Stochastic Transformer-based Approach for Post-Traumatic Stress Disorder Detection using Audio Recording of Clinical Interviews	Post-traumatic stress disorder (PTSD) is a mental disorder that can be developed after witnessing or experiencing extremely traumatic events. PTSD can affect anyone, regardless of ethnicity, or culture. An estimated one in every eleven people will experience PTSD during their lifetime. The Clinician-Administered PTSD S...	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	442,346
2111.11652	CoDiM: Learning with Noisy Labels via Contrastive Semi-Supervised Learning	Labels are costly and sometimes unreliable. Noisy label learning, semi-supervised learning, and contrastive learning are three different strategies for designing learning processes requiring less annotation cost. Semi-supervised learning and contrastive learning have been recently demonstrated to improve learning strat...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	267,729
2303.03915	The BigScience ROOTS Corpus: A 1.6TB Composite Multilingual Dataset	As language models grow ever larger, the need for large-scale high-quality text datasets has never been more pressing, especially in multilingual settings. The BigScience workshop, a 1-year international and multidisciplinary initiative, was formed with the goal of researching and training large language models as a va...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	349,890
1304.1503	Interval Influence Diagrams	We describe a mechanism for performing probabilistic reasoning in influence diagrams using interval rather than point valued probabilities. We derive the procedures for node removal (corresponding to conditional expectation) and arc reversal (corresponding to Bayesian conditioning) in influence diagrams where lower bou...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	23,536
1912.02532	Iterative Policy-Space Expansion in Reinforcement Learning	Humans and animals solve a difficult problem much more easily when they are presented with a sequence of problems that starts simple and slowly increases in difficulty. We explore this idea in the context of reinforcement learning. Rather than providing the agent with an externally provided curriculum of progressively ...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	156,374
2102.10365	Analyzing Overfitting under Class Imbalance in Neural Networks for Image Segmentation	Class imbalance poses a challenge for developing unbiased, accurate predictive models. In particular, in image segmentation neural networks may overfit to the foreground samples from small structures, which are often heavily under-represented in the training set, leading to poor generalization. In this study, we provid...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	221,070
2410.21564	Mitigating Gradient Overlap in Deep Residual Networks with Gradient Normalization for Improved Non-Convex Optimization	In deep learning, Residual Networks (ResNets) have proven effective in addressing the vanishing gradient problem, allowing for the successful training of very deep networks. However, skip connections in ResNets can lead to gradient overlap, where gradients from both the learned transformation and the skip connection co...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	503,300
2407.15837	Towards Latent Masked Image Modeling for Self-Supervised Visual Representation Learning	Masked Image Modeling (MIM) has emerged as a promising method for deriving visual representations from unlabeled image data by predicting missing pixels from masked portions of images. It excels in region-aware learning and provides strong initializations for various tasks, but struggles to capture high-level semantics...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	475,354
2301.07464	CLIPTER: Looking at the Bigger Picture in Scene Text Recognition	Reading text in real-world scenarios often requires understanding the context surrounding it, especially when dealing with poor-quality text. However, current scene text recognizers are unaware of the bigger picture as they operate on cropped text images. In this study, we harness the representative capabilities of mod...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	340,924
2311.10215	Predictive Minds: LLMs As Atypical Active Inference Agents	Large language models (LLMs) like GPT are often conceptualized as passive predictors, simulators, or even stochastic parrots. We instead conceptualize LLMs by drawing on the theory of active inference originating in cognitive science and neuroscience. We examine similarities and differences between traditional active i...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	408,445
2404.02948	PiSSA: Principal Singular Values and Singular Vectors Adaptation of Large Language Models	To parameter-efficiently fine-tune (PEFT) large language models (LLMs), the low-rank adaptation (LoRA) method approximates the model changes $\Delta W \in \mathbb{R}^{m \times n}$ through the product of two matrices $A \in \mathbb{R}^{m \times r}$ and $B \in \mathbb{R}^{r \times n}$, where $r \ll \min(m, n)$, $A$ is in...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	444,068
1607.07215	DeepWarp: Photorealistic Image Resynthesis for Gaze Manipulation	In this work, we consider the task of generating highly-realistic images of a given face with a redirected gaze. We treat this problem as a specific instance of conditional image generation and suggest a new deep architecture that can handle this task very well as revealed by numerical comparison with prior art and a u...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	58,990
2109.06120	LiDAR Odometry Methodologies for Autonomous Driving: A Survey	Vehicle odometry is an essential component of an automated driving system as it computes the vehicle's position and orientation. The odometry module has a higher demand and impact in urban areas where the global navigation satellite system (GNSS) signal is weak and noisy. Traditional visual odometry methods suffer from...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	255,055
1412.4430	On the relation between optimal transport and Schr\"odinger bridges: A stochastic control viewpoint	We take a new look at the relation between the optimal transport problem and the Schr\"{o}dinger bridge problem from the stochastic control perspective. We show that the connections are richer and deeper than described in existing literature. In particular: a) We give an elementary derivation of the Benamou-Brenier flu...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	38,392
1911.09358	Gliding vertex on the horizontal bounding box for multi-oriented object detection	Object detection has recently experienced substantial progress. Yet, the widely adopted horizontal bounding box representation is not appropriate for ubiquitous oriented objects such as objects in aerial images and scene texts. In this paper, we propose a simple yet effective framework to detect multi-oriented objects....	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	154,499
2110.13377	Instant Response Few-shot Object Detection with Meta Strategy and Explicit Localization Inference	Aiming at recognizing and localizing the object of novel categories by a few reference samples, few-shot object detection (FSOD) is a quite challenging task. Previous works often depend on the fine-tuning process to transfer their model to the novel category and rarely consider the defect of fine-tuning, resulting in m...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	263,155
2405.02310	Simulating the aftermath of Northern European Enclosure Dam (NEED) break and flooding of European coast	The Northern European Enclosure Dam (NEED) is a hypothetical project to prevent flooding in European countries following the rising ocean level due to melting arctic glaciers. This project involves the construction of two large dams between Scotland and Norway, as well as England and France. The anticipated cost of thi...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	451,702
2303.09100	Patch-Prompt Aligned Bayesian Prompt Tuning for Vision-Language Models	For downstream applications of vision-language pre-trained models, there has been significant interest in constructing effective prompts. Existing works on prompt engineering, which either require laborious manual designs or optimize the prompt tuning as a point estimation problem, may fail to describe diverse characte...	false	false	false	false	false	false	true	false	true	false	false	true	false	false	false	false	false	false	351,911
2009.01798	Ramifications of Approximate Posterior Inference for Bayesian Deep Learning in Adversarial and Out-of-Distribution Settings	Deep neural networks have been successful in diverse discriminative classification tasks, although, they are poorly calibrated often assigning high probability to misclassified predictions. Potential consequences could lead to trustworthiness and accountability of the models when deployed in real applications, where pr...	false	false	false	false	true	false	true	false	false	true	false	false	false	false	false	false	false	false	194,396
2502.09411	ImageRAG: Dynamic Image Retrieval for Reference-Guided Image Generation	Diffusion models enable high-quality and diverse visual content synthesis. However, they struggle to generate rare or unseen concepts. To address this challenge, we explore the usage of Retrieval-Augmented Generation (RAG) with image generation models. We propose ImageRAG, a method that dynamically retrieves relevant i...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	533,439
1811.09022	Three-dimensional Optical Coherence Tomography Image Denoising through Multi-input Fully-Convolutional Networks	In recent years, there has been a growing interest in applying convolutional neural networks (CNNs) to low-level vision tasks such as denoising and super-resolution. Due to the coherent nature of the image formation process, optical coherence tomography (OCT) images are inevitably affected by noise. This paper proposes...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	114,177
2006.16811	Path Integral Based Convolution and Pooling for Graph Neural Networks	Graph neural networks (GNNs) extends the functionality of traditional neural networks to graph-structured data. Similar to CNNs, an optimized design of graph convolution and pooling is key to success. Borrowing ideas from physics, we propose a path integral based graph neural networks (PAN) for classification and regre...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	184,918
2006.10541	Exact posterior distributions of wide Bayesian neural networks	Recent work has shown that the prior over functions induced by a deep Bayesian neural network (BNN) behaves as a Gaussian process (GP) as the width of all layers becomes large. However, many BNN applications are concerned with the BNN function space posterior. While some empirical evidence of the posterior convergence ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	182,922
2002.12920	Automatic Perturbation Analysis for Scalable Certified Robustness and Beyond	Linear relaxation based perturbation analysis (LiRPA) for neural networks, which computes provable linear bounds of output neurons given a certain amount of input perturbation, has become a core component in robustness verification and certified defense. The majority of LiRPA-based methods focus on simple feed-forward ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	166,169
1912.02065	Safety and Robustness in Decision Making: Deep Bayesian Recurrent Neural Networks for Somatic Variant Calling in Cancer	The genomic profile underlying an individual tumor can be highly informative in the creation of a personalized cancer treatment strategy for a given patient; a practice known as precision oncology. This involves next generation sequencing of a tumor sample and the subsequent identification of genomic aberrations, such ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	156,248
2202.04427	Revisiting QMIX: Discriminative Credit Assignment by Gradient Entropy Regularization	In cooperative multi-agent systems, agents jointly take actions and receive a team reward instead of individual rewards. In the absence of individual reward signals, credit assignment mechanisms are usually introduced to discriminate the contributions of different agents so as to achieve effective cooperation. Recently...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	279,552
1507.01380	Finding influential spreaders from human activity beyond network location	Most centralities proposed for identifying influential spreaders on social networks to either spread a message or to stop an epidemic require the full topological information of the network on which spreading occurs. In practice, however, collecting all connections between agents in social networks can be hardly achiev...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	44,863
2404.05678	Flexible Fairness-Aware Learning via Inverse Conditional Permutation	Equalized odds, as a popular notion of algorithmic fairness, aims to ensure that sensitive variables, such as race and gender, do not unfairly influence the algorithm's prediction when conditioning on the true outcome. Despite rapid advancements, current research primarily focuses on equalized odds violations caused by...	false	false	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	445,171
1201.2207	Multi-sensor Information Processing using Prediction Market-based Belief Aggregation	We consider the problem of information fusion from multiple sensors of different types with the objective of improving the confidence of inference tasks, such as object classification, performed from the data collected by the sensors. We propose a novel technique based on distributed belief aggregation using a multi-ag...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	13,763
1909.12471	DMM-Net: Differentiable Mask-Matching Network for Video Object Segmentation	In this paper, we propose the differentiable mask-matching network (DMM-Net) for solving the video object segmentation problem where the initial object masks are provided. Relying on the Mask R-CNN backbone, we extract mask proposals per frame and formulate the matching between object templates and proposals at one tim...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	147,139
2402.10645	Can Separators Improve Chain-of-Thought Prompting?	Chain-of-thought (CoT) prompting is a simple and effective method for improving the reasoning capabilities of Large Language Models (LLMs). The basic idea of CoT is to let LLMs break down their thought processes step-by-step by putting exemplars in the input prompt. However, the densely structured prompt exemplars of C...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	430,049
2409.14216	R-AIF: Solving Sparse-Reward Robotic Tasks from Pixels with Active Inference and World Models	Although research has produced promising results demonstrating the utility of active inference (AIF) in Markov decision processes (MDPs), there is relatively less work that builds AIF models in the context of environments and problems that take the form of partially observable Markov decision processes (POMDPs). In POM...	false	false	false	false	true	false	true	true	false	false	false	true	false	false	false	false	false	false	490,378
0911.4207	An information theoretic approach to statistical dependence: copula information	We discuss the connection between information and copula theories by showing that a copula can be employed to decompose the information content of a multivariate distribution into marginal and dependence components, with the latter quantified by the mutual information. We define the information excess as a measure of d...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	4,991
2407.00657	Improving Real-Time Music Accompaniment Separation with MMDenseNet	Music source separation aims to separate polyphonic music into different types of sources. Most existing methods focus on enhancing the quality of separated results by using a larger model structure, rendering them unsuitable for deployment on edge devices. Moreover, these methods may produce low-quality output when th...	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	468,956
1407.4709	Flow for Meta Control	The psychological state of flow has been linked to optimizing human performance. A key condition of flow emergence is a match between the human abilities and complexity of the task. We propose a simple computational model of flow for Artificial Intelligence (AI) agents. The model factors the standard agent-environment ...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	34,723
2303.07074	On Lyapunov functions for open Hegselmann-Krause dynamics	In this paper, we provide a formulation of an open Hegselmann-Krause (HK) dynamics where agents can join and leave the system during the interactions. We consider a stochastic framework where the time instants corresponding to arrivals and departures are determined by homogeneous Poisson processes. Then, we provide a s...	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	false	351,105
2410.13180	Secrecy Sum-Rate Maximization for Active IRS-Assisted MIMO-OFDM SWIPT System	The propagation loss of RF signals is a significant issue in simultaneous wireless information and power transfer (SWIPT) systems. Additionally, ensuring information security is crucial due to the broadcasting nature of wireless channels. To address these challenges, we exploit the potential of active intelligent refle...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	499,404
2109.03859	Leveraging Code Clones and Natural Language Processing for Log Statement Prediction	Software developers embed logging statements inside the source code as an imperative duty in modern software development as log files are necessary for tracking down runtime system issues and troubleshooting system management tasks. Prior research has emphasized the importance of logging statements in the operation and...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	254,192
2412.18827	PhyloGen: Language Model-Enhanced Phylogenetic Inference via Graph Structure Generation	Phylogenetic trees elucidate evolutionary relationships among species, but phylogenetic inference remains challenging due to the complexity of combining continuous (branch lengths) and discrete parameters (tree topology). Traditional Markov Chain Monte Carlo methods face slow convergence and computational burdens. Exis...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	520,591
2301.03363	Tuning Path Tracking Controllers for Autonomous Cars Using Reinforcement Learning	This paper proposes an adaptable path tracking control system based on Reinforcement Learning (RL) for autonomous cars. A four-parameter controller shapes the behavior of the vehicle to navigate on lane changes and roundabouts. The tuning of the tracker uses an educated Q-Learning algorithm to minimize the lateral and ...	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	false	false	339,775
2005.01771	Hybrid $L_\infty\times\ell_\infty$-Performance Analysis and Control of Linear Time-Varying Impulsive and Switched Positive Systems	Recent works have shown that the $L_1$ and $L_\infty$-gains are natural performance criteria for linear positive systems as they can be characterized using linear programs. Those performance measures have also been extended to linear positive impulsive and switched systems through the concept of hybrid $L_1\times\ell_1...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	175,666
1911.06643	Forgetting to learn logic programs	Most program induction approaches require predefined, often hand-engineered, background knowledge (BK). To overcome this limitation, we explore methods to automatically acquire BK through multi-task learning. In this approach, a learner adds learned programs to its BK so that they can be reused to help learn other prog...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	true	153,594
2307.13604	Cloud Render Farm Services Discovery Using NLP And Ontology Based Knowledge Graph	Cloud render farm services are the animation domain specific cloud services Platform-as-a-Service (PaaS) type of cloud services that provides a complete platform to render the animation files. However, identifying the render farm services that is cost effective and also matches the functional requirements that changes ...	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	false	true	381,640
2101.10196	A Hybrid Approach to Measure Semantic Relatedness in Biomedical Concepts	Objective: This work aimed to demonstrate the effectiveness of a hybrid approach based on Sentence BERT model and retrofitting algorithm to compute relatedness between any two biomedical concepts. Materials and Methods: We generated concept vectors by encoding concept preferred terms using ELMo, BERT, and Sentence BERT...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	216,850
2311.15561	ET3D: Efficient Text-to-3D Generation via Multi-View Distillation	Recent breakthroughs in text-to-image generation has shown encouraging results via large generative models. Due to the scarcity of 3D assets, it is hardly to transfer the success of text-to-image generation to that of text-to-3D generation. Existing text-to-3D generation methods usually adopt the paradigm of DreamFusio...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	410,556
1809.05996	Devil in the Details: Towards Accurate Single and Multiple Human Parsing	Human parsing has received considerable interest due to its wide application potentials. Nevertheless, it is still unclear how to develop an accurate human parsing system in an efficient and elegant way. In this paper, we identify several useful properties, including feature resolution, global context information and e...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	107,930

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