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
1810.04635	Multimodal Speech Emotion Recognition Using Audio and Text	Speech emotion recognition is a challenging task, and extensive reliance has been placed on models that use audio features in building well-performing classifiers. In this paper, we propose a novel deep dual recurrent encoder model that utilizes text data and audio signals simultaneously to obtain a better understandin...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	110,083
2110.11443	Off-Dynamics Inverse Reinforcement Learning from Hetero-Domain	We propose an approach for inverse reinforcement learning from hetero-domain which learns a reward function in the simulator, drawing on the demonstrations from the real world. The intuition behind the method is that the reward function should not only be oriented to imitate the experts, but should encourage actions ad...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	262,483
1512.01926	Thinking Required	There exists a theory of a single general-purpose learning algorithm which could explain the principles its operation. It assumes the initial rough architecture, a small library of simple innate circuits which are prewired at birth. and proposes that all significant mental algorithms are learned. Given current understa...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	49,887
1701.05574	Harnessing Cognitive Features for Sarcasm Detection	In this paper, we propose a novel mechanism for enriching the feature vector, for the task of sarcasm detection, with cognitive features extracted from eye-movement patterns of human readers. Sarcasm detection has been a challenging research problem, and its importance for NLP applications such as review summarization,...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	67,002
2102.02291	Nearest Neighbor-based Importance Weighting	Importance weighting is widely applicable in machine learning in general and in techniques dealing with data covariate shift problems in particular. A novel, direct approach to determine such importance weighting is presented. It relies on a nearest neighbor classification scheme and is relatively straightforward to im...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	218,364
2009.08709	Progressive Semantic-Aware Style Transformation for Blind Face Restoration	Face restoration is important in face image processing, and has been widely studied in recent years. However, previous works often fail to generate plausible high quality (HQ) results for real-world low quality (LQ) face images. In this paper, we propose a new progressive semantic-aware style transformation framework, ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	196,328
2010.15208	Identifying Entangled Physics Relationships through Sparse Matrix Decomposition to Inform Plasma Fusion Design	A sustainable burn platform through inertial confinement fusion (ICF) has been an ongoing challenge for over 50 years. Mitigating engineering limitations and improving the current design involves an understanding of the complex coupling of physical processes. While sophisticated simulations codes are used to model ICF ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	203,691
2303.09151	Performance Analysis of Passive Retro-Reflector Based Tracking in Free-Space Optical Communications with Pointing Errors	In this correspondence, we propose a diversity-achieving retroreflector-based fine tracking system for free-space optical (FSO) communications. We show that multiple retroreflectors deployed around the communication telescope at the aerial vehicle save the payload capacity and enhance the outage performance of the fine...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	351,930
1205.2653	L2 Regularization for Learning Kernels	The choice of the kernel is critical to the success of many learning algorithms but it is typically left to the user. Instead, the training data can be used to learn the kernel by selecting it out of a given family, such as that of non-negative linear combinations of p base kernels, constrained by a trace or L1 regular...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	15,958
2409.08147	LLM-POTUS Score: A Framework of Analyzing Presidential Debates with Large Language Models	Large language models have demonstrated remarkable capabilities in natural language processing, yet their application to political discourse analysis remains underexplored. This paper introduces a novel approach to evaluating presidential debate performances using LLMs, addressing the longstanding challenge of objectiv...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	487,787
2109.09074	Efficient Urban-scale Point Clouds Segmentation with BEV Projection	Point clouds analysis has grasped researchers' eyes in recent years, while 3D semantic segmentation remains a problem. Most deep point clouds models directly conduct learning on 3D point clouds, which will suffer from the severe sparsity and extreme data processing load in urban-scale data. To tackle the challenge, we ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	256,146
2404.03566	PointInfinity: Resolution-Invariant Point Diffusion Models	We present PointInfinity, an efficient family of point cloud diffusion models. Our core idea is to use a transformer-based architecture with a fixed-size, resolution-invariant latent representation. This enables efficient training with low-resolution point clouds, while allowing high-resolution point clouds to be gener...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	444,306
1804.10942	Learning Data Dependency with Communication Cost	In this paper, we consider the problem of recovering a graph that represents the statistical data dependency among nodes for a set of data samples generated by nodes, which provides the basic structure to perform an inference task, such as MAP (maximum a posteriori). This problem is referred to as structure learning. W...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	96,265
2405.10301	Conformal Alignment: Knowing When to Trust Foundation Models with Guarantees	Before deploying outputs from foundation models in high-stakes tasks, it is imperative to ensure that they align with human values. For instance, in radiology report generation, reports generated by a vision-language model must align with human evaluations before their use in medical decision-making. This paper present...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	454,711
2205.07881	Developing patient-driven artificial intelligence based on personal rankings of care decision making steps	We propose and experimentally motivate a new methodology to support decision-making processes in healthcare with artificial intelligence based on personal rankings of care decision making steps that can be identified with our methodology, questionnaire data and its statistical patterns. Our longitudinal quantitative cr...	true	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	296,755
1703.00723	Secrecy and Robustness for Active Attack in Secure Network Coding and its Application to Network Quantum Key Distribution	In network coding, we discuss the effect of sequential error injection on information leakage. We show that there is no improvement when the operations in the network are linear operations. However, when the operations in the network contains non-linear operations, we find a counterexample to improve Eve's obtained inf...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	69,214
2410.04733	PredFormer: Transformers Are Effective Spatial-Temporal Predictive Learners	Spatiotemporal predictive learning methods generally fall into two categories: recurrent-based approaches, which face challenges in parallelization and performance, and recurrent-free methods, which employ convolutional neural networks (CNNs) as encoder-decoder architectures. These methods benefit from strong inductive...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	495,417
cs/0301023	A semantic framework for preference handling in answer set programming	We provide a semantic framework for preference handling in answer set programming. To this end, we introduce preference preserving consequence operators. The resulting fixpoint characterizations provide us with a uniform semantic framework for characterizing preference handling in existing approaches. Although our appr...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	537,786
2111.03690	Do we still need ImageNet pre-training in remote sensing scene classification?	Due to the scarcity of labeled data, using supervised models pre-trained on ImageNet is a de facto standard in remote sensing scene classification. Recently, the availability of larger high resolution remote sensing (HRRS) image datasets and progress in self-supervised learning have brought up the questions of whether ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	265,245
2012.08298	Noisy Deductive Reasoning: How Humans Construct Math, and How Math Constructs Universes	We present a computational model of mathematical reasoning according to which mathematics is a fundamentally stochastic process. That is, on our model, whether or not a given formula is deemed a theorem in some axiomatic system is not a matter of certainty, but is instead governed by a probability distribution. We then...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	211,734
1706.08564	Illuminating Pedestrians via Simultaneous Detection & Segmentation	Pedestrian detection is a critical problem in computer vision with significant impact on safety in urban autonomous driving. In this work, we explore how semantic segmentation can be used to boost pedestrian detection accuracy while having little to no impact on network efficiency. We propose a segmentation infusion ne...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	76,008
2009.08219	Deep Learning Approaches to Classification of Production Technology for 19th Century Books	Cultural research is dedicated to understanding the processes of knowledge dissemination and the social and technological practices in the book industry. Research on children books in the 19th century can be supported by computer systems. Specifically, the advances in digital image processing seem to offer great opport...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	196,169
2308.07707	Fast Machine Unlearning Without Retraining Through Selective Synaptic Dampening	Machine unlearning, the ability for a machine learning model to forget, is becoming increasingly important to comply with data privacy regulations, as well as to remove harmful, manipulated, or outdated information. The key challenge lies in forgetting specific information while protecting model performance on the rema...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	385,613
2004.01946	Weakly-Supervised Mesh-Convolutional Hand Reconstruction in the Wild	We introduce a simple and effective network architecture for monocular 3D hand pose estimation consisting of an image encoder followed by a mesh convolutional decoder that is trained through a direct 3D hand mesh reconstruction loss. We train our network by gathering a large-scale dataset of hand action in YouTube vide...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	171,064
2407.00342	KPC-cF: Aspect-Based Sentiment Analysis via Implicit-Feature Alignment with Corpus Filtering	Investigations into Aspect-Based Sentiment Analysis (ABSA) for Korean industrial reviews are notably lacking in the existing literature. Our research proposes an intuitive and effective framework for ABSA in low-resource languages such as Korean. It optimizes prediction labels by integrating translated benchmark and un...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	468,819
2412.19374	A Review of Resilience Enhancement Measures for Hydrogen-penetrated Multi-energy Systems	Energy supply for electricity and heat sectors accounts for more than 40% of global carbon emissions in 2023, which brings great pressure for achieving net-zero carbon emission targets in the future. Under the above background, hydrogen-penetrated multi-energy systems (HMESs) have received wide attention due to their p...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	520,814
2210.03378	UU-Tax at SemEval-2022 Task 3: Improving the generalizability of language models for taxonomy classification through data augmentation	This paper presents our strategy to address the SemEval-2022 Task 3 PreTENS: Presupposed Taxonomies Evaluating Neural Network Semantics. The goal of the task is to identify if a sentence is deemed acceptable or not, depending on the taxonomic relationship that holds between a noun pair contained in the sentence. For su...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	322,010
2210.03435	IDPL: Intra-subdomain adaptation adversarial learning segmentation method based on Dynamic Pseudo Labels	Unsupervised domain adaptation(UDA) has been applied to image semantic segmentation to solve the problem of domain offset. However, in some difficult categories with poor recognition accuracy, the segmentation effects are still not ideal. To this end, in this paper, Intra-subdomain adaptation adversarial learning segme...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	322,035
2403.03463	FLAME Diffuser: Wildfire Image Synthesis using Mask Guided Diffusion	Wildfires are a significant threat to ecosystems and human infrastructure, leading to widespread destruction and environmental degradation. Recent advancements in deep learning and generative models have enabled new methods for wildfire detection and monitoring. However, the scarcity of annotated wildfire images limits...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	435,206
2211.08486	Scalar Invariant Networks with Zero Bias	Just like weights, bias terms are the learnable parameters of many popular machine learning models, including neural networks. Biases are thought to enhance the representational power of neural networks, enabling them to solve a variety of tasks in computer vision. However, we argue that biases can be disregarded for s...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	330,644
2211.12486	Shortcomings of Top-Down Randomization-Based Sanity Checks for Evaluations of Deep Neural Network Explanations	While the evaluation of explanations is an important step towards trustworthy models, it needs to be done carefully, and the employed metrics need to be well-understood. Specifically model randomization testing is often overestimated and regarded as a sole criterion for selecting or discarding certain explanation metho...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	332,124
2109.14441	An Improved BAT Algorithm for Solving Job Scheduling Problems in Hotels and Restaurants	One popular example of metaheuristic algorithms from the swarm intelligence family is the Bat algorithm (BA). The algorithm was first presented in 2010 by Yang and quickly demonstrated its efficiency in comparison with other common algorithms. The BA is based on echolocation in bats. The BA uses automatic zooming to st...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	257,969
cs/0504011	Average Coset Weight Distribution of Combined LDPC Matrix Ensemble	In this paper, the average coset weight distribution (ACWD) of structured ensembles of LDPC (Low-density Parity-Check) matrix, which is called combined ensembles, is discussed. A combined ensemble is composed of a set of simpler ensembles such as a regular bipartite ensemble. Two classes of combined ensembles have prim...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	538,639
cs/0607047	PAC Classification based on PAC Estimates of Label Class Distributions	A standard approach in pattern classification is to estimate the distributions of the label classes, and then to apply the Bayes classifier to the estimates of the distributions in order to classify unlabeled examples. As one might expect, the better our estimates of the label class distributions, the better the result...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	539,575
2204.04217	Feature-enhanced Adversarial Semi-supervised Semantic Segmentation Network for Pulmonary Embolism Annotation	This study established a feature-enhanced adversarial semi-supervised semantic segmentation model to automatically annotate pulmonary embolism lesion areas in computed tomography pulmonary angiogram (CTPA) images. In current studies, all of the PE CTPA image segmentation methods are trained by supervised learning. Howe...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	290,577
2405.10145	Deep Koopman Operator-Informed Safety Command Governor for Autonomous Vehicles	Modeling of nonlinear behaviors with physical-based models poses challenges. However, Koopman operator maps the original nonlinear system into an infinite-dimensional linear space to achieve global linearization of the nonlinear system through input and output data, which derives an absolute equivalent linear represent...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	454,662
1901.02610	Performance Analysis and Dynamic Evolution of Deep Convolutional Neural Network for Nonlinear Inverse Scattering	The solution of nonlinear electromagnetic (EM) inverse scattering problems is typically hindered by several challenges such as ill-posedness, strong nonlinearity, and high computational costs. Recently, deep learning has been demonstrated to be a promising tool in addressing these challenges. In particular, it is possi...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	118,249
2308.14104	Towards Generalizable Neural Solvers for Vehicle Routing Problems via Ensemble with Transferrable Local Policy	Machine learning has been adapted to help solve NP-hard combinatorial optimization problems. One prevalent way is learning to construct solutions by deep neural networks, which has been receiving more and more attention due to the high efficiency and less requirement for expert knowledge. However, many neural construct...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	388,192
2407.20172	LatentArtiFusion: An Effective and Efficient Histological Artifacts Restoration Framework	Histological artifacts pose challenges for both pathologists and Computer-Aided Diagnosis (CAD) systems, leading to errors in analysis. Current approaches for histological artifact restoration, based on Generative Adversarial Networks (GANs) and pixel-level Diffusion Models, suffer from performance limitations and comp...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	477,069
1803.10916	Attention-based End-to-End Models for Small-Footprint Keyword Spotting	In this paper, we propose an attention-based end-to-end neural approach for small-footprint keyword spotting (KWS), which aims to simplify the pipelines of building a production-quality KWS system. Our model consists of an encoder and an attention mechanism. The encoder transforms the input signal into a high level rep...	false	false	true	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	93,789
2410.16943	FlightAR: AR Flight Assistance Interface with Multiple Video Streams and Object Detection Aimed at Immersive Drone Control	The swift advancement of unmanned aerial vehicle (UAV) technologies necessitates new standards for developing human-drone interaction (HDI) interfaces. Most interfaces for HDI, especially first-person view (FPV) goggles, limit the operator's ability to obtain information from the environment. This paper presents a nove...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	501,255
1910.12734	A Semi-Automated Approach for Information Extraction, Classification and Analysis of Unstructured Data	In this paper, we show how Quantitative Narrative Analysis and simple Natural Language Processing techniques apply to the extraction and categorization of data in a sample case study of the Diary of the former President of the Italian Republic (PoR), Giorgio Napolitano. The Diary contains a record of all his institutio...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	151,182
2006.16869	On Finite Entailment of Non-Local Queries in Description Logics	We study the problem of finite entailment of ontology-mediated queries. Going beyond local queries, we allow transitive closure over roles. We focus on ontologies formulated in the description logics ALCOI and ALCOQ, extended with transitive closure. For both logics, we show 2EXPTIME upper bounds for finite entailment ...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	true	false	184,938
2209.04895	Backtesting Trading Strategies with GAN To Avoid Overfitting	Many works have shown the overfitting hazard of selecting a trading strategy based only on good IS (in sample) performance. But most of them have merely shown such phenomena exist without offering ways to avoid them. We propose an approach to avoid overfitting: A good (meaning non-overfitting) trading strategy should s...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	316,919
2405.05438	Information Extraction from Historical Well Records Using A Large Language Model	To reduce environmental risks and impacts from orphaned wells (abandoned oil and gas wells), it is essential to first locate and then plug these wells. Although some historical documents are available, they are often unstructured, not cleaned, and outdated. Additionally, they vary widely by state and type. Manual readi...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	452,914
2502.10647	A Power Transform	Power transforms, such as the Box-Cox transform and Tukey's ladder of powers, are a fundamental tool in mathematics and statistics. These transforms are primarily used for normalizing and standardizing datasets, effectively by raising values to a power. In this work I present a novel power transform, and I show that it...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	533,983
2105.03462	Necessary and Sufficient Girth Conditions for Tanner Graphs of Quasi-Cyclic LDPC Codes	This paper revisits the connection between the girth of a protograph-based LDPC code given by a parity-check matrix and the properties of powers of the product between the matrix and its transpose in order to obtain the necessary and sufficient conditions for a code to have given girth between 6 and 12, and to show how...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	234,154
0710.1879	Cyclotomic FFTs with Reduced Additive Complexities Based on a Novel Common Subexpression Elimination Algorithm	In this paper, we first propose a novel common subexpression elimination (CSE) algorithm for matrix-vector multiplications over characteristic-2 fields. As opposed to previously proposed CSE algorithms, which usually focus on complexity savings due to recurrences of subexpressions, our CSE algorithm achieves two types ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	761
0801.3049	Spatial-Spectral Joint Detection for Wideband Spectrum Sensing in Cognitive Radio Networks	Spectrum sensing is an essential functionality that enables cognitive radios to detect spectral holes and opportunistically use under-utilized frequency bands without causing harmful interference to primary networks. Since individual cognitive radios might not be able to reliably detect weak primary signals due to chan...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	1,177
2306.00114	The Canadian Cropland Dataset: A New Land Cover Dataset for Multitemporal Deep Learning Classification in Agriculture	Monitoring land cover using remote sensing is vital for studying environmental changes and ensuring global food security through crop yield forecasting. Specifically, multitemporal remote sensing imagery provides relevant information about the dynamics of a scene, which has proven to lead to better land cover classific...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	369,871
2410.18882	A Survey of Multimodal Sarcasm Detection	Sarcasm is a rhetorical device that is used to convey the opposite of the literal meaning of an utterance. Sarcasm is widely used on social media and other forms of computer-mediated communication motivating the use of computational models to identify it automatically. While the clear majority of approaches to sarcasm ...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	502,070
2301.00301	Generalized PTR: User-Friendly Recipes for Data-Adaptive Algorithms with Differential Privacy	The ''Propose-Test-Release'' (PTR) framework is a classic recipe for designing differentially private (DP) algorithms that are data-adaptive, i.e. those that add less noise when the input dataset is nice. We extend PTR to a more general setting by privately testing data-dependent privacy losses rather than local sensit...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	338,855
2002.11018	Breaking Batch Normalization for better explainability of Deep Neural Networks through Layer-wise Relevance Propagation	The lack of transparency of neural networks stays a major break for their use. The Layerwise Relevance Propagation technique builds heat-maps representing the relevance of each input in the model s decision. The relevance spreads backward from the last to the first layer of the Deep Neural Network. Layer-wise Relevance...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	165,584
2209.06569	The Embeddings World and Artificial General Intelligence	From early days, a key and controversial question inside the artificial intelligence community was whether Artificial General Intelligence (AGI) is achievable. AGI is the ability of machines and computer programs to achieve human-level intelligence and do all tasks that a human being can. While there exist a number of ...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	317,440
1702.04054	Matrix Completion Based Localization in the Internet of Things Network	In order to make a proper reaction to the collected information from internet of things (IoT) devices, location information of things should be available at the data center. One challenge for the massive IoT networks is to identify the location map of whole sensor nodes from partially observed distance information. In ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	68,213
2007.04101	On Learning Semantic Representations for Million-Scale Free-Hand Sketches	In this paper, we study learning semantic representations for million-scale free-hand sketches. This is highly challenging due to the domain-unique traits of sketches, e.g., diverse, sparse, abstract, noisy. We propose a dual-branch CNNRNN network architecture to represent sketches, which simultaneously encodes both th...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	186,258
2101.03929	ORDNet: Capturing Omni-Range Dependencies for Scene Parsing	Learning to capture dependencies between spatial positions is essential to many visual tasks, especially the dense labeling problems like scene parsing. Existing methods can effectively capture long-range dependencies with self-attention mechanism while short ones by local convolution. However, there is still much gap ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	215,028
1605.08470	A Feature based Approach for Video Compression	It is a high cost problem for panoramic image stitching via image matching algorithm and not practical for real-time performance. In this paper, we take full advantage ofHarris corner invariant characterization method light intensity parallel meaning, translation and rotation, and made a realtime panoramic image stitch...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	56,439
2203.04822	A high-precision underwater object detection based on joint self-supervised deblurring and improved spatial transformer network	Deep learning-based underwater object detection (UOD) remains a major challenge due to the degraded visibility and difficulty to obtain sufficient underwater object images captured from various perspectives for training. To address these issues, this paper presents a high-precision UOD based on joint self-supervised de...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	284,618
2411.01000	Enhancing Model-Based Step Adaptation for Push Recovery through Reinforcement Learning of Step Timing and Region	This paper introduces a new approach to enhance the robustness of humanoid walking under strong perturbations, such as substantial pushes. Effective recovery from external disturbances requires bipedal robots to dynamically adjust their stepping strategies, including footstep positions and timing. Unlike most advanced ...	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	504,868
2412.17231	FedMeld: A Model-dispersal Federated Learning Framework for Space-ground Integrated Networks	To bridge the digital divide, the space-ground integrated networks (SGINs), which will be a key component of the six-generation (6G) mobile networks, are expected to deliver artificial intelligence (AI) services to every corner of the world. One mission of SGINs is to support federated learning (FL) at a global scale. ...	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	true	519,877
1512.08969	Evaluating Go Game Records for Prediction of Player Attributes	We propose a way of extracting and aggregating per-move evaluations from sets of Go game records. The evaluations capture different aspects of the games such as played patterns or statistic of sente/gote sequences. Using machine learning algorithms, the evaluations can be utilized to predict different relevant target v...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	50,557
2311.05794	An Experimental Design for Anytime-Valid Causal Inference on Multi-Armed Bandits	Experimentation is crucial for managers to rigorously quantify the value of a change and determine if it leads to a statistically significant improvement over the status quo. As companies increasingly mandate that all changes undergo experimentation before widespread release, two challenges arise: (1) minimizing the pr...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	406,705
2308.00425	Discourse-Aware Text Simplification: From Complex Sentences to Linked Propositions	Sentences that present a complex syntax act as a major stumbling block for downstream Natural Language Processing applications whose predictive quality deteriorates with sentence length and complexity. The task of Text Simplification (TS) may remedy this situation. It aims to modify sentences in order to make them easi...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	382,935
2007.12620	A Novel Ensemble Deep Learning Model for Stock Prediction Based on Stock Prices and News	In recent years, machine learning and deep learning have become popular methods for financial data analysis, including financial textual data, numerical data, and graphical data. This paper proposes to use sentiment analysis to extract useful information from multiple textual data sources and a blending ensemble deep l...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	188,875
1911.02085	Path-Based Contextualization of Knowledge Graphs for Textual Entailment	In this paper, we introduce the problem of knowledge graph contextualization -- that is, given a specific NLP task, the problem of extracting meaningful and relevant sub-graphs from a given knowledge graph. The task in the case of this paper is the textual entailment problem, and the context is a relevant sub-graph for...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	152,270
2408.13798	Selectively Dilated Convolution for Accuracy-Preserving Sparse Pillar-based Embedded 3D Object Detection	Pillar-based 3D object detection has gained traction in self-driving technology due to its speed and accuracy facilitated by the artificial densification of pillars for GPU-friendly processing. However, dense pillar processing fundamentally wastes computation since it ignores the inherent sparsity of pillars derived fr...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	483,296
2401.14210	At the junction between deep learning and statistics of extremes: formalizing the landslide hazard definition	The most adopted definition of landslide hazard combines spatial information about landslide location (susceptibility), threat (intensity), and frequency (return period). Only the first two elements are usually considered and estimated when working over vast areas. Even then, separate models constitute the standard, wi...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	424,010
2207.03800	FastLTS: Non-Autoregressive End-to-End Unconstrained Lip-to-Speech Synthesis	Unconstrained lip-to-speech synthesis aims to generate corresponding speeches from silent videos of talking faces with no restriction on head poses or vocabulary. Current works mainly use sequence-to-sequence models to solve this problem, either in an autoregressive architecture or a flow-based non-autoregressive archi...	false	false	true	false	false	false	false	false	true	false	false	true	false	false	false	false	false	true	306,981
1810.07307	Solving Tree Problems with Category Theory	Artificial Intelligence (AI) has long pursued models, theories, and techniques to imbue machines with human-like general intelligence. Yet even the currently predominant data-driven approaches in AI seem to be lacking humans' unique ability to solve wide ranges of problems. This situation begs the question of the exist...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	110,612
2310.02700	Insights of using Control Theory for minimizing Induced Seismicity in Underground Reservoirs	Deep Geothermal Energy, Carbon Capture, and Storage and Hydrogen Storage have significant potential to meet the large-scale needs of the energy sector and reduce the CO$_2$ emissions. However, the injection of fluids into the earth's crust, upon which these activities rely, can lead to the formation of new seismogenic ...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	396,961
2501.19314	An Efficient Approach for Machine Translation on Low-resource Languages: A Case Study in Vietnamese-Chinese	Despite the rise of recent neural networks in machine translation, those networks do not work well if the training data is insufficient. In this paper, we proposed an approach for machine translation in low-resource languages such as Vietnamese-Chinese. Our proposed method leveraged the power of the multilingual pre-tr...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	529,107
1702.07898	Learning Deep NBNN Representations for Robust Place Categorization	This paper presents an approach for semantic place categorization using data obtained from RGB cameras. Previous studies on visual place recognition and classification have shown that, by considering features derived from pre-trained Convolutional Neural Networks (CNNs) in combination with part-based classification mod...	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	68,861
2410.12847	ACCEPT: Adaptive Codebook for Composite and Efficient Prompt Tuning	Prompt Tuning has been a popular Parameter-Efficient Fine-Tuning method attributed to its remarkable performance with few updated parameters on various large-scale pretrained Language Models (PLMs). Traditionally, each prompt has been considered indivisible and updated independently, leading the parameters increase pro...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	499,235
1601.07087	Greedy Subspace Pursuit for Joint Sparse Recovery	In this paper, we address the sparse multiple measurement vector (MMV) problem where the objective is to recover a set of sparse nonzero row vectors or indices of a signal matrix from incomplete measurements. Ideally, regardless of the number of columns in the signal matrix, the sparsity (k) plus one measurements is su...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	51,377
1810.08564	Nonparametric Bayesian Lomax delegate racing for survival analysis with competing risks	We propose Lomax delegate racing (LDR) to explicitly model the mechanism of survival under competing risks and to interpret how the covariates accelerate or decelerate the time to event. LDR explains non-monotonic covariate effects by racing a potentially infinite number of sub-risks, and consequently relaxes the ubiqu...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	110,847
2402.10392	Pretext Training Algorithms for Event Sequence Data	Pretext training followed by task-specific fine-tuning has been a successful approach in vision and language domains. This paper proposes a self-supervised pretext training framework tailored to event sequence data. We introduce a novel alignment verification task that is specialized to event sequences, building on goo...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	429,940
2010.01192	Correcting Experience Replay for Multi-Agent Communication	We consider the problem of learning to communicate using multi-agent reinforcement learning (MARL). A common approach is to learn off-policy, using data sampled from a replay buffer. However, messages received in the past may not accurately reflect the current communication policy of each agent, and this complicates le...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	true	false	false	false	198,554
2403.14465	CathFlow: Self-Supervised Segmentation of Catheters in Interventional Ultrasound Using Optical Flow and Transformers	In minimally invasive endovascular procedures, contrast-enhanced angiography remains the most robust imaging technique. However, it is at the expense of the patient and clinician's health due to prolonged radiation exposure. As an alternative, interventional ultrasound has notable benefits such as being radiation-free,...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	440,092
2403.14691	Large Language Models and User Trust: Consequence of Self-Referential Learning Loop and the Deskilling of Healthcare Professionals	This paper explores the evolving relationship between clinician trust in LLMs, the transformation of data sources from predominantly human-generated to AI-generated content, and the subsequent impact on the precision of LLMs and clinician competence. One of the primary concerns identified is the potential feedback loop...	false	false	false	false	true	false	false	false	false	false	false	false	false	true	false	false	false	false	440,205
2112.06380	Robust Voting Rules from Algorithmic Robust Statistics	Maximum likelihood estimation furnishes powerful insights into voting theory, and the design of voting rules. However the MLE can usually be badly corrupted by a single outlying sample. This means that a single voter or a group of colluding voters can vote strategically and drastically affect the outcome. Motivated by ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	271,153
2305.17611	Bayesian Decision Making to Localize Visual Queries in 2D	This report describes our approach for the EGO4D 2023 Visual Query 2D Localization Challenge. Our method aims to reduce the number of False Positives (FP) that occur because of high similarity between the visual crop and the proposed bounding boxes from the baseline's Region Proposal Network (RPN). Our method uses a tr...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	368,675
2209.07385	Resilient Communication Scheme for Distributed Decision of InterconnectingNetworks of Microgrids	Networking of microgrids can provide the operational flexibility needed for the increasing number of DERs deployed at the distribution level and supporting end-use demand when there is loss of the bulk power system. But, networked microgrids are vulnerable to cyber-physical attacks and faults due to the complex interco...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	317,725
1911.01067	Blind Network Revenue Management and Bandits with Knapsacks under Limited Switches	Our work is motivated by a common business constraint in online markets. While firms respect the advantages of dynamic pricing and price experimentation, they must limit the number of price changes (i.e., switches) to be within some budget due to various practical reasons. We study both the classical price-based networ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	152,007
2412.14366	Surrealistic-like Image Generation with Vision-Language Models	Recent advances in generative AI make it convenient to create different types of content, including text, images, and code. In this paper, we explore the generation of images in the style of paintings in the surrealism movement using vision-language generative models, including DALL-E, Deep Dream Generator, and DreamSt...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	518,666
1712.01238	Learning by Asking Questions	We introduce an interactive learning framework for the development and testing of intelligent visual systems, called learning-by-asking (LBA). We explore LBA in context of the Visual Question Answering (VQA) task. LBA differs from standard VQA training in that most questions are not observed during training time, and t...	false	false	false	false	false	false	true	false	true	false	false	true	false	false	false	false	false	false	86,070
2105.07581	Vision Transformers are Robust Learners	Transformers, composed of multiple self-attention layers, hold strong promises toward a generic learning primitive applicable to different data modalities, including the recent breakthroughs in computer vision achieving state-of-the-art (SOTA) standard accuracy. What remains largely unexplored is their robustness evalu...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	235,479
2302.08687	VEGETA: Vertically-Integrated Extensions for Sparse/Dense GEMM Tile Acceleration on CPUs	Deep Learning (DL) acceleration support in CPUs has recently gained a lot of traction, with several companies (Arm, Intel, IBM) announcing products with specialized matrix engines accessible via GEMM instructions. CPUs are pervasive and need to handle diverse requirements across DL workloads running in edge/HPC/cloud p...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	true	346,142
1810.12345	Analyzing Ideological Communities in Congressional Voting Networks	We here study the behavior of political party members aiming at identifying how ideological communities are created and evolve over time in diverse (fragmented and non-fragmented) party systems. Using public voting data of both Brazil and the US, we propose a methodology to identify and characterize ideological communi...	false	false	false	true	false	false	false	false	false	false	false	false	false	true	false	false	false	false	111,738
2205.11914	An Adaptive Contrastive Learning Model for Spike Sorting	Brain-computer interfaces (BCIs), is ways for electronic devices to communicate directly with the brain. For most medical-type brain-computer interface tasks, the activity of multiple units of neurons or local field potentials is sufficient for decoding. But for BCIs used in neuroscience research, it is important to se...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	298,333
2402.16479	Edge Detectors Can Make Deep Convolutional Neural Networks More Robust	Deep convolutional neural networks (DCNN for short) are vulnerable to examples with small perturbations. Improving DCNN's robustness is of great significance to the safety-critical applications, such as autonomous driving and industry automation. Inspired by the principal way that human eyes recognize objects, i.e., la...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	432,590
2310.10533	Label-efficient Segmentation via Affinity Propagation	Weakly-supervised segmentation with label-efficient sparse annotations has attracted increasing research attention to reduce the cost of laborious pixel-wise labeling process, while the pairwise affinity modeling techniques play an essential role in this task. Most of the existing approaches focus on using the local ap...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	400,262
2305.13204	Improving Isochronous Machine Translation with Target Factors and Auxiliary Counters	To translate speech for automatic dubbing, machine translation needs to be isochronous, i.e. translated speech needs to be aligned with the source in terms of speech durations. We introduce target factors in a transformer model to predict durations jointly with target language phoneme sequences. We also introduce auxil...	false	false	true	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	366,398
1202.0077	Datasets as Interacting Particle Systems: a Framework for Clustering	In this paper we propose a framework inspired by interacting particle physics and devised to perform clustering on multidimensional datasets. To this end, any given dataset is modeled as an interacting particle system, under the assumption that each element of the dataset corresponds to a different particle and that pa...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	14,040
1507.03761	Effects of Relay Selection Strategies on the Spectral Efficiency of Wireless Systems with Half- and Full-duplex Nodes	This work proposes an analytical framework to study how relay selection strategies perform in half- and full-duplex deployments by combining renewal theory and stochastic geometry. Specifically, we assume that the network nodes -- operating in either half- or full-duplex mode -- are scattered according to a two-dimensi...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	45,102
2502.08668	Style Extraction on Text Embeddings Using VAE and Parallel Dataset	This study investigates the stylistic differences among various Bible translations using a Variational Autoencoder (VAE) model. By embedding textual data into high-dimensional vectors, the study aims to detect and analyze stylistic variations between translations, with a specific focus on distinguishing the American St...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	533,114
2403.16303	Large Language Models in Biomedical and Health Informatics: A Review with Bibliometric Analysis	Large Language Models (LLMs) have rapidly become important tools in Biomedical and Health Informatics (BHI), enabling new ways to analyze data, treat patients, and conduct research. This study aims to provide a comprehensive overview of LLM applications in BHI, highlighting their transformative potential and addressing...	false	false	false	true	true	false	false	false	true	false	false	false	false	false	false	false	false	true	440,970
2402.06390	Deepfake for the Good: Generating Avatars through Face-Swapping with Implicit Deepfake Generation	Numerous emerging deep-learning techniques have had a substantial impact on computer graphics. Among the most promising breakthroughs are the rise of Neural Radiance Fields (NeRFs) and Gaussian Splatting (GS). NeRFs encode the object's shape and color in neural network weights using a handful of images with known camer...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	428,287
2003.09556	Appearance Fusion of Multiple Cues for Video Co-localization	This work addresses the joint object discovery problem in videos while utilizing multiple object-related cues. In contrast to the usual spatial fusion approach, a novel appearance fusion approach is presented here. Specifically, this paper proposes an effective fusion process of different GMMs derived from multiple cue...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	169,083
2312.17300	Improving Intrusion Detection with Domain-Invariant Representation Learning in Latent Space	Domain generalization focuses on leveraging knowledge from multiple related domains with ample training data and labels to enhance inference on unseen in-distribution (IN) and out-of-distribution (OOD) domains. In our study, we introduce a two-phase representation learning technique using multi-task learning. This appr...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	418,706

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