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
2410.11505	LoGS: Visual Localization via Gaussian Splatting with Fewer Training Images	Visual localization involves estimating a query image's 6-DoF (degrees of freedom) camera pose, which is a fundamental component in various computer vision and robotic tasks. This paper presents LoGS, a vision-based localization pipeline utilizing the 3D Gaussian Splatting (GS) technique as scene representation. This n...	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	498,595
2010.01410	Code to Comment "Translation": Data, Metrics, Baselining & Evaluation	The relationship of comments to code, and in particular, the task of generating useful comments given the code, has long been of interest. The earliest approaches have been based on strong syntactic theories of comment-structures, and relied on textual templates. More recently, researchers have applied deep learning me...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	true	198,641
2405.15563	Heterogeneous virus classification using a functional deep learning model based on transmission electron microscopy images (Preprint)	Viruses are submicroscopic agents that can infect all kinds of lifeforms and use their hosts' living cells to replicate themselves. Despite having some of the simplest genetic structures among all living beings, viruses are highly adaptable, resilient, and given the right conditions, are capable of causing unforeseen c...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	456,994
2109.00348	The Popularity-Homophily Index: A new way to measure Homophily in Directed Graphs	In networks, the well-documented tendency for people with similar characteristics to form connections is known as the principle of homophily. Being able to quantify homophily into a number has a significant real-world impact, ranging from government fund-allocation to finetuning the parameters in a sociological model. ...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	true	253,080
1609.05130	SemanticFusion: Dense 3D Semantic Mapping with Convolutional Neural Networks	Ever more robust, accurate and detailed mapping using visual sensing has proven to be an enabling factor for mobile robots across a wide variety of applications. For the next level of robot intelligence and intuitive user interaction, maps need extend beyond geometry and appearence - they need to contain semantics. We ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	61,081
2501.18734	STaleX: A Spatiotemporal-Aware Adaptive Auto-scaling Framework for Microservices	While cloud environments and auto-scaling solutions have been widely applied to traditional monolithic applications, they face significant limitations when it comes to microservices-based architectures. Microservices introduce additional challenges due to their dynamic and spatiotemporal characteristics, which require ...	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	true	528,833
2210.00943	Simple Pooling Front-ends For Efficient Audio Classification	Recently, there has been increasing interest in building efficient audio neural networks for on-device scenarios. Most existing approaches are designed to reduce the size of audio neural networks using methods such as model pruning. In this work, we show that instead of reducing model size using complex methods, elimin...	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	321,067
1711.11585	High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs	We present a new method for synthesizing high-resolution photo-realistic images from semantic label maps using conditional generative adversarial networks (conditional GANs). Conditional GANs have enabled a variety of applications, but the results are often limited to low-resolution and still far from realistic. In thi...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	true	85,808
2304.14889	Large-scale multidisciplinary design optimization of the NASA lift-plus-cruise concept using a novel aircraft design framework	The conceptual design of eVTOL aircraft is a high-dimensional optimization problem that involves large numbers of continuous design parameters. Therefore, eVTOL design method would benefit from numerical optimization algorithms capable of systematically searching these high-dimensional parameters spaces, using comprehe...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	361,133
2304.04225	Transformer Utilization in Medical Image Segmentation Networks	Owing to success in the data-rich domain of natural images, Transformers have recently become popular in medical image segmentation. However, the pairing of Transformers with convolutional blocks in varying architectural permutations leaves their relative effectiveness to open interpretation. We introduce Transformer A...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	357,138
2010.07902	Entropic proofs of Singleton bounds for quantum error-correcting codes	We show that a relatively simple reasoning using von Neumann entropy inequalities yields a robust proof of the quantum Singleton bound for quantum error-correcting codes (QECC). For entanglement-assisted quantum error-correcting codes (EAQECC) and catalytic codes (CQECC), a type of generalized quantum Singleton bound [...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	200,983
2106.07239	Fair Clustering Under a Bounded Cost	Clustering is a fundamental unsupervised learning problem where a dataset is partitioned into clusters that consist of nearby points in a metric space. A recent variant, fair clustering, associates a color with each point representing its group membership and requires that each color has (approximately) equal represent...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	240,836
2203.15219	AR Point&Click: An Interface for Setting Robot Navigation Goals	This paper considers the problem of designating navigation goal locations for interactive mobile robots. We propose a point-and-click interface, implemented with an Augmented Reality (AR) headset. The cameras on the AR headset are used to detect natural pointing gestures performed by the user. The selected goal is visu...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	288,293
1906.00451	Exact inference in structured prediction	Structured prediction can be thought of as a simultaneous prediction of multiple labels. This is often done by maximizing a score function on the space of labels, which decomposes as a sum of pairwise and unary potentials. The above is naturally modeled with a graph, where edges and vertices are related to pairwise and...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	133,404
0906.5397	Asymptotically Optimal Policies for Hard-deadline Scheduling over Fading Channels	A hard-deadline, opportunistic scheduling problem in which $B$ bits must be transmitted within $T$ time-slots over a time-varying channel is studied: the transmitter must decide how many bits to serve in each slot based on knowledge of the current channel but without knowledge of the channel in future slots, with the o...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	4,002
2205.15764	SymFormer: End-to-end symbolic regression using transformer-based architecture	Many real-world problems can be naturally described by mathematical formulas. The task of finding formulas from a set of observed inputs and outputs is called symbolic regression. Recently, neural networks have been applied to symbolic regression, among which the transformer-based ones seem to be the most promising. Af...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	true	false	false	299,863
2403.10768	Task-Driven Manipulation with Reconfigurable Parallel Robots	ReachBot, a proposed robotic platform, employs extendable booms as limbs for mobility in challenging environments, such as martian caves. When attached to the environment, ReachBot acts as a parallel robot, with reconfiguration driven by the ability to detach and re-place the booms. This ability enables manipulation-fo...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	438,345
2410.04887	Wide Neural Networks Trained with Weight Decay Provably Exhibit Neural Collapse	Deep neural networks (DNNs) at convergence consistently represent the training data in the last layer via a highly symmetric geometric structure referred to as neural collapse. This empirical evidence has spurred a line of theoretical research aimed at proving the emergence of neural collapse, mostly focusing on the un...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	495,487
2303.11734	Unlocking Layer-wise Relevance Propagation for Autoencoders	Autoencoders are a powerful and versatile tool often used for various problems such as anomaly detection, image processing and machine translation. However, their reconstructions are not always trivial to explain. Therefore, we propose a fast explainability solution by extending the Layer-wise Relevance Propagation met...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	352,985
2201.05947	Universal Online Learning: an Optimistically Universal Learning Rule	We study the subject of universal online learning with non-i.i.d. processes for bounded losses. The notion of an universally consistent learning was defined by Hanneke in an effort to study learning theory under minimal assumptions, where the objective is to obtain low long-run average loss for any target function. We ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	275,561
1602.05310	Large Scale Kernel Learning using Block Coordinate Descent	We demonstrate that distributed block coordinate descent can quickly solve kernel regression and classification problems with millions of data points. Armed with this capability, we conduct a thorough comparison between the full kernel, the Nystr\"om method, and random features on three large classification tasks from ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	52,238
2405.12669	A Survey on Multi-modal Machine Translation: Tasks, Methods and Challenges	In recent years, multi-modal machine translation has attracted significant interest in both academia and industry due to its superior performance. It takes both textual and visual modalities as inputs, leveraging visual context to tackle the ambiguities in source texts. In this paper, we begin by offering an exhaustive...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	455,612
2109.11788	Parameter-free Reduction of the Estimation Bias in Deep Reinforcement Learning for Deterministic Policy Gradients	Approximation of the value functions in value-based deep reinforcement learning induces overestimation bias, resulting in suboptimal policies. We show that when the reinforcement signals received by the agents have a high variance, deep actor-critic approaches that overcome the overestimation bias lead to a substantial...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	257,058
2501.12971	On Universal Decoding over Discrete Additive Channels by Noise Guessing	We study universal decoding over parametric discrete additive channels. Our decoders are variants of noise guessing decoders that use estimators for the probability of a noise sequence, when the actual channel law is unknown. A deterministic version produces noise sequences in a fixed order, and a randomised one draws ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	526,505
2011.13200	Unsupervised Word Translation Pairing using Refinement based Point Set Registration	Cross-lingual alignment of word embeddings play an important role in knowledge transfer across languages, for improving machine translation and other multi-lingual applications. Current unsupervised approaches rely on similarities in geometric structure of word embedding spaces across languages, to learn structure-pres...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	208,408
2007.11491	Preconditioned Gradient Descent Algorithm for Inverse Filtering on Spatially Distributed Networks	Graph filters and their inverses have been widely used in denoising, smoothing, sampling, interpolating and learning. Implementation of an inverse filtering procedure on spatially distributed networks (SDNs) is a remarkable challenge, as each agent on an SDN is equipped with a data processing subsystem with limited cap...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	188,568
2403.04195	Fill-and-Spill: Deep Reinforcement Learning Policy Gradient Methods for Reservoir Operation Decision and Control	Changes in demand, various hydrological inputs, and environmental stressors are among the issues that water managers and policymakers face on a regular basis. These concerns have sparked interest in applying different techniques to determine reservoir operation policy decisions. As the resolution of the analysis increa...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	435,501
1705.00399	Matrix completion with queries	In many applications, e.g., recommender systems and traffic monitoring, the data comes in the form of a matrix that is only partially observed and low rank. A fundamental data-analysis task for these datasets is matrix completion, where the goal is to accurately infer the entries missing from the matrix. Even when the ...	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	false	false	72,672
2306.14941	SIMMF: Semantics-aware Interactive Multiagent Motion Forecasting for Autonomous Vehicle Driving	Autonomous vehicles require motion forecasting of their surrounding multiagents (pedestrians and vehicles) to make optimal decisions for navigation. The existing methods focus on techniques to utilize the positions and velocities of these agents and fail to capture semantic information from the scene. Moreover, to miti...	false	false	false	false	true	false	true	true	false	false	false	true	false	false	false	false	false	false	375,876
1512.04310	Attitudes towards Refugees in Light of the Paris Attacks	The Paris attacks prompted a massive response on social media including Twitter. This paper explores the immediate response of English speakers on Twitter towards Middle Eastern refugees in Europe. We show that antagonism towards refugees is mostly coming from the United States and is mostly partisan.	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	50,123
2405.06460	ProCIS: A Benchmark for Proactive Retrieval in Conversations	The field of conversational information seeking, which is rapidly gaining interest in both academia and industry, is changing how we interact with search engines through natural language interactions. Existing datasets and methods are mostly evaluating reactive conversational information seeking systems that solely pro...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	453,299
2406.11678	TourRank: Utilizing Large Language Models for Documents Ranking with a Tournament-Inspired Strategy	Large Language Models (LLMs) are increasingly employed in zero-shot documents ranking, yielding commendable results. However, several significant challenges still persist in LLMs for ranking: (1) LLMs are constrained by limited input length, precluding them from processing a large number of documents simultaneously; (2...	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	465,000
2306.14917	Towards Enriched Controllability for Educational Question Generation	Question Generation (QG) is a task within Natural Language Processing (NLP) that involves automatically generating questions given an input, typically composed of a text and a target answer. Recent work on QG aims to control the type of generated questions so that they meet educational needs. A remarkable example of co...	false	false	false	false	true	false	true	false	true	false	false	false	false	true	false	false	false	false	375,865
1801.04581	Voliro: An Omnidirectional Hexacopter With Tiltable Rotors	Extending the maneuverability of unmanned areal vehicles promises to yield a considerable increase in the areas in which these systems can be used. Some such applications are the performance of more complicated inspection tasks and the generation of complex uninterrupted movements of an attached camera. In this paper w...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	88,309
1907.03241	ASCNet: Adaptive-Scale Convolutional Neural Networks for Multi-Scale Feature Learning	Extracting multi-scale information is key to semantic segmentation. However, the classic convolutional neural networks (CNNs) encounter difficulties in achieving multi-scale information extraction: expanding convolutional kernel incurs the high computational cost and using maximum pooling sacrifices image information. ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	137,816
2406.15050	Tri-VQA: Triangular Reasoning Medical Visual Question Answering for Multi-Attribute Analysis	The intersection of medical Visual Question Answering (Med-VQA) is a challenging research topic with advantages including patient engagement and clinical expert involvement for second opinions. However, existing Med-VQA methods based on joint embedding fail to explain whether their provided results are based on correct...	false	false	false	false	true	false	true	false	true	false	false	true	false	false	false	false	false	false	466,602
2302.09466	RePrompt: Automatic Prompt Editing to Refine AI-Generative Art Towards Precise Expressions	Generative AI models have shown impressive ability to produce images with text prompts, which could benefit creativity in visual art creation and self-expression. However, it is unclear how precisely the generated images express contexts and emotions from the input texts. We explored the emotional expressiveness of AI-...	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	346,439
2306.07003	High-speed Autonomous Racing using Trajectory-aided Deep Reinforcement Learning	The classical method of autonomous racing uses real-time localisation to follow a precalculated optimal trajectory. In contrast, end-to-end deep reinforcement learning (DRL) can train agents to race using only raw LiDAR scans. While classical methods prioritise optimization for high-performance racing, DRL approaches h...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	372,838
2211.13461	Highest-performance Stream Processing	We present the stream processing library that achieves the highest performance of existing OCaml streaming libraries, attaining the speed and memory efficiency of hand-written state machines. It supports finite and infinite streams with the familiar declarative interface, of any combination of map, filter, take(while),...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	true	332,482
1702.08717	An Extensive Technique to Detect and Analyze Melanoma: A Challenge at the International Symposium on Biomedical Imaging (ISBI) 2017	An automated method to detect and analyze the melanoma is presented to improve diagnosis which will leads to the exact treatment. Image processing techniques such as segmentation, feature descriptors and classification models are involved in this method. In the First phase the lesion region is segmented using CIELAB Co...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	69,053
2410.07286	Benchmarking Data Heterogeneity Evaluation Approaches for Personalized Federated Learning	There is growing research interest in measuring the statistical heterogeneity of clients' local datasets. Such measurements are used to estimate the suitability for collaborative training of personalized federated learning (PFL) models. Currently, these research endeavors are taking place in silos and there is a lack o...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	496,571
1705.03192	Low-Complexity Decoding for Symmetric, Neighboring and Consecutive Side-information Index Coding Problems	The capacity of symmetric, neighboring and consecutive side-information single unicast index coding problems (SNC-SUICP) with number of messages equal to the number of receivers was given by Maleki, Cadambe and Jafar. For these index coding problems, an optimal index code construction by using Vandermonde matrices was ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	73,137
2210.00947	An efficient topology optimization based on multigrid assisted reanalysis for heat transfer problem	To improve the computational efficiency of heat transfer topology optimization, a Multigrid Assisted Reanalysis (MGAR) method is proposed in this study. The MGAR not only significantly improves the computational efficiency, but also relieves the hardware burden, and thus can efficiently solve large-scale heat transfer ...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	321,070
2405.14232	FloodDamageCast: Building Flood Damage Nowcasting with Machine Learning and Data Augmentation	Near-real time estimation of damage to buildings and infrastructure, referred to as damage nowcasting in this study, is crucial for empowering emergency responders to make informed decisions regarding evacuation orders and infrastructure repair priorities during disaster response and recovery. Here, we introduce FloodD...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	456,323
2409.13321	SLaVA-CXR: Small Language and Vision Assistant for Chest X-ray Report Automation	Inspired by the success of large language models (LLMs), there is growing research interest in developing LLMs in the medical domain to assist clinicians. However, for hospitals, using closed-source commercial LLMs involves privacy issues, and developing open-source public LLMs requires large-scale computational resour...	false	false	false	false	true	false	true	false	true	false	false	true	false	false	false	false	false	false	489,942
2103.17084	DA-DETR: Domain Adaptive Detection Transformer with Information Fusion	The recent detection transformer (DETR) simplifies the object detection pipeline by removing hand-crafted designs and hyperparameters as employed in conventional two-stage object detectors. However, how to leverage the simple yet effective DETR architecture in domain adaptive object detection is largely neglected. Insp...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	227,778
2407.04736	SCDM: Unified Representation Learning for EEG-to-fNIRS Cross-Modal Generation in MI-BCIs	Hybrid motor imagery brain-computer interfaces (MI-BCIs), which integrate both electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) signals, outperform those based solely on EEG. However, simultaneously recording EEG and fNIRS signals is highly challenging due to the difficulty of colocating b...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	470,683
1806.06685	Solving the Steiner Tree Problem in graphs with Variable Neighborhood Descent	The Steiner Tree Problem (STP) in graphs is an important problem with various applications in many areas such as design of integrated circuits, evolution theory, networking, etc. In this paper, we propose an algorithm to solve the STP. The algorithm includes a reducer and a solver using Variable Neighborhood Descent (V...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	100,750
2007.12622	BMBC:Bilateral Motion Estimation with Bilateral Cost Volume for Video Interpolation	Video interpolation increases the temporal resolution of a video sequence by synthesizing intermediate frames between two consecutive frames. We propose a novel deep-learning-based video interpolation algorithm based on bilateral motion estimation. First, we develop the bilateral motion network with the bilateral cost ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	188,876
2212.11636	Towards Causal Credit Assignment	Adequately assigning credit to actions for future outcomes based on their contributions is a long-standing open challenge in Reinforcement Learning. The assumptions of the most commonly used credit assignment method are disadvantageous in tasks where the effects of decisions are not immediately evident. Furthermore, th...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	337,840
1901.10590	Throttling Malware Families in 2D	Malicious software are categorized into families based on their static and dynamic characteristics, infection methods, and nature of threat. Visual exploration of malware instances and families in a low dimensional space helps in giving a first overview about dependencies and relationships among these instances, detect...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	120,056
1912.10160	AMUSED: A Multi-Stream Vector Representation Method for Use in Natural Dialogue	The problem of building a coherent and non-monotonous conversational agent with proper discourse and coverage is still an area of open research. Current architectures only take care of semantic and contextual information for a given query and fail to completely account for syntactic and external knowledge which are cru...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	158,244
2410.01673	MaxSAT decoders for arbitrary CSS codes	Quantum error correction (QEC) is essential for operating quantum computers in the presence of noise. Here, we accurately decode arbitrary Calderbank-Shor-Steane (CSS) codes via the maximum satisfiability (MaxSAT) problem. We show how to map quantum maximum likelihood problem of CSS codes of arbitrary geometry and pari...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	493,881
1602.08349	Relational lattices via duality	The natural join and the inner union combine in different ways tables of a relational database. Tropashko [18] observed that these two operations are the meet and join in a class of lattices-called the relational lattices- and proposed lattice theory as an alternative algebraic approach to databases. Aiming at query op...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	true	52,632
2007.06317	IntegralAction: Pose-driven Feature Integration for Robust Human Action Recognition in Videos	Most current action recognition methods heavily rely on appearance information by taking an RGB sequence of entire image regions as input. While being effective in exploiting contextual information around humans, e.g., human appearance and scene category, they are easily fooled by out-of-context action videos where the...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	186,982
2411.09242	FluidML: Fast and Memory Efficient Inference Optimization	Machine learning models deployed on edge devices have enabled numerous exciting new applications, such as humanoid robots, AR glasses, and autonomous vehicles. However, the computing resources available on these edge devices are not catching up with the ever-growing number of parameters in these models. As the models b...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	508,180
2409.07067	Edge Modeling Activation Free Fourier Network for Spacecraft Image Denoising	Spacecraft image denoising is a crucial basic technology closely related to aerospace research. However, the existing deep learning-based image denoising methods lack deep consideration of the characteristics of spacecraft image. To address the aforementioned shortcomings, we analyses spacecraft noise image and identif...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	487,372
2211.09953	Towards Explaining Subjective Ground of Individuals on Social Media	Large-scale language models have been reducing the gap between machines and humans in understanding the real world, yet understanding an individual's theory of mind and behavior from text is far from being resolved. This research proposes a neural model -- Subjective Ground Attention -- that learns subjective grounds...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	331,158
2203.12071	WayFAST: Navigation with Predictive Traversability in the Field	We present a self-supervised approach for learning to predict traversable paths for wheeled mobile robots that require good traction to navigate. Our algorithm, termed WayFAST (Waypoint Free Autonomous Systems for Traversability), uses RGB and depth data, along with navigation experience, to autonomously generate trave...	false	false	false	false	true	false	true	true	false	false	true	true	false	false	false	false	false	false	287,126
2411.11932	Reviving Dormant Memories: Investigating Catastrophic Forgetting in Language Models through Rationale-Guidance Difficulty	Although substantial efforts have been made to mitigate catastrophic forgetting in continual learning, the intrinsic mechanisms are not well understood. In this paper, we discover that when a forgetting model passively receives an externally provided partial appropriate rationale, its performance on the forgotten task ...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	509,240
2103.04350	Syntax-BERT: Improving Pre-trained Transformers with Syntax Trees	Pre-trained language models like BERT achieve superior performances in various NLP tasks without explicit consideration of syntactic information. Meanwhile, syntactic information has been proved to be crucial for the success of NLP applications. However, how to incorporate the syntax trees effectively and efficiently i...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	223,611
1909.10555	Automatic Mouse Embryo Brain Ventricle & Body Segmentation and Mutant Classification From Ultrasound Data Using Deep Learning	High-frequency ultrasound (HFU) is well suited for imaging embryonic mice in vivo because it is non-invasive and real-time. Manual segmentation of the brain ventricles (BVs) and whole body from 3D HFU images is time-consuming and requires specialized training. This paper presents a deep-learning-based segmentation pipe...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	146,575
1906.06195	R2D2: Repeatable and Reliable Detector and Descriptor	Interest point detection and local feature description are fundamental steps in many computer vision applications. Classical methods for these tasks are based on a detect-then-describe paradigm where separate handcrafted methods are used to first identify repeatable keypoints and then represent them with a local descri...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	135,233
2205.05862	AdaVAE: Exploring Adaptive GPT-2s in Variational Auto-Encoders for Language Modeling	Variational Auto-Encoder (VAE) has become the de-facto learning paradigm in achieving representation learning and generation for natural language at the same time. Nevertheless, existing VAE-based language models either employ elementary RNNs, which is not powerful to handle complex works in the multi-task situation, o...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	296,065
1906.11711	Reducing Popularity Bias in Recommendation Over Time	Many recommendation algorithms suffer from popularity bias: a small number of popular items being recommended too frequently, while other items get insufficient exposure. Research in this area so far has concentrated on a one-shot representation of this bias, and on algorithms to improve the diversity of individual rec...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	136,731
2309.01242	On input-to-state stability verification of identified models obtained by Koopman operator	This paper proposes a class of basis functions for realizing the input-to-state stability verification of identified models obtained from the true system (assumed to be input-to-state stable) using the Koopman operator. The formulated input-to-state stability conditions are in the form of linear matrix inequalities. Tw...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	389,605
2210.05254	Deep Spectro-temporal Artifacts for Detecting Synthesized Speech	The Audio Deep Synthesis Detection (ADD) Challenge has been held to detect generated human-like speech. With our submitted system, this paper provides an overall assessment of track 1 (Low-quality Fake Audio Detection) and track 2 (Partially Fake Audio Detection). In this paper, spectro-temporal artifacts were detected...	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	322,777
2404.13660	Trojan Detection in Large Language Models: Insights from The Trojan Detection Challenge	Large Language Models (LLMs) have demonstrated remarkable capabilities in various domains, but their vulnerability to trojan or backdoor attacks poses significant security risks. This paper explores the challenges and insights gained from the Trojan Detection Competition 2023 (TDC2023), which focused on identifying and...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	448,387
2402.06149	HeadStudio: Text to Animatable Head Avatars with 3D Gaussian Splatting	Creating digital avatars from textual prompts has long been a desirable yet challenging task. Despite the promising results achieved with 2D diffusion priors, current methods struggle to create high-quality and consistent animated avatars efficiently. Previous animatable head models like FLAME have difficulty in accura...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	428,185
1209.6037	Reproduction of Images by Gamut Mapping and Creation of New Test Charts in Prepress Process	With the advent of digital images the problem of keeping picture visualization uniformity arises because each printing or scanning device has its own color chart. So, universal color profiles are made by ICC to bring uniformity in various types of devices. Keeping that color profile in mind various new color charts are...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	18,788
2203.12311	Self-supervised HDR Imaging from Motion and Exposure Cues	Recent High Dynamic Range (HDR) techniques extend the capabilities of current cameras where scenes with a wide range of illumination can not be accurately captured with a single low-dynamic-range (LDR) image. This is generally accomplished by capturing several LDR images with varying exposure values whose information i...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	287,224
1908.07957	DISCo: Deep learning, Instance Segmentation, and Correlations for cell segmentation in calcium imaging	Calcium imaging is one of the most important tools in neurophysiology as it enables the observation of neuronal activity for hundreds of cells in parallel and at single-cell resolution. In order to use the data gained with calcium imaging, it is necessary to extract individual cells and their activity from the recordin...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	142,425
2007.04422	IQ-VQA: Intelligent Visual Question Answering	Even though there has been tremendous progress in the field of Visual Question Answering, models today still tend to be inconsistent and brittle. To this end, we propose a model-independent cyclic framework which increases consistency and robustness of any VQA architecture. We train our models to answer the original qu...	false	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	186,342
2403.06093	Enhancing 3D Object Detection with 2D Detection-Guided Query Anchors	Multi-camera-based 3D object detection has made notable progress in the past several years. However, we observe that there are cases (e.g. faraway regions) in which popular 2D object detectors are more reliable than state-of-the-art 3D detectors. In this paper, to improve the performance of query-based 3D object detect...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	436,298
2312.05148	Shape-aware Segmentation of the Placenta in BOLD Fetal MRI Time Series	Blood oxygen level dependent (BOLD) MRI time series with maternal hyperoxia can assess placental oxygenation and function. Measuring precise BOLD changes in the placenta requires accurate temporal placental segmentation and is confounded by fetal and maternal motion, contractions, and hyperoxia-induced intensity change...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	413,961
2203.01785	On Learning Contrastive Representations for Learning with Noisy Labels	Deep neural networks are able to memorize noisy labels easily with a softmax cross-entropy (CE) loss. Previous studies attempted to address this issue focus on incorporating a noise-robust loss function to the CE loss. However, the memorization issue is alleviated but still remains due to the non-robust CE loss. To add...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	283,510
2403.10745	iDb-RRT: Sampling-based Kinodynamic Motion Planning with Motion Primitives and Trajectory Optimization	Rapidly-exploring Random Trees (RRT) and its variations have emerged as a robust and efficient tool for finding collision-free paths in robotic systems. However, adding dynamic constraints makes the motion planning problem significantly harder, as it requires solving two-value boundary problems (computationally expensi...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	438,330
2501.13690	Variational U-Net with Local Alignment for Joint Tumor Extraction and Registration (VALOR-Net) of Breast MRI Data Acquired at Two Different Field Strengths	Background: Multiparametric breast MRI data might improve tumor diagnostics, characterization, and treatment planning. Accurate alignment and delineation of images acquired at different field strengths such as 3T and 7T, remain challenging research tasks. Purpose: To address alignment challenges and enable consistent t...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	526,779
2402.06976	Neural Rearrangement Planning for Object Retrieval from Confined Spaces Perceivable by Robot's In-hand RGB-D Sensor	Rearrangement planning for object retrieval tasks from confined spaces is a challenging problem, primarily due to the lack of open space for robot motion and limited perception. Several traditional methods exist to solve object retrieval tasks, but they require overhead cameras for perception and a time-consuming exhau...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	428,517
2008.07645	Deep Learning Based Source Separation Applied To Choir Ensembles	Choral singing is a widely practiced form of ensemble singing wherein a group of people sing simultaneously in polyphonic harmony. The most commonly practiced setting for choir ensembles consists of four parts; Soprano, Alto, Tenor and Bass (SATB), each with its own range of fundamental frequencies (F$0$s). The task of...	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	192,168
1910.05587	How to Not Measure Disentanglement	To evaluate disentangled representations several metrics have been proposed. However, theoretical guarantees for conventional metrics of disentanglement are missing. Moreover, conventional metrics do not have a consistent correlation with the outcomes of qualitative studies. In this paper we analyze metrics of disentan...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	149,111
2112.09574	Super-resolution reconstruction of cytoskeleton image based on A-net deep learning network	To date, live-cell imaging at the nanometer scale remains challenging. Even though super-resolution microscopy methods have enabled visualization of subcellular structures below the optical resolution limit, the spatial resolution is still far from enough for the structural reconstruction of biomolecules in vivo (i.e. ...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	272,191
2104.05228	SuperSim: a test set for word similarity and relatedness in Swedish	Language models are notoriously difficult to evaluate. We release SuperSim, a large-scale similarity and relatedness test set for Swedish built with expert human judgments. The test set is composed of 1,360 word-pairs independently judged for both relatedness and similarity by five annotators. We evaluate three differe...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	229,645
1703.05158	A simulation technique for slurries interacting with moving parts and deformable solids with applications	A numerical method for particle-laden fluids interacting with a deformable solid domain and mobile rigid parts is proposed and implemented in a full engineering system. The fluid domain is modeled with a lattice Boltzmann representation, the particles and rigid parts are modeled with a discrete element representation, ...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	70,031
2112.01683	TransZero: Attribute-guided Transformer for Zero-Shot Learning	Zero-shot learning (ZSL) aims to recognize novel classes by transferring semantic knowledge from seen classes to unseen ones. Semantic knowledge is learned from attribute descriptions shared between different classes, which act as strong priors for localizing object attributes that represent discriminative region featu...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	269,570
2308.08628	Learning the meanings of function words from grounded language using a visual question answering model	Interpreting a seemingly-simple function word like "or", "behind", or "more" can require logical, numerical, and relational reasoning. How are such words learned by children? Prior acquisition theories have often relied on positing a foundation of innate knowledge. Yet recent neural-network based visual question answer...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	385,969
2404.03687	DRIVE: Dual Gradient-Based Rapid Iterative Pruning	Modern deep neural networks (DNNs) consist of millions of parameters, necessitating high-performance computing during training and inference. Pruning is one solution that significantly reduces the space and time complexities of DNNs. Traditional pruning methods that are applied post-training focus on streamlining infer...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	444,367
2211.00387	Reasoning on Property Graphs with Graph Generating Dependencies	Graph Generating Dependencies (GGDs) informally express constraints between two (possibly different) graph patterns which enforce relationships on both graph's data (via property value constraints) and its structure (via topological constraints). Graph Generating Dependencies (GGDs) can express tuple- and equality-gene...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	327,857
2111.06291	Related Work on Image Quality Assessment	Due to the existence of quality degradations introduced in various stages of visual signal acquisition, compression, transmission and display, image quality assessment (IQA) plays a vital role in image-based applications. According to whether the reference image is complete and available, image quality evaluation can b...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	266,032
2106.05234	Do Transformers Really Perform Bad for Graph Representation?	The Transformer architecture has become a dominant choice in many domains, such as natural language processing and computer vision. Yet, it has not achieved competitive performance on popular leaderboards of graph-level prediction compared to mainstream GNN variants. Therefore, it remains a mystery how Transformers cou...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	240,021
2406.04419	TSCMamba: Mamba Meets Multi-View Learning for Time Series Classification	Time series classification (TSC) on multivariate time series is a critical problem. We propose a novel multi-view approach integrating frequency-domain and time-domain features to provide complementary contexts for TSC. Our method fuses continuous wavelet transform spectral features with temporal convolutional or multi...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	461,673
1411.6841	Multimedia IPP Codes with Efficient Tracing	Binary multimedia identifiable parent property codes (binary $t$-MIPPCs) are used in multimedia fingerprinting schemes where the identification of users taking part in the averaging collusion attack to illegally redistribute content is required. In this paper, we first introduce a binary strong multimedia identifiable ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	37,879
2501.13555	Instantaneous Core Loss -- Cycle-by-cycle Modeling of Power Magnetics in PWM DC-AC Converters	Nowadays, PWM excitation is one of the most common waveforms seen by magnetic components in power electronic converters. Core loss modeling approaches such as the improved Generalized Steinmetz Equation (iGSE) or the loss map based on the composite waveform hypothesis (CWH) generally process the PWM excitation piecewis...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	526,735
1812.07102	Deep Learning with Attention to Predict Gestational Age of the Fetal Brain	Fetal brain imaging is a cornerstone of prenatal screening and early diagnosis of congenital anomalies. Knowledge of fetal gestational age is the key to the accurate assessment of brain development. This study develops an attention-based deep learning model to predict gestational age of the fetal brain. The proposed mo...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	116,747
2502.10187	Reinforcement Learning based Constrained Optimal Control: an Interpretable Reward Design	This paper presents an interpretable reward design framework for reinforcement learning based constrained optimal control problems with state and terminal constraints. The problem is formalized within a standard partially observable Markov decision process framework. The reward function is constructed from four weighte...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	533,767
2105.01685	Witnessing Bell violations through probabilistic negativity	Bell's theorem shows that no hidden-variable model can explain the measurement statistics of a quantum system shared between two parties, thus ruling out a classical (local) understanding of nature. In this work we demonstrate that by relaxing the positivity restriction in the hidden-variable probability distribution i...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	233,586
1511.07927	Principal Basis Analysis in Sparse Representation	This article introduces a new signal analysis method, which can be interpreted as a principal component analysis in sparse decomposition of the signal. The method, called principal basis analysis, is based on a novel criterion: reproducibility of component which is an intrinsic characteristic of regularity in natural s...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	49,483
2107.11300	Applying Evolutionary Algorithms Successfully: A Guide Gained from Real-world Applications	Metaheuristics (MHs) in general and Evolutionary Algorithms (EAs) in particular are well known tools for successful optimization of difficult problems. But when is their application meaningful and how does one approach such a project as a novice? How do you avoid beginner's mistakes or use the design possibilities of a...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	247,557
2212.08607	MURMUR: Modular Multi-Step Reasoning for Semi-Structured Data-to-Text Generation	Prompting large language models has enabled significant recent progress in multi-step reasoning over text. However, when applied to text generation from semi-structured data (e.g., graphs or tables), these methods typically suffer from low semantic coverage, hallucination, and logical inconsistency. We propose MURMUR, ...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	336,807
1904.08994	From GAN to WGAN	This paper explains the math behind a generative adversarial network (GAN) model and why it is hard to be trained. Wasserstein GAN is intended to improve GANs' training by adopting a smooth metric for measuring the distance between two probability distributions.	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	128,238

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