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
2104.04794	Robust Egocentric Photo-realistic Facial Expression Transfer for Virtual Reality	Social presence, the feeling of being there with a real person, will fuel the next generation of communication systems driven by digital humans in virtual reality (VR). The best 3D video-realistic VR avatars that minimize the uncanny effect rely on person-specific (PS) models. However, these PS models are time-consumin...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	229,500
2304.11464	Model-Free Learning of Two-Stage Beamformers for Passive IRS-Aided Network Design	Electronically tunable metasurfaces, or Intelligent Reflective Surfaces (IRSs), are a popular technology for achieving high spectral efficiency in modern wireless systems by shaping channels using a multitude of tunable passive reflective elements. Capitalizing on key practical limitations of IRS-aided beamforming pert...	false	false	false	false	false	false	false	false	false	true	true	false	false	false	false	false	false	false	359,825
1610.09038	Professor Forcing: A New Algorithm for Training Recurrent Networks	The Teacher Forcing algorithm trains recurrent networks by supplying observed sequence values as inputs during training and using the network's own one-step-ahead predictions to do multi-step sampling. We introduce the Professor Forcing algorithm, which uses adversarial domain adaptation to encourage the dynamics of th...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	62,993
1912.10985	BackPACK: Packing more into backprop	Automatic differentiation frameworks are optimized for exactly one thing: computing the average mini-batch gradient. Yet, other quantities such as the variance of the mini-batch gradients or many approximations to the Hessian can, in theory, be computed efficiently, and at the same time as the gradient. While these qua...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	158,456
2303.15319	Automated visual inspection of CMS HGCAL silicon sensor surface using an ensemble of a deep convolutional autoencoder and classifier	More than a thousand 8" silicon sensors will be visually inspected to look for anomalies on their surface during the quality control preceding assembly into the High-Granularity Calorimeter for the CMS experiment at CERN. A deep learning-based algorithm that pre-selects potentially anomalous images of the sensor surfac...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	354,449
2412.20674	Blockchain-Empowered Cyber-Secure Federated Learning for Trustworthy Edge Computing	Federated Learning (FL) is a privacy-preserving distributed machine learning scheme, where each participant data remains on the participating devices and only the local model generated utilizing the local computational power is transmitted throughout the database. However, the distributed computational nature of FL cre...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	true	521,311
2412.00312	Raw Audio Classification with Cosine Convolutional Neural Network (CosCovNN)	This study explores the field of audio classification from raw waveform using Convolutional Neural Networks (CNNs), a method that eliminates the need for extracting specialised features in the pre-processing step. Unlike recent trends in literature, which often focuses on designing frontends or filters for only the ini...	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	512,595
1611.05977	Robust and Scalable Column/Row Sampling from Corrupted Big Data	Conventional sampling techniques fall short of drawing descriptive sketches of the data when the data is grossly corrupted as such corruptions break the low rank structure required for them to perform satisfactorily. In this paper, we present new sampling algorithms which can locate the informative columns in presence ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	64,105
2111.12123	MICS : Multi-steps, Inverse Consistency and Symmetric deep learning registration network	Deformable registration consists of finding the best dense correspondence between two different images. Many algorithms have been published, but the clinical application was made difficult by the high calculation time needed to solve the optimisation problem. Deep learning overtook this limitation by taking advantage o...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	267,869
1607.07956	Joint Embedding of Hierarchical Categories and Entities for Concept Categorization and Dataless Classification	Due to the lack of structured knowledge applied in learning distributed representation of cate- gories, existing work cannot incorporate category hierarchies into entity information. We propose a framework that embeds entities and categories into a semantic space by integrating structured knowledge and taxonomy hierarc...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	59,092
2204.00184	Nondeterminism subject to output commitment in combinatorial filters	We study a class of filters -- discrete finite-state transition systems employed as incremental stream transducers -- that have application to robotics: e.g., to model combinatorial estimators and also as concise encodings of feedback plans/policies. The present paper examines their minimization problem under some new ...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	289,171
2111.02358	VLMo: Unified Vision-Language Pre-Training with Mixture-of-Modality-Experts	We present a unified Vision-Language pretrained Model (VLMo) that jointly learns a dual encoder and a fusion encoder with a modular Transformer network. Specifically, we introduce Mixture-of-Modality-Experts (MoME) Transformer, where each block contains a pool of modality-specific experts and a shared self-attention la...	false	false	false	false	false	false	true	false	true	false	false	true	false	false	false	false	false	false	264,848
2410.14949	2-Rectifications are Enough for Straight Flows: A Theoretical Insight into Wasserstein Convergence	Diffusion models have emerged as a powerful tool for image generation and denoising. Typically, generative models learn a trajectory between the starting noise distribution and the target data distribution. Recently Liu et al. (2023b) designed a novel alternative generative model Rectified Flow (RF), which aims to lear...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	500,279
cs/0611144	Coding Improves the Optimal Delay-Throughput Trade-offs in Mobile Ad-Hoc Networks: Two-Dimensional I.I.D. Mobility Models	In this paper, we investigate the delay-throughput trade-offs in mobile ad-hoc networks under two-dimensional i.i.d. mobility models. We consider two mobility time-scales: (i) Fast mobility where node mobility is at the same time-scale as data transmissions; (ii) Slow mobility where node mobility is assumed to occur at...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	539,920
2308.05961	Compositional Learning in Transformer-Based Human-Object Interaction Detection	Human-object interaction (HOI) detection is an important part of understanding human activities and visual scenes. The long-tailed distribution of labeled instances is a primary challenge in HOI detection, promoting research in few-shot and zero-shot learning. Inspired by the combinatorial nature of HOI triplets, some ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	384,971
2401.14184	Friendly Attacks to Improve Channel Coding Reliability	This paper introduces a novel approach called "friendly attack" aimed at enhancing the performance of error correction channel codes. Inspired by the concept of adversarial attacks, our method leverages the idea of introducing slight perturbations to the neural network input, resulting in a substantial impact on the ne...	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	424,001
2404.02003	AUTODIFF: Autoregressive Diffusion Modeling for Structure-based Drug Design	Structure-based drug design (SBDD), which aims to generate molecules that can bind tightly to the target protein, is an essential problem in drug discovery, and previous approaches have achieved initial success. However, most existing methods still suffer from invalid local structure or unrealistic conformation issues,...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	443,683
1903.07189	Weighted Mean Curvature	In image processing tasks, spatial priors are essential for robust computations, regularization, algorithmic design and Bayesian inference. In this paper, we introduce weighted mean curvature (WMC) as a novel image prior and present an efficient computation scheme for its discretization in practical image processing ap...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	124,559
2301.03456	UB3: Best Beam Identification in Millimeter Wave Systems via Pure Exploration Unimodal Bandits	Millimeter wave (mmWave) communications have a broad spectrum and can support data rates in the order of gigabits per second, as envisioned in 5G systems. However, they cannot be used for long distances due to their sensitivity to attenuation loss. To enable their use in the 5G network, it requires that the transmissio...	false	false	false	false	true	false	true	false	false	true	false	false	false	false	false	false	false	false	339,809
2307.07487	DreamTeacher: Pretraining Image Backbones with Deep Generative Models	In this work, we introduce a self-supervised feature representation learning framework DreamTeacher that utilizes generative networks for pre-training downstream image backbones. We propose to distill knowledge from a trained generative model into standard image backbones that have been well engineered for specific per...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	379,426
2204.11564	Maximum Mean Discrepancy Distributionally Robust Nonlinear Chance-Constrained Optimization with Finite-Sample Guarantee	This paper is motivated by addressing open questions in distributionally robust chance-constrained programs (DRCCP) using the popular Wasserstein ambiguity sets. Specifically, the computational techniques for those programs typically place restrictive assumptions on the constraint functions and the size of the Wasserst...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	293,195
2303.09728	The Cascaded Forward Algorithm for Neural Network Training	Backpropagation algorithm has been widely used as a mainstream learning procedure for neural networks in the past decade, and has played a significant role in the development of deep learning. However, there exist some limitations associated with this algorithm, such as getting stuck in local minima and experiencing va...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	352,160
2407.11072	MaPPing Your Model: Assessing the Impact of Adversarial Attacks on LLM-based Programming Assistants	LLM-based programming assistants offer the promise of programming faster but with the risk of introducing more security vulnerabilities. Prior work has studied how LLMs could be maliciously fine-tuned to suggest vulnerabilities more often. With the rise of agentic LLMs, which may use results from an untrusted third par...	false	false	false	false	true	false	false	false	false	false	false	false	true	false	false	false	false	false	473,287
2410.20672	Relaxed Recursive Transformers: Effective Parameter Sharing with Layer-wise LoRA	Large language models (LLMs) are expensive to deploy. Parameter sharing offers a possible path towards reducing their size and cost, but its effectiveness in modern LLMs remains fairly limited. In this work, we revisit "layer tying" as form of parameter sharing in Transformers, and introduce novel methods for convertin...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	502,911
2403.00329	Learning with Logical Constraints but without Shortcut Satisfaction	Recent studies in neuro-symbolic learning have explored the integration of logical knowledge into deep learning via encoding logical constraints as an additional loss function. However, existing approaches tend to vacuously satisfy logical constraints through shortcuts, failing to fully exploit the knowledge. In this p...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	433,946
2303.04545	A robust method for reliability updating with equality information using sequential adaptive importance sampling	Reliability updating refers to a problem that integrates Bayesian updating technique with structural reliability analysis and cannot be directly solved by structural reliability methods (SRMs) when it involves equality information. The state-of-the-art approaches transform equality information into inequality informati...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	350,127
2010.12201	A Simulation Study on Turnpikes in Stochastic LQ Optimal Control	This paper presents a simulation study on turnpike phenomena in stochastic optimal control problems. We employ the framework of Polynomial Chaos Expansions (PCE) to investigate the presence of turnpikes in stochastic LQ problems. Our findings indicate that turnpikes can be observed in the evolution of PCE coefficients ...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	202,605
1905.12464	Approaching Adaptation Guided Retrieval in Case-Based Reasoning through Inference in Undirected Graphical Models	In Case-Based Reasoning, when the similarity assumption does not hold, the retrieval of a set of cases structurally similar to the query does not guarantee to get a reusable or revisable solution. Knowledge about the adaptability of solutions has to be exploited, in order to define a method for adaptation-guided retrie...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	132,777
2206.11708	Reinforcement Learning under Partial Observability Guided by Learned Environment Models	In practical applications, we can rarely assume full observability of a system's environment, despite such knowledge being important for determining a reactive control system's precise interaction with its environment. Therefore, we propose an approach for reinforcement learning (RL) in partially observable environment...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	304,346
2210.04994	Sampling-based inference for large linear models, with application to linearised Laplace	Large-scale linear models are ubiquitous throughout machine learning, with contemporary application as surrogate models for neural network uncertainty quantification; that is, the linearised Laplace method. Alas, the computational cost associated with Bayesian linear models constrains this method's application to small...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	322,654
2004.01588	HandVoxNet: Deep Voxel-Based Network for 3D Hand Shape and Pose Estimation from a Single Depth Map	3D hand shape and pose estimation from a single depth map is a new and challenging computer vision problem with many applications. The state-of-the-art methods directly regress 3D hand meshes from 2D depth images via 2D convolutional neural networks, which leads to artefacts in the estimations due to perspective distor...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	170,954
2409.14720	ControlEdit: A MultiModal Local Clothing Image Editing Method	Multimodal clothing image editing refers to the precise adjustment and modification of clothing images using data such as textual descriptions and visual images as control conditions, which effectively improves the work efficiency of designers and reduces the threshold for user design. In this paper, we propose a new i...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	490,600
2403.04221	Why Online Reinforcement Learning is Causal	Reinforcement learning (RL) and causal modelling naturally complement each other. The goal of causal modelling is to predict the effects of interventions in an environment, while the goal of reinforcement learning is to select interventions that maximize the rewards the agent receives from the environment. Reinforcemen...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	435,512
2401.05339	MicroGlam: Microscopic Skin Image Dataset with Cosmetics	In this paper, we present a cosmetic-specific skin image dataset. It consists of skin images from $45$ patches ($5$ skin patches each from $9$ participants) of size $8mm^*8mm$ under three cosmetic products (i.e., foundation, blusher, and highlighter). We designed a novel capturing device inspired by Light Stage. Using ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	420,722
2312.07615	Optimizing Likelihood-free Inference using Self-supervised Neural Symmetry Embeddings	Likelihood-free inference is quickly emerging as a powerful tool to perform fast/effective parameter estimation. We demonstrate a technique of optimizing likelihood-free inference to make it even faster by marginalizing symmetries in a physical problem. In this approach, physical symmetries, for example, time-translati...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	414,996
1701.04950	On the error probability of stochastic decision and stochastic decoding	This paper investigates the error probability of a stochastic decision and the way in which it differs from the error probability of an optimal decision, i.e., the maximum a posteriori decision. This paper calls attention to the fact that the error probability of a stochastic decision with the a posteriori distribution...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	66,918
1612.02685	Nonlinear 1-Bit Precoding for Massive MU-MIMO with Higher-Order Modulation	Massive multi-user (MU) multiple-input multiple- output (MIMO) is widely believed to be a core technology for the upcoming fifth-generation (5G) wireless communication standards. The use of low-precision digital-to-analog converters (DACs) in MU-MIMO base stations is of interest because it reduces the power consumption...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	65,265
1002.2240	A Generalization of the Chow-Liu Algorithm and its Application to Statistical Learning	We extend the Chow-Liu algorithm for general random variables while the previous versions only considered finite cases. In particular, this paper applies the generalization to Suzuki's learning algorithm that generates from data forests rather than trees based on the minimum description length by balancing the fitness ...	false	false	false	false	true	false	true	false	false	true	false	false	false	false	false	false	false	false	5,678
2103.14858	Video Rescaling Networks with Joint Optimization Strategies for Downscaling and Upscaling	This paper addresses the video rescaling task, which arises from the needs of adapting the video spatial resolution to suit individual viewing devices. We aim to jointly optimize video downscaling and upscaling as a combined task. Most recent studies focus on image-based solutions, which do not consider temporal inform...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	226,995
2404.02394	Cohort-Individual Cooperative Learning for Multimodal Cancer Survival Analysis	Recently, we have witnessed impressive achievements in cancer survival analysis by integrating multimodal data, e.g., pathology images and genomic profiles. However, the heterogeneity and high dimensionality of these modalities pose significant challenges for extracting discriminative representations while maintaining ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	443,838
2005.08545	Joint Index Coding and Incentive Design for Selfish Clients	The index coding problem includes a server, a group of clients, and a set of data chunks. While each client wants a subset of the data chunks and already has another subset as its side information, the server transmits some uncoded data chunks or coded data chunks to the clients over a noiseless broadcast channel. The ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	177,652
2205.11229	Exploration of the possibility of infusing Social Media Trends into generating NFT Recommendations	Recommendations Systems have been identified to be one of the integral elements of driving sales in e-commerce sites. The utilization of opinion mining data extracted from trends has been attempted to improve the recommendations that can be provided by baseline methods in this research when user-click data is lacking o...	false	false	false	true	true	true	true	false	false	false	false	false	false	false	false	false	false	false	298,055
1008.3597	Quantization of Discrete Probability Distributions	We study the problem of quantization of discrete probability distributions, arising in universal coding, as well as other applications. We show, that in many situations this problem can be reduced to the covering problem for the unit simplex. This setting yields precise asymptotic characterization in the high-rate regi...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	7,322
2502.07840	TranSplat: Surface Embedding-guided 3D Gaussian Splatting for Transparent Object Manipulation	Transparent object manipulation remains a significant challenge in robotics due to the difficulty of acquiring accurate and dense depth measurements. Conventional depth sensors often fail with transparent objects, resulting in incomplete or erroneous depth data. Existing depth completion methods struggle with interfram...	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	532,791
1909.04850	Towards Assume-Guarantee Profiles for Autonomous Vehicles	Rules or specifications for autonomous vehicles are currently formulated on a case-by-case basis, and put together in a rather ad-hoc fashion. As a step towards eliminating this practice, we propose a systematic procedure for generating a set of supervisory specifications for self-driving cars that are 1) associated wi...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	true	144,922
2105.04656	Distribution-free calibration guarantees for histogram binning without sample splitting	We prove calibration guarantees for the popular histogram binning (also called uniform-mass binning) method of Zadrozny and Elkan [2001]. Histogram binning has displayed strong practical performance, but theoretical guarantees have only been shown for sample split versions that avoid 'double dipping' the data. We demon...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	234,572
2310.15737	Semantic-Preserving Image Coding based on Conditional Diffusion Models	Semantic communication, rather than on a bit-by-bit recovery of the transmitted messages, focuses on the meaning and the goal of the communication itself. In this paper, we propose a novel semantic image coding scheme that preserves the semantic content of an image, while ensuring a good trade-off between coding rate a...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	402,428
2012.10267	ReINTEL Challenge 2020: A Multimodal Ensemble Model for Detecting Unreliable Information on Vietnamese SNS	In this paper, we present our methods for unrealiable information identification task at VLSP 2020 ReINTEL Challenge. The task is to classify a piece of information into reliable or unreliable category. We propose a novel multimodal ensemble model which combines two multimodal models to solve the task. In each multimod...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	212,301
1710.11301	A generalized parsing framework for Abstract Grammars	This technical report presents a general framework for parsing a variety of grammar formalisms. We develop a grammar formalism, called an Abstract Grammar, which is general enough to represent grammars at many levels of the hierarchy, including Context Free Grammars, Minimalist Grammars, and Generalized Context-free Gr...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	true	83,562
2303.10138	Generate, Transform, Answer: Question Specific Tool Synthesis for Tabular Data	Tabular question answering (TQA) presents a challenging setting for neural systems by requiring joint reasoning of natural language with large amounts of semi-structured data. Unlike humans who use programmatic tools like filters to transform data before processing, language models in TQA process tables directly, resul...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	352,323
2306.11377	HabiCrowd: A High Performance Simulator for Crowd-Aware Visual Navigation	Visual navigation, a foundational aspect of Embodied AI (E-AI), has been significantly studied in the past few years. While many 3D simulators have been introduced to support visual navigation tasks, scarcely works have been directed towards combining human dynamics, creating the gap between simulation and real-world a...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	374,575
2303.04506	Radio astronomical images object detection and segmentation: A benchmark on deep learning methods	In recent years, deep learning has been successfully applied in various scientific domains. Following these promising results and performances, it has recently also started being evaluated in the domain of radio astronomy. In particular, since radio astronomy is entering the Big Data era, with the advent of the largest...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	350,116
2109.08494	What we see and What we don't see: Imputing Occluded Crowd Structures from Robot Sensing	We consider the navigation of mobile robots in crowded environments, for which onboard sensing of the crowd is typically limited by occlusions. We address the problem of inferring the human occupancy in the space around the robot, in blind spots, beyond the range of its sensing capabilities. This problem is rather unex...	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	255,919
2001.09753	Race, Gender and Beauty: The Effect of Information Provision on Online Hiring Biases	We conduct a study of hiring bias on a simulation platform where we ask Amazon MTurk participants to make hiring decisions for a mathematically intensive task. Our findings suggest hiring biases against Black workers and less attractive workers and preferences towards Asian workers female workers and more attractive wo...	false	false	false	true	false	false	false	false	false	false	false	false	false	true	false	false	false	false	161,661
2207.13500	Modelling Social Context for Fake News Detection: A Graph Neural Network Based Approach	Detection of fake news is crucial to ensure the authenticity of information and maintain the news ecosystems reliability. Recently, there has been an increase in fake news content due to the recent proliferation of social media and fake content generation techniques such as Deep Fake. The majority of the existing modal...	false	false	false	true	false	true	false	false	true	false	false	false	false	false	false	false	false	false	310,325
2404.07508	An advanced 1D physics-based model for PEM hydrogen fuel cells with enhanced overvoltage prediction	A one-dimensional, dynamic, two-phase, isothermal and finite-difference model of proton exchange membrane fuel cell (PEMFC) systems has been developed. It is distinct from most existing models which are either fast but imprecise, such as lumped-parameter models, or detailed but computationally intensive, such as comput...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	445,862
2312.05019	Vision-based Learning for Drones: A Survey	Drones as advanced cyber-physical systems are undergoing a transformative shift with the advent of vision-based learning, a field that is rapidly gaining prominence due to its profound impact on drone autonomy and functionality. Different from existing task-specific surveys, this review offers a comprehensive overview ...	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	false	413,920
1505.05613	Parallel Streaming Signature EM-tree: A Clustering Algorithm for Web Scale Applications	The proliferation of the web presents an unsolved problem of automatically analyzing billions of pages of natural language. We introduce a scalable algorithm that clusters hundreds of millions of web pages into hundreds of thousands of clusters. It does this on a single mid-range machine using efficient algorithms and ...	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	false	true	43,322
2103.06758	ENTRUST: Argument Reframing with Language Models and Entailment	Framing involves the positive or negative presentation of an argument or issue depending on the audience and goal of the speaker (Entman 1983). Differences in lexical framing, the focus of our work, can have large effects on peoples' opinions and beliefs. To make progress towards reframing arguments for positive effect...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	224,400
2407.17482	Reinforcement Learning from Human Feedback: Whose Culture, Whose Values, Whose Perspectives?	We argue for the epistemic and ethical advantages of pluralism in Reinforcement Learning from Human Feedback (RLHF) in the context of Large Language Models (LLM). Drawing on social epistemology and pluralist philosophy of science, we suggest ways in which RHLF can be made more responsive to human needs and how we can a...	true	false	false	false	true	false	false	false	true	false	false	false	false	true	false	false	false	false	475,998
1903.05817	A New Approach for Distributed Hypothesis Testing with Extensions to Byzantine-Resilience	We study a setting where a group of agents, each receiving partially informative private observations, seek to collaboratively learn the true state (among a set of hypotheses) that explains their joint observation profiles over time. To solve this problem, we propose a distributed learning rule that differs fundamental...	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	124,240
1904.06915	GraphTSNE: A Visualization Technique for Graph-Structured Data	We present GraphTSNE, a novel visualization technique for graph-structured data based on t-SNE. The growing interest in graph-structured data increases the importance of gaining human insight into such datasets by means of visualization. Among the most popular visualization techniques, classical t-SNE is not suitable o...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	127,668
1801.02553	Gaussian 1-2-1 Networks: Capacity Results for mmWave Communications	This paper proposes a new model for wireless relay networks referred to as "1-2-1 network", where two nodes can communicate only if they point "beams" at each other, while if they do not point beams at each other, no signal can be exchanged or interference can be generated. This model is motivated by millimeter wave co...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	87,941
1911.06003	Training a code-switching language model with monolingual data	A lack of code-switching data complicates the training of code-switching (CS) language models. We propose an approach to train such CS language models on monolingual data only. By constraining and normalizing the output projection matrix in RNN-based language models, we bring embeddings of different languages closer to...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	153,432
1911.07626	Convex Formulation of Overparameterized Deep Neural Networks	Analysis of over-parameterized neural networks has drawn significant attention in recentyears. It was shown that such systems behave like convex systems under various restrictedsettings, such as for two-level neural networks, and when learning is only restricted locally inthe so-called neural tangent kernel space aroun...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	153,916
2407.20147	Quantum Machine Learning Architecture Search via Deep Reinforcement Learning	The rapid advancement of quantum computing (QC) and machine learning (ML) has given rise to the burgeoning field of quantum machine learning (QML), aiming to capitalize on the strengths of quantum computing to propel ML forward. Despite its promise, crafting effective QML models necessitates profound expertise to strik...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	true	false	true	477,060
1505.03654	Neural Network with Unbounded Activation Functions is Universal Approximator	This paper presents an investigation of the approximation property of neural networks with unbounded activation functions, such as the rectified linear unit (ReLU), which is the new de-facto standard of deep learning. The ReLU network can be analyzed by the ridgelet transform with respect to Lizorkin distributions. By ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	true	false	false	43,093
1612.08388	Clustering Algorithms: A Comparative Approach	Many real-world systems can be studied in terms of pattern recognition tasks, so that proper use (and understanding) of machine learning methods in practical applications becomes essential. While a myriad of classification methods have been proposed, there is no consensus on which methods are more suitable for a given ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	66,064
2408.02248	ReDel: A Toolkit for LLM-Powered Recursive Multi-Agent Systems	Recently, there has been increasing interest in using Large Language Models (LLMs) to construct complex multi-agent systems to perform tasks such as compiling literature reviews, drafting consumer reports, and planning vacations. Many tools and libraries exist for helping create such systems, however none support recur...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	true	478,562
2204.05626	X-DETR: A Versatile Architecture for Instance-wise Vision-Language Tasks	In this paper, we study the challenging instance-wise vision-language tasks, where the free-form language is required to align with the objects instead of the whole image. To address these tasks, we propose X-DETR, whose architecture has three major components: an object detector, a language encoder, and vision-languag...	false	false	false	false	false	false	true	false	true	false	false	true	false	false	false	false	false	false	291,093
2412.00378	Bi-Band ECoGNet for ECoG Decoding on Classification Task	In the application of brain-computer interface (BCI), being able to accurately decode brain signals is a critical task. For the multi-class classification task of brain signal ECoG, how to improve the classification accuracy is one of the current research hotspots. ECoG acquisition uses a high-density electrode array a...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	512,625
2501.06524	Multi-View Factorizing and Disentangling: A Novel Framework for Incomplete Multi-View Multi-Label Classification	Multi-view multi-label classification (MvMLC) has recently garnered significant research attention due to its wide range of real-world applications. However, incompleteness in views and labels is a common challenge, often resulting from data collection oversights and uncertainties in manual annotation. Furthermore, the...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	524,017
1404.7717	A Checklist for the Evaluation of Pedestrian Simulation Software Functionalities	The employment of micro-simulation (agent-based) tools in the phase of design of public and private spaces and facilities and for the definition of transport schemes that impact on pedestrian flows, thanks to their achieved accuracy and predictive capacity, has become a consolidated practice. These instruments provide ...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	32,716
2412.19978	MAKIMA: Tuning-free Multi-Attribute Open-domain Video Editing via Mask-Guided Attention Modulation	Diffusion-based text-to-image (T2I) models have demonstrated remarkable results in global video editing tasks. However, their focus is primarily on global video modifications, and achieving desired attribute-specific changes remains a challenging task, specifically in multi-attribute editing (MAE) in video. Contemporar...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	521,040
2403.05297	PEEB: Part-based Image Classifiers with an Explainable and Editable Language Bottleneck	CLIP-based classifiers rely on the prompt containing a {class name} that is known to the text encoder. Therefore, they perform poorly on new classes or the classes whose names rarely appear on the Internet (e.g., scientific names of birds). For fine-grained classification, we propose PEEB - an explainable and editable ...	false	false	false	false	true	false	false	false	true	false	false	true	false	false	false	false	false	false	435,948
1808.02550	Collaborative Planning for Mixed-Autonomy Lane Merging	Driving is a social activity: drivers often indicate their intent to change lanes via motion cues. We consider mixed-autonomy traffic where a Human-driven Vehicle (HV) and an Autonomous Vehicle (AV) drive together. We propose a planning framework where the degree to which the AV considers the other agent's reward is co...	false	false	false	false	true	false	false	true	false	false	false	false	false	false	true	false	false	false	104,791
2303.02472	ESD: Expected Squared Difference as a Tuning-Free Trainable Calibration Measure	Studies have shown that modern neural networks tend to be poorly calibrated due to over-confident predictions. Traditionally, post-processing methods have been used to calibrate the model after training. In recent years, various trainable calibration measures have been proposed to incorporate them directly into the tra...	false	false	false	false	true	false	true	false	true	false	false	true	false	false	false	false	false	false	349,375
2412.07171	Breaking the Stage Barrier: A Novel Single-Stage Approach to Long Context Extension for Large Language Models	Recently, Large language models (LLMs) have revolutionized Natural Language Processing (NLP). Pretrained LLMs, due to limited training context size, struggle with handling long token sequences, limiting their performance on various downstream tasks. Current solutions toward long context modeling often employ multi-stag...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	515,541
2101.04185	PEng4NN: An Accurate Performance Estimation Engine for Efficient Automated Neural Network Architecture Search	Neural network (NN) models are increasingly used in scientific simulations, AI, and other high performance computing (HPC) fields to extract knowledge from datasets. Each dataset requires tailored NN model architecture, but designing structures by hand is a time-consuming and error-prone process. Neural architecture se...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	215,080
2312.09895	Generative Context-aware Fine-tuning of Self-supervised Speech Models	When performing tasks like automatic speech recognition or spoken language understanding for a given utterance, access to preceding text or audio provides contextual information can improve performance. Considering the recent advances in generative large language models (LLM), we hypothesize that an LLM could generate ...	false	false	true	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	415,922
2010.12647	Extracting Body Text from Academic PDF Documents for Text Mining	Accurate extraction of body text from PDF-formatted academic documents is essential in text-mining applications for deeper semantic understandings. The objective is to extract complete sentences in the body text into a txt file with the original sentence flow and paragraph boundaries. Existing tools for extracting text...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	202,780
2103.08590	Interpretability of a Deep Learning Model in the Application of Cardiac MRI Segmentation with an ACDC Challenge Dataset	Cardiac Magnetic Resonance (CMR) is the most effective tool for the assessment and diagnosis of a heart condition, which malfunction is the world's leading cause of death. Software tools leveraging Artificial Intelligence already enhance radiologists and cardiologists in heart condition assessment but their lack of tra...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	224,942
2303.04642	Forecasting the movements of Bitcoin prices: an application of machine learning algorithms	Cryptocurrencies, such as Bitcoin, are one of the most controversial and complex technological innovations in today's financial system. This study aims to forecast the movements of Bitcoin prices at a high degree of accuracy. To this aim, four different Machine Learning (ML) algorithms are applied, namely, the Support ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	350,166
2006.08177	Dissimilarity Mixture Autoencoder for Deep Clustering	The dissimilarity mixture autoencoder (DMAE) is a neural network model for feature-based clustering that incorporates a flexible dissimilarity function and can be integrated into any kind of deep learning architecture. It internally represents a dissimilarity mixture model (DMM) that extends classical methods like K-Me...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	182,097
2407.10476	Kinetic Typography Diffusion Model	This paper introduces a method for realistic kinetic typography that generates user-preferred animatable 'text content'. We draw on recent advances in guided video diffusion models to achieve visually-pleasing text appearances. To do this, we first construct a kinetic typography dataset, comprising about 600K videos. O...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	473,003
1503.05521	Nonparametric Detection of Nonlinearly Mixed Pixels and Endmember Estimation in Hyperspectral Images	Mixing phenomena in hyperspectral images depend on a variety of factors such as the resolution of observation devices, the properties of materials, and how these materials interact with incident light in the scene. Different parametric and nonparametric models have been considered to address hyperspectral unmixing prob...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	41,253
1704.03591	Bayesian Optimal Data Detector for mmWave OFDM System with Low-Resolution ADC	Orthogonal frequency division multiplexing (OFDM) has been widely used in communication systems operating in the millimeter wave (mmWave) band to combat frequency-selective fading and achieve multi-Gbps transmissions, such as IEEE 802.15.3c and IEEE 802.11ad. For mmWave systems with ultra high sampling rate requirement...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	71,656
2308.06703	Understanding the robustness difference between stochastic gradient descent and adaptive gradient methods	Stochastic gradient descent (SGD) and adaptive gradient methods, such as Adam and RMSProp, have been widely used in training deep neural networks. We empirically show that while the difference between the standard generalization performance of models trained using these methods is small, those trained using SGD exhibit...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	385,230
2105.11832	Estimating Redundancy in Clinical Text	The current mode of use of Electronic Health Record (EHR) elicits text redundancy. Clinicians often populate new documents by duplicating existing notes, then updating accordingly. Data duplication can lead to a propagation of errors, inconsistencies and misreporting of care. Therefore, quantifying information redundan...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	236,835
1201.6358	Deterministic Polynomial-Time Algorithms for Designing Short DNA Words	Designing short DNA words is a problem of constructing a set (i.e., code) of n DNA strings (i.e., words) with the minimum length such that the Hamming distance between each pair of words is at least k and the n words satisfy a set of additional constraints. This problem has applications in, e.g., DNA self-assembly and ...	false	true	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	14,003
2410.01899	The potential of LLM-generated reports in DevSecOps	Alert fatigue is a common issue faced by software teams using the DevSecOps paradigm. The overwhelming number of warnings and alerts generated by security and code scanning tools, particularly in smaller teams where resources are limited, leads to desensitization and diminished responsiveness to security warnings, pote...	false	false	false	false	true	false	false	false	false	false	false	false	true	false	false	false	false	true	494,005
1208.5842	Tenacious tagging of images via Mellin monomials	We describe a method for attaching persistent metadata to an image. The method can be interpreted as a template-based blind watermarking scheme, robust to common editing operations, namely: cropping, rotation, scaling, stretching, shearing, compression, printing, scanning, noise, and color removal. Robustness is achiev...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	18,290
1203.2508	Pneumatic Pressure Cell with Twin Diaphragms Embedding Spherical Corrugations in a Dual Diaphragm Structure	Thin metallic shallow spherical diaphragms are being used for measuring pneumatic pressure in process industries. The drift in vertex realized due to application of pressure is transformed into electrical signal and this is calibrated for pressure. We now propose a modified structure for the pressure cell by having dou...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	14,833
2501.18049	Joint Pricing and Resource Allocation: An Optimal Online-Learning Approach	We study an online learning problem on dynamic pricing and resource allocation, where we make joint pricing and inventory decisions to maximize the overall net profit. We consider the stochastic dependence of demands on the price, which complicates the resource allocation process and introduces significant non-convexit...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	528,544
1206.4632	A Complete Analysis of the l_1,p Group-Lasso	The Group-Lasso is a well-known tool for joint regularization in machine learning methods. While the l_{1,2} and the l_{1,\infty} version have been studied in detail and efficient algorithms exist, there are still open questions regarding other l_{1,p} variants. We characterize conditions for solutions of the l_{1,p} G...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	16,683
2209.04135	SPT-NRTL: A physics-guided machine learning model to predict thermodynamically consistent activity coefficients	The availability of property data is one of the major bottlenecks in the development of chemical processes, often requiring time-consuming and expensive experiments or limiting the design space to a small number of known molecules. This bottleneck has been the motivation behind the continuing development of predictive ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	316,695
1309.2078	Direct off-line robot programming via a common CAD package	This paper focuses on intuitive and direct off-line robot programming from a CAD drawing running on a common 3-D CAD package. It explores the most suitable way to represent robot motion in a CAD drawing, how to automatically extract such motion data from the drawing, make the mapping of data from the virtual (CAD model...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	26,921
1706.05083	Ensembling Factored Neural Machine Translation Models for Automatic Post-Editing and Quality Estimation	This work presents a novel approach to Automatic Post-Editing (APE) and Word-Level Quality Estimation (QE) using ensembles of specialized Neural Machine Translation (NMT) systems. Word-level features that have proven effective for QE are included as input factors, expanding the representation of the original source and...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	75,440
2110.05682	Provably Efficient Reinforcement Learning in Decentralized General-Sum Markov Games	This paper addresses the problem of learning an equilibrium efficiently in general-sum Markov games through decentralized multi-agent reinforcement learning. Given the fundamental difficulty of calculating a Nash equilibrium (NE), we instead aim at finding a coarse correlated equilibrium (CCE), a solution concept that ...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	true	false	false	false	260,354
1901.06359	ULDor: A Universal Lesion Detector for CT Scans with Pseudo Masks and Hard Negative Example Mining	Automatic lesion detection from computed tomography (CT) scans is an important task in medical imaging analysis. It is still very challenging due to similar appearances (e.g. intensity and texture) between lesions and other tissues, making it especially difficult to develop a universal lesion detector. Instead of devel...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	118,981

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