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
2204.10238	HEATGait: Hop-Extracted Adjacency Technique in Graph Convolution based Gait Recognition	Biometric authentication using gait has become a promising field due to its unobtrusive nature. Recent approaches in model-based gait recognition techniques utilize spatio-temporal graphs for the elegant extraction of gait features. However, existing methods often rely on multi-scale operators for extracting long-range...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	292,719
1708.02910	TPC Together with Overlapped Time Domain Multiplexing System Based on Turbo Structure	Overlapped time domain multiplexing (OvTDM) is a novel technique for utilizing inter-symbol interference (ISI) to benefit a communication system. We implement the OvTDM technique based on turbo structure and associate a turbo product code (TPC) to construct a novel coded turbo-structure OvTDM system. Two schemes of the...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	78,681
2210.16554	End-to-end Spoken Language Understanding with Tree-constrained Pointer Generator	End-to-end spoken language understanding (SLU) suffers from the long-tail word problem. This paper exploits contextual biasing, a technique to improve the speech recognition of rare words, in end-to-end SLU systems. Specifically, a tree-constrained pointer generator (TCPGen), a powerful and efficient biasing model comp...	false	false	true	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	327,375
1903.06031	Audiovisual Speaker Tracking using Nonlinear Dynamical Systems with Dynamic Stream Weights	Data fusion plays an important role in many technical applications that require efficient processing of multimodal sensory observations. A prominent example is audiovisual signal processing, which has gained increasing attention in automatic speech recognition, speaker localization and related tasks. If appropriately c...	false	false	true	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	124,297
2308.06149	Gaussian Process Regression for Maximum Entropy Distribution	Maximum-Entropy Distributions offer an attractive family of probability densities suitable for moment closure problems. Yet finding the Lagrange multipliers which parametrize these distributions, turns out to be a computational bottleneck for practical closure settings. Motivated by recent success of Gaussian processes...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	385,047
2205.03947	High-Resolution UAV Image Generation for Sorghum Panicle Detection	The number of panicles (or heads) of Sorghum plants is an important phenotypic trait for plant development and grain yield estimation. The use of Unmanned Aerial Vehicles (UAVs) enables the capability of collecting and analyzing Sorghum images on a large scale. Deep learning can provide methods for estimating phenotypi...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	295,478
2502.09615	RigAnything: Template-Free Autoregressive Rigging for Diverse 3D Assets	We present RigAnything, a novel autoregressive transformer-based model, which makes 3D assets rig-ready by probabilistically generating joints, skeleton topologies, and assigning skinning weights in a template-free manner. Unlike most existing auto-rigging methods, which rely on predefined skeleton template and are lim...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	533,513
1605.02346	Chained Predictions Using Convolutional Neural Networks	In this paper, we present an adaptation of the sequence-to-sequence model for structured output prediction in vision tasks. In this model the output variables for a given input are predicted sequentially using neural networks. The prediction for each output variable depends not only on the input but also on the previou...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	55,619
cs/0307002	AWESOME: A General Multiagent Learning Algorithm that Converges in Self-Play and Learns a Best Response Against Stationary Opponents	A satisfactory multiagent learning algorithm should, {\em at a minimum}, learn to play optimally against stationary opponents and converge to a Nash equilibrium in self-play. The algorithm that has come closest, WoLF-IGA, has been proven to have these two properties in 2-player 2-action repeated games--assuming that th...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	false	false	true	537,905
2005.07662	Guided interactive image segmentation using machine learning and color based data set clustering	We present a novel approach that combines machine learning based interactive image segmentation using supervoxels with a clustering method for the automated identification of similarly colored images in large data sets which enables a guided reuse of classifiers. Our approach solves the problem of significant color var...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	177,343
1809.05680	A New Multi-vehicle Trajectory Generator to Simulate Vehicle-to-Vehicle Encounters	Generating multi-vehicle trajectories from existing limited data can provide rich resources for autonomous vehicle development and testing. This paper introduces a multi-vehicle trajectory generator (MTG) that can encode multi-vehicle interaction scenarios (called driving encounters) into an interpretable representatio...	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	107,843
1601.01356	From Word Embeddings to Item Recommendation	Social network platforms can use the data produced by their users to serve them better. One of the services these platforms provide is recommendation service. Recommendation systems can predict the future preferences of users using their past preferences. In the recommendation systems literature there are various techn...	false	false	false	true	false	true	true	false	true	false	false	false	false	false	false	false	false	false	50,736
1901.01445	The Oxford Multimotion Dataset: Multiple SE(3) Motions with Ground Truth	Datasets advance research by posing challenging new problems and providing standardized methods of algorithm comparison. High-quality datasets exist for many important problems in robotics and computer vision, including egomotion estimation and motion/scene segmentation, but not for techniques that estimate every motio...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	117,975
1205.6228	Structure and Overlaps of Communities in Networks	One of the main organizing principles in real-world social, information and technological networks is that of network communities, where sets of nodes organize into densely linked clusters. Even though detection of such communities is of great interest, understanding the structure communities in large networks remains ...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	16,212
2305.03851	Large Language Models in Sport Science & Medicine: Opportunities, Risks and Considerations	This paper explores the potential opportunities, risks, and challenges associated with the use of large language models (LLMs) in sports science and medicine. LLMs are large neural networks with transformer style architectures trained on vast amounts of textual data, and typically refined with human feedback. LLMs can ...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	362,533
2304.13427	Training-Free Location-Aware Text-to-Image Synthesis	Current large-scale generative models have impressive efficiency in generating high-quality images based on text prompts. However, they lack the ability to precisely control the size and position of objects in the generated image. In this study, we analyze the generative mechanism of the stable diffusion model and prop...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	360,575
2007.03848	Dynamic Graph Representation Learning for Video Dialog via Multi-Modal Shuffled Transformers	Given an input video, its associated audio, and a brief caption, the audio-visual scene aware dialog (AVSD) task requires an agent to indulge in a question-answer dialog with a human about the audio-visual content. This task thus poses a challenging multi-modal representation learning and reasoning scenario, advancemen...	false	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	186,183
1805.11213	Bi-Directional Neural Machine Translation with Synthetic Parallel Data	Despite impressive progress in high-resource settings, Neural Machine Translation (NMT) still struggles in low-resource and out-of-domain scenarios, often failing to match the quality of phrase-based translation. We propose a novel technique that combines back-translation and multilingual NMT to improve performance in ...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	98,865
2111.14036	Pre-training Recommender Systems via Reinforced Attentive Multi-relational Graph Neural Network	Recently, Graph Neural Networks (GNNs) have proven their effectiveness for recommender systems. Existing studies have applied GNNs to capture collaborative relations in the data. However, in real-world scenarios, the relations in a recommendation graph can be of various kinds. For example, two movies may be associated ...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	268,470
1601.03822	On the consistency of inversion-free parameter estimation for Gaussian random fields	Gaussian random fields are a powerful tool for modeling environmental processes. For high dimensional samples, classical approaches for estimating the covariance parameters require highly challenging and massive computations, such as the evaluation of the Cholesky factorization or solving linear systems. Recently, Anit...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	50,953
2403.11509	DEE: Dual-stage Explainable Evaluation Method for Text Generation	Automatic methods for evaluating machine-generated texts hold significant importance due to the expanding applications of generative systems. Conventional methods tend to grapple with a lack of explainability, issuing a solitary numerical score to signify the assessment outcome. Recent advancements have sought to mitig...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	438,736
2410.09040	AttnGCG: Enhancing Jailbreaking Attacks on LLMs with Attention Manipulation	This paper studies the vulnerabilities of transformer-based Large Language Models (LLMs) to jailbreaking attacks, focusing specifically on the optimization-based Greedy Coordinate Gradient (GCG) strategy. We first observe a positive correlation between the effectiveness of attacks and the internal behaviors of the mode...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	497,403
1805.10927	Scalable and Robust Community Detection with Randomized Sketching	This article explores and analyzes the unsupervised clustering of large partially observed graphs. We propose a scalable and provable randomized framework for clustering graphs generated from the stochastic block model. The clustering is first applied to a sub-matrix of the graph's adjacency matrix associated with a re...	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	false	false	98,802
2010.14495	Are wider nets better given the same number of parameters?	Empirical studies demonstrate that the performance of neural networks improves with increasing number of parameters. In most of these studies, the number of parameters is increased by increasing the network width. This begs the question: Is the observed improvement due to the larger number of parameters, or is it due t...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	203,463
2306.00833	When Does Bottom-up Beat Top-down in Hierarchical Community Detection?	Hierarchical clustering of networks consists in finding a tree of communities, such that lower levels of the hierarchy reveal finer-grained community structures. There are two main classes of algorithms tackling this problem. Divisive ($\textit{top-down}$) algorithms recursively partition the nodes into two communities...	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	false	false	370,179
1702.05821	Post-edit Analysis of Collective Biography Generation	Text generation is increasingly common but often requires manual post-editing where high precision is critical to end users. However, manual editing is expensive so we want to ensure this effort is focused on high-value tasks. And we want to maintain stylistic consistency, a particular challenge in crowd settings. We p...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	68,478
2109.09943	Identifiability of Chemical Reaction Networks with Intrinsic and Extrinsic Noise from Stationary Distributions	Many biological systems can be modeled as a chemical reaction network with unknown parameters. Data available to identify these parameters are often in the form of a stationary distribution, such as that obtained from measurements of a cell population. In this work, we introduce a framework for analyzing the identifiab...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	256,456
2202.14005	Deep, Deep Learning with BART	Purpose: To develop a deep-learning-based image reconstruction framework for reproducible research in MRI. Methods: The BART toolbox offers a rich set of implementations of calibration and reconstruction algorithms for parallel imaging and compressed sensing. In this work, BART was extended by a non-linear operator f...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	282,817
1904.12682	An efficient branch-and-cut algorithm for approximately submodular function maximization	When approaching to problems in computer science, we often encounter situations where a subset of a finite set maximizing some utility function needs to be selected. Some of such utility functions are known to be approximately submodular. For the problem of maximizing an approximately submodular function (ASFM problem)...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	129,198
2310.14944	An Event based Prediction Suffix Tree	This article introduces the Event based Prediction Suffix Tree (EPST), a biologically inspired, event-based prediction algorithm. The EPST learns a model online based on the statistics of an event based input and can make predictions over multiple overlapping patterns. The EPST uses a representation specific to event b...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	true	false	false	402,091
2404.15166	Pixels and Predictions: Potential of GPT-4V in Meteorological Imagery Analysis and Forecast Communication	Generative AI, such as OpenAI's GPT-4V large-language model, has rapidly entered mainstream discourse. Novel capabilities in image processing and natural-language communication may augment existing forecasting methods. Large language models further display potential to better communicate weather hazards in a style hone...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	448,968
1203.3484	Intracluster Moves for Constrained Discrete-Space MCMC	This paper addresses the problem of sampling from binary distributions with constraints. In particular, it proposes an MCMC method to draw samples from a distribution of the set of all states at a specified distance from some reference state. For example, when the reference state is the vector of zeros, the algorithm c...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	14,932
2405.08709	Multi-Task Private Semantic Communication	We study a multi-task private semantic communication problem, in which an encoder has access to an information source arbitrarily correlated with some latent private data. A user has $L$ tasks with priorities. The encoder designs a message to be revealed which is called the semantic of the information source. Due to th...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	454,188
0811.1000	Hard and Soft Spherical-Bound Stack decoder for MIMO systems	Classical ML decoders of MIMO systems like the sphere decoder, the Schnorr-Euchner algorithm, the Fano and the stack decoders suffer of high complexity for high number of antennas and large constellation sizes. We propose in this paper a novel sequential algorithm which combines the stack algorithm search strategy and ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	2,643
cs/0611022	Multirobot rendezvous with visibility sensors in nonconvex environments	This paper presents a coordination algorithm for mobile autonomous robots. Relying upon distributed sensing the robots achieve rendezvous, that is, they move to a common location. Each robot is a point mass moving in a nonconvex environment according to an omnidirectional kinematic model. Each robot is equipped with li...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	539,852
cmp-lg/9606009	Modularizing Contexted Constraints	This paper describes a method for compiling a constraint-based grammar into a potentially more efficient form for processing. This method takes dependent disjunctions within a constraint formula and factors them into non-interacting groups whenever possible by determining their independence. When a group of dependent d...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	536,575
1910.14025	Graph Neural News Recommendation with Long-term and Short-term Interest Modeling	With the information explosion of news articles, personalized news recommendation has become important for users to quickly find news that they are interested in. Existing methods on news recommendation mainly include collaborative filtering methods which rely on direct user-item interactions and content based methods ...	false	false	false	false	false	true	true	false	true	false	false	false	false	false	false	false	false	false	151,537
2206.07674	Summarizing Labeled Multi-Graphs	Real-world graphs can be difficult to interpret and visualize beyond a certain size. To address this issue, graph summarization aims to simplify and shrink a graph, while maintaining its high-level structure and characteristics. Most summarization methods are designed for homogeneous, undirected, simple graphs; however...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	false	302,836
2103.04683	Learning Graph Neural Networks with Positive and Unlabeled Nodes	Graph neural networks (GNNs) are important tools for transductive learning tasks, such as node classification in graphs, due to their expressive power in capturing complex interdependency between nodes. To enable graph neural network learning, existing works typically assume that labeled nodes, from two or multiple cla...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	223,725
2408.10807	DisMix: Disentangling Mixtures of Musical Instruments for Source-level Pitch and Timbre Manipulation	Existing work on pitch and timbre disentanglement has been mostly focused on single-instrument music audio, excluding the cases where multiple instruments are presented. To fill the gap, we propose DisMix, a generative framework in which the pitch and timbre representations act as modular building blocks for constructi...	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	482,031
1806.04725	Accurate Detection of Inner Ears in Head CTs Using a Deep Volume-to-Volume Regression Network with False Positive Suppression and a Shape-Based Constraint	Cochlear implants (CIs) are neural prosthetics which are used to treat patients with hearing loss. CIs use an array of electrodes which are surgically inserted into the cochlea to stimulate the auditory nerve endings. After surgery, CIs need to be programmed. Studies have shown that the spatial relationship between the...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	100,302
1910.00089	Mining Uncertain Event Data in Process Mining	Nowadays, more and more process data are automatically recorded by information systems, and made available in the form of event logs. Process mining techniques enable process-centric analysis of data, including automatically discovering process models and checking if event data conform to a certain model. In this paper...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	147,567
1908.05864	Generating Random Parameters in Feedforward Neural Networks with Random Hidden Nodes: Drawbacks of the Standard Method and How to Improve It	The standard method of generating random weights and biases in feedforward neural networks with random hidden nodes, selects them both from the uniform distribution over the same fixed interval. In this work, we show the drawbacks of this approach and propose a new method of generating random parameters. This method en...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	true	false	false	141,842
1505.04427	The Best of Both Worlds: Combining Data-independent and Data-driven Approaches for Action Recognition	Motivated by the success of data-driven convolutional neural networks (CNNs) in object recognition on static images, researchers are working hard towards developing CNN equivalents for learning video features. However, learning video features globally has proven to be quite a challenge due to its high dimensionality, t...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	43,190
2410.16937	MOSAIK 3.0: Combining Time-Stepped and Discrete Event Simulation	Co-simulation is commonly used for the analysis of complex cyber-physical energy systems (CPES). Different domain-specific simulation tools and modeling approaches are used to simulate all or parts of the system. The co-simulation framework mosaik is a powerful tool to couple these simulation tools and models. This pap...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	501,250
2104.08440	Learning on a Budget via Teacher Imitation	Deep Reinforcement Learning (RL) techniques can benefit greatly from leveraging prior experience, which can be either self-generated or acquired from other entities. Action advising is a framework that provides a flexible way to transfer such knowledge in the form of actions between teacher-student peers. However, due ...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	230,797
2110.11240	A Systematic Review on the Detection of Fake News Articles	It has been argued that fake news and the spread of false information pose a threat to societies throughout the world, from influencing the results of elections to hindering the efforts to manage the COVID-19 pandemic. To combat this threat, a number of Natural Language Processing (NLP) approaches have been developed. ...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	262,405
2311.00048	SC-MIL: Sparsely Coded Multiple Instance Learning for Whole Slide Image Classification	Multiple Instance Learning (MIL) has been widely used in weakly supervised whole slide image (WSI) classification. Typical MIL methods include a feature embedding part, which embeds the instances into features via a pre-trained feature extractor, and an MIL aggregator that combines instance embeddings into predictions....	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	404,492
cs/0311050	Data mining and Privacy in Public Sector using Intelligent Agents (discussion paper)	The public sector comprises government agencies, ministries, education institutions, health providers and other types of government, commercial and not-for-profit organisations. Unlike commercial enterprises, this environment is highly heterogeneous in all aspects. This forms a complex network which is not always optim...	false	false	false	false	true	true	false	false	false	false	false	false	false	true	true	false	false	false	538,044
2405.00220	Context-Aware Mobile Network Performance Prediction Using Network & Remote Sensing Data	Accurate estimation of Network Performance is crucial for several tasks in telecom networks. Telecom networks regularly serve a vast number of radio nodes. Each radio node provides services to end-users in the associated coverage areas. The task of predicting Network Performance for telecom networks necessitates consid...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	450,835
2402.03761	Deep Learning-Based Correction and Unmixing of Hyperspectral Images for Brain Tumor Surgery	Hyperspectral Imaging (HSI) for fluorescence-guided brain tumor resection enables visualization of differences between tissues that are not distinguishable to humans. This augmentation can maximize brain tumor resection, improving patient outcomes. However, much of the processing in HSI uses simplified linear methods t...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	427,178
1311.4294	Exponential Approximation of Bandlimited Functions from Average Oversampling	Weighted average sampling is more practical and numerically more stable than sampling at single points as in the classical Shannon sampling framework. Using the frame theory, one can completely reconstruct a bandlimited function from its suitably-chosen average sample data. When only finitely many sample data are avail...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	28,482
2007.01821	Regularization of the movement of a material point along a flat trajectory: application to robotics problems	The control problem of the working tool movement along a predefined trajectory is considered. The integral of kinetic energy and weighted inertia forces for the whole period of motion is considered as a cost functional. The trajectory is assumed to be planar and defined in advance. The problem is reduced to a system of...	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	true	185,544
2406.13629	InstructRAG: Instructing Retrieval-Augmented Generation via Self-Synthesized Rationales	Retrieval-augmented generation (RAG) has shown promising potential to enhance the accuracy and factuality of language models (LMs). However, imperfect retrievers or noisy corpora can introduce misleading or even erroneous information to the retrieved contents, posing a significant challenge to the generation quality. E...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	465,926
2106.14189	A direct Jacobian total Lagrangian explicit dynamics finite element algorithm for real-time simulation of hyperelastic materials	This paper presents a novel direct Jacobian total Lagrangian explicit dynamics (DJ-TLED) finite element algorithm for real-time nonlinear mechanics simulation. The nodal force contributions are expressed using only the Jacobian operator, instead of the deformation gradient tensor and finite deformation tensor, for fewe...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	243,324
2011.03902	Learning Neural Event Functions for Ordinary Differential Equations	The existing Neural ODE formulation relies on an explicit knowledge of the termination time. We extend Neural ODEs to implicitly defined termination criteria modeled by neural event functions, which can be chained together and differentiated through. Neural Event ODEs are capable of modeling discrete and instantaneous ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	205,393
2202.13474	Interpretable Concept-based Prototypical Networks for Few-Shot Learning	Few-shot learning aims at recognizing new instances from classes with limited samples. This challenging task is usually alleviated by performing meta-learning on similar tasks. However, the resulting models are black-boxes. There has been growing concerns about deploying black-box machine learning models and FSL is not...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	282,621
2206.03869	"GAN I hire you?" -- A System for Personalized Virtual Job Interview Training	Job interviews are usually high-stakes social situations where professional and behavioral skills are required for a satisfactory outcome. Professional job interview trainers give educative feedback about the shown behavior according to common standards. This feedback can be helpful concerning the improvement of behavi...	true	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	301,436
1806.02782	Training Augmentation with Adversarial Examples for Robust Speech Recognition	This paper explores the use of adversarial examples in training speech recognition systems to increase robustness of deep neural network acoustic models. During training, the fast gradient sign method is used to generate adversarial examples augmenting the original training data. Different from conventional data augmen...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	99,846
2003.11571	Learning Layout and Style Reconfigurable GANs for Controllable Image Synthesis	With the remarkable recent progress on learning deep generative models, it becomes increasingly interesting to develop models for controllable image synthesis from reconfigurable inputs. This paper focuses on a recent emerged task, layout-to-image, to learn generative models that are capable of synthesizing photo-reali...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	169,646
2206.12099	A novel approach for glaucoma classification by wavelet neural networks using graph-based, statisitcal features of qualitatively improved images	In this paper, we have proposed a new glaucoma classification approach that employs a wavelet neural network (WNN) on optimally enhanced retinal images features. To avoid tedious and error prone manual analysis of retinal images by ophthalmologists, computer aided diagnosis (CAD) substantially aids in robust diagnosis....	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	304,478
2104.11927	Anomaly Detection for Solder Joints Using $\beta$-VAE	In the assembly process of printed circuit boards (PCB), most of the errors are caused by solder joints in Surface Mount Devices (SMD). In the literature, traditional feature extraction based methods require designing hand-crafted features and rely on the tiered RGB illumination to detect solder joint errors, whereas t...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	232,062
2310.13385	Tuna: Instruction Tuning using Feedback from Large Language Models	Instruction tuning of open-source large language models (LLMs) like LLaMA, using direct outputs from more powerful LLMs such as Instruct-GPT and GPT-4, has proven to be a cost-effective way to align model behaviors with human preferences. However, the instruction-tuned model has only seen one response per instruction, ...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	401,430
2409.06349	Improving Conditional Level Generation using Automated Validation in Match-3 Games	Generative models for level generation have shown great potential in game production. However, they often provide limited control over the generation, and the validity of the generated levels is unreliable. Despite this fact, only a few approaches that learn from existing data provide the users with ways of controlling...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	487,094
2212.03853	Clustering with Neural Network and Index	A new model called Clustering with Neural Network and Index (CNNI) is introduced. CNNI uses a Neural Network to cluster data points. Training of the Neural Network mimics supervised learning, with an internal clustering evaluation index acting as the loss function. An experiment is conducted to test the feasibility of ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	335,252
2308.16810	Atlas of Science Collaboration, 1971-2020	The evolving landscape of interinstitutional collaborative research across 15 natural science disciplines is explored using the open data sourced from OpenAlex. This extensive exploration spans the years from 1971 to 2020, facilitating a thorough investigation of leading scientific output producers and their collaborat...	false	false	false	true	false	false	false	false	false	false	false	false	false	true	false	false	false	true	389,121
2211.09423	DexPoint: Generalizable Point Cloud Reinforcement Learning for Sim-to-Real Dexterous Manipulation	We propose a sim-to-real framework for dexterous manipulation which can generalize to new objects of the same category in the real world. The key of our framework is to train the manipulation policy with point cloud inputs and dexterous hands. We propose two new techniques to enable joint learning on multiple objects a...	false	false	false	false	false	false	true	true	false	false	false	true	false	false	false	false	false	false	330,972
1805.09613	A0C: Alpha Zero in Continuous Action Space	A core novelty of Alpha Zero is the interleaving of tree search and deep learning, which has proven very successful in board games like Chess, Shogi and Go. These games have a discrete action space. However, many real-world reinforcement learning domains have continuous action spaces, for example in robotic control, na...	false	false	false	false	true	false	true	true	false	false	true	false	false	false	false	false	false	false	98,459
2005.00087	Revisiting Unsupervised Relation Extraction	Unsupervised relation extraction (URE) extracts relations between named entities from raw text without manually-labelled data and existing knowledge bases (KBs). URE methods can be categorised into generative and discriminative approaches, which rely either on hand-crafted features or surface form. However, we demonstr...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	175,120
2406.11397	DistPred: A Distribution-Free Probabilistic Inference Method for Regression and Forecasting	Traditional regression and prediction tasks often only provide deterministic point estimates. To estimate the distribution or uncertainty of the response variable, traditional methods either assume that the posterior distribution of samples follows a Gaussian process or require thousands of forward passes for sample ge...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	464,872
1106.5536	Spreading paths in partially observed social networks	Understanding how and how far information, behaviors, or pathogens spread in social networks is an important problem, having implications for both predicting the size of epidemics, as well as for planning effective interventions. There are, however, two main challenges for inferring spreading paths in real-world networ...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	11,037
2409.17470	Tactile Probabilistic Contact Dynamics Estimation of Unknown Objects	We study the problem of rapidly identifying contact dynamics of unknown objects in partially known environments. The key innovation of our method is a novel formulation of the contact dynamics estimation problem as the joint estimation of contact geometries and physical parameters. We leverage DeepSDF, a compact and ex...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	491,815
2305.10449	Cooperation Is All You Need	Going beyond 'dendritic democracy', we introduce a 'democracy of local processors', termed Cooperator. Here we compare their capabilities when used in permutation-invariant neural networks for reinforcement learning (RL), with machine learning algorithms based on Transformers, such as ChatGPT. Transformers are based on...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	true	false	false	365,074
2407.19472	Combined CNN and ViT features off-the-shelf: Another astounding baseline for recognition	We apply pre-trained architectures, originally developed for the ImageNet Large Scale Visual Recognition Challenge, for periocular recognition. These architectures have demonstrated significant success in various computer vision tasks beyond the ones for which they were designed. This work builds on our previous study ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	476,802
1909.01101	Multi-agent Learning for Neural Machine Translation	Conventional Neural Machine Translation (NMT) models benefit from the training with an additional agent, e.g., dual learning, and bidirectional decoding with one agent decoding from left to right and the other decoding in the opposite direction. In this paper, we extend the training framework to the multi-agent scenari...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	143,812
2311.13739	OASIS: Offsetting Active Reconstruction Attacks in Federated Learning	Federated Learning (FL) has garnered significant attention for its potential to protect user privacy while enhancing model training efficiency. For that reason, FL has found its use in various domains, from healthcare to industrial engineering, especially where data cannot be easily exchanged due to sensitive informati...	false	false	false	false	true	false	false	false	false	false	false	false	true	false	false	false	false	false	409,854
2406.01382	Do Large Language Models Perform the Way People Expect? Measuring the Human Generalization Function	What makes large language models (LLMs) impressive is also what makes them hard to evaluate: their diversity of uses. To evaluate these models, we must understand the purposes they will be used for. We consider a setting where these deployment decisions are made by people, and in particular, people's beliefs about wher...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	460,291
2502.03724	MD-BERT: Action Recognition in Dark Videos via Dynamic Multi-Stream Fusion and Temporal Modeling	Action recognition in dark, low-light (under-exposed) or noisy videos is a challenging task due to visibility degradation, which can hinder critical spatiotemporal details. This paper proposes MD-BERT, a novel multi-stream approach that integrates complementary pre-processing techniques such as gamma correction and his...	true	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	true	530,833
2003.06798	StarNet: towards Weakly Supervised Few-Shot Object Detection	Few-shot detection and classification have advanced significantly in recent years. Yet, detection approaches require strong annotation (bounding boxes) both for pre-training and for adaptation to novel classes, and classification approaches rarely provide localization of objects in the scene. In this paper, we introduc...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	168,233
2010.09231	CT-CPP: Coverage Path Planning for 3D Terrain Reconstruction Using Dynamic Coverage Trees	This letter addresses the 3D coverage path planning (CPP) problem for terrain reconstruction of unknown obstacle rich environments. Due to sensing limitations, the proposed method, called CT-CPP, performs layered scanning of the 3D region to collect terrain data, where the traveling sequence is optimized using the conc...	false	false	false	false	true	false	false	true	false	false	true	false	false	false	false	false	false	false	201,454
1805.00154	Consensus-based Distributed Quantile Estimation in Sensor Networks	A quantile is defined as a value below which random draws from a given distribution falls with a given probability. In a centralized setting where the cumulative distribution function (CDF) is unknown, the empirical CDF (ECDF) can be used to estimate such quantiles after aggregating the data. In a fully distributed sen...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	true	96,369
2305.11463	Generative Sliced MMD Flows with Riesz Kernels	Maximum mean discrepancy (MMD) flows suffer from high computational costs in large scale computations. In this paper, we show that MMD flows with Riesz kernels $K(x,y) = - \\|x-y\\|^r$, $r \in (0,2)$ have exceptional properties which allow their efficient computation. We prove that the MMD of Riesz kernels, which is also...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	365,542
1910.05299	Privacy-Preserving Multi-Party Contextual Bandits	Contextual bandits are online learners that, given an input, select an arm and receive a reward for that arm. They use the reward as a learning signal and aim to maximize the total reward over the inputs. Contextual bandits are commonly used to solve recommendation or ranking problems. This paper considers a learning s...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	149,015
1907.00477	Analyzing Utility of Visual Context in Multimodal Speech Recognition Under Noisy Conditions	Multimodal learning allows us to leverage information from multiple sources (visual, acoustic and text), similar to our experience of the real world. However, it is currently unclear to what extent auxiliary modalities improve performance over unimodal models, and under what circumstances the auxiliary modalities are u...	false	false	true	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	137,055
2009.09149	Co-Evolution of Multi-Robot Controllers and Task Cues for Off-World Open Pit Mining	Robots are ideal for open-pit mining on the Moon as its a dull, dirty, and dangerous task. The challenge is to scale up productivity with an ever-increasing number of robots. This paper presents a novel method for developing scalable controllers for use in multi-robot excavation and site-preparation scenarios. The cont...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	false	false	196,465
2401.07784	Certifiable Mutual Localization and Trajectory Planning for Bearing-Based Robot Swarm	Bearing measurements,as the most common modality in nature, have recently gained traction in multi-robot systems to enhance mutual localization and swarm collaboration. Despite their advantages, challenges such as sensory noise, obstacle occlusion, and uncoordinated swarm motion persist in real-world scenarios, potenti...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	421,656
2304.00152	Learning the Distribution of Errors in Stereo Matching for Joint Disparity and Uncertainty Estimation	We present a new loss function for joint disparity and uncertainty estimation in deep stereo matching. Our work is motivated by the need for precise uncertainty estimates and the observation that multi-task learning often leads to improved performance in all tasks. We show that this can be achieved by requiring the dis...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	355,573
2312.13218	FiFAR: A Fraud Detection Dataset for Learning to Defer	Public dataset limitations have significantly hindered the development and benchmarking of learning to defer (L2D) algorithms, which aim to optimally combine human and AI capabilities in hybrid decision-making systems. In such systems, human availability and domain-specific concerns introduce difficulties, while obtain...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	417,240
2210.06759	Outlier-Robust Group Inference via Gradient Space Clustering	Traditional machine learning models focus on achieving good performance on the overall training distribution, but they often underperform on minority groups. Existing methods can improve the worst-group performance, but they can have several limitations: (i) they require group annotations, which are often expensive and...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	323,433
2305.16491	SAMoSSA: Multivariate Singular Spectrum Analysis with Stochastic Autoregressive Noise	The well-established practice of time series analysis involves estimating deterministic, non-stationary trend and seasonality components followed by learning the residual stochastic, stationary components. Recently, it has been shown that one can learn the deterministic non-stationary components accurately using multiv...	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	368,111
2409.14306	LLMs are One-Shot URL Classifiers and Explainers	Malicious URL classification represents a crucial aspect of cyber security. Although existing work comprises numerous machine learning and deep learning-based URL classification models, most suffer from generalisation and domain-adaptation issues arising from the lack of representative training datasets. Furthermore, t...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	490,415
2304.05257	Multi-granulariy Time-based Transformer for Knowledge Tracing	In this paper, we present a transformer architecture for predicting student performance on standardized tests. Specifically, we leverage students historical data, including their past test scores, study habits, and other relevant information, to create a personalized model for each student. We then use these models to ...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	357,552
2202.02537	Multidimensional Cybersecurity Framework for Strategic Foresight	Cybersecurity is now at the forefront of most organisational digital transformative agendas and National economic, social and political programmes. Hence its impact to society can no longer be seen to be one dimensional. The rise in National cybersecurity laws and regulations is a good indicator of its perceived import...	false	false	false	true	false	false	true	false	false	false	false	false	true	false	false	false	false	false	278,851
1202.6042	A Regularized Graph Layout Framework for Dynamic Network Visualization	Many real-world networks, including social and information networks, are dynamic structures that evolve over time. Such dynamic networks are typically visualized using a sequence of static graph layouts. In addition to providing a visual representation of the network structure at each time step, the sequence should pre...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	true	14,602
2311.15647	Bandits Meet Mechanism Design to Combat Clickbait in Online Recommendation	We study a strategic variant of the multi-armed bandit problem, which we coin the strategic click-bandit. This model is motivated by applications in online recommendation where the choice of recommended items depends on both the click-through rates and the post-click rewards. Like in classical bandits, rewards follow a...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	410,595
1504.02531	HEp-2 Cell Image Classification with Deep Convolutional Neural Networks	Efficient Human Epithelial-2 (HEp-2) cell image classification can facilitate the diagnosis of many autoimmune diseases. This paper presents an automatic framework for this classification task, by utilizing the deep convolutional neural networks (CNNs) which have recently attracted intensive attention in visual recogni...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	41,937
2405.16693	Detection of decision-making manipulation in the pairwise comparisons method	Most decision-making models, including the pairwise comparison method, assume the decision-makers honesty. However, it is easy to imagine a situation where a decision-maker tries to manipulate the ranking results. This paper presents three simple manipulation methods in the pairwise comparison method. We then try to de...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	457,555
2011.03316	Generative adversarial training of product of policies for robust and adaptive movement primitives	In learning from demonstrations, many generative models of trajectories make simplifying assumptions of independence. Correctness is sacrificed in the name of tractability and speed of the learning phase. The ignored dependencies, which often are the kinematic and dynamic constraints of the system, are then only rest...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	205,209
1810.01575	Deep Fundamental Matrix Estimation without Correspondences	Estimating fundamental matrices is a classic problem in computer vision. Traditional methods rely heavily on the correctness of estimated key-point correspondences, which can be noisy and unreliable. As a result, it is difficult for these methods to handle image pairs with large occlusion or significantly different cam...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	true	109,426
2212.09961	Uncertainty Quantification of MLE for Entity Ranking with Covariates	This paper concerns with statistical estimation and inference for the ranking problems based on pairwise comparisons with additional covariate information such as the attributes of the compared items. Despite extensive studies, few prior literatures investigate this problem under the more realistic setting where covari...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	337,271

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