Split (2)

train · 433k rows

id stringlengths 9 16	title stringlengths 4 278	abstract stringlengths 3 4.08k	cs.HC bool 2 classes	cs.CE bool 2 classes	cs.SD bool 2 classes	cs.SI bool 2 classes	cs.AI bool 2 classes	cs.IR bool 2 classes	cs.LG bool 2 classes	cs.RO bool 2 classes	cs.CL bool 2 classes	cs.IT bool 2 classes	cs.SY bool 2 classes	cs.CV bool 2 classes	cs.CR bool 2 classes	cs.CY bool 2 classes	cs.MA bool 2 classes	cs.NE bool 2 classes	cs.DB bool 2 classes	Other bool 2 classes	__index_level_0__ int64 0 541k
2410.22554	Remote Sensing for Weed Detection and Control	Italian ryegrass is a grass weed commonly found in winter wheat fields that are competitive with winter wheat for moisture and nutrients. Ryegrass can cause substantial reductions in yield and grain quality if not properly controlled with the use of herbicides. To control the cost and environmental impact we detect wee...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	503,669
2206.07391	"Why Here and Not There?" -- Diverse Contrasting Explanations of Dimensionality Reduction	Dimensionality reduction is a popular preprocessing and a widely used tool in data mining. Transparency, which is usually achieved by means of explanations, is nowadays a widely accepted and crucial requirement of machine learning based systems like classifiers and recommender systems. However, transparency of dimensio...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	302,728
1908.07967	Multi-Antenna Relaying and Reconfigurable Intelligent Surfaces: End-to-End SNR and Achievable Rate	In this report, we summarize the end-to-end signal-to-noise ratio and the rate of half-duplex, full-duplex, amplify-and-forward, and decode-and-forward relay-aided communications, and well as the signal-to-noise ratio and the rate of the emerging technology known as reconfigurable intelligent surfaces.	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	142,428
1812.04662	Unsupervised domain-agnostic identification of product names in social media posts	Product name recognition is a significant practical problem, spurred by the greater availability of platforms for discussing products such as social media and product review functionalities of online marketplaces. Customers, product manufacturers and online marketplaces may want to identify product names in unstructure...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	116,254
2311.00213	Consistent Video-to-Video Transfer Using Synthetic Dataset	We introduce a novel and efficient approach for text-based video-to-video editing that eliminates the need for resource-intensive per-video-per-model finetuning. At the core of our approach is a synthetic paired video dataset tailored for video-to-video transfer tasks. Inspired by Instruct Pix2Pix's image transfer via ...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	404,549
2202.02476	Semantic Similarity Computing Model Based on Multi Model Fine-Grained Nonlinear Fusion	Natural language processing (NLP) task has achieved excellent performance in many fields, including semantic understanding, automatic summarization, image recognition and so on. However, most of the neural network models for NLP extract the text in a fine-grained way, which is not conducive to grasp the meaning of the ...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	278,829
2011.01990	Graph Enhanced High Dimensional Kernel Regression	In this paper, the flexibility, versatility and predictive power of kernel regression are combined with now lavishly available network data to create regression models with even greater predictive performances. Building from previous work featuring generalized linear models built in the presence of network cohesion dat...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	204,781
2102.11485	Generalized Equivariance and Preferential Labeling for GNN Node Classification	Existing graph neural networks (GNNs) largely rely on node embeddings, which represent a node as a vector by its identity, type, or content. However, graphs with unattributed nodes widely exist in real-world applications (e.g., anonymized social networks). Previous GNNs either assign random labels to nodes (which intro...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	221,435
2006.06337	A Simple Traffic Signal Control Using Queue Length Information	Developments in sensor technologies, especially emerging connected and autonomous vehicles, facilitate better queue length (QL) measurements on signalized intersection approaches in real time. Currently there are very limited methods that utilize QL information in real-time to enhance the performance of signalized inte...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	181,393
1903.02482	LF-PPL: A Low-Level First Order Probabilistic Programming Language for Non-Differentiable Models	We develop a new Low-level, First-order Probabilistic Programming Language (LF-PPL) suited for models containing a mix of continuous, discrete, and/or piecewise-continuous variables. The key success of this language and its compilation scheme is in its ability to automatically distinguish parameters the density functio...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	123,498
1712.08409	Detection and Tracking of General Movable Objects in Large 3D Maps	This paper studies the problem of detection and tracking of general objects with long-term dynamics, observed by a mobile robot moving in a large environment. A key problem is that due to the environment scale, it can only observe a subset of the objects at any given time. Since some time passes between observations of...	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	87,193
2210.00364	DCI-ES: An Extended Disentanglement Framework with Connections to Identifiability	In representation learning, a common approach is to seek representations which disentangle the underlying factors of variation. Eastwood & Williams (2018) proposed three metrics for quantifying the quality of such disentangled representations: disentanglement (D), completeness (C) and informativeness (I). In this work,...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	320,838
2310.17329	Tightening continuity bounds for entropies and bounds on quantum capacities	Uniform continuity bounds on entropies are generally expressed in terms of a single distance measure between a pair of probability distributions or quantum states, typically, the total variation distance or trace distance. However, if an additional distance measure between the probability distributions or states is kno...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	403,088
2311.08059	FS-Net: Full Scale Network and Adaptive Threshold for Improving Extraction of Micro-Retinal Vessel Structures	Retinal vascular segmentation, a widely researched topic in biomedical image processing, aims to reduce the workload of ophthalmologists in treating and detecting retinal disorders. Segmenting retinal vessels presents unique challenges; previous techniques often failed to effectively segment branches and microvascular ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	407,574
1907.11643	Training products of expert capsules with mixing by dynamic routing	This study develops an unsupervised learning algorithm for products of expert capsules with dynamic routing. Analogous to binary-valued neurons in Restricted Boltzmann Machines, the magnitude of a squashed capsule firing takes values between zero and one, representing the probability of the capsule being on. This analo...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	true	false	false	139,906
1501.05810	Ultrascale Simulations of Non-smooth Granular Dynamics	This article presents new algorithms for massively parallel granular dynamics simulations on distributed memory architectures using a domain partitioning approach. Collisions are modelled with hard contacts in order to hide their micro-dynamics and thus to extend the time and length scales that can be simulated. The mu...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	39,530
2006.07616	SDCOR: Scalable Density-based Clustering for Local Outlier Detection in Massive-Scale Datasets	This paper presents a batch-wise density-based clustering approach for local outlier detection in massive-scale datasets. Unlike the well-known traditional algorithms, which assume that all the data is memory-resident, our proposed method is scalable and processes the input data chunk-by-chunk within the confines of a ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	181,880
2301.01216	An end-to-end multi-scale network for action prediction in videos	In this paper, we develop an efficient multi-scale network to predict action classes in partial videos in an end-to-end manner. Unlike most existing methods with offline feature generation, our method directly takes frames as input and further models motion evolution on two different temporal scales.Therefore, we solve...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	339,171
2110.01167	Trustworthy AI: From Principles to Practices	The rapid development of Artificial Intelligence (AI) technology has enabled the deployment of various systems based on it. However, many current AI systems are found vulnerable to imperceptible attacks, biased against underrepresented groups, lacking in user privacy protection. These shortcomings degrade user experien...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	258,669
2105.01859	Function4D: Real-time Human Volumetric Capture from Very Sparse Consumer RGBD Sensors	Human volumetric capture is a long-standing topic in computer vision and computer graphics. Although high-quality results can be achieved using sophisticated off-line systems, real-time human volumetric capture of complex scenarios, especially using light-weight setups, remains challenging. In this paper, we propose a ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	233,651
2411.17374	Fairness And Performance In Harmony: Data Debiasing Is All You Need	Fairness in both machine learning (ML) predictions and human decisions is critical, with ML models prone to algorithmic and data bias, and human decisions affected by subjectivity and cognitive bias. This study investigates fairness using a real-world university admission dataset with 870 profiles, leveraging three ML ...	false	false	false	false	true	true	false	false	true	false	false	false	false	false	false	false	false	false	511,415
2305.14718	Leftover Lunch: Advantage-based Offline Reinforcement Learning for Language Models	Reinforcement Learning with Human Feedback (RLHF) is the most prominent method for Language Model (LM) alignment. However, RLHF is an unstable and data-hungry process that continually requires new high-quality LM-generated data for finetuning. We introduce Advantage-Leftover Lunch RL (A-LoL), a new class of offline pol...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	367,209
2104.05411	Epigenetic evolution of deep convolutional models	In this study, we build upon a previously proposed neuroevolution framework to evolve deep convolutional models. Specifically, the genome encoding and the crossover operator are extended to make them applicable to layered networks. We also propose a convolutional layer layout which allows kernels of different shapes an...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	229,714
2010.05985	NEMO: Frequentist Inference Approach to Constrained Linguistic Typology Feature Prediction in SIGTYP 2020 Shared Task	This paper describes the NEMO submission to SIGTYP 2020 shared task which deals with prediction of linguistic typological features for multiple languages using the data derived from World Atlas of Language Structures (WALS). We employ frequentist inference to represent correlations between typological features and use ...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	200,317
2311.16410	Reduced-order modeling for parameterized PDEs via implicit neural representations	We present a new data-driven reduced-order modeling approach to efficiently solve parametrized partial differential equations (PDEs) for many-query problems. This work is inspired by the concept of implicit neural representation (INR), which models physics signals in a continuous manner and independent of spatial/tempo...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	410,874
1904.00682	Standardized Assessment of Automatic Segmentation of White Matter Hyperintensities and Results of the WMH Segmentation Challenge	Quantification of cerebral white matter hyperintensities (WMH) of presumed vascular origin is of key importance in many neurological research studies. Currently, measurements are often still obtained from manual segmentations on brain MR images, which is a laborious procedure. Automatic WMH segmentation methods exist, ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	125,934
2407.11288	Zero-Shot Adaptation for Approximate Posterior Sampling of Diffusion Models in Inverse Problems	Diffusion models have emerged as powerful generative techniques for solving inverse problems. Despite their success in a variety of inverse problems in imaging, these models require many steps to converge, leading to slow inference time. Recently, there has been a trend in diffusion models for employing sophisticated n...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	473,382
1810.07362	Learning in Non-convex Games with an Optimization Oracle	We consider online learning in an adversarial, non-convex setting under the assumption that the learner has an access to an offline optimization oracle. In the general setting of prediction with expert advice, Hazan et al. (2016) established that in the optimization-oracle model, online learning requires exponentially ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	110,625
2304.05527	Black Box Variational Inference with a Deterministic Objective: Faster, More Accurate, and Even More Black Box	Automatic differentiation variational inference (ADVI) offers fast and easy-to-use posterior approximation in multiple modern probabilistic programming languages. However, its stochastic optimizer lacks clear convergence criteria and requires tuning parameters. Moreover, ADVI inherits the poor posterior uncertainty est...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	357,659
2012.10517	Machine learning applications using diffusion tensor imaging of human brain: A PubMed literature review	We performed a PubMed search to find 148 papers published between January 2010 and December 2019 related to human brain, Diffusion Tensor Imaging (DTI), and Machine Learning (ML). The studies focused on healthy cohorts (n = 15), mental health disorders (n = 25), tumor (n = 19), trauma (n = 5), dementia (n = 24), develo...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	212,361
2004.13291	Evaluating the Rainbow DQN Agent in Hanabi with Unseen Partners	Hanabi is a cooperative game that challenges exist-ing AI techniques due to its focus on modeling the mental states ofother players to interpret and predict their behavior. While thereare agents that can achieve near-perfect scores in the game byagreeing on some shared strategy, comparatively little progresshas been ma...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	true	false	false	174,504
2103.01534	Towards Efficiently Diversifying Dialogue Generation via Embedding Augmentation	Dialogue generation models face the challenge of producing generic and repetitive responses. Unlike previous augmentation methods that mostly focus on token manipulation and ignore the essential variety within a single sample using hard labels, we propose to promote the generation diversity of the neural dialogue model...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	222,658
2310.05000	The Reinforce Policy Gradient Algorithm Revisited	We revisit the Reinforce policy gradient algorithm from the literature. Note that this algorithm typically works with cost returns obtained over random length episodes obtained from either termination upon reaching a goal state (as with episodic tasks) or from instants of visit to a prescribed recurrent state (in the c...	false	false	false	false	true	false	true	false	false	false	true	false	false	false	false	false	false	false	397,930
1811.11926	Autoconj: Recognizing and Exploiting Conjugacy Without a Domain-Specific Language	Deriving conditional and marginal distributions using conjugacy relationships can be time consuming and error prone. In this paper, we propose a strategy for automating such derivations. Unlike previous systems which focus on relationships between pairs of random variables, our system (which we call Autoconj) operates ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	114,883
1201.3900	Elasticity on Ontology Matching of Folksodriven Structure Network	Nowadays folksonomy tags are used not just for personal organization, but for communication and sharing between people sharing their own local interests. In this paper is considered the new concept structure called "Folksodriven" to represent folksonomies. The Folksodriven Structure Network (FSN) was thought as folkson...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	true	13,882
1709.05433	Grade Prediction with Temporal Course-wise Influence	There is a critical need to develop new educational technology applications that analyze the data collected by universities to ensure that students graduate in a timely fashion (4 to 6 years); and they are well prepared for jobs in their respective fields of study. In this paper, we present a novel approach for analyzi...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	80,865
1811.07130	Batch DropBlock Network for Person Re-identification and Beyond	Since the person re-identification task often suffers from the problem of pose changes and occlusions, some attentive local features are often suppressed when training CNNs. In this paper, we propose the Batch DropBlock (BDB) Network which is a two branch network composed of a conventional ResNet-50 as the global branc...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	113,682
1505.06550	MSPKmerCounter: A Fast and Memory Efficient Approach for K-mer Counting	A major challenge in next-generation genome sequencing (NGS) is to assemble massive overlapping short reads that are randomly sampled from DNA fragments. To complete assembling, one needs to finish a fundamental task in many leading assembly algorithms: counting the number of occurrences of k-mers (length-k substrings ...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	43,441
1907.09511	Universal Person Re-Identification	Most state-of-the-art person re-identification (re-id) methods depend on supervised model learning with a large set of cross-view identity labelled training data. Even worse, such trained models are limited to only the same-domain deployment with significantly degraded cross-domain generalization capability, i.e. "doma...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	139,380
2102.09923	Back to Prior Knowledge: Joint Event Causality Extraction via Convolutional Semantic Infusion	Joint event and causality extraction is a challenging yet essential task in information retrieval and data mining. Recently, pre-trained language models (e.g., BERT) yield state-of-the-art results and dominate in a variety of NLP tasks. However, these models are incapable of imposing external knowledge in domain-specif...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	220,928
1909.01214	Better Rewards Yield Better Summaries: Learning to Summarise Without References	Reinforcement Learning (RL) based document summarisation systems yield state-of-the-art performance in terms of ROUGE scores, because they directly use ROUGE as the rewards during training. However, summaries with high ROUGE scores often receive low human judgement. To find a better reward function that can guide RL to...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	143,849
2305.02757	Multi-Domain Learning From Insufficient Annotations	Multi-domain learning (MDL) refers to simultaneously constructing a model or a set of models on datasets collected from different domains. Conventional approaches emphasize domain-shared information extraction and domain-private information preservation, following the shared-private framework (SP models), which offers ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	362,161
2103.00452	EKMP: Generalized Imitation Learning with Adaptation, Nonlinear Hard Constraints and Obstacle Avoidance	As a user-friendly and straightforward solution for robot trajectory generation, imitation learning has been viewed as a vital direction in the context of robot skill learning. In contrast to unconstrained imitation learning which ignores possible internal and external constraints arising from environments and robot ki...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	222,284
1904.03901	Multi-View Matrix Completion for Multi-Label Image Classification	There is growing interest in multi-label image classification due to its critical role in web-based image analytics-based applications, such as large-scale image retrieval and browsing. Matrix completion has recently been introduced as a method for transductive (semi-supervised) multi-label classification, and has seve...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	126,875
1802.06054	Learning Patterns for Detection with Multiscale Scan Statistics	This paper addresses detecting anomalous patterns in images, time-series, and tensor data when the location and scale of the pattern is unknown a priori. The multiscale scan statistic convolves the proposed pattern with the image at various scales and returns the maximum of the resulting tensor. Scale corrected multisc...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	90,577
2412.19191	Biology Instructions: A Dataset and Benchmark for Multi-Omics Sequence Understanding Capability of Large Language Models	Large language models have already demonstrated their formidable capabilities in general domains, ushering in a revolutionary transformation. However, exploring and exploiting the extensive knowledge of these models to comprehend multi-omics biology remains underexplored. To fill this research gap, we first introduce B...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	520,751
2306.06844	Provably Efficient Bayesian Optimization with Unknown Gaussian Process Hyperparameter Estimation	Gaussian process (GP) based Bayesian optimization (BO) is a powerful method for optimizing black-box functions efficiently. The practical performance and theoretical guarantees of this approach depend on having the correct GP hyperparameter values, which are usually unknown in advance and need to be estimated from the ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	372,776
2402.17304	Probing Multimodal Large Language Models for Global and Local Semantic Representations	The advancement of Multimodal Large Language Models (MLLMs) has greatly accelerated the development of applications in understanding integrated texts and images. Recent works leverage image-caption datasets to train MLLMs, achieving state-of-the-art performance on image-to-text tasks. However, there are few studies exp...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	432,934
2502.05964	Revisiting Gradient-based Uncertainty for Monocular Depth Estimation	Monocular depth estimation, similar to other image-based tasks, is prone to erroneous predictions due to ambiguities in the image, for example, caused by dynamic objects or shadows. For this reason, pixel-wise uncertainty assessment is required for safety-critical applications to highlight the areas where the predictio...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	531,861
1808.02180	Instance-Dependent PU Learning by Bayesian Optimal Relabeling	When learning from positive and unlabelled data, it is a strong assumption that the positive observations are randomly sampled from the distribution of $X$ conditional on $Y = 1$, where X stands for the feature and Y the label. Most existing algorithms are optimally designed under the assumption. However, for many real...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	104,720
2302.04643	A Novel Approach for Auto-Formulation of Optimization Problems	In the Natural Language for Optimization (NL4Opt) NeurIPS 2022 competition, competitors focus on improving the accessibility and usability of optimization solvers, with the aim of subtask 1: recognizing the semantic entities that correspond to the components of the optimization problem; subtask 2: generating formulatio...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	344,772
2409.18462	Latent Representation Learning for Multimodal Brain Activity Translation	Neuroscience employs diverse neuroimaging techniques, each offering distinct insights into brain activity, from electrophysiological recordings such as EEG, which have high temporal resolution, to hemodynamic modalities such as fMRI, which have increased spatial precision. However, integrating these heterogeneous data ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	492,269
2403.14773	StreamingT2V: Consistent, Dynamic, and Extendable Long Video Generation from Text	Text-to-video diffusion models enable the generation of high-quality videos that follow text instructions, making it easy to create diverse and individual content. However, existing approaches mostly focus on high-quality short video generation (typically 16 or 24 frames), ending up with hard-cuts when naively extended...	false	false	false	false	true	false	true	false	true	false	false	true	false	false	false	false	false	true	440,247
1001.2076	Fast-Group-Decodable STBCs via Codes over GF(4)	In this paper we construct low decoding complexity STBCs by using the Pauli matrices as linear dispersion matrices. In this case the Hurwitz-Radon orthogonality condition is shown to be easily checked by transferring the problem to $\mathbb{F}_4$ domain. The problem of constructing low decoding complexity STBCs is show...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	5,351
2203.07912	Scalable Bigraphical Lasso: Two-way Sparse Network Inference for Count Data	Classically, statistical datasets have a larger number of data points than features ($n > p$). The standard model of classical statistics caters for the case where data points are considered conditionally independent given the parameters. However, for $n\approx p$ or $p > n$ such models are poorly determined. Kalaitzis...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	285,605
2410.17913	Deep learning for model correction of dynamical systems with data scarcity	We present a deep learning framework for correcting existing dynamical system models utilizing only a scarce high-fidelity data set. In many practical situations, one has a low-fidelity model that can capture the dynamics reasonably well but lacks high resolution, due to the inherent limitation of the model and the com...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	501,663
2409.01856	Robust Second-order LiDAR Bundle Adjustment Algorithm Using Mean Squared Group Metric	The bundle adjustment (BA) algorithm is a widely used nonlinear optimization technique in the backend of Simultaneous Localization and Mapping (SLAM) systems. By leveraging the co-view relationships of landmarks from multiple perspectives, the BA method constructs a joint estimation model for both poses and landmarks, ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	485,486
1406.5370	Spectral Ranking using Seriation	We describe a seriation algorithm for ranking a set of items given pairwise comparisons between these items. Intuitively, the algorithm assigns similar rankings to items that compare similarly with all others. It does so by constructing a similarity matrix from pairwise comparisons, using seriation methods to reorder t...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	34,024
1310.8107	Scalable Frames and Convex Geometry	The recently introduced and characterized scalable frames can be considered as those frames which allow for perfect preconditioning in the sense that the frame vectors can be rescaled to yield a tight frame. In this paper we define $m$-scalability, a refinement of scalability based on the number of non-zero weights use...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	28,082
2401.10166	VMamba: Visual State Space Model	Designing computationally efficient network architectures remains an ongoing necessity in computer vision. In this paper, we adapt Mamba, a state-space language model, into VMamba, a vision backbone with linear time complexity. At the core of VMamba is a stack of Visual State-Space (VSS) blocks with the 2D Selective Sc...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	422,508
1602.04567	Adversarial Top-$K$ Ranking	We study the top-$K$ ranking problem where the goal is to recover the set of top-$K$ ranked items out of a large collection of items based on partially revealed preferences. We consider an adversarial crowdsourced setting where there are two population sets, and pairwise comparison samples drawn from one of the populat...	false	false	false	false	false	true	true	false	false	true	false	false	false	false	false	false	false	false	52,158
2209.14105	Exploring the Relationship between Architecture and Adversarially Robust Generalization	Adversarial training has been demonstrated to be one of the most effective remedies for defending adversarial examples, yet it often suffers from the huge robustness generalization gap on unseen testing adversaries, deemed as the adversarially robust generalization problem. Despite the preliminary understandings devote...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	320,143
2105.14576	StyTr$^2$: Image Style Transfer with Transformers	The goal of image style transfer is to render an image with artistic features guided by a style reference while maintaining the original content. Owing to the locality in convolutional neural networks (CNNs), extracting and maintaining the global information of input images is difficult. Therefore, traditional neural s...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	237,729
0910.2029	A Framework For Intelligent Multi Agent System Based Neural Network Classification Model	TIntelligent multi agent systems have great potentials to use in different purposes and research areas. One of the important issues to apply intelligent multi agent systems in real world and virtual environment is to develop a framework that support machine learning model to reflect the whole complexity of the real wor...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	true	false	false	4,708
0805.2199	Constraint Complexity of Realizations of Linear Codes on Arbitrary Graphs	A graphical realization of a linear code C consists of an assignment of the coordinates of C to the vertices of a graph, along with a specification of linear state spaces and linear ``local constraint'' codes to be associated with the edges and vertices, respectively, of the graph. The $\k$-complexity of a graphical re...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	1,767
2201.01251	Multi-Stage Episodic Control for Strategic Exploration in Text Games	Text adventure games present unique challenges to reinforcement learning methods due to their combinatorially large action spaces and sparse rewards. The interplay of these two factors is particularly demanding because large action spaces require extensive exploration, while sparse rewards provide limited feedback. Thi...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	274,198
1707.05427	Visually Aligned Word Embeddings for Improving Zero-shot Learning	Zero-shot learning (ZSL) highly depends on a good semantic embedding to connect the seen and unseen classes. Recently, distributed word embeddings (DWE) pre-trained from large text corpus have become a popular choice to draw such a connection. Compared with human defined attributes, DWEs are more scalable and easier to...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	77,228
1801.00820	Stratified Transfer Learning for Cross-domain Activity Recognition	In activity recognition, it is often expensive and time-consuming to acquire sufficient activity labels. To solve this problem, transfer learning leverages the labeled samples from the source domain to annotate the target domain which has few or none labels. Existing approaches typically consider learning a global doma...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	87,630
2311.12526	Neural Network Pruning by Gradient Descent	The rapid increase in the parameters of deep learning models has led to significant costs, challenging computational efficiency and model interpretability. In this paper, we introduce a novel and straightforward neural network pruning framework that incorporates the Gumbel-Softmax technique. This framework enables the ...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	409,366
2306.15755	Adversarial Backdoor Attack by Naturalistic Data Poisoning on Trajectory Prediction in Autonomous Driving	In autonomous driving, behavior prediction is fundamental for safe motion planning, hence the security and robustness of prediction models against adversarial attacks are of paramount importance. We propose a novel adversarial backdoor attack against trajectory prediction models as a means of studying their potential v...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	376,137
2104.01558	Perspective-corrected Spatial Referring Expression Generation for Human-Robot Interaction	Intelligent robots designed to interact with humans in real scenarios need to be able to refer to entities actively by natural language. In spatial referring expression generation, the ambiguity is unavoidable due to the diversity of reference frames, which will lead to an understanding gap between humans and robots. T...	true	false	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	228,394
2412.14312	Stealing That Free Lunch: Exposing the Limits of Dyna-Style Reinforcement Learning	Dyna-style off-policy model-based reinforcement learning (DMBRL) algorithms are a family of techniques for generating synthetic state transition data and thereby enhancing the sample efficiency of off-policy RL algorithms. This paper identifies and investigates a surprising performance gap observed when applying DMBRL ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	518,650
2207.11075	RealFlow: EM-based Realistic Optical Flow Dataset Generation from Videos	Obtaining the ground truth labels from a video is challenging since the manual annotation of pixel-wise flow labels is prohibitively expensive and laborious. Besides, existing approaches try to adapt the trained model on synthetic datasets to authentic videos, which inevitably suffers from domain discrepancy and hinder...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	309,490
2111.13330	ArchRepair: Block-Level Architecture-Oriented Repairing for Deep Neural Networks	Over the past few years, deep neural networks (DNNs) have achieved tremendous success and have been continuously applied in many application domains. However, during the practical deployment in the industrial tasks, DNNs are found to be erroneous-prone due to various reasons such as overfitting, lacking robustness to r...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	268,266
1901.01356	Exponential Strong Converse for Successive Refinement with Causal Decoder Side Information	We consider the $k$-user successive refinement problem with causal decoder side information and derive an exponential strong converse theorem. The rate-distortion region for the problem can be derived as a straightforward extension of the two-user case by Maor and Merhav (2008). We show that for any rate-distortion tup...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	117,957
1606.03153	Unsupervised Learning of Word-Sequence Representations from Scratch via Convolutional Tensor Decomposition	Unsupervised text embeddings extraction is crucial for text understanding in machine learning. Word2Vec and its variants have received substantial success in mapping words with similar syntactic or semantic meaning to vectors close to each other. However, extracting context-aware word-sequence embedding remains a chall...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	57,061
1712.05134	Learning Compact Recurrent Neural Networks with Block-Term Tensor Decomposition	Recurrent Neural Networks (RNNs) are powerful sequence modeling tools. However, when dealing with high dimensional inputs, the training of RNNs becomes computational expensive due to the large number of model parameters. This hinders RNNs from solving many important computer vision tasks, such as Action Recognition in ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	86,700
2408.09075	Improving Rare Word Translation With Dictionaries and Attention Masking	In machine translation, rare words continue to be a problem for the dominant encoder-decoder architecture, especially in low-resource and out-of-domain translation settings. Human translators solve this problem with monolingual or bilingual dictionaries. In this paper, we propose appending definitions from a bilingual ...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	481,268
2308.09616	Far3D: Expanding the Horizon for Surround-view 3D Object Detection	Recently 3D object detection from surround-view images has made notable advancements with its low deployment cost. However, most works have primarily focused on close perception range while leaving long-range detection less explored. Expanding existing methods directly to cover long distances poses challenges such as h...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	386,362
2405.16296	Neural Network-Based Tracking and 3D Reconstruction of Baseball Pitch Trajectories from Single-View 2D Video	In this paper, we present a neural network-based approach for tracking and reconstructing the trajectories of baseball pitches from 2D video footage to 3D coordinates. We utilize OpenCV's CSRT algorithm to accurately track the baseball and fixed reference points in 2D video frames. These tracked pixel coordinates are t...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	457,352
1701.02593	A Simple and Accurate Syntax-Agnostic Neural Model for Dependency-based Semantic Role Labeling	We introduce a simple and accurate neural model for dependency-based semantic role labeling. Our model predicts predicate-argument dependencies relying on states of a bidirectional LSTM encoder. The semantic role labeler achieves competitive performance on English, even without any kind of syntactic information and onl...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	66,572
2201.01222	The cluster structure function	For each partition of a data set into a given number of parts there is a partition such that every part is as much as possible a good model (an "algorithmic sufficient statistic") for the data in that part. Since this can be done for every number between one and the number of data, the result is a function, the cluster...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	274,187
2208.03444	AFE-CNN: 3D Skeleton-based Action Recognition with Action Feature Enhancement	Existing 3D skeleton-based action recognition approaches reach impressive performance by encoding handcrafted action features to image format and decoding by CNNs. However, such methods are limited in two ways: a) the handcrafted action features are difficult to handle challenging actions, and b) they generally require...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	311,785
2202.02340	Selective Network Linearization for Efficient Private Inference	Private inference (PI) enables inference directly on cryptographically secure data.While promising to address many privacy issues, it has seen limited use due to extreme runtimes. Unlike plaintext inference, where latency is dominated by FLOPs, in PI non-linear functions (namely ReLU) are the bottleneck. Thus, practica...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	278,767
1907.04649	Quantifying the pathways to life using assembly spaces	We have developed the concept of pathway assembly to explore the amount of extrinsic information required to build an object. To quantify this information in an agnostic way, we present a method to determine the amount of pathway assembly information contained within such an object by deconstructing the object into its...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	138,161
2304.08134	Tackling Face Verification Edge Cases: In-Depth Analysis and Human-Machine Fusion Approach	Nowadays, face recognition systems surpass human performance on several datasets. However, there are still edge cases that the machine can't correctly classify. This paper investigates the effect of a combination of machine and human operators in the face verification task. First, we look closer at the edge cases for s...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	358,602
2309.02978	Helper Recommendation with seniority control in Online Health Community	Online health communities (OHCs) are forums where patients with similar conditions communicate their experiences and provide moral support. Social support in OHCs plays a crucial role in easing and rehabilitating patients. However, many time-sensitive questions from patients often remain unanswered due to the multitude...	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	false	390,232
2406.03611	FedPylot: Navigating Federated Learning for Real-Time Object Detection in Internet of Vehicles	The Internet of Vehicles (IoV) emerges as a pivotal component for autonomous driving and intelligent transportation systems (ITS), by enabling low-latency big data processing in a dense interconnected network that comprises vehicles, infrastructures, pedestrians and the cloud. Autonomous vehicles are heavily reliant on...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	true	461,302
2410.07971	Generalizable and Animatable Gaussian Head Avatar	In this paper, we propose Generalizable and Animatable Gaussian head Avatar (GAGAvatar) for one-shot animatable head avatar reconstruction. Existing methods rely on neural radiance fields, leading to heavy rendering consumption and low reenactment speeds. To address these limitations, we generate the parameters of 3D G...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	496,885
1611.05594	SCA-CNN: Spatial and Channel-wise Attention in Convolutional Networks for Image Captioning	Visual attention has been successfully applied in structural prediction tasks such as visual captioning and question answering. Existing visual attention models are generally spatial, i.e., the attention is modeled as spatial probabilities that re-weight the last conv-layer feature map of a CNN encoding an input image....	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	64,046
2303.14771	Prototype-Sample Relation Distillation: Towards Replay-Free Continual Learning	In Continual learning (CL) balancing effective adaptation while combating catastrophic forgetting is a central challenge. Many of the recent best-performing methods utilize various forms of prior task data, e.g. a replay buffer, to tackle the catastrophic forgetting problem. Having access to previous task data can be r...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	354,243
1701.08947	Sparse phase retrieval of one-dimensional signals by Prony's method	In this paper, we show that sparse signals f representable as a linear combination of a finite number N of spikes at arbitrary real locations or as a finite linear combination of B-splines of order m with arbitrary real knots can be almost surely recovered from O(N^2) Fourier intensity measurements up to trivial ambigu...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	67,562
2210.11941	DIICAN: Dual Time-scale State-Coupled Co-estimation of SOC, SOH and RUL for Lithium-Ion Batteries	Accurate co-estimations of battery states, such as state-of-charge (SOC), state-of-health (SOH,) and remaining useful life (RUL), are crucial to the battery management systems to assure safe and reliable management. Although the external properties of the battery charge with the aging degree, batteries' degradation mec...	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	325,516
2105.06409	SyntheticFur dataset for neural rendering	We introduce a new dataset called SyntheticFur built specifically for machine learning training. The dataset consists of ray traced synthetic fur renders with corresponding rasterized input buffers and simulation data files. We procedurally generated approximately 140,000 images and 15 simulations with Houdini. The ima...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	235,114
2104.06910	Towards a framework for evaluating the safety, acceptability and efficacy of AI systems for health: an initial synthesis	The potential presented by Artificial Intelligence (AI) for healthcare has long been recognised by the technical community. More recently, this potential has been recognised by policymakers, resulting in considerable public and private investment in the development of AI for healthcare across the globe. Despite this, e...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	230,230
1604.07095	Deep Learning for Reward Design to Improve Monte Carlo Tree Search in ATARI Games	Monte Carlo Tree Search (MCTS) methods have proven powerful in planning for sequential decision-making problems such as Go and video games, but their performance can be poor when the planning depth and sampling trajectories are limited or when the rewards are sparse. We present an adaptation of PGRD (policy-gradient fo...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	55,041
1707.01777	Improved Selective Harmonic Elimination for Reducing Torque Harmonics of Induction Motors in Wide DC Bus Voltage Variations	Conventionally, Selective Harmonic Elimination (SHE) method in 2-level inverters, finds best switching angles to reach first voltage harmonic to reference level and eliminate other harmonics, simultaneously. Considering Induction Motor (IM) as the inverter load, and wide DC bus voltage variations, the inverter must ope...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	76,593
1812.02316	Skin Lesions Classification Using Convolutional Neural Networks in Clinical Images	Skin lesions are conditions that appear on a patient due to many different reasons. One of these can be because of an abnormal growth in skin tissue, defined as cancer. This disease plagues more than 14.1 million patients and had been the cause of more than 8.2 million deaths, worldwide. Therefore, the construction of ...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	115,729
2405.03409	LightTR: A Lightweight Framework for Federated Trajectory Recovery	With the proliferation of GPS-equipped edge devices, huge trajectory data is generated and accumulated in various domains, motivating a variety of urban applications. Due to the limited acquisition capabilities of edge devices, a lot of trajectories are recorded at a low sampling rate, which may lead to the effectivene...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	452,174
2006.00878	Bi-directional Exponential Angular Triplet Loss for RGB-Infrared Person Re-Identification	RGB-Infrared person re-identification (RGB-IR Re- ID) is a cross-modality matching problem, where the modality discrepancy is a big challenge. Most existing works use Euclidean metric based constraints to resolve the discrepancy between features of images from different modalities. However, these methods are incapable ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	179,598

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