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
2302.07986	On the Detection and Quantification of Nonlinearity via Statistics of the Gradients of a Black-Box Model	Detection and identification of nonlinearity is a task of high importance for structural dynamics. Detecting nonlinearity in a structure, which has been designed to operate in its linear region, might indicate the existence of damage. Therefore, it is important, even for safety reasons, to detect when a structure exhib...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	345,891
0909.1605	Kernel Spectral Curvature Clustering (KSCC)	Multi-manifold modeling is increasingly used in segmentation and data representation tasks in computer vision and related fields. While the general problem, modeling data by mixtures of manifolds, is very challenging, several approaches exist for modeling data by mixtures of affine subspaces (which is often referred to...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	4,433
2110.03528	Decoding ECoG signal into 3D hand translation using deep learning	Motor brain-computer interfaces (BCIs) are a promising technology that may enable motor-impaired people to interact with their environment. Designing real-time and accurate BCI is crucial to make such devices useful, safe, and easy to use by patients in a real-life environment. Electrocorticography (ECoG)-based BCIs em...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	259,533
1908.06267	Message Passing Attention Networks for Document Understanding	Graph neural networks have recently emerged as a very effective framework for processing graph-structured data. These models have achieved state-of-the-art performance in many tasks. Most graph neural networks can be described in terms of message passing, vertex update, and readout functions. In this paper, we represen...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	141,956
2103.10807	Linear Coding for AWGN channels with Noisy Output Feedback via Dynamic Programming	The optimal coding scheme for communicating a Gaussian message over an Additive White Gaussian noise (AWGN) channel with AWGN output feedback, with a limited number of transmissions is unknown. Even if we restrict the scope of the coding scheme to linear schemes, still, deriving the optimal coding scheme is a challengi...	false	false	false	false	false	false	false	false	false	true	true	false	false	false	false	false	false	false	225,573
2410.00601	$k$-local Graphs	In 2017 Day et al. introduced the notion of locality as a structural complexity-measure for patterns in the field of pattern matching established by Angluin in 1980. In 2019 Casel et al. showed that determining the locality of an arbitrary pattern is NP-complete. Inspired by hierarchical clustering, we extend the notio...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	true	493,441
2107.00816	Few-shot Learning for Unsupervised Feature Selection	We propose a few-shot learning method for unsupervised feature selection, which is a task to select a subset of relevant features in unlabeled data. Existing methods usually require many instances for feature selection. However, sufficient instances are often unavailable in practice. The proposed method can select a su...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	244,280
2305.14908	PURR: Efficiently Editing Language Model Hallucinations by Denoising Language Model Corruptions	The remarkable capabilities of large language models have been accompanied by a persistent drawback: the generation of false and unsubstantiated claims commonly known as "hallucinations". To combat this issue, recent research has introduced approaches that involve editing and attributing the outputs of language models,...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	367,326
1909.13126	Feature Level Fusion from Facial Attributes for Face Recognition	We introduce a deep convolutional neural networks (CNN) architecture to classify facial attributes and recognize face images simultaneously via a shared learning paradigm to improve the accuracy for facial attribute prediction and face recognition performance. In this method, we use facial attributes as an auxiliary so...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	147,334
2003.00225	Comparison of Distal Teacher Learning with Numerical and Analytical Methods to Solve Inverse Kinematics for Rigid-Body Mechanisms	Several publications are concerned with learning inverse kinematics, however, their evaluation is often limited and none of the proposed methods is of practical relevance for rigid-body kinematics with a known forward model. We argue that for rigid-body kinematics one of the first proposed machine learning (ML) solutio...	false	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	166,232
2106.12362	A new Video Synopsis Based Approach Using Stereo Camera	In today's world, the amount of data produced in every field has increased at an unexpected level. In the face of increasing data, the importance of data processing has increased remarkably. Our resource topic is on the processing of video data, which has an important place in increasing data, and the production of sum...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	242,706
2103.00719	LocalDrop: A Hybrid Regularization for Deep Neural Networks	In neural networks, developing regularization algorithms to settle overfitting is one of the major study areas. We propose a new approach for the regularization of neural networks by the local Rademacher complexity called LocalDrop. A new regularization function for both fully-connected networks (FCNs) and convolutiona...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	222,374
2202.06385	Sample-Efficient Reinforcement Learning with loglog(T) Switching Cost	We study the problem of reinforcement learning (RL) with low (policy) switching cost - a problem well-motivated by real-life RL applications in which deployments of new policies are costly and the number of policy updates must be low. In this paper, we propose a new algorithm based on stage-wise exploration and adaptiv...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	280,202
2306.12432	Interpretation of immunofluorescence slides by deep learning techniques: anti-nuclear antibodies case study	Nowadays, diseases are increasing in numbers and severity by the hour. Immunity diseases, affecting 8\% of the world population in 2017 according to the World Health Organization (WHO), is a field in medicine worth attention due to the high rate of disease occurrence classified under this category. This work presents a...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	374,938
2403.15476	Learning to Infer Generative Template Programs for Visual Concepts	People grasp flexible visual concepts from a few examples. We explore a neurosymbolic system that learns how to infer programs that capture visual concepts in a domain-general fashion. We introduce Template Programs: programmatic expressions from a domain-specific language that specify structural and parametric pattern...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	true	440,593
2109.02284	Uncertainty-Aware Balancing for Multilingual and Multi-Domain Neural Machine Translation Training	Learning multilingual and multi-domain translation model is challenging as the heterogeneous and imbalanced data make the model converge inconsistently over different corpora in real world. One common practice is to adjust the share of each corpus in the training, so that the learning process is balanced and low-resour...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	253,695
0906.4643	The Poisson Channel with Side Information	The continuous-time, peak-limited, infinite-bandwidth Poisson channel with spurious counts is considered. It is shown that if the times at which the spurious counts occur are known noncausally to the transmitter but not to the receiver, then the capacity is equal to that of the Poisson channel with no spurious counts. ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	3,964
1806.08472	Learning a High Fidelity Pose Invariant Model for High-resolution Face Frontalization	Face frontalization refers to the process of synthesizing the frontal view of a face from a given profile. Due to self-occlusion and appearance distortion in the wild, it is extremely challenging to recover faithful results and preserve texture details in a high-resolution. This paper proposes a High Fidelity Pose Inva...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	101,159
2111.15002	LEGS: Learning Efficient Grasp Sets for Exploratory Grasping	While deep learning has enabled significant progress in designing general purpose robot grasping systems, there remain objects which still pose challenges for these systems. Recent work on Exploratory Grasping has formalized the problem of systematically exploring grasps on these adversarial objects and explored a mult...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	268,770
1810.07810	LadderNet: Multi-path networks based on U-Net for medical image segmentation	U-Net has been providing state-of-the-art performance in many medical image segmentation problems. Many modifications have been proposed for U-Net, such as attention U-Net, recurrent residual convolutional U-Net (R2-UNet), and U-Net with residual blocks or blocks with dense connections. However, all these modifications...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	110,702
2304.06107	PATMAT: Person Aware Tuning of Mask-Aware Transformer for Face Inpainting	Generative models such as StyleGAN2 and Stable Diffusion have achieved state-of-the-art performance in computer vision tasks such as image synthesis, inpainting, and de-noising. However, current generative models for face inpainting often fail to preserve fine facial details and the identity of the person, despite crea...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	357,855
1402.0525	A Deterministic Annealing Approach to Witsenhausen's Counterexample	This paper proposes a numerical method, based on information theoretic ideas, to a class of distributed control problems. As a particular test case, the well-known and numerically "over-mined" problem of decentralized control and implicit communication, commonly referred to as Witsenhausen's counterexample, is consider...	false	false	false	false	false	false	false	false	false	true	true	false	false	false	false	false	false	false	30,569
1809.08417	Implementation of Fuzzy C-Means and Possibilistic C-Means Clustering Algorithms, Cluster Tendency Analysis and Cluster Validation	In this paper, several two-dimensional clustering scenarios are given. In those scenarios, soft partitioning clustering algorithms (Fuzzy C-means (FCM) and Possibilistic c-means (PCM)) are applied. Afterward, VAT is used to investigate the clustering tendency visually, and then in order of checking cluster validation, ...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	108,502
1712.01769	State-of-the-art Speech Recognition With Sequence-to-Sequence Models	Attention-based encoder-decoder architectures such as Listen, Attend, and Spell (LAS), subsume the acoustic, pronunciation and language model components of a traditional automatic speech recognition (ASR) system into a single neural network. In previous work, we have shown that such architectures are comparable to stat...	false	false	true	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	86,170
2109.04598	Automatic Portrait Video Matting via Context Motion Network	Automatic portrait video matting is an under-constrained problem. Most state-of-the-art methods only exploit the semantic information and process each frame individually. Their performance is compromised due to the lack of temporal information between the frames. To solve this problem, we propose the context motion net...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	254,457
1902.04729	Accurate 3D Cell Segmentation using Deep Feature and CRF Refinement	We consider the problem of accurately identifying cell boundaries and labeling individual cells in confocal microscopy images, specifically, 3D image stacks of cells with tagged cell membranes. Precise identification of cell boundaries, their shapes, and quantifying inter-cellular space leads to a better understanding ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	121,409
2502.05032	News about Global North considered Truthful! The Geo-political Veracity Gradient in Global South News	While there has been much research into developing AI techniques for fake news detection aided by various benchmark datasets, it has often been pointed out that fake news in different geo-political regions traces different contours. In this work we uncover, through analytical arguments and empirical evidence, the exist...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	531,410
1301.2319	Planning and Acting under Uncertainty: A New Model for Spoken Dialogue Systems	Uncertainty plays a central role in spoken dialogue systems. Some stochastic models like Markov decision process (MDP) are used to model the dialogue manager. But the partially observable system state and user intention hinder the natural representation of the dialogue state. MDP-based system degrades fast when uncerta...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	20,994
2210.04193	Predicting fluid-structure interaction with graph neural networks	We present a rotation equivariant, quasi-monolithic graph neural network framework for the reduced-order modeling of fluid-structure interaction systems. With the aid of an arbitrary Lagrangian-Eulerian formulation, the system states are evolved temporally with two sub-networks. The movement of the mesh is reduced to t...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	322,351
2108.07482	G-DetKD: Towards General Distillation Framework for Object Detectors via Contrastive and Semantic-guided Feature Imitation	In this paper, we investigate the knowledge distillation (KD) strategy for object detection and propose an effective framework applicable to both homogeneous and heterogeneous student-teacher pairs. The conventional feature imitation paradigm introduces imitation masks to focus on informative foreground areas while exc...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	250,929
2201.11630	Automatic Classification of Neuromuscular Diseases in Children Using Photoacoustic Imaging	Neuromuscular diseases (NMDs) cause a significant burden for both healthcare systems and society. They can lead to severe progressive muscle weakness, muscle degeneration, contracture, deformity and progressive disability. The NMDs evaluated in this study often manifest in early childhood. As subtypes of disease, e.g. ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	277,349
2501.09046	Learning Hemodynamic Scalar Fields on Coronary Artery Meshes: A Benchmark of Geometric Deep Learning Models	Coronary artery disease, caused by the narrowing of coronary vessels due to atherosclerosis, is the leading cause of death worldwide. The diagnostic gold standard, fractional flow reserve (FFR), measures the trans-stenotic pressure ratio during maximal vasodilation but is invasive and costly. This has driven the develo...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	524,997
2012.02232	Graph Convolutional Neural Networks for Body Force Prediction	Many scientific and engineering processes produce spatially unstructured data. However, most data-driven models require a feature matrix that enforces both a set number and order of features for each sample. They thus cannot be easily constructed for an unstructured dataset. Therefore, a graph based data-driven model t...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	209,693
2207.09135	Shrinking the Semantic Gap: Spatial Pooling of Local Moment Invariants for Copy-Move Forgery Detection	Copy-move forgery is a manipulation of copying and pasting specific patches from and to an image, with potentially illegal or unethical uses. Recent advances in the forensic methods for copy-move forgery have shown increasing success in detection accuracy and robustness. However, for images with high self-similarity or...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	308,808
2502.08995	PixLift: Accelerating Web Browsing via AI Upscaling	Accessing the internet in regions with expensive data plans and limited connectivity poses significant challenges, restricting information access and economic growth. Images, as a major contributor to webpage sizes, exacerbate this issue, despite advances in compression formats like WebP and AVIF. The continued growth ...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	533,261
2110.00135	UserIdentifier: Implicit User Representations for Simple and Effective Personalized Sentiment Analysis	Global models are trained to be as generalizable as possible, with user invariance considered desirable since the models are shared across multitudes of users. As such, these models are often unable to produce personalized responses for individual users, based on their data. Contrary to widely-used personalization tech...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	258,293
2306.01705	The Information Pathways Hypothesis: Transformers are Dynamic Self-Ensembles	Transformers use the dense self-attention mechanism which gives a lot of flexibility for long-range connectivity. Over multiple layers of a deep transformer, the number of possible connectivity patterns increases exponentially. However, very few of these contribute to the performance of the network, and even fewer are ...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	370,553
2312.12339	Value Explicit Pretraining for Learning Transferable Representations	We propose Value Explicit Pretraining (VEP), a method that learns generalizable representations for transfer reinforcement learning. VEP enables learning of new tasks that share similar objectives as previously learned tasks, by learning an encoder for objective-conditioned representations, irrespective of appearance c...	false	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	416,918
2311.10176	Scalable Multi-Robot Motion Planning Using Guidance-Informed Hypergraphs	In this work, we present a multi-robot planning framework that leverages guidance about the problem to efficiently search the planning space. This guidance captures when coordination between robots is necessary, allowing us to decompose the intractably large multi-robot search space while limiting risk of inter-robot c...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	408,436
1905.11934	Downlink Coverage and Rate Analysis of an Aerial User in Vertical Heterogeneous Networks (VHetNets)	In this paper, we analyze the downlink coverage probability and rate of an aerial user in vertical HetNets (VHetNets) comprising aerial base stations (aerial-BSs) and terrestrial-BSs. The locations of terrestrial-BSs are modeled as an infinite 2-D Poisson Point Process (PPP), while the locations of aerial-BSs are model...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	132,599
2110.04005	KaraSinger: Score-Free Singing Voice Synthesis with VQ-VAE using Mel-spectrograms	In this paper, we propose a novel neural network model called KaraSinger for a less-studied singing voice synthesis (SVS) task named score-free SVS, in which the prosody and melody are spontaneously decided by machine. KaraSinger comprises a vector-quantized variational autoencoder (VQ-VAE) that compresses the Mel-spec...	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	259,714
2203.10493	Depth Estimation by Combining Binocular Stereo and Monocular Structured-Light	It is well known that the passive stereo system cannot adapt well to weak texture objects, e.g., white walls. However, these weak texture targets are very common in indoor environments. In this paper, we present a novel stereo system, which consists of two cameras (an RGB camera and an IR camera) and an IR speckle proj...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	286,563
1811.04477	Unifying Gaussian LWF and AMP Chain Graphs to Model Interference	An intervention may have an effect on units other than those to which it was administered. This phenomenon is called interference and it usually goes unmodeled. In this paper, we propose to combine Lauritzen-Wermuth-Frydenberg and Andersson-Madigan-Perlman chain graphs to create a new class of causal models that can re...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	113,102
2404.03701	Predictive Analytics of Varieties of Potatoes	We explore the application of machine learning algorithms specifically to enhance the selection process of Russet potato clones in breeding trials by predicting their suitability for advancement. This study addresses the challenge of efficiently identifying high-yield, disease-resistant, and climate-resilient potato va...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	444,371
1203.0251	Bayesian Posteriors Without Bayes' Theorem	The classical Bayesian posterior arises naturally as the unique solution of several different optimization problems, without the necessity of interpreting data as conditional probabilities and then using Bayes' Theorem. For example, the classical Bayesian posterior is the unique posterior that minimizes the loss of Sha...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	14,684
1808.06910	Scalable Population Synthesis with Deep Generative Modeling	Population synthesis is concerned with the generation of synthetic yet realistic representations of populations. It is a fundamental problem in the modeling of transport where the synthetic populations of micro-agents represent a key input to most agent-based models. In this paper, a new methodological framework for ho...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	false	false	false	105,635
1911.01775	Distance-Based Network Partitioning	A new method for identifying communities in networks is proposed. Reference nodes, either selected using a priory information about the network or according to relevant node measurements, are obtained so as to indicate putative communities. Distance vectors between each network node and the reference nodes are then use...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	152,200
2404.07792	Nostra Domina at EvaLatin 2024: Improving Latin Polarity Detection through Data Augmentation	This paper describes submissions from the team Nostra Domina to the EvaLatin 2024 shared task of emotion polarity detection. Given the low-resource environment of Latin and the complexity of sentiment in rhetorical genres like poetry, we augmented the available data through automatic polarity annotation. We present two...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	445,978
2106.09132	Multivariate Pair Trading by Volatility & Model Adaption Trade-off	Pair trading is one of the most discussed topics among financial researches. Despite a growing base of work, portfolio management for multivariate time series is rarely discussed. On the other hand, most researches focus on refining strategy rules instead of finding the optimal portfolio weight. In this paper, we broug...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	241,549
1806.01016	Hierarchical Bi-level Multi-Objective Evolution of Single- and Multi-layer Echo State Network Autoencoders for Data Representations	Echo State Network (ESN) presents a distinguished kind of recurrent neural networks. It is built upon a sparse, random and large hidden infrastructure called reservoir. ESNs have succeeded in dealing with several non-linear problems such as prediction, classification, etc. Thanks to its rich dynamics, ESN is used as an...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	99,464
2112.13491	A Compact Neural Network-based Algorithm for Robust Image Watermarking	Digital image watermarking seeks to protect the digital media information from unauthorized access, where the message is embedded into the digital image and extracted from it, even some noises or distortions are applied under various data processing including lossy image compression and interactive content editing. Tra...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	273,254
1608.08434	Multi-Class Multi-Object Tracking using Changing Point Detection	This paper presents a robust multi-class multi-object tracking (MCMOT) formulated by a Bayesian filtering framework. Multi-object tracking for unlimited object classes is conducted by combining detection responses and changing point detection (CPD) algorithm. The CPD model is used to observe abrupt or abnormal changes ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	60,356
1811.07498	Robust Visual Tracking using Multi-Frame Multi-Feature Joint Modeling	It remains a huge challenge to design effective and efficient trackers under complex scenarios, including occlusions, illumination changes and pose variations. To cope with this problem, a promising solution is to integrate the temporal consistency across consecutive frames and multiple feature cues in a unified model....	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	113,792
1612.03769	Context-aware Sentiment Word Identification: sentiword2vec	Traditional sentiment analysis often uses sentiment dictionary to extract sentiment information in text and classify documents. However, emerging informal words and phrases in user generated content call for analysis aware to the context. Usually, they have special meanings in a particular context. Because of its great...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	65,416
1912.00392	An Efficient Multi-fidelity Bayesian Optimization Approach for Analog Circuit Synthesis	This paper presents an efficient multi-fidelity Bayesian optimization approach for analog circuit synthesis. The proposed method can significantly reduce the overall computational cost by fusing the simple but potentially inaccurate low-fidelity model and a few accurate but expensive high-fidelity data. Gaussian Proces...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	155,757
2311.06227	Does Differential Privacy Prevent Backdoor Attacks in Practice?	Differential Privacy (DP) was originally developed to protect privacy. However, it has recently been utilized to secure machine learning (ML) models from poisoning attacks, with DP-SGD receiving substantial attention. Nevertheless, a thorough investigation is required to assess the effectiveness of different DP techniq...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	406,855
1706.00493	Personalized Pancreatic Tumor Growth Prediction via Group Learning	Tumor growth prediction, a highly challenging task, has long been viewed as a mathematical modeling problem, where the tumor growth pattern is personalized based on imaging and clinical data of a target patient. Though mathematical models yield promising results, their prediction accuracy may be limited by the absence ...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	74,625
2303.11649	CoopInit: Initializing Generative Adversarial Networks via Cooperative Learning	Numerous research efforts have been made to stabilize the training of the Generative Adversarial Networks (GANs), such as through regularization and architecture design. However, we identify the instability can also arise from the fragile balance at the early stage of adversarial learning. This paper proposes the CoopI...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	352,944
1705.10130	An Automatic Contextual Analysis and Clustering Classifiers Ensemble approach to Sentiment Analysis	Products reviews are one of the major resources to determine the public sentiment. The existing literature on reviews sentiment analysis mainly utilizes supervised paradigm, which needs labeled data to be trained on and suffers from domain-dependency. This article addresses these issues by describes a completely automa...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	74,341
1704.00648	Soft-to-Hard Vector Quantization for End-to-End Learning Compressible Representations	We present a new approach to learn compressible representations in deep architectures with an end-to-end training strategy. Our method is based on a soft (continuous) relaxation of quantization and entropy, which we anneal to their discrete counterparts throughout training. We showcase this method for two challenging a...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	71,120
2011.12470	Emotional Semantics-Preserved and Feature-Aligned CycleGAN for Visual Emotion Adaptation	Thanks to large-scale labeled training data, deep neural networks (DNNs) have obtained remarkable success in many vision and multimedia tasks. However, because of the presence of domain shift, the learned knowledge of the well-trained DNNs cannot be well generalized to new domains or datasets that have few labels. Unsu...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	true	208,171
2007.04238	Predicting the Accuracy of a Few-Shot Classifier	In the context of few-shot learning, one cannot measure the generalization ability of a trained classifier using validation sets, due to the small number of labeled samples. In this paper, we are interested in finding alternatives to answer the question: is my classifier generalizing well to previously unseen data? We ...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	186,293
1805.01934	Learning to See in the Dark	Imaging in low light is challenging due to low photon count and low SNR. Short-exposure images suffer from noise, while long exposure can induce blur and is often impractical. A variety of denoising, deblurring, and enhancement techniques have been proposed, but their effectiveness is limited in extreme conditions, suc...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	true	96,729
2406.05331	Autonomous Robotic Assembly: From Part Singulation to Precise Assembly	Imagine a robot that can assemble a functional product from the individual parts presented in any configuration to the robot. Designing such a robotic system is a complex problem which presents several open challenges. To bypass these challenges, the current generation of assembly systems is built with a lot of system ...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	462,081
1311.2276	A Quantitative Evaluation Framework for Missing Value Imputation Algorithms	We consider the problem of quantitatively evaluating missing value imputation algorithms. Given a dataset with missing values and a choice of several imputation algorithms to fill them in, there is currently no principled way to rank the algorithms using a quantitative metric. We develop a framework based on treating i...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	28,304
2201.06574	Neural Computed Tomography	Motion during acquisition of a set of projections can lead to significant motion artifacts in computed tomography reconstructions despite fast acquisition of individual views. In cases such as cardiac imaging, motion may be unavoidable and evaluating motion may be of clinical interest. Reconstructing images with reduce...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	275,761
2403.16208	Convergence analysis of OT-Flow for sample generation	Deep generative models aim to learn the underlying distribution of data and generate new ones. Despite the diversity of generative models and their high-quality generation performance in practice, most of them lack rigorous theoretical convergence proofs. In this work, we aim to establish some convergence results for O...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	440,927
1901.04199	Proceedings of the 2nd Symposium on Problem-solving, Creativity and Spatial Reasoning in Cognitive Systems, ProSocrates 2017	This book contains the accepted papers at ProSocrates 2017 Symposium: Problem-solving,Creativity and Spatial Reasoning in Cognitive Systems. ProSocrates 2017 symposium was held at the Hansewissenschaftkolleg (HWK) of Advanced Studies in Delmenhorst, 20-21July 2017. This was the second edition of this symposium which ai...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	118,562
2412.10121	Familiarity: Better Evaluation of Zero-Shot Named Entity Recognition by Quantifying Label Shifts in Synthetic Training Data	Zero-shot named entity recognition (NER) is the task of detecting named entities of specific types (such as 'Person' or 'Medicine') without any training examples. Current research increasingly relies on large synthetic datasets, automatically generated to cover tens of thousands of distinct entity types, to train zero-...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	516,795
2206.03111	Medical Image Registration via Neural Fields	Image registration is an essential step in many medical image analysis tasks. Traditional methods for image registration are primarily optimization-driven, finding the optimal deformations that maximize the similarity between two images. Recent learning-based methods, trained to directly predict transformations between...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	301,151
2104.03465	Nutribullets Hybrid: Multi-document Health Summarization	We present a method for generating comparative summaries that highlights similarities and contradictions in input documents. The key challenge in creating such summaries is the lack of large parallel training data required for training typical summarization systems. To this end, we introduce a hybrid generation approac...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	229,071
1809.09446	Nested cross-validation when selecting classifiers is overzealous for most practical applications	When selecting a classification algorithm to be applied to a particular problem, one has to simultaneously select the best algorithm for that dataset \emph{and} the best set of hyperparameters for the chosen model. The usual approach is to apply a nested cross-validation procedure; hyperparameter selection is performed...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	108,715
1309.4291	Models and algorithms for skip-free Markov decision processes on trees	We introduce a class of models for multidimensional control problems which we call skip-free Markov decision processes on trees. We describe and analyse an algorithm applicable to Markov decision processes of this type that are skip-free in the negative direction. Starting with the finite average cost case, we show tha...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	27,095
1907.09111	Aggregating Probabilistic Judgments	In this paper we explore the application of methods for classical judgment aggregation in pooling probabilistic opinions on logically related issues. For this reason, we first modify the Boolean judgment aggregation framework in the way that allows handling probabilistic judgments and then define probabilistic aggregat...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	true	false	false	false	139,271
2112.14792	Graph Neural Networks for Communication Networks: Context, Use Cases and Opportunities	Graph neural networks (GNN) have shown outstanding applications in many fields where data is fundamentally represented as graphs (e.g., chemistry, biology, recommendation systems). In this vein, communication networks comprise many fundamental components that are naturally represented in a graph-structured manner (e.g....	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	273,611
2307.10316	CPCM: Contextual Point Cloud Modeling for Weakly-supervised Point Cloud Semantic Segmentation	We study the task of weakly-supervised point cloud semantic segmentation with sparse annotations (e.g., less than 0.1% points are labeled), aiming to reduce the expensive cost of dense annotations. Unfortunately, with extremely sparse annotated points, it is very difficult to extract both contextual and object informat...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	380,516
2009.06996	Light Can Hack Your Face! Black-box Backdoor Attack on Face Recognition Systems	Deep neural networks (DNN) have shown great success in many computer vision applications. However, they are also known to be susceptible to backdoor attacks. When conducting backdoor attacks, most of the existing approaches assume that the targeted DNN is always available, and an attacker can always inject a specific p...	false	false	false	false	true	false	false	false	false	false	false	true	true	false	false	false	false	false	195,807
2206.15183	Neural Network Assisted Depth Map Packing for Compression Using Standard Hardware Video Codecs	Depth maps are needed by various graphics rendering and processing operations. Depth map streaming is often necessary when such operations are performed in a distributed system and it requires in most cases fast performing compression, which is why video codecs are often used. Hardware implementations of standard video...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	305,513
2010.03331	Multi-label classification of promotions in digital leaflets using textual and visual information	Product descriptions in e-commerce platforms contain detailed and valuable information about retailers assortment. In particular, coding promotions within digital leaflets are of great interest in e-commerce as they capture the attention of consumers by showing regular promotions for different products. However, this i...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	199,372
2401.09407	Deciphering Textual Authenticity: A Generalized Strategy through the Lens of Large Language Semantics for Detecting Human vs. Machine-Generated Text	With the recent proliferation of Large Language Models (LLMs), there has been an increasing demand for tools to detect machine-generated text. The effective detection of machine-generated text face two pertinent problems: First, they are severely limited in generalizing against real-world scenarios, where machine-gener...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	422,244
2109.08706	Online Traffic Routing: Deterministic Limits and Data-driven Enhancements	Over the past decade, GPS enabled traffic applications, such as Google Maps and Waze, have become ubiquitous and have had a significant influence on billions of daily commuters' travel patterns. A consequence of the online route suggestions of such applications, e.g., via greedy routing, has often been an increase in t...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	255,984
2009.08058	MultAV: Multiplicative Adversarial Videos	The majority of adversarial machine learning research focuses on additive attacks, which add adversarial perturbation to input data. On the other hand, unlike image recognition problems, only a handful of attack approaches have been explored in the video domain. In this paper, we propose a novel attack method against v...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	196,119
2411.16121	DP-CDA: An Algorithm for Enhanced Privacy Preservation in Dataset Synthesis Through Randomized Mixing	In recent years, the growth of data across various sectors, including healthcare, security, finance, and education, has created significant opportunities for analysis and informed decision-making. However, these datasets often contain sensitive and personal information, which raises serious privacy concerns. Protecting...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	510,903
2312.17118	Fully Sparse 3D Occupancy Prediction	Occupancy prediction plays a pivotal role in autonomous driving. Previous methods typically construct dense 3D volumes, neglecting the inherent sparsity of the scene and suffering from high computational costs. To bridge the gap, we introduce a novel fully sparse occupancy network, termed SparseOcc. SparseOcc initially...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	418,626
2002.00181	Fine-Tuning BERT for Schema-Guided Zero-Shot Dialogue State Tracking	We present our work on Track 4 in the Dialogue System Technology Challenges 8 (DSTC8). The DSTC8-Track 4 aims to perform dialogue state tracking (DST) under the zero-shot settings, in which the model needs to generalize on unseen service APIs given a schema definition of these target APIs. Serving as the core for many ...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	162,282
2404.00915	Scalable 3D Registration via Truncated Entry-wise Absolute Residuals	Given an input set of $3$D point pairs, the goal of outlier-robust $3$D registration is to compute some rotation and translation that align as many point pairs as possible. This is an important problem in computer vision, for which many highly accurate approaches have been recently proposed. Despite their impressive pe...	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	443,165
2112.14706	Intersection focused Situation Coverage-based Verification and Validation Framework for Autonomous Vehicles Implemented in CARLA	Autonomous Vehicles (AVs) i.e., self-driving cars, operate in a safety critical domain, since errors in the autonomous driving software can lead to huge losses. Statistically, road intersections which are a part of the AVs operational design domain (ODD), have some of the highest accident rates. Hence, testing AVs to t...	false	false	false	false	true	false	false	true	false	false	false	false	false	false	true	false	false	true	273,592
2310.16134	The Evolution from Design to Verification of the Antenna System and Mechanisms in the AcubeSAT mission	AcubeSAT is an open-source CubeSat mission aiming to explore the effects of microgravity and radiation on eukaryotic cells using a compact microfluidic LoC platform. It is developed by SpaceDot, a volunteer, interdisciplinary student team at the Aristotle University of Thessaloniki and supported by the "Fly Your Satell...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	402,589
2004.03028	Learning Generative Models of Shape Handles	We present a generative model to synthesize 3D shapes as sets of handles -- lightweight proxies that approximate the original 3D shape -- for applications in interactive editing, shape parsing, and building compact 3D representations. Our model can generate handle sets with varying cardinality and different types of ha...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	true	171,414
1810.04873	Deep Bi-Dense Networks for Image Super-Resolution	This paper proposes Deep Bi-Dense Networks (DBDN) for single image super-resolution. Our approach extends previous intra-block dense connection approaches by including novel inter-block dense connections. In this way, feature information propagates from a single dense block to all subsequent blocks, instead of to a sin...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	110,122
1707.05429	Distributed Bi-level Energy Allocation Mechanism with Grid Constraints and Hidden User Information	A novel distributed energy allocation mechanism for Distribution System Operator (DSO) market through a bi-level iterative auction is proposed. With the locational marginal price at the substation node known, the DSO runs an upper level auction with aggregators as intermediate agents competing for energy. This DSO leve...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	77,230
2410.12878	Towards More Effective Table-to-Text Generation: Assessing In-Context Learning and Self-Evaluation with Open-Source Models	Table processing, a key task in natural language processing, has significantly benefited from recent advancements in language models (LMs). However, the capabilities of LMs in table-to-text generation, which transforms structured data into coherent narrative text, require an in-depth investigation, especially with curr...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	499,262
2411.15430	The Landscape of Data Reuse in Interactive Information Retrieval: Motivations, Sources, and Evaluation of Reusability	Sharing and reusing research data can effectively reduce redundant efforts in data collection and curation, especially for small labs and research teams conducting human-centered system research, and enhance the replicability of evaluation experiments. Building a sustainable data reuse process and culture relies on fra...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	true	510,607
2010.01845	Unbiased Gradient Estimation for Variational Auto-Encoders using Coupled Markov Chains	The variational auto-encoder (VAE) is a deep latent variable model that has two neural networks in an autoencoder-like architecture; one of them parameterizes the model's likelihood. Fitting its parameters via maximum likelihood (ML) is challenging since the computation of the marginal likelihood involves an intractabl...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	198,812
2301.06157	Cooperative Concurrent Games	In rational verification, the aim is to verify which temporal logic properties will obtain in a multi-agent system, under the assumption that agents ("players") in the system choose strategies for acting that form a game theoretic equilibrium. Preferences are typically defined by assuming that agents act in pursuit of ...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	true	340,565
1905.12198	Ensuring Readability and Data-fidelity using Head-modifier Templates in Deep Type Description Generation	A type description is a succinct noun compound which helps human and machines to quickly grasp the informative and distinctive information of an entity. Entities in most knowledge graphs (KGs) still lack such descriptions, thus calling for automatic methods to supplement such information. However, existing generative m...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	132,691
2210.10459	Estimating the coverage in 3d reconstructions of the colon from colonoscopy videos	Colonoscopy is the most common procedure for early detection and removal of polyps, a critical component of colorectal cancer prevention. Insufficient visual coverage of the colon surface during the procedure often results in missed polyps. To mitigate this issue, reconstructing the 3D surfaces of the colon in order to...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	324,931
2301.12025	Cross-Architectural Positive Pairs improve the effectiveness of Self-Supervised Learning	Existing self-supervised techniques have extreme computational requirements and suffer a substantial drop in performance with a reduction in batch size or pretraining epochs. This paper presents Cross Architectural - Self Supervision (CASS), a novel self-supervised learning approach that leverages Transformer and CNN s...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	342,355
2305.17174	From Dogwhistles to Bullhorns: Unveiling Coded Rhetoric with Language Models	Dogwhistles are coded expressions that simultaneously convey one meaning to a broad audience and a second one, often hateful or provocative, to a narrow in-group; they are deployed to evade both political repercussions and algorithmic content moderation. For example, in the sentence 'we need to end the cosmopolitan exp...	false	false	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	368,441
2308.07316	Jurassic World Remake: Bringing Ancient Fossils Back to Life via Zero-Shot Long Image-to-Image Translation	With a strong understanding of the target domain from natural language, we produce promising results in translating across large domain gaps and bringing skeletons back to life. In this work, we use text-guided latent diffusion models for zero-shot image-to-image translation (I2I) across large domain gaps (longI2I), wh...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	385,468

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