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
1904.09482	Improving Multi-Task Deep Neural Networks via Knowledge Distillation for Natural Language Understanding	This paper explores the use of knowledge distillation to improve a Multi-Task Deep Neural Network (MT-DNN) (Liu et al., 2019) for learning text representations across multiple natural language understanding tasks. Although ensemble learning can improve model performance, serving an ensemble of large DNNs such as MT-DNN...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	128,391
1607.06290	Confidence-Weighted Local Expression Predictions for Occlusion Handling in Expression Recognition and Action Unit detection	Fully-Automatic Facial Expression Recognition (FER) from still images is a challenging task as it involves handling large interpersonal morphological differences, and as partial occlusions can occasionally happen. Furthermore, labelling expressions is a time-consuming process that is prone to subjectivity, thus the var...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	58,871
2408.06021	ClickAttention: Click Region Similarity Guided Interactive Segmentation	Interactive segmentation algorithms based on click points have garnered significant attention from researchers in recent years. However, existing studies typically use sparse click maps as model inputs to segment specific target objects, which primarily affect local regions and have limited abilities to focus on the wh...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	480,047
1412.5240	Minimization of Transformed $L_1$ Penalty: Closed Form Representation and Iterative Thresholding Algorithms	The transformed $l_1$ penalty (TL1) functions are a one parameter family of bilinear transformations composed with the absolute value function. When acting on vectors, the TL1 penalty interpolates $l_0$ and $l_1$ similar to $l_p$ norm ($p \in (0,1)$). In our companion paper, we showed that TL1 is a robust sparsity prom...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	38,467
1802.04664	Recovering Loss to Followup Information Using Denoising Autoencoders	Loss to followup is a significant issue in healthcare and has serious consequences for a study's validity and cost. Methods available at present for recovering loss to followup information are restricted by their expressive capabilities and struggle to model highly non-linear relations and complex interactions. In this...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	90,272
2208.04882	Unsupervised Question Clarity Prediction Through Retrieved Item Coherency	Despite recent progress on conversational systems, they still do not perform smoothly and coherently when faced with ambiguous requests. When questions are unclear, conversational systems should have the ability to ask clarifying questions, rather than assuming a particular interpretation or simply responding that they...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	312,252
2405.09292	Attribute reduction algorithm of rough sets based on spatial optimization	Rough set is one of the important methods for rule acquisition and attribute reduction. The current goal of rough set attribute reduction focuses more on minimizing the number of reduced attributes, but ignores the spatial similarity between reduced and decision attributes, which may lead to problems such as increased ...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	454,351
0901.0062	Cores of Cooperative Games in Information Theory	Cores of cooperative games are ubiquitous in information theory, and arise most frequently in the characterization of fundamental limits in various scenarios involving multiple users. Examples include classical settings in network information theory such as Slepian-Wolf source coding and multiple access channels, class...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	2,869
1707.06887	A Distributional Perspective on Reinforcement Learning	In this paper we argue for the fundamental importance of the value distribution: the distribution of the random return received by a reinforcement learning agent. This is in contrast to the common approach to reinforcement learning which models the expectation of this return, or value. Although there is an established ...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	77,505
1906.00127	Multi-objective Bayesian Optimization using Pareto-frontier Entropy	This paper studies an entropy-based multi-objective Bayesian optimization (MBO). The entropy search is successful approach to Bayesian optimization. However, for MBO, existing entropy-based methods ignore trade-off among objectives or introduce unreliable approximations. We propose a novel entropy-based MBO called Pare...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	133,272
2412.09065	Multi-view Clustering via Unified Multi-kernel Learning and Matrix Factorization	Multi-view clustering has become increasingly important due to the multi-source character of real-world data. Among existing multi-view clustering methods, multi-kernel clustering and matrix factorization-based multi-view clustering have gained widespread attention as mainstream approaches. However, multi-kernel cluste...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	516,349
2006.15131	An Advert Creation System for 3D Product Placements	Over the past decade, the evolution of video-sharing platforms has attracted a significant amount of investments on contextual advertising. The common contextual advertising platforms utilize the information provided by users to integrate 2D visual ads into videos. The existing platforms face many technical challenges ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	184,423
2410.16712	DENOASR: Debiasing ASRs through Selective Denoising	Automatic Speech Recognition (ASR) systems have been examined and shown to exhibit biases toward particular groups of individuals, influenced by factors such as demographic traits, accents, and speech styles. Noise can disproportionately impact speakers with certain accents, dialects, or speaking styles, leading to bia...	false	false	true	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	501,157
1404.7659	Analysis-by-Synthesis Quantization for Compressed Sensing Measurements	We consider a resource-limited scenario where a sensor that uses compressed sensing (CS) collects a low number of measurements in order to observe a sparse signal, and the measurements are subsequently quantized at a low bit-rate followed by transmission or storage. For such a scenario, we design new algorithms for sou...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	32,711
2307.04663	Increasing Flips per Second and Speed of p-Computers by Using Dilute Magnetic Semiconductors to Implement Binary Stochastic Neurons	Probabilistic computing with binary stochastic neurons (BSN) implemented with low- or zero-energy barrier nanoscale ferromagnets (LBMs) possessing in-plane magnetic anisotropy has emerged as an efficient paradigm for solving computationally hard problems. The fluctuating magnetization of an LBM at room temperature enco...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	378,480
2401.10189	Chem-FINESE: Validating Fine-Grained Few-shot Entity Extraction through Text Reconstruction	Fine-grained few-shot entity extraction in the chemical domain faces two unique challenges. First, compared with entity extraction tasks in the general domain, sentences from chemical papers usually contain more entities. Moreover, entity extraction models usually have difficulty extracting entities of long-tailed type...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	422,515
2210.01257	Testing predictions of representation cost theory with CNNs	It is widely acknowledged that trained convolutional neural networks (CNNs) have different levels of sensitivity to signals of different frequency. In particular, a number of empirical studies have documented CNNs sensitivity to low-frequency signals. In this work we show with theory and experiments that this observed ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	321,184
2308.14161	Intergrated Segmentation and Detection Models for Dentex Challenge 2023	Dental panoramic x-rays are commonly used in dental diagnosing. With the development of deep learning, auto detection of diseases from dental panoramic x-rays can help dentists to diagnose diseases more efficiently.The Dentex Challenge 2023 is a competition for automatic detection of abnormal teeth along with their enu...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	388,215
2502.03065	Scientometric Analysis of the German IR Community within TREC & CLEF	Within this study, the influence of the German Information Retrieval community on the retrieval campaigns Text Retrieval Conference (TREC) and Conference and Labs of the Evaluation Forum (CLEF) between 2000 and 2022 was analyzed based on metadata provided by OpenAlex and further metadata extracted with the GROBID frame...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	true	530,583
2501.18632	Towards Safe AI Clinicians: A Comprehensive Study on Large Language Model Jailbreaking in Healthcare	Large language models (LLMs) are increasingly utilized in healthcare applications. However, their deployment in clinical practice raises significant safety concerns, including the potential spread of harmful information. This study systematically assesses the vulnerabilities of six LLMs to three advanced black-box jail...	false	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	528,787
cs/0605038	An Unfolding-Based Semantics for Logic Programming with Aggregates	The paper presents two equivalent definitions of answer sets for logic programs with aggregates. These definitions build on the notion of unfolding of aggregates, and they are aimed at creating methodologies to translate logic programs with aggregates to normal logic programs or positive programs, whose answer set sema...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	539,439
2307.08970	A Unifying Framework for Differentially Private Sums under Continual Observation	We study the problem of maintaining a differentially private decaying sum under continual observation. We give a unifying framework and an efficient algorithm for this problem for \emph{any sufficiently smooth} function. Our algorithm is the first differentially private algorithm that does not have a multiplicative err...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	380,002
2112.01591	PLSUM: Generating PT-BR Wikipedia by Summarizing Multiple Websites	Wikipedia is an important free source of intelligible knowledge. Despite that, Brazilian Portuguese Wikipedia still lacks descriptions for many subjects. In an effort to expand the Brazilian Wikipedia, we contribute PLSum, a framework for generating wiki-like abstractive summaries from multiple descriptive websites. Th...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	269,544
2008.08791	Facial movement synergies and Action Unit detection from distal wearable Electromyography and Computer Vision	Distal facial Electromyography (EMG) can be used to detect smiles and frowns with reasonable accuracy. It capitalizes on volume conduction to detect relevant muscle activity, even when the electrodes are not placed directly on the source muscle. The main advantage of this method is to prevent occlusion and obstruction ...	true	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	192,506
2204.08187	Securing Signal-free Intersections against Strategic Jamming Attacks: A Macroscopic Approach	We consider the security-by-design of a signal-free intersection for connected and autonomous vehicles in the face of strategic jamming attacks. We use a fluid model to characterize macroscopic traffic flow through the intersection, where the saturation rate is derived from a vehicle coordination algorithm. We model ja...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	291,999
1901.00532	Adversarial Robustness May Be at Odds With Simplicity	Current techniques in machine learning are so far are unable to learn classifiers that are robust to adversarial perturbations. However, they are able to learn non-robust classifiers with very high accuracy, even in the presence of random perturbations. Towards explaining this gap, we highlight the hypothesis that $\te...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	117,796
2306.12698	Interferometric lensless imaging: rank-one projections of image frequencies with speckle illuminations	Lensless illumination single-pixel imaging with a multicore fiber (MCF) is a computational imaging technique that enables potential endoscopic observations of biological samples at cellular scale. In this work, we show that this technique is tantamount to collecting multiple symmetric rank-one projections (SROP) of an ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	375,037
2305.12169	Learning to Compose Representations of Different Encoder Layers towards Improving Compositional Generalization	Recent studies have shown that sequence-to-sequence (seq2seq) models struggle with compositional generalization (CG), i.e., the ability to systematically generalize to unseen compositions of seen components. There is mounting evidence that one of the reasons hindering CG is the representation of the encoder uppermost l...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	365,871
2306.08951	MLonMCU: TinyML Benchmarking with Fast Retargeting	While there exist many ways to deploy machine learning models on microcontrollers, it is non-trivial to choose the optimal combination of frameworks and targets for a given application. Thus, automating the end-to-end benchmarking flow is of high relevance nowadays. A tool called MLonMCU is proposed in this paper and d...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	373,616
2407.04506	Balancing Operator's Risk Averseness in Model Predictive Control of a Reservoir System	Model Predictive Control (MPC) is an optimal control strategy suited for flood control of water resources infrastructure. Despite many studies on reservoir flood control and their theoretical contribution, optimisation methodologies have not been widely applied in real-time operation due to disparities between research...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	470,587
1909.13488	Oblique Decision Trees from Derivatives of ReLU Networks	We show how neural models can be used to realize piece-wise constant functions such as decision trees. The proposed architecture, which we call locally constant networks, builds on ReLU networks that are piece-wise linear and hence their associated gradients with respect to the inputs are locally constant. We formally ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	147,443
1809.04234	Sampled in Pairs and Driven by Text: A New Graph Embedding Framework	In graphs with rich texts, incorporating textual information with structural information would benefit constructing expressive graph embeddings. Among various graph embedding models, random walk (RW)-based is one of the most popular and successful groups. However, it is challenged by two issues when applied on graphs w...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	107,512
2304.08506	When SAM Meets Medical Images: An Investigation of Segment Anything Model (SAM) on Multi-phase Liver Tumor Segmentation	Learning to segmentation without large-scale samples is an inherent capability of human. Recently, Segment Anything Model (SAM) performs the significant zero-shot image segmentation, attracting considerable attention from the computer vision community. Here, we investigate the capability of SAM for medical image analys...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	358,745
2207.09688	Intrinsic dimension estimation for discrete metrics	Real world-datasets characterized by discrete features are ubiquitous: from categorical surveys to clinical questionnaires, from unweighted networks to DNA sequences. Nevertheless, the most common unsupervised dimensional reduction methods are designed for continuous spaces, and their use for discrete spaces can lead t...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	308,987
1910.00652	Automated Weed Detection in Aerial Imagery with Context	In this paper, we demonstrate the ability to discriminate between cultivated maize plant and grass or grass-like weed image segments using the context surrounding the image segments. While convolutional neural networks have brought state of the art accuracies within object detection, errors arise when objects in differ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	147,718
2306.11759	Deep Learning Accelerator in Loop Reliability Evaluation for Autonomous Driving	The reliability of deep learning accelerators (DLAs) used in autonomous driving systems has significant impact on the system safety. However, the DLA reliability is usually evaluated with low-level metrics like mean square errors of the output which remains rather different from the high-level metrics like total distan...	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	true	374,705
2406.15797	Synergistic Deep Graph Clustering Network	Employing graph neural networks (GNNs) to learn cohesive and discriminative node representations for clustering has shown promising results in deep graph clustering. However, existing methods disregard the reciprocal relationship between representation learning and structure augmentation. This study suggests that enhan...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	466,872
1011.1519	Fuzzy Controller for Matrix Converter System to Improve its Quality of Output	In this paper, Fuzzy Logic controller is developed for ac/ac Matrix Converter. Furthermore, Total Harmonic Distortion is reduced significantly. Space Vector Algorithm is a method to improve power quality of the converter output. But its quality is limited to 86.7%.We are introduced a Cross coupled DQ axis controller to...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	8,155
2205.15223	Prompting ELECTRA: Few-Shot Learning with Discriminative Pre-Trained Models	Pre-trained masked language models successfully perform few-shot learning by formulating downstream tasks as text infilling. However, as a strong alternative in full-shot settings, discriminative pre-trained models like ELECTRA do not fit into the paradigm. In this work, we adapt prompt-based few-shot learning to ELECT...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	299,648
2112.13492	Vision Transformer for Small-Size Datasets	Recently, the Vision Transformer (ViT), which applied the transformer structure to the image classification task, has outperformed convolutional neural networks. However, the high performance of the ViT results from pre-training using a large-size dataset such as JFT-300M, and its dependence on a large dataset is inter...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	273,255
1704.01617	Part of Speech Based Term Weighting for Information Retrieval	Automatic language processing tools typically assign to terms so-called weights corresponding to the contribution of terms to information content. Traditionally, term weights are computed from lexical statistics, e.g., term frequencies. We propose a new type of term weight that is computed from part of speech (POS) n-g...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	71,295
2407.20229	Improving 2D Feature Representations by 3D-Aware Fine-Tuning	Current visual foundation models are trained purely on unstructured 2D data, limiting their understanding of 3D structure of objects and scenes. In this work, we show that fine-tuning on 3D-aware data improves the quality of emerging semantic features. We design a method to lift semantic 2D features into an efficient 3...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	477,095
1711.06983	Enhanced Group Sparse Beamforming for Green Cloud-RAN: A Random Matrix Approach	Group sparse beamforming is a general framework to minimize the network power consumption for cloud radio access networks (Cloud-RANs), which, however, suffers high computational complexity. In particular, a complex optimization problem needs to be solved to obtain the remote radio head (RRH) ordering criterion in each...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	84,896
2310.14527	Marginal Nodes Matter: Towards Structure Fairness in Graphs	In social network, a person located at the periphery region (marginal node) is likely to be treated unfairly when compared with the persons at the center. While existing fairness works on graphs mainly focus on protecting sensitive attributes (e.g., age and gender), the fairness incurred by the graph structure should a...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	401,896
2101.03164	E(3)-Equivariant Graph Neural Networks for Data-Efficient and Accurate Interatomic Potentials	This work presents Neural Equivariant Interatomic Potentials (NequIP), an E(3)-equivariant neural network approach for learning interatomic potentials from ab-initio calculations for molecular dynamics simulations. While most contemporary symmetry-aware models use invariant convolutions and only act on scalars, NequIP ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	214,835
1908.08184	Report on the First Knowledge Graph Reasoning Challenge 2018 -- Toward the eXplainable AI System	A new challenge for knowledge graph reasoning started in 2018. Deep learning has promoted the application of artificial intelligence (AI) techniques to a wide variety of social problems. Accordingly, being able to explain the reason for an AI decision is becoming important to ensure the secure and safe use of AI techni...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	142,483
1811.02234	Semantic bottleneck for computer vision tasks	This paper introduces a novel method for the representation of images that is semantic by nature, addressing the question of computation intelligibility in computer vision tasks. More specifically, our proposition is to introduce what we call a semantic bottleneck in the processing pipeline, which is a crossing point i...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	true	false	false	112,547
2209.09124	DMMGAN: Diverse Multi Motion Prediction of 3D Human Joints using Attention-Based Generative Adverserial Network	Human motion prediction is a fundamental part of many human-robot applications. Despite the recent progress in human motion prediction, most studies simplify the problem by predicting the human motion relative to a fixed joint and/or only limit their model to predict one possible future motion. While due to the complex...	false	false	false	false	false	false	true	true	false	false	false	true	false	false	false	false	false	false	318,394
1906.01921	Expectation Propagation Detector for Extra-Large Scale Massive MIMO	The order-of-magnitude increase in the dimension of antenna arrays, which forms extra-large-scale massive multiple-input-multiple-output (MIMO) systems, enables substantial improvement in spectral efficiency, energy efficiency, and spatial resolution. However, practical challenges, such as excessive computational compl...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	133,883
2208.03963	MetaGraspNet: A Large-Scale Benchmark Dataset for Scene-Aware Ambidextrous Bin Picking via Physics-based Metaverse Synthesis	Autonomous bin picking poses significant challenges to vision-driven robotic systems given the complexity of the problem, ranging from various sensor modalities, to highly entangled object layouts, to diverse item properties and gripper types. Existing methods often address the problem from one perspective. Diverse ite...	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	311,955
1204.6346	Magic Sets for Disjunctive Datalog Programs	In this paper, a new technique for the optimization of (partially) bound queries over disjunctive Datalog programs with stratified negation is presented. The technique exploits the propagation of query bindings and extends the Magic Set (MS) optimization technique. An important feature of disjunctive Datalog is nonmo...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	15,708
1602.01132	Interactive algorithms: from pool to stream	We consider interactive algorithms in the pool-based setting, and in the stream-based setting. Interactive algorithms observe suggested elements (representing actions or queries), and interactively select some of them and receive responses. Pool-based algorithms can select elements at any order, while stream-based algo...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	51,663
2304.00011	A variance reduction strategy for numerical random homogenization based on the equivalent inclusion method	Using the equivalent inclusion method (a method strongly related to the Hashin-Shtrikman variational principle) as a surrogate model, we propose a variance reduction strategy for the numerical homogenization of random composites made of inclusions (or rather inhomogeneities) embedded in a homogeneous matrix. The effici...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	355,528
2312.16478	Noisy Correspondence Learning with Self-Reinforcing Errors Mitigation	Cross-modal retrieval relies on well-matched large-scale datasets that are laborious in practice. Recently, to alleviate expensive data collection, co-occurring pairs from the Internet are automatically harvested for training. However, it inevitably includes mismatched pairs, \ie, noisy correspondences, undermining sup...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	418,400
1303.1456	A Probabilistic Algorithm for Calculating Structure: Borrowing from Simulated Annealing	We have developed a general Bayesian algorithm for determining the coordinates of points in a three-dimensional space. The algorithm takes as input a set of probabilistic constraints on the coordinates of the points, and an a priori distribution for each point location. The output is a maximum-likelihood estimate of th...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	22,671
1810.04611	Scalar MSCR Codes via the Product Matrix Construction	An $(n,k,d)$ cooperative regenerating code provides the optimal-bandwidth repair for any $t~(t\!>\!1)$ node failures in a cooperative way. In particular, an MSCR (minimum storage cooperative regenerating) code retains the same storage overhead as an $(n,k)$ MDS code. Suppose each node stores $\alpha$ symbols which indi...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	110,078
1808.08999	Harnessing Historical Corrections to build Test Collections for Named Entity Disambiguation	Matching mentions of persons to the actual persons (the name disambiguation problem) is central for several digital library applications. Scientists have been working on algorithms to create this matching for decades without finding a universal solution. One problem is that test collections for this problem are often s...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	true	106,081
2305.17192	Live American Sign Language Letter Classification with Convolutional Neural Networks	This project is centered around building a neural network that is able to recognize ASL letters in images, particularly within the scope of a live video feed. Initial testing results came up short of expectations when both the convolutional network and VGG16 transfer learning approaches failed to generalize in settings...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	true	false	false	368,452
2406.03072	Local to Global: Learning Dynamics and Effect of Initialization for Transformers	In recent years, transformer-based models have revolutionized deep learning, particularly in sequence modeling. To better understand this phenomenon, there is a growing interest in using Markov input processes to study transformers. However, our current understanding in this regard remains limited with many fundamental...	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	461,079
2307.11838	Data-Induced Interactions of Sparse Sensors	Large-dimensional empirical data in science and engineering frequently has low-rank structure and can be represented as a combination of just a few eigenmodes. Because of this structure, we can use just a few spatially localized sensor measurements to reconstruct the full state of a complex system. The quality of this ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	381,050
1810.03167	Unsupervised Neural Word Segmentation for Chinese via Segmental Language Modeling	Previous traditional approaches to unsupervised Chinese word segmentation (CWS) can be roughly classified into discriminative and generative models. The former uses the carefully designed goodness measures for candidate segmentation, while the latter focuses on finding the optimal segmentation of the highest generative...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	109,751
2006.12856	PRIPEL: Privacy-Preserving Event Log Publishing Including Contextual Information	Event logs capture the execution of business processes in terms of executed activities and their execution context. Since logs contain potentially sensitive information about the individuals involved in the process, they should be pre-processed before being published to preserve the individuals' privacy. However, exist...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	true	183,734
1703.07865	Weight Design of Distributed Approximate Newton Algorithms for Constrained Optimization	Motivated by economic dispatch and linearly-constrained resource allocation problems, this paper proposes a novel Distributed Approx-Newton algorithm that approximates the standard Newton optimization method. A main property of this distributed algorithm is that it only requires agents to exchange constant-size communi...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	true	70,467
2408.07642	Boosting Unconstrained Face Recognition with Targeted Style Adversary	While deep face recognition models have demonstrated remarkable performance, they often struggle on the inputs from domains beyond their training data. Recent attempts aim to expand the training set by relying on computationally expensive and inherently challenging image-space augmentation of image generation modules. ...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	480,671
2105.06582	Handwriting Recognition with Novelty	This paper introduces an agent-centric approach to handle novelty in the visual recognition domain of handwriting recognition (HWR). An ideal transcription agent would rival or surpass human perception, being able to recognize known and new characters in an image, and detect any stylistic changes that may occur within ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	235,167
1903.12161	Fast video object segmentation with Spatio-Temporal GANs	Learning descriptive spatio-temporal object models from data is paramount for the task of semi-supervised video object segmentation. Most existing approaches mainly rely on models that estimate the segmentation mask based on a reference mask at the first frame (aided sometimes by optical flow or the previous mask). The...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	125,657
2412.01525	Take Your Steps: Hierarchically Efficient Pulmonary Disease Screening via CT Volume Compression	Deep learning models are widely used to process Computed Tomography (CT) data in the automated screening of pulmonary diseases, significantly reducing the workload of physicians. However, the three-dimensional nature of CT volumes involves an excessive number of voxels, which significantly increases the complexity of m...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	513,153
2005.09030	Effective Learning of a GMRF Mixture Model	Learning a Gaussian Mixture Model (GMM) is hard when the number of parameters is too large given the amount of available data. As a remedy, we propose restricting the GMM to a Gaussian Markov Random Field Mixture Model (GMRF-MM), as well as a new method for estimating the latter's sparse precision (i.e., inverse covari...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	177,789
2109.12814	Investigating Non-local Features for Neural Constituency Parsing	Thanks to the strong representation power of neural encoders, neural chart-based parsers have achieved highly competitive performance by using local features. Recently, it has been shown that non-local features in CRF structures lead to improvements. In this paper, we investigate injecting non-local features into the t...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	257,426
2209.07637	Library transfer between distinct Laser-Induced Breakdown Spectroscopy systems with shared standards	The mutual incompatibility of distinct spectroscopic systems is among the most limiting factors in Laser-Induced Breakdown Spectroscopy (LIBS). The cost related to setting up a new LIBS system is increased, as its extensive calibration is required. Solving the problem would enable inter-laboratory reference measurement...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	317,824
2112.01578	Invariant Priors for Bayesian Quadrature	Bayesian quadrature (BQ) is a model-based numerical integration method that is able to increase sample efficiency by encoding and leveraging known structure of the integration task at hand. In this paper, we explore priors that encode invariance of the integrand under a set of bijective transformations in the input dom...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	269,536
2402.14800	Not All Experts are Equal: Efficient Expert Pruning and Skipping for Mixture-of-Experts Large Language Models	A pivotal advancement in the progress of large language models (LLMs) is the emergence of the Mixture-of-Experts (MoE) LLMs. Compared to traditional LLMs, MoE LLMs can achieve higher performance with fewer parameters, but it is still hard to deploy them due to their immense parameter sizes. Different from previous weig...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	431,839
2303.09875	A Dynamic Multi-Scale Voxel Flow Network for Video Prediction	The performance of video prediction has been greatly boosted by advanced deep neural networks. However, most of the current methods suffer from large model sizes and require extra inputs, e.g., semantic/depth maps, for promising performance. For efficiency consideration, in this paper, we propose a Dynamic Multi-scale ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	352,228
2309.13136	Contextual Emotion Estimation from Image Captions	Emotion estimation in images is a challenging task, typically using computer vision methods to directly estimate people's emotions using face, body pose and contextual cues. In this paper, we explore whether Large Language Models (LLMs) can support the contextual emotion estimation task, by first captioning images, the...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	394,070
2103.09904	Fused Deep Features Based Classification Framework for COVID-19 Classification with Optimized MLP	The new type of Coronavirus disease called COVID-19 continues to spread quite rapidly. Although it shows some specific symptoms, this disease, which can show different symptoms in almost every individual, has caused hundreds of thousands of patients to die. Although healthcare professionals work hard to prevent further...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	225,281
1803.08460	Towards Universal Representation for Unseen Action Recognition	Unseen Action Recognition (UAR) aims to recognise novel action categories without training examples. While previous methods focus on inner-dataset seen/unseen splits, this paper proposes a pipeline using a large-scale training source to achieve a Universal Representation (UR) that can generalise to a more realistic Cro...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	true	93,264
2312.12223	Self-Supervised Detection of Perfect and Partial Input-Dependent Symmetries	Group equivariance can overly constrain models if the symmetries in the group differ from those observed in data. While common methods address this by determining the appropriate level of symmetry at the dataset level, they are limited to supervised settings and ignore scenarios in which multiple levels of symmetry co-...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	416,880
2408.12333	GRATR: Zero-Shot Evidence Graph Retrieval-Augmented Trustworthiness Reasoning	Trustworthiness reasoning aims to enable agents in multiplayer games with incomplete information to identify potential allies and adversaries, thereby enhancing decision-making. In this paper, we introduce the graph retrieval-augmented trustworthiness reasoning (GRATR) framework, which retrieves observable evidence fro...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	482,690
1612.03103	A Systematic and Semi-Automatic Safety-Based Test Case Generation Approach Based on Systems-Theoretic Process Analysis	Software safety is a crucial aspect during the development of modern safety-critical systems. Software is becoming responsible for most of the critical functions of systems. Therefore, the software components in the systems need to be tested extensively against their safety requirements to ensure a high level of system...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	true	65,318
2208.12878	DETERRENT: Detecting Trojans using Reinforcement Learning	Insertion of hardware Trojans (HTs) in integrated circuits is a pernicious threat. Since HTs are activated under rare trigger conditions, detecting them using random logic simulations is infeasible. In this work, we design a reinforcement learning (RL) agent that circumvents the exponential search space and returns a m...	false	false	false	false	true	false	true	false	false	false	false	false	true	false	false	false	false	false	314,877
2101.01100	Wasserstein barycenters are NP-hard to compute	Computing Wasserstein barycenters (a.k.a. Optimal Transport barycenters) is a fundamental problem in geometry which has recently attracted considerable attention due to many applications in data science. While there exist polynomial-time algorithms in any fixed dimension, all known running times suffer exponentially in...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	214,283
2008.10749	Breaking the Communities: Characterizing community changing users using text mining and graph machine learning on Twitter	Even though the Internet and social media have increased the amount of news and information people can consume, most users are only exposed to content that reinforces their positions and isolates them from other ideological communities. This environment has real consequences with great impact on our lives like severe p...	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	false	false	193,075
1501.04298	A Hybrid Approach to Web Service Recommendation Based on QoS-Aware Rating and Ranking	As the number of Web services with the same or similar functions increases steadily on the Internet, nowadays more and more service consumers pay great attention to the non-functional properties of Web services, also known as quality of service (QoS), when finding and selecting appropriate Web services. For most of the...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	true	39,353
2403.13809	Predicting Confinement Effect of Carbon Fiber Reinforced Polymers on Strength of Concrete using Metaheuristics-based Artificial Neural Networks	This article deals with the study of predicting the confinement effect of carbon fiber reinforced polymers (CFRPs) on concrete cylinder strength using metaheuristics-based artificial neural networks. A detailed database of 708 CFRP confined concrete cylinders is developed from previously published research with informa...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	false	false	439,792
2404.11768	Tensor-Networks-based Learning of Probabilistic Cellular Automata Dynamics	Algorithms developed to solve many-body quantum problems, like tensor networks, can turn into powerful quantum-inspired tools to tackle problems in the classical domain. In this work, we focus on matrix product operators, a prominent numerical technique to study many-body quantum systems, especially in one dimension. I...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	447,600
1901.03637	Subcarrier Pairing as Channel Gain Tailoring: Joint Resource Allocation for Relay-Assisted Secure OFDMA with Untrusted Users	Joint resource allocation involving optimization of subcarrier allocation, subcarrier pairing (SCP), and power allocation in a cooperative secure orthogonal frequency division multiple access (OFDMA) communication system with untrusted users is considered. Both amplify and forward (AF), and decode and forward (DF) mode...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	118,451
1609.05160	Energy-Efficient Resource Allocation for SWIPT in Multiple Access Channels	In this paper, we study optimal resource allocation strategies for simultaneous information and power transfer (SWIPT) focusing on the system energy efficiency. We consider two-user multiple access channels in which energy harvesting (EH) and information decoding (ID) nodes are spatially separated. We formulate optimiz...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	61,087
2212.05104	Max filtering with reflection groups	Given a finite-dimensional real inner product space V and a finite subgroup G of linear isometries, max filtering affords a bilipschitz Euclidean embedding of the orbit space V/G. We identify the max filtering maps of minimum distortion in the setting where G is a reflection group. Our analysis involves an interplay be...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	335,674
2002.11833	Policy Evaluation Networks	Many reinforcement learning algorithms use value functions to guide the search for better policies. These methods estimate the value of a single policy while generalizing across many states. The core idea of this paper is to flip this convention and estimate the value of many policies, for a single set of states. This ...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	165,838
cs/0305017	Cluster-based Specification Techniques in Dempster-Shafer Theory	When reasoning with uncertainty there are many situations where evidences are not only uncertain but their propositions may also be weakly specified in the sense that it may not be certain to which event a proposition is referring. It is then crucial not to combine such evidences in the mistaken belief that they are re...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	false	false	537,833
2104.02057	An Empirical Study of Training Self-Supervised Vision Transformers	This paper does not describe a novel method. Instead, it studies a straightforward, incremental, yet must-know baseline given the recent progress in computer vision: self-supervised learning for Vision Transformers (ViT). While the training recipes for standard convolutional networks have been highly mature and robust,...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	228,576
1302.2645	Geometrical complexity of data approximators	There are many methods developed to approximate a cloud of vectors embedded in high-dimensional space by simpler objects: starting from principal points and linear manifolds to self-organizing maps, neural gas, elastic maps, various types of principal curves and principal trees, and so on. For each type of approximator...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	21,959
2405.01198	Towards Interpretable Reinforcement Learning with Constrained Normalizing Flow Policies	Reinforcement learning policies are typically represented by black-box neural networks, which are non-interpretable and not well-suited for safety-critical domains. To address both of these issues, we propose constrained normalizing flow policies as interpretable and safe-by-construction policy models. We achieve safet...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	451,269
2410.12470	Learning to Predict Usage Options of Product Reviews with LLM-Generated Labels	Annotating large datasets can be challenging. However, crowd-sourcing is often expensive and can lack quality, especially for non-trivial tasks. We propose a method of using LLMs as few-shot learners for annotating data in a complex natural language task where we learn a standalone model to predict usage options for pr...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	499,046
2310.16355	RedCoast: A Lightweight Tool to Automate Distributed Training of LLMs on Any GPU/TPUs	The recent progress of AI can be largely attributed to large language models (LLMs). However, their escalating memory requirements introduce challenges for machine learning (ML) researchers and engineers. Addressing this requires developers to partition a large model to distribute it across multiple GPUs or TPUs. This ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	402,690
1904.08035	Residual or Gate? Towards Deeper Graph Neural Networks for Inductive Graph Representation Learning	In this paper, we study the problem of node representation learning with graph neural networks. We present a graph neural network class named recurrent graph neural network (RGNN), that address the shortcomings of prior methods. By using recurrent units to capture the long-term dependency across layers, our methods can...	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	false	false	127,948
1907.03386	Further results on some classes of permutation polynomials over finite fields	Let $\mathbb{F}_q$ denote the finite fields with $q$ elements. The permutation behavior of several classes of infinite families of permutation polynomials over finite fields have been studied in recent years. In this paper, we continue with their studies, and get some further results about the permutation properties of...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	137,848
2405.09934	Detecting Domain Shift in Multiple Instance Learning for Digital Pathology Using Fr\'echet Domain Distance	Multiple-instance learning (MIL) is an attractive approach for digital pathology applications as it reduces the costs related to data collection and labelling. However, it is not clear how sensitive MIL is to clinically realistic domain shifts, i.e., differences in data distribution that could negatively affect perform...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	454,589
2308.11761	KnowledGPT: Enhancing Large Language Models with Retrieval and Storage Access on Knowledge Bases	Large language models (LLMs) have demonstrated impressive impact in the field of natural language processing, but they still struggle with several issues regarding, such as completeness, timeliness, faithfulness and adaptability. While recent efforts have focuses on connecting LLMs with external knowledge sources, the ...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	387,261
2312.08079	Extending Whisper with prompt tuning to target-speaker ASR	Target-speaker automatic speech recognition (ASR) aims to transcribe the desired speech of a target speaker from multi-talker overlapped utterances. Most of the existing target-speaker ASR (TS-ASR) methods involve either training from scratch or fully fine-tuning a pre-trained model, leading to significant training cos...	false	false	true	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	415,192

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