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
2305.12927	Exploring Speaker-Related Information in Spoken Language Understanding for Better Speaker Diarization	Speaker diarization(SD) is a classic task in speech processing and is crucial in multi-party scenarios such as meetings and conversations. Current mainstream speaker diarization approaches consider acoustic information only, which result in performance degradation when encountering adverse acoustic conditions. In this ...	false	false	true	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	366,257
1906.00562	Learning to Self-Train for Semi-Supervised Few-Shot Classification	Few-shot classification (FSC) is challenging due to the scarcity of labeled training data (e.g. only one labeled data point per class). Meta-learning has shown to achieve promising results by learning to initialize a classification model for FSC. In this paper we propose a novel semi-supervised meta-learning method cal...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	133,434
2304.04452	Neural Residual Radiance Fields for Streamably Free-Viewpoint Videos	The success of the Neural Radiance Fields (NeRFs) for modeling and free-view rendering static objects has inspired numerous attempts on dynamic scenes. Current techniques that utilize neural rendering for facilitating free-view videos (FVVs) are restricted to either offline rendering or are capable of processing only b...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	357,225
2012.14766	Graph-based non-linear least squares optimization for visual place recognition in changing environments	Visual place recognition is an important subproblem of mobile robot localization. Since it is a special case of image retrieval, the basic source of information is the pairwise similarity of image descriptors. However, the embedding of the image retrieval problem in this robotic task provides additional structure that ...	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	213,602
2106.02974	Enhancing Taxonomy Completion with Concept Generation via Fusing Relational Representations	Automatic construction of a taxonomy supports many applications in e-commerce, web search, and question answering. Existing taxonomy expansion or completion methods assume that new concepts have been accurately extracted and their embedding vectors learned from the text corpus. However, one critical and fundamental cha...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	239,111
2110.05517	Learnability of the output distributions of local quantum circuits	There is currently a large interest in understanding the potential advantages quantum devices can offer for probabilistic modelling. In this work we investigate, within two different oracle models, the probably approximately correct (PAC) learnability of quantum circuit Born machines, i.e., the output distributions of ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	260,297
2406.06820	Adapters Strike Back	Adapters provide an efficient and lightweight mechanism for adapting trained transformer models to a variety of different tasks. However, they have often been found to be outperformed by other adaptation mechanisms, including low-rank adaptation. In this paper, we provide an in-depth study of adapters, their internal s...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	462,778
2007.03227	Extracting the fundamental diagram from aerial footage	Efficient traffic monitoring is playing a fundamental role in successfully tackling congestion in transportation networks. Congestion is strongly correlated with two measurable characteristics, the demand and the network density that impact the overall system behavior. At large, this system behavior is characterized th...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	185,997
2311.16501	Context-Aware Indoor Point Cloud Object Generation through User Instructions	Indoor scene modification has emerged as a prominent area within computer vision, particularly for its applications in Augmented Reality (AR) and Virtual Reality (VR). Traditional methods often rely on pre-existing object databases and predetermined object positions, limiting their flexibility and adaptability to new s...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	410,926
1905.13654	Exact Convergence Rates of the Neural Tangent Kernel in the Large Depth Limit	Recent work by Jacot et al. (2018) has shown that training a neural network using gradient descent in parameter space is related to kernel gradient descent in function space with respect to the Neural Tangent Kernel (NTK). Lee et al. (2019) built on this result by establishing that the output of a neural network traine...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	133,204
2302.08284	ClaPIM: Scalable Sequence CLAssification using Processing-In-Memory	DNA sequence classification is a fundamental task in computational biology with vast implications for applications such as disease prevention and drug design. Therefore, fast high-quality sequence classifiers are significantly important. This paper introduces ClaPIM, a scalable DNA sequence classification architecture ...	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	346,008
1706.06942	Graphcut Texture Synthesis for Single-Image Superresolution	Texture synthesis has proven successful at imitating a wide variety of textures. Adding additional constraints (in the form of a low-resolution version of the texture to be synthesized) makes it possible to use texture synthesis methods for texture superresolution.	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	75,767
1810.09672	Capacity Degradation with Modeling Hardware Impairment in Large Intelligent Surface	In this paper, we consider capacity degradations stemming from potential hardware impairments (HWI) of newly proposed Large Intelligent Surface (LIS) systems. Without HWI, the utility of surface-area (the first-order derivative of the capacity with respect to surface-area) is shown to be proportional to the inverse of ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	111,106
2210.09090	Modeling the Lighting in Scenes as Style for Auto White-Balance Correction	Style may refer to different concepts (e.g. painting style, hairstyle, texture, color, filter, etc.) depending on how the feature space is formed. In this work, we propose a novel idea of interpreting the lighting in the single- and multi-illuminant scenes as the concept of style. To verify this idea, we introduce an e...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	324,420
2301.11305	DetectGPT: Zero-Shot Machine-Generated Text Detection using Probability Curvature	The increasing fluency and widespread usage of large language models (LLMs) highlight the desirability of corresponding tools aiding detection of LLM-generated text. In this paper, we identify a property of the structure of an LLM's probability function that is useful for such detection. Specifically, we demonstrate th...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	342,084
2106.10129	Towards Robotic Laboratory Automation Plug & Play: The "LAPP" Framework	Increasing the level of automation in pharmaceutical laboratories and production facilities plays a crucial role in delivering medicine to patients. However, the particular requirements of this field make it challenging to adapt cutting-edge technologies present in other industries. This article provides an overview of...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	241,907
2411.13607	VioPose: Violin Performance 4D Pose Estimation by Hierarchical Audiovisual Inference	Musicians delicately control their bodies to generate music. Sometimes, their motions are too subtle to be captured by the human eye. To analyze how they move to produce the music, we need to estimate precise 4D human pose (3D pose over time). However, current state-of-the-art (SoTA) visual pose estimation algorithms s...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	509,856
2308.00180	General Anomaly Detection of Underwater Gliders Validated by Large-scale Deployment Datasets	Underwater gliders have been widely used in oceanography for a range of applications. However, unpredictable events like shark strikes or remora attachments can lead to abnormal glider behavior or even loss of the instrument. This paper employs an anomaly detection algorithm to assess operational conditions of underwat...	false	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	382,843
2202.13295	Efficient Attribute Unlearning: Towards Selective Removal of Input Attributes from Feature Representations	Recently, the enactment of privacy regulations has promoted the rise of the machine unlearning paradigm. Existing studies of machine unlearning mainly focus on sample-wise unlearning, such that a learnt model will not expose user's privacy at the sample level. Yet we argue that such ability of selective removal should ...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	282,551
2103.11181	Adaptive deep density approximation for Fokker-Planck equations	In this paper we present an adaptive deep density approximation strategy based on KRnet (ADDA-KR) for solving the steady-state Fokker-Planck (F-P) equations. F-P equations are usually high-dimensional and defined on an unbounded domain, which limits the application of traditional grid based numerical methods. With the ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	225,703
2202.12348	Bayesian Deep Learning for Graphs	The adaptive processing of structured data is a long-standing research topic in machine learning that investigates how to automatically learn a mapping from a structured input to outputs of various nature. Recently, there has been an increasing interest in the adaptive processing of graphs, which led to the development...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	282,195
2004.10341	DRMap: A Generic DRAM Data Mapping Policy for Energy-Efficient Processing of Convolutional Neural Networks	Many convolutional neural network (CNN) accelerators face performance- and energy-efficiency challenges which are crucial for embedded implementations, due to high DRAM access latency and energy. Recently, some DRAM architectures have been proposed to exploit subarray-level parallelism for decreasing the access latency...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	173,610
2305.19787	DeepMerge: Deep-Learning-Based Region-Merging for Image Segmentation	Image segmentation aims to partition an image according to the objects in the scene and is a fundamental step in analysing very high spatial-resolution (VHR) remote sensing imagery. Current methods struggle to effectively consider land objects with diverse shapes and sizes. Additionally, the determination of segmentati...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	369,689
1809.07006	Using Eigencentrality to Estimate Joint, Conditional and Marginal Probabilities from Mixed-Variable Data: Method and Applications	The ability to estimate joint, conditional and marginal probability distributions over some set of variables is of great utility for many common machine learning tasks. However, estimating these distributions can be challenging, particularly in the case of data containing a mix of discrete and continuous variables. Thi...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	108,186
1907.12416	Quadruply Stochastic Gradients for Large Scale Nonlinear Semi-Supervised AUC Optimization	Semi-supervised learning is pervasive in real-world applications, where only a few labeled data are available and large amounts of instances remain unlabeled. Since AUC is an important model evaluation metric in classification, directly optimizing AUC in semi-supervised learning scenario has drawn much attention in the...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	140,123
1312.7056	Development of Display Ads Retrieval System to Match Publisher's Contents	The technological transformation and automation of digital content delivery has revolutionized the media industry. Advertising landscape is gradually shifting its traditional media forms to the emergent of Internet advertising. In this paper, the types of internet advertising to be discussed on are contextual and spons...	false	false	false	false	false	true	false	false	false	false	false	false	false	true	false	false	false	false	29,448
2202.12752	Synthesizing Photorealistic Images with Deep Generative Learning	The goal of this thesis is to present my research contributions towards solving various visual synthesis and generation tasks, comprising image translation, image completion, and completed scene decomposition. This thesis consists of five pieces of work, each of which presents a new learning-based approach for synthesi...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	282,350
2103.08036	Power Control in Spectrum Sharing Systems with Almost-Zero Inter-System Signaling Overhead	Power allocation in spectrum sharing systems is challenging due to excessive interference that the secondary system could impose on the primary system. Therefore, an interference threshold constraint is considered to regulate the secondary system's activity. However, the primary receivers should measure the interferenc...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	224,774
2404.12819	Unveiling the Ambiguity in Neural Inverse Rendering: A Parameter Compensation Analysis	Inverse rendering aims to reconstruct the scene properties of objects solely from multiview images. However, it is an ill-posed problem prone to producing ambiguous estimations deviating from physically accurate representations. In this paper, we utilize Neural Microfacet Fields (NMF), a state-of-the-art neural inverse...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	448,047
2404.12174	Claim Check-Worthiness Detection: How Well do LLMs Grasp Annotation Guidelines?	The increasing threat of disinformation calls for automating parts of the fact-checking pipeline. Identifying text segments requiring fact-checking is known as claim detection (CD) and claim check-worthiness detection (CW), the latter incorporating complex domain-specific criteria of worthiness and often framed as a ra...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	447,772
1612.05340	Automatic Labelling of Topics with Neural Embeddings	Topics generated by topic models are typically represented as list of terms. To reduce the cognitive overhead of interpreting these topics for end-users, we propose labelling a topic with a succinct phrase that summarises its theme or idea. Using Wikipedia document titles as label candidates, we compute neural embeddin...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	65,666
2203.08396	Towards Formalizing HRI Data Collection Processes	Within the human-robot interaction (HRI) community, many researchers have focused on the careful design of human-subjects studies. However, other parts of the community, e.g., the technical advances community, also need to do human-subjects studies to collect data to train their models, in ways that require user studie...	true	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	false	285,770
2408.12013	Detection of Under-represented Samples Using Dynamic Batch Training for Brain Tumor Segmentation from MR Images	Brain tumors in magnetic resonance imaging (MR) are difficult, time-consuming, and prone to human error. These challenges can be resolved by developing automatic brain tumor segmentation methods from MR images. Various deep-learning models based on the U-Net have been proposed for the task. These deep-learning models a...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	482,546
2409.00206	RING#: PR-by-PE Global Localization with Roto-translation Equivariant Gram Learning	Global localization using onboard perception sensors, such as cameras and LiDARs, is crucial in autonomous driving and robotics applications when GPS signals are unreliable. Most approaches achieve global localization by sequential place recognition (PR) and pose estimation (PE). Some methods train separate models for ...	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	484,823
1912.00402	Bayesian Optimization Approach for Analog Circuit Synthesis Using Neural Network	Bayesian optimization with Gaussian process as surrogate model has been successfully applied to analog circuit synthesis. In the traditional Gaussian process regression model, the kernel functions are defined explicitly. The computational complexity of training is O(N 3 ), and the computation complexity of prediction i...	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	155,759
2110.11338	VLDeformer: Vision-Language Decomposed Transformer for Fast Cross-Modal Retrieval	Cross-model retrieval has emerged as one of the most important upgrades for text-only search engines (SE). Recently, with powerful representation for pairwise text-image inputs via early interaction, the accuracy of vision-language (VL) transformers has outperformed existing methods for text-image retrieval. However, w...	false	false	false	false	false	true	false	false	true	false	false	true	false	false	false	false	false	false	262,450
0901.4830	On the Relationship Between the Multi-antenna Secrecy Communications and Cognitive Radio Communications	This paper studies the capacity of the multi-antenna or multiple-input multiple-output (MIMO) secrecy channels with multiple eavesdroppers having single/multiple antennas. It is known that the MIMO secrecy capacity is achievable with the optimal transmit covariance matrix that maximizes the minimum difference between t...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	3,082
2302.02752	Baseline Method for the Sport Task of MediaEval 2022 with 3D CNNs using Attention Mechanisms	This paper presents the baseline method proposed for the Sports Video task part of the MediaEval 2022 benchmark. This task proposes two subtasks: stroke classification from trimmed videos, and stroke detection from untrimmed videos. This baseline addresses both subtasks. We propose two types of 3D-CNN architectures to ...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	true	344,103
2212.05056	Testing Human Ability To Detect Deepfake Images of Human Faces	Deepfakes are computationally-created entities that falsely represent reality. They can take image, video, and audio modalities, and pose a threat to many areas of systems and societies, comprising a topic of interest to various aspects of cybersecurity and cybersafety. In 2020 a workshop consulting AI experts from aca...	true	false	false	false	false	false	false	false	false	false	false	true	true	true	false	false	false	false	335,664
2312.14464	Adaptive Differential Evolution with Diversification: Addressing Optimization Challenges	The existing variants of the Differential Evolution (DE) algorithm come with certain limitations, such as poor local search and susceptibility to premature convergence. This study introduces Adaptive Differential Evolution with Diversification (ADED), a method that dynamically modifies the neighborhood structure by eva...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	true	417,647
2109.01397	Occlusion-Invariant Rotation-Equivariant Semi-Supervised Depth Based Cross-View Gait Pose Estimation	Accurate estimation of three-dimensional human skeletons from depth images can provide important metrics for healthcare applications, especially for biomechanical gait analysis. However, there exist inherent problems associated with depth images captured from a single view. The collected data is greatly affected by occ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	253,426
2304.11732	Quantile Extreme Gradient Boosting for Uncertainty Quantification	As the availability, size and complexity of data have increased in recent years, machine learning (ML) techniques have become popular for modeling. Predictions resulting from applying ML models are often used for inference, decision-making, and downstream applications. A crucial yet often overlooked aspect of ML is unc...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	359,941
2007.15182	Visual Analysis of Discrimination in Machine Learning	The growing use of automated decision-making in critical applications, such as crime prediction and college admission, has raised questions about fairness in machine learning. How can we decide whether different treatments are reasonable or discriminatory? In this paper, we investigate discrimination in machine learnin...	true	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	189,594
2109.02914	Scale-invariant representation of machine learning	The success of machine learning has resulted from its structured representation of data. Similar data have close internal representations as compressed codes for classification or emerged labels for clustering. We observe that the frequency of internal codes or labels follows power laws in both supervised and unsupervi...	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	253,896
1206.4604	Learning the Experts for Online Sequence Prediction	Online sequence prediction is the problem of predicting the next element of a sequence given previous elements. This problem has been extensively studied in the context of individual sequence prediction, where no prior assumptions are made on the origin of the sequence. Individual sequence prediction algorithms work qu...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	16,656
2107.09362	Protecting Semantic Segmentation Models by Using Block-wise Image Encryption with Secret Key from Unauthorized Access	Since production-level trained deep neural networks (DNNs) are of a great business value, protecting such DNN models against copyright infringement and unauthorized access is in a rising demand. However, conventional model protection methods focused only the image classification task, and these protection methods were ...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	247,011
2106.07849	Simon Says: Evaluating and Mitigating Bias in Pruned Neural Networks with Knowledge Distillation	In recent years the ubiquitous deployment of AI has posed great concerns in regards to algorithmic bias, discrimination, and fairness. Compared to traditional forms of bias or discrimination caused by humans, algorithmic bias generated by AI is more abstract and unintuitive therefore more difficult to explain and mitig...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	241,075
2105.13204	Pose2Drone: A Skeleton-Pose-based Framework for Human-Drone Interaction	Drones have become a common tool, which is utilized in many tasks such as aerial photography, surveillance, and delivery. However, operating a drone requires more and more interaction with the user. A natural and safe method for Human-Drone Interaction (HDI) is using gestures. In this paper, we introduce an HDI framewo...	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	237,233
2402.03163	Linguistic features for sentence difficulty prediction in ABSA	One of the challenges of natural language understanding is to deal with the subjectivity of sentences, which may express opinions and emotions that add layers of complexity and nuance. Sentiment analysis is a field that aims to extract and analyze these subjective elements from text, and it can be applied at different ...	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	426,880
2104.00185	Less is More: Accelerating Faster Neural Networks Straight from JPEG	Most image data available are often stored in a compressed format, from which JPEG is the most widespread. To feed this data on a convolutional neural network (CNN), a preliminary decoding process is required to obtain RGB pixels, demanding a high computational load and memory usage. For this reason, the design of CNNs...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	227,903
1404.2655	Open problem: Tightness of maximum likelihood semidefinite relaxations	We have observed an interesting, yet unexplained, phenomenon: Semidefinite programming (SDP) based relaxations of maximum likelihood estimators (MLE) tend to be tight in recovery problems with noisy data, even when MLE cannot exactly recover the ground truth. Several results establish tightness of SDP based relaxations...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	32,226
2203.10432	Interpretability of Fine-grained Classification of Sadness and Depression	While sadness is a human emotion that people experience at certain times throughout their lives, inflicting them with emotional disappointment and pain, depression is a longer term mental illness which impairs social, occupational, and other vital regions of functioning making it a much more serious issue and needs to ...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	286,534
2210.09739	Real-Time Multi-Modal Semantic Fusion on Unmanned Aerial Vehicles with Label Propagation for Cross-Domain Adaptation	Unmanned aerial vehicles (UAVs) equipped with multiple complementary sensors have tremendous potential for fast autonomous or remote-controlled semantic scene analysis, e.g., for disaster examination. Here, we propose a UAV system for real-time semantic inference and fusion of multiple sensor modalities. Semantic segme...	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	324,647
1909.00743	Blended Integrated Open Data: dados abertos p\'ublicos integrados	While several public institutions provide its data openly, the effort required to access, integrate and query this data is too high, reducing the amount of possible dataset users. The Blended Integrated Open Data (BIOD) project has as objective to ease the access to public Open Data. It integrates and makes available m...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	true	143,709
1601.07140	COCO-Text: Dataset and Benchmark for Text Detection and Recognition in Natural Images	This paper describes the COCO-Text dataset. In recent years large-scale datasets like SUN and Imagenet drove the advancement of scene understanding and object recognition. The goal of COCO-Text is to advance state-of-the-art in text detection and recognition in natural images. The dataset is based on the MS COCO datase...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	51,385
1308.6659	Spatio-spectral Formulation and Design of Spatially-Varying Filters for Signal Estimation on the 2-Sphere	In this paper, we present an optimal filter for the enhancement or estimation of signals on the 2-sphere corrupted by noise, when both the signal and noise are realizations of anisotropic processes on the 2-sphere. The estimation of such a signal in the spatial or spectral domain separately can be shown to be inadequat...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	26,732
2305.15937	Visually grounded few-shot word acquisition with fewer shots	We propose a visually grounded speech model that acquires new words and their visual depictions from just a few word-image example pairs. Given a set of test images and a spoken query, we ask the model which image depicts the query word. Previous work has simplified this problem by either using an artificial setting wi...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	367,860
2209.13435	Scaling Laws For Deep Learning Based Image Reconstruction	Deep neural networks trained end-to-end to map a measurement of a (noisy) image to a clean image perform excellent for a variety of linear inverse problems. Current methods are only trained on a few hundreds or thousands of images as opposed to the millions of examples deep networks are trained on in other domains. In ...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	319,895
2012.02676	Community detection using fast low-cardinality semidefinite programming	Modularity maximization has been a fundamental tool for understanding the community structure of a network, but the underlying optimization problem is nonconvex and NP-hard to solve. State-of-the-art algorithms like the Louvain or Leiden methods focus on different heuristics to help escape local optima, but they still ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	209,846
2208.03217	Distance-based detection of out-of-distribution silent failures for Covid-19 lung lesion segmentation	Automatic segmentation of ground glass opacities and consolidations in chest computer tomography (CT) scans can potentially ease the burden of radiologists during times of high resource utilisation. However, deep learning models are not trusted in the clinical routine due to failing silently on out-of-distribution (OOD...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	311,715
1201.1588	Upper Bound on the Capacity of Gaussian Channels with Noisy Feedback	We consider an additive Gaussian channel with additive Gaussian noise feedback. We derive an upper bound on the n-block capacity (defined by Cover [1]). It is shown that this upper bound can be obtained by solving a convex optimization problem. With stationarity assumptions on Gaussian noise processes, we characterize ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	13,719
1004.4949	Reed Muller Sensing Matrices and the LASSO	We construct two families of deterministic sensing matrices where the columns are obtained by exponentiating codewords in the quaternary Delsarte-Goethals code $DG(m,r)$. This method of construction results in sensing matrices with low coherence and spectral norm. The first family, which we call Delsarte-Goethals frame...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	6,313
1801.10087	The Benefits of Population Diversity in Evolutionary Algorithms: A Survey of Rigorous Runtime Analyses	Population diversity is crucial in evolutionary algorithms to enable global exploration and to avoid poor performance due to premature convergence. This book chapter reviews runtime analyses that have shown benefits of population diversity, either through explicit diversity mechanisms or through naturally emerging dive...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	89,225
2010.15153	On the Optimality and Convergence Properties of the Iterative Learning Model Predictive Controller	In this technical note we analyse the performance improvement and optimality properties of the Learning Model Predictive Control (LMPC) strategy for linear deterministic systems. The LMPC framework is a policy iteration scheme where closed-loop trajectories are used to update the control policy for the next execution o...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	203,677
2206.11142	Discussion of `Multiscale Fisher's Independence Test for Multivariate Dependence'	We discuss how MultiFIT, the Multiscale Fisher's Independence Test for Multivariate Dependence proposed by Gorsky and Ma (2022), compares to existing linear-time kernel tests based on the Hilbert-Schmidt independence criterion (HSIC). We highlight the fact that the levels of the kernel tests at any finite sample size c...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	304,174
2410.15387	Deep Class-guided Hashing for Multi-label Cross-modal Retrieval	Deep hashing, due to its low cost and efficient retrieval advantages, is widely valued in cross-modal retrieval. However, existing cross-modal hashing methods either explore the relationships between data points, which inevitably leads to intra-class dispersion, or explore the relationships between data points and cate...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	500,514
1811.03194	AdVersarial: Perceptual Ad Blocking meets Adversarial Machine Learning	Perceptual ad-blocking is a novel approach that detects online advertisements based on their visual content. Compared to traditional filter lists, the use of perceptual signals is believed to be less prone to an arms race with web publishers and ad networks. We demonstrate that this may not be the case. We describe att...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	112,775
2301.02962	Bayesian Graphical Entity Resolution Using Exchangeable Random Partition Priors	Entity resolution (record linkage or deduplication) is the process of identifying and linking duplicate records in databases. In this paper, we propose a Bayesian graphical approach for entity resolution that links records to latent entities, where the prior representation on the linkage structure is exchangeable. Firs...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	339,643
2106.10836	Active Learning for Deep Neural Networks on Edge Devices	When dealing with deep neural network (DNN) applications on edge devices, continuously updating the model is important. Although updating a model with real incoming data is ideal, using all of them is not always feasible due to limits, such as labeling and communication costs. Thus, it is necessary to filter and select...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	true	242,177
2306.08359	Hierarchical Task Network Planning for Facilitating Cooperative Multi-Agent Reinforcement Learning	Exploring sparse reward multi-agent reinforcement learning (MARL) environments with traps in a collaborative manner is a complex task. Agents typically fail to reach the goal state and fall into traps, which affects the overall performance of the system. To overcome this issue, we present SOMARL, a framework that uses ...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	373,387
2501.07532	Investigating Large Language Models in Inferring Personality Traits from User Conversations	Large Language Models (LLMs) are demonstrating remarkable human like capabilities across diverse domains, including psychological assessment. This study evaluates whether LLMs, specifically GPT-4o and GPT-4o mini, can infer Big Five personality traits and generate Big Five Inventory-10 (BFI-10) item scores from user co...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	524,427
2206.02609	Real-World Image Super-Resolution by Exclusionary Dual-Learning	Real-world image super-resolution is a practical image restoration problem that aims to obtain high-quality images from in-the-wild input, has recently received considerable attention with regard to its tremendous application potentials. Although deep learning-based methods have achieved promising restoration quality o...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	300,959
1906.01199	Exploring Phoneme-Level Speech Representations for End-to-End Speech Translation	Previous work on end-to-end translation from speech has primarily used frame-level features as speech representations, which creates longer, sparser sequences than text. We show that a naive method to create compressed phoneme-like speech representations is far more effective and efficient for translation than traditio...	false	false	true	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	133,634
2405.09137	On Convergence of the Iteratively Preconditioned Gradient-Descent (IPG) Observer	This paper considers the observer design problem for discrete-time nonlinear dynamical systems with sampled measurement data. Earlier, the recently proposed Iteratively Preconditioned Gradient-Descent (IPG) observer, a Newton-type observer, has been empirically shown to have improved robustness against measurement nois...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	454,312
2411.01238	Efficient Sparse Training with Structured Dropout	Dropout is a common regularisation technique in deep learning that improves generalisation. Even though it introduces sparsity and thus potential for higher throughput, it usually cannot bring speed-ups on GPUs due to its unstructured nature. In this project, I experiment with SparseDrop, a structured, hardware-friendl...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	504,976
1907.06498	An Efficient Framework for Visible-Infrared Cross Modality Person Re-Identification	Visible-infrared cross-modality person re-identification (VI-ReId) is an essential task for video surveillance in poorly illuminated or dark environments. Despite many recent studies on person re-identification in the visible domain (ReId), there are few studies dealing specifically with VI-ReId. Besides challenges tha...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	138,638
1301.1218	Finding the True Frequent Itemsets	Frequent Itemsets (FIs) mining is a fundamental primitive in data mining. It requires to identify all itemsets appearing in at least a fraction $\theta$ of a transactional dataset $\mathcal{D}$. Often though, the ultimate goal of mining $\mathcal{D}$ is not an analysis of the dataset \emph{per se}, but the understandin...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	true	true	20,835
1911.09891	Performance Effectiveness of Multimedia Information Search Using the Epsilon-Greedy Algorithm	In the search and retrieval of multimedia objects, it is impractical to either manually or automatically extract the contents for indexing since most of the multimedia contents are not machine extractable, while manual extraction tends to be highly laborious and time-consuming. However, by systematically capturing and ...	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	true	154,661
2410.09474	Distilling Invariant Representations with Dual Augmentation	Knowledge distillation (KD) has been widely used to transfer knowledge from large, accurate models (teachers) to smaller, efficient ones (students). Recent methods have explored enforcing consistency by incorporating causal interpretations to distill invariant representations. In this work, we extend this line of resea...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	497,613
2103.07640	Generating Unrestricted Adversarial Examples via Three Parameters	Deep neural networks have been shown to be vulnerable to adversarial examples deliberately constructed to misclassify victim models. As most adversarial examples have restricted their perturbations to $L_{p}$-norm, existing defense methods have focused on these types of perturbations and less attention has been paid to...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	224,653
2012.09771	End-to-end Deep Object Tracking with Circular Loss Function for Rotated Bounding Box	The task object tracking is vital in numerous applications such as autonomous driving, intelligent surveillance, robotics, etc. This task entails the assigning of a bounding box to an object in a video stream, given only the bounding box for that object on the first frame. In 2015, a new type of video object tracking (...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	212,168
2411.00164	A Recipe for Geometry-Aware 3D Mesh Transformers	Utilizing patch-based transformers for unstructured geometric data such as polygon meshes presents significant challenges, primarily due to the absence of a canonical ordering and variations in input sizes. Prior approaches to handling 3D meshes and point clouds have either relied on computationally intensive node-leve...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	504,462
1403.4174	A Receding Horizon Approach to Multi-Agent Planning from Local LTL Specifications	We study the problem of control synthesis for multi-agent systems, to achieve complex, high-level, long-term goals that are assigned to each agent individually. As the agents might not be capable of satisfying their respective goals by themselves, requests for other agents' collaborations are a part of the task descrip...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	31,628
2305.15021	EmbodiedGPT: Vision-Language Pre-Training via Embodied Chain of Thought	Embodied AI is a crucial frontier in robotics, capable of planning and executing action sequences for robots to accomplish long-horizon tasks in physical environments. In this work, we introduce EmbodiedGPT, an end-to-end multi-modal foundation model for embodied AI, empowering embodied agents with multi-modal understa...	false	false	false	false	true	false	true	true	false	false	false	true	false	false	false	false	false	false	367,400
2412.05161	DNF: Unconditional 4D Generation with Dictionary-based Neural Fields	While remarkable success has been achieved through diffusion-based 3D generative models for shapes, 4D generative modeling remains challenging due to the complexity of object deformations over time. We propose DNF, a new 4D representation for unconditional generative modeling that efficiently models deformable shapes w...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	514,716
2210.00698	NAS-based Recursive Stage Partial Network (RSPNet) for Light-Weight Semantic Segmentation	Current NAS-based semantic segmentation methods focus on accuracy improvements rather than light-weight design. In this paper, we proposed a two-stage framework to design our NAS-based RSPNet model for light-weight semantic segmentation. The first architecture search determines the inner cell structure, and the second ...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	320,962
2208.06344	Modelleme ve Simulasyon	Computer modeling and simulation is used to analyze system behavior and evaluate strategies for operating in descriptive or predictive modes. In this part of the book, modeling and simulation approaches that have been proposed since the 1970s have been tried to be presented. Simulation models used in social sciences, r...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	312,685
2411.12752	A Library Perspective on Supervised Text Processing in Digital Libraries: An Investigation in the Biomedical Domain	Digital libraries that maintain extensive textual collections may want to further enrich their content for certain downstream applications, e.g., building knowledge graphs, semantic enrichment of documents, or implementing novel access paths. All of these applications require some text processing, either to identify re...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	true	509,523
1702.06381	Compressive Channel Estimation and Multi-user Detection in C-RAN	This paper considers the channel estimation (CE) and multi-user detection (MUD) problems in cloud radio access network (C-RAN). Assuming that active users are sparse in the network, we solve CE and MUD problems with compressed sensing (CS) technology to greatly reduce the long identification pilot overhead. A mixed L{2...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	68,598
2304.00202	Improving Fast Adversarial Training with Prior-Guided Knowledge	Fast adversarial training (FAT) is an efficient method to improve robustness. However, the original FAT suffers from catastrophic overfitting, which dramatically and suddenly reduces robustness after a few training epochs. Although various FAT variants have been proposed to prevent overfitting, they require high traini...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	355,595
2209.07592	Explicit Tradeoffs between Adversarial and Natural Distributional Robustness	Several existing works study either adversarial or natural distributional robustness of deep neural networks separately. In practice, however, models need to enjoy both types of robustness to ensure reliability. In this work, we bridge this gap and show that in fact, explicit tradeoffs exist between adversarial and nat...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	317,806
2212.14319	Gaussian Process Priors for Systems of Linear Partial Differential Equations with Constant Coefficients	Partial differential equations (PDEs) are important tools to model physical systems and including them into machine learning models is an important way of incorporating physical knowledge. Given any system of linear PDEs with constant coefficients, we propose a family of Gaussian process (GP) priors, which we call EPGP...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	338,580
2007.06104	Neural disambiguation of lemma and part of speech in morphologically rich languages	We consider the problem of disambiguating the lemma and part of speech of ambiguous words in morphologically rich languages. We propose a method for disambiguating ambiguous words in context, using a large un-annotated corpus of text, and a morphological analyser -- with no manual disambiguation or data annotation. We ...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	186,898
1809.00101	Attentive Crowd Flow Machines	Traffic flow prediction is crucial for urban traffic management and public safety. Its key challenges lie in how to adaptively integrate the various factors that affect the flow changes. In this paper, we propose a unified neural network module to address this problem, called Attentive Crowd Flow Machine~(ACFM), which ...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	106,494
2408.08690	Explore-then-Commit Algorithms for Decentralized Two-Sided Matching Markets	Online learning in a decentralized two-sided matching markets, where the demand-side (players) compete to match with the supply-side (arms), has received substantial interest because it abstracts out the complex interactions in matching platforms (e.g. UpWork, TaskRabbit). However, past works assume that each arm knows...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	481,109
2303.04599	Point Cloud Classification Using Content-based Transformer via Clustering in Feature Space	Recently, there have been some attempts of Transformer in 3D point cloud classification. In order to reduce computations, most existing methods focus on local spatial attention, but ignore their content and fail to establish relationships between distant but relevant points. To overcome the limitation of local spatial ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	350,150
2011.02066	University of Washington at TREC 2020 Fairness Ranking Track	InfoSeeking Lab's FATE (Fairness Accountability Transparency Ethics) group at University of Washington participated in 2020 TREC Fairness Ranking Track. This report describes that track, assigned data and tasks, our group definitions, and our results. Our approach to bringing fairness in retrieval and re-ranking tasks ...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	204,806
2106.00641	SpanNER: Named Entity Re-/Recognition as Span Prediction	Recent years have seen the paradigm shift of Named Entity Recognition (NER) systems from sequence labeling to span prediction. Despite its preliminary effectiveness, the span prediction model's architectural bias has not been fully understood. In this paper, we first investigate the strengths and weaknesses when the sp...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	238,197
1908.04383	Apache Spark Accelerated Deep Learning Inference for Large Scale Satellite Image Analytics	The shear volumes of data generated from earth observation and remote sensing technologies continue to make major impact; leaping key geospatial applications into the dual data and compute intensive era. As a consequence, this rapid advancement poses new computational and data processing challenges. We implement a nove...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	141,460
1401.0061	On dually flat general $(\alpha,\beta)$-metrics	In this work, the dual flatness, which is connected with Statistics and Information geometry, of general $(\alpha,\beta)$-metrics (a new class of Finsler metrics) is studied. A nice characterization for such metrics to be dually flat under some suitable conditions is provided and all the solutions are completely determ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	29,525

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