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
2109.03383	DeepZensols: Deep Natural Language Processing Framework	Reproducing results in publications by distributing publicly available source code is becoming ever more popular. Given the difficulty of reproducing machine learning (ML) experiments, there have been significant efforts in reducing the variance of these results. As in any science, the ability to consistently reproduce...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	254,043
2301.09522	Optimising Event-Driven Spiking Neural Network with Regularisation and Cutoff	Spiking neural network (SNN), as the next generation of artificial neural network (ANN), offer a closer mimicry of natural neural networks and hold promise for significant improvements in computational efficiency. However, the current SNN is trained to infer over a fixed duration, overlooking the potential of dynamic i...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	341,524
2403.06417	Enhanced Sparsification via Stimulative Training	Sparsification-based pruning has been an important category in model compression. Existing methods commonly set sparsity-inducing penalty terms to suppress the importance of dropped weights, which is regarded as the suppressed sparsification paradigm. However, this paradigm inactivates the dropped parts of networks cau...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	436,442
2501.17377	ASAP: Learning Generalizable Online Bin Packing via Adaptive Selection After Pruning	Recently, deep reinforcement learning (DRL) has achieved promising results in solving online 3D Bin Packing Problems (3D-BPP). However, these DRL-based policies may perform poorly on new instances due to distribution shift. Besides generalization, we also consider adaptation, completely overlooked by previous work, whi...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	528,313
1909.03372	ShapeBots: Shape-changing Swarm Robots	We introduce shape-changing swarm robots. A swarm of self-transformable robots can both individually and collectively change their configuration to display information, actuate objects, act as tangible controllers, visualize data, and provide physical affordances. ShapeBots is a concept prototype of shape-changing swar...	true	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	144,454
2011.12388	Multiple Transmit Power Levels based NOMA for Massive Machine-type Communications	This paper proposes a tractable solution for integrating non-orthogonal multiple access (NOMA) into massive machine-type communications (mMTC) to increase the uplink connectivity. Multiple transmit power levels are provided at the user end to enable open-loop power control, which is absent from the traditional uplink N...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	208,137
2410.04684	Combining Structural and Unstructured Data: A Topic-based Finite Mixture Model for Insurance Claim Prediction	Modeling insurance claim amounts and classifying claims into different risk levels are critical yet challenging tasks. Traditional predictive models for insurance claims often overlook the valuable information embedded in claim descriptions. This paper introduces a novel approach by developing a joint mixture model tha...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	495,395
0905.0079	Multiple-Bases Belief-Propagation Decoding of High-Density Cyclic Codes	We introduce a new method for decoding short and moderate length linear block codes with dense parity-check matrix representations of cyclic form, termed multiple-bases belief-propagation (MBBP). The proposed iterative scheme makes use of the fact that a code has many structurally diverse parity-check matrices, capable...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	3,626
2208.03313	A Non-Asymptotic Framework for Approximate Message Passing in Spiked Models	Approximate message passing (AMP) emerges as an effective iterative paradigm for solving high-dimensional statistical problems. However, prior AMP theory -- which focused mostly on high-dimensional asymptotics -- fell short of predicting the AMP dynamics when the number of iterations surpasses $o\big(\frac{\log n}{\log...	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	311,741
2411.07457	DecoPrompt : Decoding Prompts Reduces Hallucinations when Large Language Models Meet False Premises	While large language models (LLMs) have demonstrated increasing power, they have also called upon studies on their hallucinated outputs that deviate from factually correct statements. In this paper, we focus on one important scenario of false premises, where LLMs are distracted by misaligned claims although the model p...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	507,532
1612.02109	A Generalized Mixed-Integer Convex Program for Multilegged Footstep Planning on Uneven Terrain	Robot footstep planning strategies can be divided in two main approaches: discrete searches and continuous optimizations. While discrete searches have been broadly applied, continuous optimizations approaches have been restricted for humanoid platforms. This article introduces a generalized continuous-optimization appr...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	65,184
2104.14754	Exploiting Spatial Dimensions of Latent in GAN for Real-time Image Editing	Generative adversarial networks (GANs) synthesize realistic images from random latent vectors. Although manipulating the latent vectors controls the synthesized outputs, editing real images with GANs suffers from i) time-consuming optimization for projecting real images to the latent vectors, ii) or inaccurate embeddin...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	232,931
1401.5899	Kernel Least Mean Square with Adaptive Kernel Size	Kernel adaptive filters (KAF) are a class of powerful nonlinear filters developed in Reproducing Kernel Hilbert Space (RKHS). The Gaussian kernel is usually the default kernel in KAF algorithms, but selecting the proper kernel size (bandwidth) is still an open important issue especially for learning with small sample s...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	30,270
1902.07636	Contributive Social Capital Extraction From Different Types of Online Data Sources	It is a recurring problem of online communication that the properties of unknown people are hard to assess. This may lead to various issues such as the spread of `fake news' from untrustworthy sources. In sociology the sum of (social) resources available to a person through their social network is often described as so...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	122,026
1910.11563	Metric Classification Network in Actual Face Recognition Scene	In order to make facial features more discriminative, some new models have recently been proposed. However, almost all of these models use the traditional face verification method, where the cosine operation is performed using the features of the bottleneck layer output. However, each of these models needs to change a ...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	150,823
2403.07832	DeliGrasp: Inferring Object Properties with LLMs for Adaptive Grasp Policies	Large language models (LLMs) can provide rich physical descriptions of most worldly objects, allowing robots to achieve more informed and capable grasping. We leverage LLMs' common sense physical reasoning and code-writing abilities to infer an object's physical characteristics$\unicode{x2013}$mass $m$, friction coeffi...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	437,043
2208.13064	A Diversity-Aware Domain Development Methodology	The development of domain ontological models, though being a mature research arena backed by well-established methodologies, still suffer from two key shortcomings. Firstly, the issues concerning the semantic persistency of ontology concepts and their flexible reuse in domain development employing existing approaches. ...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	true	false	314,941
2207.04655	Personalizing Federated Medical Image Segmentation via Local Calibration	Medical image segmentation under federated learning (FL) is a promising direction by allowing multiple clinical sites to collaboratively learn a global model without centralizing datasets. However, using a single model to adapt to various data distributions from different sites is extremely challenging. Personalized FL...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	307,268
2411.07725	ALOcc: Adaptive Lifting-based 3D Semantic Occupancy and Cost Volume-based Flow Prediction	Vision-based semantic occupancy and flow prediction plays a crucial role in providing spatiotemporal cues for real-world tasks, such as autonomous driving. Existing methods prioritize higher accuracy to cater to the demands of these tasks. In this work, we strive to improve performance by introducing a series of target...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	507,656
2408.04189	Artificial Intelligence based Approach for Identification and Mitigation of Cyber-Attacks in Wide-Area Control of Power Systems	We propose a generative adversarial network (GAN) based deep learning method that serves the dual role of both identification and mitigation of cyber-attacks in wide-area damping control loops of power systems. Two specific types of attacks considered are false data injection and denial-of-service (DoS). Unlike existin...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	479,280
2105.13318	Synthetic Data Generation for Grammatical Error Correction with Tagged Corruption Models	Synthetic data generation is widely known to boost the accuracy of neural grammatical error correction (GEC) systems, but existing methods often lack diversity or are too simplistic to generate the broad range of grammatical errors made by human writers. In this work, we use error type tags from automatic annotation to...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	237,268
2306.06102	Backup Plan Constrained Model Predictive Control with Guaranteed Stability	This article proposes and evaluates a new safety concept called backup plan safety for path planning of autonomous vehicles under mission uncertainty using model predictive control (MPC). Backup plan safety is defined as the ability to complete an alternative mission when the primary mission is aborted. To include this...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	372,454
1611.04655	Motion Estimated-Compensated Reconstruction with Preserved-Features in Free-Breathing Cardiac MRI	To develop an efficient motion-compensated reconstruction technique for free-breathing cardiac magnetic resonance imaging (MRI) that allows high-quality images to be reconstructed from multiple undersampled single-shot acquisitions. The proposed method is a joint image reconstruction and motion correction method consis...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	63,879
2203.09279	Transfer learning for cross-modal demand prediction of bike-share and public transit	The urban transportation system is a combination of multiple transport modes, and the interdependencies across those modes exist. This means that the travel demand across different travel modes could be correlated as one mode may receive demand from or create demand for another mode, not to mention natural correlations...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	286,100
2012.03243	V2I-Based Platooning Design with Delay Awareness	This paper studies the vehicle platooning system based on vehicle-to-infrastructure (V2I) communication, where all the vehicles in the platoon upload their driving state information to the roadside unit (RSU), and RSU makes the platoon control decisions with the assistance of edge computing. By addressing the delay con...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	210,047
2205.06871	Near-Negative Distinction: Giving a Second Life to Human Evaluation Datasets	Precisely assessing the progress in natural language generation (NLG) tasks is challenging, and human evaluation to establish a preference in a model's output over another is often necessary. However, human evaluation is usually costly, difficult to reproduce, and non-reusable. In this paper, we propose a new and simpl...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	296,379
2212.05606	Transductive Linear Probing: A Novel Framework for Few-Shot Node Classification	Few-shot node classification is tasked to provide accurate predictions for nodes from novel classes with only few representative labeled nodes. This problem has drawn tremendous attention for its projection to prevailing real-world applications, such as product categorization for newly added commodity categories on an ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	335,834
2407.09486	ENOVA: Autoscaling towards Cost-effective and Stable Serverless LLM Serving	Since the increasing popularity of large language model (LLM) backend systems, it is common and necessary to deploy stable serverless serving of LLM on multi-GPU clusters with autoscaling. However, there exist challenges because the diversity and co-location of applications in multi-GPU clusters will lead to low servic...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	472,585
2005.02865	An accurate methodology for surface tension modeling in OpenFOAM	In this paper a numerical methodology for surface tension modeling is presented, with an emphasis on the implementation in the OpenFOAM framework. The methodology relies on a combination of (i) a well-balanced approach based on the Ghost Fluid Method (GFM), including the jump of density and pressure directly in the num...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	175,993
1812.00049	The Indus Script and Economics. A Role for Indus Seals and Tablets in Rationing and Administration of Labor	The Indus script remains one of the last major undeciphered scripts of the ancient world. We focus here on Indus inscriptions on a group of miniature tablets discovered by Meadow and Kenoyer in Harappa in 1997. By drawing parallels with proto-Elamite and proto-Cuneiform inscriptions, we explore how these miniature tabl...	false	false	false	true	false	false	false	false	true	false	false	false	false	true	false	false	false	false	115,140
2501.17704	Inferring Implicit Goals Across Differing Task Models	One of the significant challenges to generating value-aligned behavior is to not only account for the specified user objectives but also any implicit or unspecified user requirements. The existence of such implicit requirements could be particularly common in settings where the user's understanding of the task model ma...	false	false	false	false	true	false	false	true	false	false	true	false	false	false	false	false	false	false	528,425
2306.08729	Towards vision-based dual arm robotic fruit harvesting	Interest in agricultural robotics has increased considerably in recent years due to benefits such as improvement in productivity and labor reduction. However, current problems associated with unstructured environments make the development of robotic harvesters challenging. Most research in agricultural robotics focuses...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	373,513
2111.07158	Robust Deep Reinforcement Learning for Extractive Legal Summarization	Automatic summarization of legal texts is an important and still a challenging task since legal documents are often long and complicated with unusual structures and styles. Recent advances of deep models trained end-to-end with differentiable losses can well-summarize natural text, yet when applied to legal domain, the...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	266,294
1501.03002	An Improvement to the Domain Adaptation Bound in a PAC-Bayesian context	This paper provides a theoretical analysis of domain adaptation based on the PAC-Bayesian theory. We propose an improvement of the previous domain adaptation bound obtained by Germain et al. in two ways. We first give another generalization bound tighter and easier to interpret. Moreover, we provide a new analysis of t...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	39,236
2412.07228	T-TIME: Test-Time Information Maximization Ensemble for Plug-and-Play BCIs	Objective: An electroencephalogram (EEG)-based brain-computer interface (BCI) enables direct communication between the human brain and a computer. Due to individual differences and non-stationarity of EEG signals, such BCIs usually require a subject-specific calibration session before each use, which is time-consuming ...	true	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	515,578
2405.13077	GPT-4 Jailbreaks Itself with Near-Perfect Success Using Self-Explanation	Research on jailbreaking has been valuable for testing and understanding the safety and security issues of large language models (LLMs). In this paper, we introduce Iterative Refinement Induced Self-Jailbreak (IRIS), a novel approach that leverages the reflective capabilities of LLMs for jailbreaking with only black-bo...	false	false	false	false	true	false	false	false	true	false	false	false	true	false	false	false	false	false	455,796
1804.00117	Multi-label Learning with Missing Labels using Mixed Dependency Graphs	This work focuses on the problem of multi-label learning with missing labels (MLML), which aims to label each test instance with multiple class labels given training instances that have an incomplete/partial set of these labels. The key point to handle missing labels is propagating the label information from provided l...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	93,944
2410.13853	AutoAL: Automated Active Learning with Differentiable Query Strategy Search	As deep learning continues to evolve, the need for data efficiency becomes increasingly important. Considering labeling large datasets is both time-consuming and expensive, active learning (AL) provides a promising solution to this challenge by iteratively selecting the most informative subsets of examples to train dee...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	499,730
1709.04579	Autonomous Extracting a Hierarchical Structure of Tasks in Reinforcement Learning and Multi-task Reinforcement Learning	Reinforcement learning (RL), while often powerful, can suffer from slow learning speeds, particularly in high dimensional spaces. The autonomous decomposition of tasks and use of hierarchical methods hold the potential to significantly speed up learning in such domains. This paper proposes a novel practical method that...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	80,691
2012.04224	KNN-enhanced Deep Learning Against Noisy Labels	Supervised learning on Deep Neural Networks (DNNs) is data hungry. Optimizing performance of DNN in the presence of noisy labels has become of paramount importance since collecting a large dataset will usually bring in noisy labels. Inspired by the robustness of K-Nearest Neighbors (KNN) against data noise, in this wor...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	210,387
1406.1476	A Context-aware Delayed Agglomeration Framework for Electron Microscopy Segmentation	Electron Microscopy (EM) image (or volume) segmentation has become significantly important in recent years as an instrument for connectomics. This paper proposes a novel agglomerative framework for EM segmentation. In particular, given an over-segmented image or volume, we propose a novel framework for accurately clust...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	33,635
1309.3752	Novel Repair-by-Transfer Codes and Systematic Exact-MBR Codes with Lower Complexities and Smaller Field Sizes	The $(n,k,d)$ regenerating code is a class of $(n,k)$ erasure codes with the capability to recover a lost code fragment from other $d$ existing code fragments. This paper concentrates on the design of exact regenerating codes at Minimum Bandwidth Regenerating (MBR) points. For $d=n-1$, a class of $(n,k,d=n-1)$ Exact-MB...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	27,043
2012.10545	A 3D GAN for Improved Large-pose Facial Recognition	Facial recognition using deep convolutional neural networks relies on the availability of large datasets of face images. Many examples of identities are needed, and for each identity, a large variety of images are needed in order for the network to learn robustness to intra-class variation. In practice, such datasets a...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	212,369
2209.12427	Learning Continuous Control Policies for Information-Theoretic Active Perception	This paper proposes a method for learning continuous control policies for active landmark localization and exploration using an information-theoretic cost. We consider a mobile robot detecting landmarks within a limited sensing range, and tackle the problem of learning a control policy that maximizes the mutual informa...	false	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	319,532
2401.02884	MsDC-DEQ-Net: Deep Equilibrium Model (DEQ) with Multi-scale Dilated Convolution for Image Compressive Sensing (CS)	Compressive sensing (CS) is a technique that enables the recovery of sparse signals using fewer measurements than traditional sampling methods. To address the computational challenges of CS reconstruction, our objective is to develop an interpretable and concise neural network model for reconstructing natural images us...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	419,876
2201.12451	Extracting Finite Automata from RNNs Using State Merging	One way to interpret the behavior of a blackbox recurrent neural network (RNN) is to extract from it a more interpretable discrete computational model, like a finite state machine, that captures its behavior. In this work, we propose a new method for extracting finite automata from RNNs inspired by the state merging pa...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	277,648
2405.04396	Predicting Transonic Flowfields in Non-Homogeneous Unstructured Grids Using Autoencoder Graph Convolutional Networks	This paper focuses on addressing challenges posed by non-homogeneous unstructured grids, commonly used in Computational Fluid Dynamics (CFD). Their prevalence in CFD scenarios has motivated the exploration of innovative approaches for generating reduced-order models. The core of our approach centers on geometric deep l...	false	true	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	452,558
2305.15216	Open Source High Fidelity Modeling of a Type 5 Wind Turbine Drivetrain for Grid Integration	The increasing integration of renewable energy resources in evolving bulk power system (BPS) is impacting the system inertia. Type-5 wind turbine generation has the potential to behave like a traditional synchronous generator and can help improve system inertia. Hydraulic torque converter (TC) and gearbox with torque l...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	367,508
2403.16043	Semantic Is Enough: Only Semantic Information For NeRF Reconstruction	Recent research that combines implicit 3D representation with semantic information, like Semantic-NeRF, has proven that NeRF model could perform excellently in rendering 3D structures with semantic labels. This research aims to extend the Semantic Neural Radiance Fields (Semantic-NeRF) model by focusing solely on seman...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	440,847
2207.04214	Adaptive Structural Similarity Preserving for Unsupervised Cross Modal Hashing	Cross-modal hashing is an important approach for multimodal data management and application. Existing unsupervised cross-modal hashing algorithms mainly rely on data features in pre-trained models to mine their similarity relationships. However, their optimization objectives are based on the static metric between the o...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	307,124
0903.1624	Instanton-based Techniques for Analysis and Reduction of Error Floors of LDPC Codes	We describe a family of instanton-based optimization methods developed recently for the analysis of the error floors of low-density parity-check (LDPC) codes. Instantons are the most probable configurations of the channel noise which result in decoding failures. We show that the general idea and the respective optimiza...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	3,317
2011.14277	Intrinsic Knowledge Evaluation on Chinese Language Models	Recent NLP tasks have benefited a lot from pre-trained language models (LM) since they are able to encode knowledge of various aspects. However, current LM evaluations focus on downstream performance, hence lack to comprehensively inspect in which aspect and to what extent have they encoded knowledge. This paper addres...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	208,726
1502.07996	Sparse Time-Frequency Representation for Signals with Fast Varying Instantaneous Frequency	Time-frequency distributions have been used to provide high resolution representation in a large number of signal processing applications. However, high resolution and accurate instantaneous frequency (IF) estimation usually depend on the employed distribution and complexity of signal phase function. To ensure an effic...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	40,633
2201.01490	Debiased Learning from Naturally Imbalanced Pseudo-Labels	Pseudo-labels are confident predictions made on unlabeled target data by a classifier trained on labeled source data. They are widely used for adapting a model to unlabeled data, e.g., in a semi-supervised learning setting. Our key insight is that pseudo-labels are naturally imbalanced due to intrinsic data similarit...	false	false	false	false	false	false	true	false	true	false	false	true	false	false	false	false	false	false	274,270
2206.04783	ReFace: Real-time Adversarial Attacks on Face Recognition Systems	Deep neural network based face recognition models have been shown to be vulnerable to adversarial examples. However, many of the past attacks require the adversary to solve an input-dependent optimization problem using gradient descent which makes the attack impractical in real-time. These adversarial examples are also...	false	false	false	false	false	false	true	false	false	false	false	true	true	false	false	false	false	false	301,763
2203.12552	Organic log-domain integrator synapse	Synapses play a critical role in memory, learning, and cognition. Their main functions include converting pre-synaptic voltage spikes to post-synaptic currents, as well as scaling the input signal. Several brain-inspired architectures have been proposed to emulate the behavior of biological synapses. While these are us...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	287,307
2402.14568	LLM-DA: Data Augmentation via Large Language Models for Few-Shot Named Entity Recognition	Despite the impressive capabilities of large language models (LLMs), their performance on information extraction tasks is still not entirely satisfactory. However, their remarkable rewriting capabilities and extensive world knowledge offer valuable insights to improve these tasks. In this paper, we propose $LLM-DA$, a ...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	431,739
2309.07139	A Traffic Management Framework for On-Demand Urban Air Mobility Systems	Urban Air Mobility (UAM) offers a solution to current traffic congestion by providing on-demand air mobility in urban areas. Effective traffic management is crucial for efficient operation of UAM systems, especially for high-demand scenarios. In this paper, we present a centralized traffic management framework for on-d...	false	false	false	false	false	false	false	true	false	false	true	false	false	false	true	false	false	true	391,670
2410.07695	Test-Time Intensity Consistency Adaptation for Shadow Detection	Shadow detection is crucial for accurate scene understanding in computer vision, yet it is challenged by the diverse appearances of shadows caused by variations in illumination, object geometry, and scene context. Deep learning models often struggle to generalize to real-world images due to the limited size and diversi...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	496,753
2309.12756	Towards an MLOps Architecture for XAI in Industrial Applications	Machine learning (ML) has become a popular tool in the industrial sector as it helps to improve operations, increase efficiency, and reduce costs. However, deploying and managing ML models in production environments can be complex. This is where Machine Learning Operations (MLOps) comes in. MLOps aims to streamline thi...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	393,922
2501.04286	Mapping the Edge of Chaos: Fractal-Like Boundaries in The Trainability of Decoder-Only Transformer Models	In the realm of fractal geometry, intricate structures emerge from simple iterative processes that partition parameter spaces into regions of stability and instability. Likewise, training large language models involves iteratively applying update functions, such as Adam, where even slight hyperparameter adjustments can...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	523,165
2201.11987	Computer-aided Recognition and Assessment of a Porous Bioelastomer on Ultrasound Images for Regenerative Medicine Applications	Biodegradable elastic scaffolds have attracted more and more attention in the field of soft tissue repair and tissue engineering. These scaffolds made of porous bioelastomers support tissue ingrowth along with their own degradation. It is necessary to develop a computer-aided analyzing method based on ultrasound images...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	277,483
1807.05185	Model Reconstruction from Model Explanations	We show through theory and experiment that gradient-based explanations of a model quickly reveal the model itself. Our results speak to a tension between the desire to keep a proprietary model secret and the ability to offer model explanations. On the theoretical side, we give an algorithm that provably learns a two-la...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	102,877
1508.03599	Efficient Redundancy Techniques for Latency Reduction in Cloud Systems	In cloud computing systems, assigning a task to multiple servers and waiting for the earliest copy to finish is an effective method to combat the variability in response time of individual servers, and reduce latency. But adding redundancy may result in higher cost of computing resources, as well as an increase in queu...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	46,015
2402.18884	Supervised Contrastive Representation Learning: Landscape Analysis with Unconstrained Features	Recent findings reveal that over-parameterized deep neural networks, trained beyond zero training-error, exhibit a distinctive structural pattern at the final layer, termed as Neural-collapse (NC). These results indicate that the final hidden-layer outputs in such networks display minimal within-class variations over t...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	433,603
2406.13337	Medical Spoken Named Entity Recognition	Spoken Named Entity Recognition (NER) aims to extracting named entities from speech and categorizing them into types like person, location, organization, etc. In this work, we present VietMed-NER - the first spoken NER dataset in the medical domain. To our best knowledge, our real-world dataset is the largest spoken NE...	false	false	true	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	465,806
2211.15428	Explanation on Pretraining Bias of Finetuned Vision Transformer	As the number of fine tuning of pretrained models increased, understanding the bias of pretrained model is essential. However, there is little tool to analyse transformer architecture and the interpretation of the attention maps is still challenging. To tackle the interpretability, we propose Input-Attribution and Atte...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	333,260
1805.02932	Cooperative Control of Multiple Agents with Unknown High-frequency Gain Signs under Unbalanced and Switching Topologies	Existing results on cooperative control of multi-agent systems with unknown control directions require that the underlying topology is either fixed with a strongly connected graph or switching between different strongly connected graphs. Furthermore, in most cases the graph is assumed to be balanced. This paper propose...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	96,952
2003.02638	Metric-Based Imitation Learning Between Two Dissimilar Anthropomorphic Robotic Arms	The development of autonomous robotic systems that can learn from human demonstrations to imitate a desired behavior - rather than being manually programmed - has huge technological potential. One major challenge in imitation learning is the correspondence problem: how to establish corresponding states and actions betw...	false	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	166,991
2402.16517	Discovering Artificial Viscosity Models for Discontinuous Galerkin Approximation of Conservation Laws using Physics-Informed Machine Learning	Finite element-based high-order solvers of conservation laws offer large accuracy but face challenges near discontinuities due to the Gibbs phenomenon. Artificial viscosity is a popular and effective solution to this problem based on physical insight. In this work, we present a physics-informed machine learning algorit...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	432,601
2303.01150	Multi-UAV Adaptive Path Planning Using Deep Reinforcement Learning	Efficient aerial data collection is important in many remote sensing applications. In large-scale monitoring scenarios, deploying a team of unmanned aerial vehicles (UAVs) offers improved spatial coverage and robustness against individual failures. However, a key challenge is cooperative path planning for the UAVs to e...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	348,848
1707.06992	Ideological Sublations: Resolution of Dialectic in Population-based Optimization	A population-based optimization algorithm was designed, inspired by two main thinking modes in philosophy, both based on dialectic concept and thesis-antithesis paradigm. They impose two different kinds of dialectics. Idealistic and materialistic antitheses are formulated as optimization models. Based on the models, th...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	true	false	true	77,527
2305.13168	LLMs for Knowledge Graph Construction and Reasoning: Recent Capabilities and Future Opportunities	This paper presents an exhaustive quantitative and qualitative evaluation of Large Language Models (LLMs) for Knowledge Graph (KG) construction and reasoning. We engage in experiments across eight diverse datasets, focusing on four representative tasks encompassing entity and relation extraction, event extraction, link...	false	false	false	false	true	true	true	false	true	false	false	false	false	false	false	false	true	false	366,378
2303.12660	Structural Measures of Resilience for Supply Chains	We investigate the structural factors that drive cascading failures in production networks, focusing on quantifying these risks with a topological resilience metric corresponding to the largest exogenous systemic shock that the production network can withstand, such that almost all of the network survives with high pro...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	353,330
1901.10258	RED-Attack: Resource Efficient Decision based Attack for Machine Learning	Due to data dependency and model leakage properties, Deep Neural Networks (DNNs) exhibit several security vulnerabilities. Several security attacks exploited them but most of them require the output probability vector. These attacks can be mitigated by concealing the output probability vector. To address this limitatio...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	119,975
2306.05150	Bayesian Optimization of Expensive Nested Grey-Box Functions	We consider the problem of optimizing a grey-box objective function, i.e., nested function composed of both black-box and white-box functions. A general formulation for such grey-box problems is given, which covers the existing grey-box optimization formulations as special cases. We then design an optimism-driven algor...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	372,078
2305.19525	Discovering New Interpretable Conservation Laws as Sparse Invariants	Discovering conservation laws for a given dynamical system is important but challenging. In a theorist setup (differential equations and basis functions are both known), we propose the Sparse Invariant Detector (SID), an algorithm that auto-discovers conservation laws from differential equations. Its algorithmic simpli...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	369,566
2206.10942	List-Decodable Covariance Estimation	We give the first polynomial time algorithm for \emph{list-decodable covariance estimation}. For any $\alpha > 0$, our algorithm takes input a sample $Y \subseteq \mathbb{R}^d$ of size $n\geq d^{\mathsf{poly}(1/\alpha)}$ obtained by adversarially corrupting an $(1-\alpha)n$ points in an i.i.d. sample $X$ of size $n$ fr...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	304,094
1906.00748	Improving Minimal Gated Unit for Sequential Data	In order to obtain a model which can process sequential data related to machine translation and speech recognition faster and more accurately, we propose adopting Chrono Initializer as the initialization method of Minimal Gated Unit. We evaluated the method with two tasks: adding task and copy task. As a result of the ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	true	false	false	133,501
2205.14761	Modeling Disagreement in Automatic Data Labelling for Semi-Supervised Learning in Clinical Natural Language Processing	Computational models providing accurate estimates of their uncertainty are crucial for risk management associated with decision making in healthcare contexts. This is especially true since many state-of-the-art systems are trained using the data which has been labelled automatically (self-supervised mode) and tend to o...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	299,479
1903.03642	Improved Robustness and Safety for Autonomous Vehicle Control with Adversarial Reinforcement Learning	To improve efficiency and reduce failures in autonomous vehicles, research has focused on developing robust and safe learning methods that take into account disturbances in the environment. Existing literature in robust reinforcement learning poses the learning problem as a two player game between the autonomous system...	false	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	123,777
2202.08176	Bias and unfairness in machine learning models: a systematic literature review	One of the difficulties of artificial intelligence is to ensure that model decisions are fair and free of bias. In research, datasets, metrics, techniques, and tools are applied to detect and mitigate algorithmic unfairness and bias. This study aims to examine existing knowledge on bias and unfairness in Machine Learni...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	280,790
2309.13081	Transitioning To The Digital Generation Case Studies (Previous Digital Point Studies In Japan Cases:1993-2023)	In this paper, we discuss at The 8th International Workshop on Application of Big Data for Computational Social Science, October 26-29, 2023, Venice, Italy. To achieve the realization of the Global and Innovation Gateway for All (GIGA) initiative (2019), proposed in December 2019 by the Primary and Secondary Education ...	false	false	false	true	false	false	false	false	false	false	false	false	false	true	false	false	false	false	394,051
2306.09177	Dis-AE: Multi-domain & Multi-task Generalisation on Real-World Clinical Data	Clinical data is often affected by clinically irrelevant factors such as discrepancies between measurement devices or differing processing methods between sites. In the field of machine learning (ML), these factors are known as domains and the distribution differences they cause in the data are known as domain shifts. ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	373,704
2306.12133	Fundamental Performance Bounds for Carrier Phase Positioning in Cellular Networks	The carrier phase of cellular signals can be utilized for highly accurate positioning, with the potential for orders-of-magnitude performance improvements compared to standard time-difference-of-arrival positioning. Due to the integer ambiguities, standard performance evaluation tools such as the Cram\'er-Rao bound (CR...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	374,842
cs/0011007	Tree-gram Parsing: Lexical Dependencies and Structural Relations	This paper explores the kinds of probabilistic relations that are important in syntactic disambiguation. It proposes that two widely used kinds of relations, lexical dependencies and structural relations, have complementary disambiguation capabilities. It presents a new model based on structural relations, the Tree-gra...	true	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	537,247
1306.6671	Extended Subspace Error Localization for Rate-Adaptive Distributed Source Coding	A subspace-based approach for rate-adaptive distributed source coding (DSC) based on discrete Fourier transform (DFT) codes is developed. Punctured DFT codes can be used to implement rate-adaptive source coding, however they perform poorly after even moderate puncturing since the performance of the subspace error local...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	25,494
2211.15406	Automated Detection of Dolphin Whistles with Convolutional Networks and Transfer Learning	Effective conservation of maritime environments and wildlife management of endangered species require the implementation of efficient, accurate and scalable solutions for environmental monitoring. Ecoacoustics offers the advantages of non-invasive, long-duration sampling of environmental sounds and has the potential to...	false	false	true	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	333,245
2109.00141	Storing Multi-model Data in RDBMSs based on Reinforcement Learning	How to manage various data in a unified way is a significant research topic in the field of databases. To address this problem, researchers have proposed multi-model databases to support multiple data models in a uniform platform with a single unified query language. However, since relational databases are predominant ...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	253,017
2203.02656	Deep Partial Multiplex Network Embedding	Network embedding is an effective technique to learn the low-dimensional representations of nodes in networks. Real-world networks are usually with multiplex or having multi-view representations from different relations. Recently, there has been increasing interest in network embedding on multiplex data. However, most ...	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	false	false	283,817
2204.03919	Network Shuffling: Privacy Amplification via Random Walks	Recently, it is shown that shuffling can amplify the central differential privacy guarantees of data randomized with local differential privacy. Within this setup, a centralized, trusted shuffler is responsible for shuffling by keeping the identities of data anonymous, which subsequently leads to stronger privacy guara...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	true	false	290,477
2105.13975	Relation Matters in Sampling: A Scalable Multi-Relational Graph Neural Network for Drug-Drug Interaction Prediction	Sampling is an established technique to scale graph neural networks to large graphs. Current approaches however assume the graphs to be homogeneous in terms of relations and ignore relation types, critically important in biomedical graphs. Multi-relational graphs contain various types of relations that usually come wit...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	237,474
2310.05473	Sentence-level Prompts Benefit Composed Image Retrieval	Composed image retrieval (CIR) is the task of retrieving specific images by using a query that involves both a reference image and a relative caption. Most existing CIR models adopt the late-fusion strategy to combine visual and language features. Besides, several approaches have also been suggested to generate a pseud...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	398,167
1709.04770	The Arbitrarily Varying Broadcast Channel with Degraded Message Sets with Causal Side Information at the Encoder	In this work, we study the arbitrarily varying broadcast channel (AVBC), when state information is available at the transmitter in a causal manner. We establish inner and outer bounds on both the random code capacity region and the deterministic code capacity region with degraded message sets. The capacity region is th...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	80,730
2307.00660	Minimum Levels of Interpretability for Artificial Moral Agents	As artificial intelligence (AI) models continue to scale up, they are becoming more capable and integrated into various forms of decision-making systems. For models involved in moral decision-making, also known as artificial moral agents (AMA), interpretability provides a way to trust and understand the agent's interna...	false	false	false	false	true	false	false	false	false	false	false	false	false	true	false	false	false	false	377,082
2302.09572	Rethinking Data-Free Quantization as a Zero-Sum Game	Data-free quantization (DFQ) recovers the performance of quantized network (Q) without accessing the real data, but generates the fake sample via a generator (G) by learning from full-precision network (P) instead. However, such sample generation process is totally independent of Q, specialized as failing to consider t...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	346,479
2209.11615	Robust Domain Adaptation for Machine Reading Comprehension	Most domain adaptation methods for machine reading comprehension (MRC) use a pre-trained question-answer (QA) construction model to generate pseudo QA pairs for MRC transfer. Such a process will inevitably introduce mismatched pairs (i.e., noisy correspondence) due to i) the unavailable QA pairs in target documents, an...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	319,248
2110.09574	Multilingual Domain Adaptation for NMT: Decoupling Language and Domain Information with Adapters	Adapter layers are lightweight, learnable units inserted between transformer layers. Recent work explores using such layers for neural machine translation (NMT), to adapt pre-trained models to new domains or language pairs, training only a small set of parameters for each new setting (language pair or domain). In this ...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	261,849
2102.08058	Capacity-Achieving Private Information Retrieval Schemes from Uncoded Storage Constrained Servers with Low Sub-packetization	This paper investigates reducing sub-packetization of capacity-achieving schemes for uncoded Storage Constrained Private Information Retrieval (SC-PIR) systems. In the SC-PIR system, a user aims to retrieve one out of $K$ files from $N$ servers while revealing nothing about its identity to any individual server, in whi...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	220,326
2406.12605	Attack and Defense of Deep Learning Models in the Field of Web Attack Detection	The challenge of WAD (web attack detection) is growing as hackers continuously refine their methods to evade traditional detection. Deep learning models excel in handling complex unknown attacks due to their strong generalization and adaptability. However, they are vulnerable to backdoor attacks, where contextually irr...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	465,483

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