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
1909.05746	Sams-Net: A Sliced Attention-based Neural Network for Music Source Separation	Convolutional Neural Network (CNN) or Long short-term memory (LSTM) based models with the input of spectrogram or waveforms are commonly used for deep learning based audio source separation. In this paper, we propose a Sliced Attention-based neural network (Sams-Net) in the spectrogram domain for the music source separ...	false	false	true	false	false	true	true	false	false	false	false	false	false	false	false	false	false	false	145,196
2304.11805	OGMN: Occlusion-guided Multi-task Network for Object Detection in UAV Images	Occlusion between objects is one of the overlooked challenges for object detection in UAV images. Due to the variable altitude and angle of UAVs, occlusion in UAV images happens more frequently than that in natural scenes. Compared to occlusion in natural scene images, occlusion in UAV images happens with feature confu...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	359,979
2407.13025	From Principles to Practices: Lessons Learned from Applying Partnership on AI's (PAI) Synthetic Media Framework to 11 Use Cases	2023 was the year the world woke up to generative AI, and 2024 is the year policymakers are responding more firmly. Importantly, this policy momentum is taking place alongside real world creation and distribution of synthetic media. Social media platforms, news organizations, dating apps, image generation companies, an...	false	false	false	false	true	false	false	false	false	false	false	false	false	true	false	false	false	false	474,201
2302.00775	Model Monitoring and Robustness of In-Use Machine Learning Models: Quantifying Data Distribution Shifts Using Population Stability Index	Safety goes first. Meeting and maintaining industry safety standards for robustness of artificial intelligence (AI) and machine learning (ML) models require continuous monitoring for faults and performance drops. Deep learning models are widely used in industrial applications, e.g., computer vision, but the susceptibil...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	343,334
1506.00746	Lagrangian Duality in 3D SLAM: Verification Techniques and Optimal Solutions	State-of-the-art techniques for simultaneous localization and mapping (SLAM) employ iterative nonlinear optimization methods to compute an estimate for robot poses. While these techniques often work well in practice, they do not provide guarantees on the quality of the estimate. This paper shows that Lagrangian duality...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	43,707
2310.04752	A Unified Generalization Analysis of Re-Weighting and Logit-Adjustment for Imbalanced Learning	Real-world datasets are typically imbalanced in the sense that only a few classes have numerous samples, while many classes are associated with only a few samples. As a result, a na\"ive ERM learning process will be biased towards the majority classes, making it difficult to generalize to the minority classes. To addre...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	397,802
2402.15513	Investigating the Generalizability of Physiological Characteristics of Anxiety	Recent works have demonstrated the effectiveness of machine learning (ML) techniques in detecting anxiety and stress using physiological signals, but it is unclear whether ML models are learning physiological features specific to stress. To address this ambiguity, we evaluated the generalizability of physiological feat...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	432,168
1809.06201	Player Experience Extraction from Gameplay Video	The ability to extract the sequence of game events for a given player's play-through has traditionally required access to the game's engine or source code. This serves as a barrier to researchers, developers, and hobbyists who might otherwise benefit from these game logs. In this paper we present two approaches to deri...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	107,996
2303.13495	ReVersion: Diffusion-Based Relation Inversion from Images	Diffusion models gain increasing popularity for their generative capabilities. Recently, there have been surging needs to generate customized images by inverting diffusion models from exemplar images, and existing inversion methods mainly focus on capturing object appearances (i.e., the "look"). However, how to invert ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	353,688
1706.08001	Temporal-related Convolutional-Restricted-Boltzmann-Machine capable of learning relational order via reinforcement learning procedure?	In this article, we extend the conventional framework of convolutional-Restricted-Boltzmann-Machine to learn highly abstract features among abitrary number of time related input maps by constructing a layer of multiplicative units, which capture the relations among inputs. In many cases, more than two maps are strongly...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	75,930
2009.09334	Quantifying Uncertainty in Risk Assessment using Fuzzy Theory	Risk specialists are trying to understand risk better and use complex models for risk assessment, while many risks are not yet well understood. The lack of empirical data and complex causal and outcome relationships make it difficult to estimate the degree to which certain risk types are exposed. Traditional risk model...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	196,543
2501.09665	Design-Agnostic Distributed Timing Fault Injection Monitor With End-to-End Design Automation	Fault injection attacks induce hardware failures in circuits and exploit these faults to compromise the security of the system. It has been demonstrated that FIAs can bypass system security mechanisms, cause faulty outputs, and gain access to secret information. Certain types of FIAs can be mounted with little effort b...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	525,220
2307.11344	DEFTri: A Few-Shot Label Fused Contextual Representation Learning For Product Defect Triage in e-Commerce	Defect Triage is a time-sensitive and critical process in a large-scale agile software development lifecycle for e-commerce. Inefficiencies arising from human and process dependencies in this domain have motivated research in automated approaches using machine learning to accurately assign defects to qualified teams. T...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	true	380,875
1809.09528	ComQA: A Community-sourced Dataset for Complex Factoid Question Answering with Paraphrase Clusters	To bridge the gap between the capabilities of the state-of-the-art in factoid question answering (QA) and what users ask, we need large datasets of real user questions that capture the various question phenomena users are interested in, and the diverse ways in which these questions are formulated. We introduce ComQA, a...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	108,727
2102.06019	Reinforcement Learning For Constraint Satisfaction Game Agents (15-Puzzle, Minesweeper, 2048, and Sudoku)	In recent years, reinforcement learning has seen interest because of deep Q-Learning, where the model is a convolutional neural network. Deep Q-Learning has shown promising results in games such as Atari and AlphaGo. Instead of learning the entire Q-table, it learns an estimate of the Q function that determines a state...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	219,614
2212.14012	Characteristics-Informed Neural Networks for Forward and Inverse Hyperbolic Problems	We propose characteristics-informed neural networks (CINN), a simple and efficient machine learning approach for solving forward and inverse problems involving hyperbolic PDEs. Like physics-informed neural networks (PINN), CINN is a meshless machine learning solver with universal approximation capabilities. Unlike PINN...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	338,473
2211.05119	The $[1,0]$-twisted generalized Reed-Solomon code	In this paper, we not only give the parity check matrix of the $[1,0]$-twisted generalized Reed-Solomon (in short, TGRS) code, but also determine the weight distribution. Especially, we show that the $[1,0]$-TGRS code is not GRS or EGRS. Furthermore, we present a sufficient and necessary condition for any punctured cod...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	329,444
1703.03401	Identifying User Survival Types via Clustering of Censored Social Network Data	The goal of cluster analysis in survival data is to identify clusters that are decidedly associated with the survival outcome. Previous research has explored this problem primarily in the medical domain with relatively small datasets, but the need for such a clustering methodology could arise in other domains with larg...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	69,728
1908.09995	Temporal Reasoning Graph for Activity Recognition	Despite great success has been achieved in activity analysis, it still has many challenges. Most existing work in activity recognition pay more attention to design efficient architecture or video sampling strategy. However, due to the property of fine-grained action and long term structure in video, activity recognitio...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	142,993
1604.06020	Constructive Preference Elicitation by Setwise Max-margin Learning	In this paper we propose an approach to preference elicitation that is suitable to large configuration spaces beyond the reach of existing state-of-the-art approaches. Our setwise max-margin method can be viewed as a generalization of max-margin learning to sets, and can produce a set of "diverse" items that can be use...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	54,889
1307.2674	Error Rate Bounds in Crowdsourcing Models	Crowdsourcing is an effective tool for human-powered computation on many tasks challenging for computers. In this paper, we provide finite-sample exponential bounds on the error rate (in probability and in expectation) of hyperplane binary labeling rules under the Dawid-Skene crowdsourcing model. The bounds can be appl...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	25,735
2310.02960	CoDA: Collaborative Novel Box Discovery and Cross-modal Alignment for Open-vocabulary 3D Object Detection	Open-vocabulary 3D Object Detection (OV-3DDet) aims to detect objects from an arbitrary list of categories within a 3D scene, which remains seldom explored in the literature. There are primarily two fundamental problems in OV-3DDet, i.e., localizing and classifying novel objects. This paper aims at addressing the two p...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	397,061
2412.12836	A Survey on Recommendation Unlearning: Fundamentals, Taxonomy, Evaluation, and Open Questions	Recommender systems have become increasingly influential in shaping user behavior and decision-making, highlighting their growing impact in various domains. Meanwhile, the widespread adoption of machine learning models in recommender systems has raised significant concerns regarding user privacy and security. As compli...	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	false	false	518,050
2412.01109	Revisiting Absence withSymptoms that T Show up Decades Later to Recover Empty Categories	This paper explores null elements in English, Chinese, and Korean Penn treebanks. Null elements contain important syntactic and semantic information, yet they have typically been treated as entities to be removed during language processing tasks, particularly in constituency parsing. Thus, we work towards the removal a...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	512,957
2001.04050	Relational State-Space Model for Stochastic Multi-Object Systems	Real-world dynamical systems often consist of multiple stochastic subsystems that interact with each other. Modeling and forecasting the behavior of such dynamics are generally not easy, due to the inherent hardness in understanding the complicated interactions and evolutions of their constituents. This paper introduce...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	160,137
1005.3358	The Role of Provenance Management in Accelerating the Rate of Astronomical Research	The availability of vast quantities of data through electronic archives has transformed astronomical research. It has also enabled the creation of new products, models and simulations, often from distributed input data and models, that are themselves made electronically available. These products will only provide maxim...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	6,516
2105.12235	Acquisition and analysis of crowd-sourced traffic data	Crowd-sourced traffic data offer great promise in environmental modeling. However, archives of such traffic data are typically not made available for research; instead, the data must be acquired in real time. The objective of this paper is to present methods we developed for acquiring and analyzing time series of real-...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	236,943
2001.06631	Graph Ordering: Towards the Optimal by Learning	Graph representation learning has achieved a remarkable success in many graph-based applications, such as node classification, link prediction, and community detection. These models are usually designed to preserve the vertex information at different granularity and reduce the problems in discrete space to some machine...	false	false	false	true	true	false	true	false	false	false	false	false	false	false	false	false	false	false	160,844
1509.03488	Verbs Taking Clausal and Non-Finite Arguments as Signals of Modality - Revisiting the Issue of Meaning Grounded in Syntax	We revisit Levin's theory about the correspondence of verb meaning and syntax and infer semantic classes from a large syntactic classification of more than 600 German verbs taking clausal and non-finite arguments. Grasping the meaning components of Levin-classes is known to be hard. We address this challenge by setting...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	46,836
2107.08446	Frobenius statistical manifolds & geometric invariants	In this paper, we explicitly prove that statistical manifolds, related to exponential families and with flat structure connection have a Frobenius manifold structure. This latter object, at the interplay of beautiful interactions between topology and quantum field theory, raises natural questions, concerning the existe...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	246,734
1608.08130	Scheduling Refresh Queries for Keeping Results from a SPARQL Endpoint Up-to-Date (Extended Version)	Many datasets change over time. As a consequence, long-running applications that cache and repeatedly use query results obtained from a SPARQL endpoint may resubmit the queries regularly to ensure up-to-dateness of the results. While this approach may be feasible if the number of such regular refresh queries is managea...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	60,315
2412.18518	Bayesian Optimization of Bilevel Problems	Bilevel optimization, a hierarchical mathematical framework where one optimization problem is nested within another, has emerged as a powerful tool for modeling complex decision-making processes in various fields such as economics, engineering, and machine learning. This paper focuses on bilevel optimization where both...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	520,451
2501.03881	An LSTM-based Test Selection Method for Self-Driving Cars	Self-driving cars require extensive testing, which can be costly in terms of time. To optimize this process, simple and straightforward tests should be excluded, focusing on challenging tests instead. This study addresses the test selection problem for lane-keeping systems for self-driving cars. Road segment features, ...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	true	523,027
1803.02940	The Advantage of Doubling: A Deep Reinforcement Learning Approach to Studying the Double Team in the NBA	During the 2017 NBA playoffs, Celtics coach Brad Stevens was faced with a difficult decision when defending against the Cavaliers: "Do you double and risk giving up easy shots, or stay at home and do the best you can?" It's a tough call, but finding a good defensive strategy that effectively incorporates doubling can m...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	92,159
2402.13610	Data-driven Discovery with Large Generative Models	With the accumulation of data at an unprecedented rate, its potential to fuel scientific discovery is growing exponentially. This position paper urges the Machine Learning (ML) community to exploit the capabilities of large generative models (LGMs) to develop automated systems for end-to-end data-driven discovery -- a ...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	431,338
2406.00007	PyTorch-IE: Fast and Reproducible Prototyping for Information Extraction	The objective of Information Extraction (IE) is to derive structured representations from unstructured or semi-structured documents. However, developing IE models is complex due to the need of integrating several subtasks. Additionally, representation of data among varied tasks and transforming datasets into task-speci...	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	459,645
2407.11701	Novel Artistic Scene-Centric Datasets for Effective Transfer Learning in Fragrant Spaces	Olfaction, often overlooked in cultural heritage studies, holds profound significance in shaping human experiences and identities. Examining historical depictions of olfactory scenes can offer valuable insights into the role of smells in history. We show that a transfer-learning approach using weakly labeled training d...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	473,583
1908.10125	Proactive Intention Recognition for Joint Human-Robot Search and Rescue Missions through Monte-Carlo Planning in POMDP Environments	Proactively perceiving others' intentions is a crucial skill to effectively interact in unstructured, dynamic and novel environments. This work proposes a first step towards embedding this skill in support robots for search and rescue missions. Predicting the responders' intentions, indeed, will enable exploration appr...	true	false	false	false	true	false	true	true	false	false	false	false	false	false	false	false	false	false	143,027
1907.01099	Predicting Treatment Initiation from Clinical Time Series Data via Graph-Augmented Time-Sensitive Model	Many computational models were proposed to extract temporal patterns from clinical time series for each patient and among patient group for predictive healthcare. However, the common relations among patients (e.g., share the same doctor) were rarely considered. In this paper, we represent patients and clinicians relati...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	137,222
1904.13094	Detecting Adversarial Examples through Nonlinear Dimensionality Reduction	Deep neural networks are vulnerable to adversarial examples, i.e., carefully-perturbed inputs aimed to mislead classification. This work proposes a detection method based on combining non-linear dimensionality reduction and density estimation techniques. Our empirical findings show that the proposed approach is able to...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	129,299
2406.03082	Learning Solutions of Stochastic Optimization Problems with Bayesian Neural Networks	Mathematical solvers use parametrized Optimization Problems (OPs) as inputs to yield optimal decisions. In many real-world settings, some of these parameters are unknown or uncertain. Recent research focuses on predicting the value of these unknown parameters using available contextual features, aiming to decrease deci...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	461,083
2203.10400	Status Updating with an Energy Harvesting Sensor under Partial Battery Knowledge	We consider status updating under inexact knowledge of the battery level of an energy harvesting (EH) sensor that sends status updates about a random process to users via a cache-enabled edge node. More precisely, the control decisions are performed by relying only on the battery level knowledge captured from the last ...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	true	286,520
1408.4072	Indexing Cost Sensitive Prediction	Predictive models are often used for real-time decision making. However, typical machine learning techniques ignore feature evaluation cost, and focus solely on the accuracy of the machine learning models obtained utilizing all the features available. We develop algorithms and indexes to support cost-sensitive predicti...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	true	true	35,436
2410.14547	Surpassing the fundamental limits of distillation with catalysts	Quantum resource distillation is a fundamental task in quantum information science. Minimizing the distillation overhead, i.e., the amount of noisy source states required to produce some desired output state within some target error, is crucial for the scalability of quantum computation and communication. Here, we show...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	500,069
2307.07507	MGit: A Model Versioning and Management System	Models derived from other models are extremely common in machine learning (ML) today. For example, transfer learning is used to create task-specific models from "pre-trained" models through finetuning. This has led to an ecosystem where models are related to each other, sharing structure and often even parameter values...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	379,431
1310.3970	Green Communication via Power-optimized HARQ Protocols	Recently, efficient use of energy has become an essential research topic for green communication. This paper studies the effect of optimal power controllers on the performance of delay-sensitive communication setups utilizing hybrid automatic repeat request (HARQ). The results are obtained for repetition time diversity...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	27,781
cs/0601126	Approximate Linear Time ML Decoding on Tail-Biting Trellises in Two Rounds	A linear time approximate maximum likelihood decoding algorithm on tail-biting trellises is prsented, that requires exactly two rounds on the trellis. This is an adaptation of an algorithm proposed earlier with the advantage that it reduces the time complexity from O(mlogm) to O(m) where m is the number of nodes in the...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	539,243
2006.04198	EnK: Encoding time-information in convolution	Recent development in deep learning techniques has attracted attention in decoding and classification in EEG signals. Despite several efforts utilizing different features of EEG signals, a significant research challenge is to use time-dependent features in combination with local and global features. There have been sev...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	180,600
2306.02824	COMET: Learning Cardinality Constrained Mixture of Experts with Trees and Local Search	The sparse Mixture-of-Experts (Sparse-MoE) framework efficiently scales up model capacity in various domains, such as natural language processing and vision. Sparse-MoEs select a subset of the "experts" (thus, only a portion of the overall network) for each input sample using a sparse, trainable gate. Existing sparse g...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	371,067
2406.01570	Single Trajectory Conformal Prediction	We study the performance of risk-controlling prediction sets (RCPS), an empirical risk minimization-based formulation of conformal prediction, with a single trajectory of temporally correlated data from an unknown stochastic dynamical system. First, we use the blocking technique to show that RCPS attains performance gu...	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	460,369
1206.4679	Factorized Asymptotic Bayesian Hidden Markov Models	This paper addresses the issue of model selection for hidden Markov models (HMMs). We generalize factorized asymptotic Bayesian inference (FAB), which has been recently developed for model selection on independent hidden variables (i.e., mixture models), for time-dependent hidden variables. As with FAB in mixture model...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	16,730
2305.09390	Comprehensively Analyzing the Impact of Cyberattacks on Power Grids	The increasing digitalization of power grids and especially the shift towards IP-based communication drastically increase the susceptibility to cyberattacks, potentially leading to blackouts and physical damage. Understanding the involved risks, the interplay of communication and physical assets, and the effects of cyb...	false	false	false	false	false	false	false	false	false	false	true	false	true	false	false	false	false	false	364,620
2201.05959	Master Equation for Discrete-Time Stackelberg Mean Field Games with single leader	In this paper, we consider a discrete-time Stackelberg mean field game with a leader and an infinite number of followers. The leader and the followers each observe types privately that evolve as conditionally independent controlled Markov processes. The leader commits to a dynamic policy and the followers best respond ...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	true	275,565
2210.09671	Not All Poisons are Created Equal: Robust Training against Data Poisoning	Data poisoning causes misclassification of test time target examples by injecting maliciously crafted samples in the training data. Existing defenses are often effective only against a specific type of targeted attack, significantly degrade the generalization performance, or are prohibitive for standard deep learning p...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	324,624
1905.03635	Practical Algebraic Attack on DAGS	DAGS scheme is a key encapsulation mechanism (KEM) based on quasi-dyadic alternant codes that was submitted to NIST standardization process for a quantum resistant public key algorithm. Recently an algebraic attack was devised by Barelli and Couvreur (Asiacrypt 2018) that efficiently recovers the private key. It shows ...	false	false	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	130,240
1806.06004	Partially-Supervised Image Captioning	Image captioning models are becoming increasingly successful at describing the content of images in restricted domains. However, if these models are to function in the wild - for example, as assistants for people with impaired vision - a much larger number and variety of visual concepts must be understood. To address t...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	100,605
2409.10535	Learning Co-Speech Gesture Representations in Dialogue through Contrastive Learning: An Intrinsic Evaluation	In face-to-face dialogues, the form-meaning relationship of co-speech gestures varies depending on contextual factors such as what the gestures refer to and the individual characteristics of speakers. These factors make co-speech gesture representation learning challenging. How can we learn meaningful gestures represen...	false	false	true	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	488,782
2202.07652	Predicting on the Edge: Identifying Where a Larger Model Does Better	Much effort has been devoted to making large and more accurate models, but relatively little has been put into understanding which examples are benefiting from the added complexity. In this paper, we demonstrate and analyze the surprisingly tight link between a model's predictive uncertainty on individual examples and ...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	280,620
2310.17394	PSP: Pre-Training and Structure Prompt Tuning for Graph Neural Networks	Graph Neural Networks (GNNs) are powerful in learning semantics of graph data. Recently, a new paradigm "pre-train and prompt" has shown promising results in adapting GNNs to various tasks with less supervised data. The success of such paradigm can be attributed to the more consistent objectives of pre-training and tas...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	403,116
2211.11201	Self-Supervised 3D Traversability Estimation with Proxy Bank Guidance	Traversability estimation for mobile robots in off-road environments requires more than conventional semantic segmentation used in constrained environments like on-road conditions. Recently, approaches to learning a traversability estimation from past driving experiences in a self-supervised manner are arising as they ...	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	331,629
2311.12253	The limitation of neural nets for approximation and optimization	We are interested in assessing the use of neural networks as surrogate models to approximate and minimize objective functions in optimization problems. While neural networks are widely used for machine learning tasks such as classification and regression, their application in solving optimization problems has been limi...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	409,257
2402.02297	Denoising Diffusion-Based Control of Nonlinear Systems	We propose a novel approach based on Denoising Diffusion Probabilistic Models (DDPMs) to control nonlinear dynamical systems. DDPMs are the state-of-art of generative models that have achieved success in a wide variety of sampling tasks. In our framework, we pose the feedback control problem as a generative task of dra...	false	false	false	false	false	false	true	true	false	false	true	false	false	false	false	false	false	false	426,476
2302.03679	How Reliable is Your Regression Model's Uncertainty Under Real-World Distribution Shifts?	Many important computer vision applications are naturally formulated as regression problems. Within medical imaging, accurate regression models have the potential to automate various tasks, helping to lower costs and improve patient outcomes. Such safety-critical deployment does however require reliable estimation of m...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	344,434
1401.7239	Contexts of diffusion: Adoption of research synthesis in Social Work and Women's Studies	Texts reveal the subjects of interest in research fields, and the values, beliefs, and practices of researchers. In this study, texts are examined through bibliometric mapping and topic modeling to provide a birds eye view of the social dynamics associated with the diffusion of research synthesis methods in the context...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	true	30,435
1203.4043	Your Facebook Deactivated Friend or a Cloaked Spy (Extended Abstract)	With over 750 million active users, Facebook is the most famous social networking website. One particular aspect of Facebook widely discussed in the news and heavily researched in academic circles is the privacy of its users. In this paper we introduce a zero day privacy loophole in Facebook. We call this the deactivat...	false	false	false	true	false	false	false	false	false	false	false	false	false	true	false	false	false	false	15,011
2501.06151	PySpatial: A High-Speed Whole Slide Image Pathomics Toolkit	Whole Slide Image (WSI) analysis plays a crucial role in modern digital pathology, enabling large-scale feature extraction from tissue samples. However, traditional feature extraction pipelines based on tools like CellProfiler often involve lengthy workflows, requiring WSI segmentation into patches, feature extraction ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	523,857
2411.08798	Learning Gaussian Multi-Index Models with Gradient Flow: Time Complexity and Directional Convergence	This work focuses on the gradient flow dynamics of a neural network model that uses correlation loss to approximate a multi-index function on high-dimensional standard Gaussian data. Specifically, the multi-index function we consider is a sum of neurons $f^(x) \!=\! \sum_{j=1}^k \! \sigma^(v_j^T x)$ where $v_1, \dots...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	true	false	false	508,020
2011.11484	A Theory on AI Uncertainty Based on Rademacher Complexity and Shannon Entropy	In this paper, we present a theoretical discussion on AI deep learning neural network uncertainty investigation based on the classical Rademacher complexity and Shannon entropy. First it is shown that the classical Rademacher complexity and Shannon entropy is closely related by quantity by definitions. Secondly based o...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	207,838
2405.05959	Self-Supervised Learning of Time Series Representation via Diffusion Process and Imputation-Interpolation-Forecasting Mask	Time Series Representation Learning (TSRL) focuses on generating informative representations for various Time Series (TS) modeling tasks. Traditional Self-Supervised Learning (SSL) methods in TSRL fall into four main categories: reconstructive, adversarial, contrastive, and predictive, each with a common challenge of s...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	453,124
2406.02983	FREA: Feasibility-Guided Generation of Safety-Critical Scenarios with Reasonable Adversariality	Generating safety-critical scenarios, which are essential yet difficult to collect at scale, offers an effective method to evaluate the robustness of autonomous vehicles (AVs). Existing methods focus on optimizing adversariality while preserving the naturalness of scenarios, aiming to achieve a balance through data-dri...	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	false	461,037
2405.00401	Optimized Drug Design using Multi-Objective Evolutionary Algorithms with SELFIES	Computer aided drug design is a promising approach to reduce the tremendous costs, i.e. time and resources, for developing new medicinal drugs. It finds application in aiding the traversal of the vast chemical space of potentially useful compounds. In this paper, we deploy multi-objective evolutionary algorithms, namel...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	450,908
2104.14102	Comparing Visual Reasoning in Humans and AI	Recent advances in natural language processing and computer vision have led to AI models that interpret simple scenes at human levels. Yet, we do not have a complete understanding of how humans and AI models differ in their interpretation of more complex scenes. We created a dataset of complex scenes that contained hum...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	232,712
1902.05829	Deeply Supervised Multimodal Attentional Translation Embeddings for Visual Relationship Detection	Detecting visual relationships, i.e. <Subject, Predicate, Object> triplets, is a challenging Scene Understanding task approached in the past via linguistic priors or spatial information in a single feature branch. We introduce a new deeply supervised two-branch architecture, the Multimodal Attentional Translation Embed...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	121,631
1911.02727	Understanding Knowledge Distillation in Non-autoregressive Machine Translation	Non-autoregressive machine translation (NAT) systems predict a sequence of output tokens in parallel, achieving substantial improvements in generation speed compared to autoregressive models. Existing NAT models usually rely on the technique of knowledge distillation, which creates the training data from a pretrained a...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	152,437
2307.14361	A Hybrid Machine Learning Model for Classifying Gene Mutations in Cancer using LSTM, BiLSTM, CNN, GRU, and GloVe	In our study, we introduce a novel hybrid ensemble model that synergistically combines LSTM, BiLSTM, CNN, GRU, and GloVe embeddings for the classification of gene mutations in cancer. This model was rigorously tested using Kaggle's Personalized Medicine: Redefining Cancer Treatment dataset, demonstrating exceptional pe...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	381,902
2409.16298	BetterBodies: Reinforcement Learning guided Diffusion for Antibody Sequence Design	Antibodies offer great potential for the treatment of various diseases. However, the discovery of therapeutic antibodies through traditional wet lab methods is expensive and time-consuming. The use of generative models in designing antibodies therefore holds great promise, as it can reduce the time and resources requir...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	491,286
2501.08591	OpenMLDB: A Real-Time Relational Data Feature Computation System for Online ML	Efficient and consistent feature computation is crucial for a wide range of online ML applications. Typically, feature computation is divided into two distinct phases, i.e., offline stage for model training and online stage for model serving. These phases often rely on execution engines with different interface languag...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	true	false	524,831
2002.10543	Variational Wasserstein Barycenters for Geometric Clustering	We propose to compute Wasserstein barycenters (WBs) by solving for Monge maps with variational principle. We discuss the metric properties of WBs and explore their connections, especially the connections of Monge WBs, to K-means clustering and co-clustering. We also discuss the feasibility of Monge WBs on unbalanced me...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	165,430
2112.12508	From Procedures, Objects, Actors, Components, Services, to Agents -- A Comparative Analysis of the History and Evolution of Programming Abstractions	The objective of this chapter is to propose some retrospective analysis of the evolution of programming abstractions, from {\em procedures}, {\em objects}, {\em actors}, {\em components}, {\em services}, up to {\em agents}, %have some compare concepts of software component and of agent (and multi-agent system), %The me...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	272,980
1606.05706	Improving Agreement and Disagreement Identification in Online Discussions with A Socially-Tuned Sentiment Lexicon	We study the problem of agreement and disagreement detection in online discussions. An isotonic Conditional Random Fields (isotonic CRF) based sequential model is proposed to make predictions on sentence- or segment-level. We automatically construct a socially-tuned lexicon that is bootstrapped from existing general-pu...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	57,456
2101.10028	Correctable Erasure Patterns in Product Topologies	Locality enables storage systems to recover failed nodes from small subsets of surviving nodes. The setting where nodes are partitioned into subsets, each allowing for local recovery, is well understood. In this work we consider a generalization introduced by Gopalan et al., where, viewing the codewords as arrays, cons...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	216,797
2501.11938	Navigating Robot Swarm Through a Virtual Tube with Flow-Adaptive Distribution Control	With the rapid development of robot swarm technology and its diverse applications, navigating robot swarms through complex environments has emerged as a critical research direction. To ensure safe navigation and avoid potential collisions with obstacles, the concept of virtual tubes has been introduced to define safe a...	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	526,108
1710.02315	Simulation-based Validation of Smart Grids - Status Quo and Future Research Trends	Smart grid systems are characterized by high complexity due to interactions between a traditional passive network and active power electronic components, coupled using communication links. Additionally, automation and information technology plays an important role in order to operate and optimize such cyber-physical en...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	82,157
2203.12867	Revisiting Digital Twins: Origins, Fundamentals and Practices	The Digital Twins (DT) has quickly become a hot topic since it was proposed. It not only appears in all kinds of commercial propaganda, but also is widely quoted by academic circles. However, there are misstatements and misuse of the term DT in business and academy. This paper revisits Digital Twins and defines it to b...	false	true	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	287,427
2004.01149	Stopping explosion by penalising transmission to hubs in scale-free spatial random graphs	We study the spread of information in finite and infinite inhomogeneous spatial random graphs. We assume that each edge has a transmission cost that is a product of an i.i.d. random variable L and a penalty factor: edges between vertices of expected degrees w_1 and w_2 are penalised by a factor of (w_1w_2)^\mu for all ...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	170,829
1209.1688	Rank Centrality: Ranking from Pair-wise Comparisons	The question of aggregating pair-wise comparisons to obtain a global ranking over a collection of objects has been of interest for a very long time: be it ranking of online gamers (e.g. MSR's TrueSkill system) and chess players, aggregating social opinions, or deciding which product to sell based on transactions. In mo...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	18,458
2202.03634	Multi-Agent Path Finding with Prioritized Communication Learning	Multi-agent pathfinding (MAPF) has been widely used to solve large-scale real-world problems, e.g., automation warehouses. The learning-based, fully decentralized framework has been introduced to alleviate real-time problems and simultaneously pursue optimal planning policy. However, existing methods might generate sig...	false	false	false	false	false	false	true	true	false	false	false	false	false	false	true	false	false	true	279,285
2211.06654	PMDS Array Codes With Small Sub-packetization, Small Repair Bandwidth/Rebuilding Access	Partial maximum distance separable (PMDS) codes are a kind of erasure codes where the nodes are divided into multiple groups with each forming an MDS code with a smaller code length, thus they allow repairing a failed node with only a few helper nodes and can correct all erasure patterns that are information-theoretica...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	329,972
2304.11079	Academic Writing with GPT-3.5: Reflections on Practices, Efficacy and Transparency	The debate around the use of GPT 3.5 has been a popular topic among academics since the release of ChatGPT. Whilst some have argued for the advantages of GPT 3.5 in enhancing academic writing, others have raised concerns such as plagiarism, the spread of false information, and ecological issues. The need for finding wa...	true	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	359,667
2311.07361	The Impact of Large Language Models on Scientific Discovery: a Preliminary Study using GPT-4	In recent years, groundbreaking advancements in natural language processing have culminated in the emergence of powerful large language models (LLMs), which have showcased remarkable capabilities across a vast array of domains, including the understanding, generation, and translation of natural language, and even tasks...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	407,294
2409.15365	Novel Saliency Analysis for the Forward Forward Algorithm	Incorporating the Forward Forward algorithm into neural network training represents a transformative shift from traditional methods, introducing a dual forward mechanism that streamlines the learning process by bypassing the complexities of derivative propagation. This method is noted for its simplicity and efficiency ...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	490,889
2208.01511	Unimodal Mono-Partite Matching in a Bandit Setting	We tackle a new emerging problem, which is finding an optimal monopartite matching in a weighted graph. The semi-bandit version, where a full matching is sampled at each iteration, has been addressed by \cite{ADMA}, creating an algorithm with an expected regret matching $O(\frac{L\log(L)}{\Delta}\log(T))$ with $2L$ pla...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	311,184
1610.01860	Distortion Varieties	The distortion varieties of a given projective variety are parametrized by duplicating coordinates and multiplying them with monomials. We study their degrees and defining equations. Exact formulas are obtained for the case of one-parameter distortions. These are based on Chow polytopes and Gr\"obner bases. Multi-param...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	62,020
2004.05199	Hamiltonian Dynamics for Real-World Shape Interpolation	We revisit the classical problem of 3D shape interpolation and propose a novel, physically plausible approach based on Hamiltonian dynamics. While most prior work focuses on synthetic input shapes, our formulation is designed to be applicable to real-world scans with imperfect input correspondences and various types of...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	172,114
1803.00185	Facial Expression Recognition Based on Complexity Perception Classification Algorithm	Facial expression recognition (FER) has always been a challenging issue in computer vision. The different expressions of emotion and uncontrolled environmental factors lead to inconsistencies in the complexity of FER and variability of between expression categories, which is often overlooked in most facial expression r...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	91,616
2306.09251	Towards Faster Non-Asymptotic Convergence for Diffusion-Based Generative Models	Diffusion models, which convert noise into new data instances by learning to reverse a Markov diffusion process, have become a cornerstone in contemporary generative modeling. While their practical power has now been widely recognized, the theoretical underpinnings remain far from mature. In this work, we develop a sui...	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	373,732
2404.14968	CenterArt: Joint Shape Reconstruction and 6-DoF Grasp Estimation of Articulated Objects	Precisely grasping and reconstructing articulated objects is key to enabling general robotic manipulation. In this paper, we propose CenterArt, a novel approach for simultaneous 3D shape reconstruction and 6-DoF grasp estimation of articulated objects. CenterArt takes RGB-D images of the scene as input and first predic...	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	448,893
2405.12788	What Have We Achieved on Non-autoregressive Translation?	Recent advances have made non-autoregressive (NAT) translation comparable to autoregressive methods (AT). However, their evaluation using BLEU has been shown to weakly correlate with human annotations. Limited research compares non-autoregressive translation and autoregressive translation comprehensively, leaving uncer...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	455,654
1812.03410	Binary Input Layer: Training of CNN models with binary input data	For the efficient execution of deep convolutional neural networks (CNN) on edge devices, various approaches have been presented which reduce the bit width of the network parameters down to 1 bit. Binarization of the first layer was always excluded, as it leads to a significant error increase. Here, we present the novel...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	116,005
1801.09927	Diagnose like a Radiologist: Attention Guided Convolutional Neural Network for Thorax Disease Classification	This paper considers the task of thorax disease classification on chest X-ray images. Existing methods generally use the global image as input for network learning. Such a strategy is limited in two aspects. 1) A thorax disease usually happens in (small) localized areas which are disease specific. Training CNNs using g...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	89,198

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