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
2209.13052	Training Efficient Controllers via Analytic Policy Gradient	Control design for robotic systems is complex and often requires solving an optimization to follow a trajectory accurately. Online optimization approaches like Model Predictive Control (MPC) have been shown to achieve great tracking performance, but require high computing power. Conversely, learning-based offline optim...	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	false	319,753
2302.09422	Neural Attention Memory	We propose a novel perspective of the attention mechanism by reinventing it as a memory architecture for neural networks, namely Neural Attention Memory (NAM). NAM is a memory structure that is both readable and writable via differentiable linear algebra operations. We explore three use cases of NAM: memory-augmented n...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	346,418
2001.10893	Privacy-Preserving Gaussian Process Regression -- A Modular Approach to the Application of Homomorphic Encryption	Much of machine learning relies on the use of large amounts of data to train models to make predictions. When this data comes from multiple sources, for example when evaluation of data against a machine learning model is offered as a service, there can be privacy issues and legal concerns over the sharing of data. Full...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	161,929
2311.16751	MultiCBR: Multi-view Contrastive Learning for Bundle Recommendation	Bundle recommendation seeks to recommend a bundle of related items to users to improve both user experience and the profits of platform. Existing bundle recommendation models have progressed from capturing only user-bundle interactions to the modeling of multiple relations among users, bundles and items. CrossCBR, in p...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	411,034
1908.11722	Fact-Checking Meets Fauxtography: Verifying Claims About Images	The recent explosion of false claims in social media and on the Web in general has given rise to a lot of manual fact-checking initiatives. Unfortunately, the number of claims that need to be fact-checked is several orders of magnitude larger than what humans can handle manually. Thus, there has been a lot of research ...	false	false	false	false	true	true	false	false	true	false	false	true	false	false	false	false	false	false	143,452
1202.5820	Tag-Aware Recommender Systems: A State-of-the-art Survey	In the past decade, Social Tagging Systems have attracted increasing attention from both physical and computer science communities. Besides the underlying structure and dynamics of tagging systems, many efforts have been addressed to unify tagging information to reveal user behaviors and preferences, extract the latent...	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	false	14,588
1402.0068	Radiation Pattern of Patch Antenna with Slits	The Microstrip antenna has been commercially used in many applications, such as direct broadcast satellite service, mobile satellite communications, global positioning system, medical hyperthermia usage, etc. The patch antenna of the size reduction at a given operating frequency is obtained. Mobile personal communicati...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	30,531
1905.00424	An ADMM Based Framework for AutoML Pipeline Configuration	We study the AutoML problem of automatically configuring machine learning pipelines by jointly selecting algorithms and their appropriate hyper-parameters for all steps in supervised learning pipelines. This black-box (gradient-free) optimization with mixed integer & continuous variables is a challenging problem. We pr...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	129,466
2109.03478	Cross-Site Severity Assessment of COVID-19 from CT Images via Domain Adaptation	Early and accurate severity assessment of Coronavirus disease 2019 (COVID-19) based on computed tomography (CT) images offers a great help to the estimation of intensive care unit event and the clinical decision of treatment planning. To augment the labeled data and improve the generalization ability of the classificat...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	254,084
2201.05697	An efficient aggregation method for the symbolic representation of temporal data	Symbolic representations are a useful tool for the dimension reduction of temporal data, allowing for the efficient storage of and information retrieval from time series. They can also enhance the training of machine learning algorithms on time series data through noise reduction and reduced sensitivity to hyperparamet...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	275,455
2210.10108	Parallel Inversion of Neural Radiance Fields for Robust Pose Estimation	We present a parallelized optimization method based on fast Neural Radiance Fields (NeRF) for estimating 6-DoF pose of a camera with respect to an object or scene. Given a single observed RGB image of the target, we can predict the translation and rotation of the camera by minimizing the residual between pixels rendere...	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	324,787
cmp-lg/9505019	Measuring semantic complexity	We define {\em semantic complexity} using a new concept of {\em meaning automata}. We measure the semantic complexity of understanding of prepositional phrases, of an "in depth understanding system", and of a natural language interface to an on-line calendar. We argue that it is possible to measure some semantic comple...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	536,378
2410.10591	Online waveform selection for cognitive radar	Designing a cognitive radar system capable of adapting its parameters is challenging, particularly when tasked with tracking a ballistic missile throughout its entire flight. In this work, we focus on proposing adaptive algorithms that select waveform parameters in an online fashion. Our novelty lies in formulating the...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	498,149
2102.11976	Learner-Private Convex Optimization	Convex optimization with feedback is a framework where a learner relies on iterative queries and feedback to arrive at the minimizer of a convex function. It has gained considerable popularity thanks to its scalability in large-scale optimization and machine learning. The repeated interactions, however, expose the lear...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	221,576
2312.08533	World Models via Policy-Guided Trajectory Diffusion	World models are a powerful tool for developing intelligent agents. By predicting the outcome of a sequence of actions, world models enable policies to be optimised via on-policy reinforcement learning (RL) using synthetic data, i.e. in "in imagination". Existing world models are autoregressive in that they interleave ...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	415,331
1801.08439	Analyzing Similarity in Mathematical Content To Enhance the Detection of Academic Plagiarism	Despite the effort put into the detection of academic plagiarism, it continues to be a ubiquitous problem spanning all disciplines. Various tools have been developed to assist human inspectors by automatically identifying suspicious documents. However, to our knowledge currently none of these tools use mathematical con...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	88,952
1803.06657	Sdf-GAN: Semi-supervised Depth Fusion with Multi-scale Adversarial Networks	Refining raw disparity maps from different algorithms to exploit their complementary advantages is still challenging. Uncertainty estimation and complex disparity relationships among pixels limit the accuracy and robustness of existing methods and there is no standard method for fusion of different kinds of depth data....	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	92,889
2401.04575	Let's Go Shopping (LGS) -- Web-Scale Image-Text Dataset for Visual Concept Understanding	Vision and vision-language applications of neural networks, such as image classification and captioning, rely on large-scale annotated datasets that require non-trivial data-collecting processes. This time-consuming endeavor hinders the emergence of large-scale datasets, limiting researchers and practitioners to a smal...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	420,474
1302.6832	Model-Based Diagnosis with Qualitative Temporal Uncertainty	In this paper we describe a framework for model-based diagnosis of dynamic systems, which extends previous work in this field by using and expressing temporal uncertainty in the form of qualitative interval relations a la Allen. Based on a logical framework extended by qualitative and quantitative temporal constraints ...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	22,462
2310.12842	Model-agnostic variable importance for predictive uncertainty: an entropy-based approach	In order to trust the predictions of a machine learning algorithm, it is necessary to understand the factors that contribute to those predictions. In the case of probabilistic and uncertainty-aware models, it is necessary to understand not only the reasons for the predictions themselves, but also the reasons for the mo...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	401,176
1901.06863	Reed-Solomon Codes over Fields of Characteristic Zero	We study Reed--Solomon codes over arbitrary fields, inspired by several recent papers dealing with Gabidulin codes over fields of characteristic zero. Over the field of rational numbers, we derive bounds on the coefficient growth during encoding and the bit complexity of decoding, which is polynomial in the code length...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	119,107
2306.09361	MFSN: Multi-perspective Fusion Search Network For Pre-training Knowledge in Speech Emotion Recognition	Speech Emotion Recognition (SER) is an important research topic in human-computer interaction. Many recent works focus on directly extracting emotional cues through pre-trained knowledge, frequently overlooking considerations of appropriateness and comprehensiveness. Therefore, we propose a novel framework for pre-trai...	false	false	true	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	373,798
1709.03742	Dependencies: Formalising Semantic Catenae for Information Retrieval	Building machines that can understand text like humans is an AI-complete problem. A great deal of research has already gone into this, with astounding results, allowing everyday people to discuss with their telephones, or have their reading materials analysed and classified by computers. A prerequisite for processing t...	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	80,522
2304.00569	Stability Bounds for Learning-Based Adaptive Control of Discrete-Time Multi-Dimensional Stochastic Linear Systems with Input Constraints	We consider the problem of adaptive stabilization for discrete-time, multi-dimensional linear systems with bounded control input constraints and unbounded stochastic disturbances, where the parameters of the true system are unknown. To address this challenge, we propose a certainty-equivalent control scheme which combi...	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	355,734
2012.01012	Information Theory in Density Destructors	Density destructors are differentiable and invertible transforms that map multivariate PDFs of arbitrary structure (low entropy) into non-structured PDFs (maximum entropy). Multivariate Gaussianization and multivariate equalization are specific examples of this family, which break down the complexity of the original PD...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	209,304
2301.03656	Towards Multifaceted Human-Centered AI	Human-centered AI workflows involve stakeholders with multiple roles interacting with each other and automated agents to accomplish diverse tasks. In this paper, we call for a holistic view when designing support mechanisms, such as interaction paradigms, interfaces, and systems, for these multifaceted workflows.	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	true	false	339,855
1704.01308	Flexibility Analysis for Smart Grid Demand Response	Flexibility is a key enabler for the smart grid, required to facilitate Demand Side Management (DSM) programs, managing electrical consumption to reduce peaks, balance renewable generation and provide ancillary services to the grid. Flexibility analysis is required to identify and quantify the available electrical load...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	71,240
2205.02548	Rate-Splitting Multiple Access for 6G -- Part I: Principles, Applications and Future Works	This letter is the first part of a three-part tutorial focusing on rate-splitting multiple access (RSMA) for 6G. As Part I of the tutorial, the letter presents the basics of RSMA and its applications in light of 6G. To begin with, we first delineate the design principle and basic transmission frameworks of downlink and...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	294,977
1910.00696	Improvement of Multiparametric MR Image Segmentation by Augmenting the Data with Generative Adversarial Networks for Glioma Patients	Every year thousands of patients are diagnosed with a glioma, a type of malignant brain tumor. Physicians use MR images as a key tool in the diagnosis and treatment of these patients. Neural networks show great potential to aid physicians in the medical image analysis. This study investigates the use of varying amounts...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	147,733
2111.12159	Rhythm is a Dancer: Music-Driven Motion Synthesis with Global Structure	Synthesizing human motion with a global structure, such as a choreography, is a challenging task. Existing methods tend to concentrate on local smooth pose transitions and neglect the global context or the theme of the motion. In this work, we present a music-driven motion synthesis framework that generates long-term s...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	267,885
1907.10889	Performance Evaluation of Two-layer lossless HDR Coding using Histogram Packing Technique under Various Tone-mapping Operators	We proposed a lossless two-layer HDR coding method using a histogram packing technique. The proposed method was demonstrated to outperform the normative JPEG XT encoder, under the use of the default tone-mapping operator. However, the performance under various tone-mapping operators has not been discussed. In this pape...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	139,728
2407.06613	Sparse-DeRF: Deblurred Neural Radiance Fields from Sparse View	Recent studies construct deblurred neural radiance fields (DeRF) using dozens of blurry images, which are not practical scenarios if only a limited number of blurry images are available. This paper focuses on constructing DeRF from sparse-view for more pragmatic real-world scenarios. As observed in our experiments, est...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	471,466
2405.12654	Utilizing Description Logics for Global Explanations of Heterogeneous Graph Neural Networks	Graph Neural Networks (GNNs) are effective for node classification in graph-structured data, but they lack explainability, especially at the global level. Current research mainly utilizes subgraphs of the input as local explanations or generates new graphs as global explanations. However, these graph-based methods are ...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	455,605
2003.00779	Data-Driven Control of Unknown Systems: A Linear Programming Approach	We consider the problem of discounted optimal state-feedback regulation for general unknown deterministic discrete-time systems. It is well known that open-loop instability of systems, non-quadratic cost functions and complex nonlinear dynamics, as well as the on-policy behavior of many reinforcement learning (RL) algo...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	166,415
2405.18047	2BP: 2-Stage Backpropagation	As Deep Neural Networks (DNNs) grow in size and complexity, they often exceed the memory capacity of a single accelerator, necessitating the sharding of model parameters across multiple accelerators. Pipeline parallelism is a commonly used sharding strategy for training large DNNs. However, current implementations of p...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	true	458,253
2501.14568	Hybrid Quantum-Classical Multi-Agent Pathfinding	Multi-Agent Path Finding (MAPF) focuses on determining conflict-free paths for multiple agents navigating through a shared space to reach specified goal locations. This problem becomes computationally challenging, particularly when handling large numbers of agents, as frequently encountered in practical applications li...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	527,169
2106.12693	Deep Learning for Network Traffic Classification	Monitoring network traffic to identify content, services, and applications is an active research topic in network traffic control systems. While modern firewalls provide the capability to decrypt packets, this is not appealing for privacy advocates. Hence, identifying any information from encrypted traffic is a challen...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	true	242,802
2102.06802	Blind stain separation using model-aware generative learning and its applications on fluorescence microscopy images	Multiple stains are usually used to highlight biological substances in biomedical image analysis. To decompose multiple stains for co-localization quantification, blind source separation is usually performed. Prior model-based stain separation methods usually rely on stains' spatial distributions over an image and may ...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	219,882
2103.08233	Robust MAML: Prioritization task buffer with adaptive learning process for model-agnostic meta-learning	Model agnostic meta-learning (MAML) is a popular state-of-the-art meta-learning algorithm that provides good weight initialization of a model given a variety of learning tasks. The model initialized by provided weight can be fine-tuned to an unseen task despite only using a small amount of samples and within a few adap...	false	false	false	false	true	false	true	false	false	false	true	false	false	false	false	false	false	false	224,839
2104.05382	Dual Discriminator Adversarial Distillation for Data-free Model Compression	Knowledge distillation has been widely used to produce portable and efficient neural networks which can be well applied on edge devices for computer vision tasks. However, almost all top-performing knowledge distillation methods need to access the original training data, which usually has a huge size and is often unava...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	229,707
2202.12737	Alpha-NML Universal Predictors	Inspired by the connection between classical regret measures employed in universal prediction and R\'{e}nyi divergence, we introduce a new class of universal predictors that depend on a real parameter $\alpha\geq 1$. This class interpolates two well-known predictors, the mixture estimators, that include the Laplace and...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	282,346
1306.5326	Cryptanalysis of a non-commutative key exchange protocol	In the papers by Alvarez et al. and Pathak and Sanghi a non-commutative based public key exchange is described. A similiar version of it has also been patented (US7184551). In this paper we present a polynomial time attack that breaks the variants of the protocol presented in the two papers. Moreover we show that break...	false	false	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	25,395
2004.04315	Large Arabic Twitter Dataset on COVID-19	The 2019 coronavirus disease (COVID-19), emerged late December 2019 in China, is now rapidly spreading across the globe. At the time of writing this paper, the number of global confirmed cases has passed two millions and half with over 180,000 fatalities. Many countries have enforced strict social distancing policies t...	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	false	false	false	171,840
2302.14727	Automatically Classifying Emotions based on Text: A Comparative Exploration of Different Datasets	Emotion Classification based on text is a task with many applications which has received growing interest in recent years. This paper presents a preliminary study with the goal to help researchers and practitioners gain insight into relatively new datasets as well as emotion classification in general. We focus on three...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	348,413
2009.09767	Ranky : An Approach to Solve Distributed SVD on Large Sparse Matrices	Singular Value Decomposition (SVD) is a well studied research topic in many fields and applications from data mining to image processing. Data arising from these applications can be represented as a matrix where it is large and sparse. Most existing algorithms are used to calculate singular values, left and right singu...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	196,681
1905.12879	Multi-Objective Generalized Linear Bandits	In this paper, we study the multi-objective bandits (MOB) problem, where a learner repeatedly selects one arm to play and then receives a reward vector consisting of multiple objectives. MOB has found many real-world applications as varied as online recommendation and network routing. On the other hand, these applicati...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	132,915
2409.18512	EmoPro: A Prompt Selection Strategy for Emotional Expression in LM-based Speech Synthesis	Recent advancements in speech synthesis models, trained on extensive datasets, have demonstrated remarkable zero-shot capabilities. These models can control content, timbre, and emotion in generated speech based on prompt inputs. Despite these advancements, the choice of prompts significantly impacts the output quality...	false	false	true	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	492,290
2011.11479	TSP: Temporally-Sensitive Pretraining of Video Encoders for Localization Tasks	Due to the large memory footprint of untrimmed videos, current state-of-the-art video localization methods operate atop precomputed video clip features. These features are extracted from video encoders typically trained for trimmed action classification tasks, making such features not necessarily suitable for temporal ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	207,836
2411.04269	Increasing the scalability of graph convolution for FPGA-implemented event-based vision	Event cameras are becoming increasingly popular as an alternative to traditional frame-based vision sensors, especially in mobile robotics. Taking full advantage of their high temporal resolution, high dynamic range, low power consumption and sparsity of event data, which only reflects changes in the observed scene, re...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	506,199
2212.00697	Simultaneously Transmitting and Reflecting RIS-Aided Mobile Edge Computing: Computation Rate Maximization	In this paper, the novel simultaneously transmitting and reflecting (STAR) reconfigurable intelligent surface (RIS), which enables full-space coverage on users located on both sides of the surface, is investigated in the multi-user mobile edge computing (MEC) system. A computation rate maximization problem is formulate...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	334,154
1207.4807	Finite Alphabet Iterative Decoders, Part II: Improved Guaranteed Error Correction of LDPC Codes via Iterative Decoder Diversity	Recently, we introduced a new class of finite alphabet iterative decoders (FAIDs) for low-density parity-check (LDPC) codes. These decoders are capable of surpassing belief propagation in the error floor region on the Binary Symmetric channel with much lower complexity. In this paper, we introduce a a novel scheme to f...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	17,669
2411.08059	Segmentized quarantine policy for managing a tradeoff between containment of infectious disease and social cost of quarantine	By the end of 2021, COVID-19 had spread to over 230 countries, with over 5.4 million deaths. To contain its spread, many countries implemented non-pharmaceutical interventions, notably contact tracing and self-quarantine policies. However, these measures came with significant social costs, highlighting the need for mor...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	507,763
2010.04482	Word Level Language Identification in English Telugu Code Mixed Data	In a multilingual or sociolingual configuration Intra-sentential Code Switching (ICS) or Code Mixing (CM) is frequently observed nowadays. In the world, most of the people know more than one language. CM usage is especially apparent in social media platforms. Moreover, ICS is particularly significant in the context of ...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	199,756
2007.11186	Instance-aware Self-supervised Learning for Nuclei Segmentation	Due to the wide existence and large morphological variances of nuclei, accurate nuclei instance segmentation is still one of the most challenging tasks in computational pathology. The annotating of nuclei instances, requiring experienced pathologists to manually draw the contours, is extremely laborious and expensive, ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	188,489
1910.11669	Learning Task-Oriented Grasping from Human Activity Datasets	We propose to leverage a real-world, human activity RGB dataset to teach a robot Task-Oriented Grasping (TOG). We develop a model that takes as input an RGB image and outputs a hand pose and configuration as well as an object pose and a shape. We follow the insight that jointly estimating hand and object poses increase...	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	150,853
1109.2954	A New Framework for Network Disruption	Traditional network disruption approaches focus on disconnecting or lengthening paths in the network. We present a new framework for network disruption that attempts to reroute flow through critical vertices via vertex deletion, under the assumption that this will render those vertices vulnerable to future attacks. We ...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	12,150
2304.00988	The Music Annotation Pattern	The annotation of music content is a complex process to represent due to its inherent multifaceted, subjectivity, and interdisciplinary nature. Numerous systems and conventions for annotating music have been developed as independent standards over the past decades. Little has been done to make them interoperable, which...	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	355,887
2008.09727	Seasonal-adjustment Based Feature Selection Method for Large-scale Search Engine Logs	Search engine logs have a great potential in tracking and predicting outbreaks of infectious disease. More precisely, one can use the search volume of some search terms to predict the infection rate of an infectious disease in nearly real-time. However, conducting accurate and stable prediction of outbreaks using searc...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	192,803
1906.11994	Cascade-BGNN: Toward Efficient Self-supervised Representation Learning on Large-scale Bipartite Graphs	Bipartite graphs have been used to represent data relationships in many data-mining applications such as in E-commerce recommendation systems. Since learning in graph space is more complicated than in Euclidian space, recent studies have extensively utilized neural nets to effectively and efficiently embed a graph's no...	false	false	false	true	true	false	true	false	false	false	false	false	false	false	false	false	false	false	136,817
2310.03339	Probabilistic Forecasting of Day-Ahead Electricity Prices and their Volatility with LSTMs	Accurate forecasts of electricity prices are crucial for the management of electric power systems and the development of smart applications. European electricity prices have risen substantially and became highly volatile after the Russian invasion of Ukraine, challenging established forecasting methods. Here, we presen...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	397,243
2110.15489	GalilAI: Out-of-Task Distribution Detection using Causal Active Experimentation for Safe Transfer RL	Out-of-distribution (OOD) detection is a well-studied topic in supervised learning. Extending the successes in supervised learning methods to the reinforcement learning (RL) setting, however, is difficult due to the data generating process - RL agents actively query their environment for data, and the data are a functi...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	263,894
1811.09919	A Method for Analysis of Patient Speech in Dialogue for Dementia Detection	We present an approach to automatic detection of Alzheimer's type dementia based on characteristics of spontaneous spoken language dialogue consisting of interviews recorded in natural settings. The proposed method employs additive logistic regression (a machine learning boosting method) on content-free features extrac...	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	114,360
2411.03445	Solving Trojan Detection Competitions with Linear Weight Classification	Neural networks can conceal malicious Trojan backdoors that allow a trigger to covertly change the model behavior. Detecting signs of these backdoors, particularly without access to any triggered data, is the subject of ongoing research and open challenges. In one common formulation of the problem, we are given a set o...	false	false	false	false	true	false	true	false	true	false	false	true	true	false	false	false	false	false	505,897
2406.17789	Spanish and LLM Benchmarks: is MMLU Lost in Translation?	The evaluation of Large Language Models (LLMs) is a key element in their continuous improvement process and many benchmarks have been developed to assess the performance of LLMs in different tasks and topics. As LLMs become adopted worldwide, evaluating them in languages other than English is increasingly important. Ho...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	467,726
1902.00230	Some Enumeration Problems in the Duplication-Loss Model of Genome Rearrangement	Tandem-duplication-random-loss (TDRL) is an important genome rearrangement operation studied in evolutionary biology. This paper investigates some of the formal properties of TDRL operations on the symmetric group (the space of permutations over an $ n $-set). In particular, the cardinality of `balls' of radius one in ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	120,360
1710.08015	Bringing Semantic Structures to User Intent Detection in Online Medical Queries	The Internet has revolutionized healthcare by offering medical information ubiquitously to patients via web search. The healthcare status, complex medical information needs of patients are expressed diversely and implicitly in their medical text queries. Aiming to better capture a focused picture of user's medical-rela...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	83,030
2305.12816	Farewell to Aimless Large-scale Pretraining: Influential Subset Selection for Language Model	Pretrained language models have achieved remarkable success in various natural language processing tasks. However, pretraining has recently shifted toward larger models and larger data, and this has resulted in significant computational and energy costs. In this paper, we propose Influence Subset Selection (ISS) for la...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	366,204
2303.13846	Feature Separation and Recalibration for Adversarial Robustness	Deep neural networks are susceptible to adversarial attacks due to the accumulation of perturbations in the feature level, and numerous works have boosted model robustness by deactivating the non-robust feature activations that cause model mispredictions. However, we claim that these malicious activations still contain...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	353,857
2107.07869	Nearest neighbor Methods and their Applications in Design of 5G & Beyond Wireless Networks	In this paper, we present an overview of Nearest neighbor (NN) methods, which are frequently employed for solving classification problems using supervised learning. The article concisely introduces the theoretical background, algorithmic, and implementation aspects along with the key applications. From an application s...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	246,553
2404.11127	D-Aug: Enhancing Data Augmentation for Dynamic LiDAR Scenes	Creating large LiDAR datasets with pixel-level labeling poses significant challenges. While numerous data augmentation methods have been developed to reduce the reliance on manual labeling, these methods predominantly focus on static scenes and they overlook the importance of data augmentation for dynamic scenes, which...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	447,392
2103.02583	Simulating time to event prediction with spatiotemporal echocardiography deep learning	Integrating methods for time-to-event prediction with diagnostic imaging modalities is of considerable interest, as accurate estimates of survival requires accounting for censoring of individuals within the observation period. New methods for time-to-event prediction have been developed by extending the cox-proportiona...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	223,009
1702.06831	Using Redescription Mining to Relate Clinical and Biological Characteristics of Cognitively Impaired and Alzheimer's Disease Patients	We used redescription mining to find interpretable rules revealing associations between those determinants that provide insights about the Alzheimer's disease (AD). We extended the CLUS-RM redescription mining algorithm to a constraint-based redescription mining (CBRM) setting, which enables several modes of targeted e...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	68,682
2006.06896	A New Perspective on Learning Context-Specific Independence	Local structure such as context-specific independence (CSI) has received much attention in the probabilistic graphical model (PGM) literature, as it facilitates the modeling of large complex systems, as well as for reasoning with them. In this paper, we provide a new perspective on how to learn CSIs from data. We propo...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	181,601
2406.08756	Optimizing Large Model Training through Overlapped Activation Recomputation	Large model training has been using recomputation to alleviate the memory pressure and pipelining to exploit the parallelism of data, tensor, and devices. The existing recomputation approaches may incur up to 40% overhead when training real-world models, e.g., the GPT model with 22B parameters. This is because they are...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	463,609
2310.11657	ChatGPT-guided Semantics for Zero-shot Learning	Zero-shot learning (ZSL) aims to classify objects that are not observed or seen during training. It relies on class semantic description to transfer knowledge from the seen classes to the unseen classes. Existing methods of obtaining class semantics include manual attributes or automatic word vectors from language mode...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	400,723
2410.09675	COrAL: Order-Agnostic Language Modeling for Efficient Iterative Refinement	Iterative refinement has emerged as an effective paradigm for enhancing the capabilities of large language models (LLMs) on complex tasks. However, existing approaches typically implement iterative refinement at the application or prompting level, relying on autoregressive (AR) modeling. The sequential token generation...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	497,712
2011.07557	Learn an Effective Lip Reading Model without Pains	Lip reading, also known as visual speech recognition, aims to recognize the speech content from videos by analyzing the lip dynamics. There have been several appealing progress in recent years, benefiting much from the rapidly developed deep learning techniques and the recent large-scale lip-reading datasets. Most exis...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	206,598
2303.00890	Comparison of High-Dimensional Bayesian Optimization Algorithms on BBOB	Bayesian Optimization (BO) is a class of black-box, surrogate-based heuristics that can efficiently optimize problems that are expensive to evaluate, and hence admit only small evaluation budgets. BO is particularly popular for solving numerical optimization problems in industry, where the evaluation of objective funct...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	348,737
2108.10219	Study of Proximal Normalized Subband Adaptive Algorithm for Acoustic Echo Cancellation	In this paper, we propose a novel normalized subband adaptive filter algorithm suited for sparse scenarios, which combines the proportionate and sparsity-aware mechanisms. The proposed algorithm is derived based on the proximal forward-backward splitting and the soft-thresholding methods. We analyze the mean and mean s...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	251,831
2211.08672	Mitigating Urban-Rural Disparities in Contrastive Representation Learning with Satellite Imagery	Satellite imagery is being leveraged for many societally critical tasks across climate, economics, and public health. Yet, because of heterogeneity in landscapes (e.g. how a road looks in different places), models can show disparate performance across geographic areas. Given the important potential of disparities in al...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	330,724
2202.10678	Sequential Information Design: Markov Persuasion Process and Its Efficient Reinforcement Learning	In today's economy, it becomes important for Internet platforms to consider the sequential information design problem to align its long term interest with incentives of the gig service providers. This paper proposes a novel model of sequential information design, namely the Markov persuasion processes (MPPs), where a s...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	true	281,620
cs/0509028	Projecting the Forward Rate Flow onto a Finite Dimensional Manifold	Given a Heath-Jarrow-Morton (HJM) interest rate model $\mathcal{M}$ and a parametrized family of finite dimensional forward rate curves $\mathcal{G}$, this paper provides a technique for projecting the infinite dimensional forward rate curve $r_{t}$ given by $\mathcal{M}$ onto the finite dimensional manifold $\mathcal{...	false	true	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	538,948
2409.13425	Procedure Model for Building Knowledge Graphs for Industry Applications	Enterprise knowledge graphs combine business data and organizational knowledge by means of a semantic network of concepts, properties, individuals and relationships. The graph-based integration of previously unconnected information with domain knowledge provides new insights and enables intelligent business application...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	489,987
2002.09369	Performance Evaluation of Adaptive Cooperative NOMA Protocol at Road Junctions	Vehicular communications (VCs) protocols offer useful contributions in the context of accident prevention thanks to the transmission of alert messages. This is even truer at road intersections since these areas exhibit higher collision risks and accidents rate. On the other hand, non-orthogonal multiple access (NOMA) h...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	165,043
2205.11836	Charon: a FrameNet Annotation Tool for Multimodal Corpora	This paper presents Charon, a web tool for annotating multimodal corpora with FrameNet categories. Annotation can be made for corpora containing both static images and video sequences paired - or not - with text sequences. The pipeline features, besides the annotation interface, corpus import and pre-processing tools.	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	298,307
2206.12450	Fast and Optimal Adaptive Tracking Control: A Novel Meta-Reinforcement Learning via Conditional Generative Adversarial Net	The control of nonlinear systems with unknown dynamics has been a significant field of research for many years. This paper presents a novel data-driven optimal adaptive control structure with less control effort and faster adaptation than standard adaptive control counterparts. The proposed control structure utilizes t...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	304,603
2406.18770	ADO-LLM: Analog Design Bayesian Optimization with In-Context Learning of Large Language Models	Analog circuit design requires substantial human expertise and involvement, which is a significant roadblock to design productivity. Bayesian Optimization (BO), a popular machine learning based optimization strategy, has been leveraged to automate analog design given its applicability across various circuit topologies ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	468,163
2310.06213	GeoLLM: Extracting Geospatial Knowledge from Large Language Models	The application of machine learning (ML) in a range of geospatial tasks is increasingly common but often relies on globally available covariates such as satellite imagery that can either be expensive or lack predictive power. Here we explore the question of whether the vast amounts of knowledge found in Internet langua...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	398,471
2411.17746	UVCG: Leveraging Temporal Consistency for Universal Video Protection	The security risks of AI-driven video editing have garnered significant attention. Although recent studies indicate that adding perturbations to images can protect them from malicious edits, directly applying image-based methods to perturb each frame in a video becomes ineffective, as video editing techniques leverage ...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	511,579
2403.10182	Fast and reliable uncertainty quantification with neural network ensembles for industrial image classification	Image classification with neural networks (NNs) is widely used in industrial processes, situations where the model likely encounters unknown objects during deployment, i.e., out-of-distribution (OOD) data. Worryingly, NNs tend to make confident yet incorrect predictions when confronted with OOD data. To increase the mo...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	438,092
2104.13284	An optimal control approach to determine resistance-type boundary conditions from in-vivo data for cardiovascular simulations	The choice of appropriate boundary conditions is a fundamental step in computational fluid dynamics (CFD) simulations of the cardiovascular system. Boundary conditions, in fact, highly affect the computed pressure and flow rates, and consequently haemodynamic indicators such as wall shear stress, which are of clinical ...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	232,459
1706.05825	A Divide and Conquer Approach to Cooperative Distributed Model Predictive Control	This paper is concerned with the design of cooperative distributed Model Predictive Control (MPC) for linear systems. Motivated by the special structure of the distributed models in some existing literature, we propose to apply a state transformation to the original system and global cost function. This has major impli...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	75,582
2110.13479	Zero-Shot Action Recognition from Diverse Object-Scene Compositions	This paper investigates the problem of zero-shot action recognition, in the setting where no training videos with seen actions are available. For this challenging scenario, the current leading approach is to transfer knowledge from the image domain by recognizing objects in videos using pre-trained networks, followed b...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	263,199
2207.09087	Is Vertical Logistic Regression Privacy-Preserving? A Comprehensive Privacy Analysis and Beyond	We consider vertical logistic regression (VLR) trained with mini-batch gradient descent -- a setting which has attracted growing interest among industries and proven to be useful in a wide range of applications including finance and medical research. We provide a comprehensive and rigorous privacy analysis of VLR in a ...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	308,792
1206.6443	Isoelastic Agents and Wealth Updates in Machine Learning Markets	Recently, prediction markets have shown considerable promise for developing flexible mechanisms for machine learning. In this paper, agents with isoelastic utilities are considered. It is shown that the costs associated with homogeneous markets of agents with isoelastic utilities produce equilibrium prices correspondin...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	16,978
2409.18482	HSTFL: A Heterogeneous Federated Learning Framework for Misaligned Spatiotemporal Forecasting	Spatiotemporal forecasting has emerged as an indispensable building block of diverse smart city applications, such as intelligent transportation and smart energy management. Recent advancements have uncovered that the performance of spatiotemporal forecasting can be significantly improved by integrating knowledge in ge...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	492,280
2211.04310	Safety-Critical Ergodic Exploration in Cluttered Environments via Control Barrier Functions	In this paper, we address the problem of safe trajectory planning for autonomous search and exploration in constrained, cluttered environments. Guaranteeing safe (collision-free) trajectories is a challenging problem that has garnered significant due to its importance in the successful utilization of robots in search a...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	329,205
2101.09603	Optimistic and Adaptive Lagrangian Hedging	In online learning an algorithm plays against an environment with losses possibly picked by an adversary at each round. The generality of this framework includes problems that are not adversarial, for example offline optimization, or saddle point problems (i.e. min max optimization). However, online algorithms are typi...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	216,651
2301.09249	Exploring Active 3D Object Detection from a Generalization Perspective	To alleviate the high annotation cost in LiDAR-based 3D object detection, active learning is a promising solution that learns to select only a small portion of unlabeled data to annotate, without compromising model performance. Our empirical study, however, suggests that mainstream uncertainty-based and diversity-based...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	341,447
1202.0835	Reducibility of joint relay positioning and flow optimization problem	This paper shows how to reduce the otherwise hard joint relay positioning and flow optimization problem into a sequence a two simpler decoupled problems. We consider a class of wireless multicast hypergraphs mainly characterized by their hyperarc rate functions, that are increasing and convex in power, and decreasing i...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	14,126

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