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
1908.01997	Learning Cross-Modal Deep Representations for Multi-Modal MR Image Segmentation	Multi-modal magnetic resonance imaging (MRI) is essential in clinics for comprehensive diagnosis and surgical planning. Nevertheless, the segmentation of multi-modal MR images tends to be time-consuming and challenging. Convolutional neural network (CNN)-based multi-modal MR image analysis commonly proceeds with multip...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	140,906
1604.00117	Domain Adaptation of Recurrent Neural Networks for Natural Language Understanding	The goal of this paper is to use multi-task learning to efficiently scale slot filling models for natural language understanding to handle multiple target tasks or domains. The key to scalability is reducing the amount of training data needed to learn a model for a new task. The proposed multi-task model delivers bette...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	53,977
2406.02921	Text Injection for Neural Contextual Biasing	Neural contextual biasing effectively improves automatic speech recognition (ASR) for crucial phrases within a speaker's context, particularly those that are infrequent in the training data. This work proposes contextual text injection (CTI) to enhance contextual ASR. CTI leverages not only the paired speech-text data,...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	true	false	false	461,006
1905.11828	AsymDPOP: Complete Inference for Asymmetric Distributed Constraint Optimization Problems	Asymmetric distributed constraint optimization problems (ADCOPs) are an emerging model for coordinating agents with personal preferences. However, the existing inference-based complete algorithms which use local eliminations cannot be applied to ADCOPs, as the parent agents are required to transfer their private functi...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	132,561
2008.04224	The Cone epsilon-Dominance: An Approach for Evolutionary Multiobjective Optimization	We propose the cone epsilon-dominance approach to improve convergence and diversity in multiobjective evolutionary algorithms (MOEAs). A cone-eps-MOEA is presented and compared with MOEAs based on the standard Pareto relation (NSGA-II, NSGA-II*, SPEA2, and a clustered NSGA-II) and on the epsilon-dominance (eps-MOEA). T...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	191,176
2311.16851	Edge AI for Internet of Energy: Challenges and Perspectives	The digital landscape of the Internet of Energy (IoE) is on the brink of a revolutionary transformation with the integration of edge Artificial Intelligence (AI). This comprehensive review elucidates the promise and potential that edge AI holds for reshaping the IoE ecosystem. Commencing with a meticulously curated res...	false	false	false	false	true	false	false	false	false	false	false	false	false	true	false	false	false	true	411,070
2208.03659	Fast Online and Relational Tracking	To overcome challenges in multiple object tracking task, recent algorithms use interaction cues alongside motion and appearance features. These algorithms use graph neural networks or transformers to extract interaction features that lead to high computation costs. In this paper, a novel interaction cue based on geomet...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	311,858
2303.07758	Traffic4cast at NeurIPS 2022 -- Predict Dynamics along Graph Edges from Sparse Node Data: Whole City Traffic and ETA from Stationary Vehicle Detectors	The global trends of urbanization and increased personal mobility force us to rethink the way we live and use urban space. The Traffic4cast competition series tackles this problem in a data-driven way, advancing the latest methods in machine learning for modeling complex spatial systems over time. In this edition, our ...	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	false	false	351,369
1712.06935	Mining Smart Card Data for Travelers' Mini Activities	In the context of public transport modeling and simulation, we address the problem of mismatch between simulated transit trips and observed ones. We point to the weakness of the current travel demand modeling process; the trips it generates are over-optimistic and do not reflect the real passenger choices. We introduce...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	86,964
2011.13241	The Devil is in the Boundary: Exploiting Boundary Representation for Basis-based Instance Segmentation	Pursuing a more coherent scene understanding towards real-time vision applications, single-stage instance segmentation has recently gained popularity, achieving a simpler and more efficient design than its two-stage counterparts. Besides, its global mask representation often leads to superior accuracy to the two-stage ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	208,423
2403.09488	Rectifying Demonstration Shortcut in In-Context Learning	Large language models (LLMs) are able to solve various tasks with only a few demonstrations utilizing their in-context learning (ICL) abilities. However, LLMs often rely on their pre-trained semantic priors of demonstrations rather than on the input-label relationships to proceed with ICL prediction. In this work, we t...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	437,784
2406.12946	Instruction Data Generation and Unsupervised Adaptation for Speech Language Models	In this paper, we propose three methods for generating synthetic samples to train and evaluate multimodal large language models capable of processing both text and speech inputs. Addressing the scarcity of samples containing both modalities, synthetic data generation emerges as a crucial strategy to enhance the perform...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	465,640
2301.05427	Building a Fuel Moisture Model for the Coupled Fire-Atmosphere Model WRF-SFIRE from Data: From Kalman Filters to Recurrent Neural Networks	The current fuel moisture content (FMC) subsystems in WRF-SFIRE and its workflow system WRFx use a time-lag differential equation model with assimilation of data from FMC sensors on Remote Automated Weather Stations (RAWS) by the extended augmented Kalman filter. But the quality of the result is constrained by the limi...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	340,346
2404.09779	A replica analysis of under-bagging	Under-bagging (UB), which combines under-sampling and bagging, is a popular ensemble learning method for training classifiers on an imbalanced data. Using bagging to reduce the increased variance caused by the reduction in sample size due to under-sampling is a natural approach. However, it has recently been pointed ou...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	446,826
2212.11922	SupeRGB-D: Zero-shot Instance Segmentation in Cluttered Indoor Environments	Object instance segmentation is a key challenge for indoor robots navigating cluttered environments with many small objects. Limitations in 3D sensing capabilities often make it difficult to detect every possible object. While deep learning approaches may be effective for this problem, manually annotating 3D data for s...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	337,918
2012.08733	Exploiting Sample Uncertainty for Domain Adaptive Person Re-Identification	Many unsupervised domain adaptive (UDA) person re-identification (ReID) approaches combine clustering-based pseudo-label prediction with feature fine-tuning. However, because of domain gap, the pseudo-labels are not always reliable and there are noisy/incorrect labels. This would mislead the feature representation lear...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	211,851
2005.13450	Spatially Coupled Codes with Sub-Block Locality: Joint Finite Length-Asymptotic Design Approach	SC-LDPC codes with sub-block locality can be decoded locally at the level of sub-blocks that are much smaller than the full code block, thus providing fast access to the coded information. The same code can also be decoded globally using the entire code block, for increased data reliability. In this paper, we pursue th...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	179,013
2210.05038	Fighting FIRe with FIRE: Assessing the Validity of Text-to-Video Retrieval Benchmarks	Searching troves of videos with textual descriptions is a core multimodal retrieval task. Owing to the lack of a purpose-built dataset for text-to-video retrieval, video captioning datasets have been re-purposed to evaluate models by (1) treating captions as positive matches to their respective videos and (2) assuming ...	false	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	322,673
2312.01679	Adversarial Medical Image with Hierarchical Feature Hiding	Deep learning based methods for medical images can be easily compromised by adversarial examples (AEs), posing a great security flaw in clinical decision-making. It has been discovered that conventional adversarial attacks like PGD which optimize the classification logits, are easy to distinguish in the feature space, ...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	412,547
2106.01809	Exploring Distantly-Labeled Rationales in Neural Network Models	Recent studies strive to incorporate various human rationales into neural networks to improve model performance, but few pay attention to the quality of the rationales. Most existing methods distribute their models' focus to distantly-labeled rationale words entirely and equally, while ignoring the potential important ...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	238,633
2004.05849	MLPSVM:A new parallel support vector machine to multi-label learning	Multi-label learning has attracted the attention of the machine learning community. The problem conversion method Binary Relevance converts a familiar single label into a multi-label algorithm. The binary relevance method is widely used because of its simple structure and efficient algorithm. But binary relevance does ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	172,341
2007.06032	Probabilistic Jacobian-based Saliency Maps Attacks	Neural network classifiers (NNCs) are known to be vulnerable to malicious adversarial perturbations of inputs including those modifying a small fraction of the input features named sparse or $L_0$ attacks. Effective and fast $L_0$ attacks, such as the widely used Jacobian-based Saliency Map Attack (JSMA) are practical ...	false	false	false	false	false	false	true	false	false	false	false	true	true	false	false	false	false	false	186,872
2407.09779	Layout-and-Retouch: A Dual-stage Framework for Improving Diversity in Personalized Image Generation	Personalized text-to-image (P-T2I) generation aims to create new, text-guided images featuring the personalized subject with a few reference images. However, balancing the trade-off relationship between prompt fidelity and identity preservation remains a critical challenge. To address the issue, we propose a novel P-T2...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	472,712
2103.02542	Modularity and Mutual Information in Networks: Two Sides of the Same Coin	Modularity, first proposed by [Newman and Girvan, 2004], is one of the most popular ways to quantify the significance of community structure in complex networks. It can serve as both a standard benchmark to compare different community detection algorithms, and an optimization objective to detect communities itself. Pre...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	222,996
2412.09612	Olympus: A Universal Task Router for Computer Vision Tasks	We introduce Olympus, a new approach that transforms Multimodal Large Language Models (MLLMs) into a unified framework capable of handling a wide array of computer vision tasks. Utilizing a controller MLLM, Olympus delegates over 20 specialized tasks across images, videos, and 3D objects to dedicated modules. This inst...	false	false	false	false	true	false	false	false	true	false	false	true	false	false	false	false	false	false	516,558
2108.00819	Active Learning in Gaussian Process State Space Model	We investigate active learning in Gaussian Process state-space models (GPSSM). Our problem is to actively steer the system through latent states by determining its inputs such that the underlying dynamics can be optimally learned by a GPSSM. In order that the most informative inputs are selected, we employ mutual infor...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	248,843
2409.12884	Hypersphere Secure Sketch Revisited: Probabilistic Linear Regression Attack on IronMask in Multiple Usage	Protection of biometric templates is a critical and urgent area of focus. IronMask demonstrates outstanding recognition performance while protecting facial templates against existing known attacks. In high-level, IronMask can be conceptualized as a fuzzy commitment scheme building on the hypersphere directly. We devise...	false	false	false	false	false	false	false	false	false	false	false	true	true	false	false	false	false	false	489,754
2501.07013	Sthymuli: a Static Educational Robot. Leveraging the Thymio II Platform	The use of robots in education represents a challenge for teachers and a fixed vision of what robots can do for students. This paper presents the development of Sthymuli, a static educational robot designed to explore new classroom interactions between robots, students and teachers. We propose the use of the Thymio II ...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	524,222
2011.06192	Motion Generation Using Bilateral Control-Based Imitation Learning with Autoregressive Learning	Robots that can execute various tasks automatically on behalf of humans are becoming an increasingly important focus of research in the field of robotics. Imitation learning has been studied as an efficient and high-performance method, and imitation learning based on bilateral control has been proposed as a method that...	false	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	206,165
2103.06742	Visibility-aware Trajectory Optimization with Application to Aerial Tracking	The visibility of targets determines performance and even success rate of various applications, such as active slam, exploration, and target tracking. Therefore, it is crucial to take the visibility of targets into explicit account in trajectory planning. In this paper, we propose a general metric for target visibility...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	224,396
1706.07351	An approach to reachability analysis for feed-forward ReLU neural networks	We study the reachability problem for systems implemented as feed-forward neural networks whose activation function is implemented via ReLU functions. We draw a correspondence between establishing whether some arbitrary output can ever be outputed by a neural system and linear problems characterising a neural system of...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	true	75,829
1908.05926	Empirical Bayesian Mixture Models for Medical Image Translation	Automatically generating one medical imaging modality from another is known as medical image translation, and has numerous interesting applications. This paper presents an interpretable generative modelling approach to medical image translation. By allowing a common model for group-wise normalisation and segmentation o...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	141,863
1909.09390	SPSC: a new execution policy for exploring discrete-time stochastic simulations	In this paper, we introduce a new method called SPSC (Simulation, Partitioning, Selection, Cloning) to estimate efficiently the probability of possible solutions in stochastic simulations. This method can be applied to any type of simulation, however it is particularly suitable for multi-agent-based simulations (MABS)....	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	true	146,245
1901.04641	SISC: End-to-end Interpretable Discovery Radiomics-Driven Lung Cancer Prediction via Stacked Interpretable Sequencing Cells	Objective: Lung cancer is the leading cause of cancer-related death worldwide. Computer-aided diagnosis (CAD) systems have shown significant promise in recent years for facilitating the effective detection and classification of abnormal lung nodules in computed tomography (CT) scans. While hand-engineered radiomic feat...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	118,634
1407.0323	Laboratories of Oligarchy? How the Iron Law Extends to Peer Production	Peer production projects like Wikipedia have inspired voluntary associations, collectives, social movements, and scholars to embrace open online collaboration as a model of democratic organization. However, many peer production projects exhibit entrenched leadership and deep inequalities, suggesting that they may not f...	false	false	false	true	false	false	false	false	false	false	false	false	false	true	false	false	false	false	34,318
2112.04527	Building Quantum Field Theories Out of Neurons	An approach to field theory is studied in which fields are comprised of $N$ constituent random neurons. Gaussian theories arise in the infinite-$N$ limit when neurons are independently distributed, via the Central Limit Theorem, while interactions arise due to finite-$N$ effects or non-independently distributed neurons...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	270,551
2108.11942	Machine Learning for Mediation in Armed Conflicts	Today's conflicts are becoming increasingly complex, fluid and fragmented, often involving a host of national and international actors with multiple and often divergent interests. This development poses significant challenges for conflict mediation, as mediators struggle to make sense of conflict dynamics, such as the ...	false	false	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	252,341
2007.13106	Detection and Annotation of Plant Organs from Digitized Herbarium Scans using Deep Learning	As herbarium specimens are increasingly becoming digitized and accessible in online repositories, advanced computer vision techniques are being used to extract information from them. The presence of certain plant organs on herbarium sheets is useful information in various scientific contexts and automatic recognition o...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	189,024
1403.0057	Real-time Topic-aware Influence Maximization Using Preprocessing	Influence maximization is the task of finding a set of seed nodes in a social network such that the influence spread of these seed nodes based on certain influence diffusion model is maximized. Topic-aware influence diffusion models have been recently proposed to address the issue that influence between a pair of users...	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	false	false	31,261
2206.09672	Adaptive Domain Interest Network for Multi-domain Recommendation	Industrial recommender systems usually hold data from multiple business scenarios and are expected to provide recommendation services for these scenarios simultaneously. In the retrieval step, the topK high-quality items selected from a large number of corpus usually need to be various for multiple scenarios. Take Alib...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	303,656
2409.18295	Enhancing Lossy Compression Through Cross-Field Information for Scientific Applications	Lossy compression is one of the most effective methods for reducing the size of scientific data containing multiple data fields. It reduces information density through prediction or transformation techniques to compress the data. Previous approaches use local information from a single target field when predicting targe...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	true	492,178
2303.14090	Physics-informed neural networks in the recreation of hydrodynamic simulations from dark matter	Physics-informed neural networks have emerged as a coherent framework for building predictive models that combine statistical patterns with domain knowledge. The underlying notion is to enrich the optimization loss function with known relationships to constrain the space of possible solutions. Hydrodynamic simulations ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	353,946
1112.5370	Enhancing Support for Knowledge Works: A relatively unexplored vista of computing research	Let us envision a new class of IT systems, the "Support Systems for Knowledge Works" or SSKW. An SSKW can be defined as a system built for providing comprehensive support to human knowledge-workers while performing instances of complex knowledge-works of a particular type within a particular domain of professional acti...	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	13,564
2210.09496	CEIP: Combining Explicit and Implicit Priors for Reinforcement Learning with Demonstrations	Although reinforcement learning has found widespread use in dense reward settings, training autonomous agents with sparse rewards remains challenging. To address this difficulty, prior work has shown promising results when using not only task-specific demonstrations but also task-agnostic albeit somewhat related demons...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	324,552
2204.13973	Using 3D Shadows to Detect Object Hiding Attacks on Autonomous Vehicle Perception	Autonomous Vehicles (AVs) are mostly reliant on LiDAR sensors which enable spatial perception of their surroundings and help make driving decisions. Recent works demonstrated attacks that aim to hide objects from AV perception, which can result in severe consequences. 3D shadows, are regions void of measurements in 3D ...	false	false	false	false	false	false	false	false	false	false	false	true	true	false	false	false	false	false	294,009
2104.12186	Random Spreading for Unsourced MAC with Power Diversity	We propose an improvement of the random spreading approach with polar codes for unsourced multiple access. Each user encodes its message by a polar code, and the coded bits are then spread using a random spreading sequence. The proposed approach divides the active users into different groups, and employs different powe...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	232,139
1905.13271	Exploring Computational User Models for Agent Policy Summarization	AI agents are being developed to support high stakes decision-making processes from driving cars to prescribing drugs, making it increasingly important for human users to understand their behavior. Policy summarization methods aim to convey strengths and weaknesses of such agents by demonstrating their behavior in a su...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	133,050
2502.01043	Multiphysics Continuous Shape Optimization of the TAP Reactor Components	The Transatomic Power (TAP) reactor has an unusual design for a molten salt reactor technology, building upon the foundation laid by the Molten Salt Reactor Experiment (MSRE). This design introduces three key modifications to enhance efficiency and compactness: a revised fuel salt composition, an alternative moderator ...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	529,663
2212.09049	Perfectly Covert Communication with a Reflective Panel	This work considers the problem of \emph{perfect} covert communication in wireless networks. Specifically, harnessing an Intelligent Reflecting Surface (IRS), we turn our attention to schemes that allow the transmitter to completely hide the communication, with \emph{zero energy} at the unwanted listener (Willie) and h...	false	false	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	336,979
1011.5115	Optimal Utility-Energy tradeoff in Delay Constrained Random Access Networks	Rate, energy and delay are three main parameters of interest in ad-hoc networks. In this paper, we discuss the problem of maximizing network utility and minimizing energy consumption while satisfying a given transmission delay constraint for each packet. We formulate this problem in the standard convex optimization for...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	8,311
1502.05871	Robust CS reconstruction based on appropriate minimization norm	Noise robust compressive sensing algorithm is considered. This algorithm allows an efficient signal reconstruction in the presence of different types of noise due to the possibility to change minimization norm. For instance, the commonly used l1 and l2 norms, provide good results in case of Laplace and Gaussian noise. ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	40,420
2202.08504	Finding Representative Sampling Subsets in Sensor Graphs using Time Series Similarities	With the increasing use of IoT-enabled sensors, it is important to have effective methods for querying the sensors. For example, in a dense network of battery-driven temperature sensors, it is often possible to query (sample) just a subset of the sensors at any given time, since the values of the non-sampled sensors ca...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	280,899
2405.08392	Neuromorphic Robust Estimation of Nonlinear Dynamical Systems Applied to Satellite Rendezvous	State estimation of nonlinear dynamical systems has long aimed to balance accuracy, computational efficiency, robustness, and reliability. The rapid evolution of various industries has amplified the demand for estimation frameworks that satisfy all these factors. This study introduces a neuromorphic approach for robust...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	true	false	false	454,080
2103.06124	Semantically Constrained Memory Allocation (SCMA) for Embedding in Efficient Recommendation Systems	Deep learning-based models are utilized to achieve state-of-the-art performance for recommendation systems. A key challenge for these models is to work with millions of categorical classes or tokens. The standard approach is to learn end-to-end, dense latent representations or embeddings for each token. The resulting e...	false	false	false	false	true	true	true	false	false	false	false	false	false	false	false	false	false	false	224,199
2404.06325	Automatically Learning HTN Methods from Landmarks	Hierarchical Task Network (HTN) planning usually requires a domain engineer to provide manual input about how to decompose a planning problem. Even HTN-MAKER, a well-known method-learning algorithm, requires a domain engineer to annotate the tasks with information about what to learn. We introduce CURRICULAMA, an HTN m...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	445,418
2501.09887	FLORA: Formal Language Model Enables Robust Training-free Zero-shot Object Referring Analysis	Object Referring Analysis (ORA), commonly known as referring expression comprehension, requires the identification and localization of specific objects in an image based on natural descriptions. Unlike generic object detection, ORA requires both accurate language understanding and precise visual localization, making it...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	525,305
2211.02842	A Machine Learning-based Framework for Predictive Maintenance of Semiconductor Laser for Optical Communication	Semiconductor lasers, one of the key components for optical communication systems, have been rapidly evolving to meet the requirements of next generation optical networks with respect to high speed, low power consumption, small form factor etc. However, these demands have brought severe challenges to the semiconductor ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	328,718
2310.05927	The evolution of cooperation in a mobile population on random networks: Network topology matters only for low-degree networks	We consider a finite structured population of mobile individuals that strategically explore a network using a Markov movement model and interact with each other via a public goods game. We extend the model of Erovenko et al. (2019) from complete, circle, and star graphs to various random networks to further investigate...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	398,356
2010.09388	Learning Parameter Distributions to Detect Concept Drift in Data Streams	Data distributions in streaming environments are usually not stationary. In order to maintain a high predictive quality at all times, online learning models need to adapt to distributional changes, which are known as concept drift. The timely and robust identification of concept drift can be difficult, as we never have...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	201,519
2202.11525	GIFT: Graph-guIded Feature Transfer for Cold-Start Video Click-Through Rate Prediction	Short video has witnessed rapid growth in the past few years in e-commerce platforms like Taobao. To ensure the freshness of the content, platforms need to release a large number of new videos every day, making conventional click-through rate (CTR) prediction methods suffer from the item cold-start problem. In this pap...	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	false	281,918
2002.05515	Improving Deep Learning For Airbnb Search	The application of deep learning to search ranking was one of the most impactful product improvements at Airbnb. But what comes next after you launch a deep learning model? In this paper we describe the journey beyond, discussing what we refer to as the ABCs of improving search: A for architecture, B for bias and C for...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	163,919
2412.04591	MetaFormer: High-fidelity Metalens Imaging via Aberration Correcting Transformers	Metalens is an emerging optical system with an irreplaceable merit in that it can be manufactured in ultra-thin and compact sizes, which shows great promise of various applications such as medical imaging and augmented/virtual reality (AR/VR). Despite its advantage in miniaturization, its practicality is constrained by...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	514,474
1105.0155	Optimal Decoding Algorithm for Asynchronous Physical-Layer Network Coding	A key issue in physical-layer network coding (PNC) is how to deal with the asynchrony between signals transmitted by multiple transmitters. That is, symbols transmitted by different transmitters could arrive at the receiver with symbol misalignment as well as relative carrier-phase offset. In this paper, 1) we propose ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	10,196
2411.00205	Compositional Automata Embeddings for Goal-Conditioned Reinforcement Learning	Goal-conditioned reinforcement learning is a powerful way to control an AI agent's behavior at runtime. That said, popular goal representations, e.g., target states or natural language, are either limited to Markovian tasks or rely on ambiguous task semantics. We propose representing temporal goals using compositions o...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	true	504,483
1309.0261	Multi-Column Deep Neural Networks for Offline Handwritten Chinese Character Classification	Our Multi-Column Deep Neural Networks achieve best known recognition rates on Chinese characters from the ICDAR 2011 and 2013 offline handwriting competitions, approaching human performance.	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	26,773
2011.10173	Exploring Global Information for Session-based Recommendation	Session-based recommendation (SBR) is a challenging task, which aims at recommending items based on anonymous behavior sequences. Most existing SBR studies model the user preferences based only on the current session while neglecting the item-transition information from the other sessions, which suffer from the inabili...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	207,426
2212.14720	Learning from Data Streams: An Overview and Update	The literature on machine learning in the context of data streams is vast and growing. However, many of the defining assumptions regarding data-stream learning tasks are too strong to hold in practice, or are even contradictory such that they cannot be met in the contexts of supervised learning. Algorithms are chosen a...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	338,716
2004.04719	On Linear Stochastic Approximation: Fine-grained Polyak-Ruppert and Non-Asymptotic Concentration	We undertake a precise study of the asymptotic and non-asymptotic properties of stochastic approximation procedures with Polyak-Ruppert averaging for solving a linear system $\bar{A} \theta = \bar{b}$. When the matrix $\bar{A}$ is Hurwitz, we prove a central limit theorem (CLT) for the averaged iterates with fixed step...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	171,972
1704.00917	Deriving Probability Density Functions from Probabilistic Functional Programs	The probability density function of a probability distribution is a fundamental concept in probability theory and a key ingredient in various widely used machine learning methods. However, the necessary framework for compiling probabilistic functional programs to density functions has only recently been developed. In t...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	71,169
2110.12763	SSMF: Shifting Seasonal Matrix Factorization	Given taxi-ride counts information between departure and destination locations, how can we forecast their future demands? In general, given a data stream of events with seasonal patterns that innovate over time, how can we effectively and efficiently forecast future events? In this paper, we propose Shifting Seasonal M...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	262,956
2209.08604	An Interactive Knowledge-based Multi-objective Evolutionary Algorithm Framework for Practical Optimization Problems	Experienced users often have useful knowledge and intuition in solving real-world optimization problems. User knowledge can be formulated as inter-variable relationships to assist an optimization algorithm in finding good solutions faster. Such inter-variable interactions can also be automatically learned from high-per...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	318,187
1810.10910	Une approche totalement instanci\'ee pour la planification HTN	Many planning techniques have been developed to allow autonomous systems to act and make decisions based on their perceptions of the environment. Among these techniques, HTN ({\it Hierarchical Task Network}) planning is one of the most used in practice. Unlike classical approaches of planning. HTN operates by decomposi...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	111,395
2108.03340	Tiny Neural Models for Seq2Seq	Semantic parsing models with applications in task oriented dialog systems require efficient sequence to sequence (seq2seq) architectures to be run on-device. To this end, we propose a projection based encoder-decoder model referred to as pQRNN-MAtt. Studies based on projection methods were restricted to encoder-only mo...	false	false	true	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	249,623
2008.00463	Structural Causal Models Are (Solvable by) Credal Networks	A structural causal model is made of endogenous (manifest) and exogenous (latent) variables. We show that endogenous observations induce linear constraints on the probabilities of the exogenous variables. This allows to exactly map a causal model into a credal network. Causal inferences, such as interventions and count...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	190,019
2305.04123	Transform-Equivariant Consistency Learning for Temporal Sentence Grounding	This paper addresses the temporal sentence grounding (TSG). Although existing methods have made decent achievements in this task, they not only severely rely on abundant video-query paired data for training, but also easily fail into the dataset distribution bias. To alleviate these limitations, we introduce a novel Eq...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	362,647
2108.05276	Trading Complexity for Sparsity in Random Forest Explanations	Random forests have long been considered as powerful model ensembles in machine learning. By training multiple decision trees, whose diversity is fostered through data and feature subsampling, the resulting random forest can lead to more stable and reliable predictions than a single decision tree. This however comes at...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	250,258
2403.19507	SineNet: Learning Temporal Dynamics in Time-Dependent Partial Differential Equations	We consider using deep neural networks to solve time-dependent partial differential equations (PDEs), where multi-scale processing is crucial for modeling complex, time-evolving dynamics. While the U-Net architecture with skip connections is commonly used by prior studies to enable multi-scale processing, our analysis ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	442,375
2405.02961	JOSENet: A Joint Stream Embedding Network for Violence Detection in Surveillance Videos	The increasing proliferation of video surveillance cameras and the escalating demand for crime prevention have intensified interest in the task of violence detection within the research community. Compared to other action recognition tasks, violence detection in surveillance videos presents additional issues, such as t...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	451,994
2311.01907	BoschAI @ PLABA 2023: Leveraging Edit Operations in End-to-End Neural Sentence Simplification	Automatic simplification can help laypeople to comprehend complex scientific text. Language models are frequently applied to this task by translating from complex to simple language. In this paper, we describe our system based on Llama 2, which ranked first in the PLABA shared task addressing the simplification of biom...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	405,225
2405.17506	Subspace Node Pruning	Efficiency of neural network inference is undeniably important in a time where commercial use of AI models increases daily. Node pruning is the art of removing computational units such as neurons, filters, attention heads, or even entire layers to significantly reduce inference time while retaining network performance....	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	true	false	false	457,975
2112.08791	Beamspace MIMO for Satellite Swarms	Systems of small distributed satellites in low Earth orbit (LEO) transmitting cooperatively to a multiple antenna ground station (GS) are investigated. These satellite swarms have the benefit of much higher spatial separation in the transmit antennas than traditional big satellites with antenna arrays, promising a mass...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	271,939
2403.01318	High-Dimensional Tail Index Regression: with An Application to Text Analyses of Viral Posts in Social Media	Motivated by the empirical observation of power-law distributions in the credits (e.g., "likes") of viral social media posts, we introduce a high-dimensional tail index regression model and propose methods for estimation and inference of its parameters. First, we present a regularized estimator, establish its consisten...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	434,359
0910.0650	Capacity Region of a State Dependent Degraded Broadcast Channel with Noncausal Transmitter CSI	This paper has been withdrawn due to a mistake in the previous version.	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	4,625
2001.01049	Optimal Binary Linear Codes from Maximal Arcs	The binary Hamming codes with parameters $[2^m-1, 2^m-1-m, 3]$ are perfect. Their extended codes have parameters $[2^m, 2^m-1-m, 4]$ and are distance-optimal. The first objective of this paper is to construct a class of binary linear codes with parameters $[2^{m+s}+2^s-2^m,2^{m+s}+2^s-2^m-2m-2,4]$, which have better in...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	159,388
1909.10400	Robot Navigation in Crowds by Graph Convolutional Networks with Attention Learned from Human Gaze	Safe and efficient crowd navigation for mobile robot is a crucial yet challenging task. Previous work has shown the power of deep reinforcement learning frameworks to train efficient policies. However, their performance deteriorates when the crowd size grows. We suggest that this can be addressed by enabling the networ...	false	false	false	false	true	false	false	true	false	false	false	true	false	false	false	false	false	false	146,534
2212.07126	Explainability of Text Processing and Retrieval Methods: A Critical Survey	Deep Learning and Machine Learning based models have become extremely popular in text processing and information retrieval. However, the non-linear structures present inside the networks make these models largely inscrutable. A significant body of research has focused on increasing the transparency of these models. Thi...	false	false	false	false	true	true	false	false	true	false	false	false	false	false	false	false	false	false	336,313
2110.08776	Self-Supervised U-Net for Segmenting Flat and Sessile Polyps	Colorectal Cancer(CRC) poses a great risk to public health. It is the third most common cause of cancer in the US. Development of colorectal polyps is one of the earliest signs of cancer. Early detection and resection of polyps can greatly increase survival rate to 90%. Manual inspection can cause misdetections because...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	261,554
2212.04917	TRBLLmaker -- Transformer Reads Between Lyrics Lines maker	Even for us, it can be challenging to comprehend the meaning of songs. As part of this project, we explore the process of generating the meaning of songs. Despite the widespread use of text-to-text models, few attempts have been made to achieve a similar objective. Songs are primarily studied in the context of sentimen...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	335,620
1711.08513	Calibration for the (Computationally-Identifiable) Masses	As algorithms increasingly inform and influence decisions made about individuals, it becomes increasingly important to address concerns that these algorithms might be discriminatory. The output of an algorithm can be discriminatory for many reasons, most notably: (1) the data used to train the algorithm might be biased...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	85,222
2204.04844	HFL at SemEval-2022 Task 8: A Linguistics-inspired Regression Model with Data Augmentation for Multilingual News Similarity	This paper describes our system designed for SemEval-2022 Task 8: Multilingual News Article Similarity. We proposed a linguistics-inspired model trained with a few task-specific strategies. The main techniques of our system are: 1) data augmentation, 2) multi-label loss, 3) adapted R-Drop, 4) samples reconstruction wit...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	290,810
2212.01610	Exploring Stochastic Autoregressive Image Modeling for Visual Representation	Autoregressive language modeling (ALM) have been successfully used in self-supervised pre-training in Natural language processing (NLP). However, this paradigm has not achieved comparable results with other self-supervised approach in computer vision (e.g., contrastive learning, mask image modeling). In this paper, we ...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	334,501
2310.10893	Active Learning Framework for Cost-Effective TCR-Epitope Binding Affinity Prediction	T cell receptors (TCRs) are critical components of adaptive immune systems, responsible for responding to threats by recognizing epitope sequences presented on host cell surface. Computational prediction of binding affinity between TCRs and epitope sequences using machine/deep learning has attracted intense attention r...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	400,422
2303.03373	Detecting Human-Object Contact in Images	Humans constantly contact objects to move and perform tasks. Thus, detecting human-object contact is important for building human-centered artificial intelligence. However, there exists no robust method to detect contact between the body and the scene from an image, and there exists no dataset to learn such a detector....	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	349,703
1710.08601	Homophily and minority size explain perception biases in social networks	People's perceptions about the size of minority groups in social networks can be biased, often showing systematic over- or underestimation. These social perception biases are often attributed to biased cognitive or motivational processes. Here we show that both over- and underestimation of the size of a minority group ...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	83,102
2312.01345	Introducing Modelling, Analysis and Control of Three-Phase Electrical Systems Using Geometric Algebra	State-of-the-art techniques for modeling, analysis and control of three-phase electrical systems belong to the real-valued multi-input/multi-output (MIMO) domain, or to the complex-valued nonlinear single-input/single-output (SISO) domain. In order to complement both domains while simplifying complexity and offering ne...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	412,414
2401.14923	Reinforcement Learning Interventions on Boundedly Rational Human Agents in Frictionful Tasks	Many important behavior changes are frictionful; they require individuals to expend effort over a long period with little immediate gratification. Here, an artificial intelligence (AI) agent can provide personalized interventions to help individuals stick to their goals. In these settings, the AI agent must personalize...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	424,262
2104.06470	Joint Matrix Completion and Compressed Sensing for State Estimation in Low-observable Distribution System	Limited measurement availability at the distribution grid presents challenges for state estimation and situational awareness. This paper combines the advantages of two sparsity-based state estimation approaches (matrix completion and compressive sensing) that have been proposed recently to address the challenge of unob...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	230,081
2310.04948	TEMPO: Prompt-based Generative Pre-trained Transformer for Time Series Forecasting	The past decade has witnessed significant advances in time series modeling with deep learning. While achieving state-of-the-art results, the best-performing architectures vary highly across applications and domains. Meanwhile, for natural language processing, the Generative Pre-trained Transformer (GPT) has demonstrate...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	397,899
2409.09704	AlpaPICO: Extraction of PICO Frames from Clinical Trial Documents Using LLMs	In recent years, there has been a surge in the publication of clinical trial reports, making it challenging to conduct systematic reviews. Automatically extracting Population, Intervention, Comparator, and Outcome (PICO) from clinical trial studies can alleviate the traditionally time-consuming process of manually scru...	false	false	false	false	false	true	true	false	true	false	false	false	false	false	false	false	false	false	488,434
2309.15979	Clinical Trial Recommendations Using Semantics-Based Inductive Inference and Knowledge Graph Embeddings	Designing a new clinical trial entails many decisions, such as defining a cohort and setting the study objectives to name a few, and therefore can benefit from recommendations based on exhaustive mining of past clinical trial records. Here, we propose a novel recommendation methodology, based on neural embeddings train...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	395,172

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