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
2405.17604	LoRA-XS: Low-Rank Adaptation with Extremely Small Number of Parameters	The rapid expansion of large language models (LLMs) has underscored the need for parameter-efficient fine-tuning methods, with LoRA (Low-Rank Adaptation) emerging as a popular solution. Although LoRA reduces the number of trainable parameters, serving multiple (task or user-specific) LoRA modules on top of a base model...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	458,019
2402.04442	Evaluating Embeddings for One-Shot Classification of Doctor-AI Consultations	Effective communication between healthcare providers and patients is crucial to providing high-quality patient care. In this work, we investigate how Doctor-written and AI-generated texts in healthcare consultations can be classified using state-of-the-art embeddings and one-shot classification systems. By analyzing em...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	427,452
2402.15796	Construction and application of artificial intelligence crowdsourcing map based on multi-track GPS data	In recent years, the rapid development of high-precision map technology combined with artificial intelligence has ushered in a new development opportunity in the field of intelligent vehicles. High-precision map technology is an important guarantee for intelligent vehicles to achieve autonomous driving. However, due to...	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	432,291
1709.01720	Temporal Pattern Discovery for Accurate Sepsis Diagnosis in ICU Patients	Sepsis is a condition caused by the body's overwhelming and life-threatening response to infection, which can lead to tissue damage, organ failure, and finally death. Common signs and symptoms include fever, increased heart rate, increased breathing rate, and confusion. Sepsis is difficult to predict, diagnose, and tre...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	80,141
2009.11129	Cosine Similarity of Multimodal Content Vectors for TV Programmes	Multimodal information originates from a variety of sources: audiovisual files, textual descriptions, and metadata. We show how one can represent the content encoded by each individual source using vectors, how to combine the vectors via middle and late fusion techniques, and how to compute the semantic similarities be...	false	false	false	false	false	true	true	false	true	false	false	false	false	false	false	false	false	true	197,081
2306.05545	AI Enhanced Control Engineering Methods	AI and machine learning based approaches are becoming ubiquitous in almost all engineering fields. Control engineering cannot escape this trend. In this paper, we explore how AI tools can be useful in control applications. The core tool we focus on is automatic differentiation. Two immediate applications are linearizat...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	372,237
2306.11734	Few-Shot Rotation-Invariant Aerial Image Semantic Segmentation	Few-shot aerial image segmentation is a challenging task that involves precisely parsing objects in query aerial images with limited annotated support. Conventional matching methods without consideration of varying object orientations can fail to activate same-category objects with different orientations. Moreover, con...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	374,694
2101.07191	Quantification of Disaggregation Difficulty with Respect to the Number of Meters	A promising approach toward efficient energy management is non-intrusive load monitoring (NILM), that is to extract the consumption profiles of appliances within a residence by analyzing the aggregated consumption signal. Among efficient NILM methods are event-based algorithms in which events of the aggregated signal a...	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	215,962
2312.12680	Trajectory Approximation of Video Based on Phase Correlation for Forward Facing Camera	In this paper, we introduce an innovative approach for extracting trajectories from a camera sensor in GPS-denied environments, leveraging visual odometry. The system takes video footage captured by a forward-facing camera mounted on a vehicle as input, with the output being a chain code representing the camera's traje...	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	true	417,055
1608.08609	A new fast algorithm for reproducing complex networks with community structure	In this paper, we introduce a new algorithm allowing for generation of networks with heterogeneity of both node degrees and community sizes. The quality and efficiency of the algorithm is analyzed and compared to the other, so far the most popular algorithm which was proposed by Lancichinetti et al. We discuss the adva...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	60,379
2201.09541	Image features of a splashing drop on a solid surface extracted using a feedforward neural network	This article reports nonintuitive characteristic of a splashing drop on a solid surface discovered through extracting image features using a feedforward neural network (FNN). Ethanol of area-equivalent radius about 1.29 mm was dropped from impact heights ranging from 4 cm to 60 cm (splashing threshold 20 cm) and impact...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	276,711
1210.7543	Exploiting Sparse Dynamics For Bandwidth Reduction In Cooperative Sensing Systems	Recently, there has been a significant interest in developing cooperative sensing systems for certain types of wireless applications. In such systems, a group of sensing nodes periodically collect measurements about the signals being observed in the given geographical region and transmit these measurements to a central...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	19,445
1808.00588	Weather Classification: A new multi-class dataset, data augmentation approach and comprehensive evaluations of Convolutional Neural Networks	Weather conditions often disrupt the proper functioning of transportation systems. Present systems either deploy an array of sensors or use an in-vehicle camera to predict weather conditions. These solutions have resulted in incremental cost and limited scope. To ensure smooth operation of all transportation services i...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	104,417
2403.07232	Tractable Joint Prediction and Planning over Discrete Behavior Modes for Urban Driving	Significant progress has been made in training multimodal trajectory forecasting models for autonomous driving. However, effectively integrating these models with downstream planners and model-based control approaches is still an open problem. Although these models have conventionally been evaluated for open-loop predi...	false	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	436,790
2205.14850	Play it by Ear: Learning Skills amidst Occlusion through Audio-Visual Imitation Learning	Humans are capable of completing a range of challenging manipulation tasks that require reasoning jointly over modalities such as vision, touch, and sound. Moreover, many such tasks are partially-observed; for example, taking a notebook out of a backpack will lead to visual occlusion and require reasoning over the hist...	false	false	true	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	299,514
1605.00322	Adaptive Modulation in Network-coded Two-way Relay Channel: A Supermodular Game Approach	We study the adaptive modulation (AM) problem in a network-coded two-way relay channel (NC-TWRC), where each of the two users controls its own bit rate in the $m$-ary quadrature amplitude modulation ($m$-QAM) to minimize the transmission error rate and enhance the spectral efficiency. We show that there exists a strate...	false	false	false	false	false	false	false	false	false	true	true	false	false	false	false	false	false	false	55,327
2407.03391	Soft Begging: Modular and Efficient Shielding of LLMs against Prompt Injection and Jailbreaking based on Prompt Tuning	Prompt injection (both direct and indirect) and jailbreaking are now recognized as significant issues for large language models (LLMs), particularly due to their potential for harm in application-integrated contexts. This extended abstract explores a novel approach to protecting LLMs from such attacks, termed "soft beg...	false	false	false	false	true	false	false	false	true	false	false	false	true	false	false	false	false	false	470,145
2302.04062	Machine Learning for Synthetic Data Generation: A Review	Machine learning heavily relies on data, but real-world applications often encounter various data-related issues. These include data of poor quality, insufficient data points leading to under-fitting of machine learning models, and difficulties in data access due to concerns surrounding privacy, safety, and regulations...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	344,574
2008.01825	Robust Reinforcement Learning using Adversarial Populations	Reinforcement Learning (RL) is an effective tool for controller design but can struggle with issues of robustness, failing catastrophically when the underlying system dynamics are perturbed. The Robust RL formulation tackles this by adding worst-case adversarial noise to the dynamics and constructing the noise distribu...	false	false	false	false	false	false	true	true	false	false	false	false	false	false	true	false	false	false	190,442
2112.15354	Statistical Device Activity Detection for OFDM-based Massive Grant-Free Access	Existing works on grant-free access, proposed to support massive machine-type communication (mMTC) for the Internet of things (IoT), mainly concentrate on narrow band systems under flat fading. However, little is known about massive grant-free access for wideband systems under frequency-selective fading. This paper inv...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	273,760
1912.11692	Thermostatic control for demand response using distributed averaging and deep neural networks	Smart buildings are the need of the day with increasing demand-supply ratios and deficiency to generate considerably. In any modern non-industrial infrastructure, these demands mainly comprise of thermostatically controlled loads (TCLs), which can be manoeuvred. TCL loads like air-conditioner, heater, refrigerator, are...	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	158,640
2202.08141	FUN-SIS: a Fully UNsupervised approach for Surgical Instrument Segmentation	Automatic surgical instrument segmentation of endoscopic images is a crucial building block of many computer-assistance applications for minimally invasive surgery. So far, state-of-the-art approaches completely rely on the availability of a ground-truth supervision signal, obtained via manual annotation, thus expensiv...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	280,777
1804.00770	VerdictDB: Universalizing Approximate Query Processing	Despite 25 years of research in academia, approximate query processing (AQP) has had little industrial adoption. One of the major causes of this slow adoption is the reluctance of traditional vendors to make radical changes to their legacy codebases, and the preoccupation of newer vendors (e.g., SQL-on-Hadoop products)...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	94,107
2104.11882	Incremental Few-shot Text Classification with Multi-round New Classes: Formulation, Dataset and System	Text classification is usually studied by labeling natural language texts with relevant categories from a predefined set. In the real world, new classes might keep challenging the existing system with limited labeled data. The system should be intelligent enough to recognize upcoming new classes with a few examples. In...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	232,044
2005.00107	Activity Detection from Wearable Electromyogram Sensors using Hidden Markov Model	Surface electromyography (sEMG) has gained significant importance during recent advancements in consumer electronics for healthcare systems, gesture analysis and recognition and sign language communication. For such a system, it is imperative to determine the regions of activity in a continuously recorded sEMG signal. ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	175,125
2005.07782	Unbiased Deep Reinforcement Learning: A General Training Framework for Existing and Future Algorithms	In recent years deep neural networks have been successfully applied to the domains of reinforcement learning \cite{bengio2009learning,krizhevsky2012imagenet,hinton2006reducing}. Deep reinforcement learning \cite{mnih2015human} is reported to have the advantage of learning effective policies directly from high-dimension...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	177,380
2010.07190	Towards Resistant Audio Adversarial Examples	Adversarial examples tremendously threaten the availability and integrity of machine learning-based systems. While the feasibility of such attacks has been observed first in the domain of image processing, recent research shows that speech recognition is also susceptible to adversarial attacks. However, reliably bridgi...	false	false	true	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	200,735
2405.04428	BBK: a simpler, faster algorithm for enumerating maximal bicliques in large sparse bipartite graphs	Bipartite graphs are a prevalent modeling tool for real-world networks, capturing interactions between vertices of two different types. Within this framework, bicliques emerge as crucial structures when studying dense subgraphs: they are sets of vertices such that all vertices of the first type interact with all vertic...	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	true	452,568
1509.08571	High Probability Guarantees in Repeated Games: Theory and Applications in Information Theory	We introduce a "high probability" framework for repeated games with incomplete information. In our non-equilibrium setting, players aim to guarantee a certain payoff with high probability, rather than in expected value. We provide a high probability counterpart of the classical result of Mertens and Zamir for the zero-...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	47,384
1104.1472	Gaussian Affine Feature Detector	A new method is proposed to get image features' geometric information. Using Gaussian as an input signal, a theoretical optimal solution to calculate feature's affine shape is proposed. Based on analytic result of a feature model, the method is different from conventional iterative approaches. From the model, feature's...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	9,914
2209.00671	Deep reinforcement learning for quantum multiparameter estimation	Estimation of physical quantities is at the core of most scientific research and the use of quantum devices promises to enhance its performances. In real scenarios, it is fundamental to consider that the resources are limited and Bayesian adaptive estimation represents a powerful approach to efficiently allocate, durin...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	315,654
2211.08703	SATVSR: Scenario Adaptive Transformer for Cross Scenarios Video Super-Resolution	Video Super-Resolution (VSR) aims to recover sequences of high-resolution (HR) frames from low-resolution (LR) frames. Previous methods mainly utilize temporally adjacent frames to assist the reconstruction of target frames. However, in the real world, there is a lot of irrelevant information in adjacent frames of vide...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	330,737
2411.19181	Large width penalization for neural network-based prediction interval estimation	Forecasting accuracy in highly uncertain environments is challenging due to the stochastic nature of systems. Deterministic forecasting provides only point estimates and cannot capture potential outcomes. Therefore, probabilistic forecasting has gained significant attention due to its ability to quantify uncertainty, w...	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	512,149
2308.04896	Why Data Science Projects Fail	Data Science is a modern Data Intelligence practice, which is the core of many businesses and helps businesses build smart strategies around to deal with businesses challenges more efficiently. Data Science practice also helps in automating business processes using the algorithm, and it has several other benefits, whic...	false	false	false	false	false	false	true	false	false	false	false	false	false	true	false	false	true	false	384,604
2407.02813	Data Overfitting for On-Device Super-Resolution with Dynamic Algorithm and Compiler Co-Design	Deep neural networks (DNNs) are frequently employed in a variety of computer vision applications. Nowadays, an emerging trend in the current video distribution system is to take advantage of DNN's overfitting properties to perform video resolution upscaling. By splitting videos into chunks and applying a super-resoluti...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	469,894
1808.09551	Explaining Character-Aware Neural Networks for Word-Level Prediction: Do They Discover Linguistic Rules?	Character-level features are currently used in different neural network-based natural language processing algorithms. However, little is known about the character-level patterns those models learn. Moreover, models are often compared only quantitatively while a qualitative analysis is missing. In this paper, we investi...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	106,211
2301.08781	GBOSE: Generalized Bandit Orthogonalized Semiparametric Estimation	In sequential decision-making scenarios i.e., mobile health recommendation systems revenue management contextual multi-armed bandit algorithms have garnered attention for their performance. But most of the existing algorithms are built on the assumption of a strictly parametric reward model mostly linear in nature. In ...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	341,281
2205.13451	Follow-the-Perturbed-Leader for Adversarial Markov Decision Processes with Bandit Feedback	We consider regret minimization for Adversarial Markov Decision Processes (AMDPs), where the loss functions are changing over time and adversarially chosen, and the learner only observes the losses for the visited state-action pairs (i.e., bandit feedback). While there has been a surge of studies on this problem using ...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	298,940
2307.07262	MorphPiece : A Linguistic Tokenizer for Large Language Models	Tokenization is a critical part of modern NLP pipelines. However, contemporary tokenizers for Large Language Models are based on statistical analysis of text corpora, without much consideration to the linguistic features. I propose a linguistically motivated tokenization scheme, MorphPiece, which is based partly on mor...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	379,337
2408.06883	Diffusion Model for Slate Recommendation	Slate recommendation is a technique commonly used on streaming platforms and e-commerce sites to present multiple items together. A significant challenge with slate recommendation is managing the complex combinatorial choice space. Traditional methods often simplify this problem by assuming users engage with only one i...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	480,382
2312.11549	Label-Free Multivariate Time Series Anomaly Detection	Anomaly detection in multivariate time series (MTS) has been widely studied in one-class classification (OCC) setting. The training samples in OCC are assumed to be normal, which is difficult to guarantee in practical situations. Such a case may degrade the performance of OCC-based anomaly detection methods which fit t...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	416,631
1905.11987	Automated Ground Truth Estimation For Automotive Radar Tracking Applications With Portable GNSS And IMU Devices	Baseline generation for tracking applications is a difficult task when working with real world radar data. Data sparsity usually only allows an indirect way of estimating the original tracks as most objects' centers are not represented in the data. This article proposes an automated way of acquiring reference trajector...	false	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	132,614
2209.10693	Stochastic Future Prediction in Real World Driving Scenarios	Uncertainty plays a key role in future prediction. The future is uncertain. That means there might be many possible futures. A future prediction method should cover the whole possibilities to be robust. In autonomous driving, covering multiple modes in the prediction part is crucially important to make safety-critical ...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	318,942
1109.4521	Controlling centrality in complex networks	Spectral centrality measures allow to identify influential individuals in social groups, to rank Web pages by their popularity, and even to determine the impact of scientific researches. The centrality score of a node within a network crucially depends on the entire pattern of connections, so that the usual approach is...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	12,253
1806.05502	Scrutinizing and De-Biasing Intuitive Physics with Neural Stethoscopes	Visually predicting the stability of block towers is a popular task in the domain of intuitive physics. While previous work focusses on prediction accuracy, a one-dimensional performance measure, we provide a broader analysis of the learned physical understanding of the final model and how the learning process can be g...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	100,487
1910.10345	Unsupervised Dual Adversarial Learning for Anomaly Detection in Colonoscopy Video Frames	The automatic detection of frames containing polyps from a colonoscopy video sequence is an important first step for a fully automated colonoscopy analysis tool. Typically, such detection system is built using a large annotated data set of frames with and without polyps, which is expensive to be obtained. In this paper...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	150,465
2302.12966	SUPS: A Simulated Underground Parking Scenario Dataset for Autonomous Driving	Automatic underground parking has attracted considerable attention as the scope of autonomous driving expands. The auto-vehicle is supposed to obtain the environmental information, track its location, and build a reliable map of the scenario. Mainstream solutions consist of well-trained neural networks and simultaneous...	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	347,752
1512.00413	Are We Approaching the Fundamental Limits of Wireless Network Densification?	The single most important factor behind the data rate increases experienced by users of wireless networks over the past few decades has been densification, namely adding more base stations and access points and thus getting more spatial reuse of the spectrum. This trend is set to continue into 5G and presumably beyond....	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	49,708
2303.14926	Continuous Intermediate Token Learning with Implicit Motion Manifold for Keyframe Based Motion Interpolation	Deriving sophisticated 3D motions from sparse keyframes is a particularly challenging problem, due to continuity and exceptionally skeletal precision. The action features are often derivable accurately from the full series of keyframes, and thus, leveraging the global context with transformers has been a promising data...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	354,291
1010.0417	Visual-hint Boundary to Segment Algorithm for Image Segmentation	Image segmentation has been a very active research topic in image analysis area. Currently, most of the image segmentation algorithms are designed based on the idea that images are partitioned into a set of regions preserving homogeneous intra-regions and inhomogeneous inter-regions. However, human visual intuition doe...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	7,763
2407.14811	Decoupled Prompt-Adapter Tuning for Continual Activity Recognition	Action recognition technology plays a vital role in enhancing security through surveillance systems, enabling better patient monitoring in healthcare, providing in-depth performance analysis in sports, and facilitating seamless human-AI collaboration in domains such as manufacturing and assistive technologies. The dyna...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	474,916
2410.05448	Task Diversity Shortens the ICL Plateau	In-context learning (ICL) describes a language model's ability to generate outputs based on a set of input demonstrations and a subsequent query. To understand this remarkable capability, researchers have studied simplified, stylized models. These studies have consistently observed long loss plateaus, during which mode...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	495,739
2410.09230	Improving semantic understanding in speech language models via brain-tuning	Speech language models align with human brain responses to natural language to an impressive degree. However, current models rely heavily on low-level speech features, indicating they lack brain-relevant semantics which limits their utility as model organisms of semantic processing in the brain. In this work, we addres...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	497,493
2412.10096	Reward Machine Inference for Robotic Manipulation	Learning from Demonstrations (LfD) and Reinforcement Learning (RL) have enabled robot agents to accomplish complex tasks. Reward Machines (RMs) enhance RL's capability to train policies over extended time horizons by structuring high-level task information. In this work, we introduce a novel LfD approach for learning R...	false	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	516,783
2203.15245	Robust Structured Declarative Classifiers for 3D Point Clouds: Defending Adversarial Attacks with Implicit Gradients	Deep neural networks for 3D point cloud classification, such as PointNet, have been demonstrated to be vulnerable to adversarial attacks. Current adversarial defenders often learn to denoise the (attacked) point clouds by reconstruction, and then feed them to the classifiers as input. In contrast to the literature, we ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	288,308
2305.18200	Contextual Knowledge Learning For Dialogue Generation	Incorporating conversational context and knowledge into dialogue generation models has been essential for improving the quality of the generated responses. The context, comprising utterances from previous dialogue exchanges, is used as a source of content for response generation and as a means of selecting external kno...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	368,890
2105.00824	A Survey of Recent Abstract Summarization Techniques	This paper surveys several recent abstract summarization methods: T5, Pegasus, and ProphetNet. We implement the systems in two languages: English and Indonesian languages. We investigate the impact of pre-training models (one T5, three Pegasuses, three ProphetNets) on several Wikipedia datasets in English and Indonesia...	false	false	false	false	false	true	true	false	true	false	false	false	false	false	false	false	false	false	233,345
2403.01694	Tac-Man: Tactile-Informed Prior-Free Manipulation of Articulated Objects	Integrating robots into human-centric environments such as homes, necessitates advanced manipulation skills as robotic devices will need to engage with articulated objects like doors and drawers. Key challenges in robotic manipulation of articulated objects are the unpredictability and diversity of these objects' inter...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	434,536
1805.01890	RMDL: Random Multimodel Deep Learning for Classification	The continually increasing number of complex datasets each year necessitates ever improving machine learning methods for robust and accurate categorization of these data. This paper introduces Random Multimodel Deep Learning (RMDL): a new ensemble, deep learning approach for classification. Deep learning models have ac...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	true	false	false	96,721
1805.00249	Nugget Proposal Networks for Chinese Event Detection	Neural network based models commonly regard event detection as a word-wise classification task, which suffer from the mismatch problem between words and event triggers, especially in languages without natural word delimiters such as Chinese. In this paper, we propose Nugget Proposal Networks (NPNs), which can solve the...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	96,389
2110.00385	Neural Dependency Coding inspired Multimodal Fusion	Information integration from different modalities is an active area of research. Human beings and, in general, biological neural systems are quite adept at using a multitude of signals from different sensory perceptive fields to interact with the environment and each other. Recent work in deep fusion models via neural ...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	true	false	false	258,368
2410.01293	SurgeoNet: Realtime 3D Pose Estimation of Articulated Surgical Instruments from Stereo Images using a Synthetically-trained Network	Surgery monitoring in Mixed Reality (MR) environments has recently received substantial focus due to its importance in image-based decisions, skill assessment, and robot-assisted surgery. Tracking hands and articulated surgical instruments is crucial for the success of these applications. Due to the lack of annotated d...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	493,700
2404.14869	EEGEncoder: Advancing BCI with Transformer-Based Motor Imagery Classification	Brain-computer interfaces (BCIs) harness electroencephalographic signals for direct neural control of devices, offering a significant benefit for individuals with motor impairments. Traditional machine learning methods for EEG-based motor imagery (MI) classification encounter challenges such as manual feature extractio...	true	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	448,850
0704.3886	A Note on Ontology and Ordinary Language	We argue for a compositional semantics grounded in a strongly typed ontology that reflects our commonsense view of the world and the way we talk about it. Assuming such a structure we show that the semantics of various natural language phenomena may become nearly trivial.	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	122
2208.10244	Unit Testing for Concepts in Neural Networks	Many complex problems are naturally understood in terms of symbolic concepts. For example, our concept of "cat" is related to our concepts of "ears" and "whiskers" in a non-arbitrary way. Fodor (1998) proposes one theory of concepts, which emphasizes symbolic representations related via constituency structures. Whether...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	313,981
1601.07888	Stabilization of systems with asynchronous sensors and controllers	We study the stabilization of networked control systems with asynchronous sensors and controllers. Offsets between the sensor and controller clocks are unknown and modeled as parametric uncertainty. First we consider multi-input linear systems and provide a sufficient condition for the existence of linear time-invarian...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	51,475
2106.15239	Generating the Graph Gestalt: Kernel-Regularized Graph Representation Learning	Recent work on graph generative models has made remarkable progress towards generating increasingly realistic graphs, as measured by global graph features such as degree distribution, density, and clustering coefficients. Deep generative models have also made significant advances through better modelling of the local c...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	243,666
1708.07157	Evaluation Measures for Relevance and Credibility in Ranked Lists	Recent discussions on alternative facts, fake news, and post truth politics have motivated research on creating technologies that allow people not only to access information, but also to assess the credibility of the information presented to them by information retrieval systems. Whereas technology is in place for filt...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	79,441
2108.04126	Improved Feature Importance Computations for Tree Models: Shapley vs. Banzhaf	Shapley values are one of the main tools used to explain predictions of tree ensemble models. The main alternative to Shapley values are Banzhaf values that have not been understood equally well. In this paper we make a step towards filling this gap, providing both experimental and theoretical comparison of these model...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	249,900
2111.02086	Multilingual Machine Translation Systems from Microsoft for WMT21 Shared Task	This report describes Microsoft's machine translation systems for the WMT21 shared task on large-scale multilingual machine translation. We participated in all three evaluation tracks including Large Track and two Small Tracks where the former one is unconstrained and the latter two are fully constrained. Our model sub...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	264,761
2502.11338	WRT-SAM: Foundation Model-Driven Segmentation for Generalized Weld Radiographic Testing	Radiographic testing is a fundamental non-destructive evaluation technique for identifying weld defects and assessing quality in industrial applications due to its high-resolution imaging capabilities. Over the past decade, deep learning techniques have significantly advanced weld defect identification in radiographic ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	534,310
1605.06894	DLAU: A Scalable Deep Learning Accelerator Unit on FPGA	As the emerging field of machine learning, deep learning shows excellent ability in solving complex learning problems. However, the size of the networks becomes increasingly large scale due to the demands of the practical applications, which poses significant challenge to construct a high performance implementations of...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	true	false	true	56,209
2410.08576	A Theoretical Framework for AI-driven data quality monitoring in high-volume data environments	This paper presents a theoretical framework for an AI-driven data quality monitoring system designed to address the challenges of maintaining data quality in high-volume environments. We examine the limitations of traditional methods in managing the scale, velocity, and variety of big data and propose a conceptual appr...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	497,175
1707.05031	Residual Features and Unified Prediction Network for Single Stage Detection	Recently, a lot of single stage detectors using multi-scale features have been actively proposed. They are much faster than two stage detectors that use region proposal networks (RPN) without much degradation in the detection performances. However, the feature maps in the lower layers close to the input which are respo...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	77,155
2012.15463	Learned Multi-Resolution Variable-Rate Image Compression with Octave-based Residual Blocks	Recently deep learning-based image compression has shown the potential to outperform traditional codecs. However, most existing methods train multiple networks for multiple bit rates, which increase the implementation complexity. In this paper, we propose a new variable-rate image compression framework, which employs g...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	213,795
2409.17717	Behaviour4All: in-the-wild Facial Behaviour Analysis Toolkit	In this paper, we introduce Behavior4All, a comprehensive, open-source toolkit for in-the-wild facial behavior analysis, integrating Face Localization, Valence-Arousal Estimation, Basic Expression Recognition and Action Unit Detection, all within a single framework. Available in both CPU-only and GPU-accelerated versio...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	491,949
2404.16294	LLM-Based Section Identifiers Excel on Open Source but Stumble in Real World Applications	Electronic health records (EHR) even though a boon for healthcare practitioners, are growing convoluted and longer every day. Sifting around these lengthy EHRs is taxing and becomes a cumbersome part of physician-patient interaction. Several approaches have been proposed to help alleviate this prevalent issue either vi...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	449,427
1907.09408	A Survey of Deep Learning-based Object Detection	Object detection is one of the most important and challenging branches of computer vision, which has been widely applied in peoples life, such as monitoring security, autonomous driving and so on, with the purpose of locating instances of semantic objects of a certain class. With the rapid development of deep learning ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	139,350
1711.02144	A Joint 3D-2D based Method for Free Space Detection on Roads	In this paper, we address the problem of road segmentation and free space detection in the context of autonomous driving. Traditional methods either use 3-dimensional (3D) cues such as point clouds obtained from LIDAR, RADAR or stereo cameras or 2-dimensional (2D) cues such as lane markings, road boundaries and object ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	84,010
1908.05528	Vector spaces as Kripke frames	In recent years, the compositional distributional approach in computational linguistics has opened the way for an integration of the \emph{lexical} aspects of meaning into Lambek's type-logical grammar program. This approach is based on the observation that a sound semantics for the associative, commutative and unital ...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	true	141,738
1906.03691	Interpreting Age Effects of Human Fetal Brain from Spontaneous fMRI using Deep 3D Convolutional Neural Networks	Understanding human fetal neurodevelopment is of great clinical importance as abnormal development is linked to adverse neuropsychiatric outcomes after birth. Recent advances in functional Magnetic Resonance Imaging (fMRI) have provided new insight into development of the human brain before birth, but these studies hav...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	134,459
1008.3926	Stochastic blockmodels and community structure in networks	Stochastic blockmodels have been proposed as a tool for detecting community structure in networks as well as for generating synthetic networks for use as benchmarks. Most blockmodels, however, ignore variation in vertex degree, making them unsuitable for applications to real-world networks, which typically display broa...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	7,346
2209.14089	Combining Reinforcement Learning and Tensor Networks, with an Application to Dynamical Large Deviations	We present a framework to integrate tensor network (TN) methods with reinforcement learning (RL) for solving dynamical optimisation tasks. We consider the RL actor-critic method, a model-free approach for solving RL problems, and introduce TNs as the approximators for its policy and value functions. Our "actor-critic w...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	320,136
2004.12508	Noisy Adaptive Group Testing using Bayesian Sequential Experimental Design	When the infection prevalence of a disease is low, Dorfman showed 80 years ago that testing groups of people can prove more efficient than testing people individually. Our goal in this paper is to propose new group testing algorithms that can operate in a noisy setting (tests can be mistaken) to decide adaptively (look...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	174,270
1605.06848	Nonnegative Matrix Factorization Requires Irrationality	Nonnegative matrix factorization (NMF) is the problem of decomposing a given nonnegative $n \times m$ matrix $M$ into a product of a nonnegative $n \times d$ matrix $W$ and a nonnegative $d \times m$ matrix $H$. A longstanding open question, posed by Cohen and Rothblum in 1993, is whether a rational matrix $M$ always h...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	56,199
2208.07576	Object Discovery via Contrastive Learning for Weakly Supervised Object Detection	Weakly Supervised Object Detection (WSOD) is a task that detects objects in an image using a model trained only on image-level annotations. Current state-of-the-art models benefit from self-supervised instance-level supervision, but since weak supervision does not include count or location information, the most common ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	313,083
2201.05984	In Situ Answer Sentence Selection at Web-scale	Current answer sentence selection (AS2) applied in open-domain question answering (ODQA) selects answers by ranking a large set of possible candidates, i.e., sentences, extracted from the retrieved text. In this paper, we present Passage-based Extracting Answer Sentence In-place (PEASI), a novel design for AS2 optimize...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	275,579
1006.1149	The diversity-multiplexing tradeoff of the symmetric MIMO 2-user interference channel	The fundamental diversity-multiplexing tradeoff (DMT) of the quasi-static fading, symmetric $2$-user MIMO interference channel (IC) with channel state information at the transmitters (CSIT) and a short term average power constraint is obtained. The general case is considered where the interference-to-noise ratio (INR) ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	6,683
2205.01940	Towards Theoretical Analysis of Transformation Complexity of ReLU DNNs	This paper aims to theoretically analyze the complexity of feature transformations encoded in piecewise linear DNNs with ReLU layers. We propose metrics to measure three types of complexities of transformations based on the information theory. We further discover and prove the strong correlation between the complexity ...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	294,776
2401.11126	CARE: Ensemble Adversarial Robustness Evaluation Against Adaptive Attackers for Security Applications	Ensemble defenses, are widely employed in various security-related applications to enhance model performance and robustness. The widespread adoption of these techniques also raises many questions: Are general ensembles defenses guaranteed to be more robust than individuals? Will stronger adaptive attacks defeat existin...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	422,884
2310.20447	Efficient Bayesian Learning Curve Extrapolation using Prior-Data Fitted Networks	Learning curve extrapolation aims to predict model performance in later epochs of training, based on the performance in earlier epochs. In this work, we argue that, while the inherent uncertainty in the extrapolation of learning curves warrants a Bayesian approach, existing methods are (i) overly restrictive, and/or (i...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	404,396
1405.0915	Reasoning with Probabilistic Logics	The interest in the combination of probability with logics for modeling the world has rapidly increased in the last few years. One of the most effective approaches is the Distribution Semantics which was adopted by many logic programming languages and in Descripion Logics. In this paper, we illustrate the work we have ...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	32,821
2305.14890	HARD: Hard Augmentations for Robust Distillation	Knowledge distillation (KD) is a simple and successful method to transfer knowledge from a teacher to a student model solely based on functional activity. However, current KD has a few shortcomings: it has recently been shown that this method is unsuitable to transfer simple inductive biases like shift equivariance, st...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	367,317
1904.03406	Towards Massive MIMO 2.0: Understanding spatial correlation, interference suppression, and pilot contamination	Since the seminal paper by Marzetta from 2010, Massive MIMO has changed from being a theoretical concept with an infinite number of antennas to a practical technology. The key concepts are adopted in 5G and base stations (BSs) with $M=64$ full-digital transceivers have been commercially deployed in sub-6\,GHz bands. Th...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	126,708
1803.06915	Exploiting symmetry in network analysis	Virtually all network analyses involve structural measures between pairs of vertices, or of the vertices themselves, and the large amount of symmetry present in real-world complex networks is inherited by such measures. This has practical consequences which have not yet been explored in full generality, nor systematica...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	92,929
2002.09547	Stochastic Normalizing Flows	We introduce stochastic normalizing flows, an extension of continuous normalizing flows for maximum likelihood estimation and variational inference (VI) using stochastic differential equations (SDEs). Using the theory of rough paths, the underlying Brownian motion is treated as a latent variable and approximated, enabl...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	165,092
2304.08113	Analysis of Interpolating Regression Models and the Double Descent Phenomenon	A regression model with more parameters than data points in the training data is overparametrized and has the capability to interpolate the training data. Based on the classical bias-variance tradeoff expressions, it is commonly assumed that models which interpolate noisy training data are poor to generalize. In some c...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	358,595
2112.04937	DVHN: A Deep Hashing Framework for Large-scale Vehicle Re-identification	In this paper, we make the very first attempt to investigate the integration of deep hash learning with vehicle re-identification. We propose a deep hash-based vehicle re-identification framework, dubbed DVHN, which substantially reduces memory usage and promotes retrieval efficiency while reserving nearest neighbor se...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	270,691
1907.03938	Deep Learning-Aided Dynamic Read Thresholds Design For Multi-Level-Cell Flash Memories	The practical NAND flash memory suffers from various non-stationary noises that are difficult to be predicted. Furthermore, the data retention noise induced channel offset is unknown during the readback process. This severely affects the data recovery from the memory cell. In this paper, we first propose a novel recurr...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	137,972
2410.03861	Refinement of Monocular Depth Maps via Multi-View Differentiable Rendering	The accurate reconstruction of per-pixel depth for an image is vital for many tasks in computer graphics, computer vision, and robotics. In this paper, we present a novel approach to generate view consistent and detailed depth maps from a number of posed images. We leverage advances in monocular depth estimation, which...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	495,012

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