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
2105.08872	Combating Ambiguity for Hash-code Learning in Medical Instance Retrieval	When encountering a dubious diagnostic case, medical instance retrieval can help radiologists make evidence-based diagnoses by finding images containing instances similar to a query case from a large image database. The similarity between the query case and retrieved similar cases is determined by visual features extra...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	235,892
2202.08955	R2-D2: Repetitive Reprediction Deep Decipher for Semi-Supervised Deep Learning	Most recent semi-supervised deep learning (deep SSL) methods used a similar paradigm: use network predictions to update pseudo-labels and use pseudo-labels to update network parameters iteratively. However, they lack theoretical support and cannot explain why predictions are good candidates for pseudo-labels in the dee...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	281,039
2407.12220	Questionable practices in machine learning	Evaluating modern ML models is hard. The strong incentive for researchers and companies to report a state-of-the-art result on some metric often leads to questionable research practices (QRPs): bad practices which fall short of outright research fraud. We describe 44 such practices which can undermine reported results,...	false	false	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	473,818
2009.09282	Reducing false-positive biopsies with deep neural networks that utilize local and global information in screening mammograms	Breast cancer is the most common cancer in women, and hundreds of thousands of unnecessary biopsies are done around the world at a tremendous cost. It is crucial to reduce the rate of biopsies that turn out to be benign tissue. In this study, we build deep neural networks (DNNs) to classify biopsied lesions as being ei...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	196,523
2012.15484	Seeing is Knowing! Fact-based Visual Question Answering using Knowledge Graph Embeddings	Fact-based Visual Question Answering (FVQA), a challenging variant of VQA, requires a QA-system to include facts from a diverse knowledge graph (KG) in its reasoning process to produce an answer. Large KGs, especially common-sense KGs, are known to be incomplete, i.e., not all non-existent facts are always incorrect. T...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	213,807
2008.03953	On construction and (non)existence of $c$-(almost) perfect nonlinear functions	Functions with low differential uniformity have relevant applications in cryptography. Recently, functions with low $c$-differential uniformity attracted lots of attention. In particular, so-called APcN and PcN functions (generalization of APN and PN functions) have been investigated. Here, we provide a characterizatio...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	191,082
2403.06414	Evolving Knowledge Distillation with Large Language Models and Active Learning	Large language models (LLMs) have demonstrated remarkable capabilities across various NLP tasks. However, their computational costs are prohibitively high. To address this issue, previous research has attempted to distill the knowledge of LLMs into smaller models by generating annotated data. Nonetheless, these works h...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	436,441
2012.06957	Open-World Class Discovery with Kernel Networks	We study an Open-World Class Discovery problem in which, given labeled training samples from old classes, we need to discover new classes from unlabeled test samples. There are two critical challenges to addressing this paradigm: (a) transferring knowledge from old to new classes, and (b) incorporating knowledge learne...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	211,284
2007.12619	Channel-Level Variable Quantization Network for Deep Image Compression	Deep image compression systems mainly contain four components: encoder, quantizer, entropy model, and decoder. To optimize these four components, a joint rate-distortion framework was proposed, and many deep neural network-based methods achieved great success in image compression. However, almost all convolutional neur...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	188,874
2204.07684	Circuit-theoretic Line Outage Distribution Factor	This work presents the design of AC line outage distribution factor created from the circuit-theoretic power flow models. Experiment results are shown to demonstrate its efficacy in quantifying the impact of line outages on the grid, and its resulting potential for fast contingency screening.	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	291,799
2011.03882	Multi-Modal Learning of Keypoint Predictive Models for Visual Object Manipulation	Humans have impressive generalization capabilities when it comes to manipulating objects and tools in completely novel environments. These capabilities are, at least partially, a result of humans having internal models of their bodies and any grasped object. How to learn such body schemas for robots remains an open pro...	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	false	205,383
2105.02956	Reconstruction of Convex Polytope Compositions from 3D Point-clouds	Reconstructing a composition (union) of convex polytopes that perfectly fits the corresponding input point-cloud is a hard optimization problem with interesting applications in reverse engineering and rigid body dynamics simulations. We propose a pipeline that first extracts a set of planes, then partitions the input p...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	233,982
1205.4781	An Achievable Rate Region for Three-Pair Interference Channels with Noise	An achievable rate region for certain noisy three-user-pair interference channels is proposed. The channel class under consideration generalizes the three-pair deterministic interference channel (3-DIC) in the same way as the Telatar-Tse noisy two-pair interference channel generalizes the El Gamal-Costa injective chann...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	16,116
2312.12954	TADAP: Trajectory-Aided Drivable area Auto-labeling with Pre-trained self-supervised features in winter driving conditions	Detection of the drivable area in all conditions is crucial for autonomous driving and advanced driver assistance systems. However, the amount of labeled data in adverse driving conditions is limited, especially in winter, and supervised methods generalize poorly to conditions outside the training distribution. For eas...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	417,165
2110.02127	Rate Splitting Multiple Access for Semi-Grant-Free Transmissions	Enabled by hybrid grant-based (GB) and grant-free (GF) transmission techniques, GF users of internet of things (IoT) devices and massive machine-type communications (mMTC) meet opportunities to share wireless resources with GB users. In this paper, we propose a rate splitting multiple access (RSMA) strategy for an emer...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	259,013
2412.01820	Towards Universal Soccer Video Understanding	As a globally celebrated sport, soccer has attracted widespread interest from fans all over the world. This paper aims to develop a comprehensive multi-modal framework for soccer video understanding. Specifically, we make the following contributions in this paper: (i) we introduce SoccerReplay-1988, the largest multi-m...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	513,277
2104.04543	Understanding Event-Generation Networks via Uncertainties	Following the growing success of generative neural networks in LHC simulations, the crucial question is how to control the networks and assign uncertainties to their event output. We show how Bayesian normalizing flow or invertible networks capture uncertainties from the training and turn them into an uncertainty on th...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	229,415
1902.10807	autoAx: An Automatic Design Space Exploration and Circuit Building Methodology utilizing Libraries of Approximate Components	Approximate computing is an emerging paradigm for developing highly energy-efficient computing systems such as various accelerators. In the literature, many libraries of elementary approximate circuits have already been proposed to simplify the design process of approximate accelerators. Because these libraries contain...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	122,772
1803.00211	Securing OFDM-Based Wireless Links Using Temporal Artificial-Noise Injection	We investigate the physical layer security of wireless single-input single-output orthogonal-division multiplexing (OFDM) when a transmitter, which we refer to as Alice, sends her information to a receiver, which we refer to as Bob, in the presence of an eavesdropping node, Eve. To prevent information leakage, Alice se...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	91,627
2409.02465	DetectiveQA: Evaluating Long-Context Reasoning on Detective Novels	With the rapid advancement of Large Language Models (LLMs), long-context information understanding and processing have become a hot topic in academia and industry. However, benchmarks for evaluating the ability of LLMs to handle long-context information do not seem to have kept pace with the development of LLMs. Despit...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	485,714
2109.06161	Single-Stage Keypoint-Based Category-Level Object Pose Estimation from an RGB Image	Prior work on 6-DoF object pose estimation has largely focused on instance-level processing, in which a textured CAD model is available for each object being detected. Category-level 6-DoF pose estimation represents an important step toward developing robotic vision systems that operate in unstructured, real-world scen...	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	255,069
1602.04433	Unsupervised Domain Adaptation with Residual Transfer Networks	The recent success of deep neural networks relies on massive amounts of labeled data. For a target task where labeled data is unavailable, domain adaptation can transfer a learner from a different source domain. In this paper, we propose a new approach to domain adaptation in deep networks that can jointly learn adapti...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	52,132
2204.03028	Software Testing, AI and Robotics (STAIR) Learning Lab	In this paper we presented the Software Testing, AI and Robotics (STAIR) Learning Lab. STAIR is an initiative started at the University of Innsbruck to bring robotics, Artificial Intelligence (AI) and software testing into schools. In the lab physical and virtual learning units are developed in parallel and in sync wit...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	290,157
2303.16342	Language-Guided Audio-Visual Source Separation via Trimodal Consistency	We propose a self-supervised approach for learning to perform audio source separation in videos based on natural language queries, using only unlabeled video and audio pairs as training data. A key challenge in this task is learning to associate the linguistic description of a sound-emitting object to its visual featur...	false	false	false	false	true	false	false	false	true	false	false	true	false	false	false	false	false	false	354,824
1710.01257	Deep learning for source camera identification on mobile devices	In the present paper, we propose a source camera identification method for mobile devices based on deep learning. Recently, convolutional neural networks (CNNs) have shown a remarkable performance on several tasks such as image recognition, video analysis or natural language processing. A CNN consists on a set of layer...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	81,991
1904.09092	Weakly Supervised Adversarial Domain Adaptation for Semantic Segmentation in Urban Scenes	Semantic segmentation, a pixel-level vision task, is developed rapidly by using convolutional neural networks (CNNs). Training CNNs requires a large amount of labeled data, but manually annotating data is difficult. For emancipating manpower, in recent years, some synthetic datasets are released. However, they are stil...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	128,279
2302.12538	UnbiasedNets: A Dataset Diversification Framework for Robustness Bias Alleviation in Neural Networks	Performance of trained neural network (NN) models, in terms of testing accuracy, has improved remarkably over the past several years, especially with the advent of deep learning. However, even the most accurate NNs can be biased toward a specific output classification due to the inherent bias in the available training ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	347,608
2207.08865	Romanus: Robust Task Offloading in Modular Multi-Sensor Autonomous Driving Systems	Due to the high performance and safety requirements of self-driving applications, the complexity of modern autonomous driving systems (ADS) has been growing, instigating the need for more sophisticated hardware which could add to the energy footprint of the ADS platform. Addressing this, edge computing is poised to enc...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	true	308,710
2407.06418	System stabilization with policy optimization on unstable latent manifolds	Stability is a basic requirement when studying the behavior of dynamical systems. However, stabilizing dynamical systems via reinforcement learning is challenging because only little data can be collected over short time horizons before instabilities are triggered and data become meaningless. This work introduces a rei...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	471,386
2402.04553	Curvature-Informed SGD via General Purpose Lie-Group Preconditioners	We present a novel approach to accelerate stochastic gradient descent (SGD) by utilizing curvature information obtained from Hessian-vector products or finite differences of parameters and gradients, similar to the BFGS algorithm. Our approach involves two preconditioners: a matrix-free preconditioner and a low-rank ap...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	427,499
2012.01696	FairBatch: Batch Selection for Model Fairness	Training a fair machine learning model is essential to prevent demographic disparity. Existing techniques for improving model fairness require broad changes in either data preprocessing or model training, rendering themselves difficult-to-adopt for potentially already complex machine learning systems. We address this p...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	209,496
2204.04533	Motion Artifacts Correction from Single-Channel EEG and fNIRS Signals using Novel Wavelet Packet Decomposition in Combination with Canonical Correlation Analysis	The electroencephalogram (EEG) and functional near-infrared spectroscopy (fNIRS) signals, highly non-stationary in nature, greatly suffers from motion artifacts while recorded using wearable sensors. This paper proposes two robust methods: i) Wavelet packet decomposition (WPD), and ii) WPD in combination with canonical...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	290,690
2307.05364	Neural network analysis of neutron and X-ray reflectivity data: Incorporating prior knowledge for tackling the phase problem	Due to the lack of phase information, determining the physical parameters of multilayer thin films from measured neutron and X-ray reflectivity curves is, on a fundamental level, an underdetermined inverse problem. This so-called phase problem poses limitations on standard neural networks, constraining the range and nu...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	378,710
2412.18248	Detection and Forecasting of Parkinson Disease Progression from Speech Signal Features Using MultiLayer Perceptron and LSTM	Accurate diagnosis of Parkinson disease, especially in its early stages, can be a challenging task. The application of machine learning techniques helps improve the diagnostic accuracy of Parkinson disease detection but only few studies have presented work towards the prediction of disease progression. In this research...	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	520,337
2411.05010	Scattered Forest Search: Smarter Code Space Exploration with LLMs	We propose a novel approach to scaling LLM inference for code generation. We frame code generation as a black box optimization problem within the code space, and employ optimization-inspired techniques to enhance exploration. Specifically, we introduce Scattered Forest Search to enhance solution diversity while searchi...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	true	506,508
2201.00814	Vision Transformer Slimming: Multi-Dimension Searching in Continuous Optimization Space	This paper explores the feasibility of finding an optimal sub-model from a vision transformer and introduces a pure vision transformer slimming (ViT-Slim) framework. It can search a sub-structure from the original model end-to-end across multiple dimensions, including the input tokens, MHSA and MLP modules with state-o...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	274,073
2304.00485	Graph Mining for Cybersecurity: A Survey	The explosive growth of cyber attacks nowadays, such as malware, spam, and intrusions, caused severe consequences on society. Securing cyberspace has become an utmost concern for organizations and governments. Traditional Machine Learning (ML) based methods are extensively used in detecting cyber threats, but they hard...	false	false	false	true	true	false	false	false	false	false	false	false	true	false	false	false	false	false	355,706
1305.7480	Path diversity improves the identification of influential spreaders	Identifying influential spreaders in complex networks is a crucial problem which relates to wide applications. Many methods based on the global information such as $k$-shell and PageRank have been applied to rank spreaders. However, most of related previous works overwhelmingly focus on the number of paths for propagat...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	24,918
1010.2551	Fractional Repetition Codes for Repair in Distributed Storage Systems	We introduce a new class of exact Minimum-Bandwidth Regenerating (MBR) codes for distributed storage systems, characterized by a low-complexity uncoded repair process that can tolerate multiple node failures. These codes consist of the concatenation of two components: an outer MDS code followed by an inner repetition c...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	7,887
2403.05384	A Data Augmentation Pipeline to Generate Synthetic Labeled Datasets of 3D Echocardiography Images using a GAN	Due to privacy issues and limited amount of publicly available labeled datasets in the domain of medical imaging, we propose an image generation pipeline to synthesize 3D echocardiographic images with corresponding ground truth labels, to alleviate the need for data collection and for laborious and error-prone human la...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	435,986
2311.14782	Understanding the Role of Textual Prompts in LLM for Time Series Forecasting: an Adapter View	In the burgeoning domain of Large Language Models (LLMs), there is a growing interest in applying LLM to time series forecasting, with multiple studies focused on leveraging textual prompts to further enhance the predictive prowess. This study aims to understand how and why the integration of textual prompts into LLM c...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	410,261
1811.08010	Stackelberg GAN: Towards Provable Minimax Equilibrium via Multi-Generator Architectures	We study the problem of alleviating the instability issue in the GAN training procedure via new architecture design. The discrepancy between the minimax and maximin objective values could serve as a proxy for the difficulties that the alternating gradient descent encounters in the optimization of GANs. In this work, we...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	113,920
2010.09842	Robot Design With Neural Networks, MILP Solvers and Active Learning	Central to the design of many robot systems and their controllers is solving a constrained blackbox optimization problem. This paper presents CNMA, a new method of solving this problem that is conservative in the number of potentially expensive blackbox function evaluations; allows specifying complex, even recursive co...	false	false	false	false	true	false	true	true	false	false	false	false	false	false	false	false	false	false	201,675
1509.01659	Gravitational Clustering	The downfall of many supervised learning algorithms, such as neural networks, is the inherent need for a large amount of training data. Although there is a lot of buzz about big data, there is still the problem of doing classification from a small dataset. Other methods such as support vector machines, although capable...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	46,638
1902.03680	Learning From Noisy Labels By Regularized Estimation Of Annotator Confusion	The predictive performance of supervised learning algorithms depends on the quality of labels. In a typical label collection process, multiple annotators provide subjective noisy estimates of the "truth" under the influence of their varying skill-levels and biases. Blindly treating these noisy labels as the ground trut...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	121,173
1907.01684	Solvents based model reduction of linear systems	Model order reduction is the approximation of dynamical systems into equivalent systems with smaller order. Model reduction has been studied extensively for different types of systems. In this paper, we present two methods for multi input multi output linear systems. These methods are based on solvents, also called blo...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	137,405
1011.5364	Optimizing On-Line Advertising	We want to find the optimal strategy for displaying advertisements e.g. banners, videos, in given locations at given times under some realistic dynamic constraints. Our primary goal is to maximize the expected revenue in a given period of time, i.e. the total profit produced by the impressions, which depends on profit-...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	8,328
1704.02556	Management of Cascading Outage Risk Based on Risk Gradient and Markovian Tree Search	Since cascading outages are major threats to power systems, it is important to reduce the risk of potential cascading outages. In this paper, a risk management method of cascading outages based on Markovian tree search is proposed. With the tree expansion on the cascading outage risk, risk gradient is computed efficien...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	71,468
1401.3467	Planning over Chain Causal Graphs for Variables with Domains of Size 5 Is NP-Hard	Recently, considerable focus has been given to the problem of determining the boundary between tractable and intractable planning problems. In this paper, we study the complexity of planning in the class C_n of planning problems, characterized by unary operators and directed path causal graphs. Although this is one of ...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	29,873
1705.06149	Parallel-in-Space-and-Time Simulation of the Three-Dimensional, Unsteady Navier-Stokes Equations for Incompressible Flow	In this paper we combine the Parareal parallel-in-time method together with spatial parallelization and investigate this space-time parallel scheme by means of solving the three-dimensional incompressible Navier-Stokes equations. Parallelization of time stepping provides a new direction of parallelization and allows to...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	73,601
1905.04994	Governance by Glass-Box: Implementing Transparent Moral Bounds for AI Behaviour	Artificial Intelligence (AI) applications are being used to predict and assess behaviour in multiple domains, such as criminal justice and consumer finance, which directly affect human well-being. However, if AI is to improve people's lives, then people must be able to trust AI, which means being able to understand wha...	false	false	false	false	true	false	false	false	false	false	false	false	false	true	false	false	false	true	130,613
2212.05294	Variational Speech Waveform Compression to Catalyze Semantic Communications	We propose a novel neural waveform compression method to catalyze emerging speech semantic communications. By introducing nonlinear transform and variational modeling, we effectively capture the dependencies within speech frames and estimate the probabilistic distribution of the speech feature more accurately, giving r...	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	335,740
1709.04825	General problem solving with category theory	This paper proposes a formal cognitive framework for problem solving based on category theory. We introduce cognitive categories, which are categories with exactly one morphism between any two objects. Objects in these categories are interpreted as states and morphisms as transformations between states. Moreover, cogni...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	80,738
2409.16797	Scalable Ensemble Diversification for OOD Generalization and Detection	Training a diverse ensemble of models has several practical applications such as providing candidates for model selection with better out-of-distribution (OOD) generalization, and enabling the detection of OOD samples via Bayesian principles. An existing approach to diverse ensemble training encourages the models to di...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	491,513
2304.01484	Mapping Degeneration Meets Label Evolution: Learning Infrared Small Target Detection with Single Point Supervision	Training a convolutional neural network (CNN) to detect infrared small targets in a fully supervised manner has gained remarkable research interests in recent years, but is highly labor expensive since a large number of per-pixel annotations are required. To handle this problem, in this paper, we make the first attempt...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	356,092
2501.07245	Depth and Image Fusion for Road Obstacle Detection Using Stereo Camera	This paper is devoted to the detection of objects on a road, performed with a combination of two methods based on both the use of depth information and video analysis of data from a stereo camera. Since neither the time of the appearance of an object on the road, nor its size and shape is known in advance, ML/DL-based ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	524,324
1911.11776	Noise Robust Generative Adversarial Networks	Generative adversarial networks (GANs) are neural networks that learn data distributions through adversarial training. In intensive studies, recent GANs have shown promising results for reproducing training images. However, in spite of noise, they reproduce images with fidelity. As an alternative, we propose a novel fa...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	155,223
2410.05362	LLMs Are In-Context Bandit Reinforcement Learners	Large Language Models (LLMs) excel at in-context learning (ICL), a supervised learning technique that relies on adding annotated examples to the model context. We investigate a contextual bandit version of in-context reinforcement learning (ICRL), where models learn in-context, online, from external reward, instead of ...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	495,707
2408.15608	Geometry-guided Feature Learning and Fusion for Indoor Scene Reconstruction	In addition to color and textural information, geometry provides important cues for 3D scene reconstruction. However, current reconstruction methods only include geometry at the feature level thus not fully exploiting the geometric information. In contrast, this paper proposes a novel geometry integration mechanism f...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	484,003
2311.01444	LabelFormer: Object Trajectory Refinement for Offboard Perception from LiDAR Point Clouds	A major bottleneck to scaling-up training of self-driving perception systems are the human annotations required for supervision. A promising alternative is to leverage "auto-labelling" offboard perception models that are trained to automatically generate annotations from raw LiDAR point clouds at a fraction of the cost...	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	405,041
2204.04558	Gradient-Based Trajectory Optimization With Learned Dynamics	Trajectory optimization methods have achieved an exceptional level of performance on real-world robots in recent years. These methods heavily rely on accurate analytical models of the dynamics, yet some aspects of the physical world can only be captured to a limited extent. An alternative approach is to leverage machin...	false	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	290,699
2011.00621	Semantic Task Planning for Service Robots in Open World	In this paper, we present a planning system based on semantic reasoning for a general-purpose service robot, which is aimed at behaving more intelligently in domains that contain incomplete information, under-specified goals, and dynamic changes. First, Two kinds of data are generated by Natural Language Processing mod...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	204,300
2408.10717	Accelerated training of deep learning surrogate models for surface displacement and flow, with application to MCMC-based history matching of CO2 storage operations	Deep learning surrogate modeling shows great promise for subsurface flow applications, but the training demands can be substantial. Here we introduce a new surrogate modeling framework to predict CO2 saturation, pressure and surface displacement for use in the history matching of carbon storage operations. Rather than ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	481,995
2111.02330	Geodesic statistics for random network families	A key task in the study of networked systems is to derive local and global properties that impact connectivity, synchronizability, and robustness. Computing shortest paths or geodesics in the network yields measures of node centrality and network connectivity that can contribute to explain such phenomena. We derive an ...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	264,837
1907.07844	Growing a Brain: Fine-Tuning by Increasing Model Capacity	CNNs have made an undeniable impact on computer vision through the ability to learn high-capacity models with large annotated training sets. One of their remarkable properties is the ability to transfer knowledge from a large source dataset to a (typically smaller) target dataset. This is usually accomplished through f...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	138,982
1905.02869	Automatic Inference of Minimalist Grammars using an SMT-Solver	We introduce (1) a novel parser for Minimalist Grammars (MG), encoded as a system of first-order logic formulae that may be evaluated using an SMT-solver, and (2) a novel procedure for inferring Minimalist Grammars using this parser. The input to this procedure is a sequence of sentences that have been annotated with s...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	130,066
1801.01239	How to Beat Science and Influence People: Policy Makers and Propaganda in Epistemic Networks	In their recent book Merchants of Doubt [New York:Bloomsbury 2010], Naomi Oreskes and Erik Conway describe the "tobacco strategy", which was used by the tobacco industry to influence policy makers regarding the health risks of tobacco products. The strategy involved two parts, consisting of (1) promoting and sharing in...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	87,695
2406.09147	Weakly-supervised anomaly detection for multimodal data distributions	Weakly-supervised anomaly detection can outperform existing unsupervised methods with the assistance of a very small number of labeled anomalies, which attracts increasing attention from researchers. However, existing weakly-supervised anomaly detection methods are limited as these methods do not factor in the multimod...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	463,788
2312.11669	Prediction and Control in Continual Reinforcement Learning	Temporal difference (TD) learning is often used to update the estimate of the value function which is used by RL agents to extract useful policies. In this paper, we focus on value function estimation in continual reinforcement learning. We propose to decompose the value function into two components which update at dif...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	416,673
2301.12047	Backpropagation of Unrolled Solvers with Folded Optimization	The integration of constrained optimization models as components in deep networks has led to promising advances on many specialized learning tasks. A central challenge in this setting is backpropagation through the solution of an optimization problem, which typically lacks a closed form. One typical strategy is algorit...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	342,368
2303.15193	CoCon: A Data Set on Combined Contextualized Research Artifact Use	In the wake of information overload in academia, methodologies and systems for search, recommendation, and prediction to aid researchers in identifying relevant research are actively studied and developed. Existing work, however, is limited in terms of granularity, focusing only on the level of papers or a single type ...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	true	354,398
1607.00117	All Your Cards Are Belong To Us: Understanding Online Carding Forums	Underground online forums are platforms that enable trades of illicit services and stolen goods. Carding forums, in particular, are known for being focused on trading financial information. However, little evidence exists about the sellers that are present on carding forums, the precise types of products they advertise...	false	false	false	true	false	false	false	false	false	false	false	false	true	true	false	false	false	false	58,031
2108.13304	Extracting Qualitative Causal Structure with Transformer-Based NLP	Qualitative causal relationships compactly express the direction, dependency, temporal constraints, and monotonicity constraints of discrete or continuous interactions in the world. In everyday or academic language, we may express interactions between quantities (e.g., sleep decreases stress), between discrete events o...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	252,771
2205.04376	EigenNoise: A Contrastive Prior to Warm-Start Representations	In this work, we present a naive initialization scheme for word vectors based on a dense, independent co-occurrence model and provide preliminary results that suggest it is competitive and warrants further investigation. Specifically, we demonstrate through information-theoretic minimum description length (MDL) probing...	false	false	false	false	false	false	true	false	true	true	false	false	false	false	false	false	false	false	295,625
2202.13331	Topology-Preserving Segmentation Network: A Deep Learning Segmentation Framework for Connected Component	Medical image segmentation, which aims to automatically extract anatomical or pathological structures, plays a key role in computer-aided diagnosis and disease analysis. Despite the problem has been widely studied, existing methods are prone to topological errors. In medical imaging, the topology of the structure, such...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	282,566
2103.07423	Radiomic Deformation and Textural Heterogeneity (R-DepTH) Descriptor to characterize Tumor Field Effect: Application to Survival Prediction in Glioblastoma	The concept of tumor field effect implies that cancer is a systemic disease with its impact way beyond the visible tumor confines. For instance, in Glioblastoma (GBM), an aggressive brain tumor, the increase in intracranial pressure due to tumor burden often leads to brain herniation and poor outcomes. Our work is base...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	224,589
2108.02838	Two-Stage Sector Rotation Methodology Using Machine Learning and Deep Learning Techniques	Market indicators such as CPI and GDP have been widely used over decades to identify the stage of business cycles and also investment attractiveness of sectors given market conditions. In this paper, we propose a two-stage methodology that consists of predicting ETF prices for each sector using market indicators and ra...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	249,465
2404.06114	Communication-Efficient Large-Scale Distributed Deep Learning: A Comprehensive Survey	With the rapid growth in the volume of data sets, models, and devices in the domain of deep learning, there is increasing attention on large-scale distributed deep learning. In contrast to traditional distributed deep learning, the large-scale scenario poses new challenges that include fault tolerance, scalability of a...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	445,331
2202.08942	Enhanced DeepONet for Modeling Partial Differential Operators Considering Multiple Input Functions	Machine learning, especially deep learning is gaining much attention due to the breakthrough performance in various cognitive applications. Recently, neural networks (NN) have been intensively explored to model partial differential equations as NN can be viewed as universal approximators for nonlinear functions. A deep...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	281,035
1908.05604	Generative Question Refinement with Deep Reinforcement Learning in Retrieval-based QA System	In real-world question-answering (QA) systems, ill-formed questions, such as wrong words, ill word order, and noisy expressions, are common and may prevent the QA systems from understanding and answering them accurately. In order to eliminate the effect of ill-formed questions, we approach the question refinement task ...	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	false	false	141,758
2105.14711	CTSpine1K: A Large-Scale Dataset for Spinal Vertebrae Segmentation in Computed Tomography	Spine-related diseases have high morbidity and cause a huge burden of social cost. Spine imaging is an essential tool for noninvasively visualizing and assessing spinal pathology. Segmenting vertebrae in computed tomography (CT) images is the basis of quantitative medical image analysis for clinical diagnosis and surge...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	237,778
2207.10157	Visual Knowledge Tracing	Each year, thousands of people learn new visual categorization tasks -- radiologists learn to recognize tumors, birdwatchers learn to distinguish similar species, and crowd workers learn how to annotate valuable data for applications like autonomous driving. As humans learn, their brain updates the visual features it e...	true	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	309,150
1608.02505	Analysis and Control of Aircraft Longitudinal Dynamics with Large Flight Envelopes	The paper contributes towards the development of a unified control approach for longitudinal aircraft dynamics with large flight envelopes. Prior to the control design, we analyze the existence and the uniqueness of the equilibrium orientation along a reference velocity. We show that shape symmetries and aerodynamic st...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	59,567
2403.18373	BAM: Box Abstraction Monitors for Real-time OoD Detection in Object Detection	Out-of-distribution (OoD) detection techniques for deep neural networks (DNNs) become crucial thanks to their filtering of abnormal inputs, especially when DNNs are used in safety-critical applications and interact with an open and dynamic environment. Nevertheless, integrating OoD detection into state-of-the-art (SOTA...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	441,913
2406.11781	DiffMM: Multi-Modal Diffusion Model for Recommendation	The rise of online multi-modal sharing platforms like TikTok and YouTube has enabled personalized recommender systems to incorporate multiple modalities (such as visual, textual, and acoustic) into user representations. However, addressing the challenge of data sparsity in these systems remains a key issue. To address ...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	465,054
2302.05905	Single Motion Diffusion	Synthesizing realistic animations of humans, animals, and even imaginary creatures, has long been a goal for artists and computer graphics professionals. Compared to the imaging domain, which is rich with large available datasets, the number of data instances for the motion domain is limited, particularly for the anima...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	true	345,215
1708.01162	Good Applications for Crummy Entity Linkers? The Case of Corpus Selection in Digital Humanities	Over the last decade we have made great progress in entity linking (EL) systems, but performance may vary depending on the context and, arguably, there are even principled limitations preventing a "perfect" EL system. This also suggests that there may be applications for which current "imperfect" EL is already very use...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	78,343
2208.01613	Principles of Query Visualization	Query Visualization (QV) is the problem of transforming a given query into a graphical representation that helps humans understand its meaning. This task is notably different from designing a Visual Query Language (VQL) that helps a user compose a query. This article discusses the principles of relational query visuali...	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	311,207
2405.08306	Flight Path Optimization with Optimal Control Method	This paper is based on a crucial issue in the aviation world: how to optimize the trajectory and controls given to the aircraft in order to optimize flight time and fuel consumption. This study aims to provide elements of a response to this problem and to define, under certain simplifying assumptions, an optimal respon...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	454,055
2405.16610	The devil is in discretization discrepancy. Robustifying Differentiable NAS with Single-Stage Searching Protocol	Neural Architecture Search (NAS) has been widely adopted to design neural networks for various computer vision tasks. One of its most promising subdomains is differentiable NAS (DNAS), where the optimal architecture is found in a differentiable manner. However, gradient-based methods suffer from the discretization erro...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	true	false	false	457,519
2305.02893	APR: Online Distant Point Cloud Registration Through Aggregated Point Cloud Reconstruction	For many driving safety applications, it is of great importance to accurately register LiDAR point clouds generated on distant moving vehicles. However, such point clouds have extremely different point density and sensor perspective on the same object, making registration on such point clouds very hard. In this paper, ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	362,205
1901.02307	Complexity Bounds for the Controllability of Temporal Networks with Conditions, Disjunctions, and Uncertainty	In temporal planning, many different temporal network formalisms are used to model real world situations. Each of these formalisms has different features which affect how easy it is to determine whether the underlying network of temporal constraints is consistent. While many of the simpler models have been well-studied...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	118,171
2206.13603	BeamsNet: A data-driven Approach Enhancing Doppler Velocity Log Measurements for Autonomous Underwater Vehicle Navigation	Autonomous underwater vehicles (AUV) perform various applications such as seafloor mapping and underwater structure health monitoring. Commonly, an inertial navigation system aided by a Doppler velocity log (DVL) is used to provide the vehicle's navigation solution. In such fusion, the DVL provides the velocity vector ...	false	false	false	false	false	false	true	true	false	false	true	false	false	false	false	false	false	false	305,019
2212.05598	Recurrent Vision Transformers for Object Detection with Event Cameras	We present Recurrent Vision Transformers (RVTs), a novel backbone for object detection with event cameras. Event cameras provide visual information with sub-millisecond latency at a high-dynamic range and with strong robustness against motion blur. These unique properties offer great potential for low-latency object de...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	335,830
2412.07129	StyleMark: A Robust Watermarking Method for Art Style Images Against Black-Box Arbitrary Style Transfer	Arbitrary Style Transfer (AST) achieves the rendering of real natural images into the painting styles of arbitrary art style images, promoting art communication. However, misuse of unauthorized art style images for AST may infringe on artists' copyrights. One countermeasure is robust watermarking, which tracks image pr...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	515,518
2205.01966	Non-Autoregressive Machine Translation: It's Not as Fast as it Seems	Efficient machine translation models are commercially important as they can increase inference speeds, and reduce costs and carbon emissions. Recently, there has been much interest in non-autoregressive (NAR) models, which promise faster translation. In parallel to the research on NAR models, there have been successful...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	294,786
2102.12245	Estimation of Continuous Blood Pressure from PPG via a Federated Learning Approach	Ischemic heart disease is the highest cause of mortality globally each year. This not only puts a massive strain on the lives of those affected but also on the public healthcare systems. To understand the dynamics of the healthy and unhealthy heart doctors commonly use electrocardiogram (ECG) and blood pressure (BP) re...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	221,668
2303.05518	Computably Continuous Reinforcement-Learning Objectives are PAC-learnable	In reinforcement learning, the classic objectives of maximizing discounted and finite-horizon cumulative rewards are PAC-learnable: There are algorithms that learn a near-optimal policy with high probability using a finite amount of samples and computation. In recent years, researchers have introduced objectives and co...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	350,500
2207.02296	A Tutorial on the Spectral Theory of Markov Chains	Markov chains are a class of probabilistic models that have achieved widespread application in the quantitative sciences. This is in part due to their versatility, but is compounded by the ease with which they can be probed analytically. This tutorial provides an in-depth introduction to Markov chains, and explores the...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	306,468
2102.01048	Secrecy: Secure collaborative analytics on secret-shared data	We present a relational MPC framework for secure collaborative analytics on private data with no information leakage. Our work targets challenging use cases where data owners may not have private resources to participate in the computation, thus, they need to securely outsource the data analysis to untrusted third part...	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	true	false	217,986

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