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
1308.5906	Biological effects and equivalent doses in radiotherapy: a software solution	The limits of TDF (time, dose, and fractionation) and linear quadratic models have been known for a long time. Medical physicists and physicians are required to provide fast and reliable interpretations regarding the delivered doses or any future prescriptions relating to treatment changes. We therefore propose a calcu...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	26,672
2309.07822	CATfOOD: Counterfactual Augmented Training for Improving Out-of-Domain Performance and Calibration	In recent years, large language models (LLMs) have shown remarkable capabilities at scale, particularly at generating text conditioned on a prompt. In our work, we investigate the use of LLMs to augment training data of small language models~(SLMs) with automatically generated counterfactual~(CF) instances -- i.e. mini...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	391,918
2111.07524	PatchGraph: In-hand tactile tracking with learned surface normals	We address the problem of tracking 3D object poses from touch during in-hand manipulations. Specifically, we look at tracking small objects using vision-based tactile sensors that provide high-dimensional tactile image measurements at the point of contact. While prior work has relied on a-priori information about the o...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	266,403
2309.15681	Tactile-based Active Inference for Force-Controlled Peg-in-Hole Insertions	Reinforcement Learning (RL) has shown great promise for efficiently learning force control policies in peg-in-hole tasks. However, robots often face difficulties due to visual occlusions by the gripper and uncertainties in the initial grasping pose of the peg. These challenges often restrict force-controlled insertion ...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	395,069
1212.2547	Information spreading with aging in heterogeneous populations	We study the critical properties of a model of information spreading based on the SIS epidemic model. Spreading rates decay with time, as ruled by two parameters, $\epsilon$ and $l$, that can be either constant or randomly distributed in the population. The spreading dynamics is developed on top of Erd\"os-Renyi networ...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	20,326
2307.03200	Transcribing Educational Videos Using Whisper: A preliminary study on using AI for transcribing educational videos	Videos are increasingly being used for e-learning, and transcripts are vital to enhance the learning experience. The costs and delays of generating transcripts can be alleviated by automatic speech recognition (ASR) systems. In this article, we quantify the transcripts generated by whisper for 25 educational videos and...	false	false	false	false	true	false	false	false	false	false	false	false	false	true	false	false	false	true	377,958
2401.11143	Density Adaptive Attention is All You Need: Robust Parameter-Efficient Fine-Tuning Across Multiple Modalities	We propose the Multi-Head Density Adaptive Attention Mechanism (DAAM), a novel probabilistic attention framework that can be used for Parameter-Efficient Fine-tuning (PEFT), and the Density Adaptive Transformer (DAT), designed to enhance information aggregation across multiple modalities, including Speech, Text, and Vi...	false	false	true	false	true	false	true	false	true	false	false	true	false	false	false	false	false	false	422,888
2501.17755	AI Governance through Markets	This paper argues that market governance mechanisms should be considered a key approach in the governance of artificial intelligence (AI), alongside traditional regulatory frameworks. While current governance approaches have predominantly focused on regulation, we contend that market-based mechanisms offer effective in...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	528,441
2408.02632	SEAS: Self-Evolving Adversarial Safety Optimization for Large Language Models	As large language models (LLMs) continue to advance in capability and influence, ensuring their security and preventing harmful outputs has become crucial. A promising approach to address these concerns involves training models to automatically generate adversarial prompts for red teaming. However, the evolving subtlet...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	478,700
2301.06289	Strong Converses using Typical Changes of Measures and Asymptotic Markov Chains	The paper presents exponentially-strong converses for source-coding, channel coding, and hypothesis testing problems. More specifically, it presents alternative proofs for the well-known exponentially-strong converse bounds for almost lossless source-coding with side-information and for channel coding over a discrete m...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	340,611
1701.03647	Restricted Boltzmann Machines with Gaussian Visible Units Guided by Pairwise Constraints	Restricted Boltzmann machines (RBMs) and their variants are usually trained by contrastive divergence (CD) learning, but the training procedure is an unsupervised learning approach, without any guidances of the background knowledge. To enhance the expression ability of traditional RBMs, in this paper, we propose pairwi...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	66,742
1311.1013	Interference Alignment (IA) and Coordinated Multi-Point (CoMP) with IEEE802.11ac feedback compression: testbed results	We have implemented interference alignment (IA) and joint transmission coordinated multipoint (CoMP) on a wireless testbed using the feedback compression scheme of the new 802.11ac standard. The performance as a function of the frequency domain granularity is assessed. Realistic throughput gains are obtained by probing...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	28,200
1802.06058	Variance-based Gradient Compression for Efficient Distributed Deep Learning	Due to the substantial computational cost, training state-of-the-art deep neural networks for large-scale datasets often requires distributed training using multiple computation workers. However, by nature, workers need to frequently communicate gradients, causing severe bottlenecks, especially on lower bandwidth conne...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	90,579
2309.09916	Learning Nonparametric High-Dimensional Generative Models: The Empirical-Beta-Copula Autoencoder	By sampling from the latent space of an autoencoder and decoding the latent space samples to the original data space, any autoencoder can simply be turned into a generative model. For this to work, it is necessary to model the autoencoder's latent space with a distribution from which samples can be obtained. Several si...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	392,792
2307.10337	Are you in a Masquerade? Exploring the Behavior and Impact of Large Language Model Driven Social Bots in Online Social Networks	As the capabilities of Large Language Models (LLMs) emerge, they not only assist in accomplishing traditional tasks within more efficient paradigms but also stimulate the evolution of social bots. Researchers have begun exploring the implementation of LLMs as the driving core of social bots, enabling more efficient and...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	380,523
2402.16153	ChatMusician: Understanding and Generating Music Intrinsically with LLM	While Large Language Models (LLMs) demonstrate impressive capabilities in text generation, we find that their ability has yet to be generalized to music, humanity's creative language. We introduce ChatMusician, an open-source LLM that integrates intrinsic musical abilities. It is based on continual pre-training and fin...	false	false	true	false	true	false	true	false	true	false	false	false	false	false	false	false	false	true	432,445
1705.01040	Maximum Resilience of Artificial Neural Networks	The deployment of Artificial Neural Networks (ANNs) in safety-critical applications poses a number of new verification and certification challenges. In particular, for ANN-enabled self-driving vehicles it is important to establish properties about the resilience of ANNs to noisy or even maliciously manipulated sensory ...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	true	72,786
2010.10150	Local Knowledge Powered Conversational Agents	State-of-the-art conversational agents have advanced significantly in conjunction with the use of large transformer-based language models. However, even with these advancements, conversational agents still lack the ability to produce responses that are informative and coherent with the local context. In this work, we p...	true	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	201,797
2011.10100	Efficient Consensus Model based on Proximal Gradient Method applied to Convolutional Sparse Problems	Convolutional sparse representation (CSR), shift-invariant model for inverse problems, has gained much attention in the fields of signal/image processing, machine learning and computer vision. The most challenging problems in CSR implies the minimization of a composite function of the form $min_x \sum_i f_i(x) + g(x)$,...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	207,412
1012.5208	Texture feature extraction in the spatial-frequency domain for content-based image retrieval	The advent of large scale multimedia databases has led to great challenges in content-based image retrieval (CBIR). Even though CBIR is considered an emerging field of research, however it constitutes a strong background for new methodologies and systems implementations. Therefore, many research contributions are focus...	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	true	8,635
1204.5431	Robust Head Pose Estimation Using Contourlet Transform	Estimating pose of the head is an important preprocessing step in many pattern recognition and computer vision systems such as face recognition. Since the performance of the face recognition systems is greatly affected by the poses of the face, how to estimate the accurate pose of the face in human face image is still ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	15,652
1909.00482	A Semi-Automated Usability Evaluation Framework for Interactive Image Segmentation Systems	For complex segmentation tasks, the achievable accuracy of fully automated systems is inherently limited. Specifically, when a precise segmentation result is desired for a small amount of given data sets, semi-automatic methods exhibit a clear benefit for the user. The optimization of human computer interaction (HCI) i...	true	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	143,643
1711.05929	Defense against Universal Adversarial Perturbations	Recent advances in Deep Learning show the existence of image-agnostic quasi-imperceptible perturbations that when applied to `any' image can fool a state-of-the-art network classifier to change its prediction about the image label. These `Universal Adversarial Perturbations' pose a serious threat to the success of Deep...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	84,678
2208.14536	MultiCoNER: A Large-scale Multilingual dataset for Complex Named Entity Recognition	We present MultiCoNER, a large multilingual dataset for Named Entity Recognition that covers 3 domains (Wiki sentences, questions, and search queries) across 11 languages, as well as multilingual and code-mixing subsets. This dataset is designed to represent contemporary challenges in NER, including low-context scenari...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	315,343
2310.03739	Aligning Text-to-Image Diffusion Models with Reward Backpropagation	Text-to-image diffusion models have recently emerged at the forefront of image generation, powered by very large-scale unsupervised or weakly supervised text-to-image training datasets. Due to their unsupervised training, controlling their behavior in downstream tasks, such as maximizing human-perceived image quality, ...	false	false	false	false	true	false	true	true	false	false	false	true	false	false	false	false	false	false	397,395
2410.13085	MMed-RAG: Versatile Multimodal RAG System for Medical Vision Language Models	Artificial Intelligence (AI) has demonstrated significant potential in healthcare, particularly in disease diagnosis and treatment planning. Recent progress in Medical Large Vision-Language Models (Med-LVLMs) has opened up new possibilities for interactive diagnostic tools. However, these models often suffer from factu...	false	false	false	false	false	false	true	false	true	false	false	true	false	false	false	false	false	false	499,360
1905.10568	Scalable Block-Diagonal Locality-Constrained Projective Dictionary Learning	We propose a novel structured discriminative block-diagonal dictionary learning method, referred to as scalable Locality-Constrained Projective Dictionary Learning (LC-PDL), for efficient representation and classification. To improve the scalability by saving both training and testing time, our LC-PDL aims at learning ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	132,104
2107.05235	Position-enhanced and Time-aware Graph Convolutional Network for Sequential Recommendations	Most of the existing deep learning-based sequential recommendation approaches utilize the recurrent neural network architecture or self-attention to model the sequential patterns and temporal influence among a user's historical behavior and learn the user's preference at a specific time. However, these methods have two...	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	false	245,715
2010.02840	Semantic Evaluation for Text-to-SQL with Distilled Test Suites	We propose test suite accuracy to approximate semantic accuracy for Text-to-SQL models. Our method distills a small test suite of databases that achieves high code coverage for the gold query from a large number of randomly generated databases. At evaluation time, it computes the denotation accuracy of the predicted qu...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	199,185
1405.6296	Four Classes of Morphogenetic Collective Systems	We studied the roles of morphogenetic principles---heterogeneity of components, dynamic differentiation/re-differentiation of components, and local information sharing among components---in the self-organization of morphogenetic collective systems. By incrementally introducing these principles to collectives, we define...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	33,369
1804.06964	GNAS: A Greedy Neural Architecture Search Method for Multi-Attribute Learning	A key problem in deep multi-attribute learning is to effectively discover the inter-attribute correlation structures. Typically, the conventional deep multi-attribute learning approaches follow the pipeline of manually designing the network architectures based on task-specific expertise prior knowledge and careful netw...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	true	false	false	95,421
1903.08552	Traversing the noise of dynamic mini-batch sub-sampled loss functions: A visual guide	Mini-batch sub-sampling in neural network training is unavoidable, due to growing data demands, memory-limited computational resources such as graphical processing units (GPUs), and the dynamics of on-line learning. In this study we specifically distinguish between static mini-batch sub-sampled loss functions, where mi...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	124,853
2310.16331	Brain-Inspired Reservoir Computing Using Memristors with Tunable Dynamics and Short-Term Plasticity	Recent advancements in reservoir computing research have created a demand for analog devices with dynamics that can facilitate the physical implementation of reservoirs, promising faster information processing while consuming less energy and occupying a smaller area footprint. Studies have demonstrated that dynamic mem...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	402,679
1811.11686	Compliant Fluidic Control Structures: Concept and Synthesis Approach	The concept and synthesis approach for planar Compliant Fluidic Control Structures (CFCSs), monolithic flexible continua with embedded functional pores, is presented in this manuscript. Such structures are envisioned to find application in biomedicine as tunable microuidic devices for drug/nutrient delivery. The functi...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	114,834
1802.03275	Slice Sampling Particle Belief Propagation	Inference in continuous label Markov random fields is a challenging task. We use particle belief propagation (PBP) for solving the inference problem in continuous label space. Sampling particles from the belief distribution is typically done by using Metropolis-Hastings Markov chain Monte Carlo methods which involves s...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	89,939
2402.02307	Joint Activity and Data Detection for Massive Grant-Free Access Using Deterministic Non-Orthogonal Signatures	Grant-free access is a key enabler for connecting wireless devices with low latency and low signaling overhead in massive machine-type communications (mMTC). For massive grant-free access, user-specific signatures are uniquely assigned to mMTC devices. In this paper, we first derive a sufficient condition for the succe...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	426,481
0903.0952	Definition of Strange Attractor in Benard problem for Generalized Couette Cell	For movements of the viscous continuous flow in generalized Couette cell the dynamic system describing the central limiting variety is received.	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	3,289
2311.13469	Span-Based Optimal Sample Complexity for Average Reward MDPs	We study the sample complexity of learning an $\varepsilon$-optimal policy in an average-reward Markov decision process (MDP) under a generative model. We establish the complexity bound $\widetilde{O}\left(SA\frac{H}{\varepsilon^2} \right)$, where $H$ is the span of the bias function of the optimal policy and $SA$ is t...	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	409,747
2007.03203	Learning Combined Set Covering and Traveling Salesman Problem	The Traveling Salesman Problem is one of the most intensively studied combinatorial optimization problems due both to its range of real-world applications and its computational complexity. When combined with the Set Covering Problem, it raises even more issues related to tractability and scalability. We study a combine...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	185,985
2103.01658	Minimizing Information Leakage of Abrupt Changes in Stochastic Systems	This work investigates the problem of analyzing privacy of abrupt changes for general Markov processes. These processes may be affected by changes, or exogenous signals, that need to remain private. Privacy refers to the disclosure of information of these changes through observations of the underlying Markov chain. In ...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	222,706
2209.10222	Fairness Reprogramming	Despite a surge of recent advances in promoting machine Learning (ML) fairness, the existing mainstream approaches mostly require retraining or finetuning the entire weights of the neural network to meet the fairness criteria. However, this is often infeasible in practice for those large-scale trained models due to lar...	false	false	false	false	true	false	true	false	false	false	false	false	false	true	false	false	false	false	318,792
1808.08575	Title-Guided Encoding for Keyphrase Generation	Keyphrase generation (KG) aims to generate a set of keyphrases given a document, which is a fundamental task in natural language processing (NLP). Most previous methods solve this problem in an extractive manner, while recently, several attempts are made under the generative setting using deep neural networks. However,...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	105,983
2405.15002	Private Regression via Data-Dependent Sufficient Statistic Perturbation	Sufficient statistic perturbation (SSP) is a widely used method for differentially private linear regression. SSP adopts a data-independent approach where privacy noise from a simple distribution is added to sufficient statistics. However, sufficient statistics can often be expressed as linear queries and better approx...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	456,693
2201.09451	Emotion-based Modeling of Mental Disorders on Social Media	According to the World Health Organization (WHO), one in four people will be affected by mental disorders at some point in their lives. However, in many parts of the world, patients do not actively seek professional diagnosis because of stigma attached to mental illness, ignorance of mental health and its associated sy...	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	false	false	false	276,679
1802.02223	Seeded Ising Model and Statistical Natures of Human Iris Templates	We propose a variant of Ising model, called the Seeded Ising Model, to model probabilistic nature of human iris templates. This model is an Ising model in which the values at certain lattice points are held fixed throughout Ising model evolution. Using this we show how to reconstruct the full iris template from partial...	true	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	89,737
2103.02729	Linear Bandit Algorithms with Sublinear Time Complexity	We propose two linear bandits algorithms with per-step complexity sublinear in the number of arms $K$. The algorithms are designed for applications where the arm set is extremely large and slowly changing. Our key realization is that choosing an arm reduces to a maximum inner product search (MIPS) problem, which can be...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	223,043
1407.4765	Ark: A Real-World Consensus Implementation	Ark is an implementation of a consensus algorithm similar to Paxos and Raft, designed as an improvement over the existing consensus algorithm used by MongoDB and TokuMX. Ark was designed from first principles, improving on the election algorithm used by TokuMX, to fix deficiencies in MongoDB's consensus algorithms th...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	true	34,730
2211.10284	Estimating more camera poses for ego-centric videos is essential for VQ3D	Visual queries 3D localization (VQ3D) is a task in the Ego4D Episodic Memory Benchmark. Given an egocentric video, the goal is to answer queries of the form "Where did I last see object X?", where the query object X is specified as a static image, and the answer should be a 3D displacement vector pointing to object X. ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	331,261
1808.05403	A Survey on Nonconvex Regularization Based Sparse and Low-Rank Recovery in Signal Processing, Statistics, and Machine Learning	In the past decade, sparse and low-rank recovery have drawn much attention in many areas such as signal/image processing, statistics, bioinformatics and machine learning. To achieve sparsity and/or low-rankness inducing, the $\ell_1$ norm and nuclear norm are of the most popular regularization penalties due to their co...	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	105,346
2304.11576	Exact Worst-Case Execution-Time Analysis for Implicit Model Predictive Control	We propose the first method that determines the exact worst-case execution time (WCET) for implicit linear model predictive control (MPC). Such WCET bounds are imperative when MPC is used in real time to control safety-critical systems. The proposed method applies when the quadratic programming solver in the MPC contro...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	true	359,874
1707.04406	Inner-Scene Similarities as a Contextual Cue for Object Detection	Using image context is an effective approach for improving object detection. Previously proposed methods used contextual cues that rely on semantic or spatial information. In this work, we explore a different kind of contextual information: inner-scene similarity. We present the CISS (Context by Inner Scene Similarity)...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	77,036
2104.12021	Explainable Artificial Intelligence Reveals Novel Insight into Tumor Microenvironment Conditions Linked with Better Prognosis in Patients with Breast Cancer	We investigated the data-driven relationship between features in the tumor microenvironment (TME) and the overall and 5-year survival in triple-negative breast cancer (TNBC) and non-TNBC (NTNBC) patients by using Explainable Artificial Intelligence (XAI) models. We used clinical information from patients with invasive ...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	232,086
2003.03685	Discovering contemporaneous and lagged causal relations in autocorrelated nonlinear time series datasets	The paper introduces a novel conditional independence (CI) based method for linear and nonlinear, lagged and contemporaneous causal discovery from observational time series in the causally sufficient case. Existing CI-based methods such as the PC algorithm and also common methods from other frameworks suffer from low r...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	167,320
2305.18362	Statistically Significant Concept-based Explanation of Image Classifiers via Model Knockoffs	A concept-based classifier can explain the decision process of a deep learning model by human-understandable concepts in image classification problems. However, sometimes concept-based explanations may cause false positives, which misregards unrelated concepts as important for the prediction task. Our goal is to find t...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	368,985
2401.13941	AC-Driven Series Elastic Electrohydraulic Actuator for Stable and Smooth Displacement Output	Soft electrohydraulic actuators known as HASEL actuators have attracted widespread research interest due to their outstanding dynamic performance and high output power. However, the displacement of electrohydraulic actuators usually declines with time under constant DC voltage, which hampers its prospective application...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	423,907
2001.07708	Towards Comparability in Non-Intrusive Load Monitoring: On Data and Performance Evaluation	Non-Intrusive Load Monitoring (NILM) comprises of a set of techniques that provide insights into the energy consumption of households and industrial facilities. Latest contributions show significant improvements in terms of accuracy and generalisation abilities. Despite all progress made concerning disaggregation techn...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	161,104
2111.05307	Machine-learning custom-made basis functions for partial differential equations	Spectral methods are an important part of scientific computing's arsenal for solving partial differential equations (PDEs). However, their applicability and effectiveness depend crucially on the choice of basis functions used to expand the solution of a PDE. The last decade has seen the emergence of deep learning as a ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	265,754
1110.0425	Hybrid Codes Needed for Coordination over the Point-to-Point Channel	We consider a new fundamental question regarding the point-to-point memoryless channel. The source-channel separation theorem indicates that random codebook construction for lossy source compression and channel coding can be independently constructed and paired to achieve optimal performance for coordinating a source s...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	12,460
1712.08425	Simple Methods for Scanner Drift Normalization Validated for Automatic Segmentation of Knee Magnetic Resonance Imaging - with data from the Osteoarthritis Initiative	Scanner drift is a well-known magnetic resonance imaging (MRI) artifact characterized by gradual signal degradation and scan intensity changes over time. In addition, hardware and software updates may imply abrupt changes in signal. The combined effects are particularly challenging for automatic image analysis methods ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	87,195
1808.01199	Generation Meets Recommendation: Proposing Novel Items for Groups of Users	Consider a movie studio aiming to produce a set of new movies for summer release: What types of movies it should produce? Who would the movies appeal to? How many movies should it make? Similar issues are encountered by a variety of organizations, e.g., mobile-phone manufacturers and online magazines, who have to creat...	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	false	104,532
2204.03748	Energy self-sufficient systems for monitoring sewer networks	Underground infrastructure networks form the backbone of vital supply and disposal systems. However, they are under-monitored in comparison to their value. This is due, in large part, to the lack of energy supply for monitoring and data transmission. In this paper, we investigate a novel, energy harvesting system used ...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	290,422
1607.03105	Systholic Boolean Orthonormalizer Network in Wavelet Domain for SAR Image Despeckling	We describe a novel method for removing speckle (in wavelet domain) of unknown variance from SAR images. The me-thod is based on the following procedure: We apply 1) Bidimentional Discrete Wavelet Transform (DWT-2D) to the speckled image, 2) scaling and rounding to the coefficients of the highest subbands (to obtain in...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	58,459
2009.04547	Optimal Inspection and Maintenance Planning for Deteriorating Structural Components through Dynamic Bayesian Networks and Markov Decision Processes	Civil and maritime engineering systems, among others, from bridges to offshore platforms and wind turbines, must be efficiently managed as they are exposed to deterioration mechanisms throughout their operational life, such as fatigue or corrosion. Identifying optimal inspection and maintenance policies demands the sol...	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	false	false	195,072
2206.10146	KE-RCNN: Unifying Knowledge based Reasoning into Part-level Attribute Parsing	Part-level attribute parsing is a fundamental but challenging task, which requires the region-level visual understanding to provide explainable details of body parts. Most existing approaches address this problem by adding a regional convolutional neural network (RCNN) with an attribute prediction head to a two-stage d...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	303,815
2401.12416	Enhancing Reliability of Neural Networks at the Edge: Inverted Normalization with Stochastic Affine Transformations	Bayesian Neural Networks (BayNNs) naturally provide uncertainty in their predictions, making them a suitable choice in safety-critical applications. Additionally, their realization using memristor-based in-memory computing (IMC) architectures enables them for resource-constrained edge applications. In addition to predi...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	423,362
1812.04700	Predictive Learning on Hidden Tree-Structured Ising Models	We provide high-probability sample complexity guarantees for exact structure recovery and accurate predictive learning using noise-corrupted samples from an acyclic (tree-shaped) graphical model. The hidden variables follow a tree-structured Ising model distribution, whereas the observable variables are generated by a ...	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	116,260
2501.09857	Efficient Probabilistic Assessment of Power System Resilience Using the Polynomial Chaos Expansion Method with Enhanced Stability	Increasing frequency and intensity of extreme weather events motivates the assessment of power system resilience. The random nature of these events and the resulting failures mandates probabilistic resilience assessment, but state-of-the-art methods (e.g., Monte Carlo simulation) are computationally inefficient. This p...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	525,296
2107.02643	Detecting Hypo-plastic Left Heart Syndrome in Fetal Ultrasound via Disease-specific Atlas Maps	Fetal ultrasound screening during pregnancy plays a vital role in the early detection of fetal malformations which have potential long-term health impacts. The level of skill required to diagnose such malformations from live ultrasound during examination is high and resources for screening are often limited. We present...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	244,892
2107.02569	Self-training with noisy student model and semi-supervised loss function for dcase 2021 challenge task 4	This report proposes a polyphonic sound event detection (SED) method for the DCASE 2021 Challenge Task 4. The proposed SED model consists of two stages: a mean-teacher model for providing target labels regarding weakly labeled or unlabeled data and a self-training-based noisy student model for predicting strong labels ...	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	244,870
1911.07185	Towards the Automation of Deep Image Prior	Single image inverse problem is a notoriously challenging ill-posed problem that aims to restore the original image from one of its corrupted versions. Recently, this field has been immensely influenced by the emergence of deep-learning techniques. Deep Image Prior (DIP) offers a new approach that forces the recovered ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	153,757
2312.08511	The Relative Value of Prediction in Algorithmic Decision Making	Algorithmic predictions are increasingly used to inform the allocations of goods and interventions in the public sphere. In these domains, predictions serve as a means to an end. They provide stakeholders with insights into likelihood of future events as a means to improve decision making quality, and enhance social we...	false	false	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	415,319
1808.09397	MedSTS: A Resource for Clinical Semantic Textual Similarity	The wide adoption of electronic health records (EHRs) has enabled a wide range of applications leveraging EHR data. However, the meaningful use of EHR data largely depends on our ability to efficiently extract and consolidate information embedded in clinical text where natural language processing (NLP) techniques are e...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	106,178
1809.02782	Sentiment analysis for Arabic language: A brief survey of approaches and techniques	With the emergence of Web 2.0 technology and the expansion of on-line social networks, current Internet users have the ability to add their reviews, ratings and opinions on social media and on commercial and news web sites. Sentiment analysis aims to classify these reviews reviews in an automatic way. In the literature...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	107,132
1908.00975	Y-Net: A Hybrid Deep Learning Reconstruction Framework for Photoacoustic Imaging in vivo	Photoacoustic imaging (PAI) is an emerging non-invasive imaging modality combining the advantages of deep ultrasound penetration and high optical contrast. Image reconstruction is an essential topic in PAI, which is unfortunately an ill-posed problem due to the complex and unknown optical/acoustic parameters in tissue....	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	140,641
2309.09530	Adapting Large Language Models to Domains via Reading Comprehension	We explore how continued pre-training on domain-specific corpora influences large language models, revealing that training on the raw corpora endows the model with domain knowledge, but drastically hurts its prompting ability for question answering. Taken inspiration from human learning via reading comprehension--pract...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	392,652
2203.03931	PASS: Part-Aware Self-Supervised Pre-Training for Person Re-Identification	In person re-identification (ReID), very recent researches have validated pre-training the models on unlabelled person images is much better than on ImageNet. However, these researches directly apply the existing self-supervised learning (SSL) methods designed for image classification to ReID without any adaption in th...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	284,282
2203.00836	CandidateDrug4Cancer: An Open Molecular Graph Learning Benchmark on Drug Discovery for Cancer	Anti-cancer drug discoveries have been serendipitous, we sought to present the Open Molecular Graph Learning Benchmark, named CandidateDrug4Cancer, a challenging and realistic benchmark dataset to facilitate scalable, robust, and reproducible graph machine learning research for anti-cancer drug discovery. CandidateDrug...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	283,140
2202.10324	VRL3: A Data-Driven Framework for Visual Deep Reinforcement Learning	We propose VRL3, a powerful data-driven framework with a simple design for solving challenging visual deep reinforcement learning (DRL) tasks. We analyze a number of major obstacles in taking a data-driven approach, and present a suite of design principles, novel findings, and critical insights about data-driven visual...	false	false	false	false	true	false	true	true	false	false	false	true	false	false	false	false	false	false	281,490
2502.14648	Variance Reduction Methods Do Not Need to Compute Full Gradients: Improved Efficiency through Shuffling	In today's world, machine learning is hard to imagine without large training datasets and models. This has led to the use of stochastic methods for training, such as stochastic gradient descent (SGD). SGD provides weak theoretical guarantees of convergence, but there are modifications, such as Stochastic Variance Reduc...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	535,914
2502.00190	On the Effectiveness of Random Weights in Graph Neural Networks	Graph Neural Networks (GNNs) have achieved remarkable success across diverse tasks on graph-structured data, primarily through the use of learned weights in message passing layers. In this paper, we demonstrate that random weights can be surprisingly effective, achieving performance comparable to end-to-end training co...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	529,245
1309.1585	Network-Level Cooperation in Energy Harvesting Wireless Networks	We consider a two-hop communication network consisted of a source node, a relay and a destination node in which the source and the relay node have external traffic arrivals. The relay forwards a fraction of the source node's traffic to the destination and the cooperation is performed at the network level. In addition, ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	26,879
2010.03760	Discriminatively-Tuned Generative Classifiers for Robust Natural Language Inference	While discriminative neural network classifiers are generally preferred, recent work has shown advantages of generative classifiers in term of data efficiency and robustness. In this paper, we focus on natural language inference (NLI). We propose GenNLI, a generative classifier for NLI tasks, and empirically characteri...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	199,521
2002.04525	Industry 4.0: contributions of holonic manufacturing control architectures and future challenges	The flexibility claimed by the next generation production systems induces a deep modification of the behaviour and the core itself of the control systems. Over-connectivity and data management abilities targeted by Industry 4.0 paradigm enable the emergence of more flexible and reactive control systems, based on the co...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	163,634
2410.18565	Bielik 7B v0.1: A Polish Language Model -- Development, Insights, and Evaluation	We introduce Bielik 7B v0.1, a 7-billion-parameter generative text model for Polish language processing. Trained on curated Polish corpora, this model addresses key challenges in language model development through innovative techniques. These include Weighted Instruction Cross-Entropy Loss, which balances the learning ...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	501,939
1301.3698	Modeling human dynamics of face-to-face interaction networks	Face-to-face interaction networks describe social interactions in human gatherings, and are the substrate for processes such as epidemic spreading and gossip propagation. The bursty nature of human behavior characterizes many aspects of empirical data, such as the distribution of conversation lengths, of conversations ...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	21,132
2405.00924	Zonotope-based Symbolic Controller Synthesis for Linear Temporal Logic Specifications	This paper studies the controller synthesis problem for nonlinear control systems under linear temporal logic (LTL) specifications using zonotope techniques. A local-to-global control strategy is proposed for the desired specification expressed as an LTL formula. First, a novel approach is developed to divide the state...	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	451,132
1911.03801	Human Driver Behavior Prediction based on UrbanFlow	How autonomous vehicles and human drivers share public transportation systems is an important problem, as fully automatic transportation environments are still a long way off. Understanding human drivers' behavior can be beneficial for autonomous vehicle decision making and planning, especially when the autonomous vehi...	false	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	152,763
2005.07006	Foreground-Background Ambient Sound Scene Separation	Ambient sound scenes typically comprise multiple short events occurring on top of a somewhat stationary background. We consider the task of separating these events from the background, which we call foreground-background ambient sound scene separation. We propose a deep learning-based separation framework with a suitab...	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	177,163
2409.12096	An Efficient Projection-Based Next-best-view Planning Framework for Reconstruction of Unknown Objects	Efficiently and completely capturing the three-dimensional data of an object is a fundamental problem in industrial and robotic applications. The task of next-best-view (NBV) planning is to infer the pose of the next viewpoint based on the current data, and gradually realize the complete three-dimensional reconstructio...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	489,435
1510.06153	Creating Scalable and Interactive Web Applications Using High Performance Latent Variable Models	In this project we outline a modularized, scalable system for comparing Amazon products in an interactive and informative way using efficient latent variable models and dynamic visualization. We demonstrate how our system can build on the structure and rich review information of Amazon products in order to provide a fa...	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	false	false	48,091
2308.13506	Training and Meta-Evaluating Machine Translation Evaluation Metrics at the Paragraph Level	As research on machine translation moves to translating text beyond the sentence level, it remains unclear how effective automatic evaluation metrics are at scoring longer translations. In this work, we first propose a method for creating paragraph-level data for training and meta-evaluating metrics from existing sente...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	387,948
1804.08302	Deep cross-domain building extraction for selective depth estimation from oblique aerial imagery	With the technological advancements of aerial imagery and accurate 3d reconstruction of urban environments, more and more attention has been paid to the automated analyses of urban areas. In our work, we examine two important aspects that allow live analysis of building structures in city models given oblique aerial im...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	95,740
2209.06300	PINCH: An Adversarial Extraction Attack Framework for Deep Learning Models	Adversarial extraction attacks constitute an insidious threat against Deep Learning (DL) models in-which an adversary aims to steal the architecture, parameters, and hyper-parameters of a targeted DL model. Existing extraction attack literature have observed varying levels of attack success for different DL models and ...	false	false	false	false	true	false	true	false	false	false	false	false	true	false	false	false	false	false	317,346
1009.5268	General Scaled Support Vector Machines	Support Vector Machines (SVMs) are popular tools for data mining tasks such as classification, regression, and density estimation. However, original SVM (C-SVM) only considers local information of data points on or over the margin. Therefore, C-SVM loses robustness. To solve this problem, one approach is to translate (...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	7,693
2403.16336	Predictive Inference in Multi-environment Scenarios	We address the challenge of constructing valid confidence intervals and sets in problems of prediction across multiple environments. We investigate two types of coverage suitable for these problems, extending the jackknife and split-conformal methods to show how to obtain distribution-free coverage in such non-traditio...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	440,983
1904.11481	Age of Information in Multicast Networks with Multiple Update Streams	We consider the age of information in a multicast network where there is a single source node that sends time-sensitive updates to $n$ receiver nodes. Each status update is one of two kinds: type I or type II. To study the age of information experienced by the receiver nodes for both types of updates, we consider two c...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	128,867
2011.03199	Secure Performance Analysis and Optimization for FD-NOMA Vehicular Communications	Vehicle-to-vehicle (V2V) communication appeals to increasing research interest as a result of its applications to provide safety information as well as infotainment services. The increasing demand of transmit rates and various requirements of quality of services (QoS) in vehicular communication scenarios call for the i...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	205,172
1810.09233	Scalable NoC-based Neuromorphic Hardware Learning and Inference	Bio-inspired neuromorphic hardware is a research direction to approach brain's computational power and energy efficiency. Spiking neural networks (SNN) encode information as sparsely distributed spike trains and employ spike-timing-dependent plasticity (STDP) mechanism for learning. Existing hardware implementations of...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	111,020
1807.11367	Fairly Allocating Many Goods with Few Queries	We investigate the query complexity of the fair allocation of indivisible goods. For two agents with arbitrary monotonic utilities, we design an algorithm that computes an allocation satisfying envy-freeness up to one good (EF1), a relaxation of envy-freeness, using a logarithmic number of queries. We show that the log...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	true	false	false	true	104,163
1211.4422	Continuous Models of Epidemic Spreading in Heterogeneous Dynamically Changing Random Networks	Modeling spreading processes in complex random networks plays an essential role in understanding and prediction of many real phenomena like epidemics or rumor spreading. The dynamics of such systems may be represented algorithmically by Monte-Carlo simulations on graphs or by ordinary differential equations (ODEs). Des...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	19,812

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