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
2210.15767	Gathering Strength, Gathering Storms: The One Hundred Year Study on Artificial Intelligence (AI100) 2021 Study Panel Report	In September 2021, the "One Hundred Year Study on Artificial Intelligence" project (AI100) issued the second report of its planned long-term periodic assessment of artificial intelligence (AI) and its impact on society. It was written by a panel of 17 study authors, each of whom is deeply rooted in AI research, chaired...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	327,073
2407.08842	Evaluating Nuanced Bias in Large Language Model Free Response Answers	Pre-trained large language models (LLMs) can now be easily adapted for specific business purposes using custom prompts or fine tuning. These customizations are often iteratively re-engineered to improve some aspect of performance, but after each change businesses want to ensure that there has been no negative impact on...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	472,327
2302.12927	Robot Behavior-Tree-Based Task Generation with Large Language Models	Nowadays, the behavior tree is gaining popularity as a representation for robot tasks due to its modularity and reusability. Designing behavior-tree tasks manually is time-consuming for robot end-users, thus there is a need for investigating automatic behavior-tree-based task generation. Prior behavior-tree-based task ...	false	false	false	false	true	false	false	true	true	false	false	false	false	false	false	false	false	false	347,739
1502.01975	Optimal Haplotype Assembly from High-Throughput Mate-Pair Reads	Humans have $23$ pairs of homologous chromosomes. The homologous pairs are almost identical pairs of chromosomes. For the most part, differences in homologous chromosome occur at certain documented positions called single nucleotide polymorphisms (SNPs). A haplotype of an individual is the pair of sequences of SNPs on ...	false	true	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	39,986
2212.01371	Adaptive Robust Model Predictive Control via Uncertainty Cancellation	We propose a learning-based robust predictive control algorithm that compensates for significant uncertainty in the dynamics for a class of discrete-time systems that are nominally linear with an additive nonlinear component. Such systems commonly model the nonlinear effects of an unknown environment on a nominal syste...	false	false	false	false	false	false	true	true	false	false	true	false	false	false	false	false	false	false	334,402
2406.05269	Fast assessment of non-Gaussian inputs in structural dynamics exploiting modal solutions	In various technical applications, assessing the impact of non-Gaussian processes on responses of dynamic systems is crucial. While simulating time-domain realizations offers an efficient solution for linear dynamic systems, this method proves time-consuming for finite element (FE) models, which may contain thousands t...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	462,052
2408.01970	SR-CIS: Self-Reflective Incremental System with Decoupled Memory and Reasoning	The ability of humans to rapidly learn new knowledge while retaining old memories poses a significant challenge for current deep learning models. To handle this challenge, we draw inspiration from human memory and learning mechanisms and propose the Self-Reflective Complementary Incremental System (SR-CIS). Comprising ...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	478,446
2404.16696	Report on Candidate Computational Indicators for Conscious Valenced Experience	This report enlists 13 functional conditions cashed out in computational terms that have been argued to be constituent of conscious valenced experience. These are extracted from existing empirical and theoretical literature on, among others, animal sentience, medical disorders, anaesthetics, philosophy, evolution, neur...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	449,589
2212.06727	What do Vision Transformers Learn? A Visual Exploration	Vision transformers (ViTs) are quickly becoming the de-facto architecture for computer vision, yet we understand very little about why they work and what they learn. While existing studies visually analyze the mechanisms of convolutional neural networks, an analogous exploration of ViTs remains challenging. In this pap...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	336,199
1706.09886	Optimal Control for Multi-Mode Systems with Discrete Costs	This paper studies optimal time-bounded control in multi-mode systems with discrete costs. Multi-mode systems are an important subclass of linear hybrid systems, in which there are no guards on transitions and all invariants are global. Each state has a continuous cost attached to it, which is linear in the sojourn tim...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	true	76,218
2204.10535	Alleviating Representational Shift for Continual Fine-tuning	We study a practical setting of continual learning: fine-tuning on a pre-trained model continually. Previous work has found that, when training on new tasks, the features (penultimate layer representations) of previous data will change, called representational shift. Besides the shift of features, we reveal that the in...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	292,822
2411.03217	A Personal data Value at Risk Approach	What if the main data protection vulnerability is risk management? Data Protection merges three disciplines: data protection law, information security, and risk management. Nonetheless, very little research has been made on the field of data protection risk management, where subjectivity and superficiality are the domi...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	505,815
2011.10731	LRTA: A Transparent Neural-Symbolic Reasoning Framework with Modular Supervision for Visual Question Answering	The predominant approach to visual question answering (VQA) relies on encoding the image and question with a "black-box" neural encoder and decoding a single token as the answer like "yes" or "no". Despite this approach's strong quantitative results, it struggles to come up with intuitive, human-readable forms of justi...	false	false	false	false	true	false	true	false	true	false	false	true	false	false	false	false	false	false	207,606
1810.02340	SNIP: Single-shot Network Pruning based on Connection Sensitivity	Pruning large neural networks while maintaining their performance is often desirable due to the reduced space and time complexity. In existing methods, pruning is done within an iterative optimization procedure with either heuristically designed pruning schedules or additional hyperparameters, undermining their utility...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	109,575
2101.08940	Hessian-Aware Pruning and Optimal Neural Implant	Pruning is an effective method to reduce the memory footprint and FLOPs associated with neural network models. However, existing structured-pruning methods often result in significant accuracy degradation for moderate pruning levels. To address this problem, we introduce a new Hessian Aware Pruning (HAP) method coupled...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	216,454
2006.12700	Cine Cardiac MRI Motion Artifact Reduction Using a Recurrent Neural Network	Cine cardiac magnetic resonance imaging (MRI) is widely used for diagnosis of cardiac diseases thanks to its ability to present cardiovascular features in excellent contrast. As compared to computed tomography (CT), MRI, however, requires a long scan time, which inevitably induces motion artifacts and causes patients' ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	183,675
2305.10986	Near-Field 3D Localization via MIMO Radar: Cram\'er-Rao Bound and Estimator Design	Future sixth-generation (6G) networks are envisioned to provide both sensing and communications functionalities by using densely deployed base stations (BSs) with massive antennas operating in millimeter wave (mmWave) and terahertz (THz). Due to the large number of antennas and the high frequency band, the sensing and ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	365,321
1708.02635	Anomaly Detection in Multivariate Non-stationary Time Series for Automatic DBMS Diagnosis	Anomaly detection in database management systems (DBMSs) is difficult because of increasing number of statistics (stat) and event metrics in big data system. In this paper, I propose an automatic DBMS diagnosis system that detects anomaly periods with abnormal DB stat metrics and finds causal events in the periods. Rec...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	78,623
2208.00623	Quality Evaluation of Arbitrary Style Transfer: Subjective Study and Objective Metric	Arbitrary neural style transfer is a vital topic with great research value and wide industrial application, which strives to render the structure of one image using the style of another. Recent researches have devoted great efforts on the task of arbitrary style transfer (AST) for improving the stylization quality. How...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	310,914
2206.07967	DreamNet: A Deep Riemannian Network based on SPD Manifold Learning for Visual Classification	Image set-based visual classification methods have achieved remarkable performance, via characterising the image set in terms of a non-singular covariance matrix on a symmetric positive definite (SPD) manifold. To adapt to complicated visual scenarios better, several Riemannian networks (RiemNets) for SPD matrix nonlin...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	302,950
2201.02262	A unified software/hardware scalable architecture for brain-inspired computing based on self-organizing neural models	The field of artificial intelligence has significantly advanced over the past decades, inspired by discoveries from the fields of biology and neuroscience. The idea of this work is inspired by the process of self-organization of cortical areas in the human brain from both afferent and lateral/internal connections. In t...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	274,486
2007.04030	Incorporating prior knowledge about structural constraints in model identification	Model identification is a crucial problem in chemical industries. In recent years, there has been increasing interest in learning data-driven models utilizing partial knowledge about the system of interest. Most techniques for model identification do not provide the freedom to incorporate any partial information such a...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	186,240
2206.02016	Is $L^2$ Physics-Informed Loss Always Suitable for Training Physics-Informed Neural Network?	The Physics-Informed Neural Network (PINN) approach is a new and promising way to solve partial differential equations using deep learning. The $L^2$ Physics-Informed Loss is the de-facto standard in training Physics-Informed Neural Networks. In this paper, we challenge this common practice by investigating the relatio...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	300,709
2307.14382	When Multi-Task Learning Meets Partial Supervision: A Computer Vision Review	Multi-Task Learning (MTL) aims to learn multiple tasks simultaneously while exploiting their mutual relationships. By using shared resources to simultaneously calculate multiple outputs, this learning paradigm has the potential to have lower memory requirements and inference times compared to the traditional approach o...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	381,915
1907.02907	Hybridized Threshold Clustering for Massive Data	As the size $n$ of datasets become massive, many commonly-used clustering algorithms (for example, $k$-means or hierarchical agglomerative clustering (HAC) require prohibitive computational cost and memory. In this paper, we propose a solution to these clustering problems by extending threshold clustering (TC) to probl...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	137,717
2109.11644	A Learned Stereo Depth System for Robotic Manipulation in Homes	We present a passive stereo depth system that produces dense and accurate point clouds optimized for human environments, including dark, textureless, thin, reflective and specular surfaces and objects, at 2560x2048 resolution, with 384 disparities, in 30 ms. The system consists of an algorithm combining learned stereo ...	false	false	false	false	true	false	true	true	false	false	false	true	false	false	false	false	false	false	257,009
2402.02850	An Attention Long Short-Term Memory based system for automatic classification of speech intelligibility	Speech intelligibility can be degraded due to multiple factors, such as noisy environments, technical difficulties or biological conditions. This work is focused on the development of an automatic non-intrusive system for predicting the speech intelligibility level in this latter case. The main contribution of our rese...	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	426,752
1910.07117	Analyzing the Forgetting Problem in the Pretrain-Finetuning of Dialogue Response Models	In this work, we study how the finetuning stage in the pretrain-finetune framework changes the behavior of a pretrained neural language generator. We focus on the transformer encoder-decoder model for the open-domain dialogue response generation task. Our major finding is that after standard finetuning, the model forge...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	149,521
2102.07657	Real-Time Topology Optimization in 3D via Deep Transfer Learning	The published literature on topology optimization has exploded over the last two decades to include methods that use shape and topological derivatives or evolutionary algorithms formulated on various geometric representations and parametrizations. One of the key challenges of all these methods is the massive computatio...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	220,178
2004.10066	Rigorous Explanation of Inference on Probabilistic Graphical Models	Probabilistic graphical models, such as Markov random fields (MRF), exploit dependencies among random variables to model a rich family of joint probability distributions. Sophisticated inference algorithms, such as belief propagation (BP), can effectively compute the marginal posteriors. Nonetheless, it is still diffic...	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	false	false	173,532
1608.03462	Multi-View Product Image Search Using Deep ConvNets Representations	Multi-view product image queries can improve retrieval performance over single view queries significantly. In this paper, we investigated the performance of deep convolutional neural networks (ConvNets) on multi-view product image search. First, we trained a VGG-like network to learn deep ConvNets representations of pr...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	59,676
2111.00452	Experimental Study on the Imitation of the Human Head-and-Eye Pose Using the 3-DOF Agile Eye Parallel Robot with ROS and Mediapipe Framework	In this paper, a method to mimic a human face and eyes is proposed which can be regarded as a combination of computer vision techniques and neural network concepts. From a mechanical standpoint, a 3-DOF spherical parallel robot is used which imitates the human head movement. In what concerns eye movement, a 2-DOF mecha...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	264,223
1803.09468	Clipping free attacks against artificial neural networks	During the last years, a remarkable breakthrough has been made in AI domain thanks to artificial deep neural networks that achieved a great success in many machine learning tasks in computer vision, natural language processing, speech recognition, malware detection and so on. However, they are highly vulnerable to easi...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	93,501
2405.01233	Mathematics of Differential Machine Learning in Derivative Pricing and Hedging	This article introduces the groundbreaking concept of the financial differential machine learning algorithm through a rigorous mathematical framework. Diverging from existing literature on financial machine learning, the work highlights the profound implications of theoretical assumptions within financial models on the...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	451,282
1906.00957	Symmetry-adapted generation of 3d point sets for the targeted discovery of molecules	Deep learning has proven to yield fast and accurate predictions of quantum-chemical properties to accelerate the discovery of novel molecules and materials. As an exhaustive exploration of the vast chemical space is still infeasible, we require generative models that guide our search towards systems with desired proper...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	133,554
2403.03382	Adaptive Discovering and Merging for Incremental Novel Class Discovery	One important desideratum of lifelong learning aims to discover novel classes from unlabelled data in a continuous manner. The central challenge is twofold: discovering and learning novel classes while mitigating the issue of catastrophic forgetting of established knowledge. To this end, we introduce a new paradigm cal...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	435,165
2501.11817	Toward Effective Digraph Representation Learning: A Magnetic Adaptive Propagation based Approach	The $q$-parameterized magnetic Laplacian serves as the foundation of directed graph (digraph) convolution, enabling this kind of digraph neural network (MagDG) to encode node features and structural insights by complex-domain message passing. As a generalization of undirected methods, MagDG shows superior capability in...	false	false	false	true	true	false	true	false	false	false	false	false	false	false	false	false	true	false	526,047
1911.00655	A Method for Identifying Origin of Digital Images Using a Convolution Neural Network	The rapid development of deep learning techniques has created new challenges in identifying the origin of digital images because generative adversarial networks and variational autoencoders can create plausible digital images whose contents are not present in natural scenes. In this paper, we consider the origin that c...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	151,878
2011.07365	Bayesian recurrent state space model for rs-fMRI	We propose a hierarchical Bayesian recurrent state space model for modeling switching network connectivity in resting state fMRI data. Our model allows us to uncover shared network patterns across disease conditions. We evaluate our method on the ADNI2 dataset by inferring latent state patterns corresponding to altered...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	206,524
1912.01119	Deep Bayesian Active Learning for Multiple Correct Outputs	Typical active learning strategies are designed for tasks, such as classification, with the assumption that the output space is mutually exclusive. The assumption that these tasks always have exactly one correct answer has resulted in the creation of numerous uncertainty-based measurements, such as entropy and least co...	false	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	155,989
2208.13241	Towards Accurate Reconstruction of 3D Scene Shape from A Single Monocular Image	Despite significant progress made in the past few years, challenges remain for depth estimation using a single monocular image. First, it is nontrivial to train a metric-depth prediction model that can generalize well to diverse scenes mainly due to limited training data. Thus, researchers have built large-scale relati...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	314,997
1211.4488	A Rule-Based Approach For Aligning Japanese-Spanish Sentences From A Comparable Corpora	The performance of a Statistical Machine Translation System (SMT) system is proportionally directed to the quality and length of the parallel corpus it uses. However for some pair of languages there is a considerable lack of them. The long term goal is to construct a Japanese-Spanish parallel corpus to be used for SMT,...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	19,816
2107.08411	Deformation-Aware Robotic 3D Ultrasound	Tissue deformation in ultrasound (US) imaging leads to geometrical errors when measuring tissues due to the pressure exerted by probes. Such deformation has an even larger effect on 3D US volumes as the correct compounding is limited by the inconsistent location and geometry. This work proposes a patient-specified stif...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	246,726
1912.12134	Large-scale Multi-modal Person Identification in Real Unconstrained Environments	Person identification (P-ID) under real unconstrained noisy environments is a huge challenge. In multiple-feature learning with Deep Convolutional Neural Networks (DCNNs) or Machine Learning method for large-scale person identification in the wild, the key is to design an appropriate strategy for decision layer fusion ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	158,768
1909.13055	DeepUSPS: Deep Robust Unsupervised Saliency Prediction With Self-Supervision	Deep neural network (DNN) based salient object detection in images based on high-quality labels is expensive. Alternative unsupervised approaches rely on careful selection of multiple handcrafted saliency methods to generate noisy pseudo-ground-truth labels. In this work, we propose a two-stage mechanism for robust uns...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	147,313
1804.01681	Hallucinated-IQA: No-Reference Image Quality Assessment via Adversarial Learning	No-reference image quality assessment (NR-IQA) is a fundamental yet challenging task in low-level computer vision community. The difficulty is particularly pronounced for the limited information, for which the corresponding reference for comparison is typically absent. Although various feature extraction mechanisms hav...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	94,270
2308.01127	DiffusePast: Diffusion-based Generative Replay for Class Incremental Semantic Segmentation	The Class Incremental Semantic Segmentation (CISS) extends the traditional segmentation task by incrementally learning newly added classes. Previous work has introduced generative replay, which involves replaying old class samples generated from a pre-trained GAN, to address the issues of catastrophic forgetting and pr...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	383,152
2102.04029	Non-linear frequency warping using constant-Q transformation for speech emotion recognition	In this work, we explore the constant-Q transform (CQT) for speech emotion recognition (SER). The CQT-based time-frequency analysis provides variable spectro-temporal resolution with higher frequency resolution at lower frequencies. Since lower-frequency regions of speech signal contain more emotion-related information...	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	218,967
2211.05766	Heterogeneous Randomized Response for Differential Privacy in Graph Neural Networks	Graph neural networks (GNNs) are susceptible to privacy inference attacks (PIAs), given their ability to learn joint representation from features and edges among nodes in graph data. To prevent privacy leakages in GNNs, we propose a novel heterogeneous randomized response (HeteroRR) mechanism to protect nodes' features...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	329,670
2411.00179	What Makes An Expert? Reviewing How ML Researchers Define "Expert"	Human experts are often engaged in the development of machine learning systems to collect and validate data, consult on algorithm development, and evaluate system performance. At the same time, who counts as an 'expert' and what constitutes 'expertise' is not always explicitly defined. In this work, we review 112 acade...	false	false	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	504,471
2010.09535	Cold-start Active Learning through Self-supervised Language Modeling	Active learning strives to reduce annotation costs by choosing the most critical examples to label. Typically, the active learning strategy is contingent on the classification model. For instance, uncertainty sampling depends on poorly calibrated model confidence scores. In the cold-start setting, active learning is im...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	201,579
2205.12399	Sparse Mixers: Combining MoE and Mixing to build a more efficient BERT	We combine the capacity of sparsely gated Mixture-of-Experts (MoE) with the speed and stability of linear, mixing transformations to design the Sparse Mixer encoder model. Sparse Mixer slightly outperforms (<1%) BERT on GLUE and SuperGLUE, but more importantly trains 65% faster and runs inference 61% faster. We also pr...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	298,512
2502.08262	GenIAS: Generator for Instantiating Anomalies in time Series	A recent and promising approach for building time series anomaly detection (TSAD) models is to inject synthetic samples of anomalies within real data sets. The existing injection mechanisms have significant limitations - most of them rely on ad hoc, hand-crafted strategies which fail to capture the natural diversity of...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	532,954
1904.01520	Belousov-Zhabotinsky liquid marbles in robot control	We show how to control the movement of a wheeled robot using on-board liquid marbles made of Belousov-Zhabotinsky solution droplets coated with polyethylene powder. Two stainless steel, iridium coated electrodes were inserted in a marble and the electrical potential recorded was used to control the robot's motor. We st...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	126,161
2502.06939	Generalizable automated ischaemic stroke lesion segmentation with vision transformers	Ischaemic stroke, a leading cause of death and disability, critically relies on neuroimaging for characterising the anatomical pattern of injury. Diffusion-weighted imaging (DWI) provides the highest expressivity in ischemic stroke but poses substantial challenges for automated lesion segmentation: susceptibility artef...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	532,358
2406.15398	The EM Algorithm in Information Geometry	The purpose of this thesis is to convey the basic concepts of information geometry and its applications to non-specialists and those in applied fields, assuming only a first-year undergraduate background in calculus, linear algebra, and probability theory / statistics. We first begin with an introduction to the EM algo...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	466,725
2006.04647	Neural Architecture Search without Training	The time and effort involved in hand-designing deep neural networks is immense. This has prompted the development of Neural Architecture Search (NAS) techniques to automate this design. However, NAS algorithms tend to be slow and expensive; they need to train vast numbers of candidate networks to inform the search proc...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	180,760
1503.00709	Some new insights into information decomposition in complex systems based on common information	We take a closer look at the structure of bivariate dependency induced by a pair of predictor random variables $(X_1, X_2)$ trying to synergistically, redundantly or uniquely encode a target random variable $Y$. We evaluate a recently proposed measure of redundancy based on the G\'acs-K\"{o}rner common information (Gri...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	40,738
1111.6828	Bayesian Estimation of a Gaussian source in Middleton's Class-A Impulsive Noise	The paper focuses on minimum mean square error (MMSE) Bayesian estimation for a Gaussian source impaired by additive Middleton's Class-A impulsive noise. In addition to the optimal Bayesian estimator, the paper considers also the soft-limiter and the blanker, which are two popular suboptimal estimators characterized by...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	13,224
2411.17361	Towards Robust Cross-Domain Recommendation with Joint Identifiability of User Preference	Recent cross-domain recommendation (CDR) studies assume that disentangled domain-shared and domain-specific user representations can mitigate domain gaps and facilitate effective knowledge transfer. However, achieving perfect disentanglement is challenging in practice, because user behaviors in CDR are highly complex, ...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	511,411
1606.06223	Enriched K-Tier HetNet Model to Enable the Analysis of User-Centric Small Cell Deployments	One of the principal underlying assumptions of current approaches to the analysis of heterogeneous cellular networks (HetNets) with random spatial models is the uniform distribution of users independent of the base station (BS) locations. This assumption is not quite accurate, especially for user-centric capacity-drive...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	57,545
2406.19040	On Convex Optimization with Semi-Sensitive Features	We study the differentially private (DP) empirical risk minimization (ERM) problem under the semi-sensitive DP setting where only some features are sensitive. This generalizes the Label DP setting where only the label is sensitive. We give improved upper and lower bounds on the excess risk for DP-ERM. In particular, we...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	true	468,276
2209.09168	Application of Neural Network in the Prediction of NOx Emissions from Degrading Gas Turbine	This paper is aiming to apply neural network algorithm for predicting the process response (NOx emissions) from degrading natural gas turbines. Nine different process variables, or predictors, are considered in the predictive modelling. It is found out that the model trained by neural network algorithm should use part ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	318,408
2211.07846	Category-Adaptive Label Discovery and Noise Rejection for Multi-label Image Recognition with Partial Positive Labels	As a promising solution of reducing annotation cost, training multi-label models with partial positive labels (MLR-PPL), in which merely few positive labels are known while other are missing, attracts increasing attention. Due to the absence of any negative labels, previous works regard unknown labels as negative and a...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	330,388
2402.16294	BlockFUL: Enabling Unlearning in Blockchained Federated Learning	Unlearning in Federated Learning (FL) presents significant challenges, as models grow and evolve with complex inheritance relationships. This complexity is amplified when blockchain is employed to ensure the integrity and traceability of FL, where the need to edit multiple interlinked blockchain records and update all ...	false	false	false	false	true	false	false	false	false	false	false	false	true	false	false	false	false	false	432,498
2003.07666	Inverse Design of Potential Singlet Fission Molecules using a Transfer Learning Based Approach	Singlet fission has emerged as one of the most exciting phenomena known to improve the efficiencies of different types of solar cells and has found uses in diverse optoelectronic applications. The range of available singlet fission molecules is, however, limited as to undergo singlet fission, molecules have to satisfy ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	168,502
1806.04164	Solutions of New Potential Integral Equations Using MLFMA Based on the Approximate Stable Diagonalization	We present efficient solutions of recently developed potential integral equations (PIEs) using a low-frequency implementation of the multilevel fast multipole algorithm (MLFMA). PIEs enable accurate solutions of low-frequency problems involving small objects and/or small discretization elements with respect to waveleng...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	100,158
2410.03057	How to evaluate your medical time series classification?	Medical time series (MedTS) play a critical role in many healthcare applications, such as vital sign monitoring and the diagnosis of brain and heart diseases. However, the existence of subject-specific features poses unique challenges in MedTS evaluation. Inappropriate evaluation setups that either exploit or overlook ...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	494,592
2307.08593	Artificial Intelligence for the Electron Ion Collider (AI4EIC)	The Electron-Ion Collider (EIC), a state-of-the-art facility for studying the strong force, is expected to begin commissioning its first experiments in 2028. This is an opportune time for artificial intelligence (AI) to be included from the start at this facility and in all phases that lead up to the experiments. The s...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	379,862
2005.11885	Optimization-driven Deep Reinforcement Learning for Robust Beamforming in IRS-assisted Wireless Communications	Intelligent reflecting surface (IRS) is a promising technology to assist downlink information transmissions from a multi-antenna access point (AP) to a receiver. In this paper, we minimize the AP's transmit power by a joint optimization of the AP's active beamforming and the IRS's passive beamforming. Due to uncertain ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	178,595
1905.12265	Strategies for Pre-training Graph Neural Networks	Many applications of machine learning require a model to make accurate pre-dictions on test examples that are distributionally different from training ones, while task-specific labels are scarce during training. An effective approach to this challenge is to pre-train a model on related tasks where data is abundant, and...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	132,717
2203.03878	HyperPELT: Unified Parameter-Efficient Language Model Tuning for Both Language and Vision-and-Language Tasks	The workflow of pretraining and fine-tuning has emerged as a popular paradigm for solving various NLP and V&L (Vision-and-Language) downstream tasks. With the capacity of pretrained models growing rapidly, how to perform parameter-efficient fine-tuning has become fairly important for quick transfer learning and deploym...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	284,260
1407.2742	Opinion formation in an open system and the spiral of silence	A new model is formulated of the sociological effect of the spiral of silence, introduced by Elisabeth Noelle-Neumann in 1974. The probability that a new opinion is openly expressed decreases with the difference between this new opinion and the perceived opinion of the majority. We also assume that the system is open, ...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	34,560
2005.13232	Sparse Identification of Nonlinear Dynamical Systems via Reweighted $\ell_1$-regularized Least Squares	This work proposes an iterative sparse-regularized regression method to recover governing equations of nonlinear dynamical systems from noisy state measurements. The method is inspired by the Sparse Identification of Nonlinear Dynamics (SINDy) approach of {\it [Brunton et al., PNAS, 113 (15) (2016) 3932-3937]}, which r...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	178,954
1906.04893	Efficient and Accurate Estimation of Lipschitz Constants for Deep Neural Networks	Tight estimation of the Lipschitz constant for deep neural networks (DNNs) is useful in many applications ranging from robustness certification of classifiers to stability analysis of closed-loop systems with reinforcement learning controllers. Existing methods in the literature for estimating the Lipschitz constant su...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	134,869
2406.05405	Robust Conformal Prediction Using Privileged Information	We develop a method to generate prediction sets with a guaranteed coverage rate that is robust to corruptions in the training data, such as missing or noisy variables. Our approach builds on conformal prediction, a powerful framework to construct prediction sets that are valid under the i.i.d assumption. Importantly, n...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	462,120
2005.00840	Decision Support for Intoxication Prediction Using Graph Convolutional Networks	Every day, poison control centers (PCC) are called for immediate classification and treatment recommendations if an acute intoxication is suspected. Due to the time-sensitive nature of these cases, doctors are required to propose a correct diagnosis and intervention within a minimal time frame. Usually the toxin is kno...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	175,399
2410.11782	G-Designer: Architecting Multi-agent Communication Topologies via Graph Neural Networks	Recent advancements in large language model (LLM)-based agents have demonstrated that collective intelligence can significantly surpass the capabilities of individual agents, primarily due to well-crafted inter-agent communication topologies. Despite the diverse and high-performing designs available, practitioners ofte...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	false	false	false	498,716
2108.07258	On the Opportunities and Risks of Foundation Models	AI is undergoing a paradigm shift with the rise of models (e.g., BERT, DALL-E, GPT-3) that are trained on broad data at scale and are adaptable to a wide range of downstream tasks. We call these models foundation models to underscore their critically central yet incomplete character. This report provides a thorough acc...	false	false	false	false	true	false	true	false	false	false	false	false	false	true	false	false	false	false	250,869
2209.06192	StoryDALL-E: Adapting Pretrained Text-to-Image Transformers for Story Continuation	Recent advances in text-to-image synthesis have led to large pretrained transformers with excellent capabilities to generate visualizations from a given text. However, these models are ill-suited for specialized tasks like story visualization, which requires an agent to produce a sequence of images given a correspondin...	false	false	false	false	true	false	false	false	true	false	false	true	false	false	false	false	false	false	317,320
1810.04012	Learning Converged Propagations with Deep Prior Ensemble for Image Enhancement	Enhancing visual qualities of images plays very important roles in various vision and learning applications. In the past few years, both knowledge-driven maximum a posterior (MAP) with prior modelings and fully data-dependent convolutional neural network (CNN) techniques have been investigated to address specific enhan...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	109,945
2410.15005	CAP: Data Contamination Detection via Consistency Amplification	Large language models (LLMs) are widely used, but concerns about data contamination challenge the reliability of LLM evaluations. Existing contamination detection methods are often task-specific or require extra prerequisites, limiting practicality. We propose a novel framework, Consistency Amplification-based Data Con...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	500,315
1310.2066	A Simplified Approach for Quality Management in Data Warehouse	Data warehousing is continuously gaining importance as organizations are realizing the benefits of decision oriented data bases. However, the stumbling block to this rapid development is data quality issues at various stages of data warehousing. Quality can be defined as a measure of excellence or a state free from def...	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	true	false	27,632
0705.0043	Joint Detection and Identification of an Unobservable Change in the Distribution of a Random Sequence	This paper examines the joint problem of detection and identification of a sudden and unobservable change in the probability distribution function (pdf) of a sequence of independent and identically distributed (i.i.d.) random variables to one of finitely many alternative pdf's. The objective is quick detection of the c...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	126
2208.13930	SAFE: Sensitivity-Aware Features for Out-of-Distribution Object Detection	We address the problem of out-of-distribution (OOD) detection for the task of object detection. We show that residual convolutional layers with batch normalisation produce Sensitivity-Aware FEatures (SAFE) that are consistently powerful for distinguishing in-distribution from out-of-distribution detections. We extract ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	315,172
2009.06697	A machine learning approach for efficient multi-dimensional integration	We propose a novel multi-dimensional integration algorithm using a machine learning (ML) technique. After training a ML regression model to mimic a target integrand, the regression model is used to evaluate an approximation of the integral. Then, the difference between the approximation and the true answer is calculate...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	195,716
2411.01623	FilterNet: Harnessing Frequency Filters for Time Series Forecasting	While numerous forecasters have been proposed using different network architectures, the Transformer-based models have state-of-the-art performance in time series forecasting. However, forecasters based on Transformers are still suffering from vulnerability to high-frequency signals, efficiency in computation, and bott...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	505,155
2012.09058	Towards Recognizing New Semantic Concepts in New Visual Domains	Deep learning models heavily rely on large scale annotated datasets for training. Unfortunately, datasets cannot capture the infinite variability of the real world, thus neural networks are inherently limited by the restricted visual and semantic information contained in their training set. In this thesis, we argue tha...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	211,951
2104.06797	Revisiting Light Field Rendering with Deep Anti-Aliasing Neural Network	The light field (LF) reconstruction is mainly confronted with two challenges, large disparity and the non-Lambertian effect. Typical approaches either address the large disparity challenge using depth estimation followed by view synthesis or eschew explicit depth information to enable non-Lambertian rendering, but rare...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	230,203
1803.00814	Understanding Human Mobility Flows from Aggregated Mobile Phone Data	In this paper we deal with the study of travel flows and patterns of people in large populated areas. Information about the movements of people is extracted from coarse-grained aggregated cellular network data without tracking mobile devices individually. Mobile phone data are provided by the Italian telecommunication ...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	91,751
2007.09600	EllSeg: An Ellipse Segmentation Framework for Robust Gaze Tracking	Ellipse fitting, an essential component in pupil or iris tracking based video oculography, is performed on previously segmented eye parts generated using various computer vision techniques. Several factors, such as occlusions due to eyelid shape, camera position or eyelashes, frequently break ellipse fitting algorithms...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	188,014
1904.00479	Sparse Tensor Additive Regression	Tensors are becoming prevalent in modern applications such as medical imaging and digital marketing. In this paper, we propose a sparse tensor additive regression (STAR) that models a scalar response as a flexible nonparametric function of tensor covariates. The proposed model effectively exploits the sparse and low-ra...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	125,885
2011.08769	Anatomy Prior Based U-net for Pathology Segmentation with Attention	Pathological area segmentation in cardiac magnetic resonance (MR) images plays a vital role in the clinical diagnosis of cardiovascular diseases. Because of the irregular shape and small area, pathological segmentation has always been a challenging task. We propose an anatomy prior based framework, which combines the U...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	206,988
1906.00800	Neural Network-based Object Classification by Known and Unknown Features (Based on Text Queries)	The article presents a method that improves the quality of classification of objects described by a combination of known and unknown features. The method is based on modernized Informational Neurobayesian Approach with consideration of unknown features. The proposed method was developed and trained on 1500 text queries...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	133,515
1706.09007	Strategyproof Mechanisms for Additively Separable Hedonic Games and Fractional Hedonic Games	Additively separable hedonic games and fractional hedonic games have received considerable attention. They are coalition forming games of selfish agents based on their mutual preferences. Most of the work in the literature characterizes the existence and structure of stable outcomes (i.e., partitions in coalitions), as...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	76,077
2307.00920	Node-weighted Graph Convolutional Network for Depression Detection in Transcribed Clinical Interviews	We propose a simple approach for weighting self-connecting edges in a Graph Convolutional Network (GCN) and show its impact on depression detection from transcribed clinical interviews. To this end, we use a GCN for modeling non-consecutive and long-distance semantics to classify the transcriptions into depressed or co...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	377,177
2309.13053	Using Curriculum Theory to Inform Approaches to Generative AI in Schools	In an educational landscape dramatically altered by the swift proliferation of Large Language Models, this essay interrogates the urgent this essay interrogates the urgent pedagogical modifications required in secondary schooling. Anchored in Madeline Grumet's triadic framework of curriculum inquiry, the study delineat...	false	false	false	false	true	false	false	false	false	false	false	false	false	true	false	false	false	false	394,030
1605.00252	Fast Rates for General Unbounded Loss Functions: from ERM to Generalized Bayes	We present new excess risk bounds for general unbounded loss functions including log loss and squared loss, where the distribution of the losses may be heavy-tailed. The bounds hold for general estimators, but they are optimized when applied to $\eta$-generalized Bayesian, MDL, and empirical risk minimization estimator...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	55,318
2106.07106	Alignment and Comparison of Directed Networks via Transition Couplings of Random Walks	We describe and study a transport based procedure called NetOTC (network optimal transition coupling) for the comparison and alignment of two networks. The networks of interest may be directed or undirected, weighted or unweighted, and may have distinct vertex sets of different sizes. Given two networks and a cost func...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	240,773
1202.0961	On the Capacity of a General Multiple-Access Channel and of a Cognitive Network in the Very Strong Interference Regime	The capacity of the multiple-access channel with any distribution of messages among the transmitting nodes was determined by Han in 1979 and the expression of the capacity region contains a number of rate bounds and that grows exponentially with the number of messages. We derive a more compact expression for the capaci...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	14,153

Subsets and Splits

No community queries yet

The top public SQL queries from the community will appear here once available.