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
1810.09337	Recovering Robustness in Model-Free Reinforcement learning	Reinforcement learning (RL) is used to directly design a control policy using data collected from the system. This paper considers the robustness of controllers trained via model-free RL. The discussion focuses on the standard model-based linear quadratic Gaussian (LQG) problem as a special instance of RL. A simple exa...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	111,035
1605.04253	An Empirical Study and Analysis of Generalized Zero-Shot Learning for Object Recognition in the Wild	Zero-shot learning (ZSL) methods have been studied in the unrealistic setting where test data are assumed to come from unseen classes only. In this paper, we advocate studying the problem of generalized zero-shot learning (GZSL) where the test data's class memberships are unconstrained. We show empirically that naively...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	55,846
1708.01767	Coverage Analysis in Millimeter Wave Cellular Networks with Reflections	The coverage probability of a user in a mmwave system depends on the availability of line-of-sight paths or reflected paths from any base station. Many prior works modelled blockages using random shape theory and analyzed the SIR distribution with and without interference. While, it is intuitive that the reflected path...	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	false	false	false	78,447
0711.4380	Randomness and metastability in CDMA paradigms	Code Division Multiple Access (CDMA) in which the signature code assignment to users contains a random element has recently become a cornerstone of CDMA research. The random element in the construction is particularly attractive in that it provides robustness and flexibility in application, whilst not making significan...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	963
1406.7557	Weighted Fair Multicast Multigroup Beamforming under Per-antenna Power Constraints	A multi-antenna transmitter that conveys independent sets of common data to distinct groups of users is considered. This model is known as physical layer multicasting to multiple co-channel groups. In this context, the practical constraint of a maximum permitted power level radiated by each antenna is addressed. The pe...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	34,236
1802.04337	An Ontology Based Modeling Framework for Design of Educational Technologies	Despite rapid progress, most of the educational technologies today lack a strong instructional design knowledge basis leading to questionable quality of instruction. In addition, a major challenge is to customize these educational technologies for a wide range of instructional designs. Ontologies are one of the pertine...	false	false	false	false	true	false	false	false	false	false	false	false	false	true	false	false	false	false	90,192
1901.09376	On Peak Age of Information in Data Preprocessing enabled IoT Networks	Internet of Things (IoT) has been emerging as one of the use cases permeating our daily lives in 5th Generation wireless networks, where status update packages are usually required to be timely delivered for many IoT based intelligent applications. Enabling the collected raw data to be preprocessed before transmitted t...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	119,729
1709.10053	Graph Convolutional Networks for Named Entity Recognition	In this paper we investigate the role of the dependency tree in a named entity recognizer upon using a set of GCN. We perform a comparison among different NER architectures and show that the grammar of a sentence positively influences the results. Experiments on the ontonotes dataset demonstrate consistent performance ...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	81,719
2109.10258	Arterial blood pressure waveform in liver transplant surgery possesses variability of morphology reflecting recipients' acuity and predicting short term outcomes	Background: We investigated clinical information underneath the beat-to-beat fluctuation of the arterial blood pressure (ABP) waveform morphology. We proposed the Dynamical Diffusion Map algorithm (DDMap) to quantify the variability of morphology. The underlying physiology could be the compensatory mechanisms involving...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	256,561
2501.00420	KAE: Kolmogorov-Arnold Auto-Encoder for Representation Learning	The Kolmogorov-Arnold Network (KAN) has recently gained attention as an alternative to traditional multi-layer perceptrons (MLPs), offering improved accuracy and interpretability by employing learnable activation functions on edges. In this paper, we introduce the Kolmogorov-Arnold Auto-Encoder (KAE), which integrates ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	521,659
2308.14165	Distributional Off-Policy Evaluation for Slate Recommendations	Recommendation strategies are typically evaluated by using previously logged data, employing off-policy evaluation methods to estimate their expected performance. However, for strategies that present users with slates of multiple items, the resulting combinatorial action space renders many of these methods impractical....	false	false	false	false	true	true	true	false	false	false	false	false	false	false	false	false	false	false	388,217
1905.01023	Physicist's Journeys Through the AI World - A Topical Review. There is no royal road to unsupervised learning	Artificial Intelligence (AI), defined in its most simple form, is a technological tool that makes machines intelligent. Since learning is at the core of intelligence, machine learning poses itself as a core sub-field of AI. Then there comes a subclass of machine learning, known as deep learning, to address the limitati...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	129,621
2305.01888	Fairness in AI Systems: Mitigating gender bias from language-vision models	Our society is plagued by several biases, including racial biases, caste biases, and gender bias. As a matter of fact, several years ago, most of these notions were unheard of. These biases passed through generations along with amplification have lead to scenarios where these have taken the role of expected norms by ce...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	361,834
2501.09428	AugRefer: Advancing 3D Visual Grounding via Cross-Modal Augmentation and Spatial Relation-based Referring	3D visual grounding (3DVG), which aims to correlate a natural language description with the target object within a 3D scene, is a significant yet challenging task. Despite recent advancements in this domain, existing approaches commonly encounter a shortage: a limited amount and diversity of text3D pairs available for ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	525,139
2209.04157	A Fast Algorithm for Onboard Atmospheric Powered Descent Guidance	Atmospheric powered descent guidance can be solved by successive convexification; however, its onboard application is impeded by the sharp increase in computation caused by nonlinear aerodynamic forces. The problem has to be converted into a sequence of convex subproblems instead of a single convex problem when aerodyn...	false	true	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	316,703
2305.05601	Deep Learning and Geometric Deep Learning: an introduction for mathematicians and physicists	In this expository paper we want to give a brief introduction, with few key references for further reading, to the inner functioning of the new and successfull algorithms of Deep Learning and Geometric Deep Learning with a focus on Graph Neural Networks. We go over the key ingredients for these algorithms: the score an...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	363,218
2101.11517	Investigating Bi-Level Optimization for Learning and Vision from a Unified Perspective: A Survey and Beyond	Bi-Level Optimization (BLO) is originated from the area of economic game theory and then introduced into the optimization community. BLO is able to handle problems with a hierarchical structure, involving two levels of optimization tasks, where one task is nested inside the other. In machine learning and computer visio...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	217,305
2412.21206	PERSE: Personalized 3D Generative Avatars from A Single Portrait	We present PERSE, a method for building an animatable personalized generative avatar from a reference portrait. Our avatar model enables facial attribute editing in a continuous and disentangled latent space to control each facial attribute, while preserving the individual's identity. To achieve this, our method begins...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	521,484
2412.11771	Point Cloud-Assisted Neural Image Compression	High-efficient image compression is a critical requirement. In several scenarios where multiple modalities of data are captured by different sensors, the auxiliary information from other modalities are not fully leveraged by existing image-only codecs, leading to suboptimal compression efficiency. In this paper, we inc...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	517,579
2408.04796	A Density Ratio Super Learner	The estimation of the ratio of two density probability functions is of great interest in many statistics fields, including causal inference. In this study, we develop an ensemble estimator of density ratios with a novel loss function based on super learning. We show that this novel loss function is qualified for buildi...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	479,525
cs/0303025	Algorithmic Clustering of Music	We present a fully automatic method for music classification, based only on compression of strings that represent the music pieces. The method uses no background knowledge about music whatsoever: it is completely general and can, without change, be used in different areas like linguistic classification and genomics. It...	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	537,813
1611.06660	Structure of 311 Service Requests as a Signature of Urban Location	While urban systems demonstrate high spatial heterogeneity, many urban planning, economic and political decisions heavily rely on a deep understanding of local neighborhood contexts. We show that the structure of 311 Service Requests enables one possible way of building a unique signature of the local urban context, th...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	64,233
2205.08350	RISCLESS: A Reinforcement Learning Strategy to Exploit Unused Cloud Resources	One of the main objectives of Cloud Providers (CP) is to guarantee the Service-Level Agreement (SLA) of customers while reducing operating costs. To achieve this goal, CPs have built large-scale datacenters. This leads, however, to underutilized resources and an increase in costs. A way to improve the utilization of re...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	296,901
1907.12640	Data-driven identification of dissipative linear models for nonlinear systems	We consider the problem of identifying a dissipative linear model of an unknown nonlinear system that is known to be dissipative, from time domain input-output data. We first learn an approximate linear model of the nonlinear system using standard system identification techniques and then perturb the system matrices of...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	140,157
2308.15370	Multi-Response Heteroscedastic Gaussian Process Models and Their Inference	Despite the widespread utilization of Gaussian process models for versatile nonparametric modeling, they exhibit limitations in effectively capturing abrupt changes in function smoothness and accommodating relationships with heteroscedastic errors. Addressing these shortcomings, the heteroscedastic Gaussian process (He...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	388,662
2406.08431	Diffusion Soup: Model Merging for Text-to-Image Diffusion Models	We present Diffusion Soup, a compartmentalization method for Text-to-Image Generation that averages the weights of diffusion models trained on sharded data. By construction, our approach enables training-free continual learning and unlearning with no additional memory or inference costs, since models corresponding to d...	false	false	false	false	true	false	true	false	false	false	false	true	true	false	false	false	false	false	463,484
2006.11539	On Addressing the Impact of ISO Speed upon PRNU and Forgery Detection	Photo Response Non-Uniformity (PRNU) has been used as a powerful device fingerprint for image forgery detection because image forgeries can be revealed by finding the absence of the PRNU in the manipulated areas. The correlation between an image's noise residual with the device's reference PRNU is often compared with a...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	183,278
1402.0584	NuMVC: An Efficient Local Search Algorithm for Minimum Vertex Cover	The Minimum Vertex Cover (MVC) problem is a prominent NP-hard combinatorial optimization problem of great importance in both theory and application. Local search has proved successful for this problem. However, there are two main drawbacks in state-of-the-art MVC local search algorithms. First, they select a pair of ve...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	30,598
2401.16076	Find the Cliffhanger: Multi-Modal Trailerness in Soap Operas	Creating a trailer requires carefully picking out and piecing together brief enticing moments out of a longer video, making it a challenging and time-consuming task. This requires selecting moments based on both visual and dialogue information. We introduce a multi-modal method for predicting the trailerness to assist ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	424,694
2404.09210	FedDistill: Global Model Distillation for Local Model De-Biasing in Non-IID Federated Learning	Federated Learning (FL) is a novel approach that allows for collaborative machine learning while preserving data privacy by leveraging models trained on decentralized devices. However, FL faces challenges due to non-uniformly distributed (non-iid) data across clients, which impacts model performance and its generalizat...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	446,579
1610.02273	Near-Data Processing for Differentiable Machine Learning Models	Near-data processing (NDP) refers to augmenting memory or storage with processing power. Despite its potential for acceleration computing and reducing power requirements, only limited progress has been made in popularizing NDP for various reasons. Recently, two major changes have occurred that have ignited renewed inte...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	62,075
2302.13139	Prompt-based Learning for Text Readability Assessment	We propose the novel adaptation of a pre-trained seq2seq model for readability assessment. We prove that a seq2seq model - T5 or BART - can be adapted to discern which text is more difficult from two given texts (pairwise). As an exploratory study to prompt-learn a neural network for text readability in a text-to-text ...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	347,826
2404.13853	ICST-DNET: An Interpretable Causal Spatio-Temporal Diffusion Network for Traffic Speed Prediction	Traffic speed prediction is significant for intelligent navigation and congestion alleviation. However, making accurate predictions is challenging due to three factors: 1) traffic diffusion, i.e., the spatial and temporal causality existing between the traffic conditions of multiple neighboring roads, 2) the poor inter...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	448,468
2407.05739	Multi-Bit Mechanism: A Novel Information Transmission Paradigm for Spiking Neural Networks	Since proposed, spiking neural networks (SNNs) gain recognition for their high performance, low power consumption and enhanced biological interpretability. However, while bringing these advantages, the binary nature of spikes also leads to considerable information loss in SNNs, ultimately causing performance degradatio...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	false	false	471,115
2210.03150	Towards Out-of-Distribution Adversarial Robustness	Adversarial robustness continues to be a major challenge for deep learning. A core issue is that robustness to one type of attack often fails to transfer to other attacks. While prior work establishes a theoretical trade-off in robustness against different $L_p$ norms, we show that there is potential for improvement ag...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	321,915
2012.02439	Proximal Policy Optimization Smoothed Algorithm	Proximal policy optimization (PPO) has yielded state-of-the-art results in policy search, a subfield of reinforcement learning, with one of its key points being the use of a surrogate objective function to restrict the step size at each policy update. Although such restriction is helpful, the algorithm still suffers fr...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	209,767
1910.06493	Understanding population fluctuations through volunteered geographic information and novel indicators: The experience of Rakiura, Stewart Island, New Zealand	In an era of heterogeneous data, novel methods and volunteered geographic information provide opportunities to understand how people interact with a place. However, it is not enough to simply have such heterogeneous data, instead an understanding of its usability and reliability needs to be undertaken. Here, we draw up...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	149,353
1310.8004	Online Ensemble Learning for Imbalanced Data Streams	While both cost-sensitive learning and online learning have been studied extensively, the effort in simultaneously dealing with these two issues is limited. Aiming at this challenge task, a novel learning framework is proposed in this paper. The key idea is based on the fusion of online ensemble algorithms and the stat...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	28,075
2402.01987	Online Transfer Learning for RSV Case Detection	Transfer learning has become a pivotal technique in machine learning and has proven to be effective in various real-world applications. However, utilizing this technique for classification tasks with sequential data often faces challenges, primarily attributed to the scarcity of class labels. To address this challenge,...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	426,318
2412.20834	Disentangling Preference Representation and Text Generation for Efficient Individual Preference Alignment	Aligning Large Language Models (LLMs) with general human preferences has been proved crucial in improving the interaction quality between LLMs and human. However, human values are inherently diverse among different individuals, making it insufficient to align LLMs solely with general preferences. To address this, perso...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	521,375
2005.11151	Attention Patterns Detection using Brain Computer Interfaces	The human brain provides a range of functions such as expressing emotions, controlling the rate of breathing, etc., and its study has attracted the interest of scientists for many years. As machine learning models become more sophisticated, and bio-metric data becomes more readily available through new non-invasive tec...	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	178,400
2501.05439	From Simple to Complex Skills: The Case of In-Hand Object Reorientation	Learning policies in simulation and transferring them to the real world has become a promising approach in dexterous manipulation. However, bridging the sim-to-real gap for each new task requires substantial human effort, such as careful reward engineering, hyperparameter tuning, and system identification. In this work...	false	false	false	false	true	false	true	true	false	false	false	false	false	false	false	false	false	false	523,580
2501.19088	JGHand: Joint-Driven Animatable Hand Avater via 3D Gaussian Splatting	Since hands are the primary interface in daily interactions, modeling high-quality digital human hands and rendering realistic images is a critical research problem. Furthermore, considering the requirements of interactive and rendering applications, it is essential to achieve real-time rendering and driveability of th...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	529,004
2407.11830	zIA: a GenAI-powered local auntie assists tourists in Italy	The Tourism and Destination Management Organization (DMO) industry is rapidly evolving to adapt to new technologies and traveler expectations. Generative Artificial Intelligence (AI) offers an astonishing and innovative opportunity to enhance the tourism experience by providing personalized, interactive and engaging as...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	473,638
2501.13921	The Breeze 2 Herd of Models: Traditional Chinese LLMs Based on Llama with Vision-Aware and Function-Calling Capabilities	Llama-Breeze2 (hereinafter referred to as Breeze2) is a suite of advanced multi-modal language models, available in 3B and 8B parameter configurations, specifically designed to enhance Traditional Chinese language representation. Building upon the Llama 3.2 model family, we continue the pre-training of Breeze2 on an ex...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	526,878
2109.00199	Pattern-based Acquisition of Scientific Entities from Scholarly Article Titles	We describe a rule-based approach for the automatic acquisition of salient scientific entities from Computational Linguistics (CL) scholarly article titles. Two observations motivated the approach: (i) noting salient aspects of an article's contribution in its title; and (ii) pattern regularities capturing the salient ...	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	true	253,040
2204.07485	How to Use K-means for Big Data Clustering?	K-means plays a vital role in data mining and is the simplest and most widely used algorithm under the Euclidean Minimum Sum-of-Squares Clustering (MSSC) model. However, its performance drastically drops when applied to vast amounts of data. Therefore, it is crucial to improve K-means by scaling it to big data using as...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	291,726
2406.10742	Spuriousness-Aware Meta-Learning for Learning Robust Classifiers	Spurious correlations are brittle associations between certain attributes of inputs and target variables, such as the correlation between an image background and an object class. Deep image classifiers often leverage them for predictions, leading to poor generalization on the data where the correlations do not hold. Mi...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	464,536
1809.00653	Towards Dynamic Computation Graphs via Sparse Latent Structure	Deep NLP models benefit from underlying structures in the data---e.g., parse trees---typically extracted using off-the-shelf parsers. Recent attempts to jointly learn the latent structure encounter a tradeoff: either make factorization assumptions that limit expressiveness, or sacrifice end-to-end differentiability. Us...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	106,624
0812.4889	Statistical Physics of Signal Estimation in Gaussian Noise: Theory and Examples of Phase Transitions	We consider the problem of signal estimation (denoising) from a statistical mechanical perspective, using a relationship between the minimum mean square error (MMSE), of estimating a signal, and the mutual information between this signal and its noisy version. The paper consists of essentially two parts. In the first, ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	2,856
2107.04150	MCMC Variational Inference via Uncorrected Hamiltonian Annealing	Given an unnormalized target distribution we want to obtain approximate samples from it and a tight lower bound on its (log) normalization constant log Z. Annealed Importance Sampling (AIS) with Hamiltonian MCMC is a powerful method that can be used to do this. Its main drawback is that it uses non-differentiable trans...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	245,362
1712.09213	Aircraft Fuselage Defect Detection using Deep Neural Networks	To ensure flight safety of aircraft structures, it is necessary to have regular maintenance using visual and nondestructive inspection (NDI) methods. In this paper, we propose an automatic image-based aircraft defect detection using Deep Neural Networks (DNNs). To the best of our knowledge, this is the first work for a...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	87,325
2202.03711	Rate-Distortion Theory for Strategic Semantic Communication	This paper analyzes the fundamental limit of the strategic semantic communication problem in which a transmitter obtains a limited number of indirect observation of an intrinsic semantic information source and can then influence the receiver's decoding by sending a limited number of messages to an imperfect channel. Th...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	279,316
2008.05948	Estimating the Magnitude and Phase of Automotive Radar Signals under Multiple Interference Sources with Fully Convolutional Networks	Radar sensors are gradually becoming a wide-spread equipment for road vehicles, playing a crucial role in autonomous driving and road safety. The broad adoption of radar sensors increases the chance of interference among sensors from different vehicles, generating corrupted range profiles and range-Doppler maps. In ord...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	191,661
2204.09297	Effects of Graph Convolutions in Multi-layer Networks	Graph Convolutional Networks (GCNs) are one of the most popular architectures that are used to solve classification problems accompanied by graphical information. We present a rigorous theoretical understanding of the effects of graph convolutions in multi-layer networks. We study these effects through the node classif...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	292,386
2405.08206	Beyond Theorems: A Counterexample to Potential Markov Game Criteria	There are only limited classes of multi-player stochastic games in which independent learning is guaranteed to converge to a Nash equilibrium. Markov potential games are a key example of such classes. Prior work has outlined sets of sufficient conditions for a stochastic game to qualify as a Markov potential game. Howe...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	true	454,014
1906.03118	Reliable Estimation of Individual Treatment Effect with Causal Information Bottleneck	Estimating individual level treatment effects (ITE) from observational data is a challenging and important area in causal machine learning and is commonly considered in diverse mission-critical applications. In this paper, we propose an information theoretic approach in order to find more reliable representations for e...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	134,283
2409.13688	Morphological Detection and Classification of Microplastics and Nanoplastics Emerged from Consumer Products by Deep Learning	Plastic pollution presents an escalating global issue, impacting health and environmental systems, with micro- and nanoplastics found across mediums from potable water to air. Traditional methods for studying these contaminants are labor-intensive and time-consuming, necessitating a shift towards more efficient technol...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	490,101
2405.13891	DeepNcode: Encoding-Based Protection against Bit-Flip Attacks on Neural Networks	Fault injection attacks are a potent threat against embedded implementations of neural network models. Several attack vectors have been proposed, such as misclassification, model extraction, and trojan/backdoor planting. Most of these attacks work by flipping bits in the memory where quantized model parameters are stor...	false	false	false	false	true	false	false	false	false	false	false	false	true	false	false	false	false	false	456,126
1301.0213	Compressed Sensing with Linear Correlation Between Signal and Measurement Noise	Existing convex relaxation-based approaches to reconstruction in compressed sensing assume that noise in the measurements is independent of the signal of interest. We consider the case of noise being linearly correlated with the signal and introduce a simple technique for improving compressed sensing reconstruction fro...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	20,712
cmp-lg/9404008	Principles and Implementation of Deductive Parsing	We present a system for generating parsers based directly on the metaphor of parsing as deduction. Parsing algorithms can be represented directly as deduction systems, and a single deduction engine can interpret such deduction systems so as to implement the corresponding parser. The method generalizes easily to parsers...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	536,041
2205.10969	Application of tropical optimization for solving multicriteria problems of pairwise comparisons using log-Chebyshev approximation	We consider a decision-making problem to find absolute ratings of alternatives that are compared in pairs under multiple criteria, subject to constraints in the form of two-sided bounds on ratios between the ratings. Given matrices of pairwise comparisons made according to the criteria, the problem is formulated as the...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	297,944
2410.00074	Collaborative Knowledge Distillation via a Learning-by-Education Node Community	A novel Learning-by-Education Node Community framework (LENC) for Collaborative Knowledge Distillation (CKD) is presented, which facilitates continual collective learning through effective knowledge exchanges among diverse deployed Deep Neural Network (DNN) peer nodes. These DNNs dynamically and autonomously adopt eith...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	493,225
2404.03363	Space Physiology and Technology: Musculoskeletal Adaptations, Countermeasures, and Opportunities for Wearable Systems	Space poses significant challenges for humans, leading to physiological adaptations in response to an environment vastly different from Earth. A comprehensive understanding of these physiological adaptations is needed to devise effective countermeasures to support human life in space. This narrative review first focuse...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	444,229
2011.06717	Coordinated Motion Control and Event-based Obstacle-crossing for Four Wheel-leg Independent Motor-driven Robotic System via MPC	This work presents the coordinated motion control and obstacle-crossing problem for the four wheel-leg independent motor-driven robotic systems via a model predictive control (MPC) approach based on an event-triggering mechanism. The modeling of a wheel-leg robotic control system with a dynamic supporting polygon is or...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	206,312
1909.12929	Self-Paced Video Data Augmentation with Dynamic Images Generated by Generative Adversarial Networks	There is an urgent need for an effective video classification method by means of a small number of samples. The deficiency of samples could be effectively alleviated by generating samples through Generative Adversarial Networks (GAN), but the generation of videos on a typical category remains to be underexplored since ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	147,255
2008.02610	Learning Context-Adaptive Task Constraints for Robotic Manipulation	Constraint-based control approaches offer a flexible way to specify robotic manipulation tasks and execute them on robots with many degrees of freedom. However, the specification of task constraints and their associated priorities usually requires a human-expert and often leads to tailor-made solutions for specific sit...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	190,662
2112.06134	Markov subsampling based Huber Criterion	Subsampling is an important technique to tackle the computational challenges brought by big data. Many subsampling procedures fall within the framework of importance sampling, which assigns high sampling probabilities to the samples appearing to have big impacts. When the noise level is high, those sampling procedures ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	271,069
2501.12118	Regularized dynamical parametric approximation of stiff evolution problems	Evolutionary deep neural networks have emerged as a rapidly growing field of research. This paper studies numerical integrators for such and other classes of nonlinear parametrizations $ u(t) = \Phi(\theta(t)) $, where the evolving parameters $\theta(t)$ are to be computed. The primary focus is on tackling the challeng...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	526,170
1811.07480	Global and Local Sensitivity Guided Key Salient Object Re-augmentation for Video Saliency Detection	The existing still-static deep learning based saliency researches do not consider the weighting and highlighting of extracted features from different layers, all features contribute equally to the final saliency decision-making. Such methods always evenly detect all "potentially significant regions" and unable to highl...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	113,780
2305.01801	When Newer is Not Better: Does Deep Learning Really Benefit Recommendation From Implicit Feedback?	In recent years, neural models have been repeatedly touted to exhibit state-of-the-art performance in recommendation. Nevertheless, multiple recent studies have revealed that the reported state-of-the-art results of many neural recommendation models cannot be reliably replicated. A primary reason is that existing evalu...	false	false	false	false	true	true	true	false	false	false	false	false	false	false	false	false	false	false	361,804
1212.3550	State-Dependent Multiple Access Channels with Feedback	In this paper, we examine discrete memoryless Multiple Access Channels (MACs) with two-sided feedback in the presence of two correlated channel states that are correlated in the sense of Slepian-Wolf (SW). We find achievable rate region for this channel when the states are provided non-causally to the transmitters and ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	20,404
2110.07803	Attacking Open-domain Question Answering by Injecting Misinformation	With a rise in false, inaccurate, and misleading information in propaganda, news, and social media, real-world Question Answering (QA) systems face the challenges of synthesizing and reasoning over misinformation-polluted contexts to derive correct answers. This urgency gives rise to the need to make QA systems robust ...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	261,136
2402.04777	A fast score-based search algorithm for maximal ancestral graphs using entropy	\emph{Maximal ancestral graph} (MAGs) is a class of graphical model that extend the famous \emph{directed acyclic graph} in the presence of latent confounders. Most score-based approaches to learn the unknown MAG from empirical data rely on BIC score which suffers from instability and heavy computations. We propose to ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	427,582
2010.13032	Byzantine Resilient Distributed Multi-Task Learning	Distributed multi-task learning provides significant advantages in multi-agent networks with heterogeneous data sources where agents aim to learn distinct but correlated models simultaneously.However, distributed algorithms for learning relatedness among tasks are not resilient in the presence of Byzantine agents. In t...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	202,974
2002.03407	Abstractive Summarization for Low Resource Data using Domain Transfer and Data Synthesis	Training abstractive summarization models typically requires large amounts of data, which can be a limitation for many domains. In this paper we explore using domain transfer and data synthesis to improve the performance of recent abstractive summarization methods when applied to small corpora of student reflections. F...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	163,255
1012.5815	SAPFOCS: a metaheuristic based approach to part family formation problems in group technology	This article deals with Part family formation problem which is believed to be moderately complicated to be solved in polynomial time in the vicinity of Group Technology (GT). In the past literature researchers investigated that the part family formation techniques are principally based on production flow analysis (PFA)...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	8,666
2001.04541	Visual Storytelling via Predicting Anchor Word Embeddings in the Stories	We propose a learning model for the task of visual storytelling. The main idea is to predict anchor word embeddings from the images and use the embeddings and the image features jointly to generate narrative sentences. We use the embeddings of randomly sampled nouns from the groundtruth stories as the target anchor wor...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	160,276
2305.05430	Bone Marrow Cytomorphology Cell Detection using InceptionResNetV2	Critical clinical decision points in haematology are influenced by the requirement of bone marrow cytology for a haematological diagnosis. Bone marrow cytology, however, is restricted to reference facilities with expertise, and linked to inter-observer variability which requires a long time to process that could result...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	363,147
2009.12344	A little goes a long way: Improving toxic language classification despite data scarcity	Detection of some types of toxic language is hampered by extreme scarcity of labeled training data. Data augmentation - generating new synthetic data from a labeled seed dataset - can help. The efficacy of data augmentation on toxic language classification has not been fully explored. We present the first systematic st...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	197,397
1809.06191	Multi Modal Convolutional Neural Networks for Brain Tumor Segmentation	In this work, we propose a multi-modal Convolutional Neural Network (CNN) approach for brain tumor segmentation. We investigate how to combine different modalities efficiently in the CNN framework.We adapt various fusion methods, which are previously employed on video recognition problem, to the brain tumor segmentatio...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	107,991
2401.01918	Distilling Temporal Knowledge with Masked Feature Reconstruction for 3D Object Detection	Striking a balance between precision and efficiency presents a prominent challenge in the bird's-eye-view (BEV) 3D object detection. Although previous camera-based BEV methods achieved remarkable performance by incorporating long-term temporal information, most of them still face the problem of low efficiency. One pote...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	419,542
2302.12294	SySCoRe: Synthesis via Stochastic Coupling Relations	We present SySCoRe, a MATLAB toolbox that synthesizes controllers for stochastic continuous-state systems to satisfy temporal logic specifications. Starting from a system description and a co-safe temporal logic specification, SySCoRe provides all necessary functions for synthesizing a robust controller and quantifying...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	347,506
2407.15648	TreeSBA: Tree-Transformer for Self-Supervised Sequential Brick Assembly	Inferring step-wise actions to assemble 3D objects with primitive bricks from images is a challenging task due to complex constraints and the vast number of possible combinations. Recent studies have demonstrated promising results on sequential LEGO brick assembly through the utilization of LEGO-Graph modeling to predi...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	475,269
2404.05211	Multi-level Graph Subspace Contrastive Learning for Hyperspectral Image Clustering	Hyperspectral image (HSI) clustering is a challenging task due to its high complexity. Despite subspace clustering shows impressive performance for HSI, traditional methods tend to ignore the global-local interaction in HSI data. In this study, we proposed a multi-level graph subspace contrastive learning (MLGSC) for H...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	444,985
1803.07297	Polarization and Index Modulations: a Theoretical and Practical Perspective	Radiocommunication systems have evolved significantly in recent years in order to meet present and future demands. Historically, time, frequency and more recently, spatial dimensions have been used to improve capacity and robustness. Paradoxically, radiocommunications that leverage the polarization dimension have not e...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	93,012
2009.14820	Gradient Descent-Ascent Provably Converges to Strict Local Minmax Equilibria with a Finite Timescale Separation	We study the role that a finite timescale separation parameter $\tau$ has on gradient descent-ascent in two-player non-convex, non-concave zero-sum games where the learning rate of player 1 is denoted by $\gamma_1$ and the learning rate of player 2 is defined to be $\gamma_2=\tau\gamma_1$. Existing work analyzing the r...	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	true	198,151
1403.1319	Hardware accelerated protein inference framework	Protein inference plays a vital role in the proteomics study. Two major approaches could be used to handle the problem of protein inference; top-down and bottom-up. This paper presents a framework for protein inference, which uses hardware accelerated protein inference framework for handling the most important step in ...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	31,379
2409.05024	Deep Self-Cleansing for Medical Image Segmentation with Noisy Labels	Medical image segmentation is crucial in the field of medical imaging, aiding in disease diagnosis and surgical planning. Most established segmentation methods rely on supervised deep learning, in which clean and precise labels are essential for supervision and significantly impact the performance of models. However, m...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	486,607
1304.2342	Hierarchical Evidence and Belief Functions	Dempster/Shafer (D/S) theory has been advocated as a way of representing incompleteness of evidence in a system's knowledge base. Methods now exist for propagating beliefs through chains of inference. This paper discusses how rules with attached beliefs, a common representation for knowledge in automated reasoning syst...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	23,650
2406.18992	Semi-supervised Concept Bottleneck Models	Concept Bottleneck Models (CBMs) have garnered increasing attention due to their ability to provide concept-based explanations for black-box deep learning models while achieving high final prediction accuracy using human-like concepts. However, the training of current CBMs heavily relies on the accuracy and richness of...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	468,261
1611.08661	Knowledge Graph Representation with Jointly Structural and Textual Encoding	The objective of knowledge graph embedding is to encode both entities and relations of knowledge graphs into continuous low-dimensional vector spaces. Previously, most works focused on symbolic representation of knowledge graph with structure information, which can not handle new entities or entities with few facts wel...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	64,536
2403.17603	END4Rec: Efficient Noise-Decoupling for Multi-Behavior Sequential Recommendation	In recommendation systems, users frequently engage in multiple types of behaviors, such as clicking, adding to a cart, and purchasing. However, with diversified behavior data, user behavior sequences will become very long in the short term, which brings challenges to the efficiency of the sequence recommendation model....	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	441,535
2112.07963	Towards General and Efficient Active Learning	Active learning selects the most informative samples to exploit limited annotation budgets. Existing work follows a cumbersome pipeline that repeats the time-consuming model training and batch data selection multiple times. In this paper, we challenge this status quo by proposing a novel general and efficient active le...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	271,655
1810.12343	Content Selection in Deep Learning Models of Summarization	We carry out experiments with deep learning models of summarization across the domains of news, personal stories, meetings, and medical articles in order to understand how content selection is performed. We find that many sophisticated features of state of the art extractive summarizers do not improve performance over ...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	111,737
1910.05451	Modeling Information Cascades with Self-exciting Processes via Generalized Epidemic Models	Epidemic models and self-exciting processes are two types of models used to describe diffusion phenomena online and offline. These models were originally developed in different scientific communities, and their commonalities are under-explored. This work establishes, for the first time, a general connection between the...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	149,067
1806.06055	Classification with Fairness Constraints: A Meta-Algorithm with Provable Guarantees	Developing classification algorithms that are fair with respect to sensitive attributes of the data has become an important problem due to the growing deployment of classification algorithms in various social contexts. Several recent works have focused on fairness with respect to a specific metric, modeled the correspo...	false	false	false	false	true	false	true	false	false	false	false	false	false	true	false	false	false	true	100,615
1911.08113	Hunting for Troll Comments in News Community Forums	There are different definitions of what a troll is. Certainly, a troll can be somebody who teases people to make them angry, or somebody who offends people, or somebody who wants to dominate any single discussion, or somebody who tries to manipulate people's opinion (sometimes for money), etc. The last definition is th...	false	false	false	true	false	true	false	false	true	false	false	false	false	false	false	false	false	false	154,095
2212.10950	UNIKD: UNcertainty-filtered Incremental Knowledge Distillation for Neural Implicit Representation	Recent neural implicit representations (NIRs) have achieved great success in the tasks of 3D reconstruction and novel view synthesis. However, they require the images of a scene from different camera views to be available for one-time training. This is expensive especially for scenarios with large-scale scenes and limi...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	337,666
2108.12056	Continual learning under domain transfer with sparse synaptic bursting	Existing machines are functionally specific tools that were made for easy prediction and control. Tomorrow's machines may be closer to biological systems in their mutability, resilience, and autonomy. But first they must be capable of learning and retaining new information without being exposed to it arbitrarily often....	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	252,373

Subsets and Splits

No community queries yet

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