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
1106.1820	Inferring Strategies for Sentence Ordering in Multidocument News Summarization	The problem of organizing information for multidocument summarization so that the generated summary is coherent has received relatively little attention. While sentence ordering for single document summarization can be determined from the ordering of sentences in the input article, this is not the case for multidocumen...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	10,794
2010.07722	Improving Neural Network Verification through Spurious Region Guided Refinement	We propose a spurious region guided refinement approach for robustness verification of deep neural networks. Our method starts with applying the DeepPoly abstract domain to analyze the network. If the robustness property cannot be verified, the result is inconclusive. Due to the over-approximation, the computed region ...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	200,919
2309.14347	Continuous-time control synthesis under nested signal temporal logic specifications	In this work, we propose a novel approach for the continuous-time control synthesis of nonlinear systems under nested signal temporal logic (STL) specifications. While the majority of existing literature focuses on control synthesis for STL specifications without nested temporal operators, addressing nested temporal op...	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	394,569
2012.00319	Constrained Optimization for Hybrid System Falsification and Application to Conjunctive Synthesis	The synthesis problem of a cyber-physical system (CPS) is to find an input signal under which the system's behavior satisfies a given specification. Our setting is that the specification is a formula of signal temporal logic, and furthermore, that the specification is a conjunction of different and often conflicting re...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	209,097
2408.05854	On the Robustness of Kernel Goodness-of-Fit Tests	Goodness-of-fit testing is often criticized for its lack of practical relevance; since ``all models are wrong'', the null hypothesis that the data conform to our model is ultimately always rejected when the sample size is large enough. Despite this, probabilistic models are still used extensively, raising the more pert...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	479,969
2310.07419	Multi-Concept T2I-Zero: Tweaking Only The Text Embeddings and Nothing Else	Recent advances in text-to-image diffusion models have enabled the photorealistic generation of images from text prompts. Despite the great progress, existing models still struggle to generate compositional multi-concept images naturally, limiting their ability to visualize human imagination. While several recent works...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	398,968
2410.04454	Inner-Probe: Discovering Copyright-related Data Generation in LLM Architecture	Large Language Models (LLMs) utilize extensive knowledge databases and show powerful text generation ability. However, their reliance on high-quality copyrighted datasets raises concerns about copyright infringements in generated texts. Current research often employs prompt engineering or semantic classifiers to identi...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	495,288
2412.00026	Spatial-variant causal Bayesian inference for rapid seismic ground failures and impacts estimation	Rapid and accurate estimation of post-earthquake ground failures and building damage is critical for effective post-disaster responses. Progression in remote sensing technologies has paved the way for rapid acquisition of detailed, localized data, enabling swift hazard estimation through analysis of correlation deviati...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	512,447
2110.15823	C-MADA: Unsupervised Cross-Modality Adversarial Domain Adaptation framework for medical Image Segmentation	Deep learning models have obtained state-of-the-art results for medical image analysis. However, when these models are tested on an unseen domain there is a significant performance degradation. In this work, we present an unsupervised Cross-Modality Adversarial Domain Adaptation (C-MADA) framework for medical image seg...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	264,019
1908.04466	Few Labeled Atlases are Necessary for Deep-Learning-Based Segmentation	We tackle biomedical image segmentation in the scenario of only a few labeled brain MR images. This is an important and challenging task in medical applications, where manual annotations are time-consuming. Current multi-atlas based segmentation methods use image registration to warp segments from labeled images onto a...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	141,488
2202.09981	Berman Codes: A Generalization of Reed-Muller Codes that Achieve BEC Capacity	We identify a family of binary codes whose structure is similar to Reed-Muller (RM) codes and which include RM codes as a strict subclass. The codes in this family are denoted as $C_n(r,m)$, and their duals are denoted as $B_n(r,m)$. The length of these codes is $n^m$, where $n \geq 2$, and $r$ is their `order'. When $...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	281,383
2009.10877	Symbolic Execution + Model Counting + Entropy Maximization = Automatic Search Synthesis	We present a method of automatically synthesizing steps to solve search problems. Given a specification of a search problem, our approach uses symbolic execution to analyze the specification in order to extract a set of constraints which model the problem. These constraints are used in a process called model counting, ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	197,008
2104.12945	Quantitative Risk Indices for Autonomous Vehicle Training Systems	The development of Autonomous Vehicles (AV) presents an opportunity to save and improve lives. However, achieving SAE Level 5 (full) autonomy will require overcoming many technical challenges. There is a gap in the literature regarding the measurement of safety for self-driving systems. Measuring safety and risk is par...	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	232,362
2403.11536	OCR is All you need: Importing Multi-Modality into Image-based Defect Detection System	Automatic optical inspection (AOI) plays a pivotal role in the manufacturing process, predominantly leveraging high-resolution imaging instruments for scanning purposes. It detects anomalies by analyzing image textures or patterns, making it an essential tool in industrial manufacturing and quality control. Despite its...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	438,748
2202.00563	On the Limitations of General Purpose Domain Generalisation Methods	We investigate the fundamental performance limitations of learning algorithms in several Domain Generalisation (DG) settings. Motivated by the difficulty with which previously proposed methods have in reliably outperforming Empirical Risk Minimisation (ERM), we derive upper bounds on the excess risk of ERM, and lower b...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	278,187
2008.12858	Real-world Video Adaptation with Reinforcement Learning	Client-side video players employ adaptive bitrate (ABR) algorithms to optimize user quality of experience (QoE). We evaluate recently proposed RL-based ABR methods in Facebook's web-based video streaming platform. Real-world ABR contains several challenges that requires customized designs beyond off-the-shelf RL algori...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	193,691
2206.14053	Bengali Common Voice Speech Dataset for Automatic Speech Recognition	Bengali is one of the most spoken languages in the world with over 300 million speakers globally. Despite its popularity, research into the development of Bengali speech recognition systems is hindered due to the lack of diverse open-source datasets. As a way forward, we have crowdsourced the Bengali Common Voice Speec...	false	false	true	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	305,163
2206.04140	TreeFlow: Going beyond Tree-based Gaussian Probabilistic Regression	The tree-based ensembles are known for their outstanding performance in classification and regression problems characterized by feature vectors represented by mixed-type variables from various ranges and domains. However, considering regression problems, they are primarily designed to provide deterministic responses or...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	301,517
2401.12707	Localized Data-driven Consensus Control	This paper considers a localized data-driven consensus problem for leader-follower multi-agent systems with unknown discrete-time agent dynamics, where each follower computes its local control gain using only their locally collected state and input data. Both noiseless and noisy data-driven consensus protocols are pres...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	423,471
1904.06654	The dynamic importance of nodes is poorly predicted by static network features	One of the most central questions in network science is: which nodes are most important? Often this question is answered using structural properties such as high connectedness or centrality in the network. However, static structural connectedness does not necessarily translate to dynamical importance. To demonstrate th...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	127,602
2102.10172	Channel Estimation and Data Detection Analysis of Massive MIMO with 1-Bit ADCs	We present an analytical framework for the channel estimation and the data detection in massive multiple-input multiple-output uplink systems with 1-bit analog-to-digital converters (ADCs) and i.i.d. Rayleigh fading. First, we provide closed-form expressions of the mean squared error (MSE) of the channel estimation con...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	220,994
2403.04398	MAGR: Manifold-Aligned Graph Regularization for Continual Action Quality Assessment	Action Quality Assessment (AQA) evaluates diverse skills but models struggle with non-stationary data. We propose Continual AQA (CAQA) to refine models using sparse new data. Feature replay preserves memory without storing raw inputs. However, the misalignment between static old features and the dynamically changing fe...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	435,583
2012.00924	CPF: Learning a Contact Potential Field to Model the Hand-Object Interaction	Modeling the hand-object (HO) interaction not only requires estimation of the HO pose, but also pays attention to the contact due to their interaction. Significant progress has been made in estimating hand and object separately with deep learning methods, simultaneous HO pose estimation and contact modeling has not yet...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	209,272
2501.01556	Extended Information Geometry: Large Deviation Theory, Statistical Thermodynamics, and Empirical Counting Frequencies	Combinatorics, probabilities, and measurements are fundamental to understanding information. This work explores how the application of large deviation theory (LDT) in counting phenomena leads to the emergence of various entropy functions, including Shannon's entropy, mutual information, and relative and conditional ent...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	522,116
2210.15427	Are You Stealing My Model? Sample Correlation for Fingerprinting Deep Neural Networks	An off-the-shelf model as a commercial service could be stolen by model stealing attacks, posing great threats to the rights of the model owner. Model fingerprinting aims to verify whether a suspect model is stolen from the victim model, which gains more and more attention nowadays. Previous methods always leverage the...	false	false	false	false	true	false	false	false	false	false	false	false	true	false	false	false	false	false	326,944
1809.09329	Collaborative Learning for Extremely Low Bit Asymmetric Hashing	Hashing techniques are in great demand for a wide range of real-world applications such as image retrieval and network compression. Nevertheless, existing approaches could hardly guarantee a satisfactory performance with the extremely low-bit (e.g., 4-bit) hash codes due to the severe information loss and the shrink of...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	108,684
2310.08660	Learning RL-Policies for Joint Beamforming Without Exploration: A Batch Constrained Off-Policy Approach	In this work, we consider the problem of network parameter optimization for rate maximization. We frame this as a joint optimization problem of power control, beam forming, and interference cancellation. We consider the setting where multiple Base Stations (BSs) communicate with multiple user equipment (UEs). Because o...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	399,472
2408.01892	Re-ENACT: Reinforcement Learning for Emotional Speech Generation using Actor-Critic Strategy	In this paper, we propose the first method to modify the prosodic features of a given speech signal using actor-critic reinforcement learning strategy. Our approach uses a Bayesian framework to identify contiguous segments of importance that links segments of the given utterances to perception of emotions in humans. We...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	478,409
1612.08936	Partial Membership Latent Dirichlet Allocation	Topic models (e.g., pLSA, LDA, sLDA) have been widely used for segmenting imagery. However, these models are confined to crisp segmentation, forcing a visual word (i.e., an image patch) to belong to one and only one topic. Yet, there are many images in which some regions cannot be assigned a crisp categorical label (e....	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	66,133
2110.13609	Resolving Anomalies in the Behaviour of a Modularity Inducing Problem Domain with Distributional Fitness Evaluation	Discrete gene regulatory networks (GRNs) play a vital role in the study of robustness and modularity. A common method of evaluating the robustness of GRNs is to measure their ability to regulate a set of perturbed gene activation patterns back to their unperturbed forms. Usually, perturbations are obtained by collectin...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	false	false	263,246
1008.1610	New Constant-Weight Codes from Propagation Rules	This paper proposes some simple propagation rules which give rise to new binary constant-weight codes.	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	7,233
1509.09152	Supporting interoperability of collaborative networks through engineering of a service-based Mediation Information System (MISE 2.0)	The Mediation Information System Engineering project is currently finishing its second iteration (MISE 2.0). The main objective of this scientific project is to provide any emerging collaborative situation with methods and tools to deploy a Mediation Information System (MIS). MISE 2.0 aims at defining and designing a s...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	47,456
1105.5427	Combining Lagrangian Decomposition and Excessive Gap Smoothing Technique for Solving Large-Scale Separable Convex Optimization Problems	A new algorithm for solving large-scale convex optimization problems with a separable objective function is proposed. The basic idea is to combine three techniques: Lagrangian dual decomposition, excessive gap and smoothing. The main advantage of this algorithm is that it dynamically updates the smoothness parameters w...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	10,514
2410.21473	Second-Order Analysis of CSMA Protocols for Age-of-Information Minimization	This paper introduces a general framework to analyze and optimize age-of-information (AoI) in CSMA protocols for distributed uplink transmissions. The proposed framework combines two theoretical approaches. First, it employs second-order analysis that characterizes all random processes by their respective means and tem...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	503,256
2409.13136	Federated Learning with Label-Masking Distillation	Federated learning provides a privacy-preserving manner to collaboratively train models on data distributed over multiple local clients via the coordination of a global server. In this paper, we focus on label distribution skew in federated learning, where due to the different user behavior of the client, label distrib...	false	false	false	false	false	false	true	false	false	false	false	true	true	false	false	false	false	false	489,858
2209.04356	Risk-Averse Multi-Armed Bandits with Unobserved Confounders: A Case Study in Emotion Regulation in Mobile Health	In this paper, we consider a risk-averse multi-armed bandit (MAB) problem where the goal is to learn a policy that minimizes the risk of low expected return, as opposed to maximizing the expected return itself, which is the objective in the usual approach to risk-neutral MAB. Specifically, we formulate this problem as ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	316,759
2009.11264	On the Ability and Limitations of Transformers to Recognize Formal Languages	Transformers have supplanted recurrent models in a large number of NLP tasks. However, the differences in their abilities to model different syntactic properties remain largely unknown. Past works suggest that LSTMs generalize very well on regular languages and have close connections with counter languages. In this wor...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	197,127
1411.0729	The Private and Public Correlation Cost of Three Random Variables with Collaboration	In this paper we consider the problem of generating arbitrary three-party correlations from a combination of public and secret correlations. Two parties -- called Alice and Bob -- share perfectly correlated bits that are secret from a collaborating third party, Charlie. At the same time, all three parties have access t...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	37,276
2410.13299	LLM-Rank: A Graph Theoretical Approach to Pruning Large Language Models	The evolving capabilities of large language models are accompanied by growing sizes and deployment costs, necessitating effective inference optimisation techniques. We propose a novel pruning method utilising centrality measures from graph theory, reducing both the computational requirements and the memory footprint of...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	499,471
1703.09784	Perception Driven Texture Generation	This paper investigates a novel task of generating texture images from perceptual descriptions. Previous work on texture generation focused on either synthesis from examples or generation from procedural models. Generating textures from perceptual attributes have not been well studied yet. Meanwhile, perceptual attribu...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	70,799
2403.16728	Improving Diffusion Models's Data-Corruption Resistance using Scheduled Pseudo-Huber Loss	Diffusion models are known to be vulnerable to outliers in training data. In this paper we study an alternative diffusion loss function, which can preserve the high quality of generated data like the original squared $L_{2}$ loss while at the same time being robust to outliers. We propose to use pseudo-Huber loss funct...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	441,168
1303.4247	On the efficiency of the new Italian Senate and the role of 5 Stars Movement: Comparison among different possible scenarios by means of a virtual Parliament model	The recent 2013 Italian elections are over and the situation that President Napolitano will have to settle soon for the formation of the new government is not the simplest one. After twenty years of bipolarism (more or less effective), where we were accustomed to a tight battle between two great political coalitions, t...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	22,995
2402.01203	Neural Language of Thought Models	The Language of Thought Hypothesis suggests that human cognition operates on a structured, language-like system of mental representations. While neural language models can naturally benefit from the compositional structure inherently and explicitly expressed in language data, learning such representations from non-ling...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	425,910
2304.09285	Pelphix: Surgical Phase Recognition from X-ray Images in Percutaneous Pelvic Fixation	Surgical phase recognition (SPR) is a crucial element in the digital transformation of the modern operating theater. While SPR based on video sources is well-established, incorporation of interventional X-ray sequences has not yet been explored. This paper presents Pelphix, a first approach to SPR for X-ray-guided perc...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	359,000
2305.09149	Constructing Feedback Linearizable Discretizations for Continuous-Time Systems using Retraction Maps	Control laws for continuous-time dynamical systems are most often implemented via digital controllers using a sample-and-hold technique. Numerical discretization of the continuous system is an integral part of subsequent analysis. Feedback linearizability of such sampled systems is dependent upon the choice of discreti...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	364,534
2107.07630	Evaluation of Human-AI Teams for Learned and Rule-Based Agents in Hanabi	Deep reinforcement learning has generated superhuman AI in competitive games such as Go and StarCraft. Can similar learning techniques create a superior AI teammate for human-machine collaborative games? Will humans prefer AI teammates that improve objective team performance or those that improve subjective metrics of ...	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	246,476
2106.09109	QuantumFed: A Federated Learning Framework for Collaborative Quantum Training	With the fast development of quantum computing and deep learning, quantum neural networks have attracted great attention recently. By leveraging the power of quantum computing, deep neural networks can potentially overcome computational power limitations in classic machine learning. However, when multiple quantum machi...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	241,540
1906.11559	Aerial Base Stations Deployment in 6G Cellular Networks using Tethered Drones: The Mobility and Endurance Trade-off	Airborne base stations (carried by drones) have a great potential to enhance coverage and capacity of cellular networks. Multiple scenarios and use cases will highly benefit from such technology such as (i) offloading terrestrial base stations (BSs) in dense and urban areas, and (ii) providing coverage for rural areas....	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	136,694
2007.12813	All-Optical Information Processing Capacity of Diffractive Surfaces	Precise engineering of materials and surfaces has been at the heart of some of the recent advances in optics and photonics. These advances around the engineering of materials with new functionalities have also opened up exciting avenues for designing trainable surfaces that can perform computation and machine learning ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	true	false	false	188,929
2002.07341	Joint Frame Design and Resource Allocation for Ultra-Reliable and Low-Latency Vehicular Networks	The rapid development of the fifth generation mobile communication systems accelerates the implementation of vehicle-to-everything communications. Compared with the other types of vehicular communications, vehicle-to-vehicle (V2V) communications mainly focus on the exchange of driving safety information with neighborin...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	164,437
2112.00665	Iterative Saliency Enhancement using Superpixel Similarity	Saliency Object Detection (SOD) has several applications in image analysis. The methods have evolved from image-intrinsic to object-inspired (deep-learning-based) models. When a model fail, however, there is no alternative to enhance its saliency map. We fill this gap by introducing a hybrid approach, named \textit{Ite...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	269,209
2312.11843	Enhancing Social Decision-Making of Autonomous Vehicles: A Mixed-Strategy Game Approach With Interaction Orientation Identification	The integration of Autonomous Vehicles (AVs) into existing human-driven traffic systems poses considerable challenges, especially within environments where human and machine interactions are frequent and complex, such as at unsignalized intersections. To deal with these challenges, we introduce a novel framework predic...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	416,740
1702.06011	A Downstream Crosstalk Channel Estimation Method for Mix of Legacy and Vectoring-Enabled VDSL	With the latest technology of vectoring, DSL data rates in the order of 100Mbps have become a reality that is under field deployment. The key is to cancel crosstalk from other lines, which is also known as multiuser MIMO cancellation for wireless communications. During the DSL system upgrade phase of field deployment, ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	68,515
2012.14743	BayesCard: Revitilizing Bayesian Frameworks for Cardinality Estimation	Cardinality estimation (CardEst) is an essential component in query optimizers and a fundamental problem in DBMS. A desired CardEst method should attain good algorithm performance, be stable to varied data settings, and be friendly to system deployment. However, no existing CardEst method can fulfill the three criteria...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	true	false	213,596
1611.04704	SIR Asymptotics in General Network Models	In the performance analyses of wireless networks, asymptotic quantities and properties often pro- vide useful results and insights. The asymptotic analyses become especially important when complete analytical expressions of the performance metrics of interest are not available, which is often the case if one departs fr...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	63,888
2210.08248	A Closer Look at the Calibration of Differentially Private Learners	We systematically study the calibration of classifiers trained with differentially private stochastic gradient descent (DP-SGD) and observe miscalibration across a wide range of vision and language tasks. Our analysis identifies per-example gradient clipping in DP-SGD as a major cause of miscalibration, and we show tha...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	324,058
2106.01061	Rethinking Cross-modal Interaction from a Top-down Perspective for Referring Video Object Segmentation	Referring video object segmentation (RVOS) aims to segment video objects with the guidance of natural language reference. Previous methods typically tackle RVOS through directly grounding linguistic reference over the image lattice. Such bottom-up strategy fails to explore object-level cues, easily leading to inferior ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	238,366
2502.04412	Decoder-Only LLMs are Better Controllers for Diffusion Models	Groundbreaking advancements in text-to-image generation have recently been achieved with the emergence of diffusion models. These models exhibit a remarkable ability to generate highly artistic and intricately detailed images based on textual prompts. However, obtaining desired generation outcomes often necessitates re...	false	false	false	false	true	false	false	false	true	false	false	true	false	false	false	false	false	false	531,149
1705.06908	Unbiased estimates for linear regression via volume sampling	Given a full rank matrix $X$ with more columns than rows, consider the task of estimating the pseudo inverse $X^+$ based on the pseudo inverse of a sampled subset of columns (of size at least the number of rows). We show that this is possible if the subset of columns is chosen proportional to the squared volume spanned...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	73,704
1711.04731	Evaluating prose style transfer with the Bible	In the prose style transfer task a system, provided with text input and a target prose style, produces output which preserves the meaning of the input text but alters the style. These systems require parallel data for evaluation of results and usually make use of parallel data for training. Currently, there are few pub...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	84,433
2207.01062	Distributed Online System Identification for LTI Systems Using Reverse Experience Replay	Identification of linear time-invariant (LTI) systems plays an important role in control and reinforcement learning. Both asymptotic and finite-time offline system identification are well-studied in the literature. For online system identification, the idea of stochastic-gradient descent with reverse experience replay ...	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	306,020
2403.13413	A Cox rate-and-state model for monitoring seismic hazard in the Groningen gas field	To monitor the seismic hazard in the Groningen gas field, we modify the rate-and-state model that relates changes in pore pressure to induced seismic hazard by allowing for noise in pore pressure measurements and by explicitly taking into account gas production volumes. We analyse the first and second-moment structure ...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	439,628
2412.09633	A Novel Wavelet-base Algorithm for Reconstruction of the Time-Domain Impulse Response from Band-limited Scattering Parameters with Applications	In this paper, we introduce a novel waveletbased algorithm for reconstructing time-domain impulse responses from band-limited scattering parameters (frequencydomain data) with a particular focus on ship hull applications. We establish the algorithm and demonstrate its convergence, as well as its efficiency for a class ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	516,576
1901.06086	WALL-E: An Efficient Reinforcement Learning Research Framework	There are two halves to RL systems: experience collection time and policy learning time. For a large number of samples in rollouts, experience collection time is the major bottleneck. Thus, it is necessary to speed up the rollout generation time with multi-process architecture support. Our work, dubbed WALL-E, utilizes...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	118,922
2401.04550	WaveletFormerNet: A Transformer-based Wavelet Network for Real-world Non-homogeneous and Dense Fog Removal	Although deep convolutional neural networks have achieved remarkable success in removing synthetic fog, it is essential to be able to process images taken in complex foggy conditions, such as dense or non-homogeneous fog, in the real world. However, the haze distribution in the real world is complex, and downsampling c...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	420,466
2403.01342	LM4OPT: Unveiling the Potential of Large Language Models in Formulating Mathematical Optimization Problems	In the rapidly evolving field of natural language processing, the translation of linguistic descriptions into mathematical formulation of optimization problems presents a formidable challenge, demanding intricate understanding and processing capabilities from Large Language Models (LLMs). This study compares prominent ...	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	434,368
2105.04799	A Feature Fusion-Net Using Deep Spatial Context Encoder and Nonstationary Joint Statistical Model for High Resolution SAR Image Classification	Convolutional neural networks (CNNs) have been applied to learn spatial features for high-resolution (HR) synthetic aperture radar (SAR) image classification. However, there has been little work on integrating the unique statistical distributions of SAR images which can reveal physical properties of terrain objects, in...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	234,622
2208.13653	Learning Binary and Sparse Permutation-Invariant Representations for Fast and Memory Efficient Whole Slide Image Search	Learning suitable Whole slide images (WSIs) representations for efficient retrieval systems is a non-trivial task. The WSI embeddings obtained from current methods are in Euclidean space not ideal for efficient WSI retrieval. Furthermore, most of the current methods require high GPU memory due to the simultaneous proce...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	315,104
1612.04110	Observation of dynamics inside an unlabeled live cell using bright-field photon microscopy: Evaluation of organelles' trajectories	This article presents an algorithm for the evaluation of organelles' movements inside of an unmodified live cell. We used a time-lapse image series obtained using wide-field bright-field photon transmission microscopy as an algorithm input. The benefit of the algorithm is the application of the R\'enyi information entr...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	65,480
2008.00546	A Foliated View of Transfer Learning	Transfer learning considers a learning process where a new task is solved by transferring relevant knowledge from known solutions to related tasks. While this has been studied experimentally, there lacks a foundational description of the transfer learning problem that exposes what related tasks are, and how they can be...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	190,043
2006.15373	MTStereo 2.0: improved accuracy of stereo depth estimation withMax-trees	Efficient yet accurate extraction of depth from stereo image pairs is required by systems with low power resources, such as robotics and embedded systems. State-of-the-art stereo matching methods based on convolutional neural networks require intensive computations on GPUs and are difficult to deploy on embedded system...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	184,484
2410.04731	Efficient transformer with reinforced position embedding for language models	In this paper, we propose an efficient transformer architecture that uses reinforced positional embedding to obtain superior performance with half the number of encoder decoder layers. We demonstrate that concatenating positional encoding with trainable token embeddings, normalizing columns in the token embedding matri...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	495,416
1910.12626	Model selection for deep audio source separation via clustering analysis	Audio source separation is the process of separating a mixture (e.g. a pop band recording) into isolated sounds from individual sources (e.g. just the lead vocals). Deep learning models are the state-of-the-art in source separation, given that the mixture to be separated is similar to the mixtures the deep model was tr...	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	151,152
2311.16531	Measurement and Modeling on Terahertz Channels in Rain	The Terahertz (THz) frequency band offers a wide range of bandwidths, from tens to hundreds of gigahertz (GHz) and also supports data speeds of several terabits per second (Tbps). Because of this, maintaining THz channel reliability and efficiency in adverse weather conditions is crucial. Rain, in particular, disrupts ...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	410,949
1806.03560	Semantic Correspondence: A Hierarchical Approach	Establishing semantic correspondence across images when the objects in the images have undergone complex deformations remains a challenging task in the field of computer vision. In this paper, we propose a hierarchical method to tackle this problem by first semantically targeting the foreground objects to localize the ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	100,025
2412.13835	RACQUET: Unveiling the Dangers of Overlooked Referential Ambiguity in Visual LLMs	Ambiguity resolution is key to effective communication. While humans effortlessly address ambiguity through conversational grounding strategies, the extent to which current language models can emulate these strategies remains unclear. In this work, we examine referential ambiguity in image-based question answering by i...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	518,467
2003.11902	Implementing a GPU-based parallel MAX-MIN Ant System	The MAX-MIN Ant System (MMAS) is one of the best-known Ant Colony Optimization (ACO) algorithms proven to be efficient at finding satisfactory solutions to many difficult combinatorial optimization problems. The slow-down in Moore's law, and the availability of graphics processing units (GPUs) capable of conducting gen...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	true	169,746
2201.11650	Incremental Mining of Frequent Serial Episodes Considering Multiple Occurrences	The need to analyze information from streams arises in a variety of applications. One of its fundamental research directions is to mine sequential patterns over data streams. Current studies mine series of items based on the presence of the pattern in transactions but pay no attention to the series of itemsets and thei...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	true	false	277,354
2307.10506	Is Grad-CAM Explainable in Medical Images?	Explainable Deep Learning has gained significant attention in the field of artificial intelligence (AI), particularly in domains such as medical imaging, where accurate and interpretable machine learning models are crucial for effective diagnosis and treatment planning. Grad-CAM is a baseline that highlights the most c...	false	false	false	false	false	false	false	false	false	false	false	true	false	true	false	false	false	false	380,572
2010.06969	NwQM: A neural quality assessment framework for Wikipedia	Millions of people irrespective of socioeconomic and demographic backgrounds, depend on Wikipedia articles everyday for keeping themselves informed regarding popular as well as obscure topics. Articles have been categorized by editors into several quality classes, which indicate their reliability as encyclopedic conten...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	true	200,658
2409.18170	Evaluation of Large Language Models for Summarization Tasks in the Medical Domain: A Narrative Review	Large Language Models have advanced clinical Natural Language Generation, creating opportunities to manage the volume of medical text. However, the high-stakes nature of medicine requires reliable evaluation, which remains a challenge. In this narrative review, we assess the current evaluation state for clinical summar...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	492,134
2202.03167	Bayesian Non-stationary Linear Bandits for Large-Scale Recommender Systems	Taking advantage of contextual information can potentially boost the performance of recommender systems. In the era of big data, such side information often has several dimensions. Thus, developing decision-making algorithms to cope with such a high-dimensional context in real time is essential. That is specifically ch...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	279,100
1812.10851	A Summary of Adaptation of Techniques from Search-based Optimal Multi-Agent Path Finding Solvers to Compilation-based Approach	In the multi-agent path finding problem (MAPF) we are given a set of agents each with respective start and goal positions. The task is to find paths for all agents while avoiding collisions aiming to minimize an objective function. Two such common objective functions is the sum-of-costs and the makespan. Many optimal s...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	117,450
1711.10050	Non-Orthogonal Multiple Access for mmWave Drones with Multi-Antenna Transmission	Unmanned aerial vehicles (UAVs) can be deployed as aerial base stations (BSs) for rapid establishment of communication networks during temporary events and after disasters. Since UAV-BSs are low power nodes, achieving high spectral and energy efficiency are of paramount importance. In this paper, we introduce non-ortho...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	85,501
2112.01156	A Unified Framework for Adversarial Attack and Defense in Constrained Feature Space	The generation of feasible adversarial examples is necessary for properly assessing models that work in constrained feature space. However, it remains a challenging task to enforce constraints into attacks that were designed for computer vision. We propose a unified framework to generate feasible adversarial examples t...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	269,388
2308.05758	SNR-based beaconless multi-scan link acquisition model with vibration for LEO-to-ground laser communication	We propose a link acquisition time model deeply involving the process from the transmitted power to received signal-to-noise ratio (SNR) for LEO-to-ground laser communication for the first time. Compared with the conventional acquisition models founded on geometry analysis with divergence angle threshold, utilizing SNR...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	384,903
2003.04063	Supervised Domain Adaptation using Graph Embedding	Getting deep convolutional neural networks to perform well requires a large amount of training data. When the available labelled data is small, it is often beneficial to use transfer learning to leverage a related larger dataset (source) in order to improve the performance on the small dataset (target). Among the trans...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	167,442
2408.08189	FancyVideo: Towards Dynamic and Consistent Video Generation via Cross-frame Textual Guidance	Synthesizing motion-rich and temporally consistent videos remains a challenge in artificial intelligence, especially when dealing with extended durations. Existing text-to-video (T2V) models commonly employ spatial cross-attention for text control, equivalently guiding different frame generations without frame-specific...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	480,891
1909.11524	Dual Adaptive Pyramid Network for Cross-Stain Histopathology Image Segmentation	Supervised semantic segmentation normally assumes the test data being in a similar data domain as the training data. However, in practice, the domain mismatch between the training and unseen data could lead to a significant performance drop. Obtaining accurate pixel-wise label for images in different domains is tedious...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	146,836
1810.05075	Taming the Cross Entropy Loss	We present the Tamed Cross Entropy (TCE) loss function, a robust derivative of the standard Cross Entropy (CE) loss used in deep learning for classification tasks. However, unlike other robust losses, the TCE loss is designed to exhibit the same training properties than the CE loss in noiseless scenarios. Therefore, th...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	110,160
1911.09100	Gradient Method for Continuous Influence Maximization with Budget-Saving Considerations	Continuous influence maximization (CIM) generalizes the original influence maximization by incorporating general marketing strategies: a marketing strategy mix is a vector $\boldsymbol x = (x_1,\dots,x_d)$ such that for each node $v$ in a social network, $v$ could be activated as a seed of diffusion with probability $h...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	154,404
1502.06809	Optimal Linear and Cyclic Locally Repairable Codes over Small Fields	We consider locally repairable codes over small fields and propose constructions of optimal cyclic and linear codes in terms of the dimension for a given distance and length. Four new constructions of optimal linear codes over small fields with locality properties are developed. The first two approaches give binary cyc...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	40,524
2203.13722	Probing Pre-Trained Language Models for Cross-Cultural Differences in Values	Language embeds information about social, cultural, and political values people hold. Prior work has explored social and potentially harmful biases encoded in Pre-Trained Language models (PTLMs). However, there has been no systematic study investigating how values embedded in these models vary across cultures. In this ...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	287,740
2109.05105	Towards Zero-shot Commonsense Reasoning with Self-supervised Refinement of Language Models	Can we get existing language models and refine them for zero-shot commonsense reasoning? This paper presents an initial study exploring the feasibility of zero-shot commonsense reasoning for the Winograd Schema Challenge by formulating the task as self-supervised refinement of a pre-trained language model. In contrast ...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	254,657
1710.00974	A concatenating framework of shortcut convolutional neural networks	It is well accepted that convolutional neural networks play an important role in learning excellent features for image classification and recognition. However, in tradition they only allow adjacent layers connected, limiting integration of multi-scale information. To further improve their performance, we present a conc...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	81,948
2410.18607	STTATTS: Unified Speech-To-Text And Text-To-Speech Model	Speech recognition and speech synthesis models are typically trained separately, each with its own set of learning objectives, training data, and model parameters, resulting in two distinct large networks. We propose a parameter-efficient approach to learning ASR and TTS jointly via a multi-task learning objective and ...	false	false	true	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	501,955
2212.11363	Lightweight Monocular Depth Estimation	Monocular depth estimation can play an important role in addressing the issue of deriving scene geometry from 2D images. It has been used in a variety of industries, including robots, self-driving cars, scene comprehension, 3D reconstructions, and others. The goal of our method is to create a lightweight machine-learni...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	337,766
2011.09960	Mathematical comparison of classical and quantum mechanisms in optimization under local differential privacy	Let $\varepsilon>0$. An $n$-tuple $(p_i)_{i=1}^n$ of probability vectors is called $\varepsilon$-differentially private ($\varepsilon$-DP) if $e^\varepsilon p_j-p_i$ has no negative entries for all $i,j=1,\ldots,n$. An $n$-tuple $(\rho_i)_{i=1}^n$ of density matrices is called classical-quantum $\varepsilon$-differenti...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	207,374
2408.08792	Assessing Generalization Capabilities of Malaria Diagnostic Models from Thin Blood Smears	Malaria remains a significant global health challenge, necessitating rapid and accurate diagnostic methods. While computer-aided diagnosis (CAD) tools utilizing deep learning have shown promise, their generalization to diverse clinical settings remains poorly assessed. This study evaluates the generalization capabiliti...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	481,153
2410.02811	SAC-KG: Exploiting Large Language Models as Skilled Automatic Constructors for Domain Knowledge Graphs	Knowledge graphs (KGs) play a pivotal role in knowledge-intensive tasks across specialized domains, where the acquisition of precise and dependable knowledge is crucial. However, existing KG construction methods heavily rely on human intervention to attain qualified KGs, which severely hinders the practical applicabili...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	494,476

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