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
1307.5684	Using a Dynamic Neural Field Model to Explore a Direct Collicular Inhibition Account of Inhibition of Return	When the interval between a transient ash of light (a "cue") and a second visual response signal (a "target") exceeds at least 200ms, responding is slowest in the direction indicated by the first signal. This phenomenon is commonly referred to as inhibition of return (IOR). The dynamic neural field model (DNF) has prov...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	25,968
2108.04023	DRINet: A Dual-Representation Iterative Learning Network for Point Cloud Segmentation	We present a novel and flexible architecture for point cloud segmentation with dual-representation iterative learning. In point cloud processing, different representations have their own pros and cons. Thus, finding suitable ways to represent point cloud data structure while keeping its own internal physical property s...	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	249,863
2009.11732	A Unifying Review of Deep and Shallow Anomaly Detection	Deep learning approaches to anomaly detection have recently improved the state of the art in detection performance on complex datasets such as large collections of images or text. These results have sparked a renewed interest in the anomaly detection problem and led to the introduction of a great variety of new methods...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	197,241
2402.19197	Fine Structure-Aware Sampling: A New Sampling Training Scheme for Pixel-Aligned Implicit Models in Single-View Human Reconstruction	Pixel-aligned implicit models, such as PIFu, PIFuHD, and ICON, are used for single-view clothed human reconstruction. These models need to be trained using a sampling training scheme. Existing sampling training schemes either fail to capture thin surfaces (e.g. ears, fingers) or cause noisy artefacts in reconstructed m...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	433,714
2406.13144	DialSim: A Real-Time Simulator for Evaluating Long-Term Multi-Party Dialogue Understanding of Conversation Systems	Recent advancements in Large Language Models (LLMs) have significantly enhanced the capabilities of conversation systems, making them applicable to various fields (e.g., education). Despite their progress, the evaluation of the systems often overlooks the complexities of real-world conversations, such as real-time inte...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	465,713
1506.06442	A Deep Memory-based Architecture for Sequence-to-Sequence Learning	We propose DEEPMEMORY, a novel deep architecture for sequence-to-sequence learning, which performs the task through a series of nonlinear transformations from the representation of the input sequence (e.g., a Chinese sentence) to the final output sequence (e.g., translation to English). Inspired by the recently propose...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	true	false	false	44,420
2109.13486	Exploring Teacher-Student Learning Approach for Multi-lingual Speech-to-Intent Classification	End-to-end speech-to-intent classification has shown its advantage in harvesting information from both text and speech. In this paper, we study a technique to develop such an end-to-end system that supports multiple languages. To overcome the scarcity of multi-lingual speech corpus, we exploit knowledge from a pre-trai...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	257,653
2010.03855	Dense Relational Image Captioning via Multi-task Triple-Stream Networks	We introduce dense relational captioning, a novel image captioning task which aims to generate multiple captions with respect to relational information between objects in a visual scene. Relational captioning provides explicit descriptions for each relationship between object combinations. This framework is advantageou...	false	false	false	false	true	false	false	false	true	false	false	true	false	false	false	false	false	false	199,548
2402.00856	Towards Efficient Exact Optimization of Language Model Alignment	The alignment of language models with human preferences is vital for their application in real-world tasks. The problem is formulated as optimizing the model's policy to maximize the expected reward that reflects human preferences with minimal deviation from the initial policy. While considered as a straightforward sol...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	425,751
1912.11189	Computing the Number of Equivalent Classes on $\mathcal{R}(s,n)/\mathcal{R}(k,n)$	Affine equivalent classes of Boolean functions have many applications in modern cryptography and circuit design. Previous publications have shown that affine equivalence on the entire space of Boolean functions can be computed up to 10 variables, but not on the quotient Boolean function space modulo functions of differ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	158,502
1912.12191	Explain Your Move: Understanding Agent Actions Using Specific and Relevant Feature Attribution	As deep reinforcement learning (RL) is applied to more tasks, there is a need to visualize and understand the behavior of learned agents. Saliency maps explain agent behavior by highlighting the features of the input state that are most relevant for the agent in taking an action. Existing perturbation-based approaches ...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	158,795
2201.10899	Speeding up Heterogeneous Federated Learning with Sequentially Trained Superclients	Federated Learning (FL) allows training machine learning models in privacy-constrained scenarios by enabling the cooperation of edge devices without requiring local data sharing. This approach raises several challenges due to the different statistical distribution of the local datasets and the clients' computational he...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	277,132
2311.04161	Breaking the Heavy-Tailed Noise Barrier in Stochastic Optimization Problems	We consider stochastic optimization problems with heavy-tailed noise with structured density. For such problems, we show that it is possible to get faster rates of convergence than $\mathcal{O}(K^{-2(\alpha - 1)/\alpha})$, when the stochastic gradients have finite moments of order $\alpha \in (1, 2]$. In particular, ou...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	406,122
2405.12523	Single Image Unlearning: Efficient Machine Unlearning in Multimodal Large Language Models	Machine unlearning empowers individuals with the `right to be forgotten' by removing their private or sensitive information encoded in machine learning models. However, it remains uncertain whether MU can be effectively applied to Multimodal Large Language Models (MLLMs), particularly in scenarios of forgetting the lea...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	455,561
2401.05176	Convergences and Divergences between Automatic Assessment and Human Evaluation: Insights from Comparing ChatGPT-Generated Translation and Neural Machine Translation	Large language models have demonstrated parallel and even superior translation performance compared to neural machine translation (NMT) systems. However, existing comparative studies between them mainly rely on automated metrics, raising questions into the feasibility of these metrics and their alignment with human jud...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	420,669
1506.06221	Ranking the Importance Level of Intermediaries to a Criminal using a Reliance Measure	Recent research on finding important intermediate nodes in a network suspected to contain criminal activity is highly dependent on network centrality values. Betweenness centrality, for example, is widely used to rank the nodes that act as brokers in the shortest paths connecting all source and all the end nodes in a n...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	44,397
2401.10748	Fast gradient-free activation maximization for neurons in spiking neural networks	Elements of neural networks, both biological and artificial, can be described by their selectivity for specific cognitive features. Understanding these features is important for understanding the inner workings of neural networks. For a living system, such as a neuron, whose response to a stimulus is unknown and not di...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	true	false	false	422,757
2107.01477	Byzantine-robust Federated Learning through Spatial-temporal Analysis of Local Model Updates	Federated Learning (FL) enables multiple distributed clients (e.g., mobile devices) to collaboratively train a centralized model while keeping the training data locally on the client. Compared to traditional centralized machine learning, FL offers many favorable features such as offloading operations which would usuall...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	244,497
2102.08354	Topological Deep Learning: Classification Neural Networks	Topological deep learning is a formalism that is aimed at introducing topological language to deep learning for the purpose of utilizing the minimal mathematical structures to formalize problems that arise in a generic deep learning problem. This is the first of a sequence of articles with the purpose of introducing an...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	220,421
2212.07035	MA-GCL: Model Augmentation Tricks for Graph Contrastive Learning	Contrastive learning (CL), which can extract the information shared between different contrastive views, has become a popular paradigm for vision representation learning. Inspired by the success in computer vision, recent work introduces CL into graph modeling, dubbed as graph contrastive learning (GCL). However, gener...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	336,279
2209.03116	A New Method for the High-Precision Assessment of Tumor Changes in Response to Treatment	Imaging demonstrates that preclinical and human tumors are heterogeneous, i.e. a single tumor can exhibit multiple regions that behave differently during both normal development and also in response to treatment. The large variations observed in control group tumors can obscure detection of significant therapeutic effe...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	316,405
1205.1813	Graph spectra and the detectability of community structure in networks	We study networks that display community structure -- groups of nodes within which connections are unusually dense. Using methods from random matrix theory, we calculate the spectra of such networks in the limit of large size, and hence demonstrate the presence of a phase transition in matrix methods for community dete...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	15,861
2312.14751	Hazards from Increasingly Accessible Fine-Tuning of Downloadable Foundation Models	Public release of the weights of pretrained foundation models, otherwise known as downloadable access \citep{solaiman_gradient_2023}, enables fine-tuning without the prohibitive expense of pretraining. Our work argues that increasingly accessible fine-tuning of downloadable models may increase hazards. First, we highli...	false	false	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	417,740
2006.03680	Evaluating the Disentanglement of Deep Generative Models through Manifold Topology	Learning disentangled representations is regarded as a fundamental task for improving the generalization, robustness, and interpretability of generative models. However, measuring disentanglement has been challenging and inconsistent, often dependent on an ad-hoc external model or specific to a certain dataset. To addr...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	180,394
2111.03009	Computation of Input Disturbance Sets for Constrained Output Reachability	Linear models with additive unknown-but-bounded input disturbances are extensively used to model uncertainty in robust control systems design. Typically, the disturbance set is either assumed to be known a priori or estimated from data through set-membership identification. However, the problem of computing a suitable ...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	265,026
1211.1250	Detection-Directed Sparse Estimation using Bayesian Hypothesis Test and Belief Propagation	In this paper, we propose a sparse recovery algorithm called detection-directed (DD) sparse estimation using Bayesian hypothesis test (BHT) and belief propagation (BP). In this framework, we consider the use of sparse-binary sensing matrices which has the tree-like property and the sampled-message approach for the impl...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	19,589
2309.15065	Language-EXtended Indoor SLAM (LEXIS): A Versatile System for Real-time Visual Scene Understanding	Versatile and adaptive semantic understanding would enable autonomous systems to comprehend and interact with their surroundings. Existing fixed-class models limit the adaptability of indoor mobile and assistive autonomous systems. In this work, we introduce LEXIS, a real-time indoor Simultaneous Localization and Mappi...	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	394,836
1904.02795	Generalized Lazy Search for Robot Motion Planning: Interleaving Search and Edge Evaluation via Event-based Toggles	Lazy search algorithms can efficiently solve problems where edge evaluation is the bottleneck in computation, as is the case for robotic motion planning. The optimal algorithm in this class, LazySP, lazily restricts edge evaluation to only the shortest path. Doing so comes at the expense of search effort, i.e., LazySP ...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	126,525
2208.05142	Plug-and-Play Model-Agnostic Counterfactual Policy Synthesis for Deep Reinforcement Learning based Recommendation	Recent advances in recommender systems have proved the potential of Reinforcement Learning (RL) to handle the dynamic evolution processes between users and recommender systems. However, learning to train an optimal RL agent is generally impractical with commonly sparse user feedback data in the context of recommender s...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	312,329
2207.08224	Learning with Recoverable Forgetting	Life-long learning aims at learning a sequence of tasks without forgetting the previously acquired knowledge. However, the involved training data may not be life-long legitimate due to privacy or copyright reasons. In practical scenarios, for instance, the model owner may wish to enable or disable the knowledge of spec...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	308,509
2301.09937	Explainable Deep Reinforcement Learning: State of the Art and Challenges	Interpretability, explainability and transparency are key issues to introducing Artificial Intelligence methods in many critical domains: This is important due to ethical concerns and trust issues strongly connected to reliability, robustness, auditability and fairness, and has important consequences towards keeping th...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	341,653
1806.01650	A Possibility Distribution Based Multi-Criteria Decision Algorithm for Resilient Supplier Selection Problems	Thus far, limited research has been performed on resilient supplier selection - a problem that requires simultaneous consideration of a set of numerical and linguistic evaluation criteria, which are substantially different from traditional supplier selection problem. Essentially, resilient supplier selection entails ke...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	99,599
2410.15158	Automated Segmentation and Analysis of Cone Photoreceptors in Multimodal Adaptive Optics Imaging	Accurate detection and segmentation of cone cells in the retina are essential for diagnosing and managing retinal diseases. In this study, we used advanced imaging techniques, including confocal and non-confocal split detector images from adaptive optics scanning light ophthalmoscopy (AOSLO), to analyze photoreceptors ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	500,395
2202.01470	Towards 3D Scene Reconstruction from Locally Scale-Aligned Monocular Video Depth	Existing monocular depth estimation methods have achieved excellent robustness in diverse scenes, but they can only retrieve affine-invariant depth, up to an unknown scale and shift. However, in some video-based scenarios such as video depth estimation and 3D scene reconstruction from a video, the unknown scale and shi...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	278,494
2411.16447	Model-based reinforcement corrosion prediction: Continuous calibration with Bayesian optimization and corrosion wire sensor data	Chloride-induced corrosion significantly contributes to the degradation of reinforced concrete structures, making accurate predictions of chloride migration and its effects on material durability critical. This paper explores two modeling approaches to estimate the effective diffusion coefficient for chloride transport...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	511,030
cs/0104018	Several new domain-type and boundary-type numerical discretization schemes with radial basis function	This paper is concerned with a few novel RBF-based numerical schemes discretizing partial differential equations. For boundary-type methods, we derive the indirect and direct symmetric boundary knot methods (BKM). The resulting interpolation matrix of both is always symmetric irrespective of boundary geometry and condi...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	537,319
2106.15649	Multi-Scale Spectrogram Modelling for Neural Text-to-Speech	We propose a novel Multi-Scale Spectrogram (MSS) modelling approach to synthesise speech with an improved coarse and fine-grained prosody. We present a generic multi-scale spectrogram prediction mechanism where the system first predicts coarser scale mel-spectrograms that capture the suprasegmental information in speec...	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	243,825
2312.02607	Projective Space Stern Decoding and Application to SDitH	We show that here standard decoding algorithms for generic linear codes over a finite field can speeded up by a factor which is essentially the size of the finite field by reducing it to a low weight codeword problem and working in the relevant projective space. We apply this technique to SDitH and show that the parame...	false	false	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	412,942
1703.01168	Sum-set Inequalities from Aligned Image Sets: Instruments for Robust GDoF Bounds	We present sum-set inequalities specialized to the generalized degrees of freedom (GDoF) framework. These are information theoretic lower bounds on the entropy of bounded density linear combinations of discrete, power-limited dependent random variables in terms of the joint entropies of arbitrary linear combinations of...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	69,306
2305.15842	Text-to-Motion Retrieval: Towards Joint Understanding of Human Motion Data and Natural Language	Due to recent advances in pose-estimation methods, human motion can be extracted from a common video in the form of 3D skeleton sequences. Despite wonderful application opportunities, effective and efficient content-based access to large volumes of such spatio-temporal skeleton data still remains a challenging problem....	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	367,808
2303.11634	Deep Q-Network Based Decision Making for Autonomous Driving	Currently decision making is one of the biggest challenges in autonomous driving. This paper introduces a method for safely navigating an autonomous vehicle in highway scenarios by combining deep Q-Networks and insight from control theory. A Deep Q-Network is trained in simulation to serve as a central decision-making ...	false	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	352,935
0803.0146	Polynomial time algorithms for bi-criteria, multi-objective and ratio problems in clustering and imaging. Part I: Normalized cut and ratio regions	Partitioning and grouping of similar objects plays a fundamental role in image segmentation and in clustering problems. In such problems a typical goal is to group together similar objects, or pixels in the case of image processing. At the same time another goal is to have each group distinctly dissimilar from the rest...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	1,379
1005.4292	Application Of Fuzzy System In Segmentation Of MRI Brain Tumor	Segmentation of images holds an important position in the area of image processing. It becomes more important whi le typically dealing with medical images where presurgery and post surgery decisions are required for the purpose of initiating and speeding up the recovery process. Segmentation of 3-D tumor structures fro...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	6,553
1507.02449	Finding trends and statistical patterns in name mentions in news	We extract the individual names of persons mentioned in news reports from a Philippine-based daily in the English language from 2010-2012. Names are extracted using a learning algorithm that filters adjacent capitalized words and runs it through a database of non-names grown through training. The number of mentions of ...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	44,989
2402.15039	Descripci\'on autom\'atica de secciones delgadas de rocas: una aplicaci\'on Web	The identification and characterization of various rock types is one of the fundamental activities for geology and related areas such as mining, petroleum, environment, industry and construction. Traditionally, a human specialist is responsible for analyzing and explaining details about the type, composition, texture, ...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	431,965
2310.00310	An easy zero-shot learning combination: Texture Sensitive Semantic Segmentation IceHrNet and Advanced Style Transfer Learning Strategy	We proposed an easy method of Zero-Shot semantic segmentation by using style transfer. In this case, we successfully used a medical imaging dataset (Blood Cell Imagery) to train a model for river ice semantic segmentation. First, we built a river ice semantic segmentation dataset IPC_RI_SEG using a fixed camera and cov...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	395,924
1912.13002	Opytimizer: A Nature-Inspired Python Optimizer	Optimization aims at selecting a feasible set of parameters in an attempt to solve a particular problem, being applied in a wide range of applications, such as operations research, machine learning fine-tuning, and control engineering, among others. Nevertheless, traditional iterative optimization methods use the evalu...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	158,987
1812.07868	Crack Detection Using Enhanced Thresholding on UAV based Collected Images	This paper proposes a thresholding approach for crack detection in an unmanned aerial vehicle (UAV) based infrastructure inspection system. The proposed algorithm performs recursively on the intensity histogram of UAV-taken images to exploit their crack-pixels appearing at the low intensity interval. A quantified crite...	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	116,894
1307.7562	On the convergence of weighted-average consensus	In this note we give sufficient conditions for the convergence of the iterative algorithm called weighted-average consensus in directed graphs. We study the discrete-time form of this algorithm. We use standard techniques from matrix theory to prove the main result. As a particular case one can obtain well-known result...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	26,119
2209.09898	Text2Light: Zero-Shot Text-Driven HDR Panorama Generation	High-quality HDRIs(High Dynamic Range Images), typically HDR panoramas, are one of the most popular ways to create photorealistic lighting and 360-degree reflections of 3D scenes in graphics. Given the difficulty of capturing HDRIs, a versatile and controllable generative model is highly desired, where layman users can...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	318,675
1612.08872	Context-Free Path Querying with Structural Representation of Result	Graph data model and graph databases are very popular in various areas such as bioinformatics, semantic web, and social networks. One specific problem in the area is a path querying with constraints formulated in terms of formal grammars. The query in this approach is written as grammar, and paths querying is graph par...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	66,126
2205.13921	Federated Semi-Supervised Learning with Prototypical Networks	With the increasing computing power of edge devices, Federated Learning (FL) emerges to enable model training without privacy concerns. The majority of existing studies assume the data are fully labeled on the client side. In practice, however, the amount of labeled data is often limited. Recently, federated semi-super...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	299,140
2201.00649	SAE: Sequential Anchored Ensembles	Computing the Bayesian posterior of a neural network is a challenging task due to the high-dimensionality of the parameter space. Anchored ensembles approximate the posterior by training an ensemble of neural networks on anchored losses designed for the optima to follow the Bayesian posterior. Training an ensemble, how...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	274,025
2410.07658	SeMv-3D: Towards Semantic and Mutil-view Consistency simultaneously for General Text-to-3D Generation with Triplane Priors	Recent advancements in generic 3D content generation from text prompts have been remarkable by fine-tuning text-to-image diffusion (T2I) models or employing these T2I models as priors to learn a general text-to-3D model. While fine-tuning-based methods ensure great alignment between text and generated views, i.e., sema...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	496,733
2204.02281	Design Guidelines for Inclusive Speaker Verification Evaluation Datasets	Speaker verification (SV) provides billions of voice-enabled devices with access control, and ensures the security of voice-driven technologies. As a type of biometrics, it is necessary that SV is unbiased, with consistent and reliable performance across speakers irrespective of their demographic, social and economic a...	false	false	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	289,884
1609.00461	Network clustering and community detection using modulus of families of loops	We study the structure of loops in networks using the notion of modulus of loop families. We introduce a new measure of network clustering by quantifying the richness of families of (simple) loops. Modulus tries to minimize the expected overlap among loops by spreading the expected link-usage optimally. We propose weig...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	60,481
2011.01417	Non-Equilibrium Skewness, Market Crises, and Option Pricing: Non-Linear Langevin Model of Markets with Supersymmetry	This paper presents a tractable model of non-linear dynamics of market returns using a Langevin approach. Due to non-linearity of an interaction potential, the model admits regimes of both small and large return fluctuations. Langevin dynamics are mapped onto an equivalent quantum mechanical (QM) system. Borrowing idea...	false	true	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	204,573
2105.09179	On Interpretation and Measurement of Soft Attributes for Recommendation	We address how to robustly interpret natural language refinements (or critiques) in recommender systems. In particular, in human-human recommendation settings people frequently use soft attributes to express preferences about items, including concepts like the originality of a movie plot, the noisiness of a venue, or t...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	235,994
2305.06563	Spatiotemporal Regularized Tucker Decomposition Approach for Traffic Data Imputation	In intelligent transportation systems, traffic data imputation, estimating the missing value from partially observed data is an inevitable and challenging task. Previous studies have not fully considered traffic data's multidimensionality and spatiotemporal correlations, but they are vital to traffic data recovery, esp...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	363,584
2405.00819	ICU Bloodstream Infection Prediction: A Transformer-Based Approach for EHR Analysis	We introduce RatchetEHR, a novel transformer-based framework designed for the predictive analysis of electronic health records (EHR) data in intensive care unit (ICU) settings, with a specific focus on bloodstream infection (BSI) prediction. Leveraging the MIMIC-IV dataset, RatchetEHR demonstrates superior predictive p...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	451,090
2403.10100	Efficient Multiplayer Battle Game Optimizer for Adversarial Robust Neural Architecture Search	This paper introduces a novel metaheuristic algorithm, known as the efficient multiplayer battle game optimizer (EMBGO), specifically designed for addressing complex numerical optimization tasks. The motivation behind this research stems from the need to rectify identified shortcomings in the original MBGO, particularl...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	438,055
1909.12196	Deep Video Deblurring: The Devil is in the Details	Video deblurring for hand-held cameras is a challenging task, since the underlying blur is caused by both camera shake and object motion. State-of-the-art deep networks exploit temporal information from neighboring frames, either by means of spatio-temporal transformers or by recurrent architectures. In contrast to the...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	147,049
2412.16160	Online High-Frequency Trading Stock Forecasting with Automated Feature Clustering and Radial Basis Function Neural Networks	This study presents an autonomous experimental machine learning protocol for high-frequency trading (HFT) stock price forecasting that involves a dual competitive feature importance mechanism and clustering via shallow neural network topology for fast training. By incorporating the k-means algorithm into the radial bas...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	519,382
2203.04044	Single-trajectory map equation	Community detection, the process of identifying module structures in complex systems represented on networks, is an effective tool in various fields of science. The map equation, which is an information-theoretic framework based on the random walk on a network, is a particularly popular community detection method. Desp...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	284,331
2204.11707	Optimal security hardening over a probabilistic attack graph: a case study of an industrial control system using the CySecTool tool	CySecTool is a tool that finds a cost-optimal security controls portfolio in a given budget for a probabilistic attack graph. A portfolio is a set of counter-measures, or controls, against vulnerabilities adopted for a computer system, while an attack graph is a type of a threat scenario model. In an attack graph, node...	false	false	false	false	false	false	false	false	false	false	true	false	true	false	false	false	false	false	293,238
1511.02093	Evaluation of the Hamming weights of a class of linear codes based on Gauss sums	Linear codes with a few weights have been widely investigated in recent years. In this paper, we mainly use Gauss sums to represent the Hamming weights of a class of $q$-ary linear codes under some certain conditions, where $q$ is a power of a prime. The lower bound of its minimum Hamming distance is obtained. In some ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	48,582
1103.4012	On the accuracy of language trees	Historical linguistics aims at inferring the most likely language phylogenetic tree starting from information concerning the evolutionary relatedness of languages. The available information are typically lists of homologous (lexical, phonological, syntactic) features or characters for many different languages. From t...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	9,695
1909.07587	A Hybrid Deep Learning Approach for Diagnosis of the Erythemato-Squamous Disease	The diagnosis of the Erythemato-squamous disease (ESD) is accepted as a difficult problem in dermatology. ESD is a form of skin disease. It generally causes redness of the skin and also may cause loss of skin. They are generally due to genetic or environmental factors. ESD comprises six classes of skin conditions namel...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	145,711
2401.02702	VoxelNextFusion: A Simple, Unified and Effective Voxel Fusion Framework for Multi-Modal 3D Object Detection	LiDAR-camera fusion can enhance the performance of 3D object detection by utilizing complementary information between depth-aware LiDAR points and semantically rich images. Existing voxel-based methods face significant challenges when fusing sparse voxel features with dense image features in a one-to-one manner, result...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	419,814
2112.09366	Scenario-Based Safety Assessment Framework for Automated Vehicles	Automated vehicles (AVs) are expected to increase traffic safety and traffic efficiency, among others by enabling flexible mobility-on-demand systems. This is particularly important in Singapore, being one of the world's most densely populated countries, which is why the Singaporean authorities are currently actively f...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	272,126
2012.01665	Dual-Branch Network with Dual-Sampling Modulated Dice Loss for Hard Exudate Segmentation from Colour Fundus Images	Automated segmentation of hard exudates in colour fundus images is a challenge task due to issues of extreme class imbalance and enormous size variation. This paper aims to tackle these issues and proposes a dual-branch network with dual-sampling modulated Dice loss. It consists of two branches: large hard exudate bias...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	209,485
2408.05502	GEM: Context-Aware Gaze EstiMation with Visual Search Behavior Matching for Chest Radiograph	Gaze estimation is pivotal in human scene comprehension tasks, particularly in medical diagnostic analysis. Eye-tracking technology facilitates the recording of physicians' ocular movements during image interpretation, thereby elucidating their visual attention patterns and information-processing strategies. In this pa...	false	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	479,818
2406.16151	Monte Carlo Planning for Stochastic Control on Constrained Markov Decision Processes	In the world of stochastic control, especially in economics and engineering, Markov Decision Processes (MDPs) can effectively model various stochastic decision processes, from asset management to transportation optimization. These underlying MDPs, upon closer examination, often reveal a specifically constrained causal ...	false	false	false	false	true	false	true	false	false	false	true	false	false	false	false	false	false	false	467,018
2305.10744	Online Resource Allocation in Episodic Markov Decision Processes	This paper studies a long-term resource allocation problem over multiple periods where each period requires a multi-stage decision-making process. We formulate the problem as an online allocation problem in an episodic finite-horizon constrained Markov decision process with an unknown non-stationary transition function...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	365,212
2311.14390	Directly Attention Loss Adjusted Prioritized Experience Replay	Prioritized Experience Replay (PER) enables the model to learn more about relatively important samples by artificially changing their accessed frequencies. However, this non-uniform sampling method shifts the state-action distribution that is originally used to estimate Q-value functions, which brings about the estimat...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	410,092
2112.14949	Decentralized Optimization Over the Stiefel Manifold by an Approximate Augmented Lagrangian Function	In this paper, we focus on the decentralized optimization problem over the Stiefel manifold, which is defined on a connected network of $d$ agents. The objective is an average of $d$ local functions, and each function is privately held by an agent and encodes its data. The agents can only communicate with their neighbo...	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	273,661
2211.02940	Effective Audio Classification Network Based on Paired Inverse Pyramid Structure and Dense MLP Block	Recently, massive architectures based on Convolutional Neural Network (CNN) and self-attention mechanisms have become necessary for audio classification. While these techniques are state-of-the-art, these works' effectiveness can only be guaranteed with huge computational costs and parameters, large amounts of data aug...	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	328,761
2311.06834	Osteoporosis Prediction from Hand and Wrist X-rays using Image Segmentation and Self-Supervised Learning	Osteoporosis is a widespread and chronic metabolic bone disease that often remains undiagnosed and untreated due to limited access to bone mineral density (BMD) tests like Dual-energy X-ray absorptiometry (DXA). In response to this challenge, current advancements are pivoting towards detecting osteoporosis by examining...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	407,096
2110.12786	Dictionary Learning Using Rank-One Atomic Decomposition (ROAD)	Dictionary learning aims at seeking a dictionary under which the training data can be sparsely represented. Methods in the literature typically formulate the dictionary learning problem as an optimization w.r.t. two variables, i.e., dictionary and sparse coefficients, and solve it by alternating between two stages: spa...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	262,968
2405.13686	Embedding Generalized Semantic Knowledge into Few-Shot Remote Sensing Segmentation	Few-shot segmentation (FSS) for remote sensing (RS) imagery leverages supporting information from limited annotated samples to achieve query segmentation of novel classes. Previous efforts are dedicated to mining segmentation-guiding visual cues from a constrained set of support samples. However, they still struggle to...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	456,036
1812.07069	An Atari Model Zoo for Analyzing, Visualizing, and Comparing Deep Reinforcement Learning Agents	Much human and computational effort has aimed to improve how deep reinforcement learning algorithms perform on benchmarks such as the Atari Learning Environment. Comparatively less effort has focused on understanding what has been learned by such methods, and investigating and comparing the representations learned by d...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	116,737
2309.15442	Template Model Inspired Task Space Learning for Robust Bipedal Locomotion	This work presents a hierarchical framework for bipedal locomotion that combines a Reinforcement Learning (RL)-based high-level (HL) planner policy for the online generation of task space commands with a model-based low-level (LL) controller to track the desired task space trajectories. Different from traditional end-t...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	394,969
2003.02542	Efficient and Effective Similar Subtrajectory Search with Deep Reinforcement Learning	Similar trajectory search is a fundamental problem and has been well studied over the past two decades. However, the similar subtrajectory search (SimSub) problem, aiming to return a portion of a trajectory (i.e., a subtrajectory) which is the most similar to a query trajectory, has been mostly disregarded despite that...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	true	false	166,964
2409.12390	A Novel Perspective for Multi-modal Multi-label Skin Lesion Classification	The efficacy of deep learning-based Computer-Aided Diagnosis (CAD) methods for skin diseases relies on analyzing multiple data modalities (i.e., clinical+dermoscopic images, and patient metadata) and addressing the challenges of multi-label classification. Current approaches tend to rely on limited multi-modal techniqu...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	489,562
1911.01486	Probabilistic Super-Resolution of Solar Magnetograms: Generating Many Explanations and Measuring Uncertainties	Machine learning techniques have been successfully applied to super-resolution tasks on natural images where visually pleasing results are sufficient. However in many scientific domains this is not adequate and estimations of errors and uncertainties are crucial. To address this issue we propose a Bayesian framework th...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	152,110
2006.07063	Privacy Against Adversarial Classification in Cyber-Physical Systems	For a class of Cyber-Physical Systems (CPSs), we address the problem of performing computations over the cloud without revealing private information about the structure and operation of the system. We model CPSs as a collection of input-output dynamical systems (the system operation modes). Depending on the mode the sy...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	181,675
2208.06734	An Answer Verbalization Dataset for Conversational Question Answerings over Knowledge Graphs	We introduce a new dataset for conversational question answering over Knowledge Graphs (KGs) with verbalized answers. Question answering over KGs is currently focused on answer generation for single-turn questions (KGQA) or multiple-tun conversational question answering (ConvQA). However, in a real-world scenario (e.g....	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	312,809
1812.10422	Machine Learning in Official Statistics	In the first half of 2018, the Federal Statistical Office of Germany (Destatis) carried out a "Proof of Concept Machine Learning" as part of its Digital Agenda. A major component of this was surveys on the use of machine learning methods in official statistics, which were conducted at selected national and internationa...	false	false	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	117,363
2406.09982	Constrained Motion Planning for a Robotic Endoscope Holder based on Hierarchical Quadratic Programming	Minimally Invasive Surgeries (MIS) are challenging for surgeons due to the limited field of view and constrained range of motion imposed by narrow access ports. These challenges can be addressed by robot-assisted endoscope systems which provide precise and stabilized positioning, as well as constrained and smooth motio...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	464,175
2011.13417	Generative Layout Modeling using Constraint Graphs	We propose a new generative model for layout generation. We generate layouts in three steps. First, we generate the layout elements as nodes in a layout graph. Second, we compute constraints between layout elements as edges in the layout graph. Third, we solve for the final layout using constrained optimization. For th...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	208,467
2103.09666	Multimodal End-to-End Sparse Model for Emotion Recognition	Existing works on multimodal affective computing tasks, such as emotion recognition, generally adopt a two-phase pipeline, first extracting feature representations for each single modality with hand-crafted algorithms and then performing end-to-end learning with the extracted features. However, the extracted features a...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	225,226
1407.2232	Toward Using Surrogates to Accelerate Solution of Stochastic Electricity Grid Operations Problems	Stochastic unit commitment models typically handle uncertainties in forecast demand by considering a finite number of realizations from a stochastic process model for loads. Accurate evaluations of expectations or higher moments for the quantities of interest require a prohibitively large number of model evaluations. I...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	34,512
1910.14377	Image-Guided Depth Upsampling via Hessian and TV Priors	We propose a method that combines sparse depth (LiDAR) measurements with an intensity image and to produce a dense high-resolution depth image. As there are few, but accurate, depth measurements from the scene, our method infers the remaining depth values by incorporating information from the intensity image, namely th...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	151,633
2306.14901	Phonon dynamic behaviors induced by amorphous interlayer at heterointerfaces	Interface impedes heat flow in heterostructures and the interfacial thermal resistance (ITR) has become a critical issue for thermal dissipation in electronic devices. To explore the mechanism leading to the ITR, in this work, the dynamic behaviors of phonons passing through the GaN/AlN interface with an amorphous inte...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	375,855
2304.10663	Meta Semantics: Towards better natural language understanding and reasoning	Natural language understanding is one of the most challenging topics in artificial intelligence. Deep neural network methods, particularly large language module (LLM) methods such as ChatGPT and GPT-3, have powerful flexibility to adopt informal text but are weak on logical deduction and suffer from the out-of-vocabula...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	359,500
2012.11486	Leaf Segmentation and Counting with Deep Learning: on Model Certainty, Test-Time Augmentation, Trade-Offs	Plant phenotyping tasks such as leaf segmentation and counting are fundamental to the study of phenotypic traits. Since it is well-suited for these tasks, deep supervised learning has been prevalent in recent works proposing better performing models at segmenting and counting leaves. Despite good efforts from research ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	212,650
2401.02135	PosCUDA: Position based Convolution for Unlearnable Audio Datasets	Deep learning models require large amounts of clean data to acheive good performance. To avoid the cost of expensive data acquisition, researchers use the abundant data available on the internet. This raises significant privacy concerns on the potential misuse of personal data for model training without authorisation. ...	false	false	true	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	419,614
2411.10614	To Shuffle or not to Shuffle: Auditing DP-SGD with Shuffling	Differentially Private Stochastic Gradient Descent (DP-SGD) is a popular method for training machine learning models with formal Differential Privacy (DP) guarantees. As DP-SGD processes the training data in batches, it uses Poisson sub-sampling to select batches at each step. However, due to computational and compatib...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	508,715
2407.10543	Understanding the Dependence of Perception Model Competency on Regions in an Image	While deep neural network (DNN)-based perception models are useful for many applications, these models are black boxes and their outputs are not yet well understood. To confidently enable a real-world, decision-making system to utilize such a perception model without human intervention, we must enable the system to rea...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	473,026
2403.18393	Tensor-based Graph Learning with Consistency and Specificity for Multi-view Clustering	In the context of multi-view clustering, graph learning is recognized as a crucial technique, which generally involves constructing an adaptive neighbor graph based on probabilistic neighbors, and then learning a consensus graph to for clustering. However, they are confronted with two limitations. Firstly, they often r...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	441,920

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