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
2208.07864	BERTifying Sinhala -- A Comprehensive Analysis of Pre-trained Language Models for Sinhala Text Classification	This research provides the first comprehensive analysis of the performance of pre-trained language models for Sinhala text classification. We test on a set of different Sinhala text classification tasks and our analysis shows that out of the pre-trained multilingual models that include Sinhala (XLM-R, LaBSE, and LASER)...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	313,179
1707.03739	Conflict Analysis for Pythagorean Fuzzy Information Systems with Group Decision Making	Pythagorean fuzzy sets provide stronger ability than intuitionistic fuzzy sets to model uncertainty information and knowledge, but little effort has been paid to conflict analysis of Pythagorean fuzzy information systems. In this paper, we present three types of positive, central, and negative alliances with different ...	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	false	false	76,925
1805.12118	One-at-a-time: A Meta-Learning Recommender-System for Recommendation-Algorithm Selection on Micro Level	The effectiveness of recommendation algorithms is typically assessed with evaluation metrics such as root mean square error, F1, or click through rates, calculated over entire datasets. The best algorithm is typically chosen based on these overall metrics. However, there is no single-best algorithm for all users, items...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	99,099
2107.07179	Conflict-free Cooperation Method for Connected and Automated Vehicles at Unsignalized Intersections: Graph-based Modeling and Optimality Analysis	Connected and automated vehicles have shown great potential in improving traffic mobility and reducing emissions, especially at unsignalized intersections. Previous research has shown that vehicle passing order is the key influencing factor in improving intersection traffic mobility. In this paper, we propose a graph-b...	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	246,341
2305.04027	Autonomous Navigation for Robot-assisted Intraluminal and Endovascular Procedures: A Systematic Review	Increased demand for less invasive procedures has accelerated the adoption of Intraluminal Procedures (IP) and Endovascular Interventions (EI) performed through body lumens and vessels. As navigation through lumens and vessels is quite complex, interest grows to establish autonomous navigation techniques for IP and EI ...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	362,605
1611.01142	Using a Deep Reinforcement Learning Agent for Traffic Signal Control	Ensuring transportation systems are efficient is a priority for modern society. Technological advances have made it possible for transportation systems to collect large volumes of varied data on an unprecedented scale. We propose a traffic signal control system which takes advantage of this new, high quality data, with...	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	63,327
1412.4957	Network connectivity in non-convex domains with reflections	Recent research has demonstrated the importance of boundary effects on the overall connection probability of wireless networks but has largely focused on convex deployment regions. We consider here a scenario of practical importance to wireless communications, in which one or more nodes are located outside the convex s...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	38,441
2001.04362	Multi-Source Domain Adaptation for Text Classification via DistanceNet-Bandits	Domain adaptation performance of a learning algorithm on a target domain is a function of its source domain error and a divergence measure between the data distribution of these two domains. We present a study of various distance-based measures in the context of NLP tasks, that characterize the dissimilarity between do...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	160,236
2401.08741	Fixed Point Diffusion Models	We introduce the Fixed Point Diffusion Model (FPDM), a novel approach to image generation that integrates the concept of fixed point solving into the framework of diffusion-based generative modeling. Our approach embeds an implicit fixed point solving layer into the denoising network of a diffusion model, transforming ...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	422,019
2409.15309	Joint LOS Identification and Data Association for 6G-Enabled Networked Device-Free Sensing	This paper considers networked device-free sensing in an orthogonal frequency division multiplexing (OFDM) cellular system with multipath environment, where the passive targets reflect the downlink signals to the base stations (BSs) via non-line-of-sight (NLOS) paths and/or line-of-sight (LOS) paths, and the BSs share ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	490,850
2307.12797	Causal Fair Machine Learning via Rank-Preserving Interventional Distributions	A decision can be defined as fair if equal individuals are treated equally and unequals unequally. Adopting this definition, the task of designing machine learning (ML) models that mitigate unfairness in automated decision-making systems must include causal thinking when introducing protected attributes: Following a re...	false	false	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	381,384
2206.06575	Med-DANet: Dynamic Architecture Network for Efficient Medical Volumetric Segmentation	For 3D medical image (e.g. CT and MRI) segmentation, the difficulty of segmenting each slice in a clinical case varies greatly. Previous research on volumetric medical image segmentation in a slice-by-slice manner conventionally use the identical 2D deep neural network to segment all the slices of the same case, ignori...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	302,418
2209.14945	Asynchronous Correspondences Between Hybrid Trajectory Semantics	We formalize the semantics of hybrid systems as sets of hybrid trajectories, including those generated by an hybrid transition system. We study the abstraction of hybrid trajectory semantics for verification, static analysis, and refinement. We mainly consider abstractions of hybrid semantics which establish a correspo...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	true	320,407
2405.12356	Coarse-graining conformational dynamics with multi-dimensional generalized Langevin equation: how, when, and why	A data-driven ab initio generalized Langevin equation (AIGLE) approach is developed to learn and simulate high-dimensional, heterogeneous, coarse-grained conformational dynamics. Constrained by the fluctuation-dissipation theorem, the approach can build coarse-grained models in dynamical consistency with all-atom molec...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	455,486
2402.09714	An Accelerated Distributed Stochastic Gradient Method with Momentum	In this paper, we introduce an accelerated distributed stochastic gradient method with momentum for solving the distributed optimization problem, where a group of $n$ agents collaboratively minimize the average of the local objective functions over a connected network. The method, termed ``Distributed Stochastic Moment...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	true	429,643
1605.02766	LightNet: A Versatile, Standalone Matlab-based Environment for Deep Learning	LightNet is a lightweight, versatile and purely Matlab-based deep learning framework. The idea underlying its design is to provide an easy-to-understand, easy-to-use and efficient computational platform for deep learning research. The implemented framework supports major deep learning architectures such as Multilayer P...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	true	false	false	55,669
2311.05230	ConRad: Image Constrained Radiance Fields for 3D Generation from a Single Image	We present a novel method for reconstructing 3D objects from a single RGB image. Our method leverages the latest image generation models to infer the hidden 3D structure while remaining faithful to the input image. While existing methods obtain impressive results in generating 3D models from text prompts, they do not p...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	406,518
2307.11650	Alleviating the Long-Tail Problem in Conversational Recommender Systems	Conversational recommender systems (CRS) aim to provide the recommendation service via natural language conversations. To develop an effective CRS, high-quality CRS datasets are very crucial. However, existing CRS datasets suffer from the long-tail issue, \ie a large proportion of items are rarely (or even never) menti...	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	false	false	380,983
2412.20034	Maintain Plasticity in Long-timescale Continual Test-time Adaptation	Continual test-time domain adaptation (CTTA) aims to adjust pre-trained source models to perform well over time across non-stationary target environments. While previous methods have made considerable efforts to optimize the adaptation process, a crucial question remains: can the model adapt to continually-changing env...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	521,067
1204.3436	Explaining Adaptation in Genetic Algorithms With Uniform Crossover: The Hyperclimbing Hypothesis	The hyperclimbing hypothesis is a hypothetical explanation for adaptation in genetic algorithms with uniform crossover (UGAs). Hyperclimbing is an intuitive, general-purpose, non-local search heuristic applicable to discrete product spaces with rugged or stochastic cost functions. The strength of this heuristic lie in ...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	false	false	15,494
1703.10667	TS-LSTM and Temporal-Inception: Exploiting Spatiotemporal Dynamics for Activity Recognition	Recent two-stream deep Convolutional Neural Networks (ConvNets) have made significant progress in recognizing human actions in videos. Despite their success, methods extending the basic two-stream ConvNet have not systematically explored possible network architectures to further exploit spatiotemporal dynamics within v...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	70,953
1704.05296	Coverage and Rate of Downlink Sequence Transmissions with Reliability Guarantees	Real-time distributed control is a promising application of 5G in which communication links should satisfy certain reliability guarantees. In this letter, we derive closed-form maximum average rate when a device (e.g. industrial machine) downloads a sequence of n operational commands through cellular connection, while ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	71,981
0903.4526	On the Achievable Rate of the Fading Dirty Paper Channel with Imperfect CSIT	The problem of dirty paper coding (DPC) over the (multi-antenna) fading dirty paper channel (FDPC) Y = H(X + S) + Z is considered when there is imperfect knowledge of the channel state information H at the transmitter (CSIT). The case of FDPC with positive definite (p.d.) input covariance matrix was studied by the auth...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	3,417
2403.14172	Lane level joint control of off-ramp and main line speed guidance on expressway in rainy weather	In the upstream of the exit ramp of the expressway, the speed limit difference leads to a significant deceleration of the vehicle in the area adjacent to the off-ramp. The friction coefficient of the road surface decreases under rainy weather, and the above deceleration process can easily lead to sideslip and rollover ...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	439,949
1804.07974	AI Meets Physical World -- Exploring Robot Cooking	This paper describes our recent research effort to bring the computer intelligence into the physical world so that robots could perform physically interactive manipulation tasks. Our proposed approach first gives robots the ability to learn manipulation skills by "watching" online instructional videos. After "watching"...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	95,653
2202.12440	On Learning and Testing of Counterfactual Fairness through Data Preprocessing	Machine learning has become more important in real-life decision-making but people are concerned about the ethical problems it may bring when used improperly. Recent work brings the discussion of machine learning fairness into the causal framework and elaborates on the concept of Counterfactual Fairness. In this paper,...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	282,240
1902.00918	MICIK: MIning Cross-Layer Inherent Similarity Knowledge for Deep Model Compression	State-of-the-art deep model compression methods exploit the low-rank approximation and sparsity pruning to remove redundant parameters from a learned hidden layer. However, they process each hidden layer individually while neglecting the common components across layers, and thus are not able to fully exploit the potent...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	120,542
2003.02968	A Set-Theoretic Approach to Multi-Task Execution and Prioritization	Executing multiple tasks concurrently is important in many robotic applications. Moreover, the prioritization of tasks is essential in applications where safety-critical tasks need to precede application-related objectives, in order to protect both the robot from its surroundings and vice versa. Furthermore, the possib...	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	167,086
1301.6736	A Possibilistic Model for Qualitative Sequential Decision Problems under Uncertainty in Partially Observable Environments	In this article we propose a qualitative (ordinal) counterpart for the Partially Observable Markov Decision Processes model (POMDP) in which the uncertainty, as well as the preferences of the agent, are modeled by possibility distributions. This qualitative counterpart of the POMDP model relies on a possibilistic theor...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	21,529
2412.03427	Assessing Foundation Models' Transferability to Physiological Signals in Precision Medicine	The success of precision medicine requires computational models that can effectively process and interpret diverse physiological signals across heterogeneous patient populations. While foundation models have demonstrated remarkable transfer capabilities across various domains, their effectiveness in handling individual...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	513,955
2412.20644	Uncertainty Herding: One Active Learning Method for All Label Budgets	Most active learning research has focused on methods which perform well when many labels are available, but can be dramatically worse than random selection when label budgets are small. Other methods have focused on the low-budget regime, but do poorly as label budgets increase. As the line between "low" and "high" bud...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	521,297
2006.00037	Human-Centric Active Perception for Autonomous Observation	As robot autonomy improves, robots are increasingly being considered in the role of autonomous observation systems -- free-flying cameras capable of actively tracking human activity within some predefined area of interest. In this work, we formulate the autonomous observation problem through multi-objective optimizatio...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	179,348
2407.12366	NavGPT-2: Unleashing Navigational Reasoning Capability for Large Vision-Language Models	Capitalizing on the remarkable advancements in Large Language Models (LLMs), there is a burgeoning initiative to harness LLMs for instruction following robotic navigation. Such a trend underscores the potential of LLMs to generalize navigational reasoning and diverse language understanding. However, a significant discr...	false	false	false	false	true	false	false	true	true	false	false	true	false	false	false	false	false	false	473,891
2407.15580	Annealed Multiple Choice Learning: Overcoming limitations of Winner-takes-all with annealing	We introduce Annealed Multiple Choice Learning (aMCL) which combines simulated annealing with MCL. MCL is a learning framework handling ambiguous tasks by predicting a small set of plausible hypotheses. These hypotheses are trained using the Winner-takes-all (WTA) scheme, which promotes the diversity of the predictions...	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	475,238
1910.03155	Sample Elicitation	It is important to collect credible training samples $(x,y)$ for building data-intensive learning systems (e.g., a deep learning system). Asking people to report complex distribution $p(x)$, though theoretically viable, is challenging in practice. This is primarily due to the cognitive loads required for human agents t...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	148,427
2104.02704	Blow the Dog Whistle: A Chinese Dataset for Cant Understanding with Common Sense and World Knowledge	Cant is important for understanding advertising, comedies and dog-whistle politics. However, computational research on cant is hindered by a lack of available datasets. In this paper, we propose a large and diverse Chinese dataset for creating and understanding cant from a computational linguistics perspective. We form...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	228,821
1805.00915	Trainability and Accuracy of Neural Networks: An Interacting Particle System Approach	Neural networks, a central tool in machine learning, have demonstrated remarkable, high fidelity performance on image recognition and classification tasks. These successes evince an ability to accurately represent high dimensional functions, but rigorous results about the approximation error of neural networks after tr...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	96,544
2311.15383	Visual Programming for Zero-shot Open-Vocabulary 3D Visual Grounding	3D Visual Grounding (3DVG) aims at localizing 3D object based on textual descriptions. Conventional supervised methods for 3DVG often necessitate extensive annotations and a predefined vocabulary, which can be restrictive. To address this issue, we propose a novel visual programming approach for zero-shot open-vocabula...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	410,492
1901.11162	Still out there: Modeling and Identifying Russian Troll Accounts on Twitter	There is evidence that Russia's Internet Research Agency attempted to interfere with the 2016 U.S. election by running fake accounts on Twitter - often referred to as "Russian trolls". In this work, we: 1) develop machine learning models that predict whether a Twitter account is a Russian troll within a set of 170K con...	false	false	false	true	false	false	false	false	false	false	false	false	false	true	false	false	false	false	120,184
2311.05082	Dynamic Adaptation Gains for Nonlinear Systems with Unmatched Uncertainties	We present a new direct adaptive control approach for nonlinear systems with unmatched and matched uncertainties. The method relies on adjusting the adaptation gains of individual unmatched parameters whose adaptation transients would otherwise destabilize the closed-loop system. The approach also guarantees the restor...	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	406,464
2203.03619	Adaptive Cross-Layer Attention for Image Restoration	Non-local attention module has been proven to be crucial for image restoration. Conventional non-local attention processes features of each layer separately, so it risks missing correlation between features among different layers. To address this problem, we aim to design attention modules that aggregate information fr...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	284,160
2106.10163	Steerable Partial Differential Operators for Equivariant Neural Networks	Recent work in equivariant deep learning bears strong similarities to physics. Fields over a base space are fundamental entities in both subjects, as are equivariant maps between these fields. In deep learning, however, these maps are usually defined by convolutions with a kernel, whereas they are partial differential ...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	241,921
2004.01374	Characterization of Multiple 3D LiDARs for Localization and Mapping using Normal Distributions Transform	In this work, we present a detailed comparison of ten different 3D LiDAR sensors, covering a range of manufacturers, models, and laser configurations, for the tasks of mapping and vehicle localization, using as common reference the Normal Distributions Transform (NDT) algorithm implemented in the self-driving open sour...	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	170,891
2210.01439	Boosting Few-shot Fine-grained Recognition with Background Suppression and Foreground Alignment	Few-shot fine-grained recognition (FS-FGR) aims to recognize novel fine-grained categories with the help of limited available samples. Undoubtedly, this task inherits the main challenges from both few-shot learning and fine-grained recognition. First, the lack of labeled samples makes the learned model easy to overfit....	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	321,265
2006.16709	A Survey on Recent Progress in the Theory of Evolutionary Algorithms for Discrete Optimization	The theory of evolutionary computation for discrete search spaces has made significant progress in the last ten years. This survey summarizes some of the most important recent results in this research area. It discusses fine-grained models of runtime analysis of evolutionary algorithms, highlights recent theoretical in...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	false	false	184,887
1712.02081	A family of constacyclic codes over $\mathbb{F}_{2^{m}}+u\mathbb{F}_{2^{m}}$ and its application to quantum codes	We introduce a Gray map from $\mathbb{F}_{2^{m}}+u\mathbb{F}_{2^{m}}$ to $\mathbb{F}_{2}^{2m}$ and study $(1+u)$-constacyclic codes over $\mathbb{F}_{2^{m}}+u\mathbb{F}_{2^{m}},$ where $u^{2}=0.$ It is proved that the image of a $(1+u)$-constacyclic code length $n$ over $\mathbb{F}_{2^{m}}+u\mathbb{F}_{2^{m}}$ under th...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	86,234
1903.12431	Train One Get One Free: Partially Supervised Neural Network for Bug Report Duplicate Detection and Clustering	Tracking user reported bugs requires considerable engineering effort in going through many repetitive reports and assigning them to the correct teams. This paper proposes a neural architecture that can jointly (1) detect if two bug reports are duplicates, and (2) aggregate them into latent topics. Leveraging the assump...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	125,728
2310.05479	Deep Optimal Timing Strategies for Time Series	Deciding the best future execution time is a critical task in many business activities while evolving time series forecasting, and optimal timing strategy provides such a solution, which is driven by observed data. This solution has plenty of valuable applications to reduce the operation costs. In this paper, we propos...	false	true	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	398,169
2310.18354	A Review of Reinforcement Learning for Natural Language Processing, and Applications in Healthcare	Reinforcement learning (RL) has emerged as a powerful approach for tackling complex medical decision-making problems such as treatment planning, personalized medicine, and optimizing the scheduling of surgeries and appointments. It has gained significant attention in the field of Natural Language Processing (NLP) due t...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	403,499
2410.21849	Joint Beamforming and Speaker-Attributed ASR for Real Distant-Microphone Meeting Transcription	Distant-microphone meeting transcription is a challenging task. State-of-the-art end-to-end speaker-attributed automatic speech recognition (SA-ASR) architectures lack a multichannel noise and reverberation reduction front-end, which limits their performance. In this paper, we introduce a joint beamforming and SA-ASR a...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	503,411
1403.3005	NetworKit: A Tool Suite for Large-scale Complex Network Analysis	We introduce NetworKit, an open-source software package for analyzing the structure of large complex networks. Appropriate algorithmic solutions are required to handle increasingly common large graph data sets containing up to billions of connections. We describe the methodology applied to develop scalable solutions to...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	true	31,528
1809.06958	Graph-Dependent Implicit Regularisation for Distributed Stochastic Subgradient Descent	We propose graph-dependent implicit regularisation strategies for distributed stochastic subgradient descent (Distributed SGD) for convex problems in multi-agent learning. Under the standard assumptions of convexity, Lipschitz continuity, and smoothness, we establish statistical learning rates that retain, up to logari...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	108,169
2408.15664	Auxiliary-Loss-Free Load Balancing Strategy for Mixture-of-Experts	For Mixture-of-Experts (MoE) models, an unbalanced expert load will lead to routing collapse or increased computational overhead. Existing methods commonly employ an auxiliary loss to encourage load balance, but a large auxiliary loss will introduce non-negligible interference gradients into training and thus impair th...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	484,032
2405.07573	MaskFuser: Masked Fusion of Joint Multi-Modal Tokenization for End-to-End Autonomous Driving	Current multi-modality driving frameworks normally fuse representation by utilizing attention between single-modality branches. However, the existing networks still suppress the driving performance as the Image and LiDAR branches are independent and lack a unified observation representation. Thus, this paper proposes M...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	453,770
2404.03775	A Systems Theoretic Approach to Online Machine Learning	The machine learning formulation of online learning is incomplete from a systems theoretic perspective. Typically, machine learning research emphasizes domains and tasks, and a problem solving worldview. It focuses on algorithm parameters, features, and samples, and neglects the perspective offered by considering syste...	false	false	false	false	true	false	true	false	false	false	true	false	false	false	false	false	false	false	444,400
2404.08135	SciFlow: Empowering Lightweight Optical Flow Models with Self-Cleaning Iterations	Optical flow estimation is crucial to a variety of vision tasks. Despite substantial recent advancements, achieving real-time on-device optical flow estimation remains a complex challenge. First, an optical flow model must be sufficiently lightweight to meet computation and memory constraints to ensure real-time perfor...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	446,125
1703.05916	Construction of a Japanese Word Similarity Dataset	An evaluation of distributed word representation is generally conducted using a word similarity task and/or a word analogy task. There are many datasets readily available for these tasks in English. However, evaluating distributed representation in languages that do not have such resources (e.g., Japanese) is difficult...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	70,148
1902.01903	Exponentiated Gradient Meets Gradient Descent	The (stochastic) gradient descent and the multiplicative update method are probably the most popular algorithms in machine learning. We introduce and study a new regularization which provides a unification of the additive and multiplicative updates. This regularization is derived from an hyperbolic analogue of the entr...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	120,759
2110.00644	RoomStructNet: Learning to Rank Non-Cuboidal Room Layouts From Single View	In this paper, we present a new approach to estimate the layout of a room from its single image. While recent approaches for this task use robust features learnt from data, they resort to optimization for detecting the final layout. In addition to using learnt robust features, our approach learns an additional ranking ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	258,469
2105.10381	Entropy-based Discovery of Summary Causal Graphs in Time Series	This study addresses the problem of learning a summary causal graph on time series with potentially different sampling rates. To do so, we first propose a new causal temporal mutual information measure for time series. We then show how this measure relates to an entropy reduction principle that can be seen as a special...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	236,381
2307.04569	Interpreting and generalizing deep learning in physics-based problems with functional linear models	Although deep learning has achieved remarkable success in various scientific machine learning applications, its opaque nature poses concerns regarding interpretability and generalization capabilities beyond the training data. Interpretability is crucial and often desired in modeling physical systems. Moreover, acquirin...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	378,449
2308.07501	Data-CASE: Grounding Data Regulations for Compliant Data Processing Systems	Data regulations, such as GDPR, are increasingly being adopted globally to protect against unsafe data management practices. Such regulations are, often ambiguous (with multiple valid interpretations) when it comes to defining the expected dynamic behavior of data processing systems. This paper argues that it is possib...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	385,537
2101.06864	Understanding Patterns of Users Who Repost Censored Posts on Weibo	In this study, we focus on understanding patterns of users whose repost contents would later be censored on Weibo, a counterpart of Twitter in China as a social media platform. Little is known about the way regulations and censorship work in this indigenous platform and what role it plays in affecting users' expression...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	215,863
2012.02456	Characterization of Excess Risk for Locally Strongly Convex Population Risk	We establish upper bounds for the expected excess risk of models trained by proper iterative algorithms which approximate the local minima. Unlike the results built upon the strong globally strongly convexity or global growth conditions e.g., PL-inequality, we only require the population risk to be \emph{locally} stron...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	209,773
2410.09234	Fine-Tuning In-House Large Language Models to Infer Differential Diagnosis from Radiology Reports	Radiology reports summarize key findings and differential diagnoses derived from medical imaging examinations. The extraction of differential diagnoses is crucial for downstream tasks, including patient management and treatment planning. However, the unstructured nature of these reports, characterized by diverse lingui...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	497,494
2010.00820	Discriminative and Generative Models for Anatomical Shape Analysison Point Clouds with Deep Neural Networks	We introduce deep neural networks for the analysis of anatomical shapes that learn a low-dimensional shape representation from the given task, instead of relying on hand-engineered representations. Our framework is modular and consists of several computing blocks that perform fundamental shape processing tasks. The net...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	198,405
2305.10972	Participatory Budgeting With Multiple Degrees of Projects And Ranged Approval Votes	In an indivisible participatory budgeting (PB) framework, we have a limited budget that is to be distributed among a set of projects, by aggregating the preferences of voters for the projects. All the prior work on indivisible PB assumes that each project has only one possible cost. In this work, we let each project ha...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	true	false	false	true	365,314
2003.07664	CinemAirSim: A Camera-Realistic Robotics Simulator for Cinematographic Purposes	Drones and Unmanned Aerial Vehicles (UAV's) are becoming increasingly popular in the film and entertainment industries in part because of their maneuverability and the dynamic shots and perspectives they enable. While there exists methods for controlling the position and orientation of the drones for visibility, other ...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	168,501
1806.01316	Universal Statistics of Fisher Information in Deep Neural Networks: Mean Field Approach	The Fisher information matrix (FIM) is a fundamental quantity to represent the characteristics of a stochastic model, including deep neural networks (DNNs). The present study reveals novel statistics of FIM that are universal among a wide class of DNNs. To this end, we use random weights and large width limits, which e...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	99,524
1803.08629	Generalization Challenges for Neural Architectures in Audio Source Separation	Recent work has shown that recurrent neural networks can be trained to separate individual speakers in a sound mixture with high fidelity. Here we explore convolutional neural network models as an alternative and show that they achieve state-of-the-art results with an order of magnitude fewer parameters. We also charac...	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	93,302
2112.06993	On Control Schemes of Voltage Source Converters	This paper discusses some aspects of control schemes for voltage source converters under abnormal conditions. The control schemes are developed specifically for the situations when one or more system parameters vary significantly to the extent that the system becomes unstable with a conventional controller. The paper w...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	271,342
2010.14903	A general method for estimating the prevalence of Influenza-Like-Symptoms with Wikipedia data	Influenza is an acute respiratory seasonal disease that affects millions of people worldwide and causes thousands of deaths in Europe alone. Being able to estimate in a fast and reliable way the impact of an illness on a given country is essential to plan and organize effective countermeasures, which is now possible by...	false	false	false	true	false	false	true	false	false	false	false	false	false	true	false	false	false	false	203,612
2412.19606	Enhancing Fine-grained Image Classification through Attentive Batch Training	Fine-grained image classification, which is a challenging task in computer vision, requires precise differentiation among visually similar object categories. In this paper, we propose 1) a novel module called Residual Relationship Attention (RRA) that leverages the relationships between images within each training batc...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	520,911
2109.07189	On Characterization of Finite Geometric Distributive Lattices	A Lattice is a partially ordered set where both least upper bound and greatest lower bound of any pair of elements are unique and exist within the set. K\"{o}tter and Kschischang proved that codes in the linear lattice can be used for error and erasure-correction in random networks. Codes in the linear lattice have pre...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	255,423
2311.11824	Neural Graph Collaborative Filtering Using Variational Inference	The customization of recommended content to users holds significant importance in enhancing user experiences across a wide spectrum of applications such as e-commerce, music, and shopping. Graph-based methods have achieved considerable performance by capturing user-item interactions. However, these methods tend to util...	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	false	false	409,092
2003.05980	Educational Question Mining At Scale: Prediction, Analysis and Personalization	Online education platforms enable teachers to share a large number of educational resources such as questions to form exercises and quizzes for students. With large volumes of available questions, it is important to have an automated way to quantify their properties and intelligently select them for students, enabling ...	false	false	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	167,990
1107.4600	On the Capacity of the Interference Channel with a Cognitive Relay	The InterFerence Channel with a Cognitive Relay (IFC-CR) consists of the classical interference channel with two independent source-destination pairs whose communication is aided by an additional node, referred to as the cognitive relay, that has a priori knowledge of both sources' messages. This a priori message knowl...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	11,414
2005.14028	Joint Modelling of Emotion and Abusive Language Detection	The rise of online communication platforms has been accompanied by some undesirable effects, such as the proliferation of aggressive and abusive behaviour online. Aiming to tackle this problem, the natural language processing (NLP) community has experimented with a range of techniques for abuse detection. While achievi...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	179,170
2210.01932	Regression-Based Elastic Metric Learning on Shape Spaces of Elastic Curves	We propose a metric learning paradigm, Regression-based Elastic Metric Learning (REML), which optimizes the elastic metric for geodesic regression on the manifold of discrete curves. Geodesic regression is most accurate when the chosen metric models the data trajectory close to a geodesic on the discrete curve manifold...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	321,456
1412.7188	Layered Interference Alignment: Achieving the Total DoF of MIMO X Channels	The $K\times 2$ and $2\times K$, Multiple-Input Multiple-Output (MIMO) X channel with constant channel coefficients available at all transmitters and receivers is considered. A new alignment scheme, named \emph{layered interference alignment}, is proposed in which both vector and real interference alignment are exploit...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	38,779
2406.13327	Part-aware Unified Representation of Language and Skeleton for Zero-shot Action Recognition	While remarkable progress has been made on supervised skeleton-based action recognition, the challenge of zero-shot recognition remains relatively unexplored. In this paper, we argue that relying solely on aligning label-level semantics and global skeleton features is insufficient to effectively transfer locally consis...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	465,802
2405.03411	Greedy Heuristics for Sampling-based Motion Planning in High-Dimensional State Spaces	Informed sampling techniques improve the convergence rate of sampling-based planners by guiding the sampling toward the most promising regions of the problem domain, where states that can improve the current solution are more likely to be found. However, while this approach significantly reduces the planner's explorati...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	452,175
2405.01771	Towards Predicting Collective Performance in Multi-Robot Teams	The increased deployment of multi-robot systems (MRS) in various fields has led to the need for analysis of system-level performance. However, creating consistent metrics for MRS is challenging due to the wide range of system and environmental factors, such as team size and environment size. This paper presents a new a...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	451,501
2408.01532	Contextual Cross-Modal Attention for Audio-Visual Deepfake Detection and Localization	In the digital age, the emergence of deepfakes and synthetic media presents a significant threat to societal and political integrity. Deepfakes based on multi-modal manipulation, such as audio-visual, are more realistic and pose a greater threat. Current multi-modal deepfake detectors are often based on the attention-b...	false	false	true	false	true	false	false	false	false	false	false	true	false	false	false	false	false	true	478,261
1503.00095	Task-Oriented Learning of Word Embeddings for Semantic Relation Classification	We present a novel learning method for word embeddings designed for relation classification. Our word embeddings are trained by predicting words between noun pairs using lexical relation-specific features on a large unlabeled corpus. This allows us to explicitly incorporate relation-specific information into the word e...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	40,664
2412.05570	Template-free Articulated Gaussian Splatting for Real-time Reposable Dynamic View Synthesis	While novel view synthesis for dynamic scenes has made significant progress, capturing skeleton models of objects and re-posing them remains a challenging task. To tackle this problem, in this paper, we propose a novel approach to automatically discover the associated skeleton model for dynamic objects from videos with...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	514,881
2011.07832	WikiAsp: A Dataset for Multi-domain Aspect-based Summarization	Aspect-based summarization is the task of generating focused summaries based on specific points of interest. Such summaries aid efficient analysis of text, such as quickly understanding reviews or opinions from different angles. However, due to large differences in the type of aspects for different domains (e.g., senti...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	206,690
2003.00505	Differentially Private Deep Learning with Smooth Sensitivity	Ensuring the privacy of sensitive data used to train modern machine learning models is of paramount importance in many areas of practice. One approach to study these concerns is through the lens of differential privacy. In this framework, privacy guarantees are generally obtained by perturbing models in such a way that...	false	false	false	false	true	false	true	false	false	false	false	false	true	false	false	false	false	false	166,321
2306.06622	Weakly Supervised Visual Question Answer Generation	Growing interest in conversational agents promote twoway human-computer communications involving asking and answering visual questions have become an active area of research in AI. Thus, generation of visual questionanswer pair(s) becomes an important and challenging task. To address this issue, we propose a weakly-sup...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	372,685
2307.05491	Parametric roll oscillations of a hydrodynamic Chaplygin sleigh	Biomimetic underwater robots use lateral periodic oscillatory motion to propel forward, which is seen in most fishes known as body caudal fin (BCF) propulsion. The lateral oscillatory motion makes slender-bodied fish-like robots roll unstable. Unlike the case of human-engineered aquatic robots, many species of fish can...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	378,756
2107.12846	Higher-order sliding mode observer design for linear time-invariant multivariable systems based on a new observer normal form	In various applications in the field of control engineering the estimation of the state variables of dynamic systems in the presence of unknown inputs plays an important role. Existing methods require the so-called observer matching condition to be satisfied, rely on the boundedness of the state variables or exhibit an...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	248,024
2310.17796	ControlLLM: Augment Language Models with Tools by Searching on Graphs	We present ControlLLM, a novel framework that enables large language models (LLMs) to utilize multi-modal tools for solving complex real-world tasks. Despite the remarkable performance of LLMs, they still struggle with tool invocation due to ambiguous user prompts, inaccurate tool selection and parameterization, and in...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	403,292
1401.6574	Category theory, logic and formal linguistics: some connections, old and new	We seize the opportunity of the publication of selected papers from the \emph{Logic, categories, semantics} workshop in the \emph{Journal of Applied Logic} to survey some current trends in logic, namely intuitionistic and linear type theories, that interweave categorical, geometrical and computational considerations. W...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	true	30,375
2404.18206	Enhancing Action Recognition from Low-Quality Skeleton Data via Part-Level Knowledge Distillation	Skeleton-based action recognition is vital for comprehending human-centric videos and has applications in diverse domains. One of the challenges of skeleton-based action recognition is dealing with low-quality data, such as skeletons that have missing or inaccurate joints. This paper addresses the issue of enhancing ac...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	450,167
2103.05358	PGD-based advanced nonlinear multiparametric regressions for constructing metamodels at the scarce-data limit	Regressions created from experimental or simulated data enable the construction of metamodels, widely used in a variety of engineering applications. Many engineering problems involve multi-parametric physics whose corresponding multi-parametric solutions can be viewed as a sort of computational vademecum that, once com...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	223,957
1911.02142	Intriguing Properties of Adversarial ML Attacks in the Problem Space [Extended Version]	Recent research efforts on adversarial machine learning (ML) have investigated problem-space attacks, focusing on the generation of real evasive objects in domains where, unlike images, there is no clear inverse mapping to the feature space (e.g., software). However, the design, comparison, and real-world implications ...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	152,287
2011.12328	Generalized Variational Continual Learning	Continual learning deals with training models on new tasks and datasets in an online fashion. One strand of research has used probabilistic regularization for continual learning, with two of the main approaches in this vein being Online Elastic Weight Consolidation (Online EWC) and Variational Continual Learning (VCL)....	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	208,115
2402.01665	Knowledge-Driven Deep Learning Paradigms for Wireless Network Optimization in 6G	In the sixth-generation (6G) networks, newly emerging diversified services of massive users in dynamic network environments are required to be satisfied by multi-dimensional heterogeneous resources. The resulting large-scale complicated network optimization problems are beyond the capability of model-based theoretical ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	426,126
2309.13591	Robust Distributed Learning: Tight Error Bounds and Breakdown Point under Data Heterogeneity	The theory underlying robust distributed learning algorithms, designed to resist adversarial machines, matches empirical observations when data is homogeneous. Under data heterogeneity however, which is the norm in practical scenarios, established lower bounds on the learning error are essentially vacuous and greatly m...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	394,272
2410.21153	Synthetica: Large Scale Synthetic Data for Robot Perception	Vision-based object detectors are a crucial basis for robotics applications as they provide valuable information about object localisation in the environment. These need to ensure high reliability in different lighting conditions, occlusions, and visual artifacts, all while running in real-time. Collecting and annotati...	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	503,110

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