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
2409.11370	Compact Implicit Neural Representations for Plane Wave Images	Ultrafast Plane-Wave (PW) imaging often produces artifacts and shadows that vary with insonification angles. We propose a novel approach using Implicit Neural Representations (INRs) to compactly encode multi-planar sequences while preserving crucial orientation-dependent information. To our knowledge, this is the first...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	489,126
1402.0412	Bots vs. Wikipedians, Anons vs. Logged-Ins	Wikipedia is a global crowdsourced encyclopedia that at time of writing is available in 287 languages. Wikidata is a likewise global crowdsourced knowledge base that provides shared facts to be used by Wikipedias. In the context of this research, we have developed an application and an underlying Application Programmin...	false	false	false	true	false	false	false	false	false	false	false	false	false	true	false	false	false	true	30,560
2411.19461	Robust Bayesian Scene Reconstruction by Leveraging Retrieval-Augmented Priors	Constructing 3D representations of object geometry is critical for many downstream robotics tasks, particularly tabletop manipulation problems. These representations must be built from potentially noisy partial observations. In this work, we focus on the problem of reconstructing a multi-object scene from a single RGBD...	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	512,250
2402.19289	CAMixerSR: Only Details Need More "Attention"	To satisfy the rapidly increasing demands on the large image (2K-8K) super-resolution (SR), prevailing methods follow two independent tracks: 1) accelerate existing networks by content-aware routing, and 2) design better super-resolution networks via token mixer refining. Despite directness, they encounter unavoidable ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	433,746
2405.13805	Perceptual Fairness in Image Restoration	Fairness in image restoration tasks is the desire to treat different sub-groups of images equally well. Existing definitions of fairness in image restoration are highly restrictive. They consider a reconstruction to be a correct outcome for a group (e.g., women) only if it falls within the group's set of ground truth i...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	456,090
2501.03681	SLAM: Towards Efficient Multilingual Reasoning via Selective Language Alignment	Despite the significant improvements achieved by large language models (LLMs) in English reasoning tasks, these models continue to struggle with multilingual reasoning. Recent studies leverage a full-parameter and two-stage training paradigm to teach models to first understand non-English questions and then reason. How...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	522,961
2502.07319	Learnable Residual-based Latent Denoising in Semantic Communication	A latent denoising semantic communication (SemCom) framework is proposed for robust image transmission over noisy channels. By incorporating a learnable latent denoiser into the receiver, the received signals are preprocessed to effectively remove the channel noise and recover the semantic information, thereby enhancin...	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	532,543
2410.03174	HRVMamba: High-Resolution Visual State Space Model for Dense Prediction	Recently, State Space Models (SSMs) with efficient hardware-aware designs, i.e., Mamba, have demonstrated significant potential in computer vision tasks due to their linear computational complexity with respect to token length and their global receptive field. However, Mamba's performance on dense prediction tasks, inc...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	494,661
0904.1281	Asymptotically Optimal Joint Source-Channel Coding with Minimal Delay	We present and analyze a joint source-channel coding strategy for the transmission of a Gaussian source across a Gaussian channel in n channel uses per source symbol. Among all such strategies, our scheme has the following properties: i) the resulting mean-squared error scales optimally with the signal-to-noise ratio, ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	3,505
2002.04741	Progressive Object Transfer Detection	Recent development of object detection mainly depends on deep learning with large-scale benchmarks. However, collecting such fully-annotated data is often difficult or expensive for real-world applications, which restricts the power of deep neural networks in practice. Alternatively, humans can detect new objects with ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	163,683
2305.09995	Algorithmic Decorrelation and Planted Clique in Dependent Random Graphs: The Case of Extra Triangles	We aim to understand the extent to which the noise distribution in a planted signal-plus-noise problem impacts its computational complexity. To that end, we consider the planted clique and planted dense subgraph problems, but in a different ambient graph. Instead of Erd\H{o}s-R\'enyi $G(n,p)$, which has independent edg...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	364,862
2011.14785	S2FGAN: Semantically Aware Interactive Sketch-to-Face Translation	Interactive facial image manipulation attempts to edit single and multiple face attributes using a photo-realistic face and/or semantic mask as input. In the absence of the photo-realistic image (only sketch/mask available), previous methods only retrieve the original face but ignore the potential of aiding model contr...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	208,891
2009.04042	Unconstrained Text Detection in Manga: a New Dataset and Baseline	The detection and recognition of unconstrained text is an open problem in research. Text in comic books has unusual styles that raise many challenges for text detection. This work aims to binarize text in a comic genre with highly sophisticated text styles: Japanese manga. To overcome the lack of a manga dataset with t...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	194,948
2411.10624	Weak Permission is not Well-Founded, Grounded and Stable	We consider the notion of weak permission as the failure to conclude that the opposite obligation. We investigate the issue from the point of non-monotonic reasoning, specifically logic programming and structured argumentation, and we show that it is not possible to capture weak permission in the presence of deontic co...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	508,721
2003.01653	Impact of Spatially Consistent Channels on Digital Beamforming for Millimeter-Wave Systems	The premise of massive multiple-input multiple-output (MIMO) is based around coherent transmission and detection. Majority of the vast literature on massive MIMO presents performance evaluations over simplified statistical propagation models. All such models are drop-based and do not ensure continuity of channel parame...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	166,716
1510.04104	A Preliminary Study on the Learning Informativeness of Data Subsets	Estimating the internal state of a robotic system is complex: this is performed from multiple heterogeneous sensor inputs and knowledge sources. Discretization of such inputs is done to capture saliences, represented as symbolic information, which often presents structure and recurrence. As these sequences are used to ...	false	false	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	47,890
2102.07475	Scaling Multi-Agent Reinforcement Learning with Selective Parameter Sharing	Sharing parameters in multi-agent deep reinforcement learning has played an essential role in allowing algorithms to scale to a large number of agents. Parameter sharing between agents significantly decreases the number of trainable parameters, shortening training times to tractable levels, and has been linked to more ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	false	false	false	220,118
2112.10401	Quasi-uniform designs with optimal and near-optimal uniformity constant	A design is a collection of distinct points in a given set $X$, which is assumed to be a compact subset of $R^d$, and the mesh-ratio of a design is the ratio of its fill distance to its separation radius. The uniformity constant of a sequence of nested designs is the smallest upper bound for the mesh-ratios of the desi...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	272,424
1804.02740	Facial Aging and Rejuvenation by Conditional Multi-Adversarial Autoencoder with Ordinal Regression	Facial aging and facial rejuvenation analyze a given face photograph to predict a future look or estimate a past look of the person. To achieve this, it is critical to preserve human identity and the corresponding aging progression and regression with high accuracy. However, existing methods cannot simultaneously handl...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	94,477
2104.07623	Sometimes We Want Translationese	Rapid progress in Neural Machine Translation (NMT) systems over the last few years has been driven primarily towards improving translation quality, and as a secondary focus, improved robustness to input perturbations (e.g. spelling and grammatical mistakes). While performance and robustness are important objectives, by...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	230,486
2212.10754	CoRRPUS: Code-based Structured Prompting for Neurosymbolic Story Understanding	Story generation and understanding -- as with all NLG/NLU tasks -- has seen a surge in neurosymbolic work. Researchers have recognized that, while large language models (LLMs) have tremendous utility, they can be augmented with symbolic means to be even better and to make up for any flaws that the neural networks might...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	337,590
2106.12085	A Graph-based Method for Session-based Recommendations	We present a graph-based approach for the data management tasks and the efficient operation of a system for session-based next-item recommendations. The proposed method can collect data continuously and incrementally from an ecommerce web site, thus seemingly prepare the necessary data infrastructure for the recommenda...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	242,612
2310.14863	Paraphrase Types for Generation and Detection	Current approaches in paraphrase generation and detection heavily rely on a single general similarity score, ignoring the intricate linguistic properties of language. This paper introduces two new tasks to address this shortcoming by considering paraphrase types - specific linguistic perturbations at particular text po...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	402,054
2412.11867	Transformers Use Causal World Models in Maze-Solving Tasks	Recent studies in interpretability have explored the inner workings of transformer models trained on tasks across various domains, often discovering that these networks naturally develop surprisingly structured representations. When such representations comprehensively reflect the task domain's structure, they are comm...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	517,620
1210.4235	Node Classification in Networks of Stochastic Evidence Accumulators	This paper considers a network of stochastic evidence accumulators, each represented by a drift-diffusion model accruing evidence towards a decision in continuous time by observing a noisy signal and by exchanging information with other units according to a fixed communication graph. We bring into focus the relationshi...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	19,124
2406.12658	Federated Learning with a Single Shared Image	Federated Learning (FL) enables multiple machines to collaboratively train a machine learning model without sharing of private training data. Yet, especially for heterogeneous models, a key bottleneck remains the transfer of knowledge gained from each client model with the server. One popular method, FedDF, uses distil...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	465,511
1209.1150	On dually flat Randers metrics	In this paper, I will show how to use beta-deformations to deal with dual flatness of Randers metrics. beta-deformations is a new method in Riemann-Finsler geometry, it is introduced by the author(see arxiv:1209.0845). Later on I will provide more applications of the new kind of deformations in Finsler geometry.	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	18,421
2108.12334	Explicit Good Subspace-metric Codes and Subset-metric Codes	In this paper motivated from subspace coding we introduce subspace-metric codes and subset-metric codes. These are coordinate-position independent pseudometrics and suitable for the folded codes. The half-Singleton upper bounds for linear subspace-metric codes and linear subset-metric codes are proved. Subspace distanc...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	252,471
1909.11480	Input complexity and out-of-distribution detection with likelihood-based generative models	Likelihood-based generative models are a promising resource to detect out-of-distribution (OOD) inputs which could compromise the robustness or reliability of a machine learning system. However, likelihoods derived from such models have been shown to be problematic for detecting certain types of inputs that significant...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	146,821
1906.01510	Accelerating Physics-Based Simulations Using Neural Network Proxies: An Application in Oil Reservoir Modeling	We develop a proxy model based on deep learning methods to accelerate the simulations of oil reservoirs--by three orders of magnitude--compared to industry-strength physics-based PDE solvers. This paper describes a new architectural approach to this task, accompanied by a thorough experimental evaluation on a publicly ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	133,728
2404.13454	Revolutionizing System Reliability: The Role of AI in Predictive Maintenance Strategies	The landscape of maintenance in distributed systems is rapidly evolving with the integration of Artificial Intelligence (AI). Also, as the complexity of computing continuum systems intensifies, the role of AI in predictive maintenance (Pd.M.) becomes increasingly pivotal. This paper presents a comprehensive survey of t...	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	false	true	448,303
2111.04113	Stable Lifelong Learning: Spiking neurons as a solution to instability in plastic neural networks	Synaptic plasticity poses itself as a powerful method of self-regulated unsupervised learning in neural networks. A recent resurgence of interest has developed in utilizing Artificial Neural Networks (ANNs) together with synaptic plasticity for intra-lifetime learning. Plasticity has been shown to improve the learning ...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	265,391
1409.7367	Coding of Stereo Signals by a Single Digital {\Delta}{\Sigma} Modulator	The possibility of using a single digital {\Delta}{\Sigma} modulator to simultaneously encode the two channels of a stereo signal is illustrated. From the modulated stream, the two channels can be recovered with minimal processing and no cross-talk. Notably, demultiplexing does not affect the sample-depth so that, afte...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	36,314
1202.5967	Joint Source-Channel Cooperative Transmission over Relay-Broadcast Networks	Reliable transmission of a discrete memoryless source over a multiple-relay relay-broadcast network is considered. Motivated by sensor network applications, it is assumed that the relays and the destinations all have access to side information correlated with the underlying source signal. Joint source-channel cooperati...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	14,597
2005.03197	Fair Algorithms for Hierarchical Agglomerative Clustering	Hierarchical Agglomerative Clustering (HAC) algorithms are extensively utilized in modern data science, and seek to partition the dataset into clusters while generating a hierarchical relationship between the data samples. HAC algorithms are employed in many applications, such as biology, natural language processing, a...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	176,082
2306.13761	CeBed: A Benchmark for Deep Data-Driven OFDM Channel Estimation	Deep learning has been extensively used in wireless communication problems, including channel estimation. Although several data-driven approaches exist, a fair and realistic comparison between them is difficult due to inconsistencies in the experimental conditions and the lack of a standardized experimental design. In ...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	375,388
2411.08907	From Simulators to Digital Twins for Enabling Emerging Cellular Networks: A Tutorial and Survey	Simulators are indispensable parts of the research and development necessary to advance countless industries, including cellular networks. With simulators, the evaluation, analysis, testing, and experimentation of novel designs and algorithms can be executed in a more cost-effective and convenient manner without the ri...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	true	508,066
2412.06843	Semantic Loss Guided Data Efficient Supervised Fine Tuning for Safe Responses in LLMs	Large Language Models (LLMs) generating unsafe responses to toxic prompts is a significant issue in their applications. While various efforts aim to address this safety concern, previous approaches often demand substantial human data collection or rely on the less dependable option of using another LLM to generate corr...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	515,412
2407.10874	Random Channel Ablation for Robust Hand Gesture Classification with Multimodal Biosignals	Biosignal-based hand gesture classification is an important component of effective human-machine interaction. For multimodal biosignal sensing, the modalities often face data loss due to missing channels in the data which can adversely affect the gesture classification performance. To make the classifiers robust to mis...	true	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	473,168
1507.00598	Securing Physical-Layer Communications for Cognitive Radio Networks	This article investigates the physical-layer security of cognitive radio (CR) networks, which are vulnerable to various newly arising attacks targeting on the weaknesses of CR communications and networking. We first review a range of physical-layer attacks in CR networks, including the primary user emulation, sensing f...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	44,773
2404.11819	Utilizing Adversarial Examples for Bias Mitigation and Accuracy Enhancement	We propose a novel approach to mitigate biases in computer vision models by utilizing counterfactual generation and fine-tuning. While counterfactuals have been used to analyze and address biases in DNN models, the counterfactuals themselves are often generated from biased generative models, which can introduce additio...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	447,625
2409.02368	Pluralistic Salient Object Detection	We introduce pluralistic salient object detection (PSOD), a novel task aimed at generating multiple plausible salient segmentation results for a given input image. Unlike conventional SOD methods that produce a single segmentation mask for salient objects, this new setting recognizes the inherent complexity of real-wor...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	485,668
2103.04020	NeRD: Neural Representation of Distribution for Medical Image Segmentation	We introduce Neural Representation of Distribution (NeRD) technique, a module for convolutional neural networks (CNNs) that can estimate the feature distribution by optimizing an underlying function mapping image coordinates to the feature distribution. Using NeRD, we propose an end-to-end deep learning model for medic...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	223,490
2501.10871	Enhancing User Intent for Recommendation Systems via Large Language Models	Recommendation systems play a critical role in enhancing user experience and engagement in various online platforms. Traditional methods, such as Collaborative Filtering (CF) and Content-Based Filtering (CBF), rely heavily on past user interactions or item features. However, these models often fail to capture the dynam...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	525,696
2006.12395	Binary linear codes with few weights from two-to-one functions	In this paper, we apply two-to-one functions over $\mathbb{F}_{2^n}$ in two generic constructions of binary linear codes. We consider two-to-one functions in two forms: (1) generalized quadratic functions; and (2) $\left(x^{2^t}+x\right)^e$ with $\gcd(t, n)=1$ and $\gcd\left(e, 2^n-1\right)=1$. Based on the study of th...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	183,574
2005.00069	Occlusion resistant learning of intuitive physics from videos	To reach human performance on complex tasks, a key ability for artificial systems is to understand physical interactions between objects, and predict future outcomes of a situation. This ability, often referred to as intuitive physics, has recently received attention and several methods were proposed to learn these phy...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	175,114
2101.04954	EventAnchor: Reducing Human Interactions in Event Annotation of Racket Sports Videos	The popularity of racket sports (e.g., tennis and table tennis) leads to high demands for data analysis, such as notational analysis, on player performance. While sports videos offer many benefits for such analysis, retrieving accurate information from sports videos could be challenging. In this paper, we propose Event...	true	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	215,291
2109.03483	Pose-guided Inter- and Intra-part Relational Transformer for Occluded Person Re-Identification	Person Re-Identification (Re-Id) in occlusion scenarios is a challenging problem because a pedestrian can be partially occluded. The use of local information for feature extraction and matching is still necessary. Therefore, we propose a Pose-guided inter-and intra-part relational transformer (Pirt) for occluded person...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	254,087
2105.13136	A framework for data-driven solution and parameter estimation of PDEs using conditional generative adversarial networks	This work is the first to employ and adapt the image-to-image translation concept based on conditional generative adversarial networks (cGAN) towards learning a forward and an inverse solution operator of partial differential equations (PDEs). Even though the proposed framework could be applied as a surrogate model for...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	237,220
1703.00737	Wireless Interference Identification with Convolutional Neural Networks	The steadily growing use of license-free frequency bands requires reliable coexistence management for deterministic medium utilization. For interference mitigation, proper wireless interference identification (WII) is essential. In this work we propose the first WII approach based upon deep convolutional neural network...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	69,218
1807.07803	Competition vs. Concatenation in Skip Connections of Fully Convolutional Networks	Increased information sharing through short and long-range skip connections between layers in fully convolutional networks have demonstrated significant improvement in performance for semantic segmentation. In this paper, we propose Competitive Dense Fully Convolutional Networks (CDFNet) by introducing competitive maxo...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	103,387
2008.03172	Orthologics for Cones	In applications that use knowledge representation (KR) techniques, in particular those that combine data-driven and logic methods, the domain of objects is not an abstract unstructured domain, but it exhibits a dedicated, deep structure of geometric objects. One example is the class of convex sets used to model natural...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	190,825
2309.10563	A Hierarchical Neural Framework for Classification and its Explanation in Large Unstructured Legal Documents	Automatic legal judgment prediction and its explanation suffer from the problem of long case documents exceeding tens of thousands of words, in general, and having a non-uniform structure. Predicting judgments from such documents and extracting their explanation becomes a challenging task, more so on documents with no ...	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	false	393,063
1905.09980	Perception Evaluation -- A new solar image quality metric based on the multi-fractal property of texture features	Next-generation ground-based solar observations require good image quality metrics for post-facto processing techniques. Based on the assumption that texture features in solar images are multi-fractal which can be extracted by a trained deep neural network as feature maps, a new reduced-reference objective image qualit...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	131,901
1208.4178	MOIST: A Scalable and Parallel Moving Object Indexer with School Tracking	Location-Based Service (LBS) is rapidly becoming the next ubiquitous technology for a wide range of mobile applications. To support applications that demand nearest-neighbor and history queries, an LBS spatial indexer must be able to efficiently update, query, archive and mine location records, which can be in contenti...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	18,192
2410.05266	Brain Mapping with Dense Features: Grounding Cortical Semantic Selectivity in Natural Images With Vision Transformers	Advances in large-scale artificial neural networks have facilitated novel insights into the functional topology of the brain. Here, we leverage this approach to study how semantic categories are organized in the human visual cortex. To overcome the challenge presented by the co-occurrence of multiple categories in natu...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	495,642
2204.05509	Learning Design and Construction with Varying-Sized Materials via Prioritized Memory Resets	Can a robot autonomously learn to design and construct a bridge from varying-sized blocks without a blueprint? It is a challenging task with long horizon and sparse reward -- the robot has to figure out physically stable design schemes and feasible actions to manipulate and transport blocks. Due to diverse block sizes,...	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	false	291,048
1909.01961	A Constructive Approach for Data-Driven Randomized Learning of Feedforward Neural Networks	Feedforward neural networks with random hidden nodes suffer from a problem with the generation of random weights and biases as these are difficult to set optimally to obtain a good projection space. Typically, random parameters are drawn from an interval which is fixed before or adapted during the learning process. Due...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	144,060
2402.00879	Graph Representation Learning for Contention and Interference Management in Wireless Networks	Restricted access window (RAW) in Wi-Fi 802.11ah networks manages contention and interference by grouping users and allocating periodic time slots for each group's transmissions. We will find the optimal user grouping decisions in RAW to maximize the network's worst-case user throughput. We review existing user groupin...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	425,766
2207.08283	Accelerated RRT* By Local Directional Visibility	RRT* is an efficient sampling-based motion planning algorithm. However, without taking advantages of accessible environment information, sampling-based algorithms usually result in sampling failures, generate useless nodes, and/or fail in exploring narrow passages. For this paper, in order to better utilize environment...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	308,529
1904.11830	Online Learning Algorithms for Quaternion ARMA Model	In this paper, we address the problem of adaptive learning for autoregressive moving average (ARMA) model in the quaternion domain. By transforming the original learning problem into a full information optimization task without explicit noise terms, and then solving the optimization problem using the gradient descent a...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	128,960
2104.08318	Li$_x$CoO$_2$ phase stability studied by machine learning-enabled scale bridging between electronic structure, statistical mechanics and phase field theories	Li$_xTM$O$_2$ (TM={Ni, Co, Mn}) are promising cathodes for Li-ion batteries, whose electrochemical cycling performance is strongly governed by crystal structure and phase stability as a function of Li content at the atomistic scale. Here, we use Li$_x$CoO$_2$ (LCO) as a model system to benchmark a scale-bridging framew...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	230,745
2306.06066	Multi-level Cross-modal Feature Alignment via Contrastive Learning towards Zero-shot Classification of Remote Sensing Image Scenes	Zero-shot classification of image scenes which can recognize the image scenes that are not seen in the training stage holds great promise of lowering the dependence on large numbers of labeled samples. To address the zero-shot image scene classification, the cross-modal feature alignment methods have been proposed in r...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	372,424
2306.12517	FFCV: Accelerating Training by Removing Data Bottlenecks	We present FFCV, a library for easy and fast machine learning model training. FFCV speeds up model training by eliminating (often subtle) data bottlenecks from the training process. In particular, we combine techniques such as an efficient file storage format, caching, data pre-loading, asynchronous data transfer, and ...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	374,965
2104.11746	VidTr: Video Transformer Without Convolutions	We introduce Video Transformer (VidTr) with separable-attention for video classification. Comparing with commonly used 3D networks, VidTr is able to aggregate spatio-temporal information via stacked attentions and provide better performance with higher efficiency. We first introduce the vanilla video transformer and sh...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	232,007
1907.07055	Homophily as a Process Generating Social Networks: Insights from Social Distance Attachment Model	Real-world social networks often exhibit high levels of clustering, positive degree assortativity, short average path lengths (small-world property) and right-skewed but rarely power law degree distributions. On the other hand homophily, defined as the propensity of similar agents to connect to each other, is one of th...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	138,772
0704.3019	Arbitrary Rate Permutation Modulation for the Gaussian Channel	In this paper non-group permutation modulated sequences for the Gaussian channel are considered. Without the restriction to group codes rather than subsets of group codes, arbitrary rates are achievable. The code construction utilizes the known optimal group constellations to ensure at least the same performance but ex...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	83
2301.00884	Safety Filtering for Reinforcement Learning-based Adaptive Cruise Control	Reinforcement learning (RL)-based adaptive cruise control systems (ACC) that learn and adapt to road, traffic and vehicle conditions are attractive for enhancing vehicle energy efficiency and traffic flow. However, the application of RL in safety critical systems such as ACC requires strong safety guarantees which are ...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	339,052
1704.03137	Resolution-Adaptive Hybrid MIMO Architectures for Millimeter Wave Communications	In this paper, we propose a hybrid analog-digital beamforming architecture with resolution-adaptive ADCs for millimeter wave (mmWave) receivers with large antenna arrays. We adopt array response vectors for the analog combiners and derive ADC bit-allocation (BA) solutions in closed form. The BA solutions reveal that th...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	71,575
1301.2284	Classifier Learning with Supervised Marginal Likelihood	It has been argued that in supervised classification tasks, in practice it may be more sensible to perform model selection with respect to some more focused model selection score, like the supervised (conditional) marginal likelihood, than with respect to the standard marginal likelihood criterion. However, for most Ba...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	20,959
1210.2826	An anisotropy preserving metric for DTI processing	Statistical analysis of Diffusion Tensor Imaging (DTI) data requires a computational framework that is both numerically tractable (to account for the high dimensional nature of the data) and geometric (to account for the nonlinear nature of diffusion tensors). Building upon earlier studies that have shown that a Rieman...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	19,043
2210.02713	On Optimal Learning Under Targeted Data Poisoning	Consider the task of learning a hypothesis class $\mathcal{H}$ in the presence of an adversary that can replace up to an $\eta$ fraction of the examples in the training set with arbitrary adversarial examples. The adversary aims to fail the learner on a particular target test point $x$ which is known to the adversary b...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	321,757
2011.11866	Gaussian Processes for Traffic Speed Prediction at Different Aggregation Levels	Dynamic behavior of traffic adversely affect the performance of the prediction models in intelligent transportation applications. This study applies Gaussian processes (GPs) to traffic speed prediction. Such predictions can be used by various transportation applications, such as real-time route guidance, ramp metering,...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	207,971
1808.04571	Learning A Shared Transform Model for Skull to Digital Face Image Matching	Human skull identification is an arduous task, traditionally requiring the expertise of forensic artists and anthropologists. This paper is an effort to automate the process of matching skull images to digital face images, thereby establishing an identity of the skeletal remains. In order to achieve this, a novel Share...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	105,183
1201.1345	FITS Checksum Proposal	The checksum keywords described here provide an integrity check on the information contained in FITS HDUs. (Header and Data Units are the basic components of FITS files, consisting of header keyword records followed by optional associated data records). The CHECKSUM keyword is defined to have a value that forces the 32...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	13,706
1205.5569	A Theory of Information Matching	In this work, we propose a theory for information matching. It is motivated by the observation that retrieval is about the relevance matching between two sets of properties (features), namely, the information need representation and information item representation. However, many probabilistic retrieval models rely on f...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	16,166
1907.08236	SLATE: A Super-Lightweight Annotation Tool for Experts	Many annotation tools have been developed, covering a wide variety of tasks and providing features like user management, pre-processing, and automatic labeling. However, all of these tools use Graphical User Interfaces, and often require substantial effort to install and configure. This paper presents a new annotation ...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	139,055
1602.01940	Automatic and Quantitative evaluation of attribute discovery methods	Many automatic attribute discovery methods have been developed to extract a set of visual attributes from images for various tasks. However, despite good performance in some image classification tasks, it is difficult to evaluate whether these methods discover meaningful attributes and which one is the best to find the...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	51,767
2301.00688	Active Learning for Neural Machine Translation	The machine translation mechanism translates texts automatically between different natural languages, and Neural Machine Translation (NMT) has gained attention for its rational context analysis and fluent translation accuracy. However, processing low-resource languages that lack relevant training attributes like superv...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	338,987
1704.02497	On the Linearity of Semantic Change: Investigating Meaning Variation via Dynamic Graph Models	We consider two graph models of semantic change. The first is a time-series model that relates embedding vectors from one time period to embedding vectors of previous time periods. In the second, we construct one graph for each word: nodes in this graph correspond to time points and edge weights to the similarity of th...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	71,459
2012.04028	On-Road Motion Planning for Automated Vehicles at Ulm University	The Institute of Measurement, Control and Microtechnology at Ulm University investigates advanced driver assistance systems for decades and concentrates in large parts on autonomous driving. It is well known that motion planning is a key technology for autonomous driving. It is first and foremost responsible for the sa...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	210,319
1310.2086	An Iterative Method Applied to Correct the Actual Compressor Performance to the Equivalent Performance under the Specified Reference Conditions	This paper proposes a correction method, which corrects the actual compressor performance in real operating conditions to the equivalent performance under specified reference condition. The purpose is to make fair comparisons between actual performance against design performance or reference maps under the same operati...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	27,636
1609.05486	Probabilistic Feature Selection and Classification Vector Machine	Sparse Bayesian learning is a state-of-the-art supervised learning algorithm that can choose a subset of relevant samples from the input data and make reliable probabilistic predictions. However, in the presence of high-dimensional data with irrelevant features, traditional sparse Bayesian classifiers suffer from perfo...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	61,147
2011.14874	A Simple and Effective Approach to Robust Unsupervised Bilingual Dictionary Induction	Unsupervised Bilingual Dictionary Induction methods based on the initialization and the self-learning have achieved great success in similar language pairs, e.g., English-Spanish. But they still fail and have an accuracy of 0% in many distant language pairs, e.g., English-Japanese. In this work, we show that this failu...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	208,916
2210.15779	Adapting Neural Models with Sequential Monte Carlo Dropout	The ability to adapt to changing environments and settings is essential for robots acting in dynamic and unstructured environments or working alongside humans with varied abilities or preferences. This work introduces an extremely simple and effective approach to adapting neural models in response to changing settings....	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	327,078
2206.01903	Deep Radiomic Analysis for Predicting Coronavirus Disease 2019 in Computerized Tomography and X-ray Images	This paper proposes to encode the distribution of features learned from a convolutional neural network using a Gaussian Mixture Model. These parametric features, called GMM-CNN, are derived from chest computed tomography and X-ray scans of patients with Coronavirus Disease 2019. We use the proposed GMM-CNN features as ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	300,659
2010.14091	Triple-view Convolutional Neural Networks for COVID-19 Diagnosis with Chest X-ray	The Coronavirus Disease 2019 (COVID-19) is affecting increasingly large number of people worldwide, posing significant stress to the health care systems. Early and accurate diagnosis of COVID-19 is critical in screening of infected patients and breaking the person-to-person transmission. Chest X-ray (CXR) based compute...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	203,341
1705.01015	Deep Learning for Tumor Classification in Imaging Mass Spectrometry	Motivation: Tumor classification using Imaging Mass Spectrometry (IMS) data has a high potential for future applications in pathology. Due to the complexity and size of the data, automated feature extraction and classification steps are required to fully process the data. Deep learning offers an approach to learn featu...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	72,783
2411.02694	Point processes with event time uncertainty	Point processes are widely used statistical models for uncovering the temporal patterns in dependent event data. In many applications, the event time cannot be observed exactly, calling for the incorporation of time uncertainty into the modeling of point process data. In this work, we introduce a framework to model tim...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	505,616
1406.0349	Undecidability of satisfiability in the algebra of finite binary relations with union, composition, and difference	We consider expressions built up from binary relation names using the operators union, composition, and set difference. We show that it is undecidable to test whether a given such expression $e$ is finitely satisfiable, i.e., whether there exist finite binary relations that can be substituted for the relation names so ...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	true	33,552
2112.13029	Gaussian Process Bandits with Aggregated Feedback	We consider the continuum-armed bandits problem, under a novel setting of recommending the best arms within a fixed budget under aggregated feedback. This is motivated by applications where the precise rewards are impossible or expensive to obtain, while an aggregated reward or feedback, such as the average over a subs...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	273,123
1802.02696	Improving the Universality and Learnability of Neural Programmer-Interpreters with Combinator Abstraction	To overcome the limitations of Neural Programmer-Interpreters (NPI) in its universality and learnability, we propose the incorporation of combinator abstraction into neural programing and a new NPI architecture to support this abstraction, which we call Combinatory Neural Programmer-Interpreter (CNPI). Combinator abstr...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	89,828
1711.01761	AdaBatch: Efficient Gradient Aggregation Rules for Sequential and Parallel Stochastic Gradient Methods	We study a new aggregation operator for gradients coming from a mini-batch for stochastic gradient (SG) methods that allows a significant speed-up in the case of sparse optimization problems. We call this method AdaBatch and it only requires a few lines of code change compared to regular mini-batch SGD algorithms. We p...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	83,950
2104.09713	Hierarchically Modeling Micro and Macro Behaviors via Multi-Task Learning for Conversion Rate Prediction	Conversion Rate (\emph{CVR}) prediction in modern industrial e-commerce platforms is becoming increasingly important, which directly contributes to the final revenue. In order to address the well-known sample selection bias (\emph{SSB}) and data sparsity (\emph{DS}) issues encountered during CVR modeling, the abundant ...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	231,327
2408.08056	DATTA: Towards Diversity Adaptive Test-Time Adaptation in Dynamic Wild World	Test-time adaptation (TTA) effectively addresses distribution shifts between training and testing data by adjusting models on test samples, which is crucial for improving model inference in real-world applications. However, traditional TTA methods typically follow a fixed pattern to address the dynamic data patterns (l...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	480,835
1904.01920	CubiCasa5K: A Dataset and an Improved Multi-Task Model for Floorplan Image Analysis	Better understanding and modelling of building interiors and the emergence of more impressive AR/VR technology has brought up the need for automatic parsing of floorplan images. However, there is a clear lack of representative datasets to investigate the problem further. To address this shortcoming, this paper presents...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	126,284
1609.07257	Using Neural Network Formalism to Solve Multiple-Instance Problems	Many objects in the real world are difficult to describe by a single numerical vector of a fixed length, whereas describing them by a set of vectors is more natural. Therefore, Multiple instance learning (MIL) techniques have been constantly gaining on importance throughout last years. MIL formalism represents each obj...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	61,410
2203.06668	Towards Personalized Intelligence at Scale	Personalized Intelligence (PI) is the problem of providing customized AI experiences tailored to each individual user. In many applications, PI is preferred or even required. Existing personalization approaches involve fine-tuning pre-trained models to create new customized models. However, these approaches require a s...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	285,193
2412.20193	Imitation Learning from Suboptimal Demonstrations via Meta-Learning An Action Ranker	A major bottleneck in imitation learning is the requirement of a large number of expert demonstrations, which can be expensive or inaccessible. Learning from supplementary demonstrations without strict quality requirements has emerged as a powerful paradigm to address this challenge. However, previous methods often fai...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	521,127
1104.2059	Template-based matching using weight maps	Template matching is one of the most prevalent pattern recognition methods worldwide. It has found uses in most visual concept detection fields. In this work, we investigate methods for improving template matching by adjusting the weights of different regions of the template. We compare several weight maps and test the...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	9,948

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