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
1011.5076	Application of a Quantum Ensemble Model to Linguistic Analysis	A new set of parameters to describe the word frequency behavior of texts is proposed. The analogy between the word frequency distribution and the Bose-distribution is suggested and the notion of "temperature" is introduced for this case. The calculations are made for English, Ukrainian, and the Guinean Maninka language...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	8,308
1404.2471	Yet another algorithm to compute the nonlinearity of a Boolean function	We associate to each Boolean function a polynomial whose evaluations represents the distances from all possible Boolean affine functions. Both determining the coefficients of this polynomial from the truth table of the Boolean function and computing its evaluation vector requires a worst-case complexity of $O(n2^n)$ in...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	32,217
2306.11765	About some compression algorithms	We use neural network algorithms for finding compression methods of images in the framework of iterated function systems which is a collection of the transformations of the interval $(0, 1)$ satisfying suitable properties.	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	374,710
1311.1723	On Probability Estimation via Relative Frequencies and Discount	Probability estimation is an elementary building block of every statistical data compression algorithm. In practice probability estimation is often based on relative letter frequencies which get scaled down, when their sum is too large. Such algorithms are attractive in terms of memory requirements, running time and pr...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	28,251
2302.05008	Language-Aware Multilingual Machine Translation with Self-Supervised Learning	Multilingual machine translation (MMT) benefits from cross-lingual transfer but is a challenging multitask optimization problem. This is partly because there is no clear framework to systematically learn language-specific parameters. Self-supervised learning (SSL) approaches that leverage large quantities of monolingua...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	344,894
1712.04927	Enhanced Characterness for Text Detection in the Wild	Text spotting is an interesting research problem as text may appear at any random place and may occur in various forms. Moreover, ability to detect text opens the horizons for improving many advanced computer vision problems. In this paper, we propose a novel language agnostic text detection method utilizing edge enhan...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	86,672
2103.02152	Group-wise Inhibition based Feature Regularization for Robust Classification	The convolutional neural network (CNN) is vulnerable to degraded images with even very small variations (e.g. corrupted and adversarial samples). One of the possible reasons is that CNN pays more attention to the most discriminative regions, but ignores the auxiliary features when learning, leading to the lack of featu...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	222,866
2105.02509	Speech Enhancement using Separable Polling Attention and Global Layer Normalization followed with PReLU	Single channel speech enhancement is a challenging task in speech community. Recently, various neural networks based methods have been applied to speech enhancement. Among these models, PHASEN and T-GSA achieve state-of-the-art performances on the publicly opened VoiceBank+DEMAND corpus. Both of the models reach the CO...	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	233,841
1804.02734	A Structure-Oriented Unsupervised Crawling Strategy for Social Media Sites	Existing techniques for efficiently crawling social media sites rely on URL patterns, query logs, and human supervision. This paper describes SOUrCe, a structure-oriented unsupervised crawler that uses page structures to learn how to crawl a social media site efficiently. SOUrCe consists of two stages. During its unsup...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	94,476
1705.05615	Learning Edge Representations via Low-Rank Asymmetric Projections	We propose a new method for embedding graphs while preserving directed edge information. Learning such continuous-space vector representations (or embeddings) of nodes in a graph is an important first step for using network information (from social networks, user-item graphs, knowledge bases, etc.) in many machine lear...	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	false	false	73,523
2012.08625	Learning Prediction Intervals for Model Performance	Understanding model performance on unlabeled data is a fundamental challenge of developing, deploying, and maintaining AI systems. Model performance is typically evaluated using test sets or periodic manual quality assessments, both of which require laborious manual data labeling. Automated performance prediction techn...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	211,810
2406.07080	DARA: Decomposition-Alignment-Reasoning Autonomous Language Agent for Question Answering over Knowledge Graphs	Answering Questions over Knowledge Graphs (KGQA) is key to well-functioning autonomous language agents in various real-life applications. To improve the neural-symbolic reasoning capabilities of language agents powered by Large Language Models (LLMs) in KGQA, we propose the DecompositionAlignment-Reasoning Agent (DARA)...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	462,894
2404.16206	Knowledge Graph Completion using Structural and Textual Embeddings	Knowledge Graphs (KGs) are widely employed in artificial intelligence applications, such as question-answering and recommendation systems. However, KGs are frequently found to be incomplete. While much of the existing literature focuses on predicting missing nodes for given incomplete KG triples, there remains an oppor...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	449,395
2007.14474	Construction and Usage of a Human Body Common Coordinate Framework Comprising Clinical, Semantic, and Spatial Ontologies	The National Institutes of Health's (NIH) Human Biomolecular Atlas Program (HuBMAP) aims to create a comprehensive high-resolution atlas of all the cells in the healthy human body. Multiple laboratories across the United States are collecting tissue specimens from different organs of donors who vary in sex, age, and bo...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	true	189,407
2307.00016	Overview Analysis of Recent Developments on Self-Driving Electric Vehicles	This paper provides a comprehensive overview of recent advancements in autonomous electric vehicles (AEVs) within the specified region. It elaborates on the progress and comparative analysis of diverse subsystems, including energy storage, cell balancing for battery systems, vehicle charger layouts, electric vehicle mo...	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	376,836
0901.3984	Stop the Chase	The chase procedure, an algorithm proposed 25+ years ago to fix constraint violations in database instances, has been successfully applied in a variety of contexts, such as query optimization, data exchange, and data integration. Its practicability, however, is limited by the fact that - for an arbitrary set of constra...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	3,051
2108.08983	SMedBERT: A Knowledge-Enhanced Pre-trained Language Model with Structured Semantics for Medical Text Mining	Recently, the performance of Pre-trained Language Models (PLMs) has been significantly improved by injecting knowledge facts to enhance their abilities of language understanding. For medical domains, the background knowledge sources are especially useful, due to the massive medical terms and their complicated relations...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	251,451
1811.06981	Learned Video Compression	We present a new algorithm for video coding, learned end-to-end for the low-latency mode. In this setting, our approach outperforms all existing video codecs across nearly the entire bitrate range. To our knowledge, this is the first ML-based method to do so. We evaluate our approach on standard video compression tes...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	113,632
2406.03445	Pre-trained Large Language Models Use Fourier Features to Compute Addition	Pre-trained large language models (LLMs) exhibit impressive mathematical reasoning capabilities, yet how they compute basic arithmetic, such as addition, remains unclear. This paper shows that pre-trained LLMs add numbers using Fourier features -- dimensions in the hidden state that represent numbers via a set of featu...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	461,243
2304.04960	Panoramic Image-to-Image Translation	In this paper, we tackle the challenging task of Panoramic Image-to-Image translation (Pano-I2I) for the first time. This task is difficult due to the geometric distortion of panoramic images and the lack of a panoramic image dataset with diverse conditions, like weather or time. To address these challenges, we propose...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	357,429
2006.13932	Deep Learning-based Computational Pathology Predicts Origins for Cancers of Unknown Primary	Cancer of unknown primary (CUP) is an enigmatic group of diagnoses where the primary anatomical site of tumor origin cannot be determined. This poses a significant challenge since modern therapeutics such as chemotherapy regimen and immune checkpoint inhibitors are specific to the primary tumor. Recent work has focused...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	184,078
2309.02171	A Wideband MIMO Channel Model for Aerial Intelligent Reflecting Surface-Assisted Wireless Communications	Compared to traditional intelligent reflecting surfaces(IRS), aerial IRS (AIRS) has unique advantages, such as more flexible deployment and wider service coverage. However, modeling AIRS in the channel presents new challenges due to their mobility. In this paper, a three-dimensional (3D) wideband channel model for AIRS...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	389,957
1703.06995	Spatio-Temporal Facial Expression Recognition Using Convolutional Neural Networks and Conditional Random Fields	Automated Facial Expression Recognition (FER) has been a challenging task for decades. Many of the existing works use hand-crafted features such as LBP, HOG, LPQ, and Histogram of Optical Flow (HOF) combined with classifiers such as Support Vector Machines for expression recognition. These methods often require rigorou...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	70,313
1402.5077	Group-sparse Matrix Recovery	We apply the OSCAR (octagonal selection and clustering algorithms for regression) in recovering group-sparse matrices (two-dimensional---2D---arrays) from compressive measurements. We propose a 2D version of OSCAR (2OSCAR) consisting of the $\ell_1$ norm and the pair-wise $\ell_{\infty}$ norm, which is convex but non-d...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	31,024
2104.00166	A Semidefinite Programming Approach to Discrete-time Infinite Horizon Persistent Monitoring	We investigate the problem of persistent monitoring, where a mobile agent has to survey multiple targets in an environment in order to estimate their internal states. These internal states evolve with linear stochastic dynamics and the agent can observe them with a linear observation model. However, the signal to noise...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	227,897
1605.02288	Bayesian Overlapping Community Detection in Dynamic Networks	Detecting community structures in social networks has gained considerable attention in recent years. However, lack of prior knowledge about the number of communities, and their overlapping nature have made community detection a challenging problem. Moreover, many of the existing methods only consider static networks, w...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	55,609
2203.05482	Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time	The conventional recipe for maximizing model accuracy is to (1) train multiple models with various hyperparameters and (2) pick the individual model which performs best on a held-out validation set, discarding the remainder. In this paper, we revisit the second step of this procedure in the context of fine-tuning large...	false	false	false	false	false	false	true	false	true	false	false	true	false	false	false	false	false	false	284,821
1603.01694	Intracell Interference Characterization and Cluster Inference for D2D Communication	The homogeneous poisson point process (PPP) is widely used to model temporal, spatial or both topologies of base stations (BSs) and mobile terminals (MTs). However, negative spatial correlation in BSs, due to strategical deployments, and positive spatial correlations in MTs, due to homophilic relations, cannot be captu...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	52,921
1803.11241	Improve the performance of transfer learning without fine-tuning using dissimilarity-based multi-view learning for breast cancer histology images	Breast cancer is one of the most common types of cancer and leading cancer-related death causes for women. In the context of ICIAR 2018 Grand Challenge on Breast Cancer Histology Images, we compare one handcrafted feature extractor and five transfer learning feature extractors based on deep learning. We find out that t...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	93,848
1901.05662	Two classes of linear codes with a few weights based on twisted Kloosterman sums	Linear codes with a few weights have wide applications in information security, data storage systems, consuming electronics and communication systems. Construction of the linear codes with a few weights and determination of their parameters are an important research topic in coding theory. In this paper, we construct t...	false	false	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	118,834
2104.06903	Harmonious Semantic Line Detection via Maximal Weight Clique Selection	A novel algorithm to detect an optimal set of semantic lines is proposed in this work. We develop two networks: selection network (S-Net) and harmonization network (H-Net). First, S-Net computes the probabilities and offsets of line candidates. Second, we filter out irrelevant lines through a selection-and-removal proc...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	230,229
1404.1312	Lattices over Eisenstein Integers for Compute-and-Forward	In this paper, we consider the use of lattice codes over Eisenstein integers for implementing a compute-and-forward protocol in wireless networks when channel state information is not available at the transmitter. We extend the compute-and-forward paradigm of Nazer and Gastpar to decoding Eisenstein integer combination...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	32,097
2311.05557	Exploiting Neural-Network Statistics for Low-Power DNN Inference	Specialized compute blocks have been developed for efficient DNN execution. However, due to the vast amount of data and parameter movements, the interconnects and on-chip memories form another bottleneck, impairing power and performance. This work addresses this bottleneck by contributing a low-power technique for edge...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	406,619
1903.04797	Elements of Sequential Monte Carlo	A core problem in statistics and probabilistic machine learning is to compute probability distributions and expectations. This is the fundamental problem of Bayesian statistics and machine learning, which frames all inference as expectations with respect to the posterior distribution. The key challenge is to approximat...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	124,046
2404.15199	Reinforcement Learning with Adaptive Regularization for Safe Control of Critical Systems	Reinforcement Learning (RL) is a powerful method for controlling dynamic systems, but its learning mechanism can lead to unpredictable actions that undermine the safety of critical systems. Here, we propose RL with Adaptive Regularization (RL-AR), an algorithm that enables safe RL exploration by combining the RL policy...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	448,982
2408.16340	Learned Image Transmission with Hierarchical Variational Autoencoder	In this paper, we introduce an innovative hierarchical joint source-channel coding (HJSCC) framework for image transmission, utilizing a hierarchical variational autoencoder (VAE). Our approach leverages a combination of bottom-up and top-down paths at the transmitter to autoregressively generate multiple hierarchical ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	484,299
2105.14150	Annotation Inconsistency and Entity Bias in MultiWOZ	MultiWOZ is one of the most popular multi-domain task-oriented dialog datasets, containing 10K+ annotated dialogs covering eight domains. It has been widely accepted as a benchmark for various dialog tasks, e.g., dialog state tracking (DST), natural language generation (NLG), and end-to-end (E2E) dialog modeling. In th...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	237,549
2111.15077	Unsupervised Domain Generalization for Person Re-identification: A Domain-specific Adaptive Framework	Domain generalization (DG) has attracted much attention in person re-identification (ReID) recently. It aims to make a model trained on multiple source domains generalize to an unseen target domain. Although achieving promising progress, existing methods usually need the source domains to be labeled, which could be a s...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	268,799
2111.01297	Deep neural networks as nested dynamical systems	There is an analogy that is often made between deep neural networks and actual brains, suggested by the nomenclature itself: the "neurons" in deep neural networks should correspond to neurons (or nerve cells, to avoid confusion) in the brain. We claim, however, that this analogy doesn't even type check: it is structura...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	264,512
2102.10140	BPLight-CNN: A Photonics-based Backpropagation Accelerator for Deep Learning	Training deep learning networks involves continuous weight updates across the various layers of the deep network while using a backpropagation algorithm (BP). This results in expensive computation overheads during training. Consequently, most deep learning accelerators today employ pre-trained weights and focus only on...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	220,984
2108.00402	Style Curriculum Learning for Robust Medical Image Segmentation	The performance of deep segmentation models often degrades due to distribution shifts in image intensities between the training and test data sets. This is particularly pronounced in multi-centre studies involving data acquired using multi-vendor scanners, with variations in acquisition protocols. It is challenging to ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	248,710
2408.07884	Instruct Large Language Models to Generate Scientific Literature Survey Step by Step	Abstract. Automatically generating scientific literature surveys is a valuable task that can significantly enhance research efficiency. However, the diverse and complex nature of information within a literature survey poses substantial challenges for generative models. In this paper, we design a series of prompts to sy...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	480,761
2102.07035	Model-free Representation Learning and Exploration in Low-rank MDPs	The low rank MDP has emerged as an important model for studying representation learning and exploration in reinforcement learning. With a known representation, several model-free exploration strategies exist. In contrast, all algorithms for the unknown representation setting are model-based, thereby requiring the abili...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	219,963
2404.03320	Exploring Lightweight Federated Learning for Distributed Load Forecasting	Federated Learning (FL) is a distributed learning scheme that enables deep learning to be applied to sensitive data streams and applications in a privacy-preserving manner. This paper focuses on the use of FL for analyzing smart energy meter data with the aim to achieve comparable accuracy to state-of-the-art methods f...	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	444,209
2105.07212	Generalized Nearest Neighbor Decoding for MIMO Channels with Imperfect Channel State Information	Information transmission over a multiple-input-multiple-output (MIMO) fading channel with imperfect channel state information (CSI) is investigated, under a new receiver architecture which combines the recently proposed generalized nearest neighbor decoding rule (GNNDR) and a successive procedure in the spirit of succe...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	235,362
2404.00404	Value, Representation, Information and Communication	A new analytic framework is first formalized via the usage of the Monadology (Leibniz 1898), to expand the understanding of Zermelo-Fraenkel-choice set theory (ZFC) and Von Neumann-Bernays-Godel set theory (NBG). Implicitly, the framework levels value, representation and information separately. Given the fact that ther...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	442,905
1910.09671	Coercing Machine Learning to Output Physically Accurate Results	Many machine/deep learning artificial neural networks are trained to simply be interpolation functions that map input variables to output values interpolated from the training data in a linear/nonlinear fashion. Even when the input/output pairs of the training data are physically accurate (e.g. the results of an experi...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	150,262
2502.01919	Poisson Hierarchical Indian Buffet Processes for Within and Across Group Sharing of Latent Features-With Indications for Microbiome Species Sampling Models	In this work, we present a comprehensive Bayesian posterior analysis of what we term Poisson Hierarchical Indian Buffet Processes, designed for complex random sparse count species sampling models that allow for the sharing of information across and within groups. This analysis covers a potentially infinite number of sp...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	530,089
2305.15546	Regret-Optimal Model-Free Reinforcement Learning for Discounted MDPs with Short Burn-In Time	A crucial problem in reinforcement learning is learning the optimal policy. We study this in tabular infinite-horizon discounted Markov decision processes under the online setting. The existing algorithms either fail to achieve regret optimality or have to incur a high memory and computational cost. In addition, existi...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	367,654
2410.12128	Multimodal Fusion with Relational Learning for Molecular Property Prediction	Graph based molecular representation learning is essential for accurately predicting molecular properties in drug discovery and materials science; however, it faces significant challenges due to the intricate relationships among molecules and the limited chemical knowledge utilized during training. While contrastive le...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	498,863
2309.10421	Exploring Different Levels of Supervision for Detecting and Localizing Solar Panels on Remote Sensing Imagery	This study investigates object presence detection and localization in remote sensing imagery, focusing on solar panel recognition. We explore different levels of supervision, evaluating three models: a fully supervised object detector, a weakly supervised image classifier with CAM-based localization, and a minimally su...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	393,002
1912.12355	SoftAdapt: Techniques for Adaptive Loss Weighting of Neural Networks with Multi-Part Loss Functions	Adaptive loss function formulation is an active area of research and has gained a great deal of popularity in recent years, following the success of deep learning. However, existing frameworks of adaptive loss functions often suffer from slow convergence and poor choice of weights for the loss components. Traditionally...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	158,823
2009.12326	Online Missing Value Imputation and Change Point Detection with the Gaussian Copula	Missing value imputation is crucial for real-world data science workflows. Imputation is harder in the online setting, as it requires the imputation method itself to be able to evolve over time. For practical applications, imputation algorithms should produce imputations that match the true data distribution, handle da...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	197,392
2005.03216	OTFS-NOMA based on SCMA	Orthogonal Time Frequency Space (OTFS) is a $\text{2-D}$ modulation technique that has the potential to overcome the challenges faced by orthogonal frequency division multiplexing (OFDM) in high Doppler environments. The performance of OTFS in a multi-user scenario with orthogonal multiple access (OMA) techniques has b...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	176,089
2502.12707	CausalMan: A physics-based simulator for large-scale causality	A comprehensive understanding of causality is critical for navigating and operating within today's complex real-world systems. The absence of realistic causal models with known data generating processes complicates fair benchmarking. In this paper, we present the CausalMan simulator, modeled after a real-world producti...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	535,017
1710.01852	Finite Time Identification in Unstable Linear Systems	Identification of the parameters of stable linear dynamical systems is a well-studied problem in the literature, both in the low and high-dimensional settings. However, there are hardly any results for the unstable case, especially regarding finite time bounds. For this setting, classical results on least-squares estim...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	82,074
1606.02430	On minimal distance between q-ary bent functions	The minimal Hamming distance between distinct $p$-ary bent functions of $2n$ variables is proved to be $p^n$ for any prime $p$. It is shown that the number of $p$-ary bent functions at the distance $p^n$ from the quadratic bent function is equal to $p^n(p^{n-1}+1)\cdots(p+1)(p-1)$ as $p>2$.	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	56,958
1208.2808	Analysis of a Statistical Hypothesis Based Learning Mechanism for Faster crawling	The growth of world-wide-web (WWW) spreads its wings from an intangible quantities of web-pages to a gigantic hub of web information which gradually increases the complexity of crawling process in a search engine. A search engine handles a lot of queries from various parts of this world, and the answers of it solely de...	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	false	18,070
2112.13418	Neuro-Symbolic Hierarchical Rule Induction	We propose an efficient interpretable neuro-symbolic model to solve Inductive Logic Programming (ILP) problems. In this model, which is built from a set of meta-rules organised in a hierarchical structure, first-order rules are invented by learning embeddings to match facts and body predicates of a meta-rule. To instan...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	273,237
0907.0328	Degenerate neutrality creates evolvable fitness landscapes	Understanding how systems can be designed to be evolvable is fundamental to research in optimization, evolution, and complex systems science. Many researchers have thus recognized the importance of evolvability, i.e. the ability to find new variants of higher fitness, in the fields of biological evolution and evolution...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	true	true	false	false	4,014
1705.02426	Analogical Inference for Multi-Relational Embeddings	Large-scale multi-relational embedding refers to the task of learning the latent representations for entities and relations in large knowledge graphs. An effective and scalable solution for this problem is crucial for the true success of knowledge-based inference in a broad range of applications. This paper proposes a ...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	72,991
1410.2632	Evaluation of a Conversation Management Toolkit for Multi Agent Programming	The Agent Conversation Reasoning Engine (ACRE) is intended to aid agent developers to improve the management and reliability of agent communication. To evaluate its effectiveness, a problem scenario was created that could be used to compare code written with and without the use of ACRE by groups of test subjects. Thi...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	36,631
2404.05490	Two-Person Interaction Augmentation with Skeleton Priors	Close and continuous interaction with rich contacts is a crucial aspect of human activities (e.g. hugging, dancing) and of interest in many domains like activity recognition, motion prediction, character animation, etc. However, acquiring such skeletal motion is challenging. While direct motion capture is expensive and...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	445,101
2305.12217	PromptNER: A Prompting Method for Few-shot Named Entity Recognition via k Nearest Neighbor Search	Few-shot Named Entity Recognition (NER) is a task aiming to identify named entities via limited annotated samples. Recently, prototypical networks have shown promising performance in few-shot NER. Most of prototypical networks will utilize the entities from the support set to construct label prototypes and use the quer...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	365,890
2412.02969	Unified Inductive Logic: From Formal Learning to Statistical Inference to Supervised Learning	While the traditional conception of inductive logic is Carnapian, I develop a Peircean alternative and use it to unify formal learning theory, statistics, and a significant part of machine learning: supervised learning. Some crucial standards for evaluating non-deductive inferences have been assumed separately in those...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	513,758
2109.03465	A Survey of Sound Source Localization with Deep Learning Methods	This article is a survey on deep learning methods for single and multiple sound source localization. We are particularly interested in sound source localization in indoor/domestic environment, where reverberation and diffuse noise are present. We provide an exhaustive topography of the neural-based localization literat...	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	254,079
1901.01605	Bounds on the Length of Functional PIR and Batch codes	A functional $k$-PIR code of dimension $s$ consists of $n$ servers storing linear combinations of $s$ linearly independent information symbols. Any linear combination of the $s$ information symbols can be recovered by $k$ disjoint subsets of servers. The goal is to find the smallest number of servers for given $k$ and ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	118,015
2111.13284	Ensembling of Distilled Models from Multi-task Teachers for Constrained Resource Language Pairs	This paper describes our submission to the constrained track of WMT21 shared news translation task. We focus on the three relatively low resource language pairs Bengali to and from Hindi, English to and from Hausa, and Xhosa to and from Zulu. To overcome the limitation of relatively low parallel data we train a multili...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	268,246
2307.02784	On the Spatial-Wideband Effects in Millimeter-Wave Cell-Free Massive MIMO	In this paper, we investigate the spatial-wideband effects in cell-free massive MIMO (CF-mMIMO) systems in mmWave bands. The utilization of mmWave frequencies brings challenges such as signal attenuation and the need for denser networks like ultra-dense networks (UDN) to maintain communication performance. CF-mMIMO is ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	377,812
1908.04752	Identification of relevant diffusion MRI metrics impacting cognitive functions using a novel feature selection method	Mild Traumatic Brain Injury (mTBI) is a significant public health problem. The most troubling symptoms after mTBI are cognitive complaints. Studies show measurable differences between patients with mTBI and healthy controls with respect to tissue microstructure using diffusion MRI. However, it remains unclear which dif...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	141,564
1612.05786	Predicting Completeness in Knowledge Bases	Knowledge bases such as Wikidata, DBpedia, or YAGO contain millions of entities and facts. In some knowledge bases, the correctness of these facts has been evaluated. However, much less is known about their completeness, i.e., the proportion of real facts that the knowledge bases cover. In this work, we investigate dif...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	65,726
2405.20649	Reward-based Input Construction for Cross-document Relation Extraction	Relation extraction (RE) is a fundamental task in natural language processing, aiming to identify relations between target entities in text. While many RE methods are designed for a single sentence or document, cross-document RE has emerged to address relations across multiple long documents. Given the nature of long d...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	459,451
1812.00546	Learning the progression and clinical subtypes of Alzheimer's disease from longitudinal clinical data	Alzheimer's disease (AD) is a degenerative brain disease impairing a person's ability to perform day to day activities. The clinical manifestations of Alzheimer's disease are characterized by heterogeneity in age, disease span, progression rate, impairment of memory and cognitive abilities. Due to these variabilities, ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	115,294
2403.10962	Exploiting Topological Priors for Boosting Point Cloud Generation	This paper presents an innovative enhancement to the Sphere as Prior Generative Adversarial Network (SP-GAN) model, a state-of-the-art GAN designed for point cloud generation. A novel method is introduced for point cloud generation that elevates the structural integrity and overall quality of the generated point clouds...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	438,452
1905.07961	Guiding Inferences in Connection Tableau by Recurrent Neural Networks	We present a dataset and experiments on applying recurrent neural networks (RNNs) for guiding clause selection in the connection tableau proof calculus. The RNN encodes a sequence of literals from the current branch of the partial proof tree to a hidden vector state; using it, the system selects a clause for extending ...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	true	false	true	131,373
2312.00123	Flow Matching Beyond Kinematics: Generating Jets with Particle-ID and Trajectory Displacement Information	We introduce the first generative model trained on the JetClass dataset. Our model generates jets at the constituent level, and it is a permutation-equivariant continuous normalizing flow (CNF) trained with the flow matching technique. It is conditioned on the jet type, so that a single model can be used to generate th...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	411,927
2210.11545	Transferring learned patterns from ground-based field imagery to predict UAV-based imagery for crop and weed semantic segmentation in precision crop farming	Weed and crop segmentation is becoming an increasingly integral part of precision farming that leverages the current computer vision and deep learning technologies. Research has been extensively carried out based on images captured with a camera from various platforms. Unmanned aerial vehicles (UAVs) and ground-based v...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	325,352
2005.10752	THz Precoding for 6G: Applications, Challenges, Solutions, and Opportunities	Benefiting from the ultra-wide bandwidth, terahertz (THz) communication is becoming a promising technology for future 6G networks. For THz communication, precoding is an essential technique to overcome the severe path loss of THz signals in order to support the desired coverage. In this article, we systematically inves...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	178,274
2205.11976	Universal Dependency Treebank for Odia Language	This paper presents the first publicly available treebank of Odia, a morphologically rich low resource Indian language. The treebank contains approx. 1082 tokens (100 sentences) in Odia selected from "Samantar", the largest available parallel corpora collection for Indic languages. All the selected sentences are manual...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	298,356
1710.07785	Skew cyclic and skew $(\alpha_1 + u\alpha_2 + v\alpha_3 + uv\alpha_4)$-constacyclic codes over $F_q + uF_q + vF_q + uvF_q$	In this note, we study skew cyclic and skew constacyclic codes over the ring $\mathcal{R}=F_{q}+uF_{q}+vF_{q}+uvF_{q}$ where $q=p^{m},$ $p$ is an odd prime, $u^{2}=u,~v^{2}=v,~uv=vu$. We show that Gray images of a skew cyclic and skew $\alpha$-constacyclic code of length $n$ are skew quasi-cyclic code of length $4n$ ov...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	82,989
2207.00083	DarKnight: An Accelerated Framework for Privacy and Integrity Preserving Deep Learning Using Trusted Hardware	Privacy and security-related concerns are growing as machine learning reaches diverse application domains. The data holders want to train or infer with private data while exploiting accelerators, such as GPUs, that are hosted in the cloud. Cloud systems are vulnerable to attackers that compromise the privacy of data an...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	true	305,628
1903.05239	Non-Negative Local Sparse Coding for Subspace Clustering	Subspace sparse coding (SSC) algorithms have proven to be beneficial to clustering problems. They provide an alternative data representation in which the underlying structure of the clusters can be better captured. However, most of the research in this area is mainly focused on enhancing the sparse coding part of the p...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	124,123
2406.16008	Found in the Middle: Calibrating Positional Attention Bias Improves Long Context Utilization	Large language models (LLMs), even when specifically trained to process long input contexts, struggle to capture relevant information located in the middle of their input. This phenomenon has been known as the lost-in-the-middle problem. In this work, we make three contributions. First, we set out to understand the fac...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	466,960
1508.00801	Identifying Avatar Aliases in Starcraft 2	In electronic sports, cyberathletes conceal their online training using different avatars (virtual identities), allowing them not being recognized by the opponents they may face in future competitions. In this article, we propose a method to tackle this avatar aliases identification problem. Our method trains a classif...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	45,714
2004.04305	Conversation Learner -- A Machine Teaching Tool for Building Dialog Managers for Task-Oriented Dialog Systems	Traditionally, industry solutions for building a task-oriented dialog system have relied on helping dialog authors define rule-based dialog managers, represented as dialog flows. While dialog flows are intuitively interpretable and good for simple scenarios, they fall short of performance in terms of the flexibility ne...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	171,836
2305.19591	Traffic Prediction using Artificial Intelligence: Review of Recent Advances and Emerging Opportunities	Traffic prediction plays a crucial role in alleviating traffic congestion which represents a critical problem globally, resulting in negative consequences such as lost hours of additional travel time and increased fuel consumption. Integrating emerging technologies into transportation systems provides opportunities for...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	369,599
2304.11954	Spikingformer: Spike-driven Residual Learning for Transformer-based Spiking Neural Network	Spiking neural networks (SNNs) offer a promising energy-efficient alternative to artificial neural networks, due to their event-driven spiking computation. However, state-of-the-art deep SNNs (including Spikformer and SEW ResNet) suffer from non-spike computations (integer-float multiplications) caused by the structure...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	false	false	360,032
2204.10716	Hierarchical Label-wise Attention Transformer Model for Explainable ICD Coding	International Classification of Diseases (ICD) coding plays an important role in systematically classifying morbidity and mortality data. In this study, we propose a hierarchical label-wise attention Transformer model (HiLAT) for the explainable prediction of ICD codes from clinical documents. HiLAT firstly fine-tunes ...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	292,890
1910.11529	Manipulating Node Similarity Measures in Networks	Node similarity measures quantify how similar a pair of nodes are in a network. These similarity measures turn out to be an important fundamental tool for many real world applications such as link prediction in networks, recommender systems etc. An important class of similarity measures are local similarity measures. T...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	true	150,810
2310.07749	OpenLEAF: Open-Domain Interleaved Image-Text Generation and Evaluation	This work investigates a challenging task named open-domain interleaved image-text generation, which generates interleaved texts and images following an input query. We propose a new interleaved generation framework based on prompting large-language models (LLMs) and pre-trained text-to-image (T2I) models, namely OpenL...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	399,111
1906.03815	Learning to Segment Skin Lesions from Noisy Annotations	Deep convolutional neural networks have driven substantial advancements in the automatic understanding of images. Requiring a large collection of images and their associated annotations is one of the main bottlenecks limiting the adoption of deep networks. In the task of medical image segmentation, requiring pixel-leve...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	134,508
2407.10784	AdapTable: Test-Time Adaptation for Tabular Data via Shift-Aware Uncertainty Calibrator and Label Distribution Handler	In real-world scenarios, tabular data often suffer from distribution shifts that threaten the performance of machine learning models. Despite its prevalence and importance, handling distribution shifts in the tabular domain remains underexplored due to the inherent challenges within the tabular data itself. In this sen...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	473,125
1812.05262	ELASTIC: Improving CNNs with Dynamic Scaling Policies	Scale variation has been a challenge from traditional to modern approaches in computer vision. Most solutions to scale issues have a similar theme: a set of intuitive and manually designed policies that are generic and fixed (e.g. SIFT or feature pyramid). We argue that the scaling policy should be learned from data. I...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	116,381
2310.10138	Node-based Knowledge Graph Contrastive Learning for Medical Relationship Prediction	The embedding of Biomedical Knowledge Graphs (BKGs) generates robust representations, valuable for a variety of artificial intelligence applications, including predicting drug combinations and reasoning disease-drug relationships. Meanwhile, contrastive learning (CL) is widely employed to enhance the distinctiveness of...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	false	400,109
2202.11712	Flow-based sampling in the lattice Schwinger model at criticality	Recent results suggest that flow-based algorithms may provide efficient sampling of field distributions for lattice field theory applications, such as studies of quantum chromodynamics and the Schwinger model. In this work, we provide a numerical demonstration of robust flow-based sampling in the Schwinger model at the...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	281,974
2403.11520	State-Separated SARSA: A Practical Sequential Decision-Making Algorithm with Recovering Rewards	While many multi-armed bandit algorithms assume that rewards for all arms are constant across rounds, this assumption does not hold in many real-world scenarios. This paper considers the setting of recovering bandits (Pike-Burke & Grunewalder, 2019), where the reward depends on the number of rounds elapsed since the la...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	438,741
2004.04462	FKAConv: Feature-Kernel Alignment for Point Cloud Convolution	Recent state-of-the-art methods for point cloud processing are based on the notion of point convolution, for which several approaches have been proposed. In this paper, inspired by discrete convolution in image processing, we provide a formulation to relate and analyze a number of point convolution methods. We also pro...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	171,890
1809.04017	Reducing Uncertainty of Schema Matching via Crowdsourcing with Accuracy Rates	Schema matching is a central challenge for data integration systems. Inspired by the popularity and the success of crowdsourcing platforms, we explore the use of crowdsourcing to reduce the uncertainty of schema matching. Since crowdsourcing platforms are most effective for simple questions, we assume that each Corresp...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	107,450
2109.10616	Enriching and Controlling Global Semantics for Text Summarization	Recently, Transformer-based models have been proven effective in the abstractive summarization task by creating fluent and informative summaries. Nevertheless, these models still suffer from the short-range dependency problem, causing them to produce summaries that miss the key points of document. In this paper, we att...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	256,687
2403.12995	ESM All-Atom: Multi-scale Protein Language Model for Unified Molecular Modeling	Protein language models have demonstrated significant potential in the field of protein engineering. However, current protein language models primarily operate at the residue scale, which limits their ability to provide information at the atom level. This limitation prevents us from fully exploiting the capabilities of...	false	true	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	439,430

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