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
2101.08685	ItNet: iterative neural networks with small graphs for accurate, efficient and anytime semantic segmentation	Deep neural networks have usually to be compressed and accelerated for their usage in low-power, e.g. mobile, devices. Recently, massively-parallel hardware accelerators were developed that offer high throughput and low latency at low power by utilizing in-memory computation. However, to exploit these benefits the comp...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	true	216,386
1503.01903	Partial light field tomographic reconstruction from a fixed-camera focal stack	This paper describes a novel approach to partially reconstruct high-resolution 4D light fields from a stack of differently focused photographs taken with a fixed camera. First, a focus map is calculated from this stack using a simple approach combining gradient detection and region expansion with graph-cut. Then, this ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	40,877
1911.06255	Millimeter Wave Base Stations with Cameras: Vision Aided Beam and Blockage Prediction	This paper investigates a novel research direction that leverages vision to help overcome the critical wireless communication challenges. In particular, this paper considers millimeter wave (mmWave) communication systems, which are principal components of 5G and beyond. These systems face two important challenges: (i) ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	153,492
2409.12179	Computational Dynamical Systems	We study the computational complexity theory of smooth, finite-dimensional dynamical systems. Building off of previous work, we give definitions for what it means for a smooth dynamical system to simulate a Turing machine. We then show that 'chaotic' dynamical systems (more precisely, Axiom A systems) and 'integrable' ...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	489,473
1901.01652	Randomized Tensor Ring Decomposition and Its Application to Large-scale Data Reconstruction	Dimensionality reduction is an essential technique for multi-way large-scale data, i.e., tensor. Tensor ring (TR) decomposition has become popular due to its high representation ability and flexibility. However, the traditional TR decomposition algorithms suffer from high computational cost when facing large-scale data...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	118,025
2107.01410	Maximum Entropy Weighted Independent Set Pooling for Graph Neural Networks	In this paper, we propose a novel pooling layer for graph neural networks based on maximizing the mutual information between the pooled graph and the input graph. Since the maximum mutual information is difficult to compute, we employ the Shannon capacity of a graph as an inductive bias to our pooling method. More prec...	false	false	false	false	true	false	true	false	false	true	false	false	false	false	false	true	false	false	244,479
2201.08093	AirPose: Multi-View Fusion Network for Aerial 3D Human Pose and Shape Estimation	In this letter, we present a novel markerless 3D human motion capture (MoCap) system for unstructured, outdoor environments that uses a team of autonomous unmanned aerial vehicles (UAVs) with on-board RGB cameras and computation. Existing methods are limited by calibrated cameras and off-line processing. Thus, we prese...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	276,226
2201.12126	Leveraging class abstraction for commonsense reinforcement learning via residual policy gradient methods	Enabling reinforcement learning (RL) agents to leverage a knowledge base while learning from experience promises to advance RL in knowledge intensive domains. However, it has proven difficult to leverage knowledge that is not manually tailored to the environment. We propose to use the subclass relationships present in ...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	277,536
2012.01203	Learning Delaunay Surface Elements for Mesh Reconstruction	We present a method for reconstructing triangle meshes from point clouds. Existing learning-based methods for mesh reconstruction mostly generate triangles individually, making it hard to create manifold meshes. We leverage the properties of 2D Delaunay triangulations to construct a mesh from manifold surface elements....	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	209,353
1905.06945	Uncertainty quantification of molecular property prediction using Bayesian neural network models	In chemistry, deep neural network models have been increasingly utilized in a variety of applications such as molecular property predictions, novel molecule designs, and planning chemical reactions. Despite the rapid increase in the use of state-of-the-art models and algorithms, deep neural network models often produce...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	131,116
2401.04325	RadarCam-Depth: Radar-Camera Fusion for Depth Estimation with Learned Metric Scale	We present a novel approach for metric dense depth estimation based on the fusion of a single-view image and a sparse, noisy Radar point cloud. The direct fusion of heterogeneous Radar and image data, or their encodings, tends to yield dense depth maps with significant artifacts, blurred boundaries, and suboptimal accu...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	420,391
2402.15315	On Minimal Depth in Neural Networks	A characterization of the representability of neural networks is relevant to comprehend their success in artificial intelligence. This study investigate two topics on ReLU neural network expressivity and their connection with a conjecture related to the minimum depth required for representing any continuous piecewise l...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	432,090
2409.12067	Fitting Multilevel Factor Models	We examine a special case of the multilevel factor model, with covariance given by multilevel low rank (MLR) matrix~\cite{parshakova2023factor}. We develop a novel, fast implementation of the expectation-maximization (EM) algorithm, tailored for multilevel factor models, to maximize the likelihood of the observed data....	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	489,424
2502.07175	Foreign-Object Detection in High-Voltage Transmission Line Based on Improved YOLOv8m	The safe operation of high-voltage transmission lines ensures the power grid's security. Various foreign objects attached to the transmission lines, such as balloons, kites and nesting birds, can significantly affect the safe and stable operation of high-voltage transmission lines. With the advancement of computer visi...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	532,461
2203.04547	Spectral Efficiency of Unicast and Multigroup Multicast Transmission in Cell-free Distributed Massive MIMO Systems	In this paper, we consider a joint unicast and multi-group multicast cell-free distributed massive multiple-input multiple-output (MIMO) system, while accounting for co-pilot assignment strategy based channel estimation, pilot contamination and different precoding schemes. Under the co-pilot assignment strategy, we der...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	284,507
2005.02666	Evolutionary Multi-Objective Design of SARS-CoV-2 Protease Inhibitor Candidates	Computational drug design based on artificial intelligence is an emerging research area. At the time of writing this paper, the world suffers from an outbreak of the coronavirus SARS-CoV-2. A promising way to stop the virus replication is via protease inhibition. We propose an evolutionary multi-objective algorithm (EM...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	true	false	false	175,948
2108.07538	O-HAS: Optical Hardware Accelerator Search for Boosting Both Acceleration Performance and Development Speed	The recent breakthroughs and prohibitive complexities of Deep Neural Networks (DNNs) have excited extensive interest in domain-specific DNN accelerators, among which optical DNN accelerators are particularly promising thanks to their unprecedented potential of achieving superior performance-per-watt. However, the devel...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	250,950
1909.13375	A Simple and Effective Model for Answering Multi-span Questions	Models for reading comprehension (RC) commonly restrict their output space to the set of all single contiguous spans from the input, in order to alleviate the learning problem and avoid the need for a model that generates text explicitly. However, forcing an answer to be a single span can be restrictive, and some recen...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	147,404
2204.04487	Informativeness and Invariance: Two Perspectives on Spurious Correlations in Natural Language	Spurious correlations are a threat to the trustworthiness of natural language processing systems, motivating research into methods for identifying and eliminating them. However, addressing the problem of spurious correlations requires more clarity on what they are and how they arise in language data. Gardner et al (202...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	290,673
2005.01932	ExpBERT: Representation Engineering with Natural Language Explanations	Suppose we want to specify the inductive bias that married couples typically go on honeymoons for the task of extracting pairs of spouses from text. In this paper, we allow model developers to specify these types of inductive biases as natural language explanations. We use BERT fine-tuned on MultiNLI to ``interpret'' t...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	175,713
1806.02425	Online User Assessment for Minimal Intervention During Task-Based Robotic Assistance	We propose a novel criterion for evaluating user input for human-robot interfaces for known tasks. We use the mode insertion gradient (MIG)---a tool from hybrid control theory---as a filtering criterion that instantaneously assesses the impact of user actions on a dynamic system over a time window into the future. As a...	true	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	99,778
1811.08212	Computer-Assisted Fraud Detection, From Active Learning to Reward Maximization	The automatic detection of frauds in banking transactions has been recently studied as a way to help the analysts finding fraudulent operations. Due to the availability of a human feedback, this task has been studied in the framework of active learning: the fraud predictor is allowed to sequentially call on an oracle. ...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	113,986
2010.13165	A Dynamical View on Optimization Algorithms of Overparameterized Neural Networks	When equipped with efficient optimization algorithms, the over-parameterized neural networks have demonstrated high level of performance even though the loss function is non-convex and non-smooth. While many works have been focusing on understanding the loss dynamics by training neural networks with the gradient descen...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	203,029
2208.12673	Enabling Weakly-Supervised Temporal Action Localization from On-Device Learning of the Video Stream	Detecting actions in videos have been widely applied in on-device applications. Practical on-device videos are always untrimmed with both action and background. It is desirable for a model to both recognize the class of action and localize the temporal position where the action happens. Such a task is called temporal a...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	314,819
2207.10648	A No-Code Low-Code Paradigm for Authoring Business Automations Using Natural Language	Most business process automation is still developed using traditional automation technologies such as workflow engines. These systems provide domain specific languages that require both business knowledge and programming skills to effectively use. As such, business users often lack adequate programming skills to fully ...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	309,330
2411.04219	Equivariant Graph Network Approximations of High-Degree Polynomials for Force Field Prediction	Recent advancements in equivariant deep models have shown promise in accurately predicting atomic potentials and force fields in molecular dynamics simulations. Using spherical harmonics (SH) and tensor products (TP), these equivariant networks gain enhanced physical understanding, like symmetries and many-body interac...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	506,183
2401.02034	Text2MDT: Extracting Medical Decision Trees from Medical Texts	Knowledge of the medical decision process, which can be modeled as medical decision trees (MDTs), is critical to build clinical decision support systems. However, the current MDT construction methods rely heavily on time-consuming and laborious manual annotation. In this work, we propose a novel task, Text2MDT, to expl...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	419,574
2303.06480	Knowledge Distillation for Efficient Sequences of Training Runs	In many practical scenarios -- like hyperparameter search or continual retraining with new data -- related training runs are performed many times in sequence. Current practice is to train each of these models independently from scratch. We study the problem of exploiting the computation invested in previous runs to red...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	350,863
1909.13458	Student Specialization in Deep ReLU Networks With Finite Width and Input Dimension	We consider a deep ReLU / Leaky ReLU student network trained from the output of a fixed teacher network of the same depth, with Stochastic Gradient Descent (SGD). The student network is \emph{over-realized}: at each layer $l$, the number $n_l$ of student nodes is more than that ($m_l$) of teacher. Under mild conditions...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	147,430
2110.02372	NOMA-Aided Joint Radar and Multicast-Unicast Communication Systems	The novel concept of non-orthogonal multiple access (NOMA) aided joint radar and multicast-unicast communication (Rad-MU-Com) is investigated. Employing the same spectrum resource, a multi-input-multi-output (MIMO) dual-functional radar-communication (DFRC) base station detects the radar-centric users (R-user), while t...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	259,091
1701.06307	A Tutorial on Modeling and Analysis of Dynamic Social Networks. Part I	In recent years, we have observed a significant trend towards filling the gap between social network analysis and control. This trend was enabled by the introduction of new mathematical models describing dynamics of social groups, the advancement in complex networks theory and multi-agent systems, and the development o...	false	false	false	true	false	false	false	false	false	false	true	false	false	false	true	false	false	false	67,112
2111.02987	Numerical Approximation in CFD Problems Using Physics Informed Machine Learning	The thesis focuses on various techniques to find an alternate approximation method that could be universally used for a wide range of CFD problems but with low computational cost and low runtime. Various techniques have been explored within the field of machine learning to gauge the utility in fulfilling the core ambit...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	265,020
2411.06181	Epi-NAF: Enhancing Neural Attenuation Fields for Limited-Angle CT with Epipolar Consistency Conditions	Neural field methods, initially successful in the inverse rendering domain, have recently been extended to CT reconstruction, marking a paradigm shift from traditional techniques. While these approaches deliver state-of-the-art results in sparse-view CT reconstruction, they struggle in limited-angle settings, where inp...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	507,000
1503.02877	Wideband Self-Adaptive RF Cancellation Circuit for Full-Duplex Radio: Operating Principle and Measurements	This paper presents a novel RF circuit architecture for self-interference cancellation in inband full-duplex radio transceivers. The developed canceller is able to provide wideband cancellation with waveform bandwidths in the order of 100 MHz or beyond and contains also self-adaptive or self-healing features enabling a...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	40,985
2106.04779	Point Cloud Upsampling via Disentangled Refinement	Point clouds produced by 3D scanning are often sparse, non-uniform, and noisy. Recent upsampling approaches aim to generate a dense point set, while achieving both distribution uniformity and proximity-to-surface, and possibly amending small holes, all in a single network. After revisiting the task, we propose to disen...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	239,851
2409.18304	Multi-platoon car-following models with flexible platoon sizes and communication levels	In this paper, we extend a single platoon car-following (CF) model to some multi-platoon CF models for connected and autonomous vehicles (CAVs) with flexible platoon size and communication level. Specifically, we consider forward and backward communication methods between platoons with delays. Some general results of l...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	492,184
2405.00934	Benchmarking Representations for Speech, Music, and Acoustic Events	Limited diversity in standardized benchmarks for evaluating audio representation learning (ARL) methods may hinder systematic comparison of current methods' capabilities. We present ARCH, a comprehensive benchmark for evaluating ARL methods on diverse audio classification domains, covering acoustic events, music, and s...	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	451,133
1502.04434	Invariant backpropagation: how to train a transformation-invariant neural network	In many classification problems a classifier should be robust to small variations in the input vector. This is a desired property not only for particular transformations, such as translation and rotation in image classification problems, but also for all others for which the change is small enough to retain the object ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	true	false	false	40,270
1805.04872	Kolmogorov-Sinai entropy and dissipation in driven classical Hamiltonian systems	A central concept in the connection between physics and information theory is entropy, which represents the amount of information extracted from the system by the observer performing measurements in an experiment. Indeed, Jaynes' principle of maximum entropy allows to establish the connection between entropy in statist...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	97,328
2210.05478	Aggregating Layers for Deepfake Detection	The increasing popularity of facial manipulation (Deepfakes) and synthetic face creation raises the need to develop robust forgery detection solutions. Crucially, most work in this domain assume that the Deepfakes in the test set come from the same Deepfake algorithms that were used for training the network. This is no...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	322,868
2011.05841	Linking OpenStreetMap with Knowledge Graphs -- Link Discovery for Schema-Agnostic Volunteered Geographic Information	Representations of geographic entities captured in popular knowledge graphs such as Wikidata and DBpedia are often incomplete. OpenStreetMap (OSM) is a rich source of openly available, volunteered geographic information that has a high potential to complement these representations. However, identity links between the k...	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	false	false	206,063
2008.02711	Exploring Relations in Untrimmed Videos for Self-Supervised Learning	Existing video self-supervised learning methods mainly rely on trimmed videos for model training. However, trimmed datasets are manually annotated from untrimmed videos. In this sense, these methods are not really self-supervised. In this paper, we propose a novel self-supervised method, referred to as Exploring Relati...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	190,689
2309.13662	Topology-Agnostic Detection of Temporal Money Laundering Flows in Billion-Scale Transactions	Money launderers exploit the weaknesses in detection systems by purposefully placing their ill-gotten money into multiple accounts, at different banks. That money is then layered and moved around among mule accounts to obscure the origin and the flow of transactions. Consequently, the money is integrated into the finan...	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	false	false	394,304
2004.02842	Detecting Communities in Heterogeneous Multi-Relational Networks:A Message Passing based Approach	Community is a common characteristic of networks including social networks, biological networks, computer and information networks, to name a few. Community detection is a basic step for exploring and analysing these network data. Typically, homogenous network is a type of networks which consists of only one type of ob...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	171,359
1710.02030	McDiarmid Drift Detection Methods for Evolving Data Streams	Increasingly, Internet of Things (IoT) domains, such as sensor networks, smart cities, and social networks, generate vast amounts of data. Such data are not only unbounded and rapidly evolving. Rather, the content thereof dynamically evolves over time, often in unforeseen ways. These variations are due to so-called con...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	true	false	82,093
2401.07698	Online Learning of Continuous Signed Distance Fields Using Piecewise Polynomials	Reasoning about distance is indispensable for establishing or avoiding contact in manipulation tasks. To this end, we present an online approach for learning implicit representations of signed distance using piecewise polynomial basis functions. Starting from an arbitrary prior shape, our method incrementally construct...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	421,627
2211.03075	Prediction of superconducting properties of materials based on machine learning models	The application of superconducting materials is becoming more and more widespread. Traditionally, the discovery of new superconducting materials relies on the experience of experts and a large number of "trial and error" experiments, which not only increases the cost of experiments but also prolongs the period of disco...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	328,826
1312.3061	Fast Approximate $K$-Means via Cluster Closures	$K$-means, a simple and effective clustering algorithm, is one of the most widely used algorithms in multimedia and computer vision community. Traditional $k$-means is an iterative algorithm---in each iteration new cluster centers are computed and each data point is re-assigned to its nearest center. The cluster re-ass...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	29,014
1909.01959	A Note on Data-Driven Control for SISO Feedback Linearizable Systems Without Persistency of Excitation	The paper [TF19] proposes a data-driven control technique for single-input single-output feedback linearizable systems with unknown control gain by relying on a persistency of excitation assumption. This note extends those results by showing that persistency of excitation is not necessary. We refer the readers to the p...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	144,058
2001.11586	Bio-inspired Flexible Twisting Wings Increase Lift and Efficiency of a Flapping Wing Micro Air Vehicle	We investigate the effect of wing twist flexibility on lift and efficiency of a flapping-wing micro air vehicle capable of liftoff. Wings used previously were chosen to be fully rigid due to modeling and fabrication constraints. However, biological wings are highly flexible and other micro air vehicles have successfull...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	162,108
1602.05287	Trade-off between Communication and Cooperation in the Interference Channel	We consider the problem of coding over the multi-user Interference Channel (IC). It is well-known that aligning the interfering signals results in improved achievable rates in certain setups involving more than two users. We argue that in the general interference problem, senders face a tradeoff between communicating t...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	52,235
1404.7666	Distributed Quantization for Compressed Sensing	We study distributed coding of compressed sensing (CS) measurements using vector quantizer (VQ). We develop a distributed framework for realizing optimized quantizer that enables encoding CS measurements of correlated sparse sources followed by joint decoding at a fusion center. The optimality of VQ encoder-decoder pai...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	32,713
2405.01838	A Novel Approach to Guard from Adversarial Attacks using Stable Diffusion	Recent developments in adversarial machine learning have highlighted the importance of building robust AI systems to protect against increasingly sophisticated attacks. While frameworks like AI Guardian are designed to defend against these threats, they often rely on assumptions that can limit their effectiveness. For ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	451,530
2009.03603	Evolutionary Reinforcement Learning via Cooperative Coevolutionary Negatively Correlated Search	Evolutionary algorithms (EAs) have been successfully applied to optimize the policies for Reinforcement Learning (RL) tasks due to their exploration ability. The recently proposed Negatively Correlated Search (NCS) provides a distinct parallel exploration search behavior and is expected to facilitate RL more effectivel...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	true	false	false	194,843
2311.10328	TransONet: Automatic Segmentation of Vasculature in Computed Tomographic Angiograms Using Deep Learning	Pathological alterations in the human vascular system underlie many chronic diseases, such as atherosclerosis and aneurysms. However, manually analyzing diagnostic images of the vascular system, such as computed tomographic angiograms (CTAs) is a time-consuming and tedious process. To address this issue, we propose a d...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	408,494
2008.09403	Exploiting Scene-specific Features for Object Goal Navigation	Can the intrinsic relation between an object and the room in which it is usually located help agents in the Visual Navigation Task? We study this question in the context of Object Navigation, a problem in which an agent has to reach an object of a specific class while moving in a complex domestic environment. In this p...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	192,705
2103.02155	Sensing population distribution from satellite imagery via deep learning: model selection, neighboring effect, and systematic biases	The rapid development of remote sensing techniques provides rich, large-coverage, and high-temporal information of the ground, which can be coupled with the emerging deep learning approaches that enable latent features and hidden geographical patterns to be extracted. This study marks the first attempt to cross-compare...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	222,867
2109.03890	Model Explanations via the Axiomatic Causal Lens	Explaining the decisions of black-box models is a central theme in the study of trustworthy ML. Numerous measures have been proposed in the literature; however, none of them take an axiomatic approach to causal explainability. In this work, we propose three explanation measures which aggregate the set of all but-for ca...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	254,203
2307.07295	Towards dialect-inclusive recognition in a low-resource language: are balanced corpora the answer?	ASR systems are generally built for the spoken 'standard', and their performance declines for non-standard dialects/varieties. This is a problem for a language like Irish, where there is no single spoken standard, but rather three major dialects: Ulster (Ul), Connacht (Co) and Munster (Mu). As a diagnostic to quantify ...	false	false	true	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	379,349
1411.0594	Multi-Cell Processing with Limited Cooperation: A Novel Framework to Timely Designs and Reduced CSI Feedback with General Inputs	We investigate the optimal power allocation and optimal precoding for a multi-cell-processing (MCP) framework with limited cooperation. In particular, we consider two base stations(BSs) which maximize the achievable rate for two users connecting to each BS and sharing channel state information (CSI). We propose a two w...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	37,266
2405.17596	GOI: Find 3D Gaussians of Interest with an Optimizable Open-vocabulary Semantic-space Hyperplane	3D open-vocabulary scene understanding, crucial for advancing augmented reality and robotic applications, involves interpreting and locating specific regions within a 3D space as directed by natural language instructions. To this end, we introduce GOI, a framework that integrates semantic features from 2D vision-langua...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	458,014
1804.06716	Demo of Sanskrit-Hindi SMT System	The demo proposal presents a Phrase-based Sanskrit-Hindi (SaHiT) Statistical Machine Translation system. The system has been developed on Moses. 43k sentences of Sanskrit-Hindi parallel corpus and 56k sentences of a monolingual corpus in the target language (Hindi) have been used. This system gives 57 BLEU score.	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	95,366
2010.03965	High Definition image classification in Geoscience using Machine Learning	High Definition (HD) digital photos taken with drones are widely used in the study of Geoscience. However, blurry images are often taken in collected data, and it takes a lot of time and effort to distinguish clear images from blurry ones. In this work, we apply Machine learning techniques, such as Support Vector Machi...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	199,574
1312.0317	Evolutionary Dynamics of Information Diffusion over Social Networks	Current social networks are of extremely large-scale generating tremendous information flows at every moment. How information diffuse over social networks has attracted much attention from both industry and academics. Most of the existing works on information diffusion analysis are based on machine learning methods foc...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	28,778
2305.13714	An implicit level set algorithm for hydraulic fracturing with a stress-layer asymptote	The capability to simulate a hydraulic fracturing process is an essential tool that can be used to optimize treatment design and increase the efficiency of field operations. In most practical cases, hydraulic fractures propagate in a multi-layered rock formation. As a result, there is a need to incorporate the effect o...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	366,646
2204.00511	Learning Disentangled Representations of Negation and Uncertainty	Negation and uncertainty modeling are long-standing tasks in natural language processing. Linguistic theory postulates that expressions of negation and uncertainty are semantically independent from each other and the content they modify. However, previous works on representation learning do not explicitly model this in...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	289,286
2105.14410	Machine learning moment closure models for the radiative transfer equation II: enforcing global hyperbolicity in gradient based closures	This is the second paper in a series in which we develop machine learning (ML) moment closure models for the radiative transfer equation (RTE). In our previous work \cite{huang2021gradient}, we proposed an approach to directly learn the gradient of the unclosed high order moment, which performs much better than learnin...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	237,659
2402.19449	Heavy-Tailed Class Imbalance and Why Adam Outperforms Gradient Descent on Language Models	Adam has been shown to outperform gradient descent on large language models by a larger margin than on other tasks, but it is unclear why. We show that a key factor in this performance gap is the heavy-tailed class imbalance found in language tasks. When trained with gradient descent, the loss of infrequent words decre...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	433,805
1411.1088	Expectation-Maximization for Learning Determinantal Point Processes	A determinantal point process (DPP) is a probabilistic model of set diversity compactly parameterized by a positive semi-definite kernel matrix. To fit a DPP to a given task, we would like to learn the entries of its kernel matrix by maximizing the log-likelihood of the available data. However, log-likelihood is non-co...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	37,303
2112.10307	Skin lesion segmentation and classification using deep learning and handcrafted features	Accurate diagnostics of a skin lesion is a critical task in classification dermoscopic images. In this research, we form a new type of image features, called hybrid features, which has stronger discrimination ability than single method features. This study involves a new technique where we inject the handcrafted featur...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	272,400
0904.0545	Time Hopping technique for faster reinforcement learning in simulations	This preprint has been withdrawn by the author for revision	false	false	false	false	true	false	true	true	false	false	false	false	false	false	false	false	false	false	3,472
1702.08052	Delay-Optimal Probabilistic Scheduling with Arbitrary Arrival and Adaptive Transmission	In this paper, we aim to obtain the optimal delay-power tradeoff and the corresponding optimal scheduling policy for an arbitrary i.i.d. arrival process and adaptive transmissions. The number of backlogged packets at the transmitter is known to a scheduler, who has to determine how many backlogged packets to transmit d...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	68,904
2404.12283	Enhancing Embedding Performance through Large Language Model-based Text Enrichment and Rewriting	Embedding models are crucial for various natural language processing tasks but can be limited by factors such as limited vocabulary, lack of context, and grammatical errors. This paper proposes a novel approach to improve embedding performance by leveraging large language models (LLMs) to enrich and rewrite input text ...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	447,817
1907.05688	A semi-holographic hyperdimensional representation system for hardware-friendly cognitive computing	One of the main, long-term objectives of artificial intelligence is the creation of thinking machines. To that end, substantial effort has been placed into designing cognitive systems; i.e. systems that can manipulate semantic-level information. A substantial part of that effort is oriented towards designing the mathem...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	138,430
2301.12884	Incentives to Offer Algorithmic Recourse	Due to the importance of artificial intelligence (AI) in a variety of high-stakes decisions, such as loan approval, job hiring, and criminal bail, researchers in Explainable AI (XAI) have developed algorithms to provide users with recourse for an unfavorable outcome. We analyze the incentives for a decision-maker to of...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	342,704
2307.09494	Explanation-Guided Fair Federated Learning for Transparent 6G RAN Slicing	Future zero-touch artificial intelligence (AI)-driven 6G network automation requires building trust in the AI black boxes via explainable artificial intelligence (XAI), where it is expected that AI faithfulness would be a quantifiable service-level agreement (SLA) metric along with telecommunications key performance in...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	380,197
2107.10064	Few Shots Are All You Need: A Progressive Few Shot Learning Approach for Low Resource Handwritten Text Recognition	Handwritten text recognition in low resource scenarios, such as manuscripts with rare alphabets, is a challenging problem. The main difficulty comes from the very few annotated data and the limited linguistic information (e.g. dictionaries and language models). Thus, we propose a few-shot learning-based handwriting rec...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	247,207
2409.00319	Highly-sensitive measure of complexity captures boolean networks regimes and temporal order more optimally	In this work, several random Boolean networks (RBN) are generated and analyzed from two characteristics: their time evolution diagram and their transition diagram. For this purpose, its randomness is estimated using three measures, of which Algorithmic Complexity is capable of both a) revealing transitions towards the ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	484,876
1901.01874	Mutual Context Network for Jointly Estimating Egocentric Gaze and Actions	In this work, we address two coupled tasks of gaze prediction and action recognition in egocentric videos by exploring their mutual context. Our assumption is that in the procedure of performing a manipulation task, what a person is doing determines where the person is looking at, and the gaze point reveals gaze and no...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	118,069
2009.03231	Integrating Egocentric Localization for More Realistic Point-Goal Navigation Agents	Recent work has presented embodied agents that can navigate to point-goal targets in novel indoor environments with near-perfect accuracy. However, these agents are equipped with idealized sensors for localization and take deterministic actions. This setting is practically sterile by comparison to the dirty reality of ...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	194,775
2209.07697	Selecting Stickers in Open-Domain Dialogue through Multitask Learning	With the increasing popularity of online chatting, stickers are becoming important in our online communication. Selecting appropriate stickers in open-domain dialogue requires a comprehensive understanding of both dialogues and stickers, as well as the relationship between the two types of modalities. To tackle these c...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	317,851
2310.15578	VMAF Re-implementation on PyTorch: Some Experimental Results	Based on the standard VMAF implementation we propose an implementation of VMAF using PyTorch framework. For this implementation comparisons with the standard (libvmaf) show the discrepancy $\lesssim 10^{-2}$ in VMAF units. We investigate gradients computation when using VMAF as an objective function and demonstrate tha...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	402,366
2205.14579	Leg Shaping and Event-Driven Control of a Small-Scale, Low-DoF, Two-Mode Robot	Among small-scale mobile robots, multi-modal locomotion can help compensate for limited actuator capabilities. However, supporting multiple locomotion modes or gaits in small terrestrial robots typically requires complex designs with low locomotion efficiency. In this work, legged and rolling gaits are achieved by a 10...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	299,418
2312.16451	Domain Generalization with Vital Phase Augmentation	Deep neural networks have shown remarkable performance in image classification. However, their performance significantly deteriorates with corrupted input data. Domain generalization methods have been proposed to train robust models against out-of-distribution data. Data augmentation in the frequency domain is one of s...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	418,387
2107.02842	Immuno-mimetic Deep Neural Networks (Immuno-Net)	Biomimetics has played a key role in the evolution of artificial neural networks. Thus far, in silico metaphors have been dominated by concepts from neuroscience and cognitive psychology. In this paper we introduce a different type of biomimetic model, one that borrows concepts from the immune system, for designing rob...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	true	false	false	244,957
1612.06581	Grammar rules for the isiZulu complex verb	The isiZulu verb is known for its morphological complexity, which is a subject for on-going linguistics research, as well as for prospects of computational use, such as controlled natural language interfaces, machine translation, and spellcheckers. To this end, we seek to answer the question as to what the precise gram...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	65,840
2102.08756	A three-dimensional hybrid finite element -- spectral boundary integral method for modeling earthquakes in complex unbounded domains	We present a 3D hybrid method which combines the Finite Element Method (FEM) and the Spectral Boundary Integral method (SBIM) to model nonlinear problems in unbounded domains. The flexibility of FEM is used to model the complex, heterogeneous, and nonlinear part -- such as the dynamic rupture along a fault with near fa...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	220,560
1708.04412	Resource Allocation in Shared Spectrum Access Communications for Operators with Diverse Service Requirements	In this paper, we study inter-operator spectrum sharing and intra-operator resource allocation in shared spectrum access communication systems and propose efficient dynamic solutions to address both inter-operator and intra-operator resource allocation optimization problems. For inter-operator spectrum sharing, we pres...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	78,941
1812.03173	Use Dimensionality Reduction and SVM Methods to Increase the Penetration Rate of Computer Networks	In the world today computer networks have a very important position and most of the urban and national infrastructure as well as organizations are managed by computer networks, therefore, the security of these systems against the planned attacks is of great importance. Therefore, researchers have been trying to find th...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	115,942
2402.03329	Unsupervised Salient Patch Selection for Data-Efficient Reinforcement Learning	To improve the sample efficiency of vision-based deep reinforcement learning (RL), we propose a novel method, called SPIRL, to automatically extract important patches from input images. Following Masked Auto-Encoders, SPIRL is based on Vision Transformer models pre-trained in a self-supervised fashion to reconstruct im...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	426,965
2102.01645	Generating images from caption and vice versa via CLIP-Guided Generative Latent Space Search	In this research work we present CLIP-GLaSS, a novel zero-shot framework to generate an image (or a caption) corresponding to a given caption (or image). CLIP-GLaSS is based on the CLIP neural network, which, given an image and a descriptive caption, provides similar embeddings. Differently, CLIP-GLaSS takes a caption ...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	true	false	false	218,181
2411.03857	Efficient Message Passing Architecture for GCN Training on HBM-based FPGAs with Orthogonal Topology On-Chip Networks	Graph Convolutional Networks (GCNs) are state-of-the-art deep learning models for representation learning on graphs. However, the efficient training of GCNs is hampered by constraints in memory capacity and bandwidth, compounded by the irregular data flow that results in communication bottlenecks. To address these chal...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	506,063
2307.02671	AI4OPT: AI Institute for Advances in Optimization	This article is a short introduction to AI4OPT, the NSF AI Institute for Advances in Optimization. AI4OPT fuses AI and Optimization, inspired by end-use cases in supply chains, energy systems, chip design and manufacturing, and sustainable food systems. AI4OPT also applies its "teaching the teachers" philosophy to prov...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	377,765
2102.06515	Mediastinal lymph nodes segmentation using 3D convolutional neural network ensembles and anatomical priors guiding	As lung cancer evolves, the presence of enlarged and potentially malignant lymph nodes must be assessed to properly estimate disease progression and select the best treatment strategy. Following the clinical guidelines, estimation of short-axis diameter and mediastinum station are paramount for correct diagnosis. A met...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	219,772
2401.11431	Majority or Minority: Data Imbalance Learning Method for Named Entity Recognition	Data imbalance presents a significant challenge in various machine learning (ML) tasks, particularly named entity recognition (NER) within natural language processing (NLP). NER exhibits a data imbalance with a long-tail distribution, featuring numerous minority classes (i.e., entity classes) and a single majority clas...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	422,999
1803.10996	Dihedral angle prediction using generative adversarial networks	Several dihedral angles prediction methods were developed for protein structure prediction and their other applications. However, distribution of predicted angles would not be similar to that of real angles. To address this we employed generative adversarial networks (GAN). Generative adversarial networks are composed ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	93,802
2209.14395	The Role of Metadata in Non-Fungible Tokens: Marketplace Analysis and Collection Organization	An explosion of interest in Non-Fungible Tokens (NFTs) has led to the emergence of vibrant online marketplaces that enable users to buy, sell and create digital assets. Largely considered contractual representations of digital artworks, NFTs allow ownership and authenticity to be proven through storing an asset and its...	false	false	false	false	false	true	false	false	false	true	false	false	true	false	false	false	false	false	320,226
2001.06658	Text-to-Image Generation with Attention Based Recurrent Neural Networks	Conditional image modeling based on textual descriptions is a relatively new domain in unsupervised learning. Previous approaches use a latent variable model and generative adversarial networks. While the formers are approximated by using variational auto-encoders and rely on the intractable inference that can hamper t...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	160,850
cs/0210025	An Algorithm for Pattern Discovery in Time Series	We present a new algorithm for discovering patterns in time series and other sequential data. We exhibit a reliable procedure for building the minimal set of hidden, Markovian states that is statistically capable of producing the behavior exhibited in the data -- the underlying process's causal states. Unlike conventio...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	537,710
2006.10869	Model-Aware Regularization For Learning Approaches To Inverse Problems	There are various inverse problems -- including reconstruction problems arising in medical imaging -- where one is often aware of the forward operator that maps variables of interest to the observations. It is therefore natural to ask whether such knowledge of the forward operator can be exploited in deep learning appr...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	183,026

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