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
1911.01697	Learning to flock through reinforcement	Flocks of birds, schools of fish, insects swarms are examples of coordinated motion of a group that arises spontaneously from the action of many individuals. Here, we study flocking behavior from the viewpoint of multi-agent reinforcement learning. In this setting, a learning agent tries to keep contact with the group ...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	152,178
2307.11748	BandRe: Rethinking Band-Pass Filters for Scale-Wise Object Detection Evaluation	Scale-wise evaluation of object detectors is important for real-world applications. However, existing metrics are either coarse or not sufficiently reliable. In this paper, we propose novel scale-wise metrics that strike a balance between fineness and reliability, using a filter bank consisting of triangular and trapez...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	381,006
0806.0837	Upper and Lower Bounds on Black-Box Steganography	We study the limitations of steganography when the sender is not using any properties of the underlying channel beyond its entropy and the ability to sample from it. On the negative side, we show that the number of samples the sender must obtain from the channel is exponential in the rate of the stegosystem. On the pos...	false	false	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	true	1,871
2403.02736	Bootstrapping Rare Object Detection in High-Resolution Satellite Imagery	Rare object detection is a fundamental task in applied geospatial machine learning, however is often challenging due to large amounts of high-resolution satellite or aerial imagery and few or no labeled positive samples to start with. This paper addresses the problem of bootstrapping such a rare object detection task a...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	434,922
2311.14758	Point2RBox: Combine Knowledge from Synthetic Visual Patterns for End-to-end Oriented Object Detection with Single Point Supervision	With the rapidly increasing demand for oriented object detection (OOD), recent research involving weakly-supervised detectors for learning rotated box (RBox) from the horizontal box (HBox) has attracted more and more attention. In this paper, we explore a more challenging yet label-efficient setting, namely single poin...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	410,245
2501.11299	MIFNet: Learning Modality-Invariant Features for Generalizable Multimodal Image Matching	Many keypoint detection and description methods have been proposed for image matching or registration. While these methods demonstrate promising performance for single-modality image matching, they often struggle with multimodal data because the descriptors trained on single-modality data tend to lack robustness agains...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	525,874
2411.17922	Exploring Superpixel Segmentation Methods in the Context of Citizen Science and Deforestation Detection	Tropical forests play an essential role in the planet's ecosystem, making the conservation of these biomes a worldwide priority. However, ongoing deforestation and degradation pose a significant threat to their existence, necessitating effective monitoring and the proposal of actions to mitigate the damage caused by th...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	511,655
2002.01504	Joint Power Allocation and Load Balancing Optimization for Energy-Efficient Cell-Free Massive MIMO Networks	Large-scale distributed antenna systems with many access points (APs) that serve the users by coherent joint transmission is being considered for 5G-and-beyond networks. The technology is called Cell-free Massive MIMO and can provide a more uniform service level to the users than a conventional cellular topology. For a...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	162,663
2107.03345	Post-Metering Value-Added Services for Low Voltage Electricity Users: Lessons Learned From the Italian Experience of CHAIN 2	Electrical energy consumption data accessibility for low voltage end users is one of the pillars of smart grids. In some countries, despite the presence of smart meters, a fragmentary data availability and/or the lack of standardization hinders the creation of post-metering value-added services and confines such innova...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	245,133
2004.10641	Automatic Detection of Coronavirus Disease (COVID-19) in X-ray and CT Images: A Machine Learning-Based Approach	The newly identified Coronavirus pneumonia, subsequently termed COVID-19, is highly transmittable and pathogenic with no clinically approved antiviral drug or vaccine available for treatment. The most common symptoms of COVID-19 are dry cough, sore throat, and fever. Symptoms can progress to a severe form of pneumonia ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	173,691
2010.02125	Invertible DenseNets	We introduce Invertible Dense Networks (i-DenseNets), a more parameter efficient alternative to Residual Flows. The method relies on an analysis of the Lipschitz continuity of the concatenation in DenseNets, where we enforce the invertibility of the network by satisfying the Lipschitz constraint. Additionally, we exten...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	198,911
2210.03526	A Unified Hard-Constraint Framework for Solving Geometrically Complex PDEs	We present a unified hard-constraint framework for solving geometrically complex PDEs with neural networks, where the most commonly used Dirichlet, Neumann, and Robin boundary conditions (BCs) are considered. Specifically, we first introduce the "extra fields" from the mixed finite element method to reformulate the PDE...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	322,073
2302.03994	Fully-Dynamic Approximate Decision Trees With Worst-Case Update Time Guarantees	We give the first algorithm that maintains an approximate decision tree over an arbitrary sequence of insertions and deletions of labeled examples, with strong guarantees on the worst-case running time per update request. For instance, we show how to maintain a decision tree where every vertex has Gini gain within an a...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	true	344,549
2207.02512	Identifying and Mitigating Flaws of Deep Perceptual Similarity Metrics	Measuring the similarity of images is a fundamental problem to computer vision for which no universal solution exists. While simple metrics such as the pixel-wise L2-norm have been shown to have significant flaws, they remain popular. One group of recent state-of-the-art metrics that mitigates some of those flaws are D...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	306,537
2412.02081	Let's Think Var-by-Var: Large Language Models Enable Ad Hoc Probabilistic Reasoning	A hallmark of intelligence is the ability to flesh out underspecified situations using "common sense." We propose to extract that common sense from large language models (LLMs), in a form that can feed into probabilistic inference. We focus our investigation on $\textit{guesstimation}$ questions such as "How much are A...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	513,371
2409.12839	Social impact of CAVs -- coexistence of machines and humans in the context of route choice	Suppose in a stable urban traffic system populated only by human driven vehicles (HDVs), a given proportion (e.g. 10%) is replaced by a fleet of Connected and Autonomous Vehicles (CAVs), which share information and pursue a collective goal. Suppose these vehicles are centrally coordinated and differ from HDVs only by t...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	489,738
2104.03356	Universal Spectral Adversarial Attacks for Deformable Shapes	Machine learning models are known to be vulnerable to adversarial attacks, namely perturbations of the data that lead to wrong predictions despite being imperceptible. However, the existence of "universal" attacks (i.e., unique perturbations that transfer across different data points) has only been demonstrated for ima...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	229,041
2407.11404	Mapping savannah woody vegetation at the species level with multispecral drone and hyperspectral EnMAP data	Savannahs are vital ecosystems whose sustainability is endangered by the spread of woody plants. This research targets the accurate mapping of fractional woody cover (FWC) at the species level in a South African savannah, using EnMAP hyperspectral data. Field annotations were combined with very high-resolution multispe...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	473,437
1712.02262	A New Coding/Decoding Algorithm using Fibonacci Numbers	In this paper we present a new method of coding/decoding algorithms using Fibonacci $Q$-matrices. This method is based on the blocked message matrices. The main advantage of our model is the encryption of each message matrix with different keys. Our approach will not only increase the security of information but also h...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	86,266
1811.05408	Multi-task learning for Joint Language Understanding and Dialogue State Tracking	This paper presents a novel approach for multi-task learning of language understanding (LU) and dialogue state tracking (DST) in task-oriented dialogue systems. Multi-task training enables the sharing of the neural network layers responsible for encoding the user utterance for both LU and DST and improves performance w...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	113,309
1910.00100	Deep Cooking: Predicting Relative Food Ingredient Amounts from Images	In this paper, we study the novel problem of not only predicting ingredients from a food image, but also predicting the relative amounts of the detected ingredients. We propose two prediction-based models using deep learning that output sparse and dense predictions, coupled with important semi-automatic multi-database ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	147,573
1901.02693	Model-based Stochastic Fault Detection and Diagnosis for Lithium-ion Batteries	Lithium-ion battery (Li-ion) is becoming the dominant energy storage solution in many applications such as hybrid electric and electric vehicles, due to its higher energy density and longer life cycle. For these applications, the battery should perform reliably and pose no safety threats. However, the performance of Li...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	118,261
1303.5134	Bounds on the Number of Huffman and Binary-Ternary Trees	Huffman coding is a widely used method for lossless data compression because it optimally stores data based on how often the characters occur in Huffman trees. An $n$-ary Huffman tree is a connected, cycle-lacking graph where each vertex can have either $n$ "children" vertices connecting to it, or 0 children. Vertices ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	23,058
2110.09111	Analyzing Wikipedia Membership Dataset and PredictingUnconnected Nodes in the Signed Networks	In the age of digital interaction, person-to-person relationships existing on social media may be different from the very same interactions that exist offline. Examining potential or spurious relationships between members in a social network is a fertile area of research for computer scientists -- here we examine how r...	false	false	false	true	true	false	true	false	false	false	false	false	false	false	false	false	false	false	261,683
2307.05095	ATWM: Defense against adversarial malware based on adversarial training	Deep learning technology has made great achievements in the field of image. In order to defend against malware attacks, researchers have proposed many Windows malware detection models based on deep learning. However, deep learning models are vulnerable to adversarial example attacks. Malware can generate adversarial ma...	false	false	false	false	true	false	false	false	false	false	false	false	true	false	false	false	false	false	378,622
1908.09895	Index Network	We show that existing upsampling operators can be unified using the notion of the index function. This notion is inspired by an observation in the decoding process of deep image matting where indices-guided unpooling can often recover boundary details considerably better than other upsampling operators such as bilinear...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	142,959
2106.02711	SketchGen: Generating Constrained CAD Sketches	Computer-aided design (CAD) is the most widely used modeling approach for technical design. The typical starting point in these designs is 2D sketches which can later be extruded and combined to obtain complex three-dimensional assemblies. Such sketches are typically composed of parametric primitives, such as points, l...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	true	238,986
2204.00495	Physics Informed Shallow Machine Learning for Wind Speed Prediction	The ability to predict wind is crucial for both energy production and weather forecasting. Mechanistic models that form the basis of traditional forecasting perform poorly near the ground. In this paper, we take an alternative data-driven approach based on supervised learning. We analyze a massive dataset of wind measu...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	289,280
0711.4809	Local independence of fractional Brownian motion	Let S(t,t') be the sigma-algebra generated by the differences X(s)-X(s) with s,s' in the interval(t,t'), where (X_t) is the fractional Brownian motion process with Hurst index H between 0 and 1. We prove that for any two distinct t and t' the sigma-algebras S(t-a,t+a) and S(t'-a,t'+a) are asymptotically independent as ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	976
2003.10350	Weakly Supervised 3D Human Pose and Shape Reconstruction with Normalizing Flows	Monocular 3D human pose and shape estimation is challenging due to the many degrees of freedom of the human body and thedifficulty to acquire training data for large-scale supervised learning in complex visual scenes. In this paper we present practical semi-supervised and self-supervised models that support training an...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	169,309
2202.09006	KINet: Unsupervised Forward Models for Robotic Pushing Manipulation	Object-centric representation is an essential abstraction for forward prediction. Most existing forward models learn this representation through extensive supervision (e.g., object class and bounding box) although such ground-truth information is not readily accessible in reality. To address this, we introduce KINet (K...	false	false	false	false	false	false	true	true	false	false	false	true	false	false	false	false	false	false	281,064
2010.08961	Rethinking Document-level Neural Machine Translation	This paper does not aim at introducing a novel model for document-level neural machine translation. Instead, we head back to the original Transformer model and hope to answer the following question: Is the capacity of current models strong enough for document-level translation? Interestingly, we observe that the origin...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	201,362
2410.12845	Toward Relieving Clinician Burden by Automatically Generating Progress Notes using Interim Hospital Data	Regular documentation of progress notes is one of the main contributors to clinician burden. The abundance of structured chart information in medical records further exacerbates the burden, however, it also presents an opportunity to automate the generation of progress notes. In this paper, we propose a task to automat...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	499,233
2312.16693	I2V-Adapter: A General Image-to-Video Adapter for Diffusion Models	Text-guided image-to-video (I2V) generation aims to generate a coherent video that preserves the identity of the input image and semantically aligns with the input prompt. Existing methods typically augment pretrained text-to-video (T2V) models by either concatenating the image with noised video frames channel-wise bef...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	418,470
1306.6295	Tight Lower Bound for Linear Sketches of Moments	The problem of estimating frequency moments of a data stream has attracted a lot of attention since the onset of streaming algorithms [AMS99]. While the space complexity for approximately computing the $p^{\rm th}$ moment, for $p\in(0,2]$ has been settled [KNW10], for $p>2$ the exact complexity remains open. For $p>2$ ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	25,476
1803.08069	3D Soil Compaction Mapping through Kriging-based Exploration with a Mobile Robot	This paper presents an automated method for creating spatial maps of soil condition with an outdoor mobile robot. Effective soil mapping on farms can enhance yields, reduce inputs and help protect the environment. Traditionally, data are collected manually at an arbitrary set of locations, then soil maps are constructe...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	93,187
1809.04232	Safe Exploration in Markov Decision Processes with Time-Variant Safety using Spatio-Temporal Gaussian Process	In many real-world applications (e.g., planetary exploration, robot navigation), an autonomous agent must be able to explore a space with guaranteed safety. Most safe exploration algorithms in the field of reinforcement learning and robotics have been based on the assumption that the safety features are a priori known ...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	107,511
2407.11399	Multi-Goal Motion Memory	Autonomous mobile robots (e.g., warehouse logistics robots) often need to traverse complex, obstacle-rich, and changing environments to reach multiple fixed goals (e.g., warehouse shelves). Traditional motion planners need to calculate the entire multi-goal path from scratch in response to changes in the environment, w...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	473,434
1910.05527	Regularizing Model-Based Planning with Energy-Based Models	Model-based reinforcement learning could enable sample-efficient learning by quickly acquiring rich knowledge about the world and using it to improve behaviour without additional data. Learned dynamics models can be directly used for planning actions but this has been challenging because of inaccuracies in the learned ...	false	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	149,090
2201.10859	Visualizing the Diversity of Representations Learned by Bayesian Neural Networks	Explainable Artificial Intelligence (XAI) aims to make learning machines less opaque, and offers researchers and practitioners various tools to reveal the decision-making strategies of neural networks. In this work, we investigate how XAI methods can be used for exploring and visualizing the diversity of feature repres...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	277,122
1411.7612	A Parallel Genetic Algorithm for Generalized Vertex Cover Problem	This paper presents a parallel genetic algorithm for generalised vertex cover problem (GVCP) using Hadoop Map-Reduce framework. The proposed Map-Reduce implementation helps to run the genetic algorithm for generalized vertex cover problem (GVCP) on multiple machines parallely and computes the solution in relatively sho...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	true	37,943
2308.15703	Fragment and Integrate Network (FIN): A Novel Spatial-Temporal Modeling Based on Long Sequential Behavior for Online Food Ordering Click-Through Rate Prediction	Spatial-temporal information has been proven to be of great significance for click-through rate prediction tasks in online Location-Based Services (LBS), especially in mainstream food ordering platforms such as DoorDash, Uber Eats, Meituan, and Ele.me. Modeling user spatial-temporal preferences with sequential behavior...	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	false	388,759
1609.05078	Hybrid Beamforming for Massive MIMO - A Survey	Hybrid multiple-antenna transceivers, which combine large-dimensional analog pre/postprocessing with lower-dimensional digital processing, are the most promising approach for reducing the hardware cost and training overhead in massive MIMO systems. This paper provides a comprehensive survey of the various incarnations ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	61,066
2110.08590	Automated Remote Sensing Forest Inventory Using Satellite Imagery	For many countries like Russia, Canada, or the USA, a robust and detailed tree species inventory is essential to manage their forests sustainably. Since one can not apply unmanned aerial vehicle (UAV) imagery-based approaches to large-scale forest inventory applications, the utilization of machine learning algorithms o...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	261,474
2312.06820	Extracting Self-Consistent Causal Insights from Users Feedback with LLMs and In-context Learning	Microsoft Windows Feedback Hub is designed to receive customer feedback on a wide variety of subjects including critical topics such as power and battery. Feedback is one of the most effective ways to have a grasp of users' experience with Windows and its ecosystem. However, the sheer volume of feedback received by Fee...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	414,682
2002.02693	Ready Policy One: World Building Through Active Learning	Model-Based Reinforcement Learning (MBRL) offers a promising direction for sample efficient learning, often achieving state of the art results for continuous control tasks. However, many existing MBRL methods rely on combining greedy policies with exploration heuristics, and even those which utilize principled explorat...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	162,998
2007.14658	Meta-Learning with Context-Agnostic Initialisations	Meta-learning approaches have addressed few-shot problems by finding initialisations suited for fine-tuning to target tasks. Often there are additional properties within training data (which we refer to as context), not relevant to the target task, which act as a distractor to meta-learning, particularly when the targe...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	189,467
2302.01842	A Case Study for Compliance as Code with Graphs and Language Models: Public release of the Regulatory Knowledge Graph	The paper presents a study on using language models to automate the construction of executable Knowledge Graph (KG) for compliance. The paper focuses on Abu Dhabi Global Market regulations and taxonomy, involves manual tagging a portion of the regulations, training BERT-based models, which are then applied to the rest ...	false	false	false	false	true	true	true	false	true	false	false	false	false	false	false	false	false	false	343,761
2209.02981	VGStore: A Multimodal Extension to SPARQL for Querying RDF Scene Graph	Semantic Web technology has successfully facilitated many RDF models with rich data representation methods. It also has the potential ability to represent and store multimodal knowledge bases such as multimodal scene graphs. However, most existing query languages, especially SPARQL, barely explore the implicit multimod...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	false	316,361
2109.12925	HarrisZ$^+$: Harris Corner Selection for Next-Gen Image Matching Pipelines	Due to its role in many computer vision tasks, image matching has been subjected to an active investigation by researchers, which has lead to better and more discriminant feature descriptors and to more robust matching strategies, also thanks to the advent of the deep learning and the increased computational power of t...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	257,461
1410.2324	Recommendation Scheme Based on Converging Properties for Contents Broadcasting	Popular videos are often clicked by a mount of users in a short period. With content recommendation, the popular contents could be broadcast to the potential users in wireless network, to save huge transmitting resource. In this paper, the contents propagation model is analyzed due to users' historical behavior, locati...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	true	36,608
1603.09194	Iterated Ontology Revision by Reinterpretation	Iterated applications of belief change operators are essential for different scenarios such as that of ontology evolution where new information is not presented at once but only in piecemeal fashion within a sequence. I discuss iterated applications of so called reinterpretation operators that trace conflicts between o...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	53,890
2009.07173	Graph Convolution Networks Using Message Passing and Multi-Source Similarity Features for Predicting circRNA-Disease Association	Graphs can be used to effectively represent complex data structures. Learning these irregular data in graphs is challenging and still suffers from shallow learning. Applying deep learning on graphs has recently showed good performance in many applications in social analysis, bioinformatics etc. A message passing graph ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	195,853
2404.00018	Can AI Outperform Human Experts in Creating Social Media Creatives?	Artificial Intelligence has outperformed human experts in functional tasks such as chess and baduk. How about creative tasks? This paper evaluates AI's capability in the creative domain compared to human experts, which little research has been conducted so far. We propose a novel Prompt-for-Prompt to generate social me...	true	false	false	true	true	false	false	false	false	false	false	false	false	false	false	false	false	false	442,723
1909.11899	Dynamic Parameter Estimation of Brain Mechanisms	Demystifying effective connectivity among neuronal populations has become the trend to understand the brain mechanisms of Parkinson's disease, schizophrenia, mild traumatic brain injury, and many other unlisted neurological diseases. Dynamic modeling is a state-of-the-art approach to explore various connectivities amon...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	146,962
1711.04735	Resurrecting the sigmoid in deep learning through dynamical isometry: theory and practice	It is well known that the initialization of weights in deep neural networks can have a dramatic impact on learning speed. For example, ensuring the mean squared singular value of a network's input-output Jacobian is $O(1)$ is essential for avoiding the exponential vanishing or explosion of gradients. The stronger condi...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	84,434
2104.13315	Inductive Program Synthesis over Noisy Datasets using Abstraction Refinement Based Optimization	We present a new synthesis algorithm to solve program synthesis over noisy datasets, i.e., data that may contain incorrect/corrupted input-output examples. Our algorithm uses an abstraction refinement based optimization process to synthesize programs which optimize the tradeoff between the loss over the noisy dataset a...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	232,468
2309.03748	Enhancing Pipeline-Based Conversational Agents with Large Language Models	The latest advancements in AI and deep learning have led to a breakthrough in large language model (LLM)-based agents such as GPT-4. However, many commercial conversational agent development tools are pipeline-based and have limitations in holding a human-like conversation. This paper investigates the capabilities of L...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	390,491
1809.01633	Efficient Egocentric Visual Perception Combining Eye-tracking, a Software Retina and Deep Learning	We present ongoing work to harness biological approaches to achieving highly efficient egocentric perception by combining the space-variant imaging architecture of the mammalian retina with Deep Learning methods. By pre-processing images collected by means of eye-tracking glasses to control the fixation locations of a ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	106,857
2309.09378	Dynamics of Fisheries in the Azores Islands: A Network Analysis Approach	In the context of the global seafood industry, the Azores archipelago (Portugal) plays a pivotal role due to its vast maritime domain. This study employs complex network analysis techniques to investigate the dynamics of Azores fisheries, using time series data converted into networks. We uncover associations between T...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	392,582
2306.02261	Online estimation of the hand-eye transformation from surgical scenes	Hand-eye calibration algorithms are mature and provide accurate transformation estimations for an effective camera-robot link but rely on a sufficiently wide range of calibration data to avoid errors and degenerate configurations. To solve the hand-eye problem in robotic-assisted minimally invasive surgery and also sim...	false	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	370,823
1809.10541	Novel Sparse Recovery Algorithms for 3D Debris Localization using Rotating Point Spread Function Imagery	An optical imager that exploits off-center image rotation to encode both the lateral and depth coordinates of point sources in a single snapshot can perform 3D localization and tracking of space debris. When actively illuminated, unresolved space debris, which can be regarded as a swarm of point sources, can scatter a ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	108,932
2107.01651	Hidden dependence of spreading vulnerability on topological complexity	Many dynamical phenomena in complex systems concern spreading that plays out on top of networks with changing architecture over time -- commonly known as temporal networks. A complex system's proneness to facilitate spreading phenomena, which we abbreviate as its `spreading vulnerability', is often surmised to be relat...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	244,551
2408.10360	HaSPeR: An Image Repository for Hand Shadow Puppet Recognition	Hand shadow puppetry, also known as shadowgraphy or ombromanie, is a form of theatrical art and storytelling where hand shadows are projected onto flat surfaces to create illusions of living creatures. The skilled performers create these silhouettes by hand positioning, finger movements, and dexterous gestures to resem...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	481,819
1711.10402	An Adversarial Neuro-Tensorial Approach For Learning Disentangled Representations	Several factors contribute to the appearance of an object in a visual scene, including pose, illumination, and deformation, among others. Each factor accounts for a source of variability in the data, while the multiplicative interactions of these factors emulate the entangled variability, giving rise to the rich struct...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	85,596
2106.09086	Learned Belief Search: Efficiently Improving Policies in Partially Observable Settings	Search is an important tool for computing effective policies in single- and multi-agent environments, and has been crucial for achieving superhuman performance in several benchmark fully and partially observable games. However, one major limitation of prior search approaches for partially observable environments is tha...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	241,536
2010.02275	Short-term prediction of photovoltaic power generation using Gaussian process regression	Photovoltaic (PV) power is affected by weather conditions, making the power generated from the PV systems uncertain. Solving this problem would help improve the reliability and cost effectiveness of the grid, and could help reduce reliance on fossil fuel plants. The present paper focuses on evaluating predictions of th...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	198,947
2412.07114	TT-MPD: Test Time Model Pruning and Distillation	Pruning can be an effective method of compressing large pre-trained models for inference speed acceleration. Previous pruning approaches rely on access to the original training dataset for both pruning and subsequent fine-tuning. However, access to the training data can be limited due to concerns such as data privacy a...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	515,512
1909.10005	Incremental Fairness in Two-Sided Market Platforms: On Smoothly Updating Recommendations	Major online platforms today can be thought of as two-sided markets with producers and customers of goods and services. There have been concerns that over-emphasis on customer satisfaction by the platforms may affect the well-being of the producers. To counter such issues, few recent works have attempted to incorporate...	false	false	false	true	false	false	false	false	false	false	false	false	false	true	false	false	false	false	146,425
1410.4550	Universal compression of Gaussian sources with unknown parameters	For a collection of distributions over a countable support set, the worst case universal compression formulation by Shtarkov attempts to assign a universal distribution over the support set. The formulation aims to ensure that the universal distribution does not underestimate the probability of any element in the suppo...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	36,818
2309.00464	A Theoretical and Practical Framework for Evaluating Uncertainty Calibration in Object Detection	The proliferation of Deep Neural Networks has resulted in machine learning systems becoming increasingly more present in various real-world applications. Consequently, there is a growing demand for highly reliable models in many domains, making the problem of uncertainty calibration pivotal when considering the future ...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	389,313
2310.16255	UAV-Sim: NeRF-based Synthetic Data Generation for UAV-based Perception	Tremendous variations coupled with large degrees of freedom in UAV-based imaging conditions lead to a significant lack of data in adequately learning UAV-based perception models. Using various synthetic renderers in conjunction with perception models is prevalent to create synthetic data to augment the learning in the ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	402,643
1612.03108	Low-rank matrix recovery via rank one tight frame measurements	The task of reconstructing a low rank matrix from incomplete linear measurements arises in areas such as machine learning, quantum state tomography and in the phase retrieval problem. In this note, we study the particular setup that the measurements are taken with respect to rank one matrices constructed from the eleme...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	65,319
2411.08144	Visual Tracking with Intermittent Visibility: Switched Control Design and Implementation	This paper addresses the problem of visual target tracking in scenarios where a pursuer may experience intermittent loss of visibility of the target. The design of a Switched Visual Tracker (SVT) is presented which aims to meet the competing requirements of maintaining both proximity and visibility. SVT alternates betw...	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	507,780
2110.10545	Ranking and Tuning Pre-trained Models: A New Paradigm for Exploiting Model Hubs	Model hubs with many pre-trained models (PTMs) have become a cornerstone of deep learning. Although built at a high cost, they remain \emph{under-exploited} -- practitioners usually pick one PTM from the provided model hub by popularity and then fine-tune the PTM to solve the target task. This na\"ive but common practi...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	262,189
2408.00096	From Attributes to Natural Language: A Survey and Foresight on Text-based Person Re-identification	Text-based person re-identification (Re-ID) is a challenging topic in the field of complex multimodal analysis, its ultimate aim is to recognize specific pedestrians by scrutinizing attributes/natural language descriptions. Despite the wide range of applicable areas such as security surveillance, video retrieval, perso...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	477,701
2301.10972	On the Importance of Noise Scheduling for Diffusion Models	We empirically study the effect of noise scheduling strategies for denoising diffusion generative models. There are three findings: (1) the noise scheduling is crucial for the performance, and the optimal one depends on the task (e.g., image sizes), (2) when increasing the image size, the optimal noise scheduling shift...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	true	341,984
1505.01257	A Deeper Look at Dataset Bias	The presence of a bias in each image data collection has recently attracted a lot of attention in the computer vision community showing the limits in generalization of any learning method trained on a specific dataset. At the same time, with the rapid development of deep learning architectures, the activation values of...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	42,823
2211.14498	The Impact of Racial Distribution in Training Data on Face Recognition Bias: A Closer Look	Face recognition algorithms, when used in the real world, can be very useful, but they can also be dangerous when biased toward certain demographics. So, it is essential to understand how these algorithms are trained and what factors affect their accuracy and fairness to build better ones. In this study, we shed some l...	false	false	false	false	false	false	true	false	false	false	false	true	false	true	false	false	false	false	332,863
1103.3420	Extraction of handwritten areas from colored image of bank checks by an hybrid method	One of the first step in the realization of an automatic system of check recognition is the extraction of the handwritten area. We propose in this paper an hybrid method to extract these areas. This method is based on digit recognition by Fourier descriptors and different steps of colored image processing . It requires...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	9,652
1606.04163	Intracellular regulatory control of gene expression process	This paper presents an intracellular feedback control strategy, to regulate the gene expression process dynamics. For this purpose, two types of genetic circuits are designed in order to compare concentrations of the input transcription factor (the desired input) and the protein produced by the expression of target gen...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	57,199
1905.05162	Locally Weighted Regression Pseudo-Rehearsal for Online Learning of Vehicle Dynamics	We consider the problem of online adaptation of a neural network designed to represent vehicle dynamics. The neural network model is intended to be used by an MPC control law to autonomously control the vehicle. This problem is challenging because both the input and target distributions are non-stationary, and naive ap...	false	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	130,645
2407.19144	Collaborative Adaptation for Recovery from Unforeseen Malfunctions in Discrete and Continuous MARL Domains	Cooperative multi-agent learning plays a crucial role for developing effective strategies to achieve individual or shared objectives in multi-agent teams. In real-world settings, agents may face unexpected failures, such as a robot's leg malfunctioning or a teammate's battery running out. These malfunctions decrease th...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	476,660
2303.11501	Convolutions, Transformers, and their Ensembles for the Segmentation of Organs at Risk in Radiation Treatment of Cervical Cancer	Segmentation of regions of interest in images of patients, is a crucial step in many medical procedures. Deep neural networks have proven to be particularly adept at this task. However, a key question is what type of deep neural network to choose, and whether making a certain choice makes a difference. In this work, we...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	352,872
2302.02176	An analysis of the technology acceptance model in understanding university students behavioral intention to use metaverse technologies	Metaverse can be applied in several aspects of life such as the Economy, finance, social life, working environment, healthcare, real estate, and education. In the last 2 and a half years, during the COVID-19 pandemic, universities made immediate use of learning technologies, providing students with access to online lea...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	true	343,891
2102.06128	Sequence-based Machine Learning Models in Jet Physics	Sequence-based modeling broadly refers to algorithms that act on data that is represented as an ordered set of input elements. In particular, Machine Learning algorithms with sequences as inputs have seen successfull applications to important problems, such as Natural Language Processing (NLP) and speech signal modelin...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	219,646
2111.11899	Results of improved fractional/integer order PDE-based binarization model	In this report, we present and compare the results of an improved fractional and integer order partial differential equation (PDE)-based binarization scheme. The improved model incorporates a diffusion term in addition to the edge and binary source terms from the previous formulation. Furthermore, logarithmic local con...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	267,816
1511.03570	Dimension of Marginals of Kronecker Product Models	A Kronecker product model is the set of visible marginal probability distributions of an exponential family whose sufficient statistics matrix factorizes as a Kronecker product of two matrices, one for the visible variables and one for the hidden variables. We estimate the dimension of these models by the maximum rank ...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	48,769
1111.0654	Distributed Lossy Source Coding Using Real-Number Codes	We show how real-number codes can be used to compress correlated sources, and establish a new framework for lossy distributed source coding, in which we quantize compressed sources instead of compressing quantized sources. This change in the order of binning and quantization blocks makes it possible to model correlatio...	false	false	false	false	false	false	false	false	false	true	false	true	false	false	false	false	false	true	12,884
2010.11032	Classifying Syntactic Errors in Learner Language	We present a method for classifying syntactic errors in learner language, namely errors whose correction alters the morphosyntactic structure of a sentence. The methodology builds on the established Universal Dependencies syntactic representation scheme, and provides complementary information to other error-classific...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	202,097
2011.13436	A Hierarchical Self-attentive Convolution Network for Review Modeling in Recommendation Systems	Using reviews to learn user and item representations is important for recommender system. Current review based methods can be divided into two categories: (1) the Convolution Neural Network (CNN) based models that extract n-gram features from user/item reviews; (2) the Recurrent Neural Network (RNN) based models that l...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	208,472
2208.02724	Disentangled Representation Learning for RF Fingerprint Extraction under Unknown Channel Statistics	Deep learning (DL) applied to a device's radio-frequency fingerprint~(RFF) has attracted significant attention in physical-layer authentication due to its extraordinary classification performance. Conventional DL-RFF techniques are trained by adopting maximum likelihood estimation~(MLE). Although their discriminability...	false	false	false	false	false	false	true	false	false	true	false	false	true	false	false	false	false	false	311,552
2410.02714	AlzhiNet: Traversing from 2DCNN to 3DCNN, Towards Early Detection and Diagnosis of Alzheimer's Disease	Alzheimer's disease (AD) is a progressive neurodegenerative disorder with increasing prevalence among the aging population, necessitating early and accurate diagnosis for effective disease management. In this study, we present a novel hybrid deep learning framework that integrates both 2D Convolutional Neural Networks ...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	494,409
2308.15005	Few-Shot Object Detection via Synthetic Features with Optimal Transport	Few-shot object detection aims to simultaneously localize and classify the objects in an image with limited training samples. However, most existing few-shot object detection methods focus on extracting the features of a few samples of novel classes that lack diversity. Hence, they may not be sufficient to capture the ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	388,529
2201.06219	An Empirical Study on the Overlapping Problem of Open-Domain Dialogue Datasets	Open-domain dialogue systems aim to converse with humans through text, and dialogue research has heavily relied on benchmark datasets. In this work, we observe the overlapping problem in DailyDialog and OpenSubtitles, two popular open-domain dialogue benchmark datasets. Our systematic analysis then shows that such over...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	275,649
2301.04134	Analogical Relevance Index	Focusing on the most significant features of a dataset is useful both in machine learning (ML) and data mining. In ML, it can lead to a higher accuracy, a faster learning process, and ultimately a simpler and more understandable model. In data mining, identifying significant features is essential not only for gaining a...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	339,980
2010.01996	Evaluation of company investment value based on machine learning	In this paper, company investment value evaluation models are established based on comprehensive company information. After data mining and extracting a set of 436 feature parameters, an optimal subset of features is obtained by dimension reduction through tree-based feature selection, followed by the 5-fold cross-vali...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	198,869
0906.0964	On Sparse Channel Estimation	Channel Estimation is an essential component in applications such as radar and data communication. In multi path time varying environments, it is necessary to estimate time-shifts, scale-shifts (the wideband equivalent of Doppler-shifts), and the gains/phases of each of the multiple paths. With recent advances in spars...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	3,833
2211.04772	Efficient Large-scale Audio Tagging via Transformer-to-CNN Knowledge Distillation	Audio Spectrogram Transformer models rule the field of Audio Tagging, outrunning previously dominating Convolutional Neural Networks (CNNs). Their superiority is based on the ability to scale up and exploit large-scale datasets such as AudioSet. However, Transformers are demanding in terms of model size and computation...	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	329,346
2501.04697	Grokking at the Edge of Numerical Stability	Grokking, the sudden generalization that occurs after prolonged overfitting, is a surprising phenomenon challenging our understanding of deep learning. Although significant progress has been made in understanding grokking, the reasons behind the delayed generalization and its dependence on regularization remain unclear...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	523,311

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