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
1905.08284	Enriching Pre-trained Language Model with Entity Information for Relation Classification	Relation classification is an important NLP task to extract relations between entities. The state-of-the-art methods for relation classification are primarily based on Convolutional or Recurrent Neural Networks. Recently, the pre-trained BERT model achieves very successful results in many NLP classification / sequence ...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	131,436
2306.05781	Adaptivity Complexity for Causal Graph Discovery	Causal discovery from interventional data is an important problem, where the task is to design an interventional strategy that learns the hidden ground truth causal graph $G(V,E)$ on $\|V\| = n$ nodes while minimizing the number of performed interventions. Most prior interventional strategies broadly fall into two catego...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	true	372,333
1304.6078	Automating the Dispute Resolution in Task Dependency Network	When perturbation or unexpected events do occur, agents need protocols for repairing or reforming the supply chain. Unfortunate contingency could increase too much the cost of performance, while breaching the current contract may be more efficient. In our framework the principles of contract law are applied to set pena...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	24,142
1905.09788	Multi-Sample Dropout for Accelerated Training and Better Generalization	Dropout is a simple but efficient regularization technique for achieving better generalization of deep neural networks (DNNs); hence it is widely used in tasks based on DNNs. During training, dropout randomly discards a portion of the neurons to avoid overfitting. This paper presents an enhanced dropout technique, whic...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	true	false	false	131,832
2101.07676	COTORRA: COntext-aware Testbed fOR Robotic Applications	Edge & Fog computing have received considerable attention as promising candidates for the evolution of robotic systems. In this letter, we propose COTORRA, an Edge & Fog driven robotic testbed that combines context information with robot sensor data to validate innovative concepts for robotic systems prior to being app...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	true	216,110
2403.11219	Causality from Bottom to Top: A Survey	Causality has become a fundamental approach for explaining the relationships between events, phenomena, and outcomes in various fields of study. It has invaded various fields and applications, such as medicine, healthcare, economics, finance, fraud detection, cybersecurity, education, public policy, recommender systems...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	438,598
2308.05411	Explainable AI applications in the Medical Domain: a systematic review	Artificial Intelligence in Medicine has made significant progress with emerging applications in medical imaging, patient care, and other areas. While these applications have proven successful in retrospective studies, very few of them were applied in practice.The field of Medical AI faces various challenges, in terms o...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	384,784
2403.16666	Revisiting the Sleeping Beauty problem	The Sleeping Beauty problem is a probability riddle with no definite solution for more than two decades and its solution is of great interest in many fields of knowledge. There are two main competing solutions to the problem: the halfer approach, and the thirder approach. The main reason for disagreement in the literat...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	441,144
2209.00475	REMOT: A Region-to-Whole Framework for Realistic Human Motion Transfer	Human Video Motion Transfer (HVMT) aims to, given an image of a source person, generate his/her video that imitates the motion of the driving person. Existing methods for HVMT mainly exploit Generative Adversarial Networks (GANs) to perform the warping operation based on the flow estimated from the source person image ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	315,594
2304.07139	Neuromorphic Optical Flow and Real-time Implementation with Event Cameras	Optical flow provides information on relative motion that is an important component in many computer vision pipelines. Neural networks provide high accuracy optical flow, yet their complexity is often prohibitive for application at the edge or in robots, where efficiency and latency play crucial role. To address this c...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	true	false	false	358,243
1505.02891	Ontology Based Document Clustering Using MapReduce	Nowadays, document clustering is considered as a data intensive task due to the dramatic, fast increase in the number of available documents. Nevertheless, the features that represent those documents are also too large. The most common method for representing documents is the vector space model, which represents docume...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	false	43,013
2302.03497	MMRec: Simplifying Multimodal Recommendation	This paper presents an open-source toolbox, MMRec for multimodal recommendation. MMRec simplifies and canonicalizes the process of implementing and comparing multimodal recommendation models. The objective of MMRec is to provide a unified and configurable arena that can minimize the effort in implementing and testing m...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	true	344,359
2412.07349	Disturbance Observer-Parameterized Control Barrier Function with Adaptive Safety Bounds	This letter presents a nonlinear disturbance observer-parameterized control barrier function (DOp-CBF) designed for a robust safety control system under external disturbances. This framework emphasizes that the safety bounds are relevant to the disturbances, acknowledging the critical impact of disturbances on system s...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	515,631
2309.02784	Norm Tweaking: High-performance Low-bit Quantization of Large Language Models	As the size of large language models (LLMs) continues to grow, model compression without sacrificing accuracy has become a crucial challenge for deployment. While some quantization methods, such as GPTQ, have made progress in achieving acceptable 4-bit weight-only quantization, attempts at lower-bit quantization often ...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	390,166
2409.12162	Precise Forecasting of Sky Images Using Spatial Warping	The intermittency of solar power, due to occlusion from cloud cover, is one of the key factors inhibiting its widespread use in both commercial and residential settings. Hence, real-time forecasting of solar irradiance for grid-connected photovoltaic systems is necessary to schedule and allocate resources across the gr...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	489,464
1707.05662	Learning Powers of Poisson Binomial Distributions	We introduce the problem of simultaneously learning all powers of a Poisson Binomial Distribution (PBD). A PBD of order $n$ is the distribution of a sum of $n$ mutually independent Bernoulli random variables $X_i$, where $\mathbb{E}[X_i] = p_i$. The $k$'th power of this distribution, for $k$ in a range $[m]$, is the di...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	77,271
2406.07595	VulDetectBench: Evaluating the Deep Capability of Vulnerability Detection with Large Language Models	Large Language Models (LLMs) have training corpora containing large amounts of program code, greatly improving the model's code comprehension and generation capabilities. However, sound comprehensive research on detecting program vulnerabilities, a more specific task related to code, and evaluating the performance of L...	false	false	false	false	true	false	false	false	false	false	false	false	true	false	false	false	false	true	463,134
2107.12825	Individual Survival Curves with Conditional Normalizing Flows	Survival analysis, or time-to-event modelling, is a classical statistical problem that has garnered a lot of interest for its practical use in epidemiology, demographics or actuarial sciences. Recent advances on the subject from the point of view of machine learning have been concerned with precise per-individual predi...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	248,018
2302.05138	Plan-then-Seam: Towards Efficient Table-to-Text Generation	Table-to-text generation aims at automatically generating text to help people conveniently obtain salient information in tables. Recent works explicitly decompose the generation process into content planning and surface generation stages, employing two autoregressive networks for them respectively. However, they are co...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	344,950
2008.00824	State-of-the-art Techniques in Deep Edge Intelligence	The potential held by the gargantuan volumes of data being generated across networks worldwide has been truly unlocked by machine learning techniques and more recently Deep Learning. The advantages offered by the latter have seen it rapidly becoming a framework of choice for various applications. However, the centraliz...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	true	190,128
2110.03189	Pointwise Bounds for Distribution Estimation under Communication Constraints	We consider the problem of estimating a $d$-dimensional discrete distribution from its samples observed under a $b$-bit communication constraint. In contrast to most previous results that largely focus on the global minimax error, we study the local behavior of the estimation error and provide \emph{pointwise} bounds t...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	259,405
2307.16419	Subspace Distillation for Continual Learning	An ultimate objective in continual learning is to preserve knowledge learned in preceding tasks while learning new tasks. To mitigate forgetting prior knowledge, we propose a novel knowledge distillation technique that takes into the account the manifold structure of the latent/output space of a neural network in learn...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	382,601
1709.05405	Commutativity and Commutative Pairs of Some Differential Equations	In this study, explicit differential equations representing commutative pairs of some well-known second-order linear time-varying systems have been derived. The commutativity of these systems are investigated by considering 30 second-order linear differential equations with variable coefficients. It is shown that the s...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	80,857
2412.19211	Large Language Models Meet Graph Neural Networks: A Perspective of Graph Mining	Graph mining is an important area in data mining and machine learning that involves extracting valuable information from graph-structured data. In recent years, significant progress has been made in this field through the development of graph neural networks (GNNs). However, GNNs are still deficient in generalizing to ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	520,759
2005.07376	Improving Neuroevolution Using Island Extinction and Repopulation	Neuroevolution commonly uses speciation strategies to better explore the search space of neural network architectures. One such speciation strategy is through the use of islands, which are also popular in improving performance and convergence of distributed evolutionary algorithms. However, in this approach some island...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	false	false	177,265
2206.12559	Self-supervised Context-aware Style Representation for Expressive Speech Synthesis	Expressive speech synthesis, like audiobook synthesis, is still challenging for style representation learning and prediction. Deriving from reference audio or predicting style tags from text requires a huge amount of labeled data, which is costly to acquire and difficult to define and annotate accurately. In this paper...	false	false	true	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	304,647
2007.10231	Integrating Network Embedding and Community Outlier Detection via Multiclass Graph Description	Network (or graph) embedding is the task to map the nodes of a graph to a lower dimensional vector space, such that it preserves the graph properties and facilitates the downstream network mining tasks. Real world networks often come with (community) outlier nodes, which behave differently from the regular nodes of the...	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	false	false	188,210
2312.08735	Polyper: Boundary Sensitive Polyp Segmentation	We present a new boundary sensitive framework for polyp segmentation, called Polyper. Our method is motivated by a clinical approach that seasoned medical practitioners often leverage the inherent features of interior polyp regions to tackle blurred boundaries.Inspired by this, we propose explicitly leveraging polyp re...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	415,427
1602.09118	Easy Monotonic Policy Iteration	A key problem in reinforcement learning for control with general function approximators (such as deep neural networks and other nonlinear functions) is that, for many algorithms employed in practice, updates to the policy or $Q$-function may fail to improve performance---or worse, actually cause the policy performance ...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	52,726
2101.02692	Where2Act: From Pixels to Actions for Articulated 3D Objects	One of the fundamental goals of visual perception is to allow agents to meaningfully interact with their environment. In this paper, we take a step towards that long-term goal -- we extract highly localized actionable information related to elementary actions such as pushing or pulling for articulated objects with mova...	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	214,709
2402.18128	Downstream Task Guided Masking Learning in Masked Autoencoders Using Multi-Level Optimization	Masked Autoencoder (MAE) is a notable method for self-supervised pretraining in visual representation learning. It operates by randomly masking image patches and reconstructing these masked patches using the unmasked ones. A key limitation of MAE lies in its disregard for the varying informativeness of different patche...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	433,297
2211.07687	Uncovering the Portability Limitation of Deep Learning-Based Wireless Device Fingerprints	Recent device fingerprinting approaches rely on deep learning to extract device-specific features solely from raw RF signals to identify, classify and authenticate wireless devices. One widely known issue lies in the inability of these approaches to maintain good performances when the training data and testing data are...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	330,328
1702.03401	A Minimax Algorithm Better Than Alpha-beta?: No and Yes	This paper has three main contributions to our understanding of fixed-depth minimax search: (A) A new formulation for Stockman's SSS* algorithm, based on Alpha-Beta, is presented. It solves all the perceived drawbacks of SSS, finally transforming it into a practical algorithm. In effect, we show that SSS = alpha-beta...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	68,122
2210.14985	Learning Deep Sensorimotor Policies for Vision-based Autonomous Drone Racing	Autonomous drones can operate in remote and unstructured environments, enabling various real-world applications. However, the lack of effective vision-based algorithms has been a stumbling block to achieving this goal. Existing systems often require hand-engineered components for state estimation, planning, and control...	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	false	326,754
2309.02054	An Adaptive Spatial-Temporal Local Feature Difference Method for Infrared Small-moving Target Detection	Detecting small moving targets accurately in infrared (IR) image sequences is a significant challenge. To address this problem, we propose a novel method called spatial-temporal local feature difference (STLFD) with adaptive background suppression (ABS). Our approach utilizes filters in the spatial and temporal domains...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	389,913
2005.10411	Interpretable and Accurate Fine-grained Recognition via Region Grouping	We present an interpretable deep model for fine-grained visual recognition. At the core of our method lies the integration of region-based part discovery and attribution within a deep neural network. Our model is trained using image-level object labels, and provides an interpretation of its results via the segmentation...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	178,168
1106.5264	Acquiring Correct Knowledge for Natural Language Generation	Natural language generation (NLG) systems are computer software systems that produce texts in English and other human languages, often from non-linguistic input data. NLG systems, like most AI systems, need substantial amounts of knowledge. However, our experience in two NLG projects suggests that it is difficult to ac...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	11,009
2003.02736	Claim Check-Worthiness Detection as Positive Unlabelled Learning	As the first step of automatic fact checking, claim check-worthiness detection is a critical component of fact checking systems. There are multiple lines of research which study this problem: check-worthiness ranking from political speeches and debates, rumour detection on Twitter, and citation needed detection from Wi...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	167,022
2404.11304	Dynamic Phasor Modeling of Single-Phase Grid-Forming Converters	In modern power systems, grid-forming power converters (GFMCs) have emerged as an enabling technology. However, the modeling of single-phase GFMCs faces new challenges. In particular, the nonlinear orthogonal signal generation unit, crucial for power measurement, still lacks an accurate model. To overcome the challenge...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	447,454
2208.09350	PyMIC: A deep learning toolkit for annotation-efficient medical image segmentation	Background and Objective: Open-source deep learning toolkits are one of the driving forces for developing medical image segmentation models. Existing toolkits mainly focus on fully supervised segmentation and require full and accurate pixel-level annotations that are time-consuming and difficult to acquire for segmenta...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	313,673
2406.07263	Active learning for affinity prediction of antibodies	The primary objective of most lead optimization campaigns is to enhance the binding affinity of ligands. For large molecules such as antibodies, identifying mutations that enhance antibody affinity is particularly challenging due to the combinatorial explosion of potential mutations. When the structure of the antibody-...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	462,970
2105.06253	Exploring CTC Based End-to-End Techniques for Myanmar Speech Recognition	In this work, we explore a Connectionist Temporal Classification (CTC) based end-to-end Automatic Speech Recognition (ASR) model for the Myanmar language. A series of experiments is presented on the topology of the model in which the convolutional layers are added and dropped, different depths of bidirectional long sho...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	235,068
1103.5569	An upper bound on community size in scalable community detection	It is well-known that community detection methods based on modularity optimization often fails to discover small communities. Several objective functions used for community detection therefore involve a resolution parameter that allows the detection of communities at different scales. We provide an explicit upper bound...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	9,795
2204.11640	Hybrid ISTA: Unfolding ISTA With Convergence Guarantees Using Free-Form Deep Neural Networks	It is promising to solve linear inverse problems by unfolding iterative algorithms (e.g., iterative shrinkage thresholding algorithm (ISTA)) as deep neural networks (DNNs) with learnable parameters. However, existing ISTA-based unfolded algorithms restrict the network architectures for iterative updates with the partia...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	293,219
2301.05833	Multi-Agent Coordination Fluid Flow Modeling and Experimental Evaluation	Reliability is a critical aspect of multi-agent system coordination as it ensures that the system functions correctly and consistently. If one agent in the system fails or behaves unexpectedly, it can negatively impact the performance and effectiveness of the entire system. Therefore, it is important to design and impl...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	340,459
2008.09038	Battery State of Charge Modeling for Solar PV Array using Polynomial Regression	In this manuscript, we have investigated the response of the State of Charge (SoC) and the open-circuit voltage across the dynamic battery model under the variable voltage and current during the charging cycle of the battery. These variable input voltage and current have been obtained using the variable irradiance and ...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	192,593
1609.01915	Polyp Detection and Segmentation from Video Capsule Endoscopy: A Review	Video capsule endoscopy (VCE) is used widely nowadays for visualizing the gastrointestinal (GI) tract. Capsule endoscopy exams are prescribed usually as an additional monitoring mechanism and can help in identifying polyps, bleeding, etc. To analyze the large scale video data produced by VCE exams automatic image proce...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	60,653
2411.01527	Performance Evaluation of Deep Learning Models for Water Quality Index Prediction: A Comparative Study of LSTM, TCN, ANN, and MLP	Environmental monitoring and predictive modeling of the Water Quality Index (WQI) through the assessment of the water quality.	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	505,111
2501.07563	Training-Free Motion-Guided Video Generation with Enhanced Temporal Consistency Using Motion Consistency Loss	In this paper, we address the challenge of generating temporally consistent videos with motion guidance. While many existing methods depend on additional control modules or inference-time fine-tuning, recent studies suggest that effective motion guidance is achievable without altering the model architecture or requirin...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	524,436
2209.15308	Effective Early Stopping of Point Cloud Neural Networks	Early stopping techniques can be utilized to decrease the time cost, however currently the ultimate goal of early stopping techniques is closely related to the accuracy upgrade or the ability of the neural network to generalize better on unseen data without being large or complex in structure and not directly with its ...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	320,562
2408.16725	Mini-Omni: Language Models Can Hear, Talk While Thinking in Streaming	Recent advances in language models have achieved significant progress. GPT-4o, as a new milestone, has enabled real-time conversations with humans, demonstrating near-human natural fluency. Such human-computer interaction necessitates models with the capability to perform reasoning directly with the audio modality and ...	true	false	true	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	484,427
2404.08955	Consistency analysis of refined instrumental variable methods for continuous-time system identification in closed-loop	Refined instrumental variable methods have been broadly used for identification of continuous-time systems in both open and closed-loop settings. However, the theoretical properties of these methods are still yet to be fully understood when operating in closed-loop. In this paper, we address the consistency of the simp...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	446,476
2202.07047	Vector Coded Caching Multiplicatively Boosts the Throughput of Realistic Downlink Systems	The recent introduction of vector coded caching has revealed that multi-rank transmissions in the presence of receiver-side cache content can dramatically ameliorate the file-size bottleneck of coded caching and substantially boost performance in error-free wire-like channels. We here employ large-matrix analysis to ex...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	280,407
2012.09719	Image-Based Jet Analysis	Image-based jet analysis is built upon the jet image representation of jets that enables a direct connection between high energy physics and the fields of computer vision and deep learning. Through this connection, a wide array of new jet analysis techniques have emerged. In this text, we survey jet image based classif...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	212,153
1003.0628	Linguistic Geometries for Unsupervised Dimensionality Reduction	Text documents are complex high dimensional objects. To effectively visualize such data it is important to reduce its dimensionality and visualize the low dimensional embedding as a 2-D or 3-D scatter plot. In this paper we explore dimensionality reduction methods that draw upon domain knowledge in order to achieve a b...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	5,829
1906.05685	A Focus on Neural Machine Translation for African Languages	African languages are numerous, complex and low-resourced. The datasets required for machine translation are difficult to discover, and existing research is hard to reproduce. Minimal attention has been given to machine translation for African languages so there is scant research regarding the problems that arise when ...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	135,090
2310.20656	Non-Compositionality in Sentiment: New Data and Analyses	When natural language phrases are combined, their meaning is often more than the sum of their parts. In the context of NLP tasks such as sentiment analysis, where the meaning of a phrase is its sentiment, that still applies. Many NLP studies on sentiment analysis, however, focus on the fact that sentiment computations ...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	404,470
2105.06463	Contrastive Learning of Image Representations with Cross-Video Cycle-Consistency	Recent works have advanced the performance of self-supervised representation learning by a large margin. The core among these methods is intra-image invariance learning. Two different transformations of one image instance are considered as a positive sample pair, where various tasks are designed to learn invariant repr...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	235,132
2205.04042	Incremental-DETR: Incremental Few-Shot Object Detection via Self-Supervised Learning	Incremental few-shot object detection aims at detecting novel classes without forgetting knowledge of the base classes with only a few labeled training data from the novel classes. Most related prior works are on incremental object detection that rely on the availability of abundant training samples per novel class tha...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	295,515
2003.09870	NSM Converges to a k-NN Regressor Under Loose Lipschitz Estimates	Although it is known that having accurate Lipschitz estimates is essential for certain models to deliver good predictive performance, refining this constant in practice can be a difficult task especially when the input dimension is high. In this work, we shed light on the consequences of employing loose Lipschitz bound...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	169,169
2008.00325	Bringing UMAP Closer to the Speed of Light with GPU Acceleration	The Uniform Manifold Approximation and Projection (UMAP) algorithm has become widely popular for its ease of use, quality of results, and support for exploratory, unsupervised, supervised, and semi-supervised learning. While many algorithms can be ported to a GPU in a simple and direct fashion, such efforts have result...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	189,977
1908.07247	An efficient bounded-variable nonlinear least-squares algorithm for embedded MPC	This paper presents a new approach to solve linear and nonlinear model predictive control (MPC) problems that requires small memory footprint and throughput and is particularly suitable when the model and/or controller parameters change at runtime. Typically MPC requires two phases: 1) construct an optimization problem...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	142,245
1110.5183	Diffusion of Information in Robot Swarms	This work is devoted to communication approaches, which spread information in robot swarms. These mechanisms are useful for large-scale systems and also for such cases when a limited communication equipment does not allow routing of information packages. We focus on two approaches such as virtual fields and epidemic al...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	12,755
2412.14492	FaultExplainer: Leveraging Large Language Models for Interpretable Fault Detection and Diagnosis	Machine learning algorithms are increasingly being applied to fault detection and diagnosis (FDD) in chemical processes. However, existing data-driven FDD platforms often lack interpretability for process operators and struggle to identify root causes of previously unseen faults. This paper presents FaultExplainer, an ...	false	false	false	false	true	false	true	false	false	false	true	false	false	false	false	false	false	false	518,728
2310.04516	Vulnerability Analysis of Nonlinear Control Systems to Stealthy False Data Injection Attacks	In this work, we focus on analyzing vulnerability of nonlinear dynamical control systems to stealthy false data injection attacks on sensors. We start by defining the stealthiness notion in the most general form where an attack is considered stealthy if it would be undetected by any intrusion detector, i.e., any intrus...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	397,693
1606.05694	DeepStance at SemEval-2016 Task 6: Detecting Stance in Tweets Using Character and Word-Level CNNs	This paper describes our approach for the Detecting Stance in Tweets task (SemEval-2016 Task 6). We utilized recent advances in short text categorization using deep learning to create word-level and character-level models. The choice between word-level and character-level models in each particular case was informed thr...	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	false	false	false	57,450
1309.5993	Combining smart card data and household travel survey to analyze jobs-housing relationships in Beijing	Location Based Services (LBS) provide a new perspective for spatiotemporally analyzing dynamic urban systems. Research has investigated urban dynamics using GSM (Global System for Mobile Communications), GPS (Global Positioning System), SNS (Social Networking Services) and Wi-Fi techniques. However, less attention has ...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	27,213
2309.07390	Unleashing the Power of Depth and Pose Estimation Neural Networks by Designing Compatible Endoscopic Images	Deep learning models have witnessed depth and pose estimation framework on unannotated datasets as a effective pathway to succeed in endoscopic navigation. Most current techniques are dedicated to developing more advanced neural networks to improve the accuracy. However, existing methods ignore the special properties o...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	391,758
2305.09678	Anomaly Detection Dataset for Industrial Control Systems	Over the past few decades, Industrial Control Systems (ICSs) have been targeted by cyberattacks and are becoming increasingly vulnerable as more ICSs are connected to the internet. Using Machine Learning (ML) for Intrusion Detection Systems (IDS) is a promising approach for ICS cyber protection, but the lack of suitabl...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	364,735
2304.13409	Efficient Explainable Face Verification based on Similarity Score Argument Backpropagation	Explainable Face Recognition is gaining growing attention as the use of the technology is gaining ground in security-critical applications. Understanding why two faces images are matched or not matched by a given face recognition system is important to operators, users, anddevelopers to increase trust, accountability, ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	360,565
1506.02923	Compact Shape Trees: A Contribution to the Forest of Shape Correspondences and Matching Methods	We propose a novel technique, termed compact shape trees, for computing correspondences of single-boundary 2-D shapes in O(n2) time. Together with zero or more features defined at each of n sample points on the shape's boundary, the compact shape tree of a shape comprises the O(n) collection of vectors emanating from a...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	43,988
0909.3027	Language Models for Handwritten Short Message Services	Handwriting is an alternative method for entering texts composing Short Message Services. However, a whole new language features the texts which are produced. They include for instance abbreviations and other consonantal writing which sprung up for time saving and fashion. We have collected and processed a significant ...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	4,507
2408.06776	Robust Deep Reinforcement Learning for Inverter-based Volt-Var Control in Partially Observable Distribution Networks	Inverter-based volt-var control is studied in this paper. One key issue in DRL-based approaches is the limited measurement deployment in active distribution networks, which leads to problems of a partially observable state and unknown reward. To address those problems, this paper proposes a robust DRL approach with a c...	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	false	false	480,338
2007.06902	Enabling Adaptive and Enhanced Acoustic Sensing Using Nonlinear Dynamics	Transmission of real-time data is strongly increasing due to remote processing of sensor data, among other things. A route to meet this demand is adaptive sensing, in which sensors acquire only relevant information using pre-processing at sensor level. We present here adaptive acoustic sensors based on mechanical oscil...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	187,167
2005.05079	A Survey on Sampling and Profiling over Big Data (Technical Report)	Due to the development of internet technology and computer science, data is exploding at an exponential rate. Big data brings us new opportunities and challenges. On the one hand, we can analyze and mine big data to discover hidden information and get more potential value. On the other hand, the 5V characteristic of bi...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	176,637
2202.03674	Trained Model in Supervised Deep Learning is a Conditional Risk Minimizer	We proved that a trained model in supervised deep learning minimizes the conditional risk for each input (Theorem 2.1). This property provided insights into the behavior of trained models and established a connection between supervised and unsupervised learning in some cases. In addition, when the labels are intractabl...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	279,301
2306.15089	Energy Modelling and Forecasting for an Underground Agricultural Farm using a Higher Order Dynamic Mode Decomposition Approach	This paper presents an approach based on higher order dynamic mode decomposition (HODMD) to model, analyse, and forecast energy behaviour in an urban agriculture farm situated in a retrofitted London underground tunnel, where observed measurements are influenced by noisy and occasionally transient conditions. HODMD is ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	375,903
cs/0603097	On Pinsker's Type Inequalities and Csiszar's f-divergences. Part I: Second and Fourth-Order Inequalities	We study conditions on $f$ under which an $f$-divergence $D_f$ will satisfy $D_f \geq c_f V^2$ or $D_f \geq c_{2,f} V^2 + c_{4,f} V^4$, where $V$ denotes variational distance and the coefficients $c_f$, $c_{2,f}$ and $c_{4,f}$ are {\em best possible}. As a consequence, we obtain lower bounds in terms of $V$ for many we...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	539,349
1703.01664	Diversified Texture Synthesis with Feed-forward Networks	Recent progresses on deep discriminative and generative modeling have shown promising results on texture synthesis. However, existing feed-forward based methods trade off generality for efficiency, which suffer from many issues, such as shortage of generality (i.e., build one network per texture), lack of diversity (i....	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	69,409
2311.03421	Hopfield-Enhanced Deep Neural Networks for Artifact-Resilient Brain State Decoding	The study of brain states, ranging from highly synchronous to asynchronous neuronal patterns like the sleep-wake cycle, is fundamental for assessing the brain's spatiotemporal dynamics and their close connection to behavior. However, the development of new techniques to accurately identify them still remains a challeng...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	405,854
1611.00889	Designing Sparse Reliable Pose-Graph SLAM: A Graph-Theoretic Approach	In this paper, we aim to design sparse D-optimal (determinantoptimal) pose-graph SLAM problems through the synthesis of sparse graphs with the maximum weighted number of spanning trees. Characterizing graphs with the maximum number of spanning trees is an open problem in general. To tackle this problem, several new the...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	true	63,295
1305.2741	Can Human-Like Bots Control Collective Mood: Agent-Based Simulations of Online Chats	Using agent-based modeling approach, in this paper, we study self-organized dynamics of interacting agents in the presence of chat Bots. Different Bots with tunable ``human-like'' attributes, which exchange emotional messages with agents, are considered, and collective emotional behavior of agents is quantitatively ana...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	24,544
2303.01173	Resource-Constrained Station-Keeping for Helium Balloons using Reinforcement Learning	High altitude balloons have proved useful for ecological aerial surveys, atmospheric monitoring, and communication relays. However, due to weight and power constraints, there is a need to investigate alternate modes of propulsion to navigate in the stratosphere. Very recently, reinforcement learning has been proposed a...	false	false	false	false	true	false	true	true	false	false	false	false	false	false	false	false	false	false	348,857
2112.01707	TransCouplet:Transformer based Chinese Couplet Generation	Chinese couplet is a special form of poetry composed of complex syntax with ancient Chinese language. Due to the complexity of semantic and grammatical rules, creation of a suitable couplet is a formidable challenge. This paper presents a transformer-based sequence-to-sequence couplet generation model. With the utiliza...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	269,580
2406.11087	DP-MemArc: Differential Privacy Transfer Learning for Memory Efficient Language Models	Large language models have repeatedly shown outstanding performance across diverse applications. However, deploying these models can inadvertently risk user privacy. The significant memory demands during training pose a major challenge in terms of resource consumption. This substantial size places a heavy load on memor...	false	false	false	false	true	false	true	false	true	false	false	false	true	false	false	false	false	false	464,710
2002.11221	Distributed Weighted Least-squares Estimation for Networked Systems with Edge Measurements	This paper studies the problem of distributed weighted least-squares (WLS) estimation for an interconnected linear measurement network with additive noise. Two types of measurements are considered: self measurements for individual nodes, and edge measurements for the connecting nodes. Each node in the network carries o...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	165,640
1909.09132	Spoken Speech Enhancement using EEG	In this paper we demonstrate spoken speech enhancement using electroencephalography (EEG) signals using a generative adversarial network (GAN) based model, gated recurrent unit (GRU) regression based model, temporal convolutional network (TCN) regression model and finally using a mixed TCN GRU regression model. We co...	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	146,154
1607.04331	Random projections of random manifolds	Interesting data often concentrate on low dimensional smooth manifolds inside a high dimensional ambient space. Random projections are a simple, powerful tool for dimensionality reduction of such data. Previous works have studied bounds on how many projections are needed to accurately preserve the geometry of these man...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	58,601
2409.15161	A Gated Residual Kolmogorov-Arnold Networks for Mixtures of Experts	This paper introduces KAMoE, a novel Mixture of Experts (MoE) framework based on Gated Residual Kolmogorov-Arnold Networks (GRKAN). We propose GRKAN as an alternative to the traditional gating function, aiming to enhance efficiency and interpretability in MoE modeling. Through extensive experiments on digital asset mar...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	true	false	false	490,783
2010.12306	Network Classifiers Based on Social Learning	This work proposes a new way of combining independently trained classifiers over space and time. Combination over space means that the outputs of spatially distributed classifiers are aggregated. Combination over time means that the classifiers respond to streaming data during testing and continue to improve their perf...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	false	false	false	202,646
2209.07578	Pixel-wise classification in graphene-detection with tree-based machine learning algorithms	Mechanical exfoliation of graphene and its identification by optical inspection is one of the milestones in condensed matter physics that sparked the field of 2D materials. Finding regions of interest from the entire sample space and identification of layer number is a routine task potentially amenable to automatizatio...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	317,798
2011.02126	Incremental Machine Speech Chain Towards Enabling Listening while Speaking in Real-time	Inspired by a human speech chain mechanism, a machine speech chain framework based on deep learning was recently proposed for the semi-supervised development of automatic speech recognition (ASR) and text-to-speech synthesis TTS) systems. However, the mechanism to listen while speaking can be done only after receiving ...	false	false	true	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	204,829
2104.12709	Rich Semantics Improve Few-shot Learning	Human learning benefits from multi-modal inputs that often appear as rich semantics (e.g., description of an object's attributes while learning about it). This enables us to learn generalizable concepts from very limited visual examples. However, current few-shot learning (FSL) methods use numerical class labels to den...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	232,298
2007.09493	Deep Hough-Transform Line Priors	Classical work on line segment detection is knowledge-based; it uses carefully designed geometric priors using either image gradients, pixel groupings, or Hough transform variants. Instead, current deep learning methods do away with all prior knowledge and replace priors by training deep networks on large manually anno...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	187,965
2204.01952	Towards On-Board Panoptic Segmentation of Multispectral Satellite Images	With tremendous advancements in low-power embedded computing devices and remote sensing instruments, the traditional satellite image processing pipeline which includes an expensive data transfer step prior to processing data on the ground is being replaced by on-board processing of captured data. This paradigm shift en...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	289,776
1812.10613	Generative Adversarial User Model for Reinforcement Learning Based Recommendation System	There are great interests as well as many challenges in applying reinforcement learning (RL) to recommendation systems. In this setting, an online user is the environment; neither the reward function nor the environment dynamics are clearly defined, making the application of RL challenging. In this paper, we propose a ...	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	false	117,408
2110.14038	Robustness of Graph Neural Networks at Scale	Graph Neural Networks (GNNs) are increasingly important given their popularity and the diversity of applications. Yet, existing studies of their vulnerability to adversarial attacks rely on relatively small graphs. We address this gap and study how to attack and defend GNNs at scale. We propose two sparsity-aware first...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	263,389
2408.05146	Performative Prediction on Games and Mechanism Design	Agents often have individual goals which depend on a group's actions. If agents trust a forecast of collective action and adapt strategically, such prediction can influence outcomes non-trivially, resulting in a form of performative prediction. This effect is ubiquitous in scenarios ranging from pandemic predictions to...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	false	false	true	479,673
2204.10377	Contrastive Test-Time Adaptation	Test-time adaptation is a special setting of unsupervised domain adaptation where a trained model on the source domain has to adapt to the target domain without accessing source data. We propose a novel way to leverage self-supervised contrastive learning to facilitate target feature learning, along with an online pseu...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	292,759
2402.10618	Enhancing Role-playing Systems through Aggressive Queries: Evaluation and Improvement	The advent of Large Language Models (LLMs) has propelled dialogue generation into new realms, particularly in the field of role-playing systems (RPSs). While enhanced with ordinary role-relevant training dialogues, existing LLM-based RPSs still struggle to align with roles when handling intricate and trapped queries in...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	430,037

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