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
1809.08370	Semi-Supervised Sequence Modeling with Cross-View Training	Unsupervised representation learning algorithms such as word2vec and ELMo improve the accuracy of many supervised NLP models, mainly because they can take advantage of large amounts of unlabeled text. However, the supervised models only learn from task-specific labeled data during the main training phase. We therefore ...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	108,484
2007.07761	Self-Supervised Representation Learning for Detection of ACL Tear Injury in Knee MR Videos	The success of deep learning based models for computer vision applications requires large scale human annotated data which are often expensive to generate. Self-supervised learning, a subset of unsupervised learning, handles this problem by learning meaningful features from unlabeled image or video data. In this paper,...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	187,432
1707.08470	Implicit Entity Linking in Tweets	Over the years, Twitter has become one of the largest communication platforms providing key data to various applications such as brand monitoring, trend detection, among others. Entity linking is one of the major tasks in natural language understanding from tweets and it associates entity mentions in text to correspond...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	77,838
2501.01377	Training Medical Large Vision-Language Models with Abnormal-Aware Feedback	Existing Medical Large Vision-Language Models (Med-LVLMs), which encapsulate extensive medical knowledge, demonstrate excellent capabilities in understanding medical images and responding to human queries based on these images. However, there remain challenges in visual localization in medical images, which is crucial ...	false	false	false	false	true	false	true	false	true	false	false	true	false	false	false	false	false	false	522,040
2210.05394	Computationally-efficient initialisation of GPs: The generalised variogram method	We present a computationally-efficient strategy to initialise the hyperparameters of a Gaussian process (GP) avoiding the computation of the likelihood function. Our strategy can be used as a pretraining stage to find initial conditions for maximum-likelihood (ML) training, or as a standalone method to compute hyperpar...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	322,834
2406.13115	Dynamic Walking on Highly Underactuated Point Foot Humanoids: Closing the Loop between HZD and HLIP	Realizing bipedal locomotion on humanoid robots with point feet is especially challenging due to their highly underactuated nature, high degrees of freedom, and hybrid dynamics resulting from impacts. With the goal of addressing this challenging problem, this paper develops a control framework for realizing dynamic loc...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	465,696
2108.12014	Semi-Decentralized Network Slicing for Reliable V2V Service Provisioning: A Model-free Deep Reinforcement Learning Approach	Applying of network slicing in vehicular networks becomes a promising paradigm to support emerging Vehicle-to-Vehicle (V2V) applications with diverse quality of service (QoS) requirements. However, achieving effective network slicing in dynamic vehicular communications still faces many challenges, particularly time-var...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	252,366
2202.05903	Can We Talk? An Exploratory Study of Gender and Network Ties in a Local Government Setting	We explore the influence of gender and formal organizational status on the formation of discussion ties. Network data, gathered through surveying employees from a municipal organization in the United States, garnered a 92% response rate (n=143). Results of exponential random graph modeling indicate women supervisors ar...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	280,022
2502.11521	DeFiScope: Detecting Various DeFi Price Manipulations with LLM Reasoning	DeFi (Decentralized Finance) is one of the most important applications of today's cryptocurrencies and smart contracts. It manages hundreds of billions in Total Value Locked (TVL) on-chain, yet it remains susceptible to common DeFi price manipulation attacks. Despite state-of-the-art (SOTA) systems like DeFiRanger and ...	false	false	false	false	true	false	false	false	false	false	false	false	true	false	false	false	false	false	534,424
1902.05786	Effective distribution of codewords for Low Density Parity Check Cycle codes in the presence of disorder	We review the zeta-function representation of codewords allowed by a parity-check code based on a bipartite graph, and then investigate the effect of disorder on the effective distribution of codewords. The randomness (or disorder) is implemented by sampling the graph from an ensemble of random graphs, and computing th...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	121,624
2304.03164	Synthesizing Anyone, Anywhere, in Any Pose	We address the task of in-the-wild human figure synthesis, where the primary goal is to synthesize a full body given any region in any image. In-the-wild human figure synthesis has long been a challenging and under-explored task, where current methods struggle to handle extreme poses, occluding objects, and complex bac...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	356,691
2004.03281	Teacher-Class Network: A Neural Network Compression Mechanism	To reduce the overwhelming size of Deep Neural Networks (DNN) teacher-student methodology tries to transfer knowledge from a complex teacher network to a simple student network. We instead propose a novel method called the teacher-class network consisting of a single teacher and multiple student networks (i.e. class of...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	171,503
2305.14846	Introducing Competition to Boost the Transferability of Targeted Adversarial Examples through Clean Feature Mixup	Deep neural networks are widely known to be susceptible to adversarial examples, which can cause incorrect predictions through subtle input modifications. These adversarial examples tend to be transferable between models, but targeted attacks still have lower attack success rates due to significant variations in decisi...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	367,293
1702.01488	Output Impedance Diffusion into Lossy Power Lines	Output impedances are inherent elements of power sources in the electrical grids. In this paper, we give an answer to the following question: What is the effect of output impedances on the inductivity of the power network? To address this question, we propose a measure to evaluate the inductivity of a power grid, and w...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	67,824
2409.14089	Quantum enhanced stratification of Breast Cancer: exploring quantum expressivity for real omics data	Quantum Machine Learning (QML) is considered one of the most promising applications of Quantum Computing in the Noisy Intermediate Scale Quantum (NISQ) era for the impact it is thought to have in the near future. Although promising theoretical assumptions, the exploration of how QML could foster new discoveries in Medi...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	490,320
2002.04766	Task-Robust Model-Agnostic Meta-Learning	Meta-learning methods have shown an impressive ability to train models that rapidly learn new tasks. However, these methods only aim to perform well in expectation over tasks coming from some particular distribution that is typically equivalent across meta-training and meta-testing, rather than considering worst-case t...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	163,696
1905.12145	Optimal approximation for unconstrained non-submodular minimization	Submodular function minimization is well studied, and existing algorithms solve it exactly or up to arbitrary accuracy. However, in many applications, such as structured sparse learning or batch Bayesian optimization, the objective function is not exactly submodular, but close. In this case, no theoretical guarantees e...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	132,667
cs/0610016	Norm Based Causal Reasoning in Textual Corpus	Truth based entailments are not sufficient for a good comprehension of NL. In fact, it can not deduce implicit information necessary to understand a text. On the other hand, norm based entailments are able to reach this goal. This idea was behind the development of Frames (Minsky 75) and Scripts (Schank 77, Schank 79) ...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	539,757
2010.03240	Bias and Debias in Recommender System: A Survey and Future Directions	While recent years have witnessed a rapid growth of research papers on recommender system (RS), most of the papers focus on inventing machine learning models to better fit user behavior data. However, user behavior data is observational rather than experimental. This makes various biases widely exist in the data, inclu...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	199,336
2311.09590	MARformer: An Efficient Metal Artifact Reduction Transformer for Dental CBCT Images	Cone Beam Computed Tomography (CBCT) plays a key role in dental diagnosis and surgery. However, the metal teeth implants could bring annoying metal artifacts during the CBCT imaging process, interfering diagnosis and downstream processing such as tooth segmentation. In this paper, we develop an efficient Transformer to...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	408,195
2205.11680	HiPAL: A Deep Framework for Physician Burnout Prediction Using Activity Logs in Electronic Health Records	Burnout is a significant public health concern affecting nearly half of the healthcare workforce. This paper presents the first end-to-end deep learning framework for predicting physician burnout based on electronic health record (EHR) activity logs, digital traces of physician work activities that are available in any...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	298,232
2208.12634	Wrangler for the Emergency Events Database: A Tool for Geocoding and Analysis of a Global Disaster Dataset	There is an increasing need for precise location information on historical disasters, such as mass casualty events caused by weather or earthquakes, but existing disaster datasets often do not provide geographic coordinates of past events. Here we describe a new tool, the Wrangler for the Emergency Events Database (WEE...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	314,801
2209.06053	Towards Understanding the Overfitting Phenomenon of Deep Click-Through Rate Prediction Models	Deep learning techniques have been applied widely in industrial recommendation systems. However, far less attention has been paid to the overfitting problem of models in recommendation systems, which, on the contrary, is recognized as a critical issue for deep neural networks. In the context of Click-Through Rate (CTR)...	false	false	false	false	true	true	true	false	false	false	false	false	false	false	false	false	false	false	317,278
1402.6555	The effect of interdependence on the percolation of interdependent networks	Two stochastic models are proposed to generate a system composed of two interdependent scale-free (SF) or Erd\H{o}s-R\'{e}nyi (ER) networks where interdependent nodes are connected with exponential or power-law relation, as well as different dependence strength, respectively. Each subnetwork grows through the addition ...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	31,188
2308.03582	WIKITIDE: A Wikipedia-Based Timestamped Definition Pairs Dataset	A fundamental challenge in the current NLP context, dominated by language models, comes from the inflexibility of current architectures to 'learn' new information. While model-centric solutions like continual learning or parameter-efficient fine tuning are available, the question still remains of how to reliably identi...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	384,090
2009.05782	CIA_NITT at WNUT-2020 Task 2: Classification of COVID-19 Tweets Using Pre-trained Language Models	This paper presents our models for WNUT 2020 shared task2. The shared task2 involves identification of COVID-19 related informative tweets. We treat this as binary text classification problem and experiment with pre-trained language models. Our first model which is based on CT-BERT achieves F1-score of 88.7% and second...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	195,425
2006.00866	You say Normalizing Flows I see Bayesian Networks	Normalizing flows have emerged as an important family of deep neural networks for modelling complex probability distributions. In this note, we revisit their coupling and autoregressive transformation layers as probabilistic graphical models and show that they reduce to Bayesian networks with a pre-defined topology and...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	179,594
2202.07023	Exhaustivity and anti-exhaustivity in the RSA framework: Testing the effect of prior beliefs	During communication, the interpretation of utterances is sensitive to a listener's probabilistic prior beliefs, something which is captured by one currently influential model of pragmatics, the Rational Speech Act (RSA) framework. In this paper we focus on cases when this sensitivity to priors leads to counterintuitiv...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	280,400
1910.12166	Improved Zeroth-Order Variance Reduced Algorithms and Analysis for Nonconvex Optimization	Two types of zeroth-order stochastic algorithms have recently been designed for nonconvex optimization respectively based on the first-order techniques SVRG and SARAH/SPIDER. This paper addresses several important issues that are still open in these methods. First, all existing SVRG-type zeroth-order algorithms suffer ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	150,990
1907.12377	IntentGC: a Scalable Graph Convolution Framework Fusing Heterogeneous Information for Recommendation	The remarkable progress of network embedding has led to state-of-the-art algorithms in recommendation. However, the sparsity of user-item interactions (i.e., explicit preferences) on websites remains a big challenge for predicting users' behaviors. Although research efforts have been made in utilizing some auxiliary in...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	140,105
2108.09792	UltraBot: Autonomous Mobile Robot for Indoor UV-C Disinfection with Non-trivial Shape of Disinfection Zone	The paper focuses on the development of an autonomous disinfection robot UltraBot to reduce COVID-19 transmission along with other harmful bacteria and viruses. The motivation behind the research is to develop such a robot that is capable of performing disinfection tasks without the use of harmful sprays and chemicals ...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	251,707
2410.23294	Exploiting Risk-Aversion and Size-dependent fees in FX Trading with Fitted Natural Actor-Critic	In recent years, the popularity of artificial intelligence has surged due to its widespread application in various fields. The financial sector has harnessed its advantages for multiple purposes, including the development of automated trading systems designed to interact autonomously with markets to pursue different ai...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	503,982
1204.2651	Cooperative Cognitive Networks: Optimal, Distributed and Low-Complexity Algorithms	This paper considers the cooperation between a cognitive system and a primary system where multiple cognitive base stations (CBSs) relay the primary user's (PU) signals in exchange for more opportunity to transmit their own signals. The CBSs use amplify-and-forward (AF) relaying and coordinated beamforming to relay the...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	15,430
2404.07140	Characterising directed and undirected metrics of high-order interdependence	Systems of interest for theoretical or experimental work often exhibit high-order interactions, corresponding to statistical interdependencies in groups of variables that cannot be reduced to dependencies in subsets of them. While still under active development, the framework of partial information decomposition (PID) ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	445,729
2305.11683	Sensing of inspiration events from speech: comparison of deep learning and linguistic methods	Respiratory chest belt sensor can be used to measure the respiratory rate and other respiratory health parameters. Virtual Respiratory Belt, VRB, algorithms estimate the belt sensor waveform from speech audio. In this paper we compare the detection of inspiration events (IE) from respiratory belt sensor data using a no...	false	false	true	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	365,651
2204.03771	Q-learning with online random forests	$Q$-learning is the most fundamental model-free reinforcement learning algorithm. Deployment of $Q$-learning requires approximation of the state-action value function (also known as the $Q$-function). In this work, we provide online random forests as $Q$-function approximators and propose a novel method wherein the ran...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	290,429
2103.00464	NLP-CUET@LT-EDI-EACL2021: Multilingual Code-Mixed Hope Speech Detection using Cross-lingual Representation Learner	In recent years, several systems have been developed to regulate the spread of negativity and eliminate aggressive, offensive or abusive contents from the online platforms. Nevertheless, a limited number of researches carried out to identify positive, encouraging and supportive contents. In this work, our goal is to id...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	222,289
1903.10955	GS3D: An Efficient 3D Object Detection Framework for Autonomous Driving	We present an efficient 3D object detection framework based on a single RGB image in the scenario of autonomous driving. Our efforts are put on extracting the underlying 3D information in a 2D image and determining the accurate 3D bounding box of the object without point cloud or stereo data. Leveraging the off-the-she...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	125,401
2403.04819	Automating the Information Extraction from Semi-Structured Interview Transcripts	This paper explores the development and application of an automated system designed to extract information from semi-structured interview transcripts. Given the labor-intensive nature of traditional qualitative analysis methods, such as coding, there exists a significant demand for tools that can facilitate the analysi...	false	false	false	true	false	true	false	false	true	false	false	false	false	true	false	false	false	false	435,757
2011.11884	SMG: A Shuffling Gradient-Based Method with Momentum	We combine two advanced ideas widely used in optimization for machine learning: shuffling strategy and momentum technique to develop a novel shuffling gradient-based method with momentum, coined Shuffling Momentum Gradient (SMG), for non-convex finite-sum optimization problems. While our method is inspired by momentum ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	207,979
cmp-lg/9407018	Generating Multilingual Documents from a Knowledge Base: The TECHDOC Project	TECHDOC is an implemented system demonstrating the feasibility of generating multilingual technical documents on the basis of a language-independent knowledge base. Its application domain is user and maintenance instructions, which are produced from underlying plan structures representing the activities, the participat...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	536,137
2007.02929	IMU Preintegrated Features for Efficient Deep Inertial Odometry	MEMS Inertial Measurement Units (IMUs) as ubiquitous proprioceptive motion measurement devices are available on various everyday gadgets and robotic platforms. Nevertheless, the direct inference of geometrical transformations or odometry based on these data alone is a challenging task. This is due to the hard-to-model ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	185,905
2204.00050	Leakage Localization in Water Distribution Networks: A Model-Based Approach	The paper studies the problem of leakage localization in water distribution networks. For the case of a single pipe that suffers from a single leak, by taking recourse to pressure and flow measurements, and assuming those are noiseless, we provide a closed-form expression for leak localization, leak exponent and leak c...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	289,112
2404.00284	A Likelihood Ratio Test of Genetic Relationship among Languages	Lexical resemblances among a group of languages indicate that the languages could be genetically related, i.e., they could have descended from a common ancestral language. However, such resemblances can arise by chance and, hence, need not always imply an underlying genetic relationship. Many tests of significance base...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	442,850
2306.12383	Sample Complexity for Quadratic Bandits: Hessian Dependent Bounds and Optimal Algorithms	In stochastic zeroth-order optimization, a problem of practical relevance is understanding how to fully exploit the local geometry of the underlying objective function. We consider a fundamental setting in which the objective function is quadratic, and provide the first tight characterization of the optimal Hessian-dep...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	374,924
2004.04002	Transfer learning and subword sampling for asymmetric-resource one-to-many neural translation	There are several approaches for improving neural machine translation for low-resource languages: Monolingual data can be exploited via pretraining or data augmentation; Parallel corpora on related language pairs can be used via parameter sharing or transfer learning in multilingual models; Subword segmentation and reg...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	171,754
2212.08153	FiDO: Fusion-in-Decoder optimized for stronger performance and faster inference	Fusion-in-Decoder (FiD) is a powerful retrieval-augmented language model that sets the state-of-the-art on many knowledge-intensive NLP tasks. However, the architecture used for FiD was chosen by making minimal modifications to a standard T5 model, which our analysis shows to be highly suboptimal for a retrieval-augmen...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	336,644
2209.15172	Understanding Pure CLIP Guidance for Voxel Grid NeRF Models	We explore the task of text to 3D object generation using CLIP. Specifically, we use CLIP for guidance without access to any datasets, a setting we refer to as pure CLIP guidance. While prior work has adopted this setting, there is no systematic study of mechanics for preventing adversarial generations within CLIP. We ...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	true	320,497
2109.07826	Directed degree corrected mixed membership model and estimating community memberships in directed networks	This paper considers the problem of modeling and estimating community memberships of nodes in a directed network where every row (column) node is associated with a vector determining its membership in each row (column) community. To model such directed network, we propose directed degree corrected mixed membership (DiD...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	255,665
2306.13817	The Double Helix inside the NLP Transformer	We introduce a framework for analyzing various types of information in an NLP Transformer. In this approach, we distinguish four layers of information: positional, syntactic, semantic, and contextual. We also argue that the common practice of adding positional information to semantic embedding is sub-optimal and propos...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	375,412
2204.09290	Human-Object Interaction Detection via Disentangled Transformer	Human-Object Interaction Detection tackles the problem of joint localization and classification of human object interactions. Existing HOI transformers either adopt a single decoder for triplet prediction, or utilize two parallel decoders to detect individual objects and interactions separately, and compose triplets by...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	292,381
1203.5181	$k$-MLE: A fast algorithm for learning statistical mixture models	We describe $k$-MLE, a fast and efficient local search algorithm for learning finite statistical mixtures of exponential families such as Gaussian mixture models. Mixture models are traditionally learned using the expectation-maximization (EM) soft clustering technique that monotonically increases the incomplete (expec...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	15,091
2008.12321	Learning Representations of Endoscopic Videos to Detect Tool Presence Without Supervision	In this work, we explore whether it is possible to learn representations of endoscopic video frames to perform tasks such as identifying surgical tool presence without supervision. We use a maximum mean discrepancy (MMD) variational autoencoder (VAE) to learn low-dimensional latent representations of endoscopic videos ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	193,540
2407.10993	The Effects of Embodiment and Personality Expression on Learning in LLM-based Educational Agents	This work investigates how personality expression and embodiment affect personality perception and learning in educational conversational agents. We extend an existing personality-driven conversational agent framework by integrating LLM-based conversation support tailored to an educational application. We describe a us...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	true	473,220
2111.11055	Dense Uncertainty Estimation via an Ensemble-based Conditional Latent Variable Model	Uncertainty estimation has been extensively studied in recent literature, which can usually be classified as aleatoric uncertainty and epistemic uncertainty. In current aleatoric uncertainty estimation frameworks, it is often neglected that the aleatoric uncertainty is an inherent attribute of the data and can only be ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	267,535
2109.07424	SupCL-Seq: Supervised Contrastive Learning for Downstream Optimized Sequence Representations	While contrastive learning is proven to be an effective training strategy in computer vision, Natural Language Processing (NLP) is only recently adopting it as a self-supervised alternative to Masked Language Modeling (MLM) for improving sequence representations. This paper introduces SupCL-Seq, which extends the super...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	255,516
2106.02342	ASCNet: Self-supervised Video Representation Learning with Appearance-Speed Consistency	We study self-supervised video representation learning, which is a challenging task due to 1) lack of labels for explicit supervision; 2) unstructured and noisy visual information. Existing methods mainly use contrastive loss with video clips as the instances and learn visual representation by discriminating instances ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	238,839
2109.12319	A Graph-Based Neural Model for End-to-End Frame Semantic Parsing	Frame semantic parsing is a semantic analysis task based on FrameNet which has received great attention recently. The task usually involves three subtasks sequentially: (1) target identification, (2) frame classification and (3) semantic role labeling. The three subtasks are closely related while previous studies model...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	257,239
1509.08745	Compression of Deep Neural Networks on the Fly	Thanks to their state-of-the-art performance, deep neural networks are increasingly used for object recognition. To achieve these results, they use millions of parameters to be trained. However, when targeting embedded applications the size of these models becomes problematic. As a consequence, their usage on smartphon...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	true	false	false	47,405
2408.13010	A Web-Based Solution for Federated Learning with LLM-Based Automation	Federated Learning (FL) offers a promising approach for collaborative machine learning across distributed devices. However, its adoption is hindered by the complexity of building reliable communication architectures and the need for expertise in both machine learning and network programming. This paper presents a compr...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	482,977
2310.10308	Time integration schemes based on neural networks for solving partial differential equations on coarse grids	The accuracy of solving partial differential equations (PDEs) on coarse grids is greatly affected by the choice of discretization schemes. In this work, we propose to learn time integration schemes based on neural networks which satisfy three distinct sets of mathematical constraints, i.e., unconstrained, semi-constrai...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	400,168
2109.03344	SoMIAP: Social media images analysis and prediction framework	The personal photos captured and submitted by users on social networks can provide several interesting insights about the location of the user, which is a key indicator of their daily activities. This information is invaluable for security organisations, especially for security monitoring and tracking criminal activiti...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	254,031
1809.06360	The Parallelization of Riccati Recursion	A method is presented for parallelizing the computation of solutions to discrete-time, linear-quadratic, finite-horizon optimal control problems, which we will refer to as LQR problems. This class of problem arises frequently in robotic trajectory optimization. For very complicated robots, the size of these resulting p...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	108,039
2402.08344	Implicit Bias in Noisy-SGD: With Applications to Differentially Private Training	Training Deep Neural Networks (DNNs) with small batches using Stochastic Gradient Descent (SGD) yields superior test performance compared to larger batches. The specific noise structure inherent to SGD is known to be responsible for this implicit bias. DP-SGD, used to ensure differential privacy (DP) in DNNs' training,...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	429,067
1707.06128	Geometric Analysis of Observability of Target Object Shape Using Location-Unknown Distance Sensors	We geometrically analyze the problem of estimating parameters related to the shape and size of a two-dimensional target object on the plane by using randomly distributed distance sensors whose locations are unknown. Based on the analysis using geometric probability, we discuss the observability of these parameters: whi...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	77,355
2408.08223	On the Asymptotic Rate of Optimal Codes that Correct Tandem Duplications for Nanopore Sequencing	We study codes that can correct backtracking errors during nanopore sequencing. In this channel, a sequence of length $n$ over an alphabet of size $q$ is being read by a sliding window of length $\ell$, where from each window we obtain only its composition. Backtracking errors cause some windows to repeat, hence manife...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	480,910
2011.13698	Lightweight U-Net for High-Resolution Breast Imaging	We study the fully convolutional neural networks in the context of malignancy detection for breast cancer screening. We work on a supervised segmentation task looking for an acceptable compromise between the precision of the network and the computational complexity.	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	208,567
1504.06650	Learning Dictionaries for Named Entity Recognition using Minimal Supervision	This paper describes an approach for automatic construction of dictionaries for Named Entity Recognition (NER) using large amounts of unlabeled data and a few seed examples. We use Canonical Correlation Analysis (CCA) to obtain lower dimensional embeddings (representations) for candidate phrases and classify these phra...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	42,425
2311.12655	Hand-Eye Calibration	Whenever a sensor is mounted on a robot hand it is important to know the relationship between the sensor and the hand. The problem of determining this relationship is referred to as hand-eye calibration, which is important in at least two types of tasks: (i) map sensor centered measurements into the robot workspace and...	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	409,421
1207.7147	A Calculus of Looping Sequences with Local Rules	In this paper we present a variant of the Calculus of Looping Sequences (CLS for short) with global and local rewrite rules. While global rules, as in CLS, are applied anywhere in a given term, local rules can only be applied in the compartment on which they are defined. Local rules are dynamic: they can be added, move...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	17,830
2112.15483	Cloud Removal from Satellite Images	In this report, we have analyzed available cloud detection technique using sentinel hub. We have also implemented spatial attention generative adversarial network and improved quality of generated image compared to previous solution [7].	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	273,798
2311.18820	Adversarial Attacks and Defenses for Wireless Signal Classifiers using CDI-aware GANs	We introduce a Channel Distribution Information (CDI)-aware Generative Adversarial Network (GAN), designed to address the unique challenges of adversarial attacks in wireless communication systems. The generator in this CDI-aware GAN maps random input noise to the feature space, generating perturbations intended to dec...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	411,832
2301.07175	Scaffold-Based Multi-Objective Drug Candidate Optimization	In therapeutic design, balancing various physiochemical properties is crucial for molecule development, similar to how Multiparameter Optimization (MPO) evaluates multiple variables to meet a primary goal. While many molecular features can now be predicted using \textit{in silico} methods, aiding early drug development...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	340,845
2412.10751	p-Mean Regret for Stochastic Bandits	In this work, we extend the concept of the $p$-mean welfare objective from social choice theory (Moulin 2004) to study $p$-mean regret in stochastic multi-armed bandit problems. The $p$-mean regret, defined as the difference between the optimal mean among the arms and the $p$-mean of the expected rewards, offers a flex...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	517,087
2304.09930	Stopping Criteria for Value Iteration on Stochastic Games with Quantitative Objectives	A classic solution technique for Markov decision processes (MDP) and stochastic games (SG) is value iteration (VI). Due to its good practical performance, this approximative approach is typically preferred over exact techniques, even though no practical bounds on the imprecision of the result could be given until recen...	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	false	false	359,225
1502.00741	Dynamical And-Or Graph Learning for Object Shape Modeling and Detection	This paper studies a novel discriminative part-based model to represent and recognize object shapes with an "And-Or graph". We define this model consisting of three layers: the leaf-nodes with collaborative edges for localizing local parts, the or-nodes specifying the switch of leaf-nodes, and the root-node encoding th...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	39,865
2401.16421	Two Stones Hit One Bird: Bilevel Positional Encoding for Better Length Extrapolation	In this work, we leverage the intrinsic segmentation of language sequences and design a new positional encoding method called Bilevel Positional Encoding (BiPE). For each position, our BiPE blends an intra-segment encoding and an inter-segment encoding. The intra-segment encoding identifies the locations within a segme...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	424,817
1405.0603	Extracting Family Relationship Networks from Novels	We present an approach to the extraction of family relations from literary narrative, which incorporates a technique for utterance attribution proposed recently by Elson and McKeown (2010). In our work this technique is used in combination with the detection of vocatives - the explicit forms of address used by the char...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	32,784
2206.05649	TileGen: Tileable, Controllable Material Generation and Capture	Recent methods (e.g. MaterialGAN) have used unconditional GANs to generate per-pixel material maps, or as a prior to reconstruct materials from input photographs. These models can generate varied random material appearance, but do not have any mechanism to constrain the generated material to a specific category or to c...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	302,079
2309.04730	Integrated Robotics Networks with Co-optimization of Drone Placement and Air-Ground Communications	Terrestrial robots, i.e., unmanned ground vehicles (UGVs), and aerial robots, i.e., unmanned aerial vehicles (UAVs), operate in separate spaces. To exploit their complementary features (e.g., fields of views, communication links, computing capabilities), a promising paradigm termed integrated robotics network emerges, ...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	true	390,828
2012.11420	TechTexC: Classification of Technical Texts using Convolution and Bidirectional Long Short Term Memory Network	This paper illustrates the details description of technical text classification system and its results that developed as a part of participation in the shared task TechDofication 2020. The shared task consists of two sub-tasks: (i) first task identify the coarse-grained technical domain of given text in a specified lan...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	212,637
2305.01213	Integrated Sensing and Communication in Coordinated Cellular Networks	Integrated sensing and communication (ISAC) is a promising technique to provide sensing services in future wireless networks. Numerous existing works have adopted a monostatic radar architecture to realize ISAC, i.e., employing the same base station (BS) to transmit the ISAC signal and receive the echo. Yet, the concur...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	361,602
2210.15286	Interplay between exogenous triggers and endogenous behavioral changes in contagion processes on social networks	In recent years, statistical physics' methodologies have proven extremely successful in offering insights into the mechanisms that govern social interactions. However, the question of whether these models are able to capture trends observed in real-world datasets is hardly addressed in the current literature. With this...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	326,890
2501.07670	A Survey of Early Exit Deep Neural Networks in NLP	Deep Neural Networks (DNNs) have grown increasingly large in size to achieve state of the art performance across a wide range of tasks. However, their high computational requirements make them less suitable for resource-constrained applications. Also, real-world datasets often consist of a mixture of easy and complex s...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	524,465
2311.14944	Dynamical State Feedback Control for Linear Input Delay Systems, Part I: Dissipative Stabilization via Semidefinite Programming	We propose an SDP-based framework to address the stabilization of input delay systems while taking into account dissipative constraints. A key to our approach is the introduction of the concept of parameterized linear dynamical state feedbacks (LDSFs), which draws inspiration from recent advancements in the analyses of...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	410,321
2308.13174	Interactive segmentation in aerial images: a new benchmark and an open access web-based tool	Deep learning has gradually become powerful in segmenting and classifying aerial images. However, in remote sensing applications, the lack of training datasets and the difficulty of accuracy assessment have always been challenges for the deep learning based classification. In recent years, interactive semantic segmenta...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	387,808
2203.17042	IITD-DBAI: Multi-Stage Retrieval with Pseudo-Relevance Feedback and Query Reformulation	Resolving the contextual dependency is one of the most challenging tasks in the Conversational system. Our submission to CAsT-2021 aimed to preserve the key terms and the context in all subsequent turns and use classical Information retrieval methods. It was aimed to pull as relevant documents as possible from the corp...	false	false	false	false	true	true	false	false	false	false	false	false	false	true	false	false	false	false	289,022
2303.07392	Efficient Bayesian Physics Informed Neural Networks for Inverse Problems via Ensemble Kalman Inversion	Bayesian Physics Informed Neural Networks (B-PINNs) have gained significant attention for inferring physical parameters and learning the forward solutions for problems based on partial differential equations. However, the overparameterized nature of neural networks poses a computational challenge for high-dimensional p...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	351,230
2311.12066	EditShield: Protecting Unauthorized Image Editing by Instruction-guided Diffusion Models	Text-to-image diffusion models have emerged as an evolutionary for producing creative content in image synthesis. Based on the impressive generation abilities of these models, instruction-guided diffusion models can edit images with simple instructions and input images. While they empower users to obtain their desired ...	false	false	false	false	false	false	false	false	false	false	false	true	true	false	false	false	false	false	409,189
cs/0703088	Plot 94 in ambiance X-Window	<PLOT > is a collection of routines to draw surfaces, contours and so on. In this work we are presenting a version, that functions over work stations with the operative system UNIX, that count with the graphic ambiance X-WINDOW with the tools XLIB and OSF/MOTIF. This implant was realized for the work stations DEC 5000-...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	540,242
1811.09026	Bandits with Temporal Stochastic Constraints	We study the effect of impairment on stochastic multi-armed bandits and develop new ways to mitigate it. Impairment effect is the phenomena where an agent only accrues reward for an action if they have played it at least a few times in the recent past. It is practically motivated by repetition and recency effects in do...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	114,178
1805.07833	Wasserstein regularization for sparse multi-task regression	We focus in this paper on high-dimensional regression problems where each regressor can be associated to a location in a physical space, or more generally a generic geometric space. Such problems often employ sparse priors, which promote models using a small subset of regressors. To increase statistical power, the so-c...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	97,955
2202.12107	From Natural Language to Simulations: Applying GPT-3 Codex to Automate Simulation Modeling of Logistics Systems	Our work is the first attempt to apply Natural Language Processing to automate the development of simulation models of systems vitally important for logistics. We demonstrated that the framework built on top of the fine-tuned GPT-3 Codex, a Transformer-based language model, could produce functionally valid simulations ...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	282,109
1102.3866	Treatment of Semantic Heterogeneity in Information Retrieval	The first step to handle semantic heterogeneity should be the attempt to enrich the semantic information about documents, i.e. to fill up the gaps in the documents meta-data automatically. Section 2 describes a set of cascading deductive and heuristic extraction rules, which were developed in the project CARMEN for the...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	9,275
1606.09072	Resolution- and throughput-enhanced spectroscopy using high-throughput computational slit	There exists a fundamental tradeoff between spectral resolution and the efficiency or throughput for all optical spectrometers. The primary factors affecting the spectral resolution and throughput of an optical spectrometer are the size of the entrance aperture and the optical power of the focusing element. Thus far co...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	57,942
1806.10250	Hierarchical Coded Computation	Coded computation is a method to mitigate "stragglers" in distributed computing systems through the use of error correction coding that has lately received significant attention. First used in vector-matrix multiplication, the range of application was later extended to include matrix-matrix multiplication, heterogeneou...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	101,511
1711.03580	First Results from Using Game Refinement Measure and Learning Coefficient in Scrabble	This paper explores the entertainment experience and learning experience in Scrabble. It proposes a new measure from the educational point of view, which we call learning coefficient, based on the balance between the learner's skill and the challenge in Scrabble. Scrabble variants, generated using different size of boa...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	84,239
1704.04792	Locating Power Flow Solution Space Boundaries: A Numerical Polynomial Homotopy Approach	The solution space of any set of power flow equations may contain different number of real-valued solutions. The boundaries that separate these regions are referred to as power flow solution space boundaries. Knowledge of these boundaries is important as they provide a measure for voltage stability. Traditionally, cont...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	71,889
2306.14245	FedSampling: A Better Sampling Strategy for Federated Learning	Federated learning (FL) is an important technique for learning models from decentralized data in a privacy-preserving way. Existing FL methods usually uniformly sample clients for local model learning in each round. However, different clients may have significantly different data sizes, and the clients with more data c...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	375,603
1701.02302	A Homological Theory of Functions	In computational complexity, a complexity class is given by a set of problems or functions, and a basic challenge is to show separations of complexity classes $A \not= B$ especially when $A$ is known to be a subset of $B$. In this paper we introduce a homological theory of functions that can be used to establish comple...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	66,533

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