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
2204.00424	Comparison of convolutional neural networks for cloudy optical images reconstruction from single or multitemporal joint SAR and optical images	With the increasing availability of optical and synthetic aperture radar (SAR) images thanks to the Sentinel constellation, and the explosion of deep learning, new methods have emerged in recent years to tackle the reconstruction of optical images that are impacted by clouds. In this paper, we focus on the evaluation o...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	289,250
2008.00512	Integrated monitoring of ice in selected Swiss lakes. Final project report	Various lake observables, including lake ice, are related to climate and climate change and provide a good opportunity for long-term monitoring. Lakes (and as part of them lake ice) is therefore considered an Essential Climate Variable (ECV) of the Global Climate Observing System (GCOS). Following the need for an integ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	190,032
2109.09692	Modeling Regime Shifts in Multiple Time Series	We investigate the problem of discovering and modeling regime shifts in an ecosystem comprising multiple time series known as co-evolving time series. Regime shifts refer to the changing behaviors exhibited by series at different time intervals. Learning these changing behaviors is a key step toward time series forecas...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	256,363
2406.17190	Sound Tagging in Infant-centric Home Soundscapes	Certain environmental noises have been associated with negative developmental outcomes for infants and young children. Though classifying or tagging sound events in a domestic environment is an active research area, previous studies focused on data collected from a non-stationary microphone placed in the environment or...	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	467,460
1801.08573	Etymo: A New Discovery Engine for AI Research	We present Etymo (https://etymo.io), a discovery engine to facilitate artificial intelligence (AI) research and development. It aims to help readers navigate a large number of AI-related papers published every week by using a novel form of search that finds relevant papers and displays related papers in a graphical int...	false	false	false	true	true	true	false	false	false	false	false	false	false	false	false	false	false	false	88,963
2003.00063	Bio-Inspired Modality Fusion for Active Speaker Detection	Human beings have developed fantastic abilities to integrate information from various sensory sources exploring their inherent complementarity. Perceptual capabilities are therefore heightened, enabling, for instance, the well-known "cocktail party" and McGurk effects, i.e., speech disambiguation from a panoply of soun...	false	false	true	false	false	false	true	false	false	false	false	true	false	false	false	true	false	false	166,183
1812.04405	Conditional Variational Autoencoder for Neural Machine Translation	We explore the performance of latent variable models for conditional text generation in the context of neural machine translation (NMT). Similar to Zhang et al., we augment the encoder-decoder NMT paradigm by introducing a continuous latent variable to model features of the translation process. We extend this model wit...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	116,211
2405.14896	Study on spike-and-wave detection in epileptic signals using t-location-scale distribution and the K-nearest neighbors classifier	Pattern classification in electroencephalography (EEG) signals is an important problem in biomedical engineering since it enables the detection of brain activity, particularly the early detection of epileptic seizures. In this paper, we propose a k-nearest neighbors classification for epileptic EEG signals based on a t...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	456,657
2406.07662	Progress Towards Decoding Visual Imagery via fNIRS	We demonstrate the possibility of reconstructing images from fNIRS brain activity and start building a prototype to match the required specs. By training an image reconstruction model on downsampled fMRI data, we discovered that cm-scale spatial resolution is sufficient for image generation. We obtained 71% retrieval a...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	463,147
1602.00828	Learning a Deep Model for Human Action Recognition from Novel Viewpoints	Recognizing human actions from unknown and unseen (novel) views is a challenging problem. We propose a Robust Non-Linear Knowledge Transfer Model (R-NKTM) for human action recognition from novel views. The proposed R-NKTM is a deep fully-connected neural network that transfers knowledge of human actions from any unknow...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	51,624
1406.0303	A Superposition Calculus for Abductive Reasoning	We present a modification of the superposition calculus that is meant to generate consequences of sets of first-order axioms. This approach is proven to be sound and deductive-complete in the presence of redundancy elimination rules, provided the considered consequences are built on a given finite set of ground terms, ...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	33,549
2204.07772	SETTI: A Self-supervised Adversarial Malware Detection Architecture in an IoT Environment	In recent years, malware detection has become an active research topic in the area of Internet of Things (IoT) security. The principle is to exploit knowledge from large quantities of continuously generated malware. Existing algorithms practice available malware features for IoT devices and lack real-time prediction be...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	291,841
2409.16632	Functional Stochastic Gradient MCMC for Bayesian Neural Networks	Classical parameter-space Bayesian inference for Bayesian neural networks (BNNs) suffers from several unresolved prior issues, such as knowledge encoding intractability and pathological behaviours in deep networks, which can lead to improper posterior inference. To address these issues, functional Bayesian inference ha...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	491,429
2305.12914	IMBUE: In-Memory Boolean-to-CUrrent Inference ArchitecturE for Tsetlin Machines	In-memory computing for Machine Learning (ML) applications remedies the von Neumann bottlenecks by organizing computation to exploit parallelism and locality. Non-volatile memory devices such as Resistive RAM (ReRAM) offer integrated switching and storage capabilities showing promising performance for ML applications. ...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	true	366,250
1608.04533	Improving link prediction in complex networks by adaptively exploiting multiple structural features of networks	So far, many network-structure-based link prediction methods have been proposed. However, these methods only highlight one or two structural features of networks, and then use the methods to predict missing links in different networks. The performances of these existing methods are not always satisfied in all cases sin...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	59,848
2111.05113	Membership Inference Attacks Against Self-supervised Speech Models	Recently, adapting the idea of self-supervised learning (SSL) on continuous speech has started gaining attention. SSL models pre-trained on a huge amount of unlabeled audio can generate general-purpose representations that benefit a wide variety of speech processing tasks. Despite their ubiquitous deployment, however, ...	false	false	true	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	265,699
2006.05558	Hermitian-Lifted Codes	In this paper, we construct codes for local recovery of erasures with high availability and constant-bounded rate from the Hermitian curve. These new codes, called Hermitian-lifted codes, are evaluation codes with evaluation set being the set of $\mathbb{F}_{q^2}$-rational points on the affine curve. The novelty is in ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	181,107
2007.01391	Secure Beamforming and Ergodic Secrecy Rate Analysis for Amplify-and-Forward Relay Networks with Wireless Powered Jammer	In this correspondence, we consider an amplify-and-forward relay network in which relayed information is overheard by an eavesdropper. In order to confound the eavesdropper, a wireless-powered jammer is also considered which harvests energy from a multiple-antenna source. We proposed a new secure beamforming scheme in ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	185,413
2012.08787	Query expansion with artificially generated texts	A well-known way to improve the performance of document retrieval is to expand the user's query. Several approaches have been proposed in the literature, and some of them are considered as yielding state-of-the-art results in IR. In this paper, we explore the use of text generation to automatically expand the queries. ...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	211,867
1302.4975	Refining Reasoning in Qualitative Probabilistic Networks	In recent years there has been a spate of papers describing systems for probabilisitic reasoning which do not use numerical probabilities. In some cases the simple set of values used by these systems make it impossible to predict how a probability will change or which hypothesis is most likely given certain evidence. T...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	22,249
cs/0606084	The Completeness of Propositional Resolution: A Simple and Constructive<br> Proof	It is well known that the resolution method (for propositional logic) is complete. However, completeness proofs found in the literature use an argument by contradiction showing that if a set of clauses is unsatisfiable, then it must have a resolution refutation. As a consequence, none of these proofs actually gives an ...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	539,532
2104.12898	SGNet: A Super-class Guided Network for Image Classification and Object Detection	Most classification models treat different object classes in parallel and the misclassifications between any two classes are treated equally. In contrast, human beings can exploit high-level information in making a prediction of an unknown object. Inspired by this observation, the paper proposes a super-class guided ne...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	232,348
2303.06212	Weighted Notions of Fairness with Binary Supermodular Chores	We study the problem of allocating indivisible chores among agents with binary supermodular cost functions. In other words, each chore has a marginal cost of $0$ or $1$ and chores exhibit increasing marginal costs (or decreasing marginal utilities). In this note, we combine the techniques of Viswanathan and Zick (2022)...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	350,740
2004.13278	VD-BERT: A Unified Vision and Dialog Transformer with BERT	Visual dialog is a challenging vision-language task, where a dialog agent needs to answer a series of questions through reasoning on the image content and dialog history. Prior work has mostly focused on various attention mechanisms to model such intricate interactions. By contrast, in this work, we propose VD-BERT, a ...	false	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	174,500
1901.11090	Neuroevolution with Perceptron Turing Machines	We introduce the perceptron Turing machine and show how it can be used to create a system of neuroevolution. Advantages of this approach include automatic scaling of solutions to larger problem sizes, the ability to experiment with hand-coded solutions, and an enhanced potential for understanding evolved solutions. Han...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	120,163
2206.08094	Deep Neural Imputation: A Framework for Recovering Incomplete Brain Recordings	Neuroscientists and neuroengineers have long relied on multielectrode neural recordings to study the brain. However, in a typical experiment, many factors corrupt neural recordings from individual electrodes, including electrical noise, movement artifacts, and faulty manufacturing. Currently, common practice is to disc...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	true	false	false	303,005
2101.08458	UNIT: Unifying Tensorized Instruction Compilation	Because of the increasing demand for computation in DNN, researchers develope both hardware and software mechanisms to reduce the compute and memory burden. A widely adopted approach is to use mixed precision data types. However, it is hard to leverage mixed precision without hardware support because of the overhead of...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	216,326
2001.09765	Model-assisted cohort selection with bias analysis for generating large-scale cohorts from the EHR for oncology research	Objective Electronic health records (EHRs) are a promising source of data for health outcomes research in oncology. A challenge in using EHR data is that selecting cohorts of patients often requires information in unstructured parts of the record. Machine learning has been used to address this, but even high-performing...	false	false	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	161,663
2112.10229	On Causal Inference for Data-free Structured Pruning	Neural networks (NNs) are making a large impact both on research and industry. Nevertheless, as NNs' accuracy increases, it is followed by an expansion in their size, required number of compute operations and energy consumption. Increase in resource consumption results in NNs' reduced adoption rate and real-world deplo...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	272,381
2109.12052	Free Energy Principle for State and Input Estimation of a Quadcopter Flying in Wind	The free energy principle from neuroscience provides a brain-inspired perception scheme through a data-driven model learning algorithm called Dynamic Expectation Maximization (DEM). This paper aims at introducing an experimental design to provide the first experimental confirmation of the usefulness of DEM as a state a...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	257,141
1408.1750	Time-Asynchronous Gaussian Multiple Access Relay Channel with Correlated Sources	We study the transmission of a set of correlated sources $(U_1,\cdots,U_K)$ over a Gaussian multiple access relay channel with time asynchronism between the encoders. We assume that the maximum possible offset ${\mathsf{d_{max}}}(n)$ between the transmitters grows without bound as the block length $n \rightarrow \infty...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	35,204
2101.02118	Do We Really Need Deep Learning Models for Time Series Forecasting?	Time series forecasting is a crucial task in machine learning, as it has a wide range of applications including but not limited to forecasting electricity consumption, traffic, and air quality. Traditional forecasting models rely on rolling averages, vector auto-regression and auto-regressive integrated moving averages...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	214,530
2003.09093	Masked Face Recognition Dataset and Application	In order to effectively prevent the spread of COVID-19 virus, almost everyone wears a mask during coronavirus epidemic. This almost makes conventional facial recognition technology ineffective in many cases, such as community access control, face access control, facial attendance, facial security checks at train statio...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	168,955
2411.19527	DisCoRD: Discrete Tokens to Continuous Motion via Rectified Flow Decoding	Human motion, inherently continuous and dynamic, presents significant challenges for generative models. Despite their dominance, discrete quantization methods, such as VQ-VAEs, suffer from inherent limitations, including restricted expressiveness and frame-wise noise artifacts. Continuous approaches, while producing sm...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	512,285
2202.11985	Can deep neural networks learn process model structure? An assessment framework and analysis	Predictive process monitoring concerns itself with the prediction of ongoing cases in (business) processes. Prediction tasks typically focus on remaining time, outcome, next event or full case suffix prediction. Various methods using machine and deep learning havebeen proposed for these tasks in recent years. Especiall...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	282,077
2410.02995	Task-free Lifelong Robot Learning with Retrieval-based Weighted Local Adaptation	A fundamental objective in intelligent robotics is to move towards lifelong learning robot that can learn and adapt to unseen scenarios over time. However, continually learning new tasks would introduce catastrophic forgetting problems due to data distribution shifts. To mitigate this, we store a subset of data from pr...	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	false	494,557
1810.10798	A Gaussian Process perspective on Convolutional Neural Networks	In this paper we cast the well-known convolutional neural network in a Gaussian process perspective. In this way we hope to gain additional insights into the performance of convolutional networks, in particular understand under what circumstances they tend to perform well and what assumptions are implicitly made in the...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	111,366
2010.16088	Cross-Domain Sentiment Classification with Contrastive Learning and Mutual Information Maximization	Contrastive learning (CL) has been successful as a powerful representation learning method. In this work we propose CLIM: Contrastive Learning with mutual Information Maximization, to explore the potential of CL on cross-domain sentiment classification. To the best of our knowledge, CLIM is the first to adopt contrasti...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	203,964
2104.11620	GuideBP: Guiding Backpropagation Through Weaker Pathways of Parallel Logits	Convolutional neural networks often generate multiple logits and use simple techniques like addition or averaging for loss computation. But this allows gradients to be distributed equally among all paths. The proposed approach guides the gradients of backpropagation along weakest concept representations. A weakness sco...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	231,971
2105.03791	Enhancing Transformers with Gradient Boosted Decision Trees for NLI Fine-Tuning	Transfer learning has become the dominant paradigm for many natural language processing tasks. In addition to models being pretrained on large datasets, they can be further trained on intermediate (supervised) tasks that are similar to the target task. For small Natural Language Inference (NLI) datasets, language model...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	234,263
2103.05611	Optimal Pricing with a Single Point	We study the following fundamental data-driven pricing problem. How can/should a decision-maker price its product based on data at a single historical price? How valuable is such data? We consider a decision-maker who optimizes over (potentially randomized) pricing policies to maximize the worst-case ratio of the reven...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	224,032
1711.02079	Cone Detection using a Combination of LiDAR and Vision-based Machine Learning	The classification and the position estimation of objects become more and more relevant as the field of robotics is expanding in diverse areas of society. In this Bachelor Thesis, we developed a cone detection algorithm for an autonomous car using a LiDAR sensor and a colour camera. By evaluating simple constraints, th...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	84,001
2011.00718	Fundamental Limits of Obfuscation for Linear Gaussian Dynamical Systems: An Information-Theoretic Approach	In this paper, we study the fundamental limits of obfuscation in terms of privacy-distortion tradeoffs for linear Gaussian dynamical systems via an information-theoretic approach. Particularly, we obtain analytical formulas that capture the fundamental privacy-distortion tradeoffs when privacy masks are to be added to ...	false	false	false	false	false	false	true	false	false	true	true	false	true	false	false	false	false	false	204,340
1911.06172	Word-level Lexical Normalisation using Context-Dependent Embeddings	Lexical normalisation (LN) is the process of correcting each word in a dataset to its canonical form so that it may be more easily and more accurately analysed. Most lexical normalisation systems operate at the character-level, while word-level models are seldom used. Recent language models offer solutions to the drawb...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	153,468
2409.17693	Spatial embedding promotes a specific form of modularity with low entropy and heterogeneous spectral dynamics	Understanding how biological constraints shape neural computation is a central goal of computational neuroscience. Spatially embedded recurrent neural networks provide a promising avenue to study how modelled constraints shape the combined structural and functional organisation of networks over learning. Prior work has...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	491,939
2009.07414	Ground-truth or DAER: Selective Re-query of Secondary Information	Many vision tasks use secondary information at inference time -- a seed -- to assist a computer vision model in solving a problem. For example, an initial bounding box is needed to initialize visual object tracking. To date, all such work makes the assumption that the seed is a good one. However, in practice, from crow...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	195,913
1512.08650	An Importance Sampling Scheme for Models in a Strong External Field	We propose Monte Carlo methods to estimate the partition function of the two-dimensional Ising model in the presence of an external magnetic field. The estimation is done in the dual of the Forney factor graph representing the model. The proposed methods can efficiently compute an estimate of the partition function in ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	50,533
2312.02218	WavePlanes: Compact Hex Planes for Dynamic Novel View Synthesis	Dynamic Novel View Synthesis (Dynamic NVS) enhances NVS technologies to model moving 3-D scenes. However, current methods are resource intensive and challenging to compress. To address this, we present WavePlanes, a fast and more compact hex plane representation, applicable to both Neural Radiance Fields and Gaussian S...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	412,769
1906.10718	Active Learning Solution on Distributed Edge Computing	Industry 4.0 becomes possible through the convergence between Operational and Information Technologies. All the requirements to realize the convergence is integrated on the Fog Platform. Fog Platform is introduced between the cloud server and edge devices when the unprecedented generation of data causes the burden of t...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	136,496
2412.05820	Geomagnetic and Inertial Combined Navigation Approach Based on Flexible Correction-Model Predictive Control Algorithm	This paper proposes a geomagnetic and inertial combined navigation approach based on the flexible correction-model predictive control algorithm (Fc-MPC). This approach aims to overcome the limitations of existing combined navigation methods that require prior geomagnetic maps and the inertial navigation drift of long-r...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	514,982
2309.09296	Model-based Subsampling for Knowledge Graph Completion	Subsampling is effective in Knowledge Graph Embedding (KGE) for reducing overfitting caused by the sparsity in Knowledge Graph (KG) datasets. However, current subsampling approaches consider only frequencies of queries that consist of entities and their relations. Thus, the existing subsampling potentially underestimat...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	392,549
2404.12888	Learn2Talk: 3D Talking Face Learns from 2D Talking Face	Speech-driven facial animation methods usually contain two main classes, 3D and 2D talking face, both of which attract considerable research attention in recent years. However, to the best of our knowledge, the research on 3D talking face does not go deeper as 2D talking face, in the aspect of lip-synchronization (lip-...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	true	448,075
2406.10569	MDA: An Interpretable and Scalable Multi-Modal Fusion under Missing Modalities and Intrinsic Noise Conditions	Multi-modal learning has shown exceptional performance in various tasks, especially in medical applications, where it integrates diverse medical information for comprehensive diagnostic evidence. However, there still are several challenges in multi-modal learning, 1. Heterogeneity between modalities, 2. uncertainty in ...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	464,463
2208.00905	A quantitative and constructive proof of Willems' Fundamental Lemma and its implications	Willems' Fundamental Lemma provides a powerful data-driven parametrization of all trajectories of a controllable linear time-invariant system based on one trajectory with persistently exciting (PE) input. In this paper, we present a novel proof of this result which is inspired by the classical adaptive control literatu...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	311,014
2103.05905	VideoMoCo: Contrastive Video Representation Learning with Temporally Adversarial Examples	MoCo is effective for unsupervised image representation learning. In this paper, we propose VideoMoCo for unsupervised video representation learning. Given a video sequence as an input sample, we improve the temporal feature representations of MoCo from two perspectives. First, we introduce a generator to drop out seve...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	true	224,123
2409.05030	Some Results on Neural Network Stability, Consistency, and Convergence: Insights into Non-IID Data, High-Dimensional Settings, and Physics-Informed Neural Networks	This paper addresses critical challenges in machine learning, particularly the stability, consistency, and convergence of neural networks under non-IID data, distribution shifts, and high-dimensional settings. We provide new theoretical results on uniform stability for neural networks with dynamic learning rates in non...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	486,612
1602.08186	Search by Ideal Candidates: Next Generation of Talent Search at LinkedIn	One key challenge in talent search is how to translate complex criteria of a hiring position into a search query. This typically requires deep knowledge on which skills are typically needed for the position, what are their alternatives, which companies are likely to have such candidates, etc. However, listing examples ...	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	false	52,610
2010.15327	Do Wide and Deep Networks Learn the Same Things? Uncovering How Neural Network Representations Vary with Width and Depth	A key factor in the success of deep neural networks is the ability to scale models to improve performance by varying the architecture depth and width. This simple property of neural network design has resulted in highly effective architectures for a variety of tasks. Nevertheless, there is limited understanding of effe...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	203,732
1608.08465	Intelligent Pinning Based Cooperative Secondary Control of Distributed Generators for Microgrid in Islanding Operation Mode	Motivated by the fact that the location(s) and structural properties of the pinning node(s) affect the algebraic connectivity of a network with respect to the reference value and thereby, its dynamic performance, this paper studies the application of intelligent single and multiple pinning of distributed cooperative se...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	60,364
2403.19721	Computationally and Memory-Efficient Robust Predictive Analytics Using Big Data	In the current data-intensive era, big data has become a significant asset for Artificial Intelligence (AI), serving as a foundation for developing data-driven models and providing insight into various unknown fields. This study navigates through the challenges of data uncertainties, storage limitations, and predictive...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	442,448
cmp-lg/9410003	Principle Based Semantics for HPSG	The paper presents a constraint based semantic formalism for HPSG. The advantages of the formlism are shown with respect to a grammar for a fragment of German that deals with (i) quantifier scope ambiguities triggered by scrambling and/or movement and (ii) ambiguities that arise from the collective/distributive distinc...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	536,185
2405.15926	Dissecting the Interplay of Attention Paths in a Statistical Mechanics Theory of Transformers	Despite the remarkable empirical performance of Transformers, their theoretical understanding remains elusive. Here, we consider a deep multi-head self-attention network, that is closely related to Transformers yet analytically tractable. We develop a statistical mechanics theory of Bayesian learning in this model, der...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	457,165
2308.07791	Informed Named Entity Recognition Decoding for Generative Language Models	Ever-larger language models with ever-increasing capabilities are by now well-established text processing tools. Alas, information extraction tasks such as named entity recognition are still largely unaffected by this progress as they are primarily based on the previous generation of encoder-only transformer models. He...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	385,648
1710.09236	Complete classification for simple root cyclic codes over local rings $\mathbb{Z}_{p^s}[v]/\langle v^2-pv\rangle$	Let $p$ be a prime integer, $n,s\geq 2$ be integers satisfying ${\rm gcd}(p,n)=1$, and denote $R=\mathbb{Z}_{p^s}[v]/\langle v^2-pv\rangle$. Then $R$ is a local non-principal ideal ring of $p^{2s}$ elements. First, the structure of any cyclic code over $R$ of length $n$ and a complete classification of all these codes ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	83,178
2405.10448	Dynamic In-context Learning with Conversational Models for Data Extraction and Materials Property Prediction	The advent of natural language processing and large language models (LLMs) has revolutionized the extraction of data from unstructured scholarly papers. However, ensuring data trustworthiness remains a significant challenge. In this paper, we introduce PropertyExtractor, an open-source tool that leverages advanced conv...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	454,764
2008.04355	A Two-Stage Metaheuristic Algorithm for the Dynamic Vehicle Routing Problem in Industry 4.0 approach	Industry 4.0 is a concept that assists companies in developing a modern supply chain (MSC) system when they are faced with a dynamic process. Because Industry 4.0 focuses on mobility and real-time integration, it is a good framework for a dynamic vehicle routing problem (DVRP). This research works on DVRP. The aim of t...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	191,194
1206.4636	Modeling Latent Variable Uncertainty for Loss-based Learning	We consider the problem of parameter estimation using weakly supervised datasets, where a training sample consists of the input and a partially specified annotation, which we refer to as the output. The missing information in the annotation is modeled using latent variables. Previous methods overburden a single distrib...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	16,687
2107.01113	Measuring Information Leakage in Non-stochastic Brute-Force Guessing	This paper proposes an operational measure of non-stochastic information leakage to formalize privacy against a brute-force guessing adversary. The information is measured by non-probabilistic uncertainty of uncertain variables, the non-stochastic counterparts of random variables. For $X$ that is related to released da...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	244,375
2009.11942	Online Learning With Adaptive Rebalancing in Nonstationary Environments	An enormous and ever-growing volume of data is nowadays becoming available in a sequential fashion in various real-world applications. Learning in nonstationary environments constitutes a major challenge, and this problem becomes orders of magnitude more complex in the presence of class imbalance. We provide new insigh...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	197,285
2405.13356	Large Language Models (LLMs) Assisted Wireless Network Deployment in Urban Settings	The advent of Large Language Models (LLMs) has revolutionized language understanding and human-like text generation, drawing interest from many other fields with this question in mind: What else are the LLMs capable of? Despite their widespread adoption, ongoing research continues to explore new ways to integrate LLMs ...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	455,906
2009.07872	Energy and Flow Effects of Optimal Automated Driving in Mixed Traffic: Vehicle-in-the-Loop Experimental Results	This paper experimentally demonstrates the effectiveness of an anticipative car-following algorithm in reducing energy use of gasoline engine and electric Connected and Automated Vehicles (CAV), without sacrificing safety and traffic flow. We propose a Vehicle-in-the-Loop (VIL) testing environment in which experimental...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	196,065
1810.03763	Cubic Regularization with Momentum for Nonconvex Optimization	Momentum is a popular technique to accelerate the convergence in practical training, and its impact on convergence guarantee has been well-studied for first-order algorithms. However, such a successful acceleration technique has not yet been proposed for second-order algorithms in nonconvex optimization.In this paper, ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	109,878
1701.00365	Beam-On-Graph: Simultaneous Channel Estimation for mmWave MIMO Systems with Multiple Users	This paper is concerned with the channel estimation problem in multi-user millimeter wave (mmWave) wireless systems with large antenna arrays. We develop a novel simultaneous-estimation with iterative fountain training (SWIFT) framework, in which multiple users estimate their channels at the same time and the required ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	66,265
2108.05575	Kicktionary-LOME: A Domain-Specific Multilingual Frame Semantic Parsing Model for Football Language	This technical report introduces an adapted version of the LOME frame semantic parsing model (Xia et al., EACL 2021) which is capable of automatically annotating texts according to the "Kicktionary" domain-specific framenet resource. Several methods for training a model even with limited available training data are pro...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	250,343
2101.10717	Combining Deep Generative Models and Multi-lingual Pretraining for Semi-supervised Document Classification	Semi-supervised learning through deep generative models and multi-lingual pretraining techniques have orchestrated tremendous success across different areas of NLP. Nonetheless, their development has happened in isolation, while the combination of both could potentially be effective for tackling task-specific labelled ...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	217,024
2411.14513	Towards a Middleware for Large Language Models	Large language models have gained widespread popularity for their ability to process natural language inputs and generate insights derived from their training data, nearing the qualities of true artificial intelligence. This advancement has prompted enterprises worldwide to integrate LLMs into their services. So far, t...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	true	510,215
2108.11127	AutoShape: Real-Time Shape-Aware Monocular 3D Object Detection	Existing deep learning-based approaches for monocular 3D object detection in autonomous driving often model the object as a rotated 3D cuboid while the object's geometric shape has been ignored. In this work, we propose an approach for incorporating the shape-aware 2D/3D constraints into the 3D detection framework. Spe...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	252,105
1308.5374	Dynamic Reasoning Systems	A {\it dynamic reasoning system} (DRS) is an adaptation of a conventional formal logical system that explicitly portrays reasoning as a temporal activity, with each extralogical input to the system and each inference rule application being viewed as occurring at a distinct time step. Every DRS incorporates some well-de...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	26,636
2105.11606	Centimeter-Wave Free-Space Time-of-Flight Imaging	Depth cameras are emerging as a cornerstone modality with diverse applications that directly or indirectly rely on measured depth, including personal devices, robotics, and self-driving vehicles. Although time-of-flight (ToF) methods have fueled these applications, the precision and robustness of ToF methods is limited...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	236,757
1912.04696	The Unfairness of Popularity Bias in Music Recommendation: A Reproducibility Study	Research has shown that recommender systems are typically biased towards popular items, which leads to less popular items being underrepresented in recommendations. The recent work of Abdollahpouri et al. in the context of movie recommendations has shown that this popularity bias leads to unfair treatment of both long-...	false	false	false	true	false	true	true	false	false	false	false	false	false	false	false	false	false	false	156,908
2008.04071	On Controllability of AI	Invention of artificial general intelligence is predicted to cause a shift in the trajectory of human civilization. In order to reap the benefits and avoid pitfalls of such powerful technology it is important to be able to control it. However, possibility of controlling artificial general intelligence and its more adva...	false	false	false	false	true	false	false	false	false	false	false	false	false	true	false	false	false	false	191,120
1401.3941	Network Coding for $3$s$/n$t Sum-Networks	A sum-network is a directed acyclic network where each source independently generates one symbol from a given field $\mathbb F$ and each terminal wants to receive the sum $($over $\mathbb F)$ of the source symbols. For sum-networks with two sources or two terminals, the solvability is characterized by the connection co...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	30,024
2009.10401	Dynamic Fusion based Federated Learning for COVID-19 Detection	Medical diagnostic image analysis (e.g., CT scan or X-Ray) using machine learning is an efficient and accurate way to detect COVID-19 infections. However, sharing diagnostic images across medical institutions is usually not allowed due to the concern of patients' privacy. This causes the issue of insufficient datasets ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	196,889
2106.06080	Gradual Domain Adaptation in the Wild:When Intermediate Distributions are Absent	We focus on the problem of domain adaptation when the goal is shifting the model towards the target distribution, rather than learning domain invariant representations. It has been shown that under the following two assumptions: (a) access to samples from intermediate distributions, and (b) samples being annotated with...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	240,344
1805.03257	Multimodal Hierarchical Reinforcement Learning Policy for Task-Oriented Visual Dialog	Creating an intelligent conversational system that understands vision and language is one of the ultimate goals in Artificial Intelligence (AI)~\cite{winograd1972understanding}. Extensive research has focused on vision-to-language generation, however, limited research has touched on combining these two modalities in a ...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	97,009
2211.15868	Kinematic-aware Hierarchical Attention Network for Human Pose Estimation in Videos	Previous video-based human pose estimation methods have shown promising results by leveraging aggregated features of consecutive frames. However, most approaches compromise accuracy to mitigate jitter or do not sufficiently comprehend the temporal aspects of human motion. Furthermore, occlusion increases uncertainty be...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	333,417
2409.14320	Exploring the Use of Contingency for Nuclear Electrical Studies	This paper examines the use of contingency analysis for a nuclear power plant to determine its potential benefits for the nuclear industry. Various N-1 contingencies were analyzed for a model of an existing nuclear plant, primarily inspecting voltage violations resulting from a failure. Remedial Actions Schemes were su...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	490,421
2009.14114	Projection-Free Adaptive Gradients for Large-Scale Optimization	The complexity in large-scale optimization can lie in both handling the objective function and handling the constraint set. In this respect, stochastic Frank-Wolfe algorithms occupy a unique position as they alleviate both computational burdens, by querying only approximate first-order information from the objective an...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	197,941
2307.10214	Time for aCTIon: Automated Analysis of Cyber Threat Intelligence in the Wild	Cyber Threat Intelligence (CTI) plays a crucial role in assessing risks and enhancing security for organizations. However, the process of extracting relevant information from unstructured text sources can be expensive and time-consuming. Our empirical experience shows that existing tools for automated structured CTI ex...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	380,467
2407.05721	PsycoLLM: Enhancing LLM for Psychological Understanding and Evaluation	Mental health has attracted substantial attention in recent years and LLM can be an effective technology for alleviating this problem owing to its capability in text understanding and dialogue. However, existing research in this domain often suffers from limitations, such as training on datasets lacking crucial prior k...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	471,107
1407.8513	Link overlap, viability, and mutual percolation in multiplex networks	Many real-world complex systems are best modeled by multiplex networks. The multiplexity has proved to have broad impact on the system's structure and function. Most theoretical studies on multiplex networks to date, however, have largely ignored the effect of link overlap across layers despite strong empirical evidenc...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	35,043
1802.00156	The Great Division	When information flow fails, when Democrats and Republicans do not talk to each other, when Israelis and Palestinians do not talk to each other, and when North Koreans and South Koreans do not talk to each other, mis-perceptions, biases and fake news arise. In this paper we present an in-depth study of political polari...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	89,358
1309.2254	Design of Two Dimensional Unipolar (Optical) Orthogonal Codes Through One Dimensional Unipolar (Optical) Orthogonal Codes	In this paper, an algorithm for construction of multiple sets of two dimensional (2D) or matrix unipolar (optical) orthogonal codes has been proposed. Representations of these 2D codes in difference of positions representation (DoPR) have also been discussed along-with conventional weighted positions representation (WP...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	26,941
2006.02713	Self-paced Contrastive Learning with Hybrid Memory for Domain Adaptive Object Re-ID	Domain adaptive object re-ID aims to transfer the learned knowledge from the labeled source domain to the unlabeled target domain to tackle the open-class re-identification problems. Although state-of-the-art pseudo-label-based methods have achieved great success, they did not make full use of all valuable information ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	180,123
2009.04612	How Political is the Spread of COVID-19 in the United States? An Analysis using Transportation and Weather Data	We investigate the difference in the spread of COVID-19 between the states won by Donald Trump (Red) and the states won by Hillary Clinton (Blue) in the 2016 presidential election, by mining transportation patterns of US residents from March 2020 to July 2020. To ensure a fair comparison, we first use a K-means cluster...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	195,095
2102.04577	Bayesian Poroelastic Aquifer Characterization from InSAR Surface Deformation Data Part II: Quantifying the Uncertainty	Uncertainty quantification of groundwater (GW) aquifer parameters is critical for efficient management and sustainable extraction of GW resources. These uncertainties are introduced by the data, model, and prior information on the parameters. Here we develop a Bayesian inversion framework that uses Interferometric Synt...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	219,154
2309.00074	On the Safety of Connected Cruise Control: Analysis and Synthesis with Control Barrier Functions	Connected automated vehicles have shown great potential to improve the efficiency of transportation systems in terms of passenger comfort, fuel economy, stability of driving behavior and mitigation of traffic congestions. Yet, to deploy these vehicles and leverage their benefits, the underlying algorithms must ensure t...	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	389,188
2203.15721	On Decoding Strategies for Neural Text Generators	When generating text from probabilistic models, the chosen decoding strategy has a profound effect on the resulting text. Yet the properties elicited by various decoding strategies do not always transfer across natural language generation tasks. For example, while mode-seeking methods like beam search perform remarkabl...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	288,510
1708.03027	Using Deep Neural Networks to Automate Large Scale Statistical Analysis for Big Data Applications	Statistical analysis (SA) is a complex process to deduce population properties from analysis of data. It usually takes a well-trained analyst to successfully perform SA, and it becomes extremely challenging to apply SA to big data applications. We propose to use deep neural networks to automate the SA process. In parti...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	78,703
2103.03432	Network Consensus with Privacy: A Secret Sharing Method	In this work, inspired by secret sharing schemes, we introduce a privacy-preserving approach for network consensus, by which all nodes in a network can reach an agreement on their states without exposing the individual state to neighbors. With the privacy degree defined for the agents, the proposed method makes the net...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	223,274

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