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
2112.03033	Unsupervised Law Article Mining based on Deep Pre-Trained Language Representation Models with Application to the Italian Civil Code	Modeling law search and retrieval as prediction problems has recently emerged as a predominant approach in law intelligence. Focusing on the law article retrieval task, we present a deep learning framework named LamBERTa, which is designed for civil-law codes, and specifically trained on the Italian civil code. To our ...	false	false	false	false	true	true	false	false	true	false	false	false	false	false	false	false	false	false	270,065
1103.1665	The Role of Singular Control in Frictionless Atom Cooling in a Harmonic Trapping Potential	In this article we study the frictionless cooling of atoms trapped in a harmonic potential, while minimizing the transient energy of the system. We show that in the case of unbounded control, this goal is achieved by a singular control, which is also the time-minimal solution for a "dual" problem, where the energy is h...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	9,534
2001.10964	Examining the Benefits of Capsule Neural Networks	Capsule networks are a recently developed class of neural networks that potentially address some of the deficiencies with traditional convolutional neural networks. By replacing the standard scalar activations with vectors, and by connecting the artificial neurons in a new way, capsule networks aim to be the next great...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	161,942
1906.01272	Evaluation of an AI system for the automated detection of glaucoma from stereoscopic optic disc photographs: the European Optic Disc Assessment Study	Objectives: To evaluate the performance of a deep learning based Artificial Intelligence (AI) software for detection of glaucoma from stereoscopic optic disc photographs, and to compare this performance to the performance of a large cohort of ophthalmologists and optometrists. Methods: A retrospective study evaluatin...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	133,662
2406.01757	Position: Cracking the Code of Cascading Disparity Towards Marginalized Communities	The rise of foundation models holds immense promise for advancing AI, but this progress may amplify existing risks and inequalities, leaving marginalized communities behind. In this position paper, we discuss that disparities towards marginalized communities - performance, representation, privacy, robustness, interpret...	false	false	false	false	true	false	true	false	false	false	false	false	false	true	false	false	false	false	460,445
2408.14379	Synergistic and Efficient Edge-Host Communication for Energy Harvesting Wireless Sensor Networks	There is an increasing demand for intelligent processing on ultra-low-power internet of things (IoT) device. Recent works have shown substantial efficiency boosts by executing inferences directly on the IoT device (node) rather than transmitting data. However, the computation and power demands of Deep Neural Network (D...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	true	483,513
2205.03380	Multi-mode Tensor Train Factorization with Spatial-spectral Regularization for Remote Sensing Images Recovery	Tensor train (TT) factorization and corresponding TT rank, which can well express the low-rankness and mode correlations of higher-order tensors, have attracted much attention in recent years. However, TT factorization based methods are generally not sufficient to characterize low-rankness along each mode of third-orde...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	295,268
2002.08676	Learning with Differentiable Perturbed Optimizers	Machine learning pipelines often rely on optimization procedures to make discrete decisions (e.g., sorting, picking closest neighbors, or shortest paths). Although these discrete decisions are easily computed, they break the back-propagation of computational graphs. In order to expand the scope of learning problems tha...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	164,835
2009.07567	U-Net with Graph Based Smoothing Regularizer for Small Vessel Segmentation on Fundus Image	The detection of retinal blood vessels, especially the changes of small vessel condition is the most important indicator to identify the vascular network of the human body. Existing techniques focused mainly on shape of the large vessels, which is not appropriate for the disconnected small and isolated vessels. Paying ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	195,980
0910.0456	Sharp Sufficient Conditions on Exact Sparsity Pattern Recovery	Consider the $n$-dimensional vector $y=X\be+\e$, where $\be \in \R^p$ has only $k$ nonzero entries and $\e \in \R^n$ is a Gaussian noise. This can be viewed as a linear system with sparsity constraints, corrupted by noise. We find a non-asymptotic upper bound on the probability that the optimal decoder for $\beta$ decl...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	4,616
2501.15570	ARWKV: Pretrain is not what we need, an RNN-Attention-Based Language Model Born from Transformer	As is known, hybrid quadratic and subquadratic attention models in multi-head architectures have surpassed both Transformer and Linear RNN models , with these works primarily focusing on reducing KV complexity and improving efficiency. For further research on expressiveness, we introduce our series of models distilled ...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	527,614
2109.03079	GOLD: Improving Out-of-Scope Detection in Dialogues using Data Augmentation	Practical dialogue systems require robust methods of detecting out-of-scope (OOS) utterances to avoid conversational breakdowns and related failure modes. Directly training a model with labeled OOS examples yields reasonable performance, but obtaining such data is a resource-intensive process. To tackle this limited-da...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	253,947
2212.03088	An Empirical Study on the Efficacy of Deep Active Learning for Image Classification	Deep Active Learning (DAL) has been advocated as a promising method to reduce labeling costs in supervised learning. However, existing evaluations of DAL methods are based on different settings, and their results are controversial. To tackle this issue, this paper comprehensively evaluates 19 existing DAL methods in a ...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	334,992
1212.5656	High-precision camera distortion measurements with a "calibration harp"	This paper addresses the high precision measurement of the distortion of a digital camera from photographs. Traditionally, this distortion is measured from photographs of a flat pattern which contains aligned elements. Nevertheless, it is nearly impossible to fabricate a very flat pattern and to validate its flatness. ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	20,575
2406.12580	Behavior-Dependent Linear Recurrent Units for Efficient Sequential Recommendation	Sequential recommender systems aims to predict the users' next interaction through user behavior modeling with various operators like RNNs and attentions. However, existing models generally fail to achieve the three golden principles for sequential recommendation simultaneously, i.e., training efficiency, low-cost infe...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	465,473
2304.09913	MARS: Model-agnostic Biased Object Removal without Additional Supervision for Weakly-Supervised Semantic Segmentation	Weakly-supervised semantic segmentation aims to reduce labeling costs by training semantic segmentation models using weak supervision, such as image-level class labels. However, most approaches struggle to produce accurate localization maps and suffer from false predictions in class-related backgrounds (i.e., biased ob...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	359,218
2408.17387	Bayesian Optimization for Non-Convex Two-Stage Stochastic Optimization Problems	Bayesian optimization is a sample-efficient method for solving expensive, black-box optimization problems. Stochastic programming concerns optimization under uncertainty where, typically, average performance is the quantity of interest. In the first stage of a two-stage problem, here-and-now decisions must be made in t...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	484,689
2102.11786	QuPeL: Quantized Personalization with Applications to Federated Learning	Traditionally, federated learning (FL) aims to train a single global model while collaboratively using multiple clients and a server. Two natural challenges that FL algorithms face are heterogeneity in data across clients and collaboration of clients with {\em diverse resources}. In this work, we introduce a \textit{qu...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	221,525
2106.09672	The 2021 Image Similarity Dataset and Challenge	This paper introduces a new benchmark for large-scale image similarity detection. This benchmark is used for the Image Similarity Challenge at NeurIPS'21 (ISC2021). The goal is to determine whether a query image is a modified copy of any image in a reference corpus of size 1~million. The benchmark features a variety of...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	241,745
2403.07715	Intra-video Positive Pairs in Self-Supervised Learning for Ultrasound	Self-supervised learning (SSL) is one strategy for addressing the paucity of labelled data in medical imaging by learning representations from unlabelled images. Contrastive and non-contrastive SSL methods produce learned representations that are similar for pairs of related images. Such pairs are commonly constructed ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	436,997
2109.00780	Non-Photorealistic Rendering of Layered Materials: A Multispectral Approach	We present multispectral rendering techniques for visualizing layered materials found in biological specimens. We are the first to use acquired data from the near-infrared and ultraviolet spectra for non-photorealistic rendering (NPR). Several plant and animal species are more comprehensively understood by multispectra...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	253,220
2305.05656	Cover Your Bases: How to Minimize the Sequencing Coverage in DNA Storage Systems	Although the expenses associated with DNA sequencing have been rapidly decreasing, the current cost of sequencing information stands at roughly $120/GB, which is dramatically more expensive than reading from existing archival storage solutions today. In this work, we aim to reduce not only the cost but also the latency...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	363,236
2202.13649	GausSetExpander: A Simple Approach for Entity Set Expansion	Entity Set Expansion is an important NLP task that aims at expanding a small set of entities into a larger one with items from a large pool of candidates. In this paper, we propose GausSetExpander, an unsupervised approach based on optimal transport techniques. We propose to re-frame the problem as choosing the entity ...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	282,701
2403.04322	Memetic Differential Evolution Methods for Semi-Supervised Clustering	In this paper, we propose an extension for semi-supervised Minimum Sum-of-Squares Clustering (MSSC) problems of MDEClust, a memetic framework based on the Differential Evolution paradigm for unsupervised clustering. In semi-supervised MSSC, background knowledge is available in the form of (instance-level) "must-link" a...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	true	false	false	435,557
2311.18684	Handling Cost and Constraints with Off-Policy Deep Reinforcement Learning	By reusing data throughout training, off-policy deep reinforcement learning algorithms offer improved sample efficiency relative to on-policy approaches. For continuous action spaces, the most popular methods for off-policy learning include policy improvement steps where a learned state-action ($Q$) value function is m...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	411,775
2012.09962	Addressing Feature Suppression in Unsupervised Visual Representations	Contrastive learning is one of the fastest growing research areas in machine learning due to its ability to learn useful representations without labeled data. However, contrastive learning is susceptible to feature suppression, i.e., it may discard important information relevant to the task of interest, and learn irrel...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	212,213
1912.04473	Development of a Tendon Driven Variable Stiffness Continuum Robot with Layer Jamming	The purpose of this research is to design, fabricate and test a tendon driven a continuum soft robot with three modular segments, each of which has a tunable stiffness enabled by layer jamming technology. Compared with previous studies, the robotic arm design of this project has a modular structure, which means the len...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	156,850
2406.11703	Unveiling Multiple Descents in Unsupervised Autoencoders	The phenomenon of double descent has challenged the traditional bias-variance trade-off in supervised learning but remains unexplored in unsupervised learning, with some studies arguing for its absence. In this study, we first demonstrate analytically that double descent does not occur in linear unsupervised autoencode...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	465,012
2007.02363	Aligning Partially Overlapping Point Sets: an Inner Approximation Algorithm	Aligning partially overlapping point sets where there is no prior information about the value of the transformation is a challenging problem in computer vision. To achieve this goal, we first reduce the objective of the robust point matching algorithm to a function of a low dimensional variable. The resulting function,...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	185,711
2111.10892	Deep Image Prior using Stein's Unbiased Risk Estimator: SURE-DIP	Deep learning algorithms that rely on extensive training data are revolutionizing image recovery from ill-posed measurements. Training data is scarce in many imaging applications, including ultra-high-resolution imaging. The deep image prior (DIP) algorithm was introduced for single-shot image recovery, completely elim...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	267,479
1611.01579	Decentralized Caching and Coded Delivery with Distinct Cache Capacities	Decentralized proactive caching and coded delivery is studied in a content delivery network, where each user is equipped with a cache memory, not necessarily of equal capacity. Cache memories are filled in advance during the off-peak traffic period in a decentralized manner, i.e., without the knowledge of the number of...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	63,394
1105.0087	Higher weights of Grassmann codes in terms of properties of Schubert unions	We describe the higher weights of the Grassmann codes $G(2,m)$ over finite fields ${\mathbb F}_q$ in terms of properties of Schubert unions, and in each case we determine the weight as the minimum of two explicit polynomial expressions in $q$.	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	10,190
2411.04315	Theoretically informed selection of latent activation in autoencoder based recommender systems	Autoencoders may lend themselves to the design of more accurate and computationally efficient recommender systems by distilling sparse high-dimensional data into dense lower-dimensional latent representations. However, designing these systems remains challenging due to the lack of theoretical guidance. This work addres...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	506,218
2302.05016	Is Multimodal Vision Supervision Beneficial to Language?	Vision (image and video) - Language (VL) pre-training is the recent popular paradigm that achieved state-of-the-art results on multi-modal tasks like image-retrieval, video-retrieval, visual question answering etc. These models are trained in an unsupervised way and greatly benefit from the complementary modality super...	false	false	false	false	true	false	false	false	true	false	false	true	false	false	false	false	false	false	344,897
1504.04244	Throughput Maximization in Multi-Hop Wireless Networks under Secrecy Constraint	This paper analyzes the throughput of industrial communication networks under a secrecy constraint. The proposed scenario is composed by sensors that measure some relevant information of the plant that is first processed by aggregator node and then sent to the control unit. The sensor measurements, their communication ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	42,120
1911.03058	Should All Cross-Lingual Embeddings Speak English?	Most of recent work in cross-lingual word embeddings is severely Anglocentric. The vast majority of lexicon induction evaluation dictionaries are between English and another language, and the English embedding space is selected by default as the hub when learning in a multilingual setting. With this work, however, we c...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	152,537
2003.04706	Communication-Efficient Distributed SGD with Error-Feedback, Revisited	We show that the convergence proof of a recent algorithm called dist-EF-SGD for distributed stochastic gradient descent with communication efficiency using error-feedback of Zheng et al. (NeurIPS 2019) is problematic mathematically. Concretely, the original error bound for arbitrary sequences of learning rate is unfort...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	167,631
1309.5304	Adaptive model predictive control with exploring property for constrained linear systems that uses basis function model parametrization	This manuscript contains technical details of recent results developed by the authors on adaptive model predictive control for constrained linear systems that exhibits exploring property and uses basis function model parametrization.	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	27,156
2010.11305	Mixed-Precision Embedding Using a Cache	In recommendation systems, practitioners observed that increase in the number of embedding tables and their sizes often leads to significant improvement in model performances. Given this and the business importance of these models to major internet companies, embedding tables for personalization tasks have grown to ter...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	true	202,192
cmp-lg/9607008	From Submit to Submitted via Submission: On Lexical Rules in Large-Scale Lexicon Acquisition	This paper deals with the discovery, representation, and use of lexical rules (LRs) during large-scale semi-automatic computational lexicon acquisition. The analysis is based on a set of LRs implemented and tested on the basis of Spanish and English business- and finance-related corpora. We show that, though the use of...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	536,606
0903.5328	A Stochastic View of Optimal Regret through Minimax Duality	We study the regret of optimal strategies for online convex optimization games. Using von Neumann's minimax theorem, we show that the optimal regret in this adversarial setting is closely related to the behavior of the empirical minimization algorithm in a stochastic process setting: it is equal to the maximum, over jo...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	3,447
2401.13531	QAGait: Revisit Gait Recognition from a Quality Perspective	Gait recognition is a promising biometric method that aims to identify pedestrians from their unique walking patterns. Silhouette modality, renowned for its easy acquisition, simple structure, sparse representation, and convenient modeling, has been widely employed in controlled in-the-lab research. However, as gait re...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	423,757
2206.08364	Interaction-Grounded Learning with Action-inclusive Feedback	Consider the problem setting of Interaction-Grounded Learning (IGL), in which a learner's goal is to optimally interact with the environment with no explicit reward to ground its policies. The agent observes a context vector, takes an action, and receives a feedback vector, using this information to effectively optimiz...	true	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	303,107
2010.14440	Robust Skeletonization for Plant Root Structure Reconstruction from MRI	Structural reconstruction of plant roots from MRI is challenging, because of low resolution and low signal-to-noise ratio of the 3D measurements which may lead to disconnectivities and wrongly connected roots. We propose a two-stage approach for this task. The first stage is based on semantic root vs. soil segmentation...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	203,439
1911.02212	The gradient complexity of linear regression	We investigate the computational complexity of several basic linear algebra primitives, including largest eigenvector computation and linear regression, in the computational model that allows access to the data via a matrix-vector product oracle. We show that for polynomial accuracy, $\Theta(d)$ calls to the oracle are...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	152,309
2312.04535	Trajeglish: Traffic Modeling as Next-Token Prediction	A longstanding challenge for self-driving development is simulating dynamic driving scenarios seeded from recorded driving logs. In pursuit of this functionality, we apply tools from discrete sequence modeling to model how vehicles, pedestrians and cyclists interact in driving scenarios. Using a simple data-driven toke...	false	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	413,710
2307.04263	Thriving Innovation Ecosystems: Synergy Among Stakeholders, Tools, and People	An innovation ecosystem is a multi-stakeholder environment, where different stakeholders interact to solve complex socio-technical challenges. We explored how stakeholders use digital tools, human resources, and their combination to gather information and make decisions in innovation ecosystems. To comprehensively unde...	false	false	false	true	false	false	false	false	false	false	false	false	false	true	false	false	false	false	378,340
2011.07048	Using Graph Neural Networks to Reconstruct Ancient Documents	In recent years, machine learning and deep learning approaches such as artificial neural networks have gained in popularity for the resolution of automatic puzzle resolution problems. Indeed, these methods are able to extract high-level representations from images, and then can be trained to separate matching image pie...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	206,424
2301.06648	Neuromorphic High-Frequency 3D Dancing Pose Estimation in Dynamic Environment	As a beloved sport worldwide, dancing is getting integrated into traditional and virtual reality-based gaming platforms nowadays. It opens up new opportunities in the technology-mediated dancing space. These platforms primarily rely on passive and continuous human pose estimation as an input capture mechanism. Existing...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	340,698
2204.08615	Poisons that are learned faster are more effective	Imperceptible poisoning attacks on entire datasets have recently been touted as methods for protecting data privacy. However, among a number of defenses preventing the practical use of these techniques, early-stopping stands out as a simple, yet effective defense. To gauge poisons' vulnerability to early-stopping, we b...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	292,150
2006.13806	X-ModalNet: A Semi-Supervised Deep Cross-Modal Network for Classification of Remote Sensing Data	This paper addresses the problem of semi-supervised transfer learning with limited cross-modality data in remote sensing. A large amount of multi-modal earth observation images, such as multispectral imagery (MSI) or synthetic aperture radar (SAR) data, are openly available on a global scale, enabling parsing global ur...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	184,028
2112.11629	Convolutional neural network based on transfer learning for breast cancer screening	Breast cancer is the most common cancer in the world and the most prevalent cause of death among women worldwide. Nevertheless, it is also one of the most treatable malignancies if detected early. In this paper, a deep convolutional neural network-based algorithm is proposed to aid in accurately identifying breast canc...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	272,751
2208.13780	Autoinverse: Uncertainty Aware Inversion of Neural Networks	Neural networks are powerful surrogates for numerous forward processes. The inversion of such surrogates is extremely valuable in science and engineering. The most important property of a successful neural inverse method is the performance of its solutions when deployed in the real world, i.e., on the native forward pr...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	315,140
2008.03107	Helix: Algorithm/Architecture Co-design for Accelerating Nanopore Genome Base-calling	Nanopore genome sequencing is the key to enabling personalized medicine, global food security, and virus surveillance. The state-of-the-art base-callers adopt deep neural networks (DNNs) to translate electrical signals generated by nanopore sequencers to digital DNA symbols. A DNN-based base-caller consumes $44.5\%$ of...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	190,808
2003.03908	A Mathematical Framework for IMU Error Propagation with Applications to Preintegration	To fuse information from inertial measurement units (IMU) with other sensors one needs an accurate model for IMU error propagation in terms of position, velocity and orientation, a triplet we call extended pose. In this paper we leverage a nontrivial result, namely log-linearity of inertial navigation equations based o...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	167,401
2201.10015	Automatic Recognition and Digital Documentation of Cultural Heritage Hemispherical Domes using Images	Advancements in optical metrology has enabled documentation of dense 3D point clouds of cultural heritage sites. For large scale and continuous digital documentation, processing of dense 3D point clouds becomes computationally cumbersome, and often requires additional hardware for data management, increasing the time c...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	276,849
2011.09212	On the use of Self-supervised Pre-trained Acoustic and Linguistic Features for Continuous Speech Emotion Recognition	Pre-training for feature extraction is an increasingly studied approach to get better continuous representations of audio and text content. In the present work, we use wav2vec and camemBERT as self-supervised learned models to represent our data in order to perform continuous emotion recognition from speech (SER) on Al...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	207,123
2005.12553	Efficient Use of heuristics for accelerating XCS-based Policy Learning in Markov Games	In Markov games, playing against non-stationary opponents with learning ability is still challenging for reinforcement learning (RL) agents, because the opponents can evolve their policies concurrently. This increases the complexity of the learning task and slows down the learning speed of the RL agents. This paper pro...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	true	true	false	true	178,770
2304.07309	Incentivising Building Data Availability and Accessibility Using Tokenized Data Assets	Smart cities are data driven and collect data from a variety of sources. Certain types of data such as building data is under-represented and remains harder to find despite its value. Our goal is to incentivise the stakeholders to make building data easier to avail by turning it into an asset. We use tokenized building...	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	true	false	358,305
2010.05149	SDE-AWB: a Generic Solution for 2nd International Illumination Estimation Challenge	We propose a neural network-based solution for three different tracks of 2nd International Illumination Estimation Challenge (chromaticity.iitp.ru). Our method is built on pre-trained Squeeze-Net backbone, differential 2D chroma histogram layer and a shallow MLP utilizing Exif information. By combining semantic feature...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	200,006
2011.14427	Architectural Adversarial Robustness: The Case for Deep Pursuit	Despite their unmatched performance, deep neural networks remain susceptible to targeted attacks by nearly imperceptible levels of adversarial noise. While the underlying cause of this sensitivity is not well understood, theoretical analyses can be simplified by reframing each layer of a feed-forward network as an appr...	false	false	false	false	false	false	true	false	false	false	false	true	true	false	false	false	false	false	208,774
1303.6935	Efficiently Using Second Order Information in Large l1 Regularization Problems	We propose a novel general algorithm LHAC that efficiently uses second-order information to train a class of large-scale l1-regularized problems. Our method executes cheap iterations while achieving fast local convergence rate by exploiting the special structure of a low-rank matrix, constructed via quasi-Newton approx...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	23,304
2410.06493	BiC-MPPI: Goal-Pursuing, Sampling-Based Bidirectional Rollout Clustering Path Integral for Trajectory Optimization	This paper introduces the Bidirectional Clustered MPPI (BiC-MPPI) algorithm, a novel trajectory optimization method aimed at enhancing goal-directed guidance within the Model Predictive Path Integral (MPPI) framework. BiC-MPPI incorporates bidirectional dynamics approximations and a new guide cost mechanism, improving ...	false	false	false	false	true	false	false	true	false	false	true	false	false	false	false	false	false	false	496,228
1908.07689	A false data injection attack method for generator dynamic state estimation	Accurate and reliable dynamic state quantities of generators are very important for real-time monitoring and control of the power system. The emergence of cyber attacks has brought new challenges to the state estimation of generators. Especially, false data injection (FDI) attacks deteriorate the accuracy of state esti...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	142,346
1701.07481	Learning Word-Like Units from Joint Audio-Visual Analysis	Given a collection of images and spoken audio captions, we present a method for discovering word-like acoustic units in the continuous speech signal and grounding them to semantically relevant image regions. For example, our model is able to detect spoken instances of the word 'lighthouse' within an utterance and assoc...	false	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	67,296
1708.02031	Learning Uncertain Convolutional Features for Accurate Saliency Detection	Deep convolutional neural networks (CNNs) have delivered superior performance in many computer vision tasks. In this paper, we propose a novel deep fully convolutional network model for accurate salient object detection. The key contribution of this work is to learn deep uncertain convolutional features (UCF), which en...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	78,508
2211.11513	DSLOB: A Synthetic Limit Order Book Dataset for Benchmarking Forecasting Algorithms under Distributional Shift	In electronic trading markets, limit order books (LOBs) provide information about pending buy/sell orders at various price levels for a given security. Recently, there has been a growing interest in using LOB data for resolving downstream machine learning tasks (e.g., forecasting). However, dealing with out-of-distribu...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	331,760
1911.01407	Active Status Update Packet Drop Control in an Energy Harvesting Node	This paper considers an energy harvesting sensor node with battery size $B_{max}$ that recharges its battery through an incremental energy harvesting process and receives updates from a single information source in slotted time. The node actively decides to power down (OFF) or up (ON) the communication circuitry for a ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	152,088
2409.15697	dnaGrinder: a lightweight and high-capacity genomic foundation model	The task of understanding and interpreting the complex information encoded within genomic sequences remains a grand challenge in biological research and clinical applications. In this context, recent advancements in large language model research have led to the development of both encoder-only and decoder-only foundati...	false	true	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	491,017
1812.00111	A Big Data Architecture for Log Data Storage and Analysis	We propose an architecture for analysing database connection logs across different instances of databases within an intranet comprising over 10,000 users and associated devices. Our system uses Flume agents to send notifications to a Hadoop Distributed File System for long-term storage and ElasticSearch and Kibana for ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	115,162
2105.07111	Prescriptive Process Monitoring for Cost-Aware Cycle Time Reduction	Reducing cycle time is a recurrent concern in the field of business process management. Depending on the process, various interventions may be triggered to reduce the cycle time of a case, for example, using a faster shipping service in an order-to-delivery process or giving a phone call to a customer to obtain missing...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	235,319
2202.00980	Robust Training of Neural Networks Using Scale Invariant Architectures	In contrast to SGD, adaptive gradient methods like Adam allow robust training of modern deep networks, especially large language models. However, the use of adaptivity not only comes at the cost of extra memory but also raises the fundamental question: can non-adaptive methods like SGD enjoy similar benefits? In this p...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	278,330
1808.05240	Blended Coarse Gradient Descent for Full Quantization of Deep Neural Networks	Quantized deep neural networks (QDNNs) are attractive due to their much lower memory storage and faster inference speed than their regular full precision counterparts. To maintain the same performance level especially at low bit-widths, QDNNs must be retrained. Their training involves piecewise constant activation func...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	105,311
1405.6594	Density Evolution and Functional Threshold for the Noisy Min-Sum Decoder	This paper investigates the behavior of the Min-Sum decoder running on noisy devices. The aim is to evaluate the robustness of the decoder in the presence of computation noise, e.g. due to faulty logic in the processing units, which represents a new source of errors that may occur during the decoding process. To this e...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	33,397
2501.03349	FTA-FTL: A Fine-Tuned Aggregation Federated Transfer Learning Scheme for Lithology Microscopic Image Classification	Lithology discrimination is a crucial activity in characterizing oil reservoirs, and processing lithology microscopic images is an essential technique for investigating fossils and minerals and geological assessment of shale oil exploration. In this way, Deep Learning (DL) technique is a powerful approach for building ...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	522,845
1709.00308	A Comprehensive Survey of Deep Learning in Remote Sensing: Theories, Tools and Challenges for the Community	In recent years, deep learning (DL), a re-branding of neural networks (NNs), has risen to the top in numerous areas, namely computer vision (CV), speech recognition, natural language processing, etc. Whereas remote sensing (RS) possesses a number of unique challenges, primarily related to sensors and applications, inev...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	79,880
2206.12342	FEATHERS: Federated Architecture and Hyperparameter Search	Deep neural architectures have profound impact on achieved performance in many of today's AI tasks, yet, their design still heavily relies on human prior knowledge and experience. Neural architecture search (NAS) together with hyperparameter optimization (HO) helps to reduce this dependence. However, state of the art N...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	304,561
0705.4134	The Battery-Discharge-Model: A Class of Stochastic Finite Automata to Simulate Multidimensional Continued Fraction Expansion	We define an infinite stochastic state machine, the Battery-Discharge-Model (BDM), which simulates the behaviour of linear and jump complexity of the continued fraction expansion of multidimensional formal power series, a relevant security measure in the cryptanalysis of stream ciphers. We also obtain finite approxim...	false	false	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	true	290
0711.0350	Intermittent estimation of stationary time series	Let $\{X_n\}_{n=0}^{\infty}$ be a stationary real-valued time series with unknown distribution. Our goal is to estimate the conditional expectation of $X_{n+1}$ based on the observations $X_i$, $0\le i\le n$ in a strongly consistent way. Bailey and Ryabko proved that this is not possible even for ergodic binary time se...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	860
2411.09214	HateGPT: Unleashing GPT-3.5 Turbo to Combat Hate Speech on X	The widespread use of social media platforms like Twitter and Facebook has enabled people of all ages to share their thoughts and experiences, leading to an immense accumulation of user-generated content. However, alongside the benefits, these platforms also face the challenge of managing hate speech and offensive cont...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	508,173
1404.0084	A Calculus of Located Entities	We define BioScapeL, a stochastic pi-calculus in 3D-space. A novel aspect of BioScapeL is that entities have programmable locations. The programmer can specify a particular location where to place an entity, or a location relative to the current location of the entity. The motivation for the extension comes from the ne...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	31,980
1907.11620	Exploiting weak ties in trust-based recommender systems using regular equivalence	User-based Collaborative Filtering (CF) is one of the most popular approaches to create recommender systems. CF, however, suffers from data sparsity and the cold-start problem since users often rate only a small fraction of available items. One solution is to incorporate additional information into the recommendation p...	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	false	139,895
1410.6387	On Lower and Upper Bounds in Smooth Strongly Convex Optimization - A Unified Approach via Linear Iterative Methods	In this thesis we develop a novel framework to study smooth and strongly convex optimization algorithms, both deterministic and stochastic. Focusing on quadratic functions we are able to examine optimization algorithms as a recursive application of linear operators. This, in turn, reveals a powerful connection between ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	36,979
2202.11669	Refining the state-of-the-art in Machine Translation, optimizing NMT for the JA <-> EN language pair by leveraging personal domain expertise	Documenting the construction of an NMT (Neural Machine Translation) system for En/Ja based on the Transformer architecture leveraging the OpenNMT framework. A systematic exploration of corpora pre-processing, hyperparameter tuning and model architecture is carried out to obtain optimal performance. The system is evalua...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	281,950
0708.2021	Who is the best connected EC researcher? Centrality analysis of the complex network of authors in evolutionary computation	Co-authorship graphs (that is, the graph of authors linked by co-authorship of papers) are complex networks, which expresses the dynamics of a complex system. Only recently its study has started to draw interest from the EC community, the first paper dealing with it having been published two years ago. In this paper we...	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	true	false	false	552
2205.05914	Observer-Based Consensus of Nonlinear Positive Multi-Agent Systems with Saturated Control Input	This paper presents the distributed pinning consensus solution for nonlinear positive multi-agent systems with nonlinear control input by applying observer-based control protocols. The network topology is considered as a directed and fully connected structure. By considering sector input nonlinearities and various form...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	296,085
2110.12678	Nearly Tight Convergence Bounds for Semi-discrete Entropic Optimal Transport	We derive nearly tight and non-asymptotic convergence bounds for solutions of entropic semi-discrete optimal transport. These bounds quantify the stability of the dual solutions of the regularized problem (sometimes called Sinkhorn potentials) w.r.t. the regularization parameter, for which we ensure a better than Lipsc...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	262,933
1610.08870	Uniform continuity bounds for information characteristics of quantum channels depending on input dimension and on input energy	We obtain continuity bounds for basic information characteristics of quantum channels depending on their input dimension (if it is finite) and on the input energy bound (if the input dimension is infinite). We pay a special attention to the case of a multi-mode quantum oscillator as an input system. First we prove co...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	62,977
2305.07537	Saturated Non-Monotonic Activation Functions	Activation functions are essential to deep learning networks. Popular and versatile activation functions are mostly monotonic functions, some non-monotonic activation functions are being explored and show promising performance. But by introducing non-monotonicity, they also alter the positive input, which is proved to ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	true	false	false	363,939
2501.08219	Investigating Energy Efficiency and Performance Trade-offs in LLM Inference Across Tasks and DVFS Settings	Large language models (LLMs) have shown significant improvements in many natural language processing (NLP) tasks, accelerating their rapid adoption across many industries. These models are resource-intensive, requiring extensive computational resources both during training and inference, leading to increased energy con...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	524,676
1908.04109	Successive Projection Algorithm Robust to Outliers	The successive projection algorithm (SPA) is a fast algorithm to tackle separable nonnegative matrix factorization (NMF). Given a nonnegative data matrix $X$, SPA identifies an index set $\mathcal{K}$ such that there exists a nonnegative matrix $H$ with $X \approx X(:,\mathcal{K})H$. SPA has been successfully used as a...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	141,406
2303.13549	Optical Character Recognition and Transcription of Berber Signs from Images in a Low-Resource Language Amazigh	The Berber, or Amazigh language family is a low-resource North African vernacular language spoken by the indigenous Berber ethnic group. It has its own unique alphabet called Tifinagh used across Berber communities in Morocco, Algeria, and others. The Afroasiatic language Berber is spoken by 14 million people, yet lack...	false	false	false	false	true	false	true	false	true	false	false	true	false	false	false	false	false	false	353,724
1608.07672	Transceiver Design for Cooperative Non-Orthogonal Multiple Access Systems with Wireless Energy Transfer	In this paper, an energy harvesting (EH) based cooperative non-orthogonal multiple access (NOMA) system is considered, where node S simultaneously sends independent signals to a stronger node R and a weaker node D. We focus on the scenario that the direct link between S and D is too weak to meet the quality of service ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	60,252
2501.04105	DeepVIVONet: Using deep neural operators to optimize sensor locations with application to vortex-induced vibrations	We introduce DeepVIVONet, a new framework for optimal dynamic reconstruction and forecasting of the vortex-induced vibrations (VIV) of a marine riser, using field data. We demonstrate the effectiveness of DeepVIVONet in accurately reconstructing the motion of an off--shore marine riser by using sparse spatio-temporal m...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	523,098
1603.06995	Multi-Scale Convolutional Neural Networks for Time Series Classification	Time series classification (TSC), the problem of predicting class labels of time series, has been around for decades within the community of data mining and machine learning, and found many important applications such as biomedical engineering and clinical prediction. However, it still remains challenging and falls sho...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	53,570
2305.17531	Probing reaction channels via reinforcement learning	We propose a reinforcement learning based method to identify important configurations that connect reactant and product states along chemical reaction paths. By shooting multiple trajectories from these configurations, we can generate an ensemble of configurations that concentrate on the transition path ensemble. This ...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	true	368,637
2211.10701	Complementary Labels Learning with Augmented Classes	Complementary Labels Learning (CLL) arises in many real-world tasks such as private questions classification and online learning, which aims to alleviate the annotation cost compared with standard supervised learning. Unfortunately, most previous CLL algorithms were in a stable environment rather than an open and dynam...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	331,404
1710.05319	Human-centered transparency of grasping via a robot-assisted minimally invasive surgery system	We investigate grasping of rigid objects in unilateral robot-assisted minimally invasive surgery (RAMIS) in this paper. We define a human-centered transparency that quantifies natural action and perception in RAMIS. We demonstrate this human-centered transparency analysis for different values of gripper scaling - the s...	true	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	82,624
2303.00978	Leveraging Large Text Corpora for End-to-End Speech Summarization	End-to-end speech summarization (E2E SSum) is a technique to directly generate summary sentences from speech. Compared with the cascade approach, which combines automatic speech recognition (ASR) and text summarization models, the E2E approach is more promising because it mitigates ASR errors, incorporates nonverbal in...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	348,783
2412.19304	Perceive, Query & Reason: Enhancing Video QA with Question-Guided Temporal Queries	Video Question Answering (Video QA) is a challenging video understanding task that requires models to comprehend entire videos, identify the most relevant information based on contextual cues from a given question, and reason accurately to provide answers. Recent advancements in Multimodal Large Language Models (MLLMs)...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	520,790

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