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
2206.05614	Information theory of spatial network ensembles	This chapter provides a comprehensive and self-contained discussion of the most recent developments of information theory of networks. Maximum entropy models of networks are the least biased ensembles enforcing a set of constraints and are used in a number of application to produce null model of networks. Here maximum ...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	302,066
2103.05180	An Introduction to Deep Generative Modeling	Deep generative models (DGM) are neural networks with many hidden layers trained to approximate complicated, high-dimensional probability distributions using a large number of samples. When trained successfully, we can use the DGMs to estimate the likelihood of each observation and to create new samples from the underl...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	223,894
2105.04615	Differentially Private Transferrable Deep Learning with Membership-Mappings	This paper considers the problem of differentially private semi-supervised transfer and multi-task learning. The notion of \emph{membership-mapping} has been developed using measure theory basis to learn data representation via a fuzzy membership function. An alternative conception of deep autoencoder, referred to as \...	false	false	false	false	true	false	true	false	false	false	false	false	true	false	false	false	false	false	234,555
1609.00992	Performance Evaluation of a Natural Language Processing approach applied in White Collar crime investigation	In today world we are confronted with increasing amounts of information every day coming from a large variety of sources. People and co-operations are producing data on a large scale, and since the rise of the internet, e-mail and social media the amount of produced data has grown exponentially. From a law enforcement ...	false	false	false	false	false	true	false	false	false	false	false	false	false	true	false	false	false	false	60,545
2407.05674	Coding Reliable LLM-based Integrated Task and Knowledge Agents with GenieWorksheets	Large Language Models (LLMs) present an opportunity to create automated assistants that can help users navigate complex tasks. However, existing approaches have limitations in handling conditional logic, integrating knowledge sources, and consistently following instructions. Researchers and industry professionals often...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	true	471,082
2307.07813	TinyTracker: Ultra-Fast and Ultra-Low-Power Edge Vision In-Sensor for Gaze Estimation	Intelligent edge vision tasks encounter the critical challenge of ensuring power and latency efficiency due to the typically heavy computational load they impose on edge platforms.This work leverages one of the first "AI in sensor" vision platforms, IMX500 by Sony, to achieve ultra-fast and ultra-low-power end-to-end e...	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	379,551
2108.09203	Parsing Birdsong with Deep Audio Embeddings	Monitoring of bird populations has played a vital role in conservation efforts and in understanding biodiversity loss. The automation of this process has been facilitated by both sensing technologies, such as passive acoustic monitoring, and accompanying analytical tools, such as deep learning. However, machine learnin...	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	251,537
2002.08232	CoLES: Contrastive Learning for Event Sequences with Self-Supervision	We address the problem of self-supervised learning on discrete event sequences generated by real-world users. Self-supervised learning incorporates complex information from the raw data in low-dimensional fixed-length vector representations that could be easily applied in various downstream machine learning tasks. In t...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	164,685
2011.13580	A Sheaf and Topology Approach to Generating Local Branch Numbers in Digital Images	This paper concerns a theoretical approach that combines topological data analysis (TDA) and sheaf theory. Topological data analysis, a rising field in mathematics and computer science, concerns the shape of the data and has been proven effective in many scientific disciplines. Sheaf theory, a mathematics subject in al...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	208,524
2103.10175	Local Electricity Market Design Utilizing Dynamic Network Usage Tariff	The new technologies emerging in the energy sector pose new requirements for both the regulation and operation of the electricity grid. Revised tariff structures and the introduction of local markets are two approaches that could tackle the issues resulting from the increasing number of active end-users. However, a smo...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	225,371
cmp-lg/9408010	On Using Selectional Restriction in Language Models for Speech Recognition	In this paper, we investigate the use of selectional restriction -- the constraints a predicate imposes on its arguments -- in a language model for speech recognition. We use an un-tagged corpus, followed by a public domain tagger and a very simple finite state machine to obtain verb-object pairs from unrestricted Engl...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	536,159
1010.4272	Isospectral Reductions of Dynamical Networks	We present a general and flexible procedure which allows for the reduction (or expansion) of any dynamical network while preserving the spectrum of the network's adjacency matrix. Computationally, this process is simple and easily implemented for the analysis of any network. Moreover, it is possible to isospectrally re...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	7,967
2408.11481	VE-Bench: Subjective-Aligned Benchmark Suite for Text-Driven Video Editing Quality Assessment	Text-driven video editing has recently experienced rapid development. Despite this, evaluating edited videos remains a considerable challenge. Current metrics tend to fail to align with human perceptions, and effective quantitative metrics for video editing are still notably absent. To address this, we introduce VE-Ben...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	482,315
1207.1933	A Hybrid Forecast of Exchange Rate based on ARFIMA,Discrete Grey-Markov, and Fractal Kalman Model	We propose a hybrid forecast based on extended discrete grey Markov and variable dimension Kalman model and show that our hybrid model can improve much more the performance of forecast than traditional grey Markov and Kalman models. Our simulation results are given to demonstrate that our hybrid forecast method combine...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	17,347
2107.03331	KOALA: A Kalman Optimization Algorithm with Loss Adaptivity	Optimization is often cast as a deterministic problem, where the solution is found through some iterative procedure such as gradient descent. However, when training neural networks the loss function changes over (iteration) time due to the randomized selection of a subset of the samples. This randomization turns the op...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	245,127
2403.17421	MA4DIV: Multi-Agent Reinforcement Learning for Search Result Diversification	Search result diversification (SRD), which aims to ensure that documents in a ranking list cover a broad range of subtopics, is a significant and widely studied problem in Information Retrieval and Web Search. Existing methods primarily utilize a paradigm of "greedy selection", i.e., selecting one document with the hig...	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	false	false	441,460
2010.11699	Generative Model-Enhanced Human Motion Prediction	The task of predicting human motion is complicated by the natural heterogeneity and compositionality of actions, necessitating robustness to distributional shifts as far as out-of-distribution (OoD). Here we formulate a new OoD benchmark based on the Human3.6M and CMU motion capture datasets, and introduce a hybrid fra...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	202,380
1304.1018	Estimating Phoneme Class Conditional Probabilities from Raw Speech Signal using Convolutional Neural Networks	In hybrid hidden Markov model/artificial neural networks (HMM/ANN) automatic speech recognition (ASR) system, the phoneme class conditional probabilities are estimated by first extracting acoustic features from the speech signal based on prior knowledge such as, speech perception or/and speech production knowledge, and...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	true	false	false	23,429
2502.13573	Noise May Contain Transferable Knowledge: Understanding Semi-supervised Heterogeneous Domain Adaptation from an Empirical Perspective	Semi-supervised heterogeneous domain adaptation (SHDA) addresses learning across domains with distinct feature representations and distributions, where source samples are labeled while most target samples are unlabeled, with only a small fraction labeled. Moreover, there is no one-to-one correspondence between source a...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	535,426
1909.07827	Weak Edge Identification Nets for Ocean Front Detection	The ocean front has an important impact in many areas, it is meaningful to obtain accurate ocean front positioning, therefore, ocean front detection is a very important task. However, the traditional edge detection algorithm does not detect the weak edge information of the ocean front very well. In response to this pro...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	145,788
2204.11573	Joint-Modal Label Denoising for Weakly-Supervised Audio-Visual Video Parsing	This paper focuses on the weakly-supervised audio-visual video parsing task, which aims to recognize all events belonging to each modality and localize their temporal boundaries. This task is challenging because only overall labels indicating the video events are provided for training. However, an event might be labele...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	293,199
2103.00536	Towards Conversational Humor Analysis and Design	Well-defined jokes can be divided neatly into a setup and a punchline. While most works on humor today talk about a joke as a whole, the idea of generating punchlines to a setup has applications in conversational humor, where funny remarks usually occur with a non-funny context. Thus, this paper is based around two cor...	true	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	222,317
2002.01306	Linear and Fisher Separability of Random Points in the d-dimensional Spherical Layer	Stochastic separation theorems play important role in high-dimensional data analysis and machine learning. It turns out that in high dimension any point of a random set of points can be separated from other points by a hyperplane with high probability even if the number of points is exponential in terms of dimension. T...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	162,624
1003.2372	On Ergodic Secrecy Capacity for Gaussian MISO Wiretap Channels	A Gaussian multiple-input single-output (MISO) wiretap channel model is considered, where there exists a transmitter equipped with multiple antennas, a legitimate receiver and an eavesdropper each equipped with a single antenna. We study the problem of finding the optimal input covariance that achieves ergodic secrecy ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	5,901
1401.3870	Learning to Make Predictions In Partially Observable Environments Without a Generative Model	When faced with the problem of learning a model of a high-dimensional environment, a common approach is to limit the model to make only a restricted set of predictions, thereby simplifying the learning problem. These partial models may be directly useful for making decisions or may be combined together to form a more c...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	29,984
2405.15477	MagicBathyNet: A Multimodal Remote Sensing Dataset for Bathymetry Prediction and Pixel-based Classification in Shallow Waters	Accurate, detailed, and high-frequent bathymetry, coupled with complex semantic content, is crucial for the undermapped shallow seabed areas facing intense climatological and anthropogenic pressures. Current methods exploiting remote sensing images to derive bathymetry or seabed classes mainly exploit non-open data. Th...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	456,956
2409.07853	Improve Machine Learning carbon footprint using Nvidia GPU and Mixed Precision training for classification models -- Part I	This is the 1st part of the dissertation for my master degree and compares the power consumption using the default floating point (32bit) and Nvidia mixed precision (16bit and 32bit) while training a classification ML model. A custom PC with specific hardware was built to perform the experiments, and different ML hyper...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	487,689
2403.18868	A recommender network perspective on the informational value of critics and crowds	How do the ratings of critics and amateurs compare and how should they be combined? Previous research has produced mixed results about the first question, while the second remains unanswered. We have created a new, unique dataset, with wine ratings from critics and amateurs, and simulated a recommender system using the...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	442,111
2007.04316	The UU-Net: Reversible Face De-Identification for Visual Surveillance Video Footage	We propose a reversible face de-identification method for low resolution video data, where landmark-based techniques cannot be reliably used. Our solution is able to generate a photo realistic de-identified stream that meets the data protection regulations and can be publicly released under minimal privacy constraints....	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	186,321
1807.11622	Count-Based Exploration with the Successor Representation	In this paper we introduce a simple approach for exploration in reinforcement learning (RL) that allows us to develop theoretically justified algorithms in the tabular case but that is also extendable to settings where function approximation is required. Our approach is based on the successor representation (SR), which...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	104,211
2010.16143	HyperText: Endowing FastText with Hyperbolic Geometry	Natural language data exhibit tree-like hierarchical structures such as the hypernym-hyponym relations in WordNet. FastText, as the state-of-the-art text classifier based on shallow neural network in Euclidean space, may not model such hierarchies precisely with limited representation capacity. Considering that hyperbo...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	203,981
1708.00531	End-to-End Neural Segmental Models for Speech Recognition	Segmental models are an alternative to frame-based models for sequence prediction, where hypothesized path weights are based on entire segment scores rather than a single frame at a time. Neural segmental models are segmental models that use neural network-based weight functions. Neural segmental models have achieved c...	false	false	true	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	78,222
2406.16148	Towards Open Respiratory Acoustic Foundation Models: Pretraining and Benchmarking	Respiratory audio, such as coughing and breathing sounds, has predictive power for a wide range of healthcare applications, yet is currently under-explored. The main problem for those applications arises from the difficulty in collecting large labeled task-specific data for model development. Generalizable respiratory ...	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	467,016
2204.13666	Schr\"odinger's FP: Dynamic Adaptation of Floating-Point Containers for Deep Learning Training	The transfer of tensors from/to memory during neural network training dominates time and energy. To improve energy efficiency and performance, research has been exploring ways to use narrower data representations. So far, these attempts relied on user-directed trial-and-error to achieve convergence. We present methods ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	293,894
1705.07561	Detection Estimation and Grid matching of Multiple Targets with Single Snapshot Measurements	In this work, we explore the problems of detecting the number of narrow-band, far-field targets and estimating their corresponding directions from single snapshot measurements. The principles of sparse signal recovery (SSR) are used for the single snapshot detection and estimation of multiple targets. In the SSR framew...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	73,861
1507.02954	An Iterative Receiver for OFDM With Sparsity-Based Parametric Channel Estimation	In this work we design a receiver that iteratively passes soft information between the channel estimation and data decoding stages. The receiver incorporates sparsity-based parametric channel estimation. State-of-the-art sparsity-based iterative receivers simplify the channel estimation problem by restricting the multi...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	45,033
2008.06125	One Size Does Not Fit All: A Study of Badge Behavior in Stack Overflow	Badges are endemic to online interaction sites, from Question and Answer (Q&A) websites to ride sharing, as systems for rewarding participants for their contributions. This paper studies how badge design affects people's contributions and behavior over time. Past work has shown that badges "steer" people's behavior tow...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	true	191,703
1407.3077	Charge Scheduling of an Energy Storage System under Time-of-use Pricing and a Demand Charge	A real-coded genetic algorithm is used to schedule the charging of an energy storage system (ESS), operated in tandem with renewable power by an electricity consumer who is subject to time-of-use pricing and a demand charge. Simulations based on load and generation profiles of typical residential customers show that an...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	34,594
1712.10195	Growing Attributed Networks through Local Processes	This paper proposes an attributed network growth model. Despite the knowledge that individuals use limited resources to form connections to similar others, we lack an understanding of how local and resource-constrained mechanisms explain the emergence of rich structural properties found in real-world networks. We make ...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	87,468
2203.12573	SerialTrack: ScalE and Rotation Invariant Augmented Lagrangian Particle Tracking	We present a new particle tracking algorithm to accurately resolve large deformation and rotational motion fields, which takes advantage of both local and global particle tracking algorithms. We call this method the ScalE and Rotation Invariant Augmented Lagrangian Particle Tracking (SerialTrack). This method builds an...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	287,313
1610.01132	A Non-generative Framework and Convex Relaxations for Unsupervised Learning	We give a novel formal theoretical framework for unsupervised learning with two distinctive characteristics. First, it does not assume any generative model and based on a worst-case performance metric. Second, it is comparative, namely performance is measured with respect to a given hypothesis class. This allows to avo...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	61,929
2405.17251	GenWarp: Single Image to Novel Views with Semantic-Preserving Generative Warping	Generating novel views from a single image remains a challenging task due to the complexity of 3D scenes and the limited diversity in the existing multi-view datasets to train a model on. Recent research combining large-scale text-to-image (T2I) models with monocular depth estimation (MDE) has shown promise in handling...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	457,828
2502.12065	Formalizing Complex Mathematical Statements with LLMs: A Study on Mathematical Definitions	Thanks to their linguistic capabilities, LLMs offer an opportunity to bridge the gap between informal mathematics and formal languages through autoformalization. However, it is still unclear how well LLMs generalize to sophisticated and naturally occurring mathematical statements. To address this gap, we investigate th...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	true	534,670
2105.02320	Iterative Human and Automated Identification of Wildlife Images	Camera trapping is increasingly used to monitor wildlife, but this technology typically requires extensive data annotation. Recently, deep learning has significantly advanced automatic wildlife recognition. However, current methods are hampered by a dependence on large static data sets when wildlife data is intrinsical...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	233,782
2009.07799	On the Curse of Memory in Recurrent Neural Networks: Approximation and Optimization Analysis	We study the approximation properties and optimization dynamics of recurrent neural networks (RNNs) when applied to learn input-output relationships in temporal data. We consider the simple but representative setting of using continuous-time linear RNNs to learn from data generated by linear relationships. Mathematical...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	196,049
1409.4044	A new approach in machine learning	In this technical report we presented a novel approach to machine learning. Once the new framework is presented, we will provide a simple and yet very powerful learning algorithm which will be benchmark on various dataset. The framework we proposed is based on booleen circuits; more specifically the classifier produc...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	36,035
1208.2900	On Achievable Degrees of Freedom for MIMO X Channels	In this paper, the achievable DoF of MIMO X channels for constant channel coefficients with $M_t$ antennas at transmitter $t$ and $N_r$ antennas at receiver $r$ ($t,r=1,2$) is studied. A spatial interference alignment and cancelation scheme is proposed to achieve the maximum DoF of the MIMO X channels. The scenario of ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	18,073
1412.1024	Degree correlations in signed social networks	We investigate degree correlations in two online social networks where users are connected through different types of links. We find that, while subnetworks in which links have a positive connotation, such as endorsement and trust, are characterized by assortative mixing by degree, networks in which links have a negati...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	38,067
2309.02165	PCFGaze: Physics-Consistent Feature for Appearance-based Gaze Estimation	Although recent deep learning based gaze estimation approaches have achieved much improvement, we still know little about how gaze features are connected to the physics of gaze. In this paper, we try to answer this question by analyzing the gaze feature manifold. Our analysis revealed the insight that the geodesic dist...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	389,954
2404.13522	Error Analysis of Shapley Value-Based Model Explanations: An Informative Perspective	Shapley value attribution (SVA) is an increasingly popular explainable AI (XAI) method, which quantifies the contribution of each feature to the model's output. However, recent work has shown that most existing methods to implement SVAs have some drawbacks, resulting in biased or unreliable explanations that fail to co...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	448,330
2309.08353	Continual Learning with Deep Streaming Regularized Discriminant Analysis	Continual learning is increasingly sought after in real world machine learning applications, as it enables learning in a more human-like manner. Conventional machine learning approaches fail to achieve this, as incrementally updating the model with non-identically distributed data leads to catastrophic forgetting, wher...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	392,136
2005.13179	Structural Control Analysis of System Dynamics Models	Structural control theory could be applied to study the control principles of social, economic and managerial systems. System Dynamics (SD) is the target field in social-economic sciences for endogenizing this theory, a subject that provides modeling solutions to real-world problems. SD models adopt diagrammatic repres...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	178,936
2101.05357	Towards Creating a Deployable Grasp Type Probability Estimator for a Prosthetic Hand	For lower arm amputees, prosthetic hands promise to restore most of physical interaction capabilities. This requires to accurately predict hand gestures capable of grabbing varying objects and execute them timely as intended by the user. Current approaches often rely on physiological signal inputs such as Electromyogra...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	215,400
1903.06873	Secure Control under Partial Observability with Temporal Logic Constraints	This paper studies the synthesis of control policies for an agent that has to satisfy a temporal logic specification in a partially observable environment, in the presence of an adversary. The interaction of the agent (defender) with the adversary is modeled as a partially observable stochastic game. The search for pol...	false	false	false	false	false	false	false	false	false	false	true	false	true	false	false	false	false	true	124,481
2501.03786	KAnoCLIP: Zero-Shot Anomaly Detection through Knowledge-Driven Prompt Learning and Enhanced Cross-Modal Integration	Zero-shot anomaly detection (ZSAD) identifies anomalies without needing training samples from the target dataset, essential for scenarios with privacy concerns or limited data. Vision-language models like CLIP show potential in ZSAD but have limitations: relying on manually crafted fixed textual descriptions or anomaly...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	522,988
2111.12299	EH-DNAS: End-to-End Hardware-aware Differentiable Neural Architecture Search	In hardware-aware Differentiable Neural Architecture Search (DNAS), it is challenging to compute gradients of hardware metrics to perform architecture search. Existing works rely on linear approximations with limited support to customized hardware accelerators. In this work, we propose End-to-end Hardware-aware DNAS (E...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	267,926
1901.03461	Dialog System Technology Challenge 7	This paper introduces the Seventh Dialog System Technology Challenges (DSTC), which use shared datasets to explore the problem of building dialog systems. Recently, end-to-end dialog modeling approaches have been applied to various dialog tasks. The seventh DSTC (DSTC7) focuses on developing technologies related to end...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	118,413
2412.14323	The Role of Handling Attributive Nouns in Improving Chinese-To-English Machine Translation	Translating between languages with drastically different grammatical conventions poses challenges, not just for human interpreters but also for machine translation systems. In this work, we specifically target the translation challenges posed by attributive nouns in Chinese, which frequently cause ambiguities in Englis...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	518,652
1210.4840	Lifted Relax, Compensate and then Recover: From Approximate to Exact Lifted Probabilistic Inference	We propose an approach to lifted approximate inference for first-order probabilistic models, such as Markov logic networks. It is based on performing exact lifted inference in a simplified first-order model, which is found by relaxing first-order constraints, and then compensating for the relaxation. These simplified m...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	19,169
2502.10694	Simulations of Common Unsupervised Domain Adaptation Algorithms for Image Classification	Traditional machine learning assumes that training and test sets are derived from the same distribution; however, this assumption does not always hold in practical applications. This distribution disparity can lead to severe performance drops when the trained model is used in new data sets. Domain adaptation (DA) is a ...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	534,003
2009.08100	How-to Present News on Social Media: A Causal Analysis of Editing News Headlines for Boosting User Engagement	To reach a broader audience and optimize traffic toward news articles, media outlets commonly run social media accounts and share their content with a short text summary. Despite its importance of writing a compelling message in sharing articles, the research community does not own a sufficient understanding of what ki...	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	false	false	false	196,133
1106.0664	The Complexity of Reasoning about Spatial Congruence	In the recent literature of Artificial Intelligence, an intensive research effort has been spent, for various algebras of qualitative relations used in the representation of temporal and spatial knowledge, on the problem of classifying the computational complexity of reasoning problems for subsets of algebras. The main...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	10,699
2307.02865	PLIERS: a Popularity-Based Recommender System for Content Dissemination in Online Social Networks	In this paper, we propose a novel tag-based recommender system called PLIERS, which relies on the assumption that users are mainly interested in items and tags with similar popularity to those they already own. PLIERS is aimed at reaching a good tradeoff between algorithmic complexity and the level of personalization o...	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	false	377,844
1901.10233	Reconstruction of 3D Porous Media From 2D Slices	In many branches of earth sciences, the problem of rock study on the micro-level arises. However, a significant number of representative samples is not always feasible. Thus the problem of the generation of samples with similar properties becomes actual. In this paper, we propose a novel deep learning architecture for ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	119,968
2110.00717	Mobile Manipulation Leveraging Multiple Views	While both navigation and manipulation are challenging topics in isolation, many tasks require the ability to both navigate and manipulate in concert. To this end, we propose a mobile manipulation system that leverages novel navigation and shape completion methods to manipulate an object with a mobile robot. Our system...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	258,503
1605.07273	LDPC Codes Based on the Space of Symmetric Matrices over Finite Fields	In this paper, we present a new method for explicitly constructing regular low-density parity-check (LDPC) codes based on $\mathbb{S}_{n}(\mathbb{F}_{q})$, the space of $n\times n$ symmetric matrices over $\mathbb{F}_{q}$. Using this method, we obtain two classes of binary LDPC codes, $\cal{C}(n,q)$ and $\cal{C}^{T}(n,...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	56,273
2101.10730	Model-free Data-Driven simulation of inelastic materials using structured data sets, tangent space information and transition rules	Model-free data-driven computational mechanics replaces phenomenological constitutive functions by numerical simulations based on data sets of representative samples in stress-strain space. The distance of strain and stress pairs from the data set is minimized, subject to equilibrium and compatibility constraints. Alth...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	217,029
1209.5625	Managing Complex Structured Data In a Fast Evolving Environment	Criminal data comes in a variety of formats, mandated by state, federal, and international standards. Specifying the data in a unified fashion is necessary for any system that intends to integrate with state, federal, and international law enforcement agencies. However, the contents, format, and structure of the data i...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	18,752
2008.13173	LIMSI_UPV at SemEval-2020 Task 9: Recurrent Convolutional Neural Network for Code-mixed Sentiment Analysis	This paper describes the participation of LIMSI UPV team in SemEval-2020 Task 9: Sentiment Analysis for Code-Mixed Social Media Text. The proposed approach competed in SentiMix Hindi-English subtask, that addresses the problem of predicting the sentiment of a given Hindi-English code-mixed tweet. We propose Recurrent C...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	193,783
2412.03561	FLAIR: VLM with Fine-grained Language-informed Image Representations	CLIP has shown impressive results in aligning images and texts at scale. However, its ability to capture detailed visual features remains limited because CLIP matches images and texts at a global level. To address this issue, we propose FLAIR, Fine-grained Language-informed Image Representations, an approach that utili...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	514,003
2103.01716	Self-restrained Triplet Loss for Accurate Masked Face Recognition	Using the face as a biometric identity trait is motivated by the contactless nature of the capture process and the high accuracy of the recognition algorithms. After the current COVID-19 pandemic, wearing a face mask has been imposed in public places to keep the pandemic under control. However, face occlusion due to we...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	222,724
1312.6079	An Improved Outer Bound on the Storage-Repair-Bandwidth Tradeoff of Exact-Repair Regenerating Codes	In this paper we establish an improved outer bound on the storage-repair-bandwidth tradeoff of regenerating codes under exact repair. The result shows that in particular, it is not possible to construct exact-repair regenerating codes that asymptotically achieve the tradeoff that holds for functional repair. While this...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	29,301
2207.13695	A detailed introduction to density-based topology optimisation of fluid flow problems with implementation in MATLAB	This article presents a detailed introduction to density-based topology optimisation of fluid flow problems. The goal is to allow new students and researchers to quickly get started in the research area and to skip many of the initial steps, often consuming unnecessarily long time from the scientific advancement of the...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	310,375
2007.06355	Multiple Sound Sources Localization from Coarse to Fine	How to visually localize multiple sound sources in unconstrained videos is a formidable problem, especially when lack of the pairwise sound-object annotations. To solve this problem, we develop a two-stage audiovisual learning framework that disentangles audio and visual representations of different categories from com...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	186,994
1805.09960	Phrase Table as Recommendation Memory for Neural Machine Translation	Neural Machine Translation (NMT) has drawn much attention due to its promising translation performance recently. However, several studies indicate that NMT often generates fluent but unfaithful translations. In this paper, we propose a method to alleviate this problem by using a phrase table as recommendation memory. T...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	98,540
1908.05832	Transferable Contrastive Network for Generalized Zero-Shot Learning	Zero-shot learning (ZSL) is a challenging problem that aims to recognize the target categories without seen data, where semantic information is leveraged to transfer knowledge from some source classes. Although ZSL has made great progress in recent years, most existing approaches are easy to overfit the sources classes...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	141,826
2004.01612	Towards a Parallel-in-Time Calculation of Time-Periodic Solutions with Unknown Period	This paper presents a novel parallel-in-time algorithm able to compute time-periodic solutions of problems where the period is not given. Exploiting the idea of the multiple shooting method, the proposed approach calculates the initial values at each subinterval as well as the corresponding period iteratively. As in th...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	170,963
1910.12713	Few-shot Video-to-Video Synthesis	Video-to-video synthesis (vid2vid) aims at converting an input semantic video, such as videos of human poses or segmentation masks, to an output photorealistic video. While the state-of-the-art of vid2vid has advanced significantly, existing approaches share two major limitations. First, they are data-hungry. Numerous ...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	true	151,175
2410.17858	Blendify -- Python rendering framework for Blender	With the rapid growth of the volume of research fields like computer vision and computer graphics, researchers require effective and user-friendly rendering tools to visualize results. While advanced tools like Blender offer powerful capabilities, they also require a significant effort to master. This technical report ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	501,643
2207.08547	Few-shot Fine-grained Image Classification via Multi-Frequency Neighborhood and Double-cross Modulation	Traditional fine-grained image classification typically relies on large-scale training samples with annotated ground-truth. However, some sub-categories have few available samples in real-world applications, and current few-shot models still have difficulty in distinguishing subtle differences among fine-grained catego...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	308,623
1702.06463	Predicting non-linear dynamics by stable local learning in a recurrent spiking neural network	Brains need to predict how the body reacts to motor commands. It is an open question how networks of spiking neurons can learn to reproduce the non-linear body dynamics caused by motor commands, using local, online and stable learning rules. Here, we present a supervised learning scheme for the feedforward and recurren...	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	true	false	false	68,619
2109.11330	Quantum algorithms for group convolution, cross-correlation, and equivariant transformations	Group convolutions and cross-correlations, which are equivariant to the actions of group elements, are commonly used in mathematics to analyze or take advantage of symmetries inherent in a given problem setting. Here, we provide efficient quantum algorithms for performing linear group convolutions and cross-correlation...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	256,911
1903.09122	Finite Sample Analysis of Stochastic System Identification	In this paper, we analyze the finite sample complexity of stochastic system identification using modern tools from machine learning and statistics. An unknown discrete-time linear system evolves over time under Gaussian noise without external inputs. The objective is to recover the system parameters as well as the Kalm...	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	124,982
2303.06519	Lossless Point Cloud Geometry and Attribute Compression Using a Learned Conditional Probability Model	In recent years, we have witnessed the presence of point cloud data in many aspects of our life, from immersive media, autonomous driving to healthcare, although at the cost of a tremendous amount of data. In this paper, we present an efficient lossless point cloud compression method that uses sparse tensor-based deep ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	350,877
2104.11867	Exploring Multi-dimensional Data via Subset Embedding	Multi-dimensional data exploration is a classic research topic in visualization. Most existing approaches are designed for identifying record patterns in dimensional space or subspace. In this paper, we propose a visual analytics approach to exploring subset patterns. The core of the approach is a subset embedding netw...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	232,041
2204.05735	GARF: Gaussian Activated Radiance Fields for High Fidelity Reconstruction and Pose Estimation	Despite Neural Radiance Fields (NeRF) showing compelling results in photorealistic novel views synthesis of real-world scenes, most existing approaches require accurate prior camera poses. Although approaches for jointly recovering the radiance field and camera pose exist (BARF), they rely on a cumbersome coarse-to-fin...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	291,126
1307.0974	On Secure Source Coding with Side Information at the Encoder	We consider a secure source coding problem with side information (S.I.) at the decoder and the eavesdropper. The encoder has a source that it wishes to describe with limited distortion through a rate limited link to a legitimate decoder. The message sent is also observed by the eavesdropper. The encoder aims to minimiz...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	25,592
2112.11294	Extending CLIP for Category-to-image Retrieval in E-commerce	E-commerce provides rich multimodal data that is barely leveraged in practice. One aspect of this data is a category tree that is being used in search and recommendation. However, in practice, during a user's session there is often a mismatch between a textual and a visual representation of a given category. Motivated ...	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	true	272,670
1505.01335	Comparing persistence diagrams through complex vectors	The natural pseudo-distance of spaces endowed with filtering functions is precious for shape classification and retrieval; its optimal estimate coming from persistence diagrams is the bottleneck distance, which unfortunately suffers from combinatorial explosion. A possible algebraic representation of persistence diagra...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	42,828
2405.08681	Achieving Fairness Through Channel Pruning for Dermatological Disease Diagnosis	Numerous studies have revealed that deep learning-based medical image classification models may exhibit bias towards specific demographic attributes, such as race, gender, and age. Existing bias mitigation methods often achieve high level of fairness at the cost of significant accuracy degradation. In response to this ...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	454,179
2005.02576	Towards Concise, Machine-discovered Proofs of G\"odel's Two Incompleteness Theorems	There is an increasing interest in applying recent advances in AI to automated reasoning, as it may provide useful heuristics in reasoning over formalisms in first-order, second-order, or even meta-logics. To facilitate this research, we present MATR, a new framework for automated theorem proving explicitly designed to...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	175,915
2304.11111	Inducing anxiety in large language models can induce bias	Large language models (LLMs) are transforming research on machine learning while galvanizing public debates. Understanding not only when these models work well and succeed but also why they fail and misbehave is of great societal relevance. We propose to turn the lens of psychiatry, a framework used to describe and mod...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	359,687
2209.00189	Federated Learning with Label Distribution Skew via Logits Calibration	Traditional federated optimization methods perform poorly with heterogeneous data (ie, accuracy reduction), especially for highly skewed data. In this paper, we investigate the label distribution skew in FL, where the distribution of labels varies across clients. First, we investigate the label distribution skew from a...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	315,507
2411.02715	CIT: Rethinking Class-incremental Semantic Segmentation with a Class Independent Transformation	Class-incremental semantic segmentation (CSS) requires that a model learn to segment new classes without forgetting how to segment previous ones: this is typically achieved by distilling the current knowledge and incorporating the latest data. However, bypassing iterative distillation by directly transferring outputs o...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	505,629
2112.08547	Learning Rich Representation of Keyphrases from Text	In this work, we explore how to train task-specific language models aimed towards learning rich representation of keyphrases from text documents. We experiment with different masking strategies for pre-training transformer language models (LMs) in discriminative as well as generative settings. In the discriminative set...	false	false	false	false	false	true	true	false	true	false	false	false	false	false	false	false	false	false	271,830
2411.18224	KANs for Computer Vision: An Experimental Study	This paper presents an experimental study of Kolmogorov-Arnold Networks (KANs) applied to computer vision tasks, particularly image classification. KANs introduce learnable activation functions on edges, offering flexible non-linear transformations compared to traditional pre-fixed activation functions with specific ne...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	511,778
2108.07895	Adaptive Convolutions with Per-pixel Dynamic Filter Atom	Applying feature dependent network weights have been proved to be effective in many fields. However, in practice, restricted by the enormous size of model parameters and memory footprints, scalable and versatile dynamic convolutions with per-pixel adapted filters are yet to be fully explored. In this paper, we address ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	251,051
2310.06201	Compressing Context to Enhance Inference Efficiency of Large Language Models	Large language models (LLMs) achieved remarkable performance across various tasks. However, they face challenges in managing long documents and extended conversations, due to significantly increased computational requirements, both in memory and inference time, and potential context truncation when the input exceeds th...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	398,465
2104.03838	Speech Denoising Without Clean Training Data: A Noise2Noise Approach	This paper tackles the problem of the heavy dependence of clean speech data required by deep learning based audio-denoising methods by showing that it is possible to train deep speech denoising networks using only noisy speech samples. Conventional wisdom dictates that in order to achieve good speech denoising performa...	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	229,189
2207.04623	Deep neural network based adaptive learning for switched systems	In this paper, we present a deep neural network based adaptive learning (DNN-AL) approach for switched systems. Currently, deep neural network based methods are actively developed for learning governing equations in unknown dynamic systems, but their efficiency can degenerate for switching systems, where structural cha...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	307,257

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