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
1510.01135	Feedback and Time are Essential for the Optimal Control of Computing Systems	The performance, reliability, cost, size and energy usage of computing systems can be improved by one or more orders of magnitude by the systematic use of modern control and optimization methods. Computing systems rely on the use of feedback algorithms to schedule tasks, data and resources, but the models that are used...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	47,585
2405.15083	MuDreamer: Learning Predictive World Models without Reconstruction	The DreamerV3 agent recently demonstrated state-of-the-art performance in diverse domains, learning powerful world models in latent space using a pixel reconstruction loss. However, while the reconstruction loss is essential to Dreamer's performance, it also necessitates modeling unnecessary information. Consequently, ...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	456,734
1711.02198	Regret Bounds and Regimes of Optimality for User-User and Item-Item Collaborative Filtering	We consider an online model for recommendation systems, with each user being recommended an item at each time-step and providing 'like' or 'dislike' feedback. Each user may be recommended a given item at most once. A latent variable model specifies the user preferences: both users and items are clustered into types. Al...	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	84,017
1812.06110	Dopamine: A Research Framework for Deep Reinforcement Learning	Deep reinforcement learning (deep RL) research has grown significantly in recent years. A number of software offerings now exist that provide stable, comprehensive implementations for benchmarking. At the same time, recent deep RL research has become more diverse in its goals. In this paper we introduce Dopamine, a new...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	116,541
1902.04208	MaCow: Masked Convolutional Generative Flow	Flow-based generative models, conceptually attractive due to tractability of both the exact log-likelihood computation and latent-variable inference, and efficiency of both training and sampling, has led to a number of impressive empirical successes and spawned many advanced variants and theoretical investigations. Des...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	121,287
2408.15916	Multi-modal Adversarial Training for Zero-Shot Voice Cloning	A text-to-speech (TTS) model trained to reconstruct speech given text tends towards predictions that are close to the average characteristics of a dataset, failing to model the variations that make human speech sound natural. This problem is magnified for zero-shot voice cloning, a task that requires training data with...	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	484,126
2104.14072	Nonlinear Level Set Learning for Function Approximation on Sparse Data with Applications to Parametric Differential Equations	A dimension reduction method based on the "Nonlinear Level set Learning" (NLL) approach is presented for the pointwise prediction of functions which have been sparsely sampled. Leveraging geometric information provided by the Implicit Function Theorem, the proposed algorithm effectively reduces the input dimension to t...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	232,698
2305.19889	Evaluating Machine Learning Models with NERO: Non-Equivariance Revealed on Orbits	Proper evaluations are crucial for better understanding, troubleshooting, interpreting model behaviors and further improving model performance. While using scalar-based error metrics provides a fast way to overview model performance, they are often too abstract to display certain weak spots and lack information regardi...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	369,721
2408.12466	WCEbleedGen: A wireless capsule endoscopy dataset and its benchmarking for automatic bleeding classification, detection, and segmentation	Computer-based analysis of Wireless Capsule Endoscopy (WCE) is crucial. However, a medically annotated WCE dataset for training and evaluation of automatic classification, detection, and segmentation of bleeding and non-bleeding frames is currently lacking. The present work focused on development of a medically annotat...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	482,746
2103.13762	Mining Energy-Related Practices in Robotics Software	Robots are becoming more and more commonplace in many industry settings. This successful adoption can be partly attributed to (1) their increasingly affordable cost and (2) the possibility of developing intelligent, software-driven robots. Unfortunately, robotics software consumes significant amounts of energy. Moreove...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	true	226,602
1103.5535	A Lattice Compress-and-Forward Scheme	We present a nested lattice-code-based strategy that achieves the random-coding based Compress-and-Forward (CF) rate for the three node Gaussian relay channel. To do so, we first outline a lattice-based strategy for the $(X+Z_1,X+Z_2)$ Wyner-Ziv lossy source-coding with side-information problem in Gaussian noise, a re-...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	9,792
1611.08946	Exponential Separation of Quantum Communication and Classical Information	We exhibit a Boolean function for which the quantum communication complexity is exponentially larger than the classical information complexity. An exponential separation in the other direction was already known from the work of Kerenidis et. al. [SICOMP 44, pp. 1550-1572], hence our work implies that these two complexi...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	64,591
1910.09763	Stochastic Feedforward Neural Networks: Universal Approximation	In this chapter we take a look at the universal approximation question for stochastic feedforward neural networks. In contrast to deterministic networks, which represent mappings from a set of inputs to a set of outputs, stochastic networks represent mappings from a set of inputs to a set of probability distributions o...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	true	false	false	150,299
2405.14270	Sparse $L^1$-Autoencoders for Scientific Data Compression	Scientific datasets present unique challenges for machine learning-driven compression methods, including more stringent requirements on accuracy and mitigation of potential invalidating artifacts. Drawing on results from compressed sensing and rate-distortion theory, we introduce effective data compression methods by d...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	true	456,344
2406.07694	A PRISMA Driven Systematic Review of Publicly Available Datasets for Benchmark and Model Developments for Industrial Defect Detection	Recent advancements in quality control across various industries have increasingly utilized the integration of video cameras and image processing for effective defect detection. A critical barrier to progress is the scarcity of comprehensive datasets featuring annotated defects, which are essential for developing and r...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	463,165
2308.14969	Uncovering the Hidden Cost of Model Compression	In an age dominated by resource-intensive foundation models, the ability to efficiently adapt to downstream tasks is crucial. Visual Prompting (VP), drawing inspiration from the prompting techniques employed in Large Language Models (LLMs), has emerged as a pivotal method for transfer learning in the realm of computer ...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	388,512
2208.09104	A Causality-Based Learning Approach for Discovering the Underlying Dynamics of Complex Systems from Partial Observations with Stochastic Parameterization	Discovering the underlying dynamics of complex systems from data is an important practical topic. Constrained optimization algorithms are widely utilized and lead to many successes. Yet, such purely data-driven methods may bring about incorrect physics in the presence of random noise and cannot easily handle the situat...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	313,587
1810.09401	Alternating Linear Bandits for Online Matrix-Factorization Recommendation	We consider the problem of online collaborative filtering in the online setting, where items are recommended to the users over time. At each time step, the user (selected by the environment) consumes an item (selected by the agent) and provides a rating of the selected item. In this paper, we propose a novel algorithm ...	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	false	111,051
1910.12795	Learning Data Manipulation for Augmentation and Weighting	Manipulating data, such as weighting data examples or augmenting with new instances, has been increasingly used to improve model training. Previous work has studied various rule- or learning-based approaches designed for specific types of data manipulation. In this work, we propose a new method that supports learning d...	false	false	false	false	false	false	true	false	true	false	false	true	false	false	false	false	false	false	151,200
1807.09077	Optional Stopping with Bayes Factors: a categorization and extension of folklore results, with an application to invariant situations	It is often claimed that Bayesian methods, in particular Bayes factor methods for hypothesis testing, can deal with optional stopping. We first give an overview, using elementary probability theory, of three different mathematical meanings that various authors give to this claim: (1) stopping rule independence, (2) pos...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	103,653
1702.05952	Interplay between social influence and competitive strategical games in multiplex networks	We present a model that takes into account the coupling between evolutionary game dynamics and social influence. Importantly, social influence and game dynamics take place in different domains, which we model as different layers of a multiplex network. We show that the coupling between these dynamical processes can lea...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	68,504
2210.12158	Graph Coloring via Neural Networks for Haplotype Assembly and Viral Quasispecies Reconstruction	Understanding genetic variation, e.g., through mutations, in organisms is crucial to unravel their effects on the environment and human health. A fundamental characterization can be obtained by solving the haplotype assembly problem, which yields the variation across multiple copies of chromosomes. Variations among fas...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	325,609
2307.01390	Adversarial Learning in Real-World Fraud Detection: Challenges and Perspectives	Data economy relies on data-driven systems and complex machine learning applications are fueled by them. Unfortunately, however, machine learning models are exposed to fraudulent activities and adversarial attacks, which threaten their security and trustworthiness. In the last decade or so, the research interest on adv...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	377,331
1803.03849	Learning to Localize Sound Source in Visual Scenes	Visual events are usually accompanied by sounds in our daily lives. We pose the question: Can the machine learn the correspondence between visual scene and the sound, and localize the sound source only by observing sound and visual scene pairs like human? In this paper, we propose a novel unsupervised algorithm to addr...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	true	92,333
2311.08883	Distilling Rule-based Knowledge into Large Language Models	Large language models (LLMs) have shown incredible performance in completing various real-world tasks. The current paradigm of knowledge learning for LLMs is mainly based on learning from examples, in which LLMs learn the internal rule implicitly from a certain number of supervised examples. However, this learning para...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	407,904
2312.10421	On the Use of Walsh Domain Equalizer for Performance Enhancement of MIMO-OFDM Communication Systems	The purpose of this article is to investigate the viability of Multi-Carrier Modulation (MCM) systems based on the Fast Walsh Hadamard Transform (FWHT). In addition, a nonlinear Joint Low-Complexity Optimized Zero Forcing Successive Interference Cancellation (JLCOZF-SIC) equalizer is proposed. To that end, general equa...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	416,160
2409.06192	NOVI : Chatbot System for University Novice with BERT and LLMs	To mitigate the difficulties of university freshmen in adapting to university life, we developed NOVI, a chatbot system based on GPT-4o. This system utilizes post and comment data from SKKU 'Everytime', a university community site. Developed using LangChain, NOVI's performance has been evaluated with a BLEU score, Perp...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	487,033
2307.06484	Single-Class Target-Specific Attack against Interpretable Deep Learning Systems	In this paper, we present a novel Single-class target-specific Adversarial attack called SingleADV. The goal of SingleADV is to generate a universal perturbation that deceives the target model into confusing a specific category of objects with a target category while ensuring highly relevant and accurate interpretation...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	379,080
2404.11577	Towards Reliable Empirical Machine Unlearning Evaluation: A Cryptographic Game Perspective	Machine unlearning updates machine learning models to remove information from specific training samples, complying with data protection regulations that allow individuals to request the removal of their personal data. Despite the recent development of numerous unlearning algorithms, reliable evaluation of these algorit...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	447,544
2412.01348	Hierarchical Object-Oriented POMDP Planning for Object Rearrangement	We present an online planning framework for solving multi-object rearrangement problems in partially observable, multi-room environments. Current object rearrangement solutions, primarily based on Reinforcement Learning or hand-coded planning methods, often lack adaptability to diverse challenges. To address this limit...	false	false	false	false	true	false	true	true	false	false	false	false	false	false	false	false	false	false	513,075
1806.10556	Every Pixel Counts: Unsupervised Geometry Learning with Holistic 3D Motion Understanding	Learning to estimate 3D geometry in a single image by watching unlabeled videos via deep convolutional network has made significant process recently. Current state-of-the-art (SOTA) methods, are based on the learning framework of rigid structure-from-motion, where only 3D camera ego motion is modeled for geometry estim...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	101,561
2102.11176	Deep Reinforcement Learning for Dynamic Spectrum Sharing of LTE and NR	In this paper, a proactive dynamic spectrum sharing scheme between 4G and 5G systems is proposed. In particular, a controller decides on the resource split between NR and LTE every subframe while accounting for future network states such as high interference subframes and multimedia broadcast single frequency network (...	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	false	true	221,348
1611.08358	Kannada Spell Checker with Sandhi Splitter	Spelling errors are introduced in text either during typing, or when the user does not know the correct phoneme or grapheme. If a language contains complex words like sandhi where two or more morphemes join based on some rules, spell checking becomes very tedious. In such situations, having a spell checker with sandhi ...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	64,492
2406.16490	eagerlearners at SemEval2024 Task 5: The Legal Argument Reasoning Task in Civil Procedure	This study investigates the performance of the zero-shot method in classifying data using three large language models, alongside two models with large input token sizes and the two pre-trained models on legal data. Our main dataset comes from the domain of U.S. civil procedure. It includes summaries of legal cases, spe...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	467,150
2201.12048	The FreshPRINCE: A Simple Transformation Based Pipeline Time Series Classifier	There have recently been significant advances in the accuracy of algorithms proposed for time series classification (TSC). However, a commonly asked question by real world practitioners and data scientists less familiar with the research topic, is whether the complexity of the algorithms considered state of the art is ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	277,506
2303.02839	Single-shot phase retrieval: a holography-driven problem in Sobolev space	The phase-shifting digital holography (PSDH) is a widely used approach for recovering signals by their interference (with reference waves) intensity measurements. Such measurements are traditionally from multiple shots (corresponding to multiple reference waves). However, the imaging of dynamic signals requires a singl...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	349,498
1506.01186	Cyclical Learning Rates for Training Neural Networks	It is known that the learning rate is the most important hyper-parameter to tune for training deep neural networks. This paper describes a new method for setting the learning rate, named cyclical learning rates, which practically eliminates the need to experimentally find the best values and schedule for the global lea...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	true	false	false	43,776
2208.03496	Smart Explorer: Recognizing Objects in Dense Clutter via Interactive Exploration	Recognizing objects in dense clutter accurately plays an important role to a wide variety of robotic manipulation tasks including grasping, packing, rearranging and many others. However, conventional visual recognition models usually miss objects because of the significant occlusion among instances and causes incorrect...	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	311,800
1505.07717	Exploring multimodal data fusion through joint decompositions with flexible couplings	A Bayesian framework is proposed to define flexible coupling models for joint tensor decompositions of multiple data sets. Under this framework, a natural formulation of the data fusion problem is to cast it in terms of a joint maximum a posteriori (MAP) estimator. Data driven scenarios of joint posterior distributions...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	43,565
2403.01663	PillarGen: Enhancing Radar Point Cloud Density and Quality via Pillar-based Point Generation Network	In this paper, we present a novel point generation model, referred to as Pillar-based Point Generation Network (PillarGen), which facilitates the transformation of point clouds from one domain into another. PillarGen can produce synthetic point clouds with enhanced density and quality based on the provided input point ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	434,526
cmp-lg/9702001	SCREEN: Learning a Flat Syntactic and Semantic Spoken Language Analysis Using Artificial Neural Networks	In this paper, we describe a so-called screening approach for learning robust processing of spontaneously spoken language. A screening approach is a flat analysis which uses shallow sequences of category representations for analyzing an utterance at various syntactic, semantic and dialog levels. Rather than using a dee...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	536,688
2212.06397	Style-Label-Free: Cross-Speaker Style Transfer by Quantized VAE and Speaker-wise Normalization in Speech Synthesis	Cross-speaker style transfer in speech synthesis aims at transferring a style from source speaker to synthesised speech of a target speaker's timbre. Most previous approaches rely on data with style labels, but manually-annotated labels are expensive and not always reliable. In response to this problem, we propose Styl...	false	false	true	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	336,104
1812.10308	Hierarchical Genetic Algorithms with evolving objective functions	We propose a framework of genetic algorithms which use multi-level hierarchies to solve an optimization problem by searching over the space of simpler objective functions. We solve a variant of Travelling Salesman Problem called \texttt{soft-TSP} and show that when the constraints on the overall objective function are ...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	true	false	false	117,334
1905.00256	Entanglement Access Control for the Quantum Internet	Quantum entanglement is a crucial element of establishing the entangled network structure of the quantum Internet. Here we define a method to achieve controlled entanglement access in the quantum Internet. The proposed model defines different levels of entanglement accessibility for the users of the quantum network. Th...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	129,427
2003.10477	Distilling Knowledge from Graph Convolutional Networks	Existing knowledge distillation methods focus on convolutional neural networks (CNNs), where the input samples like images lie in a grid domain, and have largely overlooked graph convolutional networks (GCN) that handle non-grid data. In this paper, we propose to our best knowledge the first dedicated approach to disti...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	169,345
2206.02094	Using Connectome Features to Constrain Echo State Networks	We report an improvement to the conventional Echo State Network (ESN) across three benchmark chaotic time-series prediction tasks using fruit fly connectome data alone. We also investigate the impact of key connectome-derived structural features on prediction performance -- uniquely bridging neurobiological structure a...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	true	false	false	300,743
2211.03757	Discrete Distribution Estimation under User-level Local Differential Privacy	We study discrete distribution estimation under user-level local differential privacy (LDP). In user-level $\varepsilon$-LDP, each user has $m\ge1$ samples and the privacy of all $m$ samples must be preserved simultaneously. We resolve the following dilemma: While on the one hand having more samples per user should pro...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	329,029
1702.05911	Efficient Large-scale Approximate Nearest Neighbor Search on the GPU	We present a new approach for efficient approximate nearest neighbor (ANN) search in high dimensional spaces, extending the idea of Product Quantization. We propose a two-level product and vector quantization tree that reduces the number of vector comparisons required during tree traversal. Our approach also includes a...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	68,498
2501.10841	Practical and Ready-to-Use Methodology to Assess the re-identification Risk in Anonymized Datasets	To prove that a dataset is sufficiently anonymized, many privacy policies suggest that a re-identification risk assessment be performed, but do not provide a precise methodology for doing so, leaving the industry alone with the problem. This paper proposes a practical and ready-to-use methodology for re-identification ...	false	false	false	false	true	false	false	false	false	false	false	false	true	false	false	false	true	false	525,682
2409.00147	MultiMath: Bridging Visual and Mathematical Reasoning for Large Language Models	The rapid development of large language models (LLMs) has spurred extensive research into their domain-specific capabilities, particularly mathematical reasoning. However, most open-source LLMs focus solely on mathematical reasoning, neglecting the integration with visual injection, despite the fact that many mathemati...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	484,809
2412.01262	Do Large Language Models with Reasoning and Acting Meet the Needs of Task-Oriented Dialogue?	Large language models (LLMs) gained immense popularity due to their impressive capabilities in unstructured conversations. However, they underperform compared to previous approaches in task-oriented dialogue (TOD), wherein reasoning and accessing external information are crucial. Empowering LLMs with advanced prompting...	true	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	513,034
2106.10978	Attribute Selection using Contranominal Scales	Formal Concept Analysis (FCA) allows to analyze binary data by deriving concepts and ordering them in lattices. One of the main goals of FCA is to enable humans to comprehend the information that is encapsulated in the data; however, the large size of concept lattices is a limiting factor for the feasibility of underst...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	242,236
1611.00709	A Novel Hybrid Beamforming Algorithm with Unified Analog Beamforming by Subspace Construction Based on Partial CSI for Massive MIMO-OFDM Systems	Hybrid beamforming (HB) has been widely studied for reducing the number of costly radio frequency (RF) chains in massive multiple-input multiple-output (MIMO) systems. However, previous works on HB are limited to a single user equipment (UE) or a single group of UEs, employing the frequency-flat first-level analog beam...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	63,270
1909.09227	An Introduction to Quaternion-Valued Recurrent Projection Neural Networks	Hypercomplex-valued neural networks, including quaternion-valued neural networks, can treat multi-dimensional data as a single entity. In this paper, we introduce the quaternion-valued recurrent projection neural networks (QRPNNs). Briefly, QRPNNs are obtained by combining the non-local projection learning with the qua...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	146,190
1803.09211	Bernoulli Embeddings for Graphs	Just as semantic hashing can accelerate information retrieval, binary valued embeddings can significantly reduce latency in the retrieval of graphical data. We introduce a simple but effective model for learning such binary vectors for nodes in a graph. By imagining the embeddings as independent coin flips of varying b...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	93,455
2409.17167	StressPrompt: Does Stress Impact Large Language Models and Human Performance Similarly?	Human beings often experience stress, which can significantly influence their performance. This study explores whether Large Language Models (LLMs) exhibit stress responses similar to those of humans and whether their performance fluctuates under different stress-inducing prompts. To investigate this, we developed a no...	true	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	491,688
1808.08926	Opportunistic Treating Interference as Noise	We consider a $K$-user interference network with $M$ states, where each transmitter has $M$ messages and over State $m$, Receiver $k$ wishes to decode the first $\pi_k(m) \in \{1,2,\cdots,M\}$ messages from its desired transmitter. This problem of channel with states models opportunistic communications, where more mess...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	106,064
2410.17834	Non-intrusive Speech Quality Assessment with Diffusion Models Trained on Clean Speech	Diffusion models have found great success in generating high quality, natural samples of speech, but their potential for density estimation for speech has so far remained largely unexplored. In this work, we leverage an unconditional diffusion model trained only on clean speech for the assessment of speech quality. We ...	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	501,635
2205.08459	Dynamic Recognition of Speakers for Consent Management by Contrastive Embedding Replay	Voice assistants overhear conversations and a consent management mechanism is required. Consent management can be implemented using speaker recognition. Users that do not give consent enrol their voice and all their further recordings are discarded. Building speaker recognition-based consent management is challenging a...	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	296,950
2109.07296	Predicting Anti-Asian Hateful Users on Twitter during COVID-19	We investigate predictors of anti-Asian hate among Twitter users throughout COVID-19. With the rise of xenophobia and polarization that has accompanied widespread social media usage in many nations, online hate has become a major social issue, attracting many researchers. Here, we apply natural language processing tech...	false	false	false	true	false	false	false	false	false	false	false	false	false	true	false	false	false	false	255,470
2010.12619	Learning Implicitly with Noisy Data in Linear Arithmetic	Robust learning in expressive languages with real-world data continues to be a challenging task. Numerous conventional methods appeal to heuristics without any assurances of robustness. While probably approximately correct (PAC) Semantics offers strong guarantees, learning explicit representations is not tractable, eve...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	202,760
1704.07179	A Simple Proof of Fast Polarization	Fast polarization is an important and useful property of polar codes. It was proved for the binary polarizing $2 \times 2$ kernel by Arikan and Telatar. The proof was later generalized by Sasoglu. We give a simplified proof.	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	72,316
1604.03626	Privacy & Social Media in the Context of the Arab Gulf	Theories of privacy and how it relates to the use of Information Communication Technology (ICT) have been a topic of research for decades. However, little attention has been paid to the perception of privacy from the perspective of technology users in the Middle East. In this paper, we delve into interpretations of pri...	false	false	false	true	false	false	false	false	false	false	false	false	false	true	false	false	false	false	54,538
2110.05078	State Estimation Using a Network of Distributed Observers With Unknown Inputs	State estimation for a class of linear time-invariant systems with distributed output measurements (distributed sensors) and unknown inputs is addressed in this paper. The objective is to design a network of observers such that the state vector of the entire system can be estimated, while each observer (or node) has ac...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	260,157
1511.06438	Joint Word Representation Learning using a Corpus and a Semantic Lexicon	Methods for learning word representations using large text corpora have received much attention lately due to their impressive performance in numerous natural language processing (NLP) tasks such as, semantic similarity measurement, and word analogy detection. Despite their success, these data-driven word representatio...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	49,251
2405.09582	AD-Aligning: Emulating Human-like Generalization for Cognitive Domain Adaptation in Deep Learning	Domain adaptation is pivotal for enabling deep learning models to generalize across diverse domains, a task complicated by variations in presentation and cognitive nuances. In this paper, we introduce AD-Aligning, a novel approach that combines adversarial training with source-target domain alignment to enhance general...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	454,465
1105.2214	An improved mathematical model of social group competition	An improved mathematical model of social group competition is proposed. The utility obtained by a member of a certain group from each other member is assumed to be group size-dependent. Obtained results are close to available census data. It is shown that a significant fraction of population can be affiliated in a grou...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	10,326
2111.06057	Characterization of Frequent Online Shoppers using Statistical Learning with Sparsity	Developing shopping experiences that delight the customer requires businesses to understand customer taste. This work reports a method to learn the shopping preferences of frequent shoppers to an online gift store by combining ideas from retail analytics and statistical learning with sparsity. Shopping activity is repr...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	265,973
2410.12577	On the Utility of Domain Modeling Assistance with Large Language Models	Model-driven engineering (MDE) simplifies software development through abstraction, yet challenges such as time constraints, incomplete domain understanding, and adherence to syntactic constraints hinder the design process. This paper presents a study to evaluate the usefulness of a novel approach utilizing large langu...	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	499,093
2310.06155	CoQuest: Exploring Research Question Co-Creation with an LLM-based Agent	Developing novel research questions (RQs) often requires extensive literature reviews, especially in interdisciplinary fields. To support RQ development through human-AI co-creation, we leveraged Large Language Models (LLMs) to build an LLM-based agent system named CoQuest. We conducted an experiment with 20 HCI resear...	true	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	398,443
2212.01959	INGeo: Accelerating Instant Neural Scene Reconstruction with Noisy Geometry Priors	We present a method that accelerates reconstruction of 3D scenes and objects, aiming to enable instant reconstruction on edge devices such as mobile phones and AR/VR headsets. While recent works have accelerated scene reconstruction training to minute/second-level on high-end GPUs, there is still a large gap to the goa...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	334,630
2207.09372	On Decentralizing Federated Reinforcement Learning in Multi-Robot Scenarios	Federated Learning (FL) allows for collaboratively aggregating learned information across several computing devices and sharing the same amongst them, thereby tackling issues of privacy and the need of huge bandwidth. FL techniques generally use a central server or cloud for aggregating the models received from the dev...	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	false	308,882
2208.10165	Exploring Task-oriented Communication in Multi-agent System: A Deep Reinforcement Learning Approach	The multi-agent system (MAS) enables the sharing of capabilities among agents, such that collaborative tasks can be accomplished with high scalability and efficiency. MAS is increasingly widely applied in various fields. Meanwhile, the large-scale and time-sensitive data transmission between agents brings challenges to...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	313,957
2109.01182	COVID-19 Vaccine Hesitancy and Information Diffusion: An Agent-based Modeling Approach	Despite the unprecedented success in the rapid development of several effective vaccines against the Cov-SARS-2, global vaccination rollout efforts suffer from vaccine distribution inequality and vaccine acceptance, leading to insufficient public immunity provided by the vaccine products. While a major current focus in...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	false	false	false	253,360
2206.07150	Attacks on Perception-Based Control Systems: Modeling and Fundamental Limits	We study the performance of perception-based control systems in the presence of attacks, and provide methods for modeling and analysis of their resiliency to stealthy attacks on both physical and perception-based sensing. Specifically, we consider a general setup with a nonlinear affine physical plant controlled with a...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	302,615
2207.09089	Balancing the trade-off between cost and reliability for wireless sensor networks: a multi-objective optimized deployment method	The deployment of the sensor nodes (SNs) always plays a decisive role in the system performance of wireless sensor networks (WSNs). In this work, we propose an optimal deployment method for practical heterogeneous WSNs which gives a deep insight into the trade-off between the reliability and deployment cost. Specifical...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	true	308,794
1606.04232	DCNNs on a Diet: Sampling Strategies for Reducing the Training Set Size	Large-scale supervised classification algorithms, especially those based on deep convolutional neural networks (DCNNs), require vast amounts of training data to achieve state-of-the-art performance. Decreasing this data requirement would significantly speed up the training process and possibly improve generalization. M...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	57,219
2010.10962	World impact of kernel European Union 9 countries from Google matrix analysis of the world trade network	We use the United Nations COMTRADE database for analysis of the multiproduct world trade network. With this data, considered for years 2012-2018, we determined the world trade impact of the Kernel of EU 9 countries (KEU9), being Austria, Belgium, France, Germany, Italy, Luxembourg, Netherlands, Portugal, Spain, conside...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	202,068
1708.05279	Designing and building the mlpack open-source machine learning library	mlpack is an open-source C++ machine learning library with an emphasis on speed and flexibility. Since its original inception in 2007, it has grown to be a large project implementing a wide variety of machine learning algorithms, from standard techniques such as decision trees and logistic regression to modern techniqu...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	79,104
1306.5609	Partial Spreads in Random Network Coding	Following the approach by R. K\"otter and F. R. Kschischang, we study network codes as families of k-dimensional linear subspaces of a vector space F_q^n, q being a prime power and F_q the finite field with q elements. In particular, following an idea in finite projective geometry, we introduce a class of network codes...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	25,423
2412.04244	GigaHands: A Massive Annotated Dataset of Bimanual Hand Activities	Understanding bimanual human hand activities is a critical problem in AI and robotics. We cannot build large models of bimanual activities because existing datasets lack the scale, coverage of diverse hand activities, and detailed annotations. We introduce GigaHands, a massive annotated dataset capturing 34 hours of bi...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	514,321
1608.01017	Automated X-ray Image Analysis for Cargo Security: Critical Review and Future Promise	We review the relatively immature field of automated image analysis for X-ray cargo imagery. There is increasing demand for automated analysis methods that can assist in the inspection and selection of containers, due to the ever-growing volumes of traded cargo and the increasing concerns that customs- and security-rel...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	59,370
2102.02000	A generalised feature for low level vision	This papers presents a novel quantised transform (the Sinclair-Town or ST transform for short) that subsumes the rolls of both edge-detector, MSER style region detector and corner detector. The transform is similar to the $unsharp$ transform but the difference from the local mean is quantised to 3 values (dark-neutral-...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	218,296
1207.1425	Qualitative Decision Making Under Possibilistic Uncertainty: Toward more discriminating criteria	The aim of this paper is to propose a generalization of previous approaches in qualitative decision making. Our work is based on the binary possibilistic utility (PU), which is a possibilistic counterpart of Expected Utility (EU).We first provide a new axiomatization of PU and study its relation with the lexicographic ...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	17,304
2401.07923	Word Boundary Information Isn't Useful for Encoder Language Models	All existing transformer-based approaches to NLP using subword tokenisation algorithms encode whitespace (word boundary information) through the use of special space symbols (such as \#\# or \_) forming part of tokens. These symbols have been shown to a) lead to reduced morphological validity of tokenisations, and b) g...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	421,696
1804.08049	Semi-supervised User Geolocation via Graph Convolutional Networks	Social media user geolocation is vital to many applications such as event detection. In this paper, we propose GCN, a multiview geolocation model based on Graph Convolutional Networks, that uses both text and network context. We compare GCN to the state-of-the-art, and to two baselines we propose, and show that our mod...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	95,674
2403.00665	Complex-Valued Neural Network based Federated Learning for Multi-user Indoor Positioning Performance Optimization	In this article, the use of channel state information (CSI) for indoor positioning is studied. In the considered model, a server equipped with several antennas sends pilot signals to users, while each user uses the received pilot signals to estimate channel states for user positioning. To this end, we formulate the pos...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	434,058
2502.06727	Application of Artificial Intelligence (AI) in Civil Engineering	Hard computing generally deals with precise data, which provides ideal solutions to problems. However, in the civil engineering field, amongst other disciplines, that is not always the case as real-world systems are continuously changing. Here lies the need to explore soft computing methods and artificial intelligence ...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	532,204
2308.02632	Generation of Realistic Synthetic Raw Radar Data for Automated Driving Applications using Generative Adversarial Networks	The main approaches for simulating FMCW radar are based on ray tracing, which is usually computationally intensive and do not account for background noise. This work proposes a faster method for FMCW radar simulation capable of generating synthetic raw radar data using generative adversarial networks (GAN). The code an...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	383,718
2402.09124	Finding Densest Subgraphs with Edge-Color Constraints	We consider a variant of the densest subgraph problem in networks with single or multiple edge attributes. For example, in a social network, the edge attributes may describe the type of relationship between users, such as friends, family, or acquaintances, or different types of communication. For conceptual simplicity,...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	429,378
2406.03216	Choice of PEFT Technique in Continual Learning: Prompt Tuning is Not All You Need	Recent Continual Learning (CL) methods have combined pretrained Transformers with prompt tuning, a parameter-efficient fine-tuning (PEFT) technique. We argue that the choice of prompt tuning in prior works was an undefended and unablated decision, which has been uncritically adopted by subsequent research, but warrants...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	461,146
2411.03556	VQ-ACE: Efficient Policy Search for Dexterous Robotic Manipulation via Action Chunking Embedding	Dexterous robotic manipulation remains a significant challenge due to the high dimensionality and complexity of hand movements required for tasks like in-hand manipulation and object grasping. This paper addresses this issue by introducing Vector Quantized Action Chunking Embedding (VQ-ACE), a novel framework that comp...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	505,945
2310.01022	Subtractor-Based CNN Inference Accelerator	This paper presents a novel method to boost the performance of CNN inference accelerators by utilizing subtractors. The proposed CNN preprocessing accelerator relies on sorting, grouping, and rounding the weights to create combinations that allow for the replacement of one multiplication operation and addition operatio...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	396,245
2406.07314	Rethinking the impact of noisy labels in graph classification: A utility and privacy perspective	Graph neural networks based on message-passing mechanisms have achieved advanced results in graph classification tasks. However, their generalization performance degrades when noisy labels are present in the training data. Most existing noisy labeling approaches focus on the visual domain or graph node classification t...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	462,993
2403.16464	Training Generative Adversarial Network-Based Vocoder with Limited Data Using Augmentation-Conditional Discriminator	A generative adversarial network (GAN)-based vocoder trained with an adversarial discriminator is commonly used for speech synthesis because of its fast, lightweight, and high-quality characteristics. However, this data-driven model requires a large amount of training data incurring high data-collection costs. This fac...	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	441,056
2402.03978	Reconfigurable Power Converters with Increased Utilization for Unbalanced Power Distribution System Applications	A low-cost reconfiguration stage connected at the output of balanced three-phase, multi-terminal ac/dc/ac converters can increase the feasible set of power injections substantially, increasing converter utilization and therefore achieving a lower system cost. However, the approach has yet to be explored for phase unbal...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	427,278
2112.06307	Image-to-Height Domain Translation for Synthetic Aperture Sonar	Observations of seabed texture with synthetic aperture sonar are dependent upon several factors. In this work, we focus on collection geometry with respect to isotropic and anisotropic textures. The low grazing angle of the collection geometry, combined with orientation of the sonar path relative to anisotropic texture...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	271,124
1710.00132	Dense RGB-D semantic mapping with Pixel-Voxel neural network	For intelligent robotics applications, extending 3D mapping to 3D semantic mapping enables robots to, not only localize themselves with respect to the scene's geometrical features but also simultaneously understand the higher level meaning of the scene contexts. Most previous methods focus on geometric 3D reconstructio...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	81,811
2212.02936	M-VADER: A Model for Diffusion with Multimodal Context	We introduce M-VADER: a diffusion model (DM) for image generation where the output can be specified using arbitrary combinations of images and text. We show how M-VADER enables the generation of images specified using combinations of image and text, and combinations of multiple images. Previously, a number of successfu...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	334,941
2209.10369	Approximating the full-field temperature evolution in 3D electronic systems from randomized "Minecraft" systems	Neural Networks as fast physics simulators have a large potential for many engineering design tasks. Prerequisites for a wide-spread application are an easy-to-use workflow for generating training datasets in a reasonable time, and the capability of the network to generalize to unseen systems. In contrast to most previ...	false	true	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	318,840

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