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
2407.13948	Assurance of AI Systems From a Dependability Perspective	We outline the principles of classical assurance for computer-based systems that pose significant risks. We then consider application of these principles to systems that employ Artificial Intelligence (AI) and Machine Learning (ML). A key element in this "dependability" perspective is a requirement to have near-compl...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	474,579
1807.06610	Learning Noise-Invariant Representations for Robust Speech Recognition	Despite rapid advances in speech recognition, current models remain brittle to superficial perturbations to their inputs. Small amounts of noise can destroy the performance of an otherwise state-of-the-art model. To harden models against background noise, practitioners often perform data augmentation, adding artificial...	false	false	true	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	103,153
2203.15351	Random Geometric Graph: Some recent developments and perspectives	The Random Geometric Graph (RGG) is a random graph model for network data with an underlying spatial representation. Geometry endows RGGs with a rich dependence structure and often leads to desirable properties of real-world networks such as the small-world phenomenon and clustering. Originally introduced to model wire...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	288,363
1208.4161	Robust Distributed Maximum Likelihood Estimation with Dependent Quantized Data	In this paper, we consider distributed maximum likelihood estimation (MLE) with dependent quantized data under the assumption that the structure of the joint probability density function (pdf) is known, but it contains unknown deterministic parameters. The parameters may include different vector parameters correspondin...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	18,179
2110.08927	MARTINI: Smart Meter Driven Estimation of HVAC Schedules and Energy Savings Based on WiFi Sensing and Clustering	HVAC systems account for a significant portion of building energy use. Nighttime setback scheduling is an energy conservation measure where cooling and heating setpoints are increased and decreased respectively during unoccupied periods with the goal of obtaining energy savings. However, knowledge of a building's real ...	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	261,609
2412.10273	Probabilistic Inverse Cameras: Image to 3D via Multiview Geometry	We introduce a hierarchical probabilistic approach to go from a 2D image to multiview 3D: a diffusion "prior" models the unseen 3D geometry, which then conditions a diffusion "decoder" to generate novel views of the subject. We use a pointmap-based geometric representation in a multiview image format to coordinate the ...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	516,853
1006.3679	Segmentation of Natural Images by Texture and Boundary Compression	We present a novel algorithm for segmentation of natural images that harnesses the principle of minimum description length (MDL). Our method is based on observations that a homogeneously textured region of a natural image can be well modeled by a Gaussian distribution and the region boundary can be effectively coded by...	false	false	false	false	false	false	true	false	false	true	false	true	false	false	false	false	false	false	6,834
1402.1834	The Generalized Statistical Complexity of PolSAR Data	This paper presents and discusses the use of a new feature for PolSAR imagery: the Generalized Statistical Complexity. This measure is able to capture the disorder of the data by means of the entropy, as well as its departure from a reference distribution. The latter component is obtained by measuring a stochastic dist...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	30,716
2402.19076	Pointing out the Shortcomings of Relation Extraction Models with Semantically Motivated Adversarials	In recent years, large language models have achieved state-of-the-art performance across various NLP tasks. However, investigations have shown that these models tend to rely on shortcut features, leading to inaccurate predictions and causing the models to be unreliable at generalization to out-of-distribution (OOD) sam...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	433,671
1702.03629	Model-Free MLE Estimation for Online Rotor Angle Stability Assessment with PMU Data	Recent research has demonstrated that the rotor angle stability can be assessed by identifying the sign of the system maximal Lyapunov exponent (MLE). A positive (negative) MLE implies unstable (stable) rotor angle dynamics. However, because the MLE may fluctuate between positive and negative values for a long time aft...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	68,165
2001.09360	Robust Submodular Minimization with Applications to Cooperative Modeling	Robust Optimization is becoming increasingly important in machine learning applications. This paper studies the problem of robust submodular minimization subject to combinatorial constraints. Constrained Submodular Minimization arises in several applications such as co-operative cuts in image segmentation, co-operative...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	161,545
2410.06746	Cluster-wise Graph Transformer with Dual-granularity Kernelized Attention	In the realm of graph learning, there is a category of methods that conceptualize graphs as hierarchical structures, utilizing node clustering to capture broader structural information. While generally effective, these methods often rely on a fixed graph coarsening routine, leading to overly homogeneous cluster represe...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	496,336
1901.04277	Natural Disasters Detection in Social Media and Satellite imagery: a survey	The analysis of natural disaster-related multimedia content got great attention in recent years. Being one of the most important sources of information, social media have been crawled over the years to collect and analyze disaster-related multimedia content. Satellite imagery has also been widely explored for disasters...	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	false	118,576
2307.16115	IWEK: An Interpretable What-If Estimator for Database Knobs	The knobs of modern database management systems have significant impact on the performance of the systems. With the development of cloud databases, an estimation service for knobs is urgently needed to improve the performance of database. Unfortunately, few attentions have been paid to estimate the performance of certa...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	382,478
2402.07025	Generalization Error of Graph Neural Networks in the Mean-field Regime	This work provides a theoretical framework for assessing the generalization error of graph neural networks in the over-parameterized regime, where the number of parameters surpasses the quantity of data points. We explore two widely utilized types of graph neural networks: graph convolutional neural networks and messag...	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	428,535
0909.3384	Comparing Single and Multiobjective Evolutionary Approaches to the Inventory and Transportation Problem	EVITA, standing for Evolutionary Inventory and Transportation Algorithm, is a two-level methodology designed to address the Inventory and Transportation Problem (ITP) in retail chains. The top level uses an evolutionary algorithm to obtain delivery patterns for each shop on a weekly basis so as to minimise the inventor...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	4,522
1206.5292	Markov Logic in Infinite Domains	Combining first-order logic and probability has long been a goal of AI. Markov logic (Richardson & Domingos, 2006) accomplishes this by attaching weights to first-order formulas and viewing them as templates for features of Markov networks. Unfortunately, it does not have the full power of first-order logic, because it...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	16,829
2209.02075	The SZ flux-mass ($Y$-$M$) relation at low halo masses: improvements with symbolic regression and strong constraints on baryonic feedback	Feedback from active galactic nuclei (AGN) and supernovae can affect measurements of integrated SZ flux of halos ($Y_\mathrm{SZ}$) from CMB surveys, and cause its relation with the halo mass ($Y_\mathrm{SZ}-M$) to deviate from the self-similar power-law prediction of the virial theorem. We perform a comprehensive study...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	316,096
2204.04916	A Token-level Contrastive Framework for Sign Language Translation	Sign Language Translation (SLT) is a promising technology to bridge the communication gap between the deaf and the hearing people. Recently, researchers have adopted Neural Machine Translation (NMT) methods, which usually require large-scale corpus for training, to achieve SLT. However, the publicly available SLT corpu...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	290,846
2106.08863	Unbiased Methods for Multi-Goal Reinforcement Learning	In multi-goal reinforcement learning (RL) settings, the reward for each goal is sparse, and located in a small neighborhood of the goal. In large dimension, the probability of reaching a reward vanishes and the agent receives little learning signal. Methods such as Hindsight Experience Replay (HER) tackle this issue by...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	241,452
2112.11111	Developing and Validating Semi-Markov Occupancy Generative Models: A Technical Report	This report documents recent technical work on developing and validating stochastic occupancy models in commercial buildings, performed by the Pacific Northwest National Laboratory (PNNL) as part of the Sensor Impact Evaluation and Verification project under the U.S. Department of Energy (DOE) Building Technologies Off...	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	272,611
2208.08726	Efficient Signed Graph Sampling via Balancing & Gershgorin Disc Perfect Alignment	A basic premise in graph signal processing (GSP) is that a graph encoding pairwise (anti-)correlations of the targeted signal as edge weights is exploited for graph filtering. However, existing fast graph sampling schemes are designed and tested only for positive graphs describing positive correlations. In this paper, ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	313,456
1306.0886	$\propto$SVM for learning with label proportions	We study the problem of learning with label proportions in which the training data is provided in groups and only the proportion of each class in each group is known. We propose a new method called proportion-SVM, or $\propto$SVM, which explicitly models the latent unknown instance labels together with the known group ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	24,996
2311.12760	High-resolution Image-based Malware Classification using Multiple Instance Learning	This paper proposes a novel method of classifying malware into families using high-resolution greyscale images and multiple instance learning to overcome adversarial binary enlargement. Current methods of visualisation-based malware classification largely rely on lossy transformations of inputs such as resizing to hand...	false	false	false	false	false	false	true	false	false	false	false	true	true	false	false	false	false	false	409,462
1611.04558	Google's Multilingual Neural Machine Translation System: Enabling Zero-Shot Translation	We propose a simple solution to use a single Neural Machine Translation (NMT) model to translate between multiple languages. Our solution requires no change in the model architecture from our base system but instead introduces an artificial token at the beginning of the input sentence to specify the required target lan...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	63,867
1912.12383	Efficient Top-k Vulnerable Nodes Detection in Uncertain Graphs	Uncertain graphs have been widely used to model complex linked data in many real-world applications, such as guaranteed-loan networks and power grids, where a node or edge may be associated with a probability. In these networks, a node usually has a certain chance of default or breakdown due to self-factors or the infl...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	158,829
2009.08770	Probably Approximately Correct Explanations of Machine Learning Models via Syntax-Guided Synthesis	We propose a novel approach to understanding the decision making of complex machine learning models (e.g., deep neural networks) using a combination of probably approximately correct learning (PAC) and a logic inference methodology called syntax-guided synthesis (SyGuS). We prove that our framework produces explanation...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	196,339
2501.16813	Multimodal Magic Elevating Depression Detection with a Fusion of Text and Audio Intelligence	This study proposes an innovative multimodal fusion model based on a teacher-student architecture to enhance the accuracy of depression classification. Our designed model addresses the limitations of traditional methods in feature fusion and modality weight allocation by introducing multi-head attention mechanisms and ...	false	false	true	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	528,118
2304.10859	Text2Time: Transformer-based Article Time Period Prediction	The task of predicting the publication period of text documents, such as news articles, is an important but less studied problem in the field of natural language processing. Predicting the year of a news article can be useful in various contexts, such as historical research, sentiment analysis, and media monitoring. In...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	359,589
2310.10781	BanglaNLP at BLP-2023 Task 1: Benchmarking different Transformer Models for Violence Inciting Text Detection in Bengali	This paper presents the system that we have developed while solving this shared task on violence inciting text detection in Bangla. We explain both the traditional and the recent approaches that we have used to make our models learn. Our proposed system helps to classify if the given text contains any threat. We studie...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	400,385
2411.12977	MindForge: Empowering Embodied Agents with Theory of Mind for Lifelong Collaborative Learning	Contemporary embodied agents powered by large language models (LLMs), such as Voyager, have shown promising capabilities in individual learning within open-ended environments like Minecraft. However, when powered by open LLMs, they struggle with basic tasks even after domain-specific fine-tuning. We present MindForge, ...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	509,622
2103.11833	AutoSpace: Neural Architecture Search with Less Human Interference	Current neural architecture search (NAS) algorithms still require expert knowledge and effort to design a search space for network construction. In this paper, we consider automating the search space design to minimize human interference, which however faces two challenges: the explosive complexity of the exploration s...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	225,962
2405.15540	Bundle Neural Networks for message diffusion on graphs	The dominant paradigm for learning on graph-structured data is message passing. Despite being a strong inductive bias, the local message passing mechanism suffers from pathological issues such as over-smoothing, over-squashing, and limited node-level expressivity. To address these limitations we propose Bundle Neural N...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	456,982
1707.08040	A Simple Exponential Family Framework for Zero-Shot Learning	We present a simple generative framework for learning to predict previously unseen classes, based on estimating class-attribute-gated class-conditional distributions. We model each class-conditional distribution as an exponential family distribution and the parameters of the distribution of each seen/unseen class are d...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	77,734
2401.07697	Data vs. Model Machine Learning Fairness Testing: An Empirical Study	Although several fairness definitions and bias mitigation techniques exist in the literature, all existing solutions evaluate fairness of Machine Learning (ML) systems after the training stage. In this paper, we take the first steps towards evaluating a more holistic approach by testing for fairness both before and aft...	false	false	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	true	421,626
2204.02492	Towards End-to-end Unsupervised Speech Recognition	Unsupervised speech recognition has shown great potential to make Automatic Speech Recognition (ASR) systems accessible to every language. However, existing methods still heavily rely on hand-crafted pre-processing. Similar to the trend of making supervised speech recognition end-to-end, we introduce wav2vec-U 2.0 whic...	false	false	true	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	289,965
2006.10782	AI Feynman 2.0: Pareto-optimal symbolic regression exploiting graph modularity	We present an improved method for symbolic regression that seeks to fit data to formulas that are Pareto-optimal, in the sense of having the best accuracy for a given complexity. It improves on the previous state-of-the-art by typically being orders of magnitude more robust toward noise and bad data, and also by discov...	false	false	false	false	true	false	true	false	false	true	false	false	false	false	false	false	false	false	182,997
2211.12732	Wild-Places: A Large-Scale Dataset for Lidar Place Recognition in Unstructured Natural Environments	Many existing datasets for lidar place recognition are solely representative of structured urban environments, and have recently been saturated in performance by deep learning based approaches. Natural and unstructured environments present many additional challenges for the tasks of long-term localisation but these env...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	332,213
2006.04027	Efficient Architecture Search for Continual Learning	Continual learning with neural networks is an important learning framework in AI that aims to learn a sequence of tasks well. However, it is often confronted with three challenges: (1) overcome the catastrophic forgetting problem, (2) adapt the current network to new tasks, and meanwhile (3) control its model complexit...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	180,526
2312.02137	MANUS: Markerless Grasp Capture using Articulated 3D Gaussians	Understanding how we grasp objects with our hands has important applications in areas like robotics and mixed reality. However, this challenging problem requires accurate modeling of the contact between hands and objects. To capture grasps, existing methods use skeletons, meshes, or parametric models that does not repr...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	412,707
1304.0920	Information-Preserving Markov Aggregation	We present a sufficient condition for a non-injective function of a Markov chain to be a second-order Markov chain with the same entropy rate as the original chain. This permits an information-preserving state space reduction by merging states or, equivalently, lossless compression of a Markov source on a sample-by-sam...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	23,424
2204.02874	ECLIPSE: Efficient Long-range Video Retrieval using Sight and Sound	We introduce an audiovisual method for long-range text-to-video retrieval. Unlike previous approaches designed for short video retrieval (e.g., 5-15 seconds in duration), our approach aims to retrieve minute-long videos that capture complex human actions. One challenge of standard video-only approaches is the large com...	false	false	true	false	true	false	false	false	true	false	false	true	false	false	false	false	false	false	290,111
2406.16675	Decentralized and Centralized IDD Schemes for Cell-Free Networks	In this paper, we propose iterative interference cancellation schemes with access points selection (APs-Sel) for cell-free massive multiple-input multiple-output (CF-mMIMO) systems. Closed-form expressions for centralized and decentralized linear minimum mean square error (LMMSE) receive filters with APs-Sel are derive...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	467,221
2202.05695	Positive-Unlabeled Domain Adaptation	Domain Adaptation methodologies have shown to effectively generalize from a labeled source domain to a label scarce target domain. Previous research has either focused on unlabeled domain adaptation without any target supervision or semi-supervised domain adaptation with few labeled target examples per class. On the ot...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	279,953
2411.00686	Latent Paraphrasing: Perturbation on Layers Improves Knowledge Injection in Language Models	As Large Language Models (LLMs) are increasingly deployed in specialized domains with continuously evolving knowledge, the need for timely and precise knowledge injection has become essential. Fine-tuning with paraphrased data is a common approach to enhance knowledge injection, yet it faces two significant challenges:...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	504,700
2103.14404	ReaDmE: Read-Rate Based Dynamic Execution Scheduling for Intermittent RF-Powered Devices	This paper presents a method for remotely and dynamically determining the execution schedule of long-running tasks on intermittently powered devices such as computational RFID. Our objective is to prevent brown-out events caused by sudden power-loss due to the intermittent nature of the powering channel. We formulate, ...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	226,845
2309.11224	Leveraging Diversity in Online Interactions	This paper addresses the issue of connecting people online to help them find support with their day-to-day problems. We make use of declarative norms for mediating online interactions, and we specifically focus on the issue of leveraging diversity when connecting people. We run pilots at different university sites, and...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	393,327
2009.00540	Training Deep Neural Networks with Constrained Learning Parameters	Today's deep learning models are primarily trained on CPUs and GPUs. Although these models tend to have low error, they consume high power and utilize large amount of memory owing to double precision floating point learning parameters. Beyond the Moore's law, a significant portion of deep learning tasks would run on ed...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	194,068
1801.10198	Generating Wikipedia by Summarizing Long Sequences	We show that generating English Wikipedia articles can be approached as a multi- document summarization of source documents. We use extractive summarization to coarsely identify salient information and a neural abstractive model to generate the article. For the abstractive model, we introduce a decoder-only architectur...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	89,244
1610.08815	A Deeper Look into Sarcastic Tweets Using Deep Convolutional Neural Networks	Sarcasm detection is a key task for many natural language processing tasks. In sentiment analysis, for example, sarcasm can flip the polarity of an "apparently positive" sentence and, hence, negatively affect polarity detection performance. To date, most approaches to sarcasm detection have treated the task primarily a...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	62,969
2410.15373	DynaVINS++: Robust Visual-Inertial State Estimator in Dynamic Environments by Adaptive Truncated Least Squares and Stable State Recovery	Despite extensive research in robust visual-inertial navigation systems~(VINS) in dynamic environments, many approaches remain vulnerable to objects that suddenly start moving, which are referred to as \textit{abruptly dynamic objects}. In addition, most approaches have considered the effect of dynamic objects only at ...	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	500,508
2411.17126	From Machine Learning to Machine Unlearning: Complying with GDPR's Right to be Forgotten while Maintaining Business Value of Predictive Models	Recent privacy regulations (e.g., GDPR) grant data subjects the `Right to Be Forgotten' (RTBF) and mandate companies to fulfill data erasure requests from data subjects. However, companies encounter great challenges in complying with the RTBF regulations, particularly when asked to erase specific training data from the...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	511,315
2401.08998	Attack and Reset for Unlearning: Exploiting Adversarial Noise toward Machine Unlearning through Parameter Re-initialization	With growing concerns surrounding privacy and regulatory compliance, the concept of machine unlearning has gained prominence, aiming to selectively forget or erase specific learned information from a trained model. In response to this critical need, we introduce a novel approach called Attack-and-Reset for Unlearning (...	false	false	false	false	false	false	true	false	false	false	false	true	true	false	false	false	false	false	422,109
1807.02502	Maximizing Welfare in Social Networks under a Utility Driven Influence Diffusion Model	Motivated by applications such as viral marketing, the problem of influence maximization (IM) has been extensively studied in the literature. The goal is to select a small number of users to adopt an item such that it results in a large cascade of adoptions by others. Existing works have three key limitations. (1) They...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	102,281
2112.01579	Fast Neural Representations for Direct Volume Rendering	Despite the potential of neural scene representations to effectively compress 3D scalar fields at high reconstruction quality, the computational complexity of the training and data reconstruction step using scene representation networks limits their use in practical applications. In this paper, we analyze whether scene...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	269,537
1810.02954	Adapting to Unknown Noise Distribution in Matrix Denoising	We consider the problem of estimating an unknown matrix $\boldsymbol{X}\in {\mathbb R}^{m\times n}$, from observations $\boldsymbol{Y} = \boldsymbol{X}+\boldsymbol{W}$ where $\boldsymbol{W}$ is a noise matrix with independent and identically distributed entries, as to minimize estimation error measured in operator norm...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	109,699
1406.6647	Extract Secrets from Wireless Channel: A New Shape-based Approach	Existing secret key extraction techniques use quantization to map wireless channel amplitudes to secret bits. This pa- per shows that such techniques are highly prone to environ- ment and local noise effects: They have very high mismatch rates between the two nodes that measure the channel be- tween them. This paper ad...	false	false	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	34,142
2402.16181	How Can LLM Guide RL? A Value-Based Approach	Reinforcement learning (RL) has become the de facto standard practice for sequential decision-making problems by improving future acting policies with feedback. However, RL algorithms may require extensive trial-and-error interactions to collect useful feedback for improvement. On the other hand, recent developments in...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	432,458
1809.07405	Distances for WiFi Based Topological Indoor Mapping	For localization and mapping of indoor environments through WiFi signals, locations are often represented as likelihoods of the received signal strength indicator. In this work we compare various measures of distance between such likelihoods in combination with different methods for estimation and representation. In pa...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	108,269
2408.09130	Gaussian in the Dark: Real-Time View Synthesis From Inconsistent Dark Images Using Gaussian Splatting	3D Gaussian Splatting has recently emerged as a powerful representation that can synthesize remarkable novel views using consistent multi-view images as input. However, we notice that images captured in dark environments where the scenes are not fully illuminated can exhibit considerable brightness variations and multi...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	481,294
2306.08780	Explaining Explainability: Towards Deeper Actionable Insights into Deep Learning through Second-order Explainability	Explainability plays a crucial role in providing a more comprehensive understanding of deep learning models' behaviour. This allows for thorough validation of the model's performance, ensuring that its decisions are based on relevant visual indicators and not biased toward irrelevant patterns existing in training data....	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	373,539
2004.00005	Perception of emergent epidemic of COVID-2019 / SARS CoV-2 on the Polish Internet	We study the perception of COVID-2019 epidemic in Polish society using quantitative analysis of its digital footprints on the Internet (on Twitter, Google, YouTube, Wikipedia and electronic media represented by Event Registry) from January 2020 to 12.03.2020 (before and after official introduction to Poland on 04.03.20...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	170,491
1806.07569	A Distributed Second-Order Algorithm You Can Trust	Due to the rapid growth of data and computational resources, distributed optimization has become an active research area in recent years. While first-order methods seem to dominate the field, second-order methods are nevertheless attractive as they potentially require fewer communication rounds to converge. However, th...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	100,965
2406.16350	A Survey on Intent-aware Recommender Systems	Many modern online services feature personalized recommendations. A central challenge when providing such recommendations is that the reason why an individual user accesses the service may change from visit to visit or even during an ongoing usage session. To be effective, a recommender system should therefore aim to t...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	467,099
1906.11884	Identifying Emotions from Walking using Affective and Deep Features	We present a new data-driven model and algorithm to identify the perceived emotions of individuals based on their walking styles. Given an RGB video of an individual walking, we extract his/her walking gait in the form of a series of 3D poses. Our goal is to exploit the gait features to classify the emotional state of ...	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	136,774
1304.0682	Sparse Signal Processing with Linear and Nonlinear Observations: A Unified Shannon-Theoretic Approach	We derive fundamental sample complexity bounds for recovering sparse and structured signals for linear and nonlinear observation models including sparse regression, group testing, multivariate regression and problems with missing features. In general, sparse signal processing problems can be characterized in terms of t...	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	23,404
2009.10778	On Data Augmentation for Extreme Multi-label Classification	In this paper, we focus on data augmentation for the extreme multi-label classification (XMC) problem. One of the most challenging issues of XMC is the long tail label distribution where even strong models suffer from insufficient supervision. To mitigate such label bias, we propose a simple and effective augmentation ...	false	false	false	false	true	true	true	false	true	false	false	false	false	false	false	false	false	false	196,979
2209.08742	Integrative Feature and Cost Aggregation with Transformers for Dense Correspondence	We present a novel architecture for dense correspondence. The current state-of-the-art are Transformer-based approaches that focus on either feature descriptors or cost volume aggregation. However, they generally aggregate one or the other but not both, though joint aggregation would boost each other by providing infor...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	318,249
2305.08302	t-RAIN: Robust generalization under weather-aliasing label shift attacks	In the classical supervised learning settings, classifiers are fit with the assumption of balanced label distributions and produce remarkable results on the same. In the real world, however, these assumptions often bend and in turn adversely impact model performance. Identifying bad learners in skewed target distributi...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	364,236
1509.08881	Building Subject-aligned Comparable Corpora and Mining it for Truly Parallel Sentence Pairs	Parallel sentences are a relatively scarce but extremely useful resource for many applications including cross-lingual retrieval and statistical machine translation. This research explores our methodology for mining such data from previously obtained comparable corpora. The task is highly practical since non-parallel m...	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	47,419
1901.01153	Demystifying Multi-Faceted Video Summarization: Tradeoff Between Diversity,Representation, Coverage and Importance	This paper addresses automatic summarization of videos in a unified manner. In particular, we propose a framework for multi-faceted summarization for extractive, query base and entity summarization (summarization at the level of entities like objects, scenes, humans and faces in the video). We investigate several summa...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	117,928
1809.05309	On Plans With Loops and Noise	In an influential paper, Levesque proposed a formal specification for analysing the correctness of program-like plans, such as conditional plans, iterative plans, and knowledge-based plans. He motivated a logical characterisation within the situation calculus that included binary sensing actions. While the characterisa...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	true	false	false	true	107,772
2411.06398	Do you want to play a game? Learning to play Tic-Tac-Toe in Hypermedia Environments	We demonstrate the integration of Transfer Learning into a hypermedia Multi-Agent System using the Multi-Agent MicroServices (MAMS) architectural style. Agents use RDF knowledge stores to reason over information and apply Reinforcement Learning techniques to learn how to interact with a Tic-Tac-Toe API. Agents form adv...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	507,099
1709.06429	Neural Networks for Text Correction and Completion in Keyboard Decoding	Despite the ubiquity of mobile and wearable text messaging applications, the problem of keyboard text decoding is not tackled sufficiently in the light of the enormous success of the deep learning Recurrent Neural Network (RNN) and Convolutional Neural Networks (CNN) for natural language understanding. In particular, c...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	81,098
1601.00987	Stimulation-based control of dynamic brain networks	The ability to modulate brain states using targeted stimulation is increasingly being employed to treat neurological disorders and to enhance human performance. Despite the growing interest in brain stimulation as a form of neuromodulation, much remains unknown about the network-level impact of these focal perturbation...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	50,697
2310.07871	Hierarchical Pretraining on Multimodal Electronic Health Records	Pretraining has proven to be a powerful technique in natural language processing (NLP), exhibiting remarkable success in various NLP downstream tasks. However, in the medical domain, existing pretrained models on electronic health records (EHR) fail to capture the hierarchical nature of EHR data, limiting their general...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	399,158
1501.03093	MultiGain: A controller synthesis tool for MDPs with multiple mean-payoff objectives	We present MultiGain, a tool to synthesize strategies for Markov decision processes (MDPs) with multiple mean-payoff objectives. Our models are described in PRISM, and our tool uses the existing interface and simulator of PRISM. Our tool extends PRISM by adding novel algorithms for multiple mean-payoff objectives, and ...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	39,244
2401.11098	Neural auto-designer for enhanced quantum kernels	Quantum kernels hold great promise for offering computational advantages over classical learners, with the effectiveness of these kernels closely tied to the design of the quantum feature map. However, the challenge of designing effective quantum feature maps for real-world datasets, particularly in the absence of suff...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	422,870
1608.01946	Iterative Learning of Answer Set Programs from Context Dependent Examples	In recent years, several frameworks and systems have been proposed that extend Inductive Logic Programming (ILP) to the Answer Set Programming (ASP) paradigm. In ILP, examples must all be explained by a hypothesis together with a given background knowledge. In existing systems, the background knowledge is the same for ...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	59,486
1802.09197	AI4AI: Quantitative Methods for Classifying Host Species from Avian Influenza DNA Sequence	Avian Influenza breakouts cause millions of dollars in damage each year globally, especially in Asian countries such as China and South Korea. The impact magnitude of a breakout directly correlates to time required to fully understand the influenza virus, particularly the interspecies pathogenicity. The procedure requi...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	91,291
2411.04744	Respecting the limit:Bayesian optimization with a bound on the optimal value	In many real-world optimization problems, we have prior information about what objective function values are achievable. In this paper, we study the scenario that we have either exact knowledge of the minimum value or a, possibly inexact, lower bound on its value. We propose bound-aware Bayesian optimization (BABO), a ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	506,399
2212.03176	Domain Adaptation and Generalization on Functional Medical Images: A Systematic Survey	Machine learning algorithms have revolutionized different fields, including natural language processing, computer vision, signal processing, and medical data processing. Despite the excellent capabilities of machine learning algorithms in various tasks and areas, the performance of these models mainly deteriorates when...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	335,014
1804.01118	Synthesizing Programs for Images using Reinforced Adversarial Learning	Advances in deep generative networks have led to impressive results in recent years. Nevertheless, such models can often waste their capacity on the minutiae of datasets, presumably due to weak inductive biases in their decoders. This is where graphics engines may come in handy since they abstract away low-level detail...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	94,176
2108.12898	Generating Answer Candidates for Quizzes and Answer-Aware Question Generators	In education, open-ended quiz questions have become an important tool for assessing the knowledge of students. Yet, manually preparing such questions is a tedious task, and thus automatic question generation has been proposed as a possible alternative. So far, the vast majority of research has focused on generating the...	false	false	false	false	true	true	true	false	true	false	false	false	false	true	false	false	false	false	252,641
2303.09750	Measurement Optimization under Uncertainty using Deep Reinforcement Learning	Optimal sensor placement enhances the efficiency of a variety of applications for monitoring dynamical systems. It has been established that deterministic solutions to the sensor placement problem are insufficient due to the many uncertainties in system input and parameters that affect system response sensor measuremen...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	352,171
1802.05130	Multi-Task Learning for Extraction of Adverse Drug Reaction Mentions from Tweets	Adverse drug reactions (ADRs) are one of the leading causes of mortality in health care. Current ADR surveillance systems are often associated with a substantial time lag before such events are officially published. On the other hand, online social media such as Twitter contain information about ADR events in real-time...	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	90,381
2303.08774	GPT-4 Technical Report	We report the development of GPT-4, a large-scale, multimodal model which can accept image and text inputs and produce text outputs. While less capable than humans in many real-world scenarios, GPT-4 exhibits human-level performance on various professional and academic benchmarks, including passing a simulated bar exam...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	351,771
2407.00492	Fast Gibbs sampling for the local and global trend Bayesian exponential smoothing model	In Smyl et al. [Local and global trend Bayesian exponential smoothing models. International Journal of Forecasting, 2024.], a generalised exponential smoothing model was proposed that is able to capture strong trends and volatility in time series. This method achieved state-of-the-art performance in many forecasting ta...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	468,883
1908.04509	On the Complexity of Checking Transactional Consistency	Transactions simplify concurrent programming by enabling computations on shared data that are isolated from other concurrent computations and are resilient to failures. Modern databases provide different consistency models for transactions corresponding to different tradeoffs between consistency and availability. In th...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	true	141,503
2406.06027	HOLMES: Hyper-Relational Knowledge Graphs for Multi-hop Question Answering using LLMs	Given unstructured text, Large Language Models (LLMs) are adept at answering simple (single-hop) questions. However, as the complexity of the questions increase, the performance of LLMs degrade. We believe this is due to the overhead associated with understanding the complex question followed by filtering and aggregati...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	462,403
2412.11306	Unimodal and Multimodal Static Facial Expression Recognition for Virtual Reality Users with EmoHeVRDB	In this study, we explored the potential of utilizing Facial Expression Activations (FEAs) captured via the Meta Quest Pro Virtual Reality (VR) headset for Facial Expression Recognition (FER) in VR settings. Leveraging the EmojiHeroVR Database (EmoHeVRDB), we compared several unimodal approaches and achieved up to 73.0...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	517,351
2312.12583	Observation-Augmented Contextual Multi-Armed Bandits for Robotic Search and Exploration	We introduce a new variant of contextual multi-armed bandits (CMABs) called observation-augmented CMABs (OA-CMABs) wherein a robot uses extra outcome observations from an external information source, e.g. humans. In OA-CMABs, external observations are a function of context features and thus provide evidence on top of o...	false	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	417,011
2310.02557	Generalization in diffusion models arises from geometry-adaptive harmonic representations	Deep neural networks (DNNs) trained for image denoising are able to generate high-quality samples with score-based reverse diffusion algorithms. These impressive capabilities seem to imply an escape from the curse of dimensionality, but recent reports of memorization of the training set raise the question of whether th...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	396,900
2205.00974	Cross Cryptocurrency Relationship Mining for Bitcoin Price Prediction	Blockchain finance has become a part of the world financial system, most typically manifested in the attention to the price of Bitcoin. However, a great deal of work is still limited to using technical indicators to capture Bitcoin price fluctuation, with little consideration of historical relationships and interaction...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	294,438
2502.12742	3D Shape-to-Image Brownian Bridge Diffusion for Brain MRI Synthesis from Cortical Surfaces	Despite recent advances in medical image generation, existing methods struggle to produce anatomically plausible 3D structures. In synthetic brain magnetic resonance images (MRIs), characteristic fissures are often missing, and reconstructed cortical surfaces appear scattered rather than densely convoluted. To address ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	535,029
1704.07899	Reinforcement Learning-based Thermal Comfort Control for Vehicle Cabins	Vehicle climate control systems aim to keep passengers thermally comfortable. However, current systems control temperature rather than thermal comfort and tend to be energy hungry, which is of particular concern when considering electric vehicles. This paper poses energy-efficient vehicle comfort control as a Markov De...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	72,439
2205.10101	MSTRIQ: No Reference Image Quality Assessment Based on Swin Transformer with Multi-Stage Fusion	Measuring the perceptual quality of images automatically is an essential task in the area of computer vision, as degradations on image quality can exist in many processes from image acquisition, transmission to enhancing. Many Image Quality Assessment(IQA) algorithms have been designed to tackle this problem. However, ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	297,560
1907.06361	Micro, Meso, Macro: the effect of triangles on communities in networks	Meso-scale structures (communities) are used to understand the macro-scale properties of complex networks, such as their functionality and formation mechanisms. Micro-scale structures are known to exist in most complex networks (e.g., large number of triangles or motifs), but they are absent in the simple random-graph ...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	138,604
1909.09598	Street Crossing Aid Using Light-weight CNNs for the Visually Impaired	In this paper, we address an issue that the visually impaired commonly face while crossing intersections and propose a solution that takes form as a mobile application. The application utilizes a deep learning convolutional neural network model, LytNetV2, to output necessary information that the visually impaired may l...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	146,306
2312.12340	Scalable Geometric Fracture Assembly via Co-creation Space among Assemblers	Geometric fracture assembly presents a challenging practical task in archaeology and 3D computer vision. Previous methods have focused solely on assembling fragments based on semantic information, which has limited the quantity of objects that can be effectively assembled. Therefore, there is a need to develop a scalab...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	416,919

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