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
2203.08559	Learning to Generate Synthetic Training Data using Gradient Matching and Implicit Differentiation	Using huge training datasets can be costly and inconvenient. This article explores various data distillation techniques that can reduce the amount of data required to successfully train deep networks. Inspired by recent ideas, we suggest new data distillation techniques based on generative teaching networks, gradient m...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	true	false	false	285,840
2310.10841	A Machine Learning-based Algorithm for Automated Detection of Frequency-based Events in Recorded Time Series of Sensor Data	Automated event detection has emerged as one of the fundamental practices to monitor the behavior of technical systems by means of sensor data. In the automotive industry, these methods are in high demand for tracing events in time series data. For assessing the active vehicle safety systems, a diverse range of driving...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	400,404
2405.13874	Affine-based Deformable Attention and Selective Fusion for Semi-dense Matching	Identifying robust and accurate correspondences across images is a fundamental problem in computer vision that enables various downstream tasks. Recent semi-dense matching methods emphasize the effectiveness of fusing relevant cross-view information through Transformer. In this paper, we propose several improvements up...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	456,123
2103.08827	Semi-Supervised Graph-to-Graph Translation	Graph translation is very promising research direction and has a wide range of potential real-world applications. Graph is a natural structure for representing relationship and interactions, and its translation can encode the intrinsic semantic changes of relationships in different scenarios. However, despite its seemi...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	224,997
1302.1300	Kriging Interpolation Filter to Reduce High Density Salt and Pepper Noise	Image denoising is a critical issue in the field of digital image processing. This paper proposes a novel Salt & Pepper noise suppression by developing a Kriging Interpolation Filter (KIF) for image denoising. Gray-level images degraded with Salt & Pepper noise have been considered. A sequential search for noise detect...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	21,797
2405.04079	Leveraging swarm capabilities to assist other systems	Most studies in swarm robotics treat the swarm as an isolated system of interest. We argue that the prevailing view of swarms as self-sufficient, independent systems limits the scope of potential applications for swarm robotics. A robot swarm could act as a support in an heterogeneous system comprising other robots and...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	452,436
1803.01368	Finite Length Analysis of Irregular Repetition Slotted ALOHA in the Waterfall Region	A finite length analysis is introduced for irregular repetition slotted ALOHA (IRSA) that enables to accurately estimate its performance in the moderate-to-high packet loss probability regime, i.e., in the so-called waterfall region. The analysis is tailored to the collision channel model, which enables mapping the des...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	91,860
1804.06061	Improving Deep Binary Embedding Networks by Order-aware Reweighting of Triplets	In this paper, we focus on triplet-based deep binary embedding networks for image retrieval task. The triplet loss has been shown to be most effective for the ranking problem. However, most of the previous works treat the triplets equally or select the hard triplets based on the loss. Such strategies do not consider th...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	95,220
1909.13121	How to Evaluate Machine Learning Approaches for Combinatorial Optimization: Application to the Travelling Salesman Problem	Combinatorial optimization is the field devoted to the study and practice of algorithms that solve NP-hard problems. As Machine Learning (ML) and deep learning have popularized, several research groups have started to use ML to solve combinatorial optimization problems, such as the well-known Travelling Salesman Proble...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	147,330
1504.03916	Challenges and some new directions in channel coding	Three areas of ongoing research in channel coding are surveyed, and recent developments are presented in each area: spatially coupled Low-Density Parity-Check (LDPC) codes, non-binary LDPC codes, and polar coding.	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	42,082
2305.14492	Sociocultural Norm Similarities and Differences via Situational Alignment and Explainable Textual Entailment	Designing systems that can reason across cultures requires that they are grounded in the norms of the contexts in which they operate. However, current research on developing computational models of social norms has primarily focused on American society. Here, we propose a novel approach to discover and compare descript...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	367,057
2312.15113	Identifying built environment factors influencing driver yielding behavior at unsignalized intersections: A naturalistic open-source dataset collected in Minnesota	Many factors influence the yielding result of a driver-pedestrian interaction, including traffic volume, vehicle speed, roadway characteristics, etc. While individual aspects of these interactions have been explored, comprehensive, naturalistic studies, particularly those considering the built environment's influence o...	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	false	false	417,876
1609.01571	Best-Buddies Similarity - Robust Template Matching using Mutual Nearest Neighbors	We propose a novel method for template matching in unconstrained environments. Its essence is the Best-Buddies Similarity (BBS), a useful, robust, and parameter-free similarity measure between two sets of points. BBS is based on counting the number of Best-Buddies Pairs (BBPs)--pairs of points in source and target sets...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	60,611
1808.03793	Document Informed Neural Autoregressive Topic Models	Context information around words helps in determining their actual meaning, for example "networks" used in contexts of artificial neural networks or biological neuron networks. Generative topic models infer topic-word distributions, taking no or only little context into account. Here, we extend a neural autoregressive ...	false	false	false	false	false	true	true	false	true	false	false	false	false	false	false	false	false	false	104,997
1506.01603	A Certified Universal Gathering Algorithm for Oblivious Mobile Robots	We present a new algorithm for the problem of universal gathering mobile oblivious robots (that is, starting from any initial configuration that is not bivalent, using any number of robots, the robots reach in a finite number of steps the same position, not known beforehand) without relying on a common chirality. We gi...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	true	43,815
2312.02125	TPPoet: Transformer-Based Persian Poem Generation using Minimal Data and Advanced Decoding Techniques	Recent advances in language models (LMs), have demonstrated significant efficacy in tasks related to the arts and humanities. While LMs have exhibited exceptional performance across a wide range of natural language processing tasks, there are notable challenges associated with their utilization on small datasets and th...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	412,699
1903.11968	On the stability of periodic binary sequences with zone restriction	Traditional global stability measure for sequences is hard to determine because of large search space. We propose the $k$-error linear complexity with a zone restriction for measuring the local stability of sequences. Accordingly, we can efficiently determine the global stability by studying a local stability for these...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	125,612
2101.07195	A New Approach for Automatic Segmentation and Evaluation of Pigmentation Lesion by using Active Contour Model and Speeded Up Robust Features	Digital image processing techniques have wide applications in different scientific fields including the medicine. By use of image processing algorithms, physicians have been more successful in diagnosis of different diseases and have achieved much better treatment results. In this paper, we propose an automatic method ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	215,963
1507.05487	Outage and Capacity Comparisons For Ground Relaying Systems Using Stochastic Geometry	Concurrent cooperative transmission for relaying purposes in mobile communication networks is relevant in current institutional systems with limited infrastructure, and and may be viewed as a potential range-extension mechanism for future commercial networks, including vehicular autonomous networking. The complexity of...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	45,291
1906.11882	From Data Quality to Model Quality: an Exploratory Study on Deep Learning	Nowadays, people strive to improve the accuracy of deep learning models. However, very little work has focused on the quality of data sets. In fact, data quality determines model quality. Therefore, it is important for us to make research on how data quality affects on model quality. In this paper, we mainly consider f...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	136,772
1909.03814	Parameter Tuning for Self-optimizing Software at Scale	Efficiency of self-optimizing systems is heavily dependent on their optimization strategies, e.g., choosing exact or approximate solver. A choice of such a strategy, in turn, is influenced by numerous factors, such as re-optimization time, size of the problem, optimality constraints, etc. Exact solvers are domain-indep...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	144,608
2406.13770	Elliptical Attention	Pairwise dot-product self-attention is key to the success of transformers that achieve state-of-the-art performance across a variety of applications in language and vision. This dot-product self-attention computes attention weights among the input tokens using Euclidean distance, which makes the model prone to represen...	false	false	false	false	true	false	true	false	true	false	false	true	false	false	false	false	false	false	465,992
2208.13065	Towards Improving Unit Commitment Economics: An Add-On Tailor for Renewable Energy and Reserve Predictions	Generally, day-ahead unit commitment (UC) is conducted in a predict-then-optimize process: it starts by predicting the renewable energy source (RES) availability and system reserve requirements; given the predictions, the UC model is then optimized to determine the economic operation plans. In fact, predictions within ...	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	314,942
2206.07764	SAVi++: Towards End-to-End Object-Centric Learning from Real-World Videos	The visual world can be parsimoniously characterized in terms of distinct entities with sparse interactions. Discovering this compositional structure in dynamic visual scenes has proven challenging for end-to-end computer vision approaches unless explicit instance-level supervision is provided. Slot-based models levera...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	302,872
1303.2449	Using qualia information to identify lexical semantic classes in an unsupervised clustering task	Acquiring lexical information is a complex problem, typically approached by relying on a number of contexts to contribute information for classification. One of the first issues to address in this domain is the determination of such contexts. The work presented here proposes the use of automatically obtained FORMAL rol...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	22,838
2202.08082	Formulating Beurling LASSO for Source Separation via Proximal Gradient Iteration	Beurling LASSO generalizes the LASSO problem to finite Radon measures regularized via their total variation. Despite its theoretical appeal, this space is hard to parametrize, which poses an algorithmic challenge. We propose a formulation of continuous convolutional source separation with Beurling LASSO that avoids the...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	280,759
2109.00077	Interactive Machine Comprehension with Dynamic Knowledge Graphs	Interactive machine reading comprehension (iMRC) is machine comprehension tasks where knowledge sources are partially observable. An agent must interact with an environment sequentially to gather necessary knowledge in order to answer a question. We hypothesize that graph representations are good inductive biases, whic...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	252,990
2412.15514	PolySmart @ TRECVid 2024 Medical Video Question Answering	Video Corpus Visual Answer Localization (VCVAL) includes question-related video retrieval and visual answer localization in the videos. Specifically, we use text-to-text retrieval to find relevant videos for a medical question based on the similarity of video transcript and answers generated by GPT4. For the visual ans...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	519,149
2410.22177	Analyzing Multimodal Interaction Strategies for LLM-Assisted Manipulation of 3D Scenes	As more applications of large language models (LLMs) for 3D content for immersive environments emerge, it is crucial to study user behaviour to identify interaction patterns and potential barriers to guide the future design of immersive content creation and editing systems which involve LLMs. In an empirical user study...	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	503,528
2310.00458	Forced oscillation source localization from generator measurements	Malfunctioning equipment, erroneous operating conditions or periodic load variations can cause periodic disturbances that would persist over time, creating an undesirable transfer of energy across the system -- an effect referred to as forced oscillations. Wide-area oscillations may damage assets, trigger inadvertent t...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	395,991
2409.13079	Embedding Geometries of Contrastive Language-Image Pre-Training	Since the publication of CLIP, the approach of using InfoNCE loss for contrastive pre-training has become widely popular for bridging two or more modalities. Despite its wide adoption, CLIP's original design choices of L2 normalization and cosine similarity logit have rarely been revisited. We have systematically exper...	false	false	false	false	false	false	true	false	true	false	false	true	false	false	false	false	false	false	489,838
2305.12557	Confidence-aware Personalized Federated Learning via Variational Expectation Maximization	Federated Learning (FL) is a distributed learning scheme to train a shared model across clients. One common and fundamental challenge in FL is that the sets of data across clients could be non-identically distributed and have different sizes. Personalized Federated Learning (PFL) attempts to solve this challenge via lo...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	366,056
2407.19300	CoLiDR: Concept Learning using Aggregated Disentangled Representations	Interpretability of Deep Neural Networks using concept-based models offers a promising way to explain model behavior through human-understandable concepts. A parallel line of research focuses on disentangling the data distribution into its underlying generative factors, in turn explaining the data generation process. W...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	476,728
1509.04200	Simple Approximations of Semialgebraic Sets and their Applications to Control	Many uncertainty sets encountered in control systems analysis and design can be expressed in terms of semialgebraic sets, that is as the intersection of sets described by means of polynomial inequalities. Important examples are for instance the solution set of linear matrix inequalities or the Schur/Hurwitz stability d...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	46,901
2203.00172	Enhancing Local Feature Learning for 3D Point Cloud Processing using Unary-Pairwise Attention	We present a simple but effective attention named the unary-pairwise attention (UPA) for modeling the relationship between 3D point clouds. Our idea is motivated by the analysis that the standard self-attention (SA) that operates globally tends to produce almost the same attention maps for different query positions, re...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	282,902
1902.09904	Diagnosis of Alzheimer's Disease via Multi-modality 3D Convolutional Neural Network	Alzheimer's Disease (AD) is one of the most concerned neurodegenerative diseases. In the last decade, studies on AD diagnosis attached great significance to artificial intelligence (AI)-based diagnostic algorithms. Among the diverse modality imaging data, T1-weighted MRI and 18F-FDGPET are widely researched for this ta...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	122,540
2404.07969	An End-to-End Structure with Novel Position Mechanism and Improved EMD for Stock Forecasting	As a branch of time series forecasting, stock movement forecasting is one of the challenging problems for investors and researchers. Since Transformer was introduced to analyze financial data, many researchers have dedicated themselves to forecasting stock movement using Transformer or attention mechanisms. However, ex...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	446,049
2202.08325	A Data-Augmentation Is Worth A Thousand Samples: Exact Quantification From Analytical Augmented Sample Moments	Data-Augmentation (DA) is known to improve performance across tasks and datasets. We propose a method to theoretically analyze the effect of DA and study questions such as: how many augmented samples are needed to correctly estimate the information encoded by that DA? How does the augmentation policy impact the final p...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	280,831
2005.13480	Constrained H-infinity Consensus with Nonidentical Constraints	This note considers the constrained H-infinity consensus of multi-agent networks with nonidentical constraint sets. An improved distributed algorithm is adopted and a nonlinear controlled output function is defined to evaluate the effect of disturbances. Then, it is shown that the constrained H-infinity consensus can b...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	179,022
2312.03133	Predicting Bone Degradation Using Vision Transformer and Synthetic Cellular Microstructures Dataset	Bone degradation, especially for astronauts in microgravity conditions, is crucial for space exploration missions since the lower applied external forces accelerate the diminution in bone stiffness and strength substantially. Although existing computational models help us understand this phenomenon and possibly restric...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	413,146
2405.07841	Sample Selection Bias in Machine Learning for Healthcare	While machine learning algorithms hold promise for personalised medicine, their clinical adoption remains limited, partly due to biases that can compromise the reliability of predictions. In this paper, we focus on sample selection bias (SSB), a specific type of bias where the study population is less representative of...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	453,885
1908.05232	Agent-based simulator of dynamic flood-people interactions	This paper presents a new simulator for dynamic modelling of interactions between flooding and people in crowded areas. The simulator is developed in FLAMEGPU (a Flexible Large scale Agent-based Modelling Environment for the GPU), which allows to model multiple agent interactions while benefitting from the speed-up of ...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	141,668
2006.13551	Network connectivity under a probabilistic node failure model	Centrality metrics have been widely applied to identify the nodes in a graph whose removal is effective in decomposing the graph into smaller sub-components. The node--removal process is generally used to test network robustness against failures. Most of the available studies assume that the node removal task is always...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	true	183,951
2412.05290	Memristor-Based Selective Convolutional Circuit for High-Density Salt-and-Pepper Noise Removal	In this article, we propose a memristor-based selective convolutional (MSC) circuit for salt-and-pepper (SAP) noise removal. We implement its algorithm using memristors in analog circuits. In experiments, we build the MSC model and benchmark it against a ternary selective convolutional (TSC) model. Results show that th...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	true	514,771
2212.00784	Improving Zero-Shot Models with Label Distribution Priors	Labeling large image datasets with attributes such as facial age or object type is tedious and sometimes infeasible. Supervised machine learning methods provide a highly accurate solution, but require manual labels which are often unavailable. Zero-shot models (e.g., CLIP) do not require manual labels but are not as ac...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	334,192
2103.13680	Decentralized Coordination Between Economic Dispatch and Demand Response in Multi-Energy Systems	In this paper, we investigate the problem of coordination between economic dispatch (ED) and demand response (DR) in multi-energy systems (MESs), aiming to improve the economic utility and reduce the waste of energy in MESs. Since multiple energy sources are coupled through energy hubs (EHs), the supply-demand constrai...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	226,577
2205.10238	Visualizing and Explaining Language Models	During the last decade, Natural Language Processing has become, after Computer Vision, the second field of Artificial Intelligence that was massively changed by the advent of Deep Learning. Regardless of the architecture, the language models of the day need to be able to process or generate text, as well as predict mis...	true	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	297,625
2402.15921	Pretraining Strategy for Neural Potentials	We propose a mask pretraining method for Graph Neural Networks (GNNs) to improve their performance on fitting potential energy surfaces, particularly in water systems. GNNs are pretrained by recovering spatial information related to masked-out atoms from molecules, then transferred and finetuned on atomic forcefields. ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	432,331
2402.17019	Leveraging Large Language Models for Learning Complex Legal Concepts through Storytelling	Making legal knowledge accessible to non-experts is crucial for enhancing general legal literacy and encouraging civic participation in democracy. However, legal documents are often challenging to understand for people without legal backgrounds. In this paper, we present a novel application of large language models (LL...	true	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	432,809
2412.05672	Graph with Sequence: Broad-Range Semantic Modeling for Fake News Detection	The rapid proliferation of fake news on social media threatens social stability, creating an urgent demand for more effective detection methods. While many promising approaches have emerged, most rely on content analysis with limited semantic depth, leading to suboptimal comprehension of news content.To address this li...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	514,922
2302.08160	The Inadequacy of Shapley Values for Explainability	This paper develops a rigorous argument for why the use of Shapley values in explainable AI (XAI) will necessarily yield provably misleading information about the relative importance of features for predictions. Concretely, this paper demonstrates that there exist classifiers, and associated predictions, for which the ...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	345,968
2203.11076	Collaborative Learning for Cyberattack Detection in Blockchain Networks	This article aims to study intrusion attacks and then develop a novel cyberattack detection framework to detect cyberattacks at the network layer (e.g., Brute Password and Flooding of Transactions) of blockchain networks. Specifically, we first design and implement a blockchain network in our laboratory. This blockchai...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	286,788
2309.12056	BELT:Bootstrapping Electroencephalography-to-Language Decoding and Zero-Shot Sentiment Classification by Natural Language Supervision	This paper presents BELT, a novel model and learning framework for the pivotal topic of brain-to-language translation research. The translation from noninvasive brain signals into readable natural language has the potential to promote the application scenario as well as the development of brain-computer interfaces (BCI...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	393,650
2011.08783	A Spiking Neural Network (SNN) for detecting High Frequency Oscillations (HFOs) in the intraoperative ECoG	To achieve seizure freedom, epilepsy surgery requires the complete resection of the epileptogenic brain tissue. In intraoperative ECoG recordings, high frequency oscillations (HFOs) generated by epileptogenic tissue can be used to tailor the resection margin. However, automatic detection of HFOs in real-time remains an...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	206,994
2307.13755	Training-based Model Refinement and Representation Disagreement for Semi-Supervised Object Detection	Semi-supervised object detection (SSOD) aims to improve the performance and generalization of existing object detectors by utilizing limited labeled data and extensive unlabeled data. Despite many advances, recent SSOD methods are still challenged by inadequate model refinement using the classical exponential moving av...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	381,688
2205.09721	HyperAid: Denoising in hyperbolic spaces for tree-fitting and hierarchical clustering	The problem of fitting distances by tree-metrics has received significant attention in the theoretical computer science and machine learning communities alike, due to many applications in natural language processing, phylogeny, cancer genomics and a myriad of problem areas that involve hierarchical clustering. Despite ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	297,391
2302.12301	An Aligned Multi-Temporal Multi-Resolution Satellite Image Dataset for Change Detection Research	This paper presents an aligned multi-temporal and multi-resolution satellite image dataset for research in change detection. We expect our dataset to be useful to researchers who want to fuse information from multiple satellites for detecting changes on the surface of the earth that may not be fully visible in any sing...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	347,509
cs/0610100	A Mobile Transient Internet Architecture	This paper describes a new architecture for transient mobile networks destined to merge existing and future network architectures, communication implementations and protocol operations by introducing a new paradigm to data delivery and identification. The main goal of our research is to enable seamless end-to-end commu...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	539,794
2010.14778	DNA: Differentiable Network-Accelerator Co-Search	Powerful yet complex deep neural networks (DNNs) have fueled a booming demand for efficient DNN solutions to bring DNN-powered intelligence into numerous applications. Jointly optimizing the networks and their accelerators are promising in providing optimal performance. However, the great potential of such solutions ha...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	203,570
2406.17182	Debiased Recommendation with Noisy Feedback	Ratings of a user to most items in recommender systems are usually missing not at random (MNAR), largely because users are free to choose which items to rate. To achieve unbiased learning of the prediction model under MNAR data, three typical solutions have been proposed, including error-imputation-based (EIB), inverse...	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	false	467,453
1901.06294	Estimating Noisy Order Statistics	This paper proposes an estimation framework to assess the performance of sorting over perturbed/noisy data. In particular, the recovering accuracy is measured in terms of Minimum Mean Square Error (MMSE) between the values of the sorting function computed on data without perturbation and the estimator that operates on ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	118,968
1711.10870	Sparse Photometric 3D Face Reconstruction Guided by Morphable Models	We present a novel 3D face reconstruction technique that leverages sparse photometric stereo (PS) and latest advances on face registration/modeling from a single image. We observe that 3D morphable faces approach provides a reasonable geometry proxy for light position calibration. Specifically, we develop a robust opti...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	85,687
2402.14296	Mitigating Biases of Large Language Models in Stance Detection with Counterfactual Augmented Calibration	Stance detection is critical for understanding the underlying position or attitude expressed toward a topic. Large language models (LLMs) have demonstrated significant advancements across various natural language processing tasks including stance detection, however, their performance in stance detection is limited by b...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	431,613
2212.07552	Structurally aware 3D gas distribution mapping using belief propagation: a real-time algorithm for robotic deployment	This paper proposes a new 3D gas distribution mapping technique based on the local message passing of Gaussian belief propagation that is capable of resolving in real time, concentration estimates in 3D space whilst accounting for the obstacle information within the scenario, the first of its kind in the literature. Th...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	336,433
1310.1799	Linear Precoding Based on Polynomial Expansion: Large-Scale Multi-Cell MIMO Systems	Large-scale MIMO systems can yield a substantial improvement in spectral efficiency for future communication systems. Due to the finer spatial resolution achieved by a huge number of antennas at the base stations, these systems have shown to be robust to inter-user interference and the use of linear precoding is asympt...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	27,597
2408.07337	KIND: Knowledge Integration and Diversion in Diffusion Models	Pre-trained models have become the preferred backbone due to the expansion of model parameters, with techniques like Parameter-Efficient Fine-Tuning (PEFTs) typically fixing the parameters of these models. However, pre-trained models may not always be optimal, especially when there are discrepancies between training ta...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	480,545
2305.18632	Graph Rewriting for Graph Neural Networks	Given graphs as input, Graph Neural Networks (GNNs) support the inference of nodes, edges, attributes, or graph properties. Graph Rewriting investigates the rule-based manipulation of graphs to model complex graph transformations. We propose that, therefore, (i) graph rewriting subsumes GNNs and could serve as formal m...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	true	false	false	369,170
2502.08080	NLI under the Microscope: What Atomic Hypothesis Decomposition Reveals	Decomposition of text into atomic propositions is a flexible framework allowing for the closer inspection of input and output text. We use atomic decomposition of hypotheses in two natural language reasoning tasks, traditional NLI and defeasible NLI, to form atomic sub-problems, or granular inferences that models must ...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	532,883
2502.08397	Strong bounds for large-scale Minimum Sum-of-Squares Clustering	Clustering is a fundamental technique in data analysis and machine learning, used to group similar data points together. Among various clustering methods, the Minimum Sum-of-Squares Clustering (MSSC) is one of the most widely used. MSSC aims to minimize the total squared Euclidean distance between data points and their...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	533,003
2211.09260	Task-aware Retrieval with Instructions	We study the problem of retrieval with instructions, where users of a retrieval system explicitly describe their intent along with their queries. We aim to develop a general-purpose task-aware retrieval system using multi-task instruction tuning, which can follow human-written instructions to find the best documents fo...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	330,918
2404.09271	VRS-NeRF: Visual Relocalization with Sparse Neural Radiance Field	Visual relocalization is a key technique to autonomous driving, robotics, and virtual/augmented reality. After decades of explorations, absolute pose regression (APR), scene coordinate regression (SCR), and hierarchical methods (HMs) have become the most popular frameworks. However, in spite of high efficiency, APRs an...	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	446,605
1910.03019	Flood Detection On Low Cost Orbital Hardware	Satellite imaging is a critical technology for monitoring and responding to natural disasters such as flooding. Despite the capabilities of modern satellites, there is still much to be desired from the perspective of first response organisations like UNICEF. Two main challenges are rapid access to data, and the ability...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	148,388
2206.08781	Reinforcement Learning for Economic Policy: A New Frontier?	Agent-based computational economics is a field with a rich academic history, yet one which has struggled to enter mainstream policy design toolboxes, plagued by the challenges associated with representing a complex and dynamic reality. The field of Reinforcement Learning (RL), too, has a rich history, and has recently ...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	true	false	false	false	303,294
2204.00872	Calibration window selection based on change-point detection for forecasting electricity prices	We employ a recently proposed change-point detection algorithm, the Narrowest-Over-Threshold (NOT) method, to select subperiods of past observations that are similar to the currently recorded values. Then, contrarily to the traditional time series approach in which the most recent $\tau$ observations are taken as the c...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	289,416
0901.3987	Improved Delay Estimates for a Queueing Model for Random Linear Coding for Unicast	Consider a lossy communication channel for unicast with zero-delay feedback. For this communication scenario, a simple retransmission scheme is optimum with respect to delay. An alternative approach is to use random linear coding in automatic repeat-request (ARQ) mode. We extend the work of Shrader and Ephremides, by d...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	3,052
cs/0006032	Estimation of English and non-English Language Use on the WWW	The World Wide Web has grown so big, in such an anarchic fashion, that it is difficult to describe. One of the evident intrinsic characteristics of the World Wide Web is its multilinguality. Here, we present a technique for estimating the size of a language-specific corpus given the frequency of commonly occurring word...	true	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	537,138
2301.08868	Computationally Efficient 3D MRI Reconstruction with Adaptive MLP	Compared with 2D MRI, 3D MRI provides superior volumetric spatial resolution and signal-to-noise ratio. However, it is more challenging to reconstruct 3D MRI images. Current methods are mainly based on convolutional neural networks (CNN) with small kernels, which are difficult to scale up to have sufficient fitting pow...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	341,324
2403.08843	Fuzzy Fault Trees Formalized	Fault tree analysis is a vital method of assessing safety risks. It helps to identify potential causes of accidents, assess their likelihood and severity, and suggest preventive measures. Quantitative analysis of fault trees is often done via the dependability metrics that compute the system's failure behaviour over ti...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	437,513
2205.06548	Meta Balanced Network for Fair Face Recognition	Although deep face recognition has achieved impressive progress in recent years, controversy has arisen regarding discrimination based on skin tone, questioning their deployment into real-world scenarios. In this paper, we aim to systematically and scientifically study this bias from both data and algorithm aspects. Fi...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	296,280
1410.6289	Signal inference with unknown response: Calibration-uncertainty renormalized estimator	The calibration of a measurement device is crucial for every scientific experiment, where a signal has to be inferred from data. We present CURE, the calibration uncertainty renormalized estimator, to reconstruct a signal and simultaneously the instrument's calibration from the same data without knowing the exact calib...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	36,972
2401.14248	On generalisability of segment anything model for nuclear instance segmentation in histology images	Pre-trained on a large and diverse dataset, the segment anything model (SAM) is the first promptable foundation model in computer vision aiming at object segmentation tasks. In this work, we evaluate SAM for the task of nuclear instance segmentation performance with zero-shot learning and finetuning. We compare SAM wit...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	424,022
2305.02323	Correlation-Driven Multi-Level Multimodal Learning for Anomaly Detection on Multiple Energy Sources	Advanced metering infrastructure (AMI) has been widely used as an intelligent energy consumption measurement system. Electric power was the representative energy source that can be collected by AMI; most existing studies to detect abnormal energy consumption have focused on a single energy source, i.e., power. Recently...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	361,995
2009.09084	Intimate Partner Violence and Injury Prediction From Radiology Reports	Intimate partner violence (IPV) is an urgent, prevalent, and under-detected public health issue. We present machine learning models to assess patients for IPV and injury. We train the predictive algorithms on radiology reports with 1) IPV labels based on entry to a violence prevention program and 2) injury labels provi...	false	false	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	196,437
2412.14401	The One RING: a Robotic Indoor Navigation Generalist	Modern robots vary significantly in shape, size, and sensor configurations used to perceive and interact with their environments. However, most navigation policies are embodiment-specific; a policy learned using one robot's configuration does not typically gracefully generalize to another. Even small changes in the bod...	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	518,680
2302.01582	Controlling for Stereotypes in Multimodal Language Model Evaluation	We propose a methodology and design two benchmark sets for measuring to what extent language-and-vision language models use the visual signal in the presence or absence of stereotypes. The first benchmark is designed to test for stereotypical colors of common objects, while the second benchmark considers gender stereot...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	343,671
1106.0680	Learning Geometrically-Constrained Hidden Markov Models for Robot Navigation: Bridging the Topological-Geometrical Gap	Hidden Markov models (HMMs) and partially observable Markov decision processes (POMDPs) provide useful tools for modeling dynamical systems. They are particularly useful for representing the topology of environments such as road networks and office buildings, which are typical for robot navigation and planning. The wor...	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	false	10,712
2003.08904	RAB: Provable Robustness Against Backdoor Attacks	Recent studies have shown that deep neural networks (DNNs) are vulnerable to adversarial attacks, including evasion and backdoor (poisoning) attacks. On the defense side, there have been intensive efforts on improving both empirical and provable robustness against evasion attacks; however, the provable robustness again...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	168,902
1903.07792	Differentially Private Consensus-Based Distributed Optimization	Data privacy is an important concern in learning, when datasets contain sensitive information about individuals. This paper considers consensus-based distributed optimization under data privacy constraints. Consensus-based optimization consists of a set of computational nodes arranged in a graph, each having a local ob...	false	false	false	true	false	false	true	false	false	false	false	false	true	false	false	false	false	false	124,698
1711.00953	Automatic Query Image Disambiguation for Content-Based Image Retrieval	Query images presented to content-based image retrieval systems often have various different interpretations, making it difficult to identify the search objective pursued by the user. We propose a technique for overcoming this ambiguity, while keeping the amount of required user interaction at a minimum. To achieve thi...	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	false	83,805
2105.04830	Jerk-limited Real-time Trajectory Generation with Arbitrary Target States	We present Ruckig, an algorithm for Online Trajectory Generation (OTG) respecting third-order constraints and complete kinematic target states. Given any initial state of a system with multiple Degrees of Freedom (DoFs), Ruckig calculates a time-optimal trajectory to an arbitrary target state defined by its position, v...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	234,631
2004.09215	CatNet: Class Incremental 3D ConvNets for Lifelong Egocentric Gesture Recognition	Egocentric gestures are the most natural form of communication for humans to interact with wearable devices such as VR/AR helmets and glasses. A major issue in such scenarios for real-world applications is that may easily become necessary to add new gestures to the system e.g., a proper VR system should allow users to ...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	173,285
2310.05365	Molecular De Novo Design through Transformer-based Reinforcement Learning	In this work, we introduce a method to fine-tune a Transformer-based generative model for molecular de novo design. Leveraging the superior sequence learning capacity of Transformers over Recurrent Neural Networks (RNNs), our model can generate molecular structures with desired properties effectively. In contrast to th...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	398,115
2405.14043	Attitudes Towards Migration in a COVID-19 Context: Testing a Behavioral Immune System Hypothesis with Twitter Data	The COVID-19 outbreak implied many changes in the daily life of most of the world's population for a long time, prompting severe restrictions on sociality. The Behavioral Immune System (BIS) suggests that when facing pathogens, a psychological mechanism would be activated that, among other things, would generate an inc...	false	false	false	true	false	false	false	false	false	false	false	false	false	true	false	false	false	false	456,217
1106.0254	Conflict-Directed Backjumping Revisited	In recent years, many improvements to backtracking algorithms for solving constraint satisfaction problems have been proposed. The techniques for improving backtracking algorithms can be conveniently classified as look-ahead schemes and look-back schemes. Unfortunately, look-ahead and look-back schemes are not entirely...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	10,662
2006.14407	Snitches Get Stitches: On The Difficulty of Whistleblowing	One of the most critical security protocol problems for humans is when you are betraying a trust, perhaps for some higher purpose, and the world can turn against you if you're caught. In this short paper, we report on efforts to enable whistleblowers to leak sensitive documents to journalists more safely. Following a s...	false	false	false	true	false	false	false	false	false	false	false	false	false	true	false	false	false	true	184,215
2111.04794	Deep Learning Approach for Aggressive Driving Behaviour Detection	Driving behaviour is one of the primary causes of road crashes and accidents, and these can be decreased by identifying and minimizing aggressive driving behaviour. This study identifies the timesteps when a driver in different circumstances (rush, mental conflicts, reprisal) begins to drive aggressively. An observer (...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	265,591
2104.13948	Applying Convolutional Neural Networks for Stock Market Trends Identification	In this paper we apply a specific type ANNs - convolutional neural networks (CNNs) - to the problem of finding start and endpoints of trends, which are the optimal points for entering and leaving the market. We aim to explore long-term trends, which last several months, not days. The key distinction of our model is tha...	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	false	false	232,661
1607.00659	Robust Deep Appearance Models	This paper presents a novel Robust Deep Appearance Models to learn the non-linear correlation between shape and texture of face images. In this approach, two crucial components of face images, i.e. shape and texture, are represented by Deep Boltzmann Machines and Robust Deep Boltzmann Machines (RDBM), respectively. The...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	58,122
2312.15770	A Recipe for Scaling up Text-to-Video Generation with Text-free Videos	Diffusion-based text-to-video generation has witnessed impressive progress in the past year yet still falls behind text-to-image generation. One of the key reasons is the limited scale of publicly available data (e.g., 10M video-text pairs in WebVid10M vs. 5B image-text pairs in LAION), considering the high cost of vid...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	418,127
2006.09016	Acoustic prediction of flowrate: varying liquid jet stream onto a free surface	Information on liquid jet stream flow is crucial in many real world applications. In a large number of cases, these flows fall directly onto free surfaces (e.g. pools), creating a splash with accompanying splashing sounds. The sound produced is supplied by energy interactions between the liquid jet stream and the passi...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	182,394

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