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
2111.00670	Comparative Explanations of Recommendations	As recommendation is essentially a comparative (or ranking) process, a good explanation should illustrate to users why an item is believed to be better than another, i.e., comparative explanations about the recommended items. Ideally, after reading the explanations, a user should reach the same ranking of items as the ...	false	false	false	false	true	true	false	false	true	false	false	false	false	false	false	false	false	false	264,299
2501.04467	A Histologic Dataset of Normal and Atypical Mitotic Figures on Human Breast Cancer (AMi-Br)	Assessment of the density of mitotic figures (MFs) in histologic tumor sections is an important prognostic marker for many tumor types, including breast cancer. Recently, it has been reported in multiple works that the quantity of MFs with an atypical morphology (atypical MFs, AMFs) might be an independent prognostic c...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	true	false	523,234
1709.08536	Towards An Adaptive Compliant Aerial Manipulator for Contact-Based Interaction	As roles for unmanned aerial vehicles (UAV) continue to diversify, the ability to sense and interact closely with the environment becomes increasingly important. Within this paper we report on the initial flight tests of a novel adaptive compliant actuator which will allow a UAV to carry out such tasks as the "pick and...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	81,495
2402.13432	DrBenchmark: A Large Language Understanding Evaluation Benchmark for French Biomedical Domain	The biomedical domain has sparked a significant interest in the field of Natural Language Processing (NLP), which has seen substantial advancements with pre-trained language models (PLMs). However, comparing these models has proven challenging due to variations in evaluation protocols across different models. A fair so...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	431,239
2112.13432	New Methods & Metrics for LFQA tasks	Long-form question answering (LFQA) tasks require retrieving the documents pertinent to a query, using them to form a paragraph-length answer. Despite considerable progress in LFQA modeling, fundamental issues impede its progress: i) train/validation/test dataset overlap, ii) absence of automatic metrics and iii) gener...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	273,240
2211.04166	Spiking sampling network for image sparse representation and dynamic vision sensor data compression	Sparse representation has attracted great attention because it can greatly save storage resources and find representative features of data in a low-dimensional space. As a result, it may be widely applied in engineering domains including feature extraction, compressed sensing, signal denoising, picture clustering, and ...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	329,159
2307.01387	ALBERTI, a Multilingual Domain Specific Language Model for Poetry Analysis	The computational analysis of poetry is limited by the scarcity of tools to automatically analyze and scan poems. In a multilingual settings, the problem is exacerbated as scansion and rhyme systems only exist for individual languages, making comparative studies very challenging and time consuming. In this work, we pre...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	377,329
1911.04970	Robust and Fast Automatic Modulation Classification with CNN under Multipath Fading Channels	Automatic modulation classification (AMC) has been studied for more than a quarter of a century; however, it has been difficult to design a classifier that operates successfully under changing multipath fading conditions and other impairments. Recently, deep learning (DL)-based methods are adopted by AMC systems and ma...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	153,133
2108.05762	Multimodal analysis of the predictability of hand-gesture properties	Embodied conversational agents benefit from being able to accompany their speech with gestures. Although many data-driven approaches to gesture generation have been proposed in recent years, it is still unclear whether such systems can consistently generate gestures that convey meaning. We investigate which gesture pro...	true	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	250,403
1211.3831	Objective Improvement in Information-Geometric Optimization	Information-Geometric Optimization (IGO) is a unified framework of stochastic algorithms for optimization problems. Given a family of probability distributions, IGO turns the original optimization problem into a new maximization problem on the parameter space of the probability distributions. IGO updates the parameter ...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	19,756
2303.04757	Error Correcting Codes From General Linear Groups	The parameters of the AG codes on general linear groups are found. The hyperplane sections having the minimum (or maximum) number of rational points are determined.	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	350,211
2002.09018	Scalable Second Order Optimization for Deep Learning	Optimization in machine learning, both theoretical and applied, is presently dominated by first-order gradient methods such as stochastic gradient descent. Second-order optimization methods, that involve second derivatives and/or second order statistics of the data, are far less prevalent despite strong theoretical pro...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	164,931
2404.00034	Investigating Similarities Across Decentralized Financial (DeFi) Services	We explore the adoption of graph representation learning (GRL) algorithms to investigate similarities across services offered by Decentralized Finance (DeFi) protocols. Following existing literature, we use Ethereum transaction data to identify the DeFi building blocks. These are sets of protocol-specific smart contrac...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	442,734
2310.20703	Vanishing Gradients in Reinforcement Finetuning of Language Models	Pretrained language models are commonly aligned with human preferences and downstream tasks via reinforcement finetuning (RFT), which refers to maximizing a (possibly learned) reward function using policy gradient algorithms. This work identifies a fundamental optimization obstacle in RFT: we prove that the expected gr...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	404,484
1504.06077	Open Data Platform for Knowledge Access in Plant Health Domain : VESPA Mining	Important data are locked in ancient literature. It would be uneconomic to produce these data again and today or to extract them without the help of text mining technologies. Vespa is a text mining project whose aim is to extract data on pest and crops interactions, to model and predict attacks on crops, and to reduce ...	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	42,367
2101.11363	KoreALBERT: Pretraining a Lite BERT Model for Korean Language Understanding	A Lite BERT (ALBERT) has been introduced to scale up deep bidirectional representation learning for natural languages. Due to the lack of pretrained ALBERT models for Korean language, the best available practice is the multilingual model or resorting back to the any other BERT-based model. In this paper, we develop and...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	217,254
2006.04014	Medical Concept Normalization in User Generated Texts by Learning Target Concept Embeddings	Medical concept normalization helps in discovering standard concepts in free-form text i.e., maps health-related mentions to standard concepts in a vocabulary. It is much beyond simple string matching and requires a deep semantic understanding of concept mentions. Recent research approach concept normalization as eithe...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	180,521
2012.04144	Improved Swarm Engineering: Aligning Intuition and Analysis	We present a set of metrics intended to supplement designer intuitions when designing swarm-robotic systems, increase accuracy in extrapolating swarm behavior from algorithmic descriptions and small test experiments, and lead to faster and less costly design cycles. We build on previous works studying self-organizing b...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	210,357
1201.3466	Detecting community structure in networks using edge prediction methods	Community detection and edge prediction are both forms of link mining: they are concerned with discovering the relations between vertices in networks. Some of the vertex similarity measures used in edge prediction are closely related to the concept of community structure. We use this insight to propose a novel method f...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	13,854
2204.09640	Probabilistic AutoRegressive Neural Networks for Accurate Long-range Forecasting	Forecasting time series data is a critical area of research with applications spanning from stock prices to early epidemic prediction. While numerous statistical and machine learning methods have been proposed, real-life prediction problems often require hybrid solutions that bridge classical forecasting approaches and...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	292,509
2407.01054	Joint Pruning and Channel-wise Mixed-Precision Quantization for Efficient Deep Neural Networks	The resource requirements of deep neural networks (DNNs) pose significant challenges to their deployment on edge devices. Common approaches to address this issue are pruning and mixed-precision quantization, which lead to latency and memory occupation improvements. These optimization techniques are usually applied inde...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	469,133
2409.00410	A Hybrid Transformer-Mamba Network for Single Image Deraining	Existing deraining Transformers employ self-attention mechanisms with fixed-range windows or along channel dimensions, limiting the exploitation of non-local receptive fields. In response to this issue, we introduce a novel dual-branch hybrid Transformer-Mamba network, denoted as TransMamba, aimed at effectively captur...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	484,920
2411.01354	Online and Offline Evaluations of Collaborative Filtering and Content Based Recommender Systems	Recommender systems are widely used AI applications designed to help users efficiently discover relevant items. The effectiveness of such systems is tied to the satisfaction of both users and providers. However, user satisfaction is complex and cannot be easily framed mathematically using information retrieval and accu...	false	false	false	false	true	true	true	false	true	false	false	false	false	false	false	false	false	false	505,032
2009.02191	Dual Precision Deep Neural Network	On-line Precision scalability of the deep neural networks(DNNs) is a critical feature to support accuracy and complexity trade-off during the DNN inference. In this paper, we propose dual-precision DNN that includes two different precision modes in a single model, thereby supporting an on-line precision switch without ...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	194,490
1901.10830	Design of Polar Codes for Parallel Channels with an Average Power Constraint	Polar codes are designed for parallel binary-input additive white Gaussian noise (BiAWGN) channels with an average power constraint. The two main design choices are: the mapping between codeword bits and channels of different quality, and the power allocation under the average power constraint. Information theory sugge...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	120,116
2002.10022	Application of ERA5 and MENA simulations to predict offshore wind energy potential	This study explores wind energy resources in different locations through the Gulf of Oman and also their future variability due climate change impacts. In this regard, EC-EARTH near surface wind outputs obtained from CORDEX-MENA simulations are used for historical and future projection of the energy. The ERA5 wind data...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	165,256
2205.03911	Codes for Constrained Periodicity	Reliability is an inherent challenge for the emerging nonvolatile technology of racetrack memories, and there exists a fundamental relationship between codes designed for racetrack memories and codes with constrained periodicity. Previous works have sought to construct codes that avoid periodicity in windows, yet have ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	295,466
1612.08278	Photoacoustic imaging beyond the acoustic diffraction-limit with dynamic speckle illumination and sparse joint support recovery	In deep tissue photoacoustic imaging the spatial resolution is inherently limited by the acoustic wavelength. Recently, it was demonstrated that it is possible to surpass the acoustic diffraction limit by analyzing fluctuations in a set of photoacoustic images obtained under unknown speckle illumination patterns. Here,...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	66,053
2008.02514	Object-based Illumination Estimation with Rendering-aware Neural Networks	We present a scheme for fast environment light estimation from the RGBD appearance of individual objects and their local image areas. Conventional inverse rendering is too computationally demanding for real-time applications, and the performance of purely learning-based techniques may be limited by the meager input dat...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	190,636
2410.23690	XRDSLAM: A Flexible and Modular Framework for Deep Learning based SLAM	In this paper, we propose a flexible SLAM framework, XRDSLAM. It adopts a modular code design and a multi-process running mechanism, providing highly reusable foundational modules such as unified dataset management, 3d visualization, algorithm configuration, and metrics evaluation. It can help developers quickly build ...	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	504,141
1802.00609	An LMI Approach to Stability Analysis of Coupled Parabolic Systems	We analyze the exponential stability of distributed parameter systems. The system we consider is described by a coupled parabolic partial differential equation with spatially varying coefficients. We approximate the coefficients by splitting space domains but take into account approximation errors during stability anal...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	89,443
1904.03800	Towards Concurrent Stateful Stream Processing on Multicore Processors (Technical Report)	Recent data stream processing systems (DSPSs) can achieve excellent performance when processing large volumes of data under tight latency constraints. However, they sacrifice support for concurrent state access that eases the burden of developing stateful stream applications. Recently, some have proposed managing concu...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	126,839
1402.5988	Incremental Learning of Event Definitions with Inductive Logic Programming	Event recognition systems rely on properly engineered knowledge bases of event definitions to infer occurrences of events in time. The manual development of such knowledge is a tedious and error-prone task, thus event-based applications may benefit from automated knowledge construction techniques, such as Inductive Log...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	31,134
2501.03479	Women, Infamous, and Exotic Beings: What Honorific Usages in Wikipedia Reveal about the Socio-Cultural Norms	Honorifics serve as powerful linguistic markers that reflect social hierarchies and cultural values. This paper presents a large-scale, cross-linguistic exploration of usage of honorific pronouns in Bengali and Hindi Wikipedia articles, shedding light on how socio-cultural factors shape language. Using LLM (GPT-4o), we...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	522,888
2402.02741	Glocal Hypergradient Estimation with Koopman Operator	Gradient-based hyperparameter optimization methods update hyperparameters using hypergradients, gradients of a meta criterion with respect to hyperparameters. Previous research used two distinct update strategies: optimizing hyperparameters using global hypergradients obtained after completing model training or local h...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	426,714
2411.07570	Constructive RNNs: An Error-Recurrence Perspective on Time-Variant Zero Finding Problem Solving Under Uncertainty	When facing time-variant problems in analog computing, the desirable RNN design requires finite-time convergence and robustness with respect to various types of uncertainties, due to the time-variant nature and difficulties in implementation. It is very worthwhile to explore terminal zeroing neural networks, through ex...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	507,588
2103.13808	3D3L: Deep Learned 3D Keypoint Detection and Description for LiDARs	With the advent of powerful, light-weight 3D LiDARs, they have become the hearth of many navigation and SLAM algorithms on various autonomous systems. Pointcloud registration methods working with unstructured pointclouds such as ICP are often computationally expensive or require a good initial guess. Furthermore, 3D fe...	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	226,614
2312.13193	HCDIR: End-to-end Hate Context Detection, and Intensity Reduction model for online comments	Warning: This paper contains examples of the language that some people may find offensive. Detecting and reducing hateful, abusive, offensive comments is a critical and challenging task on social media. Moreover, few studies aim to mitigate the intensity of hate speech. While studies have shown that context-level sem...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	417,234
2108.00689	Nonlinear Controller Design with Prediction Horizon Time Reduction Applied to Unstable CSTR System	Ensuring nominal asymptotic stability of the Nonlinear Model Predictive Control controller is not trivial. Stabilizing ingredients such as terminal penalty term and terminal region are crucial in establishing the asymptotic stability. Current work presents alternate approaches namely arbitrary controller based approach...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	248,798
2006.07694	Sensorless Freehand 3D Ultrasound Reconstruction via Deep Contextual Learning	Transrectal ultrasound (US) is the most commonly used imaging modality to guide prostate biopsy and its 3D volume provides even richer context information. Current methods for 3D volume reconstruction from freehand US scans require external tracking devices to provide spatial position for every frame. In this paper, we...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	181,901
2012.15837	Using Natural Language Relations between Answer Choices for Machine Comprehension	When evaluating an answer choice for Reading Comprehension task, other answer choices available for the question and the answers of related questions about the same paragraph often provide valuable information. In this paper, we propose a method to leverage the natural language relations between the answer choices, suc...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	213,924
2108.01454	Inscriptis -- A Python-based HTML to text conversion library optimized for knowledge extraction from the Web	Inscriptis provides a library, command line client and Web service for converting HTML to plain text. Its development has been triggered by the need to obtain accurate text representations for knowledge extraction tasks that preserve the spatial alignment of text without drawing upon heavyweight, browser-based solution...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	249,031
2305.10502	EENED: End-to-End Neural Epilepsy Detection based on Convolutional Transformer	Recently Transformer and Convolution neural network (CNN) based models have shown promising results in EEG signal processing. Transformer models can capture the global dependencies in EEG signals through a self-attention mechanism, while CNN models can capture local features such as sawtooth waves. In this work, we pro...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	365,098
1703.06485	Near Optimal Hamiltonian-Control and Learning via Chattering	Many applications require solving non-linear control problems that are classically not well behaved. This paper develops a simple and efficient chattering algorithm that learns near optimal decision policies through an open-loop feedback strategy. The optimal control problem reduces to a series of linear optimization p...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	70,235
2002.06806	Reinforcement learning for the privacy preservation and manipulation of eye tracking data	In this paper, we present an approach based on reinforcement learning for eye tracking data manipulation. It is based on two opposing agents, where one tries to classify the data correctly and the second agent looks for patterns in the data, which get manipulated to hide specific information. We show that our approach ...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	164,312
2411.02764	Fast, robust approximate message passing	We give a fast, spectral procedure for implementing approximate-message passing (AMP) algorithms robustly. For any quadratic optimization problem over symmetric matrices $X$ with independent subgaussian entries, and any separable AMP algorithm $\mathcal A$, our algorithm performs a spectral pre-processing step and then...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	505,646
1709.05470	Long-Term Ensemble Learning of Visual Place Classifiers	This paper addresses the problem of cross-season visual place classification (VPC) from a novel perspective of long-term map learning. Our goal is to enable transfer learning efficiently from one season to the next, at a small constant cost, and without wasting the robot's available long-term-memory by memorizing very ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	80,880
1610.04823	To Frontalize or Not To Frontalize: Do We Really Need Elaborate Pre-processing To Improve Face Recognition?	Face recognition performance has improved remarkably in the last decade. Much of this success can be attributed to the development of deep learning techniques such as convolutional neural networks (CNNs). While CNNs have pushed the state-of-the-art forward, their training process requires a large amount of clean and co...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	62,439
2304.11717	Automatized marine vessel monitoring from sentinel-1 data using convolution neural network	The advancement of multi-channel synthetic aperture radar (SAR) system is considered as an upgraded technology for surveillance activities. SAR sensors onboard provide data for coastal ocean surveillance and a view of the oceanic surface features. Vessel monitoring has earlier been performed using Constant False Alarm ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	359,934
quant-ph/0207069	Data compression limit for an information source of interacting qubits	A system of interacting qubits can be viewed as a non-i.i.d quantum information source. A possible model of such a source is provided by a quantum spin system, in which spin-1/2 particles located at sites of a lattice interact with each other. We establish the limit for the compression of information from such a source...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	540,873
1906.09076	Inside the Echo Chamber: Disentangling network dynamics from polarization	Echo chambers are defined by the simultaneous presence of opinion polarization with respect to a controversial topic and homophily, i.e. the preference of individuals to interact with like-minded peers. While recent efforts have been devoted to detecting the presence of echo chambers in polarized debates on online soci...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	136,056
2309.06533	Hierarchical Multi-Task Learning Framework for Session-based Recommendations	While session-based recommender systems (SBRSs) have shown superior recommendation performance, multi-task learning (MTL) has been adopted by SBRSs to enhance their prediction accuracy and generalizability further. Hierarchical MTL (H-MTL) sets a hierarchical structure between prediction tasks and feeds outputs from au...	false	false	false	false	true	true	true	false	false	false	false	false	false	false	false	false	false	false	391,444
1701.03674	Fault Tolerant Control of Automotive Air Conditioning Systems using a GIMC Structure	Although model-based fault tolerant control (FTC) has become prevalent in various engineering fields, its application to air-conditioning systems is limited due to the lack of control-oriented models to characterize the phase change of refrigerant in the vapor compression cycle. The emergence of moving boundary method ...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	66,744
2408.06827	PRESENT: Zero-Shot Text-to-Prosody Control	Current strategies for achieving fine-grained prosody control in speech synthesis entail extracting additional style embeddings or adopting more complex architectures. To enable zero-shot application of pretrained text-to-speech (TTS) models, we present PRESENT (PRosody Editing without Style Embeddings or New Training)...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	480,362
2406.08105	Prediction of the Realisation of an Information Need: An EEG Study	One of the foundational goals of Information Retrieval (IR) is to satisfy searchers' Information Needs (IN). Understanding how INs physically manifest has long been a complex and elusive process. However, recent studies utilising Electroencephalography (EEG) data have provided real-time insights into the neural process...	true	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	false	false	463,356
2208.09788	FaceOff: A Video-to-Video Face Swapping System	Doubles play an indispensable role in the movie industry. They take the place of the actors in dangerous stunt scenes or scenes where the same actor plays multiple characters. The double's face is later replaced with the actor's face and expressions manually using expensive CGI technology, costing millions of dollars a...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	313,833
2308.09929	RIS-assisted High-Speed Railway Integrated Sensing and Communication System	One technology that has the potential to improve wireless communications in years to come is integrated sensing and communication (ISAC). In this study, we take advantage of reconfigurable intelligent surface's (RIS) potential advantages to achieve ISAC while using the same frequency and resources. Specifically, by usi...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	386,490
2311.09854	SurvTimeSurvival: Survival Analysis On The Patient With Multiple Visits/Records	The accurate prediction of survival times for patients with severe diseases remains a critical challenge despite recent advances in artificial intelligence. This study introduces "SurvTimeSurvival: Survival Analysis On Patients With Multiple Visits/Records", utilizing the Transformer model to not only handle the comple...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	true	408,342
2302.11077	Impact of Event Encoding and Dissimilarity Measures on Traffic Crash Characterization Based on Sequence of Events	Crash sequence analysis has been shown in prior studies to be useful for characterizing crashes and identifying safety countermeasures. Sequence analysis is highly domain-specific, but its various techniques have not been evaluated for adaptation to crash sequences. This paper evaluates the impact of encoding and dissi...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	347,079
2102.07954	AlphaNet: Improved Training of Supernets with Alpha-Divergence	Weight-sharing neural architecture search (NAS) is an effective technique for automating efficient neural architecture design. Weight-sharing NAS builds a supernet that assembles all the architectures as its sub-networks and jointly trains the supernet with the sub-networks. The success of weight-sharing NAS heavily re...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	220,288
2412.04158	LossVal: Efficient Data Valuation for Neural Networks	Assessing the importance of individual training samples is a key challenge in machine learning. Traditional approaches retrain models with and without specific samples, which is computationally expensive and ignores dependencies between data points. We introduce LossVal, an efficient data valuation method that computes...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	514,288
2402.10128	GES: Generalized Exponential Splatting for Efficient Radiance Field Rendering	Advancements in 3D Gaussian Splatting have significantly accelerated 3D reconstruction and generation. However, it may require a large number of Gaussians, which creates a substantial memory footprint. This paper introduces GES (Generalized Exponential Splatting), a novel representation that employs Generalized Exponen...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	true	429,833
2403.17392	Swarm navigation of cyborg-insects in unknown obstructed soft terrain	Cyborg insects refer to hybrid robots that integrate living insects with miniature electronic controllers to enable robotic-like programmable control. These creatures exhibit advantages over conventional robots in adaption to complex terrain and sustained energy efficiency. Nevertheless, there is a lack of literature o...	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	441,448
1810.12193	Pyramidal Person Re-IDentification via Multi-Loss Dynamic Training	Most existing Re-IDentification (Re-ID) methods are highly dependent on precise bounding boxes that enable images to be aligned with each other. However, due to the challenging practical scenarios, current detection models often produce inaccurate bounding boxes, which inevitably degenerate the performance of existing ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	111,714
2203.14031	Medicinal Boxes Recognition on a Deep Transfer Learning Augmented Reality Mobile Application	Taking medicines is a fundamental aspect to cure illnesses. However, studies have shown that it can be hard for patients to remember the correct posology. More aggravating, a wrong dosage generally causes the disease to worsen. Although, all relevant instructions for a medicine are summarized in the corresponding patie...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	287,850
2403.08928	Neuromorphic force-control in an industrial task: validating energy and latency benefits	As robots become smarter and more ubiquitous, optimizing the power consumption of intelligent compute becomes imperative towards ensuring the sustainability of technological advancements. Neuromorphic computing hardware makes use of biologically inspired neural architectures to achieve energy and latency improvements c...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	437,541
1910.10563	Domain Bridge for Unpaired Image-to-Image Translation and Unsupervised Domain Adaptation	Image-to-image translation architectures may have limited effectiveness in some circumstances. For example, while generating rainy scenarios, they may fail to model typical traits of rain as water drops, and this ultimately impacts the synthetic images realism. With our method, called domain bridge, web-crawled data ar...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	150,526
2405.18393	A Critique of Snapshot Isolation	The support for transactions is an essential part of a database management system (DBMS). Without this support, the developers are burdened with ensuring atomic execution of a transaction despite failures as well as concurrent accesses to the database by other transactions. Ideally, a transactional system provides seri...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	458,408
1811.06407	Neural Predictive Belief Representations	Unsupervised representation learning has succeeded with excellent results in many applications. It is an especially powerful tool to learn a good representation of environments with partial or noisy observations. In partially observable domains it is important for the representation to encode a belief state, a sufficie...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	113,520
2106.05731	Leveraged Weighted Loss for Partial Label Learning	As an important branch of weakly supervised learning, partial label learning deals with data where each instance is assigned with a set of candidate labels, whereas only one of them is true. Despite many methodology studies on learning from partial labels, there still lacks theoretical understandings of their risk cons...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	240,206
1902.03906	Investigations on Increased Data rate Differential Space-Time Block Codes for Single Carrier Wireless Systems	In this thesis, we study differential modulation schemes which do not need channel knowledge at the transmitter nor at the receiver. First, we consider single-antenna systems and investigate the use of Differential Amplitude Phase Shift Keying (DAPSK) modulation. We study the use of Multiple-Symbol Differential Detecti...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	121,221
2110.11611	Error-correcting neural networks for semi-Lagrangian advection in the level-set method	We present a machine learning framework that blends image super-resolution technologies with passive, scalar transport in the level-set method. Here, we investigate whether we can compute on-the-fly, data-driven corrections to minimize numerical viscosity in the coarse-mesh evolution of an interface. The proposed syste...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	262,548
1510.04183	Mathematical Foundations for Designing and Development of Intelligent Systems of Information Analysis	This article is an attempt to combine different ways of working with sets of objects and their classes for designing and development of artificial intelligent systems (AIS) of analysis information, using object-oriented programming (OOP). This paper contains analysis of basic concepts of OOP and their relation with set...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	47,895
1912.03573	Deep Variable-Block Chain with Adaptive Variable Selection	The architectures of deep neural networks (DNN) rely heavily on the underlying grid structure of variables, for instance, the lattice of pixels in an image. For general high dimensional data with variables not associated with a grid, the multi-layer perceptron and deep belief network are often used. However, it is freq...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	156,625
2409.18026	ReliOcc: Towards Reliable Semantic Occupancy Prediction via Uncertainty Learning	Vision-centric semantic occupancy prediction plays a crucial role in autonomous driving, which requires accurate and reliable predictions from low-cost sensors. Although having notably narrowed the accuracy gap with LiDAR, there is still few research effort to explore the reliability in predicting semantic occupancy fr...	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	492,074
2101.10801	Global-Local Propagation Network for RGB-D Semantic Segmentation	Depth information matters in RGB-D semantic segmentation task for providing additional geometric information to color images. Most existing methods exploit a multi-stage fusion strategy to propagate depth feature to the RGB branch. However, at the very deep stage, the propagation in a simple element-wise addition manne...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	217,055
2311.11476	Empowering remittance management in the digitised landscape: A real-time Data-Driven Decision Support with predictive abilities for financial transactions	The advent of Blockchain technology (BT) revolutionised the way remittance transactions are recorded. Banks and remittance organisations have shown a growing interest in exploring blockchain's potential advantages over traditional practices. This paper presents a data-driven predictive decision support approach as an i...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	408,962
2206.11021	Monte Carlo Methods for Industry 4.0 Applications	The fourth industrial revolution and the digital transformation, commonly known as Industry 4.0, is exponentially progressing in recent years. Connected computers, devices, and intelligent machines communicate with each other and interact with the environment to require only a minimum of human intervention. An importan...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	304,122
2302.13700	Imaginary Voice: Face-styled Diffusion Model for Text-to-Speech	The goal of this work is zero-shot text-to-speech synthesis, with speaking styles and voices learnt from facial characteristics. Inspired by the natural fact that people can imagine the voice of someone when they look at his or her face, we introduce a face-styled diffusion text-to-speech (TTS) model within a unified f...	false	false	true	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	348,035
1701.08462	TipTop: (Almost) Exact Solutions for Influence Maximization in Billion-scale Networks	In this paper, we study the Cost-aware Target Viral Marketing (CTVM) problem, a generalization of Influence Maximization (IM). CTVM asks for the most cost-effective users to influence the most relevant users. In contrast to the vast literature, we attempt to offer exact solutions. As the problem is NP-hard, thus, exact...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	67,473
1302.3609	Real Time Estimation of Bayesian Networks	For real time evaluation of a Bayesian network when there is not sufficient time to obtain an exact solution, a guaranteed response time, approximate solution is required. It is shown that nontraditional methods utilizing estimators based on an archive of trial solutions and genetic search can provide an approximate so...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	22,075
1802.05701	Inverting The Generator Of A Generative Adversarial Network (II)	Generative adversarial networks (GANs) learn a deep generative model that is able to synthesise novel, high-dimensional data samples. New data samples are synthesised by passing latent samples, drawn from a chosen prior distribution, through the generative model. Once trained, the latent space exhibits interesting prop...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	90,488
2302.04126	Predicting the performance of hybrid ventilation in buildings using a multivariate attention-based biLSTM Encoder-Decoder neural network	Hybrid ventilation is an energy-efficient solution to provide fresh air for most climates, given that it has a reliable control system. To operate such systems optimally, a high-fidelity control-oriented modesl is required. It should enable near-real time forecast of the indoor air temperature based on operational cond...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	344,600
2212.14032	On Implicit Bias in Overparameterized Bilevel Optimization	Many problems in machine learning involve bilevel optimization (BLO), including hyperparameter optimization, meta-learning, and dataset distillation. Bilevel problems consist of two nested sub-problems, called the outer and inner problems, respectively. In practice, often at least one of these sub-problems is overparam...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	338,476
2103.08878	Learning without gradient descent encoded by the dynamics of a neurobiological model	The success of state-of-the-art machine learning is essentially all based on different variations of gradient descent algorithms that minimize some version of a cost or loss function. A fundamental limitation, however, is the need to train these systems in either supervised or unsupervised ways by exposing them to typi...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	true	false	false	225,013
2502.11766	Warmup-Distill: Bridge the Distribution Mismatch between Teacher and Student before Knowledge Distillation	The widespread deployment of Large Language Models (LLMs) is hindered by the high computational demands, making knowledge distillation (KD) crucial for developing compact smaller ones. However, the conventional KD methods endure the distribution mismatch issue between the teacher and student models, leading to the poor...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	534,536
2202.02635	Multilingual Hate Speech and Offensive Content Detection using Modified Cross-entropy Loss	The number of increased social media users has led to a lot of people misusing these platforms to spread offensive content and use hate speech. Manual tracking the vast amount of posts is impractical so it is necessary to devise automated methods to identify them quickly. Large language models are trained on a lot of d...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	278,884
2006.08661	Predicting Livelihood Indicators from Community-Generated Street-Level Imagery	Major decisions from governments and other large organizations rely on measurements of the populace's well-being, but making such measurements at a broad scale is expensive and thus infrequent in much of the developing world. We propose an inexpensive, scalable, and interpretable approach to predict key livelihood indi...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	182,252
1904.07629	Causality Extraction based on Self-Attentive BiLSTM-CRF with Transferred Embeddings	Causality extraction from natural language texts is a challenging open problem in artificial intelligence. Existing methods utilize patterns, constraints, and machine learning techniques to extract causality, heavily depending on domain knowledge and requiring considerable human effort and time for feature engineering....	false	false	false	false	false	true	true	false	true	false	false	false	false	false	false	false	false	false	127,846
1410.2592	Transmit without regrets: Online optimization in MIMO-OFDM cognitive radio systems	In this paper, we examine cognitive radio systems that evolve dynamically over time due to changing user and environmental conditions. To combine the advantages of orthogonal frequency division multiplexing (OFDM) and multiple-input, multiple-output (MIMO) technologies, we consider a MIMO-OFDM cognitive radio network w...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	36,628
2412.11934	Stepwise Reasoning Error Disruption Attack of LLMs	Large language models (LLMs) have made remarkable strides in complex reasoning tasks, but their safety and robustness in reasoning processes remain underexplored. Existing attacks on LLM reasoning are constrained by specific settings or lack of imperceptibility, limiting their feasibility and generalizability. To addre...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	517,644
2007.04790	MO-PaDGAN: Generating Diverse Designs with Multivariate Performance Enhancement	Deep generative models have proven useful for automatic design synthesis and design space exploration. However, they face three challenges when applied to engineering design: 1) generated designs lack diversity, 2) it is difficult to explicitly improve all the performance measures of generated designs, and 3) existing ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	186,475
2004.12316	Towards Persona-Based Empathetic Conversational Models	Empathetic conversational models have been shown to improve user satisfaction and task outcomes in numerous domains. In Psychology, persona has been shown to be highly correlated to personality, which in turn influences empathy. In addition, our empirical analysis also suggests that persona plays an important role in e...	true	false	false	false	true	true	false	false	true	false	false	false	false	false	false	false	false	false	174,209
1810.08332	Zero and Few Shot Learning with Semantic Feature Synthesis and Competitive Learning	Zero-shot learning (ZSL) is made possible by learning a projection function between a feature space and a semantic space (e.g.,~an attribute space). Key to ZSL is thus to learn a projection that is robust against the often large domain gap between the seen and unseen class domains. In this work, this is achieved by uns...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	110,802
2002.07877	CBIR using features derived by Deep Learning	In a Content Based Image Retrieval (CBIR) System, the task is to retrieve similar images from a large database given a query image. The usual procedure is to extract some useful features from the query image, and retrieve images which have similar set of features. For this purpose, a suitable similarity measure is chos...	false	false	false	false	false	true	true	false	false	false	false	true	false	false	false	false	false	false	164,591
2305.01122	Learning Controllable Adaptive Simulation for Multi-resolution Physics	Simulating the time evolution of physical systems is pivotal in many scientific and engineering problems. An open challenge in simulating such systems is their multi-resolution dynamics: a small fraction of the system is extremely dynamic, and requires very fine-grained resolution, while a majority of the system is cha...	false	true	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	361,560
1505.01728	Integrating K-means with Quadratic Programming Feature Selection	Several data mining problems are characterized by data in high dimensions. One of the popular ways to reduce the dimensionality of the data is to perform feature selection, i.e, select a subset of relevant and non-redundant features. Recently, Quadratic Programming Feature Selection (QPFS) has been proposed which formu...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	42,875
2410.17390	Revealing The Secret Power: How Algorithms Can Influence Content Visibility on Social Media	Online social media platforms significantly influence public debates by shaping the information users encounter. Content visibility on these platforms is regulated by recommendation algorithms designed to maximize user engagement using individual-level data, including personal preferences and interactions. These algori...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	501,436
2003.00952	Bayesian Neural Networks With Maximum Mean Discrepancy Regularization	Bayesian Neural Networks (BNNs) are trained to optimize an entire distribution over their weights instead of a single set, having significant advantages in terms of, e.g., interpretability, multi-task learning, and calibration. Because of the intractability of the resulting optimization problem, most BNNs are either sa...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	166,498
2202.00088	Reinforcement Learning with Heterogeneous Data: Estimation and Inference	Reinforcement Learning (RL) has the promise of providing data-driven support for decision-making in a wide range of problems in healthcare, education, business, and other domains. Classical RL methods focus on the mean of the total return and, thus, may provide misleading results in the setting of the heterogeneous pop...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	278,017

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