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
2312.01638	J-Net: Improved U-Net for Terahertz Image Super-Resolution	Terahertz (THz) waves are electromagnetic waves in the 0.1 to 10 THz frequency range, and THz imaging is utilized in a range of applications, including security inspections, biomedical fields, and the non-destructive examination of materials. However, THz images have low resolution due to the long wavelength of THz wav...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	412,525
2311.04933	Evaluating Large Language Models in Ophthalmology	Purpose: The performance of three different large language models (LLMS) (GPT-3.5, GPT-4, and PaLM2) in answering ophthalmology professional questions was evaluated and compared with that of three different professional populations (medical undergraduates, medical masters, and attending physicians). Methods: A 100-item...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	406,410
1102.4612	Spatially-Coupled MacKay-Neal Codes and Hsu-Anastasopoulos Codes	Kudekar et al. recently proved that for transmission over the binary erasure channel (BEC), spatial coupling of LDPC codes increases the BP threshold of the coupled ensemble to the MAP threshold of the underlying LDPC codes. One major drawback of the capacity-achieving spatially-coupled LDPC codes is that one needs to ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	9,319
1701.04028	Using data-compressors for statistical analysis of problems on homogeneity testing and classification	Nowadays data compressors are applied to many problems of text analysis, but many such applications are developed outside of the framework of mathematical statistics. In this paper we overcome this obstacle and show how several methods of classical mathematical statistics can be developed based on applications of the d...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	66,798
2307.08041	Planting a SEED of Vision in Large Language Model	We present SEED, an elaborate image tokenizer that empowers Large Language Models (LLMs) with the emergent ability to SEE and Draw at the same time. Research on image tokenizers has previously reached an impasse, as frameworks employing quantized visual tokens have lost prominence due to subpar performance and converge...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	379,647
2208.06308	Developing a Philosophical Framework for Fair Machine Learning: Lessons From The Case of Algorithmic Collusion	Fair machine learning research has been primarily concerned with classification tasks that result in discrimination. However, as machine learning algorithms are applied in new contexts the harms and injustices that result are qualitatively different than those presently studied. The existing research paradigm in machin...	false	false	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	312,672
2110.02204	Learning Sense-Specific Static Embeddings using Contextualised Word Embeddings as a Proxy	Contextualised word embeddings generated from Neural Language Models (NLMs), such as BERT, represent a word with a vector that considers the semantics of the target word as well its context. On the other hand, static word embeddings such as GloVe represent words by relatively low-dimensional, memory- and compute-effici...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	259,040
2310.05150	From Data to Dialogue: Leveraging the Structure of Knowledge Graphs for Conversational Exploratory Search	Exploratory search is an open-ended information retrieval process that aims at discovering knowledge about a topic or domain rather than searching for a specific answer or piece of information. Conversational interfaces are particularly suitable for supporting exploratory search, allowing users to refine queries and ex...	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	398,009
2209.01963	Modeling User Repeat Consumption Behavior for Online Novel Recommendation	Given a user's historical interaction sequence, online novel recommendation suggests the next novel the user may be interested in. Online novel recommendation is important but underexplored. In this paper, we concentrate on recommending online novels to new users of an online novel reading platform, whose first visits ...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	316,062
2402.01950	ConRF: Zero-shot Stylization of 3D Scenes with Conditioned Radiation Fields	Most of the existing works on arbitrary 3D NeRF style transfer required retraining on each single style condition. This work aims to achieve zero-shot controlled stylization in 3D scenes utilizing text or visual input as conditioning factors. We introduce ConRF, a novel method of zero-shot stylization. Specifically, du...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	426,299
2301.12340	Incremental Value and Interpretability of Radiomics Features of Both Lung and Epicardial Adipose Tissue for Detecting the Severity of COVID-19 Infection	Epicardial adipose tissue (EAT) is known for its pro-inflammatory properties and association with Coronavirus Disease 2019 (COVID-19) severity. However, current EAT segmentation methods do not consider positional information. Additionally, the detection of COVID-19 severity lacks consideration for EAT radiomics feature...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	342,494
1007.3254	Distinguishing Fact from Fiction: Pattern Recognition in Texts Using Complex Networks	We establish concrete mathematical criteria to distinguish between different kinds of written storytelling, fictional and non-fictional. Specifically, we constructed a semantic network from both novels and news stories, with $N$ independent words as vertices or nodes, and edges or links allotted to words occurring with...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	7,076
2404.07827	iPREFER: An Intelligent Parameter Extractor based on Features for BSIM-CMG Models	This paper introduces an innovative parameter extraction method for BSIM-CMG compact models, seamlessly integrating curve feature extraction and machine learning techniques. This method offers a promising solution for bridging the division between TCAD and compact model, significantly contributing to the Design Technol...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	445,990
2212.08570	Audio-based AI classifiers show no evidence of improved COVID-19 screening over simple symptoms checkers	Recent work has reported that AI classifiers trained on audio recordings can accurately predict severe acute respiratory syndrome coronavirus 2 (SARSCoV2) infection status. Here, we undertake a large scale study of audio-based deep learning classifiers, as part of the UK governments pandemic response. We collect and an...	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	336,795
2309.15821	LGMCTS: Language-Guided Monte-Carlo Tree Search for Executable Semantic Object Rearrangement	We introduce a novel approach to the executable semantic object rearrangement problem. In this challenge, a robot seeks to create an actionable plan that rearranges objects within a scene according to a pattern dictated by a natural language description. Unlike existing methods such as StructFormer and StructDiffusion,...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	395,131
2106.13451	Collision Avoidance for Unmanned Aerial Vehicles in the Presence of Static and Moving Obstacles	This paper presents a new collision avoidance procedure for unmanned aerial vehicles in the presence of static and moving obstacles. The proposed procedure is based on a new form of local parametrized guidance vector fields, called collision avoidance vector fields, that produce smooth and intuitive maneuvers around ob...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	243,083
2409.17759	LGFN: Lightweight Light Field Image Super-Resolution using Local Convolution Modulation and Global Attention Feature Extraction	Capturing different intensity and directions of light rays at the same scene Light field (LF) can encode the 3D scene cues into a 4D LF image which has a wide range of applications (i.e. post-capture refocusing and depth sensing). LF image super-resolution (SR) aims to improve the image resolution limited by the perfor...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	491,970
2212.02935	A multi-language toolkit for the semi-automated checking of research outputs	This article presents a free and open source toolkit that supports the semi-automated checking of research outputs (SACRO) for privacy disclosure within secure data environments. SACRO is a framework that applies best-practice principles-based statistical disclosure control (SDC) techniques on-the-fly as researchers co...	false	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	true	334,940
1704.01631	Multitask Learning with Low-Level Auxiliary Tasks for Encoder-Decoder Based Speech Recognition	End-to-end training of deep learning-based models allows for implicit learning of intermediate representations based on the final task loss. However, the end-to-end approach ignores the useful domain knowledge encoded in explicit intermediate-level supervision. We hypothesize that using intermediate representations as ...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	71,296
1901.04078	Periodic Analog Channel Estimation Aided Beamforming for Massive MIMO Systems	Analog beamforming is an attractive and cost-effective solution to exploit the benefits of massive multiple-input-multiple-output systems, by requiring only one up/down-conversion chain. However, the presence of only one chain imposes a significant overhead in estimating the channel state information required for beamf...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	118,548
1511.09033	The Multiverse Loss for Robust Transfer Learning	Deep learning techniques are renowned for supporting effective transfer learning. However, as we demonstrate, the transferred representations support only a few modes of separation and much of its dimensionality is unutilized. In this work, we suggest to learn, in the source domain, multiple orthogonal classifiers. We ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	49,615
2411.03224	Interpretable Predictive Models for Healthcare via Rational Logistic Regression	The healthcare sector has experienced a rapid accumulation of digital data recently, especially in the form of electronic health records (EHRs). EHRs constitute a precious resource that IS researchers could utilize for clinical applications (e.g., morbidity prediction). Deep learning seems like the obvious choice to ex...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	505,818
2211.09070	Towards Computationally Verifiable Semantic Grounding for Language Models	The paper presents an approach to semantic grounding of language models (LMs) that conceptualizes the LM as a conditional model generating text given a desired semantic message formalized as a set of entity-relationship triples. It embeds the LM in an auto-encoder by feeding its output to a semantic parser whose output...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	330,862
2209.10444	Off-Policy Risk Assessment in Markov Decision Processes	Addressing such diverse ends as safety alignment with human preferences, and the efficiency of learning, a growing line of reinforcement learning research focuses on risk functionals that depend on the entire distribution of returns. Recent work on \emph{off-policy risk assessment} (OPRA) for contextual bandits introdu...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	318,862
2408.10271	Data-Driven Fire Modeling: Learning First Arrival Times and Model Parameters with Neural Networks	Data-driven techniques are being increasingly applied to complement physics-based models in fire science. However, the lack of sufficiently large datasets continues to hinder the application of certain machine learning techniques. In this paper, we use simulated data to investigate the ability of neural networks to par...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	481,792
2109.14429	Minimal Expected Regret in Linear Quadratic Control	We consider the problem of online learning in Linear Quadratic Control systems whose state transition and state-action transition matrices $A$ and $B$ may be initially unknown. We devise an online learning algorithm and provide guarantees on its expected regret. This regret at time $T$ is upper bounded (i) by $\widetil...	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	257,966
1708.03918	Learning Deep Neural Networks for Vehicle Re-ID with Visual-spatio-temporal Path Proposals	Vehicle re-identification is an important problem and has many applications in video surveillance and intelligent transportation. It gains increasing attention because of the recent advances of person re-identification techniques. However, unlike person re-identification, the visual differences between pairs of vehicle...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	78,847
2410.10037	GALA: Geometry-Aware Local Adaptive Grids for Detailed 3D Generation	We propose GALA, a novel representation of 3D shapes that (i) excels at capturing and reproducing complex geometry and surface details, (ii) is computationally efficient, and (iii) lends itself to 3D generative modelling with modern, diffusion-based schemes. The key idea of GALA is to exploit both the global sparsity o...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	497,882
1910.06271	Organ-based Chronological Age Estimation based on 3D MRI Scans	Individuals age differently depending on a multitude of different factors such as lifestyle, medical history and genetics. Often, the global chronological age is not indicative of the true ageing process. An organ-based age estimation would yield a more accurate health state assessment. In this work, we propose a new d...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	149,301
2407.06866	ChatGPT Doesn't Trust Chargers Fans: Guardrail Sensitivity in Context	While the biases of language models in production are extensively documented, the biases of their guardrails have been neglected. This paper studies how contextual information about the user influences the likelihood of an LLM to refuse to execute a request. By generating user biographies that offer ideological and dem...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	471,563
2304.08320	On Fast-Converged Deep Reinforcement Learning for Optimal Dispatch of Large-Scale Power Systems under Transient Security Constraints	Power system optimal dispatch with transient security constraints is commonly represented as Transient Security-Constrained Optimal Power Flow (TSC-OPF). Deep Reinforcement Learning (DRL)-based TSC-OPF trains efficient decision-making agents that are adaptable to various scenarios and provide solution results quickly. ...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	358,673
2304.01426	Conformalized Unconditional Quantile Regression	We develop a predictive inference procedure that combines conformal prediction (CP) with unconditional quantile regression (QR) -- a commonly used tool in econometrics that involves regressing the recentered influence function (RIF) of the quantile functional over input covariates. Unlike the more widely-known conditio...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	356,064
2312.01476	Optimizing Context-Enhanced Relational Joins	Collecting data, extracting value, and combining insights from relational and context-rich multi-modal sources in data processing pipelines presents a challenge for traditional relational DBMS. While relational operators allow declarative and optimizable query specification, they are limited to data transformations uns...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	true	false	412,453
1306.3976	Lifting $\ell_q$-optimization thresholds	In this paper we look at a connection between the $\ell_q,0\leq q\leq 1$, optimization and under-determined linear systems of equations with sparse solutions. The case $q=1$, or in other words $\ell_1$ optimization and its a connection with linear systems has been thoroughly studied in last several decades; in fact, es...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	25,274
1906.10121	Metaheuristics optimized feedforward neural networks for efficient stock price prediction	The prediction of stock prices is an important task in economics, investment and making financial decisions. This has, for decades, spurred the interest of many researchers to make focused contributions to the design of accurate stock price predictive models; of which some have been utilized to predict the next day ope...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	136,365
1409.7472	The Expected Optimal Labeling Order Problem for Crowdsourced Joins and Entity Resolution	In the SIGMOD 2013 conference, we published a paper extending our earlier work on crowdsourced entity resolution to improve crowdsourced join processing by exploiting transitive relationships [Wang et al. 2013]. The VLDB 2014 conference has a paper that follows up on our previous work [Vesdapunt et al., 2014], which po...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	36,326
2307.08716	Enforcing 3D Topological Constraints in Composite Objects via Implicit Functions	Medical applications often require accurate 3D representations of complex organs with multiple parts, such as the heart and spine. Their individual parts must adhere to specific topological constraints to ensure proper functionality. Yet, there are very few mechanisms in the deep learning literature to achieve this goa...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	379,919
2410.10276	Intelligent Reflecting Surface-Assisted Symbiotic Radio Systems: A Double-Reflection Covert Communication Design	We investigate covert communication in an intelligent reflecting surface (IRS)-assisted symbiotic radio (SR) system under the parasitic SR (PSR) and the commensal SR (CSR) cases, where an IRS is exploited to create a double reflection link for legitimate users and degrade the detection performance of the warden (W). Sp...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	497,992
1602.04918	Multi-Sensor Surface Analysis for Robotic Ironing	Robotic manipulation of deformable objects remains a challenging task. One such task is to iron a piece of cloth autonomously. Given a roughly flattened cloth, the goal is to have an ironing plan that can iteratively apply a regular iron to remove all the major wrinkles by a robot. We present a novel solution to analyz...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	52,195
2412.00639	Needle: A Generative-AI Powered Monte Carlo Method for Answering Complex Natural Language Queries on Multi-modal Data	Multi-modal data, such as image data sets, often miss the detailed descriptions that properly capture the rich information encoded in them. This makes answering complex natural language queries a major challenge in these domains. In particular, unlike the traditional nearest-neighbor search, where the tuples and the qu...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	false	512,745
2410.18312	Countering Autonomous Cyber Threats	With the capability to write convincing and fluent natural language and generate code, Foundation Models present dual-use concerns broadly and within the cyber domain specifically. Generative AI has already begun to impact cyberspace through a broad illicit marketplace for assisting malware development and social engin...	false	false	false	false	true	false	false	false	false	false	false	false	true	true	false	false	false	false	501,829
2309.10309	Bridging Zero-shot Object Navigation and Foundation Models through Pixel-Guided Navigation Skill	Zero-shot object navigation is a challenging task for home-assistance robots. This task emphasizes visual grounding, commonsense inference and locomotion abilities, where the first two are inherent in foundation models. But for the locomotion part, most works still depend on map-based planning approaches. The gap betwe...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	392,955
2110.14019	Reliable and Trustworthy Machine Learning for Health Using Dataset Shift Detection	Unpredictable ML model behavior on unseen data, especially in the health domain, raises serious concerns about its safety as repercussions for mistakes can be fatal. In this paper, we explore the feasibility of using state-of-the-art out-of-distribution detectors for reliable and trustworthy diagnostic predictions. We ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	263,381
1602.03145	A New Spatio-Spectral Morphological Segmentation For Multi-Spectral Remote-Sensing Images	A general framework of spatio-spectral segmentation for multi-spectral images is introduced in this paper. The method is based on classification-driven stochastic watershed (WS) by Monte Carlo simulations, and it gives more regular and reliable contours than standard WS. The present approach is decomposed into several ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	51,965
2206.14798	Generalized Permutants and Graph GENEOs	In this paper we establish a bridge between Topological Data Analysis and Geometric Deep Learning, adapting the topological theory of group equivariant non-expansive operators (GENEOs) to act on the space of all graphs weighted on vertices or edges. This is done by showing how the general concept of GENEO can be used t...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	305,399
2501.08466	A Short-Term Predict-Then-Cluster Framework for Meal Delivery Services	Micro-delivery services offer promising solutions for on-demand city logistics, but their success relies on efficient real-time delivery operations and fleet management. On-demand meal delivery platforms seek to optimize real-time operations based on anticipatory insights into citywide demand distributions. To address ...	false	false	false	false	true	false	false	false	false	false	false	false	false	true	false	false	false	false	524,773
1501.05198	Memory and burstiness in dynamic networks	A discrete-time random process is described which can generate bursty sequences of events. A Bernoulli process, where the probability of an event occurring at time $t$ is given by a fixed probability $x$, is modified to include a memory effect where the event probability is increased proportionally to the number of eve...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	39,459
2004.13122	Development of a Machine-Learning System to Classify Lung CT Scan Images into Normal/COVID-19 Class	Recently, the lung infection due to Coronavirus Disease (COVID-19) affected a large human group worldwide and the assessment of the infection rate in the lung is essential for treatment planning. This research aims to propose a Machine-Learning-System (MLS) to detect the COVID-19 infection using the CT scan Slices (CTS...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	174,446
2107.01785	Improved Bounds for Codes Correcting Insertions and Deletions	This paper studies the cardinality of codes correcting insertions and deletions. We give improved upper and lower bounds on code size. Our upper bound is obtained by utilizing the asymmetric property of list decoding for insertions and deletions and can be seen as analogous to the Elias bound in the Hamming metric. Our...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	244,592
2101.07046	Mind the Gap when Conditioning Amortised Inference in Sequential Latent-Variable Models	Amortised inference enables scalable learning of sequential latent-variable models (LVMs) with the evidence lower bound (ELBO). In this setting, variational posteriors are often only partially conditioned. While the true posteriors depend, e.g., on the entire sequence of observations, approximate posteriors are only in...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	215,928
2302.13417	Training neural networks with structured noise improves classification and generalization	The beneficial role of noise-injection in learning is a consolidated concept in the field of artificial neural networks, suggesting that even biological systems might take advantage of similar mechanisms to optimize their performance. The training-with-noise algorithm proposed by Gardner and collaborators is an emblema...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	347,943
2307.06267	Physics-informed Machine Learning for Calibrating Macroscopic Traffic Flow Models	Well-calibrated traffic flow models are fundamental to understanding traffic phenomena and designing control strategies. Traditional calibration has been developed base on optimization methods. In this paper, we propose a novel physics-informed, learning-based calibration approach that achieves performances comparable ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	379,015
2412.09177	Weighted Poisson-disk Resampling on Large-Scale Point Clouds	For large-scale point cloud processing, resampling takes the important role of controlling the point number and density while keeping the geometric consistency. % in related tasks. However, current methods cannot balance such different requirements. Particularly with large-scale point clouds, classical methods often st...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	516,386
1507.00257	From Causes for Database Queries to Repairs and Model-Based Diagnosis and Back	In this work we establish and investigate connections between causes for query answers in databases, database repairs wrt. denial constraints, and consistency-based diagnosis. The first two are relatively new research areas in databases, and the third one is an established subject in knowledge representation. We show h...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	true	true	44,739
2409.10277	Cognitive Kernel: An Open-source Agent System towards Generalist Autopilots	We introduce Cognitive Kernel, an open-source agent system towards the goal of generalist autopilots. Unlike copilot systems, which primarily rely on users to provide essential state information (e.g., task descriptions) and assist users by answering questions or auto-completing contents, autopilot systems must complet...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	488,684
2404.03740	Randomized Greedy Methods for Weak Submodular Sensor Selection with Robustness Considerations	We study a pair of budget- and performance-constrained weak submodular maximization problems. For computational efficiency, we explore the use of stochastic greedy algorithms which limit the search space via random sampling instead of the standard greedy procedure which explores the entire feasible search space. We pro...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	444,387
2206.00390	Attention-embedded Quadratic Network (Qttention) for Effective and Interpretable Bearing Fault Diagnosis	Bearing fault diagnosis is of great importance to decrease the damage risk of rotating machines and further improve economic profits. Recently, machine learning, represented by deep learning, has made great progress in bearing fault diagnosis. However, applying deep learning to such a task still faces a major problem. ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	300,108
2212.00881	Investigating Deep Learning Model Calibration for Classification Problems in Mechanics	Recently, there has been a growing interest in applying machine learning methods to problems in engineering mechanics. In particular, there has been significant interest in applying deep learning techniques to predicting the mechanical behavior of heterogeneous materials and structures. Researchers have shown that deep...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	334,227
1909.08876	Understanding the Information needs of Social Scientists in Germany	The information needs of social science researchers are manifold and almost studied in every decade since the 1950s. With this paper, we contribute to this series and present the results of three studies. We asked 367 social science researchers in Germany for their information needs and identified needs in different ca...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	true	146,086
2306.02174	Training Data Attribution for Diffusion Models	Diffusion models have become increasingly popular for synthesizing high-quality samples based on training datasets. However, given the oftentimes enormous sizes of the training datasets, it is difficult to assess how training data impact the samples produced by a trained diffusion model. The difficulty of relating diff...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	370,784
1201.4049	Parameter Identification in a Probabilistic Setting	Parameter identification problems are formulated in a probabilistic language, where the randomness reflects the uncertainty about the knowledge of the true values. This setting allows conceptually easily to incorporate new information, e.g. through a measurement, by connecting it to Bayes's theorem. The unknown quantit...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	13,891
2104.15060	DriveGAN: Towards a Controllable High-Quality Neural Simulation	Realistic simulators are critical for training and verifying robotics systems. While most of the contemporary simulators are hand-crafted, a scaleable way to build simulators is to use machine learning to learn how the environment behaves in response to an action, directly from data. In this work, we aim to learn to si...	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	233,041
2402.02718	Denoising Time Cycle Modeling for Recommendation	Recently, modeling temporal patterns of user-item interactions have attracted much attention in recommender systems. We argue that existing methods ignore the variety of temporal patterns of user behaviors. We define the subset of user behaviors that are irrelevant to the target item as noises, which limits the perform...	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	false	false	426,701
1907.11885	Effective and efficient ROI-wise visual encoding using an end-to-end CNN regression model and selective optimization	Recently, visual encoding based on functional magnetic resonance imaging (fMRI) have realized many achievements with the rapid development of deep network computation. Visual encoding model is aimed at predicting brain activity in response to presented image stimuli. Currently, visual encoding is accomplished mainly by...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	139,972
1803.03827	Face2Text: Collecting an Annotated Image Description Corpus for the Generation of Rich Face Descriptions	The past few years have witnessed renewed interest in NLP tasks at the interface between vision and language. One intensively-studied problem is that of automatically generating text from images. In this paper, we extend this problem to the more specific domain of face description. Unlike scene descriptions, face descr...	false	false	false	false	true	false	false	false	true	false	false	true	false	false	false	false	false	false	92,326
2007.01720	Qualitative Analysis of Monte Carlo Dropout	In this report, we present qualitative analysis of Monte Carlo (MC) dropout method for measuring model uncertainty in neural network (NN) models. We first consider the sources of uncertainty in NNs, and briefly review Bayesian Neural Networks (BNN), the group of Bayesian approaches to tackle uncertainties in NNs. After...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	185,517
2405.20350	Linear Function Approximation as a Computationally Efficient Method to Solve Classical Reinforcement Learning Challenges	Neural Network based approximations of the Value function make up the core of leading Policy Based methods such as Trust Regional Policy Optimization (TRPO) and Proximal Policy Optimization (PPO). While this adds significant value when dealing with very complex environments, we note that in sufficiently low State and a...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	459,308
2101.10578	Malware Detection Using Frequency Domain-Based Image Visualization and Deep Learning	We propose a novel method to detect and visualize malware through image classification. The executable binaries are represented as grayscale images obtained from the count of N-grams (N=2) of bytes in the Discrete Cosine Transform (DCT) domain and a neural network is trained for malware detection. A shallow neural netw...	false	false	false	false	false	false	true	false	false	false	false	true	true	false	false	false	false	false	216,988
1808.07784	Time-Agnostic Prediction: Predicting Predictable Video Frames	Prediction is arguably one of the most basic functions of an intelligent system. In general, the problem of predicting events in the future or between two waypoints is exceedingly difficult. However, most phenomena naturally pass through relatively predictable bottlenecks---while we cannot predict the precise trajector...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	105,812
2303.02387	Towards a Unified Theoretical Understanding of Non-contrastive Learning via Rank Differential Mechanism	Recently, a variety of methods under the name of non-contrastive learning (like BYOL, SimSiam, SwAV, DINO) show that when equipped with some asymmetric architectural designs, aligning positive pairs alone is sufficient to attain good performance in self-supervised visual learning. Despite some understandings of some sp...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	349,333
2207.04914	Team CERBERUS Wins the DARPA Subterranean Challenge: Technical Overview and Lessons Learned	This article presents the CERBERUS robotic system-of-systems, which won the DARPA Subterranean Challenge Final Event in 2021. The Subterranean Challenge was organized by DARPA with the vision to facilitate the novel technologies necessary to reliably explore diverse underground environments despite the grueling challen...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	307,365
2107.08149	Dual Quaternion-Based Visual Servoing for Grasping Moving Objects	This paper presents a new dual quaternion-based formulation for pose-based visual servoing. Extending our previous work on local contact moment (LoCoMo) based grasp planning, we demonstrate grasping of arbitrarily moving objects in 3D space. Instead of using the conventional axis-angle parameterization, dual quaternion...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	246,635
2404.06400	Dynamic Deep Learning Based Super-Resolution For The Shallow Water Equations	Using the nonlinear shallow water equations as benchmark, we demonstrate that a simulation with the ICON-O ocean model with a 20km resolution that is frequently corrected by a U-net-type neural network can achieve discretization errors of a simulation with 10km resolution. The network, originally developed for image-ba...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	445,452
2408.03728	A Convex-optimization-based Layer-wise Post-training Pruner for Large Language Models	Pruning is a critical strategy for compressing trained large language models (LLMs), aiming at substantial memory conservation and computational acceleration without compromising performance. However, existing pruning methods often necessitate inefficient retraining for billion-scale LLMs or rely on heuristic methods s...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	479,129
1709.01353	Learning Non-Metric Visual Similarity for Image Retrieval	Measuring visual similarity between two or more instances within a data distribution is a fundamental task in image retrieval. Theoretically, non-metric distances are able to generate a more complex and accurate similarity model than metric distances, provided that the non-linear data distribution is precisely captured...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	80,066
2110.05423	Using Document Similarity Methods to create Parallel Datasets for Code Translation	Translating source code from one programming language to another is a critical, time-consuming task in modernizing legacy applications and codebases. Recent work in this space has drawn inspiration from the software naturalness hypothesis by applying natural language processing techniques towards automating the code tr...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	260,272
2007.02141	Off-Policy Exploitability-Evaluation in Two-Player Zero-Sum Markov Games	Off-policy evaluation (OPE) is the problem of evaluating new policies using historical data obtained from a different policy. In the recent OPE context, most studies have focused on single-player cases, and not on multi-player cases. In this study, we propose OPE estimators constructed by the doubly robust and double r...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	185,648
1808.00150	Depth Estimation via Affinity Learned with Convolutional Spatial Propagation Network	Depth estimation from a single image is a fundamental problem in computer vision. In this paper, we propose a simple yet effective convolutional spatial propagation network (CSPN) to learn the affinity matrix for depth prediction. Specifically, we adopt an efficient linear propagation model, where the propagation is pe...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	104,319
2403.09750	Meta-Cognitive Analysis: Evaluating Declarative and Procedural Knowledge in Datasets and Large Language Models	Declarative knowledge and procedural knowledge are two key parts in meta-cognitive theory, and these two hold significant importance in pre-training and inference of LLMs. However, a comprehensive analysis comparing these two types of knowledge is lacking, primarily due to challenges in definition, probing and quantita...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	437,904
2002.08332	Designing spontaneous behavioral switching via chaotic itinerancy	Chaotic itinerancy is a frequently observed phenomenon in high-dimensional and nonlinear dynamical systems, and it is characterized by the random transitions among multiple quasi-attractors. Several studies have revealed that chaotic itinerancy has been observed in brain activity, and it is considered to play a critica...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	164,727
1706.00043	Biased Importance Sampling for Deep Neural Network Training	Importance sampling has been successfully used to accelerate stochastic optimization in many convex problems. However, the lack of an efficient way to calculate the importance still hinders its application to Deep Learning. In this paper, we show that the loss value can be used as an alternative importance metric, an...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	74,548
2212.03868	Deep Learning for Brain Age Estimation: A Systematic Review	Over the years, Machine Learning models have been successfully employed on neuroimaging data for accurately predicting brain age. Deviations from the healthy brain aging pattern are associated to the accelerated brain aging and brain abnormalities. Hence, efficient and accurate diagnosis techniques are required for eli...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	335,262
1909.08184	Transfer Learning with Dynamic Adversarial Adaptation Network	The recent advances in deep transfer learning reveal that adversarial learning can be embedded into deep networks to learn more transferable features to reduce the distribution discrepancy between two domains. Existing adversarial domain adaptation methods either learn a single domain discriminator to align the global ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	145,893
2101.06662	Intact-VAE: Estimating Treatment Effects under Unobserved Confounding	NOTE: This preprint has a flawed theoretical formulation. Please avoid it and refer to the ICLR22 publication https://openreview.net/forum?id=q7n2RngwOM. Also, arXiv:2109.15062 contains some new ideas on unobserved Confounding. As an important problem of causal inference, we discuss the identification and estimation ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	215,799
2502.11382	A Physics-Informed Blur Learning Framework for Imaging Systems	Accurate blur estimation is essential for high-performance imaging across various applications. Blur is typically represented by the point spread function (PSF). In this paper, we propose a physics-informed PSF learning framework for imaging systems, consisting of a simple calibration followed by a learning process. Ou...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	534,336
2205.11116	Summarize and Generate to Back-translate: Unsupervised Translation of Programming Languages	Back-translation is widely known for its effectiveness in neural machine translation when there is little to no parallel data. In this approach, a source-to-target model is coupled with a target-to-source model trained in parallel. The target-to-source model generates noisy sources, while the source-to-target model is ...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	true	298,006
2412.02612	GLM-4-Voice: Towards Intelligent and Human-Like End-to-End Spoken Chatbot	We introduce GLM-4-Voice, an intelligent and human-like end-to-end spoken chatbot. It supports both Chinese and English, engages in real-time voice conversations, and varies vocal nuances such as emotion, intonation, speech rate, and dialect according to user instructions. GLM-4-Voice uses an ultra-low bitrate (175bps)...	false	false	true	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	513,604
cs/0307072	Camera Calibration: a USU Implementation	The task of camera calibration is to estimate the intrinsic and extrinsic parameters of a camera model. Though there are some restricted techniques to infer the 3-D information about the scene from uncalibrated cameras, effective camera calibration procedures will open up the possibility of using a wide range of existi...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	537,945
1609.03478	Boosting Nodes for Improving the Spread of Influence	Information diffusion in networks has received a lot of recent attention. Most previous work addresses the influence maximization problem of selecting an appropriate set of seed nodes to initiate the diffusion process so that the largest number of nodes is reached. Since the seed selection problem is NP hard, most solu...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	60,887
2310.13103	AVTENet: Audio-Visual Transformer-based Ensemble Network Exploiting Multiple Experts for Video Deepfake Detection	Forged content shared widely on social media platforms is a major social problem that requires increased regulation and poses new challenges to the research community. The recent proliferation of hyper-realistic deepfake videos has drawn attention to the threat of audio and visual forgeries. Most previous work on detec...	false	false	true	false	true	false	true	false	false	false	false	true	false	false	false	false	false	true	401,295
2006.01791	SaliencyMix: A Saliency Guided Data Augmentation Strategy for Better Regularization	Advanced data augmentation strategies have widely been studied to improve the generalization ability of deep learning models. Regional dropout is one of the popular solutions that guides the model to focus on less discriminative parts by randomly removing image regions, resulting in improved regularization. However, su...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	179,869
2305.12859	Flying Adversarial Patches: Manipulating the Behavior of Deep Learning-based Autonomous Multirotors	Autonomous flying robots, e.g. multirotors, often rely on a neural network that makes predictions based on a camera image. These deep learning (DL) models can compute surprising results if applied to input images outside the training domain. Adversarial attacks exploit this fault, for example, by computing small images...	false	false	false	false	true	false	false	true	false	false	false	false	true	false	false	false	false	false	366,226
2006.05720	Extrapolation for Large-batch Training in Deep Learning	Deep learning networks are typically trained by Stochastic Gradient Descent (SGD) methods that iteratively improve the model parameters by estimating a gradient on a very small fraction of the training data. A major roadblock faced when increasing the batch size to a substantial fraction of the training data for improv...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	181,181
2006.08866	Probabilistic Optimal Transport based on Collective Graphical Models	Optimal Transport (OT) is being widely used in various fields such as machine learning and computer vision, as it is a powerful tool for measuring the similarity between probability distributions and histograms. In previous studies, OT has been defined as the minimum cost to transport probability mass from one probabil...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	182,337
2004.10705	A Committee of Convolutional Neural Networks for Image Classication in the Concurrent Presence of Feature and Label Noise	Image classification has become a ubiquitous task. Models trained on good quality data achieve accuracy which in some application domains is already above human-level performance. Unfortunately, real-world data are quite often degenerated by the noise existing in features and/or labels. There are quite many papers that...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	173,707
2104.13298	Self-distillation with Batch Knowledge Ensembling Improves ImageNet Classification	The recent studies of knowledge distillation have discovered that ensembling the "dark knowledge" from multiple teachers or students contributes to creating better soft targets for training, but at the cost of significantly more computations and/or parameters. In this work, we present BAtch Knowledge Ensembling (BAKE) ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	232,463
2011.13032	Like a Researcher Stating Broader Impact For the Very First Time	In requiring that a statement of broader impact accompany all submissions for this year's conference, the NeurIPS program chairs made ethics part of the stake in groundbreaking AI research. While there is precedent from other fields and increasing awareness within the NeurIPS community, this paper seeks to answer the q...	false	false	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	208,343
1904.12331	Support Vector Regression via a Combined Reward Cum Penalty Loss Function	In this paper, we introduce a novel combined reward cum penalty loss function to handle the regression problem. The proposed combined reward cum penalty loss function penalizes the data points which lie outside the $\epsilon$-tube of the regressor and also assigns reward for the data points which lie inside of the $\ep...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	129,079
2410.10184	Eliminating the Language Bias for Visual Question Answering with fine-grained Causal Intervention	Despite the remarkable advancements in Visual Question Answering (VQA), the challenge of mitigating the language bias introduced by textual information remains unresolved. Previous approaches capture language bias from a coarse-grained perspective. However, the finer-grained information within a sentence, such as conte...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	497,960
2303.16411	Unlocking Masked Autoencoders as Loss Function for Image and Video Restoration	Image and video restoration has achieved a remarkable leap with the advent of deep learning. The success of deep learning paradigm lies in three key components: data, model, and loss. Currently, many efforts have been devoted to the first two while seldom study focuses on loss function. With the question ``are the de f...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	354,844

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