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
2303.11963	NEMTO: Neural Environment Matting for Novel View and Relighting Synthesis of Transparent Objects	We propose NEMTO, the first end-to-end neural rendering pipeline to model 3D transparent objects with complex geometry and unknown indices of refraction. Commonly used appearance modeling such as the Disney BSDF model cannot accurately address this challenging problem due to the complex light paths bending through refr...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	353,085
1601.07267	Evolutionary stability implies asymptotic stability under multiplicative weights	We show that evolutionarily stable states in general (nonlinear) population games (which can be viewed as continuous vector fields constrained on a polytope) are asymptotically stable under a multiplicative weights dynamic (under appropriate choices of a parameter called the learning rate or step size, which we demonst...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	51,408
2205.07208	Fine-tuning Pre-trained Language Models for Few-shot Intent Detection: Supervised Pre-training and Isotropization	It is challenging to train a good intent classifier for a task-oriented dialogue system with only a few annotations. Recent studies have shown that fine-tuning pre-trained language models with a small amount of labeled utterances from public benchmarks in a supervised manner is extremely helpful. However, we find that ...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	296,514
2108.04107	Identifying Wetland Areas in Historical Maps using Deep Convolutional Neural Networks	1) The local environment and land usages have changed a lot during the past one hundred years. Historical documents and materials are crucial in understanding and following these changes. Historical documents are, therefore, an important piece in the understanding of the impact and consequences of land usage change. Th...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	249,894
1712.07632	Deep Learning with Lung Segmentation and Bone Shadow Exclusion Techniques for Chest X-Ray Analysis of Lung Cancer	The recent progress of computing, machine learning, and especially deep learning, for image recognition brings a meaningful effect for automatic detection of various diseases from chest X-ray images (CXRs). Here efficiency of lung segmentation and bone shadow exclusion techniques is demonstrated for analysis of 2D CXRs...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	87,073
2502.01436	Towards Safer Chatbots: A Framework for Policy Compliance Evaluation of Custom GPTs	Large Language Models (LLMs) have gained unprecedented prominence, achieving widespread adoption across diverse domains and integrating deeply into society. The capability to fine-tune general-purpose LLMs, such as Generative Pre-trained Transformers (GPT), for specific tasks has facilitated the emergence of numerous C...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	529,855
2306.04334	Echoes from Alexandria: A Large Resource for Multilingual Book Summarization	In recent years, research in text summarization has mainly focused on the news domain, where texts are typically short and have strong layout features. The task of full-book summarization presents additional challenges which are hard to tackle with current resources, due to their limited size and availability in Englis...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	371,698
2409.03621	Attend First, Consolidate Later: On the Importance of Attention in Different LLM Layers	In decoder-based LLMs, the representation of a given layer serves two purposes: as input to the next layer during the computation of the current token; and as input to the attention mechanism of future tokens. In this work, we show that the importance of the latter role might be overestimated. To show that, we start by...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	486,106
2309.05273	Formalizing Multimedia Recommendation through Multimodal Deep Learning	Recommender systems (RSs) offer personalized navigation experiences on online platforms, but recommendation remains a challenging task, particularly in specific scenarios and domains. Multimodality can help tap into richer information sources and construct more refined user/item profiles for recommendations. However, e...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	391,029
2208.12559	Physics-Aware Neural Networks for Boundary Layer Linear Problems	Physics-Informed Neural Networks (PINNs) are machine learning tools that approximate the solution of general partial differential equations (PDEs) by adding them in some form as terms of the loss/cost function of a Neural Network. Most pieces of work in the area of PINNs tackle non-linear PDEs. Nevertheless, many inter...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	314,771
2502.12454	Benchmarking Zero-Shot Facial Emotion Annotation with Large Language Models: A Multi-Class and Multi-Frame Approach in DailyLife	This study investigates the feasibility and performance of using large language models (LLMs) to automatically annotate human emotions in everyday scenarios. We conducted experiments on the DailyLife subset of the publicly available FERV39k dataset, employing the GPT-4o-mini model for rapid, zero-shot labeling of key f...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	534,872
1204.1596	An Intelligent Location Management approaches in GSM Mobile Network	Location management refers to the problem of updating and searching the current location of mobile nodes in a wireless network. To make it efficient, the sum of update costs of location database must be minimized. Previous work relying on fixed location databases is unable to fully exploit the knowledge of user mobilit...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	15,329
2206.07852	Performance analysis of coreset selection for quantum implementation of K-Means clustering algorithm	Quantum computing is anticipated to offer immense computational capabilities which could provide efficient solutions to many data science problems. However, the current generation of quantum devices are small and noisy, which makes it difficult to process large data sets relevant for practical problems. Coreset selecti...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	302,912
2410.05630	Navigating Inflation in Ghana: How Can Machine Learning Enhance Economic Stability and Growth Strategies	Inflation remains a persistent challenge for many African countries. This research investigates the critical role of machine learning (ML) in understanding and managing inflation in Ghana, emphasizing its significance for the country's economic stability and growth. Utilizing a comprehensive dataset spanning from 2010 ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	495,832
2112.08935	MVSS-Net: Multi-View Multi-Scale Supervised Networks for Image Manipulation Detection	As manipulating images by copy-move, splicing and/or inpainting may lead to misinterpretation of the visual content, detecting these sorts of manipulations is crucial for media forensics. Given the variety of possible attacks on the content, devising a generic method is nontrivial. Current deep learning based methods a...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	271,984
2010.15316	Multiple Sclerosis Severity Classification From Clinical Text	Multiple Sclerosis (MS) is a chronic, inflammatory and degenerative neurological disease, which is monitored by a specialist using the Expanded Disability Status Scale (EDSS) and recorded in unstructured text in the form of a neurology consult note. An EDSS measurement contains an overall "EDSS" score and several funct...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	203,729
2112.01767	MT-TransUNet: Mediating Multi-Task Tokens in Transformers for Skin Lesion Segmentation and Classification	Recent advances in automated skin cancer diagnosis have yielded performance on par with board-certified dermatologists. However, these approaches formulated skin cancer diagnosis as a simple classification task, dismissing the potential benefit from lesion segmentation. We argue that an accurate lesion segmentation can...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	269,608
1302.4931	An Algebraic Semantics for Possibilistic Logic	The first contribution of this paper is the presentation of a Pavelka - like formulation of possibilistic logic in which the language is naturally enriched by two connectives which represent negation (eg) and a new type of conjunction (otimes). The space of truth values for this logic is the lattice of possibility func...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	22,205
2105.10598	Embracing New Techniques in Deep Learning for Estimating Image Memorability	Various work has suggested that the memorability of an image is consistent across people, and thus can be treated as an intrinsic property of an image. Using computer vision models, we can make specific predictions about what people will remember or forget. While older work has used now-outdated deep learning architect...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	236,444
1802.02547	Learning One Convolutional Layer with Overlapping Patches	We give the first provably efficient algorithm for learning a one hidden layer convolutional network with respect to a general class of (potentially overlapping) patches. Additionally, our algorithm requires only mild conditions on the underlying distribution. We prove that our framework captures commonly used schemes ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	89,788
2409.09359	Symbolic Regression with a Learned Concept Library	We present a novel method for symbolic regression (SR), the task of searching for compact programmatic hypotheses that best explain a dataset. The problem is commonly solved using genetic algorithms; we show that we can enhance such methods by inducing a library of abstract textual concepts. Our algorithm, called LaSR,...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	true	false	true	488,288
2203.01228	Estimating average causal effects from patient trajectories	In medical practice, treatments are selected based on the expected causal effects on patient outcomes. Here, the gold standard for estimating causal effects are randomized controlled trials; however, such trials are costly and sometimes even unethical. Instead, medical practice is increasingly interested in estimating ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	283,303
2106.00643	A survey of machine learning-based physics event generation	Event generators in high-energy nuclear and particle physics play an important role in facilitating studies of particle reactions. We survey the state-of-the-art of machine learning (ML) efforts at building physics event generators. We review ML generative models used in ML-based event generators and their specific cha...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	238,198
1811.06047	Looking at the Driver/Rider in Autonomous Vehicles to Predict Take-Over Readiness	Continuous estimation the driver's take-over readiness is critical for safe and timely transfer of control during the failure modes of autonomous vehicles. In this paper, we propose a data-driven approach for estimating the driver's take-over readiness based purely on observable cues from in-vehicle vision sensors. We ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	113,436
1809.08053	Galois Hulls of Linear Codes over Finite Fields	The $\ell$-Galois hull $h_{\ell}(C)$ of an $[n,k]$ linear code $C$ over a finite field $\mathbb{F}_q$ is the intersection of $C$ and $C^{{\bot}_{\ell}}$, where $C^{\bot_{\ell}}$ denotes the $\ell$-Galois dual of $C$ which introduced by Fan and Zhang (2017). The $\ell$- Galois LCD code is a linear code $C$ with $h_{\ell...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	108,417
2109.04026	Learning Performance Bounds for Safety-Critical Systems	As the complexity of control systems increases, the need for systematic methods to guarantee their efficacy grows as well. However, direct testing of these systems is oftentimes costly, difficult, or impractical. As a result, the test and evaluation ideal would be to verify the efficacy of a system simulator and use th...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	254,259
1507.01282	Empowering Kids to Create and Share Programmable Media	This article reflects on the first eight months of existence of the Scratch Online Community by discussing the design rationale and learning theories underlying Scratch and its website.	true	false	false	true	false	false	false	false	false	false	false	false	false	true	false	false	false	false	44,845
2405.14016	Towards a Unified Framework for Evaluating Explanations	The challenge of creating interpretable models has been taken up by two main research communities: ML researchers primarily focused on lower-level explainability methods that suit the needs of engineers, and HCI researchers who have more heavily emphasized user-centered approaches often based on participatory design me...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	456,201
2304.13290	Improving Conversational Passage Re-ranking with View Ensemble	This paper presents ConvRerank, a conversational passage re-ranker that employs a newly developed pseudo-labeling approach. Our proposed view-ensemble method enhances the quality of pseudo-labeled data, thus improving the fine-tuning of ConvRerank. Our experimental evaluation on benchmark datasets shows that combining ...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	360,532
2310.02971	Prompting and Adapter Tuning for Self-supervised Encoder-Decoder Speech Model	Prompting and adapter tuning have emerged as efficient alternatives to fine-tuning (FT) methods. However, existing studies on speech prompting focused on classification tasks and failed on more complex sequence generation tasks. Besides, adapter tuning is primarily applied with a focus on encoder-only self-supervised m...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	397,067
2009.07525	Detectability of hierarchical communities in networks	We study the problem of recovering a planted hierarchy of partitions in a network. The detectability of a single planted partition has previously been analysed in detail and a phase transition has been identified below which the partition cannot be detected. Here we show that, in the hierarchical setting, there exist a...	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	false	false	195,961
2204.04438	Guided deep learning by subaperture decomposition: ocean patterns from SAR imagery	Spaceborne synthetic aperture radar can provide meters scale images of the ocean surface roughness day or night in nearly all weather conditions. This makes it a unique asset for many geophysical applications. Sentinel 1 SAR wave mode vignettes have made possible to capture many important oceanic and atmospheric phenom...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	290,658
1402.4618	Passive Dynamics in Mean Field Control	Mean-field models are a popular tool in a variety of fields. They provide an understanding of the impact of interactions among a large number of particles or people or other "self-interested agents", and are an increasingly popular tool in distributed control. This paper considers a particular randomized distributed ...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	30,982
1505.03540	Brain Tumor Segmentation with Deep Neural Networks	In this paper, we present a fully automatic brain tumor segmentation method based on Deep Neural Networks (DNNs). The proposed networks are tailored to glioblastomas (both low and high grade) pictured in MR images. By their very nature, these tumors can appear anywhere in the brain and have almost any kind of shape, si...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	43,085
1409.7963	MoDeep: A Deep Learning Framework Using Motion Features for Human Pose Estimation	In this work, we propose a novel and efficient method for articulated human pose estimation in videos using a convolutional network architecture, which incorporates both color and motion features. We propose a new human body pose dataset, FLIC-motion, that extends the FLIC dataset with additional motion features. We ap...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	true	false	false	36,375
1005.5141	On Recursive Edit Distance Kernels with Application to Time Series Classification	This paper proposes some extensions to the work on kernels dedicated to string or time series global alignment based on the aggregation of scores obtained by local alignments. The extensions we propose allow to construct, from classical recursive definition of elastic distances, recursive edit distance (or time-warp) k...	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	false	6,589
2206.02889	Conditional Seq2Seq model for the time-dependent two-level system	We apply the deep learning neural network architecture to the two-level system in quantum optics to solve the time-dependent Schrodinger equation. By carefully designing the network structure and tuning parameters, above 90 percent accuracy in super long-term predictions can be achieved in the case of random electric f...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	301,065
2501.10666	Speech Emotion Detection Based on MFCC and CNN-LSTM Architecture	Emotion detection techniques have been applied to multiple cases mainly from facial image features and vocal audio features, of which the latter aspect is disputed yet not only due to the complexity of speech audio processing but also the difficulties of extracting appropriate features. Part of the SAVEE and RAVDESS da...	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	525,608
2206.15079	Prediction of Dilatory Behavior in eLearning: A Comparison of Multiple Machine Learning Models	Procrastination, the irrational delay of tasks, is a common occurrence in online learning. Potential negative consequences include higher risk of drop-outs, increased stress, and reduced mood. Due to the rise of learning management systems and learning analytics, indicators of such behavior can be detected, enabling pr...	false	false	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	305,480
2102.11488	Senone-aware Adversarial Multi-task Training for Unsupervised Child to Adult Speech Adaptation	Acoustic modeling for child speech is challenging due to the high acoustic variability caused by physiological differences in the vocal tract. The dearth of publicly available datasets makes the task more challenging. In this work, we propose a feature adaptation approach by exploiting adversarial multi-task training t...	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	221,437
2410.08903	Dynamic Benchmarks: Spatial and Temporal Alignment for ADS Performance Evaluation	Deployed SAE level 4+ Automated Driving Systems (ADS) without a human driver are currently operational ride-hailing fleets on surface streets in the United States. This current use case and future applications of this technology will determine where and when the fleets operate, potentially resulting in a divergence fro...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	497,337
2012.01982	Tensor Data Scattering and the Impossibility of Slicing Theorem	This paper proposes a standard way to represent sparse tensors. A broad theoretical framework for tensor data scattering methods used in various deep learning frameworks is established. This paper presents a theorem that is very important for performance analysis and accelerator optimization for implementing data scatt...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	209,605
2305.17940	Learning Conditional Attributes for Compositional Zero-Shot Learning	Compositional Zero-Shot Learning (CZSL) aims to train models to recognize novel compositional concepts based on learned concepts such as attribute-object combinations. One of the challenges is to model attributes interacted with different objects, e.g., the attribute ``wet" in ``wet apple" and ``wet cat" is different. ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	368,808
2310.17021	Streaming Factor Trajectory Learning for Temporal Tensor Decomposition	Practical tensor data is often along with time information. Most existing temporal decomposition approaches estimate a set of fixed factors for the objects in each tensor mode, and hence cannot capture the temporal evolution of the objects' representation. More important, we lack an effective approach to capture such e...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	402,961
1511.08977	Fundamental Limits of Training-Based Multiuser MIMO Systems	In this paper, we endeavour to seek a fundamental understanding of the potentials and limitations of training-based multiuser multiple-input multiple-output (MIMO) systems. In a multiuser MIMO system, users are geographically separated. So, the near-far effect plays an indispensable role in channel fading. The existing...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	49,607
2310.14079	To Copy, or not to Copy; That is a Critical Issue of the Output Softmax Layer in Neural Sequential Recommenders	Recent studies suggest that the existing neural models have difficulty handling repeated items in sequential recommendation tasks. However, our understanding of this difficulty is still limited. In this study, we substantially advance this field by identifying a major source of the problem: the single hidden state embe...	false	false	false	false	true	true	true	false	false	false	false	false	false	false	false	false	false	false	401,702
2107.02525	Semantic Segmentation Alternative Technique: Segmentation Domain Generation	Detecting objects of interest in images was always a compelling task to automate. In recent years this task was more and more explored using deep learning techniques, mostly using region-based convolutional networks. In this project we propose an alternative semantic segmentation technique making use of Generative Adve...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	244,856
2211.01458	Towards Zero-Shot Code-Switched Speech Recognition	In this work, we seek to build effective code-switched (CS) automatic speech recognition systems (ASR) under the zero-shot setting where no transcribed CS speech data is available for training. Previously proposed frameworks which conditionally factorize the bilingual task into its constituent monolingual parts are a p...	false	false	true	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	328,231
2212.02988	PRISM: Probabilistic Real-Time Inference in Spatial World Models	We introduce PRISM, a method for real-time filtering in a probabilistic generative model of agent motion and visual perception. Previous approaches either lack uncertainty estimates for the map and agent state, do not run in real-time, do not have a dense scene representation or do not model agent dynamics. Our solutio...	false	false	false	false	false	false	true	true	false	false	false	true	false	false	false	false	false	false	334,956
2110.05324	Learnable Adaptive Cosine Estimator (LACE) for Image Classification	In this work, we propose a new loss to improve feature discriminability and classification performance. Motivated by the adaptive cosine/coherence estimator (ACE), our proposed method incorporates angular information that is inherently learned by artificial neural networks. Our learnable ACE (LACE) transforms the data ...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	260,243
2409.13175	RPAF: A Reinforcement Prediction-Allocation Framework for Cache Allocation in Large-Scale Recommender Systems	Modern recommender systems are built upon computation-intensive infrastructure, and it is challenging to perform real-time computation for each request, especially in peak periods, due to the limited computational resources. Recommending by user-wise result caches is widely used when the system cannot afford a real-tim...	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	false	489,881
1604.03306	A sharp bound on RIC in generalized orthogonal matching pursuit	Generalized orthogonal matching pursuit (gOMP) algorithm has received much attention in recent years as a natural extension of orthogonal matching pursuit. It is used to recover sparse signals in compressive sensing. In this paper, a new bound is obtained for the exact reconstruction of every $K$-sparse signal via the ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	54,486
2311.15809	From deepfake to deep useful: risks and opportunities through a systematic literature review	Deepfake videos are defined as a resulting media from the synthesis of different persons images and videos, mostly faces, replacing a real one. The easy spread of such videos leads to elevated misinformation and represents a threat to society and democracy today. The present study aims to collect and analyze the releva...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	410,646
1805.09575	Primal-Dual Wasserstein GAN	We introduce Primal-Dual Wasserstein GAN, a new learning algorithm for building latent variable models of the data distribution based on the primal and the dual formulations of the optimal transport (OT) problem. We utilize the primal formulation to learn a flexible inference mechanism and to create an optimal approxim...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	98,453
2006.11584	Calibration of Model Uncertainty for Dropout Variational Inference	The model uncertainty obtained by variational Bayesian inference with Monte Carlo dropout is prone to miscalibration. In this paper, different logit scaling methods are extended to dropout variational inference to recalibrate model uncertainty. Expected uncertainty calibration error (UCE) is presented as a metric to me...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	183,293
2502.05784	Propagation of Chaos for Mean-Field Langevin Dynamics and its Application to Model Ensemble	Mean-field Langevin dynamics (MFLD) is an optimization method derived by taking the mean-field limit of noisy gradient descent for two-layer neural networks in the mean-field regime. Recently, the propagation of chaos (PoC) for MFLD has gained attention as it provides a quantitative characterization of the optimization...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	531,776
2410.14766	Evaluating Quantized Large Language Models for Code Generation on Low-Resource Language Benchmarks	Democratization of AI is an important topic within the broader topic of the digital divide. This issue is relevant to LLMs, which are becoming popular as AI co-pilots but suffer from a lack of accessibility due to high computational demand. In this study, we evaluate whether quantization is a viable approach toward ena...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	true	500,204
2410.00860	Enhancing Web Spam Detection through a Blockchain-Enabled Crowdsourcing Mechanism	The proliferation of spam on the Web has necessitated the development of machine learning models to automate their detection. However, the dynamic nature of spam and the sophisticated evasion techniques employed by spammers often lead to low accuracy in these models. Traditional machine-learning approaches struggle to ...	false	false	false	true	false	false	false	false	false	false	false	false	true	false	false	false	false	false	493,515
2103.01903	Semantic Relation Reasoning for Shot-Stable Few-Shot Object Detection	Few-shot object detection is an imperative and long-lasting problem due to the inherent long-tail distribution of real-world data. Its performance is largely affected by the data scarcity of novel classes. But the semantic relation between the novel classes and the base classes is constant regardless of the data availa...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	222,785
2309.09196	Efficient Pyramid Channel Attention Network for Pathological Myopia Recognition	Pathological myopia (PM) is the leading ocular disease for impaired vision worldwide. Clinically, the characteristic of pathology distribution in PM is global-local on the fundus image, which plays a significant role in assisting clinicians in diagnosing PM. However, most existing deep neural networks focused on design...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	392,508
2402.07180	MAGNETO: Edge AI for Human Activity Recognition -- Privacy and Personalization	Human activity recognition (HAR) is a well-established field, significantly advanced by modern machine learning (ML) techniques. While companies have successfully integrated HAR into consumer products, they typically rely on a predefined activity set, which limits personalizations at the user level (edge devices). Desp...	false	false	false	false	true	false	true	false	false	false	false	false	true	false	false	false	false	false	428,596
2311.08150	The Hyperdimensional Transform for Distributional Modelling, Regression and Classification	Hyperdimensional computing (HDC) is an increasingly popular computing paradigm with immense potential for future intelligent applications. Although the main ideas already took form in the 1990s, HDC recently gained significant attention, especially in the field of machine learning and data science. Next to efficiency, ...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	true	407,609
2309.05019	SA-Solver: Stochastic Adams Solver for Fast Sampling of Diffusion Models	Diffusion Probabilistic Models (DPMs) have achieved considerable success in generation tasks. As sampling from DPMs is equivalent to solving diffusion SDE or ODE which is time-consuming, numerous fast sampling methods built upon improved differential equation solvers are proposed. The majority of such techniques consid...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	390,929
2105.01560	Broadly Applicable Targeted Data Sample Omission Attacks	We introduce a novel clean-label targeted poisoning attack on learning mechanisms. While classical poisoning attacks typically corrupt data via addition, modification and omission, our attack focuses on data omission only. Our attack misclassifies a single, targeted test sample of choice, without manipulating that samp...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	233,553
1706.04692	Bias and high-dimensional adjustment in observational studies of peer effects	Peer effects, in which the behavior of an individual is affected by the behavior of their peers, are posited by multiple theories in the social sciences. Other processes can also produce behaviors that are correlated in networks and groups, thereby generating debate about the credibility of observational (i.e. nonexper...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	75,379
1203.5485	BlinkDB: Queries with Bounded Errors and Bounded Response Times on Very Large Data	In this paper, we present BlinkDB, a massively parallel, sampling-based approximate query engine for running ad-hoc, interactive SQL queries on large volumes of data. The key insight that BlinkDB builds on is that one can often make reasonable decisions in the absence of perfect answers. For example, reliably detecting...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	true	15,115
2302.07458	CUTS: Neural Causal Discovery from Irregular Time-Series Data	Causal discovery from time-series data has been a central task in machine learning. Recently, Granger causality inference is gaining momentum due to its good explainability and high compatibility with emerging deep neural networks. However, most existing methods assume structured input data and degenerate greatly when ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	345,741
2008.00715	Learning to Drive (L2D) as a Low-Cost Benchmark for Real-World Reinforcement Learning	We present Learning to Drive (L2D), a low-cost benchmark for real-world reinforcement learning (RL). L2D involves a simple and reproducible experimental setup where an RL agent has to learn to drive a Donkey car around three miniature tracks, given only monocular image observations and speed of the car. The agent has t...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	190,091
2403.02236	Interpretable Models for Detecting and Monitoring Elevated Intracranial Pressure	Detecting elevated intracranial pressure (ICP) is crucial in diagnosing and managing various neurological conditions. These fluctuations in pressure are transmitted to the optic nerve sheath (ONS), resulting in changes to its diameter, which can then be detected using ultrasound imaging devices. However, interpreting s...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	434,737
2312.06971	CCM: Adding Conditional Controls to Text-to-Image Consistency Models	Consistency Models (CMs) have showed a promise in creating visual content efficiently and with high quality. However, the way to add new conditional controls to the pretrained CMs has not been explored. In this technical report, we consider alternative strategies for adding ControlNet-like conditional control to CMs an...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	414,745
2412.06284	Your Data Is Not Perfect: Towards Cross-Domain Out-of-Distribution Detection in Class-Imbalanced Data	Previous OOD detection systems only focus on the semantic gap between ID and OOD samples. Besides the semantic gap, we are faced with two additional gaps: the domain gap between source and target domains, and the class-imbalance gap between different classes. In fact, similar objects from different domains should belon...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	515,188
2003.10719	Multi-Feature Discrete Collaborative Filtering for Fast Cold-start Recommendation	Hashing is an effective technique to address the large-scale recommendation problem, due to its high computation and storage efficiency on calculating the user preferences on items. However, existing hashing-based recommendation methods still suffer from two important problems: 1) Their recommendation process mainly re...	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	false	169,421
2010.07218	Peridynamics-based discrete element method (PeriDEM) model of granular systems involving breakage of arbitrarily shaped particles	Usage, manipulation, transport, delivery, and mixing of granular or particulate media, comprised of spherical or polyhedral particles, is commonly encountered in industrial sectors of construction (cement and rock fragments), pharmaceutics (tablets), and transportation (ballast). Elucidating particulate media's behavio...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	200,741
1503.01910	Sequential Relevance Maximization with Binary Feedback	Motivated by online settings where users can provide explicit feedback about the relevance of products that are sequentially presented to them, we look at the recommendation process as a problem of dynamically optimizing this relevance feedback. Such an algorithm optimizes the fine tradeoff between presenting the produ...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	40,878
2211.04659	When is Momentum Extragradient Optimal? A Polynomial-Based Analysis	The extragradient method has gained popularity due to its robust convergence properties for differentiable games. Unlike single-objective optimization, game dynamics involve complex interactions reflected by the eigenvalues of the game vector field's Jacobian scattered across the complex plane. This complexity can caus...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	329,314
2502.01329	Benchmarking Different QP Formulations and Solvers for Dynamic Quadrupedal Walking	Quadratic Programs (QPs) are widely used in the control of walking robots, especially in Model Predictive Control (MPC) and Whole-Body Control (WBC). In both cases, the controller design requires the formulation of a QP and the selection of a suitable QP solver, both requiring considerable time and expertise. While com...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	529,805
1811.12039	EV-SegNet: Semantic Segmentation for Event-based Cameras	Event cameras, or Dynamic Vision Sensor (DVS), are very promising sensors which have shown several advantages over frame based cameras. However, most recent work on real applications of these cameras is focused on 3D reconstruction and 6-DOF camera tracking. Deep learning based approaches, which are leading the state-o...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	114,921
2207.02368	Text Enriched Sparse Hyperbolic Graph Convolutional Networks	Heterogeneous networks, which connect informative nodes containing text with different edge types, are routinely used to store and process information in various real-world applications. Graph Neural Networks (GNNs) and their hyperbolic variants provide a promising approach to encode such networks in a low-dimensional ...	false	false	false	true	false	true	true	false	false	false	false	false	false	false	false	false	false	false	306,489
2103.01598	Spatial Attention Point Network for Deep-learning-based Robust Autonomous Robot Motion Generation	Deep learning provides a powerful framework for automated acquisition of complex robotic motions. However, despite a certain degree of generalization, the need for vast amounts of training data depending on the work-object position is an obstacle to industrial applications. Therefore, a robot motion-generation model th...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	222,682
2501.06458	O1 Replication Journey -- Part 3: Inference-time Scaling for Medical Reasoning	Building upon our previous investigations of O1 replication (Part 1: Journey Learning [Qin et al., 2024] and Part 2: Distillation [Huang et al., 2024]), this work explores the potential of inference-time scaling in large language models (LLMs) for medical reasoning tasks, ranging from diagnostic decision-making to trea...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	523,990
2307.01209	Multi-Dialectal Representation Learning of Sinitic Phonology	Machine learning techniques have shown their competence for representing and reasoning in symbolic systems such as language and phonology. In Sinitic Historical Phonology, notable tasks that could benefit from machine learning include the comparison of dialects and reconstruction of proto-languages systems. Motivated b...	false	false	false	true	false	false	true	false	true	false	false	false	false	false	false	false	false	false	377,275
2208.14662	AWADA: Attention-Weighted Adversarial Domain Adaptation for Object Detection	Object detection networks have reached an impressive performance level, yet a lack of suitable data in specific applications often limits it in practice. Typically, additional data sources are utilized to support the training task. In these, however, domain gaps between different data sources pose a challenge in deep l...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	315,387
2206.14322	An Empirical Study of Challenges in Converting Deep Learning Models	There is an increase in deploying Deep Learning (DL)-based software systems in real-world applications. Usually DL models are developed and trained using DL frameworks that have their own internal mechanisms/formats to represent and train DL models, and usually those formats cannot be recognized by other frameworks. Mo...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	305,242
1901.08079	A Question-Entailment Approach to Question Answering	One of the challenges in large-scale information retrieval (IR) is to develop fine-grained and domain-specific methods to answer natural language questions. Despite the availability of numerous sources and datasets for answer retrieval, Question Answering (QA) remains a challenging problem due to the difficulty of the ...	false	false	false	false	true	true	true	false	true	false	false	false	false	false	false	false	false	false	119,374
2310.09998	SeUNet-Trans: A Simple yet Effective UNet-Transformer Model for Medical Image Segmentation	Automated medical image segmentation is becoming increasingly crucial to modern clinical practice, driven by the growing demand for precise diagnosis, the push towards personalized treatment plans, and the advancements in machine learning algorithms, especially the incorporation of deep learning methods. While convolut...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	400,036
2202.05385	Cyclops: Open Platform for Scale Truck Platooning	Cyclops, introduced in this paper, is an open research platform for everyone that wants to validate novel ideas and approaches in the area of self-driving heavy-duty vehicle platooning. The platform consists of multiple 1/14 scale semi-trailer trucks, a scale proving ground, and associated computing, communication and ...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	279,860
2502.00562	Assessment of ChatGPT for Engineering Statics Analysis	Large language models (LLMs) such as OpenAI's ChatGPT hold potential for automating engineering analysis, yet their reliability in solving multi-step statics problems remains uncertain. This study evaluates the performance of ChatGPT-4o and ChatGPT-o1-preview on foundational statics tasks, from simple calculations of N...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	529,437
1711.08333	A correlational analysis of multiagent sensorimotor interactions: clustering autonomous and controllable entities	A first step to reach Theory of Mind (ToM) abilities (attribution of beliefs to others) in synthetic agents through sensorimotor interactions, would be to tag sensory data with agent typology and action intentions: autonomous agent X moved an object under the box. We propose a dual arm robotic setup in which ToM could ...	false	false	false	false	true	false	false	true	false	false	false	false	false	false	true	false	false	false	85,186
2302.07594	Toward matrix multiplication for deep learning inference on the Xilinx Versal	The remarkable positive impact of Deep Neural Networks on many Artificial Intelligence (AI) tasks has led to the development of various high performance algorithms as well as specialized processors and accelerators. In this paper we address this scenario by demonstrating that the principles underlying the modern realiz...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	345,778
2309.11497	FreeU: Free Lunch in Diffusion U-Net	In this paper, we uncover the untapped potential of diffusion U-Net, which serves as a "free lunch" that substantially improves the generation quality on the fly. We initially investigate the key contributions of the U-Net architecture to the denoising process and identify that its main backbone primarily contributes t...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	393,427
1102.4021	Privacy Preserving Spam Filtering	Email is a private medium of communication, and the inherent privacy constraints form a major obstacle in developing effective spam filtering methods which require access to a large amount of email data belonging to multiple users. To mitigate this problem, we envision a privacy preserving spam filtering system, where ...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	9,290
1910.07089	Challenges of Human-Aware AI Systems	From its inception, AI has had a rather ambivalent relationship to humans---swinging between their augmentation and replacement. Now, as AI technologies enter our everyday lives at an ever increasing pace, there is a greater need for AI systems to work synergistically with humans. To do this effectively, AI systems mus...	true	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	149,515
1805.11054	A Poisson Gamma Probabilistic Model for Latent Node-group Memberships in Dynamic Networks	We present a probabilistic model for learning from dynamic relational data, wherein the observed interactions among networked nodes are modeled via the Bernoulli Poisson link function, and the underlying network structure are characterized by nonnegative latent node-group memberships, which are assumed to be gamma dist...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	98,831
1412.7437	Compressibility of positive semidefinite factorizations and quantum models	We investigate compressibility of the dimension of positive semidefinite matrices while approximately preserving their pairwise inner products. This can either be regarded as compression of positive semidefinite factorizations of nonnegative matrices or (if the matrices are subject to additional normalization constrain...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	38,805
1503.05849	Deep Transform: Time-Domain Audio Error Correction via Probabilistic Re-Synthesis	In the process of recording, storage and transmission of time-domain audio signals, errors may be introduced that are difficult to correct in an unsupervised way. Here, we train a convolutional deep neural network to re-synthesize input time-domain speech signals at its output layer. We then use this abstract transform...	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	true	false	false	41,291
2405.13993	AutoLCZ: Towards Automatized Local Climate Zone Mapping from Rule-Based Remote Sensing	Local climate zones (LCZs) established a standard classification system to categorize the landscape universe for improved urban climate studies. Existing LCZ mapping is guided by human interaction with geographic information systems (GIS) or modelled from remote sensing (RS) data. GIS-based methods do not scale to larg...	false	true	false	false	false	true	true	false	false	false	false	true	false	false	false	false	false	false	456,185
2303.09780	Mpox-AISM: AI-Mediated Super Monitoring for Mpox and Like-Mpox	Swift and accurate diagnosis for earlier-stage monkeypox (mpox) patients is crucial to avoiding its spread. However, the similarities between common skin disorders and mpox and the need for professional diagnosis unavoidably impaired the diagnosis of earlier-stage mpox patients and contributed to mpox outbreak. To addr...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	352,186
2102.08583	A Discrete-Time Switching System Analysis of Q-learning	This paper develops a novel control-theoretic framework to analyze the non-asymptotic convergence of Q-learning. We show that the dynamics of asynchronous Q-learning with a constant step-size can be naturally formulated as a discrete-time stochastic affine switching system. Moreover, the evolution of the Q-learning est...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	220,510
1507.01316	Delay-aware data transmission of multi-carrier communications in the presence of renewable energy	In the paper, we investigate the delay-aware data transmission in renewable energy aided multi-carrier system. Besides utilizing the local renewables, the transmitter can also purchase grid power. By scheduling the amount of transmitted data (The data are stored in a buffer before transmission), the sub-carrier allocat...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	44,858
2412.01555	Optimizing Domain-Specific Image Retrieval: A Benchmark of FAISS and Annoy with Fine-Tuned Features	Approximate Nearest Neighbor search is one of the keys to high-scale data retrieval performance in many applications. The work is a bridge between feature extraction and ANN indexing through fine-tuning a ResNet50 model with various ANN methods: FAISS and Annoy. We evaluate the systems with respect to indexing time, me...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	513,168

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