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
2408.09646	Debiased Contrastive Representation Learning for Mitigating Dual Biases in Recommender Systems	In recommender systems, popularity and conformity biases undermine recommender effectiveness by disproportionately favouring popular items, leading to their over-representation in recommendation lists and causing an unbalanced distribution of user-item historical data. We construct a causal graph to address both biases...	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	false	false	481,522
1102.2837	Efficient Promotion Strategies in Hierarchical Organizations	The Peter principle has been recently investigated by means of an agent-based simulation and its validity has been numerically corroborated. It has been confirmed that, within certain conditions, it can really influence in a negative way the efficiency of a pyramidal organization adopting meritocratic promotions. It wa...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	9,179
2001.10888	Cross-Layer Scheduling and Beamforming in Smart-Grid Powered Cellular Networks With Heterogeneous Energy Coordination	User scheduling, beamforming and energy coordination are investigated in smart-grid powered cellular networks (SGPCNs), where the base stations are powered by a smart grid and natural renewable energy sources. Heterogeneous energy coordination is considered in SGPCNs, namely energy merchandizing with the smart grid and...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	161,928
1706.05374	Expected Policy Gradients	We propose expected policy gradients (EPG), which unify stochastic policy gradients (SPG) and deterministic policy gradients (DPG) for reinforcement learning. Inspired by expected sarsa, EPG integrates across the action when estimating the gradient, instead of relying only on the action in the sampled trajectory. We es...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	75,503
1103.5348	Precoding for Outage Probability Minimization on Block Fading Channels	The outage probability limit is a fundamental and achievable lower bound on the word error rate of coded communication systems affected by fading. This limit is mainly determined by two parameters: the diversity order and the coding gain. With linear precoding, full diversity on a block fading channel can be achieved w...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	9,780
2202.00514	Analyzing Community-aware Centrality Measures Using The Linear Threshold Model	Targeting influential nodes in complex networks allows fastening or hindering rumors, epidemics, and electric blackouts. Since communities are prevalent in real-world networks, community-aware centrality measures exploit this information to target influential nodes. Researches show that they compare favorably with clas...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	278,169
2205.09898	Let the Model Decide its Curriculum for Multitask Learning	Curriculum learning strategies in prior multi-task learning approaches arrange datasets in a difficulty hierarchy either based on human perception or by exhaustively searching the optimal arrangement. However, human perception of difficulty may not always correlate well with machine interpretation leading to poor perfo...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	297,463
2011.07586	Uncertainty as a Form of Transparency: Measuring, Communicating, and Using Uncertainty	Algorithmic transparency entails exposing system properties to various stakeholders for purposes that include understanding, improving, and contesting predictions. Until now, most research into algorithmic transparency has predominantly focused on explainability. Explainability attempts to provide reasons for a machine...	true	false	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	206,604
2206.09010	LIMO: Latent Inceptionism for Targeted Molecule Generation	Generation of drug-like molecules with high binding affinity to target proteins remains a difficult and resource-intensive task in drug discovery. Existing approaches primarily employ reinforcement learning, Markov sampling, or deep generative models guided by Gaussian processes, which can be prohibitively slow when ge...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	303,391
2412.16925	Quantifying Public Response to COVID-19 Events: Introducing the Community Sentiment and Engagement Index	This study introduces the Community Sentiment and Engagement Index (CSEI), developed to capture nuanced public sentiment and engagement variations on social media, particularly in response to major events related to COVID-19. Constructed with diverse sentiment indicators, CSEI integrates features like engagement, daily...	false	false	false	true	true	false	true	false	true	false	false	false	false	true	false	false	false	false	519,751
2404.16548	Cross-Domain Spatial Matching for Camera and Radar Sensor Data Fusion in Autonomous Vehicle Perception System	In this paper, we propose a novel approach to address the problem of camera and radar sensor fusion for 3D object detection in autonomous vehicle perception systems. Our approach builds on recent advances in deep learning and leverages the strengths of both sensors to improve object detection performance. Precisely, we...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	449,535
1401.5636	Causal Discovery in a Binary Exclusive-or Skew Acyclic Model: BExSAM	Discovering causal relations among observed variables in a given data set is a major objective in studies of statistics and artificial intelligence. Recently, some techniques to discover a unique causal model have been explored based on non-Gaussianity of the observed data distribution. However, most of these are limit...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	30,215
1806.02400	A Comparative Study on Unsupervised Domain Adaptation Approaches for Coffee Crop Mapping	In this work, we investigate the application of existing unsupervised domain adaptation (UDA) approaches to the task of transferring knowledge between crop regions having different coffee patterns. Given a geographical region with fully mapped coffee plantations, we observe that this knowledge can be used to train a cl...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	99,770
1909.08964	To Detect Irregular Trade Behaviors In Stock Market By Using Graph Based Ranking Methods	To detect the irregular trade behaviors in the stock market is the important problem in machine learning field. These irregular trade behaviors are obviously illegal. To detect these irregular trade behaviors in the stock market, data scientists normally employ the supervised learning techniques. In this paper, we empl...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	146,104
2405.16310	An Empirical Exploration of Trust Dynamics in LLM Supply Chains	With the widespread proliferation of AI systems, trust in AI is an important and timely topic to navigate. Researchers so far have largely employed a myopic view of this relationship. In particular, a limited number of relevant trustors (e.g., end-users) and trustees (i.e., AI systems) have been considered, and empiric...	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	457,357
2111.12489	Repeated-root Constacyclic Codes with Optimal Locality	A code is called a locally repairable code (LRC) if any code symbol is a function of a small fraction of other code symbols. When a locally repairable code is employed in a distributed storage systems, an erased symbol can be recovered by accessing only a small number of other symbols, and hence alleviating the network...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	267,981
1207.7144	Information and Estimation over Binomial and Negative Binomial Models	In recent years, a number of results have been developed which connect information measures and estimation measures under various models, including, predominently, Gaussian and Poisson models. More recent results due to Taborda and Perez-Cruz relate the relative entropy to certain mismatched estimation errors in the co...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	17,829
1109.0687	Performance of distributed mechanisms for flow admission in wireless adhoc networks	Given a wireless network where some pairs of communication links interfere with each other, we study sufficient conditions for determining whether a given set of minimum bandwidth quality-of-service (QoS) requirements can be satisfied. We are especially interested in algorithms which have low communication overhead and...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	11,957
2102.10130	Image Classification using CNN for Traffic Signs in Pakistan	The autonomous automotive industry is one of the largest and most conventional projects worldwide, with many technology companies effectively designing and orienting their products towards automobile safety and accuracy. These products are performing very well over the roads in developed countries. But can fail in the ...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	220,979
2411.12181	Enhancing Low Dose Computed Tomography Images Using Consistency Training Techniques	Diffusion models have significant impact on wide range of generative tasks, especially on image inpainting and restoration. Although the improvements on aiming for decreasing number of function evaluations (NFE), the iterative results are still computationally expensive. Consistency models are as a new family of genera...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	509,325
2405.12701	OLAPH: Improving Factuality in Biomedical Long-form Question Answering	In the medical domain, numerous scenarios necessitate the long-form generation ability of large language models (LLMs). Specifically, when addressing patients' questions, it is essential that the model's response conveys factual claims, highlighting the need for an automated method to evaluate those claims. Thus, we in...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	455,618
2311.03551	Context Unlocks Emotions: Text-based Emotion Classification Dataset Auditing with Large Language Models	The lack of contextual information in text data can make the annotation process of text-based emotion classification datasets challenging. As a result, such datasets often contain labels that fail to consider all the relevant emotions in the vocabulary. This misalignment between text inputs and labels can degrade the p...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	405,890
2405.01538	Multi-Space Alignments Towards Universal LiDAR Segmentation	A unified and versatile LiDAR segmentation model with strong robustness and generalizability is desirable for safe autonomous driving perception. This work presents M3Net, a one-of-a-kind framework for fulfilling multi-task, multi-dataset, multi-modality LiDAR segmentation in a universal manner using just a single set ...	false	false	false	false	false	false	true	true	false	false	false	true	false	false	false	false	false	false	451,392
1902.02629	SAPSAM - Sparsely Annotated Pathological Sign Activation Maps - A novel approach to train Convolutional Neural Networks on lung CT scans using binary labels only	Chronic Pulmonary Aspergillosis (CPA) is a complex lung disease caused by infection with Aspergillus. Computed tomography (CT) images are frequently requested in patients with suspected and established disease, but the radiological signs on CT are difficult to quantify making accurate follow-up challenging. We propose ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	120,918
1912.11176	Unsupervised Learning of Graph Hierarchical Abstractions with Differentiable Coarsening and Optimal Transport	Hierarchical abstractions are a methodology for solving large-scale graph problems in various disciplines. Coarsening is one such approach: it generates a pyramid of graphs whereby the one in the next level is a structural summary of the prior one. With a long history in scientific computing, many coarsening strategies...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	158,497
2204.07980	Does Recommend-Revise Produce Reliable Annotations? An Analysis on Missing Instances in DocRED	DocRED is a widely used dataset for document-level relation extraction. In the large-scale annotation, a \textit{recommend-revise} scheme is adopted to reduce the workload. Within this scheme, annotators are provided with candidate relation instances from distant supervision, and they then manually supplement and remov...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	291,922
2301.00057	A Mapping of Assurance Techniques for Learning Enabled Autonomous Systems to the Systems Engineering Lifecycle	Learning enabled autonomous systems provide increased capabilities compared to traditional systems. However, the complexity of and probabilistic nature in the underlying methods enabling such capabilities present challenges for current systems engineering processes for assurance, and test, evaluation, verification, and...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	true	338,781
2404.00099	Efficient and Sharp Off-Policy Evaluation in Robust Markov Decision Processes	We study the evaluation of a policy under best- and worst-case perturbations to a Markov decision process (MDP), using transition observations from the original MDP, whether they are generated under the same or a different policy. This is an important problem when there is the possibility of a shift between historical ...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	442,762
2305.09057	Self-Supervised Pretraining on Paired Sequences of fMRI Data for Transfer Learning to Brain Decoding Tasks	In this work we introduce a self-supervised pretraining framework for transformers on functional Magnetic Resonance Imaging (fMRI) data. First, we pretrain our architecture on two self-supervised tasks simultaneously to teach the model a general understanding of the temporal and spatial dynamics of human auditory corte...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	364,494
1611.08229	Fast Orthonormal Sparsifying Transforms Based on Householder Reflectors	Dictionary learning is the task of determining a data-dependent transform that yields a sparse representation of some observed data. The dictionary learning problem is non-convex, and usually solved via computationally complex iterative algorithms. Furthermore, the resulting transforms obtained generally lack structure...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	64,467
2211.01839	HyperSound: Generating Implicit Neural Representations of Audio Signals with Hypernetworks	Implicit neural representations (INRs) are a rapidly growing research field, which provides alternative ways to represent multimedia signals. Recent applications of INRs include image super-resolution, compression of high-dimensional signals, or 3D rendering. However, these solutions usually focus on visual data, and a...	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	true	false	false	328,380
2401.11491	BA-LINS: A Frame-to-Frame Bundle Adjustment for LiDAR-Inertial Navigation	Bundle Adjustment (BA) has been proven to improve the accuracy of the LiDAR mapping. However, the BA method has not yet been properly employed in a dead-reckoning navigation system. In this paper, we present a frame-to-frame (F2F) BA for LiDAR-inertial navigation, named BA-LINS. Based on the direct F2F point-cloud asso...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	423,026
2401.02274	ShapeAug: Occlusion Augmentation for Event Camera Data	Recently, Dynamic Vision Sensors (DVSs) sparked a lot of interest due to their inherent advantages over conventional RGB cameras. These advantages include a low latency, a high dynamic range and a low energy consumption. Nevertheless, the processing of DVS data using Deep Learning (DL) methods remains a challenge, part...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	419,655
2010.01494	PTUM: Pre-training User Model from Unlabeled User Behaviors via Self-supervision	User modeling is critical for many personalized web services. Many existing methods model users based on their behaviors and the labeled data of target tasks. However, these methods cannot exploit useful information in unlabeled user behavior data, and their performance may be not optimal when labeled data is scarce. M...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	198,675
2403.00189	The Road to Next-Generation Multiple Access: A 50-Year Tutorial Review	The evolution of wireless communications has been significantly influenced by remarkable advancements in multiple access (MA) technologies over the past five decades, shaping the landscape of modern connectivity. Within this context, a comprehensive tutorial review is presented, focusing on representative MA techniques...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	433,886
cs/0003043	Automatic Classification of Text Databases through Query Probing	Many text databases on the web are "hidden" behind search interfaces, and their documents are only accessible through querying. Search engines typically ignore the contents of such search-only databases. Recently, Yahoo-like directories have started to manually organize these databases into categories that users can br...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	false	537,056
2012.11369	Flexible, Non-parametric Modeling Using Regularized Neural Networks	Non-parametric, additive models are able to capture complex data dependencies in a flexible, yet interpretable way. However, choosing the format of the additive components often requires non-trivial data exploration. Here, as an alternative, we propose PrAda-net, a one-hidden-layer neural network, trained with proximal...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	212,620
2403.05564	Promoting Fair Vaccination Strategies Through Influence Maximization: A Case Study on COVID-19 Spread	The aftermath of the Covid-19 pandemic saw more severe outcomes for racial minority groups and economically-deprived communities. Such disparities can be explained by several factors, including unequal access to healthcare, as well as the inability of low income groups to reduce their mobility due to work or social obl...	false	false	false	true	false	false	false	false	false	false	false	false	false	true	false	false	false	false	436,060
2108.11513	Learning Effective and Efficient Embedding via an Adaptively-Masked Twins-based Layer	Embedding learning for categorical features is crucial for the deep learning-based recommendation models (DLRMs). Each feature value is mapped to an embedding vector via an embedding learning process. Conventional methods configure a fixed and uniform embedding size to all feature values from the same feature field. Ho...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	252,196
2405.05363	LOC-ZSON: Language-driven Object-Centric Zero-Shot Object Retrieval and Navigation	In this paper, we present LOC-ZSON, a novel Language-driven Object-Centric image representation for object navigation task within complex scenes. We propose an object-centric image representation and corresponding losses for visual-language model (VLM) fine-tuning, which can handle complex object-level queries. In addi...	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	452,889
2007.07591	Learning Invariances for Interpretability using Supervised VAE	We propose to learn model invariances as a means of interpreting a model. This is motivated by a reverse engineering principle. If we understand a problem, we may introduce inductive biases in our model in the form of invariances. Conversely, when interpreting a complex supervised model, we can study its invariances to...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	187,381
2003.02306	Reduced Dilation-Erosion Perceptron for Binary Classification	Dilation and erosion are two elementary operations from mathematical morphology, a non-linear lattice computing methodology widely used for image processing and analysis. The dilation-erosion perceptron (DEP) is a morphological neural network obtained by a convex combination of a dilation and an erosion followed by the...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	166,901
2206.06126	Robust Time Series Denoising with Learnable Wavelet Packet Transform	Signal denoising is a key preprocessing step for many applications, as the performance of a learning task is closely related to the quality of the input data. In this paper, we apply a signal processing based deep neural network architecture, a learnable extension of the wavelet packet transform. As main advantages, th...	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	302,262
2502.08106	PoGDiff: Product-of-Gaussians Diffusion Models for Imbalanced Text-to-Image Generation	Diffusion models have made significant advancements in recent years. However, their performance often deteriorates when trained or fine-tuned on imbalanced datasets. This degradation is largely due to the disproportionate representation of majority and minority data in image-text pairs. In this paper, we propose a gene...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	532,892
2404.12292	Reducing Bias in Pre-trained Models by Tuning while Penalizing Change	Deep models trained on large amounts of data often incorporate implicit biases present during training time. If later such a bias is discovered during inference or deployment, it is often necessary to acquire new data and retrain the model. This behavior is especially problematic in critical areas such as autonomous dr...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	447,822
2003.04593	3D printed cable-driven continuum robots with generally routed cables: modeling and experiments	Continuum robots are becoming increasingly popular for applications which require the robots to deform and change shape, while also being compliant. A cable-driven continuum robot is one of the most commonly used type. Typical cable driven continuum robots consist of a flexible backbone with spacer disks attached to th...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	167,596
1710.05233	Learners that Use Little Information	We study learning algorithms that are restricted to using a small amount of information from their input sample. We introduce a category of learning algorithms we term $d$-bit information learners, which are algorithms whose output conveys at most $d$ bits of information of their input. A central theme in this work is ...	false	false	false	false	true	false	true	false	false	true	false	false	true	false	false	false	false	false	82,604
2401.08649	Deep Pulse-Coupled Neural Networks	Spiking Neural Networks (SNNs) capture the information processing mechanism of the brain by taking advantage of spiking neurons, such as the Leaky Integrate-and-Fire (LIF) model neuron, which incorporates temporal dynamics and transmits information via discrete and asynchronous spikes. However, the simplified biologica...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	true	false	false	421,972
1707.01939	High-Performance FPGA Implementation of Equivariant Adaptive Separation via Independence Algorithm for Independent Component Analysis	Independent Component Analysis (ICA) is a dimensionality reduction technique that can boost efficiency of machine learning models that deal with probability density functions, e.g. Bayesian neural networks. Algorithms that implement adaptive ICA converge slower than their nonadaptive counterparts, however, they are cap...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	76,620
2410.00054	Transferable Unsupervised Outlier Detection Framework for Human Semantic Trajectories	Semantic trajectories, which enrich spatial-temporal data with textual information such as trip purposes or location activities, are key for identifying outlier behaviors critical to healthcare, social security, and urban planning. Traditional outlier detection relies on heuristic rules, which requires domain knowledge...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	493,213
1811.03519	Few-shot learning with attention-based sequence-to-sequence models	End-to-end approaches have recently become popular as a means of simplifying the training and deployment of speech recognition systems. However, they often require large amounts of data to perform well on large vocabulary tasks. With the aim of making end-to-end approaches usable by a broader range of researchers, we e...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	112,858
2202.09631	Confidence-rich Localization and Mapping based on Particle Filter for Robotic Exploration	This paper mainly studies the localization and mapping of range sensing robots in the confidence-rich map (CRM) and then extends it to provide a full state estimate for information-theoretic exploration. Most previous works about active simultaneous localization and mapping and exploration always assumed the known robo...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	281,260
2109.12969	Challenging the Semi-Supervised VAE Framework for Text Classification	Semi-Supervised Variational Autoencoders (SSVAEs) are widely used models for data efficient learning. In this paper, we question the adequacy of the standard design of sequence SSVAEs for the task of text classification as we exhibit two sources of overcomplexity for which we provide simplifications. These simplificati...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	257,476
2106.10456	Humble Teachers Teach Better Students for Semi-Supervised Object Detection	We propose a semi-supervised approach for contemporary object detectors following the teacher-student dual model framework. Our method is featured with 1) the exponential moving averaging strategy to update the teacher from the student online, 2) using plenty of region proposals and soft pseudo-labels as the student's ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	242,024
2103.06467	Pavement Distress Detection and Segmentation using YOLOv4 and DeepLabv3 on Pavements in the Philippines	Road transport infrastructure is critical for safe, fast, economical, and reliable mobility within the whole country that is conducive to a productive society. However, roads tend to deteriorate over time due to natural causes in the environment and repeated traffic loads. Pavement Distress (PD) detection is essential ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	224,310
2003.01916	Optimal Deep Learning for Robot Touch	This article illustrates the application of deep learning to robot touch by considering a basic yet fundamental capability: estimating the relative pose of part of an object in contact with a tactile sensor. We begin by surveying deep learning applied to tactile robotics, focussing on optical tactile sensors, which hel...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	166,804
2308.00958	Isolation and Induction: Training Robust Deep Neural Networks against Model Stealing Attacks	Despite the broad application of Machine Learning models as a Service (MLaaS), they are vulnerable to model stealing attacks. These attacks can replicate the model functionality by using the black-box query process without any prior knowledge of the target victim model. Existing stealing defenses add deceptive perturba...	false	false	false	false	true	false	false	false	false	false	false	false	true	false	false	false	false	false	383,094
2410.11463	Advanced Persistent Threats (APT) Attribution Using Deep Reinforcement Learning	The development of the DRL model for malware attribution involved extensive research, iterative coding, and numerous adjustments based on the insights gathered from predecessor models and contemporary research papers. This preparatory work was essential to establish a robust foundation for the model, ensuring it could ...	false	false	false	false	true	false	true	false	false	false	false	false	true	false	false	false	false	false	498,577
2106.09330	A Simple Generative Network	Generative neural networks are able to mimic intricate probability distributions such as those of handwritten text, natural images, etc. Since their inception several models were proposed. The most successful of these were based on adversarial (GAN), auto-encoding (VAE) and maximum mean discrepancy (MMD) relatively com...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	true	false	false	241,630
2408.10524	XCB: an effective contextual biasing approach to bias cross-lingual phrases in speech recognition	Contextualized ASR models have been demonstrated to effectively improve the recognition accuracy of uncommon phrases when a predefined phrase list is available. However, these models often struggle with bilingual settings, which are prevalent in code-switching speech recognition. In this study, we make the initial atte...	false	false	true	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	481,891
2312.04282	Adaptive Recursive Query Optimization	Performance-critical industrial applications, including large-scale program, network, and distributed system analyses, are increasingly reliant on recursive queries for data analysis. Yet traditional relational algebra-based query optimization techniques do not scale well to recursive query processing due to the iterat...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	true	413,621
2204.10648	Exposure Correction Model to Enhance Image Quality	Exposure errors in an image cause a degradation in the contrast and low visibility in the content. In this paper, we address this problem and propose an end-to-end exposure correction model in order to handle both under- and overexposure errors with a single model. Our model contains an image encoder, consecutive resid...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	292,867
1106.4232	Approximate controllability for linear degenerate parabolic problems with bilinear control	In this work we study the global approximate multiplicative controllability for the linear degenerate parabolic Cauchy-Neumann problem $$ \{{array}{l} \displaystyle{v_t-(a(x) v_x)_x =\alpha (t,x)v\,\,\qquad {in} \qquad Q_T \,=\,(0,T)\times(-1,1)} [2.5ex] \displaystyle{a(x)v_x(t,x)\|_{x=\pm 1} = 0\,\,\qquad\qquad\qquad\,...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	10,934
1612.02203	A Functional Regression approach to Facial Landmark Tracking	Linear regression is a fundamental building block in many face detection and tracking algorithms, typically used to predict shape displacements from image features through a linear mapping. This paper presents a Functional Regression solution to the least squares problem, which we coin Continuous Regression, resulting ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	65,206
2401.14241	New Algorithms for Computing Sibson Capacity and Arimoto Capacity	The Sibson and Arimoto capacity, which are based on the Sibson and Arimoto mutual information (MI) of order {\alpha}, respectively, are well-known generalizations of the channel capacity C. In this study, we derive novel alternating optimization algorithms for computing these capacities by providing new variational cha...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	424,020
1901.05657	Certainty Driven Consistency Loss on Multi-Teacher Networks for Semi-Supervised Learning	One of the successful approaches in semi-supervised learning is based on the consistency regularization. Typically, a student model is trained to be consistent with teacher prediction for the inputs under different perturbations. To be successful, the prediction targets given by teacher should have good quality, otherw...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	118,833
2408.09845	Predicting Long-term Dynamics of Complex Networks via Identifying Skeleton in Hyperbolic Space	Learning complex network dynamics is fundamental for understanding, modeling, and controlling real-world complex systems. Though great efforts have been made to predict the future states of nodes on networks, the capability of capturing long-term dynamics remains largely limited. This is because they overlook the fact ...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	481,615
2203.07774	An Empirical Study of Market Inefficiencies in Uniswap and SushiSwap	Decentralized exchanges are revolutionizing finance. With their ever-growing increase in popularity, a natural question that begs to be asked is: how efficient are these new markets? We find that nearly 30% of analyzed trades are executed at an unfavorable rate. Additionally, we observe that, especially during the De...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	285,556
1809.08925	Constrained Exploration and Recovery from Experience Shaping	We consider the problem of reinforcement learning under safety requirements, in which an agent is trained to complete a given task, typically formalized as the maximization of a reward signal over time, while concurrently avoiding undesirable actions or states, associated to lower rewards, or penalties. The constructio...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	108,621
2204.09595	Exploring Continuous Integrate-and-Fire for Adaptive Simultaneous Speech Translation	Simultaneous speech translation (SimulST) is a challenging task aiming to translate streaming speech before the complete input is observed. A SimulST system generally includes two components: the pre-decision that aggregates the speech information and the policy that decides to read or write. While recent works had pro...	false	false	true	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	292,490
2412.05976	Lightweight Spatial Embedding for Vision-based 3D Occupancy Prediction	Occupancy prediction has garnered increasing attention in recent years for its comprehensive fine-grained environmental representation and strong generalization to open-set objects. However, cumbersome voxel features and 3D convolution operations inevitably introduce large overheads in both memory and computation, obst...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	515,046
2110.01389	A Survey of Selected Algorithms Used in Military Applications from the Viewpoints of Dataflow and GaAs	This is a short survey of ten algorithms that are often used for military purposes, followed by analysis of their potential suitability for dataflow and GaAs, which are a specific architecture and technology for supercomputers on a chip, respectively. Whenever an algorithm or a device is used in military settings, it...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	258,750
2412.05711	On an Analytical Inversion Formula for the Modulo Radon Transform	This paper proves a novel analytical inversion formula for the so-called modulo Radon transform (MRT), which models a recently proposed approach to one-shot high dynamic range tomography. It is based on the solution of a Poisson problem linking the Laplacian of the Radon transform (RT) of a function to its MRT in combi...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	514,938
2112.02828	PP-MSVSR: Multi-Stage Video Super-Resolution	Different from the Single Image Super-Resolution(SISR) task, the key for Video Super-Resolution(VSR) task is to make full use of complementary information across frames to reconstruct the high-resolution sequence. Since images from different frames with diverse motion and scene, accurately aligning multiple frames and ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	269,981
2107.06563	Multi-Label Generalized Zero Shot Learning for the Classification of Disease in Chest Radiographs	Despite the success of deep neural networks in chest X-ray (CXR) diagnosis, supervised learning only allows the prediction of disease classes that were seen during training. At inference, these networks cannot predict an unseen disease class. Incorporating a new class requires the collection of labeled data, which is n...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	246,130
2112.07917	SPTS: Single-Point Text Spotting	Existing scene text spotting (i.e., end-to-end text detection and recognition) methods rely on costly bounding box annotations (e.g., text-line, word-level, or character-level bounding boxes). For the first time, we demonstrate that training scene text spotting models can be achieved with an extremely low-cost annotati...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	271,639
1608.07897	Using k-nearest neighbors to construct cancelable minutiae templates	Fingerprint is widely used in a variety of applications. Security measures have to be taken to protect the privacy of fingerprint data. Cancelable biometrics is proposed as an effective mechanism of using and protecting biometrics. In this paper we propose a new method of constructing cancelable fingerprint template by...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	60,285
1905.05934	EigenDamage: Structured Pruning in the Kronecker-Factored Eigenbasis	Reducing the test time resource requirements of a neural network while preserving test accuracy is crucial for running inference on resource-constrained devices. To achieve this goal, we introduce a novel network reparameterization based on the Kronecker-factored eigenbasis (KFE), and then apply Hessian-based structure...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	130,860
1809.07043	NICT's Corpus Filtering Systems for the WMT18 Parallel Corpus Filtering Task	This paper presents the NICT's participation in the WMT18 shared parallel corpus filtering task. The organizers provided 1 billion words German-English corpus crawled from the web as part of the Paracrawl project. This corpus is too noisy to build an acceptable neural machine translation (NMT) system. Using the clean d...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	108,197
1307.0201	Simulating Ability: Representing Skills in Games	Throughout the history of games, representing the abilities of the various agents acting on behalf of the players has been a central concern. With increasingly sophisticated games emerging, these simulations have become more realistic, but the underlying mechanisms are still, to a large extent, of an ad hoc nature. Thi...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	25,532
2501.01811	QuantumBind-RBFE: Accurate Relative Binding Free Energy Calculations Using Neural Network Potentials	Accurate prediction of protein-ligand binding affinities is crucial in drug discovery, particularly during hit-to-lead and lead optimization phases, however, limitations in ligand force fields continue to impact prediction accuracy. In this work, we validate relative binding free energy (RBFE) accuracy using neural net...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	522,222
2110.07582	Network Representation Learning: From Preprocessing, Feature Extraction to Node Embedding	Network representation learning (NRL) advances the conventional graph mining of social networks, knowledge graphs, and complex biomedical and physics information networks. Over dozens of network representation learning algorithms have been reported in the literature. Most of them focus on learning node embeddings for h...	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	false	false	261,046
1812.01874	Learning to Take Directions One Step at a Time	We present a method to generate a video sequence given a single image. Because items in an image can be animated in arbitrarily many different ways, we introduce as control signal a sequence of motion strokes. Such control signal can be automatically transferred from other videos, e.g., via bounding box tracking. Each ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	115,637
2306.04928	Underwater Intention Recognition using Head Motion and Throat Vibration for Supernumerary Robotic Assistance	This study presents a multi-modal mechanism for recognizing human intentions while diving underwater, aiming to achieve natural human-robot interactions through an underwater superlimb for diving assistance. The underwater environment severely limits the divers' capabilities in intention expression, which becomes more ...	true	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	371,969
2412.08289	k-HyperEdge Medoids for Clustering Ensemble	Clustering ensemble has been a popular research topic in data science due to its ability to improve the robustness of the single clustering method. Many clustering ensemble methods have been proposed, most of which can be categorized into clustering-view and sample-view methods. The clustering-view method is generally ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	516,030
2404.04654	Music Recommendation Based on Facial Emotion Recognition	Introduction: Music provides an incredible avenue for individuals to express their thoughts and emotions, while also serving as a delightful mode of entertainment for enthusiasts and music lovers. Objectives: This paper presents a comprehensive approach to enhancing the user experience through the integration of emotio...	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	false	444,740
2004.08773	Safe Screening Rules for $\ell_0$-Regression	We give safe screening rules to eliminate variables from regression with $\ell_0$ regularization or cardinality constraint. These rules are based on guarantees that a feature may or may not be selected in an optimal solution. The screening rules can be computed from a convex relaxation solution in linear time, without ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	173,167
2209.09240	Distributed Semi-supervised Fuzzy Regression with Interpolation Consistency Regularization	Recently, distributed semi-supervised learning (DSSL) algorithms have shown their effectiveness in leveraging unlabeled samples over interconnected networks, where agents cannot share their original data with each other and can only communicate non-sensitive information with their neighbors. However, existing DSSL algo...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	318,442
2306.16643	Cautious explorers generate more future academic impact	Some scientists are more likely to explore unfamiliar research topics while others tend to exploit existing ones. In previous work, correlations have been found between scientists' topic choices and their career performances. However, literature has yet to untangle the intricate interplay between scientific impact and ...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	true	376,430
2411.01897	LE-PDE++: Mamba for accelerating PDEs Simulations	Partial Differential Equations are foundational in modeling science and natural systems such as fluid dynamics and weather forecasting. The Latent Evolution of PDEs method is designed to address the computational intensity of classical and deep learning-based PDE solvers by proposing a scalable and efficient alternativ...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	505,275
2004.09780	Strong Consistency, Graph Laplacians, and the Stochastic Block Model	Spectral clustering has become one of the most popular algorithms in data clustering and community detection. We study the performance of classical two-step spectral clustering via the graph Laplacian to learn the stochastic block model. Our aim is to answer the following question: when is spectral clustering via the g...	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	false	false	173,453
2404.12832	COIN: Counterfactual inpainting for weakly supervised semantic segmentation for medical images	Deep learning is dramatically transforming the field of medical imaging and radiology, enabling the identification of pathologies in medical images, including computed tomography (CT) and X-ray scans. However, the performance of deep learning models, particularly in segmentation tasks, is often limited by the need for ...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	448,052
2211.00842	Delivery by Drones with Arbitrary Energy Consumption Models: A New Formulation Approach	This paper presents a new approach for formulating the delivery problem by drones with general energy consumption models where the drones visit a set of places to deliver parcels to customers. Drones can perform multiple trips that start and end at a central depot while visiting several customers along their paths. The...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	328,019
2108.02170	Curriculum learning for language modeling	Language Models like ELMo and BERT have provided robust representations of natural language, which serve as the language understanding component for a diverse range of downstream tasks.Curriculum learning is a method that employs a structured training regime instead, which has been leveraged in computer vision and mach...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	249,240
2501.10610	Automated Water Irrigation System	This paper presents the design and implementation of an automated water irrigation system aimed at optimizing plant care through precision moisture monitoring and controlled water delivery. The system uses a capacitive soil moisture sensor, an ADC (analog-to-digital converter), and a relay-driven water pump to ensure p...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	525,587
2104.06512	Trust and Safety	Robotics in Australia have a long history of conforming with safety standards and risk managed practices. This chapter articulates the current state of trust and safety in robotics including society's expectations, safety management systems and system safety as well as emerging issues and methods for ensuring safety in...	true	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	230,099
2411.02783	BrainBits: How Much of the Brain are Generative Reconstruction Methods Using?	When evaluating stimuli reconstruction results it is tempting to assume that higher fidelity text and image generation is due to an improved understanding of the brain or more powerful signal extraction from neural recordings. However, in practice, new reconstruction methods could improve performance for at least three...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	505,655
1912.11652	Confounder Selection via Support Intersection	Confounding matters in almost all observational studies that focus on causality. In order to eliminate bias caused by connfounders, oftentimes a substantial number of features need to be collected in the analysis. In this case, large p small n problem can arise and dimensional reduction technique is required. However, ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	158,621
2403.17994	Solution for Point Tracking Task of ICCV 1st Perception Test Challenge 2023	This report proposes an improved method for the Tracking Any Point (TAP) task, which tracks any physical surface through a video. Several existing approaches have explored the TAP by considering the temporal relationships to obtain smooth point motion trajectories, however, they still suffer from the cumulative error c...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	441,719
2108.00422	An Effective and Robust Detector for Logo Detection	In recent years, intellectual property (IP), which represents literary, inventions, artistic works, etc, gradually attract more and more people's attention. Particularly, with the rise of e-commerce, the IP not only represents the product design and brands, but also represents the images/videos displayed on e-commerce ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	248,719

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