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
2110.10289	On Coordinate Decoding for Keypoint Estimation Tasks	A series of 2D (and 3D) keypoint estimation tasks are built upon heatmap coordinate representation, i.e. a probability map that allows for learnable and spatially aware encoding and decoding of keypoint coordinates on grids, even allowing for sub-pixel coordinate accuracy. In this report, we aim to reproduce the findin...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	262,091
2202.03271	Spectro Temporal EEG Biomarkers For Binary Emotion Classification	Electroencephalogram (EEG) is one of the most reliable physiological signal for emotion detection. Being non-stationary in nature, EEGs are better analysed by spectro temporal representations. Standard features like Discrete Wavelet Transformation (DWT) can represent temporal changes in spectral dynamics of an EEG, but...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	279,143
1501.01678	LeoTask: a fast, flexible and reliable framework for computational research	LeoTask is a Java library for computation-intensive and time-consuming research tasks. It automatically executes tasks in parallel on multiple CPU cores on a computing facility. It uses a configuration file to enable automatic exploration of parameter space and flexible aggregation of results, and therefore allows rese...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	39,104
0711.2873	Trellis Computations	For a certain class of functions, the distribution of the function values can be calculated in the trellis or a sub-trellis. The forward/backward recursion known from the BCJR algorithm is generalized to compute the moments of these distributions. In analogy to the symbol probabilities, by introducing a constraint at a...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	917
2409.15675	Northeast Materials Database (NEMAD): Enabling Discovery of High Transition Temperature Magnetic Compounds	The discovery of novel magnetic materials with greater operating temperature ranges and optimized performance is essential for advanced applications. Current data-driven approaches are challenging and limited due to the lack of accurate, comprehensive, and feature-rich databases. This study aims to address this challen...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	491,006
2410.04322	Toward Debugging Deep Reinforcement Learning Programs with RLExplorer	Deep reinforcement learning (DRL) has shown success in diverse domains such as robotics, computer games, and recommendation systems. However, like any other software system, DRL-based software systems are susceptible to faults that pose unique challenges for debugging and diagnosing. These faults often result in unexpe...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	true	495,236
2103.00034	Beyond Perturbation Stability: LP Recovery Guarantees for MAP Inference on Noisy Stable Instances	Several works have shown that perturbation stable instances of the MAP inference problem in Potts models can be solved exactly using a natural linear programming (LP) relaxation. However, most of these works give few (or no) guarantees for the LP solutions on instances that do not satisfy the relatively strict perturba...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	222,123
2412.13724	USEFUSE: Utile Stride for Enhanced Performance in Fused Layer Architecture of Deep Neural Networks	Convolutional Neural Networks (CNNs) are crucial in various applications, but their deployment on resource-constrained edge devices poses challenges. This study presents the Sum-of-Products (SOP) units for convolution, which utilize low-latency left-to-right bit-serial arithmetic to minimize response time and enhance o...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	518,420
2005.12123	Feature Robust Optimal Transport for High-dimensional Data	Optimal transport is a machine learning problem with applications including distribution comparison, feature selection, and generative adversarial networks. In this paper, we propose feature-robust optimal transport (FROT) for high-dimensional data, which solves high-dimensional OT problems using feature selection to a...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	178,646
2210.11476	Encoding nonlinear and unsteady aerodynamics of limit cycle oscillations using nonlinear sparse Bayesian learning	This paper investigates the applicability of a recently-proposed nonlinear sparse Bayesian learning (NSBL) algorithm to identify and estimate the complex aerodynamics of limit cycle oscillations. NSBL provides a semi-analytical framework for determining the data-optimal sparse model nested within a (potentially) over-p...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	325,329
2410.11053	Fair Interest Rates Are Impossible for Lending Pools: Results from Options Pricing	Cryptocurrency lending pools are services that allow lenders to pool together assets in one cryptocurrency and loan it out to borrowers who provide collateral worth more (than the loan) in a separate cryptocurrency. Borrowers can repay their loans to reclaim their collateral unless their loan was liquidated, which happ...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	498,357
2005.08746	Improving Named Entity Recognition in Tor Darknet with Local Distance Neighbor Feature	Name entity recognition in noisy user-generated texts is a difficult task usually enhanced by incorporating an external resource of information, such as gazetteers. However, gazetteers are task-specific, and they are expensive to build and maintain. This paper adopts and improves the approach of Aguilar et al. by prese...	false	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	177,718
2204.11159	Explainable Fairness in Recommendation	Existing research on fairness-aware recommendation has mainly focused on the quantification of fairness and the development of fair recommendation models, neither of which studies a more substantial problem--identifying the underlying reason of model disparity in recommendation. This information is critical for recomme...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	293,052
2312.17670	Benchmarking the CoW with the TopCoW Challenge: Topology-Aware Anatomical Segmentation of the Circle of Willis for CTA and MRA	The Circle of Willis (CoW) is an important network of arteries connecting major circulations of the brain. Its vascular architecture is believed to affect the risk, severity, and clinical outcome of serious neuro-vascular diseases. However, characterizing the highly variable CoW anatomy is still a manual and time-consu...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	418,829
1602.08791	The BigDAWG Architecture	BigDAWG is a polystore system designed to work on complex problems that naturally span across different processing or storage engines. BigDAWG provides an architecture that supports diverse database systems working with different data models, support for the competing notions of location transparency and semantic compl...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	52,691
2311.07634	ActiveDC: Distribution Calibration for Active Finetuning	The pretraining-finetuning paradigm has gained popularity in various computer vision tasks. In this paradigm, the emergence of active finetuning arises due to the abundance of large-scale data and costly annotation requirements. Active finetuning involves selecting a subset of data from an unlabeled pool for annotation...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	407,427
1401.5857	COLIN: Planning with Continuous Linear Numeric Change	In this paper we describe COLIN, a forward-chaining heuristic search planner, capable of reasoning with COntinuous LINear numeric change, in addition to the full temporal semantics of PDDL. Through this work we make two advances to the state-of-the-art in terms of expressive reasoning capabilities of planners: the hand...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	30,257
1810.04892	Automata for Infinite Argumentation Structures	The theory of abstract argumentation frameworks (afs) has, in the main, focused on finite structures, though there are many significant contexts where argumentation can be regarded as a process involving infinite objects. To address this limitation, in this paper we propose a novel approach for describing infinite afs ...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	110,130
2010.09594	Multi-Modal Super Resolution for Dense Microscopic Particle Size Estimation	Particle Size Analysis (PSA) is an important process carried out in a number of industries, which can significantly influence the properties of the final product. A ubiquitous instrument for this purpose is the Optical Microscope (OM). However, OMs are often prone to drawbacks like low resolution, small focal depth, an...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	201,604
1702.07507	Use Generalized Representations, But Do Not Forget Surface Features	Only a year ago, all state-of-the-art coreference resolvers were using an extensive amount of surface features. Recently, there was a paradigm shift towards using word embeddings and deep neural networks, where the use of surface features is very limited. In this paper, we show that a simple SVM model with surface feat...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	68,798
2308.05187	Exploring the Interplay of Interference and Queues in Unlicensed Spectrum Bands for UAV Networks	In this paper, we present an analytical framework to explore the interplay of signal interference and transmission queue management, and their impacts on the performance of unmanned aerial vehicles (UAVs) when operating in the unlicensed spectrum bands. In particular, we develop a comprehensive framework to investigate...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	384,708
2411.15206	Conditional Distribution Learning on Graphs	Leveraging the diversity and quantity of data provided by various graph-structured data augmentations while preserving intrinsic semantic information is challenging. Additionally, successive layers in graph neural network (GNN) tend to produce more similar node embeddings, while graph contrastive learning aims to incre...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	510,491
2406.12830	What Are the Odds? Language Models Are Capable of Probabilistic Reasoning	Language models (LM) are capable of remarkably complex linguistic tasks; however, numerical reasoning is an area in which they frequently struggle. An important but rarely evaluated form of reasoning is understanding probability distributions. In this paper, we focus on evaluating the probabilistic reasoning capabiliti...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	465,590
1107.0020	Learning to Order BDD Variables in Verification	The size and complexity of software and hardware systems have significantly increased in the past years. As a result, it is harder to guarantee their correct behavior. One of the most successful methods for automated verification of finite-state systems is model checking. Most of the current model-checking systems use ...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	11,094
2410.08027	Private Language Models via Truncated Laplacian Mechanism	Deep learning models for NLP tasks are prone to variants of privacy attacks. To prevent privacy leakage, researchers have investigated word-level perturbations, relying on the formal guarantees of differential privacy (DP) in the embedding space. However, many existing approaches either achieve unsatisfactory performan...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	496,915
2206.05407	Opportunistic Routing aided Cooperative Communication MRC Network with Energy-Harvesting Nodes	In this paper, we consider a multi-hop cooperative network founded on two energy-harvesting (EH) decode-and-forward (DF) relays which are provided with harvest-store-use (HSU) architecture to harvest energy from the ambience using the energy buffers. For the sake of boosting the data delivery in this network, maximal r...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	301,994
1405.6136	An evolutionary computational based approach towards automatic image registration	Image registration is a key component of various image processing operations which involve the analysis of different image data sets. Automatic image registration domains have witnessed the application of many intelligent methodologies over the past decade; however inability to properly model object shape as well as co...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	true	false	false	33,340
2501.02018	Safeguarding Large Language Models in Real-time with Tunable Safety-Performance Trade-offs	Large Language Models (LLMs) have been shown to be susceptible to jailbreak attacks, or adversarial attacks used to illicit high risk behavior from a model. Jailbreaks have been exploited by cybercriminals and blackhat actors to cause significant harm, highlighting the critical need to safeguard widely-deployed models....	false	false	false	false	true	false	true	false	true	false	false	false	true	false	false	false	false	false	522,313
2402.09409	Seasons's Greetings by AD	We use Algorithmic Differentiation (AD) to implement type-generic tangent and adjoint versions of $$ y=\sum_{i=0}^{n-1} x_{2 i} \cdot x_{2 i+1} $$ in C++. We run an instantiation for char-arithmetic and we print the gradient at $(101~77~114~114~32~121~109~88~115~97)^T$ to std::cout, yielding the output ``Merry Xmas''. ...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	429,505
2310.13378	ScalableMap: Scalable Map Learning for Online Long-Range Vectorized HD Map Construction	We propose a novel end-to-end pipeline for online long-range vectorized high-definition (HD) map construction using on-board camera sensors. The vectorized representation of HD maps, employing polylines and polygons to represent map elements, is widely used by downstream tasks. However, previous schemes designed with r...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	401,426
1805.00254	Joint Bootstrapping Machines for High Confidence Relation Extraction	Semi-supervised bootstrapping techniques for relationship extraction from text iteratively expand a set of initial seed instances. Due to the lack of labeled data, a key challenge in bootstrapping is semantic drift: if a false positive instance is added during an iteration, then all following iterations are contaminate...	false	false	false	false	true	true	true	false	true	false	false	false	false	false	false	true	false	false	96,393
1712.05319	Deep CNN ensembles and suggestive annotations for infant brain MRI segmentation	Precise 3D segmentation of infant brain tissues is an essential step towards comprehensive volumetric studies and quantitative analysis of early brain developement. However, computing such segmentations is very challenging, especially for 6-month infant brain, due to the poor image quality, among other difficulties inh...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	86,724
2403.13178	Fast Value Tracking for Deep Reinforcement Learning	Reinforcement learning (RL) tackles sequential decision-making problems by creating agents that interacts with their environment. However, existing algorithms often view these problem as static, focusing on point estimates for model parameters to maximize expected rewards, neglecting the stochastic dynamics of agent-en...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	439,503
2210.16490	Harmonic Tutte polynomials of matroids II	In this work, we introduce the harmonic generalization of the $m$-tuple weight enumerators of codes over finite Frobenius rings. A harmonic version of the MacWilliams-type identity for $m$-tuple weight enumerators of codes over finite Frobenius ring is also given. Moreover, we define the demi-matroid analogue of well-k...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	327,350
2412.03417	Learning Semantic Association Rules from Internet of Things Data	Association Rule Mining (ARM) is the task of discovering commonalities in data in the form of logical implications. ARM is used in the Internet of Things (IoT) for different tasks including monitoring and decision-making. However, existing methods give limited consideration to IoT-specific requirements such as heteroge...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	513,951
2411.06719	Shallow Signed Distance Functions for Kinematic Collision Bodies	We present learning-based implicit shape representations designed for real-time avatar collision queries arising in the simulation of clothing. Signed distance functions (SDFs) have been used for such queries for many years due to their computational efficiency. Recently deep neural networks have been used for implicit...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	507,235
2311.15627	Phonetic-aware speaker embedding for far-field speaker verification	When a speaker verification (SV) system operates far from the sound sourced, significant challenges arise due to the interference of noise and reverberation. Studies have shown that incorporating phonetic information into speaker embedding can improve the performance of text-independent SV. Inspired by this observation...	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	410,591
2404.07533	IITP-VDLand: A Comprehensive Dataset on Decentraland Parcels	This paper presents IITP-VDLand, a comprehensive dataset of Decentraland parcels sourced from diverse platforms. Unlike existing datasets which have limited attributes and records, IITP-VDLand offers a rich array of attributes, encompassing parcel characteristics, trading history, past activities, transactions, and soc...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	true	445,872
2402.03202	Leveraging IRS Induced Time Delay for Enhanced Physical Layer Security in VLC Systems	Indoor visible light communication (VLC) is considered secure against attackers outside the confined area where the light propagates, but it is still susceptible to interception from inside the coverage area. A new technology, intelligent reflecting surfaces (IRS), has been recently introduced, offering a way to enhanc...	false	false	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	426,903
2109.12860	Classifying Dyads for Militarized Conflict Analysis	Understanding the origins of militarized conflict is a complex, yet important undertaking. Existing research seeks to build this understanding by considering bi-lateral relationships between entity pairs (dyadic causes) and multi-lateral relationships among multiple entities (systemic causes). The aim of this work is t...	false	false	false	true	false	false	true	false	true	false	false	false	false	false	false	false	false	false	257,438
2411.04876	Non-Euclidean Mixture Model for Social Network Embedding	It is largely agreed that social network links are formed due to either homophily or social influence. Inspired by this, we aim at understanding the generation of links via providing a novel embedding-based graph formation model. Different from existing graph representation learning, where link generation probabilities...	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	false	false	506,453
2305.05833	A Statistical Model of Bipartite Networks: Application to Cosponsorship in the United States Senate	Many networks in political and social research are bipartite, with edges connecting exclusively across two distinct types of nodes. A common example includes cosponsorship networks, in which legislators are connected indirectly through the bills they support. Yet most existing network models are designed for unipartite...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	363,298
2008.13406	Rotational analysis of ChaCha permutation	We show that the underlying permutation of ChaCha20 stream cipher does not behave as a random permutation for up to 17 rounds with respect to rotational cryptanalysis. In particular, we derive a lower and an upper bound for the rotational probability through ChaCha quarter round, we show how to extend the bound to a fu...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	193,839
1909.06541	Scalable Gaussian Process Classification with Additive Noise for Various Likelihoods	Gaussian process classification (GPC) provides a flexible and powerful statistical framework describing joint distributions over function space. Conventional GPCs however suffer from (i) poor scalability for big data due to the full kernel matrix, and (ii) intractable inference due to the non-Gaussian likelihoods. Henc...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	145,406
1811.10791	Accurate, Data-Efficient Learning from Noisy, Choice-Based Labels for Inherent Risk Scoring	Inherent risk scoring is an important function in anti-money laundering, used for determining the riskiness of an individual during onboarding $\textit{before}$ fraudulent transactions occur. It is, however, often fraught with two challenges: (1) inconsistent notions of what constitutes as high or low risk by experts a...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	114,589
1708.09666	Generating Video Descriptions with Topic Guidance	Generating video descriptions in natural language (a.k.a. video captioning) is a more challenging task than image captioning as the videos are intrinsically more complicated than images in two aspects. First, videos cover a broader range of topics, such as news, music, sports and so on. Second, multiple topics could co...	false	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	79,818
1307.0127	Concentration and Confidence for Discrete Bayesian Sequence Predictors	Bayesian sequence prediction is a simple technique for predicting future symbols sampled from an unknown measure on infinite sequences over a countable alphabet. While strong bounds on the expected cumulative error are known, there are only limited results on the distribution of this error. We prove tight high-probabil...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	25,525
2010.08525	Analogous Process Structure Induction for Sub-event Sequence Prediction	Computational and cognitive studies of event understanding suggest that identifying, comprehending, and predicting events depend on having structured representations of a sequence of events and on conceptualizing (abstracting) its components into (soft) event categories. Thus, knowledge about a known process such as "b...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	201,202
2108.00454	SSPU-Net: Self-Supervised Point Cloud Upsampling via Differentiable Rendering	Point clouds obtained from 3D sensors are usually sparse. Existing methods mainly focus on upsampling sparse point clouds in a supervised manner by using dense ground truth point clouds. In this paper, we propose a self-supervised point cloud upsampling network (SSPU-Net) to generate dense point clouds without using gr...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	248,724
2210.06375	Superpolynomial Lower Bounds for Decision Tree Learning and Testing	We establish new hardness results for decision tree optimization problems, adding to a line of work that dates back to Hyafil and Rivest in 1976. We prove, under randomized ETH, superpolynomial lower bounds for two basic problems: given an explicit representation of a function $f$ and a generator for a distribution $\m...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	323,258
2407.01791	{\mu}-Bench: A Vision-Language Benchmark for Microscopy Understanding	Recent advances in microscopy have enabled the rapid generation of terabytes of image data in cell biology and biomedical research. Vision-language models (VLMs) offer a promising solution for large-scale biological image analysis, enhancing researchers' efficiency, identifying new image biomarkers, and accelerating hy...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	469,450
2108.11332	Self-optimizing adaptive optics control with Reinforcement Learning for high-contrast imaging	Current and future high-contrast imaging instruments require extreme adaptive optics (XAO) systems to reach contrasts necessary to directly image exoplanets. Telescope vibrations and the temporal error induced by the latency of the control loop limit the performance of these systems. One way to reduce these effects is ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	252,156
2210.05674	Semi-supervised detection of structural damage using Variational Autoencoder and a One-Class Support Vector Machine	In recent years, Artificial Neural Networks (ANNs) have been introduced in Structural Health Monitoring (SHM) systems. A semi-supervised method with a data-driven approach allows the ANN training on data acquired from an undamaged structural condition to detect structural damages. In standard approaches, after the trai...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	322,959
2204.03829	Does the Market of Citations Reward Reproducible Work?	The field of bibliometrics, studying citations and behavior, is critical to the discussion of reproducibility. Citations are one of the primary incentive and reward systems for academic work, and so we desire to know if this incentive rewards reproducible work. Yet to the best of our knowledge, only one work has attemp...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	true	290,446
2410.23736	MoTaDual: Modality-Task Dual Alignment for Enhanced Zero-shot Composed Image Retrieval	Composed Image Retrieval (CIR) is a challenging vision-language task, utilizing bi-modal (image+text) queries to retrieve target images. Despite the impressive performance of supervised CIR, the dependence on costly, manually-labeled triplets limits its scalability and zero-shot capability. To address this issue, zero-...	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	false	504,158
2005.09139	Over-the-Air Computation Systems: Optimal Design with Sum-Power Constraint	Over-the-air computation (AirComp), which leverages the superposition property of wireless multiple-access channel (MAC) and the mathematical tool of function representation, has been considered as a promising technique for effective collection and computation of massive sensor data in wireless Big Data applications. I...	false	false	false	false	false	false	false	false	false	true	true	false	false	false	false	false	false	false	177,826
1304.0383	An Efficient Bilinear Pairing-Free Certificateless Two-Party Authenticated Key Agreement Protocol in the eCK Model	Recent study on certificateless authenticated key agreement focuses on bilinear pairing-free certificateless authenticated key agreement protocol. Yet it has got limitations in the aspect of computational amount. So it is important to reduce the number of the scalar multiplication over elliptic curve group in bilinear ...	false	false	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	23,386
2205.14461	Collaborative likelihood-ratio estimation over graphs	Assuming we have iid observations from two unknown probability density functions (pdfs), $p$ and $q$, the likelihood-ratio estimation (LRE) is an elegant approach to compare the two pdfs only by relying on the available data. In this paper, we introduce the first -to the best of our knowledge-graph-based extension of t...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	299,372
2404.02403	Benchmarking Large Language Models for Persian: A Preliminary Study Focusing on ChatGPT	This paper explores the efficacy of large language models (LLMs) for Persian. While ChatGPT and consequent LLMs have shown remarkable performance in English, their efficiency for more low-resource languages remains an open question. We present the first comprehensive benchmarking study of LLMs across diverse Persian la...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	443,842
2409.02795	Towards a Unified View of Preference Learning for Large Language Models: A Survey	Large Language Models (LLMs) exhibit remarkably powerful capabilities. One of the crucial factors to achieve success is aligning the LLM's output with human preferences. This alignment process often requires only a small amount of data to efficiently enhance the LLM's performance. While effective, research in this area...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	485,833
1808.03374	Fast computation of the principal components of genotype matrices in Julia	Finding the largest few principal components of a matrix of genetic data is a common task in genome-wide association studies (GWASs), both for dimensionality reduction and for identifying unwanted factors of variation. We describe a simple random matrix model for matrices that arise in GWASs, showing that the singular ...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	104,927
2212.09069	Masked Wavelet Representation for Compact Neural Radiance Fields	Neural radiance fields (NeRF) have demonstrated the potential of coordinate-based neural representation (neural fields or implicit neural representation) in neural rendering. However, using a multi-layer perceptron (MLP) to represent a 3D scene or object requires enormous computational resources and time. There have be...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	336,986
1706.04859	Sobolev Training for Neural Networks	At the heart of deep learning we aim to use neural networks as function approximators - training them to produce outputs from inputs in emulation of a ground truth function or data creation process. In many cases we only have access to input-output pairs from the ground truth, however it is becoming more common to have...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	75,403
2409.05202	A Survey on Mixup Augmentations and Beyond	As Deep Neural Networks have achieved thrilling breakthroughs in the past decade, data augmentations have garnered increasing attention as regularization techniques when massive labeled data are unavailable. Among existing augmentations, Mixup and relevant data-mixing methods that convexly combine selected samples and ...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	486,675
2410.22229	Cora: Accelerating Stateful Network Applications with SmartNICs	With the growing performance requirements on networked applications, there is a new trend of offloading stateful network applications to SmartNICs to improve performance and reduce the total cost of ownership. However, offloading stateful network applications is non-trivial due to state operation complexity, state reso...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	true	503,551
2102.01013	End2End Acoustic to Semantic Transduction	In this paper, we propose a novel end-to-end sequence-to-sequence spoken language understanding model using an attention mechanism. It reliably selects contextual acoustic features in order to hypothesize semantic contents. An initial architecture capable of extracting all pronounced words and concepts from acoustic sp...	false	false	true	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	217,980
2006.06438	GAIT-prop: A biologically plausible learning rule derived from backpropagation of error	Traditional backpropagation of error, though a highly successful algorithm for learning in artificial neural network models, includes features which are biologically implausible for learning in real neural circuits. An alternative called target propagation proposes to solve this implausibility by using a top-down model...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	true	false	false	181,429
2308.00236	Partitioned Saliency Ranking with Dense Pyramid Transformers	In recent years, saliency ranking has emerged as a challenging task focusing on assessing the degree of saliency at instance-level. Being subjective, even humans struggle to identify the precise order of all salient instances. Previous approaches undertake the saliency ranking by directly sorting the rank scores of sal...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	382,865
1807.03232	Robust Heartbeat Detection from Multimodal Data via CNN-based Generalizable Information Fusion	Objective: Heartbeat detection remains central to cardiac disease diagnosis and management, and is traditionally performed based on electrocardiogram (ECG). To improve robustness and accuracy of detection, especially, in certain critical-care scenarios, the use of additional physiological signals such as arterial blood...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	102,476
2201.02410	Auction-Based Ex-Post-Payment Incentive Mechanism Design for Horizontal Federated Learning with Reputation and Contribution Measurement	Federated learning trains models across devices with distributed data, while protecting the privacy and obtaining a model similar to that of centralized ML. A large number of workers with data and computing power are the foundation of federal learning. However, the inevitable costs prevent self-interested workers from ...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	true	false	false	false	274,535
2303.09713	CHAMPAGNE: Learning Real-world Conversation from Large-Scale Web Videos	Visual information is central to conversation: body gestures and physical behaviour, for example, contribute to meaning that transcends words alone. To date, however, most neural conversational models are limited to just text. We introduce CHAMPAGNE, a generative model of conversations that can account for visual conte...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	352,156
2311.12399	A Survey of Graph Meets Large Language Model: Progress and Future Directions	Graph plays a significant role in representing and analyzing complex relationships in real-world applications such as citation networks, social networks, and biological data. Recently, Large Language Models (LLMs), which have achieved tremendous success in various domains, have also been leveraged in graph-related task...	false	false	false	true	false	false	true	false	true	false	false	false	false	false	false	false	false	false	409,319
2005.01556	Compose Like Humans: Jointly Improving the Coherence and Novelty for Modern Chinese Poetry Generation	Chinese poetry is an important part of worldwide culture, and classical and modern sub-branches are quite different. The former is a unique genre and has strict constraints, while the latter is very flexible in length, optional to have rhymes, and similar to modern poetry in other languages. Thus, it requires more to c...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	175,607
1904.08918	Attentive Single-Tasking of Multiple Tasks	In this work we address task interference in universal networks by considering that a network is trained on multiple tasks, but performs one task at a time, an approach we refer to as "single-tasking multiple tasks". The network thus modifies its behaviour through task-dependent feature adaptation, or task attention. T...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	128,216
1904.00247	Classification of Motorcycles using Extracted Images of Traffic Monitoring Videos	Due to the great growth of motorcycles in the urban fleet and the growth of the study on its behavior and of how this vehicle affects the flow of traffic becomes necessary the development of tools and techniques different from the conventional ones to identify its presence in the traffic flow and be able to extract you...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	125,829
2302.13132	Hierarchical Needs-driven Agent Learning Systems: From Deep Reinforcement Learning To Diverse Strategies	The needs describe the necessities for a system to survive and evolve, which arouses an agent to action toward a goal, giving purpose and direction to behavior. Based on Maslow hierarchy of needs, an agent needs to satisfy a certain amount of needs at the current level as a condition to arise at the next stage -- upgra...	false	false	false	false	true	false	true	true	false	false	true	false	false	false	true	false	false	false	347,823
2402.02658	Multi-step Problem Solving Through a Verifier: An Empirical Analysis on Model-induced Process Supervision	Process supervision, using a trained verifier to evaluate the intermediate steps generated by a reasoner, has demonstrated significant improvements in multi-step problem solving. In this paper, to avoid the expensive effort of human annotation on the verifier training data, we introduce Model-induced Process Supervisio...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	426,673
2406.10563	Privacy-Preserving Heterogeneous Federated Learning for Sensitive Healthcare Data	In the realm of healthcare where decentralized facilities are prevalent, machine learning faces two major challenges concerning the protection of data and models. The data-level challenge concerns the data privacy leakage when centralizing data with sensitive personal information. While the model-level challenge arises...	false	false	false	false	true	false	true	false	false	false	false	false	true	false	false	false	false	false	464,462
1912.02096	Learning Multi-Object Tracking and Segmentation from Automatic Annotations	In this work we contribute a novel pipeline to automatically generate training data, and to improve over state-of-the-art multi-object tracking and segmentation (MOTS) methods. Our proposed track mining algorithm turns raw street-level videos into high-fidelity MOTS training data, is scalable and overcomes the need of ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	156,255
1911.00926	Learning Algorithmic Solutions to Symbolic Planning Tasks with a Neural Computer Architecture	A key feature of intelligent behavior is the ability to learn abstract strategies that transfer to unfamiliar problems. Therefore, we present a novel architecture, based on memory-augmented networks, that is inspired by the von Neumann and Harvard architectures of modern computers. This architecture enables the learnin...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	true	false	false	151,956
2307.07439	Atlas-Based Interpretable Age Prediction In Whole-Body MR Images	Age prediction is an important part of medical assessments and research. It can aid in detecting diseases as well as abnormal ageing by highlighting potential discrepancies between chronological and biological age. To improve understanding of age-related changes in various body parts, we investigate the ageing of the h...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	379,413
2105.09536	On the $\alpha$-lazy version of Markov chains in estimation and testing problems	Given access to a single long trajectory generated by an unknown irreducible Markov chain $M$, we simulate an $\alpha$-lazy version of $M$ which is ergodic. This enables us to generalize recent results on estimation and identity testing that were stated for ergodic Markov chains in a way that allows fully empirical inf...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	236,094
2501.16624	More Efficient Sybil Detection Mechanisms Leveraging Resistance of Users to Attack Requests	We investigate the problem of sybil (fake account) detection in social networks from a graph algorithms perspective, where graph structural information is used to classify users as sybil and benign. We introduce the novel notion of user resistance to attack requests (friendship requests from sybil accounts). Building o...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	528,036
1911.07690	Leveraging Decentralized Artificial Intelligence to Enhance Resilience of Energy Networks	This paper reintroduces the notion of resilience in the context of recent issues originated from climate change triggered events including severe hurricanes and wildfires. A recent example is PG&E's forced power outage to contain wildfire risk which led to widespread power disruption. This paper focuses on answering tw...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	true	false	false	true	153,939
nlin/0511015	Combinatorial Approach to Object Analysis	We present a perceptional mathematical model for image and signal analysis. A resemblance measure is defined, and submitted to an innovating combinatorial optimization algorithm. Numerical Simulations are also presented	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	540,789
2306.11879	Open-Domain Text Evaluation via Contrastive Distribution Methods	Recent advancements in open-domain text generation, driven by the power of large pre-trained language models (LLMs), have demonstrated remarkable performance. However, assessing these models' generation quality remains a challenge. In this paper, we introduce a novel method for evaluating open-domain text generation ca...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	374,743
2407.00186	DCSM 2.0: Deep Conditional Shape Models for Data Efficient Segmentation	Segmentation is often the first step in many medical image analyses workflows. Deep learning approaches, while giving state-of-the-art accuracies, are data intensive and do not scale well to low data regimes. We introduce Deep Conditional Shape Models 2.0, which uses an edge detector, along with an implicit shape funct...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	468,758
2312.13240	Efficient Verification-Based Face Identification	We study the problem of performing face verification with an efficient neural model $f$. The efficiency of $f$ stems from simplifying the face verification problem from an embedding nearest neighbor search into a binary problem; each user has its own neural network $f$. To allow information sharing between different in...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	417,247
2409.19581	DiMB-RE: Mining the Scientific Literature for Diet-Microbiome Associations	Motivation: The gut microbiota has recently emerged as a key factor that underpins certain connections between diet and human health. A tremendous amount of knowledge has been amassed from experimental studies on diet, human metabolism and microbiome. However, this evidence remains mostly buried in scientific publicati...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	492,750
2404.06311	DRE: Generating Recommendation Explanations by Aligning Large Language Models at Data-level	Recommendation systems play a crucial role in various domains, suggesting items based on user behavior.However, the lack of transparency in presenting recommendations can lead to user confusion. In this paper, we introduce Data-level Recommendation Explanation (DRE), a non-intrusive explanation framework for black-box ...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	445,414
1912.11580	A Study of the Learnability of Relational Properties: Model Counting Meets Machine Learning (MCML)	This paper introduces the MCML approach for empirically studying the learnability of relational properties that can be expressed in the well-known software design language Alloy. A key novelty of MCML is quantification of the performance of and semantic differences among trained machine learning (ML) models, specifical...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	158,598
2307.08286	Going Beyond Linear Mode Connectivity: The Layerwise Linear Feature Connectivity	Recent work has revealed many intriguing empirical phenomena in neural network training, despite the poorly understood and highly complex loss landscapes and training dynamics. One of these phenomena, Linear Mode Connectivity (LMC), has gained considerable attention due to the intriguing observation that different solu...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	379,747
2110.08642	Local Advantage Actor-Critic for Robust Multi-Agent Deep Reinforcement Learning	Policy gradient methods have become popular in multi-agent reinforcement learning, but they suffer from high variance due to the presence of environmental stochasticity and exploring agents (i.e., non-stationarity), which is potentially worsened by the difficulty in credit assignment. As a result, there is a need for a...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	true	false	false	false	261,497
1410.6903	Choice of Mel Filter Bank in Computing MFCC of a Resampled Speech	Mel Frequency Cepstral Coefficients (MFCCs) are the most popularly used speech features in most speech and speaker recognition applications. In this paper, we study the effect of resampling a speech signal on these speech features. We first derive a relationship between the MFCC param- eters of the resampled speech and...	false	false	true	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	37,019
2012.03244	Covert Communication in Intelligent Reflecting Surface-Assisted NOMA Systems: Design, Analysis, and Optimization	In this paper, we investigate covert communication in an intelligent reflecting surface (IRS)-assisted non-orthogonal multiple access (NOMA) system, where a legitimate transmitter (Alice) applies NOMA for downlink and uplink transmissions with a covert user (Bob) and a public user (Roy) aided by an IRS. Specifically, w...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	210,048
2404.05458	Teaching Higher-Order Logic Using Isabelle	We present a formalization of higher-order logic in the Isabelle proof assistant, building directly on the foundational framework Isabelle/Pure and developed to be as small and readable as possible. It should therefore serve as a good introduction for someone looking into learning about higher-order logic and proof ass...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	445,093
2006.15469	End-to-End AI-Based Point-of-Care Diagnosis System for Classifying Respiratory Illnesses and Early Detection of COVID-19	Respiratory symptoms can be a caused by different underlying conditions, and are often caused by viral infections, such as Influenza-like illnesses or other emerging viruses like the Coronavirus. These respiratory viruses, often, have common symptoms, including coughing, high temperature, congested nose, and difficulty...	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	184,514
2011.09892	Data Representing Ground-Truth Explanations to Evaluate XAI Methods	Explainable artificial intelligence (XAI) methods are currently evaluated with approaches mostly originated in interpretable machine learning (IML) research that focus on understanding models such as comparison against existing attribution approaches, sensitivity analyses, gold set of features, axioms, or through demon...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	207,352
1811.02834	Fused Gromov-Wasserstein distance for structured objects: theoretical foundations and mathematical properties	Optimal transport theory has recently found many applications in machine learning thanks to its capacity for comparing various machine learning objects considered as distributions. The Kantorovitch formulation, leading to the Wasserstein distance, focuses on the features of the elements of the objects but treat them in...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	112,701
2401.15975	StableIdentity: Inserting Anybody into Anywhere at First Sight	Recent advances in large pretrained text-to-image models have shown unprecedented capabilities for high-quality human-centric generation, however, customizing face identity is still an intractable problem. Existing methods cannot ensure stable identity preservation and flexible editability, even with several images for...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	424,671

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