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
1906.02570	On typical encodings of multivariate ergodic sources	We show that the typical coordinate-wise encoding of multivariate ergodic source into prescribed alphabets has the entropy profile close to the convolution of the entropy profile of the source and the modular polymatroid that is determined by the cardinalities of the output alphabets. We show that the proportion of the...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	134,110
1508.03721	A Comparative Study on Regularization Strategies for Embedding-based Neural Networks	This paper aims to compare different regularization strategies to address a common phenomenon, severe overfitting, in embedding-based neural networks for NLP. We chose two widely studied neural models and tasks as our testbed. We tried several frequently applied or newly proposed regularization strategies, including pe...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	46,033
2112.11393	A Survey on Perfectly-Secure Verifiable Secret-Sharing	Verifiable Secret-Sharing (VSS) is a fundamental primitive in secure distributed computing. It is used as a building block in several distributed computing tasks, such as Byzantine agreement and secure multi-party computation. In this article, we consider VSS schemes with perfect security, tolerating computationally un...	false	false	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	true	272,690
1807.05473	Codes with hierarchical locality from covering maps of curves	Locally recoverable (LRC) codes provide ways of recovering erased coordinates of the codeword without having to access each of the remaining coordinates. A subfamily of LRC codes with hierarchical locality (H-LRC codes) provides added flexibility to the construction by introducing several tiers of recoverability for co...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	102,929
2103.09100	A massively parallel explicit solver for elasto-dynamic problems exploiting octree meshes	Typical areas of application of explicit dynamics are impact, crash test, and most importantly, wave propagation simulations. Due to the numerically highly demanding nature of these problems, efficient automatic mesh generators and transient solvers are required. To this end, a parallel explicit solver exploiting the a...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	225,082
1601.03829	Cellular Communications on License-Exempt Spectrum: A Tutorial	A traditional cellular system (e.g., LTE) operates only on the licensed spectrum. This tutorial explains the concept of cellular communications on both licensed and license-exempt spectrum under a unified architecture. The purpose to extend a cellular system into the bandwidth-rich license-exempt spectrum is to form a ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	50,954
2312.02142	Object Recognition as Next Token Prediction	We present an approach to pose object recognition as next token prediction. The idea is to apply a language decoder that auto-regressively predicts the text tokens from image embeddings to form labels. To ground this prediction process in auto-regression, we customize a non-causal attention mask for the decoder, incorp...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	412,710
2210.06002	Face Super-Resolution with Progressive Embedding of Multi-scale Face Priors	The face super-resolution (FSR) task is to reconstruct high-resolution face images from low-resolution inputs. Recent works have achieved success on this task by utilizing facial priors such as facial landmarks. Most existing methods pay more attention to global shape and structure information, but less to local textur...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	323,101
2209.13113	FG-UAP: Feature-Gathering Universal Adversarial Perturbation	Deep Neural Networks (DNNs) are susceptible to elaborately designed perturbations, whether such perturbations are dependent or independent of images. The latter one, called Universal Adversarial Perturbation (UAP), is very attractive for model robustness analysis, since its independence of input reveals the intrinsic c...	false	false	false	false	false	false	true	false	false	false	false	true	true	false	false	false	false	false	319,776
2107.01205	HandVoxNet++: 3D Hand Shape and Pose Estimation using Voxel-Based Neural Networks	3D hand shape and pose estimation from a single depth map is a new and challenging computer vision problem with many applications. Existing methods addressing it directly regress hand meshes via 2D convolutional neural networks, which leads to artefacts due to perspective distortions in the images. To address the limit...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	244,410
2401.14498	Predictive Analysis for Optimizing Port Operations	Maritime transport is a pivotal logistics mode for the long-distance and bulk transportation of goods. However, the intricate planning involved in this mode is often hindered by uncertainties, including weather conditions, cargo diversity, and port dynamics, leading to increased costs. Consequently, accurate estimation...	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	424,113
2405.16219	Deep Causal Generative Models with Property Control	Generating data with properties of interest by external users while following the right causation among its intrinsic factors is important yet has not been well addressed jointly. This is due to the long-lasting challenge of jointly identifying key latent variables, their causal relations, and their correlation with pr...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	457,313
2211.09066	Teaching Algorithmic Reasoning via In-context Learning	Large language models (LLMs) have shown increasing in-context learning capabilities through scaling up model and data size. Despite this progress, LLMs are still unable to solve algorithmic reasoning problems. While providing a rationale with the final answer has led to further improvements in multi-step reasoning prob...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	330,859
1609.06693	SoftTarget Regularization: An Effective Technique to Reduce Over-Fitting in Neural Networks	Deep neural networks are learning models with a very high capacity and therefore prone to over-fitting. Many regularization techniques such as Dropout, DropConnect, and weight decay all attempt to solve the problem of over-fitting by reducing the capacity of their respective models (Srivastava et al., 2014), (Wan et al...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	61,335
2304.05175	Sufficient Conditions for the Exact Relaxation of Complementarity Constraints for Storages in Multi-period OPF Problems	Storage-concerned Optimal Power Flow (OPF) with complementarity constraints is highly non-convex and intractable. In this paper, we propose two generalized sufficient conditions which guarantee no simultaneous charging and discharging (SCD) in the relaxed multi-period OPF excluding the complementarity constraints. More...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	357,526
2304.06495	An embedding for EEG signals learned using a triplet loss	Neurophysiological time series recordings like the electroencephalogram (EEG) or local field potentials are obtained from multiple sensors. They can be decoded by machine learning models in order to estimate the ongoing brain state of a patient or healthy user. In a brain-computer interface (BCI), this decoded brain st...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	357,997
2106.15944	A survey on computational spectral reconstruction methods from RGB to hyperspectral imaging	Hyperspectral imaging enables versatile applications due to its competence in capturing abundant spatial and spectral information, which are crucial for identifying substances. However, the devices for acquiring hyperspectral images are expensive and complicated. Therefore, many alternative spectral imaging methods hav...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	243,922
2501.00464	Addressing Challenges in Data Quality and Model Generalization for Malaria Detection	Malaria remains a significant global health burden, particularly in resource-limited regions where timely and accurate diagnosis is critical to effective treatment and control. Deep Learning (DL) has emerged as a transformative tool for automating malaria detection and it offers high accuracy and scalability. However, ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	521,675
2210.07565	Multitask Pre-training of Modular Prompt for Chinese Few-Shot Learning	Prompt tuning is a parameter-efficient approach to adapting pre-trained language models to downstream tasks. Although prompt tuning has been shown to match the performance of full model tuning when training data is sufficient, it tends to struggle in few-shot learning settings. In this paper, we present Multi-task Pre-...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	323,772
2301.02593	Multi-Agent Reinforcement Learning for Fast-Timescale Demand Response of Residential Loads	To integrate high amounts of renewable energy resources, electrical power grids must be able to cope with high amplitude, fast timescale variations in power generation. Frequency regulation through demand response has the potential to coordinate temporally flexible loads, such as air conditioners, to counteract these v...	false	false	false	false	true	false	true	false	false	false	true	false	false	false	true	false	false	false	339,542
2301.06544	Distinguish Sense from Nonsense: Out-of-Scope Detection for Virtual Assistants	Out of Scope (OOS) detection in Conversational AI solutions enables a chatbot to handle a conversation gracefully when it is unable to make sense of the end-user query. Accurately tagging a query as out-of-domain is particularly hard in scenarios when the chatbot is not equipped to handle a topic which has semantic ove...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	340,670
1709.05435	An Integrated System for Perception-Driven Autonomy with Modular Robots	The theoretical ability of modular robots to reconfigure in response to complex tasks in a priori unknown environments has frequently been cited as an advantage and remains a major motivator for work in the field. We present a modular robot system capable of autonomously completing high-level tasks by reactively reconf...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	80,866
2010.15268	Understanding the Pathologies of Approximate Policy Evaluation when Combined with Greedification in Reinforcement Learning	Despite empirical success, the theory of reinforcement learning (RL) with value function approximation remains fundamentally incomplete. Prior work has identified a variety of pathological behaviours that arise in RL algorithms that combine approximate on-policy evaluation and greedification. One prominent example is p...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	203,713
1801.04623	Sex differences in network controllability as a predictor of executive function in youth	Executive function emerges late in development and displays different developmental trends in males and females. Sex differences in executive function in youth have been linked to vulnerability to psychopathology as well as to behaviors that impinge on health. Yet, the neurobiological basis of these differences is not ...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	88,317
1808.01204	Learning Overparameterized Neural Networks via Stochastic Gradient Descent on Structured Data	Neural networks have many successful applications, while much less theoretical understanding has been gained. Towards bridging this gap, we study the problem of learning a two-layer overparameterized ReLU neural network for multi-class classification via stochastic gradient descent (SGD) from random initialization. In ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	104,535
2307.07681	Data-centric Operational Design Domain Characterization for Machine Learning-based Aeronautical Products	We give a first rigorous characterization of Operational Design Domains (ODDs) for Machine Learning (ML)-based aeronautical products. Unlike in other application sectors (such as self-driving road vehicles) where ODD development is scenario-based, our approach is data-centric: we propose the dimensions along which the ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	379,499
2103.09555	Textile Taxonomy and Classification Using Pulling and Twisting	Identification of textile properties is an important milestone toward advanced robotic manipulation tasks that consider interaction with clothing items such as assisted dressing, laundry folding, automated sewing, textile recycling and reusing. Despite the abundance of work considering this class of deformable objects,...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	225,196
1707.09093	How Often Should CSI be Updated for Massive MIMO Systems with Massive Connectivity?	Massive multiple-input multiple-output (MIMO) systems need to support massive connectivity for the application of the Internet of things (IoT). The overhead of channel state information (CSI) acquisition becomes a bottleneck in the system performance due to the increasing number of users. An intermittent estimation sch...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	77,944
2412.04473	Take Package as Language: Anomaly Detection Using Transformer	Network data packet anomaly detection faces numerous challenges, including exploring new anomaly supervision signals, researching weakly supervised anomaly detection, and improving model interpretability. This paper proposes NIDS-GPT, a GPT-based causal language model for network intrusion detection. Unlike previous wo...	false	false	false	false	true	false	false	false	false	false	false	false	true	false	false	false	false	false	514,428
2301.01917	Flying Bird Object Detection Algorithm in Surveillance Video Based on Motion Information	A Flying Bird Object Detection algorithm Based on Motion Information (FBOD-BMI) is proposed to solve the problem that the features of the object are not obvious in a single frame, and the size of the object is small (low Signal-to-Noise Ratio (SNR)) in surveillance video. Firstly, a ConvLSTM-PAN model structure is desi...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	339,364
1905.06287	Output-Constrained Bayesian Neural Networks	Bayesian neural network (BNN) priors are defined in parameter space, making it hard to encode prior knowledge expressed in function space. We formulate a prior that incorporates functional constraints about what the output can or cannot be in regions of the input space. Output-Constrained BNNs (OC-BNN) represent an int...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	130,946
1910.10781	Hierarchical Transformers for Long Document Classification	BERT, which stands for Bidirectional Encoder Representations from Transformers, is a recently introduced language representation model based upon the transfer learning paradigm. We extend its fine-tuning procedure to address one of its major limitations - applicability to inputs longer than a few hundred words, such as...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	150,577
1907.10213	Image Super-Resolution Using a Wavelet-based Generative Adversarial Network	In this paper, we consider the problem of super-resolution recons-truction. This is a hot topic because super-resolution reconstruction has a wide range of applications in the medical field, remote sensing monitoring, and criminal investigation. Compared with traditional algorithms, the current super-resolution reconst...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	139,562
2008.08072	AssembleNet++: Assembling Modality Representations via Attention Connections	We create a family of powerful video models which are able to: (i) learn interactions between semantic object information and raw appearance and motion features, and (ii) deploy attention in order to better learn the importance of features at each convolutional block of the network. A new network component named peer-a...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	true	false	false	192,313
2209.04862	Adaptive Perturbation-Based Gradient Estimation for Discrete Latent Variable Models	The integration of discrete algorithmic components in deep learning architectures has numerous applications. Recently, Implicit Maximum Likelihood Estimation (IMLE, Niepert, Minervini, and Franceschi 2021), a class of gradient estimators for discrete exponential family distributions, was proposed by combining implicit ...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	true	false	false	316,909
2111.08073	Learning Robust Scheduling with Search and Attention	Allocating physical layer resources to users based on channel quality, buffer size, requirements and constraints represents one of the central optimization problems in the management of radio resources. The solution space grows combinatorially with the cardinality of each dimension making it hard to find optimal soluti...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	true	266,557
2203.02131	3D endoscopic depth estimation using 3D surface-aware constraints	Robotic-assisted surgery allows surgeons to conduct precise surgical operations with stereo vision and flexible motor control. However, the lack of 3D spatial perception limits situational awareness during procedures and hinders mastering surgical skills in the narrow abdominal space. Depth estimation, as a representat...	false	false	false	false	true	false	false	true	false	false	false	true	false	false	false	false	false	false	283,652
2407.02772	Gradient descent with generalized Newton's method	We propose the generalized Newton's method (GeN) -- a Hessian-informed approach that applies to any optimizer such as SGD and Adam, and covers the Newton-Raphson method as a sub-case. Our method automatically and dynamically selects the learning rate that accelerates the convergence, without the intensive tuning of the...	false	false	false	false	false	false	true	false	true	false	false	true	false	false	false	false	false	false	469,876
cs/0703002	Integral Biomathics: A Post-Newtonian View into the Logos of Bios (On the New Meaning, Relations and Principles of Life in Science)	This work is an attempt for a state-of-the-art survey of natural and life sciences with the goal to define the scope and address the central questions of an original research program. It is focused on the phenomena of emergence, adaptive dynamics and evolution of self-assembling, self-organizing, self-maintaining and s...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	true	540,209
1804.09788	Multi-Layer Sparse Coding: The Holistic Way	The recently proposed multi-layer sparse model has raised insightful connections between sparse representations and convolutional neural networks (CNN). In its original conception, this model was restricted to a cascade of convolutional synthesis representations. In this paper, we start by addressing a more general mod...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	96,035
1904.13353	Object Contour and Edge Detection with RefineContourNet	A ResNet-based multi-path refinement CNN is used for object contour detection. For this task, we prioritise the effective utilization of the high-level abstraction capability of a ResNet, which leads to state-of-the-art results for edge detection. Keeping our focus in mind, we fuse the high, mid and low-level features ...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	129,367
2411.16642	Preventing Jailbreak Prompts as Malicious Tools for Cybercriminals: A Cyber Defense Perspective	Jailbreak prompts pose a significant threat in AI and cybersecurity, as they are crafted to bypass ethical safeguards in large language models, potentially enabling misuse by cybercriminals. This paper analyzes jailbreak prompts from a cyber defense perspective, exploring techniques like prompt injection and context ma...	false	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	511,100
1204.1624	UCB Algorithm for Exponential Distributions	We introduce in this paper a new algorithm for Multi-Armed Bandit (MAB) problems. A machine learning paradigm popular within Cognitive Network related topics (e.g., Spectrum Sensing and Allocation). We focus on the case where the rewards are exponentially distributed, which is common when dealing with Rayleigh fading c...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	15,333
2106.15420	Spiking-GAN: A Spiking Generative Adversarial Network Using Time-To-First-Spike Coding	Spiking Neural Networks (SNNs) have shown great potential in solving deep learning problems in an energy-efficient manner. However, they are still limited to simple classification tasks. In this paper, we propose Spiking-GAN, the first spike-based Generative Adversarial Network (GAN). It employs a kind of temporal codi...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	true	false	false	243,765
2407.01606	On Discrete Prompt Optimization for Diffusion Models	This paper introduces the first gradient-based framework for prompt optimization in text-to-image diffusion models. We formulate prompt engineering as a discrete optimization problem over the language space. Two major challenges arise in efficiently finding a solution to this problem: (1) Enormous Domain Space: Setting...	false	false	false	false	true	false	true	false	true	false	false	true	false	false	false	false	false	false	469,382
2009.03984	Automatic feature-preserving size field for 3D mesh generation	This paper presents a methodology aiming at easing considerably the generation of high-quality meshes for complex 3D domains. We show that the whole mesh generation process can be controlled with only five parameters to generate in one stroke quality meshes for arbitrary geometries. The main idea is to build a meshsize...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	194,933
1908.09092	Fairness Warnings and Fair-MAML: Learning Fairly with Minimal Data	Motivated by concerns surrounding the fairness effects of sharing and transferring fair machine learning tools, we propose two algorithms: Fairness Warnings and Fair-MAML. The first is a model-agnostic algorithm that provides interpretable boundary conditions for when a fairly trained model may not behave fairly on sim...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	142,752
2105.12272	Provable Representation Learning for Imitation with Contrastive Fourier Features	In imitation learning, it is common to learn a behavior policy to match an unknown target policy via max-likelihood training on a collected set of target demonstrations. In this work, we consider using offline experience datasets - potentially far from the target distribution - to learn low-dimensional state representa...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	236,958
2108.00475	Self-supervised Learning with Local Attention-Aware Feature	In this work, we propose a novel methodology for self-supervised learning for generating global and local attention-aware visual features. Our approach is based on training a model to differentiate between specific image transformations of an input sample and the patched images. Utilizing this approach, the proposed me...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	248,729
2305.17478	Deep Variational Lesion-Deficit Mapping	Causal mapping of the functional organisation of the human brain requires evidence of \textit{necessity} available at adequate scale only from pathological lesions of natural origin. This demands inferential models with sufficient flexibility to capture both the observable distribution of pathological damage and the un...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	368,612
2112.02128	MIMO Networks with One-Bit ADCs: Receiver Design and Communication Strategies	High resolution analog to digital converters (ADCs) are conventionally used at the receiver terminals to store an accurate digital representation of the received signal, thereby allowing for reliable decoding of transmitted messages. However, in a wide range of applications, such as communication over millimeter wave a...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	269,735
2303.11126	Robustifying Token Attention for Vision Transformers	Despite the success of vision transformers (ViTs), they still suffer from significant drops in accuracy in the presence of common corruptions, such as noise or blur. Interestingly, we observe that the attention mechanism of ViTs tends to rely on few important tokens, a phenomenon we call token overfocusing. More critic...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	352,713
2401.12486	Quaternary codes and their binary images	Recently, simplicial complexes are used in constructions of several infinite families of minimal and optimal linear codes by Hyun {\em et al.} Building upon their research, in this paper more linear codes over the ring $\mathbb{Z}_4$ are constructed by simplicial complexes. Specifically, the Lee weight distributions of...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	423,398
2108.13153	LIGAR: Lightweight General-purpose Action Recognition	Growing amount of different practical tasks in a video understanding problem has addressed the great challenge aiming to design an universal solution, which should be available for broad masses and suitable for the demanding edge-oriented inference. In this paper we are focused on designing a network architecture and a...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	252,726
1705.05922	LCDet: Low-Complexity Fully-Convolutional Neural Networks for Object Detection in Embedded Systems	Deep convolutional Neural Networks (CNN) are the state-of-the-art performers for object detection task. It is well known that object detection requires more computation and memory than image classification. Thus the consolidation of a CNN-based object detection for an embedded system is more challenging. In this work, ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	73,565
2009.13239	Scalable Transfer Learning with Expert Models	Transfer of pre-trained representations can improve sample efficiency and reduce computational requirements for new tasks. However, representations used for transfer are usually generic, and are not tailored to a particular distribution of downstream tasks. We explore the use of expert representations for transfer with...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	197,677
1503.04404	Predicting Item Popularity: Analysing Local Clustering Behaviour of Users	Predicting the popularity of items in rating networks is an interesting but challenging problem. This is especially so when an item has first appeared and has received very few ratings. In this paper, we propose a novel approach to predicting the future popularity of new items in rating networks, defining a new biparti...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	41,158
2209.05477	Adaptive 3D Localization of 2D Freehand Ultrasound Brain Images	Two-dimensional (2D) freehand ultrasound is the mainstay in prenatal care and fetal growth monitoring. The task of matching corresponding cross-sectional planes in the 3D anatomy for a given 2D ultrasound brain scan is essential in freehand scanning, but challenging. We propose AdLocUI, a framework that Adaptively Loca...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	317,107
2405.02334	Rad4XCNN: a new agnostic method for post-hoc global explanation of CNN-derived features by means of radiomics	In recent years, machine learning-based clinical decision support systems (CDSS) have played a key role in the analysis of several medical conditions. Despite their promising capabilities, the lack of transparency in AI models poses significant challenges, particularly in medical contexts where reliability is a mandato...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	451,719
2105.13782	How to Split: the Effect of Word Segmentation on Gender Bias in Speech Translation	Having recognized gender bias as a major issue affecting current translation technologies, researchers have primarily attempted to mitigate it by working on the data front. However, whether algorithmic aspects concur to exacerbate unwanted outputs remains so far under-investigated. In this work, we bring the analysis o...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	237,402
1606.04721	Personality Traits and Echo Chambers on Facebook	In online social networks, users tend to select information that adhere to their system of beliefs and to form polarized groups of like minded people. Polarization as well as its effects on online social interactions have been extensively investigated. Still, the relation between group formation and personality traits ...	true	false	false	true	false	false	false	false	true	false	false	false	false	true	false	false	false	false	57,303
2502.12453	UniMatch: Universal Matching from Atom to Task for Few-Shot Drug Discovery	Drug discovery is crucial for identifying candidate drugs for various diseases.However, its low success rate often results in a scarcity of annotations, posing a few-shot learning problem. Existing methods primarily focus on single-scale features, overlooking the hierarchical molecular structures that determine differe...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	534,871
2205.09977	FairNorm: Fair and Fast Graph Neural Network Training	Graph neural networks (GNNs) have been demonstrated to achieve state-of-the-art for a number of graph-based learning tasks, which leads to a rise in their employment in various domains. However, it has been shown that GNNs may inherit and even amplify bias within training data, which leads to unfair results towards cer...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	297,499
1402.4732	Efficient Inference of Gaussian Process Modulated Renewal Processes with Application to Medical Event Data	The episodic, irregular and asynchronous nature of medical data render them difficult substrates for standard machine learning algorithms. We would like to abstract away this difficulty for the class of time-stamped categorical variables (or events) by modeling them as a renewal process and inferring a probability dens...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	30,990
1704.05815	Headphones on the wire	We analyze a dataset providing the complete information on the effective plays of thousands of music listeners during several months. Our analysis confirms a number of properties previously highlighted by research based on interviews and questionnaires, but also uncover new statistical patterns, both at the individual ...	false	false	false	true	false	false	false	false	false	false	false	false	false	true	false	false	false	false	72,078
2305.18657	Representation Of Lexical Stylistic Features In Language Models' Embedding Space	The representation space of pretrained Language Models (LMs) encodes rich information about words and their relationships (e.g., similarity, hypernymy, polysemy) as well as abstract semantic notions (e.g., intensity). In this paper, we demonstrate that lexical stylistic notions such as complexity, formality, and figura...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	369,179
2305.12916	Towards higher-order accurate mass lumping in explicit isogeometric analysis for structural dynamics	We present a mass lumping approach based on an isogeometric Petrov-Galerkin method that preserves higher-order spatial accuracy in explicit dynamics calculations irrespective of the polynomial degree of the spline approximation. To discretize the test function space, our method uses an approximate dual basis, whose fun...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	366,251
2306.00402	Discriminative Deep Feature Visualization for Explainable Face Recognition	Despite the huge success of deep convolutional neural networks in face recognition (FR) tasks, current methods lack explainability for their predictions because of their "black-box" nature. In recent years, studies have been carried out to give an interpretation of the decision of a deep FR system. However, the affinit...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	369,998
2208.06900	Convolutional Spiking Neural Networks for Detecting Anticipatory Brain Potentials Using Electroencephalogram	Spiking neural networks (SNNs) are receiving increased attention because they mimic synaptic connections in biological systems and produce spike trains, which can be approximated by binary values for computational efficiency. Recently, the addition of convolutional layers to combine the feature extraction power of conv...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	312,862
2402.05400	Optimizing for ROC Curves on Class-Imbalanced Data by Training over a Family of Loss Functions	Although binary classification is a well-studied problem in computer vision, training reliable classifiers under severe class imbalance remains a challenging problem. Recent work has proposed techniques that mitigate the effects of training under imbalance by modifying the loss functions or optimization methods. While ...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	427,846
2210.04213	Towards Understanding and Boosting Adversarial Transferability from a Distribution Perspective	Transferable adversarial attacks against Deep neural networks (DNNs) have received broad attention in recent years. An adversarial example can be crafted by a surrogate model and then attack the unknown target model successfully, which brings a severe threat to DNNs. The exact underlying reasons for the transferability...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	322,359
2311.13319	Deep Learning for Vascular Segmentation and Applications in Phase Contrast Tomography Imaging	Automated blood vessel segmentation is vital for biomedical imaging, as vessel changes indicate many pathologies. Still, precise segmentation is difficult due to the complexity of vascular structures, anatomical variations across patients, the scarcity of annotated public datasets, and the quality of images. We present...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	409,704
cs/0011032	Top-down induction of clustering trees	An approach to clustering is presented that adapts the basic top-down induction of decision trees method towards clustering. To this aim, it employs the principles of instance based learning. The resulting methodology is implemented in the TIC (Top down Induction of Clustering trees) system for first order clustering. ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	537,259
2209.03005	Knowledge-enhanced Iterative Instruction Generation and Reasoning for Knowledge Base Question Answering	Multi-hop Knowledge Base Question Answering(KBQA) aims to find the answer entity in a knowledge base which is several hops from the topic entity mentioned in the question. Existing Retrieval-based approaches first generate instructions from the question and then use them to guide the multi-hop reasoning on the knowledg...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	316,372
2308.10740	We Don't Need No Adam, All We Need Is EVE: On The Variance of Dual Learning Rate And Beyond	In the rapidly advancing field of deep learning, optimising deep neural networks is paramount. This paper introduces a novel method, Enhanced Velocity Estimation (EVE), which innovatively applies different learning rates to distinct components of the gradients. By bifurcating the learning rate, EVE enables more nuanced...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	386,862
2306.17187	An Intelligent Mechanism for Monitoring and Detecting Intrusions in IoT Devices	The current amount of IoT devices and their limitations has come to serve as a motivation for malicious entities to take advantage of such devices and use them for their own gain. To protect against cyberattacks in IoT devices, Machine Learning techniques can be applied to Intrusion Detection Systems. Moreover, privacy...	false	false	false	false	true	false	false	false	false	false	false	false	true	false	false	false	false	true	376,627
2404.15734	ODMixer: Fine-grained Spatial-temporal MLP for Metro Origin-Destination Prediction	Metro Origin-Destination (OD) prediction is a crucial yet challenging spatial-temporal prediction task in urban computing, which aims to accurately forecast cross-station ridership for optimizing metro scheduling and enhancing overall transport efficiency. Analyzing fine-grained and comprehensive relations among statio...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	449,215
2404.08676	ALERT: A Comprehensive Benchmark for Assessing Large Language Models' Safety through Red Teaming	When building Large Language Models (LLMs), it is paramount to bear safety in mind and protect them with guardrails. Indeed, LLMs should never generate content promoting or normalizing harmful, illegal, or unethical behavior that may contribute to harm to individuals or society. This principle applies to both normal an...	false	false	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	446,343
2209.15475	Point Cloud Quality Assessment using 3D Saliency Maps	Point cloud quality assessment (PCQA) has become an appealing research field in recent days. Considering the importance of saliency detection in quality assessment, we propose an effective full-reference PCQA metric which makes the first attempt to utilize the saliency information to facilitate quality prediction, call...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	320,626
2406.11012	Connecting the Dots: Evaluating Abstract Reasoning Capabilities of LLMs Using the New York Times Connections Word Game	The New York Times Connections game has emerged as a popular and challenging pursuit for word puzzle enthusiasts. We collect 438 Connections games to evaluate the performance of state-of-the-art large language models (LLMs) against expert and novice human players. Our results show that even the best performing LLM, Cla...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	464,669
2301.08846	Regeneration Learning: A Learning Paradigm for Data Generation	Machine learning methods for conditional data generation usually build a mapping from source conditional data X to target data Y. The target Y (e.g., text, speech, music, image, video) is usually high-dimensional and complex, and contains information that does not exist in source data, which hinders effective and effic...	false	false	false	false	true	false	true	false	true	false	false	true	false	false	false	false	false	false	341,315
2009.08948	A New Citation Recommendation Strategy Based on Term Functions in Related Studies Section	Purpose: Researchers frequently encounter the following problems when writing scientific articles: (1) Selecting appropriate citations to support the research idea is challenging. (2) The literature review is not conducted extensively, which leads to working on a research problem that others have well addressed. This s...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	196,400
1311.6091	A Primal-Dual Method for Training Recurrent Neural Networks Constrained by the Echo-State Property	We present an architecture of a recurrent neural network (RNN) with a fully-connected deep neural network (DNN) as its feature extractor. The RNN is equipped with both causal temporal prediction and non-causal look-ahead, via auto-regression (AR) and moving-average (MA), respectively. The focus of this paper is a prima...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	true	false	false	28,622
2110.07840	ESPnet2-TTS: Extending the Edge of TTS Research	This paper describes ESPnet2-TTS, an end-to-end text-to-speech (E2E-TTS) toolkit. ESPnet2-TTS extends our earlier version, ESPnet-TTS, by adding many new features, including: on-the-fly flexible pre-processing, joint training with neural vocoders, and state-of-the-art TTS models with extensions like full-band E2E text-...	false	false	true	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	261,153
2309.01875	Gradient Domain Diffusion Models for Image Synthesis	Diffusion models are getting popular in generative image and video synthesis. However, due to the diffusion process, they require a large number of steps to converge. To tackle this issue, in this paper, we propose to perform the diffusion process in the gradient domain, where the convergence becomes faster. There are ...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	true	389,838
2209.03473	Higher-order Clustering and Pooling for Graph Neural Networks	Graph Neural Networks achieve state-of-the-art performance on a plethora of graph classification tasks, especially due to pooling operators, which aggregate learned node embeddings hierarchically into a final graph representation. However, they are not only questioned by recent work showing on par performance with rand...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	316,504
2409.19255	DENEB: A Hallucination-Robust Automatic Evaluation Metric for Image Captioning	In this work, we address the challenge of developing automatic evaluation metrics for image captioning, with a particular focus on robustness against hallucinations. Existing metrics are often inadequate for handling hallucinations, primarily due to their limited ability to compare candidate captions with multifaceted ...	false	false	false	false	true	false	false	false	true	false	false	true	false	false	false	false	false	false	492,603
2412.06484	Small Languages, Big Models: A Study of Continual Training on Languages of Norway	Training large language models requires vast amounts of data, posing a challenge for less widely spoken languages like Norwegian and even more so for truly low-resource languages like Northern S\'ami. To address this issue, we present a novel three-stage continual training approach that substantially improves the downs...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	515,263
1803.07870	Reservoir computing approaches for representation and classification of multivariate time series	Classification of multivariate time series (MTS) has been tackled with a large variety of methodologies and applied to a wide range of scenarios. Reservoir Computing (RC) provides efficient tools to generate a vectorial, fixed-size representation of the MTS that can be further processed by standard classifiers. Despite...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	93,147
2303.06198	Deflated HeteroPCA: Overcoming the curse of ill-conditioning in heteroskedastic PCA	This paper is concerned with estimating the column subspace of a low-rank matrix $\boldsymbol{X}^\star \in \mathbb{R}^{n_1\times n_2}$ from contaminated data. How to obtain optimal statistical accuracy while accommodating the widest range of signal-to-noise ratios (SNRs) becomes particularly challenging in the presence...	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	350,732
2107.06907	TGIF: Tree-Graph Integrated-Format Parser for Enhanced UD with Two-Stage Generic- to Individual-Language Finetuning	We present our contribution to the IWPT 2021 shared task on parsing into enhanced Universal Dependencies. Our main system component is a hybrid tree-graph parser that integrates (a) predictions of spanning trees for the enhanced graphs with (b) additional graph edges not present in the spanning trees. We also adopt a f...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	246,232
2310.04787	HI-SLAM: Monocular Real-time Dense Mapping with Hybrid Implicit Fields	In this letter, we present a neural field-based real-time monocular mapping framework for accurate and dense Simultaneous Localization and Mapping (SLAM). Recent neural mapping frameworks show promising results, but rely on RGB-D or pose inputs, or cannot run in real-time. To address these limitations, our approach int...	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	397,816
2005.11401	Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks	Large pre-trained language models have been shown to store factual knowledge in their parameters, and achieve state-of-the-art results when fine-tuned on downstream NLP tasks. However, their ability to access and precisely manipulate knowledge is still limited, and hence on knowledge-intensive tasks, their performance ...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	178,456
1307.7382	Learning Frames from Text with an Unsupervised Latent Variable Model	We develop a probabilistic latent-variable model to discover semantic frames---types of events and their participants---from corpora. We present a Dirichlet-multinomial model in which frames are latent categories that explain the linking of verb-subject-object triples, given document-level sparsity. We analyze what the...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	26,098
2205.00840	Traction of Interlocking Spikes on a Granular Material	The interlock drive system generates traction by inserting narrow articulated spikes into the ground and by leveraging the soil's strength to resist horizontal draft forces. The system promises high tractive performance in low gravity environments where tires have little traction for lack of weight. At Earth and Space ...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	294,396
2302.02272	Divide and Compose with Score Based Generative Models	While score based generative models, or diffusion models, have found success in image synthesis, they are often coupled with text data or image label to be able to manipulate and conditionally generate images. Even though manipulation of images by changing the text prompt is possible, our understanding of the text embe...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	343,933
2412.11408	Federated Domain Generalization with Label Smoothing and Balanced Decentralized Training	In this paper, we propose a novel approach, Federated Domain Generalization with Label Smoothing and Balanced Decentralized Training (FedSB), to address the challenges of data heterogeneity within a federated learning framework. FedSB utilizes label smoothing at the client level to prevent overfitting to domain-specifi...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	517,391
2409.03111	What is Normal? A Big Data Observational Science Model of Anonymized Internet Traffic	Understanding what is normal is a key aspect of protecting a domain. Other domains invest heavily in observational science to develop models of normal behavior to better detect anomalies. Recent advances in high performance graph libraries, such as the GraphBLAS, coupled with supercomputers enables processing of the tr...	false	false	false	true	false	false	false	false	false	false	false	false	true	true	false	false	false	true	485,919
2203.02860	Compartmental Models for COVID-19 and Control via Policy Interventions	We demonstrate an approach to replicate and forecast the spread of the SARS-CoV-2 (COVID-19) pandemic using the toolkit of probabilistic programming languages (PPLs). Our goal is to study the impact of various modeling assumptions and motivate policy interventions enacted to limit the spread of infectious diseases. Usi...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	283,888
1608.05997	OFDM Without CP in Massive MIMO	We study the possibility of removing the cyclic prefix (CP) overhead from orthogonal frequency division multiplexing (OFDM) in massive multiple-input multiple-output (MIMO) systems. We consider the uplink transmission while our results are applicable to the downlink as well. The absence of CP increases the spectral eff...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	60,052

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