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
2203.10165	Privacy-Preserving Reinforcement Learning Beyond Expectation	Cyber and cyber-physical systems equipped with machine learning algorithms such as autonomous cars share environments with humans. In such a setting, it is important to align system (or agent) behaviors with the preferences of one or more human users. We consider the case when an agent has to learn behaviors in an unkn...	false	false	false	false	true	false	true	false	false	false	true	false	true	false	false	false	false	false	286,422
2303.18005	Artificial Intelligence in Ovarian Cancer Histopathology: A Systematic Review	Purpose - To characterise and assess the quality of published research evaluating artificial intelligence (AI) methods for ovarian cancer diagnosis or prognosis using histopathology data. Methods - A search of PubMed, Scopus, Web of Science, CENTRAL, and WHO-ICTRP was conducted up to 19/05/2023. The inclusion criteria ...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	355,431
1202.5398	Mod-CSA: Modularity optimization by conformational space annealing	We propose a new modularity optimization method, Mod-CSA, based on stochastic global optimization algorithm, conformational space annealing (CSA). Our method outperforms simulated annealing in terms of both efficiency and accuracy, finding higher modularity partitions with less computational resources required. The hig...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	14,551
2412.04826	Pushing Rendering Boundaries: Hard Gaussian Splatting	3D Gaussian Splatting (3DGS) has demonstrated impressive Novel View Synthesis (NVS) results in a real-time rendering manner. During training, it relies heavily on the average magnitude of view-space positional gradients to grow Gaussians to reduce rendering loss. However, this average operation smooths the positional g...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	514,583
2301.04116	Minimizing the Age of Information Over an Erasure Channel for Random Packet Arrivals With a Storage Option at the Transmitter	We consider a time slotted communication system consisting of a base station (BS) and a user. At each time slot an update packet arrives at the BS with probability $p$, and the BS successfully transmits the update packet with probability $q$ over an erasure channel. We assume that the BS has a unit size buffer where it...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	339,974
2201.06922	Computational Rational Engineering and Development: Synergies and Opportunities	Research and development in computer technology and computational methods have resulted in a wide variety of valuable tools for Computer-Aided Engineering (CAE) and Industrial Engineering. However, despite the exponential increase in computational capabilities and Artificial Intelligence (AI) methods, many of the visio...	false	false	false	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	275,875
0901.4147	Determination of Minimal Sets of Control Places for Safe Petri Nets	Our objective is to design a controlled system with a simple method for discrete event systems based on Petri nets. It is possible to construct the Petri net model of a system and the specification separately. By synchronous composition of both models, the desired functioning closed loop model is deduced. Often uncontr...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	3,060
1711.11438	SyGuS-Comp 2017: Results and Analysis	Syntax-Guided Synthesis (SyGuS) is the computational problem of finding an implementation f that meets both a semantic constraint given by a logical formula phi in a background theory T, and a syntactic constraint given by a grammar G, which specifies the allowed set of candidate implementations. Such a synthesis probl...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	85,779
2305.17140	Interactive Model Expansion in an Observable Environment	Many practical problems can be understood as the search for a state of affairs that extends a fixed partial state of affairs, the \emph{environment}, while satisfying certain conditions that are formally specified. Such problems are found in, e.g., engineering, law or economics. We study this class of problems in a c...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	368,427
2404.12784	Contrastive Gaussian Clustering: Weakly Supervised 3D Scene Segmentation	We introduce Contrastive Gaussian Clustering, a novel approach capable of provide segmentation masks from any viewpoint and of enabling 3D segmentation of the scene. Recent works in novel-view synthesis have shown how to model the appearance of a scene via a cloud of 3D Gaussians, and how to generate accurate images fr...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	448,032
2302.03488	APAM: Adaptive Pre-training and Adaptive Meta Learning in Language Model for Noisy Labels and Long-tailed Learning	Practical natural language processing (NLP) tasks are commonly long-tailed with noisy labels. Those problems challenge the generalization and robustness of complex models such as Deep Neural Networks (DNNs). Some commonly used resampling techniques, such as oversampling or undersampling, could easily lead to overfittin...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	344,352
2209.15392	Improving the Efficiency of Payments Systems Using Quantum Computing	High-value payment systems (HVPSs) are typically liquidity-intensive as the payment requests are indivisible and settled on a gross basis. Finding the right order in which payments should be processed to maximize the liquidity efficiency of these systems is an $NP$-hard combinatorial optimization problem, which quantum...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	320,589
2006.00059	Towards a Human-Centred Cognitive Model of Visuospatial Complexity in Everyday Driving	We develop a human-centred, cognitive model of visuospatial complexity in everyday, naturalistic driving conditions. With a focus on visual perception, the model incorporates quantitative, structural, and dynamic attributes identifiable in the chosen context; the human-centred basis of the model lies in its behavioural...	true	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	179,354
2312.05897	PSCR: Patches Sampling-based Contrastive Regression for AIGC Image Quality Assessment	In recent years, Artificial Intelligence Generated Content (AIGC) has gained widespread attention beyond the computer science community. Due to various issues arising from continuous creation of AI-generated images (AIGI), AIGC image quality assessment (AIGCIQA), which aims to evaluate the quality of AIGIs from human p...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	414,287
2411.16776	SynDiff-AD: Improving Semantic Segmentation and End-to-End Autonomous Driving with Synthetic Data from Latent Diffusion Models	In recent years, significant progress has been made in collecting large-scale datasets to improve segmentation and autonomous driving models. These large-scale datasets are often dominated by common environmental conditions such as "Clear and Day" weather, leading to decreased performance in under-represented condition...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	511,174
2407.03621	The Mysterious Case of Neuron 1512: Injectable Realignment Architectures Reveal Internal Characteristics of Meta's Llama 2 Model	Large Language Models (LLMs) have an unrivaled and invaluable ability to "align" their output to a diverse range of human preferences, by mirroring them in the text they generate. The internal characteristics of such models, however, remain largely opaque. This work presents the Injectable Realignment Model (IRM) as a ...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	470,223
2406.13282	Understanding the RoPE Extensions of Long-Context LLMs: An Attention Perspective	Enabling LLMs to handle lengthy context is currently a research hotspot. Most LLMs are built upon rotary position embedding (RoPE), a popular position encoding method. Therefore, a prominent path is to extrapolate the RoPE trained on comparably short texts to far longer texts. A heavy bunch of efforts have been dedicat...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	465,787
2206.00282	Needle In A Haystack, Fast: Benchmarking Image Perceptual Similarity Metrics At Scale	The advent of the internet, followed shortly by the social media made it ubiquitous in consuming and sharing information between anyone with access to it. The evolution in the consumption of media driven by this change, led to the emergence of images as means to express oneself, convey information and convince others e...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	300,065
2002.12412	Formal Synthesis of Monitoring and Detection Systems for Secure CPS Implementations	We consider the problem of securing a given control loop implementation of a cyber-physical system (CPS) in the presence of Man-in-the-Middle attacks on data exchange between plant and controller over a compromised network. To this end, there exist various detection schemes that provide mathematical guarantees against ...	false	false	false	false	false	false	false	false	false	false	true	false	true	false	false	false	false	false	166,020
2008.01944	Optimal Pooling Matrix Design for Group Testing with Dilution (Row Degree) Constraints	In this paper, we consider the problem of designing optimal pooling matrix for group testing (for example, for COVID-19 virus testing) with the constraint that no more than $r>0$ samples can be pooled together, which we call "dilution constraint". This problem translates to designing a matrix with elements being either...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	190,477
2103.12827	Fisher Task Distance and Its Application in Neural Architecture Search	We formulate an asymmetric (or non-commutative) distance between tasks based on Fisher Information Matrices, called Fisher task distance. This distance represents the complexity of transferring the knowledge from one task to another. We provide a proof of consistency for our distance through theorems and experiments on...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	226,293
1705.00645	A General Framework For Task-Oriented Network Inference	We present a brief introduction to a flexible, general network inference framework which models data as a network space, sampled to optimize network structure to a particular task. We introduce a formal problem statement related to influence maximization in networks, where the network structure is not given as input, b...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	72,723
2011.08618	Theory-guided Auto-Encoder for Surrogate Construction and Inverse Modeling	A Theory-guided Auto-Encoder (TgAE) framework is proposed for surrogate construction and is further used for uncertainty quantification and inverse modeling tasks. The framework is built based on the Auto-Encoder (or Encoder-Decoder) architecture of convolutional neural network (CNN) via a theory-guided training proces...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	206,935
2208.03650	A Game-Theoretic Perspective of Generalization in Reinforcement Learning	Generalization in reinforcement learning (RL) is of importance for real deployment of RL algorithms. Various schemes are proposed to address the generalization issues, including transfer learning, multi-task learning and meta learning, as well as the robust and adversarial reinforcement learning. However, there is not ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	311,857
2208.12459	Meta Objective Guided Disambiguation for Partial Label Learning	Partial label learning (PLL) is a typical weakly supervised learning framework, where each training instance is associated with a candidate label set, among which only one label is valid. To solve PLL problems, typically methods try to perform disambiguation for candidate sets by either using prior knowledge, such as s...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	314,731
2108.01110	Batch Normalization Preconditioning for Neural Network Training	Batch normalization (BN) is a popular and ubiquitous method in deep learning that has been shown to decrease training time and improve generalization performance of neural networks. Despite its success, BN is not theoretically well understood. It is not suitable for use with very small mini-batch sizes or online learni...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	248,924
2410.09690	FAMOUS: High-Fidelity Monocular 3D Human Digitization Using View Synthesis	The advancement in deep implicit modeling and articulated models has significantly enhanced the process of digitizing human figures in 3D from just a single image. While state-of-the-art methods have greatly improved geometric precision, the challenge of accurately inferring texture remains, particularly in obscured ar...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	497,721
2302.06992	Hard-aware Instance Adaptive Self-training for Unsupervised Cross-domain Semantic Segmentation	The divergence between labeled training data and unlabeled testing data is a significant challenge for recent deep learning models. Unsupervised domain adaptation (UDA) attempts to solve such problem. Recent works show that self-training is a powerful approach to UDA. However, existing methods have difficulty in balanc...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	345,600
2308.04595	Quantization Aware Factorization for Deep Neural Network Compression	Tensor decomposition of convolutional and fully-connected layers is an effective way to reduce parameters and FLOP in neural networks. Due to memory and power consumption limitations of mobile or embedded devices, the quantization step is usually necessary when pre-trained models are deployed. A conventional post-train...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	384,467
1205.4133	Constrained Overcomplete Analysis Operator Learning for Cosparse Signal Modelling	We consider the problem of learning a low-dimensional signal model from a collection of training samples. The mainstream approach would be to learn an overcomplete dictionary to provide good approximations of the training samples using sparse synthesis coefficients. This famous sparse model has a less well known counte...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	16,065
1004.2304	Spatio-Temporal Graphical Model Selection	We consider the problem of estimating the topology of spatial interactions in a discrete state, discrete time spatio-temporal graphical model where the interactions affect the temporal evolution of each agent in a network. Among other models, the susceptible, infected, recovered ($SIR$) model for interaction events fal...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	6,163
1810.08169	Exploiting High-Level Semantics for No-Reference Image Quality Assessment of Realistic Blur Images	To guarantee a satisfying Quality of Experience (QoE) for consumers, it is required to measure image quality efficiently and reliably. The neglect of the high-level semantic information may result in predicting a clear blue sky as bad quality, which is inconsistent with human perception. Therefore, in this paper, we ta...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	110,771
2301.04850	Understanding Difficulty-based Sample Weighting with a Universal Difficulty Measure	Sample weighting is widely used in deep learning. A large number of weighting methods essentially utilize the learning difficulty of training samples to calculate their weights. In this study, this scheme is called difficulty-based weighting. Two important issues arise when explaining this scheme. First, a unified diff...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	340,191
2409.18486	Evaluation of OpenAI o1: Opportunities and Challenges of AGI	This comprehensive study evaluates the performance of OpenAI's o1-preview large language model across a diverse array of complex reasoning tasks, spanning multiple domains, including computer science, mathematics, natural sciences, medicine, linguistics, and social sciences. Through rigorous testing, o1-preview demonst...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	492,281
1902.06178	Iterated Belief Base Revision: A Dynamic Epistemic Logic Approach	AGM's belief revision is one of the main paradigms in the study of belief change operations. In this context, belief bases (prioritised bases) have been largely used to specify the agent's belief state - whether representing the agent's `explicit beliefs' or as a computational model for her belief state. While the conn...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	121,701
1806.02847	A Simple Method for Commonsense Reasoning	Commonsense reasoning is a long-standing challenge for deep learning. For example, it is difficult to use neural networks to tackle the Winograd Schema dataset (Levesque et al., 2011). In this paper, we present a simple method for commonsense reasoning with neural networks, using unsupervised learning. Key to our metho...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	99,855
2110.00175	DualNet: Continual Learning, Fast and Slow	According to Complementary Learning Systems (CLS) theory~\citep{mcclelland1995there} in neuroscience, humans do effective \emph{continual learning} through two complementary systems: a fast learning system centered on the hippocampus for rapid learning of the specifics and individual experiences, and a slow learning sy...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	258,308
2111.06012	Kronecker Factorization for Preventing Catastrophic Forgetting in Large-scale Medical Entity Linking	Multi-task learning is useful in NLP because it is often practically desirable to have a single model that works across a range of tasks. In the medical domain, sequential training on tasks may sometimes be the only way to train models, either because access to the original (potentially sensitive) data is no longer ava...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	265,954
2408.03923	Fast Sprite Decomposition from Animated Graphics	This paper presents an approach to decomposing animated graphics into sprites, a set of basic elements or layers. Our approach builds on the optimization of sprite parameters to fit the raster video. For efficiency, we assume static textures for sprites to reduce the search space while preventing artifacts using a text...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	479,198
2110.00639	The Proportional Integral Notch and Coleman Blade Effective Wind Speed Estimators and Their Similarities	The estimation of the rotor effective wind speed is used in modern wind turbines to provide advanced power and load control capabilities. However, with the ever increasing rotor sizes, the wind field over the rotor surface shows a higher degree of spatial variation. A single effective wind speed estimation therefore li...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	258,466
2403.07332	LKM-UNet: Large Kernel Vision Mamba UNet for Medical Image Segmentation	In clinical practice, medical image segmentation provides useful information on the contours and dimensions of target organs or tissues, facilitating improved diagnosis, analysis, and treatment. In the past few years, convolutional neural networks (CNNs) and Transformers have dominated this area, but they still suffer ...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	436,841
2211.03977	Assemble Them All: Physics-Based Planning for Generalizable Assembly by Disassembly	Assembly planning is the core of automating product assembly, maintenance, and recycling for modern industrial manufacturing. Despite its importance and long history of research, planning for mechanical assemblies when given the final assembled state remains a challenging problem. This is due to the complexity of deali...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	true	329,095
2001.06362	Rumor Detection on Social Media with Bi-Directional Graph Convolutional Networks	Social media has been developing rapidly in public due to its nature of spreading new information, which leads to rumors being circulated. Meanwhile, detecting rumors from such massive information in social media is becoming an arduous challenge. Therefore, some deep learning methods are applied to discover rumors thro...	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	false	false	160,786
2107.07067	MeNToS: Tracklets Association with a Space-Time Memory Network	We propose a method for multi-object tracking and segmentation (MOTS) that does not require fine-tuning or per benchmark hyperparameter selection. The proposed method addresses particularly the data association problem. Indeed, the recently introduced HOTA metric, that has a better alignment with the human visual asses...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	246,300
1805.11119	Adding New Tasks to a Single Network with Weight Transformations using Binary Masks	Visual recognition algorithms are required today to exhibit adaptive abilities. Given a deep model trained on a specific, given task, it would be highly desirable to be able to adapt incrementally to new tasks, preserving scalability as the number of new tasks increases, while at the same time avoiding catastrophic for...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	98,841
1611.07507	Variational Intrinsic Control	In this paper we introduce a new unsupervised reinforcement learning method for discovering the set of intrinsic options available to an agent. This set is learned by maximizing the number of different states an agent can reliably reach, as measured by the mutual information between the set of options and option termin...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	64,361
2403.16964	GSDF: 3DGS Meets SDF for Improved Rendering and Reconstruction	Presenting a 3D scene from multiview images remains a core and long-standing challenge in computer vision and computer graphics. Two main requirements lie in rendering and reconstruction. Notably, SOTA rendering quality is usually achieved with neural volumetric rendering techniques, which rely on aggregated point/prim...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	441,259
2404.14709	SC-HVPPNet: Spatial and Channel Hybrid-Attention Video Post-Processing Network with CNN and Transformer	Convolutional Neural Network (CNN) and Transformer have attracted much attention recently for video post-processing (VPP). However, the interaction between CNN and Transformer in existing VPP methods is not fully explored, leading to inefficient communication between the local and global extracted features. In this pap...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	448,781
1203.4176	SignsWorld; Deeping Into the Silence World and Hearing Its Signs (State of the Art)	Automatic speech processing systems are employed more and more often in real environments. Although the underlying speech technology is mostly language independent, differences between languages with respect to their structure and grammar have substantial effect on the recognition systems performance. In this paper, we...	false	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	15,021
2109.05483	ART-SLAM: Accurate Real-Time 6DoF LiDAR SLAM	Real-time six degree-of-freedom pose estimation with ground vehicles represents a relevant and well studied topic in robotics, due to its many applications, such as autonomous driving and 3D mapping. Although some systems exist already, they are either not accurate or they struggle in real-time setting. In this paper, ...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	254,809
2104.01832	Task-Independent Knowledge Makes for Transferable Representations for Generalized Zero-Shot Learning	Generalized Zero-Shot Learning (GZSL) targets recognizing new categories by learning transferable image representations. Existing methods find that, by aligning image representations with corresponding semantic labels, the semantic-aligned representations can be transferred to unseen categories. However, supervised by ...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	228,495
2312.06164	ReshapeIT: Reliable Shape Interaction with Implicit Template for Anatomical Structure Reconstruction	Shape modeling of volumetric medical images is crucial for quantitative analysis and surgical planning in computer-aided diagnosis. To alleviate the burden of expert clinicians, reconstructed shapes are typically obtained from deep learning models, such as Convolutional Neural Networks (CNNs) or transformer-based archi...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	414,399
1909.08254	Advances in Big Data Bio Analytics	Delivering effective data analytics is of crucial importance to the interpretation of the multitude of biological datasets currently generated by an ever increasing number of high throughput techniques. Logic programming has much to offer in this area. Here, we detail advances that highlight two of the strengths of log...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	true	145,931
2304.14545	Augmented balancing weights as linear regression	We provide a novel characterization of augmented balancing weights, also known as automatic debiased machine learning (AutoDML). These popular doubly robust or de-biased machine learning estimators combine outcome modeling with balancing weights - weights that achieve covariate balance directly in lieu of estimating an...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	361,013
2111.15016	Joint Modeling of Code-Switched and Monolingual ASR via Conditional Factorization	Conversational bilingual speech encompasses three types of utterances: two purely monolingual types and one intra-sententially code-switched type. In this work, we propose a general framework to jointly model the likelihoods of the monolingual and code-switch sub-tasks that comprise bilingual speech recognition. By def...	false	false	true	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	268,774
2303.07932	A Kernel-Based Identification Approach to LPV Feedforward: With Application to Motion Systems	The increasing demands for motion control result in a situation where Linear Parameter-Varying (LPV) dynamics have to be taken into account. Inverse-model feedforward control for LPV motion systems is challenging, since the inverse of an LPV system is often dynamically dependent on the scheduling sequence. The aim of t...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	351,444
2501.03221	RW-Net: Enhancing Few-Shot Point Cloud Classification with a Wavelet Transform Projection-based Network	In the domain of 3D object classification, a fundamental challenge lies in addressing the scarcity of labeled data, which limits the applicability of traditional data-intensive learning paradigms. This challenge is particularly pronounced in few-shot learning scenarios, where the objective is to achieve robust generali...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	522,797
2006.07548	Guided Transformer: Leveraging Multiple External Sources for Representation Learning in Conversational Search	Asking clarifying questions in response to ambiguous or faceted queries has been recognized as a useful technique for various information retrieval systems, especially conversational search systems with limited bandwidth interfaces. Analyzing and generating clarifying questions have been studied recently but the accura...	false	false	false	false	false	true	true	false	true	false	false	false	false	false	false	false	false	false	181,844
2012.14058	Asymptotic Achievability of the Cram\'er-Rao Lower Bound of Channel Estimation for Reconfigurable Intelligent Surface Aided Communication Systems	To achieve the joint active and passive beamforming gains in the reconfigurable intelligent surface assisted millimeter wave system, the reflected cascade channel needs to be accurately estimated. Many strategies have been proposed in the literature to solve this issue. However, whether the Cram\'er-Rao lower bound (CR...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	213,396
2012.03532	Deep Policy Networks for NPC Behaviors that Adapt to Changing Design Parameters in Roguelike Games	Recent advances in Deep Reinforcement Learning (DRL) have largely focused on improving the performance of agents with the aim of replacing humans in known and well-defined environments. The use of these techniques as a game design tool for video game production, where the aim is instead to create Non-Player Character (...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	210,154
2305.19512	Fine-grained Text Style Transfer with Diffusion-Based Language Models	Diffusion probabilistic models have shown great success in generating high-quality images controllably, and researchers have tried to utilize this controllability into text generation domain. Previous works on diffusion-based language models have shown that they can be trained without external knowledge (such as pre-tr...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	369,561
1909.03590	Does Order Matter? An Empirical Study on Generating Multiple Keyphrases as a Sequence	Recently, concatenating multiple keyphrases as a target sequence has been proposed as a new learning paradigm for keyphrase generation. Existing studies concatenate target keyphrases in different orders but no study has examined the effects of ordering on models' behavior. In this paper, we propose several orderings fo...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	144,542
2404.04267	What AIs are not Learning (and Why)	Today's robots do not learn the general skills needed for such services as providing home care, being nursing assistants, or doing household chores. Addressing such aspirational goals requires improving how AIs and robots are created. Today's mainstream AIs are not created by agents learning from experiences doing real...	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	444,573
2003.06566	On the benefits of defining vicinal distributions in latent space	The vicinal risk minimization (VRM) principle is an empirical risk minimization (ERM) variant that replaces Dirac masses with vicinal functions. There is strong numerical and theoretical evidence showing that VRM outperforms ERM in terms of generalization if appropriate vicinal functions are chosen. Mixup Training (MT)...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	168,157
2301.06031	A Review on the effectiveness of Dimensional Reduction with Computational Forensics: An Application on Malware Analysis	The Android operating system is pervasively adopted as the operating system platform of choice for smart devices. However, the strong adoption has also resulted in exponential growth in the number of Android based malicious software or malware. To deal with such cyber threats as part of cyber investigation and digital ...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	340,526
2301.10862	Learning Gradients of Convex Functions with Monotone Gradient Networks	While much effort has been devoted to deriving and analyzing effective convex formulations of signal processing problems, the gradients of convex functions also have critical applications ranging from gradient-based optimization to optimal transport. Recent works have explored data-driven methods for learning convex ob...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	341,926
2405.11958	Exploring Commonalities in Explanation Frameworks: A Multi-Domain Survey Analysis	This study presents insights gathered from surveys and discussions with specialists in three domains, aiming to find essential elements for a universal explanation framework that could be applied to these and other similar use cases. The insights are incorporated into a software tool that utilizes GP algorithms, known ...	true	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	455,358
2005.01939	From Image Collections to Point Clouds with Self-supervised Shape and Pose Networks	Reconstructing 3D models from 2D images is one of the fundamental problems in computer vision. In this work, we propose a deep learning technique for 3D object reconstruction from a single image. Contrary to recent works that either use 3D supervision or multi-view supervision, we use only single view images with no po...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	175,717
1811.00210	Online Planner Selection with Graph Neural Networks and Adaptive Scheduling	Automated planning is one of the foundational areas of AI. Since no single planner can work well for all tasks and domains, portfolio-based techniques have become increasingly popular in recent years. In particular, deep learning emerges as a promising methodology for online planner selection. Owing to the recent devel...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	112,035
2207.05820	Exploiting Social Graph Networks for Emotion Prediction	Emotion prediction plays an essential role in mental health and emotion-aware computing. The complex nature of emotion resulting from its dependency on a person's physiological health, mental state, and his surroundings makes its prediction a challenging task. In this work, we utilize mobile sensing data to predict hap...	false	false	false	true	false	false	true	false	true	false	true	false	false	false	false	false	false	false	307,674
2112.11282	VW-SDK: Efficient Convolutional Weight Mapping Using Variable Windows for Processing-In-Memory Architectures	With their high energy efficiency, processing-in-memory (PIM) arrays are increasingly used for convolutional neural network (CNN) inference. In PIM-based CNN inference, the computational latency and energy are dependent on how the CNN weights are mapped to the PIM array. A recent study proposed shifted and duplicated k...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	true	false	false	272,667
2412.16031	Learning sparsity-promoting regularizers for linear inverse problems	This paper introduces a novel approach to learning sparsity-promoting regularizers for solving linear inverse problems. We develop a bilevel optimization framework to select an optimal synthesis operator, denoted as $B$, which regularizes the inverse problem while promoting sparsity in the solution. The method leverage...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	519,339
2307.16082	EnrichEvent: Enriching Social Data with Contextual Information for Emerging Event Extraction	Social platforms have emerged as crucial platforms for disseminating information and discussing real-life social events, offering researchers an excellent opportunity to design and implement novel event detection frameworks. However, most existing approaches only exploit keyword burstiness or network structures to dete...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	382,465
2410.20019	Attacks against Abstractive Text Summarization Models through Lead Bias and Influence Functions	Large Language Models have introduced novel opportunities for text comprehension and generation. Yet, they are vulnerable to adversarial perturbations and data poisoning attacks, particularly in tasks like text classification and translation. However, the adversarial robustness of abstractive text summarization models ...	false	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	502,610
2001.04693	Balancing the composition of word embeddings across heterogenous data sets	Word embeddings capture semantic relationships based on contextual information and are the basis for a wide variety of natural language processing applications. Notably these relationships are solely learned from the data and subsequently the data composition impacts the semantic of embeddings which arguably can lead t...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	160,322
1507.06346	Evaluation of Spectral Learning for the Identification of Hidden Markov Models	Hidden Markov models have successfully been applied as models of discrete time series in many fields. Often, when applied in practice, the parameters of these models have to be estimated. The currently predominating identification methods, such as maximum-likelihood estimation and especially expectation-maximization, a...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	45,380
2301.10919	Joint action loss for proximal policy optimization	PPO (Proximal Policy Optimization) is a state-of-the-art policy gradient algorithm that has been successfully applied to complex computer games such as Dota 2 and Honor of Kings. In these environments, an agent makes compound actions consisting of multiple sub-actions. PPO uses clipping to restrict policy updates. Alth...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	341,954
1906.01171	Understanding the Limitations of Conditional Generative Models	Class-conditional generative models hold promise to overcome the shortcomings of their discriminative counterparts. They are a natural choice to solve discriminative tasks in a robust manner as they jointly optimize for predictive performance and accurate modeling of the input distribution. In this work, we investigate...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	133,624
2402.17372	Coupled Laplacian Eigenmaps for Locally-Aware 3D Rigid Point Cloud Matching	Point cloud matching, a crucial technique in computer vision, medical and robotics fields, is primarily concerned with finding correspondences between pairs of point clouds or voxels. In some practical scenarios, emphasizing local differences is crucial for accurately identifying a correct match, thereby enhancing the ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	432,959
1606.07947	Sequence-Level Knowledge Distillation	Neural machine translation (NMT) offers a novel alternative formulation of translation that is potentially simpler than statistical approaches. However to reach competitive performance, NMT models need to be exceedingly large. In this paper we consider applying knowledge distillation approaches (Bucila et al., 2006; Hi...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	true	false	false	57,803
2312.16230	Navigating Decision Landscapes: The Impact of Principals on Decision-Making Dynamics	We explored decision-making dynamics in social systems, referencing the 'herd behavior' from prior studies where individuals follow preceding choices without understanding the underlying reasons. While previous research highlighted a preference for the optimal choice without external influences, our study introduced pr...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	418,318
2107.13966	Artificial Intelligence in Achieving Sustainable Development Goals	This perspective illustrates some of the AI applications that can accelerate the achievement of SDGs and also highlights some of the considerations that could hinder the efforts towards them. This emphasizes the importance of establishing standard AI guidelines and regulations for the beneficial applications of AI.	false	false	false	false	false	false	true	true	false	false	false	false	false	true	false	false	false	false	248,342
2308.08841	Machine Learning-Assisted Discovery of Flow Reactor Designs	Additive manufacturing has enabled the fabrication of advanced reactor geometries, permitting larger, more complex design spaces. Identifying promising configurations within such spaces presents a significant challenge for current approaches. Furthermore, existing parameterisations of reactor geometries are low-dimensi...	false	true	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	386,060
2410.22388	ET-Flow: Equivariant Flow-Matching for Molecular Conformer Generation	Predicting low-energy molecular conformations given a molecular graph is an important but challenging task in computational drug discovery. Existing state-of-the-art approaches either resort to large scale transformer-based models that diffuse over conformer fields, or use computationally expensive methods to generate ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	503,623
2203.00636	Distributional Reinforcement Learning for Scheduling of Chemical Production Processes	Reinforcement Learning (RL) has recently received significant attention from the process systems engineering and control communities. Recent works have investigated the application of RL to identify optimal scheduling decision in the presence of uncertainty. In this work, we present a RL methodology tailored to efficie...	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	283,071
2410.17521	Diffusion Priors for Variational Likelihood Estimation and Image Denoising	Real-world noise removal is crucial in low-level computer vision. Due to the remarkable generation capabilities of diffusion models, recent attention has shifted towards leveraging diffusion priors for image restoration tasks. However, existing diffusion priors-based methods either consider simple noise types or rely o...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	501,503
1309.5931	Data Mining using Unguided Symbolic Regression on a Blast Furnace Dataset	In this paper a data mining approach for variable selection and knowledge extraction from datasets is presented. The approach is based on unguided symbolic regression (every variable present in the dataset is treated as the target variable in multiple regression runs) and a novel variable relevance metric for genetic p...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	27,209
2201.02071	Simplicial cascades are orchestrated by the multidimensional geometry of neuronal complexes	Cascades arise in many contexts (e.g., neuronal avalanches, social contagions, and system failures). Despite evidence that propagations often involve higher-order dependencies, cascade theory has largely focused on models with pairwise/dyadic interactions. Here, we develop a simplicial threshold model (STM) for nonline...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	274,444
2411.10831	Neighboring Slice Noise2Noise: Self-Supervised Medical Image Denoising from Single Noisy Image Volume	In the last few years, with the rapid development of deep learning technologies, supervised methods based on convolutional neural networks have greatly enhanced the performance of medical image denoising. However, these methods require large quantities of noisy-clean image pairs for training, which greatly limits their...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	508,810
2310.01105	Energy-Guided Continuous Entropic Barycenter Estimation for General Costs	Optimal transport (OT) barycenters are a mathematically grounded way of averaging probability distributions while capturing their geometric properties. In short, the barycenter task is to take the average of a collection of probability distributions w.r.t. given OT discrepancies. We propose a novel algorithm for approx...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	396,281
1511.03745	Grounding of Textual Phrases in Images by Reconstruction	Grounding (i.e. localizing) arbitrary, free-form textual phrases in visual content is a challenging problem with many applications for human-computer interaction and image-text reference resolution. Few datasets provide the ground truth spatial localization of phrases, thus it is desirable to learn from data with no or...	false	false	false	false	false	false	true	false	true	false	false	true	false	false	false	false	false	false	48,791
2410.21952	On the Robustness of Adversarial Training Against Uncertainty Attacks	In learning problems, the noise inherent to the task at hand hinders the possibility to infer without a certain degree of uncertainty. Quantifying this uncertainty, regardless of its wide use, assumes high relevance for security-sensitive applications. Within these scenarios, it becomes fundamental to guarantee good (i...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	503,447
1105.2790	On the equivalence of Hopfield Networks and Boltzmann Machines	A specific type of neural network, the Restricted Boltzmann Machine (RBM), is implemented for classification and feature detection in machine learning. RBM is characterized by separate layers of visible and hidden units, which are able to learn efficiently a generative model of the observed data. We study a "hybrid" ve...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	10,360
2402.02964	Mixed Noise and Posterior Estimation with Conditional DeepGEM	Motivated by indirect measurements and applications from nanometrology with a mixed noise model, we develop a novel algorithm for jointly estimating the posterior and the noise parameters in Bayesian inverse problems. We propose to solve the problem by an expectation maximization (EM) algorithm. Based on the current no...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	426,802
2107.03789	Homogenizing Entropy Across Different Environmental Conditions: A Universally Applicable Method for Transforming Continuous Variables	In classical information theory, a causal relationship between two variables is typically modelled by assuming that, for every possible state of one of the variables, there exists a particular distribution of states of the second variable. Let us call these two variables the causal and caused variables, respectively. W...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	245,259
1211.2197	What is the Nature of Chinese MicroBlogging: Unveiling the Unique Features of Tencent Weibo	China has the largest number of online users in the world and about 20% internet users are from China. This is a huge, as well as a mysterious, market for IT industry due to various reasons such as culture difference. Twitter is the largest microblogging service in the world and Tencent Weibo is one of the largest micr...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	19,659
2404.03126	GaSpCT: Gaussian Splatting for Novel CT Projection View Synthesis	We present GaSpCT, a novel view synthesis and 3D scene representation method used to generate novel projection views for Computer Tomography (CT) scans. We adapt the Gaussian Splatting framework to enable novel view synthesis in CT based on limited sets of 2D image projections and without the need for Structure from Mo...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	444,128
2406.09014	Deep learning empowered sensor fusion boosts infant movement classification	To assess the integrity of the developing nervous system, the Prechtl general movement assessment (GMA) is recognized for its clinical value in diagnosing neurological impairments in early infancy. GMA has been increasingly augmented through machine learning approaches intending to scale-up its application, circumvent ...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	463,723
2311.15142	Testable Learning with Distribution Shift	We revisit the fundamental problem of learning with distribution shift, in which a learner is given labeled samples from training distribution $D$, unlabeled samples from test distribution $D'$ and is asked to output a classifier with low test error. The standard approach in this setting is to bound the loss of a class...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	410,398
2210.04373	Contrastive Representation Learning for Conversational Question Answering over Knowledge Graphs	This paper addresses the task of conversational question answering (ConvQA) over knowledge graphs (KGs). The majority of existing ConvQA methods rely on full supervision signals with a strict assumption of the availability of gold logical forms of queries to extract answers from the KG. However, creating such a gold lo...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	322,433

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