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
2410.00699	Investigating the Impact of Model Complexity in Large Language Models	Large Language Models (LLMs) based on the pre-trained fine-tuning paradigm have become pivotal in solving natural language processing tasks, consistently achieving state-of-the-art performance. Nevertheless, the theoretical understanding of how model complexity influences fine-tuning performance remains challenging and...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	493,466
2202.01108	Learning to reason about and to act on physical cascading events	Reasoning and interacting with dynamic environments is a fundamental problem in AI, but it becomes extremely challenging when actions can trigger cascades of cross-dependent events. We introduce a new supervised learning setup called {\em Cascade} where an agent is shown a video of a physically simulated dynamic scene,...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	278,369
1508.02865	Maximum Entropy Vector Kernels for MIMO system identification	Recent contributions have framed linear system identification as a nonparametric regularized inverse problem. Relying on $\ell_2$-type regularization which accounts for the stability and smoothness of the impulse response to be estimated, these approaches have been shown to be competitive w.r.t classical parametric met...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	45,950
1907.04839	Fast geodesic shooting for landmark matching using CUDA	Landmark matching via geodesic shooting is a prerequisite task for numerous registration based applications in biomedicine. Geodesic shooting has been developed as one solution approach and formulates the diffeomorphic registration as an optimal control problem under the Hamiltonian framework. In this framework, with l...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	138,213
1710.10774	Sequence-to-Sequence ASR Optimization via Reinforcement Learning	Despite the success of sequence-to-sequence approaches in automatic speech recognition (ASR) systems, the models still suffer from several problems, mainly due to the mismatch between the training and inference conditions. In the sequence-to-sequence architecture, the model is trained to predict the grapheme of the cur...	false	false	true	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	83,470
2404.08567	CATP: Cross-Attention Token Pruning for Accuracy Preserved Multimodal Model Inference	In response to the rising interest in large multimodal models, we introduce Cross-Attention Token Pruning (CATP), a precision-focused token pruning method. Our approach leverages cross-attention layers in multimodal models, exemplified by BLIP-2, to extract valuable information for token importance determination. CATP ...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	446,295
1709.07121	Discrete-Time Polar Opinion Dynamics with Susceptibility	This paper considers a discrete-time opinion dynamics model in which each individual's susceptibility to being influenced by others is dependent on her current opinion. We assume that the social network has time-varying topology and that the opinions are scalars on a continuous interval. We first propose a general opin...	false	false	false	true	false	false	false	false	false	false	true	false	false	false	true	false	false	false	81,227
1603.03357	Cognitive Green Radio for Energy-Aware Communications	In 5G networks, the number of connected devices, data rate and data volume per area, as well as the variety of QoS requirements, will attain unprecedented scales. The achievement of these goals will rely on new technologies and disruptive changes in network architecture and node design. Energy efficiency is believed to...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	53,106
2105.03767	Aerospace Sliding Mode Control Toolbox: Relative Degree Approach with Resource Prospector Lander and Launch Vehicle Case Studies	Conventional Sliding mode control and observation techniques are widely used in aerospace applications, including aircrafts, UAVs, launch vehicles, missile interceptors, and hypersonic missiles. This work is dedicated to creating a MATLAB-based sliding mode controller design and simulation software toolbox that aims to...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	234,257
1901.10461	The effect of a physical robot on vocabulary learning	This study investigates the effect of a physical robot taking the role of a teacher or exercise partner in a language learning exercise. In order to investigate this, an application was developed enabling a 2:nd language learning vocabulary exercise in three different conditions. In the first condition the learner woul...	true	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	120,026
2403.01491	Ultimate linear block and convolutional codes	Codes considered as structures within unit schemes greatly extends the availability of linear block and convolutional codes and allows the construction of these codes to required length, rate, distance and type. Properties of a code emanate from properties of the unit from which it was derived. Orthogonal units, units ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	434,448
2012.07483	On the Treatment of Optimization Problems with L1 Penalty Terms via Multiobjective Continuation	We present a novel algorithm that allows us to gain detailed insight into the effects of sparsity in linear and nonlinear optimization, which is of great importance in many scientific areas such as image and signal processing, medical imaging, compressed sensing, and machine learning (e.g., for the training of neural n...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	211,470
2410.12208	Vehicle Localization in GPS-Denied Scenarios Using Arc-Length-Based Map Matching	Automated driving systems face challenges in GPS-denied situations. To address this issue, kinematic dead reckoning is implemented using measurements from the steering angle, steering rate, yaw rate, and wheel speed sensors onboard the vehicle. However, dead reckoning methods suffer from drift. This paper provides an a...	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	498,905
2206.09766	Quantitative CT texture-based method to predict diagnosis and prognosis of fibrosing interstitial lung disease patterns	Purpose: To utilize high-resolution quantitative CT (QCT) imaging features for prediction of diagnosis and prognosis in fibrosing interstitial lung diseases (ILD). Approach: 40 ILD patients (20 usual interstitial pneumonia (UIP), 20 non-UIP pattern ILD) were classified by expert consensus of 2 radiologists and followed...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	303,682
2407.06778	A BERT-based Empirical Study of Privacy Policies' Compliance with GDPR	Since its implementation in May 2018, the General Data Protection Regulation (GDPR) has prompted businesses to revisit and revise their data handling practices to ensure compliance. The privacy policy, which serves as the primary means of informing users about their privacy rights and the data practices of companies, h...	false	false	false	false	true	false	false	false	false	false	false	false	true	false	false	false	false	false	471,529
2210.08868	Cerebrovascular Segmentation via Vessel Oriented Filtering Network	Accurate cerebrovascular segmentation from Magnetic Resonance Angiography (MRA) and Computed Tomography Angiography (CTA) is of great significance in diagnosis and treatment of cerebrovascular pathology. Due to the complexity and topology variability of blood vessels, complete and accurate segmentation of vascular netw...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	324,329
2212.02941	Safe Imitation Learning of Nonlinear Model Predictive Control for Flexible Robots	Flexible robots may overcome some of the industry's major challenges, such as enabling intrinsically safe human-robot collaboration and achieving a higher payload-to-mass ratio. However, controlling flexible robots is complicated due to their complex dynamics, which include oscillatory behavior and a high-dimensional s...	false	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	334,943
2406.14947	LiCS: Navigation using Learned-imitation on Cluttered Space	In this letter, we propose a robust and fast navigation system in a narrow indoor environment for UGV (Unmanned Ground Vehicle) using 2D LiDAR and odometry. We used behavior cloning with Transformer neural network to learn the optimization-based baseline algorithm. We inject Gaussian noise during expert demonstration t...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	466,548
2005.01979	Demand-Side Scheduling Based on Multi-Agent Deep Actor-Critic Learning for Smart Grids	We consider the problem of demand-side energy management, where each household is equipped with a smart meter that is able to schedule home appliances online. The goal is to minimize the overall cost under a real-time pricing scheme. While previous works have introduced centralized approaches in which the scheduling al...	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	175,725
2309.10298	Learning Orbitally Stable Systems for Diagrammatically Teaching	Diagrammatic Teaching is a paradigm for robots to acquire novel skills, whereby the user provides 2D sketches over images of the scene to shape the robot's motion. In this work, we tackle the problem of teaching a robot to approach a surface and then follow cyclic motion on it, where the cycle of the motion can be arbi...	false	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	392,949
2410.09902	Multi class activity classification in videos using Motion History Image generation	Human action recognition has been a topic of interest across multiple fields ranging from security to entertainment systems. Tracking the motion and identifying the action being performed on a real time basis is necessary for critical security systems. In entertainment, especially gaming, the need for immediate respons...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	497,821
2312.07425	Deep Internal Learning: Deep Learning from a Single Input	Deep learning, in general, focuses on training a neural network from large labeled datasets. Yet, in many cases there is value in training a network just from the input at hand. This is particularly relevant in many signal and image processing problems where training data is scarce and diversity is large on the one han...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	414,921
1902.10182	Obstacle-aware Adaptive Informative Path Planning for UAV-based Target Search	Target search with unmanned aerial vehicles (UAVs) is relevant problem to many scenarios, e.g., search and rescue (SaR). However, a key challenge is planning paths for maximal search efficiency given flight time constraints. To address this, we propose the Obstacle-aware Adaptive Informative Path Planning (OA-IPP) algo...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	122,603
2210.05156	Task-Aware Specialization for Efficient and Robust Dense Retrieval for Open-Domain Question Answering	Given its effectiveness on knowledge-intensive natural language processing tasks, dense retrieval models have become increasingly popular. Specifically, the de-facto architecture for open-domain question answering uses two isomorphic encoders that are initialized from the same pretrained model but separately parameteri...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	322,726
2106.03033	Graph Belief Propagation Networks	With the wide-spread availability of complex relational data, semi-supervised node classification in graphs has become a central machine learning problem. Graph neural networks are a recent class of easy-to-train and accurate methods for this problem that map the features in the neighborhood of a node to its label, but...	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	false	false	239,139
2412.20796	FastCHGNet: Training one Universal Interatomic Potential to 1.5 Hours with 32 GPUs	Graph neural network universal interatomic potentials (GNN-UIPs) have demonstrated remarkable generalization and transfer capabilities in material discovery and property prediction. These models can accelerate molecular dynamics (MD) simulation by several orders of magnitude while maintaining \textit{ab initio} accurac...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	521,358
1801.03562	Discrete symbolic optimization and Boltzmann sampling by continuous neural dynamics: Gradient Symbolic Computation	Gradient Symbolic Computation is proposed as a means of solving discrete global optimization problems using a neurally plausible continuous stochastic dynamical system. Gradient symbolic dynamics involves two free parameters that must be adjusted as a function of time to obtain the global maximizer at the end of the co...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	88,114
1210.4749	An Auction Approach to Distributed Power Allocation for Multiuser Cooperative Networks	This paper studies a wireless network where multiple users cooperate with each other to improve the overall network performance. Our goal is to design an optimal distributed power allocation algorithm that enables user cooperation, in particular, to guide each user on the decision of transmission mode selection and rel...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	19,158
2502.08646	Poly-Autoregressive Prediction for Modeling Interactions	We introduce a simple framework for predicting the behavior of an agent in multi-agent settings. In contrast to autoregressive (AR) tasks, such as language processing, our focus is on scenarios with multiple agents whose interactions are shaped by physical constraints and internal motivations. To this end, we propose P...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	533,099
1610.04576	Kernel Alignment Inspired Linear Discriminant Analysis	Kernel alignment measures the degree of similarity between two kernels. In this paper, inspired from kernel alignment, we propose a new Linear Discriminant Analysis (LDA) formulation, kernel alignment LDA (kaLDA). We first define two kernels, data kernel and class indicator kernel. The problem is to find a subspace to ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	62,408
2501.12690	Growth strategies for arbitrary DAG neural architectures	Deep learning has shown impressive results obtained at the cost of training huge neural networks. However, the larger the architecture, the higher the computational, financial, and environmental costs during training and inference. We aim at reducing both training and inference durations. We focus on Neural Architectur...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	526,406
2306.05949	Evaluating the Social Impact of Generative AI Systems in Systems and Society	Generative AI systems across modalities, ranging from text (including code), image, audio, and video, have broad social impacts, but there is no official standard for means of evaluating those impacts or for which impacts should be evaluated. In this paper, we present a guide that moves toward a standard approach in ev...	false	false	false	false	true	false	false	false	false	false	false	false	false	true	false	false	false	false	372,384
2501.10160	CSSDM Ontology to Enable Continuity of Care Data Interoperability	The rapid advancement of digital technologies and recent global pandemic scenarios have led to a growing focus on how these technologies can enhance healthcare service delivery and workflow to address crises. Action plans that consolidate existing digital transformation programs are being reviewed to establish core inf...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	525,418
cs/0501090	Stochastic Iterative Decoders	This paper presents a stochastic algorithm for iterative error control decoding. We show that the stochastic decoding algorithm is an approximation of the sum-product algorithm. When the code's factor graph is a tree, as with trellises, the algorithm approaches maximum a-posteriori decoding. We also demonstrate a stoch...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	538,526
2405.14452	JointRF: End-to-End Joint Optimization for Dynamic Neural Radiance Field Representation and Compression	Neural Radiance Field (NeRF) excels in photo-realistically static scenes, inspiring numerous efforts to facilitate volumetric videos. However, rendering dynamic and long-sequence radiance fields remains challenging due to the significant data required to represent volumetric videos. In this paper, we propose a novel en...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	456,435
2502.08071	Collaborative Filtering Meets Spectrum Shift: Connecting User-Item Interaction with Graph-Structured Side Information	Graph Neural Network (GNN) has demonstrated their superiority in collaborative filtering, where the user-item (U-I) interaction bipartite graph serves as the fundamental data format. However, when graph-structured side information (e.g., multimodal similarity graphs or social networks) is integrated into the U-I bipart...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	532,879
2210.04431	Scientific Machine Learning for Modeling and Simulating Complex Fluids	The formulation of rheological constitutive equations -- models that relate internal stresses and deformations in complex fluids -- is a critical step in the engineering of systems involving soft materials. While data-driven models provide accessible alternatives to expensive first-principles models and less accurate e...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	322,452
2407.18897	Small Molecule Optimization with Large Language Models	Recent advancements in large language models have opened new possibilities for generative molecular drug design. We present Chemlactica and Chemma, two language models fine-tuned on a novel corpus of 110M molecules with computed properties, totaling 40B tokens. These models demonstrate strong performance in generating ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	true	false	false	476,552
1911.05584	Tensor Decomposition with Relational Constraints for Predicting Multiple Types of MicroRNA-disease Associations	MicroRNAs (miRNAs) play crucial roles in multifarious biological processes associated with human diseases. Identifying potential miRNA-disease associations contributes to understanding the molecular mechanisms of miRNA-related diseases. Most of the existing computational methods mainly focus on predicting whether a miR...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	153,305
1001.2554	A new proof of Delsarte, Goethals and Mac Williams theorem on minimal weight codewords of generalized Reed-Muller code	We give a new proof of Delsarte, Goethals and Mac williams theorem on minimal weight codewords of generalized Reed-Muller codes published in 1970. To prove this theorem, we consider intersection of support of minimal weight codewords with affine hyperplanes and we proceed by recursion.	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	5,397
2407.04215	T2IShield: Defending Against Backdoors on Text-to-Image Diffusion Models	While text-to-image diffusion models demonstrate impressive generation capabilities, they also exhibit vulnerability to backdoor attacks, which involve the manipulation of model outputs through malicious triggers. In this paper, for the first time, we propose a comprehensive defense method named T2IShield to detect, lo...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	470,471
2010.10901	On Information Asymmetry in Competitive Multi-Agent Reinforcement Learning: Convergence and Optimality	In this work, we study the system of interacting non-cooperative two Q-learning agents, where one agent has the privilege of observing the other's actions. We show that this information asymmetry can lead to a stable outcome of population learning, which generally does not occur in an environment of general independent...	false	false	false	false	false	false	true	false	false	false	true	false	false	false	true	false	false	true	202,049
2002.03054	Extrapolation Towards Imaginary $0$-Nearest Neighbour and Its Improved Convergence Rate	$k$-nearest neighbour ($k$-NN) is one of the simplest and most widely-used methods for supervised classification, that predicts a query's label by taking weighted ratio of observed labels of $k$ objects nearest to the query. The weights and the parameter $k \in \mathbb{N}$ regulate its bias-variance trade-off, and the ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	163,118
2304.08466	Synthetic Data from Diffusion Models Improves ImageNet Classification	Deep generative models are becoming increasingly powerful, now generating diverse high fidelity photo-realistic samples given text prompts. Have they reached the point where models of natural images can be used for generative data augmentation, helping to improve challenging discriminative tasks? We show that large-sca...	false	false	false	false	true	false	true	false	true	false	false	true	false	false	false	false	false	false	358,720
2301.02064	Single-round Self-supervised Distributed Learning using Vision Transformer	Despite the recent success of deep learning in the field of medicine, the issue of data scarcity is exacerbated by concerns about privacy and data ownership. Distributed learning approaches, including federated learning, have been investigated to address these issues. However, they are hindered by the need for cumberso...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	339,403
1506.05143	Interference-Nulling Time-Reversal Beamforming for mm-Wave Massive MIMO in Multi-User Frequency-Selective Indoor Channels	Millimeter wave (mm-wave) and massive MIMO have been proposed for next generation wireless systems. However, there are many open problems for the implementation of those technologies. In particular, beamforming is necessary in mm-wave systems in order to counter high propagation losses. However, conventional beamsteeri...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	44,258
2001.11846	Quaternion-Valued Recurrent Projection Neural Networks on Unit Quaternions	Hypercomplex-valued neural networks, including quaternion-valued neural networks, can treat multi-dimensional data as a single entity. In this paper, we present the quaternion-valued recurrent projection neural networks (QRPNNs). Briefly, QRPNNs are obtained by combining the non-local projection learning with the quate...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	true	false	false	162,187
1203.4870	Variational Bayesian algorithm for quantized compressed sensing	Compressed sensing (CS) is on recovery of high dimensional signals from their low dimensional linear measurements under a sparsity prior and digital quantization of the measurement data is inevitable in practical implementation of CS algorithms. In the existing literature, the quantization error is modeled typically as...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	15,064
2110.14020	The Difficulty of Passive Learning in Deep Reinforcement Learning	Learning to act from observational data without active environmental interaction is a well-known challenge in Reinforcement Learning (RL). Recent approaches involve constraints on the learned policy or conservative updates, preventing strong deviations from the state-action distribution of the dataset. Although these m...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	263,382
2210.03112	A New Path: Scaling Vision-and-Language Navigation with Synthetic Instructions and Imitation Learning	Recent studies in Vision-and-Language Navigation (VLN) train RL agents to execute natural-language navigation instructions in photorealistic environments, as a step towards robots that can follow human instructions. However, given the scarcity of human instruction data and limited diversity in the training environments...	false	false	false	false	false	false	true	true	true	false	false	true	false	false	false	false	false	false	321,901
2106.03354	AI without networks	Contemporary Artificial Intelligence (AI) stands on two legs: large training data corpora and many-parameter artificial neural networks (ANNs). The data corpora are needed to represent the complexity and heterogeneity of the world. The role of the networks is less transparent due to the obscure dependence of the networ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	239,285
2310.08256	Impact of Co-occurrence on Factual Knowledge of Large Language Models	Large language models (LLMs) often make factually incorrect responses despite their success in various applications. In this paper, we hypothesize that relying heavily on simple co-occurrence statistics of the pre-training corpora is one of the main factors that cause factual errors. Our results reveal that LLMs are vu...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	399,325
2404.13764	Using Adaptive Empathetic Responses for Teaching English	Existing English-teaching chatbots rarely incorporate empathy explicitly in their feedback, but empathetic feedback could help keep students engaged and reduce learner anxiety. Toward this end, we propose the task of negative emotion detection via audio, for recognizing empathetic feedback opportunities in language lea...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	448,431
2302.04917	ChemVise: Maximizing Out-of-Distribution Chemical Detection with the Novel Application of Zero-Shot Learning	Accurate chemical sensors are vital in medical, military, and home safety applications. Training machine learning models to be accurate on real world chemical sensor data requires performing many diverse, costly experiments in controlled laboratory settings to create a data set. In practice even expensive, large data s...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	344,864
2502.12446	Multi-Attribute Steering of Language Models via Targeted Intervention	Inference-time intervention (ITI) has emerged as a promising method for steering large language model (LLM) behavior in a particular direction (e.g., improving helpfulness) by intervening on token representations without costly updates to the LLM's parameters. However, existing ITI approaches fail to scale to multi-att...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	534,867
2405.10793	CCTNet: A Circular Convolutional Transformer Network for LiDAR-based Place Recognition Handling Movable Objects Occlusion	Place recognition is a fundamental task for robotic application, allowing robots to perform loop closure detection within simultaneous localization and mapping (SLAM), and achieve relocalization on prior maps. Current range image-based networks use single-column convolution to maintain feature invariance to shifts in i...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	454,887
2211.05533	GREENER: Graph Neural Networks for News Media Profiling	We study the problem of profiling news media on the Web with respect to their factuality of reporting and bias. This is an important but under-studied problem related to disinformation and "fake news" detection, but it addresses the issue at a coarser granularity compared to looking at an individual article or an indiv...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	329,586
1809.01906	Model-Based Regularization for Deep Reinforcement Learning with Transcoder Networks	This paper proposes a new optimization objective for value-based deep reinforcement learning. We extend conventional Deep Q-Networks (DQNs) by adding a model-learning component yielding a transcoder network. The prediction errors for the model are included in the basic DQN loss as additional regularizers. This augmente...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	106,918
1612.08220	Understanding Neural Networks through Representation Erasure	While neural networks have been successfully applied to many natural language processing tasks, they come at the cost of interpretability. In this paper, we propose a general methodology to analyze and interpret decisions from a neural model by observing the effects on the model of erasing various parts of the represen...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	66,046
1707.09108	Ensemble Performance of Biometric Authentication Systems Based on Secret Key Generation	We study the ensemble performance of biometric authentication systems, based on secret key generation, which work as follows. In the enrollment stage, an individual provides a biometric signal that is mapped into a secret key and a helper message, the former being prepared to become available to the system at a later t...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	77,952
1805.04152	Training Recurrent Neural Networks via Dynamical Trajectory-Based Optimization	This paper introduces a new method to train recurrent neural networks using dynamical trajectory-based optimization. The optimization method utilizes a projected gradient system (PGS) and a quotient gradient system (QGS) to determine the feasible regions of an optimization problem and search the feasible regions for lo...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	97,180
1501.04867	Conditional Information Inequalities and Combinatorial Applications	We show that the inequality $H(A \mid B,X) + H(A \mid B,Y) \le H(A\mid B)$ for jointly distributed random variables $A,B,X,Y$, which does not hold in general case, holds under some natural condition on the support of the probability distribution of $A,B,X,Y$. This result generalizes a version of the conditional Ingleto...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	39,427
2311.17958	CommunityAI: Towards Community-based Federated Learning	Federated Learning (FL) has emerged as a promising paradigm to train machine learning models collaboratively while preserving data privacy. However, its widespread adoption faces several challenges, including scalability, heterogeneous data and devices, resource constraints, and security concerns. Despite its promise, ...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	true	411,496
1709.04427	Contrast Enhancement of Brightness-Distorted Images by Improved Adaptive Gamma Correction	As an efficient image contrast enhancement (CE) tool, adaptive gamma correction (AGC) was previously proposed by relating gamma parameter with cumulative distribution function (CDF) of the pixel gray levels within an image. ACG deals well with most dimmed images, but fails for globally bright images and the dimmed imag...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	80,664
2311.02625	New structure of channel coding: serial concatenation of Polar codes	In this paper, we introduce a new coding and decoding structure for enhancing the reliability and performance of polar codes, specifically at low error rates. We achieve this by concatenating two polar codes in series to create robust error-correcting codes. The primary objective here is to optimize the behavior of ind...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	405,519
2012.12978	Eurythmic Dancing with Plants -- Measuring Plant Response to Human Body Movement in an Anthroposophic Environment	This paper describes three experiments measuring interaction of humans with garden plants. In particular, body movement of a human conducting eurythmic dances near the plants (beetroots, tomatoes, lettuce) is correlated with the action potential measured by a plant SpikerBox, a device measuring the electrical activity ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	213,065
2307.03783	Neural Abstraction-Based Controller Synthesis and Deployment	Abstraction-based techniques are an attractive approach for synthesizing correct-by-construction controllers to satisfy high-level temporal requirements. A main bottleneck for successful application of these techniques is the memory requirement, both during controller synthesis and in controller deployment. We propos...	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	378,147
2311.10387	Stable Attractors for Neural networks classification via Ordinary Differential Equations (SA-nODE)	A novel approach for supervised classification is presented which sits at the intersection of machine learning and dynamical systems theory. At variance with other methodologies that employ ordinary differential equations for classification purposes, the untrained model is a priori constructed to accommodate for a set ...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	408,519
2212.05129	Measuring Data	We identify the task of measuring data to quantitatively characterize the composition of machine learning data and datasets. Similar to an object's height, width, and volume, data measurements quantify different attributes of data along common dimensions that support comparison. Several lines of research have proposed ...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	335,681
2010.12412	SmBoP: Semi-autoregressive Bottom-up Semantic Parsing	The de-facto standard decoding method for semantic parsing in recent years has been to autoregressively decode the abstract syntax tree of the target program using a top-down depth-first traversal. In this work, we propose an alternative approach: a Semi-autoregressive Bottom-up Parser (SmBoP) that constructs at decodi...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	202,684
1911.07548	Optimal Control over Multiple Input Lossy Channels	The performance of control systems with input packet losses on the controller to plant communication channel is analysed. The main contribution of this work is a proof that linear optimal control systems operating with UDP-like communication protocols have a larger quadratic cost than the same systems operating with TC...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	153,893
1310.6119	Asynchronous Rumour Spreading in Social and Signed Topologies	In this paper, we present an experimental analysis of the asynchronous push & pull rumour spreading protocol. This protocol is, to date, the best-performing rumour spreading protocol for simple, scalable, and robust information dissemination in distributed systems. We analyse the effect that multiple parameters have on...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	27,949
2303.01526	Semantic Attention Flow Fields for Monocular Dynamic Scene Decomposition	From video, we reconstruct a neural volume that captures time-varying color, density, scene flow, semantics, and attention information. The semantics and attention let us identify salient foreground objects separately from the background across spacetime. To mitigate low resolution semantic and attention features, we c...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	349,005
2406.08887	Low-Overhead Channel Estimation via 3D Extrapolation for TDD mmWave Massive MIMO Systems Under High-Mobility Scenarios	In time division duplexing (TDD) millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) systems, downlink channel state information (CSI) can be obtained from uplink channel estimation thanks to channel reciprocity. However, under high-mobility scenarios, frequent uplink channel estimation is needed due...	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	false	false	463,675
2205.01930	Explainable Anomaly Detection for Industrial Control System Cybersecurity	Industrial Control Systems (ICSs) are becoming more and more important in managing the operation of many important systems in smart manufacturing, such as power stations, water supply systems, and manufacturing sites. While massive digital data can be a driving force for system performance, data security has raised ser...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	294,772
1903.00317	TamperNN: Efficient Tampering Detection of Deployed Neural Nets	Neural networks are powering the deployment of embedded devices and Internet of Things. Applications range from personal assistants to critical ones such as self-driving cars. It has been shown recently that models obtained from neural nets can be trojaned ; an attacker can then trigger an arbitrary model behavior faci...	false	false	false	false	false	false	true	false	false	false	false	true	true	false	false	false	false	false	122,994
2311.10472	End-to-end autoencoding architecture for the simultaneous generation of medical images and corresponding segmentation masks	Despite the increasing use of deep learning in medical image segmentation, acquiring sufficient training data remains a challenge in the medical field. In response, data augmentation techniques have been proposed; however, the generation of diverse and realistic medical images and their corresponding masks remains a di...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	408,540
2210.00939	Improving Sample Quality of Diffusion Models Using Self-Attention Guidance	Denoising diffusion models (DDMs) have attracted attention for their exceptional generation quality and diversity. This success is largely attributed to the use of class- or text-conditional diffusion guidance methods, such as classifier and classifier-free guidance. In this paper, we present a more comprehensive persp...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	321,064
cs/0611094	Reducing Order Enforcement Cost in Complex Query Plans	Algorithms that exploit sort orders are widely used to implement joins, grouping, duplicate elimination and other set operations. Query optimizers traditionally deal with sort orders by using the notion of interesting orders. The number of interesting orders is unfortunately factorial in the number of participating att...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	539,890
2409.05367	Diagnostic Reasoning in Natural Language: Computational Model and Application	Diagnostic reasoning is a key component of expert work in many domains. It is a hard, time-consuming activity that requires expertise, and AI research has investigated the ways automated systems can support this process. Yet, due to the complexity of natural language, the applications of AI for diagnostic reasoning to ...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	486,744
1106.4577	Interactive Execution Monitoring of Agent Teams	There is an increasing need for automated support for humans monitoring the activity of distributed teams of cooperating agents, both human and machine. We characterize the domain-independent challenges posed by this problem, and describe how properties of domains influence the challenges and their solutions. We will c...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	true	false	false	false	10,960
2211.15502	ToothInpaintor: Tooth Inpainting from Partial 3D Dental Model and 2D Panoramic Image	In orthodontic treatment, a full tooth model consisting of both the crown and root is indispensable in making the treatment plan. However, acquiring tooth root information to obtain the full tooth model from CBCT images is sometimes restricted due to the massive radiation of CBCT scanning. Thus, reconstructing the full...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	333,296
2204.08624	Topology and geometry of data manifold in deep learning	Despite significant advances in the field of deep learning in applications to various fields, explaining the inner processes of deep learning models remains an important and open question. The purpose of this article is to describe and substantiate the geometric and topological view of the learning process of neural ne...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	292,155
1812.11842	Do GANs leave artificial fingerprints?	In the last few years, generative adversarial networks (GAN) have shown tremendous potential for a number of applications in computer vision and related fields. With the current pace of progress, it is a sure bet they will soon be able to generate high-quality images and videos, virtually indistinguishable from real on...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	117,639
2006.09732	High order low-bit Sigma-Delta quantization for fusion frames	We construct high order low-bit Sigma-Delta $(\Sigma \Delta)$ quantizers for the vector-valued setting of fusion frames. We prove that these $\Sigma \Delta$ quantizers can be stably implemented to quantize fusion frame measurements on subspaces $W_n$ using $\log_2( {\rm dim}(W_n)+1)$ bits per measurement. Signal recons...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	182,639
2111.00409	Kernel-based Impulse Response Identification with Side-Information on Steady-State Gain	In this paper, we consider the problem of system identification when side-information is available on the steady-state (or DC) gain of the system. We formulate a general nonparametric identification method as an infinite-dimensional constrained convex program over the reproducing kernel Hilbert space (RKHS) of stable i...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	264,208
2405.15151	NeB-SLAM: Neural Blocks-based Salable RGB-D SLAM for Unknown Scenes	Neural implicit representations have recently demonstrated considerable potential in the field of visual simultaneous localization and mapping (SLAM). This is due to their inherent advantages, including low storage overhead and representation continuity. However, these methods necessitate the size of the scene as input...	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	true	456,770
2002.09841	SetRank: A Setwise Bayesian Approach for Collaborative Ranking from Implicit Feedback	The recent development of online recommender systems has a focus on collaborative ranking from implicit feedback, such as user clicks and purchases. Different from explicit ratings, which reflect graded user preferences, the implicit feedback only generates positive and unobserved labels. While considerable efforts hav...	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	false	165,201
2411.12043	A comparative analysis for different finite element types in strain-gradient elasticity simulations performed on Firedrake and FEniCS	The layer-upon-layer approach in additive manufacturing, open or closed cells in polymeric or metallic foams involve an intrinsic microstructure tailored to the underlying applications. Homogenization of such architectured materials creates metamaterials modeled by higher-gradient models, specifically when the microstr...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	509,272
1812.00498	Permutations Unlabeled beyond Sampling Unknown	A recent unlabeled sampling result by Unnikrishnan, Haghighatshoar and Vetterli states that with probability one over iid Gaussian matrices $A$, any $x$ can be uniquely recovered from an unknown permutation of $y = A x$ as soon as $A$ has at least twice as many rows as columns. We show that this condition on $A$ implie...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	115,278
2211.07955	IntegratedPIFu: Integrated Pixel Aligned Implicit Function for Single-view Human Reconstruction	We propose IntegratedPIFu, a new pixel aligned implicit model that builds on the foundation set by PIFuHD. IntegratedPIFu shows how depth and human parsing information can be predicted and capitalised upon in a pixel-aligned implicit model. In addition, IntegratedPIFu introduces depth oriented sampling, a novel trainin...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	330,429
2404.06283	LLMs' Reading Comprehension Is Affected by Parametric Knowledge and Struggles with Hypothetical Statements	The task of reading comprehension (RC), often implemented as context-based question answering (QA), provides a primary means to assess language models' natural language understanding (NLU) capabilities. Yet, when applied to large language models (LLMs) with extensive built-in world knowledge, this method can be decepti...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	445,405
1602.01541	Fundamental Limits in Multi-image Alignment	The performance of multi-image alignment, bringing different images into one coordinate system, is critical in many applications with varied signal-to-noise ratio (SNR) conditions. A great amount of effort is being invested into developing methods to solve this problem. Several important questions thus arise, including...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	51,712
2405.11914	PT43D: A Probabilistic Transformer for Generating 3D Shapes from Single Highly-Ambiguous RGB Images	Generating 3D shapes from single RGB images is essential in various applications such as robotics. Current approaches typically target images containing clear and complete visual descriptions of the object, without considering common realistic cases where observations of objects that are largely occluded or truncated. ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	455,342
2308.06453	Multi-Label Knowledge Distillation	Existing knowledge distillation methods typically work by imparting the knowledge of output logits or intermediate feature maps from the teacher network to the student network, which is very successful in multi-class single-label learning. However, these methods can hardly be extended to the multi-label learning scenar...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	385,145
2107.05319	Human-like Relational Models for Activity Recognition in Video	Video activity recognition by deep neural networks is impressive for many classes. However, it falls short of human performance, especially for challenging to discriminate activities. Humans differentiate these complex activities by recognising critical spatio-temporal relations among explicitly recognised objects and ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	245,741
1606.05060	Pruning Random Forests for Prediction on a Budget	We propose to prune a random forest (RF) for resource-constrained prediction. We first construct a RF and then prune it to optimize expected feature cost & accuracy. We pose pruning RFs as a novel 0-1 integer program with linear constraints that encourages feature re-use. We establish total unimodularity of the constra...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	57,353
1611.02401	Divide and Conquer Networks	We consider the learning of algorithmic tasks by mere observation of input-output pairs. Rather than studying this as a black-box discrete regression problem with no assumption whatsoever on the input-output mapping, we concentrate on tasks that are amenable to the principle of divide and conquer, and study what are it...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	63,560
2111.02687	CoreLM: Coreference-aware Language Model Fine-Tuning	Language Models are the underpin of all modern Natural Language Processing (NLP) tasks. The introduction of the Transformers architecture has contributed significantly into making Language Modeling very effective across many NLP task, leading to significant advancements in the field. However, Transformers come with a b...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	264,946
2502.04358	Position: Scaling LLM Agents Requires Asymptotic Analysis with LLM Primitives	Decomposing hard problems into subproblems often makes them easier and more efficient to solve. With large language models (LLMs) crossing critical reliability thresholds for a growing slate of capabilities, there is an increasing effort to decompose systems into sets of LLM-based agents, each of whom can be delegated ...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	true	false	true	531,102

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