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
2106.08927	On the long-term learning ability of LSTM LMs	We inspect the long-term learning ability of Long Short-Term Memory language models (LSTM LMs) by evaluating a contextual extension based on the Continuous Bag-of-Words (CBOW) model for both sentence- and discourse-level LSTM LMs and by analyzing its performance. We evaluate on text and speech. Sentence-level models us...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	241,482
1504.06700	Preferential Multi-Context Systems	Multi-context systems (MCS) presented by Brewka and Eiter can be considered as a promising way to interlink decentralized and heterogeneous knowledge contexts. In this paper, we propose preferential multi-context systems (PMCS), which provide a framework for incorporating a total preorder relation over contexts in a mu...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	42,438
2103.01400	Smoothness Analysis of Adversarial Training	Deep neural networks are vulnerable to adversarial attacks. Recent studies about adversarial robustness focus on the loss landscape in the parameter space since it is related to optimization and generalization performance. These studies conclude that the difficulty of adversarial training is caused by the non-smoothnes...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	222,603
2305.16222	Incomplete Multimodal Learning for Complex Brain Disorders Prediction	Recent advancements in the acquisition of various brain data sources have created new opportunities for integrating multimodal brain data to assist in early detection of complex brain disorders. However, current data integration approaches typically need a complete set of biomedical data modalities, which may not alway...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	367,968
2402.11494	Graph Out-of-Distribution Generalization via Causal Intervention	Out-of-distribution (OOD) generalization has gained increasing attentions for learning on graphs, as graph neural networks (GNNs) often exhibit performance degradation with distribution shifts. The challenge is that distribution shifts on graphs involve intricate interconnections between nodes, and the environment labe...	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	false	false	430,423
2306.06530	Use of Robust DOB/CDOB Compensation to Improve Autonomous Vehicle Path Following Performance in the Presence of Model Uncertainty, CAN Bus Delays and External Disturbances	A path tracking control system is chosen as the proof-of-concept demonstration application in this paper. A disturbance observer (DOB) is embedded within the steering to path error automated driving loop to handle uncertain parameters such as vehicle mass, vehicle velocities and road friction coefficient and to reject ...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	372,646
2208.09047	Machine learning algorithms for three-dimensional mean-curvature computation in the level-set method	We propose a data-driven mean-curvature solver for the level-set method. This work is the natural extension to $\mathbb{R}^3$ of our two-dimensional strategy in [DOI: 10.1007/s10915-022-01952-2][1] and the hybrid inference system of [DOI: 10.1016/j.jcp.2022.111291][2]. However, in contrast to [1,2], which built resolut...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	313,572
2206.02789	Efficient and Accurate Physics-aware Multiplex Graph Neural Networks for 3D Small Molecules and Macromolecule Complexes	Recent advances in applying Graph Neural Networks (GNNs) to molecular science have showcased the power of learning three-dimensional (3D) structure representations with GNNs. However, most existing GNNs suffer from the limitations of insufficient modeling of diverse interactions, computational expensive operations, and...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	301,033
1806.02873	Medical Concept Embedding with Time-Aware Attention	Embeddings of medical concepts such as medication, procedure and diagnosis codes in Electronic Medical Records (EMRs) are central to healthcare analytics. Previous work on medical concept embedding takes medical concepts and EMRs as words and documents respectively. Nevertheless, such models miss out the temporal natur...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	99,862
2011.10396	Double Self-weighted Multi-view Clustering via Adaptive View Fusion	Multi-view clustering has been applied in many real-world applications where original data often contain noises. Some graph-based multi-view clustering methods have been proposed to try to reduce the negative influence of noises. However, previous graph-based multi-view clustering methods treat all features equally eve...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	207,500
2402.06884	Low-Rank Approximation of Structural Redundancy for Self-Supervised Learning	We study the data-generating mechanism for reconstructive SSL to shed light on its effectiveness. With an infinite amount of labeled samples, we provide a sufficient and necessary condition for perfect linear approximation. The condition reveals a full-rank component that preserves the label classes of Y, along with a ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	428,472
2010.04880	Designing for Recommending Intermediate States in A Scientific Workflow Management System	To process a large amount of data sequentially and systematically, proper management of workflow components (i.e., modules, data, configurations, associations among ports and links) in a Scientific Workflow Management System (SWfMS) is inevitable. Managing data with provenance in a SWfMS to support reusability of workf...	true	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	199,899
1411.1125	Distributed Low-Rank Estimation Based on Joint Iterative Optimization in Wireless Sensor Networks	This paper proposes a novel distributed reduced--rank scheme and an adaptive algorithm for distributed estimation in wireless sensor networks. The proposed distributed scheme is based on a transformation that performs dimensionality reduction at each agent of the network followed by a reduced-dimension parameter vector...	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	37,310
2012.09157	LIREx: Augmenting Language Inference with Relevant Explanation	Natural language explanations (NLEs) are a special form of data annotation in which annotators identify rationales (most significant text tokens) when assigning labels to data instances, and write out explanations for the labels in natural language based on the rationales. NLEs have been shown to capture human reasonin...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	211,979
2211.09741	Learning 4DVAR inversion directly from observations	Variational data assimilation and deep learning share many algorithmic aspects in common. While the former focuses on system state estimation, the latter provides great inductive biases to learn complex relationships. We here design a hybrid architecture learning the assimilation task directly from partial and noisy ob...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	331,080
2006.13991	Controversial information spreads faster and further in Reddit	Online users discuss and converse about all sorts of topics on social networks. Facebook, Twitter, Reddit are among many other networks where users can have this freedom of information sharing. The abundance of information shared over these networks makes them an attractive area for investigating all aspects of human b...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	184,087
2307.02502	Math Agents: Computational Infrastructure, Mathematical Embedding, and Genomics	The advancement in generative AI could be boosted with more accessible mathematics. Beyond human-AI chat, large language models (LLMs) are emerging in programming, algorithm discovery, and theorem proving, yet their genomics application is limited. This project introduces Math Agents and mathematical embedding as fresh...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	377,721
1711.03906	D-SLATS: Distributed Simultaneous Localization and Time Synchronization	Through the last decade, we have witnessed a surge of Internet of Things (IoT) devices, and with that a greater need to choreograph their actions across both time and space. Although these two problems, namely time synchronization and localization, share many aspects in common, they are traditionally treated separately...	false	false	false	false	false	false	true	true	false	false	true	false	false	false	false	false	false	true	84,294
1202.3757	Identifiability of Causal Graphs using Functional Models	This work addresses the following question: Under what assumptions on the data generating process can one infer the causal graph from the joint distribution? The approach taken by conditional independence-based causal discovery methods is based on two assumptions: the Markov condition and faithfulness. It has been show...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	14,429
2204.00754	Homography Loss for Monocular 3D Object Detection	Monocular 3D object detection is an essential task in autonomous driving. However, most current methods consider each 3D object in the scene as an independent training sample, while ignoring their inherent geometric relations, thus inevitably resulting in a lack of leveraging spatial constraints. In this paper, we prop...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	289,372
2402.07350	Antagonistic AI	The vast majority of discourse around AI development assumes that subservient, "moral" models aligned with "human values" are universally beneficial -- in short, that good AI is sycophantic AI. We explore the shadow of the sycophantic paradigm, a design space we term antagonistic AI: AI systems that are disagreeable, r...	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	428,670
1507.08711	Beyond Gauss: Image-Set Matching on the Riemannian Manifold of PDFs	State-of-the-art image-set matching techniques typically implicitly model each image-set with a Gaussian distribution. Here, we propose to go beyond these representations and model image-sets as probability distribution functions (PDFs) using kernel density estimators. To compare and match image-sets, we exploit Csisza...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	45,591
1710.02726	Image Matching Using SIFT, SURF, BRIEF and ORB: Performance Comparison for Distorted Images	Fast and robust image matching is a very important task with various applications in computer vision and robotics. In this paper, we compare the performance of three different image matching techniques, i.e., SIFT, SURF, and ORB, against different kinds of transformations and deformations such as scaling, rotation, noi...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	82,212
2401.05041	Learning to Configure Mathematical Programming Solvers by Mathematical Programming	We discuss the issue of finding a good mathematical programming solver configuration for a particular instance of a given problem, and we propose a two-phase approach to solve it. In the first phase we learn the relationships between the instance, the configuration and the performance of the configured solver on the gi...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	420,628
1109.5665	PDDL2.1 - The Art of the Possible? Commentary on Fox and Long	PDDL2.1 was designed to push the envelope of what planning algorithms can do, and it has succeeded. It adds two important features: durative actions,which take time (and may have continuous effects); and objective functions for measuring the quality of plans. The concept of durative actions is flawed; and the treatment...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	12,336
2401.06059	Investigating Data Contamination for Pre-training Language Models	Language models pre-trained on web-scale corpora demonstrate impressive capabilities on diverse downstream tasks. However, there is increasing concern whether such capabilities might arise from evaluation datasets being included in the pre-training corpus -- a phenomenon known as \textit{data contamination} -- in a man...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	421,001
2502.11203	Multiscale autonomous forecasting of plasma systems' dynamics using neural networks	Plasma systems exhibit complex multiscale dynamics, resolving which poses significant challenges for conventional numerical simulations. Machine learning (ML) offers an alternative by learning data-driven representations of these dynamics. Yet existing ML time-stepping models suffer from error accumulation, instability...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	534,252
1908.05402	Shield Synthesis for Real: Enforcing Safety in Cyber-Physical Systems	Cyber-physical systems are often safety-critical in that violations of safety properties may lead to catastrophes. We propose a method to enforce the safety of systems with real-valued signals by synthesizing a runtime enforcer called the shield. Whenever the system violates a property, the shield, composed with the sy...	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	true	141,707
2402.01067	Assessing Patient Eligibility for Inspire Therapy through Machine Learning and Deep Learning Models	Inspire therapy is an FDA-approved internal neurostimulation treatment for obstructive sleep apnea. However, not all patients respond to this therapy, posing a challenge even for experienced otolaryngologists to determine candidacy. This paper makes the first attempt to leverage both machine learning and deep learning ...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	425,840
2312.06134	Order Matters in the Presence of Dataset Imbalance for Multilingual Learning	In this paper, we empirically study the optimization dynamics of multi-task learning, particularly focusing on those that govern a collection of tasks with significant data imbalance. We present a simple yet effective method of pre-training on high-resource tasks, followed by fine-tuning on a mixture of high/low-resour...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	414,384
2403.04750	JAX-SPH: A Differentiable Smoothed Particle Hydrodynamics Framework	Particle-based fluid simulations have emerged as a powerful tool for solving the Navier-Stokes equations, especially in cases that include intricate physics and free surfaces. The recent addition of machine learning methods to the toolbox for solving such problems is pushing the boundary of the quality vs. speed tradeo...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	435,711
2106.02487	Debiasing a First-order Heuristic for Approximate Bi-level Optimization	Approximate bi-level optimization (ABLO) consists of (outer-level) optimization problems, involving numerical (inner-level) optimization loops. While ABLO has many applications across deep learning, it suffers from time and memory complexity proportional to the length $r$ of its inner optimization loop. To address this...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	238,886
2109.10601	Efficient Context-Aware Network for Abdominal Multi-organ Segmentation	The contextual information, presented in abdominal CT scan, is relative consistent. In order to make full use of the overall 3D context, we develop a whole-volume-based coarse-to-fine framework for efficient and effective abdominal multi-organ segmentation. We propose a new efficientSegNet network, which is composed of...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	256,682
2201.05382	Mental Health Assessment for the Chatbots	Previous researches on dialogue system assessment usually focus on the quality evaluation (e.g. fluency, relevance, etc) of responses generated by the chatbots, which are local and technical metrics. For a chatbot which responds to millions of online users including minors, we argue that it should have a healthy mental...	true	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	275,375
2201.05286	Demystifying Swarm Learning: A New Paradigm of Blockchain-based Decentralized Federated Learning	Federated learning (FL) is an emerging promising privacy-preserving machine learning paradigm and has raised more and more attention from researchers and developers. FL keeps users' private data on devices and exchanges the gradients of local models to cooperatively train a shared Deep Learning (DL) model on central cu...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	275,345
2412.01701	FathomVerse: A community science dataset for ocean animal discovery	Can computer vision help us explore the ocean? The ultimate challenge for computer vision is to recognize any visual phenomena, more than only the objects and animals humans encounter in their terrestrial lives. Previous datasets have explored everyday objects and fine-grained categories humans see frequently. We prese...	true	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	513,223
2303.01378	A Vision for Semantically Enriched Data Science	The recent efforts in automation of machine learning or data science has achieved success in various tasks such as hyper-parameter optimization or model selection. However, key areas such as utilizing domain knowledge and data semantics are areas where we have seen little automation. Data Scientists have long leveraged...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	true	false	348,943
1703.04391	Extrinsic Calibration of 3D Range Finder and Camera without Auxiliary Object or Human Intervention	Fusion of heterogeneous extroceptive sensors is the most effient and effective way to representing the environment precisely, as it overcomes various defects of each homogeneous sensor. The rigid transformation (aka. extrinsic parameters) of heterogeneous sensory systems should be available before precisely fusing the ...	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	69,893
2001.02870	Hybrid Multiple Attention Network for Semantic Segmentation in Aerial Images	Semantic segmentation in very high resolution (VHR) aerial images is one of the most challenging tasks in remote sensing image understanding. Most of the current approaches are based on deep convolutional neural networks (DCNNs). However, standard convolution with local receptive fields fails in modeling global depende...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	159,826
2101.08674	DAF:re: A Challenging, Crowd-Sourced, Large-Scale, Long-Tailed Dataset For Anime Character Recognition	In this work we tackle the challenging problem of anime character recognition. Anime, referring to animation produced within Japan and work derived or inspired from it. For this purpose we present DAF:re (DanbooruAnimeFaces:revamped), a large-scale, crowd-sourced, long-tailed dataset with almost 500 K images spread acr...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	216,383
1704.06461	A Theory of Nonlinear Signal-Noise Interactions in Wavelength Division Multiplexed Coherent Systems	A general theory of nonlinear signal-noise interactions for wavelength division multiplexed fiber-optic coherent transmission systems is presented. This theory is based on the regular perturbation treatment of the nonlinear Schrodinger equation, which governs the wave propagation in the optical fiber, and is exact up t...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	72,178
2207.00415	AI in 6G: Energy-Efficient Distributed Machine Learning for Multilayer Heterogeneous Networks	Adept network management is key for supporting extremely heterogeneous applications with stringent quality of service (QoS) requirements; this is more so when envisioning the complex and ultra-dense 6G mobile heterogeneous network (HetNet). From both the environmental and economical perspectives, non-homogeneous QoS de...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	305,745
1807.05576	Semantic Search by Latent Ontological Features	Both named entities and keywords are important in defining the content of a text in which they occur. In particular, people often use named entities in information search. However, named entities have ontological features, namely, their aliases, classes, and identifiers, which are hidden from their textual appearance. ...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	102,951
2403.15385	LATTE3D: Large-scale Amortized Text-To-Enhanced3D Synthesis	Recent text-to-3D generation approaches produce impressive 3D results but require time-consuming optimization that can take up to an hour per prompt. Amortized methods like ATT3D optimize multiple prompts simultaneously to improve efficiency, enabling fast text-to-3D synthesis. However, they cannot capture high-frequen...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	true	440,523
2403.05773	Unveiling Ancient Maya Settlements Using Aerial LiDAR Image Segmentation	Manual identification of archaeological features in LiDAR imagery is labor-intensive, costly, and requires archaeological expertise. This paper shows how recent advancements in deep learning (DL) present efficient solutions for accurately segmenting archaeological structures in aerial LiDAR images using the YOLOv8 neur...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	436,149
2004.07922	Light-Weighted CNN for Text Classification	For management, documents are categorized into a specific category, and to do these, most of the organizations use manual labor. In today's automation era, manual efforts on such a task are not justified, and to avoid this, we have so many software out there in the market. However, efficiency and minimal resource consu...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	172,903
2412.17292	AV-EmoDialog: Chat with Audio-Visual Users Leveraging Emotional Cues	In human communication, both verbal and non-verbal cues play a crucial role in conveying emotions, intentions, and meaning beyond words alone. These non-linguistic information, such as facial expressions, eye contact, voice tone, and pitch, are fundamental elements of effective interactions, enriching conversations by ...	true	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	519,911
2006.04152	BERT Loses Patience: Fast and Robust Inference with Early Exit	In this paper, we propose Patience-based Early Exit, a straightforward yet effective inference method that can be used as a plug-and-play technique to simultaneously improve the efficiency and robustness of a pretrained language model (PLM). To achieve this, our approach couples an internal-classifier with each layer o...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	180,580
2405.12783	Epanechnikov Variational Autoencoder	In this paper, we bridge Variational Autoencoders (VAEs) [17] and kernel density estimations (KDEs) [25 ],[23] by approximating the posterior by KDEs and deriving an upper bound of the Kullback-Leibler (KL) divergence in the evidence lower bound (ELBO). The flexibility of KDEs makes the optimization of posteriors in VA...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	455,651
2401.06430	Mutual Distillation Learning For Person Re-Identification	With the rapid advancements in deep learning technologies, person re-identification (ReID) has witnessed remarkable performance improvements. However, the majority of prior works have traditionally focused on solving the problem via extracting features solely from a single perspective, such as uniform partitioning, har...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	421,153
2012.15036	SGD Distributional Dynamics of Three Layer Neural Networks	With the rise of big data analytics, multi-layer neural networks have surfaced as one of the most powerful machine learning methods. However, their theoretical mathematical properties are still not fully understood. Training a neural network requires optimizing a non-convex objective function, typically done using stoc...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	213,678
2309.01825	LoopTune: Optimizing Tensor Computations with Reinforcement Learning	Advanced compiler technology is crucial for enabling machine learning applications to run on novel hardware, but traditional compilers fail to deliver performance, popular auto-tuners have long search times and expert-optimized libraries introduce unsustainable costs. To address this, we developed LoopTune, a deep rein...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	389,821
2202.03856	Class Density and Dataset Quality in High-Dimensional, Unstructured Data	We provide a definition for class density that can be used to measure the aggregate similarity of the samples within each of the classes in a high-dimensional, unstructured dataset. We then put forth several candidate methods for calculating class density and analyze the correlation between the values each method produ...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	279,365
2108.13320	Neural HMMs are all you need (for high-quality attention-free TTS)	Neural sequence-to-sequence TTS has achieved significantly better output quality than statistical speech synthesis using HMMs. However, neural TTS is generally not probabilistic and uses non-monotonic attention. Attention failures increase training time and can make synthesis babble incoherently. This paper describes h...	true	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	252,773
2406.16638	Feature Fusion for Human Activity Recognition using Parameter-Optimized Multi-Stage Graph Convolutional Network and Transformer Models	Human activity recognition (HAR) is a crucial area of research that involves understanding human movements using computer and machine vision technology. Deep learning has emerged as a powerful tool for this task, with models such as Convolutional Neural Networks (CNNs) and Transformers being employed to capture various...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	467,210
2202.08064	Learning a Single Neuron for Non-monotonic Activation Functions	We study the problem of learning a single neuron $\mathbf{x}\mapsto \sigma(\mathbf{w}^T\mathbf{x})$ with gradient descent (GD). All the existing positive results are limited to the case where $\sigma$ is monotonic. However, it is recently observed that non-monotonic activation functions outperform the traditional monot...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	true	false	false	280,752
1910.10942	A Recurrent Variational Autoencoder for Speech Enhancement	This paper presents a generative approach to speech enhancement based on a recurrent variational autoencoder (RVAE). The deep generative speech model is trained using clean speech signals only, and it is combined with a nonnegative matrix factorization noise model for speech enhancement. We propose a variational expect...	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	true	false	false	150,635
2109.13977	Risk averse non-stationary multi-armed bandits	This paper tackles the risk averse multi-armed bandits problem when incurred losses are non-stationary. The conditional value-at-risk (CVaR) is used as the objective function. Two estimation methods are proposed for this objective function in the presence of non-stationary losses, one relying on a weighted empirical di...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	257,808
2106.06468	Locally Sparse Neural Networks for Tabular Biomedical Data	Tabular datasets with low-sample-size or many variables are prevalent in biomedicine. Practitioners in this domain prefer linear or tree-based models over neural networks since the latter are harder to interpret and tend to overfit when applied to tabular datasets. To address these neural networks' shortcomings, we pro...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	240,491
2309.13609	Vulnerabilities in Video Quality Assessment Models: The Challenge of Adversarial Attacks	No-Reference Video Quality Assessment (NR-VQA) plays an essential role in improving the viewing experience of end-users. Driven by deep learning, recent NR-VQA models based on Convolutional Neural Networks (CNNs) and Transformers have achieved outstanding performance. To build a reliable and practical assessment system...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	394,283
2412.11689	Just a Simple Transformation is Enough for Data Protection in Vertical Federated Learning	Vertical Federated Learning (VFL) aims to enable collaborative training of deep learning models while maintaining privacy protection. However, the VFL procedure still has components that are vulnerable to attacks by malicious parties. In our work, we consider feature reconstruction attacks, a common risk targeting inpu...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	517,537
2203.10649	Coordinate Invariant User-Guided Constrained Path Planning with Reactive Rapidly Expanding Plane-Oriented Escaping Trees	As collaborative robots move closer to human environments, motion generation and reactive planning strategies that allow for elaborate task execution with minimal easy-to-implement guidance whilst coping with changes in the environment is of paramount importance. In this paper, we present a novel approach for generatin...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	286,629
1908.06402	eSports Pro-Players Behavior During the Game Events: Statistical Analysis of Data Obtained Using the Smart Chair	Today's competition between the professional eSports teams is so strong that in-depth analysis of players' performance literally crucial for creating a powerful team. There are two main approaches to such an estimation: obtaining features and metrics directly from the in-game data or collecting detailed information abo...	true	false	false	false	true	false	false	false	false	false	false	false	false	true	false	false	false	false	142,012
2302.01622	Private, fair and accurate: Training large-scale, privacy-preserving AI models in medical imaging	Artificial intelligence (AI) models are increasingly used in the medical domain. However, as medical data is highly sensitive, special precautions to ensure its protection are required. The gold standard for privacy preservation is the introduction of differential privacy (DP) to model training. Prior work indicates th...	false	false	false	false	true	false	true	false	false	false	false	true	true	false	false	false	false	false	343,686
1801.07988	Understanding news story chains using information retrieval and network clustering techniques	Content analysis of news stories (whether manual or automatic) is a cornerstone of the communication studies field. However, much research is conducted at the level of individual news articles, despite the fact that news events (especially significant ones) are frequently presented as "stories" by news outlets: chains ...	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	false	88,887
1806.00239	Private Streaming with Convolutional Codes	Recently, information-theoretic private information retrieval (PIR) from coded storage systems has gained a lot of attention, and a general star product PIR scheme was proposed. In this paper, the star product scheme is adopted, with appropriate modifications, to the case of private (e.g., video) streaming. It is assum...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	99,270
1908.04718	Micro-architectural Analysis of OLAP: Limitations and Opportunities	Understanding micro-architectural behavior is profound in efficiently using hardware resources. Recent work has shown that, despite being aggressively optimized for modern hardware, in-memory online transaction processing (OLTP) systems severely underutilize their core micro-architecture resources [25]. Online analytic...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	true	141,553
1906.09652	Secure Multi-party Computation for Cloud-based Control	In this chapter, we will explore the cloud-outsourced privacy-preserving computation of a controller on encrypted measurements from a (possibly distributed) system, taking into account the challenges introduced by the dynamical nature of the data. The privacy notion used in this work is that of cryptographic multi-part...	false	false	false	false	false	false	false	false	false	false	true	false	true	false	false	false	false	false	136,225
2212.10564	Re-evaluating the Need for Multimodal Signals in Unsupervised Grammar Induction	Are multimodal inputs necessary for grammar induction? Recent work has shown that multimodal training inputs can improve grammar induction. However, these improvements are based on comparisons to weak text-only baselines that were trained on relatively little textual data. To determine whether multimodal inputs are nee...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	337,530
2301.11518	Online Learning in Stackelberg Games with an Omniscient Follower	We study the problem of online learning in a two-player decentralized cooperative Stackelberg game. In each round, the leader first takes an action, followed by the follower who takes their action after observing the leader's move. The goal of the leader is to learn to minimize the cumulative regret based on the histor...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	342,176
2303.12755	Text Semantics to Image Generation: A method of building facades design base on Stable Diffusion model	Stable Diffusion model has been extensively employed in the study of archi-tectural image generation, but there is still an opportunity to enhance in terms of the controllability of the generated image content. A multi-network combined text-to-building facade image generating method is proposed in this work. We first f...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	353,383
2110.15954	Limiting fluctuation and trajectorial stability of multilayer neural networks with mean field training	The mean field (MF) theory of multilayer neural networks centers around a particular infinite-width scaling, where the learning dynamics is closely tracked by the MF limit. A random fluctuation around this infinite-width limit is expected from a large-width expansion to the next order. This fluctuation has been studied...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	264,053
2201.12918	How Correlated are Community-aware and Classical Centrality Measures in Complex Networks?	Unlike classical centrality measures, recently developed community-aware centrality measures use a network's community structure to identify influential nodes in complex networks. This paper investigates their relationship on a set of fifty real-world networks originating from various domains. Results show that classic...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	277,834
2111.07104	A strong baseline for image and video quality assessment	In this work, we present a simple yet effective unified model for perceptual quality assessment of image and video. In contrast to existing models which usually consist of complex network architecture, or rely on the concatenation of multiple branches of features, our model achieves a comparable performance by applying...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	266,278
1702.04539	Time-Invariant LDPC Convolutional Codes	Spatially coupled codes have been shown to universally achieve the capacity for a large class of channels. Many variants of such codes have been introduced to date. We discuss a further such variant that is particularly simple and is determined by a very small number of parameters. More precisely, we consider time-inva...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	68,274
2211.08191	Improved disentangled speech representations using contrastive learning in factorized hierarchical variational autoencoder	Leveraging the fact that speaker identity and content vary on different time scales, \acrlong{fhvae} (\acrshort{fhvae}) uses different latent variables to symbolize these two attributes. Disentanglement of these attributes is carried out by different prior settings of the corresponding latent variables. For the prior o...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	330,515
2005.00619	Probing Contextual Language Models for Common Ground with Visual Representations	The success of large-scale contextual language models has attracted great interest in probing what is encoded in their representations. In this work, we consider a new question: to what extent contextual representations of concrete nouns are aligned with corresponding visual representations? We design a probing model t...	false	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	175,295
1903.07781	Vulnerability Assessment of N-1 Reliable Power Systems to False Data Injection Attacks	This paper studies the vulnerability of large-scale power systems to false data injection (FDI) attacks through their physical consequences. Prior work has shown that an attacker-defender bi-level linear program (ADBLP) can be used to determine the worst-case consequences of FDI attacks aiming to maximize the physical ...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	124,693
2112.01054	Emotions are Subtle: Learning Sentiment Based Text Representations Using Contrastive Learning	Contrastive learning techniques have been widely used in the field of computer vision as a means of augmenting datasets. In this paper, we extend the use of these contrastive learning embeddings to sentiment analysis tasks and demonstrate that fine-tuning on these embeddings provides an improvement over fine-tuning on ...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	269,357
2211.09715	Physics-informed neural networks for gravity currents reconstruction from limited data	The present work investigates the use of physics-informed neural networks (PINNs) for the 3D reconstruction of unsteady gravity currents from limited data. In the PINN context, the flow fields are reconstructed by training a neural network whose objective function penalizes the mismatch between the network predictions ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	331,065
1602.03351	Adaptive Skills, Adaptive Partitions (ASAP)	We introduce the Adaptive Skills, Adaptive Partitions (ASAP) framework that (1) learns skills (i.e., temporally extended actions or options) as well as (2) where to apply them. We believe that both (1) and (2) are necessary for a truly general skill learning framework, which is a key building block needed to scale up t...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	51,990
2306.01746	An Application of Neutrosophic Sets to Decision Making	Maji et al. introduced in 2002 a method of parametric decision making using soft sets as tools and representing their tabular form as a binary matrix. In cases, however, where some or all of the parameters used for the characterization of the elements of the universal set are of fuzzy texture, their method does not giv...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	370,579
2310.16956	Datastore Design for Analysis of Police Broadcast Audio at Scale	With policing coming under greater scrutiny in recent years, researchers have begun to more thoroughly study the effects of contact between police and minority communities. Despite data archives of hundreds of thousands of recorded Broadcast Police Communications (BPC) being openly available to the public, a closer loo...	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	true	false	402,932
2106.01170	Detecting Bot-Generated Text by Characterizing Linguistic Accommodation in Human-Bot Interactions	Language generation models' democratization benefits many domains, from answering health-related questions to enhancing education by providing AI-driven tutoring services. However, language generation models' democratization also makes it easier to generate human-like text at-scale for nefarious activities, from spread...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	238,411
2306.16661	NaturalInversion: Data-Free Image Synthesis Improving Real-World Consistency	We introduce NaturalInversion, a novel model inversion-based method to synthesize images that agrees well with the original data distribution without using real data. In NaturalInversion, we propose: (1) a Feature Transfer Pyramid which uses enhanced image prior of the original data by combining the multi-scale feature...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	376,440
2007.06630	Dense Crowds Detection and Counting with a Lightweight Architecture	In the context of crowd counting, most of the works have focused on improving the accuracy without regard to the performance leading to algorithms that are not suitable for embedded applications. In this paper, we propose a lightweight convolutional neural network architecture to perform crowd detection and counting us...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	true	false	false	187,063
2410.18092	Two-Stage Radio Map Construction with Real Environments and Sparse Measurements	Radio map construction based on extensive measurements is accurate but expensive and time-consuming, while environment-aware radio map estimation reduces the costs at the expense of low accuracy. Considering accuracy and costs, a first-predict-then-correct (FPTC) method is proposed by leveraging generative adversarial ...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	501,738
1607.03057	Learning from the News: Predicting Entity Popularity on Twitter	In this work, we tackle the problem of predicting entity popularity on Twitter based on the news cycle. We apply a supervised learn- ing approach and extract four types of features: (i) signal, (ii) textual, (iii) sentiment and (iv) semantic, which we use to predict whether the popularity of a given entity will be high...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	58,453
1201.1262	A Network Approach to the French System of Legal codes - Part I: Analysis of a Dense Network	We explore one aspect of the structure of a codified legal system at the national level using a new type of representation to understand the strong or weak dependencies between the various fields of law. In Part I of this study, we analyze the graph associated with the network in which each French legal code is a verte...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	13,703
2003.04470	Data Warehouse and Decision Support on Integrated Crop Big Data	In recent years, precision agriculture is becoming very popular. The introduction of modern information and communication technologies for collecting and processing Agricultural data revolutionise the agriculture practises. This has started a while ago (early 20th century) and it is driven by the low cost of collecting...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	true	true	167,567
2105.14710	Robustifying $\ell_\infty$ Adversarial Training to the Union of Perturbation Models	Classical adversarial training (AT) frameworks are designed to achieve high adversarial accuracy against a single attack type, typically $\ell_\infty$ norm-bounded perturbations. Recent extensions in AT have focused on defending against the union of multiple perturbations but this benefit is obtained at the expense of ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	237,777
2501.05238	FOCUS: Towards Universal Foreground Segmentation	Foreground segmentation is a fundamental task in computer vision, encompassing various subdivision tasks. Previous research has typically designed task-specific architectures for each task, leading to a lack of unification. Moreover, they primarily focus on recognizing foreground objects without effectively distinguish...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	523,512
1911.00139	Device-Circuit-Architecture Co-Exploration for Computing-in-Memory Neural Accelerators	Co-exploration of neural architectures and hardware design is promising to simultaneously optimize network accuracy and hardware efficiency. However, state-of-the-art neural architecture search algorithms for the co-exploration are dedicated for the conventional von-neumann computing architecture, whose performance is ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	true	false	false	151,733
2212.05707	Human Mobility Modeling During the COVID-19 Pandemic via Deep Graph Diffusion Infomax	Non-Pharmaceutical Interventions (NPIs), such as social gathering restrictions, have shown effectiveness to slow the transmission of COVID-19 by reducing the contact of people. To support policy-makers, multiple studies have first modeled human mobility via macro indicators (e.g., average daily travel distance) and the...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	335,863
2203.16966	Human Instance Segmentation and Tracking via Data Association and Single-stage Detector	Human video instance segmentation plays an important role in computer understanding of human activities and is widely used in video processing, video surveillance, and human modeling in virtual reality. Most current VIS methods are based on Mask-RCNN framework, where the target appearance and motion information for dat...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	288,997
2312.04757	Induced Generative Adversarial Particle Transformers	In high energy physics (HEP), machine learning methods have emerged as an effective way to accurately simulate particle collisions at the Large Hadron Collider (LHC). The message-passing generative adversarial network (MPGAN) was the first model to simulate collisions as point, or ``particle'', clouds, with state-of-th...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	413,811
2308.02412	Self-Supervised Learning for WiFi CSI-Based Human Activity Recognition: A Systematic Study	Recently, with the advancement of the Internet of Things (IoT), WiFi CSI-based HAR has gained increasing attention from academic and industry communities. By integrating the deep learning technology with CSI-based HAR, researchers achieve state-of-the-art performance without the need of expert knowledge. However, the s...	true	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	383,609
1811.00911	Online Diverse Learning to Rank from Partial-Click Feedback	Learning to rank is an important problem in machine learning and recommender systems. In a recommender system, a user is typically recommended a list of items. Since the user is unlikely to examine the entire recommended list, partial feedback arises naturally. At the same time, diverse recommendations are important be...	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	false	112,223
2105.02589	Bandit based centralized matching in two-sided markets for peer to peer lending	Sequential fundraising in two sided online platforms enable peer to peer lending by sequentially bringing potential contributors, each of whose decisions impact other contributors in the market. However, understanding the dynamics of sequential contributions in online platforms for peer lending has been an open ended r...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	233,867
2108.06897	AutoChart: A Dataset for Chart-to-Text Generation Task	The analytical description of charts is an exciting and important research area with many applications in academia and industry. Yet, this challenging task has received limited attention from the computational linguistics research community. This paper proposes \textsf{AutoChart}, a large dataset for the analytical des...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	true	250,768

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