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
0806.2140	Defaults and Normality in Causal Structures	A serious defect with the Halpern-Pearl (HP) definition of causality is repaired by combining a theory of causality with a theory of defaults. In addition, it is shown that (despite a claim to the contrary) a cause according to the HP condition need not be a single conjunct. A definition of causality motivated by Wrigh...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	1,917
2106.10352	Cross-hospital Sepsis Early Detection via Semi-supervised Optimal Transport with Self-paced Ensemble	Leveraging machine learning techniques for Sepsis early detection and diagnosis has attracted increasing interest in recent years. However, most existing methods require a large amount of labeled training data, which may not be available for a target hospital that deploys a new Sepsis detection system. More seriously, ...	false	false	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	241,983
1808.01244	CornerNet: Detecting Objects as Paired Keypoints	We propose CornerNet, a new approach to object detection where we detect an object bounding box as a pair of keypoints, the top-left corner and the bottom-right corner, using a single convolution neural network. By detecting objects as paired keypoints, we eliminate the need for designing a set of anchor boxes commonly...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	104,537
2309.01412	Finite/fixed-time Stabilization of Linear Systems with States Quantization	This paper develops a homogeneity-based approach to finite/fixed-time stabilization of linear time-invariant (LTI) system with quantized measurements. A sufficient condition for finite/fixed-time stabilization of multi-input LTI system under states quantization is derived. It is shown that a homogeneous quantized state...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	389,678
2105.08131	A New Framework to Adopt Multidimensional Databases for Organizational Information System Strategies	As information becomes increasingly sizable for organizations to maintain the challenge of organizing data still remains. More importantly, the on-going process of analysing incoming data occurs on a continual basis and organizations should employ existing procedures that may not be adequate or efficient when attemptin...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	235,659
2006.03824	Scaling Equilibrium Propagation to Deep ConvNets by Drastically Reducing its Gradient Estimator Bias	Equilibrium Propagation (EP) is a biologically-inspired algorithm for convergent RNNs with a local learning rule that comes with strong theoretical guarantees. The parameter updates of the neural network during the credit assignment phase have been shown mathematically to approach the gradients provided by Backpropagat...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	180,448
2007.13475	Towards an ontology of HTTP interactions	Enterprise information systems have adopted Web-based foundations for exchanges between heterogeneous programmes. These programs provide and consume via Web APIs some resources identified by URIs, whose representations are transmitted via HTTP. Furthermore HTTP remains at the heart of all Web developments (Semantic Web...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	189,139
1611.07212	Recurrent Attention Models for Depth-Based Person Identification	We present an attention-based model that reasons on human body shape and motion dynamics to identify individuals in the absence of RGB information, hence in the dark. Our approach leverages unique 4D spatio-temporal signatures to address the identification problem across days. Formulated as a reinforcement learning tas...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	64,323
1907.02161	Seeing Under the Cover: A Physics Guided Learning Approach for In-Bed Pose Estimation	Human in-bed pose estimation has huge practical values in medical and healthcare applications yet still mainly relies on expensive pressure mapping (PM) solutions. In this paper, we introduce our novel physics inspired vision-based approach that addresses the challenging issues associated with the in-bed pose estimatio...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	137,541
2107.02306	Connectivity Matters: Neural Network Pruning Through the Lens of Effective Sparsity	Neural network pruning is a fruitful area of research with surging interest in high sparsity regimes. Benchmarking in this domain heavily relies on faithful representation of the sparsity of subnetworks, which has been traditionally computed as the fraction of removed connections (direct sparsity). This definition, how...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	244,761
2301.09042	The Shape of Explanations: A Topological Account of Rule-Based Explanations in Machine Learning	Rule-based explanations provide simple reasons explaining the behavior of machine learning classifiers at given points in the feature space. Several recent methods (Anchors, LORE, etc.) purport to generate rule-based explanations for arbitrary or black-box classifiers. But what makes these methods work in general? We i...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	341,383
2502.13172	Unveiling Privacy Risks in LLM Agent Memory	Large Language Model (LLM) agents have become increasingly prevalent across various real-world applications. They enhance decision-making by storing private user-agent interactions in the memory module for demonstrations, introducing new privacy risks for LLM agents. In this work, we systematically investigate the vuln...	false	false	false	false	true	false	false	false	false	false	false	false	true	false	false	false	false	false	535,242
2501.09411	Towards Robust and Realistic Human Pose Estimation via WiFi Signals	Robust WiFi-based human pose estimation is a challenging task that bridges discrete and subtle WiFi signals to human skeletons. This paper revisits this problem and reveals two critical yet overlooked issues: 1) cross-domain gap, i.e., due to significant variations between source-target domain pose distributions; and 2...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	525,134
2110.06559	Infinitely Divisible Noise in the Low Privacy Regime	Federated learning, in which training data is distributed among users and never shared, has emerged as a popular approach to privacy-preserving machine learning. Cryptographic techniques such as secure aggregation are used to aggregate contributions, like a model update, from all users. A robust technique for making su...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	true	260,669
1811.00907	Importance of Search and Evaluation Strategies in Neural Dialogue Modeling	We investigate the impact of search strategies in neural dialogue modeling. We first compare two standard search algorithms, greedy and beam search, as well as our newly proposed iterative beam search which produces a more diverse set of candidate responses. We evaluate these strategies in realistic full conversations ...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	112,221
2308.13534	Building Trust in Conversational AI: A Comprehensive Review and Solution Architecture for Explainable, Privacy-Aware Systems using LLMs and Knowledge Graph	Conversational AI systems have emerged as key enablers of human-like interactions across diverse sectors. Nevertheless, the balance between linguistic nuance and factual accuracy has proven elusive. In this paper, we first introduce LLMXplorer, a comprehensive tool that provides an in-depth review of over 150 Large Lan...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	387,952
1902.11155	Generalized Karush-Kuhn-Tucker Conditions for Real Continuous Optimization Problems	Most existing work focuses on the generalization of KKT for nonsmooth convex optimization problems, but this paper explores a generalized form of Karush-Kuhn-Tucker (KKT) conditions for real continuous optimization problems.	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	122,883
2206.09432	Object Localization Assistive System Based on CV and Vibrotactile Encoding	Intelligent assistive systems can navigate blind people, but most of them could only give non-intuitive cues or inefficient guidance. Based on computer vision and vibrotactile encoding, this paper presents an interactive system that provides blind people with intuitive spatial cognition. Different from the traditional ...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	303,571
2312.15516	A-SDM: Accelerating Stable Diffusion through Redundancy Removal and Performance Optimization	The Stable Diffusion Model (SDM) is a popular and efficient text-to-image (t2i) generation and image-to-image (i2i) generation model. Although there have been some attempts to reduce sampling steps, model distillation, and network quantization, these previous methods generally retain the original network architecture. ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	418,034
2501.04227	Agent Laboratory: Using LLM Agents as Research Assistants	Historically, scientific discovery has been a lengthy and costly process, demanding substantial time and resources from initial conception to final results. To accelerate scientific discovery, reduce research costs, and improve research quality, we introduce Agent Laboratory, an autonomous LLM-based framework capable o...	true	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	523,138
1903.04752	Occlusion-guided compact template learning for ensemble deep network-based pose-invariant face recognition	Concatenation of the deep network representations extracted from different facial patches helps to improve face recognition performance. However, the concatenated facial template increases in size and contains redundant information. Previous solutions aim to reduce the dimensionality of the facial template without cons...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	124,040
1912.11234	Computation Reallocation for Object Detection	The allocation of computation resources in the backbone is a crucial issue in object detection. However, classification allocation pattern is usually adopted directly to object detector, which is proved to be sub-optimal. In order to reallocate the engaged computation resources in a more efficient way, we present CR-NA...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	158,518
1909.05084	Image Segmentation using Multi-Threshold technique by Histogram Sampling	The segmentation of digital images is one of the essential steps in image processing or a computer vision system. It helps in separating the pixels into different regions according to their intensity level. A large number of segmentation techniques have been proposed, and a few of them use complex computational operati...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	144,990
2007.07054	Nonlinear Adaptive Cruise Control of Vehicular Platoons	The paper deals with the design of nonlinear adaptive cruise controllers for vehicular platoons operating on an open road or a ring-road. The constructed feedback controllers are nonlinear functions of the distance between successive vehicles and their speeds. It is shown that the proposed novel controllers guarantee s...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	187,210
1912.10068	Recommendations and User Agency: The Reachability of Collaboratively-Filtered Information	Recommender systems often rely on models which are trained to maximize accuracy in predicting user preferences. When the systems are deployed, these models determine the availability of content and information to different users. The gap between these objectives gives rise to a potential for unintended consequences, co...	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	false	158,211
1704.07139	An Aposteriorical Clusterability Criterion for $k$-Means++ and Simplicity of Clustering	We define the notion of a well-clusterable data set combining the point of view of the objective of $k$-means clustering algorithm (minimising the centric spread of data elements) and common sense (clusters shall be separated by gaps). We identify conditions under which the optimum of $k$-means objective coincides with...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	72,303
2406.10366	Improving the Validity and Practical Usefulness of AI/ML Evaluations Using an Estimands Framework	Commonly, AI or machine learning (ML) models are evaluated on benchmark datasets. This practice supports innovative methodological research, but benchmark performance can be poorly correlated with performance in real-world applications -- a construct validity issue. To improve the validity and practical usefulness of e...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	464,374
2501.11347	EndoChat: Grounded Multimodal Large Language Model for Endoscopic Surgery	Recently, Multimodal Large Language Models (MLLMs) have demonstrated their immense potential in computer-aided diagnosis and decision-making. In the context of robotic-assisted surgery, MLLMs can serve as effective tools for surgical training and guidance. However, there is still a lack of MLLMs specialized for surgica...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	525,893
1812.05477	Gaussian Process Deep Belief Networks: A Smooth Generative Model of Shape with Uncertainty Propagation	The shape of an object is an important characteristic for many vision problems such as segmentation, detection and tracking. Being independent of appearance, it is possible to generalize to a large range of objects from only small amounts of data. However, shapes represented as silhouette images are challenging to mode...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	116,421
2401.06683	DQNC2S: DQN-based Cross-stream Crisis event Summarizer	Summarizing multiple disaster-relevant data streams simultaneously is particularly challenging as existing Retrieve&Re-ranking strategies suffer from the inherent redundancy of multi-stream data and limited scalability in a multi-query setting. This work proposes an online approach to crisis timeline generation based o...	false	false	false	false	true	true	true	false	true	false	false	false	false	false	false	false	false	false	421,240
2111.06016	Synthetic Document Generator for Annotation-free Layout Recognition	Analyzing the layout of a document to identify headers, sections, tables, figures etc. is critical to understanding its content. Deep learning based approaches for detecting the layout structure of document images have been promising. However, these methods require a large number of annotated examples during training, ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	265,956
1811.02390	Local-Encoding-Preserving Secure Network Coding---Part II: Flexible Rate and Security Level	In the two-part paper, we consider the problem of secure network coding when the information rate and the security level can change over time. To efficiently solve this problem, we put forward local-encoding-preserving secure network coding, where a family of secure linear network codes (SLNCs) is called local-encoding...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	112,581
2009.00470	Data Anomaly Detection for Structural Health Monitoring of Bridges using Shapelet Transform	With the wider availability of sensor technology, a number of Structural Health Monitoring (SHM) systems are deployed to monitor civil infrastructure. The continuous monitoring provides valuable information about the structure that can help in providing a decision support system for retrofits and other structural modif...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	194,053
2008.03937	Feature Ranking for Semi-supervised Learning	The data made available for analysis are becoming more and more complex along several directions: high dimensionality, number of examples and the amount of labels per example. This poses a variety of challenges for the existing machine learning methods: coping with dataset with a large number of examples that are descr...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	191,076
2305.04835	How Do In-Context Examples Affect Compositional Generalization?	Compositional generalization--understanding unseen combinations of seen primitives--is an essential reasoning capability in human intelligence. The AI community mainly studies this capability by fine-tuning neural networks on lots of training samples, while it is still unclear whether and how in-context learning--the p...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	362,920
2407.14133	I Know About "Up"! Enhancing Spatial Reasoning in Visual Language Models Through 3D Reconstruction	Visual Language Models (VLMs) are essential for various tasks, particularly visual reasoning tasks, due to their robust multi-modal information integration, visual reasoning capabilities, and contextual awareness. However, existing \VLMs{}' visual spatial reasoning capabilities are often inadequate, struggling even wit...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	474,665
1303.6454	Partial Transfer Entropy on Rank Vectors	For the evaluation of information flow in bivariate time series, information measures have been employed, such as the transfer entropy (TE), the symbolic transfer entropy (STE), defined similarly to TE but on the ranks of the components of the reconstructed vectors, and the transfer entropy on rank vectors (TERV), simi...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	23,275
1709.07358	Non-Depth-First Search against Independent Distributions on an AND-OR Tree	Suzuki and Niida (Ann. Pure. Appl. Logic, 2015) showed the following results on independent distributions (IDs) on an AND-OR tree, where they took only depth-first algorithms into consideration. (1) Among IDs such that probability of the root having value 0 is fixed as a given r such that 0 < r < 1, if d is a maximizer...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	81,264
2407.05377	Collective Innovation in Groups of Large Language Models	Human culture relies on collective innovation: our ability to continuously explore how existing elements in our environment can be combined to create new ones. Language is hypothesized to play a key role in human culture, driving individual cognitive capacities and shaping communication. Yet the majority of models of c...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	470,951
2212.04928	P2T2: a Physically-primed deep-neural-network approach for robust $T_{2}$ distribution estimation from quantitative $T_{2}$-weighted MRI	Estimating $T_2$ relaxation time distributions from multi-echo $T_2$-weighted MRI ($T_2W$) data can provide valuable biomarkers for assessing inflammation, demyelination, edema, and cartilage composition in various pathologies, including neurodegenerative disorders, osteoarthritis, and tumors. Deep neural network (DNN)...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	335,623
cs/0008031	Bunsetsu Identification Using Category-Exclusive Rules	This paper describes two new bunsetsu identification methods using supervised learning. Since Japanese syntactic analysis is usually done after bunsetsu identification, bunsetsu identification is important for analyzing Japanese sentences. In experiments comparing the four previously available machine-learning methods ...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	537,198
1307.7138	Reconstruction of Network Coded Sources From Incomplete Datasets	In this paper, we investigate the problem of recovering source information from an incomplete set of network coded data. We first study the theoretical performance of such systems under maximum a posteriori (MAP) decoding and derive the upper bound on the probability of decoding error as a function of the system parame...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	26,069
1308.6683	Benchmarking Summarizability Processing in XML Warehouses with Complex Hierarchies	Business Intelligence plays an important role in decision making. Based on data warehouses and Online Analytical Processing, a business intelligence tool can be used to analyze complex data. Still, summarizability issues in data warehouses cause ineffective analyses that may become critical problems to businesses. To s...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	26,734
2308.09210	Efficient Algorithms for Attributed Graph Alignment with Vanishing Edge Correlation	Graph alignment refers to the task of finding the vertex correspondence between two correlated graphs of $n$ vertices. Extensive study has been done on polynomial-time algorithms for the graph alignment problem under the Erd\H{o}s-R\'enyi graph pair model, where the two graphs are Erd\H{o}s-R\'enyi graphs with edge pro...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	386,193
2406.16135	Crosslingual Capabilities and Knowledge Barriers in Multilingual Large Language Models	Large language models (LLMs) are typically multilingual due to pretraining on diverse multilingual corpora. But can these models relate corresponding concepts across languages, effectively being crosslingual? This study evaluates six state-of-the-art LLMs on inherently crosslingual tasks. We observe that while these mo...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	467,010
2502.05938	Energy-Efficient Autonomous Aerial Navigation with Dynamic Vision Sensors: A Physics-Guided Neuromorphic Approach	Vision-based object tracking is a critical component for achieving autonomous aerial navigation, particularly for obstacle avoidance. Neuromorphic Dynamic Vision Sensors (DVS) or event cameras, inspired by biological vision, offer a promising alternative to conventional frame-based cameras. These cameras can detect cha...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	531,849
2310.08419	Jailbreaking Black Box Large Language Models in Twenty Queries	There is growing interest in ensuring that large language models (LLMs) align with human values. However, the alignment of such models is vulnerable to adversarial jailbreaks, which coax LLMs into overriding their safety guardrails. The identification of these vulnerabilities is therefore instrumental in understanding ...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	399,384
2306.04839	Solving Novel Program Synthesis Problems with Genetic Programming using Parametric Polymorphism	Contemporary genetic programming (GP) systems for general program synthesis have been primarily concerned with evolving programs that can manipulate values from a standard set of primitive data types and simple indexed data structures. In contrast, human programmers do not limit themselves to a small finite set of data...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	true	371,923
2111.08679	Automatically detecting anomalous exoplanet transits	Raw light curve data from exoplanet transits is too complex to naively apply traditional outlier detection methods. We propose an architecture which estimates a latent representation of both the main transit and residual deviations with a pair of variational autoencoders. We show, using two fabricated datasets, that ou...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	true	false	false	266,776
2404.17807	Meta In-Context Learning Makes Large Language Models Better Zero and Few-Shot Relation Extractors	Relation extraction (RE) is an important task that aims to identify the relationships between entities in texts. While large language models (LLMs) have revealed remarkable in-context learning (ICL) capability for general zero and few-shot learning, recent studies indicate that current LLMs still struggle with zero and...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	450,009
2310.08582	Tree-Planner: Efficient Close-loop Task Planning with Large Language Models	This paper studies close-loop task planning, which refers to the process of generating a sequence of skills (a plan) to accomplish a specific goal while adapting the plan based on real-time observations. Recently, prompting Large Language Models (LLMs) to generate actions iteratively has become a prevalent paradigm due...	false	false	false	false	true	false	true	true	true	false	false	false	false	false	false	false	false	false	399,445
1804.07134	varrank: an R package for variable ranking based on mutual information with applications to observed systemic datasets	This article describes the R package varrank. It has a flexible implementation of heuristic approaches which perform variable ranking based on mutual information. The package is particularly suitable for exploring multivariate datasets requiring a holistic analysis. The core functionality is a general implementation of...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	95,462
2107.11911	Restless Bandits with Many Arms: Beating the Central Limit Theorem	We consider finite-horizon restless bandits with multiple pulls per period, which play an important role in recommender systems, active learning, revenue management, and many other areas. While an optimal policy can be computed, in principle, using dynamic programming, the computation required scales exponentially in t...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	247,736
0712.3329	Universal Intelligence: A Definition of Machine Intelligence	A fundamental problem in artificial intelligence is that nobody really knows what intelligence is. The problem is especially acute when we need to consider artificial systems which are significantly different to humans. In this paper we approach this problem in the following way: We take a number of well known informal...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	1,062
2102.12960	Deep learning based electrical noise removal enables high spectral optoacoustic contrast in deep tissue	Image contrast in multispectral optoacoustic tomography (MSOT) can be severely reduced by electrical noise and interference in the acquired optoacoustic signals. Signal processing techniques have proven insufficient to remove the effects of electrical noise because they typically rely on simplified models and fail to c...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	221,897
2306.07961	Differentiating Metropolis-Hastings to Optimize Intractable Densities	We develop an algorithm for automatic differentiation of Metropolis-Hastings samplers, allowing us to differentiate through probabilistic inference, even if the model has discrete components within it. Our approach fuses recent advances in stochastic automatic differentiation with traditional Markov chain coupling sche...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	373,220
2402.14547	OmniPred: Language Models as Universal Regressors	Regression is a powerful tool to accurately predict the outcome metric of a system given a set of parameters, but has traditionally been restricted to methods which are only applicable to a specific task. In this paper, we propose OmniPred, a framework for training language models as universal end-to-end regressors ove...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	true	false	431,734
2003.02392	PointLoc: Deep Pose Regressor for LiDAR Point Cloud Localization	In this paper, we present a novel end-to-end learning-based LiDAR relocalization framework, termed PointLoc, which infers 6-DoF poses directly using only a single point cloud as input, without requiring a pre-built map. Compared to RGB image-based relocalization, LiDAR frames can provide rich and robust geometric infor...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	166,926
1901.01686	Ten ways to fool the masses with machine learning	If you want to tell people the truth, make them laugh, otherwise they'll kill you. (source unclear) Machine learning and deep learning are the technologies of the day for developing intelligent automatic systems. However, a key hurdle for progress in the field is the literature itself: we often encounter papers that ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	118,033
2404.14942	Manipulating Recommender Systems: A Survey of Poisoning Attacks and Countermeasures	Recommender systems have become an integral part of online services to help users locate specific information in a sea of data. However, existing studies show that some recommender systems are vulnerable to poisoning attacks, particularly those that involve learning schemes. A poisoning attack is where an adversary inj...	false	false	false	false	false	true	true	false	false	false	false	false	true	false	false	false	false	false	448,874
2110.01882	Simultaneous Information and Energy Transmission with Finite Constellations	In this paper, the fundamental limits on the rates at which information and energy can be simultaneously transmitted over an additive white Gaussian noise channel are studied under the following assumptions: $(a)$ the channel is memoryless; $(b)$ the number of channel input symbols (constellation size) and block length...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	258,930
1909.08970	RUN through the Streets: A New Dataset and Baseline Models for Realistic Urban Navigation	Following navigation instructions in natural language requires a composition of language, action, and knowledge of the environment. Knowledge of the environment may be provided via visual sensors or as a symbolic world representation referred to as a map. Here we introduce the Realistic Urban Navigation (RUN) task, aim...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	146,106
2111.08634	NVIDIA NeMo Neural Machine Translation Systems for English-German and English-Russian News and Biomedical Tasks at WMT21	This paper provides an overview of NVIDIA NeMo's neural machine translation systems for the constrained data track of the WMT21 News and Biomedical Shared Translation Tasks. Our news task submissions for English-German (En-De) and English-Russian (En-Ru) are built on top of a baseline transformer-based sequence-to-sequ...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	266,765
2305.14787	Polarimetric Imaging for Perception	Autonomous driving and advanced driver-assistance systems rely on a set of sensors and algorithms to perform the appropriate actions and provide alerts as a function of the driving scene. Typically, the sensors include color cameras, radar, lidar and ultrasonic sensors. Strikingly however, although light polarization i...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	367,257
2107.03315	Predicting with Confidence on Unseen Distributions	Recent work has shown that the performance of machine learning models can vary substantially when models are evaluated on data drawn from a distribution that is close to but different from the training distribution. As a result, predicting model performance on unseen distributions is an important challenge. Our work co...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	245,121
1702.00500	AMR-to-text Generation with Synchronous Node Replacement Grammar	This paper addresses the task of AMR-to-text generation by leveraging synchronous node replacement grammar. During training, graph-to-string rules are learned using a heuristic extraction algorithm. At test time, a graph transducer is applied to collapse input AMRs and generate output sentences. Evaluated on SemEval-20...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	67,661
1705.06830	Exploring the structure of a real-time, arbitrary neural artistic stylization network	In this paper, we present a method which combines the flexibility of the neural algorithm of artistic style with the speed of fast style transfer networks to allow real-time stylization using any content/style image pair. We build upon recent work leveraging conditional instance normalization for multi-style transfer n...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	73,686
2309.01207	Spectral Adversarial MixUp for Few-Shot Unsupervised Domain Adaptation	Domain shift is a common problem in clinical applications, where the training images (source domain) and the test images (target domain) are under different distributions. Unsupervised Domain Adaptation (UDA) techniques have been proposed to adapt models trained in the source domain to the target domain. However, those...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	389,593
1907.11158	Cross-Lingual Transfer for Distantly Supervised and Low-resources Indonesian NER	Manually annotated corpora for low-resource languages are usually small in quantity (gold), or large but distantly supervised (silver). Inspired by recent progress of injecting pre-trained language model (LM) on many Natural Language Processing (NLP) task, we proposed to fine-tune pre-trained language model from high-r...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	139,789
2407.16907	Research on Education Big Data for Students Academic Performance Analysis based on Machine Learning	The application of the Internet in the field of education is becoming more and more popular, and a large amount of educational data is generated in the process. How to effectively use these data has always been a key issue in the field of educational data mining. In this work, a machine learning model based on Long Sho...	false	false	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	475,774
2412.14802	Stack Trace Deduplication: Faster, More Accurately, and in More Realistic Scenarios	In large-scale software systems, there are often no fully-fledged bug reports with human-written descriptions when an error occurs. In this case, developers rely on stack traces, i.e., series of function calls that led to the error. Since there can be tens and hundreds of thousands of them describing the same issue fro...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	true	518,862
2407.10307	Distributed Charging Coordination for Electric Trucks under Limited Facilities and Travel Uncertainties	In this work, we address the problem of charging coordination between electric trucks and charging stations. The problem arises from the tension between the trucks' nontrivial charging times and the stations' limited charging facilities. Our goal is to reduce the trucks' waiting times at the stations while minimizing i...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	472,930
2008.01928	Component Divide-and-Conquer for Real-World Image Super-Resolution	In this paper, we present a large-scale Diverse Real-world image Super-Resolution dataset, i.e., DRealSR, as well as a divide-and-conquer Super-Resolution (SR) network, exploring the utility of guiding SR model with low-level image components. DRealSR establishes a new SR benchmark with diverse real-world degradation p...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	190,471
2012.03600	Exploiting Intrinsic Kinematic Null Space for Supernumerary Robotic Limbs Control	Supernumerary robotic limbs (SRLs) gained increasing interest in the last years for their applicability as healthcare and assistive technologies. These devices can either support or augment human sensorimotor capabilities, allowing users to complete tasks that are more complex than those feasible for their natural limb...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	210,176
1804.06248	PM-GANs: Discriminative Representation Learning for Action Recognition Using Partial-modalities	Data of different modalities generally convey complimentary but heterogeneous information, and a more discriminative representation is often preferred by combining multiple data modalities like the RGB and infrared features. However in reality, obtaining both data channels is challenging due to many limitations. For ex...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	95,257
2006.13329	Bach or Mock? A Grading Function for Chorales in the Style of J.S. Bach	Deep generative systems that learn probabilistic models from a corpus of existing music do not explicitly encode knowledge of a musical style, compared to traditional rule-based systems. Thus, it can be difficult to determine whether deep models generate stylistically correct output without expert evaluation, but this ...	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	183,868
2307.11620	Offline Multi-Agent Reinforcement Learning with Implicit Global-to-Local Value Regularization	Offline reinforcement learning (RL) has received considerable attention in recent years due to its attractive capability of learning policies from offline datasets without environmental interactions. Despite some success in the single-agent setting, offline multi-agent RL (MARL) remains to be a challenge. The large joi...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	false	false	false	380,975
2007.05577	Vizarel: A System to Help Better Understand RL Agents	Visualization tools for supervised learning have allowed users to interpret, introspect, and gain intuition for the successes and failures of their models. While reinforcement learning practitioners ask many of the same questions, existing tools are not applicable to the RL setting. In this work, we describe our initia...	false	false	false	false	true	false	true	true	false	false	false	false	false	false	false	false	false	false	186,717
2004.12585	A Batch Normalized Inference Network Keeps the KL Vanishing Away	Variational Autoencoder (VAE) is widely used as a generative model to approximate a model's posterior on latent variables by combining the amortized variational inference and deep neural networks. However, when paired with strong autoregressive decoders, VAE often converges to a degenerated local optimum known as "post...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	174,293
2310.18794	Sequence-Level Certainty Reduces Hallucination In Knowledge-Grounded Dialogue Generation	In this work, we propose sequence-level certainty as a common theme over hallucination in Knowledge Grounded Dialogue Generation (KGDG). We explore the correlation between the level of hallucination in model responses and two types of sequence-level certainty: probabilistic certainty and semantic certainty. Empirical r...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	403,704
1303.7103	Decentralized Eigenvalue Algorithms for Distributed Signal Detection in Cognitive Networks	In this paper we derive and analyze two algorithms -- referred to as decentralized power method (DPM) and decentralized Lanczos algorithm (DLA) -- for distributed computation of one (the largest) or multiple eigenvalues of a sample covariance matrix over a wireless network. The proposed algorithms, based on sequential ...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	true	23,321
1304.2103	High-Throughput Cooperative Communication with Interference Cancellation for Two-Path Relay in Multi-source System	Relay-based cooperative communication has become a research focus in recent years because it can achieve diversity gain in wireless networks. In existing works, network coding and two-path relay are adopted to deal with the increase of network size and the half-duplex nature of relay, respectively. To further improve b...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	23,624
2012.12331	Real-Time Vehicular Wireless System-Level Simulation	Future automation and control units for advanced driver assistance systems (ADAS) will exchange sensor and kinematic data with nearby vehicles using wireless communication links to improve traffic safety. In this paper we present an accurate real-time system-level simulation for multi-vehicle communication scenarios to...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	212,890
1803.01384	Data Curation with Deep Learning [Vision]	Data curation - the process of discovering, integrating, and cleaning data - is one of the oldest, hardest, yet inevitable data management problems. Despite decades of efforts from both researchers and practitioners, it is still one of the most time consuming and least enjoyable work of data scientists. In most organiz...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	91,865
2306.01789	Edit Distance based RL for RNNT decoding	RNN-T is currently considered the industry standard in ASR due to its exceptional WERs in various benchmark tests and its ability to support seamless streaming and longform transcription. However, its biggest drawback lies in the significant discrepancy between its training and inference objectives. During training, RN...	false	false	true	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	370,608
1602.01731	Multi-Objective Framework for Dynamic Optimization of OFDMA Cellular Systems	Green cellular networking has become an important research area in recent years due to environmental and economical concerns. Switching off under-utilized BSs during off-peak traffic load conditions is a promising approach to reduce energy consumption in cellular networks. In practice, during initial cell planning, the...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	51,745
1910.08343	Automatic Data Augmentation by Learning the Deterministic Policy	Aiming to produce sufficient and diverse training samples, data augmentation has been demonstrated for its effectiveness in training deep models. Regarding that the criterion of the best augmentation is challenging to define, we in this paper present a novel learning-based augmentation method termed as DeepAugNet, whic...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	149,849
1910.01590	DPSOM: Deep Probabilistic Clustering with Self-Organizing Maps	Generating interpretable visualizations from complex data is a common problem in many applications. Two key ingredients for tackling this issue are clustering and representation learning. However, current methods do not yet successfully combine the strengths of these two approaches. Existing representation learning mod...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	147,979
2305.14039	Learning a Single Convolutional Layer Model for Low Light Image Enhancement	Low-light image enhancement (LLIE) aims to improve the illuminance of images due to insufficient light exposure. Recently, various lightweight learning-based LLIE methods have been proposed to handle the challenges of unfavorable prevailing low contrast, low brightness, etc. In this paper, we have streamlined the archi...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	366,824
1905.04771	Failure-Tolerant Connectivity Maintenance for Robot Swarms	Connectivity maintenance plays a key role in achieving a desired global behavior among a swarm of robots. However, connectivity maintenance in realistic environments is hampered by lack of computation resources, low communication bandwidth, robot failures, and unstable links. In this paper, we propose a novel decentral...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	130,560
2301.09077	Unleash the Potential of Image Branch for Cross-modal 3D Object Detection	To achieve reliable and precise scene understanding, autonomous vehicles typically incorporate multiple sensing modalities to capitalize on their complementary attributes. However, existing cross-modal 3D detectors do not fully utilize the image domain information to address the bottleneck issues of the LiDAR-based det...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	341,396
2203.06714	A Survey on Deep Graph Generation: Methods and Applications	Graphs are ubiquitous in encoding relational information of real-world objects in many domains. Graph generation, whose purpose is to generate new graphs from a distribution similar to the observed graphs, has received increasing attention thanks to the recent advances of deep learning models. In this paper, we conduct...	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	false	false	285,205
1410.2960	Location Spoofing Detection for VANETs by a Single Base Station in Rician Fading Channels	In this work we examine the performance of a Location Spoofing Detection System (LSDS) for vehicular networks in the realistic setting of Rician fading channels. In the LSDS, an authorized Base Station (BS) equipped with multiple antennas utilizes channel observations to identify a malicious vehicle, also equipped with...	false	false	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	36,668
1810.10096	Learning Representations in Model-Free Hierarchical Reinforcement Learning	Common approaches to Reinforcement Learning (RL) are seriously challenged by large-scale applications involving huge state spaces and sparse delayed reward feedback. Hierarchical Reinforcement Learning (HRL) methods attempt to address this scalability issue by learning action selection policies at multiple levels of te...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	111,195
2408.07791	An Efficient and Explanatory Image and Text Clustering System with Multimodal Autoencoder Architecture	We demonstrate the efficiencies and explanatory abilities of extensions to the common tools of Autoencoders and LLM interpreters, in the novel context of comparing different cultural approaches to the same international news event. We develop a new Convolutional-Recurrent Variational Autoencoder (CRVAE) model that exte...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	true	480,721
1606.03556	Human Attention in Visual Question Answering: Do Humans and Deep Networks Look at the Same Regions?	We conduct large-scale studies on `human attention' in Visual Question Answering (VQA) to understand where humans choose to look to answer questions about images. We design and test multiple game-inspired novel attention-annotation interfaces that require the subject to sharpen regions of a blurred image to answer a qu...	false	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	57,108
2312.12972	From Past to Future: Rethinking Eligibility Traces	In this paper, we introduce a fresh perspective on the challenges of credit assignment and policy evaluation. First, we delve into the nuances of eligibility traces and explore instances where their updates may result in unexpected credit assignment to preceding states. From this investigation emerges the concept of a ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	417,170
2211.11759	Learning Cooperative Oversubscription for Cloud by Chance-Constrained Multi-Agent Reinforcement Learning	Oversubscription is a common practice for improving cloud resource utilization. It allows the cloud service provider to sell more resources than the physical limit, assuming not all users would fully utilize the resources simultaneously. However, how to design an oversubscription policy that improves utilization while ...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	true	false	false	false	331,868
2402.02622	DenseFormer: Enhancing Information Flow in Transformers via Depth Weighted Averaging	The transformer architecture by Vaswani et al. (2017) is now ubiquitous across application domains, from natural language processing to speech processing and image understanding. We propose DenseFormer, a simple modification to the standard architecture that improves the perplexity of the model without increasing its s...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	426,650
2305.06936	An Option-Dependent Analysis of Regret Minimization Algorithms in Finite-Horizon Semi-Markov Decision Processes	A large variety of real-world Reinforcement Learning (RL) tasks is characterized by a complex and heterogeneous structure that makes end-to-end (or flat) approaches hardly applicable or even infeasible. Hierarchical Reinforcement Learning (HRL) provides general solutions to address these problems thanks to a convenient...	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	363,714

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