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
2010.08715	Understanding Information Processing in Human Brain by Interpreting Machine Learning Models	The thesis explores the role machine learning methods play in creating intuitive computational models of neural processing. Combined with interpretability techniques, machine learning could replace human modeler and shift the focus of human effort to extracting the knowledge from the ready-made models and articulating ...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	201,278
2403.03848	Dexterous Legged Locomotion in Confined 3D Spaces with Reinforcement Learning	Recent advances of locomotion controllers utilizing deep reinforcement learning (RL) have yielded impressive results in terms of achieving rapid and robust locomotion across challenging terrain, such as rugged rocks, non-rigid ground, and slippery surfaces. However, while these controllers primarily address challenges ...	false	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	435,357
2308.04005	Few-shot medical image classification with simple shape and texture text descriptors using vision-language models	In this work, we investigate the usefulness of vision-language models (VLMs) and large language models for binary few-shot classification of medical images. We utilize the GPT-4 model to generate text descriptors that encapsulate the shape and texture characteristics of objects in medical images. Subsequently, these GP...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	384,246
2109.11225	ChannelAugment: Improving generalization of multi-channel ASR by training with input channel randomization	End-to-end (E2E) multi-channel ASR systems show state-of-the-art performance in far-field ASR tasks by joint training of a multi-channel front-end along with the ASR model. The main limitation of such systems is that they are usually trained with data from a fixed array geometry, which can lead to degradation in accura...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	256,876
1408.7094	Improving the Effectiveness of Content Popularity Prediction Methods using Time Series Trends	We here present a simple and effective model to predict the popularity of web content. Our solution, which is the winner of two of the three tasks of the ECML/PKDD 2014 Predictive Analytics Challenge, aims at predicting user engagement metrics, such as number of visits and social network engagement, that a web page wil...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	35,693
2008.12260	Pollux: Co-adaptive Cluster Scheduling for Goodput-Optimized Deep Learning	Pollux improves scheduling performance in deep learning (DL) clusters by adaptively co-optimizing inter-dependent factors both at the per-job level and at the cluster-wide level. Most existing schedulers expect users to specify the number of resources for each job, often leading to inefficient resource use. Some recent...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	193,526
1708.02443	An Effective Feature Selection Method Based on Pair-Wise Feature Proximity for High Dimensional Low Sample Size Data	Feature selection has been studied widely in the literature. However, the efficacy of the selection criteria for low sample size applications is neglected in most cases. Most of the existing feature selection criteria are based on the sample similarity. However, the distance measures become insignificant for high dimen...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	78,590
2009.01884	Model extraction from counterfactual explanations	Post-hoc explanation techniques refer to a posteriori methods that can be used to explain how black-box machine learning models produce their outcomes. Among post-hoc explanation techniques, counterfactual explanations are becoming one of the most popular methods to achieve this objective. In particular, in addition to...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	194,411
2009.14502	Stochastic Precision Ensemble: Self-Knowledge Distillation for Quantized Deep Neural Networks	The quantization of deep neural networks (QDNNs) has been actively studied for deployment in edge devices. Recent studies employ the knowledge distillation (KD) method to improve the performance of quantized networks. In this study, we propose stochastic precision ensemble training for QDNNs (SPEQ). SPEQ is a knowledge...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	198,057
2005.06613	A framework for probabilistic weather forecast post-processing across models and lead times using machine learning	Forecasting the weather is an increasingly data intensive exercise. Numerical Weather Prediction (NWP) models are becoming more complex, with higher resolutions, and there are increasing numbers of different models in operation. While the forecasting skill of NWP models continues to improve, the number and complexity o...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	177,052
2103.16566	An Integrated Mechanical Intelligence and Control Approach Towards Flight Control of Aerobat	Our goal in this work is to expand the theory and practice of robot locomotion by addressing critical challenges associated with the robotic biomimicry of bat aerial locomotion. Bats are known for their pronounced, fast wing articulations, e.g., bats can mobilize as many as forty joints during a single wingbeat, with s...	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	227,630
1812.07692	Fast Exact Computation of Expected HyperVolume Improvement	In multi-objective Bayesian optimization and surrogate-based evolutionary algorithms, Expected HyperVolume Improvement (EHVI) is widely used as the acquisition function to guide the search approaching the Pareto front. This paper focuses on the exact calculation of EHVI given a nondominated set, for which the existing ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	116,859
0912.1023	Efficient Relay Beamforming Design with SIC Detection for Dual-Hop MIMO Relay Networks	In this paper, we consider a dual-hop Multiple Input Multiple Output (MIMO) relay wireless network, in which a source-destination pair both equipped with multiple antennas communicates through a large number of half-duplex amplify-and-forward (AF) relay terminals. Two novel linear beamforming schemes based on the match...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	5,102
2202.12524	MAMDR: A Model Agnostic Learning Method for Multi-Domain Recommendation	Large-scale e-commercial platforms in the real-world usually contain various recommendation scenarios (domains) to meet demands of diverse customer groups. Multi-Domain Recommendation (MDR), which aims to jointly improve recommendations on all domains and easily scales to thousands of domains, has attracted increasing ...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	282,275
1311.6838	Learning Prices for Repeated Auctions with Strategic Buyers	Inspired by real-time ad exchanges for online display advertising, we consider the problem of inferring a buyer's value distribution for a good when the buyer is repeatedly interacting with a seller through a posted-price mechanism. We model the buyer as a strategic agent, whose goal is to maximize her long-term surplu...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	28,690
2307.02991	ContainerGym: A Real-World Reinforcement Learning Benchmark for Resource Allocation	We present ContainerGym, a benchmark for reinforcement learning inspired by a real-world industrial resource allocation task. The proposed benchmark encodes a range of challenges commonly encountered in real-world sequential decision making problems, such as uncertainty. It can be configured to instantiate problems of ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	377,887
1308.2923	Robotic Message Ferrying for Wireless Networks using Coarse-Grained Backpressure Control	We formulate the problem of robots ferrying messages between statically-placed source and sink pairs that they can communicate with wirelessly. We first analyze the capacity region for this problem under both ideal (arbitrarily high velocity, long scheduling periods) and realistic conditions. We indicate how robots cou...	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	true	26,419
2406.13227	Controllable and Gradual Facial Blemishes Retouching via Physics-Based Modelling	Face retouching aims to remove facial blemishes, such as pigmentation and acne, and still retain fine-grain texture details. Nevertheless, existing methods just remove the blemishes but focus little on realism of the intermediate process, limiting their use more to beautifying facial images on social media rather than ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	465,758
2407.00120	Automated Web-Based Malaria Detection System with Machine Learning and Deep Learning Techniques	Malaria parasites pose a significant global health burden, causing widespread suffering and mortality. Detecting malaria infection accurately is crucial for effective treatment and control. However, existing automated detection techniques have shown limitations in terms of accuracy and generalizability. Many studies ha...	false	false	false	false	true	false	true	false	false	false	false	true	false	false	false	false	false	false	468,736
2109.09906	Audio Interval Retrieval using Convolutional Neural Networks	Modern streaming services are increasingly labeling videos based on their visual or audio content. This typically augments the use of technologies such as AI and ML by allowing to use natural speech for searching by keywords and video descriptions. Prior research has successfully provided a number of solutions for spee...	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	256,441
2203.11639	Learning Relation-Specific Representations for Few-shot Knowledge Graph Completion	Recent years have witnessed increasing interest in few-shot knowledge graph completion (FKGC), which aims to infer unseen query triples for a few-shot relation using a few reference triples about the relation. The primary focus of existing FKGC methods lies in learning relation representations that can reflect the comm...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	286,985
2405.02485	A Survey of Few-Shot Learning for Biomedical Time Series	Advancements in wearable sensor technologies and the digitization of medical records have contributed to the unprecedented ubiquity of biomedical time series data. Data-driven models have tremendous potential to assist clinical diagnosis and improve patient care by improving long-term monitoring capabilities, facilitat...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	451,779
1604.03034	M3: Scaling Up Machine Learning via Memory Mapping	To process data that do not fit in RAM, conventional wisdom would suggest using distributed approaches. However, recent research has demonstrated virtual memory's strong potential in scaling up graph mining algorithms on a single machine. We propose to use a similar approach for general machine learning. We contribute:...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	54,429
2306.10833	PTDRL: Parameter Tuning using Deep Reinforcement Learning	A variety of autonomous navigation algorithms exist that allow robots to move around in a safe and fast manner. However, many of these algorithms require parameter re-tuning when facing new environments. In this paper, we propose PTDRL, a parameter-tuning strategy that adaptively selects from a fixed set of parameters ...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	374,385
2407.08865	Single-Image Shadow Removal Using Deep Learning: A Comprehensive Survey	Shadow removal aims at restoring the image content within shadow regions, pursuing a uniform distribution of illumination that is consistent between shadow and non-shadow regions. {Comparing to other image restoration tasks, there are two unique challenges in shadow removal:} 1) The patterns of shadows are arbitrary, v...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	472,335
1704.01285	Smart Mining for Deep Metric Learning	To solve deep metric learning problems and producing feature embeddings, current methodologies will commonly use a triplet model to minimise the relative distance between samples from the same class and maximise the relative distance between samples from different classes. Though successful, the training convergence of...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	71,234
2206.12967	RF Signal Classification with Synthetic Training Data and its Real-World Performance	Neural nets are a powerful method for the classification of radio signals in the electromagnetic spectrum. These neural nets are often trained with synthetically generated data due to the lack of diverse and plentiful real RF data. However, it is often unclear how neural nets trained on synthetic data perform in real-w...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	304,797
2109.14648	A Study of Feature Selection and Extraction Algorithms for Cancer Subtype Prediction	In this work, we study and analyze different feature selection algorithms that can be used to classify cancer subtypes in case of highly varying high-dimensional data. We apply three different feature selection methods on five different types of cancers having two separate omics each. We show that the existing feature ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	258,013
2211.15931	Posterior Sampling for Continuing Environments	We develop an extension of posterior sampling for reinforcement learning (PSRL) that is suited for a continuing agent-environment interface and integrates naturally into agent designs that scale to complex environments. The approach, continuing PSRL, maintains a statistically plausible model of the environment and foll...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	333,452
2110.09109	Patch-Based Deep Autoencoder for Point Cloud Geometry Compression	The ever-increasing 3D application makes the point cloud compression unprecedentedly important and needed. In this paper, we propose a patch-based compression process using deep learning, focusing on the lossy point cloud geometry compression. Unlike existing point cloud compression networks, which apply feature extrac...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	261,681
2210.00327	Deep Recurrent Q-learning for Energy-constrained Coverage with a Mobile Robot	In this paper, we study the problem of coverage of an environment with an energy-constrained robot in the presence of multiple charging stations. As the robot's on-board power supply is limited, it might not have enough energy to cover all the points in the environment with a single charge. Instead, it will need to sto...	false	false	false	false	false	false	true	true	false	false	false	false	false	false	false	true	false	false	320,826
2501.12528	Improved Coded Caching Scheme for Multi-User Information Retrieval System	In this paper, we study the coded caching scheme for the $(L, K, M, N)$ multi-user information retrieval (MIR) system, which consists of a content library containing $N$ files, a base station (BS) with $L$ antennas that cannot access the library, and $K$ single-antenna users, each of which can cache at most $M$ files f...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	526,345
2406.13871	Robust Time Series Forecasting with Non-Heavy-Tailed Gaussian Loss-Weighted Sampler	Forecasting multivariate time series is a computationally intensive task challenged by extreme or redundant samples. Recent resampling methods aim to increase training efficiency by reweighting samples based on their running losses. However, these methods do not solve the problems caused by heavy-tailed distribution lo...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	466,034
1307.1568	Using MathML to Represent Units of Measurement for Improved Ontology Alignment	Ontologies provide a formal description of concepts and their relationships in a knowledge domain. The goal of ontology alignment is to identify semantically matching concepts and relationships across independently developed ontologies that purport to describe the same knowledge. In order to handle the widest possible ...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	25,642
2001.01826	First-Order Algorithms for Constrained Nonlinear Dynamic Games	This paper presents algorithms for non-zero sum nonlinear constrained dynamic games with full information. Such problems emerge when multiple players with action constraints and differing objectives interact with the same dynamic system. They model a wide range of applications including economics, defense, and energy s...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	159,584
2307.03637	Discovering Variable Binding Circuitry with Desiderata	Recent work has shown that computation in language models may be human-understandable, with successful efforts to localize and intervene on both single-unit features and input-output circuits. Here, we introduce an approach which extends causal mediation experiments to automatically identify model components responsibl...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	378,104
2304.04366	Learning Residual Model of Model Predictive Control via Random Forests for Autonomous Driving	One major issue in learning-based model predictive control (MPC) for autonomous driving is the contradiction between the system model's prediction accuracy and computation efficiency. The more situations a system model covers, the more complex it is, along with highly nonlinear and nonconvex properties. These issues ma...	false	false	false	false	false	false	true	true	false	false	true	false	false	false	false	false	false	false	357,198
1901.08949	Subspace Robust Wasserstein Distances	Making sense of Wasserstein distances between discrete measures in high-dimensional settings remains a challenge. Recent work has advocated a two-step approach to improve robustness and facilitate the computation of optimal transport, using for instance projections on random real lines, or a preliminary quantization of...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	119,609
2406.19670	Function+Data Flow: A Framework to Specify Machine Learning Pipelines for Digital Twinning	The development of digital twins (DTs) for physical systems increasingly leverages artificial intelligence (AI), particularly for combining data from different sources or for creating computationally efficient, reduced-dimension models. Indeed, even in very different application domains, twinning employs common techniq...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	true	468,521
2412.18826	RapGuard: Safeguarding Multimodal Large Language Models via Rationale-aware Defensive Prompting	While Multimodal Large Language Models (MLLMs) have made remarkable progress in vision-language reasoning, they are also more susceptible to producing harmful content compared to models that focus solely on text. Existing defensive prompting techniques rely on a static, unified safety guideline that fails to account fo...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	520,590
1402.4031	Estimation with Strategic Sensors	We introduce a model of estimation in the presence of strategic, self-interested sensors. We employ a game-theoretic setup to model the interaction between the sensors and the receiver. The cost function of the receiver is equal to the estimation error variance while the cost function of the sensor contains an extra te...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	true	30,925
1005.2646	An Algebraic Approach to Physical-Layer Network Coding	The problem of designing new physical-layer network coding (PNC) schemes via lattice partitions is considered. Building on a recent work by Nazer and Gastpar, who demonstrated its asymptotic gain using information-theoretic tools, we take an algebraic approach to show its potential in non-asymptotic settings. We first ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	6,491
2306.12970	A Survey of Link Prediction Algorithms	The problem of link prediction, predicting if two nodes in a network have a connection between them, is a theoretical problem with numerous field-agnostic real-world applications. This paper investigates the efficacy of three classes of link prediction algorithms: local node similarity heuristics, the global index Rand...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	375,119
2310.08187	Visual Question Generation in Bengali	The task of Visual Question Generation (VQG) is to generate human-like questions relevant to the given image. As VQG is an emerging research field, existing works tend to focus only on resource-rich language such as English due to the availability of datasets. In this paper, we propose the first Bengali Visual Question...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	399,302
2011.10007	Multi-Plane Program Induction with 3D Box Priors	We consider two important aspects in understanding and editing images: modeling regular, program-like texture or patterns in 2D planes, and 3D posing of these planes in the scene. Unlike prior work on image-based program synthesis, which assumes the image contains a single visible 2D plane, we present Box Program Induc...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	207,390
2004.11233	QUANOS- Adversarial Noise Sensitivity Driven Hybrid Quantization of Neural Networks	Deep Neural Networks (DNNs) have been shown to be vulnerable to adversarial attacks, wherein, a model gets fooled by applying slight perturbations on the input. With the advent of Internet-of-Things and the necessity to enable intelligence in embedded devices, low-power and secure hardware implementation of DNNs is vit...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	173,856
2305.15262	Revisiting Parallel Context Windows: A Frustratingly Simple Alternative and Chain-of-Thought Deterioration	We identify two crucial limitations in the evaluation of recent parallel-integrated method Parallel Context Windows (PCW), which extends the maximum context lengths of language models, e.g., 2048 for LLaMA, by harnessing window-wise attention and positional embedding techniques. We first show that a simple yet strong b...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	367,534
1905.12762	Securing Connected & Autonomous Vehicles: Challenges Posed by Adversarial Machine Learning and The Way Forward	Connected and autonomous vehicles (CAVs) will form the backbone of future next-generation intelligent transportation systems (ITS) providing travel comfort, road safety, along with a number of value-added services. Such a transformation---which will be fuelled by concomitant advances in technologies for machine learnin...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	132,866
2502.03424	Prediction of the Most Fire-Sensitive Point in Building Structures with Differentiable Agents for Thermal Simulators	Fire safety is a critical area of research in civil and mechanical engineering, particularly in ensuring the structural stability of buildings during fire events. The Most Fire-Sensitive Point (MFSP) in a structure is the location where a fire would cause the greatest impact on structural stability. Accurate prediction...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	530,721
1412.7287	On Non-Integer Linear Degrees of Freedom of Constant Two-Cell MIMO Cellular Networks	The study of degrees of freedom (DoF) of multiuser channels has led to the development of important interference managing schemes, such as interference alignment (IA) and interference neutralization. However, while the integer DoF have been widely studied in literatures, non-integer DoF are much less addressed, especia...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	38,794
2004.05234	Attend and Decode: 4D fMRI Task State Decoding Using Attention Models	Functional magnetic resonance imaging (fMRI) is a neuroimaging modality that captures the blood oxygen level in a subject's brain while the subject either rests or performs a variety of functional tasks under different conditions. Given fMRI data, the problem of inferring the task, known as task state decoding, is chal...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	172,124
1506.06981	R-CNN minus R	Deep convolutional neural networks (CNNs) have had a major impact in most areas of image understanding, including object category detection. In object detection, methods such as R-CNN have obtained excellent results by integrating CNNs with region proposal generation algorithms such as selective search. In this paper, ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	44,469
2311.09782	More Samples or More Prompts? Exploring Effective In-Context Sampling for LLM Few-Shot Prompt Engineering	While most existing works on LLM prompting techniques focus only on how to select a better set of data samples inside one single prompt input (In-Context Learning or ICL), why can not we design and leverage multiple prompts together to further improve the LLM's performance? In this work, we propose In-Context Sampling ...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	408,293
2406.09021	Contextual Distillation Model for Diversified Recommendation	The diversity of recommendation is equally crucial as accuracy in improving user experience. Existing studies, e.g., Determinantal Point Process (DPP) and Maximal Marginal Relevance (MMR), employ a greedy paradigm to iteratively select items that optimize both accuracy and diversity. However, prior methods typically ex...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	463,728
2310.20280	AutoMixer for Improved Multivariate Time-Series Forecasting on Business and IT Observability Data	The efficiency of business processes relies on business key performance indicators (Biz-KPIs), that can be negatively impacted by IT failures. Business and IT Observability (BizITObs) data fuses both Biz-KPIs and IT event channels together as multivariate time series data. Forecasting Biz-KPIs in advance can enhance ef...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	404,330
2309.10011	Universal Photorealistic Style Transfer: A Lightweight and Adaptive Approach	Photorealistic style transfer aims to apply stylization while preserving the realism and structure of input content. However, existing methods often encounter challenges such as color tone distortions, dependency on pair-wise pre-training, inefficiency with high-resolution inputs, and the need for additional constraint...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	392,834
2409.14850	GroCo: Ground Constraint for Metric Self-Supervised Monocular Depth	Monocular depth estimation has greatly improved in the recent years but models predicting metric depth still struggle to generalize across diverse camera poses and datasets. While recent supervised methods mitigate this issue by leveraging ground prior information at inference, their adaptability to self-supervised set...	false	false	false	false	true	false	true	true	false	false	false	true	false	false	false	false	false	false	490,661
2410.17758	Escaping the Forest: Sparse Interpretable Neural Networks for Tabular Data	Tabular datasets are widely used in scientific disciplines such as biology. While these disciplines have already adopted AI methods to enhance their findings and analysis, they mainly use tree-based methods due to their interpretability. At the same time, artificial neural networks have been shown to offer superior fle...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	501,602
2107.07058	A Generalized Framework for Edge-preserving and Structure-preserving Image Smoothing	Image smoothing is a fundamental procedure in applications of both computer vision and graphics. The required smoothing properties can be different or even contradictive among different tasks. Nevertheless, the inherent smoothing nature of one smoothing operator is usually fixed and thus cannot meet the various require...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	true	246,295
1701.04888	The connectivity of graphs of graphs with self-loops and a given degree sequence	`Double edge swaps' transform one graph into another while preserving the graph's degree sequence, and have thus been used in a number of popular Markov chain Monte Carlo (MCMC) sampling techniques. However, while double edge-swaps can transform, for any fixed degree sequence, any two graphs inside the classes of simpl...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	66,901
2301.13801	Cultural Differences in Friendship Network Behaviors: A Snapchat Case Study	Culture shapes people's behavior, both online and offline. Surprisingly, there is sparse research on how cultural context affects network formation and content consumption on social media. We analyzed the friendship networks and dyadic relations between content producers and consumers across 73 countries through a cult...	false	false	false	true	false	false	false	false	false	false	false	false	false	true	false	false	false	false	343,035
2103.06063	On spatial variation in the detectability and density of social media user protest supporters	Although much has been published regarding street protests on social media, few works have attempted to characterize social media users' spatial behavior in such events. The research reported here uses spatial capture-recapture methods to determine the influence of the built environment, physical proximity to protest l...	true	false	false	true	false	false	false	false	false	false	false	false	false	true	false	false	false	false	224,176
2412.10380	Challenges in Human-Agent Communication	Remarkable advancements in modern generative foundation models have enabled the development of sophisticated and highly capable autonomous agents that can observe their environment, invoke tools, and communicate with other agents to solve problems. Although such agents can communicate with users through natural languag...	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	516,892
2407.06044	Data-driven input-to-state stabilization	For the class of nonlinear input-affine systems with polynomial dynamics, we consider the problem of designing an input-to-state stabilizing controller with respect to typical exogenous signals in a feedback control system, such as actuator and process disturbances. We address this problem in a data-based setting when ...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	471,225
2206.09107	Tree-Guided Rare Feature Selection and Logic Aggregation with Electronic Health Records Data	Statistical learning with a large number of rare binary features is commonly encountered in analyzing electronic health records (EHR) data, especially in the modeling of disease onset with prior medical diagnoses and procedures. Dealing with the resulting highly sparse and large-scale binary feature matrix is notorious...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	303,432
2209.02160	Improving Assistive Robotics with Deep Reinforcement Learning	Assistive Robotics is a class of robotics concerned with aiding humans in daily care tasks that they may be inhibited from doing due to disabilities or age. While research has demonstrated that classical control methods can be used to design policies to complete these tasks, these methods can be difficult to generalize...	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	false	316,119
2209.08879	Scalable data storage for PV monitoring systems	Efficient PV research which includes a prolonged data monitoring from multiple experiments with different characteristics, requires a scalable supporting system to handle all of the collected information. This paper presents the development of a relational database for hosting all the necessary information for data mod...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	318,300
2410.13911	GraspDiffusion: Synthesizing Realistic Whole-body Hand-Object Interaction	Recent generative models can synthesize high-quality images but often fail to generate humans interacting with objects using their hands. This arises mostly from the model's misunderstanding of such interactions, and the hardships of synthesizing intricate regions of the body. In this paper, we propose GraspDiffusion, ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	499,771
1802.01073	Weighted Hamming Metric Structures	A weighted Hamming metric is introduced in [4] and it showed that the binary generalized Goppa code is a perfect code in some weighted Hamming metric. In this paper, we study the weight structures which admit the binary Hamming code and the extended binary Hamming code to be perfect codes in the weighted Hamming metric...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	89,535
1909.12233	Deep Ensemble Learning for News Stance Detection	Stance detection in fake news is an important component in news veracity assessment because this process helps fact-checking by understanding stance to a central claim from different information sources. The Fake News Challenge Stage 1 (FNC-1) held in 2017 was setup for this purpose, which involves estimating the stanc...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	147,068
2303.10087	Neural Refinement for Absolute Pose Regression with Feature Synthesis	Absolute Pose Regression (APR) methods use deep neural networks to directly regress camera poses from RGB images. However, the predominant APR architectures only rely on 2D operations during inference, resulting in limited accuracy of pose estimation due to the lack of 3D geometry constraints or priors. In this work, w...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	352,301
2103.03373	Neural model robustness for skill routing in large-scale conversational AI systems: A design choice exploration	Current state-of-the-art large-scale conversational AI or intelligent digital assistant systems in industry comprises a set of components such as Automatic Speech Recognition (ASR) and Natural Language Understanding (NLU). For some of these systems that leverage a shared NLU ontology (e.g., a centralized intent/slot sc...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	223,245
1912.05492	Neural-Symbolic Descriptive Action Model from Images: The Search for STRIPS	Recent work on Neural-Symbolic systems that learn the discrete planning model from images has opened a promising direction for expanding the scope of Automated Planning and Scheduling to the raw, noisy data. However, previous work only partially addressed this problem, utilizing the black-box neural model as the succes...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	157,118
2305.00379	Image Completion via Dual-path Cooperative Filtering	Given the recent advances with image-generating algorithms, deep image completion methods have made significant progress. However, state-of-art methods typically provide poor cross-scene generalization, and generated masked areas often contain blurry artifacts. Predictive filtering is a method for restoring images, whi...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	361,315
2106.10796	CD-SGD: Distributed Stochastic Gradient Descent with Compression and Delay Compensation	Communication overhead is the key challenge for distributed training. Gradient compression is a widely used approach to reduce communication traffic. When combining with parallel communication mechanism method like pipeline, gradient compression technique can greatly alleviate the impact of communication overhead. Howe...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	242,161
2312.09844	Small Dataset, Big Gains: Enhancing Reinforcement Learning by Offline Pre-Training with Model Based Augmentation	Offline reinforcement learning leverages pre-collected datasets of transitions to train policies. It can serve as effective initialization for online algorithms, enhancing sample efficiency and speeding up convergence. However, when such datasets are limited in size and quality, offline pre-training can produce sub-opt...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	415,902
2407.04688	Enhancing Vehicle Re-identification and Matching for Weaving Analysis	Vehicle weaving on highways contributes to traffic congestion, raises safety issues, and underscores the need for sophisticated traffic management systems. Current tools are inadequate in offering precise and comprehensive data on lane-specific weaving patterns. This paper introduces an innovative method for collecting...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	470,656
1808.09111	Unsupervised Learning of Syntactic Structure with Invertible Neural Projections	Unsupervised learning of syntactic structure is typically performed using generative models with discrete latent variables and multinomial parameters. In most cases, these models have not leveraged continuous word representations. In this work, we propose a novel generative model that jointly learns discrete syntactic ...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	106,114
2102.11462	An Interaction-aware Evaluation Method for Highly Automated Vehicles	It is important to build a rigorous verification and validation (V&V) process to evaluate the safety of highly automated vehicles (HAVs) before their wide deployment on public roads. In this paper, we propose an interaction-aware framework for HAV safety evaluation which is suitable for some highly-interactive driving ...	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	221,426
2111.02599	Leveraging Time Irreversibility with Order-Contrastive Pre-training	Label-scarce, high-dimensional domains such as healthcare present a challenge for modern machine learning techniques. To overcome the difficulties posed by a lack of labeled data, we explore an "order-contrastive" method for self-supervised pre-training on longitudinal data. We sample pairs of time segments, switch the...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	264,916
1312.3989	Classifiers With a Reject Option for Early Time-Series Classification	Early classification of time-series data in a dynamic environment is a challenging problem of great importance in signal processing. This paper proposes a classifier architecture with a reject option capable of online decision making without the need to wait for the entire time series signal to be present. The main ide...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	29,087
2402.14694	A Quick Introduction to Quantum Machine Learning for Non-Practitioners	This paper provides an introduction to quantum machine learning, exploring the potential benefits of using quantum computing principles and algorithms that may improve upon classical machine learning approaches. Quantum computing utilizes particles governed by quantum mechanics for computational purposes, leveraging pr...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	431,791
2106.02639	Singular Dynamic Mode Decompositions	This manuscript is aimed at addressing several long standing limitations of dynamic mode decompositions in the application of Koopman analysis. Principle among these limitations are the convergence of associated Dynamic Mode Decomposition algorithms and the existence of Koopman modes. To address these limitations, two ...	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	true	238,958
2305.07290	The 3rd Anti-UAV Workshop & Challenge: Methods and Results	The 3rd Anti-UAV Workshop & Challenge aims to encourage research in developing novel and accurate methods for multi-scale object tracking. The Anti-UAV dataset used for the Anti-UAV Challenge has been publicly released. There are two main differences between this year's competition and the previous two. First, we have ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	363,840
2312.09029	Some points of view on Grothendieck's inequalities	Haagerup's proof of the non commutative little Grothendieck inequality raises some questions on the commutative little inequality, and it offers a new result on scalar matrices with non negative entries. The theory of completely bounded maps implies that the commutative Grothendieck inequality follows from the little c...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	415,571
1808.06394	Faster Support Vector Machines	The time complexity of support vector machines (SVMs) prohibits training on huge data sets with millions of data points. Recently, multilevel approaches to train SVMs have been developed to allow for time-efficient training on huge data sets. While regular SVMs perform the entire training in one -- time consuming -- op...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	105,524
2406.19236	Human-Aware Vision-and-Language Navigation: Bridging Simulation to Reality with Dynamic Human Interactions	Vision-and-Language Navigation (VLN) aims to develop embodied agents that navigate based on human instructions. However, current VLN frameworks often rely on static environments and optimal expert supervision, limiting their real-world applicability. To address this, we introduce Human-Aware Vision-and-Language Navigat...	false	false	false	false	true	false	false	true	false	false	false	true	false	false	false	false	false	false	468,342
1801.10292	On the Optimal Recovery Threshold of Coded Matrix Multiplication	We provide novel coded computation strategies for distributed matrix-matrix products that outperform the recent "Polynomial code" constructions in recovery threshold, i.e., the required number of successful workers. When $m$-th fraction of each matrix can be stored in each worker node, Polynomial codes require $m^2$ su...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	89,260
2210.12573	An Efficient Nonlinear Acceleration method that Exploits Symmetry of the Hessian	Nonlinear acceleration methods are powerful techniques to speed up fixed-point iterations. However, many acceleration methods require storing a large number of previous iterates and this can become impractical if computational resources are limited. In this paper, we propose a nonlinear Truncated Generalized Conjugate ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	325,801
2405.01273	Towards Inclusive Face Recognition Through Synthetic Ethnicity Alteration	Numerous studies have shown that existing Face Recognition Systems (FRS), including commercial ones, often exhibit biases toward certain ethnicities due to under-represented data. In this work, we explore ethnicity alteration and skin tone modification using synthetic face image generation methods to increase the diver...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	451,296
2004.14107	Informative Scene Decomposition for Crowd Analysis, Comparison and Simulation Guidance	Crowd simulation is a central topic in several fields including graphics. To achieve high-fidelity simulations, data has been increasingly relied upon for analysis and simulation guidance. However, the information in real-world data is often noisy, mixed and unstructured, making it difficult for effective analysis, the...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	true	false	false	true	174,778
2203.16067	Decision-Focused Learning without Differentiable Optimization: Learning Locally Optimized Decision Losses	Decision-Focused Learning (DFL) is a paradigm for tailoring a predictive model to a downstream optimization task that uses its predictions in order to perform better on that specific task. The main technical challenge associated with DFL is that it requires being able to differentiate through the optimization problem, ...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	288,641
1807.04188	A Hardware-Software Blueprint for Flexible Deep Learning Specialization	Specialized Deep Learning (DL) acceleration stacks, designed for a specific set of frameworks, model architectures, operators, and data types, offer the allure of high performance while sacrificing flexibility. Changes in algorithms, models, operators, or numerical systems threaten the viability of specialized hardware...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	102,685
2403.00315	Axe the X in XAI: A Plea for Understandable AI	In a recent paper, Erasmus et al. (2021) defend the idea that the ambiguity of the term "explanation" in explainable AI (XAI) can be solved by adopting any of four different extant accounts of explanation in the philosophy of science: the Deductive Nomological, Inductive Statistical, Causal Mechanical, and New Mechanis...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	433,938
2410.11687	State-space models can learn in-context by gradient descent	Deep state-space models (Deep SSMs) are becoming popular as effective approaches to model sequence data. They have also been shown to be capable of in-context learning, much like transformers. However, a complete picture of how SSMs might be able to do in-context learning has been missing. In this study, we provide a d...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	true	false	false	498,673
2307.06385	Temporal Label-Refinement for Weakly-Supervised Audio-Visual Event Localization	Audio-Visual Event Localization (AVEL) is the task of temporally localizing and classifying \emph{audio-visual events}, i.e., events simultaneously visible and audible in a video. In this paper, we solve AVEL in a weakly-supervised setting, where only video-level event labels (their presence/absence, but not their loca...	false	false	true	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	379,050
0905.3347	Information Distance in Multiples	Information distance is a parameter-free similarity measure based on compression, used in pattern recognition, data mining, phylogeny, clustering, and classification. The notion of information distance is extended from pairs to multiples (finite lists). We study maximal overlap, metricity, universality, minimal overlap...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	3,732
1809.07032	Optimal Deployment of Drone Base Stations for Cellular Communication by Network-based Localization	Drone base stations can assist cellular networks in a variety of scenarios. To serve the maximum number of users in an area without apriori user distribution information, we proposed a two-stage algorithm to find the optimal deployment of drone base stations. The algorithm involves UTDOA positioning, coverage control a...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	108,193
1806.03816	Adaptive MCMC via Combining Local Samplers	Markov chain Monte Carlo (MCMC) methods are widely used in machine learning. One of the major problems with MCMC is the question of how to design chains that mix fast over the whole state space; in particular, how to select the parameters of an MCMC algorithm. Here we take a different approach and, similarly to paralle...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	100,094
1412.7028	Joint RNN-Based Greedy Parsing and Word Composition	This paper introduces a greedy parser based on neural networks, which leverages a new compositional sub-tree representation. The greedy parser and the compositional procedure are jointly trained, and tightly depends on each-other. The composition procedure outputs a vector representation which summarizes syntactically ...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	true	false	false	38,755

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