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
2209.09486	Self-supervised 3D Object Detection from Monocular Pseudo-LiDAR	There have been attempts to detect 3D objects by fusion of stereo camera images and LiDAR sensor data or using LiDAR for pre-training and only monocular images for testing, but there have been less attempts to use only monocular image sequences due to low accuracy. In addition, when depth prediction using only monocula...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	318,531
2202.05922	Deep Signatures -- Learning Invariants of Planar Curves	We propose a learning paradigm for numerical approximation of differential invariants of planar curves. Deep neural-networks' (DNNs) universal approximation properties are utilized to estimate geometric measures. The proposed framework is shown to be a preferable alternative to axiomatic constructions. Specifically, we...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	280,027
2412.07017	Asynchronous LLM Function Calling	Large language models (LLMs) use function calls to interface with external tools and data source. However, the current approach to LLM function calling is inherently synchronous, where each call blocks LLM inference, limiting LLM operation and concurrent function execution. In this work, we propose AsyncLM, a system fo...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	515,474
2312.09578	Self-Supervised Learning for Anomalous Sound Detection	State-of-the-art anomalous sound detection (ASD) systems are often trained by using an auxiliary classification task to learn an embedding space. Doing so enables the system to learn embeddings that are robust to noise and are ignoring non-target sound events but requires manually annotated meta information to be used ...	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	415,794
2106.00687	Online Detection of Vibration Anomalies Using Balanced Spiking Neural Networks	Vibration patterns yield valuable information about the health state of a running machine, which is commonly exploited in predictive maintenance tasks for large industrial systems. However, the overhead, in terms of size, complexity and power budget, required by classical methods to exploit this information is often pr...	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	true	false	false	238,217
1402.5881	Filter Bank Multicarrier for Massive MIMO	This paper introduces filter bank multicarrier (FBMC) as a potential candidate in the application of massive MIMO communication. It also points out the advantages of FBMC over OFDM (orthogonal frequency division multiplexing) in the application of massive MIMO. The absence of cyclic prefix in FBMC increases the bandwid...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	31,126
2109.13059	Trans-Encoder: Unsupervised sentence-pair modelling through self- and mutual-distillations	In NLP, a large volume of tasks involve pairwise comparison between two sequences (e.g. sentence similarity and paraphrase identification). Predominantly, two formulations are used for sentence-pair tasks: bi-encoders and cross-encoders. Bi-encoders produce fixed-dimensional sentence representations and are computation...	false	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	257,506
2105.14450	Maximizing Parallelism in Distributed Training for Huge Neural Networks	The recent Natural Language Processing techniques have been refreshing the state-of-the-art performance at an incredible speed. Training huge language models is therefore an imperative demand in both industry and academy. However, huge language models impose challenges to both hardware and software. Graphical processin...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	237,677
1209.5991	Subset Selection for Gaussian Markov Random Fields	Given a Gaussian Markov random field, we consider the problem of selecting a subset of variables to observe which minimizes the total expected squared prediction error of the unobserved variables. We first show that finding an exact solution is NP-hard even for a restricted class of Gaussian Markov random fields, calle...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	18,781
2411.02095	The evolution of volumetric video: A survey of smart transcoding and compression approaches	Volumetric video, the capture and display of three-dimensional (3D) imagery, has emerged as a revolutionary technology poised to transform the media landscape, enabling immersive experiences that transcend the limitations of traditional 2D video. One of the key challenges in this domain is the efficient delivery of the...	true	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	505,349
1611.06436	Geometrically exact beam elements and smooth contact schemes for the modeling of fiber-based materials and structures	Recently, the authors have proposed a novel all-angle beam contact (ABC) formulation that combines the advantages of existing point and line contact models in a variationally consistent manner. However, the ABC formulation has so far only been applied in combination with a special torsion-free beam model, which yields ...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	64,186
1902.01780	Learning Decision Trees Recurrently Through Communication	Integrated interpretability without sacrificing the prediction accuracy of decision making algorithms has the potential of greatly improving their value to the user. Instead of assigning a label to an image directly, we propose to learn iterative binary sub-decisions, inducing sparsity and transparency in the decision ...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	120,730
1810.07118	Lagrangian Approximations for Stochastic Reachability of a Target Tube	In this paper we examine how Lagrangian techniques can be used to compute underapproximations and overapproximation of the finite-time horizon, stochastic reach-avoid level sets for discrete-time, nonlinear systems. This approach is applicable for a generic nonlinear system without any convexity assumptions on the safe...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	110,573
2003.06713	Document Ranking with a Pretrained Sequence-to-Sequence Model	This work proposes a novel adaptation of a pretrained sequence-to-sequence model to the task of document ranking. Our approach is fundamentally different from a commonly-adopted classification-based formulation of ranking, based on encoder-only pretrained transformer architectures such as BERT. We show how a sequence-t...	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	false	168,204
1905.02649	High Frequency Residual Learning for Multi-Scale Image Classification	We present a novel high frequency residual learning framework, which leads to a highly efficient multi-scale network (MSNet) architecture for mobile and embedded vision problems. The architecture utilizes two networks: a low resolution network to efficiently approximate low frequency components and a high resolution ne...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	130,016
1803.10864	Human Emotional Facial Expression Recognition	An automatic Facial Expression Recognition (FER) model with Adaboost face detector, feature selection based on manifold learning and synergetic prototype based classifier has been proposed. Improved feature selection method and proposed classifier can achieve favorable effectiveness to performance FER in reasonable pro...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	93,778
1906.03142	HPILN: A feature learning framework for cross-modality person re-identification	Most video surveillance systems use both RGB and infrared cameras, making it a vital technique to re-identify a person cross the RGB and infrared modalities. This task can be challenging due to both the cross-modality variations caused by heterogeneous images in RGB and infrared, and the intra-modality variations cause...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	134,288
1610.02800	Cross-layer Transmission Design for Tactile Internet	To ensure the low end-to-end (E2E) delay for tactile internet, short frame structures will be used in 5G systems. As such, transmission errors with finite blocklength channel codes should be considered to guarantee the high reliability requirement. In this paper, we study cross-layer transmission optimization for tacti...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	62,157
1606.04191	Path-Following Algorithms for Beamforming and Signal Splitting in RF Energy Harvesting Networks	We consider the joint design of transmit beamforming and receive signal-splitting ratios in the downlink of a wireless network with simultaneous radio-frequency (RF) information and energy transfer. Under constraints on the signal-to-interference-plus-noise ratio (SINR) at each user and the total transmit power at the ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	57,207
2003.11303	Cylindrical Convolutional Networks for Joint Object Detection and Viewpoint Estimation	Existing techniques to encode spatial invariance within deep convolutional neural networks only model 2D transformation fields. This does not account for the fact that objects in a 2D space are a projection of 3D ones, and thus they have limited ability to severe object viewpoint changes. To overcome this limitation, w...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	169,573
2304.00342	Factorization of Multi-Agent Sampling-Based Motion Planning	Modern robotics often involves multiple embodied agents operating within a shared environment. Path planning in these cases is considerably more challenging than in single-agent scenarios. Although standard Sampling-based Algorithms (SBAs) can be used to search for solutions in the robots' joint space, this approach qu...	false	false	false	false	true	false	false	true	false	false	false	false	false	false	true	false	false	false	355,642
2404.17943	Deep Representation Learning for Forecasting Recursive and Multi-Relational Events in Temporal Networks	Understanding relations arising out of interactions among entities can be very difficult, and predicting them is even more challenging. This problem has many applications in various fields, such as financial networks and e-commerce. These relations can involve much more complexities than just involving more than two en...	false	false	false	true	true	false	true	false	false	false	false	false	false	false	false	false	false	false	450,067
2404.07924	A Parsimonious Setup for Streamflow Forecasting using CNN-LSTM	Significant strides have been made in advancing streamflow predictions, notably with the introduction of cutting-edge machine-learning models. Predominantly, Long Short-Term Memories (LSTMs) and Convolution Neural Networks (CNNs) have been widely employed in this domain. While LSTMs are applicable in both rainfall-runo...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	446,021
2312.11153	Research on Multilingual Natural Scene Text Detection Algorithm	Natural scene text detection is a significant challenge in computer vision, with tremendous potential applications in multilingual, diverse, and complex text scenarios. We propose a multilingual text detection model to address the issues of low accuracy and high difficulty in detecting multilingual text in natural scen...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	416,462
1605.08478	Model-Free Imitation Learning with Policy Optimization	In imitation learning, an agent learns how to behave in an environment with an unknown cost function by mimicking expert demonstrations. Existing imitation learning algorithms typically involve solving a sequence of planning or reinforcement learning problems. Such algorithms are therefore not directly applicable to la...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	56,440
1209.6238	Natural Language Processing - A Survey	The utility and power of Natural Language Processing (NLP) seems destined to change our technological society in profound and fundamental ways. However there are, to date, few accessible descriptions of the science of NLP that have been written for a popular audience, or even for an audience of intelligent, but uniniti...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	18,800
2106.15448	Detecting Cattle and Elk in the Wild from Space	Localizing and counting large ungulates -- hoofed mammals like cows and elk -- in very high-resolution satellite imagery is an important task for supporting ecological studies. Prior work has shown that this is feasible with deep learning based methods and sub-meter multi-spectral satellite imagery. We extend this line...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	243,772
2203.00672	Generalizable Person Re-Identification via Self-Supervised Batch Norm Test-Time Adaption	In this paper, we investigate the generalization problem of person re-identification (re-id), whose major challenge is the distribution shift on an unseen domain. As an important tool of regularizing the distribution, batch normalization (BN) has been widely used in existing methods. However, they neglect that BN is se...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	283,083
1206.0375	Some Computational Aspects of Essential Properties of Evolution and Life	While evolution has inspired algorithmic methods of heuristic optimisation, little has been done in the way of using concepts of computation to advance our understanding of salient aspects of biological phenomena. We argue that under reasonable assumptions, interesting conclusions can be drawn that are of relevance to ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	true	16,292
2109.06067	Packed Levitated Marker for Entity and Relation Extraction	Recent entity and relation extraction works focus on investigating how to obtain a better span representation from the pre-trained encoder. However, a major limitation of existing works is that they ignore the interrelation between spans (pairs). In this work, we propose a novel span representation approach, named Pack...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	255,033
cs/0007044	Managing Periodically Updated Data in Relational Databases: A Stochastic Modeling Approach	Recent trends in information management involve the periodic transcription of data onto secondary devices in a networked environment, and the proper scheduling of these transcriptions is critical for efficient data management. To assist in the scheduling process, we are interested in modeling the reduction of consisten...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	537,174
1610.00427	Rain structure transfer using an exemplar rain image for synthetic rain image generation	This letter proposes a simple method of transferring rain structures of a given exemplar rain image into a target image. Given the exemplar rain image and its corresponding masked rain image, rain patches including rain structures are extracted randomly, and then residual rain patches are obtained by subtracting those ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	61,836
1006.2322	Discovery of a missing disease spreader	This study presents a method to discover an outbreak of an infectious disease in a region for which data are missing, but which is at work as a disease spreader. Node discovery for the spread of an infectious disease is defined as discriminating between the nodes which are neighboring to a missing disease spreader node...	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	false	false	false	6,764
2306.11487	Efficient Large-scale Nonstationary Spatial Covariance Function Estimation Using Convolutional Neural Networks	Spatial processes observed in various fields, such as climate and environmental science, often occur on a large scale and demonstrate spatial nonstationarity. Fitting a Gaussian process with a nonstationary Mat\'ern covariance is challenging. Previous studies in the literature have tackled this challenge by employing s...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	374,612
2405.04324	Granite Code Models: A Family of Open Foundation Models for Code Intelligence	Large Language Models (LLMs) trained on code are revolutionizing the software development process. Increasingly, code LLMs are being integrated into software development environments to improve the productivity of human programmers, and LLM-based agents are beginning to show promise for handling complex tasks autonomou...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	true	452,532
1912.11323	Bidding in Spades	We present a Spades bidding algorithm that is superior to recreational human players and to publicly available bots. Like in Bridge, the game of Spades is composed of two independent phases, \textit{bidding} and \textit{playing}. This paper focuses on the bidding algorithm, since this phase holds a precise challenge: b...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	158,539
1304.0270	An optimal problem for relative entropy	Relative entropy is an essential tool in quantum information theory. There are so many problems which are related to relative entropy. In this article, the optimal values which are defined by $\displaystyle\max_{U\in{U(\cX_{d})}} S(U\rho{U^{\ast}}\parallel\sigma)$ and $\displaystyle\min_{U\in{U(\cX_{d})}} S(U\rho{U^{\a...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	23,382
2103.14301	Evaluation of Preprocessing Techniques for U-Net Based Automated Liver Segmentation	To extract liver from medical images is a challenging task due to similar intensity values of liver with adjacent organs, various contrast levels, various noise associated with medical images and irregular shape of liver. To address these issues, it is important to preprocess the medical images, i.e., computerized tomo...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	226,812
2502.01680	Neurosymbolic AI for Travel Demand Prediction: Integrating Decision Tree Rules into Neural Networks	Travel demand prediction is crucial for optimizing transportation planning, resource allocation, and infrastructure development, ensuring efficient mobility and economic sustainability. This study introduces a Neurosymbolic Artificial Intelligence (Neurosymbolic AI) framework that integrates decision tree (DT)-based sy...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	529,983
2404.10572	Label merge-and-split: A graph-colouring approach for memory-efficient brain parcellation	Whole brain parcellation requires inferring hundreds of segmentation labels in large image volumes and thus presents significant practical challenges for deep learning approaches. We introduce label merge-and-split, a method that first greatly reduces the effective number of labels required for learning-based whole bra...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	447,154
2011.05970	Transformers for One-Shot Visual Imitation	Humans are able to seamlessly visually imitate others, by inferring their intentions and using past experience to achieve the same end goal. In other words, we can parse complex semantic knowledge from raw video and efficiently translate that into concrete motor control. Is it possible to give a robot this same capabil...	false	false	false	false	false	false	true	true	false	false	false	true	false	false	false	false	false	false	206,096
2409.09266	TransformerMPC: Accelerating Model Predictive Control via Transformers	In this paper, we address the problem of reducing the computational burden of Model Predictive Control (MPC) for real-time robotic applications. We propose TransformerMPC, a method that enhances the computational efficiency of MPC algorithms by leveraging the attention mechanism in transformers for both online constrai...	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	488,241
0905.2200	Towards Chip-on-Chip Neuroscience: Fast Mining of Frequent Episodes Using Graphics Processors	Computational neuroscience is being revolutionized with the advent of multi-electrode arrays that provide real-time, dynamic, perspectives into brain function. Mining event streams from these chips is critical to understanding the firing patterns of neurons and to gaining insight into the underlying cellular activity. ...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	true	3,679
1902.04129	CPOI: A Compact Method to Archive Versioned RDF Triple-Sets	Large amounts of RDF/S data are produced and published lately, and several modern applications require the provision of versioning and archiving services over such datasets. In this paper we propose a novel storage index for archiving versions of such datasets, called CPOI (compact partial order index), that exploits t...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	121,266
2202.06228	Robust Deepfake On Unrestricted Media: Generation And Detection	Recent advances in deep learning have led to substantial improvements in deepfake generation, resulting in fake media with a more realistic appearance. Although deepfake media have potential application in a wide range of areas and are drawing much attention from both the academic and industrial communities, it also le...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	280,141
2006.14652	Constant-Depth and Subcubic-Size Threshold Circuits for Matrix Multiplication	Boolean circuits of McCulloch-Pitts threshold gates are a classic model of neural computation studied heavily in the late 20th century as a model of general computation. Recent advances in large-scale neural computing hardware has made their practical implementation a near-term possibility. We describe a theoretical ap...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	true	184,286
2306.17141	Filtered-Guided Diffusion: Fast Filter Guidance for Black-Box Diffusion Models	Recent advances in diffusion-based generative models have shown incredible promise for Image-to-Image translation and editing. Most recent work in this space relies on additional training or architecture-specific adjustments to the diffusion process. In this work, we show that much of this low-level control can be achi...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	376,606
2101.02373	Architectural Patterns for the Design of Federated Learning Systems	Federated learning has received fast-growing interests from academia and industry to tackle the challenges of data hungriness and privacy in machine learning. A federated learning system can be viewed as a large-scale distributed system with different components and stakeholders as numerous client devices participate i...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	214,601
1606.06352	Visualizing textual models with in-text and word-as-pixel highlighting	We explore two techniques which use color to make sense of statistical text models. One method uses in-text annotations to illustrate a model's view of particular tokens in particular documents. Another uses a high-level, "words-as-pixels" graphic to display an entire corpus. Together, these methods offer both zoomed-i...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	57,560
2105.05735	Autoencoding Under Normalization Constraints	Likelihood is a standard estimate for outlier detection. The specific role of the normalization constraint is to ensure that the out-of-distribution (OOD) regime has a small likelihood when samples are learned using maximum likelihood. Because autoencoders do not possess such a process of normalization, they often fail...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	234,907
2410.15960	AI-Driven Innovations in Modern Cloud Computing	The world has witnessed rapid technological transformation, past couple of decades and with Advent of Cloud computing the landscape evolved exponentially leading to efficient and scalable application development. Now, the past couple of years the digital ecosystem has brought in numerous innovations with integration of...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	500,812
2502.01528	SQUASH: Serverless and Distributed Quantization-based Attributed Vector Similarity Search	Vector similarity search presents significant challenges in terms of scalability for large and high-dimensional datasets, as well as in providing native support for hybrid queries. Serverless computing and cloud functions offer attractive benefits such as elasticity and cost-effectiveness, but are difficult to apply to...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	true	529,896
1707.04677	Knowledge-Guided Recurrent Neural Network Learning for Task-Oriented Action Prediction	This paper aims at task-oriented action prediction, i.e., predicting a sequence of actions towards accomplishing a specific task under a certain scene, which is a new problem in computer vision research. The main challenges lie in how to model task-specific knowledge and integrate it in the learning procedure. In this ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	77,088
2105.13962	NViSII: A Scriptable Tool for Photorealistic Image Generation	We present a Python-based renderer built on NVIDIA's OptiX ray tracing engine and the OptiX AI denoiser, designed to generate high-quality synthetic images for research in computer vision and deep learning. Our tool enables the description and manipulation of complex dynamic 3D scenes containing object meshes, material...	false	false	false	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	237,469
2211.06846	Conversational Pattern Mining using Motif Detection	The subject of conversational mining has become of great interest recently due to the explosion of social and other online media. Supplementing this explosion of text is the advancement in pre-trained language models which have helped us to leverage these sources of information. An interesting domain to analyse is conv...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	330,048
2007.00374	Performance Evaluation of UAV-enabled Cellular Networks with Battery-limited Drones	Unmanned aerial vehicles (UAVs) can be used as flying base stations (BSs) to offload Macro-BSs in hotspots. However, due to the limited battery on-board, UAVs can typically stay in operation for less than 1.5 hours. Afterward, the UAV has to fly back to a dedicated charging station that recharges/replaces the UAV's bat...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	185,102
2003.04736	Optimizing Revenue while showing Relevant Assortments at Scale	Scalable real-time assortment optimization has become essential in e-commerce operations due to the need for personalization and the availability of a large variety of items. While this can be done when there are simplistic assortment choices to be made, the optimization process becomes difficult when imposing constrai...	false	true	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	167,644
2402.07938	Large Language User Interfaces: Voice Interactive User Interfaces powered by LLMs	The evolution of Large Language Models (LLMs) has showcased remarkable capacities for logical reasoning and natural language comprehension. These capabilities can be leveraged in solutions that semantically and textually model complex problems. In this paper, we present our efforts toward constructing a framework that ...	true	false	false	false	true	false	true	false	true	false	false	false	false	false	false	false	false	false	428,906
1811.03250	ABC: Efficient Selection of Machine Learning Configuration on Large Dataset	A machine learning configuration refers to a combination of preprocessor, learner, and hyperparameters. Given a set of configurations and a large dataset randomly split into training and testing set, we study how to efficiently select the best configuration with approximately the highest testing accuracy when trained f...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	112,789
2101.09864	Applications of Deep Learning in Fundus Images: A Review	The use of fundus images for the early screening of eye diseases is of great clinical importance. Due to its powerful performance, deep learning is becoming more and more popular in related applications, such as lesion segmentation, biomarkers segmentation, disease diagnosis and image synthesis. Therefore, it is very n...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	216,741
1606.01530	Adaptive Submodular Ranking and Routing	We study a general stochastic ranking problem where an algorithm needs to adaptively select a sequence of elements so as to "cover" a random scenario (drawn from a known distribution) at minimum expected cost. The coverage of each scenario is captured by an individual submodular function, where the scenario is said to ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	56,815
2405.17345	Exploring and steering the moral compass of Large Language Models	Large Language Models (LLMs) have become central to advancing automation and decision-making across various sectors, raising significant ethical questions. This study proposes a comprehensive comparative analysis of the most advanced LLMs to assess their moral profiles. We subjected several state-of-the-art models to a...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	457,864
2411.00851	Automatic feature selection and weighting in molecular systems using Differentiable Information Imbalance	Feature selection is essential in the analysis of molecular systems and many other fields, but several uncertainties remain: What is the optimal number of features for a simplified, interpretable model that retains essential information? How should features with different units be aligned, and how should their relative...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	504,788
1610.01980	Polynomial-time Tensor Decompositions with Sum-of-Squares	We give new algorithms based on the sum-of-squares method for tensor decomposition. Our results improve the best known running times from quasi-polynomial to polynomial for several problems, including decomposing random overcomplete 3-tensors and learning overcomplete dictionaries with constant relative sparsity. We al...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	62,040
1809.06752	3D segmentation of mandible from multisectional CT scans by convolutional neural networks	Segmentation of mandibles in CT scans during virtual surgical planning is crucial for 3D surgical planning in order to obtain a detailed surface representation of the patients bone. Automatic segmentation of mandibles in CT scans is a challenging task due to large variation in their shape and size between individuals. ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	108,138
2312.12466	Users Approach on Providing Feedback for Smart Home Devices	Smart Home technology has accomplished extraordinary interest in making individuals' lives more straightforward and more relaxing as of late. Technology as of late brought about delivering numerous savvy and refined frameworks which advanced clever living innovation. In this paper, we will be investigating the behaviou...	true	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	416,968
2007.06024	The Impossibility Theorem of Machine Fairness -- A Causal Perspective	With the increasing pervasive use of machine learning in social and economic settings, there has been an interest in the notion of machine bias in the AI community. Models trained on historic data reflect biases that exist in society and propagated them to the future through their decisions. There are three prominent m...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	186,869
2112.00905	HelixMO: Sample-Efficient Molecular Optimization in Scene-Sensitive Latent Space	Efficient exploration of the chemical space to search the candidate drugs that satisfy various constraints is a fundamental task of drug discovery. Advanced deep generative methods attempt to optimize the molecules in the compact latent space instead of the discrete original space, but the mapping between the original ...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	269,289
2201.11037	RTNet: Relation Transformer Network for Diabetic Retinopathy Multi-lesion Segmentation	Automatic diabetic retinopathy (DR) lesions segmentation makes great sense of assisting ophthalmologists in diagnosis. Although many researches have been conducted on this task, most prior works paid too much attention to the designs of networks instead of considering the pathological association for lesions. Through i...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	277,169
2412.16772	Assessing Social Alignment: Do Personality-Prompted Large Language Models Behave Like Humans?	The ongoing revolution in language modelling has led to various novel applications, some of which rely on the emerging "social abilities" of large language models (LLMs). Already, many turn to the new "cyber friends" for advice during pivotal moments of their lives and trust them with their deepest secrets, implying th...	false	false	false	false	true	false	true	false	false	false	false	false	false	true	false	false	false	false	519,676
2309.16932	Symmetry Induces Structure and Constraint of Learning	Due to common architecture designs, symmetries exist extensively in contemporary neural networks. In this work, we unveil the importance of the loss function symmetries in affecting, if not deciding, the learning behavior of machine learning models. We prove that every mirror-reflection symmetry, with reflection surfac...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	395,556
1912.04441	HR-SAR-Net: A Deep Neural Network for Urban Scene Segmentation from High-Resolution SAR Data	Synthetic aperture radar (SAR) data is becoming increasingly available to a wide range of users through commercial service providers with resolutions reaching 0.5m/px. Segmenting SAR data still requires skilled personnel, limiting the potential for large-scale use. We show that it is possible to automatically and relia...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	156,837
2501.05264	Towards Balanced Continual Multi-Modal Learning in Human Pose Estimation	3D human pose estimation (3D HPE) has emerged as a prominent research topic, particularly in the realm of RGB-based methods. However, RGB images are susceptible to limitations such as sensitivity to lighting conditions and potential user discomfort. Consequently, multi-modal sensing, which leverages non-intrusive senso...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	523,526
2412.05630	Dislocation-based crystal plasticity simulation on grain-size dependence of mechanical properties in dual-phase steels	In this study, the effect of ferrite grain size on the mechanical properties and dislocation behavior of dual-phase (DP) steel is investigated using dislocation-based crystal plasticity finite element analysis. DP steel, composed of a soft ferritic phase and a hard martensitic phase, shows mechanical properties that ar...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	514,908
2105.07938	RoSmEEry: Robotic Simulated Environment for Evaluation and Benchmarking of Semantic Mapping Algorithms	Human-robot interaction requires a common understanding of the operational environment, which can be provided by a representation that blends geometric and symbolic knowledge: a semantic map. Through a semantic map the robot can interpret user commands by grounding them to its sensory observations. Semantic mapping is ...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	235,603
1803.10464	Learning Pixel-level Semantic Affinity with Image-level Supervision for Weakly Supervised Semantic Segmentation	The deficiency of segmentation labels is one of the main obstacles to semantic segmentation in the wild. To alleviate this issue, we present a novel framework that generates segmentation labels of images given their image-level class labels. In this weakly supervised setting, trained models have been known to segment l...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	93,708
1903.06969	Domain adaptation for holistic skin detection	Human skin detection in images is a widely studied topic of Computer Vision for which it is commonly accepted that analysis of pixel color or local patches may suffice. This is because skin regions appear to be relatively uniform and many argue that there is a small chromatic variation among different samples. However,...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	124,501
1908.00244	Some optimal entanglement-assisted quantum codes constructed from quaternary Hermitian linear complementary dual codes	We establish the existence of optimal maximal entanglement entanglement-assisted quantum $[[n,k,d;n-k]]_2$ codes for $(n,k,d)=(14,6,7)$, $(15,7,7)$, $(17,6,9)$, $(17,7,8)$, $(19,7,9)$ and $(20,7,10)$. These codes are obtained from quaternary Hermitian linear complementary dual codes. We also give some observation on th...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	140,466
2008.12914	Data augmentation using prosody and false starts to recognize non-native children's speech	This paper describes AaltoASR's speech recognition system for the INTERSPEECH 2020 shared task on Automatic Speech Recognition (ASR) for non-native children's speech. The task is to recognize non-native speech from children of various age groups given a limited amount of speech. Moreover, the speech being spontaneous h...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	193,708
2203.11849	A Girl Has A Name, And It's ... Adversarial Authorship Attribution for Deobfuscation	Recent advances in natural language processing have enabled powerful privacy-invasive authorship attribution. To counter authorship attribution, researchers have proposed a variety of rule-based and learning-based text obfuscation approaches. However, existing authorship obfuscation approaches do not consider the adver...	false	false	false	false	false	false	true	false	true	false	false	false	true	false	false	false	false	false	287,062
2111.03654	Asymptotically Good Quantum and Locally Testable Classical LDPC Codes	We study classical and quantum LDPC codes of constant rate obtained by the lifted product construction over non-abelian groups. We show that the obtained families of quantum LDPC codes are asymptotically good, which proves the qLDPC conjecture. Moreover, we show that the produced classical LDPC codes are also asymptoti...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	265,237
2102.12238	Inductive Bias of Multi-Channel Linear Convolutional Networks with Bounded Weight Norm	We provide a function space characterization of the inductive bias resulting from minimizing the $\ell_2$ norm of the weights in multi-channel convolutional neural networks with linear activations and empirically test our resulting hypothesis on ReLU networks trained using gradient descent. We define an induced regular...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	221,666
2007.03812	Robust Multi-Agent Multi-Armed Bandits	Recent works have shown that agents facing independent instances of a stochastic $K$-armed bandit can collaborate to decrease regret. However, these works assume that each agent always recommends their individual best-arm estimates to other agents, which is unrealistic in envisioned applications (machine faults in dist...	false	false	false	true	false	false	true	false	false	false	false	false	false	false	false	false	false	true	186,168
1901.10513	Adversarial Examples Are a Natural Consequence of Test Error in Noise	Over the last few years, the phenomenon of adversarial examples --- maliciously constructed inputs that fool trained machine learning models --- has captured the attention of the research community, especially when the adversary is restricted to small modifications of a correctly handled input. Less surprisingly, image...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	120,035
1010.2955	Robust Recovery of Subspace Structures by Low-Rank Representation	In this work we address the subspace recovery problem. Given a set of data samples (vectors) approximately drawn from a union of multiple subspaces, our goal is to segment the samples into their respective subspaces and correct the possible errors as well. To this end, we propose a novel method termed Low-Rank Represen...	false	false	false	false	false	false	true	false	false	true	false	true	false	false	false	false	false	false	7,904
2405.14185	A Structure-Aware Framework for Learning Device Placements on Computation Graphs	Computation graphs are Directed Acyclic Graphs (DAGs) where the nodes correspond to mathematical operations and are used widely as abstractions in optimizations of neural networks. The device placement problem aims to identify optimal allocations of those nodes to a set of (potentially heterogeneous) devices. Existing ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	456,295
2112.05483	Latency-Aware Multi-antenna SWIPT System with Battery-Constrained Receivers	Power splitting (PS) based simultaneous wireless information and power transfer (SWIPT) is considered in a multi-user multiple-input-single-output broadcast scenario. Specifically, we focus on jointly configuring the transmit beamforming vectors and receive PS ratios to minimize the total transmit energy of the base st...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	270,855
2410.05290	Curve Segment Neighborhood-based Vector Field Exploration	Integral curves have been widely used to represent and analyze various vector fields. In this paper, we propose a Curve Segment Neighborhood Graph (CSNG) to capture the relationships between neighboring curve segments. This graph representation enables us to adapt the fast community detection algorithm, i.e., the Louva...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	true	495,655
2208.05768	MixSKD: Self-Knowledge Distillation from Mixup for Image Recognition	Unlike the conventional Knowledge Distillation (KD), Self-KD allows a network to learn knowledge from itself without any guidance from extra networks. This paper proposes to perform Self-KD from image Mixture (MixSKD), which integrates these two techniques into a unified framework. MixSKD mutually distills feature maps...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	312,499
2309.03640	Context-Aware 3D Object Localization from Single Calibrated Images: A Study of Basketballs	Accurately localizing objects in three dimensions (3D) is crucial for various computer vision applications, such as robotics, autonomous driving, and augmented reality. This task finds another important application in sports analytics and, in this work, we present a novel method for 3D basketball localization from a si...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	390,449
2206.05276	Game-Theoretic Neyman-Pearson Detection to Combat Strategic Evasion	The security in networked systems depends greatly on recognizing and identifying adversarial behaviors. Traditional detection methods focus on specific categories of attacks and have become inadequate for increasingly stealthy and deceptive attacks that are designed to bypass detection strategically. This work aims to ...	false	false	false	false	false	false	false	false	false	true	true	false	true	false	false	false	false	true	301,951
2412.19227	Multi-view Fake News Detection Model Based on Dynamic Hypergraph	With the rapid development of online social networks and the inadequacies in content moderation mechanisms, the detection of fake news has emerged as a pressing concern for the public. Various methods have been proposed for fake news detection, including text-based approaches as well as a series of graph-based approach...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	520,766
2502.02430	A Scalable Crawling Algorithm Utilizing Noisy Change-Indicating Signals	Web refresh crawling is the problem of keeping a cache of web pages fresh, that is, having the most recent copy available when a page is requested, given a limited bandwidth available to the crawler. Under the assumption that the change and request events, resp., to each web page follow independent Poisson processes, t...	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	false	530,309
2104.07660	SCALE: Modeling Clothed Humans with a Surface Codec of Articulated Local Elements	Learning to model and reconstruct humans in clothing is challenging due to articulation, non-rigid deformation, and varying clothing types and topologies. To enable learning, the choice of representation is the key. Recent work uses neural networks to parameterize local surface elements. This approach captures locally ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	true	230,502
2207.03881	The Power of Transfer Learning in Agricultural Applications: AgriNet	Advances in deep learning and transfer learning have paved the way for various automation classification tasks in agriculture, including plant diseases, pests, weeds, and plant species detection. However, agriculture automation still faces various challenges, such as the limited size of datasets and the absence of plan...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	307,003
2501.11532	Early Stopping Bayesian Optimization for Controller Tuning	Manual tuning of performance-critical controller parameters can be tedious and sub-optimal. Bayesian Optimization (BO) is an increasingly popular practical alternative to automatically optimize controller parameters from few experiments. Standard BO practice is to evaluate the closed-loop performance of parameters prop...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	525,957
2404.16807	Improving Diversity of Commonsense Generation by Large Language Models via In-Context Learning	Generative Commonsense Reasoning (GCR) requires a model to reason about a situation using commonsense knowledge, while generating coherent sentences. Although the quality of the generated sentences is crucial, the diversity of the generation is equally important because it reflects the model's ability to use a range of...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	449,622
2205.04550	A for-loop is all you need. For solving the inverse problem in the case of personalized tumor growth modeling	Solving the inverse problem is the key step in evaluating the capacity of a physical model to describe real phenomena. In medical image computing, it aligns with the classical theme of image-based model personalization. Traditionally, a solution to the problem is obtained by performing either sampling or variational in...	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	295,673
2310.13303	Motif-Based Prompt Learning for Universal Cross-Domain Recommendation	Cross-Domain Recommendation (CDR) stands as a pivotal technology addressing issues of data sparsity and cold start by transferring general knowledge from the source to the target domain. However, existing CDR models suffer limitations in adaptability across various scenarios due to their inherent complexity. To tackle ...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	401,392
2104.07168	Data-driven Actuator Selection for Artificial Muscle-Powered Robots	Even though artificial muscles have gained popularity due to their compliant, flexible, and compact properties, there currently does not exist an easy way of making informed decisions on the appropriate actuation strategy when designing a muscle-powered robot; thus limiting the transition of such technologies into broa...	false	false	false	false	false	false	true	true	false	false	false	false	false	false	false	false	false	false	230,314

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