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
2306.16207	Inferring the Goals of Communicating Agents from Actions and Instructions	When humans cooperate, they frequently coordinate their activity through both verbal communication and non-verbal actions, using this information to infer a shared goal and plan. How can we model this inferential ability? In this paper, we introduce a model of a cooperative team where one agent, the principal, may comm...	false	false	false	false	true	false	false	true	true	false	false	false	false	false	false	false	false	false	376,314
1904.03848	Unsupervised Deep Epipolar Flow for Stationary or Dynamic Scenes	Unsupervised deep learning for optical flow computation has achieved promising results. Most existing deep-net based methods rely on image brightness consistency and local smoothness constraint to train the networks. Their performance degrades at regions where repetitive textures or occlusions occur. In this paper, we ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	126,859
2102.02852	Eliciting judgements about dependent quantities of interest: The SHELF extension and copula methods illustrated using an asthma case study	Pharmaceutical companies regularly need to make decisions about drug development programs based on the limited knowledge from early stage clinical trials. In this situation, eliciting the judgements of experts is an attractive approach for synthesising evidence on the unknown quantities of interest. When calculating th...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	218,539
1804.03269	Characterising information-theoretic storage and transfer in continuous time processes	The characterisation of information processing is an important task in complex systems science. Information dynamics is a quantitative methodology for modelling the intrinsic information processing conducted by a process represented as a time series, but to date has only been formulated in discrete time. Building on pr...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	94,584
1706.00457	NMTPY: A Flexible Toolkit for Advanced Neural Machine Translation Systems	In this paper, we present nmtpy, a flexible Python toolkit based on Theano for training Neural Machine Translation and other neural sequence-to-sequence architectures. nmtpy decouples the specification of a network from the training and inference utilities to simplify the addition of a new architecture and reduce the a...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	74,620
2411.08534	Neural Topic Modeling with Large Language Models in the Loop	Topic modeling is a fundamental task in natural language processing, allowing the discovery of latent thematic structures in text corpora. While Large Language Models (LLMs) have demonstrated promising capabilities in topic discovery, their direct application to topic modeling suffers from issues such as incomplete top...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	507,923
1904.12400	Attentive Adversarial Learning for Domain-Invariant Training	Adversarial domain-invariant training (ADIT) proves to be effective in suppressing the effects of domain variability in acoustic modeling and has led to improved performance in automatic speech recognition (ASR). In ADIT, an auxiliary domain classifier takes in equally-weighted deep features from a deep neural network ...	false	false	true	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	129,106
2408.00905	High-Impact Innovations and Hidden Gender Disparities in Inventor-Evaluator Networks	We study of millions of scientific, technological, and artistic innovations and find that the innovation gap faced by women is far from universal. No gap exists for conventional innovations. Rather, the gap is pervasively rooted in innovations that combine ideas in unexpected ways - innovations most critical to scienti...	false	false	false	true	false	false	false	false	false	false	false	false	false	true	false	false	false	true	478,020
2405.09552	ODFormer: Semantic Fundus Image Segmentation Using Transformer for Optic Nerve Head Detection	Optic nerve head (ONH) detection has been a crucial area of study in ophthalmology for years. However, the significant discrepancy between fundus image datasets, each generated using a single type of fundus camera, poses challenges to the generalizability of ONH detection approaches developed based on semantic segmenta...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	454,443
2007.04477	Good AI for the Present of Humanity Democratizing AI Governance	What do Cyberpunk and AI Ethics have to do with each other? Cyberpunk is a sub-genre of science fiction that explores the post-human relationships between human experience and technology. One similarity between AI Ethics and Cyberpunk literature is that both seek to explore future social and ethical problems that our t...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	186,364
2108.11346	Auxiliary Task Update Decomposition: The Good, The Bad and The Neutral	While deep learning has been very beneficial in data-rich settings, tasks with smaller training set often resort to pre-training or multitask learning to leverage data from other tasks. In this case, careful consideration is needed to select tasks and model parameterizations such that updates from the auxiliary tasks a...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	252,160
2105.05332	The DEVIL is in the Details: A Diagnostic Evaluation Benchmark for Video Inpainting	Quantitative evaluation has increased dramatically among recent video inpainting work, but the video and mask content used to gauge performance has received relatively little attention. Although attributes such as camera and background scene motion inherently change the difficulty of the task and affect methods differe...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	234,781
1711.09251	When Do Users Change Their Profile Information on Twitter?	We can see profile information such as name, description and location in order to know the user on social media. However, this profile information is not always fixed. If there is a change in the user's life, the profile information will be changed. In this study, we focus on user's profile information changes and anal...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	85,363
2203.09848	Gender classification by means of online uppercase handwriting: A text-dependent allographic approach	This paper presents a gender classification schema based on online handwriting. Using samples acquired with a digital tablet that captures the dynamics of the writing, it classifies the writer as a male or a female. The method proposed is allographic, regarding strokes as the structural units of handwriting. Strokes pe...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	286,312
2308.04169	Dual input neural networks for positional sound source localization	In many signal processing applications, metadata may be advantageously used in conjunction with a high dimensional signal to produce a desired output. In the case of classical Sound Source Localization (SSL) algorithms, information from a high dimensional, multichannel audio signals received by many distributed microph...	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	384,314
2409.11450	High performance Lunar landing simulations	Autonomous precision navigation to land onto the Moon relies on vision sensors. Computer vision algorithms are designed, trained and tested using synthetic simulations. High quality terrain models have been produced by Moon orbiters developed by several nations, with resolutions ranging from tens or hundreds of meters ...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	true	489,166
2407.10026	Conditional Entropies of k-Deletion/Insertion Channels	The channel output entropy of a transmitted sequence is the entropy of the possible channel outputs and similarly the channel input entropy of a received sequence is the entropy of all possible transmitted sequences. The goal of this work is to study these entropy values for the k-deletion, k-insertion channels, where ...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	472,810
2010.01236	Placement of UAV-Mounted Mobile Base Station through User Load-Feature K-means Clustering	Temporary high traffic requests in cellular networks is a challenging problem to address. Recent advances in Unmanned Aerial Vehicles applied to cover these types of traffics. UAV -Mounted Mobile Base Stations placement is a challenging problem to achieve high performance. Different approaches have been proposed; howev...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	198,570
2101.02270	Fast Parallel Newton-Raphson Power Flow Solver for Large Number of System Calculations with CPU and GPU	To analyze large sets of grid states, e.g. when evaluating the impact from the uncertainties of the renewable generation with probabilistic Monte Carlo simulation or in stationary time series simulation, large number of power flow calculations have to be performed. For the application in real-time grid operation, grid ...	false	true	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	214,566
2311.13454	Explaining high-dimensional text classifiers	Explainability has become a valuable tool in the last few years, helping humans better understand AI-guided decisions. However, the classic explainability tools are sometimes quite limited when considering high-dimensional inputs and neural network classifiers. We present a new explainability method using theoretically...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	true	false	false	409,742
2410.06412	Stochastic Sparse Sampling: A Framework for Variable-Length Medical Time Series Classification	While the majority of time series classification research has focused on modeling fixed-length sequences, variable-length time series classification (VTSC) remains critical in healthcare, where sequence length may vary among patients and events. To address this challenge, we propose $\textbf{S}$tochastic $\textbf{S}$pa...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	496,189
2102.04926	Reduction of the Beam Pointing Error for Improved Free-Space Optical Communication Link Performance	Free-space optical communication is emerging as a low-power, low-cost, and high data rate alternative to radio-frequency communication in short-to medium-range applications. However, it requires a close-to-line-of-sight link between the transmitter and the receiver. This paper proposes a robust $\cHi$ control law for f...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	219,269
2004.13903	An Auto-Encoder Strategy for Adaptive Image Segmentation	Deep neural networks are powerful tools for biomedical image segmentation. These models are often trained with heavy supervision, relying on pairs of images and corresponding voxel-level labels. However, obtaining segmentations of anatomical regions on a large number of cases can be prohibitively expensive. Thus there ...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	174,709
1711.02271	High-order Tensor Completion for Data Recovery via Sparse Tensor-train Optimization	In this paper, we aim at the problem of tensor data completion. Tensor-train decomposition is adopted because of its powerful representation ability and linear scalability to tensor order. We propose an algorithm named Sparse Tensor-train Optimization (STTO) which considers incomplete data as sparse tensor and uses fir...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	84,032
2305.03960	Beyond Rule-based Named Entity Recognition and Relation Extraction for Process Model Generation from Natural Language Text	Process-aware information systems offer extensive advantages to companies, facilitating planning, operations, and optimization of day-to-day business activities. However, the time-consuming but required step of designing formal business process models often hampers the potential of these systems. To overcome this chall...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	362,580
2111.01395	Training Certifiably Robust Neural Networks with Efficient Local Lipschitz Bounds	Certified robustness is a desirable property for deep neural networks in safety-critical applications, and popular training algorithms can certify robustness of a neural network by computing a global bound on its Lipschitz constant. However, such a bound is often loose: it tends to over-regularize the neural network an...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	264,545
1702.06103	An Improved Parametrization and Analysis of the EXP3++ Algorithm for Stochastic and Adversarial Bandits	We present a new strategy for gap estimation in randomized algorithms for multiarmed bandits and combine it with the EXP3++ algorithm of Seldin and Slivkins (2014). In the stochastic regime the strategy reduces dependence of regret on a time horizon from $(\ln t)^3$ to $(\ln t)^2$ and eliminates an additive factor of o...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	68,530
2307.13782	A Data-Driven Approach to Synthesizing Dynamics-Aware Trajectories for Underactuated Robotic Systems	We consider joint trajectory generation and tracking control for under-actuated robotic systems. A common solution is to use a layered control architecture, where the top layer uses a simplified model of system dynamics for trajectory generation, and the low layer ensures approximate tracking of this trajectory via fee...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	381,700
2407.00918	Robust and Reliable Early-Stage Website Fingerprinting Attacks via Spatial-Temporal Distribution Analysis	Website Fingerprinting (WF) attacks identify the websites visited by users by performing traffic analysis, compromising user privacy. Particularly, DL-based WF attacks demonstrate impressive attack performance. However, the effectiveness of DL-based WF attacks relies on the collected complete and pure traffic during th...	false	false	false	false	true	false	true	false	false	false	false	false	true	false	false	false	false	false	469,059
1903.03319	One-Bit Sigma-Delta MIMO Precoding	Coarsely quantized MIMO signalling methods have gained popularity in the recent developments of massive MIMO as they open up opportunities for massive MIMO implementation using cheap and power-efficient radio-frequency front-ends. This paper presents a new one-bit MIMO precoding approach using spatial Sigma-Delta ($\Si...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	123,707
2106.00328	Optimizing travel routes using temporal networks constructed from GPS data	Because of the complexity of urban transportation networks and the temporal changes in traffic conditions, it is difficult to assess real-time traffic situations. However, the development of information terminals has made it easier to obtain personal mobility information. In this study, we propose methods for evaluatin...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	238,085
2305.02577	Text Reading Order in Uncontrolled Conditions by Sparse Graph Segmentation	Text reading order is a crucial aspect in the output of an OCR engine, with a large impact on downstream tasks. Its difficulty lies in the large variation of domain specific layout structures, and is further exacerbated by real-world image degradations such as perspective distortions. We propose a lightweight, scalable...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	362,095
2403.13812	Quantitative Analysis of AI-Generated Texts in Academic Research: A Study of AI Presence in Arxiv Submissions using AI Detection Tool	Many people are interested in ChatGPT since it has become a prominent AIGC model that provides high-quality responses in various contexts, such as software development and maintenance. Misuse of ChatGPT might cause significant issues, particularly in public safety and education, despite its immense potential. The major...	false	false	false	false	true	false	true	false	true	false	false	false	false	true	false	false	false	true	439,793
1811.08996	HyperAdam: A Learnable Task-Adaptive Adam for Network Training	Deep neural networks are traditionally trained using human-designed stochastic optimization algorithms, such as SGD and Adam. Recently, the approach of learning to optimize network parameters has emerged as a promising research topic. However, these learned black-box optimizers sometimes do not fully utilize the experi...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	114,167
1807.06976	The Generalized Lasso for Sub-gaussian Measurements with Dithered Quantization	In the problem of structured signal recovery from high-dimensional linear observations, it is commonly assumed that full-precision measurements are available. Under this assumption, the recovery performance of the popular Generalized Lasso (G-Lasso) is by now well-established. In this paper, we extend these types of re...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	103,238
2405.06355	Switched Vector Field-based Guidance for General Reference Path Following in Planar Environment	Reference path following is a key component in the functioning of almost all engineered autonomous agents. Among several path following guidance methods in existing literature, vector-field-based guidance approach has got wide attention because of its simplicity and guarantee of stability under a broad class of scenari...	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	453,270
2205.06376	KASAM: Spline Additive Models for Function Approximation	Neural networks have been criticised for their inability to perform continual learning due to catastrophic forgetting and rapid unlearning of a past concept when a new concept is introduced. Catastrophic forgetting can be alleviated by specifically designed models and training techniques. This paper outlines a novel Sp...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	296,217
2405.08754	Hierarchical Resource Partitioning on Modern GPUs: A Reinforcement Learning Approach	GPU-based heterogeneous architectures are now commonly used in HPC clusters. Due to their architectural simplicity specialized for data-level parallelism, GPUs can offer much higher computational throughput and memory bandwidth than CPUs in the same generation do. However, as the available resources in GPUs have increa...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	454,204
1509.00967	A Reconfigurable Mixed-signal Implementation of a Neuromorphic ADC	We present a neuromorphic Analogue-to-Digital Converter (ADC), which uses integrate-and-fire (I&F) neurons as the encoders of the analogue signal, with modulated inhibitions to decohere the neuronal spikes trains. The architecture consists of an analogue chip and a control module. The analogue chip comprises two scan c...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	46,549
2209.15532	Joint Scheduling and Resource Allocation for Packets with Deadlines and Priorities	Cellular networks provide communication for different applications. Some applications have strict and very short latency requirements, while others require high bandwidth with varying priorities. The challenge of satisfying the requirements grows in congested traffic where some packets might miss their deadlines. Unfor...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	true	320,641
1403.4789	Structure-preserving model reduction of physical network systems by clustering	In this paper, we establish a method for model order reduction of a certain class of physical network systems. The proposed method is based on clustering of the vertices of the underlying graph, and yields a reduced order model within the same class. To capture the physical properties of the network, we allow for weigh...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	31,679
2408.06025	A novel metric for detecting quadrotor loss-of-control	Unmanned aerial vehicles (UAVs) are becoming an integral part of both industry and society. In particular, the quadrotor is now invaluable across a plethora of fields and recent developments, such as the inclusion of aerial manipulators, only extends their versatility. As UAVs become more widespread, preventing loss-of...	false	false	false	false	false	false	false	true	false	false	true	false	false	false	false	false	false	false	480,050
2304.13169	SAFE: Machine Unlearning With Shard Graphs	We present Synergy Aware Forgetting Ensemble (SAFE), a method to adapt large models on a diverse collection of data while minimizing the expected cost to remove the influence of training samples from the trained model. This process, also known as selective forgetting or unlearning, is often conducted by partitioning a ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	360,487
2408.07941	Robust Offline Active Learning on Graphs	We consider the problem of active learning on graphs, which has crucial applications in many real-world networks where labeling node responses is expensive. In this paper, we propose an offline active learning method that selects nodes to query by explicitly incorporating information from both the network structure and...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	480,787
2207.00688	Building African Voices	Modern speech synthesis techniques can produce natural-sounding speech given sufficient high-quality data and compute resources. However, such data is not readily available for many languages. This paper focuses on speech synthesis for low-resourced African languages, from corpus creation to sharing and deploying the T...	false	false	true	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	305,844
2004.02352	Deep Reinforcement Learning-Aided Random Access	We consider a system model comprised of an access point (AP) and K Internet of Things (IoT) nodes that sporadically become active in order to send data to the AP. The AP is assumed to have N time-frequency resource blocks that it can allocate to the IoT nodes that wish to send data, where N < K. The main problem is how...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	171,203
2004.03659	The Russian Drug Reaction Corpus and Neural Models for Drug Reactions and Effectiveness Detection in User Reviews	The Russian Drug Reaction Corpus (RuDReC) is a new partially annotated corpus of consumer reviews in Russian about pharmaceutical products for the detection of health-related named entities and the effectiveness of pharmaceutical products. The corpus itself consists of two parts, the raw one and the labelled one. The r...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	171,634
1911.02590	Optimizing Millions of Hyperparameters by Implicit Differentiation	We propose an algorithm for inexpensive gradient-based hyperparameter optimization that combines the implicit function theorem (IFT) with efficient inverse Hessian approximations. We present results about the relationship between the IFT and differentiating through optimization, motivating our algorithm. We use the pro...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	152,396
1705.06264	Deep Diagnostics: Applying Convolutional Neural Networks for Vessels Defects Detection	Coronary angiography is considered to be a safe tool for the evaluation of coronary artery disease and perform in approximately 12 million patients each year worldwide. [1] In most cases, angiograms are manually analyzed by a cardiologist. Actually, there are no clinical practice algorithms which could improve and auto...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	73,611
2310.16658	An Online Self-calibrating Refractive Camera Model with Application to Underwater Odometry	This work presents a camera model for refractive media such as water and its application in underwater visual-inertial odometry. The model is self-calibrating in real-time and is free of known correspondences or calibration targets. It is separable as a distortion model (dependent on refractive index $n$ and radial pix...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	402,820
0711.3726	Let's get the student into the driver's seat	Speaking a language and achieving proficiency in another one is a highly complex process which requires the acquisition of various kinds of knowledge and skills, like the learning of words, rules and patterns and their connection to communicative goals (intentions), the usual starting point. To help the learner to acqu...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	949
2403.20230	An FPGA-Based Reconfigurable Accelerator for Convolution-Transformer Hybrid EfficientViT	Vision Transformers (ViTs) have achieved significant success in computer vision. However, their intensive computations and massive memory footprint challenge ViTs' deployment on embedded devices, calling for efficient ViTs. Among them, EfficientViT, the state-of-the-art one, features a Convolution-Transformer hybrid ar...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	442,670
1603.05739	A Readability Analysis of Campaign Speeches from the 2016 US Presidential Campaign	Readability is defined as the reading level of the speech from grade 1 to grade 12. It results from the use of the REAP readability analysis (vocabulary - Collins-Thompson and Callan, 2004; syntax - Heilman et al ,2006, 2007), which use the lexical contents and grammatical structure of the sentences in a document to pr...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	53,393
2402.01989	Optimal Planning of PV and Battery Resources in Remote Microgrids Considering Degradation Costs: An Iterative Post-Optimization Correction-based Approach	The benefits of shifting to renewable energy sources have granted microgrids considerable attention, especially photovoltaic (PV) systems. However, given the inherent variable and intermittent nature of solar power, battery energy storage systems (BESS) are pivotal for a reliable and cost-effective microgrid. The optim...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	426,319
1906.07004	Improving Multi-turn Dialogue Modelling with Utterance ReWriter	Recent research has made impressive progress in single-turn dialogue modelling. In the multi-turn setting, however, current models are still far from satisfactory. One major challenge is the frequently occurred coreference and information omission in our daily conversation, making it hard for machines to understand the...	false	false	false	false	true	false	false	false	true	false	false	false	false	false	false	false	false	false	135,487
2408.07433	MagicFace: Training-free Universal-Style Human Image Customized Synthesis	Current human image customization methods leverage Stable Diffusion (SD) for its rich semantic prior. However, since SD is not specifically designed for human-oriented generation, these methods often require extensive fine-tuning on large-scale datasets, which renders them susceptible to overfitting and hinders their a...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	480,580
1909.03868	Partner Approximating Learners (PAL): Simulation-Accelerated Learning with Explicit Partner Modeling in Multi-Agent Domains	Mixed cooperative-competitive control scenarios such as human-machine interaction with individual goals of the interacting partners are very challenging for reinforcement learning agents. In order to contribute towards intuitive human-machine collaboration, we focus on problems in the continuous state and control domai...	false	false	false	false	true	false	true	false	false	false	true	false	false	false	true	false	false	false	144,627
cs/0007031	Parameter-free Model of Rank Polysemantic Distribution	A model of rank polysemantic distribution with a minimal number of fitting parameters is offered. In an ideal case a parameter-free description of the dependence on the basis of one or several immediate features of the distribution is possible.	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	537,165
2305.12959	Contrastive Predictive Autoencoders for Dynamic Point Cloud Self-Supervised Learning	We present a new self-supervised paradigm on point cloud sequence understanding. Inspired by the discriminative and generative self-supervised methods, we design two tasks, namely point cloud sequence based Contrastive Prediction and Reconstruction (CPR), to collaboratively learn more comprehensive spatiotemporal repre...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	366,274
1011.0474	Construction of New Delay-Tolerant Space-Time Codes	Perfect Space-Time Codes (STC) are optimal codes in their original construction for Multiple Input Multiple Output (MIMO) systems. Based on Cyclic Division Algebras (CDA), they are full-rate, full-diversity codes, have Non-Vanishing Determinants (NVD) and hence achieve Diversity-Multiplexing Tradeoff (DMT). In addition...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	8,103
2203.06442	Equivariant Graph Mechanics Networks with Constraints	Learning to reason about relations and dynamics over multiple interacting objects is a challenging topic in machine learning. The challenges mainly stem from that the interacting systems are exponentially-compositional, symmetrical, and commonly geometrically-constrained. Current methods, particularly the ones based on...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	285,118
2010.12794	X-Class: Text Classification with Extremely Weak Supervision	In this paper, we explore text classification with extremely weak supervision, i.e., only relying on the surface text of class names. This is a more challenging setting than the seed-driven weak supervision, which allows a few seed words per class. We opt to attack this problem from a representation learning perspectiv...	false	false	false	false	false	true	true	false	true	false	false	false	false	false	false	false	false	false	202,856
1605.08374	Kronecker Determinantal Point Processes	Determinantal Point Processes (DPPs) are probabilistic models over all subsets a ground set of $N$ items. They have recently gained prominence in several applications that rely on "diverse" subsets. However, their applicability to large problems is still limited due to the $\mathcal O(N^3)$ complexity of core tasks suc...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	56,425
2306.12456	Pushing the Limits of Machine Design: Automated CPU Design with AI	Design activity -- constructing an artifact description satisfying given goals and constraints -- distinguishes humanity from other animals and traditional machines, and endowing machines with design abilities at the human level or beyond has been a long-term pursuit. Though machines have already demonstrated their abi...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	true	374,948
2407.17914	Modelling Multimodal Integration in Human Concept Processing with Vision-and-Language Models	Representations from deep neural networks (DNNs) have proven remarkably predictive of neural activity involved in both visual and linguistic processing. Despite these successes, most studies to date concern unimodal DNNs, encoding either visual or textual input but not both. Yet, there is growing evidence that human me...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	476,174
2105.10587	Techniques Toward Optimizing Viewability in RTB Ad Campaigns Using Reinforcement Learning	Reinforcement learning (RL) is an effective technique for training decision-making agents through interactions with their environment. The advent of deep learning has been associated with highly notable successes with sequential decision making problems - such as defeating some of the highest-ranked human players at Go...	false	false	false	false	true	true	true	false	false	false	false	false	false	false	false	false	false	false	236,438
2105.00463	Unsupervised Anomaly Detection in MR Images using Multi-Contrast Information	Anomaly detection in medical imaging is to distinguish the relevant biomarkers of diseases from those of normal tissues. Deep supervised learning methods have shown potentials in various detection tasks, but its performances would be limited in medical imaging fields where collecting annotated anomaly data is limited a...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	233,231
2305.19409	Examining risks of racial biases in NLP tools for child protective services	Although much literature has established the presence of demographic bias in natural language processing (NLP) models, most work relies on curated bias metrics that may not be reflective of real-world applications. At the same time, practitioners are increasingly using algorithmic tools in high-stakes settings, with pa...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	369,511
2405.07376	Advocating Feedback Control for Human-Earth System Applications	This paper proposes a feedback control perspective for Human-Earth Systems (HESs) which essentially are complex systems that capture the interactions between humans and nature. Recent attention in HES research has been directed towards devising strategies for climate change mitigation and adaptation, aimed at achieving...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	453,688
1907.01332	Applying Transfer Learning To Deep Learned Models For EEG Analysis	The introduction of deep learning and transfer learning techniques in fields such as computer vision allowed a leap forward in the accuracy of image classification tasks. Currently there is only limited use of such techniques in neuroscience. The challenge of using deep learning methods to successfully train models in ...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	137,296
1909.11894	Social Network Analysis for Social Neuroscientists	Although social neuroscience is concerned with understanding how the brain interacts with its social environment, prevailing research in the field has primarily considered the human brain in isolation, deprived of its rich social context. Emerging work in social neuroscience that leverages tools from network analysis h...	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	false	146,958
cs/0604077	Successive Wyner-Ziv Coding Scheme and its Application to the Quadratic Gaussian CEO Problem	We introduce a distributed source coding scheme called successive Wyner-Ziv coding. We show that any point in the rate region of the quadratic Gaussian CEO problem can be achieved via the successive Wyner-Ziv coding. The concept of successive refinement in the single source coding is generalized to the distributed sour...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	539,398
1310.3366	PCG-Cut: Graph Driven Segmentation of the Prostate Central Gland	Prostate cancer is the most abundant cancer in men, with over 200,000 expected new cases and around 28,000 deaths in 2012 in the US alone. In this study, the segmentation results for the prostate central gland (PCG) in MR scans are presented. The aim of this research study is to apply a graph-based algorithm to automat...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	27,740
2308.13140	Learn With Imagination: Safe Set Guided State-wise Constrained Policy Optimization	Deep reinforcement learning (RL) excels in various control tasks, yet the absence of safety guarantees hampers its real-world applicability. In particular, explorations during learning usually results in safety violations, while the RL agent learns from those mistakes. On the other hand, safe control techniques ensure ...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	387,792
2006.10255	Calibrated Reliable Regression using Maximum Mean Discrepancy	Accurate quantification of uncertainty is crucial for real-world applications of machine learning. However, modern deep neural networks still produce unreliable predictive uncertainty, often yielding over-confident predictions. In this paper, we are concerned with getting well-calibrated predictions in regression tasks...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	182,822
2212.06368	Single Cell Training on Architecture Search for Image Denoising	Neural Architecture Search (NAS) for automatically finding the optimal network architecture has shown some success with competitive performances in various computer vision tasks. However, NAS in general requires a tremendous amount of computations. Thus reducing computational cost has emerged as an important issue. Mos...	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	336,093
1405.4507	A Multi-parent Memetic Algorithm for the Linear Ordering Problem	In this paper, we present a multi-parent memetic algorithm (denoted by MPM) for solving the classic Linear Ordering Problem (LOP). The MPM algorithm integrates in particular a multi-parent recombination operator for generating offspring solutions and a distance-and-quality based criterion for pool updating. Our MPM alg...	false	false	false	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	33,181
2006.08937	Channel Relationship Prediction with Forget-Update Module for Few-shot Classification	In this paper, we proposed a pipeline for inferring the relationship of each class in support set and a query sample using forget-update module. We first propose a novel architectural module called "channel vector sequence construction module", which boosts the performance of sequence-prediction-model-based few-shot cl...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	182,366
2104.06481	Political Polarization in Online News Consumption	Political polarization appears to be on the rise, as measured by voting behavior, general affect towards opposing partisans and their parties, and contents posted and consumed online. Research over the years has focused on the role of the Web as a driver of polarization. In order to further our understanding of the fac...	false	false	false	true	false	false	false	false	false	false	false	false	false	true	false	false	false	false	230,087
2207.02238	Improving Trustworthiness of AI Disease Severity Rating in Medical Imaging with Ordinal Conformal Prediction Sets	The regulatory approval and broad clinical deployment of medical AI have been hampered by the perception that deep learning models fail in unpredictable and possibly catastrophic ways. A lack of statistically rigorous uncertainty quantification is a significant factor undermining trust in AI results. Recent development...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	306,445
1905.04127	Design of Artificial Intelligence Agents for Games using Deep Reinforcement Learning	In order perform a large variety of tasks and to achieve human-level performance in complex real-world environments, Artificial Intelligence (AI) Agents must be able to learn from their past experiences and gain both knowledge and an accurate representation of their environment from raw sensory inputs. Traditionally, A...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	130,379
2303.11454	How (Implicit) Regularization of ReLU Neural Networks Characterizes the Learned Function -- Part II: the Multi-D Case of Two Layers with Random First Layer	Randomized neural networks (randomized NNs), where only the terminal layer's weights are optimized constitute a powerful model class to reduce computational time in training the neural network model. At the same time, these models generalize surprisingly well in various regression and classification tasks. In this pape...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	352,856
2202.11697	Stochastic Coded Offloading Scheme for Unmanned Aerial Vehicle-Assisted Edge Computing	Unmanned aerial vehicles (UAVs) have gained wide research interests due to their technological advancement and high mobility. The UAVs are equipped with increasingly advanced capabilities to run computationally intensive applications enabled by machine learning techniques. However, because of both energy and computatio...	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	281,966
2005.06149	DeepRobust: A PyTorch Library for Adversarial Attacks and Defenses	DeepRobust is a PyTorch adversarial learning library which aims to build a comprehensive and easy-to-use platform to foster this research field. It currently contains more than 10 attack algorithms and 8 defense algorithms in image domain and 9 attack algorithms and 4 defense algorithms in graph domain, under a variety...	false	false	false	false	false	false	true	false	false	false	false	false	true	false	false	false	false	false	176,928
2404.10200	TEL'M: Test and Evaluation of Language Models	Language Models have demonstrated remarkable capabilities on some tasks while failing dramatically on others. The situation has generated considerable interest in understanding and comparing the capabilities of various Language Models (LMs) but those efforts have been largely ad hoc with results that are often little m...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	446,990
1709.09041	Switching and Information Exchange in Compressed Estimation of Coupled High Dimensional Processes	Compressed Estimation approaches, such as the Generalised Compressed Kalman Filter (GCKF), reduce the computational cost and complexity of high dimensional and high frequency data assimilation problems; usually without sacrificing optimality. Configured using adequate cores, such as the Unscented Kalman Filter (UKF), t...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	81,564
2110.14549	Latent Equilibrium: A unified learning theory for arbitrarily fast computation with arbitrarily slow neurons	The response time of physical computational elements is finite, and neurons are no exception. In hierarchical models of cortical networks each layer thus introduces a response lag. This inherent property of physical dynamical systems results in delayed processing of stimuli and causes a timing mismatch between network ...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	true	false	false	263,568
2502.08514	Faithful, Unfaithful or Ambiguous? Multi-Agent Debate with Initial Stance for Summary Evaluation	Faithfulness evaluators based on large language models (LLMs) are often fooled by the fluency of the text and struggle with identifying errors in the summaries. We propose an approach to summary faithfulness evaluation in which multiple LLM-based agents are assigned initial stances (regardless of what their belief migh...	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	533,038
2410.14942	2D Basement Relief Inversion using Sparse Regularization	Basement relief gravimetry is crucial in geophysics, especially for oil exploration and mineral prospecting. It involves solving an inverse problem to infer geological model parameters from observed data. The model represents basement relief with constant-density prisms, and the data reflect gravitational anomalies fro...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	500,273
1809.09414	Triple Trustworthiness Measurement for Knowledge Graph	The Knowledge graph (KG) uses the triples to describe the facts in the real world. It has been widely used in intelligent analysis and applications. However, possible noises and conflicts are inevitably introduced in the process of constructing. And the KG based tasks or applications assume that the knowledge in the KG...	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	false	108,704
2210.09468	Chance Constrained Stochastic Optimal Control for Linear Systems with Time Varying Random Plant Parameters	We propose an open loop control scheme for linear systems with time-varying random elements in the plant's state matrix. This paper focuses on joint chance constraints for potentially time-varying target sets. Under assumption of finite and known expectation and variance, we use the one-sided Vysochanskij-Petunin inequ...	false	false	false	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	324,542
2206.04349	Deep radiomic signature with immune cell markers predicts the survival of glioma patients	Imaging biomarkers offer a non-invasive way to predict the response of immunotherapy prior to treatment. In this work, we propose a novel type of deep radiomic features (DRFs) computed from a convolutional neural network (CNN), which capture tumor characteristics related to immune cell markers and overall survival. Our...	false	false	false	false	true	false	false	false	false	false	false	true	false	false	false	false	false	false	301,593
1605.05087	Word2Vec is a special case of Kernel Correspondence Analysis and Kernels for Natural Language Processing	We show that correspondence analysis (CA) is equivalent to defining a Gini index with appropriately scaled one-hot encoding. Using this relation, we introduce a nonlinear kernel extension to CA. This extended CA gives a known analysis for natural language via specialized kernels that use an appropriate contingency tabl...	false	false	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	55,950
1803.07624	Dynamic Filtering with Large Sampling Field for ConvNets	We propose a dynamic filtering strategy with large sampling field for ConvNets (LS-DFN), where the position-specific kernels learn from not only the identical position but also multiple sampled neighbor regions. During sampling, residual learning is introduced to ease training and an attention mechanism is applied to f...	false	false	false	false	false	false	true	false	false	false	false	true	false	false	false	false	false	false	93,091
2411.16615	Graph Pooling by Local Cluster Selection	Graph pooling is a family of operations which take graphs as input and produce shrinked graphs as output. Modern graph pooling methods are trainable and, in general inserted in Graph Neural Networks (GNNs) architectures as graph shrinking operators along the (deep) processing pipeline. This work proposes a novel proced...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	511,090
1106.0518	Submodular Functions Are Noise Stable	We show that all non-negative submodular functions have high {\em noise-stability}. As a consequence, we obtain a polynomial-time learning algorithm for this class with respect to any product distribution on $\{-1,1\}^n$ (for any constant accuracy parameter $\epsilon$). Our algorithm also succeeds in the agnostic setti...	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	true	10,692
2302.13935	RangedIK: An Optimization-based Robot Motion Generation Method for Ranged-Goal Tasks	Generating feasible robot motions in real-time requires achieving multiple tasks (i.e., kinematic requirements) simultaneously. These tasks can have a specific goal, a range of equally valid goals, or a range of acceptable goals with a preference toward a specific goal. To satisfy multiple and potentially competing tas...	false	false	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	348,101
0905.4162	Google matrix, dynamical attractors and Ulam networks	We study the properties of the Google matrix generated by a coarse-grained Perron-Frobenius operator of the Chirikov typical map with dissipation. The finite size matrix approximant of this operator is constructed by the Ulam method. This method applied to the simple dynamical model creates the directed Ulam networks w...	false	false	false	false	false	true	false	false	false	false	false	false	false	false	false	false	false	false	3,770
1906.08149	Efficient privacy preservation of big data for accurate data mining	Computing technologies pervade physical spaces and human lives, and produce a vast amount of data that is available for analysis. However, there is a growing concern that potentially sensitive data may become public if the collected data are not appropriately sanitized before being released for investigation. Although ...	false	false	false	false	false	false	false	false	false	false	false	false	true	false	false	false	true	false	135,790
2301.03134	A Semi-supervised Approach for Activity Recognition from Indoor Trajectory Data	The increasingly wide usage of location aware sensors has made it possible to collect large volume of trajectory data in diverse application domains. Machine learning allows to study the activities or behaviours of moving objects (e.g., people, vehicles, robot) using such trajectory data with rich spatiotemporal inform...	false	false	false	false	true	false	true	false	false	false	false	false	false	false	false	false	false	false	339,707

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