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Showing 17,641–17,664 of 49,285 skills
- Inferscale Gpu Native Kv Injection For PersonalizeInferScale: GPU-Native KV Injection for Personalized LLM ServingVotes: 0GitHub stars: 3
- Improved Forecasting Using A Psordv Framework To Enhance Artificial Neural Network**arXiv ID:** 2402.18576 **Authors:** Sales Aribe **Published:** 2024-01-10T01:15:33Z **Abstract:** Decision making and planning have long relied heavily on AI-driven forecasts. The government and the general public are working to minimize the risks while maximizing benefits in the face of potential future public health uncertainties. This study used an improved method of forecasting utilizing the Random Descending Velocity Inertia Weight (RDV IW) technique to improve the convergence of Parti...Votes: 0GitHub stars: 3
- Improved Data Encoding For Emerging Computing Paradigms From Stochastic To Hyperdimensional Computing**arXiv ID:** 2501.02715 **Authors:** Mehran Shoushtari Moghadam, Sercan Aygun, M. Hassan Najafi **Published:** 2025-01-06T02:07:49Z **Abstract:** Data encoding is a fundamental step in emerging computing paradigms, particularly in stochastic computing (SC) and hyperdimensional computing (HDC), where it plays a crucial role in determining the overall system performance and hardware cost efficiency. This study presents an advanced encoding strategy that leverages a hardware-friendly class of l...Votes: 0GitHub stars: 3
- Image Captioning Using Deep Stacked Lstms Contextual Word Embeddings And Data Augmentation**arXiv ID:** 2102.11237 **Authors:** Sulabh Katiyar, Samir Kumar Borgohain **Published:** 2021-02-22T18:15:39Z **Abstract:** Image Captioning, or the automatic generation of descriptions for images, is one of the core problems in Computer Vision and has seen considerable progress using Deep Learning Techniques. We propose to use Inception-ResNet Convolutional Neural Network as encoder to extract features from images, Hierarchical Context based Word Embeddings for word representations and a D...Votes: 0GitHub stars: 3
- Illuminating The Space Of Beatable Lode Runner Levels Produced By Various Generative Adversarial Networks**arXiv ID:** 2101.07868 **Authors:** Kirby Steckel, Jacob Schrum **Published:** 2021-01-19T21:41:42Z **Abstract:** Generative Adversarial Networks (GANs) are capable of generating convincing imitations of elements from a training set, but the distribution of elements in the training set affects to difficulty of properly training the GAN and the quality of the outputs it produces. This paper looks at six different GANs trained on different subsets of data from the game Lode Runner. The qualit...Votes: 0GitHub stars: 3
- Homogeneous Artificial Neural Network**arXiv ID:** 2311.17973 **Authors:** Andrey Polyakov **Published:** 2023-11-29T16:16:32Z **Abstract:** The paper proposes an artificial neural network (ANN) being a global approximator for a special class of functions, which are known as generalized homogeneous. The homogeneity means a symmetry of a function with respect to a group of transformations having topological characterization of a dilation. In this paper, a class of the so-called linear dilations is considered. A homogeneous univer...Votes: 0GitHub stars: 3
- High Performance Im2win And Direct Convolutions Using Three Tensor Layouts On Simd Architectures**arXiv ID:** 2408.00278 **Authors:** Xiang Fu, Xinpeng Zhang, Jixiang Ma, Peng Zhao, Shuai Lu, Xu T. Liu **Published:** 2024-08-01T04:37:03Z **Abstract:** Convolution is the core component within deep neural networks and it is computationally intensive and time consuming. Tensor data layouts significantly impact convolution operations in terms of memory access and computational efficiency. Yet, there is still a lack of comprehensive performance characterization on data layouts on SIMD archit...Votes: 0GitHub stars: 3
- Guiding Inferences In Connection Tableau By Recurrent Neural Networks**arXiv ID:** 1905.07961 **Authors:** Bartosz Piotrowski, Josef Urban **Published:** 2019-05-20T09:47:41Z **Abstract:** We present a dataset and experiments on applying recurrent neural networks (RNNs) for guiding clause selection in the connection tableau proof calculus. The RNN encodes a sequence of literals from the current branch of the partial proof tree to a hidden vector state; using it, the system selects a clause for extending the proof tree. The training data and learning setup are ...Votes: 0GitHub stars: 3
- Genplusss A Genetic Algorithm Based Plugin For Measured Subsurface Scattering Representation**arXiv ID:** 2401.15245 **Authors:** Barış Yıldırım, Murat Kurt **Published:** 2024-01-26T23:31:53Z **Abstract:** This paper presents a plugin that adds a representation of homogeneous and heterogeneous, optically thick, translucent materials on the Blender 3D modeling tool. The working principle of this plugin is based on a combination of Genetic Algorithm (GA) and Singular Value Decomposition (SVD)-based subsurface scattering method (GenSSS). The proposed plugin has been implemented using ...Votes: 0GitHub stars: 3
- From Observation To Prediction Lstm For Vehicle Lane Change Forecasting On Highway Onofframps**arXiv ID:** 2601.14848 **Authors:** Mohamed Abouras, Catherine M. Elias **Published:** 2026-01-21T10:31:03Z **Abstract:** On and off-ramps are understudied road sections even though they introduce a higher level of variation in highway interactions. Predicting vehicles' behavior in these areas can decrease the impact of uncertainty and increase road safety. In this paper, the difference between this Area of Interest (AoI) and a straight highway section is studied. Multi-layered LSTM archite...Votes: 0GitHub stars: 3
- From Conceptual Hydrologic Models To ConceptuallyFrom Conceptual Hydrologic Models to Conceptually Interpretable Neural Networks: A Snow-Water Mass-CVotes: 0GitHub stars: 3
- Forecasting Of Nonstationary Sales Time Series Using Deep Learning**arXiv ID:** 2205.11636 **Authors:** Bohdan M. Pavlyshenko **Published:** 2022-05-23T21:06:27Z **Abstract:** The paper describes the deep learning approach for forecasting non-stationary time series with using time trend correction in a neural network model. Along with the layers for predicting sales values, the neural network model includes a subnetwork block for the prediction weight for a time trend term which is added to a predicted sales value. The time trend term is considered as a pro...Votes: 0GitHub stars: 3
- Fmrp Lean A Hipaa Compliant Ai Augmented Lims ArchSkill generated from arXiv paper 2607.20382: FMRP-LEAN: A HIPAA-Compliant AI-Augmented LIMS Architecture for End-to-End Clinical Assay Workflow OptimizationVotes: 0GitHub stars: 3
- Fast Exploration Of Weight Sharing Opportunities For Cnn Compression**arXiv ID:** 2102.01345 **Authors:** Etienne Dupuis, David Novo, Ian O'Connor, Alberto Bosio **Published:** 2021-02-02T06:45:56Z **Abstract:** The computational workload involved in Convolutional Neural Networks (CNNs) is typically out of reach for low-power embedded devices. There are a large number of approximation techniques to address this problem. These methods have hyper-parameters that need to be optimized for each CNNs using design space exploration (DSE). The goal of this work is to...Votes: 0GitHub stars: 3
- Evothink Evolving Thinking In Large Reasoning ModeSkill generated from arXiv paper 2607.19962: EvoThink: Evolving Thinking in Large Reasoning Models via Self-Pruning and Aha-Moment Preference OptimizationVotes: 0GitHub stars: 3
- Evolutionbased Feature Selection For Predicting Dissolved Oxygen Concentrations In Lakes**arXiv ID:** 2403.18923 **Authors:** Runlong Yu, Robert Ladwig, Xiang Xu, Peijun Zhu, Paul C. Hanson, Yiqun Xie, Xiaowei Jia **Published:** 2024-02-15T20:27:33Z **Abstract:** Accurate prediction of dissolved oxygen (DO) concentrations in lakes requires a comprehensive study of phenological patterns across ecosystems, highlighting the need for precise selection of interactions amongst external factors and internal physical-chemical-biological variables. This paper presents the Multi-populatio...Votes: 0GitHub stars: 3
- Empirical Learning Aided By Weak Domain Knowledge In The Form Of Feature Importance**arXiv ID:** 1005.5556 **Authors:** Ridwan Al Iqbal **Published:** 2010-05-30T19:28:01Z **Abstract:** Standard hybrid learners that use domain knowledge require stronger knowledge that is hard and expensive to acquire. However, weaker domain knowledge can benefit from prior knowledge while being cost effective. Weak knowledge in the form of feature relative importance (FRI) is presented and explained. Feature relative importance is a real valued approximation of a feature's importance provid...Votes: 0GitHub stars: 3
- Efficient Methods For Unsupervised Learning Of Probabilistic Models**arXiv ID:** 1205.4295 **Authors:** Jascha Sohl-Dickstein **Published:** 2012-05-19T04:25:04Z **Abstract:** In this thesis I develop a variety of techniques to train, evaluate, and sample from intractable and high dimensional probabilistic models. Abstract exceeds arXiv space limitations -- see PDF.Votes: 0GitHub stars: 3
- Efficient Learning Of Locomotion Skills Through The Discovery Of Diverse Environmental Trajectory Generator Priors**arXiv ID:** 2210.04819 **Authors:** Shikha Surana, Bryan Lim, Antoine Cully **Published:** 2022-10-10T16:31:11Z **Abstract:** Data-driven learning based methods have recently been particularly successful at learning robust locomotion controllers for a variety of unstructured terrains. Prior work has shown that incorporating good locomotion priors in the form of trajectory generators (TGs) is effective at efficiently learning complex locomotion skills. However, defining a good, single TG as ...Votes: 0GitHub stars: 3
- Echo State Queueing Network A New Reservoir Computing Learning Tool**arXiv ID:** 1212.6276 **Authors:** Sebastián Basterrech, Gerardo Rubino **Published:** 2012-12-26T22:31:13Z **Abstract:** In the last decade, a new computational paradigm was introduced in the field of Machine Learning, under the name of Reservoir Computing (RC). RC models are neural networks which a recurrent part (the reservoir) that does not participate in the learning process, and the rest of the system where no recurrence (no neural circuit) occurs. This approach has grown rapidly due ...Votes: 0GitHub stars: 3
- Dynamic Spiking Framework For Graph Neural Networks**arXiv ID:** 2401.05373 **Authors:** Nan Yin, Mengzhu Wang, Zhenghan Chen, Giulia De Masi, Bin Gu, Huan Xiong **Published:** 2023-12-15T12:45:47Z **Abstract:** The integration of Spiking Neural Networks (SNNs) and Graph Neural Networks (GNNs) is gradually attracting attention due to the low power consumption and high efficiency in processing the non-Euclidean data represented by graphs. However, as a common problem, dynamic graph representation learning faces challenges such as high complexi...Votes: 0GitHub stars: 3
- Do You Really Need To Pretrain Q Functions For OnlDo You Really Need to Pretrain Q-Functions for Online RL Fine-Tuning?Votes: 0GitHub stars: 3
- Deep Oscillatory Neural Network**arXiv ID:** 2405.03725 **Authors:** Nurani Rajagopal Rohan, Vigneswaran C, Sayan Ghosh, Kishore Rajendran, Gaurav A, V Srinivasa Chakravarthy **Published:** 2024-05-06T06:17:16Z **Abstract:** We propose a novel, brain-inspired deep neural network model known as the Deep Oscillatory Neural Network (DONN). Deep neural networks like the Recurrent Neural Networks indeed possess sequence processing capabilities but the internal states of the network are not designed to exhibit brain-like oscilla...Votes: 0GitHub stars: 3
- Deep Neural Regression Collapse**arXiv ID:** 2603.23805 **Authors:** Akshay Rangamani, Altay Unal **Published:** 2026-03-25T00:26:16Z **Abstract:** Neural Collapse is a phenomenon that helps identify sparse and low rank structures in deep classifiers. Recent work has extended the definition of neural collapse to regression problems, albeit only measuring the phenomenon at the last layer. In this paper, we establish that Neural Regression Collapse (NRC) also occurs below the last layer across different types of models. We s...Votes: 0GitHub stars: 3