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CLI tools, utilities, converters, workflow automation, and productivity helpers
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Showing 17,665–17,688 of 49,285 skills
- Deep Loopy Neural Network Model For Graph Structured Data Representation Learning**arXiv ID:** 1805.07504 **Authors:** Jiawei Zhang **Published:** 2018-05-19T03:33:20Z **Abstract:** Existing deep learning models may encounter great challenges in handling graph structured data. In this paper, we introduce a new deep learning model for graph data specifically, namely the deep loopy neural network. Significantly different from the previous deep models, inside the deep loopy neural network, there exist a large number of loops created by the extensive connections among nodes i...Votes: 0GitHub stars: 3
- Deep Learning For Explicitly Modeling Optimization Landscapes**arXiv ID:** 1703.07394 **Authors:** Shumeet Baluja **Published:** 2017-03-21T19:12:35Z **Abstract:** In all but the most trivial optimization problems, the structure of the solutions exhibit complex interdependencies between the input parameters. Decades of research with stochastic search techniques has shown the benefit of explicitly modeling the interactions between sets of parameters and the overall quality of the solutions discovered. We demonstrate a novel method, based on learning dee...Votes: 0GitHub stars: 3
- Cyclic Data Parallelism For Efficient Parallelism Of Deep Neural Networks**arXiv ID:** 2403.08837 **Authors:** Louis Fournier, Edouard Oyallon **Published:** 2024-03-13T08:39:21Z **Abstract:** Training large deep learning models requires parallelization techniques to scale. In existing methods such as Data Parallelism or ZeRO-DP, micro-batches of data are processed in parallel, which creates two drawbacks: the total memory required to store the model's activations peaks at the end of the forward pass, and gradients must be simultaneously averaged at the end of the...Votes: 0GitHub stars: 3
- Ctl Evaluating Generalization On Neverseen Compositional Patterns Of Known Functions And Compatibility Of Neural Representations**arXiv ID:** 2210.06350 **Authors:** Róbert Csordás, Kazuki Irie, Jürgen Schmidhuber **Published:** 2022-10-12T16:01:57Z **Abstract:** Well-designed diagnostic tasks have played a key role in studying the failure of neural nets (NNs) to generalize systematically. Famous examples include SCAN and Compositional Table Lookup (CTL). Here we introduce CTL++, a new diagnostic dataset based on compositions of unary symbolic functions. While the original CTL is used to test length generalization or ...Votes: 0GitHub stars: 3
- Continuous Pde Dynamics Forecasting With Implicit Neural Representations**arXiv ID:** 2209.14855 **Authors:** Yuan Yin, Matthieu Kirchmeyer, Jean-Yves Franceschi, Alain Rakotomamonjy, Patrick Gallinari **Published:** 2022-09-29T15:17:50Z **Abstract:** Effective data-driven PDE forecasting methods often rely on fixed spatial and / or temporal discretizations. This raises limitations in real-world applications like weather prediction where flexible extrapolation at arbitrary spatiotemporal locations is required. We address this problem by introducing a new data-dri...Votes: 0GitHub stars: 3
- Continual Learning Of A Mixed Sequence Of Similar And Dissimilar Tasks**arXiv ID:** 2112.10017 **Authors:** Zixuan Ke, Bing Liu, Xingchang Huang **Published:** 2021-12-18T22:37:30Z **Abstract:** Existing research on continual learning of a sequence of tasks focused on dealing with catastrophic forgetting, where the tasks are assumed to be dissimilar and have little shared knowledge. Some work has also been done to transfer previously learned knowledge to the new task when the tasks are similar and have shared knowledge. To the best of our knowledge, no techniqu...Votes: 0GitHub stars: 3
- Contextual Graph Markov Model A Deep And Generative Approach To Graph Processing**arXiv ID:** 1805.10636 **Authors:** Davide Bacciu, Federico Errica, Alessio Micheli **Published:** 2018-05-27T15:04:05Z **Abstract:** We introduce the Contextual Graph Markov Model, an approach combining ideas from generative models and neural networks for the processing of graph data. It founds on a constructive methodology to build a deep architecture comprising layers of probabilistic models that learn to encode the structured information in an incremental fashion. Context is diffused in...Votes: 0GitHub stars: 3
- Constitutional Midtraining Content Presence DrivesConstitutional Midtraining: Content Presence Drives Alignment GainsVotes: 0GitHub stars: 3
- Concentric Esn Assessing The Effect Of Modularity In Cycle Reservoirs**arXiv ID:** 1805.09244 **Authors:** Davide Bacciu, Andrea Bongiorno **Published:** 2018-05-23T15:58:57Z **Abstract:** The paper introduces concentric Echo State Network, an approach to design reservoir topologies that tries to bridge the gap between deterministically constructed simple cycle models and deep reservoir computing approaches. We show how to modularize the reservoir into simple unidirectional and concentric cycles with pairwise bidirectional jump connections between adjacent loo...Votes: 0GitHub stars: 3
- Combining Modelfree Qensembles And Modelbased Approaches For Informed Exploration**arXiv ID:** 1806.04552 **Authors:** Sreecharan Sankaranarayanan, Raghuram Mandyam Annasamy, Katia Sycara, Carolyn Penstein Rosé **Published:** 2018-06-12T14:24:02Z **Abstract:** Q-Ensembles are a model-free approach where input images are fed into different Q-networks and exploration is driven by the assumption that uncertainty is proportional to the variance of the output Q-values obtained. They have been shown to perform relatively well compared to other exploration strategies. Further, m...Votes: 0GitHub stars: 3
- Collaborative Storytelling With Largescale Neural Language Models**arXiv ID:** 2011.10208 **Authors:** Eric Nichols, Leo Gao, Randy Gomez **Published:** 2020-11-20T04:36:54Z **Abstract:** Storytelling plays a central role in human socializing and entertainment. However, much of the research on automatic storytelling generation assumes that stories will be generated by an agent without any human interaction. In this paper, we introduce the task of collaborative storytelling, where an artificial intelligence agent and a person collaborate to create a unique ...Votes: 0GitHub stars: 3
- Coarse To Fine Nonrigid Registration A Chain Of Scalespecific Neural Networks For Multimodal Image Alignment With Application To Remote Sensing**arXiv ID:** 1802.09816 **Authors:** Armand Zampieri, Guillaume Charpiat, Yuliya Tarabalka **Published:** 2018-02-27T10:47:06Z **Abstract:** We tackle here the problem of multimodal image non-rigid registration, which is of prime importance in remote sensing and medical imaging. The difficulties encountered by classical registration approaches include feature design and slow optimization by gradient descent. By analyzing these methods, we note the significance of the notion of scale. We desi...Votes: 0GitHub stars: 3
- Closing The Lab To Store Gap A Data Efficient PostSkill generated from arXiv paper 2607.20345: Closing the Lab-to-Store Gap: A Data-Efficient Post-Training and Experience-Driven Learning VLA Framework for Retail HumanoidsVotes: 0GitHub stars: 3
- Closedform Continuoustime Neural Models**arXiv ID:** 2106.13898 **Authors:** Ramin Hasani, Mathias Lechner, Alexander Amini, Lucas Liebenwein, Aaron Ray, Max Tschaikowski, Gerald Teschl, Daniela Rus **Published:** 2021-06-25T22:08:51Z **Abstract:** Continuous-time neural processes are performant sequential decision-makers that are built by differential equations (DE). However, their expressive power when they are deployed on computers is bottlenecked by numerical DE solvers. This limitation has significantly slowed down the scalin...Votes: 0GitHub stars: 3
- Cg World A Large Scale World State Dataset And ProCG-World: A Large-Scale World-State Dataset and Protocol for World ModelsVotes: 0GitHub stars: 3
- Can Ai Agents Really Complete Rtl To Gds Lessons FDerived from arXiv:2607.17528 - Can AI Agents Really Complete RTL-to-GDS? Lessons from Benchmarking Tool-Interactive EDA WorkflowsVotes: 0GitHub stars: 3
- Calibration Error Estimation Using Fuzzy Binning**arXiv ID:** 2305.00543 **Authors:** Geetanjali Bihani, Julia Taylor Rayz **Published:** 2023-04-30T18:06:14Z **Abstract:** Neural network-based decisions tend to be overconfident, where their raw outcome probabilities do not align with the true decision probabilities. Calibration of neural networks is an essential step towards more reliable deep learning frameworks. Prior metrics of calibration error primarily utilize crisp bin membership-based measures. This exacerbates skew in model proba...Votes: 0GitHub stars: 3
- Browser Based Arxiv ResearchBrowser-based arXiv research methodology for reliable paper discovery when API methods fail. Uses browser navigation to arXiv listing pages combined with JavaScript console extraction for structured paper metadata. Use when automated arXiv searches encounter connection issues, security blocks, SSL errors, or rate limiting.Votes: 0GitHub stars: 3
- Bm3d Vs 2layer Onn**arXiv ID:** 2103.03060 **Authors:** Junaid Malik, Serkan Kiranyaz, Mehmet Yamac, Moncef Gabbouj **Published:** 2021-03-04T14:37:23Z **Abstract:** Despite their recent success on image denoising, the need for deep and complex architectures still hinders the practical usage of CNNs. Older but computationally more efficient methods such as BM3D remain a popular choice, especially in resource-constrained scenarios. In this study, we aim to find out whether compact neural networks can learn to p...Votes: 0GitHub stars: 3
- Biomaker Ca A Biome Maker Project Using Cellular Automata**arXiv ID:** 2307.09320 **Authors:** Ettore Randazzo, Alexander Mordvintsev **Published:** 2023-07-18T15:03:40Z **Abstract:** We introduce Biomaker CA: a Biome Maker project using Cellular Automata (CA). In Biomaker CA, morphogenesis is a first class citizen and small seeds need to grow into plant-like organisms to survive in a nutrient starved environment and eventually reproduce with variation so that a biome survives for long timelines. We simulate complex biomes by means of CA rules in 2...Votes: 0GitHub stars: 3
- Biologicallyplausible Learning Algorithms Can Scale To Large Datasets**arXiv ID:** 1811.03567 **Authors:** Will Xiao, Honglin Chen, Qianli Liao, Tomaso Poggio **Published:** 2018-11-08T17:43:59Z **Abstract:** The backpropagation (BP) algorithm is often thought to be biologically implausible in the brain. One of the main reasons is that BP requires symmetric weight matrices in the feedforward and feedback pathways. To address this "weight transport problem" (Grossberg, 1987), two more biologically plausible algorithms, proposed by Liao et al. (2016) and Lillicr...Votes: 0GitHub stars: 3
- Beyond Uniform Scaling Exploring Depth Heterogeneity In Neural Architectures**arXiv ID:** 2402.12418 **Authors:** Akash Guna R. T, Arnav Chavan, Deepak Gupta **Published:** 2024-02-19T09:52:45Z **Abstract:** Conventional scaling of neural networks typically involves designing a base network and growing different dimensions like width, depth, etc. of the same by some predefined scaling factors. We introduce an automated scaling approach leveraging second-order loss landscape information. Our method is flexible towards skip connections a mainstay in modern vision trans...Votes: 0GitHub stars: 3
- Bayesian Optimization With Automatic Prior Selection For Dataefficient Direct Policy Search**arXiv ID:** 1709.06919 **Authors:** Rémi Pautrat, Konstantinos Chatzilygeroudis, Jean-Baptiste Mouret **Published:** 2017-09-20T15:04:50Z **Abstract:** One of the most interesting features of Bayesian optimization for direct policy search is that it can leverage priors (e.g., from simulation or from previous tasks) to accelerate learning on a robot. In this paper, we are interested in situations for which several priors exist but we do not know in advance which one fits best the current sit...Votes: 0GitHub stars: 3
- Bayesian Deep Convolutional Networks With Many Channels Are Gaussian Processes**arXiv ID:** 1810.05148 **Authors:** Roman Novak, Lechao Xiao, Jaehoon Lee, Yasaman Bahri, Greg Yang, Jiri Hron, Daniel A. Abolafia, Jeffrey Pennington, Jascha Sohl-Dickstein **Published:** 2018-10-11T17:49:41Z **Abstract:** There is a previously identified equivalence between wide fully connected neural networks (FCNs) and Gaussian processes (GPs). This equivalence enables, for instance, test set predictions that would have resulted from a fully Bayesian, infinitely wide trained FCN to be c...Votes: 0GitHub stars: 3