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One-stop shop to learn paperswithcode state-of-the-art research papers with access to open-source resources including machine learning models, paperswithcode, datasets, methods, evaluation tables, and code. Image by author.

To overcome this dilemma, we observe the high similarity between the input from adjacent diffusion steps and propose displaced patch parallelism, which takes advantage of the sequential nature of the diffusion process by reusing the pre-computed feature maps from the previous timestep to provide context for the current step. Recent studies have demonstrated the capabilities of LLMs to automatically conduct prompt engineering by employing a meta-prompt that incorporates the outcomes of the last trials and proposes an improved prompt. Prompt Engineering. Marketing Video Generation. It can be used to obtain complete information, so that train-from-scratch models can achieve better results than state-of-the-art models pre-trained using large datasets, the comparison results are shown in Figure 1. Additionally, a comprehensive review of the existing available dataset resources is also provided, including statistics from datasets, covering 8 language categories and spanning 32 domains.

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The existence of a maximal ideal in a general nontrivial commutative ring is tied together with the axiom of choice. We also study the problem without restriction on the optimal plan, and provide lower and upper bounds for the value of the Gromov-Wasserstein distance between Gaussian distributions. Bidirectional data accessors such as lenses, prisms and traversals are all instances of the same general 'optic' construction. Category Theory. Polyak momentum PM , also known as the heavy-ball method, is a widely used optimization method that enjoys an asymptotic optimal worst-case complexity on quadratic objectives. Optimization and Control. We introduce a numerical framework to verify the finite step convergence of first-order methods for parametric convex quadratic optimization.

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This technical report introduces TripoSR, a 3D reconstruction model leveraging transformer architecture for fast feed-forward 3D generation, producing 3D mesh from a single image in under 0. Our approach reduces memory usage by up to Our formulation directly provides a 3D model of the scene as well as depth information, but interestingly, we can seamlessly recover from it, pixel matches, relative and absolute camera. Despite the vast repository of global medical knowledge predominantly being in English, local languages are crucial for delivering tailored healthcare services, particularly in areas with limited medical resources. Despite the success in specific tasks and scenarios, existing foundation agents, empowered by large models LMs and advanced tools, still cannot generalize to different scenarios, mainly due to dramatic differences in the observations and actions across scenarios. Efficient Exploration. Imitation learning provides an efficient way to teach robots dexterous skills; however, learning complex skills robustly and generalizablely usually consumes large amounts of human demonstrations. Imitation Learning. Retrieval-augmented language models can better adapt to changes in world state and incorporate long-tail knowledge. Question Answering Retrieval.

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It can be used to obtain complete information, so that train-from-scratch models can achieve better results than state-of-the-art models pre-trained using large datasets, the comparison results are shown in Figure 1. If you want to add code to a paper, evaluation table, task or dataset then find the edit button on a particular page to modify it. A Brief Introduction to Papers With Code One-stop shop to learn about state-of-the-art research papers with access to open-source resources including machine learning models, datasets, methods, evaluation tables, and code. Language Modelling Large Language Model. The increasing size of large language models has posed challenges for deployment and raised concerns about environmental impact due to high energy consumption. Recent studies have demonstrated the capabilities of LLMs to automatically conduct prompt engineering by employing a meta-prompt that incorporates the outcomes of the last trials and proposes an improved prompt. You can read the abstract or even download the full paper from arxiv or general publications. Image from ImageNet Benchmark. The platform also provides a link to Hugging Face Spaces with GitHub repository so that you can experience how the model works. Anyone can contribute by clicking on the edit button. State of the Art State of the Art section contains benchmark machine learning models, tasks and sub-tasks Knowledge Distillation, Few-Shot Image Classification , 65, papers with code. Papers with Code have several features that enable machine learning practitioners and researchers to learn and contribute to cutting-edge technologies. You can reproduce the results by using the code, checking all the previous implementations with the model performance metrics, viewing the dataset, models, and methods used in the research paper. Prompt Engineering. Abid Ali Awan 1abidaliawan is a certified data scientist professional who loves building machine learning models.

The mission of Papers with Code is to create a free and open resource with Machine Learning papers, code, datasets, methods and evaluation tables. We also operate specialized portals for papers with code in astronomy, physics, computer sciences, mathematics and statistics. We hang out on Slack , come join us!

Papers with Code have several features that enable machine learning practitioners and researchers to learn and contribute to cutting-edge technologies. The Data page is easy to navigate and within a few minutes you can understand the modality, license information, papers published, and benchmark based on subcategories. Each method has some sort of variation that has been used to create models or used in processing the data. For example, you can add the results on the Hugging Face model, and it will show up on Papers with Code with the dataset, model, and model metrics. Paper Code. Gif from CoAtNet Conclusion Papers with Code have several features that enable machine learning practitioners and researchers to learn and contribute to cutting-edge technologies. The name tells everything. Gif from CoAtNet. The increasing size of large language models has posed challenges for deployment and raised concerns about environmental impact due to high energy consumption. The platform also provides a link to Hugging Face Spaces with GitHub repository so that you can experience how the model works. Image from Papers With Code Anyone can contribute by clicking on the edit button. It can be used to obtain complete information, so that train-from-scratch models can achieve better results than state-of-the-art models pre-trained using large datasets, the comparison results are shown in Figure 1. Language Modelling Large Language Model.