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https://openreview.net/forum?id=RwgNbIpCpk Reparameterized Multi-Resolution Convolutions for Long Sequence Modelling | OpenReview Global convolutions have shown increasing promise as powerful general-purpose sequence models. However, training long convolutions is challenging, and kernel... multi resolution convolutions long sequence https://deepai.org/publication/geometric-graph-representations-and-geometric-graph-convolutions-for-deep-learning-on-three-dimensional-3d-graphs Geometric Graph Representations and Geometric Graph Convolutions for Deep Learning on... Jun 2, 2020 - 06/02/20 - The geometry of three-dimensional (3D) graphs, consisting of nodes and edges, plays a crucial role in many important applications.... geometric graph deep learning representations convolutions https://huggingface.co/papers/2311.05908 Paper page - FlashFFTConv: Efficient Convolutions for Long Sequences with Tensor Cores Join the discussion on this paper page paper efficient convolutions https://github.com/limuhit/pseudocylindrical_convolution GitHub - limuhit/pseudocylindrical_convolution: Pseudocylindrical convolutions for Learned... Pseudocylindrical convolutions for Learned Omnidirectional Image Compression - limuhit/pseudocylindrical_convolution github pseudocylindrical convolution learned https://www.frontiersin.org/journals/applied-mathematics-and-statistics/articles/10.3389/fams.2025.1526541/full Frontiers | Mellin convolutions of products and ratios Usually, convolution refers to Laplace convolution in the literature, but Mellin convolutions can yield very ueful results. This aspect is illustrated in the... frontiers mellin convolutions products ratios https://wandb.ai/apuri/BSConv/reports/Rethinking-Depthwise-Separable-Convolutions-How-Intra-Kernel-Correlations-Lead-to-Improved-MobileNets-CVPR-2020---Vmlldzo4Njc2MjY Rethinking Depthwise Separable Convolutions: How Intra-Kernel Correlations Lead to Improved... Sep 7, 2021 - Submission for Reproducibility Challenge 2021 for the paper "Rethinking Depthwise Separable Convolutions: How Intra-Kernel Correlations Lead to Improved... rethinking separable convolutions https://deepai.org/publication/improved-variational-autoencoders-for-text-modeling-using-dilated-convolutions Improved Variational Autoencoders for Text Modeling using Dilated Convolutions | DeepAI Feb 27, 2017 - 02/27/17 - Recent work on generative modeling of text has found that variational auto-encoders (VAE) incorporating LSTM decoders perform wors... variational autoencoders improved text modeling using https://openreview.net/forum?id=eb_cpjZZ3GH Scalable and Equivariant Spherical CNNs by Discrete-Continuous (DISCO) Convolutions | OpenReview A discrete-continuous (DISCO) spherical CNN framework that is simultaneously rotationally equivariant and computationally scalable and achieves... scalable equivariant spherical cnns https://openreview.net/forum?id=OWELckerm6 Laughing Hyena Distillery: Extracting Compact Recurrences From Convolutions | OpenReview Recent advances in attention-free sequence models rely on convolutions as alternatives to the attention operator at the core of Transformers. In particular,... laughing hyena distillery extracting compact recurrences https://arxiv.org/abs/1005.2656 [1005.2656] Warped Convolutions, Rieffel Deformations and the Construction of Quantum Field Theories Abstract page for arXiv paper 1005.2656: Warped Convolutions, Rieffel Deformations and the Construction of Quantum Field Theories https://www.analyticsvidhya.com/blog/2021/11/an-introduction-to-separable-convolutions/ An Introduction to Separable Convolutions - Analytics Vidhya Nov 24, 2021 - Separable Convolutions is a process in which a single convolution can be divided into two or more convolutions to produce the same output an introduction to separable convolutions analytics vidhya https://deepai.org/publication/xceptiontime-a-novel-deep-architecture-based-on-depthwise-separable-convolutions-for-hand-gesture-classification XceptionTime: A Novel Deep Architecture based on Depthwise Separable Convolutions for Hand Gesture... Nov 9, 2019 - 11/09/19 - Capitalizing on the need for addressing the existing challenges associated with gesture recognition via sparse multichannel surfac... https://openreview.net/forum?id=H1ZaRZVKg On Improving the Numerical Stability of Winograd Convolutions | OpenReview By improving the numerical stability of Winograd convolutions, we are able to use larger tiles which provide performance benefits to convolutional neural... numerical stability improving winograd convolutions openreview https://research.google/pubs/going-deeper-with-convolutions/ Going Deeper with Convolutions going deeper convolutions https://openreview.net/forum?id=P-73JPgRs0R Effects of Graph Convolutions in Multi-layer Networks | OpenReview Theoretical and empirical insights into the performance of graph convolutions in multi-layer networks multi layer effects graph convolutions networks https://openreview.net/forum?id=aCYzMmNK6tK Sparse Convolutions on Lie Groups | OpenReview Convolutional neural networks have proven very successful for a wide range of modelling tasks. Convolutional layers embed equivariance to discrete translations... lie groups sparse convolutions openreview https://arxiv.org/abs/2210.12818 [2210.12818] Pushing the Efficiency Limit Using Structured Sparse Convolutions Abstract page for arXiv paper 2210.12818: Pushing the Efficiency Limit Using Structured Sparse Convolutions 2210 12818 pushing efficiency limit https://openreview.net/forum?id=rJPcZ3txx Faster CNNs with Direct Sparse Convolutions and Guided Pruning | OpenReview Highly-performance sparse convolution outperforms dense with only 70% sparsity. Performance model that guides training to find useful sparsity range, applied... faster cnns direct sparse convolutions https://www.baeldung.com/cs/neural-nets-strided-convolutions Neural Networks: Strided Convolutions | Baeldung on Computer Science Feb 28, 2025 - Explore the concept of strided convolutions in neural networks. neural networks convolutions baeldung computer science https://arxiv.org/abs/0910.1319 [0910.1319] Conditionally monotone independence II: Multiplicative convolutions and infinite... Abstract page for arXiv paper 0910.1319: Conditionally monotone independence II: Multiplicative convolutions and infinite divisibility independence ii 0910 1319 conditionally monotone https://openreview.net/forum?id=xSzBf1te4s On Convolutions, Intrinsic Dimension, and Diffusion Models | OpenReview The manifold hypothesis asserts that data of interest in high-dimensional ambient spaces, such as image data, lies on unknown low-dimensional submanifolds.... intrinsic dimension diffusion models convolutions openreview https://jmlr.org/papers/v26/24-0944.html Laplace Meets Moreau: Smooth Approximation to Infimal Convolutions Using Laplace's Method smooth approximation laplace meets moreau https://openreview.net/forum?id=83LJRUzXWj Convolutions Die Hard: Open-Vocabulary Segmentation with Single Frozen Convolutional CLIP |... Open-vocabulary segmentation is a challenging task requiring segmenting and recognizing objects from an open set of categories in diverse environments. One way... die hard convolutions open vocabulary https://openreview.net/forum?id=HKgRwNhI9R Symmetric Basis Convolutions for Learning Lagrangian Fluid Mechanics | OpenReview Learning physical simulations has been an essential and central aspect of many recent research efforts in machine learning, particularly for... for learning fluid mechanics symmetric basis convolutions https://openreview.net/forum?id=8e6BrwU6AjQ MoVie: Revisiting Modulated Convolutions for Visual Counting and Beyond | OpenReview This paper focuses on visual counting, which aims to predict the number of occurrences given a natural image and a query (e.g. a question or a category).... and beyond movie revisiting modulated convolutions https://arxiv.org/abs/1907.11475 [1907.11475] Single Level Feature-to-Feature Forecasting with Deformable Convolutions Abstract page for arXiv paper 1907.11475: Single Level Feature-to-Feature Forecasting with Deformable Convolutions single level 1907 11475 feature forecasting https://openreview.net/forum?id=B1lDoJSYDH Lagrangian Fluid Simulation with Continuous Convolutions | OpenReview We learn particle-based fluid simulation with convolutional networks. fluid simulation lagrangian continuous convolutions openreview https://wellcomecollection.org/works/c9jaa6mh The convolutions of the brain : a study in comparative anatomy : being an address delivered to the... The convolutions of the brain : a study in comparative anatomy : being an address delivered to the Anatomical Section of the Tenth International Medical... https://www.merriam-webster.com/dictionary/convolutions CONVOLUTIONS Definition & Meaning - Merriam-Webster The meaning of CONVOLUTION is a form or shape that is folded in curved or tortuous windings. How to use convolution in a sentence. convolutions definition meaning merriam webster https://deepai.org/publication/disco-accurate-discrete-scale-convolutions DISCO: accurate Discrete Scale Convolutions | DeepAI Jun 4, 2021 - 06/04/21 - Scale is often seen as a given, disturbing factor in many vision tasks. When doing so it is one of the factors why we need more da... disco accurate discrete scale convolutions https://openreview.net/forum?id=Cp75iRscx4A&referrer=%5Bthe%20profile%20of%20Bin%20Xiao%5D(%2Fprofile%3Fid%3D~Bin_Xiao2) CvT: Introducing Convolutions to Vision Transformers | OpenReview We present in this paper a new architecture, named Convolutional vision Transformer (CvT), that improves Vision Transformer (ViT) in performance and efficiency... cvt introducing convolutions vision transformers https://www.julialang.org/blog/2018/08/adding-newer-features-and-speeding-up-convolutions-in-flux/ GSoC 2018: Adding Newer Features and Speeding up Convolutions in Flux The official website for the Julia Language. Julia is a language that is fast, dynamic, easy to use, and open source. Click here to learn more. gsoc 2018 adding newer features https://wordnik.com/words/convolutions convolutions - definition and meaning convolutions definition meaning https://www.amazon.science/publications/minuet-accelerating-3d-sparse-convolutions-on-gpus Minuet: Accelerating 3D sparse convolutions on GPUs - Amazon Science Sparse Convolution (SC) is widely used for processing 3D point clouds that are inherently sparse. Different from dense convolution, SC preserves the sparsity... minuet accelerating 3d sparse convolutions https://openreview.net/forum?id=TRrXkVdhwi Short-Long Convolutions Help Hardware-Efficient Linear Attention to Focus on Long Sequences |... To mitigate the computational complexity in the self-attention mechanism on long sequences, linear attention utilizes computation tricks to achieve linear... https://openreview.net/forum?id=jNq-i1zd0t9 Generalized Depthwise-Separable Convolutions for Adversarially Robust and Efficient Neural Networks... We propose Generalized Depthwise-Separable convolutions as an efficient approximation of standard 2D convolutions that dramatically improve the throughput of... generalized separable convolutions https://deepai.org/publication/channelnets-compact-and-efficient-convolutional-neural-networks-via-channel-wise-convolutions ChannelNets: Compact and Efficient Convolutional Neural Networks via Channel-Wise Convolutions |... Sep 5, 2018 - 09/05/18 - Convolutional neural networks (CNNs) have shown great capability of solving various artificial intelligence tasks. However, the in... convolutional neural networks compact efficient https://wandb.ai/edorado93/DynConv/reports/Dynamic-Convolutions-Exploiting-Spatial-Sparsity-for-Faster-Inference-CVPR-2020---Vmlldzo4NzMwNzc Dynamic Convolutions: Exploiting Spatial Sparsity for Faster Inference (CVPR 2020) Sep 20, 2021 - A reproduction of the 2020 CVPR paper entitled 'Dynamic Convolutions: Exploiting Spatial Sparsity for Faster Inference ' by Thomas Verelst et. al. dynamic convolutions exploiting spatial sparsity https://deepai.org/publication/single-image-3d-hand-reconstruction-with-mesh-convolutions Single Image 3D Hand Reconstruction with Mesh Convolutions | DeepAI May 4, 2019 - 05/04/19 - Monocular 3D reconstruction of deformable objects, such as human body parts, has been typically approached by predicting parameter... single image hand reconstruction 3d mesh convolutions https://openreview.net/forum?id=reqzCYQI3Z&referrer=%5Bthe%20profile%20of%20Guanghui%20Wang%5D(%2Fprofile%3Fid%3D~Guanghui_Wang8) Depth-Wise Convolutions in Vision Transformers for Efficient Training on Small Datasets | OpenReview The Vision Transformer (ViT) leverages the Transformer's encoder to capture global information by dividing images into patches and achieves superior... https://www.qualcomm.com/research/artificial-intelligence/papers-with-code/paper-6 Gauge Equivariant Mesh CNNs: Anisotropic convolutions on geometric graphs | Research Paper from... Unlike traditional Convolutional Neural Networks (CNN), Gauge Equivariant CNNs (G-CNNs) can analyse image data on any curved space or geometry. https://huggingface.co/papers/2406.05317 Paper page - LoCoCo: Dropping In Convolutions for Long Context Compression Join the discussion on this paper page dropping in long context paper lococo convolutions