Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 20 of 33 for “"Pytorch"”.
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Converting PyTorch Models to StreamIt Pipelines
… in a GPU. Machine learning libraries such as PyTorch produce dynamic computation graphs in order to represent the forward pass of the model. PyTorch allows conversion of these dynamic graphs into static ones through just-in-time (JIT) compilation. These graphs can then be optimized further by …
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Gradient-Based Optimization of ReaxFF Parameters Using Pytorch for the Study of Silica Precipitation
… of reactive force-field parameters using Pytorch was performed. We have implemented ReaxFF potential as a Pytorch model. The model’s performance was validated against existing ReaxFF implementations. ReaxFF parameters were fitted to the dataset, which comprised 15345 geometries calculated …
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Optimizing Graph Neural Network Training on Large Graphs
… analysis of GNN training codes written using PyTorch Geometric, the most popular machine learning framework for GNNs. Through this performance analysis, I uncover significant performance bottlenecks related to neighborhood sampling and GPU data transfers. To address these issues, I create …
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From efficient high-order methods to scientific machine learning.
… programming interface that combines PyTorch and Firedrake to perform model-constrained deep learning of solutions of parameterized PDEs and PDE-constrained inverse problems. It leverages PyTorch's high-level interface for training neural networks, its automatic differentiation module …
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From efficient high-order methods to scientific machine learning.
… programming interface that combines PyTorch and Firedrake to perform model-constrained deep learning of solutions of parameterized PDEs and PDE-constrained inverse problems. It leverages PyTorch's high-level interface for training neural networks, its automatic differentiation module …
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Federated Learning for Resource Constrained Devices
… automatically allocate resources by leveraging PyTorch Lightning. To the best of my knowledge, this is the first work that uses PyTorch Lightning specifically for training, and not just inference, on edge devices.
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Graph Metrics for Improving Cybersecurity on Software Dependency Networks
… risk. We compare two deep learning frameworks (PyTorch and TensorFlow) with two traditional software frameworks (npm and PyPI), identifying critical properties of their dependency structures, which motivates several recommendations for improving software supply chain security.
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Fast Multistage Compilation of Machine Learning Computation Graphs
… of machine learning models programmed in PyTorch.
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Sparse Matrix Belief Propagation
… it in a modern deep learning framework (PyTorch), measuring the resulting massive improvement in running time, and facilitating future integration into deep learning models.
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Simplifying Equivariant GPU Kernels through Tile-based Programming
… the tiling- optimizations in just 100 lines of PyTorch-like code. We explore the performance-simplicity tradeoff with two case studies and demonstrate the practicality of our kernel workflow through downstream integration with a production model. We hope this work serves as inspiration to …
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Efficient Segment Anything on the Edge
… a 24x speedup relative to the baseline FP32 PyTorch implementation. GazeSAM runs at a speed of over 30 FPS, enabling real-time performance on an RTX 4070 GPU.
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API Knowledge Guided Test Generation for Machine Learning Libraries
… (i.e., Scikit-learn, Pandas, Numpy, Scipy, and PyTorch),then for each API, we further extract their API knowledge, i.e., API constraints and API usage patterns. Given an API, MUTester combines its API knowledge with existing test generators (e.g., search-based test generator PyEvosuite and …
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Real-Time Computed Tomography-based Medical Diagnosis Using Deep Learning
… is at least 2x faster than analogous PyTorch implementation on each platform and achieves comparable performance between CPU and FPGA, while FPGA operated at a much lower frequency.
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Biologically Interpretable, Integrative Deep Learning for Cancer Survival Analysis
… The open-source software of PASNet family in PyTorch is publicly available at <a href="https://github.com/DataX-JieHao/" target="_blank">https://github.com/DataX-JieHao/</a></p>
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Finite Gaussian Neurons: Defending Against Adversarial Attacks by Making Neural Networks Say "I Don’t Know"
… the GPL 3.0 open source license. Work done with PyTorch.</p>
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Game of threads: Enabling asynchronous poisoning attacks
… training implementation published by PyTorch. We perform a deeper analysis on a LeNet-style DNN. We also perform proof-of-concept experiments to validate our assumptions on an SGX-enabled machine. Our most powerful accuracy degradation attack makes no assumptions about the underlying …
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Algorithms & Systems for Differentiable Graphics Programming
… due to complications: existing frameworks like PyTorch are ill-suited to graphics programming both due to algorithmic problems, like discontinuities, and system-design problems that lead to poor performance & expressive power. This thesis discusses several original approaches that were developed …
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Equivariant Autoregressive Models for Molecular Generation
… inference speeds through a new implementation in PyTorch, making inference nearly 4x faster than previously. In addition, we demonstrate its effectiveness across a variety of tasks, including small molecule and protein backbone generation.
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Sampling Methods for Fast and Versatile GNN Training
… up to 1.3× speed-up for end-to-end training over PyTorch Geometric with the same sampling code. Furthermore, FlexSample extends SALIENT with highly-optimized C++ implementations of FastGCN and LADIES layer-wise sampling, which achieve 2×–5× speed-up over their respective Python implementations. …
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Efficient Systems for Large-Scale Graph Representation Learning
… stages. Combined with compiler infrastructure in PyTorch, Joestar achieves state-of-the-art GNN training throughputs for billion-edge graph datasets on a single GPU.
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