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Discovering Symbolic Models from Deep Learning w/ Inductive Bias
A general approach to distill symbolic representations of a learned deep model by introducing strong inductive biases.
symbolic-models inductive-bias graph-neural-networks graphs
Implicit Neural Representations with Periodic Activation Function
Leverage periodic activation functions for implicit neural representations & demonstrate that these networks, dubbed sinusoidal representation networks or ...
siren activation-functions tanh relu
PyTorch3D
FAIR's library of reusable components for deep learning with 3D data.
3d heterogeneous-batching batching differentiable-rendering
Intro to Jupyter Notebooks & JupyterLab with Python
Introduction to Jupyter Notebooks & JupyterLab: set-up, user-guide, and best practices with Python. This is a beginner level intro.
python jupyter-notebook ide tutorial
T5 for Sentiment Span Extraction
Exploring how T5 works and applying it for sentiment span extraction.
sentiment-analysis t5 transformers natural-language-processing
Self-Supervised Learning -- UC Berkeley Spring 2020
Lecture on self-supervised learning from CS294-158-SP20: Deep Unsupervised Learning.
self-supervised-learning video berkeley unsupervised-learning
3D Photography using Context-aware Layered Depth Inpainting
A multi-layer representation for novel view synthesis that contains hallucinated color and depth structures in regions occluded in the original view.
3d image-generation inpainting design
Question Answering with a Fine-Tuned BERT
What does it mean for BERT to achieve “human-level performance on Question Answering”?
question-answering bert fine-tuning squad
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