gladia-research-group / explanatory-learning
This is the official repository for "Explanatory Learning: Beyond Empiricism in Neural Networks".
☆14Updated 2 years ago
Alternatives and similar repositories for explanatory-learning:
Users that are interested in explanatory-learning are comparing it to the libraries listed below
- A Python package for analyzing and transforming neural latent spaces.☆44Updated 4 months ago
- How to Turn Your Knowledge Graph Embeddings into Generative Models☆51Updated 9 months ago
- Mechanistic Interpretability for Transformer Models☆50Updated 2 years ago
- Code Release for "Broken Neural Scaling Laws" (BNSL) paper☆58Updated last year
- Flexible library for merging large language models (LLMs) via evolutionary optimization.☆16Updated this week
- LENS Project☆48Updated last year
- Codebase for VAEL: Bridging Variational Autoencoders and Probabilistic Logic Programming☆20Updated last year
- Relative representations can be leveraged to enable solving tasks regarding "latent communication": from zero-shot model stitching to lat…☆57Updated last year
- A Python Library for Deep Probabilistic Modeling☆60Updated 6 months ago
- The Happy Faces Benchmark☆15Updated last year
- PyTorch and NNsight implementation of AtP* (Kramar et al 2024, DeepMind)☆18Updated 3 months ago
- ☆19Updated last week
- This repository includes code to reproduce the tables in "Loss Landscapes are All You Need: Neural Network Generalization Can Be Explaine…☆36Updated 2 years ago
- Stochastic Automatic Differentiation library for PyTorch.☆203Updated 7 months ago
- Personal implementation of ASIF by Antonio Norelli☆25Updated 11 months ago
- This repository holds the code for the NeurIPS 2022 paper, Semantic Probabilistic Layers☆27Updated last year
- Neural Networks and the Chomsky Hierarchy☆205Updated last year
- 👋 Overcomplete is a Vision-based SAE Toolbox☆51Updated 3 weeks ago
- ☆71Updated 2 months ago
- a python framework to build, learn and reason about probabilistic circuits and tensor networks☆93Updated this week
- PyTorch Explain: Interpretable Deep Learning in Python.☆154Updated 11 months ago
- ☆62Updated 2 years ago
- ☆26Updated last year
- ☆34Updated 4 months ago
- ☆90Updated 2 months ago
- Erasing concepts from neural representations with provable guarantees☆227Updated 2 months ago
- Implementation of the unbounded depth neural network from the paper Variational Inference for Infinitely Deep Neural Networks☆17Updated 2 years ago
- Code for "Bayesian Structure Learning with Generative Flow Networks"☆87Updated 3 years ago
- ☆91Updated 2 months ago
- A centralized place for deep thinking code and experiments☆82Updated last year