uvadlc / uvadlc_practicals_2022
Repository for the code assignment of the Deep Learning 1 course, Fall 2022 edition
☆19Updated 2 years ago
Alternatives and similar repositories for uvadlc_practicals_2022:
Users that are interested in uvadlc_practicals_2022 are comparing it to the libraries listed below
- An annotated implementation of the Hyena Hierarchy paper☆32Updated last year
- Extending Conformal Prediction to LLMs☆64Updated 8 months ago
- Code for "Counterfactual Token Generation in Large Language Models", Arxiv 2024.☆25Updated 3 months ago
- ☆70Updated 6 months ago
- Code associated to papers on superposition (in ML interpretability)☆28Updated 2 years ago
- ☆34Updated 3 months ago
- ☆26Updated last year
- Transformer Grammars: Augmenting Transformer Language Models with Syntactic Inductive Biases at Scale, TACL (2022)☆122Updated 4 months ago
- TorchDR - PyTorch Dimensionality Reduction☆95Updated 3 weeks ago
- Evaluation of neuro-symbolic engines☆34Updated 7 months ago
- Sparse and discrete interpretability tool for neural networks☆59Updated last year
- Code Release for "Broken Neural Scaling Laws" (BNSL) paper☆58Updated last year
- An introduction to LLM Sampling☆75Updated 2 months ago
- Materials of the Nordic Probabilistic AI School 2023.☆89Updated last year
- Understanding how features learned by neural networks evolve throughout training☆33Updated 4 months ago
- gzip Predicts Data-dependent Scaling Laws☆34Updated 9 months ago
- Code for minimum-entropy coupling.☆31Updated 8 months ago
- Exca - Execution and caching tool for python☆78Updated last week
- ☆44Updated 2 weeks ago
- ☆52Updated 5 months ago
- This repository contains a Jax implementation of conformal training corresponding to the ICLR'22 paper "learning optimal conformal classi…☆129Updated 2 years ago
- Fine-grained, dynamic control of neural network topology in JAX.☆21Updated last year
- ☆24Updated last year
- Neural Graphical models are neural network based graphical models that offer richer representation, faster inference & sampling☆28Updated last year
- ☆59Updated 2 years ago
- Official implementation of the paper "Interventions, Where and How? Experimental Design for Causal Models at Scale", NeurIPS 2022.☆19Updated 2 years ago
- ☆61Updated last year
- Implementations of growing and pruning in neural networks☆22Updated last year
- 🧠 Starter templates for doing interpretability research☆67Updated last year
- Utilities for PyTorch distributed☆23Updated this week