Alternatives and similar repositories for ensemble-everything-everywhere:

Users that are interested in ensemble-everything-everywhere are comparing it to the libraries listed below

• stanislavfort / dissect-git-re-basin
  Replicating and dissecting the git-re-basin project in one-click-replication Colabs
  ☆36Updated 2 years ago
• locuslab / T-MARS
  Code for T-MARS data filtering
  ☆35Updated last year
• MadryLab / pretraining-distribution-shift-robustness
  ☆14Updated 10 months ago
• JeanKaddour / LAWA
  Latest Weight Averaging (NeurIPS HITY 2022)
  ☆28Updated last year
• tml-epfl / sharpness-vs-generalization
  A modern look at the relationship between sharpness and generalization [ICML 2023]
  ☆43Updated last year
• fKunstner / noise-sgd-adam-sign
  ☆15Updated last year
• MadryLab / journey-TRAK
  Code for the paper "The Journey, Not the Destination: How Data Guides Diffusion Models"
  ☆20Updated last year
• tml-epfl / sam-low-rank-features
  Sharpness-Aware Minimization Leads to Low-Rank Features [NeurIPS 2023]
  ☆25Updated last year
• oripress / EntropyEnigma
  Official code for the ICML 2024 paper "The Entropy Enigma: Success and Failure of Entropy Minimization"
  ☆47Updated 8 months ago
• pilancilab / Riemannian_Preconditioned_LoRA
  source code for paper "Riemannian Preconditioned LoRA for Fine-Tuning Foundation Models"
  ☆21Updated 7 months ago
• Ping-C / optimizer
  This repository includes code to reproduce the tables in "Loss Landscapes are All You Need: Neural Network Generalization Can Be Explaine…
  ☆35Updated last year
• sail-sg / D-TRAK
  Intriguing Properties of Data Attribution on Diffusion Models (ICLR 2024)
  ☆27Updated last year
• MadryLab / dataset-interfaces
  Dataset Interfaces: Diagnosing Model Failures Using Controllable Counterfactual Generation
  ☆44Updated last year
• JonasGeiping / dataaugs
  ☆17Updated 2 years ago
• rtaori / data_feedback
  Code for the paper "Data Feedback Loops: Model-driven Amplification of Dataset Biases"
  ☆15Updated 2 years ago
• hamidkazemi22 / CLIPInversion
  What do we learn from inverting CLIP models?
  ☆49Updated 10 months ago
• YuxinWenRick / diffusion_memorization
  Official repo for Detecting, Explaining, and Mitigating Memorization in Diffusion Models (ICLR 2024)
  ☆64Updated 9 months ago
• zyushun / hessian-spectrum
  Code for the paper: Why Transformers Need Adam: A Hessian Perspective
  ☆48Updated 9 months ago
• gortizji / linearized-networks
  Source code of "What can linearized neural networks actually say about generalization?
  ☆20Updated 3 years ago
• aks2203 / easy-to-hard-data
  Pytorch Datasets for Easy-To-Hard
  ☆27Updated 3 weeks ago
• apple / ml-act
  ☆15Updated 2 months ago
• somepago / DCR
  Official Pytorch repo of CVPR'23 and NeurIPS'23 papers on understanding replication in diffusion models.
  ☆105Updated last year
• JeanKaddour / NoTrainNoGain
  Revisiting Efficient Training Algorithms For Transformer-based Language Models (NeurIPS 2023)
  ☆79Updated last year
• smonsays / hypernetwork-attention
  Official code for the paper "Attention as a Hypernetwork"
  ☆23Updated 7 months ago
• EleutherAI / w2s
  ☆20Updated 4 months ago
• vasusingla / simple-data-attribution
  A simple and efficient baseline for data attribution
  ☆11Updated last year
• JonasGeiping / fullbatchtraining
  Training vision models with full-batch gradient descent and regularization
  ☆37Updated last year
• EkdeepSLubana / MMC
  Codebase for Mechanistic Mode Connectivity
  ☆13Updated last year
• cloneofsimo / zeroshampoo
  ☆33Updated 4 months ago
• tml-epfl / why-weight-decay
  Why Do We Need Weight Decay in Modern Deep Learning? [NeurIPS 2024]
  ☆59Updated 4 months ago