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A library for making RepE control vectors
machine-learning transformers language-model sparse-autoencoders sae sparse-autoencoder saes representation-engineering
An Alibaba Cloud native microservice demo powered by Apache Dubbo and Spring Cloud Alibaba
Dyno Lab — an AI safety, alignment and interpretability research workbench for Apple Silicon. Explore activations, probes, SAEs and interventions with local inference, a Python SDK, APIs and MCP.
ai mcp alignment probes sparse-autoencoders ai-safety sae mlx swiftui aisafety apple-silicon llm mechanistic-interpretability ai-interpretability
Pivotal Token Search
tokens dataset-generation sae sparse-autoencoder dpo llm reasoning-agent llm-inference direct-preference-optimization llm-steering steering-vector phi-4 reasoning-language-models phi4 reasoning-models mech-interp phi4-mini phi-4-mini pivotal-token-search pivotal-tokens
A Deep Learning framework that analyses Windows PE files to detect malicious Softwares.
machine-learning deep-learning cnn python3 recurrent-neural-networks feature-extraction lstm neural-networks rnn feedforward-neural-network convolutional-neural-networks yara sae malware-detection fnn
Implementation of the BatchTopK activation function for training sparse autoencoders (SAEs)
Python Implementation of Zero Shot Learning Algorithms (ALE, DeViSE, ESZSL, SAE, SJE) under ZSLGBU protocol
Sparse Embedding Compression for Scalable Retrieval in Recommender Systems
Implementation of PatchSAE as presented in "Sparse autoencoders reveal selective remapping of visual concepts during adaptation"
Explore visualization tools for understanding Transformer-based large language models (LLMs)
visualization awesome interactive transformer attention-mechanism bert gemma interactive-visualizations sae sparse-autoencoder explainable-ai large-language-models llm mechanistic-interpretability
Multi-Layer Sparse Autoencoders (ICLR 2025)
Official Triton kernels for TopK and HierarchicalTopK Sparse Autoencoder decoders.
Interpreting CLIP with Hierarchical Sparse Autoencoders (ICML 2025)
Sparse Autoencoders (SAE) vs CLIP fine-tuning fun.
vit fine-tune clip sae adversarial-learning sparse-autoencoder finetune fine-tuning adversarial-attacks vision-transformer
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