A collection of academic papers/blogs/talks/projects that I read/watched/explored during the month. I also include any small (or large) personal projects that I did and any such related ML/non-ML work.
Personal Projects Paper re-implementation - “Extracting Training Data from Large Language Models” by Carlini et al., 2021. - [Github] Annotated Papers Learning Backward Compatible Embeddings Memorization Without Overfitting: Analyzing the Training Dynamics of Large Language Models Tracing Knowledge in Language Models Back to the Training Data Papers I read On the Unreasonable Effectiveness of Feature propagation in Learning on Graphs with Missing Node Features PaLM: Scaling Language Modeling with Pathways Hierarchical Text-Conditional Image Generation with CLIP Latents Socratic Models: Composing Zero-Shot Multimodal Reasoning with Language Unified Contrastive Learning in Image-Text-Label Space Improving Passage Retrieval with Zero-Shot Question Generation Exploring Dual Encoder Architectures for Question Answering Efficient Fine-Tuning of BERT Models on the Edge Fine-Tuning Transformers: Vocabulary Transfer Manipulating SGD with Data Ordering Attacks Differentially Private Fine-tuning of Language Models Extracting Training Data from Large Language Models Learning Backward Compatible Embeddings Compacter: Efficient Low-Rank Hypercomplex Adapter Layers Agreement-on-the-Line: Predicting the Performance of Neural Networks under Distribution Shift Memorization Without Overfitting: Analyzing the Training Dynamics of Large Language Models Tracing Knowledge in Language Models Back to the Training Data Blogs I read Domain Adaptation with Generative Pseudo-Labeling (GPL) Making Deep Learning Go Brrrr From First Principles Introduction to TorchScript Nonlinear Computation in Deep Linear Networks Talks I watched How GPU Computing Works
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