??Top ML Papers of the Week

1). GPT-4?- a large multimodal model with broader general knowledge and problem-solving abilities. (paper | tweet)

2). LERF (Language Embedded Radiance Fields)?- a method for grounding language embeddings from models like CLIP into NeRF; this enables open-ended language queries in 3D. (paper | tweet)

3). An Overview of Language Models?- an overview of language models covering recent developments and future directions. It also covers topics like linguistic units, structures, training methods, evaluation, and applications. (paper | tweet)

4). Tuned Lens?- a method for transformer interpretability that can trace a language model predictions as it develops layer by layer. (paper | tweet)

5). MIM (Meet in the Middle)?- a new pre-training paradigm using techniques that jointly improve training data efficiency and capabilities of LMs in the infilling task; performance improvement is shown in code generation tasks. (paper | tweet)

6). Resurrecting RNNs?- demonstrates that careful design of deep RNNs using standard signal propagation argument can recover the performance of deep state-space models on long-range reasoning tasks. (paper | tweet)

7). Universal Prompt Retrieval?- a new approach to tune a lightweight and versatile retriever to automatically retrieve prompts to improve zero-shot performance and help mitigate hallucinations. (paper | tweet)

8). Patches Are All You Need?- proposes ConvMixer, a parameter-efficient fully-convolutional model which replaces self-attention and MLP layers in ViTs with less-expressive depthwise and pointwise convolutional layers. (paper | tweet)

9). NeRFMeshing?- a compact and flexible architecture that enables easy 3D surface reconstruction from any NeRF-driven approach; distills NeRFs into geometrically-accurate 3D meshes. (paper | tweet)

10). FlexGen?- a high-throughput generation engine for running LLMs with limited GPU memory. (paper | tweet)