??Top ML Papers of the Week

The top ML Papers of the Week (June 26 - July 2):

1). LeanDojo?- an open-source Lean playground consisting of toolkits, data, models, and benchmarks for theorem proving; also develops ReProver, a retrieval augmented LLM-based prover for theorem solving using premises from a vast math library. (paper?|?tweet)

2). Extending Context Window of LLMs?- extends the context window of LLMs like LLaMA to up to 32K with minimal fine-tuning (within 1000 steps); previous methods for extending the context window are inefficient but this approach attains good performance on several tasks while being more efficient and cost-effective. (paper?|?tweet)

3). Computer Vision Through the Lens of Natural Language?- proposes a modular approach for solving computer vision problems by leveraging LLMs; the LLM is used to reason over outputs from independent and descriptive modules that provide extensive information about an image. (paper?|?tweet)

4). Visual Navigation Transformer?- a foundational model that leverages the power of pretrained models to vision-based robotic navigation; it can be used with any navigation dataset and is built on a flexible Transformer-based architecture that can tackle various navigational tasks. (paper?|?tweet)

5). Generative AI for Programming Education -?evaluates GPT-4 and ChatGPT on programming education scenarios and compares their performance with human tutors; GPT-4 outperforms ChatGPT and comes close to human tutors' performance. (paper?|?tweet)

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6). DragDiffusion?- extends interactive point-based image editing using diffusion models; it optimizes the diffusion latent to achieve precise spatial control and complete high-quality editing efficiently. (paper?|?tweet)

7). Understanding Theory-of-Mind in LLMs with LLMs?- a framework for procedurally generating evaluations with LLMs; proposes a benchmark to study the social reasoning capabilities of LLMs with LLMs. (paper?|?tweet)

8). Evaluations with No Labels?- a framework for self-supervised evaluation of LLMs by analyzing their sensitivity or invariance to transformations on input text; can be used to monitor LLM behavior on datasets streamed during live model deployment. (paper?|?tweet)

9). Long-range Language Modeling with Self-Retrieval?- an architecture and training procedure for jointly training a retrieval-augmented language model from scratch for long-range language modeling tasks. (paper?|?tweet)

10). Scaling MLPs: A Tale of Inductive Bias?- shows that the performance of MLPs improves with scale and highlights that lack of inductive bias can be compensated. (paper?|?tweet)

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