This paper investigates Large Language Models (LLMs) ability to assess the economic soundness and theoretical consistency of empirical findings in spatial econometrics. We created original and deliberately altered "counterfactual" summaries from 28 published papers (2005-2024), which were evaluated by a diverse set of LLMs. The LLMs provided qualitative assessments and structured binary classifications on variable choice, coefficient plausibility, and publication suitability. The results indicate that while LLMs can expertly assess the coherence of variable choices (with top models like GPT-4o achieving an overall F1 score of 0.87), their performance varies significantly when evaluating deeper aspects such as coefficient plausibility and overall publication suitability. The results further revealed that the choice of LLM, the specific characteristics of the paper and the interaction between these two factors significantly influence the accuracy of the assessment, particularly for nuanced judgments. These findings highlight LLMs' current strengths in assisting with initial, more surface-level checks and their limitations in performing comprehensive, deep economic reasoning, suggesting a potential assistive role in peer review that still necessitates robust human oversight.

The update equations are given above. The basic idea behind CM is that it accumulates a velocity vector in directions of persistent reduction in the objective across iterations. Directions of low-curvature which are suffering from a slow local change in their reduction, these will tend to persist across iterations and hence be amplified by the use of CM. Nesterov's Accelerated Gradient (NAG) is now described by the authors (update equations given above). While CM computes the gradient update from the current position θt, NAG first performs a partial update to θt, computing θt μvt, which is similar to θt 1, but missing the as yet unknown correction.

Whenever we train a neural network from scratch, it's weights are initialized with random values. So, if you re-run the same training job again and again, the values used to initialized the weights will keep on changing as they would be randomly generated. Now just imagine, metric of a State of the Art architecture for a given task is 80. You propose a new architecture for the same task and train your model from scratch. After you run it once (assuming all hyper-parameters were just perfect), you get 79.8 metric value.

NIPS 2018 (Montreal, Canada), or NeurIPS, as it is called now, is over, and I would like to take the opportunity to dissect one of the papers that received the Best Paper Award at this prestigious conference. The name of the paper is Neural Ordinary Differential Equations (arXiv link) and its authors are affiliated to the famous Vector Institute at the University of Toronto. In this post, I will try to explain some of the main ideas of this paper as well as discuss their potential implications for the future of the field of Deep Learning. Since the paper is quite advanced and touches on concepts such as Ordinary Differential Equations (ODE), Recurrent Neural Networks (RNN) or Normalizing Flows (NF), I suggest that you read up on these terms if you are not familiar with them, since I will not go into details on these. However, I will try to explain the ideas of the paper as intuitively as possible, so that you may get the main concepts without going too much into the technical details. If you are interested, you may read up on these details afterwards in the original paper.

Didn't click the link but didn't this paper come out a long time ago?

This paper presents a model-based planner called the Probabilistic Sulu Planner or the p-Sulu Planner, which controls stochastic systems in a goal directed manner within user-specified risk bounds. We first develop a new plan representation called a chance-constrained qualitative state plan (CCQSP), through which users can specify the desired evolution of the plant state as well as the acceptable level of risk. We then develop the p-Sulu Planner, which can tractably solve a CCQSP planning problem.