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Found 7 papers, 5 papers with code
DMLR: Data-centric Machine Learning Research -- Past, Present and Future
Drawing from discussions at the inaugural DMLR workshop at ICML 2023 and meetings prior, in this report we outline the relevance of community engagement and infrastructure development for the creation of next-generation public datasets that will advance machine learning science.
LLM-Prop: Predicting Physical And Electronic Properties Of Crystalline Solids From Their Text Descriptions
The prediction of crystal properties plays a crucial role in the crystal design process.
Cousins Of The Vendi Score: A Family Of Similarity-Based Diversity Metrics For Science And Machine Learning
Contrary to many diversity metrics in ecology, the Vendi Score accounts for similarity and does not require knowledge of the prevalence of the categories in the collection to be evaluated for diversity.
The Vendi Score: A Diversity Evaluation Metric for Machine Learning
Importantly, unlike many existing metrics in ML, the Vendi Score does not require a reference dataset or distribution over samples or labels, it is therefore general and applicable to any generative model, decoding algorithm, and dataset from any domain where similarity can be defined.
Markov Chain Score Ascent: A Unifying Framework of Variational Inference with Markovian Gradients
Minimizing the inclusive Kullback-Leibler (KL) divergence with stochastic gradient descent (SGD) is challenging since its gradient is defined as an integral over the posterior.
Augment and Reduce: Stochastic Inference for Large Categorical Distributions
It maximizes a lower bound on the marginal likelihood of the data.
Variational Inference via \chi Upper Bound Minimization
In this paper we propose CHIVI, a black-box variational inference algorithm that minimizes $D_{\chi}(p || q)$, the $\chi$-divergence from $p$ to $q$.
