We introduce visual ability into the large language model to complete the ophthalmic large language and vision assistant (OphGLM).
To address this issue, we propose a multi-stage computational framework -- ASTEROID, which lowers the data cost of MLFFs by leveraging a combination of cheap inaccurate data and expensive accurate data.
To model the complex nonlinearity in predicting molecular properties in an more end-to-end approach, we propose to encode the positional quantities with a learnable embedding that is continuous and differentiable.
Experiments show that, compared to training from scratch, fine-tuning the pretrained model can significantly improve the performance for seven molecular property prediction tasks and two force field tasks.
Recently researchers have studied input leakage problems in Federated Learning (FL) where a malicious party can reconstruct sensitive training inputs provided by users from shared gradient.
In this paper, we propose a vFL framework based on Private Set Union (PSU) that allows each party to keep sensitive membership information to itself.
Deep learning models in large-scale machine learning systems are often continuously trained with enormous data from production environments.
Two-party split learning is a popular technique for learning a model across feature-partitioned data.
With the model learnt, a beam search over the latent codes is performed to retrieve the top candidates.
With the model learnt, a beam search over the structure is performed to retrieve the top candidates for reranking.
We show that model compression can improve the population risk of a pre-trained model, by studying the tradeoff between the decrease in the generalization error and the increase in the empirical risk with model compression.
Theoretically, we prove that the proposed scheme is optimal for compressing one-hidden-layer ReLU neural networks.
Significant advances have been made recently on training neural networks, where the main challenge is in solving an optimization problem with abundant critical points.
We provide numerical experiments suggesting superiority of the proposed estimator compared to other heuristics of adding small continuous noise to all the samples and applying standard estimators tailored for purely continuous variables, and quantizing the samples and applying standard estimators tailored for purely discrete variables.
Discovering a correlation from one variable to another variable is of fundamental scientific and practical interest.
In this paper, we combine both these approaches to design new estimators of entropy and mutual information that outperform state of the art methods.
In this paper we demonstrate that the estimator is consistent and also identify an upper bound on the rate of convergence of the bias as a function of number of samples.
We conduct an axiomatic study of the problem of estimating the strength of a known causal relationship between a pair of variables.