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Found 11 papers, 3 papers with code
Revisiting Group Relative Policy Optimization: Insights into On-Policy and Off-Policy Training
We revisit Group Relative Policy Optimization (GRPO) in both on-policy and off-policy optimization regimes.
Distributional Preference Alignment of LLMs via Optimal Transport
Thanks to the one-dimensional nature of the resulting optimal transport problem and the convexity of the cost, it has a closed-form solution via sorting on empirical measures.
GP-MoLFormer: A Foundation Model For Molecular Generation
Our analyses reveal that training data memorization and novelty in generations are impacted by the quality and scale of the training data; duplication bias in training data can enhance memorization at the cost of lowering novelty.
Risk Aware Benchmarking of Large Language Models
Using this framework, we formally develop a risk-aware approach for foundation model selection given guardrails quantified by specified metrics.
Auditing and Generating Synthetic Data with Controllable Trust Trade-offs
We introduce a holistic auditing framework that comprehensively evaluates synthetic datasets and AI models.
Large-Scale Chemical Language Representations Capture Molecular Structure and Properties
Models based on machine learning can enable accurate and fast molecular property predictions, which is of interest in drug discovery and material design.
Tabular Transformers for Modeling Multivariate Time Series
This results in two architectures for tabular time series: one for learning representations that is analogous to BERT and can be pre-trained end-to-end and used in downstream tasks, and one that is akin to GPT and can be used for generation of realistic synthetic tabular sequences.
Fast Mixing of Multi-Scale Langevin Dynamics under the Manifold Hypothesis
Recently, the task of image generation has attracted much attention.
Towards Better Understanding of Adaptive Gradient Algorithms in Generative Adversarial Nets
Then we propose an adaptive variant of OSG named Optimistic Adagrad (OAdagrad) and reveal an \emph{improved} adaptive complexity $O\left(\epsilon^{-\frac{2}{1-\alpha}}\right)$, where $\alpha$ characterizes the growth rate of the cumulative stochastic gradient and $0\leq \alpha\leq 1/2$.
A Decentralized Parallel Algorithm for Training Generative Adversarial Nets
Despite recent progress on decentralized algorithms for training deep neural networks, it remains unclear whether it is possible to train GANs in a decentralized manner.
Wasserstein Barycenter Model Ensembling
In this paper we propose to perform model ensembling in a multiclass or a multilabel learning setting using Wasserstein (W.) barycenters.
