5 citations · 10 across the 8 of their papers we have counts for
8 papers
Scaling Behavior of Discrete Diffusion Language Models
Dimitri von Rütte, Janis Fluri, Omead Pooladzandi +3
Modern LLM pre-training consumes vast amounts of compute and training data, making the scaling behavior, or scaling laws, of different models a key distinguishing factor. Discrete…
Towards Composable Distributions of Latent Space Augmentations
Omead Pooladzandi, Jeffrey Jiang, Sunay Bhat +1
We propose a composable framework for latent space image augmentation that allows for easy combination of multiple augmentations. Image augmentation has been shown to be an effecti…
Generating High Fidelity Synthetic Data via Coreset selection and Entropic Regularization
Omead Pooladzandi, Pasha Khosravi, Erik Nijkamp +1
Generative models have the ability to synthesize data points drawn from the data distribution, however, not all generated samples are high quality. In this paper, we propose using…
Improving Levenberg-Marquardt Algorithm for Neural Networks
Omead Pooladzandi, Yiming Zhou
We explore the usage of the Levenberg-Marquardt (LM) algorithm for regression (non-linear least squares) and classification (generalized Gauss-Newton methods) tasks in neural netwo…
Causal Structural Hypothesis Testing and Data Generation Models
Jeffrey Jiang, Omead Pooladzandi, Sunay Bhat +1
A vast amount of expert and domain knowledge is captured by causal structural priors, yet there has been little research on testing such priors for generalization and data synthesi…
Adaptive Second Order Coresets for Data-efficient Machine Learning
Omead Pooladzandi, David Davini, Baharan Mirzasoleiman
Training machine learning models on massive datasets incurs substantial computational costs. To alleviate such costs, there has been a sustained effort to develop data-efficient tr…
De-Biasing Generative Models using Counterfactual Methods
Sunay Bhat, Jeffrey Jiang, Omead Pooladzandi +1
Variational autoencoders (VAEs) and other generative methods have garnered growing interest not just for their generative properties but also for the ability to dis-entangle a low-…
Diverse Imitation Learning via Self-Organizing Generative Models
Arash Vahabpour, Tianyi Wang, Qiujing Lu +2
Imitation learning is the task of replicating expert policy from demonstrations, without access to a reward function. This task becomes particularly challenging when the expert exh…