Mixture-of-experts (MoE) architecture has been proven a powerful method for diverse tasks in training deep models in many applications.
Our method outperforms the very LLM that was used to generate the annotated dataset -- with Few-Shot Prompting on GPT3. 5 achieving 58%, 61%, and 64% on the respective datasets, a consistently lower correction accuracy, despite using nearly 800 times as many parameters as our model.
Despite their popularity in deep learning and machine learning in general, the theoretical properties of adaptive optimizers such as Adagrad, RMSProp, Adam or AdamW are not yet fully understood.
In this setup, we seek the optimal step ordering consistent with the procedure flow graph and a given video.
Despite several successes in document understanding, the practical task for long document understanding is largely under-explored due to several challenges in computation and how to efficiently absorb long multimodal input.
Many of the existing style transfer benchmarks primarily focus on individual high-level semantic changes (e. g. positive to negative), which enable controllability at a high level but do not offer fine-grained control involving sentence structure, emphasis, and content of the sentence.
We introduce a new scalable approximation for Gaussian processes with provable guarantees which hold simultaneously over its entire parameter space.
Despite the advent of deep learning in computer vision, the general handwriting recognition problem is far from solved.
It often leads to the dependence of convergence rate on maximum Lipschitz constant of gradients across the devices.
Under this framework, the objective function can represented end-to-end as a single computational graph, which allows seamless policy gradient computation via backpropagation through the models.
Our method is based on the key insight that translation from a source to a target modality provides a method of learning joint representations using only the source modality as input.
Multimodal machine learning is a core research area spanning the language, visual and acoustic modalities.