Search Results for author:
Found 7 papers, 3 papers with code
Simulated Overparameterization
Building upon this framework, we present a novel, architecture agnostic algorithm called "majority kernels", which seamlessly integrates with predominant architectures, including Transformer models.
Deep Fusion: Efficient Network Training via Pre-trained Initializations
Finally, we validate our theoretical framework, which guides the optimal use of Deep Fusion, showing that with carefully optimized training dynamics, it significantly reduces both training time and resource consumption.
Towards Task and Architecture-Independent Generalization Gap Predictors
We also show results for architecture-independent, task-independent, and out-of-distribution generalization gap prediction tasks.
AdaNet: A Scalable and Flexible Framework for Automatically Learning Ensembles
AdaNet is a lightweight TensorFlow-based (Abadi et al., 2015) framework for automatically learning high-quality ensembles with minimal expert intervention.
Improving Neural Architecture Search Image Classifiers via Ensemble Learning
However, once a good building block is found, manual design is still required to assemble the final architecture as a combination of multiple blocks under a predefined parameter budget constraint.
Ranked #63 on
Image Classification
on CIFAR-100
(using extra training data)
Non-Adaptive Learning a Hidden Hipergraph
We give a new deterministic algorithm that non-adaptively learns a hidden hypergraph from edge-detecting queries.
On Exact Learning Monotone DNF from Membership Queries
In this paper, we study the problem of learning a monotone DNF with at most $s$ terms of size (number of variables in each term) at most $r$ ($s$ term $r$-MDNF) from membership queries.
